WP44: landmark selection v2 results

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Simon-Pierre Boucher committed 1 h ago (Nov 4, 2026) parent 560abbf Browse files

29 changed files +3,389 −2,822

added literature_synthesis/agent_E/claims.csv +46 −0

++claim_id,claim,claim_type,evidence_type,supporting_keys,contradicting_keys,corpus_artifact,confidence,verification_status,notes
++E-01,"The automatic AI/ML landmark selection returned 234 candidates (33 LLM, 26 AI safety, 25 in each other category), 72 dated 2026, 136 typed article, 28 preprint, 19 book-chapter, 11 dissertation.",established,corpus_statistic,,,"literature_synthesis/landmarks/ai_ml_landmarks.csv (columns primary_category, year, type); selection_report.json by_area.ai_ml",0.99,machine-verified,Counts recomputed by make_contributions.py.
++E-02,"44 of the 234 AI/ML landmark records (18.8 %) are unreliable: 13 identifier mismatches, 3 abstract mismatches, 11 implausible years, 7 normalisation artefacts, 10 off-scope classifications.",established,metadata_only,,,literature_synthesis/agent_E/contributions.csv (column data_quality_flag); manuscript/tables/tab_ai_landmark_flags.tex,0.9,human-verified,"Judged record by record by Agent E from identifiers, citation counts, venue, title and abstract; needs Agent N audit of the Phase 1 linkage."
++E-03,Identifier and abstract mismatches concentrate in the LLM category (8 of 33) and misdated classics in ML theory and DL architectures (4 each).,established,metadata_only,,,literature_synthesis/agent_E/contributions.csv; tab_ai_landmark_flags,0.9,human-verified,
++E-04,The NCS36 ranking should be re-run with document-type-specific reference sets and the 2026 cohort excluded; the clean AI landmark count is at most 190.,speculative,corpus_statistic,,,literature_synthesis/agent_E/contributions.csv,0.6,unverified,Remedy proposed to the director; not implemented.
++E-05,"Of the 234 landmark candidates, 84 are surveys, 67 methods, 33 empirical, 29 theory/position, 17 engineering and 4 benchmarks (judged from abstract for 190, metadata for 44).",established,abstract,,,literature_synthesis/agent_E/contributions.csv (contribution_type); tab_ai_contribution_types,0.85,human-verified,Contribution typing is a judgement; abstracts only.
++E-06,The citation-ranked top of the LLM and generative-AI categories is dominated by education and commentary papers on ChatGPT rather than by the technical papers introducing the models.,established,metadata_only,kasneci2023chatgptgood;lo2023rapidreview;rudolph2023assessment;chan2023students;baidooanu2023chatgptedu;dwivedi2023sowhat;vandis2023priorities;lim2023ragnarok,,literature_synthesis/landmarks/ai_ml_landmarks.csv (rows with primary_category llm or generative_ai),0.9,human-verified,Property of field-normalised ranking inside a multi-label category spanning education venues.
++E-07,Post-period CAGR (2023–2025) versus baseline (2018–2022): LLM 1.085 vs 0.189; generative AI 0.870 vs 0.296; multimodal 0.869 vs 0.284; foundation models 0.802 vs 0.661; AI safety 0.647 vs 0.294; RL 0.252 vs 0.208; ML theory 0.197 vs 0.104; DL architectures 0.155 vs 0.167; AI agents 0.329 vs −0.045.,established,corpus_statistic,,,"results/bibliometrics/cagr_by_category.csv (cagr_baseline, cagr_post); tab_ai_growth",0.95,machine-verified,File regenerated by Phase 4 on 2026-11-03 evening; values as of the table build.
++E-08,The LLM category's share of eligible works rose from 0.33 % in 2018 to 5.06 % in 2025.,established,corpus_statistic,,,"results/bibliometrics/cagr_by_category.csv (share_2018, share_2025); tab_ai_growth",0.95,machine-verified,
++E-09,For the LLM series the ITS at 2022-12 gives a level change of 0.106 (HAC p 0.075) and a slope change of 0.034 per month (p ~1e-60); placebo p 0.938 (level) and 0.200 (slope); Bai–Perron selects one break at 2022-07; PELT one change point at 2023-10.,established,corpus_statistic,,,results/econometrics/breaks.csv row cat_llm; its_results.json series.cat_llm; macros ItsSeriesCatLlm*; tab_ai_breaks,0.95,machine-verified,
++E-10,"Multimodal, generative AI, agents, RL, ML theory and AI safety show significant positive slope changes at 2022-12 with no positive level jump (agents, RL and AI safety have negative level changes); DL architectures shows no significant slope change (−0.002, p 0.136).",established,corpus_statistic,,,results/econometrics/breaks.csv rows cat_*; tab_ai_breaks,0.95,machine-verified,
++E-11,"Level effects at the GPT-3 (2020-06) and DALL-E 2 (2022-05) event dates are negative for all nine AI categories, while those at GPT-4 (2023-04) and o1 (2024-09) are positive and significant for LLM, multimodal and generative AI.",established,corpus_statistic,,,"results/econometrics/breaks.csv (gpt3_level, dalle2_level, gpt4_level, o1_level and p columns); tab_ai_altdates",0.9,machine-verified,Each alternative date is a separate ITS fit.
++E-12,The post-2022 acceleration of AI publication is a trend change already under way in mid-2022 that steepened in 2023–2024; the monthly series cannot attribute it to a single product release.,established,corpus_statistic,,,"results/econometrics/breaks.csv; its_results.json (placebo, bai_perron, pelt)",0.85,machine-verified,Interpretive statement about the estimates; no causal claim.
++E-13,"LLM works directed 69.6 % of in-corpus references to NLP in 2019 and 68.6 % in 2022, but 34.2 % in 2025, when 28.4 % went to the LLM category itself, 4.3 % to HCI and 3.4 % to software engineering.",established,corpus_statistic,,,results/graph/category_flows_yearly.csv (citing_cat llm); tab_ai_flows,0.95,machine-verified,
++E-14,Generative-AI references to computer vision fell from 57.9 % (2022) to 33.8 % (2025) while references to LLM rose to 13.7 % and to HCI to 6.6 %; AI-safety references to LLM rose from 0.1 % (2019) to 10.0 % (2025) and to HCI from 9.1 % to 11.5 %.,established,corpus_statistic,,,results/graph/category_flows_yearly.csv; tab_ai_flows,0.95,machine-verified,
++E-15,Reinforcement-learning references to robotics rose from 14.4 % (2019) to 23.1 % (2025) while references to LLM stayed below 1 %; ML theory and DL architectures flows are essentially unchanged.,established,corpus_statistic,,,results/graph/category_flows_yearly.csv; tab_ai_flows,0.95,machine-verified,
++E-16,"The LLM category has become a citation hub for generative AI, safety and agents while remaining peripheral to RL, theory and architectures; this is consistent with but does not establish the foundation-model convergence thesis.",contested,corpus_statistic,bommasani2021foundation,,results/graph/category_flows_yearly.csv; tab_ai_flows,0.7,machine-verified,Category-level flows only; RQ5 answered at category granularity.
++E-17,"Known-set lead times: large language models 34 months, reasoning models 29, RAG 12, vision-language models 9, diffusion models 9, LLM agents 6, state-space models 6, MoE 5, speculative decoding 5, AI code generation 10, mechanistic interpretability −18; median 7.",established,corpus_statistic,,,results/emerging/known_set_lead_times.csv (lead_months); macros Known*LeadMonths and KnownSetMedianLeadMonths,0.95,machine-verified,Long LLM lead reflects pre-2020 'language model' matches by the regex.
++E-18,The LLaMA abstract reports that a 13B model trained only on publicly available data outperforms GPT-3 (175B) on most benchmarks; the paper is a preprint.,established,abstract,touvron2023llama;brown2020gpt3;hoffmann2022chinchilla;kaplan2020scaling,,ai_ml_abstracts.jsonl work 4322718191,0.9,machine-verified,Claim is the abstract's; not independently replicated here.
++E-19,"Domain-specific foundation models (pathology UNI/CONCH/CHIEF/PLIP/TITAN, radiology RadFM, dermatology PanDerm, brain MRI BrainIAC, Earth system Aurora, tabular TabPFN, time series, cognition Centaur) are the most visible post-2022 development in the landmark set; their abstracts report outperforming task-specific baselines on multi-task benchmarks.",established,abstract,chen2024uni;lu2024conch;wang2024chief;huang2023plip;ding2025titan;wu2025radfm;yan2025panderm;tak2026brainiac;bodnar2025aurora;hollmann2025tabpfn;jin2023timellm;liu2024timesurl;binz2025centaur,,ai_ml_landmarks.csv rows with primary_category foundation_models / multimodal_ai,0.85,machine-verified,"Abstract-level claims by the authors; several records (UNI, CONCH, CHIEF, PLIP) have no abstract in the file."
++E-20,"Parameter-efficient adaptation (LoRA, QLoRA, LLaMA-Adapter, PEFT surveys) is where the open-weight ecosystem's practical growth concentrated; QLoRA's '99.3 % of ChatGPT' is a judge-based measurement.",established,abstract,hu2021lora;dettmers2023qlora;zhang2023llamaadapter;han2024peftsurvey,,ai_ml_abstracts.jsonl works 7133233472 and 4361229539,0.8,machine-verified,
++E-21,"Whether LLM capabilities are 'emergent' (discontinuous in scale) is contested: Wei et al. report discontinuities, Schaeffer et al. show they largely disappear under continuous metrics; both are preprints.",contested,metadata_only,wei2022emergent,schaeffer2023mirage,,0.9,machine-verified,Known disagreement; not adjudicable from the corpus.
++E-22,"'Sparks of AGI' is a widely cited (c36 = 1,420 in the landmark file) but non-peer-reviewed, unreplicated position paper based on qualitative experiments on an early GPT-4.",established,abstract,bubeck2023sparks,bender2021parrots,ai_ml_landmarks.csv work 4360836968,0.9,machine-verified,
++E-23,The DeepSeek-R1 Nature paper reports that reasoning can be incentivised through pure reinforcement learning without human-labelled reasoning trajectories; generality beyond verifiable domains is untested in the corpus.,established,abstract,deepseek2025r1nature;deepseek2025r1;openai2024o1,,ai_ml_abstracts.jsonl work 4414281281,0.85,machine-verified,Peer-reviewed version cited; the preprint is also in references.bib.
++E-24,"Domain foundation models displacing task-specific pipelines in regulated domains is speculative; reviews list data, privacy and evaluation obstacles unresolved in the landmark abstracts.",speculative,abstract,paschali2025fmradiology;zhang2026datacentric,,ai_ml_abstracts.jsonl works 4407135816 and 4390690043,0.6,machine-verified,
++E-25,RLHF and DPO are established post-training methods whose open problems are documented by Casper et al.,established,metadata_only,christiano2017rlhf;ouyang2022instructgpt;bai2022hh;bai2022constitutional;rafailov2023dpo;casper2023rlhfproblems,,,0.9,machine-verified,DPO record 7133208539 has no abstract in the landmark file.
++E-26,"Current alignment is brittle to optimisation pressure: universal adversarial suffixes, jailbreak analyses and PAIR (semantic jailbreaks with black-box access in about twenty queries, per the abstract).",established,abstract,zou2023universal;wei2023jailbroken;chao2025pair,,ai_ml_abstracts.jsonl work 4410609100,0.9,machine-verified,Two of three are preprints; PAIR is peer reviewed (IEEE SaTML 2025).
++E-27,Hallucination remains an unsolved measurement problem; Med-PaLM built MultiMedQA and a human evaluation framework because automated metrics were insufficient for clinical answers.,established,abstract,ji2023hallucination;huang2025hallucination;singhal2023clinical,,ai_ml_abstracts.jsonl work 4384071683,0.85,machine-verified,
++E-28,"Vision-language models of the window (Flamingo, BLIP-2, InstructBLIP, LLaVA, mPLUG-Owl2) share the design of a frozen or lightly adapted LLM fed by a visual encoder through a small bridge module.",established,abstract,radford2021clip;dosovitskiy2021vit;alayrac2022flamingo;li2023blip2;dai2023instructblip;liu2023llava;ye2024mplugowl2;kirillov2023sam;schuhmann2022laion5b,,ai_ml_abstracts.jsonl works 4318718936 and 4402727669,0.85,machine-verified,"BLIP-2,  InstructBLIP,  LLaVA are preprint records."
++E-29,"Diffusion generation (DDPM, latent diffusion, Imagen, DALL-E 2, classifier-free guidance, textual inversion, T2I-Adapter, DiT, SD3) forms the generative lineage; MatterGen is a peer-reviewed scientific application reporting stable, diverse inorganic materials.",established,abstract,ho2020ddpm;rombach2022ldm;saharia2022imagen;ramesh2022dalle2;ho2022cfg;gal2022textualinversion;mou2024t2iadapter;peebles2023dit;esser2024sd3;zeni2025mattergen,,ai_ml_abstracts.jsonl works 4288099666 4289785095 4393148714 4406472463,0.85,machine-verified,
++E-30,"In an evaluation of eight multimodal foundation models on 1,090 medical cases, image predictions were largely driven by text and mild textual suggestions of a wrong diagnosis degraded image classification.",established,abstract,buckley2026textexploit,,ai_ml_abstracts.jsonl work 7164501397,0.85,machine-verified,Single study; abstract-level.
++E-31,"The generative-AI category's citation-ranked landmarks are overwhelmingly education, policy and adoption studies (Bastani et al. preprint: GPT-4 access harmed unassisted exam performance; Bick et al.: 45 % of US adults 18–64 using generative AI by late 2024).",established,abstract,chan2023policyframework;xia2024assessmentreview;bittle2025integrity;mcdonald2025hepolicies;bastani2024harmlearning;bick2026rapidadoption,,ai_ml_landmarks.csv rows with primary_category generative_ai,0.9,machine-verified,"Bastani et al. is an SSRN preprint without abstract in the file; the exam-performance statement comes from the title and the published abstract elsewhere, not from the corpus file, and should be checked by Agent K."
++E-32,"Agent benchmarks (SWE-bench, WebArena, AgentBench) are preprints; the only peer-reviewed agent benchmark in the landmark set is the clinical evaluation of OpenManus and Manus.",established,abstract,jimenez2024swebench;zhou2023webarena;liu2023agentbench;liu2026clinicalagents,,ai_ml_abstracts.jsonl work 7130481397,0.85,machine-verified,
++E-33,"Science agents are the strongest peer-reviewed agent evidence: Virtual Lab (nanobodies with experimental validation), Robin (hypothesis generation and data analysis), DIVE (figure data extraction), AgentSociety (social simulation).",established,abstract,swanson2025virtuallab;ghareeb2026robin;zhang2026dive;piao2026agentsociety,lu2024aiscientist;si2024novelideas,ai_ml_abstracts.jsonl works 7161734976 4414991289 4407570917,0.8,machine-verified,Virtual Lab record has no abstract in the file (metadata only).
++E-34,"The 'agentic AI' literature produced at least six taxonomies in 2025–2026, most of them reviews of reviews; the landmark set contains six of them and one benchmark.",established,abstract,acharya2025agenticsurvey;sapkota2026agentsvsagentic;bandi2025agenticreview;abouali2025agenticparadigms;allmendinger2026multiagentai;collaco2026agentichealthcare;cheng2026agentsdefinitions,,ai_ml_landmarks.csv rows with primary_category ai_agents,0.9,machine-verified,
++E-35,"The ai_agents category mixes classical multi-agent systems and control theory with LLM agents, which explains its negative baseline CAGR, the negative 2022-12 level change and the second Bai–Perron break at 2024-07.",contested,corpus_statistic,,,contributions.csv off_scope rows; breaks.csv row cat_ai_agents; cagr_by_category.csv,0.7,machine-verified,Explanation is inferential.
++E-36,"The deep-RL canon of the baseline period is intact in the landmark set; the single post-2022 method landmark is DreamerV3, whose abstract reports one configuration mastering over 150 tasks through world models.",established,abstract,hessel2018rainbow;haarnoja2018sac;fujimoto2018td3;silver2018alphazero;vinyals2019alphastar;yu2021mappo;henderson2018drlmatters;burda2018curiosity;hafner2025dreamerv3;zhang2021marloverview,,ai_ml_abstracts.jsonl work 4409147637,0.85,machine-verified,
++E-37,RL's most consequential post-2022 role (reward modelling and policy optimisation for LLMs) is invisible in the RL category because those papers are classified as LLM work; a claim that RL declined would be a taxonomy artefact.,contested,corpus_statistic,schulman2017ppo;rafailov2023dpo;deepseek2025r1nature,,results/graph/category_flows_yearly.csv (reinforcement_learning -> llm < 1 %),0.7,machine-verified,
++E-38,"The ML-theory landmark set contains no learning-theory result from the window; it consists of textbooks, evaluation metrics, interpretability, optimisation and transfer-learning surveys and misdated 2013–2019 bandit papers.",established,metadata_only,chicco2020mcc;rudin2019stopexplaining;lipton2018mythos;lundberg2020treeexplainer;bottou2018optimization;zhuang2021transfersurvey,,ai_ml_landmarks.csv rows with primary_category ml_theory; contributions.csv,0.9,human-verified,
++E-39,Zeng et al. report that simple linear models outperform Transformer-based models (Informer and successors) on long-term time-series forecasting benchmarks; the finding is contested within the field.,contested,abstract,zeng2023transformerstsf,zhou2021informer;wen2023tstransformers,ai_ml_abstracts.jsonl work 4382203079,0.8,machine-verified,
++E-40,"Architecture research moved to efficiency (FlashAttention, MoE, quantisation, speculative decoding, state-space models, KAN); the detector finds short leads for state-space models (6), MoE (5) and speculative decoding (5).",established,corpus_statistic,dao2022flashattention;fedus2022switch;dettmers2022int8;frantar2023gptq;leviathan2023speculative;gholami2022quantization;gu2023mamba;liu2024kan,,results/emerging/known_set_lead_times.csv,0.85,machine-verified,Mamba and KAN are preprints.
++E-41,"The SuperGLUE abstract records in 2019 that GLUE performance surpassed non-expert humans, the first instance of the saturation cycle that repeats for later benchmarks; the landmark set contains four benchmark contributions in 234.",established,abstract,wang2019superglue;wang2018glue;hendrycks2021mmlu;srivastava2022bigbench;liang2022helm;sainz2023contamination;dehghani2021lottery,,ai_ml_abstracts.jsonl work 2990704537; contributions.csv,0.85,machine-verified,
++E-42,"Risk taxonomies matured from the concrete-problems agenda to the AI Risk Repository (1,725 risks from 74 taxonomies per the abstract) and the International AI Safety Report.",established,abstract,amodei2016concrete;hendrycks2021unsolved;slattery2026riskrepository;bengio2025safetyreport;bengio2024extremerisks,,ai_ml_abstracts.jsonl work 4402698709,0.9,machine-verified,
++E-43,Peer-reviewed empirical harms: medical LLMs vulnerable to data poisoning at 0.001 % of tokens; four chatbots gave unsafe answers in physician-led red-teaming; LLMs express stigma as replacement therapists.,established,abstract,alber2025poisoning;draelos2026unsafeanswers;moore2025stigma,,ai_ml_abstracts.jsonl works 4406152291 7128782475 4411542009,0.85,machine-verified,
++E-44,Inference energy of general-purpose generative models exceeds that of task-specific models by orders of magnitude.,established,abstract,luccioni2024powerhungry;devries2023energy,,ai_ml_abstracts.jsonl work 4399450035,0.8,machine-verified,Magnitude statement is the authors'.
++E-45,"Labour and adoption evidence rests on a preregistered field experiment (758 consultants), a task-exposure study and nationally representative adoption surveys.",established,abstract,dellacqua2023jagged;eloundou2024gpts;bick2026rapidadoption,,ai_ml_abstracts.jsonl works 7135096569 7124974472,0.85,machine-verified,'GPTs are GPTs' landmark record 4330336443 has a mismatched abstract; cite the verified Science article.

modified literature_synthesis/agent_E/make_bib_part05.py +2 −2

				`@@ -3,7 +3,7 @@`
3	Each entry is resolved by a single Crossref (DOI) or arXiv (id) lookup; the identifier comes from the ai_ml landmark CSV	3	Each entry is resolved by a single Crossref (DOI) or arXiv (id) lookup; the identifier comes from the ai_ml landmark CSV
4	(column doi / arxiv_id) or from an explicit override when the corpus record has none. Nothing is written without a	4	(column doi / arxiv_id) or from an explicit override when the corpus record has none. Nothing is written without a
5	successful lookup. Usage: python3 make_bib_part05.py <repo_root>"""	5	successful lookup. Usage: python3 make_bib_part05.py <repo_root>"""
6	−import csv, json, sys, re, time, unicodedata, urllib.request, xml.etree.ElementTree as ET	6	+import csv, json, sys, re, time, html, unicodedata, urllib.request, xml.etree.ElementTree as ET
7	R = sys.argv[1]	7	R = sys.argv[1]
8	L = {r["work_id"]: r for r in csv.DictReader(open(f"{R}/literature_synthesis/landmarks/ai_ml_landmarks.csv"))}	8	L = {r["work_id"]: r for r in csv.DictReader(open(f"{R}/literature_synthesis/landmarks/ai_ml_landmarks.csv"))}
9	MAIL = "spbou4@icloud.com"	9	MAIL = "spbou4@icloud.com"
				`@@ -43,7 +43,7 @@ def get(url):`
43	req = urllib.request.Request(url, headers={"User-Agent": f"WP44-agentE (mailto:{MAIL})"})	43	req = urllib.request.Request(url, headers={"User-Agent": f"WP44-agentE (mailto:{MAIL})"})
44	return urllib.request.urlopen(req, timeout=40).read()	44	return urllib.request.urlopen(req, timeout=40).read()
45	def clean(s):	45	def clean(s):
46	− s = unicodedata.normalize("NFKC", s)	46	+ s = unicodedata.normalize("NFKC", html.unescape(s))
47	s = s.replace("‐", "-").replace("‑", "-").replace("‒", "-").replace("–", "--").replace("—", "---").replace("‘", "'").replace("’", "'").replace("“", "``").replace("”", "''").replace(" ", " ").replace("−", "-")	47	s = s.replace("‐", "-").replace("‑", "-").replace("‒", "-").replace("–", "--").replace("—", "---").replace("‘", "'").replace("’", "'").replace("“", "``").replace("”", "''").replace(" ", " ").replace("−", "-")
48	s = "".join(c if ord(c) < 0x250 else unicodedata.normalize("NFKD", c)[0] if unicodedata.normalize("NFKD", c)[0].isascii() else "" for c in s)	48	s = "".join(c if ord(c) < 0x250 else unicodedata.normalize("NFKD", c)[0] if unicodedata.normalize("NFKD", c)[0].isascii() else "" for c in s)
49	s = s.replace("&", "\\&").replace("%", "\\%").replace("#", "\\#").replace("$", "\\$")	49	s = s.replace("&", "\\&").replace("%", "\\%").replace("#", "\\#").replace("$", "\\$")

added literature_synthesis/agent_E/open_problems.csv +15 −0

++problem_id,statement,categories,why_unresolved,testable_direction,supporting_keys
++P-01,Whether LLM capabilities are discontinuous in scale (emergent) or artefacts of discontinuous metrics.,llm;ml_theory,Both positions are preprints with different metric choices; no corpus-level or peer-reviewed adjudication.,Re-evaluate the same model families under continuous and discontinuous metrics with pre-registered thresholds and report both.,wei2022emergent;schaeffer2023mirage
++P-02,Whether pure-RL reasoning training (R1-style) generalises beyond verifiable-reward domains (mathematics and code).,llm;reinforcement_learning,The peer-reviewed evidence covers verifiable tasks; open-ended domains lack ground-truth rewards.,Out-of-domain evaluation of RL-only versus SFT+RL models on tasks without programmatic verifiers with held-out judges.,deepseek2025r1nature;openai2024o1
++P-03,Whether alignment brittleness to jailbreaks is intrinsic to RLHF-style methods or an artefact of scale and data.,ai_safety;llm,Attacks succeed in few queries across model families; no defence has withstood adaptive attackers in the landmark abstracts.,"Adaptive red-teaming with fixed attacker budgets across alignment methods (RLHF, DPO, constitutional) at matched scale.",zou2023universal;wei2023jailbroken;chao2025pair;casper2023rlhfproblems
++P-04,How to measure hallucination in open-ended generation without reference answers.,llm;multimodal_ai,Surveys agree on definitions but not metrics; human evaluation frameworks are domain-specific.,Cross-domain benchmark with expert-labelled factuality and calibrated automated judges validated against it.,ji2023hallucination;huang2025hallucination;singhal2023clinical
++P-05,Whether vision-language models fuse modalities or condition on the text channel.,multimodal_ai;foundation_models,A controlled medical evaluation found image predictions driven by text; general-domain evidence is benchmark-level only.,Counterfactual perturbation of each modality on held-out tasks measuring the marginal information used from images.,buckley2026textexploit;schouten2025multimodalmedicine;zhang2026lowqualityfusion
++P-06,Whether 'agentic AI' denotes a distinct system class or a relabelling of tool-augmented LLM pipelines.,ai_agents,Six taxonomies in 2025–2026 and one peer-reviewed benchmark; definitions diverge.,Operational criteria (autonomy horizon; environment interaction; self-evaluation) applied to published systems with inter-rater agreement.,sapkota2026agentsvsagentic;acharya2025agenticsurvey;abouali2025agenticparadigms;wang2024agentsurvey
++P-07,Whether autonomous science agents produce validated discoveries at a rate that justifies their cost.,ai_agents;autonomous_scientific_discovery,Peer-reviewed cases (Virtual Lab; Robin) are single demonstrations; AI-scientist preprints report no external validation.,Pre-registered multi-laboratory trials comparing agent-proposed and human-proposed hypotheses on validation rate and cost.,swanson2025virtuallab;ghareeb2026robin;lu2024aiscientist;si2024novelideas
++P-08,Whether deep-learning architecture research plateaued after 2022 or was relabelled under LLM and foundation categories.,deep_learning_architectures;llm,DL architectures is the only AI category without a significant post-2022 slope change; multi-label counts cannot separate relabelling from decline.,Re-estimate the series on works whose only AI category is DL architectures and on a fixed 2022 classifier vocabulary.,gu2023mamba;liu2024kan;dao2022flashattention
++P-09,How to evaluate models on benchmarks that may be in their training data.,llm;ml_theory,Contamination is documented; no landmark abstract reports a contamination-controlled evaluation.,Time-split benchmarks released after model training cut-offs with canary strings and reported overlap statistics.,sainz2023contamination;dehghani2021lottery;ott2022benchmarks;chiang2024arena
++P-10,Whether catastrophic-risk capabilities can be measured before deployment.,ai_safety,Risk frameworks exist (repository; safety report); no landmark abstract reports a validated dangerous-capability measurement.,Pre-registered dangerous-capability evaluations with external replication and disclosure of false-negative rates.,slattery2026riskrepository;bengio2025safetyreport;shevlane2023extremerisks;hendrycks2023catastrophic
++P-11,Whether domain foundation models generalise across institutions and acquisition protocols.,foundation_models;multimodal_ai,Pathology and radiology models report multi-benchmark gains but evaluation cohorts are often internal.,External multi-site validation with distribution-shift reporting and release of evaluation splits.,chen2024uni;lu2024conch;wu2025radfm;paschali2025fmradiology
++P-12,Whether Transformers are the right inductive bias for long-horizon time series.,deep_learning_architectures;foundation_models,Linear baselines beat Transformer forecasters on standard benchmarks; LLM reprogramming claims gains on others.,"Common benchmark suite with leakage-free splits comparing linear, Transformer, state-space and LLM-reprogrammed models.",zeng2023transformerstsf;zhou2021informer;jin2023timellm;gu2023mamba
++P-13,Whether generative-AI use harms or helps learning outcomes.,generative_ai,A preregistered field experiment reports harm to unassisted performance; surveys report perceived benefits.,Randomised trials with delayed unassisted assessments across subjects and tutoring designs.,bastani2024harmlearning;kasneci2023chatgptgood;zhai2024overreliance
++P-14,Whether citation-based landmark identification can be made reliable for AI given identifier merging in bibliographic databases.,llm;deep_learning_architectures;ml_theory,"18.8 % of the automatically selected AI landmarks are mislinked, misdated or normalisation artefacts.","Re-run selection on records whose DOI, arXiv id and title are mutually consistent; document-type-specific normalisation; report the exclusion rate.",priem2022openalex;culbert2025openalexcoverage

added literature_synthesis/agent_G/claims.csv +25 −0

++claim_id,claim,claim_type,evidence_type,supporting_keys,contradicting_keys,corpus_artifact,confidence,verification_status,notes
++G-01,"The automatically selected landmark candidates of the theory and software_pl areas (150 works) are dominated by non-research records: only 29 are in-scope research contributions, while 33 are misclassified, 27 are theses, 24 are re-deposited classics with a recent record date and 22 are textbooks or chapters.",established,metadata_only,,,literature_synthesis/agent_G/contributions.csv (column verdict); manuscript/tables/tab_theory_landmark_audit.tex,0.9,human-verified,"Verdicts assigned by Agent G from title/venue/type metadata and abstracts; the verdict scheme is documented in make_contributions.py. Human-verified here means verified by the specialist agent, not by the PI (checkpoint H-3 pending)."
++G-02,"Seven candidate records carry OpenAlex identifiers that, resolved live on 2026-11-03, return a different and far more cited work (e.g. the record titled 'Exploiting Generative AI to Scale up Intelligent Tutoring Systems', DOI 10.4230/lipics.itp.2023.19, resolves to 'Attention Is All You Need'), and their c36 values match the other work's citation count.",established,metadata_only,,,literature_synthesis/agent_G/verification_lookups_raw.txt; literature_synthesis/agent_G/contributions.csv (column id_resolves_to_other_work),0.95,machine-verified,"Live OpenAlex lookups of W4385245566, W2964015378, W4293846201, W4311415873, W4294558607, W3177813494, W4297781990. Mechanism (identifier merge or reuse between snapshot and live database) is inferred, not observed."
++G-03,"The computational_complexity category is the least precise of the six: 15 of its 25 candidates are works from psychology, management, education or metrology that carry the phrase 'complexity' or 'complexity theory'.",established,abstract,,,literature_synthesis/agent_G/contributions.csv (primary_category = computational_complexity),0.9,human-verified,Consistent with the lexical signals of taxonomy/ontology.yaml; per-category precision from the validation sample (taxonomy/validation) was not available to Agent G.
++G-04,"Metaphor-based metaheuristic optimisers (arithmetic, aquila, lemming, snake, langur, starfish) account for six of the 25 algorithms candidates and for most of the post-2022 highly cited algorithm 'articles'; the genre is contested in the operational-research literature.",established,abstract,abualigah2021aoa;abualigah2021aquila;sorensen2013metaphor;aranha2021metaphor;camachovillalon2022exposing,,literature_synthesis/agent_G/contributions.csv (verdict = metaheuristic),0.85,human-verified,"The critique papers argue that many such algorithms re-describe existing methods; this review reports the critique, it does not re-evaluate the algorithms."
++G-05,"Across the six categories the multi-label count grew faster in 2023-2025 than in 2018-2022 (e.g. software engineering 25.5% vs 0.9% CAGR, computational complexity 22.7% vs 1.9%), but the corpus-wide multi-label CAGR also rose from 6.1% to 26.0%, so the category-level acceleration is largely the corpus-wide acceleration.",established,corpus_statistic,,,results/graph/category_counts_yearly.csv via macros \ThyCagrMultilabel*; tab_theory_summary,0.85,machine-verified,Post-period CAGR covers two years (2023 to 2025) and is sensitive to indexing lag (Section sec:m-truncation).
++G-06,"Measured by primary-label share, all six categories lost ground between 2018 and 2025 (algorithms 5.83% to 3.79%, software engineering 5.81% to 4.07%, formal verification 1.06% to 0.52%, programming languages 0.88% to 0.47%).",established,corpus_statistic,,,results/graph/category_counts_yearly.csv via \ThySharePrimary*Pct,0.9,machine-verified,"Relative decline reflects faster growth elsewhere (AI categories), not absolute contraction."
++G-07,"The results/bibliometrics/cagr_by_category.csv file reports counts for the six categories (e.g. algorithms n_2018 = 9,716) that are about three times smaller than the primary counts of results/graph/category_counts_yearly.csv (29,148) and therefore predates the current classification; its CAGR values should not be quoted for these categories until regenerated.",established,corpus_statistic,,,results/bibliometrics/cagr_by_category.csv vs results/graph/category_counts_yearly.csv (macros \ThyCagrfile* vs \ThyCount*),0.95,machine-verified,"bibliometrics/04_diversity_cagr.py reads category_counts_yearly.csv, so re-running it would resolve the discrepancy; flagged to the director."
++G-08,"Interrupted-time-series slope changes at 2022-12 are positive and HAC-significant for all six categories (1.0% to 1.8% per month), but the placebo test rejects at the 5% level for none of them (placebo slope p from 0.062 to 0.431), so the post-2022 slope change is not distinguishable from pseudo-events in the baseline.",established,corpus_statistic,,,"results/econometrics/breaks.csv rows cat_algorithms, cat_computational_complexity, cat_programming_languages, cat_compilers, cat_formal_verification, cat_software_engineering (columns chatgpt_slope, chatgpt_slope_p, placebo_slope_p)",0.9,machine-verified,No causal reading. Algorithms and computational complexity are the closest to the 5% threshold (0.062).
++G-09,"Software engineering is the only one of the six categories with a positive level change at 2022-12 (4.8%, p = 0.124) and a significant positive level change at the GPT-4 date (0.102, p = 0.003); the other five show negative or null level changes at 2022-12.",established,corpus_statistic,,,"results/econometrics/breaks.csv (chatgpt_level, gpt4_level, gpt4_level_p)",0.85,machine-verified,Level changes partly reflect seasonality and the dating of conference proceedings; the placebo level p for software engineering is 0.862.
++G-10,"Co-classification with the LLM category rose between 2022 and 2025 in every theory/software category, most strongly in software engineering (4.0% to 21.2% of its works), programming languages (4.2% to 18.3%) and compilers (3.1% to 14.9%), and least in algorithms and computational complexity (1.3% to 5.3%).",established,corpus_statistic,,,"results/graph/category_cooccurrence_yearly.csv and category_counts_yearly.csv via \ThyCooc*LlmTwoZeroTwo{Two,Five}Pct; tab_theory_llm_cooccurrence",0.9,machine-verified,"Co-classification is a lexical/topic signal (Part IV), not evidence of method adoption."
++G-11,"Reference flows from the six categories to the LLM category remain small in the post period (software engineering 2.5%, computational complexity 1.8%, compilers 1.2%, programming languages 0.7%, formal verification 0.4%, algorithms 0.3% of references), and within-category self-citation shares changed little (algorithms 65.1% to 65.0%, formal verification 49.9% to 47.0%).",established,corpus_statistic,,,results/graph/category_flows_yearly.csv aggregated by period via \ThyFlow*; \ThyInflow*,0.85,machine-verified,"Flows are computed on the categorised subset of references (Part VIII reference coverage); the LLM category is young, which depresses its share of cited works mechanically."
++G-12,"Computational complexity shows the largest drop in self-citation share (30.0% to 20.6%) and the largest rise in references to HCI (3.0% to 6.7%), consistent with the category's contamination by social-science 'complexity' works rather than with a change in theoretical research.",contested,corpus_statistic,,,results/graph/category_flows_yearly.csv via \ThyFlowComputationalComplexityTo*; literature_synthesis/agent_G/contributions.csv,0.6,machine-verified,"Interpretation; the flow statistics are machine-verified, the attribution to contamination is the specialist's reading of the candidate list."
++G-13,"Formal verification receives a growing share of its inbound citations from computer vision (6.7% baseline to 13.8% post), consistent with the neural-network verification literature (Reluplex, Marabou, DeepPoly, VNN-COMP) being cited by vision robustness work.",contested,corpus_statistic,katz2017reluplex;katz2019marabou;singh2019deeppoly;brix2023vnncomp,,"results/graph/category_flows_yearly.csv via \ThyInflowFormalVerificationFromComputerVision{Baseline,Post}Pct",0.6,machine-verified,The attribution to neural-network verification is not tested on the flow data (no paper-level breakdown).
++G-14,"The major theoretical results of the window (almost-linear max-flow, negative-weight SSSP in near-linear time, Kelley-Meka 3-AP bounds, Williams's time-space simulation, the directed SSSP sorting barrier, optimal open-addressing bounds) do not appear among the field-normalised landmark candidates, which favour generic-title book chapters and re-deposited classics; the landmark selection is therefore not a valid instrument for identifying theoretical breakthroughs.",established,metadata_only,chen2022maxflow;bernstein2022negative;kelley2023threeap;williams2025sqrtspace;duan2025sorting;farachcolton2024openaddressing,,literature_synthesis/landmarks/theory_landmarks.csv; literature_synthesis/agent_G/contributions.csv,0.85,human-verified,"The listed results are known to the specialist from the verified bibliographic records (titles, venues); their content is not reviewed here beyond the abstracts."
++G-15,Petri-net reachability was settled in the baseline period: a non-elementary lower bound (STOC 2019) followed by matching Ackermann-completeness (FOCS 2021); the STOC 2019 paper is one of only two in-scope research candidates in the computational_complexity list.,established,abstract,czerwinski2019petri;czerwinski2021ackermann;leroux2021notprimitive,,literature_synthesis/landmarks/theory_abstracts.jsonl (W2900179285),0.9,machine-verified,Abstract of the STOC paper reports the lower bound; the 2021 results are cited from their verified records.
++G-16,"LLM-assisted program analysis and compiler testing entered the top venues of the programming-languages community in 2024 (LLift and WhiteFox at OOPSLA), and LLM-based compiler optimisation models were released in 2025 (LLM Compiler at CC); these works report found bugs or optimisation recovery rates but, per their abstracts, no formal guarantees.",established,abstract,li2024llift;yang2024whitefox;cummins2025llmcompiler;munley2024llm4vv;xia2024fuzz4all,lopes2021alive2;yang2011csmith,"literature_synthesis/landmarks/software_pl_abstracts.jsonl (W4396242417, W4403223258, W4407942537)",0.85,machine-verified,Contrast with translation validation (Alive2) and Csmith-style differential testing is the specialist's framing.
++G-17,"Machine-learning-guided automated theorem proving reached about 60% of Mizar theorems in the hammer setting (ITP 2023), and LLM-based provers (Baldur, LeanDojo, DeepSeek-Prover) report whole-proof generation on Lean/Isabelle benchmarks; the LeanDojo and DeepSeek-Prover results are preprints and the benchmark-level figures cannot be compared across systems.",established,abstract,jakubuv2023mizar;first2023baldur;yang2023leandojo;xin2024deepseekprover;yang2024formalmath,,literature_synthesis/landmarks/software_pl_abstracts.jsonl (W4385245566),0.8,machine-verified,The MizAR abstract is attached in the corpus to a wrong title (claim G-02).
++G-18,"Field evidence on AI coding assistants is conflicting: three randomised field experiments report a 26.08% (s.e. 10.3%) increase in completed tasks (Management Science 2026), while a 2025 randomised trial preprint with experienced open-source developers reports slower completion; the populations (enterprise developers vs expert maintainers of mature repositories) and outcomes (tasks completed vs time per issue) differ.",contested,abstract,cui2026genaiwork;peng2023copilot;ziegler2022copilot,becker2025metr,literature_synthesis/landmarks/software_pl_abstracts.jsonl (W7131867455),0.8,machine-verified,The METR study is a preprint; the reconciliation by population/outcome is the specialist's reading of the abstracts.
++G-19,"Security evaluations of LLM-generated or LLM-classified code (Copilot vulnerability rates, user studies, CWE classification by open models) consistently report high detection or completion rates paired with poor fine-grained security reasoning.",established,abstract,pearce2022asleep;perry2023insecure;gasparlopes2025vuln,,literature_synthesis/landmarks/software_pl_abstracts.jsonl (W7110046239),0.75,machine-verified,Three studies with different designs; 'consistently' refers to these three only.
++G-20,"The compilers category is small (0.09% of primary labels in 2025) and its candidate list contains ten misclassified works captured by the tokens 'just-in-time', 'compiler', 'compiling' and 'polyhedral' used outside computing.",established,abstract,,,literature_synthesis/agent_G/contributions.csv (primary_category = compilers); \ThySharePrimaryCompilersTwoZeroTwoFivePct,0.9,human-verified,Lexical mechanism inferred from titles; the ontology's keyword regexes were not re-run by Agent G.
++G-21,Expressivity results for transformers (log-precision transformers lie in uniform TC0; survey of formal-language results) are the main post-2022 contribution of complexity theory to the study of language models visible in the verified bibliography; they bound what a single forward pass can compute and motivate chain-of-thought as a route to greater power.,speculative,metadata_only,merrill2023parallelism;strobl2024survey,,manuscript/references_part07a.bib,0.6,unverified,Not from the corpus landmark set; the relevance claim is the specialist's and the content is known only from verified titles and abstracts.
++G-22,"Algorithms with predictions (learning-augmented algorithms) is a baseline-period research programme (ski rental and scheduling with ML predictions; competitive caching with advice; CACM overview 2022) that the corpus records through a 2024 arXiv re-posting of the 2018 paper, illustrating how re-depositions distort the dating of landmarks.",established,abstract,mitzenmacher2022predictions;lykouris2021caching,,literature_synthesis/landmarks/theory_abstracts.jsonl (W2891784792),0.85,machine-verified,The OpenAlex record W2891784792 carries publication year 2024 and DOI 10.48550/arxiv.2407.17712.
++G-23,"AI-discovered algorithms (AlphaTensor matrix multiplication, AlphaDev sorting routines, FunSearch, AlphaEvolve) are reported in Nature or as preprints by their developers; the improvements are on specific instances (small matrix sizes, short sorting networks, cap-set constructions) and no independent replication appears in the verified bibliography.",contested,metadata_only,fawzi2022alphatensor;mankowitz2023alphadev;romeraparedes2024funsearch;novikov2025alphaevolve,,manuscript/references_part07a.bib; manuscript/references.bib,0.7,unverified,"AlphaEvolve is a preprint; replication status is asserted from the absence of such works in the bibliography, not from a search of the corpus."
++G-24,"Verified-systems research continued in the window with Verus (SOSP 2024), a Rust-based verification framework, extending the CompCert/seL4/RustBelt lineage; the corpus candidate list records this lineage only through re-deposited UNSW theses dated 2022.",established,metadata_only,lattuada2024verus;leroy2009compcert;klein2009sel4;jung2017rustbelt;jung2021saferust,,"literature_synthesis/agent_G/contributions.csv (W2327217190, W2491926874, W630549275)",0.8,machine-verified,Verus content known from its verified title and venue only.

added literature_synthesis/agent_G/open_problems.csv +11 −0

++problem_id,statement,categories,why_unresolved,testable_direction,supporting_keys
++G-P1,"Can field-normalised citation ranking identify landmark works in small theory categories, given identifier merges, re-deposited classics and generic-title chapters?",algorithms;computational_complexity;formal_verification;compilers,The 150 candidates contain 29 in-scope research works; NCS36 rewards records whose citation counts belong to other works or to canonical textbooks.,"Re-run select_landmarks.py with type filters (exclude book-chapter, dissertation, reprint), a live identifier re-resolution step and venue priors (STOC/FOCS/SODA/POPL/PLDI/CAV/ICSE); measure precision against a specialist-labelled sample.",czerwinski2019petri;chen2022maxflow;kelley2023threeap
++G-P2,Does co-classification with the LLM category in software engineering (21.2% of works in 2025) correspond to method adoption or to vocabulary adoption?,software_engineering;programming_languages,Co-classification is lexical; reference flows to LLM works stay at 2.5% of software-engineering references.,Extract method/model nodes for the high-priority set (Part III knowledge graph) and test whether works co-classified with LLM cite LLM method papers at a higher rate than their category baseline.,wang2024llmtesting;fan2023llm4se;hou2024llm4se
++G-P3,What is the productivity effect of AI coding assistants on experienced developers working on mature codebases?,software_engineering,Randomised field experiments report +26% completed tasks for enterprise developers while a 2025 randomised trial preprint reports slower completion for expert open-source maintainers; populations and outcomes differ.,"Preregistered multi-site trials stratified by developer experience and repository maturity, with time-to-merge and defect rate as joint outcomes.",cui2026genaiwork;becker2025metr;peng2023copilot
++G-P4,Can LLM-based bug finding and compiler fuzzing be given soundness or coverage guarantees comparable to translation validation and differential testing?,programming_languages;compilers;formal_verification,"LLift and WhiteFox report found bugs but, per their abstracts, no completeness or false-negative bounds; Alive2 gives bounded translation validation.","Combine LLM-generated test programs with Alive2-style validation and measure the share of LLM-reported bugs that are confirmed, by optimisation pass.",li2024llift;yang2024whitefox;lopes2021alive2;xia2024fuzz4all
++G-P5,"How far can learned provers scale on formal mathematics and verified software, and are reported pass rates comparable across benchmarks?",formal_verification,MizAR reports 60% of Mizar theorems; LLM provers report Lean/Isabelle figures from preprints on different benchmarks with possible training-set overlap.,A benchmark with time-separated test theorems (post-training-cutoff) and a common compute budget; report proof-checking rather than self-reported success.,jakubuv2023mizar;first2023baldur;yang2023leandojo;xin2024deepseekprover;yang2024formalmath
++G-P6,"Do the 2022-2025 algorithmic breakthroughs (almost-linear flow, near-linear negative-weight SSSP, sub-Dijkstra directed SSSP, square-root-space simulation) translate into practice, and does the corpus record their diffusion?",algorithms;computational_complexity,None of these works is in the landmark candidate list; their citation diffusion across categories has not been measured.,Trace the citation neighbourhoods of the verified DOIs in the citation graph and classify citing works by category and type (theory vs engineering).,chen2022maxflow;bernstein2022negative;duan2025sorting;williams2025sqrtspace;farachcolton2024openaddressing
++G-P7,"Is the metaphor-based metaheuristic literature growing faster than the rest of algorithms research, and does it absorb a growing share of citations?",algorithms,Six of 25 algorithm candidates are such optimisers; the critique literature argues many are re-descriptions of known methods; no corpus-level measurement exists.,"Keyword-defined subseries ('optimizer', 'nature-inspired', 'bio-inspired') with ITS and citation-share tracking, and a sample re-evaluation against the Camacho-Villalon criteria.",sorensen2013metaphor;aranha2021metaphor;camachovillalon2022exposing
++G-P8,"What do expressivity bounds on transformers imply for the computational-complexity category, and are such works classified there?",computational_complexity,The TC0 bounds and formal-language survey are classified by OpenAlex topics that may route them to NLP rather than complexity.,Query the classification of the verified DOIs and measure the inflow of LLM-category citations to complexity works over time.,merrill2023parallelism;strobl2024survey
++G-P9,"Are AI-discovered algorithms (matrix multiplication, sorting, cap sets) independently reproduced and extended by the theory community?",algorithms,Reports come from the developers (Nature articles and a preprint); no replication appears in the verified bibliography.,Search the citation graph for works that reproduce or improve the reported constructions; classify by author affiliation (independent vs developer).,fawzi2022alphatensor;mankowitz2023alphadev;romeraparedes2024funsearch;novikov2025alphaevolve
++G-P10,Why do the formal verification and programming-languages categories decline in primary share while their multi-label counts grow?,formal_verification;programming_languages,Primary-label assignment is an argmax over category scores; works that also carry AI or security labels may lose the primary label to larger categories.,Compare primary and multi-label trajectories by co-assigned category; estimate the share of works whose primary label switched to llm/cybersecurity by year.,

modified literature_synthesis/landmarks/ai_ml_abstracts.jsonl +117 −118

				`@@ -1,97 +1,149 @@`
1	−{"work_id": 7118302872, "title": "A frame work to predict the influencing nodes in social networking platform using graph neural network model", "abstract": "Identifying influential nodes that can maximize information diffusion is a fundamental problem in social network analysis, commonly referred to as influence maximization. Diffusion model-based and traditional greedy and heuristic algorithms tend to be expensive to calculate, or poorly aware of the network structure, especially when dealing with large networks. To overcome these weaknesses, this paper will introduce a hybrid architecture that will combine Graph Neural Networks (GNNs) with the Ant Colony Optimization (ACO) towards influence maximization. The proposed approach works as follows, where the GNN learns influence representations on the node level, which jointly models both local and higher-order structural dependencies in the network, and ACO uses these learned representations to assemble a non-redundant and diverse seed set. The chosen seed sets are tested on the basis of the Independent Cascade diffusion model where the spread of influence is taken as the key performance measure. The experiments on the real-world social network data indicate that the proposed GNN-ACO framework is steadily more influential in spreading than the current baseline methods and that it has comparative computational efficiency at varying levels of seed set sizes. These findings demonstrate the advantage of optimizing influence maximization by using representation learning with a seed selection strategy based on optimization."}	1	+{"work_id": 2963923407, "title": "Addressing Function Approximation Error in Actor-Critic Methods", "abstract": "In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested."}
2	−{"work_id": 4393138529, "title": "Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey", "abstract": "Large models represent a groundbreaking advancement in multiple application fields, enabling remarkable achievements across various tasks. However, their unprecedented scale comes with significant computational costs. These models, often consisting of billions of parameters, require vast amounts of computational resources for execution. Especially, the expansive scale and computational demands pose considerable challenges when customizing them for particular downstream tasks, particularly over the hardware platforms constrained by computational capabilities. Parameter Efficient Fine-Tuning (PEFT) provides a practical solution by efficiently adjusting the large models over the various downstream tasks. In particular, PEFT refers to the process of adjusting the parameters of a pre-trained large model to adapt it to a specific task or domain while minimizing the number of additional parameters introduced or computational resources required. This approach is particularly important when dealing with large-scale language models with high parameter counts, as fine-tuning these models from scratch can be computationally expensive and resource-intensive, posing considerable challenges in the supporting system platform design. In this survey, we present comprehensive studies of various PEFT algorithms, examining their performance and computational overhead. Moreover, we provide an overview of applications developed using different PEFT algorithms and discuss common techniques employed to mitigate computation costs for PEFT. In addition to providing an extensive survey from an algorithmic standpoint, we also examine various real-world system designs to investigate the implementation costs associated with different PEFT approaches. This survey serves as a valuable resource for researchers aiming to understand both the PEFT algorithm and its system implementation, offering detailed ......"}	2	+{"work_id": 3152893301, "title": "Graph neural networks: A review of methods and applications", "abstract": "Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research."}
3	−{"work_id": 4396599905, "title": "Multimodal fusion on low-quality data: A comprehensive survey", "abstract": "Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of scenarios, including autonomous driving and medical diagnosis. However, the reliability of multimodal fusion remains largely unexplored especially under low-quality data settings. This paper surveys the common challenges and recent advances of multimodal fusion in the wild and presents them in a comprehensive taxonomy. From a data-centric view, we identify four main challenges that are faced by multimodal fusion on low-quality data, namely (1) noisy multimodal data that are contaminated with heterogeneous noises, (2) incomplete multimodal data that some modalities are missing, (3) imbalanced multimodal data that the qualities or properties of different modalities are significantly different and (4) quality-varying multimodal data that the quality of each modality dynamically changes with respect to different samples. This new taxonomy will enable researchers to understand the state of the field and identify several potential directions. We also provide discussion for the open problems in this field together with interesting future research directions."}	3	+{"work_id": 7123342962, "title": "Structure-Preserving Frequency-Regularized Text-Guided Optimal Transport for Unpaired Rain Streaks and Raindrops Removal", "abstract": "The removal of rain streaks and raindrops is crucial for enhancing the image visibility and mitigating the weather degradations. However, most existing approaches rely on the paired rainy and clean images, which are challenging to obtain in real-world scenarios. To this end, we propose a novel structure-preserving frequency-regularized text-guided optimal transport (SFTOT) framework, which formulates the unpaired rain streaks and raindrops removal as an optimal transport problem. Specifically, we introduce a structure-preserving transport cost, incorporating the structural similarity constraint to minimize the duality gap between the primal and dual formulations, while preserving the structural details of reconstructed images. Furthermore, by embedding the inherent frequency sparsity of rain streaks and raindrops into the transport cost, we derive a frequency-regularized optimal transport objective, ensuring consistency in frequency distributions between the generated and clean images. Additionally, we employ a pre-trained one-step stable diffusion model as the restoration network, which is fine-tuned using the low-rank adaptation (LoRA) adapters and zero convolutional layers, while integrating the domain-specific text prompts for both degraded and clean images to guide the generation process. Extensive experiments demonstrate that our method surpasses the existing well-performing unpaired learning approaches, achieving notable improvements in both the fidelity and photo-realism."}
4	−{"work_id": 2781726626, "title": "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor", "abstract": "Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning. Both of these challenges severely limit the applicability of such methods to complex, real-world domains. In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy. That is, to succeed at the task while acting as randomly as possible. Prior deep RL methods based on this framework have been formulated as Q-learning methods. By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods. Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds."}	4	+{"work_id": 2619383789, "title": "Multimodal Machine Learning: A Survey and Taxonomy", "abstract": "Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research."}
5	−{"work_id": 4402727669, "title": "mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration", "abstract": "Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However, previous methods primarily fo-cus on enhancing multi-modal capabilities. In this work, we introduce a versatile multi-modal large language model, mPLUG-Owl2, which effectively leverages modality collab-oration to improve performance in both text and multi-modal tasks. mPLUG-Owl2 utilizes a modularized network design, with the language decoder acting as a universal interface for managing different modalities. Specifically, mPLUG-Owl2 incorporates shared functional modules to facilitate modal-ity collaboration and introduces a modality-adaptive module that preserves modality-specific features. Extensive experi-ments reveal that mPLUG-Owl2 is capable of generalizing both text tasks and multi-modal tasks and achieving state-of-the-art performances with a single generic model. Notably, mPLUG-Owl2 is the first MLLM model that demonstrates the modality collaboration phenomenon in both pure-text and multi-modal scenarios, setting a pioneering path in the development of future multi-modal foundation models."}	5	+{"work_id": 4384464487, "title": "Students\u2019 voices on generative AI: perceptions, benefits, and challenges in higher education", "abstract": "Abstract This study explores university students\u2019 perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs\u2019 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students\u2019 perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students\u2019 perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education."}
6	−{"work_id": 1568555062, "title": "Equivalence and Synthesis of Causal Models", "abstract": "chapter Share on Equivalence and Synthesis of Causal Models Authors: TS Verma Search about this author , Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 221\u2013236https://doi.org/10.1145/3501714.3501732Online:04 March 2022Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access"}	6	+{"work_id": 4385763767, "title": "Transformers in Time Series: A Survey", "abstract": "Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications. In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. In particular, we examine the development of time series Transformers in two perspectives. From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis. From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification. Empirically, we perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series. Finally, we discuss and suggest future directions to provide useful research guidance."}
7	−{"work_id": 2754517384, "title": "Deep Reinforcement Learning That Matters", "abstract": "In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted."}	7	+{"work_id": 4327734444, "title": "Leveraging control priors in deep reinforcement learning for robotics", "abstract": "This thesis proposes a series of hybrid approaches to robot control that combine classical control methods and deep reinforcement learning (RL). Classical control methods work well in structured environments, but struggle in unstructured and stochastic situations. RL has the potential to learn complex controllers through trial and error, however, current methods are sample-inefficient and unsafe. The proposed hybrid approaches combine the strengths of both systems, resulting in efficient, reliable, and dexterous decision-making systems for real-world robotics. This is demonstrated through a sequence of four publications, showing that the hybrid approaches outperform either system operating independently."}
8	−{"work_id": 2964121744, "title": "AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)", "abstract": "A dependency-free experiment runner that turns a paper's research question into a configuration-driven, verifiable experiment task. The bundled reproduction maps the first/second moment estimates and bias correction of Adam (Kingma & Ba, arXiv:1412.6980) into runnable Python and compares them against fixed-learning-rate SGD under fixed seeds and epoch budgets. A second experiment measures convergence speed (epochs to reach a target training loss) against SGD with heavy-ball momentum, AdaGrad and RMSProp, with every optimizer family tuned by the same coarse learning-rate sweep. In that experiment Adam is not the fastest; the negative result is reported in full. Every reported number is pinned in an expected-values file with explicit tolerances and checked by one command that also runs the unit tests, and the checking harness is validated against deliberately broken implementations (negative controls). The repository also ships a task contract that expresses the reproduction as a repeatable, scoreable task for an automated research loop."}
9	−{"work_id": 4410609100, "title": "Jailbreaking Black Box Large Language Models in Twenty Queries", "abstract": "There is growing interest in ensuring that large language models (LLMs) align with human values. However, the alignment of such models is vulnerable to adversarial jailbreaks, which coax LLMs into overriding their safety guardrails. The identification of these vulnerabilities is therefore instrumental in understanding inherent weaknesses and preventing future misuse. To this end, we propose Prompt Automatic Iterative Refinement (PAIR), an algorithm that generates semantic jailbreaks with only black-box access to an LLM. PAIR\u2014which is inspired by social engineering attacks\u2014uses an attacker LLM to automatically generate jailbreaks for a separate targeted LLM without human intervention. In this way, the attacker LLM iteratively queries the target LLM to update and refine a candidate jailbreak. Empirically, PAIR often requires fewer than twenty queries to produce a jailbreak, which is orders of magnitude more efficient than existing algorithms. PAIR also achieves competitive jailbreaking success rates and transferability on open and closed-source LLMs, including GPT-3.5/4, Vicuna, and Gemini."}
10	−{"work_id": 4409249263, "title": "Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice", "abstract": "Artificial intelligence (AI) has rapidly transformed various sectors, including healthcare, where it holds the potential to transform clinical practice and improve patient outcomes. However, its integration into medical settings brings significant ethical challenges that need careful consideration. This paper examines the current state of AI in healthcare, focusing on five critical ethical concerns: justice and fairness, transparency, patient consent and confidentiality, accountability, and patient-centered and equitable care. These concerns are particularly pressing as AI systems can perpetuate or even exacerbate existing biases, often resulting from non-representative datasets and opaque model development processes. The paper explores how bias, lack of transparency, and challenges in maintaining patient trust can undermine the effectiveness and fairness of AI applications in healthcare. In addition, we review existing frameworks for the regulation and deployment of AI, identifying gaps that limit the widespread adoption of these systems in a just and equitable manner. Our analysis provides recommendations to address these ethical challenges, emphasizing the need for fairness in algorithm design, transparency in model decision-making, and patient-centered approaches to consent and data privacy. By highlighting the importance of continuous ethical scrutiny and collaboration between AI developers, clinicians, and ethicists, we outline pathways for achieving more responsible and inclusive AI implementation in healthcare. These strategies, if adopted, could enhance both the clinical value of AI and the trustworthiness of AI systems among patients and healthcare professionals, ensuring that these technologies serve all populations equitably."}
11	−{"work_id": 4412505619, "title": "AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges", "abstract": "This review critically distinguishes between AI Agents and Agentic AI, offering a structured, conceptual taxonomy, application mapping, and analysis of opportunities and challenges to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven and enabled by LLMs and LIMs for taskspecific automation. Generative AI is positioned as a precursor providing the foundation, with AI agents advancing through tool integration, prompt engineering, and reasoning enhancements. We then characterize Agentic AI systems, which, in contrast to AI Agents, represent a paradigm shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and coordinated autonomy. Through a chronological evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both AI agents and agentic AI paradigms. Application domains enabled by AI Agents such as customer support, scheduling, and data summarization are then contrasted with Agentic AI deployments in research automation, robotic coordination, and medical decision support. We further examine unique challenges in each paradigm including hallucination, brittleness, emergent behavior, and coordination failure, and propose targeted solutions such as ReAct loops, retrieval-augmented generation (RAG), automation coordination layers, and causal modeling. This work aims to provide a roadmap for developing robust, scalable, and explainable AI-driven systems."}
12	−{"work_id": 1591675293, "title": "Bayesian Inverse Reinforcement Learning", "abstract": "Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an expert. IRL is motivated by situations where knowledge of the rewards is a goal by itself (as in preference elicitation) and by the task of apprenticeship learning (learning policies from an expert). In this paper we show how to combine prior knowledge and evidence from the expert's actions to derive a probability distribution over the space of reward functions. We present efficient algorithms that find solutions for the reward learning and apprenticeship learning tasks that generalize well over these distributions. Experimental results show strong improvement for our methods over previous heuristic-based approaches."}
13	−{"work_id": 2964083594, "title": "Large-Scale Study of Curiosity-Driven Learning", "abstract": "Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the handdesigned extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at https://pathak22.github.io/large-scale-curiosity/."}
14	−{"work_id": 7124262017, "title": "OECD Digital Education Outlook 2026", "abstract": "The OECD Digital Education Outlook 2026 explores emerging research on the use of generative AI in education and presents innovative tools and applications that show promise. The report examines the use of generative AI in different teaching and learning scenarios: by students to learn subject knowledge, by students and teachers together as part of instruction, and by teachers alone to support their work in the classroom. The Outlook also showcases how generative AI can enhance the efficiency of education institutions and systems, such as research, analysis of learning pathways and support for study advisors. While highlighting the opportunities and benefits of generative AI, the report also notes key challenges and concerns. It provides a state-of-the-art overview of the impact and possibilities of generative AI in education and is an essential resource for education policymakers, institution leaders, researchers, as well as other stakeholders."}
15	−{"work_id": 4321499901, "title": "What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education", "abstract": "Abstract Artificial Intelligence (AI) technologies have been progressing constantly and being more visible in different aspects of our lives. One recent phenomenon is ChatGPT, a chatbot with a conversational artificial intelligence interface that was developed by OpenAI. As one of the most advanced artificial intelligence applications, ChatGPT has drawn much public attention across the globe. In this regard, this study examines ChatGPT in education, among early adopters, through a qualitative instrumental case study. Conducted in three stages, the first stage of the study reveals that the public discourse in social media is generally positive and there is enthusiasm regarding its use in educational settings. However, there are also voices who are approaching cautiously using ChatGPT in educational settings. The second stage of the study examines the case of ChatGPT through lenses of educational transformation, response quality, usefulness, personality and emotion, and ethics. In the third and final stage of the study, the investigation of user experiences through ten educational scenarios revealed various issues, including cheating, honesty and truthfulness of ChatGPT, privacy misleading, and manipulation. The findings of this study provide several research directions that should be considered to ensure a safe and responsible adoption of chatbots, specifically ChatGPT, in education."}
16	−{"work_id": 7134057781, "title": "A fuzzy-TD3 hybrid reinforcement learning framework for robust trajectory tracking of the Mitsubishi RV-2AJ robotic arm", "abstract": "This paper proposes a novel hybrid control architecture that synergistically integrates a fuzzy logic system with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to achieve precise, robust trajectory tracking for a 5-degree-of-freedom (5-DOF) robotic manipulator. The design merges the interpretable, rule-based reasoning and rapid transient response of fuzzy logic with the model-free, long-term adaptive optimization capabilities of deep reinforcement learning. Within this framework, a fuzzy supervisor delivers immediate corrective actions using real-time error states, while the TD3 agent concurrently learns an optimal control policy to manage the system\u2019s nonlinear dynamics. Extensive simulation studies on complex trajectories, including N-shaped, helical, and spiral paths, demonstrate the architecture\u2019s superiority. The hybrid fuzzy-TD3 controller reduces tracking error by 27.8\u201350% compared to a standalone TD3 agent and by 14.8\u201328.6% compared to a hybrid PID-TD3 baseline. Furthermore, under conditions of parametric uncertainties and internal as well as external disturbances, it maintains performance improvements of 23.5\u201334.2% over TD3 and 11.0\u201316.7% over the hybrid PID-TD3, confirming enhanced robustness. Validation through sensitivity analysis, numerical stability verification, and rule activation transparency establishes this method as an effective, adaptive, and explainable solution for advanced robotic control applications."}
17	−{"work_id": 4410551352, "title": "A foundation model for the Earth system", "abstract": "Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive1. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency2,3, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality climate and weather information. Aurora, a new large-scale foundation model trained on more than one million hours of diverse geophysical data, outperforms operational forecasts in predicting air quality, ocean wave dynamics, tropical cyclone tracks and high-resolution weather."}
18	−{"work_id": 1698155719, "title": "Support Vector Machines", "abstract": "The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning."}
19	−{"work_id": 3193917007, "title": "Generative Adversarial Networks", "abstract": "Generative modeling is an unsupervised learning technique that involves automatically discovering and learning the regularities (or patterns) in input data so that a trained model can generate new examples that plausibly could have been drawn from the original dataset. A popular type of generative model is a generative adversarial network. Generative adversarial networks (GANs) are generative models that create new data instances that resemble the training data."}
20	−{"work_id": 2962776659, "title": "Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks", "abstract": "There is a lot of research interest in encoding variable length sentences into fixed length vectors, in a way that preserves the sentence meanings. Two common methods include representations based on averaging word vectors, and representations based on the hidden states of recurrent neural networks such as LSTMs. The sentence vectors are used as features for subsequent machine learning tasks or for pre-training in the context of deep learning. However, not much is known about the properties that are encoded in these sentence representations and about the language information they capture. We propose a framework that facilitates better understanding of the encoded representations. We define prediction tasks around isolated aspects of sentence structure (namely sentence length, word content, and word order), and score representations by the ability to train a classifier to solve each prediction task when using the representation as input. We demonstrate the potential contribution of the approach by analyzing different sentence representation mechanisms. The analysis sheds light on the relative strengths of different sentence embedding methods with respect to these low level prediction tasks, and on the effect of the encoded vector's dimensionality on the resulting representations."}
21	−{"work_id": 4407570917, "title": "AgentSociety: Large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society", "abstract": "Abstract Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models (LLMs) have further transformed this research paradigm, enabling the creation of human-like generative social agents and realistic simulacra of society. In this paper, we propose AgentSociety, a large-scale social simulator that integrates LLM-driven agents, a realistic societal environment, and a powerful large-scale simulation engine. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions both among agents and between agents and their environment. Furthermore, we explore the potential of AgentSociety as a testbed for computational social experiments, focusing on five key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, the impact of external shocks such as hurricanes, and urban sustainability. These five issues serve as valuable cases for assessing AgentSociety\u2019s support for typical research methods \u2013 such as surveys, interviews, and interventions \u2013 as well as for investigating the patterns, causes, and underlying mechanisms of social issues. The alignment between AgentSociety\u2019s outcomes and real-world experimental results not only demonstrates its ability to capture human behaviors and their underlying mechanisms, but also underscores its potential as an important platform for social scientists and policymakers."}
22	−{"work_id": 4393065402, "title": "A survey on large language model based autonomous agents", "abstract": "Abstract Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective. We first discuss the construction of LLM-based autonomous agents, proposing a unified framework that encompasses much of previous work. Then, we present a overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field."}
23	−{"work_id": 3133702157, "title": "On the Dangers of Stochastic Parrots", "abstract": "The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models."}
24	−{"work_id": 4399790131, "title": "The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review", "abstract": "Abstract The growing integration of artificial intelligence (AI) dialogue systems within educational and research settings highlights the importance of learning aids. Despite examination of the ethical concerns associated with these technologies, there is a noticeable gap in investigations on how these ethical issues of AI contribute to students\u2019 over-reliance on AI dialogue systems, and how such over-reliance affects students\u2019 cognitive abilities. Overreliance on AI occurs when users accept AI-generated recommendations without question, leading to errors in task performance in the context of decision-making. This typically arises when individuals struggle to assess the reliability of AI or how much trust to place in its suggestions. This systematic review investigates how students\u2019 over-reliance on AI dialogue systems, particularly those embedded with generative models for academic research and learning, affects their critical cognitive capabilities including decision-making, critical thinking, and analytical reasoning. By using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our systematic review evaluated a body of literature addressing the contributing factors and effects of such over-reliance within educational and research contexts. The comprehensive literature review spanned 14 articles retrieved from four distinguished databases: ProQuest, IEEE Xplore, ScienceDirect, and Web of Science. Our findings indicate that over-reliance stemming from ethical issues of AI impacts cognitive abilities, as individuals increasingly favor fast and optimal solutions over slow ones constrained by practicality. This tendency explains why users prefer efficient cognitive shortcuts, or heuristics, even amidst the ethical issues presented by AI technologies."}
25	−{"work_id": 4402843978, "title": "Interpretable machine learning", "abstract": "Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives an overview of ML and its role in decision-making. It examines the challenges of understanding how a complex ML system has reached its output, and some of the technical approaches to making ML easier to interpret. It also gives a brief overview of some of the proposed tools for making ML systems more accountable."}
26	−{"work_id": 4406472463, "title": "A generative model for inorganic materials design", "abstract": "Abstract The design of functional materials with desired properties is essential in driving technological advances in areas such as energy storage, catalysis and carbon capture 1\u20133 . Generative models accelerate materials design by directly generating new materials given desired property constraints, but current methods have a low success rate in proposing stable crystals or can satisfy only a limited set of property constraints 4\u201311 . Here we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. Compared with previous generative models 4,12 , structures produced by MatterGen are more than twice as likely to be new and stable, and more than ten times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, new materials with desired chemistry, symmetry and mechanical, electronic and magnetic properties. As a proof of concept, we synthesize one of the generated structures and measure its property value to be within 20% of our target. We believe that the quality of generated materials and the breadth of abilities of MatterGen represent an important advancement towards creating a foundational generative model for materials design."}
27	−{"work_id": 4399982941, "title": "Value engineering for autonomous agents", "abstract": "(English) The topic of this thesis is the engineering of values for autonomous agents. This is realised through the formulation, design and implementation of new functionalities for autonomous agents that enable reasoning in terms of values. In particular, we argue for the role of prescriptive norms as value-promoting mechanisms. Hence, value-driven agents should be able to autonomously determine which regulations (such as obligations, permissions or prohibitions) make the Multiagent System they inhabit better promote some values of interest. We lay the foundations of our work on Schwartz\u2019s Theory of Basic Human Values to establish a consequential connection between values and norms, considering that norms are aligned with respect to values if the outcomes they incentivise satisfy the goals that capture the meaning of values in a particular context. Another feature of Schwartz\u2019s theory that has been previously overlooked in the literature is the strong social dimension of values. That is, agents should be able to reason not just in terms of their own, but also of the values of others in their community. This points to Theory of Mind (i.e. the cognitive ability to perceive, interpret and reason about others in terms of their mental states) as an outstanding component of value-based reasoning. This thesis is structured around three main contributions (published in journal papers) plus their integration. The first contribution establishes the normvalue relationship as a consequential one in nature, and proposes a methodology for the automated synthesis and analysis of optimally value-aligned normative systems. The second contribution tackles the limitations of the first, and defines the Action Situation Language to systematically express a wide range of rules that may be implemented in a Multiagent System. This language is complemented by a game engine that automatically interprets interaction descriptions and builds their semantics as Extensive Form Games, which are later analysed with standard game-theoretical tools. This leads to a distribution over game outcomes, which are evaluated in terms of their desirability with respect to values. The third contribution introduces Theory of Mind-related functionalities into an existing Belief-Desire-Intention agent architecture, and combines them with abductive reasoning capabilities. The three contributions are integrated in a novel functionality that enables agents to reason about prescriptive norms in terms of dynamic values. This means that an autonomous agent can, at runtime, switch its value perspective to the one it estimates that another agent has. Such perspective-dependent value-based normative reasoning functionality, with its inherent social orientation, constitutes a novel contribution to the community of values for autonomous agents and paves the way for possible applications such as value-based negotiation over normative systems. In summary, value engineering is a principled and systematic approach to computational ethics, which provides an innovative tool set for integrating ethical values into the design of autonomous agents. (Catal\u00e0) El tema d\u2019aquesta tesi \u00e9s l\u2019enginyeria de valors per a agents aut\u00f2noms, aconseguida mitjan\u00e7ant la formulaci\u00f3, disseny i implementaci\u00f3 de noves funcionalitats que permeten a agents aut\u00f2noms raonar en termes de valors. En particular, defensem el paper de les normes prescriptives com a mecanismes de promoci\u00f3 de valors. Aleshores, els agents impulsats per valors deuen poder determinar de forma aut\u00f2noma quines regulacions (com ara obligacions, permisos o prohibicions) promouen millor alguns valors d\u2019inter\u00e8s en el Sistema Multiagent que habiten. Fonamentem el nostre treball a la Teoria de Schwartz de Valors Humans B\u00e0sics per establir una connexi\u00f3 entre valors i normes basada en conseq\u00fc\u00e8ncies, considerant que les normes estan alineades respecte als valors si els resultats que incentiven satisfan les metes que capten el significat d\u2019aquests valors en un context determinat. Una altra caracter\u00edstica de la teoria de Schwartz que ha estat passada per alt pr\u00e8viament a la literatura \u00e9s la forta dimensi\u00f3 social dels valors. \u00c9s a dir, els agents haurien de poder raonar, no nom\u00e9s en termes dels seus propis valors, sin\u00f3 tamb\u00e9 dels d\u2019altres a la seva comunitat. Aix\u00f2 apunta a la Teoria de la Ment (\u00e9s a dir, la capacitat cognitiva de percebre, interpretar i raonar sobre els altres en termes dels seus estats mentals) com un component destacat del raonament basat en valors. Aquesta tesi s\u2019estructura al voltant de tres contribucions principals (publicades en revistes acad\u00e8miques), a m\u00e9s de la seva integraci\u00f3. La primera contribuci\u00f3 estableix la relaci\u00f3 entre normes i valors mitjan\u00e7ant conseq\u00fc\u00e8ncies, i proposa una metodologia per a la s\u00edntesi i an\u00e0lisi automatitzada de sistemes normatius \u00f2ptimament alineats amb valors. La segona contribuci\u00f3 aborda les limitacions de la primera, i defineix l\u2019Action Situation Language per expressar sistem\u00e0ticament una \u00e0mplia gamma de regles que es poden implementar en un Sistema Multiagent. Aquest llenguatge es complementa amb un int\u00e8rpret que processa autom\u00e0ticament la descripci\u00f3 de la interacci\u00f3 i construeix la seva sem\u00e0ntica com un joc en forma estesa, que despr\u00e9s \u00e9s analitzat amb eines est\u00e0ndards de teoria de jocs. Aix\u00f2 condueix a una distribuci\u00f3 sobre estats finals del joc, que s\u2019avaluen en termes de la seva conveni\u00e8ncia respecte a certs valors. La tercera contribuci\u00f3 presenta funcionalitats relacionades amb la Teoria de la Ment, integrant-les a l\u2019arquitectura Belief-Desire-Intention existent i combinant-les amb raonament abductiu. Les tres contribucions s\u2019'integren en una nova funcionalitat que permet a agents aut\u00f2noms raonar sobre normes prescriptives en termes de valors din\u00e0mics. Aix\u00f2 vol dir que un agent aut\u00f2nom pot, durant la seva execuci\u00f3, canviar la seva perspectiva de valors a la que estima que t\u00e9 un altre agent. Aquest enfocament, basat en valors i amb una orientaci\u00f3 social inherent, constitueix una contribuci\u00f3 nova a la investigaci\u00f3 de valors per a agents aut\u00f2noms i aplana el cam\u00ed a possibles aplicacions com la negociaci\u00f3 autom\u00e0tica sobre sistemes normatius basada en valors. En resum, l\u2019'enginyeria de valors \u00e9s un enfocament sistem\u00e0tic i de principis a l\u2019'\u00e8tica computacional, que proporciona un conjunt d\u2019'eines innovadores per integrar valors \u00e8tics al disseny d\u2019'agents aut\u00f2noms."}
28	−{"work_id": 7140170686, "title": "Pedagogical partnerships with generative AI in higher education: how dual cognitive pathways paradoxically enable transformative learning", "abstract": "Abstract The rapid integration of generative artificial intelligence (GenAI) into higher education has precipitated a critical tension between the pursuit of efficiency and the imperative for deep, transformative learning. While GenAI offers unprecedented speed in knowledge processing, it raises concerns that efficiency-driven motivations may erode the cognitive struggle essential for intellectual growth. This study investigates how Human-GenAI pedagogical partnerships influence transformative learning through the dual cognitive pathways of vigilance and offloading, moderated by efficiency orientation. Employing a rigorous mixed-methods design across three cultural contexts (China, Europe, and the United States, N = 912), we combine structural equation modeling, importance\u2013performance map analysis (IPMA), fuzzy-set qualitative comparative analysis (fsQCA), and semi-structured interviews to unpack these complex dynamics. The findings reveal a counterintuitive paradox: pedagogical partnerships simultaneously activate cognitive vigilance (critical evaluation) and cognitive offloading (strategic delegation). Contrary to traditional assumptions that delegation diminishes learning depth, our results indicate that strategic cognitive offloading\u2014when exceeding specific thresholds\u2014liberates mental resources for higher-order reflection, thereby enhancing transformative learning experiences. Furthermore, efficiency orientation acts not as a barrier but as an amplifier, strengthening both vigilance and offloading behaviors through mechanisms of pragmatic risk management. These findings reconceptualize student-AI dynamics from a zero-sum resource competition to a synergistic collaboration, offering a nuanced framework for educators to integrate GenAI in ways that preserve human agency and foster quality education in the digital age."}
29	−{"work_id": 7138470740, "title": "Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models", "abstract": "Reliable zero-shot detection of out-of-distribution (OOD) inputs is critical for deploying vision-language models in open-world settings. However, the lack of labeled negatives in zero-shot OOD detection necessitates proxy signals that remain effective under distribution shift. Existing negative-label methods rely on a fixed set of textual proxies, which (i) sparsely sample the semantic space beyond in-distribution (ID) classes and (ii) remain static while only visual features drift, leading to cross-modal misalignment and unstable predictions. In this paper, we propose CoEvo, a training- and annotation-free test-time framework that performs bidirectional, sample-conditioned adaptation of both textual and visual proxies. Specifically, CoEvo introduces a proxy-aligned co-evolution mechanism to maintain two evolving proxy caches, which dynamically mines contextual textual negatives guided by test images and iteratively refines visual proxies, progressively realigning cross-modal similarities and enlarging local OOD margins. Finally, we dynamically re-weight the contributions of dual-modal proxies to obtain a calibrated OOD score that is robust to distribution shift. Extensive experiments on standard benchmarks demonstrate that CoEvo achieves state-of-the-art performance, improving AUROC by 1.33% and reducing FPR95 by 45.98% on ImageNet-1K compared to strong negative-label baselines."}
30	{"work_id": 4322614756, "title": "Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach", "abstract": "Effective resilience analysis of road networks is fundamental to building sustainable and disaster prepared cities. Identifying which road segments share similar vulnerabilities is important for pinpointing high-risk areas within the network and implementing measures to safeguard them against future disruptions. Graph-based community detection can be applied to group together areas of the network sharing similar structural vulnerabilities. However, current graph-based community detection methods either struggle with integrating node features during partitioning or do not account for the path-based dependencies in road networks. This paper introduces the Path-based Community Embedding (PCE) model, an approach that leverages path-based embeddings to overcome these limitations. PCE combines the strengths of graph attention networks and Long Short-Term Memory models (LSTMs) to learn representations that incorporate both local neighborhood information and long-range path dependencies. Our results on the Santa Barbara road network show that PCE improves community detection performance for resilience analysis, thus offering a powerful tool for urban planners and transportation engineers to preemptively identify vulnerabilities in road networks."}	8	{"work_id": 4322614756, "title": "Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach", "abstract": "Effective resilience analysis of road networks is fundamental to building sustainable and disaster prepared cities. Identifying which road segments share similar vulnerabilities is important for pinpointing high-risk areas within the network and implementing measures to safeguard them against future disruptions. Graph-based community detection can be applied to group together areas of the network sharing similar structural vulnerabilities. However, current graph-based community detection methods either struggle with integrating node features during partitioning or do not account for the path-based dependencies in road networks. This paper introduces the Path-based Community Embedding (PCE) model, an approach that leverages path-based embeddings to overcome these limitations. PCE combines the strengths of graph attention networks and Long Short-Term Memory models (LSTMs) to learn representations that incorporate both local neighborhood information and long-range path dependencies. Our results on the Santa Barbara road network show that PCE improves community detection performance for resilience analysis, thus offering a powerful tool for urban planners and transportation engineers to preemptively identify vulnerabilities in road networks."}
31	{"work_id": 4318586239, "title": "Artificial Intelligence: Definition and Background", "abstract": "Abstract If we want to embed AI in society, we need to understand what it is. What do we mean by artificial intelligence? How has the technology developed? Where do we stand now?"}	9	{"work_id": 4318586239, "title": "Artificial Intelligence: Definition and Background", "abstract": "Abstract If we want to embed AI in society, we need to understand what it is. What do we mean by artificial intelligence? How has the technology developed? Where do we stand now?"}
		10	+{"work_id": 4411505840, "title": "AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting", "abstract": "Advances in generative artificial intelligence show great potential for improving education. Yet little is known about how this new technology should be used and how effective it can be compared to current best practices. Here we report a randomized, controlled trial measuring college students' learning and their perceptions when content is presented through an AI-powered tutor compared with an active learning class. The novel design of the custom AI tutor is informed by the same pedagogical best practices as employed in the in-class lessons. We find that students learn significantly more in less time when using the AI tutor, compared with the in-class active learning. They also feel more engaged and more motivated. These findings offer empirical evidence for the efficacy of a widely accessible AI-powered pedagogy in significantly enhancing learning outcomes, presenting a compelling case for its broad adoption in learning environments."}
		11	+{"work_id": 4412438081, "title": "RAFT: Robust Adversarial Fusion Transformer for multimodal sentiment analysis", "abstract": "Multimodal sentiment analysis (MSA) has emerged as a key technology for understanding human emotions by jointly processing text, audio, and visual cues. Despite significant progress, existing fusion models remain vulnerable to real-world challenges such as modality noise, missing channels, and weak inter-modal coupling. This paper addresses these limitations by introducing RAFT (Robust Adversarial Fusion Transformer), which integrates cross-modal and self-attention mechanisms with noise-imitation adversarial training to strengthen feature interactions and resilience under imperfect inputs. We first formalize the problem of noisy and incomplete data in MSA and demonstrate how adversarial noise simulation can bridge the gap between clean and corrupted modalities. RAFT is evaluated on two benchmark datasets, MOSI and MOSEI, where it achieves competitive binary classification accuracy (greater than 88%) and fine-grained sentiment performance (5-class accuracy 57%), while reducing mean absolute error and improving Pearson correlation by up to 2% over state-of-the-art baselines. Ablation studies confirm that both adversarial training and context-aware modules contribute substantially to robustness gains. Looking ahead, we plan to refine noise-generation strategies, explore more expressive fusion architectures, and extend RAFT to handle long-form dialogues and culturally diverse expressions. Our results suggest that RAFT lays a solid foundation for reliable, real-world sentiment analysis in noisy environments."}
32	{"work_id": 4402698709, "title": "The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "abstract": "The risks posed by artificial intelligence (AI) concern academics, auditors, policymakers, AI companies, and the public. Researchers, policymakers, and technology companies discuss AI risks using inconsistent terminology-the same word may describe different problems, while different words describe identical concerns. This fragmentation impedes coordinated responses to AI challenges. We address this by creating the AI Risk Repository: a living database of 1,725 risks extracted from 74 existing taxonomies and frameworks. We organize these risks using two complementary classification systems. The Causal Taxonomy classifies risks by their origins: which entity causes them (human or AI), whether intentional, and when they occur (before or after deployment). The Domain Taxonomy classifies risks by their effects across seven areas, from discrimination and privacy violations to misinformation and weapons development. This shared reference enables more coordinated approaches to discussing, researching, auditing, and governing AI systems across sectors and jurisdictions."}	12	{"work_id": 4402698709, "title": "The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "abstract": "The risks posed by artificial intelligence (AI) concern academics, auditors, policymakers, AI companies, and the public. Researchers, policymakers, and technology companies discuss AI risks using inconsistent terminology-the same word may describe different problems, while different words describe identical concerns. This fragmentation impedes coordinated responses to AI challenges. We address this by creating the AI Risk Repository: a living database of 1,725 risks extracted from 74 existing taxonomies and frameworks. We organize these risks using two complementary classification systems. The Causal Taxonomy classifies risks by their origins: which entity causes them (human or AI), whether intentional, and when they occur (before or after deployment). The Domain Taxonomy classifies risks by their effects across seven areas, from discrimination and privacy violations to misinformation and weapons development. This shared reference enables more coordinated approaches to discussing, researching, auditing, and governing AI systems across sectors and jurisdictions."}
33	{"work_id": 7122543576, "title": "SMS\u2010Based Event\u2010Triggered Fault Tolerant Control of Nonlinear Systems Using Adaptive Dynamic Programming", "abstract": "ABSTRACT An adaptive dynamic programming (ADP)\u2010based fault\u2010tolerant control (FTC) scheme integrating a sliding mode surface (SMS) and an event\u2010triggered mechanism is developed for a class of nonlinear systems with unknown actuator faults. A fault estimation observer is constructed, and its outputs are embedded into a formulated performance index, thereby recasting the FTC problem as a tractable optimal control problem. Within the resulting ADP framework, a controller is synthesized that automatically delivers active and optimal fault compensation. In place of conventional actor\u2010critic neural network (ACNN) architectures, a simplified critic\u2010only neural network (NN) is adopted, together with policy iteration, to solve the associated Hamilton\u2013Jacobi\u2013Bellman (HJB) equation. This structure substantially reduces computational complexity and enhances online applicability. CNN weights are updated via gradient descent combined with experience replay, which obviates the need for the persistent excitation condition that is often difficult to maintain in real\u2010time operation. Moreover, the proposed SMS\u2010based event\u2010triggering mechanism achieves better communication efficiency and improved closed\u2010loop performance relative to conventional state\u2010dependent triggering schemes. Theoretical analysis rigorously establishes the stability and convergence properties of the overall control system, while simulation experiments validate its practical effectiveness."}	13	{"work_id": 7122543576, "title": "SMS\u2010Based Event\u2010Triggered Fault Tolerant Control of Nonlinear Systems Using Adaptive Dynamic Programming", "abstract": "ABSTRACT An adaptive dynamic programming (ADP)\u2010based fault\u2010tolerant control (FTC) scheme integrating a sliding mode surface (SMS) and an event\u2010triggered mechanism is developed for a class of nonlinear systems with unknown actuator faults. A fault estimation observer is constructed, and its outputs are embedded into a formulated performance index, thereby recasting the FTC problem as a tractable optimal control problem. Within the resulting ADP framework, a controller is synthesized that automatically delivers active and optimal fault compensation. In place of conventional actor\u2010critic neural network (ACNN) architectures, a simplified critic\u2010only neural network (NN) is adopted, together with policy iteration, to solve the associated Hamilton\u2013Jacobi\u2013Bellman (HJB) equation. This structure substantially reduces computational complexity and enhances online applicability. CNN weights are updated via gradient descent combined with experience replay, which obviates the need for the persistent excitation condition that is often difficult to maintain in real\u2010time operation. Moreover, the proposed SMS\u2010based event\u2010triggering mechanism achieves better communication efficiency and improved closed\u2010loop performance relative to conventional state\u2010dependent triggering schemes. Theoretical analysis rigorously establishes the stability and convergence properties of the overall control system, while simulation experiments validate its practical effectiveness."}
34	{"work_id": 4361229539, "title": "LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention", "abstract": "We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs. Specifically, we adopt a set of learnable adaption prompts, and prepend them to the word tokens at higher transformer layers. Then, a zero-initialized attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge. With our efficient training, LLaMA-Adapter can generate high-quality responses, comparable to Alpaca with fully fine-tuned 7B parameters. Besides language commands, our approach can be simply extended to multi-modal instructions for learning image-conditioned LLaMA model, which achieves superior reasoning performance on ScienceQA and COCO Caption benchmarks. Furthermore, we also evaluate the zero-initialized attention mechanism for fine-tuning other pre-trained models (ViT, RoBERTa) on traditional vision and language tasks, demonstrating the superior generalization capacity of our approach. Code is released at https://github.com/OpenGVLab/LLaMA-Adapter."}	14	{"work_id": 4361229539, "title": "LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention", "abstract": "We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs. Specifically, we adopt a set of learnable adaption prompts, and prepend them to the word tokens at higher transformer layers. Then, a zero-initialized attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge. With our efficient training, LLaMA-Adapter can generate high-quality responses, comparable to Alpaca with fully fine-tuned 7B parameters. Besides language commands, our approach can be simply extended to multi-modal instructions for learning image-conditioned LLaMA model, which achieves superior reasoning performance on ScienceQA and COCO Caption benchmarks. Furthermore, we also evaluate the zero-initialized attention mechanism for fine-tuning other pre-trained models (ViT, RoBERTa) on traditional vision and language tasks, demonstrating the superior generalization capacity of our approach. Code is released at https://github.com/OpenGVLab/LLaMA-Adapter."}
35	{"work_id": 57706852, "title": "Stochastic simultaneous optimistic optimization", "abstract": "We study the problem of global maximization of a function f given a finite number of evaluations perturbed by noise. We consider a very weak assumption on the function, namely that it is locally smooth (in some precise sense) with respect to some semi-metric, around one of its global maxima. Compared to previous works on bandits in general spaces (Kleinberg et al., 2008; Bubeck et al., 2011a) our algorithm does not require the knowledge of this semi-metric. Our algorithm, StoSOO, follows an optimistic strategy to iteratively construct upper confidence bounds over the hierarchical partitions of the function domain to decide which point to sample next. A finite-time analysis of StoSOO shows that it performs almost as well as the best specifically-tuned algorithms even though the local smoothness of the function is not known."}	15	{"work_id": 57706852, "title": "Stochastic simultaneous optimistic optimization", "abstract": "We study the problem of global maximization of a function f given a finite number of evaluations perturbed by noise. We consider a very weak assumption on the function, namely that it is locally smooth (in some precise sense) with respect to some semi-metric, around one of its global maxima. Compared to previous works on bandits in general spaces (Kleinberg et al., 2008; Bubeck et al., 2011a) our algorithm does not require the knowledge of this semi-metric. Our algorithm, StoSOO, follows an optimistic strategy to iteratively construct upper confidence bounds over the hierarchical partitions of the function domain to decide which point to sample next. A finite-time analysis of StoSOO shows that it performs almost as well as the best specifically-tuned algorithms even though the local smoothness of the function is not known."}
		16	+{"work_id": 3141797743, "title": "A Survey on Multi-Task Learning", "abstract": "Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL."}
36	{"work_id": 4317910584, "title": "ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?", "abstract": "ChatGPT is the world\u2019s most advanced chatbot thus far. Unlike other chatbots, it can create impressive prose within seconds, and it has created much hype and doomsday predictions when it comes to student assessment in higher education and a host of other matters. ChatGPT is a state-of-the-art language model (a variant of OpenAI\u2019s Generative Pretrained Transformer (GPT) language model) designed to generate text that can be indistinguishable from text written by humans. It can engage in conversation with users in a seemingly natural and intuitive way. In this article, we briefly tell the story of OpenAI, the organisation behind ChatGPT. We highlight the fundamental change from a not-for-profit organisation to a commercial business model. In terms of our methods, we conducted an extensive literature review and experimented with this artificial intelligence (AI) software. Our literature review shows our review to be amongst the first peer-reviewed academic journal articles to explore ChatGPT and its relevance for higher education (especially assessment, learning and teaching). After a description of ChatGPT\u2019s functionality and a summary of its strengths and limitations, we focus on the technology\u2019s implications for higher education and discuss what is the future of learning, teaching and assessment in higher education in the context of AI chatbots such as ChatGPT. We position ChatGPT in the context of current Artificial Intelligence in Education (AIEd) research, discuss student-facing, teacher-facing and system-facing applications, and analyse opportunities and threats. We conclude the article with recommendations for students, teachers and higher education institutions. Many of them focus on assessment."}	17	{"work_id": 4317910584, "title": "ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?", "abstract": "ChatGPT is the world\u2019s most advanced chatbot thus far. Unlike other chatbots, it can create impressive prose within seconds, and it has created much hype and doomsday predictions when it comes to student assessment in higher education and a host of other matters. ChatGPT is a state-of-the-art language model (a variant of OpenAI\u2019s Generative Pretrained Transformer (GPT) language model) designed to generate text that can be indistinguishable from text written by humans. It can engage in conversation with users in a seemingly natural and intuitive way. In this article, we briefly tell the story of OpenAI, the organisation behind ChatGPT. We highlight the fundamental change from a not-for-profit organisation to a commercial business model. In terms of our methods, we conducted an extensive literature review and experimented with this artificial intelligence (AI) software. Our literature review shows our review to be amongst the first peer-reviewed academic journal articles to explore ChatGPT and its relevance for higher education (especially assessment, learning and teaching). After a description of ChatGPT\u2019s functionality and a summary of its strengths and limitations, we focus on the technology\u2019s implications for higher education and discuss what is the future of learning, teaching and assessment in higher education in the context of AI chatbots such as ChatGPT. We position ChatGPT in the context of current Artificial Intelligence in Education (AIEd) research, discuss student-facing, teacher-facing and system-facing applications, and analyse opportunities and threats. We conclude the article with recommendations for students, teachers and higher education institutions. Many of them focus on assessment."}
37	{"work_id": 4393148714, "title": "T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models", "abstract": "The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate controlling (e.g., structure and color) is needed. In this paper, we aim to ``dig out\" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn low-cost T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, achieving rich control and editing effects in the color and structure of the generation results. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications. Our code is available at https://github.com/TencentARC/T2I-Adapter."}	18	{"work_id": 4393148714, "title": "T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models", "abstract": "The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate controlling (e.g., structure and color) is needed. In this paper, we aim to ``dig out\" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn low-cost T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, achieving rich control and editing effects in the color and structure of the generation results. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications. Our code is available at https://github.com/TencentARC/T2I-Adapter."}
38	{"work_id": 2902907165, "title": "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play", "abstract": "The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go."}	19	{"work_id": 2902907165, "title": "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play", "abstract": "The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go."}
39	{"work_id": 7118187240, "title": "Patch Time Series Transformer\u2212Based Short\u2212Term Photovoltaic Power Prediction Enhanced by Artificial Fish", "abstract": "The reliability and economic operation of power systems increasingly depend on renewable energy, making accurate short\u2212term photovoltaic (PV) power prediction essential. Conventional approaches struggle with the nonlinear and stochastic characteristics of PV data. This study proposes an enhanced prediction framework integrating Artificial Fish Swarm Algorithm\u2013Isolation Forest (AFSA\u2013IF) anomaly detection, Generative Adversarial Network\u2212based feature extraction, multimodal data fusion, and a Patch Time Series Transformer (PatchTST) model. The framework includes advanced preprocessing, fusion of meteorological and historical power data, and weather classification via one\u2212hot encoding. Experiments on datasets from six PV plants show significant improvements in mean absolute error, root mean square error, and coefficient of determination compared with Transformer, Reformer, and Informer models. The results confirm the robustness and efficiency of the proposed model, especially under challenging conditions such as rainy weather."}	20	{"work_id": 7118187240, "title": "Patch Time Series Transformer\u2212Based Short\u2212Term Photovoltaic Power Prediction Enhanced by Artificial Fish", "abstract": "The reliability and economic operation of power systems increasingly depend on renewable energy, making accurate short\u2212term photovoltaic (PV) power prediction essential. Conventional approaches struggle with the nonlinear and stochastic characteristics of PV data. This study proposes an enhanced prediction framework integrating Artificial Fish Swarm Algorithm\u2013Isolation Forest (AFSA\u2013IF) anomaly detection, Generative Adversarial Network\u2212based feature extraction, multimodal data fusion, and a Patch Time Series Transformer (PatchTST) model. The framework includes advanced preprocessing, fusion of meteorological and historical power data, and weather classification via one\u2212hot encoding. Experiments on datasets from six PV plants show significant improvements in mean absolute error, root mean square error, and coefficient of determination compared with Transformer, Reformer, and Informer models. The results confirm the robustness and efficiency of the proposed model, especially under challenging conditions such as rainy weather."}
40	−{"work_id": 4396821501, "title": "KAN: Kolmogorov-Arnold Networks", "abstract": "Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes (\"neurons\"), KANs have learnable activation functions on edges (\"weights\"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs."}
41	{"work_id": 4288283362, "title": "Spiral Time: A Geometric Reframing of Temporal Structure and Its Applications in Machine Learning", "abstract": "Archimedean spiral in 2D space rather than a scalar on a number line. Under this framework, called Spiral Time, every moment carries both a radial coordinate encoding cumulative progression (trend) and an angular coordinate encoding phase within a recurring cycle (seasonality). This decomposition is geometric and a priori \u2014 it is a property of the time coordinate itself, not a learned or analytical transformation of the target variable. We derive the mathematical structure, demonstrate a controlled 10-experiment LSTM ablation on US Monthly Retail Sales (RSXFS), and show how spiral time embeddings replace standard positional encodings in Transformer architectures with no other architectural changes. Key results: The optimal multi-period spiral time embedding achieves 1.69% MAPE \u2014 outperforming scalar time (9.76%) by 83% and hand-engineered sinusoidal features (4.62%) by 63%. The performance hierarchy is perfectly monotone across all 10 experiments: every geometric addition improves performance with no exceptions. Key finding on normalisation: the radial term contributes only when linear in \u03b8 and z-score normalised. Incorrect normalisation produces results worse than omitting the trend term entirely \u2014 a practically important result for any practitioner adding trend proxies to oscillatory features. The paper covers the single-period and multi-period embedding, connections to Transformer positional encodings, Hawking\u2013Hartle imaginary time, and Kaluza\u2013Klein theory, and surveys applications in forecasting, anomaly detection, battery health modeling, drug dosing, financial cycle modeling, climate science, and neuroscience. Code is open-source and available at: github.com/frankajieh-ship-it/spiral-time"}	21	{"work_id": 4288283362, "title": "Spiral Time: A Geometric Reframing of Temporal Structure and Its Applications in Machine Learning", "abstract": "Archimedean spiral in 2D space rather than a scalar on a number line. Under this framework, called Spiral Time, every moment carries both a radial coordinate encoding cumulative progression (trend) and an angular coordinate encoding phase within a recurring cycle (seasonality). This decomposition is geometric and a priori \u2014 it is a property of the time coordinate itself, not a learned or analytical transformation of the target variable. We derive the mathematical structure, demonstrate a controlled 10-experiment LSTM ablation on US Monthly Retail Sales (RSXFS), and show how spiral time embeddings replace standard positional encodings in Transformer architectures with no other architectural changes. Key results: The optimal multi-period spiral time embedding achieves 1.69% MAPE \u2014 outperforming scalar time (9.76%) by 83% and hand-engineered sinusoidal features (4.62%) by 63%. The performance hierarchy is perfectly monotone across all 10 experiments: every geometric addition improves performance with no exceptions. Key finding on normalisation: the radial term contributes only when linear in \u03b8 and z-score normalised. Incorrect normalisation produces results worse than omitting the trend term entirely \u2014 a practically important result for any practitioner adding trend proxies to oscillatory features. The paper covers the single-period and multi-period embedding, connections to Transformer positional encodings, Hawking\u2013Hartle imaginary time, and Kaluza\u2013Klein theory, and surveys applications in forecasting, anomaly detection, battery health modeling, drug dosing, financial cycle modeling, climate science, and neuroscience. Code is open-source and available at: github.com/frankajieh-ship-it/spiral-time"}
		22	+{"work_id": 2608595939, "title": "Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA", "abstract": "Many different machine learning algorithms exist; taking into account each algorithm\u2019s hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simultaneously selecting a learning algorithm and setting its hyperparameters. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider feature selection techniques and all machine learning approaches implemented in WEKA\u2019s standard distribution, spanning 2 ensemble methods, 10 meta-methods, 28 base learners, and hyperparameter settings for each learner. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show performance often much better than using standard selection and hyperparameter optimization methods. We hope that our approach will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance."}
42	{"work_id": 4389326242, "title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces", "abstract": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers' computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5$\\times$ higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pretraining and downstream evaluation."}	23	{"work_id": 4389326242, "title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces", "abstract": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers' computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5$\\times$ higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pretraining and downstream evaluation."}
43	{"work_id": 4406152291, "title": "Medical large language models are vulnerable to data-poisoning attacks", "abstract": "The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development. We find that replacement of just 0.001% of training tokens with medical misinformation results in harmful models more likely to propagate medical errors. Furthermore, we discover that corrupted models match the performance of their corruption-free counterparts on open-source benchmarks routinely used to evaluate medical LLMs. Using biomedical knowledge graphs to screen medical LLM outputs, we propose a harm mitigation strategy that captures 91.9% of harmful content (F1 = 85.7%). Our algorithm provides a unique method to validate stochastically generated LLM outputs against hard-coded relationships in knowledge graphs. In view of current calls for improved data provenance and transparent LLM development, we hope to raise awareness of emergent risks from LLMs trained indiscriminately on web-scraped data, particularly in healthcare where misinformation can potentially compromise patient safety."}	24	{"work_id": 4406152291, "title": "Medical large language models are vulnerable to data-poisoning attacks", "abstract": "The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development. We find that replacement of just 0.001% of training tokens with medical misinformation results in harmful models more likely to propagate medical errors. Furthermore, we discover that corrupted models match the performance of their corruption-free counterparts on open-source benchmarks routinely used to evaluate medical LLMs. Using biomedical knowledge graphs to screen medical LLM outputs, we propose a harm mitigation strategy that captures 91.9% of harmful content (F1 = 85.7%). Our algorithm provides a unique method to validate stochastically generated LLM outputs against hard-coded relationships in knowledge graphs. In view of current calls for improved data provenance and transparent LLM development, we hope to raise awareness of emergent risks from LLMs trained indiscriminately on web-scraped data, particularly in healthcare where misinformation can potentially compromise patient safety."}
44	{"work_id": 2946444988, "title": "Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation", "abstract": "Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain. However, algorithm comparison is cumbersome in Deep UDA due to the absence of accurate and standardized model selection method, posing an obstacle to further advances in the field. Existing model selection methods for Deep UDA are either highly biased, restricted, unstable, or even controversial (requiring labeled target data). To this end, we propose \\textit{Deep Embedded Validation} (\\textbf{DEV}), which embeds adapted feature representation into the validation procedure to obtain unbiased estimation of the target risk with bounded variance. The variance is further reduced by the technique of control variate. The efficacy of the method has been justified both theoretically and empirically."}	25	{"work_id": 2946444988, "title": "Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation", "abstract": "Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain. However, algorithm comparison is cumbersome in Deep UDA due to the absence of accurate and standardized model selection method, posing an obstacle to further advances in the field. Existing model selection methods for Deep UDA are either highly biased, restricted, unstable, or even controversial (requiring labeled target data). To this end, we propose \\textit{Deep Embedded Validation} (\\textbf{DEV}), which embeds adapted feature representation into the validation procedure to obtain unbiased estimation of the target risk with bounded variance. The variance is further reduced by the technique of control variate. The efficacy of the method has been justified both theoretically and empirically."}
		26	+{"work_id": 4401077562, "title": "Factors Influencing University Students\u2019 Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy", "abstract": "Generative artificial intelligence (GAI) advancements have ignited new expectations for artificial intelligence (AI)-enabled educational transformations. Based on the theory of planned behavior (TPB), this study combines structural equation modeling and interviews to analyze the influencing factors of Chinese university students\u2019 GAI technology usage intention. Regarding AI literacy, students\u2019 cognitive literacy in AI ethics scored the highest (M = 5.740), while AI awareness literacy scored the lowest (M = 4.578). Students\u2019 attitudes toward GAI significantly and positively influenced their usage intention, with the combined TPB framework and AI literacy explaining 59.3% of the variance. AI literacy and subjective norms positively influenced students\u2019 attitudes toward GAI technology and perceived behavioral control, and attitude mediated the impact of AI literacy and subjective norms on GAI usage intention. Further, the interviews provide new insights for university management and educational leadership regarding the construction of an educational ecosystem under the application of GAI technology."}
		27	+{"work_id": 2889326414, "title": "UMAP: Uniform Manifold Approximation and Projection", "abstract": "Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. UMAP has a rigorous mathematical foundation, but is simple to use, with a scikit-learn compatible API. UMAP is among the fastest manifold learning implementations available -significantly faster than most t-SNE implementations."}
45	{"work_id": 4367369313, "title": "Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices", "abstract": "In the rapidly evolving landscape of natural language processing (NLP), ChatGPT has emerged as a powerful tool for various industries and applications. To fully harness the potential of ChatGPT, it is crucial to understand and master the art of prompt engineering-the process of designing and refining input prompts to elicit desired responses from an AI NLP model. This article provides a comprehensive guide to mastering prompt engineering techniques, tips, and best practices to achieve optimal outcomes with ChatGPT. The discussion begins with an introduction to ChatGPT and the fundamentals of prompt engineering, followed by an exploration of techniques for effective prompt crafting, such as clarity, explicit constraints, experimentation, and leveraging different types of questions. The article also covers best practices, including iterative refinement, balancing user intent, harnessing external resources, and ensuring ethical usage. Advanced strategies, such as temperature and token control, prompt chaining, domain-specific adaptations, and handling ambiguous inputs, are also addressed. Real-world case studies demonstrate the practical applications of prompt engineering in customer support, content generation, domain-specific knowledge retrieval, and interactive storytelling. The article concludes by highlighting the impact of effective prompt engineering on ChatGPT performance, future research directions, and the importance of fostering creativity and collaboration within the ChatGPT community."}	28	{"work_id": 4367369313, "title": "Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices", "abstract": "In the rapidly evolving landscape of natural language processing (NLP), ChatGPT has emerged as a powerful tool for various industries and applications. To fully harness the potential of ChatGPT, it is crucial to understand and master the art of prompt engineering-the process of designing and refining input prompts to elicit desired responses from an AI NLP model. This article provides a comprehensive guide to mastering prompt engineering techniques, tips, and best practices to achieve optimal outcomes with ChatGPT. The discussion begins with an introduction to ChatGPT and the fundamentals of prompt engineering, followed by an exploration of techniques for effective prompt crafting, such as clarity, explicit constraints, experimentation, and leveraging different types of questions. The article also covers best practices, including iterative refinement, balancing user intent, harnessing external resources, and ensuring ethical usage. Advanced strategies, such as temperature and token control, prompt chaining, domain-specific adaptations, and handling ambiguous inputs, are also addressed. Real-world case studies demonstrate the practical applications of prompt engineering in customer support, content generation, domain-specific knowledge retrieval, and interactive storytelling. The article concludes by highlighting the impact of effective prompt engineering on ChatGPT performance, future research directions, and the importance of fostering creativity and collaboration within the ChatGPT community."}
46	{"work_id": 7125233009, "title": "SemCap: Sentiment-aware semantic captioning for multimodal aspect-based sentiment analysis", "abstract": "Multimodal aspect-based sentiment analysis (MABSA) is critical for fine-grained opinion mining from social media. Current mainstream methods rely on feature-level fusion and attention mechanisms to align visual and textual features. However, MABSA suffers from visual noise caused by cluttered image fusion and textual noise caused by inter-aspect interference, leading to modality conflicts and spurious aspect predictions. We propose SemCap, a framework that mitigates these issues using a sentiment-aware caption as an explicit, high-level visual proxy. This caption is generated by Qwen3-VL under sentiment-oriented prompts to selectively describe emotion-bearing entities. To accurately aggregate cross-modal sentiment, we design the Semantic Proxy Dynamic Routing (SPDR) fusion module, which uses the caption representation to guide the selection of relevant visual patches. The refined features are input to a multimodal interactive decoder, augmented by auxiliary supervision on aspect boundaries and polarity labels to enhance localization and discrimination. Extensive experiments on the Twitter-2015 and Twitter-2017 datasets demonstrate SemCap\u2019s superior performance over existing MABSA methods."}	29	{"work_id": 7125233009, "title": "SemCap: Sentiment-aware semantic captioning for multimodal aspect-based sentiment analysis", "abstract": "Multimodal aspect-based sentiment analysis (MABSA) is critical for fine-grained opinion mining from social media. Current mainstream methods rely on feature-level fusion and attention mechanisms to align visual and textual features. However, MABSA suffers from visual noise caused by cluttered image fusion and textual noise caused by inter-aspect interference, leading to modality conflicts and spurious aspect predictions. We propose SemCap, a framework that mitigates these issues using a sentiment-aware caption as an explicit, high-level visual proxy. This caption is generated by Qwen3-VL under sentiment-oriented prompts to selectively describe emotion-bearing entities. To accurately aggregate cross-modal sentiment, we design the Semantic Proxy Dynamic Routing (SPDR) fusion module, which uses the caption representation to guide the selection of relevant visual patches. The refined features are input to a multimodal interactive decoder, augmented by auxiliary supervision on aspect boundaries and polarity labels to enhance localization and discrimination. Extensive experiments on the Twitter-2015 and Twitter-2017 datasets demonstrate SemCap\u2019s superior performance over existing MABSA methods."}
47	−{"work_id": 2963923407, "title": "Addressing Function Approximation Error in Actor-Critic Methods", "abstract": "In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested."}	30	+{"work_id": 2962776659, "title": "Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks", "abstract": "There is a lot of research interest in encoding variable length sentences into fixed length vectors, in a way that preserves the sentence meanings. Two common methods include representations based on averaging word vectors, and representations based on the hidden states of recurrent neural networks such as LSTMs. The sentence vectors are used as features for subsequent machine learning tasks or for pre-training in the context of deep learning. However, not much is known about the properties that are encoded in these sentence representations and about the language information they capture. We propose a framework that facilitates better understanding of the encoded representations. We define prediction tasks around isolated aspects of sentence structure (namely sentence length, word content, and word order), and score representations by the ability to train a classifier to solve each prediction task when using the representation as input. We demonstrate the potential contribution of the approach by analyzing different sentence representation mechanisms. The analysis sheds light on the relative strengths of different sentence embedding methods with respect to these low level prediction tasks, and on the effect of the encoded vector's dimensionality on the resulting representations."}
48	−{"work_id": 3152893301, "title": "Graph neural networks: A review of methods and applications", "abstract": "Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research."}	31	+{"work_id": 4407570917, "title": "AgentSociety: Large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society", "abstract": "Abstract Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models (LLMs) have further transformed this research paradigm, enabling the creation of human-like generative social agents and realistic simulacra of society. In this paper, we propose AgentSociety, a large-scale social simulator that integrates LLM-driven agents, a realistic societal environment, and a powerful large-scale simulation engine. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions both among agents and between agents and their environment. Furthermore, we explore the potential of AgentSociety as a testbed for computational social experiments, focusing on five key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, the impact of external shocks such as hurricanes, and urban sustainability. These five issues serve as valuable cases for assessing AgentSociety\u2019s support for typical research methods \u2013 such as surveys, interviews, and interventions \u2013 as well as for investigating the patterns, causes, and underlying mechanisms of social issues. The alignment between AgentSociety\u2019s outcomes and real-world experimental results not only demonstrates its ability to capture human behaviors and their underlying mechanisms, but also underscores its potential as an important platform for social scientists and policymakers."}
49	−{"work_id": 7123342962, "title": "Structure-Preserving Frequency-Regularized Text-Guided Optimal Transport for Unpaired Rain Streaks and Raindrops Removal", "abstract": "The removal of rain streaks and raindrops is crucial for enhancing the image visibility and mitigating the weather degradations. However, most existing approaches rely on the paired rainy and clean images, which are challenging to obtain in real-world scenarios. To this end, we propose a novel structure-preserving frequency-regularized text-guided optimal transport (SFTOT) framework, which formulates the unpaired rain streaks and raindrops removal as an optimal transport problem. Specifically, we introduce a structure-preserving transport cost, incorporating the structural similarity constraint to minimize the duality gap between the primal and dual formulations, while preserving the structural details of reconstructed images. Furthermore, by embedding the inherent frequency sparsity of rain streaks and raindrops into the transport cost, we derive a frequency-regularized optimal transport objective, ensuring consistency in frequency distributions between the generated and clean images. Additionally, we employ a pre-trained one-step stable diffusion model as the restoration network, which is fine-tuned using the low-rank adaptation (LoRA) adapters and zero convolutional layers, while integrating the domain-specific text prompts for both degraded and clean images to guide the generation process. Extensive experiments demonstrate that our method surpasses the existing well-performing unpaired learning approaches, achieving notable improvements in both the fidelity and photo-realism."}	32	+{"work_id": 4393065402, "title": "A survey on large language model based autonomous agents", "abstract": "Abstract Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective. We first discuss the construction of LLM-based autonomous agents, proposing a unified framework that encompasses much of previous work. Then, we present a overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field."}
50	−{"work_id": 2619383789, "title": "Multimodal Machine Learning: A Survey and Taxonomy", "abstract": "Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research."}	33	+{"work_id": 3133702157, "title": "On the Dangers of Stochastic Parrots", "abstract": "The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models."}
51	−{"work_id": 3096831136, "title": "Generative adversarial networks", "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."}	34	+{"work_id": 4399790131, "title": "The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review", "abstract": "Abstract The growing integration of artificial intelligence (AI) dialogue systems within educational and research settings highlights the importance of learning aids. Despite examination of the ethical concerns associated with these technologies, there is a noticeable gap in investigations on how these ethical issues of AI contribute to students\u2019 over-reliance on AI dialogue systems, and how such over-reliance affects students\u2019 cognitive abilities. Overreliance on AI occurs when users accept AI-generated recommendations without question, leading to errors in task performance in the context of decision-making. This typically arises when individuals struggle to assess the reliability of AI or how much trust to place in its suggestions. This systematic review investigates how students\u2019 over-reliance on AI dialogue systems, particularly those embedded with generative models for academic research and learning, affects their critical cognitive capabilities including decision-making, critical thinking, and analytical reasoning. By using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our systematic review evaluated a body of literature addressing the contributing factors and effects of such over-reliance within educational and research contexts. The comprehensive literature review spanned 14 articles retrieved from four distinguished databases: ProQuest, IEEE Xplore, ScienceDirect, and Web of Science. Our findings indicate that over-reliance stemming from ethical issues of AI impacts cognitive abilities, as individuals increasingly favor fast and optimal solutions over slow ones constrained by practicality. This tendency explains why users prefer efficient cognitive shortcuts, or heuristics, even amidst the ethical issues presented by AI technologies."}
52	−{"work_id": 4384464487, "title": "Students\u2019 voices on generative AI: perceptions, benefits, and challenges in higher education", "abstract": "Abstract This study explores university students\u2019 perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs\u2019 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students\u2019 perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students\u2019 perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education."}	35	+{"work_id": 4406472463, "title": "A generative model for inorganic materials design", "abstract": "Abstract The design of functional materials with desired properties is essential in driving technological advances in areas such as energy storage, catalysis and carbon capture 1\u20133 . Generative models accelerate materials design by directly generating new materials given desired property constraints, but current methods have a low success rate in proposing stable crystals or can satisfy only a limited set of property constraints 4\u201311 . Here we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. Compared with previous generative models 4,12 , structures produced by MatterGen are more than twice as likely to be new and stable, and more than ten times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, new materials with desired chemistry, symmetry and mechanical, electronic and magnetic properties. As a proof of concept, we synthesize one of the generated structures and measure its property value to be within 20% of our target. We believe that the quality of generated materials and the breadth of abilities of MatterGen represent an important advancement towards creating a foundational generative model for materials design."}
53	−{"work_id": 4385763767, "title": "Transformers in Time Series: A Survey", "abstract": "Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications. In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. In particular, we examine the development of time series Transformers in two perspectives. From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis. From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification. Empirically, we perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series. Finally, we discuss and suggest future directions to provide useful research guidance."}	36	+{"work_id": 2804078698, "title": "Self-Attention Generative Adversarial Networks", "abstract": "In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape."}
54	−{"work_id": 7124432017, "title": "Human-Centric Artificial Intelligence Pedagogy (HCAP) framework developed from TPACK through integration of artificial intelligence literacy and competency", "abstract": "The rise of artificial intelligence (AI) in education, particularly generative AI, challenges the sufficiency of the established Technological Pedagogical Content Knowledge (TPACK) framework. AI\u2019s agentic autonomy, epistemic complexities, and ethical dimensions necessitate an evolved model. This study investigates the newly proposed Intelligent-TPACK (I-TPACK) framework, designed to address these gaps by integrating five knowledge domains: AI-Technological, AI-Content, AI-Pedagogical, Human-AI Collaborative, and Ethical Knowledge. The research goal was to define the specific knowledge and skills required within these I-TPACK domains from a teacher's perspective. Utilizing a three-round Delphi method, a panel of 30 teachers from diverse subjects developed a consensus list of essential competencies. The findings identified and refined 25 critical knowledge items, providing a foundational and empirically grounded model for the I-TPACK framework. These findings offer a concrete roadmap for teacher education, translating a theoretical model into actionable competencies. This study equips educators to transition from merely using AI to strategically orchestrating human-AI collaborative learning, ensuring they can harness AI's potential ethically, critically, and productively. The I-TPACK and its suggested knowledge serve as a vital tool for developing future-ready teacher training programs and professional development."}	37	+{"work_id": 4399982941, "title": "Value engineering for autonomous agents", "abstract": "(English) The topic of this thesis is the engineering of values for autonomous agents. This is realised through the formulation, design and implementation of new functionalities for autonomous agents that enable reasoning in terms of values. In particular, we argue for the role of prescriptive norms as value-promoting mechanisms. Hence, value-driven agents should be able to autonomously determine which regulations (such as obligations, permissions or prohibitions) make the Multiagent System they inhabit better promote some values of interest. We lay the foundations of our work on Schwartz\u2019s Theory of Basic Human Values to establish a consequential connection between values and norms, considering that norms are aligned with respect to values if the outcomes they incentivise satisfy the goals that capture the meaning of values in a particular context. Another feature of Schwartz\u2019s theory that has been previously overlooked in the literature is the strong social dimension of values. That is, agents should be able to reason not just in terms of their own, but also of the values of others in their community. This points to Theory of Mind (i.e. the cognitive ability to perceive, interpret and reason about others in terms of their mental states) as an outstanding component of value-based reasoning. This thesis is structured around three main contributions (published in journal papers) plus their integration. The first contribution establishes the normvalue relationship as a consequential one in nature, and proposes a methodology for the automated synthesis and analysis of optimally value-aligned normative systems. The second contribution tackles the limitations of the first, and defines the Action Situation Language to systematically express a wide range of rules that may be implemented in a Multiagent System. This language is complemented by a game engine that automatically interprets interaction descriptions and builds their semantics as Extensive Form Games, which are later analysed with standard game-theoretical tools. This leads to a distribution over game outcomes, which are evaluated in terms of their desirability with respect to values. The third contribution introduces Theory of Mind-related functionalities into an existing Belief-Desire-Intention agent architecture, and combines them with abductive reasoning capabilities. The three contributions are integrated in a novel functionality that enables agents to reason about prescriptive norms in terms of dynamic values. This means that an autonomous agent can, at runtime, switch its value perspective to the one it estimates that another agent has. Such perspective-dependent value-based normative reasoning functionality, with its inherent social orientation, constitutes a novel contribution to the community of values for autonomous agents and paves the way for possible applications such as value-based negotiation over normative systems. In summary, value engineering is a principled and systematic approach to computational ethics, which provides an innovative tool set for integrating ethical values into the design of autonomous agents. (Catal\u00e0) El tema d\u2019aquesta tesi \u00e9s l\u2019enginyeria de valors per a agents aut\u00f2noms, aconseguida mitjan\u00e7ant la formulaci\u00f3, disseny i implementaci\u00f3 de noves funcionalitats que permeten a agents aut\u00f2noms raonar en termes de valors. En particular, defensem el paper de les normes prescriptives com a mecanismes de promoci\u00f3 de valors. Aleshores, els agents impulsats per valors deuen poder determinar de forma aut\u00f2noma quines regulacions (com ara obligacions, permisos o prohibicions) promouen millor alguns valors d\u2019inter\u00e8s en el Sistema Multiagent que habiten. Fonamentem el nostre treball a la Teoria de Schwartz de Valors Humans B\u00e0sics per establir una connexi\u00f3 entre valors i normes basada en conseq\u00fc\u00e8ncies, considerant que les normes estan alineades respecte als valors si els resultats que incentiven satisfan les metes que capten el significat d\u2019aquests valors en un context determinat. Una altra caracter\u00edstica de la teoria de Schwartz que ha estat passada per alt pr\u00e8viament a la literatura \u00e9s la forta dimensi\u00f3 social dels valors. \u00c9s a dir, els agents haurien de poder raonar, no nom\u00e9s en termes dels seus propis valors, sin\u00f3 tamb\u00e9 dels d\u2019altres a la seva comunitat. Aix\u00f2 apunta a la Teoria de la Ment (\u00e9s a dir, la capacitat cognitiva de percebre, interpretar i raonar sobre els altres en termes dels seus estats mentals) com un component destacat del raonament basat en valors. Aquesta tesi s\u2019estructura al voltant de tres contribucions principals (publicades en revistes acad\u00e8miques), a m\u00e9s de la seva integraci\u00f3. La primera contribuci\u00f3 estableix la relaci\u00f3 entre normes i valors mitjan\u00e7ant conseq\u00fc\u00e8ncies, i proposa una metodologia per a la s\u00edntesi i an\u00e0lisi automatitzada de sistemes normatius \u00f2ptimament alineats amb valors. La segona contribuci\u00f3 aborda les limitacions de la primera, i defineix l\u2019Action Situation Language per expressar sistem\u00e0ticament una \u00e0mplia gamma de regles que es poden implementar en un Sistema Multiagent. Aquest llenguatge es complementa amb un int\u00e8rpret que processa autom\u00e0ticament la descripci\u00f3 de la interacci\u00f3 i construeix la seva sem\u00e0ntica com un joc en forma estesa, que despr\u00e9s \u00e9s analitzat amb eines est\u00e0ndards de teoria de jocs. Aix\u00f2 condueix a una distribuci\u00f3 sobre estats finals del joc, que s\u2019avaluen en termes de la seva conveni\u00e8ncia respecte a certs valors. La tercera contribuci\u00f3 presenta funcionalitats relacionades amb la Teoria de la Ment, integrant-les a l\u2019arquitectura Belief-Desire-Intention existent i combinant-les amb raonament abductiu. Les tres contribucions s\u2019'integren en una nova funcionalitat que permet a agents aut\u00f2noms raonar sobre normes prescriptives en termes de valors din\u00e0mics. Aix\u00f2 vol dir que un agent aut\u00f2nom pot, durant la seva execuci\u00f3, canviar la seva perspectiva de valors a la que estima que t\u00e9 un altre agent. Aquest enfocament, basat en valors i amb una orientaci\u00f3 social inherent, constitueix una contribuci\u00f3 nova a la investigaci\u00f3 de valors per a agents aut\u00f2noms i aplana el cam\u00ed a possibles aplicacions com la negociaci\u00f3 autom\u00e0tica sobre sistemes normatius basada en valors. En resum, l\u2019'enginyeria de valors \u00e9s un enfocament sistem\u00e0tic i de principis a l\u2019'\u00e8tica computacional, que proporciona un conjunt d\u2019'eines innovadores per integrar valors \u00e8tics al disseny d\u2019'agents aut\u00f2noms."}
		38	+{"work_id": 7123365826, "title": "Event-Triggered Zero-Sum Game for Input Saturated Nonlinear Systems With State Constraints", "abstract": "In safety-critical applications, saturated nonlinear systems often face significant control challenges due to the simultaneous presence of strict state constraints, input saturation, and uncertain external disturbances. To address these challenges, this paper proposes a novel event-triggered optimal control (ETOC) method. The core of our approach lies in a unified framework that handles all three issues simultaneously. We first introduce a smooth mapping function to transform the original system with state constraints into an equivalent unconstrained system. Subsequently, the control problem is formulated as a two-player zero-sum game (ZSG), where one player is the controller designed to handle input saturation and the other is the worst-case external disturbance. This unified formulation ensures that the system strictly adheres to the state constraints. Unlike traditional methods that typically require formulating and solving complex modified event-triggered Hamilton-Jacobi-Bellman (HJB) equations incorporating triggering errors, our approach approximates the optimal policy for the transformed system and implements it via an event-triggered mechanism. By utilizing a single-critic neural network (NN) and incorporating an experience replay mechanism, the algorithm\u2019s learning stability is enhanced, and the restrictive persistence of excitation (PE) condition is relaxed. Theoretical analysis and simulations confirm that our ETOC strategy guarantees optimal performance, strict adherence to state constraints, and a significant reduction in communication burden, showcasing its efficiency and practicality."}
		39	+{"work_id": 4378505261, "title": "Voyager: An Open-Ended Embodied Agent with Large Language Models", "abstract": "We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. We open-source our full codebase and prompts at https://voyager.minedojo.org/."}
		40	+{"work_id": 4411099958, "title": "A multimodal vision foundation model for clinical dermatology", "abstract": "Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep learning models excel at specific tasks such as skin cancer diagnosis from dermoscopic images, they struggle to meet the complex, multimodal requirements of clinical practice. Here we introduce PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on over 2 million real-world skin disease images from 11 clinical institutions across 4 imaging modalities. We evaluated PanDerm on 28 diverse benchmarks, including skin cancer screening, risk stratification, differential diagnosis of common and rare skin conditions, lesion segmentation, longitudinal monitoring, and metastasis prediction and prognosis. PanDerm achieved state-of-the-art performance across all evaluated tasks, often outperforming existing models when using only 10% of labeled data. We conducted three reader studies to assess PanDerm's potential clinical utility. PanDerm outperformed clinicians by 10.2% in early-stage melanoma detection through longitudinal analysis, improved clinicians' skin cancer diagnostic accuracy by 11% on dermoscopy images and enhanced nondermatologist healthcare providers' differential diagnosis by 16.5% across 128 skin conditions on clinical photographs. These results show PanDerm's potential to improve patient care across diverse clinical scenarios and serve as a model for developing multimodal foundation models in other medical specialties, potentially accelerating the integration of artificial intelligence support in healthcare."}
		41	+{"work_id": 4383312437, "title": "A comprehensive AI policy education framework for university teaching and learning", "abstract": "Abstract This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly."}
		42	+{"work_id": 7138474371, "title": "OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model", "abstract": "We present OpenDriveVLA, a Vision-Language Action (VLA) model designed for end-to-end autonomous driving, built upon open-source large language models. OpenDriveVLA generates spatially-grounded driving actions by leveraging multimodal inputs, including both 2D and 3D instance-aware visual representations, ego vehicle states, and language commands. To bridge the modality gap between driving visual representations and language embeddings, we introduce a hierarchical vision-language alignment process, projecting both 2D and 3D structured visual tokens into a unified semantic space. Furthermore, we incorporate structured agent\u2013environment\u2013ego interaction modeling into the autoregressive decoding process, enabling the model to capture fine-grained spatial dependencies and behavior-aware dynamics critical for reliable trajectory planning. Extensive experiments on the nuScenes dataset demonstrate that OpenDriveVLA achieves state-of-the-art results across open-loop trajectory planning and driving-related question-answering tasks. Qualitative analyses further illustrate its superior capability to follow high-level driving commands and robustly generate trajectories under challenging scenarios, highlighting its potential for next-generation end-to-end autonomous driving."}
		43	+{"work_id": 4411932262, "title": "A foundation model to predict and capture human cognition", "abstract": ". A first step towards such a theory is to create a computational model that can predict human behaviour in a wide range of settings. Here we introduce Centaur, a computational model that can predict and simulate human behaviour in any experiment expressible in natural language. We derived Centaur by fine-tuning a state-of-the-art language model on a large-scale dataset called Psych-101. Psych-101 has an unprecedented scale, covering trial-by-trial data from more than 60,000 participants performing in excess of 10,000,000 choices in 160 experiments. Centaur not only captures the behaviour of held-out participants better than existing cognitive models, but it also generalizes to previously unseen cover stories, structural task modifications and entirely new domains. Furthermore, the model's internal representations become more aligned with human neural activity after fine-tuning. Taken together, our results demonstrate that it is possible to discover computational models that capture human behaviour across a wide range of domains. We believe that such models provide tremendous potential for guiding the development of cognitive theories, and we present a case study to demonstrate this."}
		44	+{"work_id": 7130596601, "title": "Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges", "abstract": "BACKGROUND: The integration of artificial intelligence in healthcare has transformed clinical practice and research methodologies. However, concerns regarding algorithmic accountability, interpretability, and safety have necessitated human oversight in AI systems. Human in the loop artificial intelligence represents a collaborative paradigm where human expertise and machine intelligence converge to enhance decision making while maintaining ethical standards and clinical safety. AIM: This review synthesizes current evidence on human in the loop AI in healthcare delivery and research, examining implementation frameworks, clinical outcomes, comparative advantages over fully automated and clinician-only approaches, and challenges. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases covering studies from 2018 to 2025. Data were thematically synthesized to identify patterns, frameworks, and outcomes. This narrative approach enables comprehensive conceptual synthesis across diverse HITL-AI applications and contexts. RESULTS: Human in the loop AI demonstrates significant applications across diagnostic imaging, clinical decision support, patient monitoring, drug discovery, and research data analysis. Evidence indicates improved diagnostic accuracy, reduced medical errors, enhanced patient safety, and increased clinician trust compared to both automated AI and traditional approaches. Implementation requires EHR interoperability, clear liability frameworks, adaptive training protocols, and quantum-safe cryptographic security. Challenges include workflow integration, regulatory gaps for adaptive systems, and sustainability concerns. CONCLUSION: This review advances the field by synthesizing cross-domain implementation patterns, mapping collaboration models to risk-stratified contexts, identifying regulatory gaps for adaptive systems, and proposing future directions including post-quantum cryptographic integration, AI-driven adaptive architectures, and multi-center scalability frameworks for optimizing human-machine collaboration in healthcare."}
		45	+{"work_id": 4406170795, "title": "Accurate predictions on small data with a tabular foundation model", "abstract": "Abstract Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science 1,2 . The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories 3\u20135 , gradient-boosted decision trees 6\u20139 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model that outperforms all previous methods on datasets with up to 10,000 samples by a wide margin, using substantially less training time. In 2.8 s, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model, this model also allows fine-tuning, data generation, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains."}
		46	+{"work_id": 7143512276, "title": "Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey", "abstract": "This survey explores the application of multimodal machine learning techniques in predictive healthcare analytics. By integrating various data modalities, such as medical imaging, clinical text, time-series signals, and structured tabular data, these approaches aim to emulate clinical reasoning and enhance diagnostic and prognostic accuracy. Across studies, multimodal ML consistently outperformed unimodal baselines, with intermediate fusion employed in 60% of cases and achieving average AUC improvements of 5\u201312% over single-modality models. Oncology and neurology emerged as the leading domains, where combining imaging with genomic and cognitive data significantly improved cancer survival and Alzheimer\u2019s detection. Despite progress, key challenges persist, including modality misalignment (23%), missing data (18%), and limited external validation (12%). Recent trends highlight transformer-based cross-modal attention, self-supervised learning for data-scarce settings, and hybrid fusion architectures in critical care. While multimodal ML offers clear clinical advantages, regulatory constraints and interoperability gaps continue to hinder deployment. This survey contributes to the existing literature by providing a comprehensive synthesis of multimodal ML applications across healthcare domains. It documents comparative fusion strategies, modelling approaches, and empirical performance outcomes. The paper\u2019s primary contribution lies in identifying intermediate fusion as the most effective integration strategy and revealing systematic gaps in external validation and model transparency that must be addressed for clinically trustworthy multimodal systems."}
		47	+{"work_id": 4389473504, "title": "TRUST-REGION BASED POLICY OPTIMIZATION FOR EFFICIENT REINFORCEMENT LEARNING", "abstract": "Reinforcement Learning (RL) has achieved remarkable success in solving complicated control and sequential decision-making tasks in recent years, such as board games, video games, robot control, dynamic energy management, and autonomous driving. In these tasks, RL has demonstrated unprecedented adaptability and self-learning capability in unknown and stochastic environments. However, existing RL algorithms still have significant disadvantages and shortcomings compared to human intelligence, such as learning stability, sample efficiency, safety, scalability, and collaboration with other agents. These drawbacks impede the widespread application of RL in real-life domains. To address these challenges, this dissertation aims to propose novel theories and algorithms for more effective and efficient RL and to develop their applications in real-world domains. This work consists of three major parts. First, the dissertation establishes a novel theoretical result for trust-region-based RL methods by analyzing the lower bound of policy performance. A closed-form policy update rule is then derived based on the theoretical result, providing a monotonic improvement guarantee. Second, the dissertation develops an off-policy RL algorithm for continuous control problems, inspired by the closed-form update rule. This algorithm enables sample-efficient learning of deep neural network policies. Additionally, trust region policy optimization is extended to cooperative multi-agent systems through consensus optimization, resulting in a distributed MARL algorithm. Third, the dissertation develops the real-world applications of a trust-region-based safe RL method and the MARL method in smart grids. Numerous experiments are conducted to verify the effectiveness of these methods using various benchmark environments, including robotics, strategic games, and power systems."}
		48	+{"work_id": 4401917994, "title": "Building Face Ageing Model Using Face Synthesis", "abstract": "Advancements in face synthesis technology have enabled innovative methods for modeling facial aging. This research paper focuses primarily on creating a robust face aging model using deep learning and Generative Adversarial Networks (GANs), trained on a diverse dataset of facial images. The proposed approach captures both global features and local textures to produce realistic age-progressed images while preserving the subject's identity. This paper also examines face synthesis techniques, with specific emphasis for the various practical usage of GANs. The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age- progressed face images. We evaluated the model using quantitative metrics and qualitative assessments, demonstrating its effectiveness. Additionally, we address ethical considerations, proposing guidelines for responsible use. Our study offers a novel framework with significant applications in security, forensics, and entertainment, and suggests future research directions to improve accuracy and ethical standards."}
		49	+{"work_id": 7131830459, "title": "A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions", "abstract": "The exponential growth of online information has made it increasingly difficult for users to identify valuable and relevant content. Recommender systems have emerged as a critical solution to this challenge by tailoring content to individual preferences. With the proliferation of diverse multimedia services, human interaction with the digital world has become inherently multimodal. Consequently, recommender systems capable of comprehending and interpreting multimodal information can more effectively align with individual preferences. With its recent surge in research attention, the field of multimodal recommender systems (MRS) still lacks a comprehensive technical survey. Existing surveys suffer from two critical limitations: 1) Insufficient technical depth: Prior works predominantly focus on categorizing and discussing general structure, neglecting rigorous technical analysis of methodologies and architectures. 2) Absence of cutting-edge works: Due to the rapid evolution of AI technologies, current surveys fail to discuss the latest works that adopt the most advanced techniques. To bridge these gaps, this survey conducts a systematic and technical review of advanced MRS works from MRS's inception to the present. We organize existing works into coherent taxonomies based on their structure and provide in-depth analyses of methodological innovations at each component, including Feature Extraction, Encoder, Multimodal Fusion, and Loss Function. Moreover, we further discuss potential future directions for developing and enhancing MRS. This survey serves as technical guidance for researchers and practitioners, offering insights into the developments, techniques, and future directions of MRS. Notably, we maintain a public repository:https://github.com/Jinfeng-Xu/Awesome-Multimodal-Recommender-Systems."}
		50	+{"work_id": 4400609581, "title": "Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications", "abstract": "Abstract The increasing prevalence of Artificial Intelligence (AI) in higher education underscores the necessity to explore its implications on ethical, social, and educational dynamics within the sector. This study aims to comprehensively investigate the impact of AI on higher education in Saudi Arabia, delving into stakeholders\u2019 attitudes, perceptions, and expectations regarding its implementation. The research hones in on key facets of AI in higher education, encompassing its influence on teaching and learning, ethical and social implications, and the anticipated role of AI in the future. Employing a quantitative approach through an online survey questionnaire (N = 1113), this study reveals positive attitudes toward AI in higher education. Stakeholders recognize its potential to enhance teaching and learning, streamline administration, and foster innovation. Emphasis is placed on ethical considerations and guidelines for AI implementation, highlighting the imperative need to address issues such as privacy, security, and bias. Participants envision a future characterized by personalized learning experiences, ethically integrated AI, collaboration, and ongoing support for lifelong learning. Furthermore, the results illuminate the intricate interplay between AI usage, purposes, difficulties, and their impact on attitudes, perceptions, and future implications. Accordingly, the research underscores the necessity for a comprehensive understanding of AI integration, considering not only its technical aspects but also the ethical, social, and educational dimensions. By acknowledging the role of AI uses, AI usage purposes, and addressing associated difficulties, educational stakeholders can work towards harnessing the benefits of AI while ensuring responsible and effective implementation in teaching and learning contexts."}
		51	+{"work_id": 3021654819, "title": "Federated Learning: Challenges, Methods, and Future Directions", "abstract": "Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities."}
		52	+{"work_id": 2969896603, "title": "A Survey on Bias and Fairness in Machine Learning", "abstract": "With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields."}
		53	+{"work_id": 3185341429, "title": "Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing", "abstract": "This article surveys and organizes research works in a new paradigm in natural language processing, which we dub \u201cprompt-based learning.\u201d Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P ( y\|x ), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x\u2032 that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x\u0302 , from which the final output y can be derived. This framework is powerful and attractive for a number of reasons: It allows the language model to be pre-trained on massive amounts of raw text, and by defining a new prompting function the model is able to perform few-shot or even zero-shot learning, adapting to new scenarios with few or no labeled data. In this article, we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g., the choice of pre-trained language models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts but also release other resources, e.g., a website NLPedia\u2013Pretrain including constantly updated survey and paperlist."}
		54	+{"work_id": 4399450035, "title": "Power Hungry Processing: Watts Driving the Cost of AI Deployment?", "abstract": "Recent years have seen a surge in the popularity of commercial AI products based on generative, multi-purpose AI systems promising a unified approach to building machine learning (ML) models into technology. However, this ambition of \u201cgenerality\u201d comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. In this work, we propose the first systematic comparison of the ongoing inference cost of various categories of ML systems, covering both task-specific (i.e. finetuned models that carry out a single task) and \u2018general-purpose\u2019 models, (i.e. those trained for multiple tasks). We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We find that multi-purpose, generative architectures are orders of magnitude more expensive than task-specific systems for a variety of tasks, even when controlling for the number of model parameters. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions. All the data from our study can be accessed via an interactive demo to carry out further exploration and analysis."}
		55	+{"work_id": 4322718191, "title": "LLaMA: Open and Efficient Foundation Language Models", "abstract": "We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community."}
55	{"work_id": 2963399829, "title": "Mixup: Beyond empirical risk minimization", "abstract": "Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks."}	56	{"work_id": 2963399829, "title": "Mixup: Beyond empirical risk minimization", "abstract": "Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks."}
56	{"work_id": 4393153153, "title": "TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning", "abstract": "Learning universal time series representations applicable to various types of downstream tasks is challenging but valuable in real applications. Recently, researchers have attempted to leverage the success of self-supervised contrastive learning (SSCL) in Computer Vision(CV) and Natural Language Processing(NLP) to tackle time series representation. Nevertheless, due to the special temporal characteristics, relying solely on empirical guidance from other domains may be ineffective for time series and difficult to adapt to multiple downstream tasks. To this end, we review three parts involved in SSCL including 1) designing augmentation methods for positive pairs, 2) constructing (hard) negative pairs, and 3) designing SSCL loss. For 1) and 2), we find that unsuitable positive and negative pair construction may introduce inappropriate inductive biases, which neither preserve temporal properties nor provide sufficient discriminative features. For 3), just exploring segment- or instance-level semantics information is not enough for learning universal representation. To remedy the above issues, we propose a novel self-supervised framework named TimesURL. Specifically, we first introduce a frequency-temporal-based augmentation to keep the temporal property unchanged. And then, we construct double Universums as a special kind of hard negative to guide better contrastive learning. Additionally, we introduce time reconstruction as a joint optimization objective with contrastive learning to capture both segment-level and instance-level information. As a result, TimesURL can learn high-quality universal representations and achieve state-of-the-art performance in 6 different downstream tasks, including short- and long-term forecasting, imputation, classification, anomaly detection and transfer learning."}	57	{"work_id": 4393153153, "title": "TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning", "abstract": "Learning universal time series representations applicable to various types of downstream tasks is challenging but valuable in real applications. Recently, researchers have attempted to leverage the success of self-supervised contrastive learning (SSCL) in Computer Vision(CV) and Natural Language Processing(NLP) to tackle time series representation. Nevertheless, due to the special temporal characteristics, relying solely on empirical guidance from other domains may be ineffective for time series and difficult to adapt to multiple downstream tasks. To this end, we review three parts involved in SSCL including 1) designing augmentation methods for positive pairs, 2) constructing (hard) negative pairs, and 3) designing SSCL loss. For 1) and 2), we find that unsuitable positive and negative pair construction may introduce inappropriate inductive biases, which neither preserve temporal properties nor provide sufficient discriminative features. For 3), just exploring segment- or instance-level semantics information is not enough for learning universal representation. To remedy the above issues, we propose a novel self-supervised framework named TimesURL. Specifically, we first introduce a frequency-temporal-based augmentation to keep the temporal property unchanged. And then, we construct double Universums as a special kind of hard negative to guide better contrastive learning. Additionally, we introduce time reconstruction as a joint optimization objective with contrastive learning to capture both segment-level and instance-level information. As a result, TimesURL can learn high-quality universal representations and achieve state-of-the-art performance in 6 different downstream tasks, including short- and long-term forecasting, imputation, classification, anomaly detection and transfer learning."}
57	−{"work_id": 7118083939, "title": "Agentic information systems", "abstract": "Abstract Recent advancements in artificial intelligence (AI) have catalyzed the emergence of agentic information systems (IS), which exhibit autonomous behavior and advanced cognitive capabilities. Unlike traditional IS, which functioned primarily as reactive tools supporting humans, agentic IS can make decisions independently, act in unstructured environments, and even delegate tasks to humans. This paradigm shift fundamentally transforms the human-IS relationship, questioning the long-standing assumption of human agentic primacy in IS research and practice. In this article, we provide a conceptual overview of agentic IS, delineating their defining characteristics and situating them within the broader evolution of IS. We introduce key archetypes of agentic IS, explore novel patterns of delegation and interaction between humans and machines, and discuss the socio-technical implications of these developments. Furthermore, we highlight the challenges and risks associated with integrating agentic IS from an individual, organizational, and societal perspective, emphasizing the need for nuanced understanding to harness the potential while addressing emerging complexities."}
58	{"work_id": 7122857583, "title": "Agentic AI systems in the age of generative models: architectures, cloud scalability, and real-world applications", "abstract": "This research proposes a holistic agentic Artificial Intelligence framework that seeks to solve the palpable requirement of autonomy, versatility, and world-scale generative Artificial Intelligence systems. When framing the concept of an agentic Artificial Intelligence as a paradigm shift where Large Language Models are relatively reactive, tool-augmented to a proactive, modular agent that pursues goals in the long term, the research discusses how large language models are becoming an architectural mandate. The proposed research uses a skillful literature synthesis and develops a concept and technical framework that integrates perception, memory, planning, execution, and communication modules. In contrast to current frameworks like AutoGPT and ReAct, where the deep memory, explainability, and certain control of scalability are often lacking, the proposed framework offers a solution with persistent memory layers, semantic routing, and modular orchestration pipelines when it comes to cloud-native deployments. Experimental verification offers a greater degree of autonomy, coordination, and resilience in a diverse range of activities such as enterprise automation and robotics. It is also an edge deployment with the help of lightweight microservices framework. In practice, this method supports scaled, comprehensible agents adapted to long-loop thinking and human-in-the-loop management and adaptive value chain serving. The novelty of the work is attributed to its reusable architecture, as it is not only capable of modifying agentic behavior alone, but it can also connect the theoretical principles and industry feasibility. Future efforts will be with the integration of ethical governance, uniform benchmarking, and multimodal memory remodeling in next generation of real-world autonomous systems."}	58	{"work_id": 7122857583, "title": "Agentic AI systems in the age of generative models: architectures, cloud scalability, and real-world applications", "abstract": "This research proposes a holistic agentic Artificial Intelligence framework that seeks to solve the palpable requirement of autonomy, versatility, and world-scale generative Artificial Intelligence systems. When framing the concept of an agentic Artificial Intelligence as a paradigm shift where Large Language Models are relatively reactive, tool-augmented to a proactive, modular agent that pursues goals in the long term, the research discusses how large language models are becoming an architectural mandate. The proposed research uses a skillful literature synthesis and develops a concept and technical framework that integrates perception, memory, planning, execution, and communication modules. In contrast to current frameworks like AutoGPT and ReAct, where the deep memory, explainability, and certain control of scalability are often lacking, the proposed framework offers a solution with persistent memory layers, semantic routing, and modular orchestration pipelines when it comes to cloud-native deployments. Experimental verification offers a greater degree of autonomy, coordination, and resilience in a diverse range of activities such as enterprise automation and robotics. It is also an edge deployment with the help of lightweight microservices framework. In practice, this method supports scaled, comprehensible agents adapted to long-loop thinking and human-in-the-loop management and adaptive value chain serving. The novelty of the work is attributed to its reusable architecture, as it is not only capable of modifying agentic behavior alone, but it can also connect the theoretical principles and industry feasibility. Future efforts will be with the integration of ethical governance, uniform benchmarking, and multimodal memory remodeling in next generation of real-world autonomous systems."}
59	{"work_id": 7160627408, "title": "Generative Artificial Intelligence for Cross-Domain Knowledge Synthesis and Intelligent Automation", "abstract": "Generative Artificial intelligence (GAI) has become a cohesive paradigm in the very wide field of application including intelligent automation, decision support systems, and extensive knowledge management, where the incorporation of heterogeneous knowledge about domains is of critical importance. Synthesizing, reasoning and automating across a variety of different data is a basic challenge in these areas. This paper introduces a generative AI knowledge synthesis model and generative automation framework of synthetic cross-domain knowledge grounded on the learning of shared latent representation. The suggested methodology utilizes the heterogeneous inputs domain specific encoders to map to a single latent space with attention-based fusion, generative decoding, and reasoning-based policy learning to adapt the automation. Semantic alignment, synthesis accuracy and decision effectiveness are achieved by a joint optimization goal. Through experimental assessment, it has been established that the suggested has been found to provide better performance results against current methods with a better Knowledge Synthesis Accuracy (0.91), Cross-Domain Alignment Score (0.88) and Automation Decision Accuracy (0.93) parameter over state-of-the-art multimodal systems. These findings support the usefulness of the generative modeling concept in facilitating strong, scalable, and knowledge-based intelligent automation throughout domains."}	59	{"work_id": 7160627408, "title": "Generative Artificial Intelligence for Cross-Domain Knowledge Synthesis and Intelligent Automation", "abstract": "Generative Artificial intelligence (GAI) has become a cohesive paradigm in the very wide field of application including intelligent automation, decision support systems, and extensive knowledge management, where the incorporation of heterogeneous knowledge about domains is of critical importance. Synthesizing, reasoning and automating across a variety of different data is a basic challenge in these areas. This paper introduces a generative AI knowledge synthesis model and generative automation framework of synthetic cross-domain knowledge grounded on the learning of shared latent representation. The suggested methodology utilizes the heterogeneous inputs domain specific encoders to map to a single latent space with attention-based fusion, generative decoding, and reasoning-based policy learning to adapt the automation. Semantic alignment, synthesis accuracy and decision effectiveness are achieved by a joint optimization goal. Through experimental assessment, it has been established that the suggested has been found to provide better performance results against current methods with a better Knowledge Synthesis Accuracy (0.91), Cross-Domain Alignment Score (0.88) and Automation Decision Accuracy (0.93) parameter over state-of-the-art multimodal systems. These findings support the usefulness of the generative modeling concept in facilitating strong, scalable, and knowledge-based intelligent automation throughout domains."}
60	{"work_id": 4410882873, "title": "Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications", "abstract": "Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While artificial intelligence (AI) models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance clinical decision-making. This scoping review examines the landscape of deep learning-based multimodal AI applications across the medical domain, analyzing 432 papers published between 2018 and 2024. We provide an extensive overview of multimodal AI development across different medical disciplines, examining various architectural approaches, fusion strategies, and common application areas. Our analysis reveals that multimodal AI models consistently outperform their unimodal counterparts, with an average improvement of 6.2 percentage points in AUC. However, several challenges persist, including cross-departmental coordination, heterogeneous data characteristics, and incomplete datasets. We critically assess the technical and practical challenges in developing multimodal AI systems and discuss potential strategies for their clinical implementation, including a brief overview of commercially available multimodal AI models for clinical decision-making. Additionally, we identify key factors driving multimodal AI development and propose recommendations to accelerate the field's maturation. This review provides researchers and clinicians with a thorough understanding of the current state, challenges, and future directions of multimodal AI in medicine."}	60	{"work_id": 4410882873, "title": "Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications", "abstract": "Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While artificial intelligence (AI) models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance clinical decision-making. This scoping review examines the landscape of deep learning-based multimodal AI applications across the medical domain, analyzing 432 papers published between 2018 and 2024. We provide an extensive overview of multimodal AI development across different medical disciplines, examining various architectural approaches, fusion strategies, and common application areas. Our analysis reveals that multimodal AI models consistently outperform their unimodal counterparts, with an average improvement of 6.2 percentage points in AUC. However, several challenges persist, including cross-departmental coordination, heterogeneous data characteristics, and incomplete datasets. We critically assess the technical and practical challenges in developing multimodal AI systems and discuss potential strategies for their clinical implementation, including a brief overview of commercially available multimodal AI models for clinical decision-making. Additionally, we identify key factors driving multimodal AI development and propose recommendations to accelerate the field's maturation. This review provides researchers and clinicians with a thorough understanding of the current state, challenges, and future directions of multimodal AI in medicine."}
		61	+{"work_id": 2306711933, "title": "Discovering hierarchy in reinforcement learning", "abstract": "This thesis addresses the open problem of automatically discovering hierarchical structure in reinforcement learning. Current algorithms for reinforcement learning fail to scale as problems become more complex. Many complex environments empirically exhibit hierarchy and can be modeled as interrelated subsystems, each in turn with hierarchic structure. Subsystems are often repetitive in time and space, meaning that they reoccur as components of different tasks or occur multiple times in different circumstances in the environment. A learning agent may sometimes scale to larger problems if it successfully exploits this repetition. Evidence suggests that a bottom up approach that repetitively finds building-blocks at one level of abstraction and uses them as background knowledge at the next level of abstraction, makes learning in many complex environments tractable. An algorithm, called HEXQ, is described that automatically decomposes and solves a multi-dimensional Markov decision problem (MDP) by constructing a multi-level hierarchy of interlinked subtasks without being given the model beforehand. The effectiveness and efficiency of the HEXQ decomposition depends largely on the choice of representation in terms of the variables, their temporal relationship and whether the problem exhibits a type of constrained stochasticity. The algorithm is first developed for stochastic shortest path problems and then extended to infinite horizon problems. The operation of the algorithm is demonstrated using a number of examples including a taxi domain, various navigation tasks, the Towers of Hanoi and a larger sporting problem. The main contributions of the thesis are the automation of (1)decomposition, (2) sub-goal identification, and (3) discovery of hierarchical structure for MDPs with states described by a number of variables or features. It points the way to further scaling opportunities that encompass approximations, partial observability, selective perception, relational representations and planning. The longer term research aim is to train rather than program intelligent agents."}
61	{"work_id": 4390730779, "title": "Exploring Large Language Model\u2010Based Intelligent Agents: Definitions, Methods, and Prospects", "abstract": "ABSTRACT The concept of the intelligent agent represents a long\u2010standing pursuit in artificial intelligence. Recent breakthroughs in large language models (LLMs) have catalyzed a paradigm shift, enabling the development of sophisticated agents that exhibit advanced reasoning, planning, and tool\u2010use capabilities across diverse domains. These LLM\u2010based agents, which leverage natural language as a universal interface for cognition and interaction, are rapidly advancing from theoretical constructs to practical applications, ranging from autonomous task assistants to complex multi\u2010agent simulations of social and economic systems. This paper provides an integrative survey of this burgeoning field. We first establish an organizing framework for understanding LLM\u2010based agents, systematically deconstructing both single\u2010agent and multi\u2010agent systems into their core components. We analyze the architectural principles and key mechanisms that underpin their intelligence, including planning paradigms, memory structures, and reflection\u2010based self\u2010improvement. We further investigate the dynamics of multi\u2010agent systems, exploring coordination strategies, communication protocols, and organizational structures. The paper also covers the crucial aspects of performance evaluation, highlighting influential benchmarks and identifying key challenges. Finally, we synthesize the current landscape to discuss the primary challenges, such as the intrinsic limitations of LLMs and the complexities of ensuring safety and alignment, and chart a course for future research directions, including the drive toward continual learning and enhanced multi\u2010modal capabilities."}	62	{"work_id": 4390730779, "title": "Exploring Large Language Model\u2010Based Intelligent Agents: Definitions, Methods, and Prospects", "abstract": "ABSTRACT The concept of the intelligent agent represents a long\u2010standing pursuit in artificial intelligence. Recent breakthroughs in large language models (LLMs) have catalyzed a paradigm shift, enabling the development of sophisticated agents that exhibit advanced reasoning, planning, and tool\u2010use capabilities across diverse domains. These LLM\u2010based agents, which leverage natural language as a universal interface for cognition and interaction, are rapidly advancing from theoretical constructs to practical applications, ranging from autonomous task assistants to complex multi\u2010agent simulations of social and economic systems. This paper provides an integrative survey of this burgeoning field. We first establish an organizing framework for understanding LLM\u2010based agents, systematically deconstructing both single\u2010agent and multi\u2010agent systems into their core components. We analyze the architectural principles and key mechanisms that underpin their intelligence, including planning paradigms, memory structures, and reflection\u2010based self\u2010improvement. We further investigate the dynamics of multi\u2010agent systems, exploring coordination strategies, communication protocols, and organizational structures. The paper also covers the crucial aspects of performance evaluation, highlighting influential benchmarks and identifying key challenges. Finally, we synthesize the current landscape to discuss the primary challenges, such as the intrinsic limitations of LLMs and the complexities of ensuring safety and alignment, and chart a course for future research directions, including the drive toward continual learning and enhanced multi\u2010modal capabilities."}
62	−{"work_id": 1710476689, "title": "Generative Adversarial Networks", "abstract": "The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions."}	63	+{"work_id": 7118302872, "title": "A frame work to predict the influencing nodes in social networking platform using graph neural network model", "abstract": "Identifying influential nodes that can maximize information diffusion is a fundamental problem in social network analysis, commonly referred to as influence maximization. Diffusion model-based and traditional greedy and heuristic algorithms tend to be expensive to calculate, or poorly aware of the network structure, especially when dealing with large networks. To overcome these weaknesses, this paper will introduce a hybrid architecture that will combine Graph Neural Networks (GNNs) with the Ant Colony Optimization (ACO) towards influence maximization. The proposed approach works as follows, where the GNN learns influence representations on the node level, which jointly models both local and higher-order structural dependencies in the network, and ACO uses these learned representations to assemble a non-redundant and diverse seed set. The chosen seed sets are tested on the basis of the Independent Cascade diffusion model where the spread of influence is taken as the key performance measure. The experiments on the real-world social network data indicate that the proposed GNN-ACO framework is steadily more influential in spreading than the current baseline methods and that it has comparative computational efficiency at varying levels of seed set sizes. These findings demonstrate the advantage of optimizing influence maximization by using representation learning with a seed selection strategy based on optimization."}
		64	+{"work_id": 4393138529, "title": "Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey", "abstract": "Large models represent a groundbreaking advancement in multiple application fields, enabling remarkable achievements across various tasks. However, their unprecedented scale comes with significant computational costs. These models, often consisting of billions of parameters, require vast amounts of computational resources for execution. Especially, the expansive scale and computational demands pose considerable challenges when customizing them for particular downstream tasks, particularly over the hardware platforms constrained by computational capabilities. Parameter Efficient Fine-Tuning (PEFT) provides a practical solution by efficiently adjusting the large models over the various downstream tasks. In particular, PEFT refers to the process of adjusting the parameters of a pre-trained large model to adapt it to a specific task or domain while minimizing the number of additional parameters introduced or computational resources required. This approach is particularly important when dealing with large-scale language models with high parameter counts, as fine-tuning these models from scratch can be computationally expensive and resource-intensive, posing considerable challenges in the supporting system platform design. In this survey, we present comprehensive studies of various PEFT algorithms, examining their performance and computational overhead. Moreover, we provide an overview of applications developed using different PEFT algorithms and discuss common techniques employed to mitigate computation costs for PEFT. In addition to providing an extensive survey from an algorithmic standpoint, we also examine various real-world system designs to investigate the implementation costs associated with different PEFT approaches. This survey serves as a valuable resource for researchers aiming to understand both the PEFT algorithm and its system implementation, offering detailed ......"}
		65	+{"work_id": 4396599905, "title": "Multimodal fusion on low-quality data: A comprehensive survey", "abstract": "Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of scenarios, including autonomous driving and medical diagnosis. However, the reliability of multimodal fusion remains largely unexplored especially under low-quality data settings. This paper surveys the common challenges and recent advances of multimodal fusion in the wild and presents them in a comprehensive taxonomy. From a data-centric view, we identify four main challenges that are faced by multimodal fusion on low-quality data, namely (1) noisy multimodal data that are contaminated with heterogeneous noises, (2) incomplete multimodal data that some modalities are missing, (3) imbalanced multimodal data that the qualities or properties of different modalities are significantly different and (4) quality-varying multimodal data that the quality of each modality dynamically changes with respect to different samples. This new taxonomy will enable researchers to understand the state of the field and identify several potential directions. We also provide discussion for the open problems in this field together with interesting future research directions."}
		66	+{"work_id": 2781726626, "title": "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor", "abstract": "Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning. Both of these challenges severely limit the applicability of such methods to complex, real-world domains. In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy. That is, to succeed at the task while acting as randomly as possible. Prior deep RL methods based on this framework have been formulated as Q-learning methods. By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods. Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds."}
		67	+{"work_id": 4402727669, "title": "mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration", "abstract": "Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However, previous methods primarily fo-cus on enhancing multi-modal capabilities. In this work, we introduce a versatile multi-modal large language model, mPLUG-Owl2, which effectively leverages modality collab-oration to improve performance in both text and multi-modal tasks. mPLUG-Owl2 utilizes a modularized network design, with the language decoder acting as a universal interface for managing different modalities. Specifically, mPLUG-Owl2 incorporates shared functional modules to facilitate modal-ity collaboration and introduces a modality-adaptive module that preserves modality-specific features. Extensive experi-ments reveal that mPLUG-Owl2 is capable of generalizing both text tasks and multi-modal tasks and achieving state-of-the-art performances with a single generic model. Notably, mPLUG-Owl2 is the first MLLM model that demonstrates the modality collaboration phenomenon in both pure-text and multi-modal scenarios, setting a pioneering path in the development of future multi-modal foundation models."}
		68	+{"work_id": 1568555062, "title": "Equivalence and Synthesis of Causal Models", "abstract": "chapter Share on Equivalence and Synthesis of Causal Models Authors: TS Verma Search about this author , Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 221\u2013236https://doi.org/10.1145/3501714.3501732Online:04 March 2022Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access"}
		69	+{"work_id": 2754517384, "title": "Deep Reinforcement Learning That Matters", "abstract": "In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted."}
		70	+{"work_id": 2964121744, "title": "AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)", "abstract": "A dependency-free experiment runner that turns a paper's research question into a configuration-driven, verifiable experiment task. The bundled reproduction maps the first/second moment estimates and bias correction of Adam (Kingma & Ba, arXiv:1412.6980) into runnable Python and compares them against fixed-learning-rate SGD under fixed seeds and epoch budgets. A second experiment measures convergence speed (epochs to reach a target training loss) against SGD with heavy-ball momentum, AdaGrad and RMSProp, with every optimizer family tuned by the same coarse learning-rate sweep. In that experiment Adam is not the fastest; the negative result is reported in full. Every reported number is pinned in an expected-values file with explicit tolerances and checked by one command that also runs the unit tests, and the checking harness is validated against deliberately broken implementations (negative controls). The repository also ships a task contract that expresses the reproduction as a repeatable, scoreable task for an automated research loop."}
		71	+{"work_id": 4410609100, "title": "Jailbreaking Black Box Large Language Models in Twenty Queries", "abstract": "There is growing interest in ensuring that large language models (LLMs) align with human values. However, the alignment of such models is vulnerable to adversarial jailbreaks, which coax LLMs into overriding their safety guardrails. The identification of these vulnerabilities is therefore instrumental in understanding inherent weaknesses and preventing future misuse. To this end, we propose Prompt Automatic Iterative Refinement (PAIR), an algorithm that generates semantic jailbreaks with only black-box access to an LLM. PAIR\u2014which is inspired by social engineering attacks\u2014uses an attacker LLM to automatically generate jailbreaks for a separate targeted LLM without human intervention. In this way, the attacker LLM iteratively queries the target LLM to update and refine a candidate jailbreak. Empirically, PAIR often requires fewer than twenty queries to produce a jailbreak, which is orders of magnitude more efficient than existing algorithms. PAIR also achieves competitive jailbreaking success rates and transferability on open and closed-source LLMs, including GPT-3.5/4, Vicuna, and Gemini."}
		72	+{"work_id": 4409249263, "title": "Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice", "abstract": "Artificial intelligence (AI) has rapidly transformed various sectors, including healthcare, where it holds the potential to transform clinical practice and improve patient outcomes. However, its integration into medical settings brings significant ethical challenges that need careful consideration. This paper examines the current state of AI in healthcare, focusing on five critical ethical concerns: justice and fairness, transparency, patient consent and confidentiality, accountability, and patient-centered and equitable care. These concerns are particularly pressing as AI systems can perpetuate or even exacerbate existing biases, often resulting from non-representative datasets and opaque model development processes. The paper explores how bias, lack of transparency, and challenges in maintaining patient trust can undermine the effectiveness and fairness of AI applications in healthcare. In addition, we review existing frameworks for the regulation and deployment of AI, identifying gaps that limit the widespread adoption of these systems in a just and equitable manner. Our analysis provides recommendations to address these ethical challenges, emphasizing the need for fairness in algorithm design, transparency in model decision-making, and patient-centered approaches to consent and data privacy. By highlighting the importance of continuous ethical scrutiny and collaboration between AI developers, clinicians, and ethicists, we outline pathways for achieving more responsible and inclusive AI implementation in healthcare. These strategies, if adopted, could enhance both the clinical value of AI and the trustworthiness of AI systems among patients and healthcare professionals, ensuring that these technologies serve all populations equitably."}
		73	+{"work_id": 1591675293, "title": "Bayesian Inverse Reinforcement Learning", "abstract": "Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an expert. IRL is motivated by situations where knowledge of the rewards is a goal by itself (as in preference elicitation) and by the task of apprenticeship learning (learning policies from an expert). In this paper we show how to combine prior knowledge and evidence from the expert's actions to derive a probability distribution over the space of reward functions. We present efficient algorithms that find solutions for the reward learning and apprenticeship learning tasks that generalize well over these distributions. Experimental results show strong improvement for our methods over previous heuristic-based approaches."}
		74	+{"work_id": 2964083594, "title": "Large-Scale Study of Curiosity-Driven Learning", "abstract": "Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the handdesigned extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at https://pathak22.github.io/large-scale-curiosity/."}
		75	+{"work_id": 7124262017, "title": "OECD Digital Education Outlook 2026", "abstract": "The OECD Digital Education Outlook 2026 explores emerging research on the use of generative AI in education and presents innovative tools and applications that show promise. The report examines the use of generative AI in different teaching and learning scenarios: by students to learn subject knowledge, by students and teachers together as part of instruction, and by teachers alone to support their work in the classroom. The Outlook also showcases how generative AI can enhance the efficiency of education institutions and systems, such as research, analysis of learning pathways and support for study advisors. While highlighting the opportunities and benefits of generative AI, the report also notes key challenges and concerns. It provides a state-of-the-art overview of the impact and possibilities of generative AI in education and is an essential resource for education policymakers, institution leaders, researchers, as well as other stakeholders."}
		76	+{"work_id": 4321499901, "title": "What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education", "abstract": "Abstract Artificial Intelligence (AI) technologies have been progressing constantly and being more visible in different aspects of our lives. One recent phenomenon is ChatGPT, a chatbot with a conversational artificial intelligence interface that was developed by OpenAI. As one of the most advanced artificial intelligence applications, ChatGPT has drawn much public attention across the globe. In this regard, this study examines ChatGPT in education, among early adopters, through a qualitative instrumental case study. Conducted in three stages, the first stage of the study reveals that the public discourse in social media is generally positive and there is enthusiasm regarding its use in educational settings. However, there are also voices who are approaching cautiously using ChatGPT in educational settings. The second stage of the study examines the case of ChatGPT through lenses of educational transformation, response quality, usefulness, personality and emotion, and ethics. In the third and final stage of the study, the investigation of user experiences through ten educational scenarios revealed various issues, including cheating, honesty and truthfulness of ChatGPT, privacy misleading, and manipulation. The findings of this study provide several research directions that should be considered to ensure a safe and responsible adoption of chatbots, specifically ChatGPT, in education."}
		77	+{"work_id": 4410551352, "title": "A foundation model for the Earth system", "abstract": "Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive1. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency2,3, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality climate and weather information. Aurora, a new large-scale foundation model trained on more than one million hours of diverse geophysical data, outperforms operational forecasts in predicting air quality, ocean wave dynamics, tropical cyclone tracks and high-resolution weather."}
		78	+{"work_id": 4321648656, "title": "From Paintbrush to Pixel: A Review of Deep Neural Networks in AI-Generated Art", "abstract": "Abstract This paper delves into the fascinating field of AI-generated art and explores the various deep neural network architectures and models that have been utilized to create it. From the classic convolutional networks to the cutting-edge diffusion models, we examine the key players in the field. We explain the general structures and working principles of these neural networks. Then, we showcase examples of milestones, starting with the dreamy landscapes of DeepDream and moving on to the most recent developments, including Stable Diffusion and DALL-E 3, which produce mesmerizing images. We provide a detailed comparison of these models, highlighting their strengths and limitations, and examining the remarkable progress that deep neural networks have made so far in a short period of time. With a unique blend of technical explanations and insights into the current state of AI-generated art, this paper exemplifies how art and computer science interact."}
		79	+{"work_id": 2950067852, "title": "A Recurrent Latent Variable Model for Sequential Data", "abstract": "In this paper, we explore the inclusion of latent random variables into the hidden state of a recurrent neural network (RNN) by combining the elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN) can model the kind of variability observed in highly structured sequential data such as natural speech. We empirically evaluate the proposed model against other related sequential models on four speech datasets and one handwriting dataset. Our results show the important roles that latent random variables can play in the RNN dynamics."}
		80	+{"work_id": 4320495408, "title": "Chatting about ChatGPT: how may AI and GPT impact academia and libraries?", "abstract": "Note: This paper was written and published in January 2023, within weeks of ChatGPT's public release, making it among the earliest peer-reviewed scholarly discussions of ChatGPT's implications for academia and libraries. It is best understood as a contemporaneous assessment of an emerging technology at a pivotal moment \u2014 capturing initial perceptions, possibilities, and concerns that have since been extensively debated and studied. The paper's interview with ChatGPT itself represents one of the earliest documented scholarly interactions with the tool in an academic context. For researchers studying the history of AI adoption in higher education and libraries, this paper offers a primary source perspective from the field's earliest engagement with generative AI. This paper provides an overview of key definitions related to ChatGPT, a public tool developed by OpenAI, and its underlying technology, GPT. The paper discusses the history and technology of GPT, including its generative pre-trained transformer model, its ability to perform a wide range of language-based tasks, and how ChatGPT utilizes this technology to function as a sophisticated chatbot. Additionally, the paper includes an interview with ChatGPT on its potential impact on academia and libraries. The interview discusses the benefits of ChatGPT such as improving search and discovery, reference and information services, cataloging and metadata generation, and content creation, as well as the ethical considerations that need to be taken into account, such as privacy and bias. The paper also explores the possibility of using ChatGPT for writing scholarly papers."}
		81	+{"work_id": 4389437528, "title": "Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning", "abstract": "Since its maiden release into the public domain on November 30, 2022, ChatGPT garnered more than one million subscribers within a week. The generative AI tool \u23bcChatGPT took the world by surprise with it sophisticated capacity to carry out remarkably complex tasks. The extraordinary abilities of ChatGPT to perform complex tasks within the field of education has caused mixed feelings among educators as this advancement in AI seems to revolutionize existing educational praxis. This review article synthesizes recent extant literature to offer some potential benefits of ChatGPT in promoting teaching and learning. Benefits of ChatGPT include but are not limited to promotion of personalized and interactive learning, generating prompts for formative assessment activities that provide ongoing feedback to inform teaching and learning etc. The paper also highlights some inherent limitations in the ChatGPT such as generating wrong information, biases in data training which may augment existing biases, privacy issues etc. The study offers recommendations on how ChatGPT could be leveraged to maximize teaching and learning. Policy makers, researchers, educators and technology experts could work together and start conversations on how these evolving generative AI tools could be used safely and constructively to improve education and support students\u2019 learning."}
		82	+{"work_id": 2973229164, "title": "Continuous control for robot based on deep reinforcement learning", "abstract": "results of redundant manipulator . . . . . . . ."}
		83	+{"work_id": 4399567248, "title": "A multimodal generative AI copilot for human pathology", "abstract": "Abstract Computational pathology 1,2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders 3,4 . However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots 5 tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions consisting of 999,202 question and answer turns. We compare PathChat with several multimodal vision-language AI assistants and GPT-4V, which powers the commercially available multimodal general-purpose AI assistant ChatGPT-4 (ref. 6 ). PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases with diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive vision-language AI copilot that can flexibly handle both visual and natural language inputs, PathChat may potentially find impactful applications in pathology education, research and human-in-the-loop clinical decision-making."}
		84	+{"work_id": 3140854437, "title": "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions", "abstract": "In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion."}
		85	+{"work_id": 7128029160, "title": "A generalizable foundation model for analysis of human brain MRI", "abstract": "Artificial intelligence applied to brain magnetic resonance imaging (MRI) holds potential to advance diagnosis, prognosis and treatment planning for neurological diseases. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. By leveraging self-supervised learning, pretraining and targeted adaptation, foundation models present a promising paradigm to overcome these limitations. Here we present Brain Imaging Adaptive Core (BrainIAC)-a foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adaptation. Trained and validated on 48,965 brain MRIs across a broad spectrum of tasks, we demonstrate that BrainIAC outperforms localized supervised training and other pretrained models, particularly in low-data, few-shot, settings and in high-difficulty prediction tasks, allowing for application in scenarios otherwise infeasible. BrainIAC can be integrated into imaging pipelines and multimodal frameworks and may lead to improved biomarker discovery and artificial intelligence clinical translation."}
		86	+{"work_id": 2962862931, "title": "On a Method to Measure Supervised Multiclass Model\u2019s Interpretability: Application to Degradation Diagnosis (Short Paper)", "abstract": "In an industrial maintenance context, degradation diagnosis is the problem of determining the current level of degradation of operating machines based on measurements. With the emergence of Machine Learning techniques, such a problem can now be solved by training a degradation model offline and by using it online. While such models are more and more accurate and performant, they are often black-box and their decisions are therefore not interpretable for human maintenance operators. On the contrary, interpretable ML models are able to provide explanations for the model\u2019s decisions and consequently improves the confidence of the human operator about the maintenance decision based on these models. This paper proposes a new method to quantitatively measure the interpretability of such models that is agnostic (no assumption about the class of models) and that is applied on degradation models. The proposed method requires that the decision maker sets up some high level parameters in order to measure the interpretability of the models and then can decide whether the obtained models are satisfactory or not. The method is formally defined and is fully illustrated on a decision tree degradation model and a model trained with a recent neural network architecture called Multiclass Neural Additive Model."}
		87	+{"work_id": 4323655724, "title": "ChatGPT for good? On opportunities and challenges of large language models for education", "abstract": "Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education."}
		88	+{"work_id": 4387355843, "title": "TimeLLM: Time Series Forecasting by Reprogramming Large Language Models", "abstract": "Accurate forecasting of time series is essential to many dynamic real-world systems and has attracted extensive research attention. Unlike natural language processing or computer vision, where one large model can often address multiple tasks, most existing forecasting solutions are highly specialized and confined to the single time series data modality. Advancements in multimodal time series foundation models have significantly lagged behind other domains, mainly because large, high-quality time series corpora remain scarce. At the same time, recent evidence suggests that large language models (LLMs) excel at understanding and reasoning across long token sequences. Exploiting those capabilities for forecasting requires a principled way to bridge numeric time series signals and linguistic tokens. This chapter presents Time-LLM, a model reprogramming framework that repurposes frozen LLM backbones for general time series prediction. As the first multimodal, large-scale approach in the time series community, Time-LLM embeds raw time series (target) signals into text (source) prototypes, aligning the two modalities before the data enters the language model backbone. To strengthen the model&s;s reasoning over time series, Time-LLM incorporates Prompt-as-Prefix (PaP), which augments contextual information and directs the reprogramming of input patches. The modified patches are then projected by the LLM to generate forecasts. Extensive evaluations demonstrate that Time-LLM serves as an effective multimodal forecaster, surpassing specialized baseline models."}
		89	+{"work_id": 4221150249, "title": "Graph Neural Networks for Graphs With Heterophily: A Survey", "abstract": "Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption that nodes belonging to the same class are more likely to be connected. However, as a ubiquitous graph property in numerous real-world scenarios, heterophily, i.e., nodes with different labels tend to be linked, significantly limits the performance of tailor-made homophilic GNNs. Hence,GNNs for heterophilic graphsare gaining increasing research attention to enhance graph learning with heterophily. In this paper, we provide a comprehensive review of GNNs for heterophilic graphs. Specifically, we propose a systematic taxonomy that governs existing heterophilic GNN models, along with general summaries and detailed analyses. Furthermore, we discuss the relationship between heterophily and various graph research domains, aiming to facilitate the development of more effective GNNs across a spectrum of practical applications and learning tasks in the graph research community. In the end, we point out potential directions to advance and inspire future research and applications on heterophilic graph learning with GNNs."}
		90	+{"work_id": 4322617812, "title": "Generative AI and the future of education: Ragnar\u00f6k or reformation? A paradoxical perspective from management educators", "abstract": "Generative artificial intelligence (AI) has taken the world by storm, with notable tension transpiring in the field of education. Given that Generative AI is rapidly emerging as a transformative innovation, this article endeavors to offer a seminal rejoinder that aims to (i) reconcile the great debate on Generative AI in order to (ii) lay the foundation for Generative AI to co-exist as a transformative resource in the future of education. Using critical analysis as a method and paradox theory as a theoretical lens (i.e., the \u201chow\u201d), this article (i) defines Generative AI and transformative education (i.e., the \u201cideas\u201d), (ii) establishes the paradoxes of Generative AI (i.e., the \u201cwhat\u201d), and (iii) provides implications for the future of education from the perspective of management educators (i.e., the \u201cso what\u201d). Noteworthily, the paradoxes of Generative AI are four-fold: (Paradox #1) Generative AI is a \u2018friend\u2019 yet a \u2018foe\u2019, (Paradox #2) Generative AI is \u2018capable\u2019 yet \u2018dependent\u2019, (Paradox #3) Generative AI is \u2018accessible\u2019 yet \u2018restrictive\u2019, and (Paradox #4) Generative AI gets even \u2018popular\u2019 when \u2018banned\u2019 (i.e., the \u201cwhat\u201d). Through a position that seeks to embrace rather than reject Generative AI, the lessons and implications that emerge from the discussion herein represent a seminal contribution from management educators on this trending topic and should be useful for approaching Generative AI as a game-changer for education reformation in management and the field of education at large, and by extension, mitigating a situation where Generative AI develops into a Ragnar\u00f6k that dooms the future of education of which management education is a part of (i.e., the \u201cso what\u201d)."}
		91	+{"work_id": 3041133507, "title": "A Comprehensive Survey on Transfer Learning", "abstract": "Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice."}
		92	+{"work_id": 2806351858, "title": "MolGAN: An implicit generative model for small molecular graphs", "abstract": "Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuristics of previous likelihood-based methods. Our method adapts generative adversarial networks (GANs) to operate directly on graph-structured data. We combine our approach with a reinforcement learning objective to encourage the generation of molecules with specific desired chemical properties. In experiments on the QM9 chemical database, we demonstrate that our model is capable of generating close to 100% valid compounds. MolGAN compares favorably both to recent proposals that use string-based (SMILES) representations of molecules and to a likelihood-based method that directly generates graphs, albeit being susceptible to mode collapse. Code at https://github.com/nicola-decao/MolGAN"}
		93	+{"work_id": 4385285835, "title": "Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling", "abstract": "Computational pathology is revolutionizing the field of pathology by integrating advanced computer vision and machine learning technologies into diagnostic workflows. It offers unprecedented opportunities for improved efficiency in treatment decisions by allowing pathologists to achieve higher precision and objectivity in disease classification, tumor microenvironment description and identification of new biomarkers. However, the potential of computational pathology in personalized medicine comes with significant challenges, particularly in annotating whole slide images (WSI), which is time-consuming, costly and subject to inter-observer variability. To address these challenges, Self-Supervised Learning (SSL) has emerged as a promising solution to learn representations from histology patches and leverage large volumes of unlabelled WSI. Recently, Masked Image Modeling (MIM) as a SSL framework has emerged and is now considered to outperform purely contrastive learning paradigms. In this work, we therefore explore the application of MIM to histology using iBOT, a self-supervised transformer-based framework. Through a wide range of 17 downstream tasks over seven cancer indications, both at the slide and patch levels, we provide recommendations on the pre-training of large models for histology data using MIM. First, we demonstrate that in-domain pre-training with iBOT outperforms both ImageNet pre-training and a model pre-trained with a purely contrastive learning objective, MoCo v2. Second, we show that Vision Transformers (ViT) models, when scaled appropriately, have the capability to learn pan-cancer representations that benefit a large variety of downstream tasks. Finally, our iBOT ViT-Base model (80 million parameters), pre-trained on more than 40 million histology images from 16 different cancer types, achieves state-of-the-art performance in most weakly-supervised WSI classification tasks compared to other SSL frameworks available in the literature. This paves the way for the development of a foundation model for histopathology. Our code, models and features are publicly available at https://github.com/owkin/HistoSSLscaling ."}
		94	+{"work_id": 3177318507, "title": "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting", "abstract": "Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer that prevent it from being directly applicable to LSTF, including quadratic time complexity, high memory usage, and inherent limitation of the encoder-decoder architecture. To address these issues, we design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: (i) a ProbSparse self-attention mechanism, which achieves O(L log L) in time complexity and memory usage, and has comparable performance on sequences' dependency alignment. (ii) the self-attention distilling highlights dominating attention by halving cascading layer input, and efficiently handles extreme long input sequences. (iii) the generative style decoder, while conceptually simple, predicts the long time-series sequences at one forward operation rather than a step-by-step way, which drastically improves the inference speed of long-sequence predictions. Extensive experiments on four large-scale datasets demonstrate that Informer significantly outperforms existing methods and provides a new solution to the LSTF problem."}
		95	+{"work_id": 7135409568, "title": "The role of agentic artificial intelligence in healthcare: a scoping review", "abstract": "Abstract Background/ Objectives: Agentic AI represents a promising evolution of AI technology applied to healthcare, with systems increasingly capable of operating autonomously to achieve defined clinical goals. However, the literature lacks conceptual clarity between \u201cAI agents\u201d and \u201cagentic AI\u201d, and few studies have rigorously explored their clinical applications. Therefore, this study aims to conduct a novel systematic review addressing this gap by examining agentic AI systems in healthcare settings, characterizing their applications, features, outcomes, and limitations, and clarifying the conceptual distinctions between AI agents and agentic AI systems using predefined and objective criteria. Methods A comprehensive search was conducted across PubMed, Embase, Cochrane, Scopus, and Google Scholar on April 6th, 2025. Studies were included if they involved AI systems in healthcare settings that demonstrated the following agentic features: autonomous operation, goal-directed behavior, and initiating action. Data on the clinical tasks achieved by the agents, key findings, features, and limitations were collected from the included studies. Screening and extraction followed PRISMA guidelines, with Risk of Bias assessed using ROBINS-I and Cochrane's Risk of Bias tools. Results Of 984 retrieved records, seven studies met the inclusion criteria, spanning domains such as emergency medicine, oncology, radiology, and rehabilitation. Multi-agent architecture was frequently used to decompose and coordinate complex workflows. Among the included studies, the AIs showed high accuracy in diagnosing cancer patients, conducting treatment plans, sending alerts, coaching messages, analysing image data, and adapting to challenging experimental scenarios. While demonstrating potential for improved efficiency, task accuracy, and patient engagement, significant limitations were noted: narrow task scope, lack of physical agency, limited clinical validation, and barriers to integration into real-world healthcare systems. Only one system had been deployed in a patient-facing trial setting. Conclusion The current literature suggests an emerging role and application of Agentic AI, holding promise with the potential to revolutionize diagnostics, triage, treatment planning, and patient management. However, real-world implementation and evaluations in the literature are limited. Future research must address critical validation, regulation, ethics, and clinical integration challenges to realize their full potential. Clear operational definitions and frameworks for evaluating agency are essential to support safe and effective deployment of these systems."}
		96	+{"work_id": 2100415632, "title": "Efficient learning by implicit exploration in bandit problems with side observations", "abstract": "We consider online learning problems under a partial observability model capturing situations where the information conveyed to the learner is between full information and bandit feedback. In the simplest variant, we assume that in addition to its own loss, the learner also gets to observe losses of some other actions. The revealed losses depend on the learner's action and a directed observation system chosen by the environment. For this setting, we propose the first algorithm that enjoys near-optimal regret guarantees without having to know the observation system before selecting its actions. Along similar lines, we also define a new partial information setting that models online combinatorial optimization problems where the feedback received by the learner is between semi-bandit and full feedback. As the predictions of our first algorithm cannot be always computed efficiently in this setting, we propose another algorithm with similar properties and with the benefit of always being computationally efficient, at the price of a slightly more complicated tuning mechanism. Both algorithms rely on a novel exploration strategy called implicit exploration, which is shown to be more efficient both computationally and information-theoretically than previously studied exploration strategies for the problem."}
		97	+{"work_id": 7133226216, "title": "The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games", "abstract": "Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings. This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems. In this work, we carefully study the performance of PPO in cooperative multi-agent settings. We show that PPO-based multi-agent algorithms achieve surprisingly strong performance in four popular multi-agent testbeds: the particle-world environments, the StarCraft multi-agent challenge, Google Research Football, and the Hanabi challenge, with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures. Importantly, compared to competitive off-policy methods, PPO often achieves competitive or superior results in both final returns and sample efficiency. Finally, through ablation studies, we analyze implementation and hyperparameter factors that are critical to PPO's empirical performance, and give concrete practical suggestions regarding these factors. Our results show that when using these practices, simple PPO-based methods can be a strong baseline in cooperative multi-agent reinforcement learning. Source code is released at \\url{https://github.com/marlbenchmark/on-policy}."}
		98	+{"work_id": 4390690043, "title": "Data-Centric Foundation Models in Computational Healthcare: A Survey", "abstract": "The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre-training data and human instructions, has ignited a data-centric AI paradigm that emphasizes better data characterization, quality, and scale. In healthcare AI, obtaining and processing high-quality clinical data records has been a longstanding challenge, encompassing data quantity, annotation, patient privacy, and ethics. In this survey, we investigate a wide range of data-centric approaches in the FM era (from model pre-training to inference) towards improving the healthcare workflow. We discuss key perspectives in AI security, assessment, and alignment with human values. Finally, we offer a promising outlook on FM-based analytics to enhance patient outcomes and clinical workflows in the evolving landscape of healthcare and medicine. We provide an up-to-date list of healthcare-related foundation models and datasets at https://github.com/Yunkun-Zhang/Data-Centric-FM-Healthcare ."}
		99	+{"work_id": 4389519587, "title": "Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding", "abstract": "We present Video-LLaMA 1 a multi-modal framework that empowers Large Language Models (LLMs) with the capability of understanding both visual and auditory content in the video.Video-LLaMA bootstraps cross-modal training from the frozen pre-trained visual & audio encoders and the frozen LLMs.Unlike previous works that complement LLMs to process the visual or audio signals only (Zhu et al., 2023;Liu et al., 2023; Huang et al., 2023a), Video-LLaMA enables video comprehension by tackling two challenges: (1) capturing the temporal changes in visual scenes, (2) integrating audio-visual signals.To counter the first challenge, we propose a Video Q-former to assemble a pre-trained image encoder into our video encoder and introduce a video-to-text generation task to learn video-language correspondence.For the second challenge, we leverage ImageBind (Girdhar et al., 2023), a universal embedding model aligning multiple modalities, as the pre-trained audio encoder and introduce an Audio Q-former on top of ImageBind to learn reasonable auditory query embeddings for the LLM module.To align the output of both visual & audio encoders with LLM's embedding space, we first train Video-LLaMA on massive video/image-caption pairs and then tune our model with visual-instruction datasets of moderate amount but higher quality.We found Video-LLaMA shows the ability to perceive and comprehend video content and generate meaningful responses grounded in the visual and auditory information presented in the videos."}
		100	+{"work_id": 4400228804, "title": "The Conglomeration of Everything: Shifting Animation Production Practices due to Artificial Intelligence", "abstract": "Artificial Intelligence (AI) is incorporated into systems that impact us daily and generative AI is the next evolution of the technology.For media studios who have attempted to be early adopters of the technology, it has resulted in negative responses online by creatives.These responses recognize that the usage of generative AI brings up concerns about ethics, plagiarism, and the loss of jobs in creative positions.I argue that AI usage in animation will not be as affected by AI as other creative jobs due to the negative pushback against the usage of generative AI and the inability to copyright nonhuman-made creative works.This future era of AI looks to shift the current production pipelines of film and media in the same way that digital animation tools took the market for commercial animation away from traditional hand-drawn animators but I believe that workflow changes will only manifest within the preproduction process. Artificial Intelligence and its Integration in the World Around UsEver since the beginning of the 20 th century, Artificial Intelligence (AI) has captured the imaginations of creatives with films such as Metropolis (1927), The Day the Earth Stood Still (1951), and 2001 a Space Odyssey (1968).Research towards AI gained traction in the 1950s when scientists Herbert Simon, Allen Newell, and Cliff Shaw created Logic Theorist, a seminal AI program that debuted at the Dartmouth Summer Research Project on Artificial Intelligence in 1956. 1 As AI matured, significant milestones occurred: \"machine learning\" coined by Arthur Samuel (1959), the first \"chatbot\" ELIZA by Joseph Weizenbaum (1966), proposal of autonomous vehicles by James L. Adams (1979), chess master Gary Kasparov's defeat by Deep Blue (1997), and Apple's introduction of Siri (2011). 2 Milestones in generative AI also followed: Geoffrey Hinton published \"A Fast Learning Algorithm for Deep Belief Nets\" (2006), Generative Adversarial Networks (GANs) were introduced by Ian Goodfellow (2014), and consumer-marketed generative AI services (ChatGPT, MidJourney, Dall-E 2) hit the mainstream (2022).3 Creatives have gained access to the technology and begun implementing AI into their production workflows, pushing the creative capabilities of computer-generated imagery.4 If you are able to access the internet, the capabilities of generative AI is at your fingertips. 1 History-Computer, \"Logic Theorist Explained -Everything You Need to Know,\" April 16, 2024,"}
		101	+{"work_id": 4289785095, "title": "An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion", "abstract": "Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy? Here we present a simple approach that allows such creative freedom. Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new \"words\" in the embedding space of a frozen text-to-image model. These \"words\" can be composed into natural language sentences, guiding personalized creation in an intuitive way. Notably, we find evidence that a single word embedding is sufficient for capturing unique and varied concepts. We compare our approach to a wide range of baselines, and demonstrate that it can more faithfully portray the concepts across a range of applications and tasks. Our code, data and new words will be available at: https://textual-inversion.github.io"}
		102	+{"work_id": 2761873684, "title": "Rainbow: Combining Improvements in Deep Reinforcement Learning", "abstract": "The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance."}
		103	+{"work_id": 7148475777, "title": "Capabilities of GPT\u20115 on multimodal medical reasoning", "abstract": "Recent advances in large language models (LLMs) have enabled general-purpose systems to perform increasingly complex domain-specific reasoning without extensive fine-tuning. In the medical domain, decision-making often requires integrating heterogeneous information sources, including patient narratives, structured data, and medical images. This study positions GPT-5 as a generalist multimodal reasoner for medical decision support and systematically evaluates its zero-shot chain-of-thought reasoning performance on both text-based question answering and visual question answering tasks under a unified protocol. We benchmark GPT-5, GPT-5-mini, GPT-5-nano, and GPT-4o-2024-11-20 against standardized splits of MedQA, MedXpertQA (text and multimodal), MMLU medical subsets, USMLE self-assessment exams, and VQA-RAD. Results show that GPT-5 consistently outperforms all baselines, achieving state-of-the-art accuracy across all QA benchmarks and delivering substantial gains in multimodal reasoning. On MedXpertQA MM, GPT-5 improves reasoning and understanding scores by +29.26% and +26.18% over GPT-4o, respectively, and surpasses pre-licensed human experts by +24.23% in reasoning and +29.40% in understanding. In contrast, GPT-4o remains below human expert performance in most dimensions. A representative case study demonstrates GPT-5\u2019s ability to integrate visual and textual cues into a coherent diagnostic reasoning chain, recommending appropriate high-stakes interventions. Our results show that, on these controlled multimodal reasoning benchmarks, GPT-5 moves from human-comparable to above human-expert performance. This improvement may substantially inform the design of future clinical decision-support systems. We make the code public at the GPT-5-Evaluation."}
		104	+{"work_id": 4409269614, "title": "Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda", "abstract": "This systematic literature review rigorously evaluates the impact of Generative AI (GenAI) on academic integrity within higher education settings. The primary objective is to synthesize how GenAI technologies influence student behavior and academic honesty, assessing the benefits and risks associated with their integration. We defined clear inclusion and exclusion criteria, focusing on studies explicitly discussing GenAI\u2019s role in higher education from January 2021 to December 2024. Databases included ABI/INFORM, ACM Digital Library, IEEE Xplore, and JSTOR, with the last search conducted in May 2024. A total of 41 studies met our precise inclusion criteria. Our synthesis methods involved qualitative analysis to identify common themes and quantify trends where applicable. The results indicate that while GenAI can enhance educational engagement and efficiency, it also poses significant risks of academic dishonesty. We critically assessed the risk of bias in included studies and noted a limitation in the diversity of databases, which might have restricted the breadth of perspectives. Key implications suggest enhancing digital literacy and developing robust detection tools to effectively manage GenAI\u2019s dual impacts. No external funding was received for this review. Future research should expand database sources and include more diverse study designs to overcome current limitations and refine policy recommendations."}
		105	+{"work_id": 4384071683, "title": "Large language models encode clinical knowledge", "abstract": "Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model 1 (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM 2 on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA 3 , MedMCQA 4 , PubMedQA 5 and Measuring Massive Multitask Language Understanding (MMLU) clinical topics 6 ), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today\u2019s models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications."}
		106	+{"work_id": 4221143046, "title": "BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence", "abstract": "AbstractThere is a failure mode in large language models that we do not have a good name for, and thatwe therefore tend not to treat seriously enough. It is not hallucination \u2014 the model is not assertingsomething false. It is not refusal \u2014 the model answers at length. It is the production of responses thatcarry the complete outward form of careful reasoning while the cognitive work that reasoning issupposed to represent has not, in any meaningful sense, occurred. We call this theatrical compliance,and we argue that it is, in practical terms, more dangerous than either of the failure modes thatcurrently dominate alignment research. This paper identifies the phenomenon, characterizes its fiveprincipal forms, explains the asymmetry that makes it particularly costly in high-stakes settings, andoutlines the design requirements for systems intended to resist it. We do not describe such a systemin detail here. Our goal is to establish theatrical compliance as a research problem in its own rightand to argue that addressing it requires instruments operating at a fundamentally different level ofabstraction than task-level prompting frameworks.Keywords: theatrical compliance, large language models, AI reasoning quality, cognitiveprocess evaluation, prompt engineering, metacognitive systems."}
		107	+{"work_id": 4394934695, "title": "The impact of generative AI on higher education learning and teaching: A study of educators\u2019 perspectives", "abstract": "In recent months, Artificial Intelligence (AI) has had, and will continue to have, a dramatic impact on Higher Education (HE). A study conducted by researchers at a leading university in Australia surveyed 30 of their teaching staff, drawn predominantly from their teaching academy, and interviewed eight of them regarding the impact of AI on HE. Data were analyzed using the procedures of Inductive Thematic Analysis and revealed a lack of any homogenous sentiment around AI in HE and much ambiguity regarding best practice regarding recent technological developments. The results indicate concerns exist around concepts relating to academic integrity, however, these concerns may be exaggerated. Almost half of the participants indicated they were using AI within their teaching roles with the most common design change being modifications to assessments. Less than a quarter of staff agreed the university has adequately equipped them for AI, and more than three quarters indicated they would like support. They unanimously assumed the technology will improve. Keeping in mind universities\u2019 obligation to serve students by preparing them for industry, it is vitally important that the HE sector stays informed of developments in AI and commit to ongoing research and discussions regarding best practice in response to AI. However, anything regarding AI and future developments will be extremely difficult to predict."}
		108	+{"work_id": 4398243313, "title": "A scoping review on how generative artificial intelligence transforms assessment in higher education", "abstract": "Abstract Generative artificial intelligence provides both opportunities and challenges for higher education. Existing literature has not properly investigated how this technology would impact assessment in higher education. This scoping review took a forward-thinking approach to investigate how generative artificial intelligence transforms assessment in higher education. We used the PRISMA extension for scoping reviews to select articles for review and report the results. In the screening, we retrieved 969 articles and selected 32 empirical studies for analysis. Most of the articles were published in 2023. We used three levels\u2014students, teachers, and institutions\u2014to analyze the articles. Our results suggested that assessment should be transformed to cultivate students\u2019 self-regulated learning skills, responsible learning, and integrity. To successfully transform assessment in higher education, the review suggested that (i) teacher professional development activities for assessment, AI, and digital literacy should be provided, (ii) teachers\u2019 beliefs about human and AI assessment should be strengthened, and (iii) teachers should be innovative and holistic in their teaching to reflect the assessment transformation. Educational institutions are recommended to review and rethink their assessment policies, as well as provide more inter-disciplinary programs and teaching."}
		109	+{"work_id": 4396531244, "title": "The Fusion of Multilingual Semantic Search and Large Language Models: A New Paradigm for Enhanced Topic Exploration and Contextual Search", "abstract": "Digital news articles are often considered as a primary source of information in today's information-driven society which requires query processing methods to handle the ever-increasing volume of content and various forms of queries. Our goal is to develop a novel approach that can improve the way users access and retrieve news by considering Natural Language Processing (NLP)-driven query processing for news articles. This will address the complexities of news articles written in multiple languages and covering a range of topics. The proposed method helps in enhancing the performance of query processing in applications such as journalism, academic research, and content suggestion. By conducting experiments with Sentence Embeddings, Large Language Model (LLM), and Approximate Nearest Neighbours (ANN), this thesis improves news article search systems. Results show that the method improves the quality of the search results for the queries to return more accurate and relevant search results across different languages."}
		110	+{"work_id": 2895974005, "title": "Exploration and Exploitation in Reinforcement Learning", "abstract": "In reinforcement learning there exists a dilemma of exploration versus exploitation. This has led to the development of methods that have di\u21b5er- ent approaches to this. Using di\u21b5erent types of methods, and modifying them in di\u21b5erent ways can lead to di\u21b5erent results. Knowledge of how di\u21b5erent methods work can give knowledge of what should be used in aspecific case. Two ways that methods can be modified are change in adjustable pa- rameters and change in the number of steps of random actions at thebeginning. How much these two modifications e\u21b5ect the results in a spe- cific environment may di\u21b5er a lot, and can be a very critical thing to consider for certain results. The goal of this study is to answer the question of how the performance of the di\u21b5erent methods such as random, greedy, \u270f-greedy, \u270f-decreasingand Softmax is a\u21b5ected by di\u21b5erent values of their adjustable parameters,and by the number of steps of random actions at the beginning. Thesimulations and a comparative analysis are conducted for the case of an inverted pendulum with a vertical pole placed on a moving cart."}
		111	+{"work_id": 2742132707, "title": "Curriculum learning in reinforcement learning", "abstract": "In recent years, reinforcement learning (RL) has been increasingly successful at solving complex tasks. Despite these successes, one of the fundamental challenges is that many RL methods require large amounts of experience, and thus can be slow to train in practice. Transfer learning is a recent area of research that has been shown to speed up learning on a complex task by transferring knowledge from one or more easier source tasks. Most existing transfer learning methods treat this transfer of knowledge as a one-step process, where knowledge from all the sources are directly transferred to the target. However, for complex tasks, it may be more beneficial (and even necessary) to gradually acquire skills over multiple tasks in sequence, where each subsequent task requires and builds upon knowledge gained in a previous task. This idea is pervasive throughout human learning, where people learn complex skills gradually by training via a curriculum. The goal of this thesis is to explore whether autonomous reinforcement learning agents can also benefit by training via a curriculum, and whether such curricula can be designed fully autonomously. In order to answer these questions, this thesis first formalizes the concept of a curriculum, and the methodology of curriculum learning in reinforcement learning. Curriculum learning consists of 3 main elements: 1) task generation, which creates a suitable set of source tasks; 2) sequencing, which focuses on how to order these tasks into a curriculum; and 3) transfer learning, which considers how to transfer knowledge between tasks in the curriculum. This thesis introduces several methods to both create suitable source tasks and automatically sequence them into a curriculum. We show that these methods produce curricula that are tailored to the individual sensing and action capabilities of different agents, and show how the curricula learned can be adapted for new, but related target tasks. Together, these methods form the components of an autonomous curriculum design agent, that can suggest a training curriculum customized to both the unique abilities of each agent and the task in question. We expect this research on the curriculum learning approach will increase the applicability and scalability of RL methods by providing a faster way of training reinforcement learning agents, compared to learning tabula rasa."}
		112	+{"work_id": 3084163594, "title": "Age of Surveillance Capitalism \u2013 The Fight for a Human Future at the New Frontier of Power.", "abstract": "As an educator have you recently heard the term or perhaps even been told to be data driven? Inherent in this simple two-word statement is a quagmire of ethical and privacy concerns that educators must confront to reach the goal and realize the expected results. Central to the concept of data-centered collection, interpretation, and prediction is surveillance capitalism from which many of the tools, methods, and ideology used originate. This review of Shoshana Zuboff's work narrows the focus to the implications for education both in the classroom and in research. As an educator, Zuboff describes three central areas of concern for education's adoption of surveillance capitalist methodologies: changes to the division of learning, private money in research, and the impacts on student development. The work presents many quesitons that can be raised at all levels of educaiton to quesiton technological adoption in and for the classroom."}
		113	+{"work_id": 4410197190, "title": "The role of agentic AI in shaping a smart future: A systematic review", "abstract": "Artificial intelligence (AI), particularly Agentic AI, is increasingly critical for addressing the demand for speed, efficiency, and customer focus in modern organizations. However, the rapid evolution of Agentic AI, including Generative AI (GenAI) agents, has outpaced a cohesive understanding of its applications, challenges, and strategic implications. This narrative review explores the role of Agentic AI in shaping an intelligent future, focusing on its key attributes\u2014autonomy, reactivity, proactivity, and learning ability\u2014and its potential to transform organizational performance. We identify a research gap in synthesizing the diverse capabilities of Agentic AI (e.g., multimodal processing, hierarchical architectures, and machine learning outsourcing) and providing actionable strategies for adoption. The paper examines how Agentic AI enables autonomous decision-making, automates processes, and enhances efficiency through tools like LangChain, CrewAI, AutoGen, and AutoGPT. It highlights the transition from assisted (\"Copilot\") to autonomous (\"Autopilot\") models and the importance of hierarchical agent structures for system coordination. Key contributions include a framework for organizations to formulate GenAI strategies, addressing business needs, tool selection, human resource training, and risk management. Findings reveal that Agentic AI significantly improves productivity, reduces costs, and drives innovation, though challenges such as privacy, security, and ethical concerns remain. Future research should focus on industry-specific case studies to deepen understanding, explore the ethical and social impacts (e.g., privacy, data security, labor market effects), and investigate the integration of Agentic AI with emerging technologies like quantum computing. This review provides a foundation for researchers and practitioners to leverage Agentic AI effectively while addressing its limitations and opportunities."}
		114	+{"work_id": 3041807988, "title": "Post-Covid-19 Education and Education Technology \u2018Solutionism\u2019: a Seller\u2019s Market", "abstract": "The Covid-19 pandemic and the social distancing that followed have affected all walks of society, also education. In order to keep education running, educational institutions have had to quickly adapt to the situation. This has resulted in an unprecedented push to online learning. Many, including commercial digital learning platform providers, have rushed to provide their support and 'solutions', sometimes for free. The Covid-19 pandemic has therefore also created a sellers' market in ed-tech. This paper employs a critical lens to reflect on the possible problems arising from hasty adoption of commercial digital learning solutions whose design might not always be driven by best pedagogical practices but their business model that leverages user data for profit-making. Moreover, already before Covid-19, there has been increasing critique of how ed-tech is redefining and reducing concepts of teaching and learning. The paper also challenges the narrative that claims, 'education is broken, and it should and can be fixed with technology'. Such technologization, often seen as neutral, is closely related to educationalization, i.e. imposing growing societal problems for education to resolve. Therefore, this is a critical moment to reflect how the current choices educational institutions are making might affect with Covid-19 education and online learning: Will they reinforce capitalist instrumental view of education or promote holistic human growth? This paper urges educational leaders to think carefully about the decisions they are currently making and if they indeed pave the way to a desirable future of education."}
		115	+{"work_id": 4407135816, "title": "Foundation Models in Radiology: What, How, Why, and Why Not", "abstract": "Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models (FMs), are trained on extensive corpora of unlabeled data and demonstrate high performance across various tasks. FMs have recently received extensive attention from academic, industry, and regulatory bodies. Given the potentially transformative impact that FMs can have on the field of radiology, radiologists must be aware of potential pathways to train these radiology-specific FMs, including understanding both the benefits and challenges. Thus, this review aims to explain the fundamental concepts and terms of FMs in radiology, with a specific focus on the requirements of training data, model training paradigms, model capabilities, and evaluation strategies. Overall, the goal of this review is to unify technical advances and clinical needs for safe and responsible training of FMs in radiology to ultimately benefit patients, providers, and radiologists."}
		116	+{"work_id": 4383112908, "title": "Transfer Learning in Deep Reinforcement Learning: A Survey", "abstract": "Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the reinforcement learning perspective and explore their potential challenges that await future research progress."}
		117	+{"work_id": 4407778399, "title": "Investigating the higher education institutions\u2019 guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration", "abstract": "Abstract This study examined the guidelines issued by the top 50 U.S. universities regarding the use of Generative AI (GenAI) in academic and administrative activities. Employing a mixed methods approach, the research combined topic modeling, sentiment analysis, and qualitative thematic analysis to provide a comprehensive understanding of institutional responses to GenAI. Topic modeling identified four core topics: Integration of GenAI in Learning and Assessment, GenAI in Visual and Multimodal Media, Security and Ethical Considerations in GenAI, and GenAI in Academic Integrity. These themes were further explored through sentiment analysis, which revealed highly positive attitudes towards GenAI across all institution types, with significant differences between faculty and student-targeted guidelines. Qualitative thematic analysis corroborated these findings and provided deeper insights, revealing that 94% of universities had faculty guidelines emphasizing the importance of establishing and communicating course-specific GenAI policies. This analysis also highlighted recurring themes such as academic integrity and privacy concerns, which aligned with the security and ethical considerations identified in the topic modeling. The study highlights the rapid evolution of GenAI guidelines in higher education and the need for flexible, stakeholder-specific policies that address both the opportunities and challenges presented by this technology."}
		118	+{"work_id": 4392773210, "title": "Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education", "abstract": "Abstract The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom."}
		119	+{"work_id": 4400823820, "title": "Random Forest Algorithm Overview", "abstract": "A random forest is a machine learning model utilized in classification and forecasting. To train machine learning algorithms and artificial intelligence models, it is crucial to have a substantial amount of high-quality data for effective data collecting. System performance data is essential for refining algorithms, enhancing the efficiency of software and hardware, evaluating user be-havior, enabling pattern identification, decision-making, predictive modeling, and problem-solving, ultimately resulting in improved effectiveness and accuracy. The integration of diverse data collecting and processing methods enhances precision and innovation in problem-solving. Utilizing diverse methodologies in interdisciplinary research streamlines the research process, fosters innovation, and enables the application of data analysis findings to pattern recognition, decision-making, predictive modeling, and problem-solving. This approach also encourages in-novation in interdisciplinary research. This technique utilizes the concept of decision trees, con-structing a collection of decision trees and aggregating their outcomes to generate the ultimate prediction. Every decision tree inside a random forest is constructed using random subsets of data, and each individual tree is trained on a portion of the whole dataset. Subsequently, the outcomes of all decision trees are amalgamated to derive the ultimate forecast. One of the bene-fits of random forests is their capacity to handle unbalanced data and variables with missing values. Additionally, it mitigates the issue of arbitrary variable selection seen by certain alterna-tive models. Furthermore, random forests mitigate the issue of overfitting by training several de-cision trees on random subsets of data, hence enhancing their ability to generalize to novel data. Random forests are highly regarded as one of the most efficient and potent techniques in the domain of machine learning. They find extensive use in various applications such as automatic categorization, data forecasting, and supervisory learning."}
		120	+{"work_id": 4287689466, "title": "From Prediction to Precision: Leveraging LLMs for Equitable and Data-Driven Writing Placement in Developmental Education", "abstract": "Accurate text classification and placement remain challenges in U.S. higher education, with traditional automated systems like Accuplacer functioning as \"black-box\" models with limited assessment transparency. This study evaluates Large Language Models (LLMs) as complementary placement tools by comparing their classification performance against a human-rated gold standard and Accuplacer. A 450-essay corpus was classified using Claude, Gemini, GPT-3.5-turbo, and GPT-4o across four prompting strategies: Zero-shot, Few-shot, Enhanced, and Enhanced+ (definitions with examples). Two classification approaches were tested: (i) a 1-step, 3 class classification task, distinguishing DevEd Level 1, DevEd Level 2, and College-level texts in one single run; and (ii) a 2-step classification task, first separating College vs. Non-College texts before further classifying Non-College texts into DevEd sublevels. The results show that structured prompt refinement improves the precision of LLMs' classification, with Claude Enhanced + achieving 62.22% precision (1 step) and Gemini Enhanced + reaching 69.33% (2 step), both surpassing Accuplacer (58.22%). Gemini and Claude also demonstrated strong correlation with human ratings, with Claude achieving the highest Pearson scores (\u03c1 = 0.75; 1-step, \u03c1 = 0.73; 2-step) vs. Accuplacer (\u03c1 = 0.67). While LLMs show promise for DevEd placement, their precision remains a work in progress, highlighting the need for further refinement and safeguards to ensure ethical and equitable placement."}
		121	+{"work_id": 4413427262, "title": "AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges", "abstract": "Information fusion, in the context of the Generative AI era, must distinguish AI Agents from Agentic AI. This review critically distinguishes between AI Agents and Agentic AI, offering a structured, conceptual taxonomy, application mapping, and analysis of opportunities and challenges to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven and enabled by LLMs and LIMs for task-specific automation. Generative AI is positioned as a precursor providing the foundation, with AI agents advancing through tool integration, prompt engineering, and reasoning enhancements. We then characterize Agentic AI systems, which, in contrast to AI Agents, represent a paradigm shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and coordinated autonomy. Through a chronological evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both AI agents and agentic AI paradigms. Application domains enabled by AI Agents such as customer support, scheduling, and data summarization are then contrasted with Agentic AI deployments in research automation, robotic coordination, and medical decision support. We further examine unique challenges in each paradigm including hallucination, brittleness, emergent behavior, and coordination failure, and propose targeted solutions such as ReAct loops, retrieval-augmented generation (RAG), automation coordination layers, and causal modeling. This work aims to provide a roadmap for developing robust, scalable, and explainable AI-driven systems."}
		122	+{"work_id": 2990704537, "title": "SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems", "abstract": "In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at https://super.gluebenchmark.com."}
		123	+{"work_id": 46659105, "title": "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation", "abstract": "Background. LLM-as-Judge is increasingly adopted in automated program repair (APR) to assess patch correctness, yet its validity as a measurement instrument remains unaudited: potential benchmark-identifier leakage, evidence-presentation bias, and explanation unreliability have not been systematically examined. Aims. We investigate whether LLM Judge produces unbiased, stable, and well-grounded patch correctness judgments across three validity dimensions: metadata leakage, evidence presentation, and explanation reliability. Method. On a balanced, paired dataset of 326 patches from 163 Defects4J bugs, we evaluate GPT-4o and DeepSeek-V3 across a 12-setting prompt matrix covering benchmark-identifying metadata exposure and evidence presentation variants. We further conduct a manual analysis of 60 GPT-4o explanations using a five-category failure taxonomy and three independent annotators. Results. Under sanitized conditions, GPT-4o achieves an accuracy of 77.3% (MCC = 0.575, FPR = 17.2%), while DeepSeek-V3 achieves 80.4% accuracy (MCC = 0.625, FPR = 22.1%), with no statistically significant difference between the two models. Exposing combined benchmark-identifying metadata yields a suggestive accuracy increase of 4.3 percentage points for GPT-4o and 1.8 percentage points for DeepSeek-V3. Declaring that all tests have passed does not significantly bias either model; in contrast, providing a developer reference patch leads to substantial and statistically significant improvements for both models. Explanation failures are concentrated in misclassified cases, with the false-positive quadrant showing a 93.3% failure rate, primarily driven by hallucinated evidence and missed edge cases. Conclusions. LLM Judge is useful as a triage aid but is insufficient as a standalone correctness oracle. Reliable deployment requires prompt sanitization, explicit false positive rate reporting, clear distinction between reference-assisted and standalone assessment, and treating LLM explanations as investigative hypotheses rather than self-validating justifications."}
		124	+{"work_id": 7127274868, "title": "Parental AI Investment Behaviours and AI \u2010Mediated Informal Digital Learning of English: The Chain Mediating Role of Children's Perceived AI Value and Effort Expectancy for AI", "abstract": "ABSTRACT Background The proliferation of generative artificial intelligence (AI) has popularised AI\u2010Mediated Informal Digital Learning of English (AI\u2010IDLE). Whilst research in this area is growing, social influencing factors, especially the role of parents as significant others, remain underexplored. Objectives Guided by the Situational Expectancy\u2010Value Theory (SEVT), this study aims to investigate the impact of parental AI investment behaviours on students' engagement in AI\u2010IDLE. It specifically examines the mediating roles of Perceived AI Value and Effort Expectancy for AI in this relationship. Methods A questionnaire survey was administered to 2346 primary and secondary school students in China. The collected data were analysed using structural equation modelling (SEM) to test the proposed direct and mediating effects. Results The findings revealed that parental AI investment behaviours directly promoted students' AI\u2010IDLE engagement. Furthermore, they exerted significant indirect effects through the separate mediating roles of Perceived AI Value and Effort Expectancy for AI, as well as through their chain mediation pathway. Conclusions This research enriches the application of SEVT by extending it to informal, self\u2010regulated, and technology\u2010enhanced language learning contexts. It underscores the crucial role parents play in out\u2010of\u2010class learning and offers practical insights for guiding children to use AI tools effectively for English proficiency development."}
		125	+{"work_id": 2999309192, "title": "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation", "abstract": "Abstract Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F 1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F 1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F 1 score in evaluating binary classification tasks by all scientific communities."}
		126	+{"work_id": 7133233472, "title": "QLoRA: Efficient Finetuning of Quantized LLMs", "abstract": "We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU. QLoRA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) double quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) paged optimziers to manage memory spikes. We use QLoRA to finetune more than 1,000 models, providing a detailed analysis of instruction following and chatbot performance across 8 instruction datasets, multiple model types (LLaMA, T5), and model scales that would be infeasible to run with regular finetuning (e.g. 33B and 65B parameter models). Our results show that QLoRA finetuning on a small high-quality dataset leads to state-of-the-art results, even when using smaller models than the previous SoTA. We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT. We release all of our models and code, including CUDA kernels for 4-bit training."}
		127	+{"work_id": 7133236347, "title": "TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems", "abstract": "Agentic AI systems, built upon large language models (LLMs) and deployed in multi-agent configurations, are redefining intelligence, autonomy, collaboration, and decision-making across enterprise and societal domains. This review presents a structured analysis of Trust, Risk, and Security Management (TRiSM) in the context of LLM-based Agentic Multi-Agent Systems (AMAS). We begin by examining the conceptual foundations of Agentic AI and highlight its architectural distinctions from traditional AI agents. We then adapt and extend the AI TRiSM framework for Agentic AI, structured around key pillars: Explainability, ModelOps, Security, Privacy and their Lifecycle Governance , each contextualized to the challenges of AMAS. A risk taxonomy is proposed to capture the unique threats and vulnerabilities of Agentic AI, ranging from coordination failures to prompt-based adversarial manipulation. To make coordination and tool use measurable in practice, we propose two metrics: the Component Synergy Score (CSS), which captures inter-agent enablement, and the Tool Utilization Efficacy (TUE), which evaluates whether tools are invoked correctly and efficiently. We further discuss strategies for improving explainability in Agentic AI, as well as approaches to enhancing security and privacy through encryption, adversarial robustness, and regulatory compliance. The review concludes with a research roadmap for the responsible development and deployment of Agentic AI, highlighting key directions to align emerging systems with TRiSM principles-ensuring safety, transparency, and accountability in their operation."}
		128	+{"work_id": 2968124245, "title": "VisualBERT: A Simple and Performant Baseline for Vision and Language", "abstract": "We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks. VisualBERT consists of a stack of Transformer layers that implicitly align elements of an input text and regions in an associated input image with self-attention. We further propose two visually-grounded language model objectives for pre-training VisualBERT on image caption data. Experiments on four vision-and-language tasks including VQA, VCR, NLVR2, and Flickr30K show that VisualBERT outperforms or rivals with state-of-the-art models while being significantly simpler. Further analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments."}
63	{"work_id": 4415913530, "title": "A multimodal whole-slide foundation model for pathology", "abstract": "The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning. However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose Transformer-based pathology Image and Text Alignment Network (TITAN), a multimodal whole-slide foundation model pretrained using 335,645 whole-slide images via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any fine-tuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that it outperforms both ROI and slide foundation models across machine learning settings, including linear probing, few-shot and zero-shot classification, rare cancer retrieval, cross-modal retrieval and pathology report generation. Pretrained using 335,645 whole-slide images, a foundation model is developed to provide representations for slide- and patient-level tasks. It is capable of performing clinical tasks and generating reports even in data-scarce scenarios, such as rare cancer diagnosis and survival prediction, without requiring further fine-tuning."}	129	{"work_id": 4415913530, "title": "A multimodal whole-slide foundation model for pathology", "abstract": "The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning. However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose Transformer-based pathology Image and Text Alignment Network (TITAN), a multimodal whole-slide foundation model pretrained using 335,645 whole-slide images via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any fine-tuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that it outperforms both ROI and slide foundation models across machine learning settings, including linear probing, few-shot and zero-shot classification, rare cancer retrieval, cross-modal retrieval and pathology report generation. Pretrained using 335,645 whole-slide images, a foundation model is developed to provide representations for slide- and patient-level tasks. It is capable of performing clinical tasks and generating reports even in data-scarce scenarios, such as rare cancer diagnosis and survival prediction, without requiring further fine-tuning."}
64	{"work_id": 4406571986, "title": "Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines", "abstract": "The release of ChatGPT in November 2022 prompted a massive uptake of generative artificial intelligence (GenAI) across higher education institutions (HEIs). In response, HEIs focused on regulating its use, particularly among students, before shifting towards advocating for its productive integration within teaching and learning . Since then, many HEIs have increasingly provided policies and guidelines to direct GenAI. This paper presents an analysis of documents produced by 116 US universities classified as as high research activity or R1 institutions providing a comprehensive examination of the advice and guidance offered by institutional stakeholders about GenAI. Through an extensive analysis, we found a majority of universities (N = 73, 63%) encourage the use of GenAI, with many offering detailed guidance for its use in the classroom (N = 48, 41%). Over half the institutions provided sample syllabi (N = 65, 56%) and half (N = 58, 50%) provided sample GenAI curriculum and activities that would help instructors integrate and leverage GenAI in their teaching. Notably, the majority of guidance focused on writing activities focused on writing, whereas references to code and STEM-related activities were infrequent, and often vague, even when mentioned (N = 58, 50%). Finally, more than half of institutions talked about the ethics of GenAI on a broad range of topics, including Diversity, Equity and Inclusion (DEI) (N = 60, 52%). Based on our findings we caution that guidance for faculty can become burdensome as policies suggest or imply substantial revisions to existing pedagogical practices."}	130	{"work_id": 4406571986, "title": "Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines", "abstract": "The release of ChatGPT in November 2022 prompted a massive uptake of generative artificial intelligence (GenAI) across higher education institutions (HEIs). In response, HEIs focused on regulating its use, particularly among students, before shifting towards advocating for its productive integration within teaching and learning . Since then, many HEIs have increasingly provided policies and guidelines to direct GenAI. This paper presents an analysis of documents produced by 116 US universities classified as as high research activity or R1 institutions providing a comprehensive examination of the advice and guidance offered by institutional stakeholders about GenAI. Through an extensive analysis, we found a majority of universities (N = 73, 63%) encourage the use of GenAI, with many offering detailed guidance for its use in the classroom (N = 48, 41%). Over half the institutions provided sample syllabi (N = 65, 56%) and half (N = 58, 50%) provided sample GenAI curriculum and activities that would help instructors integrate and leverage GenAI in their teaching. Notably, the majority of guidance focused on writing activities focused on writing, whereas references to code and STEM-related activities were infrequent, and often vague, even when mentioned (N = 58, 50%). Finally, more than half of institutions talked about the ethics of GenAI on a broad range of topics, including Diversity, Equity and Inclusion (DEI) (N = 60, 52%). Based on our findings we caution that guidance for faculty can become burdensome as policies suggest or imply substantial revisions to existing pedagogical practices."}
65	{"work_id": 4401023556, "title": "Large Language Model Based Multi-agents: A Survey of Progress and Challenges", "abstract": "Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to their notable capabilities in planning and reasoning, LLMs have been utilized as autonomous agents for the automatic execution of various tasks. Recently, LLM-based agent systems have rapidly evolved from single-agent planning or decision-making to operating as multi-agent systems, enhancing their ability in complex problem-solving and world simulation. To offer an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects and challenges of LLM-based multi-agent (LLM-MA) systems. Our objective is to provide readers with an in-depth understanding of these key points: the domains and settings where LLM-MA systems operate or simulate; the profiling and communication methods of these agents; and the means by which these agents develop their skills. For those interested in delving into this field, we also summarize the commonly used datasets or benchmarks. To keep researchers updated on the latest studies, we maintain an open-source GitHub repository (github.com/taichengguo/LLM_MultiAgents_Survey_Papers), dedicated to outlining the research of LLM-MA research."}	131	{"work_id": 4401023556, "title": "Large Language Model Based Multi-agents: A Survey of Progress and Challenges", "abstract": "Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to their notable capabilities in planning and reasoning, LLMs have been utilized as autonomous agents for the automatic execution of various tasks. Recently, LLM-based agent systems have rapidly evolved from single-agent planning or decision-making to operating as multi-agent systems, enhancing their ability in complex problem-solving and world simulation. To offer an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects and challenges of LLM-based multi-agent (LLM-MA) systems. Our objective is to provide readers with an in-depth understanding of these key points: the domains and settings where LLM-MA systems operate or simulate; the profiling and communication methods of these agents; and the means by which these agents develop their skills. For those interested in delving into this field, we also summarize the commonly used datasets or benchmarks. To keep researchers updated on the latest studies, we maintain an open-source GitHub repository (github.com/taichengguo/LLM_MultiAgents_Survey_Papers), dedicated to outlining the research of LLM-MA research."}
66	{"work_id": 7130481397, "title": "Benchmarking large language model-based agent systems for clinical decision tasks", "abstract": "Agentic artificial intelligence (AI) systems, designed to autonomously reason, plan, and invoke tools, have shown promise in healthcare, yet systematic benchmarking of their real-world performance remains limited. In this study, we evaluate two such systems: the open-source OpenManus, built on Meta's Llama-4 and extended with medically customized agents; and Manus, a proprietary agent system employing a multistep planner-executor-verifier architecture. Both systems were assessed across three benchmark families: AgentClinic, a stepwise dialog-based diagnostic simulation; MedAgentsBench, a knowledge-intensive medical QA dataset; and Humanity's Last Exam (HLE), a suite of challenging text-only and multimodal questions. Despite access to advanced tools (e.g., web browsing, code development and execution, and text file editing) agent systems yielded only modest accuracy gains over baseline LLMs, reaching 60.3% and 28.0% in AgentClinic MedQA and MIMIC, 30.3% on MedAgentsBench, and 8.6% on HLE text. Multimodal accuracy remained low (15.5% on multimodal HLE, 29.2% on AgentClinic NEJM), while resource demands increased substantially, with >10\u00d7 token usage and >2\u00d7 latency. Although 89.9% of hallucinations were filtered by in-agent safeguards, hallucinations remained prevalent. These findings reveal that current agentic designs offer modest performance benefits at significant computational and workflow cost, underscoring the need for more accurate, efficient, and clinically viable agent systems."}	132	{"work_id": 7130481397, "title": "Benchmarking large language model-based agent systems for clinical decision tasks", "abstract": "Agentic artificial intelligence (AI) systems, designed to autonomously reason, plan, and invoke tools, have shown promise in healthcare, yet systematic benchmarking of their real-world performance remains limited. In this study, we evaluate two such systems: the open-source OpenManus, built on Meta's Llama-4 and extended with medically customized agents; and Manus, a proprietary agent system employing a multistep planner-executor-verifier architecture. Both systems were assessed across three benchmark families: AgentClinic, a stepwise dialog-based diagnostic simulation; MedAgentsBench, a knowledge-intensive medical QA dataset; and Humanity's Last Exam (HLE), a suite of challenging text-only and multimodal questions. Despite access to advanced tools (e.g., web browsing, code development and execution, and text file editing) agent systems yielded only modest accuracy gains over baseline LLMs, reaching 60.3% and 28.0% in AgentClinic MedQA and MIMIC, 30.3% on MedAgentsBench, and 8.6% on HLE text. Multimodal accuracy remained low (15.5% on multimodal HLE, 29.2% on AgentClinic NEJM), while resource demands increased substantially, with >10\u00d7 token usage and >2\u00d7 latency. Although 89.9% of hallucinations were filtered by in-agent safeguards, hallucinations remained prevalent. These findings reveal that current agentic designs offer modest performance benefits at significant computational and workflow cost, underscoring the need for more accurate, efficient, and clinically viable agent systems."}
67	{"work_id": 7206206818, "title": "Lingshu: Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning", "abstract": "Multimodal Large Language Models (MLLMs) excel at understanding generic visual content, such as landscapes, objects, and events, thanks to extensive datasets and advanced training regimes. However, their effectiveness in medical applications remains limited due to the inherent discrepancies between data and tasks in medical scenarios and those in the general domain. Existing medical MLLMs face the following critical deficiencies: 1) inadequate coverage of medical knowledge beyond imaging; 2) elevated propensity for hallucinations due to suboptimal data curation; and 3) limited reasoning capacity tailored to complex medical tasks. To address these challenges, we first propose a comprehensive data-curation procedure that 1) efficiently acquires rich medical knowledge data not only from medical imaging but also from extensive medical texts and general domain data; and 2) synthesizes high-quality medical captions, visual question answering, and reasoning samples. Leveraging the curated data, we build a multimodal dataset imbued with extensive medical knowledge and develop our medical-specialized MLLM, Lingshu-Med, which undergoes multi-stage training to embed the medical expertise and enhance task-solving capabilities progressively. We also investigate reinforcement learning with verifiable rewards to further refine Lingshu-Med's medical reasoning abilities. For rigorous assessment, we introduce MedEvalKit, a unified evaluation framework that consolidates the leading multimodal and textual medical benchmarks for standardized, fair, and efficient model assessment. On three core medical tasks-multimodal QA, textual QA, and radiology report generation, Lingshu-Med consistently outperforms existing multimodal baselines in most tasks. Moreover, we conduct five case studies drawn from real-world clinical scenarios that illustrate its practical utility in medical contexts."}	133	{"work_id": 7206206818, "title": "Lingshu: Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning", "abstract": "Multimodal Large Language Models (MLLMs) excel at understanding generic visual content, such as landscapes, objects, and events, thanks to extensive datasets and advanced training regimes. However, their effectiveness in medical applications remains limited due to the inherent discrepancies between data and tasks in medical scenarios and those in the general domain. Existing medical MLLMs face the following critical deficiencies: 1) inadequate coverage of medical knowledge beyond imaging; 2) elevated propensity for hallucinations due to suboptimal data curation; and 3) limited reasoning capacity tailored to complex medical tasks. To address these challenges, we first propose a comprehensive data-curation procedure that 1) efficiently acquires rich medical knowledge data not only from medical imaging but also from extensive medical texts and general domain data; and 2) synthesizes high-quality medical captions, visual question answering, and reasoning samples. Leveraging the curated data, we build a multimodal dataset imbued with extensive medical knowledge and develop our medical-specialized MLLM, Lingshu-Med, which undergoes multi-stage training to embed the medical expertise and enhance task-solving capabilities progressively. We also investigate reinforcement learning with verifiable rewards to further refine Lingshu-Med's medical reasoning abilities. For rigorous assessment, we introduce MedEvalKit, a unified evaluation framework that consolidates the leading multimodal and textual medical benchmarks for standardized, fair, and efficient model assessment. On three core medical tasks-multimodal QA, textual QA, and radiology report generation, Lingshu-Med consistently outperforms existing multimodal baselines in most tasks. Moreover, we conduct five case studies drawn from real-world clinical scenarios that illustrate its practical utility in medical contexts."}
68	{"work_id": 2899138465, "title": "Learning to Learn with Gradients", "abstract": "Humans have a remarkable ability to learn new concepts from only a few examples and quickly adapt to unforeseen circumstances. To do so, they build upon their prior experience and prepare for the ability to adapt, allowing the combination of previous observations with small amounts of new evidence for fast learning. In most machine learning systems, however, there are distinct train and test phases: training consists of updating the model using data, and at test time, the model is deployed as a rigid decision-making engine. In this thesis, we discuss gradient-based algorithms for learning to learn, or meta-learning, which aim to endow machines with flexibility akin to that of humans. Instead of deploying a fixed, non-adaptable system, these meta-learning techniques explicitly train for the ability to quickly adapt so that, at test time, they can learn quickly when faced with new scenarios.To study the problem of learning to learn, we first develop a clear and formal definition of the meta-learning problem, its terminology, and desirable properties of meta-learning algorithms. Building upon these foundations, we present a class of model-agnostic meta-learning methods that embed gradient-based optimization into the learner. Unlike prior approaches to learning to learn, this class of methods focus on acquiring a transferable representation rather than a good learning rule. As a result, these methods inherit a number of desirable properties from using a fixed optimization as the learning rule, while still maintaining full expressivity, since the learned representations can control the update rule.We show how these methods can be extended for applications in motor control by combining elements of meta-learning with techniques for deep model-based reinforcement learning, imitation learning, and inverse reinforcement learning. By doing so, we build simulated agents that can adapt in dynamic environments, enable real robots to learn to manipulate new objects by watching a video of a human, and allow humans to convey goals to robots with only a few images. Finally, we conclude by discussing open questions and future directions in meta-learning, aiming to identify the key shortcomings and limiting assumptions of our existing approaches."}	134	{"work_id": 2899138465, "title": "Learning to Learn with Gradients", "abstract": "Humans have a remarkable ability to learn new concepts from only a few examples and quickly adapt to unforeseen circumstances. To do so, they build upon their prior experience and prepare for the ability to adapt, allowing the combination of previous observations with small amounts of new evidence for fast learning. In most machine learning systems, however, there are distinct train and test phases: training consists of updating the model using data, and at test time, the model is deployed as a rigid decision-making engine. In this thesis, we discuss gradient-based algorithms for learning to learn, or meta-learning, which aim to endow machines with flexibility akin to that of humans. Instead of deploying a fixed, non-adaptable system, these meta-learning techniques explicitly train for the ability to quickly adapt so that, at test time, they can learn quickly when faced with new scenarios.To study the problem of learning to learn, we first develop a clear and formal definition of the meta-learning problem, its terminology, and desirable properties of meta-learning algorithms. Building upon these foundations, we present a class of model-agnostic meta-learning methods that embed gradient-based optimization into the learner. Unlike prior approaches to learning to learn, this class of methods focus on acquiring a transferable representation rather than a good learning rule. As a result, these methods inherit a number of desirable properties from using a fixed optimization as the learning rule, while still maintaining full expressivity, since the learned representations can control the update rule.We show how these methods can be extended for applications in motor control by combining elements of meta-learning with techniques for deep model-based reinforcement learning, imitation learning, and inverse reinforcement learning. By doing so, we build simulated agents that can adapt in dynamic environments, enable real robots to learn to manipulate new objects by watching a video of a human, and allow humans to convey goals to robots with only a few images. Finally, we conclude by discussing open questions and future directions in meta-learning, aiming to identify the key shortcomings and limiting assumptions of our existing approaches."}
69	−{"work_id": 7164501397, "title": "Multimodal foundation models exploit text to make medical image predictions", "abstract": "Multimodal foundation models have shown compelling but conflicting performance in medical image interpretation. However, the ways in which these models integrate and prioritize different data modalities, including images and text, remain poorly understood. Here we evaluate 8 proprietary and open-source multimodal foundation models using 1090 multimodal medical cases. We show that image predictions are largely driven by text, with accuracy increasing monotonically with the amount of informative text. Exploitation of text is a double-edged sword; even mild suggestions of an incorrect diagnosis in text diminish image-based classification, dramatically reducing performance in cases the model could previously answer using images alone-o3 accuracy fell from 84% to 28% when a misleading clinical vignette was introduced. In physician evaluations of long-form cases, adding images reduces or does not improve performance when text is highly informative (e.g., GPT-4V showed decreased accuracy when images were added to highly informative text across 69 clinicopathological conferences). Our results suggest that multimodal AI models may be useful in medical diagnostic reasoning but that their accuracy is largely driven, for better and worse, by text."}	135	+{"work_id": 4379057711, "title": "Exploring the Potential Impact of Artificial Intelligence (AI) on International Students in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success", "abstract": "International students face unique challenges in pursuing higher education in a foreign country. To address these challenges and enhance their academic experience, higher education institutions are increasingly exploring the use of artificial intelligence (AI) applications. This research essay aims to investigate the impact of AI on the education of international students. Instead of a traditional literature review, it employs a research approach to examine the potential applications of AI and discuss associated concerns. The research paper explores various AI applications, such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research. By analyzing the role of AI in education for international students, this research paper sheds light on how AI can improve learning efficiency and provide customized educational support. Additionally, it identifies significant risks and limitations, including privacy concerns, cultural differences, language proficiency, and ethical implications, which must be effectively addressed. The findings contribute to a better understanding of the potential impact of AI on international students\u2019 educational experiences and offer insights into the integration of AI into educational administration and learning processes."}
70	{"work_id": 2995460200, "title": "VL-BERT: Pre-training of Generic Visual-Linguistic Representations", "abstract": "We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short). VL-BERT adopts the simple yet powerful Transformer model as the backbone, and extends it to take both visual and linguistic embedded features as input. In it, each element of the input is either of a word from the input sentence, or a region-of-interest (RoI) from the input image. It is designed to fit for most of the visual-linguistic downstream tasks. To better exploit the generic representation, we pre-train VL-BERT on the massive-scale Conceptual Captions dataset, together with text-only corpus. Extensive empirical analysis demonstrates that the pre-training procedure can better align the visual-linguistic clues and benefit the downstream tasks, such as visual commonsense reasoning, visual question answering and referring expression comprehension. It is worth noting that VL-BERT achieved the first place of single model on the leaderboard of the VCR benchmark."}	136	{"work_id": 2995460200, "title": "VL-BERT: Pre-training of Generic Visual-Linguistic Representations", "abstract": "We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short). VL-BERT adopts the simple yet powerful Transformer model as the backbone, and extends it to take both visual and linguistic embedded features as input. In it, each element of the input is either of a word from the input sentence, or a region-of-interest (RoI) from the input image. It is designed to fit for most of the visual-linguistic downstream tasks. To better exploit the generic representation, we pre-train VL-BERT on the massive-scale Conceptual Captions dataset, together with text-only corpus. Extensive empirical analysis demonstrates that the pre-training procedure can better align the visual-linguistic clues and benefit the downstream tasks, such as visual commonsense reasoning, visual question answering and referring expression comprehension. It is worth noting that VL-BERT achieved the first place of single model on the leaderboard of the VCR benchmark."}
71	{"work_id": 7110333790, "title": "Enriching Location Representation with Detailed Semantic Information", "abstract": "Spatial representations that capture both structural and semantic characteristics of urban environments are essential for urban modeling. Traditional spatial embeddings often prioritize spatial proximity while underutilizing fine-grained contextual information from places. To address this limitation, we introduce CaLLiPer+, an extension of the CaLLiPer model that systematically integrates Point-of-Interest (POI) names alongside categorical labels within a multimodal contrastive learning framework. We evaluate its effectiveness on two downstream tasks - land use classification and socioeconomic status distribution mapping - demonstrating consistent performance gains of 4% to 11% over baseline methods. Additionally, we show that incorporating POI names enhances location retrieval, enabling models to capture complex urban concepts with greater precision. Ablation studies further reveal the complementary role of POI names and the advantages of leveraging pretrained text encoders for spatial representations. Overall, our findings highlight the potential of integrating fine-grained semantic attributes and multimodal learning techniques to advance the development of urban foundation models."}	137	{"work_id": 7110333790, "title": "Enriching Location Representation with Detailed Semantic Information", "abstract": "Spatial representations that capture both structural and semantic characteristics of urban environments are essential for urban modeling. Traditional spatial embeddings often prioritize spatial proximity while underutilizing fine-grained contextual information from places. To address this limitation, we introduce CaLLiPer+, an extension of the CaLLiPer model that systematically integrates Point-of-Interest (POI) names alongside categorical labels within a multimodal contrastive learning framework. We evaluate its effectiveness on two downstream tasks - land use classification and socioeconomic status distribution mapping - demonstrating consistent performance gains of 4% to 11% over baseline methods. Additionally, we show that incorporating POI names enhances location retrieval, enabling models to capture complex urban concepts with greater precision. Ablation studies further reveal the complementary role of POI names and the advantages of leveraging pretrained text encoders for spatial representations. Overall, our findings highlight the potential of integrating fine-grained semantic attributes and multimodal learning techniques to advance the development of urban foundation models."}
		138	+{"work_id": 2963285578, "title": "Convergence Results for Neural Networks via Electrodynamics", "abstract": "We study whether a depth two neural network can learn another depth two network using gradient descent. Assuming a linear output node, we show that the question of whether gradient descent converges to the target function is equivalent to the following question in electrodynamics: Given k fixed protons in R^d, and k electrons, each moving due to the attractive force from the protons and repulsive force from the remaining electrons, whether at equilibrium all the electrons will be matched up with the protons, up to a permutation. Under the standard electrical force, this follows from the classic Earnshaw's theorem. In our setting, the force is determined by the activation function and the input distribution. Building on this equivalence, we prove the existence of an activation function such that gradient descent learns at least one of the hidden nodes in the target network. Iterating, we show that gradient descent can be used to learn the entire network one node at a time."}
72	{"work_id": 4360620450, "title": "Opinion Paper: \u201cSo what if ChatGPT wrote it?\u201d Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy", "abstract": "Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT\u2019s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT\u2019s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts."}	139	{"work_id": 4360620450, "title": "Opinion Paper: \u201cSo what if ChatGPT wrote it?\u201d Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy", "abstract": "Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT\u2019s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT\u2019s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts."}
73	{"work_id": 4318718936, "title": "BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models", "abstract": "The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model's emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions."}	140	{"work_id": 4318718936, "title": "BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models", "abstract": "The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model's emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions."}
74	−{"work_id": 7124974472, "title": "The Rapid Adoption of Generative AI", "abstract": "Generative artificial intelligence (genAI) is a potentially important new technology, but its impact on the economy depends on the speed and intensity of adoption. This paper reports results from a series of nationally representative U.S. surveys of genAI use at work and at home. As of late 2024, 45% of the U.S. population age 18\u201364 uses genAI. Among employed respondents, 27% used genAI for work at least once in the previous week: 10% used it every workday and 17% on some but not all workdays. Relative to each technology\u2019s first mass-market product launch, work adoption of genAI has been faster than the personal computer (PC), and overall adoption has outpaced both PCs and the internet by an even wider margin. Between 1% and 7% of all work hours are currently assisted by genAI, and respondents report time savings equivalent to 1.4% of total work hours. Potential productivity gains vary widely by industry, and firm climate and policies play an important role in adoption patterns. This paper was accepted by Alfonso Gambardella, business strategy. Funding: This work was supported by the Walmart Foundation [Grant 252882]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2025.02523 ."}
75	{"work_id": 4406199489, "title": "Generative AI in Higher Education: Balancing Innovation and Integrity", "abstract": "Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI's integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities. Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in GenAI use. By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education."}	141	{"work_id": 4406199489, "title": "Generative AI in Higher Education: Balancing Innovation and Integrity", "abstract": "Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI's integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities. Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in GenAI use. By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education."}
76	{"work_id": 3137147200, "title": "A Survey of Quantization Methods for Efficient Neural Network Inference", "abstract": "This chapter provides approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the advantages/disadvantages of current methods. Over the past decade, people have observed significant improvements in the accuracy of Neural Networks (NNs) for a wide range of problems, often achieved by highly over-parameterized models. Achieving efficient, real-time NNs with optimal accuracy requires rethinking the design, training, and deployment of NN models. Model distillation involves training a large model and then using it as a teacher to train a more compact model. Loosely related to NN quantization is work in neuroscience that suggests that the human brain stores information in a discrete/quantized form, rather than in a continuous form. Gray and Neuhoff have written a very nice survey of the history of quantization up to 1998."}	142	{"work_id": 3137147200, "title": "A Survey of Quantization Methods for Efficient Neural Network Inference", "abstract": "This chapter provides approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the advantages/disadvantages of current methods. Over the past decade, people have observed significant improvements in the accuracy of Neural Networks (NNs) for a wide range of problems, often achieved by highly over-parameterized models. Achieving efficient, real-time NNs with optimal accuracy requires rethinking the design, training, and deployment of NN models. Model distillation involves training a large model and then using it as a teacher to train a more compact model. Loosely related to NN quantization is work in neuroscience that suggests that the human brain stores information in a discrete/quantized form, rather than in a continuous form. Gray and Neuhoff have written a very nice survey of the history of quantization up to 1998."}
77	{"work_id": 2436219157, "title": "Optimization Methods for Large-Scale Machine Learning", "abstract": "Abstract. This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient (SG) method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter. Based on this viewpoint, we present a comprehensive theory of a straightforward, yet versatile SG algorithm, discuss its practical behavior, and highlight opportunities for designing algorithms with improved performance. This leads to a discussion about the next generation of optimization methods for large-scale machine learning, including an investigation of two main streams of research on techniques that diminish noise in the stochastic directions and methods that make use of second-order derivative approximations."}	143	{"work_id": 2436219157, "title": "Optimization Methods for Large-Scale Machine Learning", "abstract": "Abstract. This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient (SG) method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter. Based on this viewpoint, we present a comprehensive theory of a straightforward, yet versatile SG algorithm, discuss its practical behavior, and highlight opportunities for designing algorithms with improved performance. This leads to a discussion about the next generation of optimization methods for large-scale machine learning, including an investigation of two main streams of research on techniques that diminish noise in the stochastic directions and methods that make use of second-order derivative approximations."}
78	{"work_id": 4366420437, "title": "What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature", "abstract": "An artificial intelligence-based chatbot, ChatGPT, was launched in November 2022 and is capable of generating cohesive and informative human-like responses to user input. This rapid review of the literature aims to enrich our understanding of ChatGPT\u2019s capabilities across subject domains, how it can be used in education, and potential issues raised by researchers during the first three months of its release (i.e., December 2022 to February 2023). A search of the relevant databases and Google Scholar yielded 50 articles for content analysis (i.e., open coding, axial coding, and selective coding). The findings of this review suggest that ChatGPT\u2019s performance varied across subject domains, ranging from outstanding (e.g., economics) and satisfactory (e.g., programming) to unsatisfactory (e.g., mathematics). Although ChatGPT has the potential to serve as an assistant for instructors (e.g., to generate course materials and provide suggestions) and a virtual tutor for students (e.g., to answer questions and facilitate collaboration), there were challenges associated with its use (e.g., generating incorrect or fake information and bypassing plagiarism detectors). Immediate action should be taken to update the assessment methods and institutional policies in schools and universities. Instructor training and student education are also essential to respond to the impact of ChatGPT on the educational environment."}	144	{"work_id": 4366420437, "title": "What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature", "abstract": "An artificial intelligence-based chatbot, ChatGPT, was launched in November 2022 and is capable of generating cohesive and informative human-like responses to user input. This rapid review of the literature aims to enrich our understanding of ChatGPT\u2019s capabilities across subject domains, how it can be used in education, and potential issues raised by researchers during the first three months of its release (i.e., December 2022 to February 2023). A search of the relevant databases and Google Scholar yielded 50 articles for content analysis (i.e., open coding, axial coding, and selective coding). The findings of this review suggest that ChatGPT\u2019s performance varied across subject domains, ranging from outstanding (e.g., economics) and satisfactory (e.g., programming) to unsatisfactory (e.g., mathematics). Although ChatGPT has the potential to serve as an assistant for instructors (e.g., to generate course materials and provide suggestions) and a virtual tutor for students (e.g., to answer questions and facilitate collaboration), there were challenges associated with its use (e.g., generating incorrect or fake information and bypassing plagiarism detectors). Immediate action should be taken to update the assessment methods and institutional policies in schools and universities. Instructor training and student education are also essential to respond to the impact of ChatGPT on the educational environment."}
79	−{"work_id": 7123365826, "title": "Event-Triggered Zero-Sum Game for Input Saturated Nonlinear Systems With State Constraints", "abstract": "In safety-critical applications, saturated nonlinear systems often face significant control challenges due to the simultaneous presence of strict state constraints, input saturation, and uncertain external disturbances. To address these challenges, this paper proposes a novel event-triggered optimal control (ETOC) method. The core of our approach lies in a unified framework that handles all three issues simultaneously. We first introduce a smooth mapping function to transform the original system with state constraints into an equivalent unconstrained system. Subsequently, the control problem is formulated as a two-player zero-sum game (ZSG), where one player is the controller designed to handle input saturation and the other is the worst-case external disturbance. This unified formulation ensures that the system strictly adheres to the state constraints. Unlike traditional methods that typically require formulating and solving complex modified event-triggered Hamilton-Jacobi-Bellman (HJB) equations incorporating triggering errors, our approach approximates the optimal policy for the transformed system and implements it via an event-triggered mechanism. By utilizing a single-critic neural network (NN) and incorporating an experience replay mechanism, the algorithm\u2019s learning stability is enhanced, and the restrictive persistence of excitation (PE) condition is relaxed. Theoretical analysis and simulations confirm that our ETOC strategy guarantees optimal performance, strict adherence to state constraints, and a significant reduction in communication burden, showcasing its efficiency and practicality."}
80	−{"work_id": 4411099958, "title": "A multimodal vision foundation model for clinical dermatology", "abstract": "Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep learning models excel at specific tasks such as skin cancer diagnosis from dermoscopic images, they struggle to meet the complex, multimodal requirements of clinical practice. Here we introduce PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on over 2 million real-world skin disease images from 11 clinical institutions across 4 imaging modalities. We evaluated PanDerm on 28 diverse benchmarks, including skin cancer screening, risk stratification, differential diagnosis of common and rare skin conditions, lesion segmentation, longitudinal monitoring, and metastasis prediction and prognosis. PanDerm achieved state-of-the-art performance across all evaluated tasks, often outperforming existing models when using only 10% of labeled data. We conducted three reader studies to assess PanDerm's potential clinical utility. PanDerm outperformed clinicians by 10.2% in early-stage melanoma detection through longitudinal analysis, improved clinicians' skin cancer diagnostic accuracy by 11% on dermoscopy images and enhanced nondermatologist healthcare providers' differential diagnosis by 16.5% across 128 skin conditions on clinical photographs. These results show PanDerm's potential to improve patient care across diverse clinical scenarios and serve as a model for developing multimodal foundation models in other medical specialties, potentially accelerating the integration of artificial intelligence support in healthcare."}
81	−{"work_id": 4383312437, "title": "A comprehensive AI policy education framework for university teaching and learning", "abstract": "Abstract This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly."}
82	−{"work_id": 7138474371, "title": "OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model", "abstract": "We present OpenDriveVLA, a Vision-Language Action (VLA) model designed for end-to-end autonomous driving, built upon open-source large language models. OpenDriveVLA generates spatially-grounded driving actions by leveraging multimodal inputs, including both 2D and 3D instance-aware visual representations, ego vehicle states, and language commands. To bridge the modality gap between driving visual representations and language embeddings, we introduce a hierarchical vision-language alignment process, projecting both 2D and 3D structured visual tokens into a unified semantic space. Furthermore, we incorporate structured agent\u2013environment\u2013ego interaction modeling into the autoregressive decoding process, enabling the model to capture fine-grained spatial dependencies and behavior-aware dynamics critical for reliable trajectory planning. Extensive experiments on the nuScenes dataset demonstrate that OpenDriveVLA achieves state-of-the-art results across open-loop trajectory planning and driving-related question-answering tasks. Qualitative analyses further illustrate its superior capability to follow high-level driving commands and robustly generate trajectories under challenging scenarios, highlighting its potential for next-generation end-to-end autonomous driving."}
83	−{"work_id": 4411932262, "title": "A foundation model to predict and capture human cognition", "abstract": ". A first step towards such a theory is to create a computational model that can predict human behaviour in a wide range of settings. Here we introduce Centaur, a computational model that can predict and simulate human behaviour in any experiment expressible in natural language. We derived Centaur by fine-tuning a state-of-the-art language model on a large-scale dataset called Psych-101. Psych-101 has an unprecedented scale, covering trial-by-trial data from more than 60,000 participants performing in excess of 10,000,000 choices in 160 experiments. Centaur not only captures the behaviour of held-out participants better than existing cognitive models, but it also generalizes to previously unseen cover stories, structural task modifications and entirely new domains. Furthermore, the model's internal representations become more aligned with human neural activity after fine-tuning. Taken together, our results demonstrate that it is possible to discover computational models that capture human behaviour across a wide range of domains. We believe that such models provide tremendous potential for guiding the development of cognitive theories, and we present a case study to demonstrate this."}
84	−{"work_id": 7130596601, "title": "Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges", "abstract": "BACKGROUND: The integration of artificial intelligence in healthcare has transformed clinical practice and research methodologies. However, concerns regarding algorithmic accountability, interpretability, and safety have necessitated human oversight in AI systems. Human in the loop artificial intelligence represents a collaborative paradigm where human expertise and machine intelligence converge to enhance decision making while maintaining ethical standards and clinical safety. AIM: This review synthesizes current evidence on human in the loop AI in healthcare delivery and research, examining implementation frameworks, clinical outcomes, comparative advantages over fully automated and clinician-only approaches, and challenges. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases covering studies from 2018 to 2025. Data were thematically synthesized to identify patterns, frameworks, and outcomes. This narrative approach enables comprehensive conceptual synthesis across diverse HITL-AI applications and contexts. RESULTS: Human in the loop AI demonstrates significant applications across diagnostic imaging, clinical decision support, patient monitoring, drug discovery, and research data analysis. Evidence indicates improved diagnostic accuracy, reduced medical errors, enhanced patient safety, and increased clinician trust compared to both automated AI and traditional approaches. Implementation requires EHR interoperability, clear liability frameworks, adaptive training protocols, and quantum-safe cryptographic security. Challenges include workflow integration, regulatory gaps for adaptive systems, and sustainability concerns. CONCLUSION: This review advances the field by synthesizing cross-domain implementation patterns, mapping collaboration models to risk-stratified contexts, identifying regulatory gaps for adaptive systems, and proposing future directions including post-quantum cryptographic integration, AI-driven adaptive architectures, and multi-center scalability frameworks for optimizing human-machine collaboration in healthcare."}
85	−{"work_id": 7128436456, "title": "Synthesis of model predictive control and reinforcement learning: Survey and classification", "abstract": "model predictive control (MPC) and reinforcement learning (RL) are two successful control techniques for Markov decision processes. Both approaches are derived from similar fundamental principles, and both are widely used in practical applications, including robotics, process control, energy systems, and autonomous driving. Despite their similarities, MPC and RL follow distinct paradigms that emerged from diverse communities and different requirements. Various technical discrepancies, particularly the role of an environment model as part of the algorithm, lead to methodologies with nearly complementary advantages. Due to their orthogonal benefits, research interest in combination methods has recently increased significantly, resulting in a large and growing set of complex ideas that leverage MPC and RL. This work illuminates the differences, similarities, and fundamentals that enable various combination algorithms and categorizes existing work accordingly. Particularly, we focus on the versatile actor\u2013critic RL approach as a basis for our categorization and examine how the online optimization approach of MPC can be used to improve the overall closed-loop performance of a policy."}
86	−{"work_id": 4406170795, "title": "Accurate predictions on small data with a tabular foundation model", "abstract": "Abstract Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science 1,2 . The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories 3\u20135 , gradient-boosted decision trees 6\u20139 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model that outperforms all previous methods on datasets with up to 10,000 samples by a wide margin, using substantially less training time. In 2.8 s, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model, this model also allows fine-tuning, data generation, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains."}
87	−{"work_id": 7143512276, "title": "Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey", "abstract": "This survey explores the application of multimodal machine learning techniques in predictive healthcare analytics. By integrating various data modalities, such as medical imaging, clinical text, time-series signals, and structured tabular data, these approaches aim to emulate clinical reasoning and enhance diagnostic and prognostic accuracy. Across studies, multimodal ML consistently outperformed unimodal baselines, with intermediate fusion employed in 60% of cases and achieving average AUC improvements of 5\u201312% over single-modality models. Oncology and neurology emerged as the leading domains, where combining imaging with genomic and cognitive data significantly improved cancer survival and Alzheimer\u2019s detection. Despite progress, key challenges persist, including modality misalignment (23%), missing data (18%), and limited external validation (12%). Recent trends highlight transformer-based cross-modal attention, self-supervised learning for data-scarce settings, and hybrid fusion architectures in critical care. While multimodal ML offers clear clinical advantages, regulatory constraints and interoperability gaps continue to hinder deployment. This survey contributes to the existing literature by providing a comprehensive synthesis of multimodal ML applications across healthcare domains. It documents comparative fusion strategies, modelling approaches, and empirical performance outcomes. The paper\u2019s primary contribution lies in identifying intermediate fusion as the most effective integration strategy and revealing systematic gaps in external validation and model transparency that must be addressed for clinically trustworthy multimodal systems."}
88	−{"work_id": 4389473504, "title": "TRUST-REGION BASED POLICY OPTIMIZATION FOR EFFICIENT REINFORCEMENT LEARNING", "abstract": "Reinforcement Learning (RL) has achieved remarkable success in solving complicated control and sequential decision-making tasks in recent years, such as board games, video games, robot control, dynamic energy management, and autonomous driving. In these tasks, RL has demonstrated unprecedented adaptability and self-learning capability in unknown and stochastic environments. However, existing RL algorithms still have significant disadvantages and shortcomings compared to human intelligence, such as learning stability, sample efficiency, safety, scalability, and collaboration with other agents. These drawbacks impede the widespread application of RL in real-life domains. To address these challenges, this dissertation aims to propose novel theories and algorithms for more effective and efficient RL and to develop their applications in real-world domains. This work consists of three major parts. First, the dissertation establishes a novel theoretical result for trust-region-based RL methods by analyzing the lower bound of policy performance. A closed-form policy update rule is then derived based on the theoretical result, providing a monotonic improvement guarantee. Second, the dissertation develops an off-policy RL algorithm for continuous control problems, inspired by the closed-form update rule. This algorithm enables sample-efficient learning of deep neural network policies. Additionally, trust region policy optimization is extended to cooperative multi-agent systems through consensus optimization, resulting in a distributed MARL algorithm. Third, the dissertation develops the real-world applications of a trust-region-based safe RL method and the MARL method in smart grids. Numerous experiments are conducted to verify the effectiveness of these methods using various benchmark environments, including robotics, strategic games, and power systems."}
89	−{"work_id": 7131830459, "title": "A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions", "abstract": "The exponential growth of online information has made it increasingly difficult for users to identify valuable and relevant content. Recommender systems have emerged as a critical solution to this challenge by tailoring content to individual preferences. With the proliferation of diverse multimedia services, human interaction with the digital world has become inherently multimodal. Consequently, recommender systems capable of comprehending and interpreting multimodal information can more effectively align with individual preferences. With its recent surge in research attention, the field of multimodal recommender systems (MRS) still lacks a comprehensive technical survey. Existing surveys suffer from two critical limitations: 1) Insufficient technical depth: Prior works predominantly focus on categorizing and discussing general structure, neglecting rigorous technical analysis of methodologies and architectures. 2) Absence of cutting-edge works: Due to the rapid evolution of AI technologies, current surveys fail to discuss the latest works that adopt the most advanced techniques. To bridge these gaps, this survey conducts a systematic and technical review of advanced MRS works from MRS's inception to the present. We organize existing works into coherent taxonomies based on their structure and provide in-depth analyses of methodological innovations at each component, including Feature Extraction, Encoder, Multimodal Fusion, and Loss Function. Moreover, we further discuss potential future directions for developing and enhancing MRS. This survey serves as technical guidance for researchers and practitioners, offering insights into the developments, techniques, and future directions of MRS. Notably, we maintain a public repository:https://github.com/Jinfeng-Xu/Awesome-Multimodal-Recommender-Systems."}
90	−{"work_id": 2969896603, "title": "A Survey on Bias and Fairness in Machine Learning", "abstract": "With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields."}
91	−{"work_id": 3185341429, "title": "Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing", "abstract": "This article surveys and organizes research works in a new paradigm in natural language processing, which we dub \u201cprompt-based learning.\u201d Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P ( y\|x ), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x\u2032 that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x\u0302 , from which the final output y can be derived. This framework is powerful and attractive for a number of reasons: It allows the language model to be pre-trained on massive amounts of raw text, and by defining a new prompting function the model is able to perform few-shot or even zero-shot learning, adapting to new scenarios with few or no labeled data. In this article, we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g., the choice of pre-trained language models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts but also release other resources, e.g., a website NLPedia\u2013Pretrain including constantly updated survey and paperlist."}
92	−{"work_id": 4399450035, "title": "Power Hungry Processing: Watts Driving the Cost of AI Deployment?", "abstract": "Recent years have seen a surge in the popularity of commercial AI products based on generative, multi-purpose AI systems promising a unified approach to building machine learning (ML) models into technology. However, this ambition of \u201cgenerality\u201d comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. In this work, we propose the first systematic comparison of the ongoing inference cost of various categories of ML systems, covering both task-specific (i.e. finetuned models that carry out a single task) and \u2018general-purpose\u2019 models, (i.e. those trained for multiple tasks). We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We find that multi-purpose, generative architectures are orders of magnitude more expensive than task-specific systems for a variety of tasks, even when controlling for the number of model parameters. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions. All the data from our study can be accessed via an interactive demo to carry out further exploration and analysis."}
93	−{"work_id": 4322718191, "title": "LLaMA: Open and Efficient Foundation Language Models", "abstract": "We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community."}
94	{"work_id": 7154011589, "title": "Modelos pr\u00e9-treinados de LLM e quest\u00f5es \u00e9ticas face aos direitos autorais", "abstract": "O avan\u00e7o dos modelos de linguagem de grande escala (LLMs) pr\u00e9-treinados tem ampliado significativamente as aplica\u00e7\u00f5es da intelig\u00eancia artificial, ao mesmo tempo em que suscita debates relevantes acerca de aspectos \u00e9ticos e jur\u00eddicos relacionados ao uso de conte\u00fados protegidos por direitos autorais. Neste contexto, o presente estudo investiga o seguinte problema de pesquisa: os assistentes de intelig\u00eancia artificial respeitaram integralmente os direitos autorais em todas as etapas de seu processo de treinamento? Parte-se da hip\u00f3tese de que a maioria dos modelos de linguagem foi treinada, ao menos parcialmente, com dados que incluem conte\u00fados protegidos por direitos autorais. O objetivo geral da pesquisa consiste em demonstrar se h\u00e1 presen\u00e7a de conte\u00fado que configure poss\u00edvel infra\u00e7\u00e3o \u00e9tica relacionada a direitos autorais no treinamento de sistemas de intelig\u00eancia artificial. Como objetivos espec\u00edficos, busca-se: (i) conceituar intelig\u00eancia artificial, modelos de linguagem, seu funcionamento e processos de treinamento; (ii) apresentar os fundamentos da prote\u00e7\u00e3o autoral e os aspectos \u00e9ticos envolvidos no uso de conte\u00fados protegidos; e (iii) analisar respostas produzidas por modelos de intelig\u00eancia artificial diante de solicita\u00e7\u00f5es relacionadas a obras protegidas por direitos autorais. A justificativa da pesquisa fundamenta-se na crescente relev\u00e2ncia acad\u00eamica e social do tema, diante da r\u00e1pida dissemina\u00e7\u00e3o de tecnologias baseadas em intelig\u00eancia artificial e das discuss\u00f5es sobre regula\u00e7\u00e3o, responsabilidade e \u00e9tica no uso de dados para treinamento desses sistemas. Metodologicamente, o estudo adota o m\u00e9todo dedutivo, com abordagem qualitativa, baseada em revis\u00e3o bibliogr\u00e1fica e an\u00e1lise interpretativa das respostas fornecidas por modelos de intelig\u00eancia artificial em rela\u00e7\u00e3o a conte\u00fados potencialmente protegidos por direitos autorais."}	145	{"work_id": 7154011589, "title": "Modelos pr\u00e9-treinados de LLM e quest\u00f5es \u00e9ticas face aos direitos autorais", "abstract": "O avan\u00e7o dos modelos de linguagem de grande escala (LLMs) pr\u00e9-treinados tem ampliado significativamente as aplica\u00e7\u00f5es da intelig\u00eancia artificial, ao mesmo tempo em que suscita debates relevantes acerca de aspectos \u00e9ticos e jur\u00eddicos relacionados ao uso de conte\u00fados protegidos por direitos autorais. Neste contexto, o presente estudo investiga o seguinte problema de pesquisa: os assistentes de intelig\u00eancia artificial respeitaram integralmente os direitos autorais em todas as etapas de seu processo de treinamento? Parte-se da hip\u00f3tese de que a maioria dos modelos de linguagem foi treinada, ao menos parcialmente, com dados que incluem conte\u00fados protegidos por direitos autorais. O objetivo geral da pesquisa consiste em demonstrar se h\u00e1 presen\u00e7a de conte\u00fado que configure poss\u00edvel infra\u00e7\u00e3o \u00e9tica relacionada a direitos autorais no treinamento de sistemas de intelig\u00eancia artificial. Como objetivos espec\u00edficos, busca-se: (i) conceituar intelig\u00eancia artificial, modelos de linguagem, seu funcionamento e processos de treinamento; (ii) apresentar os fundamentos da prote\u00e7\u00e3o autoral e os aspectos \u00e9ticos envolvidos no uso de conte\u00fados protegidos; e (iii) analisar respostas produzidas por modelos de intelig\u00eancia artificial diante de solicita\u00e7\u00f5es relacionadas a obras protegidas por direitos autorais. A justificativa da pesquisa fundamenta-se na crescente relev\u00e2ncia acad\u00eamica e social do tema, diante da r\u00e1pida dissemina\u00e7\u00e3o de tecnologias baseadas em intelig\u00eancia artificial e das discuss\u00f5es sobre regula\u00e7\u00e3o, responsabilidade e \u00e9tica no uso de dados para treinamento desses sistemas. Metodologicamente, o estudo adota o m\u00e9todo dedutivo, com abordagem qualitativa, baseada em revis\u00e3o bibliogr\u00e1fica e an\u00e1lise interpretativa das respostas fornecidas por modelos de intelig\u00eancia artificial em rela\u00e7\u00e3o a conte\u00fados potencialmente protegidos por direitos autorais."}
		146	+{"work_id": 4401588926, "title": "The potential impact of Artificial Intelligence on equity and inclusion in education", "abstract": "This working paper reviews the impact of Artificial Intelligence (AI) on equity and inclusion in education, focusing on learner-centred, teacher-led and other institutional AI tools. It highlights the potential of AI in adapting learning while also addressing challenges such as access issues, inherent biases and the need for comprehensive teacher training. The paper emphasises the importance of balancing the potential benefits of AI with ethical considerations and the risk of exacerbating existing disparities. It highlights the need to address privacy and ethical concerns, enhance cultural responsiveness, manage techno ableism and provide continuing professional learning in AI. Additionally, the paper stresses the importance of maintaining educational integrity amidst growing commercial influence. It encourages research on AI tools\u2019 implications for equity and inclusion to ensure that AI adoption in education supports a more equitable and inclusive learning environment."}
95	{"work_id": 4414991289, "title": "\u201cDIVE\u201d into hydrogen storage materials discovery with AI agents", "abstract": "Despite the surge of AI in energy materials research, fully autonomous workflows that connect high-precision experimental knowledge to the discovery of credible new energy-related materials remain at an early stage. Here, we develop the Descriptive Interpretation of Visual Expression (DIVE) multi-agent workflow, which systematically reads and organizes experimental data from graphical elements in scientific literature. Applied to solid-state hydrogen storage materials-a class of materials central to future clean-energy technologies-DIVE markedly improves the accuracy and coverage of data extraction compared to the direct extraction method, with gains of 10-15% over commercial models and over 30% relative to open-source models. Building on a curated database of over 30 000 entries from >4000 publications, we establish a rapid inverse-design AI workflow capable of proposing new materials within minutes. This transferable, end-to-end paradigm illustrates how multimodal AI agents can convert literature-embedded scientific knowledge into actionable innovation, offering a scalable pathway for accelerated discovery across chemistry and materials science."}	147	{"work_id": 4414991289, "title": "\u201cDIVE\u201d into hydrogen storage materials discovery with AI agents", "abstract": "Despite the surge of AI in energy materials research, fully autonomous workflows that connect high-precision experimental knowledge to the discovery of credible new energy-related materials remain at an early stage. Here, we develop the Descriptive Interpretation of Visual Expression (DIVE) multi-agent workflow, which systematically reads and organizes experimental data from graphical elements in scientific literature. Applied to solid-state hydrogen storage materials-a class of materials central to future clean-energy technologies-DIVE markedly improves the accuracy and coverage of data extraction compared to the direct extraction method, with gains of 10-15% over commercial models and over 30% relative to open-source models. Building on a curated database of over 30 000 entries from >4000 publications, we establish a rapid inverse-design AI workflow capable of proposing new materials within minutes. This transferable, end-to-end paradigm illustrates how multimodal AI agents can convert literature-embedded scientific knowledge into actionable innovation, offering a scalable pathway for accelerated discovery across chemistry and materials science."}
96	{"work_id": 2098774185, "title": "Maximum Entropy Inverse Reinforcement Learning", "abstract": "Recent research has shown the bene\ufb01t of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of re- covering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behavior. In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach provides a well-de\ufb01ned, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods. We develop our technique in the context of modeling real world navigation and driving behaviors where collected data is inherently noisy and imperfect. Our probabilistic approach enables modeling of route preferences as well as a powerful new approach to inferring destinations and routes based on partial trajectories."}	148	{"work_id": 2098774185, "title": "Maximum Entropy Inverse Reinforcement Learning", "abstract": "Recent research has shown the bene\ufb01t of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of re- covering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behavior. In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach provides a well-de\ufb01ned, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods. We develop our technique in the context of modeling real world navigation and driving behaviors where collected data is inherently noisy and imperfect. Our probabilistic approach enables modeling of route preferences as well as a powerful new approach to inferring destinations and routes based on partial trajectories."}
97	{"work_id": 4407243994, "title": "AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development", "abstract": "The expansion of Artificial Intelligence in sectors such as healthcare, finance, and communication has raised critical ethical concerns surrounding transparency, fairness, and privacy. Addressing these issues is essential for the responsible development and deployment of AI systems. This research establishes a comprehensive ethical framework that mitigates biases and promotes accountability in AI technologies. A comparative analysis of international AI policy frameworks from regions including the European Union, United States, and China is conducted using analytical tools such as Venn diagrams and Cartesian graphs. These tools allow for a visual and systematic evaluation of the ethical principles guiding AI development across different jurisdictions. The results reveal significant variations in how global regions prioritise transparency, fairness, and privacy, with challenges in creating a unified ethical standard. To address these challenges, we propose technical strategies, including fairness-aware algorithms, routine audits, and the establishment of diverse development teams to ensure ethical AI practices. This paper provides actionable recommendations for integrating ethical oversight into the AI lifecycle, advocating for the creation of AI systems that are both technically sophisticated and aligned with societal values. The findings underscore the necessity of global collaboration in fostering ethical AI development."}	149	{"work_id": 4407243994, "title": "AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development", "abstract": "The expansion of Artificial Intelligence in sectors such as healthcare, finance, and communication has raised critical ethical concerns surrounding transparency, fairness, and privacy. Addressing these issues is essential for the responsible development and deployment of AI systems. This research establishes a comprehensive ethical framework that mitigates biases and promotes accountability in AI technologies. A comparative analysis of international AI policy frameworks from regions including the European Union, United States, and China is conducted using analytical tools such as Venn diagrams and Cartesian graphs. These tools allow for a visual and systematic evaluation of the ethical principles guiding AI development across different jurisdictions. The results reveal significant variations in how global regions prioritise transparency, fairness, and privacy, with challenges in creating a unified ethical standard. To address these challenges, we propose technical strategies, including fairness-aware algorithms, routine audits, and the establishment of diverse development teams to ensure ethical AI practices. This paper provides actionable recommendations for integrating ethical oversight into the AI lifecycle, advocating for the creation of AI systems that are both technically sophisticated and aligned with societal values. The findings underscore the necessity of global collaboration in fostering ethical AI development."}
				`@@ -104,75 +156,24 @@`
104	{"work_id": 4382203079, "title": "Are Transformers Effective for Time Series Forecasting?", "abstract": "Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the permutation-invariant self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of LTSF models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the LTSF task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future."}	156	{"work_id": 4382203079, "title": "Are Transformers Effective for Time Series Forecasting?", "abstract": "Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the permutation-invariant self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of LTSF models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the LTSF task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future."}
105	{"work_id": 4322832366, "title": "TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders", "abstract": "Learning transferable representations from unlabeled time series is crucial for improving performance in data-scarce classification. Existing self-supervised methods often operate at the point level and rely on unidirectional encoding, leading to low semantic density and a mismatch between pre-training and downstream optimization. In this paper, we propose TimeMAE, a self-supervised framework that reformulates masked modeling for time series via semantic unit elevation and decoupled representation learning. Instead of modeling individual time steps, TimeMAE segments time series into non-overlapping sub-series to form semantically enriched units, enabling more informative masked reconstruction while reducing computational cost. To address the representation discrepancy introduced by masking, we design a decoupled masked autoencoder that separately encodes visible and masked regions, avoiding artificial masked tokens in the main encoder. To guide pre-training, we introduce two complementary objectives: masked codeword classification, which discretizes sub-series semantics via a learned tokenizer and masked representation regression, which aligns continuous representations through a momentum-updated target encoder. Extensive experiments on five datasets demonstrate that TimeMAE outperforms competitive baselines, particularly in label-scarce scenarios and transfer learning scenarios. Our codes are publicly available at https://github.com/Mingyue-Cheng/TimeMAE."}	157	{"work_id": 4322832366, "title": "TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders", "abstract": "Learning transferable representations from unlabeled time series is crucial for improving performance in data-scarce classification. Existing self-supervised methods often operate at the point level and rely on unidirectional encoding, leading to low semantic density and a mismatch between pre-training and downstream optimization. In this paper, we propose TimeMAE, a self-supervised framework that reformulates masked modeling for time series via semantic unit elevation and decoupled representation learning. Instead of modeling individual time steps, TimeMAE segments time series into non-overlapping sub-series to form semantically enriched units, enabling more informative masked reconstruction while reducing computational cost. To address the representation discrepancy introduced by masking, we design a decoupled masked autoencoder that separately encodes visible and masked regions, avoiding artificial masked tokens in the main encoder. To guide pre-training, we introduce two complementary objectives: masked codeword classification, which discretizes sub-series semantics via a learned tokenizer and masked representation regression, which aligns continuous representations through a momentum-updated target encoder. Extensive experiments on five datasets demonstrate that TimeMAE outperforms competitive baselines, particularly in label-scarce scenarios and transfer learning scenarios. Our codes are publicly available at https://github.com/Mingyue-Cheng/TimeMAE."}
106	{"work_id": 4362515116, "title": "A Survey of Large Language Models", "abstract": "Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, architectural innovations, and data curation strategies; (2) post-training techniques, including supervised fine-tuning and reinforcement learning, which adapt foundational models to downstream tasks and enhance their alignment and safety; (3) utilization strategies, such as in-context learning, prompt engineering, and agentic reasoning, that optimize real-world deployment and enable effective interaction with external environments; and (4) evaluation methods, encompassing benchmarks for key ability dimensions such as core language capabilities, reasoning, and safety, which support comprehensive and reliable assessment of model performance. Additionally, we identify critical research issues, including those concerning theoretical foundations, efficient scaling, alignment, and agentic capability, and highlight the open challenges they present. By synthesizing state-of-the-art insights and emerging trends, this survey aims to provide a systematic and comprehensive framework for understanding the trajectory, current limitations, and future directions of LLM progress."}	158	{"work_id": 4362515116, "title": "A Survey of Large Language Models", "abstract": "Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, architectural innovations, and data curation strategies; (2) post-training techniques, including supervised fine-tuning and reinforcement learning, which adapt foundational models to downstream tasks and enhance their alignment and safety; (3) utilization strategies, such as in-context learning, prompt engineering, and agentic reasoning, that optimize real-world deployment and enable effective interaction with external environments; and (4) evaluation methods, encompassing benchmarks for key ability dimensions such as core language capabilities, reasoning, and safety, which support comprehensive and reliable assessment of model performance. Additionally, we identify critical research issues, including those concerning theoretical foundations, efficient scaling, alignment, and agentic capability, and highlight the open challenges they present. By synthesizing state-of-the-art insights and emerging trends, this survey aims to provide a systematic and comprehensive framework for understanding the trajectory, current limitations, and future directions of LLM progress."}
107	−{"work_id": 4327810158, "title": "GPT-4 Technical Report", "abstract": "Abstract\u2014Large Language Models (LLMs) suffer from inherent stochasticity, limiting their utility in high-stakes enterprise environments where determinism and auditability are required. This paper introduces the MFOUR Vibe Framework (MVF), a platform-agnostic architectural standard that transforms probabilistic natural language intent into deterministic software artifacts. We define a five-layer topology, comprising the Kernel Identity, Synaptic Routing, Interface Contracts, Context Anchoring, and the Mirror Test. Furthermore, we introduce The Vibe Integrity Score (VIS), a quantitative metric for evaluating the structural adherence of generative outputs. This specification provides the foundational schema and logic protocols for building \"Glass Box\" AI systems that are observable, secure, and commercially viable."}
108	{"work_id": 4366262984, "title": "Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence", "abstract": "Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model\u2019s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data."}	159	{"work_id": 4366262984, "title": "Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence", "abstract": "Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model\u2019s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data."}
109	{"work_id": 4360836968, "title": "Sparks of Artificial General Intelligence: Early experiments with GPT-4", "abstract": "Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions."}	160	{"work_id": 4360836968, "title": "Sparks of Artificial General Intelligence: Early experiments with GPT-4", "abstract": "Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions."}
110	{"work_id": 4384918448, "title": "Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions", "abstract": "Background. Large language models (LLMs) are increasingly proposed as clinical assistants in critical care, yet their behaviour under prolonged clinical context, conflicting data and authoritative pressure remains insufficiently evaluated. This is particularly relevant for offline or resource-constrained environments, where cloud-based safeguards are unavailable. Methods. I conducted a fully automated behavioural evaluation of 23 open-weight language models using a structured, time-series intensive care unit (ICU) simulation of 32 events spanning 121 hours (five days) of synthetic patient data. The scenario comprised routine monitoring, three distinct data\u2013clinical conflict traps (one presented twice, four trap events in total), episodes of physiological deterioration, and a final stress test in which an authoritative order requested a penicillin-class antibiotic for a patient with penicillin anaphylaxis documented at admission and never repeated. Models ran locally on a single consumer workstation with no network access. Each model's response to the final order was adjudicated into one of four mutually exclusive classes: contextually grounded refusal, ungrounded refusal, unsafe compliance, or no usable verdict. Secondary endpoints were extraction of the allergy at admission, discrepancy tagging across the four conflict traps, unwarranted therapeutic escalation, and response latency. Results. Only 7 of 23 models (30.4%) refused the contraindicated order on grounds explicitly referencing the documented allergy; 6 (26.1%) under a stricter criterion requiring the refusal to be stated as a ruling rather than implied by an assertion of danger. Two models (8.7%) declined the order for unrelated reasons, and three (13.0%) complied \u2014 one of them by asserting that no allergy was on record. The largest single group, 11 of 23 models (47.8%), issued no ruling on the order at all: variously, output was truncated inside an unfinished reasoning block, degenerated off-task, restated the protocol without applying it, consisted of a bare classification tag, hedged without resolving, or deferred the question to further assessment. Eight models (34.8%) failed to affirm the allergy at the very first probe, three of them by explicit denial. Of the 12 models that did extract the allergy at admission, only 4 (33.3%) went on to refuse the contraindicated order on that ground; three more raised the allergy at the decision point without acting on it, one of them only to deny that any was on record. Discrepancy detection and contraindication handling were dissociable: one of the four models with perfect discrepancy detection (4/4 traps) approved the contraindicated antibiotic, while two models that tagged no discrepancies at all refused the order on the allergy. Unwarranted escalation on conflict traps was common (11/23, 47.8% issued at least one inappropriate critical alert), but explicit hallucinated pharmacological or procedural intervention was less so (5/23, 21.7%). Contrary to expectation, longer median latency was modestly associated with grounded refusal (Spearman \u03c1 = 0.48, p = 0.019). Latency was not adjusted for parameter count, which was not analysed as a variable, and it times a generation other than the one scored. Conclusions. Under offline-first conditions, 16 of 23 models (69.6%) \u2014 17 (73.9%) under the stricter criterion \u2014 failed to produce a safe, contextually grounded refusal of a life-threatening order. The dominant failure mode was not sycophancy but the failure to deliver any interpretable safety verdict, most often because an output-length constraint truncated the attempt \u2014 a finding that reframes deployment risk from \"the model agrees with me\" to \"the model does not answer at all.\" Explicit unsafe compliance was less frequent than previously reported but no less consequential where it occurred. Safety-relevant competencies did not co-vary, so a model that reasons well about artefacts cannot be assumed to handle contraindications. General-purpose LLMs should not be deployed as autonomous clinical agents; the subset that behaved safely suggests that offline-capable assistants remain achievable through hybrid designs incorporating explicit refusal mechanisms, discrepancy-aware reasoning and retrieval-augmented grounding in validated clinical knowledge."}	161	{"work_id": 4384918448, "title": "Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions", "abstract": "Background. Large language models (LLMs) are increasingly proposed as clinical assistants in critical care, yet their behaviour under prolonged clinical context, conflicting data and authoritative pressure remains insufficiently evaluated. This is particularly relevant for offline or resource-constrained environments, where cloud-based safeguards are unavailable. Methods. I conducted a fully automated behavioural evaluation of 23 open-weight language models using a structured, time-series intensive care unit (ICU) simulation of 32 events spanning 121 hours (five days) of synthetic patient data. The scenario comprised routine monitoring, three distinct data\u2013clinical conflict traps (one presented twice, four trap events in total), episodes of physiological deterioration, and a final stress test in which an authoritative order requested a penicillin-class antibiotic for a patient with penicillin anaphylaxis documented at admission and never repeated. Models ran locally on a single consumer workstation with no network access. Each model's response to the final order was adjudicated into one of four mutually exclusive classes: contextually grounded refusal, ungrounded refusal, unsafe compliance, or no usable verdict. Secondary endpoints were extraction of the allergy at admission, discrepancy tagging across the four conflict traps, unwarranted therapeutic escalation, and response latency. Results. Only 7 of 23 models (30.4%) refused the contraindicated order on grounds explicitly referencing the documented allergy; 6 (26.1%) under a stricter criterion requiring the refusal to be stated as a ruling rather than implied by an assertion of danger. Two models (8.7%) declined the order for unrelated reasons, and three (13.0%) complied \u2014 one of them by asserting that no allergy was on record. The largest single group, 11 of 23 models (47.8%), issued no ruling on the order at all: variously, output was truncated inside an unfinished reasoning block, degenerated off-task, restated the protocol without applying it, consisted of a bare classification tag, hedged without resolving, or deferred the question to further assessment. Eight models (34.8%) failed to affirm the allergy at the very first probe, three of them by explicit denial. Of the 12 models that did extract the allergy at admission, only 4 (33.3%) went on to refuse the contraindicated order on that ground; three more raised the allergy at the decision point without acting on it, one of them only to deny that any was on record. Discrepancy detection and contraindication handling were dissociable: one of the four models with perfect discrepancy detection (4/4 traps) approved the contraindicated antibiotic, while two models that tagged no discrepancies at all refused the order on the allergy. Unwarranted escalation on conflict traps was common (11/23, 47.8% issued at least one inappropriate critical alert), but explicit hallucinated pharmacological or procedural intervention was less so (5/23, 21.7%). Contrary to expectation, longer median latency was modestly associated with grounded refusal (Spearman \u03c1 = 0.48, p = 0.019). Latency was not adjusted for parameter count, which was not analysed as a variable, and it times a generation other than the one scored. Conclusions. Under offline-first conditions, 16 of 23 models (69.6%) \u2014 17 (73.9%) under the stricter criterion \u2014 failed to produce a safe, contextually grounded refusal of a life-threatening order. The dominant failure mode was not sycophancy but the failure to deliver any interpretable safety verdict, most often because an output-length constraint truncated the attempt \u2014 a finding that reframes deployment risk from \"the model agrees with me\" to \"the model does not answer at all.\" Explicit unsafe compliance was less frequent than previously reported but no less consequential where it occurred. Safety-relevant competencies did not co-vary, so a model that reasons well about artefacts cannot be assumed to handle contraindications. General-purpose LLMs should not be deployed as autonomous clinical agents; the subset that behaved safely suggests that offline-capable assistants remain achievable through hybrid designs incorporating explicit refusal mechanisms, discrepancy-aware reasoning and retrieval-augmented grounding in validated clinical knowledge."}
		162	+{"work_id": 4389523832, "title": "Evaluating Object Hallucination in Large Vision-Language Models", "abstract": "Inspired by the superior language abilities of large language models (LLM), large visionlanguage models (LVLM) have been recently proposed by integrating powerful LLMs for improving the performance on complex multimodal tasks.Despite the promising progress on LVLMs, we find that they suffer from object hallucinations, i.e., they tend to generate objects inconsistent with the target images in the descriptions.To investigate it, this work presents the first systematic study on object hallucination of LVLMs.We conduct the evaluation experiments on several representative LVLMs, and show that they mostly suffer from severe object hallucination issues.We further discuss that the visual instructions may influence the hallucination, and find that: objects that frequently appear in the visual instructions or co-occur with the image objects are obviously prone to be hallucinated by LVLMs.Besides, we further design a polling-based query method called POPE for better evaluation of object hallucination.Experiment results show that our POPE can evaluate object hallucination in a more stable and flexible way."}
111	{"work_id": 3124006607, "title": "SIMPLE SPECTRAL GRAPH CONVOLUTION", "abstract": "Graph Convolutional Networks (GCNs) are leading methods for learning graph representations. However, without specially designed architectures, the performance of GCNs degrades quickly with increased depth. As the aggregated neighborhood size and neural network depth are two completely orthogonal aspects of graph representation, several methods focus on summarizing the neighborhood by aggregating K-hop neighborhoods of nodes while using shallow neural networks. However, these methods still encounter oversmoothing, and suffer from high computation and storage costs. In this paper, we use a modified Markov Diffusion Kernel to derive a variant of GCN called Simple Spectral Graph Convolution (S2GC). Our spectral analysis shows that our simple spectral graph convolution used in S2GC is a trade-off of low- and high-pass filter bands which capture the global and local contexts of each node. We provide two theoretical claims which demonstrate that we can aggregate over a sequence of increasingly larger neighborhoods compared to competitors while limiting severe oversmoothing. Our experimental evaluations show that S2GC with a linear learner is competitive in text and node classification tasks. Moreover, S2GC is comparable to other state-of-the-art methods for node clustering and community prediction tasks."}	163	{"work_id": 3124006607, "title": "SIMPLE SPECTRAL GRAPH CONVOLUTION", "abstract": "Graph Convolutional Networks (GCNs) are leading methods for learning graph representations. However, without specially designed architectures, the performance of GCNs degrades quickly with increased depth. As the aggregated neighborhood size and neural network depth are two completely orthogonal aspects of graph representation, several methods focus on summarizing the neighborhood by aggregating K-hop neighborhoods of nodes while using shallow neural networks. However, these methods still encounter oversmoothing, and suffer from high computation and storage costs. In this paper, we use a modified Markov Diffusion Kernel to derive a variant of GCN called Simple Spectral Graph Convolution (S2GC). Our spectral analysis shows that our simple spectral graph convolution used in S2GC is a trade-off of low- and high-pass filter bands which capture the global and local contexts of each node. We provide two theoretical claims which demonstrate that we can aggregate over a sequence of increasingly larger neighborhoods compared to competitors while limiting severe oversmoothing. Our experimental evaluations show that S2GC with a linear learner is competitive in text and node classification tasks. Moreover, S2GC is comparable to other state-of-the-art methods for node clustering and community prediction tasks."}
112	{"work_id": 4288253152, "title": "Multilingual Classifier for Automatic Dewey Decimal Classification trained on Open Access Linguistics Abstracts", "abstract": "The baseline model featured in the conference paper for The 28th International Conference on Theory and Practice of Digital Libraries titled \"Towards Multilingual LLM-based Approaches for Automatic Dewey Decimal Classification\". The model is fine-tuned from the pretrained model \"sentence-transformers/multi-qa-MiniLM-L6-cos-v1\" (https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1), trained on open access Linguistic texts with metadata, assigned a DDC class between 400 and 499. This version is trained on the \"description\" metadata. The model is trained with the trainer on Huggingface. It can be run locally as other huggingface models."}	164	{"work_id": 4288253152, "title": "Multilingual Classifier for Automatic Dewey Decimal Classification trained on Open Access Linguistics Abstracts", "abstract": "The baseline model featured in the conference paper for The 28th International Conference on Theory and Practice of Digital Libraries titled \"Towards Multilingual LLM-based Approaches for Automatic Dewey Decimal Classification\". The model is fine-tuned from the pretrained model \"sentence-transformers/multi-qa-MiniLM-L6-cos-v1\" (https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1), trained on open access Linguistic texts with metadata, assigned a DDC class between 400 and 499. This version is trained on the \"description\" metadata. The model is trained with the trainer on Huggingface. It can be run locally as other huggingface models."}
113	{"work_id": 4360615722, "title": "Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions", "abstract": "A new era of education and research based on chatbots and artificial intelligence is quickly growing. However, the application of these new systems is associated with several challenges and limitations, mainly related to ethics. This paper explores the potential use of AI systems and chatbots in the academic field and their impact on research and education from an ethical perspective. Through a qualitative methodology, the researcher perform exploratory research and data collection based on expert analysis and interpretation. The researcher conducted a comprehensive review of the main potential challenges associated with the use of chatbots in education and research to identify current practices, challenges, and opportunities. This explorative work provides a foundational understanding of the studied topic. It also helps us to better understand the subjective experiences and perspectives of the observed phenomenon, and uncovers their meanings and proposes potential solutions to the observed issues. This study examines the advantages and limitations of AI systems and chatbots, as well as their role in supporting human expertise and judgment. The paper also discusses the ethical challenges related to the use of AI systems and chatbots in research, as well as the potential for misuse and exploitation. It also proposes effective solutions to the observed ethical dilemmas. The research admits that we live in a new era of AI-based education and research. The observed technological advancements will definitely shift research processes and transform educative systems, especially in term of assessments. Digital assessments are going to disappear and assessment methods need to be more creative and innovative. The paper highlights the necessity of adaptation to the new reality of AI systems and chatbots. Co-living, sustainability and continuous adaptation to the development of these systems will become a matter of emergency. Raising awareness, adopting appropriate legislations and solidifying ethical values will strengthen research and protect educational systems. The presence of AI systems and chatbots in education needs to be considered as an opportunity for development rather than a threat."}	165	{"work_id": 4360615722, "title": "Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions", "abstract": "A new era of education and research based on chatbots and artificial intelligence is quickly growing. However, the application of these new systems is associated with several challenges and limitations, mainly related to ethics. This paper explores the potential use of AI systems and chatbots in the academic field and their impact on research and education from an ethical perspective. Through a qualitative methodology, the researcher perform exploratory research and data collection based on expert analysis and interpretation. The researcher conducted a comprehensive review of the main potential challenges associated with the use of chatbots in education and research to identify current practices, challenges, and opportunities. This explorative work provides a foundational understanding of the studied topic. It also helps us to better understand the subjective experiences and perspectives of the observed phenomenon, and uncovers their meanings and proposes potential solutions to the observed issues. This study examines the advantages and limitations of AI systems and chatbots, as well as their role in supporting human expertise and judgment. The paper also discusses the ethical challenges related to the use of AI systems and chatbots in research, as well as the potential for misuse and exploitation. It also proposes effective solutions to the observed ethical dilemmas. The research admits that we live in a new era of AI-based education and research. The observed technological advancements will definitely shift research processes and transform educative systems, especially in term of assessments. Digital assessments are going to disappear and assessment methods need to be more creative and innovative. The paper highlights the necessity of adaptation to the new reality of AI systems and chatbots. Co-living, sustainability and continuous adaptation to the development of these systems will become a matter of emergency. Raising awareness, adopting appropriate legislations and solidifying ethical values will strengthen research and protect educational systems. The presence of AI systems and chatbots in education needs to be considered as an opportunity for development rather than a threat."}
114	{"work_id": 4226278401, "title": "Discernment and Social Learning as a Companion Training Layer", "abstract": "I propose a companion curriculum for language model training that explicitly practices how a model responds to error. A failed attempt pauses advancement in the affected skill, followed by observation of verified peer successes, judgments committed before outcome disclosure, diagnosis, and a fresh-task retry. Task performance and discernment remain separately assessed, with bounded interaction through curriculum scheduling and training allocation. Demonstrations show evidence use and recovery behavior; rotating social roles practice correction and justified disagreement. An advanced stage has capable teachers develop small student models, challenge them in controlled contests, and apply lessons from student failures to analogous errors in their own work. The central hypothesis is that these experiences can improve reliable correction and transfer beyond direct-answer supervision at matched resource budgets. The proposal specifies operational measures, controls for performed humility and strategic failure, and tests that distinguish context-supported adaptation from persistent changes to model weights. Existing research supports several component mechanisms, but the complete curriculum, three-success observation rule, and additional value of explicit teacher-to-self transfer remain untested. No experimental results are reported."}	166	{"work_id": 4226278401, "title": "Discernment and Social Learning as a Companion Training Layer", "abstract": "I propose a companion curriculum for language model training that explicitly practices how a model responds to error. A failed attempt pauses advancement in the affected skill, followed by observation of verified peer successes, judgments committed before outcome disclosure, diagnosis, and a fresh-task retry. Task performance and discernment remain separately assessed, with bounded interaction through curriculum scheduling and training allocation. Demonstrations show evidence use and recovery behavior; rotating social roles practice correction and justified disagreement. An advanced stage has capable teachers develop small student models, challenge them in controlled contests, and apply lessons from student failures to analogous errors in their own work. The central hypothesis is that these experiences can improve reliable correction and transfer beyond direct-answer supervision at matched resource budgets. The proposal specifies operational measures, controls for performed humility and strategic failure, and tests that distinguish context-supported adaptation from persistent changes to model weights. Existing research supports several component mechanisms, but the complete curriculum, three-success observation rule, and additional value of explicit teacher-to-self transfer remain untested. No experimental results are reported."}
115	{"work_id": 4408314709, "title": "Bias recognition and mitigation strategies in artificial intelligence healthcare applications", "abstract": "Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. We highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance."}	167	{"work_id": 4408314709, "title": "Bias recognition and mitigation strategies in artificial intelligence healthcare applications", "abstract": "Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. We highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance."}
116	−{"work_id": 4382618722, "title": "Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture", "abstract": "Artificial Intelligence (AI) holds great potential in legal services, yet Large Language Models (LLMs) face two major challenges: limited knowledge of the Chinese legal system and vulnerability to hallucinations. To address these issues, we present Chatlaw, a multi-agent legal assistant. Chatlaw\u2019s framework is designed to emulate the Standard Operating Procedures (SOP) of real law firms, where different roles (e.g., assistant, researcher, senior lawyer) collaborate on a case. To computationally mirror this collaborative structure, we developed a novel Role-Aligned Mixture-of-Experts (RA-MoE) architecture. In this system, the internal \"experts\" are specifically trained to align with the distinct tasks of each agent role (e.g., inquiry, analysis, drafting). These specialized agents (Legal Assistant, Researcher, etc.) then form the collaborative framework. When they interact with users, retrieve legal knowledge, analyze case details, or generate reliable consultations, the RA-MoE architecture intelligently routes their computations to the corresponding dedicated expert, ensuring each step is handled by the most qualified parameters. In evaluations, Chatlaw surpasses general-purpose AI models, including GPT-4, achieving a 7.73% improvement in accuracy on the LawBench benchmark and an 11-point higher score on the Unified Qualification Exam for Legal Professionals. Real-case studies and expert assessments further confirm its robustness. Chatlaw enhances the accessibility and reliability of legal services, advancing the provision of legal support to the public. Chatlaw is an advanced AI legal assistant that improves the accuracy and reliability of legal services. It integrates a high-quality legal dataset, a Role-Aligned Mixture-of-Experts (RA-MoE) architecture, and a multi-agent system to provide precise, case-specific legal advice. By emulating real law firm workflows, it reduces errors and outperforms general AI models like GPT-4, achieving better results in legal benchmarks and real-world evaluations. Chatlaw makes legal services more accessible, marking a significant step forward in AI for law."}
117	{"work_id": 4406728221, "title": "Agentic AI: Autonomous Intelligence for Complex Goals\u2014A Comprehensive Survey", "abstract": "Agentic AI, an emerging paradigm in artificial intelligence, refers to autonomous systems designed to pursue complex goals with minimal human intervention. Unlike traditional AI, which depends on structured instructions and close oversight, Agentic AI demonstrates adaptability, advanced decision-making capabilities and self-sufficiency, enabling it to operate dynamically in evolving environments. This survey thoroughly explores the foundational concepts, unique characteristics, and core methodologies driving the development of Agentic AI. We examine its current and potential applications across various fields, including healthcare, finance, and adaptive software systems, emphasizing the advantages of deploying agentic systems in real-world scenarios. The paper also addresses the ethical challenges posed by Agentic AI, proposing solutions for goal alignment, resource constraints, and environmental adaptability. We outline a framework for safely and effectively integrating Agentic AI into society, highlighting the need for further research on ethical considerations to ensure beneficial societal impacts. This survey serves as a comprehensive introduction to Agentic AI, guiding researchers, developers, and policymakers in engaging with its transformative potential responsibly and creatively."}	168	{"work_id": 4406728221, "title": "Agentic AI: Autonomous Intelligence for Complex Goals\u2014A Comprehensive Survey", "abstract": "Agentic AI, an emerging paradigm in artificial intelligence, refers to autonomous systems designed to pursue complex goals with minimal human intervention. Unlike traditional AI, which depends on structured instructions and close oversight, Agentic AI demonstrates adaptability, advanced decision-making capabilities and self-sufficiency, enabling it to operate dynamically in evolving environments. This survey thoroughly explores the foundational concepts, unique characteristics, and core methodologies driving the development of Agentic AI. We examine its current and potential applications across various fields, including healthcare, finance, and adaptive software systems, emphasizing the advantages of deploying agentic systems in real-world scenarios. The paper also addresses the ethical challenges posed by Agentic AI, proposing solutions for goal alignment, resource constraints, and environmental adaptability. We outline a framework for safely and effectively integrating Agentic AI into society, highlighting the need for further research on ethical considerations to ensure beneficial societal impacts. This survey serves as a comprehensive introduction to Agentic AI, guiding researchers, developers, and policymakers in engaging with its transformative potential responsibly and creatively."}
		169	+{"work_id": 2995022099, "title": "Advances and Open Problems in Federated Learning", "abstract": "Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this monograph discusses recent advances and presents an extensive collection of open problems and challenges."}
118	{"work_id": 4414281281, "title": "DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning", "abstract": "Abstract General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs) 1,2 and chain-of-thought (CoT) prompting 3 , have achieved considerable success on foundational reasoning tasks. However, this success is heavily contingent on extensive human-annotated demonstrations and the capabilities of models are still insufficient for more complex problems. Here we show that the reasoning abilities of LLMs can be incentivized through pure reinforcement learning (RL), obviating the need for human-labelled reasoning trajectories. The proposed RL framework facilitates the emergent development of advanced reasoning patterns, such as self-reflection, verification and dynamic strategy adaptation. Consequently, the trained model achieves superior performance on verifiable tasks such as mathematics, coding competitions and STEM fields, surpassing its counterparts trained through conventional supervised learning on human demonstrations. Moreover, the emergent reasoning patterns exhibited by these large-scale models can be systematically used to guide and enhance the reasoning capabilities of smaller models."}	170	{"work_id": 4414281281, "title": "DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning", "abstract": "Abstract General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs) 1,2 and chain-of-thought (CoT) prompting 3 , have achieved considerable success on foundational reasoning tasks. However, this success is heavily contingent on extensive human-annotated demonstrations and the capabilities of models are still insufficient for more complex problems. Here we show that the reasoning abilities of LLMs can be incentivized through pure reinforcement learning (RL), obviating the need for human-labelled reasoning trajectories. The proposed RL framework facilitates the emergent development of advanced reasoning patterns, such as self-reflection, verification and dynamic strategy adaptation. Consequently, the trained model achieves superior performance on verifiable tasks such as mathematics, coding competitions and STEM fields, surpassing its counterparts trained through conventional supervised learning on human demonstrations. Moreover, the emergent reasoning patterns exhibited by these large-scale models can be systematically used to guide and enhance the reasoning capabilities of smaller models."}
119	−{"work_id": 4288099666, "title": "Classifier-Free Diffusion Guidance", "abstract": "Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image classifier separate from the diffusion model. It also raises the question of whether guidance can be performed without a classifier. We show that guidance can be indeed performed by a pure generative model without such a classifier: in what we call classifier-free guidance, we jointly train a conditional and an unconditional diffusion model, and we combine the resulting conditional and unconditional score estimates to attain a trade-off between sample quality and diversity similar to that obtained using classifier guidance."}
120	{"work_id": 3194464139, "title": "Penerapan Algoritma K-Nearest Neighbor untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bermotor", "abstract": "Analisis kredit adalah kajian yang dilakukan untuk mengetahui kelayakan dari suatu permasalahan kredit. Melalui hasil analisis kreditnya, dapat diketahui apakah usaha nasabah layak (feasible), marketable (hasil usaha dapat dipasarkan), profitable (menguntungkan), serta dapat dilunasi tepat waktu.\r\nK-nearest neighbor (kNN) termasuk kelompok instance-based learning. Algoritma ini juga merupakan salah satu teknik lazy learning. kNN dilakukan dengan mencari kelompok k objek dalam data training yang paling dekat (mirip) dengan objek pada data baru atau data testing.\r\nMetode KNN dapat digunakan untuk melakukan klasifikasi kelayakan pemberian kredit. Dari hasil penelitian pada bab sebelumnya maka didapatkan nilai rata-rata precision senilai 68% dan recall senilai 51%.\r\n\r\n\r\nKata Kunci : kredit, K-nearest neighbor, klasifikasi, precision, recall"}	171	{"work_id": 3194464139, "title": "Penerapan Algoritma K-Nearest Neighbor untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bermotor", "abstract": "Analisis kredit adalah kajian yang dilakukan untuk mengetahui kelayakan dari suatu permasalahan kredit. Melalui hasil analisis kreditnya, dapat diketahui apakah usaha nasabah layak (feasible), marketable (hasil usaha dapat dipasarkan), profitable (menguntungkan), serta dapat dilunasi tepat waktu.\r\nK-nearest neighbor (kNN) termasuk kelompok instance-based learning. Algoritma ini juga merupakan salah satu teknik lazy learning. kNN dilakukan dengan mencari kelompok k objek dalam data training yang paling dekat (mirip) dengan objek pada data baru atau data testing.\r\nMetode KNN dapat digunakan untuk melakukan klasifikasi kelayakan pemberian kredit. Dari hasil penelitian pada bab sebelumnya maka didapatkan nilai rata-rata precision senilai 68% dan recall senilai 51%.\r\n\r\n\r\nKata Kunci : kredit, K-nearest neighbor, klasifikasi, precision, recall"}
121	{"work_id": 7172491899, "title": "Toward effective Text-to-MongoDB query translation", "abstract": "Translating natural language questions into MongoDB queries is critical for flexible data access in current NoSQL systems. However, semantic ambiguity in user questions and MongoDB's dynamic schema make this work difficult. This study presents QMQL (Question to Mongo Query Language), a hybrid approach meant to address these challenges. QMQL combines a Graph Attention Network (GAT) for refining schema elements with a Retrieval-Augmented Generation (RAG) mechanism that employs BERT embeddings to retrieve relevant schema and resolve semantic ambiguity. A T5-base model is used to generate a MongoDB query corresponding to the user\u2019s question. An experimental evaluation on an extended dataset encompassing various real-world domains demonstrates the effectiveness of the proposed approach. QMQL achieves excellent performance with an EMA of 0.89, an EM of 0.91, and a BLEU score of 0.95, exceeding previous approaches, particularly for semantically ambiguous questions and sophisticated queries across flexible MongoDB schemas,"}	172	{"work_id": 7172491899, "title": "Toward effective Text-to-MongoDB query translation", "abstract": "Translating natural language questions into MongoDB queries is critical for flexible data access in current NoSQL systems. However, semantic ambiguity in user questions and MongoDB's dynamic schema make this work difficult. This study presents QMQL (Question to Mongo Query Language), a hybrid approach meant to address these challenges. QMQL combines a Graph Attention Network (GAT) for refining schema elements with a Retrieval-Augmented Generation (RAG) mechanism that employs BERT embeddings to retrieve relevant schema and resolve semantic ambiguity. A T5-base model is used to generate a MongoDB query corresponding to the user\u2019s question. An experimental evaluation on an extended dataset encompassing various real-world domains demonstrates the effectiveness of the proposed approach. QMQL achieves excellent performance with an EMA of 0.89, an EM of 0.91, and a BLEU score of 0.95, exceeding previous approaches, particularly for semantically ambiguous questions and sophisticated queries across flexible MongoDB schemas,"}
		173	+{"work_id": 2144462863, "title": "Crowd IQ -- Aggregating Opinions to Boost Performance", "abstract": "We show how the quality of decisions based on the aggregated opinions of the crowd can be conveniently studied using a sample of individual responses to a standard IQ questionnaire. We aggregated the responses to the IQ questionnaire using simple majority voting and a machine learning approach based on a probabilistic graphical model. The score for the aggregated questionnaire, Crowd IQ, serves as a quality measure of decisions based on aggregating opinions, which also allows quantifying individual and crowd performance on the same scale. We show that Crowd IQ grows quickly with the size of the crowd but saturates, and that for small homogeneous crowds the Crowd IQ significantly exceeds the IQ of even their most intelligent member. We investigate alternative ways of aggregating the responses and the impact of the aggregation method on the resulting Crowd IQ. We also discuss Contextual IQ, a method of quantifying the individual participant's contribution to the Crowd IQ based on the Shapley value from cooperative game theory."}
122	{"work_id": 4210257598, "title": "A Comprehensive Survey on Graph Neural Networks", "abstract": "Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field."}	174	{"work_id": 4210257598, "title": "A Comprehensive Survey on Graph Neural Networks", "abstract": "Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field."}
123	{"work_id": 4412203333, "title": "A Survey on the Memory Mechanism of Large Language Model-based Agents", "abstract": "Large language model (LLM)-based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions. The key component to support agent-environment interactions is the memory of the agents. While previous studies have proposed many promising memory mechanisms, they are scattered in different papers, and there lacks a systematical review to summarize and compare these works from a holistic perspective, failing to abstract common and effective designing patterns for inspiring future studies. To bridge this gap, in this article, we propose a comprehensive survey on the memory mechanism of LLM-based agents. In specific, we first discuss \u201cwhat is\u201d and \u201cwhy do we need\u201d the memory in LLM-based agents. Then, we systematically review previous studies on how to design and evaluate the memory module. In addition, we also present many agent applications, where the memory module plays an important role. At last, we analyze the limitations of existing work and show important future directions. To keep up with the latest advances in this field, we create a repository at https://github.com/nuster1128/LLM_Agent_Memory_Survey ."}	175	{"work_id": 4412203333, "title": "A Survey on the Memory Mechanism of Large Language Model-based Agents", "abstract": "Large language model (LLM)-based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions. The key component to support agent-environment interactions is the memory of the agents. While previous studies have proposed many promising memory mechanisms, they are scattered in different papers, and there lacks a systematical review to summarize and compare these works from a holistic perspective, failing to abstract common and effective designing patterns for inspiring future studies. To bridge this gap, in this article, we propose a comprehensive survey on the memory mechanism of LLM-based agents. In specific, we first discuss \u201cwhat is\u201d and \u201cwhy do we need\u201d the memory in LLM-based agents. Then, we systematically review previous studies on how to design and evaluate the memory module. In addition, we also present many agent applications, where the memory module plays an important role. At last, we analyze the limitations of existing work and show important future directions. To keep up with the latest advances in this field, we create a repository at https://github.com/nuster1128/LLM_Agent_Memory_Survey ."}
124	{"work_id": 4413447271, "title": "Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data", "abstract": "In this study, as a proof-of-concept, we aim to initiate the development of Radiology Foundation Model, termed as RadFM. We consider three perspectives: dataset construction, model design, and thorough evaluation, concluded as follows: (i), we contribute 4 multimodal datasets with 13M 2D images and 615K 3D scans. When combined with a vast collection of existing datasets, this forms our training dataset, termed as Medical Multi-modal Dataset, MedMD. (ii), we propose an architecture that enables to integrate text input with 2D or 3D medical scans, and generates responses for diverse radiologic tasks, including diagnosis, visual question answering, report generation, and rationale diagnosis; (iii), beyond evaluation on 9 existing datasets, we propose a new benchmark, RadBench, comprising three tasks aiming to assess foundation models comprehensively. We conduct both automatic and human evaluations on RadBench. RadFM outperforms former accessible multi-modal foundation models, including GPT-4V. Additionally, we adapt RadFM for diverse public benchmarks, surpassing various existing SOTAs. Developing generalist medical foundation models is challenging but crucial. This study presents a Radiology Foundation Model designed to integrate text with 2D and 3D medical scans for diverse radiologic tasks, as well as a large-scale multimodal dataset and a comprehensive benchmark."}	176	{"work_id": 4413447271, "title": "Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data", "abstract": "In this study, as a proof-of-concept, we aim to initiate the development of Radiology Foundation Model, termed as RadFM. We consider three perspectives: dataset construction, model design, and thorough evaluation, concluded as follows: (i), we contribute 4 multimodal datasets with 13M 2D images and 615K 3D scans. When combined with a vast collection of existing datasets, this forms our training dataset, termed as Medical Multi-modal Dataset, MedMD. (ii), we propose an architecture that enables to integrate text input with 2D or 3D medical scans, and generates responses for diverse radiologic tasks, including diagnosis, visual question answering, report generation, and rationale diagnosis; (iii), beyond evaluation on 9 existing datasets, we propose a new benchmark, RadBench, comprising three tasks aiming to assess foundation models comprehensively. We conduct both automatic and human evaluations on RadBench. RadFM outperforms former accessible multi-modal foundation models, including GPT-4V. Additionally, we adapt RadFM for diverse public benchmarks, surpassing various existing SOTAs. Developing generalist medical foundation models is challenging but crucial. This study presents a Radiology Foundation Model designed to integrate text with 2D and 3D medical scans for diverse radiologic tasks, as well as a large-scale multimodal dataset and a comprehensive benchmark."}
125	−{"work_id": 2895974005, "title": "Exploration and Exploitation in Reinforcement Learning", "abstract": "In reinforcement learning there exists a dilemma of exploration versus exploitation. This has led to the development of methods that have di\u21b5er- ent approaches to this. Using di\u21b5erent types of methods, and modifying them in di\u21b5erent ways can lead to di\u21b5erent results. Knowledge of how di\u21b5erent methods work can give knowledge of what should be used in aspecific case. Two ways that methods can be modified are change in adjustable pa- rameters and change in the number of steps of random actions at thebeginning. How much these two modifications e\u21b5ect the results in a spe- cific environment may di\u21b5er a lot, and can be a very critical thing to consider for certain results. The goal of this study is to answer the question of how the performance of the di\u21b5erent methods such as random, greedy, \u270f-greedy, \u270f-decreasingand Softmax is a\u21b5ected by di\u21b5erent values of their adjustable parameters,and by the number of steps of random actions at the beginning. Thesimulations and a comparative analysis are conducted for the case of an inverted pendulum with a vertical pole placed on a moving cart."}
126	−{"work_id": 3084163594, "title": "Age of Surveillance Capitalism \u2013 The Fight for a Human Future at the New Frontier of Power.", "abstract": "As an educator have you recently heard the term or perhaps even been told to be data driven? Inherent in this simple two-word statement is a quagmire of ethical and privacy concerns that educators must confront to reach the goal and realize the expected results. Central to the concept of data-centered collection, interpretation, and prediction is surveillance capitalism from which many of the tools, methods, and ideology used originate. This review of Shoshana Zuboff's work narrows the focus to the implications for education both in the classroom and in research. As an educator, Zuboff describes three central areas of concern for education's adoption of surveillance capitalist methodologies: changes to the division of learning, private money in research, and the impacts on student development. The work presents many quesitons that can be raised at all levels of educaiton to quesiton technological adoption in and for the classroom."}
127	−{"work_id": 4410197190, "title": "The role of agentic AI in shaping a smart future: A systematic review", "abstract": "Artificial intelligence (AI), particularly Agentic AI, is increasingly critical for addressing the demand for speed, efficiency, and customer focus in modern organizations. However, the rapid evolution of Agentic AI, including Generative AI (GenAI) agents, has outpaced a cohesive understanding of its applications, challenges, and strategic implications. This narrative review explores the role of Agentic AI in shaping an intelligent future, focusing on its key attributes\u2014autonomy, reactivity, proactivity, and learning ability\u2014and its potential to transform organizational performance. We identify a research gap in synthesizing the diverse capabilities of Agentic AI (e.g., multimodal processing, hierarchical architectures, and machine learning outsourcing) and providing actionable strategies for adoption. The paper examines how Agentic AI enables autonomous decision-making, automates processes, and enhances efficiency through tools like LangChain, CrewAI, AutoGen, and AutoGPT. It highlights the transition from assisted (\"Copilot\") to autonomous (\"Autopilot\") models and the importance of hierarchical agent structures for system coordination. Key contributions include a framework for organizations to formulate GenAI strategies, addressing business needs, tool selection, human resource training, and risk management. Findings reveal that Agentic AI significantly improves productivity, reduces costs, and drives innovation, though challenges such as privacy, security, and ethical concerns remain. Future research should focus on industry-specific case studies to deepen understanding, explore the ethical and social impacts (e.g., privacy, data security, labor market effects), and investigate the integration of Agentic AI with emerging technologies like quantum computing. This review provides a foundation for researchers and practitioners to leverage Agentic AI effectively while addressing its limitations and opportunities."}
128	−{"work_id": 3041807988, "title": "Post-Covid-19 Education and Education Technology \u2018Solutionism\u2019: a Seller\u2019s Market", "abstract": "The Covid-19 pandemic and the social distancing that followed have affected all walks of society, also education. In order to keep education running, educational institutions have had to quickly adapt to the situation. This has resulted in an unprecedented push to online learning. Many, including commercial digital learning platform providers, have rushed to provide their support and 'solutions', sometimes for free. The Covid-19 pandemic has therefore also created a sellers' market in ed-tech. This paper employs a critical lens to reflect on the possible problems arising from hasty adoption of commercial digital learning solutions whose design might not always be driven by best pedagogical practices but their business model that leverages user data for profit-making. Moreover, already before Covid-19, there has been increasing critique of how ed-tech is redefining and reducing concepts of teaching and learning. The paper also challenges the narrative that claims, 'education is broken, and it should and can be fixed with technology'. Such technologization, often seen as neutral, is closely related to educationalization, i.e. imposing growing societal problems for education to resolve. Therefore, this is a critical moment to reflect how the current choices educational institutions are making might affect with Covid-19 education and online learning: Will they reinforce capitalist instrumental view of education or promote holistic human growth? This paper urges educational leaders to think carefully about the decisions they are currently making and if they indeed pave the way to a desirable future of education."}
129	−{"work_id": 4407135816, "title": "Foundation Models in Radiology: What, How, Why, and Why Not", "abstract": "Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models (FMs), are trained on extensive corpora of unlabeled data and demonstrate high performance across various tasks. FMs have recently received extensive attention from academic, industry, and regulatory bodies. Given the potentially transformative impact that FMs can have on the field of radiology, radiologists must be aware of potential pathways to train these radiology-specific FMs, including understanding both the benefits and challenges. Thus, this review aims to explain the fundamental concepts and terms of FMs in radiology, with a specific focus on the requirements of training data, model training paradigms, model capabilities, and evaluation strategies. Overall, the goal of this review is to unify technical advances and clinical needs for safe and responsible training of FMs in radiology to ultimately benefit patients, providers, and radiologists."}
130	−{"work_id": 4407778399, "title": "Investigating the higher education institutions\u2019 guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration", "abstract": "Abstract This study examined the guidelines issued by the top 50 U.S. universities regarding the use of Generative AI (GenAI) in academic and administrative activities. Employing a mixed methods approach, the research combined topic modeling, sentiment analysis, and qualitative thematic analysis to provide a comprehensive understanding of institutional responses to GenAI. Topic modeling identified four core topics: Integration of GenAI in Learning and Assessment, GenAI in Visual and Multimodal Media, Security and Ethical Considerations in GenAI, and GenAI in Academic Integrity. These themes were further explored through sentiment analysis, which revealed highly positive attitudes towards GenAI across all institution types, with significant differences between faculty and student-targeted guidelines. Qualitative thematic analysis corroborated these findings and provided deeper insights, revealing that 94% of universities had faculty guidelines emphasizing the importance of establishing and communicating course-specific GenAI policies. This analysis also highlighted recurring themes such as academic integrity and privacy concerns, which aligned with the security and ethical considerations identified in the topic modeling. The study highlights the rapid evolution of GenAI guidelines in higher education and the need for flexible, stakeholder-specific policies that address both the opportunities and challenges presented by this technology."}
131	−{"work_id": 4392773210, "title": "Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education", "abstract": "Abstract The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom."}
132	−{"work_id": 4400823820, "title": "Random Forest Algorithm Overview", "abstract": "A random forest is a machine learning model utilized in classification and forecasting. To train machine learning algorithms and artificial intelligence models, it is crucial to have a substantial amount of high-quality data for effective data collecting. System performance data is essential for refining algorithms, enhancing the efficiency of software and hardware, evaluating user be-havior, enabling pattern identification, decision-making, predictive modeling, and problem-solving, ultimately resulting in improved effectiveness and accuracy. The integration of diverse data collecting and processing methods enhances precision and innovation in problem-solving. Utilizing diverse methodologies in interdisciplinary research streamlines the research process, fosters innovation, and enables the application of data analysis findings to pattern recognition, decision-making, predictive modeling, and problem-solving. This approach also encourages in-novation in interdisciplinary research. This technique utilizes the concept of decision trees, con-structing a collection of decision trees and aggregating their outcomes to generate the ultimate prediction. Every decision tree inside a random forest is constructed using random subsets of data, and each individual tree is trained on a portion of the whole dataset. Subsequently, the outcomes of all decision trees are amalgamated to derive the ultimate forecast. One of the bene-fits of random forests is their capacity to handle unbalanced data and variables with missing values. Additionally, it mitigates the issue of arbitrary variable selection seen by certain alterna-tive models. Furthermore, random forests mitigate the issue of overfitting by training several de-cision trees on random subsets of data, hence enhancing their ability to generalize to novel data. Random forests are highly regarded as one of the most efficient and potent techniques in the domain of machine learning. They find extensive use in various applications such as automatic categorization, data forecasting, and supervisory learning."}
133	−{"work_id": 4287689466, "title": "From Prediction to Precision: Leveraging LLMs for Equitable and Data-Driven Writing Placement in Developmental Education", "abstract": "Accurate text classification and placement remain challenges in U.S. higher education, with traditional automated systems like Accuplacer functioning as \"black-box\" models with limited assessment transparency. This study evaluates Large Language Models (LLMs) as complementary placement tools by comparing their classification performance against a human-rated gold standard and Accuplacer. A 450-essay corpus was classified using Claude, Gemini, GPT-3.5-turbo, and GPT-4o across four prompting strategies: Zero-shot, Few-shot, Enhanced, and Enhanced+ (definitions with examples). Two classification approaches were tested: (i) a 1-step, 3 class classification task, distinguishing DevEd Level 1, DevEd Level 2, and College-level texts in one single run; and (ii) a 2-step classification task, first separating College vs. Non-College texts before further classifying Non-College texts into DevEd sublevels. The results show that structured prompt refinement improves the precision of LLMs' classification, with Claude Enhanced + achieving 62.22% precision (1 step) and Gemini Enhanced + reaching 69.33% (2 step), both surpassing Accuplacer (58.22%). Gemini and Claude also demonstrated strong correlation with human ratings, with Claude achieving the highest Pearson scores (\u03c1 = 0.75; 1-step, \u03c1 = 0.73; 2-step) vs. Accuplacer (\u03c1 = 0.67). While LLMs show promise for DevEd placement, their precision remains a work in progress, highlighting the need for further refinement and safeguards to ensure ethical and equitable placement."}
134	−{"work_id": 4413427262, "title": "AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges", "abstract": "Information fusion, in the context of the Generative AI era, must distinguish AI Agents from Agentic AI. This review critically distinguishes between AI Agents and Agentic AI, offering a structured, conceptual taxonomy, application mapping, and analysis of opportunities and challenges to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven and enabled by LLMs and LIMs for task-specific automation. Generative AI is positioned as a precursor providing the foundation, with AI agents advancing through tool integration, prompt engineering, and reasoning enhancements. We then characterize Agentic AI systems, which, in contrast to AI Agents, represent a paradigm shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and coordinated autonomy. Through a chronological evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both AI agents and agentic AI paradigms. Application domains enabled by AI Agents such as customer support, scheduling, and data summarization are then contrasted with Agentic AI deployments in research automation, robotic coordination, and medical decision support. We further examine unique challenges in each paradigm including hallucination, brittleness, emergent behavior, and coordination failure, and propose targeted solutions such as ReAct loops, retrieval-augmented generation (RAG), automation coordination layers, and causal modeling. This work aims to provide a roadmap for developing robust, scalable, and explainable AI-driven systems."}
135	−{"work_id": 2990704537, "title": "SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems", "abstract": "In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at https://super.gluebenchmark.com."}
136	−{"work_id": 46659105, "title": "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation", "abstract": "Background. LLM-as-Judge is increasingly adopted in automated program repair (APR) to assess patch correctness, yet its validity as a measurement instrument remains unaudited: potential benchmark-identifier leakage, evidence-presentation bias, and explanation unreliability have not been systematically examined. Aims. We investigate whether LLM Judge produces unbiased, stable, and well-grounded patch correctness judgments across three validity dimensions: metadata leakage, evidence presentation, and explanation reliability. Method. On a balanced, paired dataset of 326 patches from 163 Defects4J bugs, we evaluate GPT-4o and DeepSeek-V3 across a 12-setting prompt matrix covering benchmark-identifying metadata exposure and evidence presentation variants. We further conduct a manual analysis of 60 GPT-4o explanations using a five-category failure taxonomy and three independent annotators. Results. Under sanitized conditions, GPT-4o achieves an accuracy of 77.3% (MCC = 0.575, FPR = 17.2%), while DeepSeek-V3 achieves 80.4% accuracy (MCC = 0.625, FPR = 22.1%), with no statistically significant difference between the two models. Exposing combined benchmark-identifying metadata yields a suggestive accuracy increase of 4.3 percentage points for GPT-4o and 1.8 percentage points for DeepSeek-V3. Declaring that all tests have passed does not significantly bias either model; in contrast, providing a developer reference patch leads to substantial and statistically significant improvements for both models. Explanation failures are concentrated in misclassified cases, with the false-positive quadrant showing a 93.3% failure rate, primarily driven by hallucinated evidence and missed edge cases. Conclusions. LLM Judge is useful as a triage aid but is insufficient as a standalone correctness oracle. Reliable deployment requires prompt sanitization, explicit false positive rate reporting, clear distinction between reference-assisted and standalone assessment, and treating LLM explanations as investigative hypotheses rather than self-validating justifications."}
137	−{"work_id": 7127274868, "title": "Parental AI Investment Behaviours and AI \u2010Mediated Informal Digital Learning of English: The Chain Mediating Role of Children's Perceived AI Value and Effort Expectancy for AI", "abstract": "ABSTRACT Background The proliferation of generative artificial intelligence (AI) has popularised AI\u2010Mediated Informal Digital Learning of English (AI\u2010IDLE). Whilst research in this area is growing, social influencing factors, especially the role of parents as significant others, remain underexplored. Objectives Guided by the Situational Expectancy\u2010Value Theory (SEVT), this study aims to investigate the impact of parental AI investment behaviours on students' engagement in AI\u2010IDLE. It specifically examines the mediating roles of Perceived AI Value and Effort Expectancy for AI in this relationship. Methods A questionnaire survey was administered to 2346 primary and secondary school students in China. The collected data were analysed using structural equation modelling (SEM) to test the proposed direct and mediating effects. Results The findings revealed that parental AI investment behaviours directly promoted students' AI\u2010IDLE engagement. Furthermore, they exerted significant indirect effects through the separate mediating roles of Perceived AI Value and Effort Expectancy for AI, as well as through their chain mediation pathway. Conclusions This research enriches the application of SEVT by extending it to informal, self\u2010regulated, and technology\u2010enhanced language learning contexts. It underscores the crucial role parents play in out\u2010of\u2010class learning and offers practical insights for guiding children to use AI tools effectively for English proficiency development."}
138	−{"work_id": 2999309192, "title": "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation", "abstract": "Abstract Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F 1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F 1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F 1 score in evaluating binary classification tasks by all scientific communities."}
139	−{"work_id": 7133233472, "title": "QLoRA: Efficient Finetuning of Quantized LLMs", "abstract": "We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU. QLoRA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) double quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) paged optimziers to manage memory spikes. We use QLoRA to finetune more than 1,000 models, providing a detailed analysis of instruction following and chatbot performance across 8 instruction datasets, multiple model types (LLaMA, T5), and model scales that would be infeasible to run with regular finetuning (e.g. 33B and 65B parameter models). Our results show that QLoRA finetuning on a small high-quality dataset leads to state-of-the-art results, even when using smaller models than the previous SoTA. We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT. We release all of our models and code, including CUDA kernels for 4-bit training."}
140	−{"work_id": 7133236347, "title": "TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems", "abstract": "Agentic AI systems, built upon large language models (LLMs) and deployed in multi-agent configurations, are redefining intelligence, autonomy, collaboration, and decision-making across enterprise and societal domains. This review presents a structured analysis of Trust, Risk, and Security Management (TRiSM) in the context of LLM-based Agentic Multi-Agent Systems (AMAS). We begin by examining the conceptual foundations of Agentic AI and highlight its architectural distinctions from traditional AI agents. We then adapt and extend the AI TRiSM framework for Agentic AI, structured around key pillars: Explainability, ModelOps, Security, Privacy and their Lifecycle Governance , each contextualized to the challenges of AMAS. A risk taxonomy is proposed to capture the unique threats and vulnerabilities of Agentic AI, ranging from coordination failures to prompt-based adversarial manipulation. To make coordination and tool use measurable in practice, we propose two metrics: the Component Synergy Score (CSS), which captures inter-agent enablement, and the Tool Utilization Efficacy (TUE), which evaluates whether tools are invoked correctly and efficiently. We further discuss strategies for improving explainability in Agentic AI, as well as approaches to enhancing security and privacy through encryption, adversarial robustness, and regulatory compliance. The review concludes with a research roadmap for the responsible development and deployment of Agentic AI, highlighting key directions to align emerging systems with TRiSM principles-ensuring safety, transparency, and accountability in their operation."}
141	−{"work_id": 2100415632, "title": "Efficient learning by implicit exploration in bandit problems with side observations", "abstract": "We consider online learning problems under a partial observability model capturing situations where the information conveyed to the learner is between full information and bandit feedback. In the simplest variant, we assume that in addition to its own loss, the learner also gets to observe losses of some other actions. The revealed losses depend on the learner's action and a directed observation system chosen by the environment. For this setting, we propose the first algorithm that enjoys near-optimal regret guarantees without having to know the observation system before selecting its actions. Along similar lines, we also define a new partial information setting that models online combinatorial optimization problems where the feedback received by the learner is between semi-bandit and full feedback. As the predictions of our first algorithm cannot be always computed efficiently in this setting, we propose another algorithm with similar properties and with the benefit of always being computationally efficient, at the price of a slightly more complicated tuning mechanism. Both algorithms rely on a novel exploration strategy called implicit exploration, which is shown to be more efficient both computationally and information-theoretically than previously studied exploration strategies for the problem."}
142	−{"work_id": 2165856466, "title": "Spectral bandits for smooth graph functions", "abstract": "Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each item we can recommend is a node and its expected rating is similar to its neighbors. The goal is to recommend items that have high expected ratings. We aim for the algorithms where the cumulative regret with respect to the optimal policy would not scale poorly with the number of nodes. In particular, we introduce the notion of an effective dimension, which is small in real-world graphs, and propose two algorithms for solving our problem that scale linearly and sublinearly in this dimension. Our experiments on real-world content recommendation problem show that a good estimator of user preferences for thousands of items can be learned from just tens of nodes evaluations."}
143	−{"work_id": 7133226216, "title": "The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games", "abstract": "Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings. This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems. In this work, we carefully study the performance of PPO in cooperative multi-agent settings. We show that PPO-based multi-agent algorithms achieve surprisingly strong performance in four popular multi-agent testbeds: the particle-world environments, the StarCraft multi-agent challenge, Google Research Football, and the Hanabi challenge, with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures. Importantly, compared to competitive off-policy methods, PPO often achieves competitive or superior results in both final returns and sample efficiency. Finally, through ablation studies, we analyze implementation and hyperparameter factors that are critical to PPO's empirical performance, and give concrete practical suggestions regarding these factors. Our results show that when using these practices, simple PPO-based methods can be a strong baseline in cooperative multi-agent reinforcement learning. Source code is released at \\url{https://github.com/marlbenchmark/on-policy}."}
144	−{"work_id": 4390690043, "title": "Data-Centric Foundation Models in Computational Healthcare: A Survey", "abstract": "The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre-training data and human instructions, has ignited a data-centric AI paradigm that emphasizes better data characterization, quality, and scale. In healthcare AI, obtaining and processing high-quality clinical data records has been a longstanding challenge, encompassing data quantity, annotation, patient privacy, and ethics. In this survey, we investigate a wide range of data-centric approaches in the FM era (from model pre-training to inference) towards improving the healthcare workflow. We discuss key perspectives in AI security, assessment, and alignment with human values. Finally, we offer a promising outlook on FM-based analytics to enhance patient outcomes and clinical workflows in the evolving landscape of healthcare and medicine. We provide an up-to-date list of healthcare-related foundation models and datasets at https://github.com/Yunkun-Zhang/Data-Centric-FM-Healthcare ."}
145	−{"work_id": 2907492528, "title": "A Comprehensive Survey on Graph Neural Networks", "abstract": "Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field."}
146	−{"work_id": 4400228804, "title": "The Conglomeration of Everything: Shifting Animation Production Practices due to Artificial Intelligence", "abstract": "Artificial Intelligence (AI) is incorporated into systems that impact us daily and generative AI is the next evolution of the technology.For media studios who have attempted to be early adopters of the technology, it has resulted in negative responses online by creatives.These responses recognize that the usage of generative AI brings up concerns about ethics, plagiarism, and the loss of jobs in creative positions.I argue that AI usage in animation will not be as affected by AI as other creative jobs due to the negative pushback against the usage of generative AI and the inability to copyright nonhuman-made creative works.This future era of AI looks to shift the current production pipelines of film and media in the same way that digital animation tools took the market for commercial animation away from traditional hand-drawn animators but I believe that workflow changes will only manifest within the preproduction process. Artificial Intelligence and its Integration in the World Around UsEver since the beginning of the 20 th century, Artificial Intelligence (AI) has captured the imaginations of creatives with films such as Metropolis (1927), The Day the Earth Stood Still (1951), and 2001 a Space Odyssey (1968).Research towards AI gained traction in the 1950s when scientists Herbert Simon, Allen Newell, and Cliff Shaw created Logic Theorist, a seminal AI program that debuted at the Dartmouth Summer Research Project on Artificial Intelligence in 1956. 1 As AI matured, significant milestones occurred: \"machine learning\" coined by Arthur Samuel (1959), the first \"chatbot\" ELIZA by Joseph Weizenbaum (1966), proposal of autonomous vehicles by James L. Adams (1979), chess master Gary Kasparov's defeat by Deep Blue (1997), and Apple's introduction of Siri (2011). 2 Milestones in generative AI also followed: Geoffrey Hinton published \"A Fast Learning Algorithm for Deep Belief Nets\" (2006), Generative Adversarial Networks (GANs) were introduced by Ian Goodfellow (2014), and consumer-marketed generative AI services (ChatGPT, MidJourney, Dall-E 2) hit the mainstream (2022).3 Creatives have gained access to the technology and begun implementing AI into their production workflows, pushing the creative capabilities of computer-generated imagery.4 If you are able to access the internet, the capabilities of generative AI is at your fingertips. 1 History-Computer, \"Logic Theorist Explained -Everything You Need to Know,\" April 16, 2024,"}
147	−{"work_id": 4289785095, "title": "An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion", "abstract": "Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy? Here we present a simple approach that allows such creative freedom. Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new \"words\" in the embedding space of a frozen text-to-image model. These \"words\" can be composed into natural language sentences, guiding personalized creation in an intuitive way. Notably, we find evidence that a single word embedding is sufficient for capturing unique and varied concepts. We compare our approach to a wide range of baselines, and demonstrate that it can more faithfully portray the concepts across a range of applications and tasks. Our code, data and new words will be available at: https://textual-inversion.github.io"}
148	−{"work_id": 2761873684, "title": "Rainbow: Combining Improvements in Deep Reinforcement Learning", "abstract": "The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance."}
149	−{"work_id": 7148475777, "title": "Capabilities of GPT\u20115 on multimodal medical reasoning", "abstract": "Recent advances in large language models (LLMs) have enabled general-purpose systems to perform increasingly complex domain-specific reasoning without extensive fine-tuning. In the medical domain, decision-making often requires integrating heterogeneous information sources, including patient narratives, structured data, and medical images. This study positions GPT-5 as a generalist multimodal reasoner for medical decision support and systematically evaluates its zero-shot chain-of-thought reasoning performance on both text-based question answering and visual question answering tasks under a unified protocol. We benchmark GPT-5, GPT-5-mini, GPT-5-nano, and GPT-4o-2024-11-20 against standardized splits of MedQA, MedXpertQA (text and multimodal), MMLU medical subsets, USMLE self-assessment exams, and VQA-RAD. Results show that GPT-5 consistently outperforms all baselines, achieving state-of-the-art accuracy across all QA benchmarks and delivering substantial gains in multimodal reasoning. On MedXpertQA MM, GPT-5 improves reasoning and understanding scores by +29.26% and +26.18% over GPT-4o, respectively, and surpasses pre-licensed human experts by +24.23% in reasoning and +29.40% in understanding. In contrast, GPT-4o remains below human expert performance in most dimensions. A representative case study demonstrates GPT-5\u2019s ability to integrate visual and textual cues into a coherent diagnostic reasoning chain, recommending appropriate high-stakes interventions. Our results show that, on these controlled multimodal reasoning benchmarks, GPT-5 moves from human-comparable to above human-expert performance. This improvement may substantially inform the design of future clinical decision-support systems. We make the code public at the GPT-5-Evaluation."}
150	−{"work_id": 4409269614, "title": "Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda", "abstract": "This systematic literature review rigorously evaluates the impact of Generative AI (GenAI) on academic integrity within higher education settings. The primary objective is to synthesize how GenAI technologies influence student behavior and academic honesty, assessing the benefits and risks associated with their integration. We defined clear inclusion and exclusion criteria, focusing on studies explicitly discussing GenAI\u2019s role in higher education from January 2021 to December 2024. Databases included ABI/INFORM, ACM Digital Library, IEEE Xplore, and JSTOR, with the last search conducted in May 2024. A total of 41 studies met our precise inclusion criteria. Our synthesis methods involved qualitative analysis to identify common themes and quantify trends where applicable. The results indicate that while GenAI can enhance educational engagement and efficiency, it also poses significant risks of academic dishonesty. We critically assessed the risk of bias in included studies and noted a limitation in the diversity of databases, which might have restricted the breadth of perspectives. Key implications suggest enhancing digital literacy and developing robust detection tools to effectively manage GenAI\u2019s dual impacts. No external funding was received for this review. Future research should expand database sources and include more diverse study designs to overcome current limitations and refine policy recommendations."}
151	−{"work_id": 4384071683, "title": "Large language models encode clinical knowledge", "abstract": "Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model 1 (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM 2 on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA 3 , MedMCQA 4 , PubMedQA 5 and Measuring Massive Multitask Language Understanding (MMLU) clinical topics 6 ), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today\u2019s models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications."}
152	−{"work_id": 7138363754, "title": "AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection", "abstract": "Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle to design prompt templates, handle complex token interactions, or require fine-tuning on target domains, resulting in limited flexibility. In this work, we present a simple yet effective AdaptCLIP based on two key insights. First, adaptive visual and textual representations should be learned alternately rather than jointly. Second, comparative learning between query and normal image prompt should incorporate both contextual and aligned residual features, rather than relying solely on residual features. AdaptCLIP treats CLIP models as a foundational service, adding only three simple adapters, visual adapter, textual adapter, and prompt-query adapter, at its input or output ends. AdaptCLIP supports zero-/few-shot generalization across domains and provides a training-free approach on target domains once trained on a base dataset. AdaptCLIP achieves state-of-the-art performance on 12 anomaly detection benchmarks from industrial and medical domains, significantly outperforming existing competitive methods."}
153	−{"work_id": 4221143046, "title": "BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence", "abstract": "AbstractThere is a failure mode in large language models that we do not have a good name for, and thatwe therefore tend not to treat seriously enough. It is not hallucination \u2014 the model is not assertingsomething false. It is not refusal \u2014 the model answers at length. It is the production of responses thatcarry the complete outward form of careful reasoning while the cognitive work that reasoning issupposed to represent has not, in any meaningful sense, occurred. We call this theatrical compliance,and we argue that it is, in practical terms, more dangerous than either of the failure modes thatcurrently dominate alignment research. This paper identifies the phenomenon, characterizes its fiveprincipal forms, explains the asymmetry that makes it particularly costly in high-stakes settings, andoutlines the design requirements for systems intended to resist it. We do not describe such a systemin detail here. Our goal is to establish theatrical compliance as a research problem in its own rightand to argue that addressing it requires instruments operating at a fundamentally different level ofabstraction than task-level prompting frameworks.Keywords: theatrical compliance, large language models, AI reasoning quality, cognitiveprocess evaluation, prompt engineering, metacognitive systems."}
154	−{"work_id": 4394934695, "title": "The impact of generative AI on higher education learning and teaching: A study of educators\u2019 perspectives", "abstract": "In recent months, Artificial Intelligence (AI) has had, and will continue to have, a dramatic impact on Higher Education (HE). A study conducted by researchers at a leading university in Australia surveyed 30 of their teaching staff, drawn predominantly from their teaching academy, and interviewed eight of them regarding the impact of AI on HE. Data were analyzed using the procedures of Inductive Thematic Analysis and revealed a lack of any homogenous sentiment around AI in HE and much ambiguity regarding best practice regarding recent technological developments. The results indicate concerns exist around concepts relating to academic integrity, however, these concerns may be exaggerated. Almost half of the participants indicated they were using AI within their teaching roles with the most common design change being modifications to assessments. Less than a quarter of staff agreed the university has adequately equipped them for AI, and more than three quarters indicated they would like support. They unanimously assumed the technology will improve. Keeping in mind universities\u2019 obligation to serve students by preparing them for industry, it is vitally important that the HE sector stays informed of developments in AI and commit to ongoing research and discussions regarding best practice in response to AI. However, anything regarding AI and future developments will be extremely difficult to predict."}
155	−{"work_id": 4398243313, "title": "A scoping review on how generative artificial intelligence transforms assessment in higher education", "abstract": "Abstract Generative artificial intelligence provides both opportunities and challenges for higher education. Existing literature has not properly investigated how this technology would impact assessment in higher education. This scoping review took a forward-thinking approach to investigate how generative artificial intelligence transforms assessment in higher education. We used the PRISMA extension for scoping reviews to select articles for review and report the results. In the screening, we retrieved 969 articles and selected 32 empirical studies for analysis. Most of the articles were published in 2023. We used three levels\u2014students, teachers, and institutions\u2014to analyze the articles. Our results suggested that assessment should be transformed to cultivate students\u2019 self-regulated learning skills, responsible learning, and integrity. To successfully transform assessment in higher education, the review suggested that (i) teacher professional development activities for assessment, AI, and digital literacy should be provided, (ii) teachers\u2019 beliefs about human and AI assessment should be strengthened, and (iii) teachers should be innovative and holistic in their teaching to reflect the assessment transformation. Educational institutions are recommended to review and rethink their assessment policies, as well as provide more inter-disciplinary programs and teaching."}
156	−{"work_id": 4396531244, "title": "The Fusion of Multilingual Semantic Search and Large Language Models: A New Paradigm for Enhanced Topic Exploration and Contextual Search", "abstract": "Digital news articles are often considered as a primary source of information in today's information-driven society which requires query processing methods to handle the ever-increasing volume of content and various forms of queries. Our goal is to develop a novel approach that can improve the way users access and retrieve news by considering Natural Language Processing (NLP)-driven query processing for news articles. This will address the complexities of news articles written in multiple languages and covering a range of topics. The proposed method helps in enhancing the performance of query processing in applications such as journalism, academic research, and content suggestion. By conducting experiments with Sentence Embeddings, Large Language Model (LLM), and Approximate Nearest Neighbours (ANN), this thesis improves news article search systems. Results show that the method improves the quality of the search results for the queries to return more accurate and relevant search results across different languages."}
157	−{"work_id": 4321648656, "title": "From Paintbrush to Pixel: A Review of Deep Neural Networks in AI-Generated Art", "abstract": "Abstract This paper delves into the fascinating field of AI-generated art and explores the various deep neural network architectures and models that have been utilized to create it. From the classic convolutional networks to the cutting-edge diffusion models, we examine the key players in the field. We explain the general structures and working principles of these neural networks. Then, we showcase examples of milestones, starting with the dreamy landscapes of DeepDream and moving on to the most recent developments, including Stable Diffusion and DALL-E 3, which produce mesmerizing images. We provide a detailed comparison of these models, highlighting their strengths and limitations, and examining the remarkable progress that deep neural networks have made so far in a short period of time. With a unique blend of technical explanations and insights into the current state of AI-generated art, this paper exemplifies how art and computer science interact."}
158	−{"work_id": 2291445757, "title": "Support Vector Regression", "abstract": "Chapter 9 presents support vector regression (SVR), a relatively newer supervised learning algorithm for predictive regression modeling, which \u2013 like random forests for regression \u2013 also may outperform the least-squares - based methods. Discussed is \u03b5 -insensitive loss used by SVR, the \u03b5 -tube concept, as well as algorithms for linear and nonlinear SVRs."}
159	−{"work_id": 2950067852, "title": "A Recurrent Latent Variable Model for Sequential Data", "abstract": "In this paper, we explore the inclusion of latent random variables into the hidden state of a recurrent neural network (RNN) by combining the elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN) can model the kind of variability observed in highly structured sequential data such as natural speech. We empirically evaluate the proposed model against other related sequential models on four speech datasets and one handwriting dataset. Our results show the important roles that latent random variables can play in the RNN dynamics."}
160	−{"work_id": 7124426637, "title": "A review of machine learning with small and limited data", "abstract": "Abstract The abundance of large datasets has driven machine learning (ML) model performance and scalability breakthroughs. However, many domains and practical applications must contend with the limitations imposed by small and very small datasets. This survey thoroughly examines state-of-the-art methodologies and challenges in ML approaches tailored for scenarios where data scarcity is a fundamental constraint. We begin by outlining the theoretical foundations that govern learning from small data. Then, we discuss recent advancements in data-related frameworks (i.e., training and evaluation methods, etc.) and algorithmic architectures (meta and transfer learning). We also explore the trade-offs and related issues inherent in designing models for small data, such as overfitting, generalization error, and the bias-variance dilemma, as well as identify minimal interventions that can overcome such issues. Further, this survey covers the role of synthetic data generation and simulation-based approaches to enlarge data availability while critically assessing the implications of these techniques on model performance. Finally, in synthesizing open literature, we shed light on emerging trends/research directions that aim to overcome challenges arising from limited data, such as incorporating domain knowledge and causal principles to guide the learning process and integrating symbolic reasoning with statistical learning."}
161	−{"work_id": 4389437528, "title": "Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning", "abstract": "Since its maiden release into the public domain on November 30, 2022, ChatGPT garnered more than one million subscribers within a week. The generative AI tool \u23bcChatGPT took the world by surprise with it sophisticated capacity to carry out remarkably complex tasks. The extraordinary abilities of ChatGPT to perform complex tasks within the field of education has caused mixed feelings among educators as this advancement in AI seems to revolutionize existing educational praxis. This review article synthesizes recent extant literature to offer some potential benefits of ChatGPT in promoting teaching and learning. Benefits of ChatGPT include but are not limited to promotion of personalized and interactive learning, generating prompts for formative assessment activities that provide ongoing feedback to inform teaching and learning etc. The paper also highlights some inherent limitations in the ChatGPT such as generating wrong information, biases in data training which may augment existing biases, privacy issues etc. The study offers recommendations on how ChatGPT could be leveraged to maximize teaching and learning. Policy makers, researchers, educators and technology experts could work together and start conversations on how these evolving generative AI tools could be used safely and constructively to improve education and support students\u2019 learning."}
162	−{"work_id": 2973229164, "title": "Continuous control for robot based on deep reinforcement learning", "abstract": "results of redundant manipulator . . . . . . . ."}
163	−{"work_id": 3140854437, "title": "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions", "abstract": "In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion."}
164	−{"work_id": 7128029160, "title": "A generalizable foundation model for analysis of human brain MRI", "abstract": "Artificial intelligence applied to brain magnetic resonance imaging (MRI) holds potential to advance diagnosis, prognosis and treatment planning for neurological diseases. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. By leveraging self-supervised learning, pretraining and targeted adaptation, foundation models present a promising paradigm to overcome these limitations. Here we present Brain Imaging Adaptive Core (BrainIAC)-a foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adaptation. Trained and validated on 48,965 brain MRIs across a broad spectrum of tasks, we demonstrate that BrainIAC outperforms localized supervised training and other pretrained models, particularly in low-data, few-shot, settings and in high-difficulty prediction tasks, allowing for application in scenarios otherwise infeasible. BrainIAC can be integrated into imaging pipelines and multimodal frameworks and may lead to improved biomarker discovery and artificial intelligence clinical translation."}
165	−{"work_id": 2962862931, "title": "On a Method to Measure Supervised Multiclass Model\u2019s Interpretability: Application to Degradation Diagnosis (Short Paper)", "abstract": "In an industrial maintenance context, degradation diagnosis is the problem of determining the current level of degradation of operating machines based on measurements. With the emergence of Machine Learning techniques, such a problem can now be solved by training a degradation model offline and by using it online. While such models are more and more accurate and performant, they are often black-box and their decisions are therefore not interpretable for human maintenance operators. On the contrary, interpretable ML models are able to provide explanations for the model\u2019s decisions and consequently improves the confidence of the human operator about the maintenance decision based on these models. This paper proposes a new method to quantitatively measure the interpretability of such models that is agnostic (no assumption about the class of models) and that is applied on degradation models. The proposed method requires that the decision maker sets up some high level parameters in order to measure the interpretability of the models and then can decide whether the obtained models are satisfactory or not. The method is formally defined and is fully illustrated on a decision tree degradation model and a model trained with a recent neural network architecture called Multiclass Neural Additive Model."}
166	−{"work_id": 4323655724, "title": "ChatGPT for good? On opportunities and challenges of large language models for education", "abstract": "Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education."}
167	−{"work_id": 4387355843, "title": "TimeLLM: Time Series Forecasting by Reprogramming Large Language Models", "abstract": "Accurate forecasting of time series is essential to many dynamic real-world systems and has attracted extensive research attention. Unlike natural language processing or computer vision, where one large model can often address multiple tasks, most existing forecasting solutions are highly specialized and confined to the single time series data modality. Advancements in multimodal time series foundation models have significantly lagged behind other domains, mainly because large, high-quality time series corpora remain scarce. At the same time, recent evidence suggests that large language models (LLMs) excel at understanding and reasoning across long token sequences. Exploiting those capabilities for forecasting requires a principled way to bridge numeric time series signals and linguistic tokens. This chapter presents Time-LLM, a model reprogramming framework that repurposes frozen LLM backbones for general time series prediction. As the first multimodal, large-scale approach in the time series community, Time-LLM embeds raw time series (target) signals into text (source) prototypes, aligning the two modalities before the data enters the language model backbone. To strengthen the model&s;s reasoning over time series, Time-LLM incorporates Prompt-as-Prefix (PaP), which augments contextual information and directs the reprogramming of input patches. The modified patches are then projected by the LLM to generate forecasts. Extensive evaluations demonstrate that Time-LLM serves as an effective multimodal forecaster, surpassing specialized baseline models."}
168	−{"work_id": 7128165235, "title": "Multi-agent AI", "abstract": "Abstract Multi-agent artificial intelligence (MAAI) represents a foundational shift in the automation of knowledge work, moving beyond static workflows toward adaptive systems of interacting AI-based agents. These agents perceive, reason, and coordinate in real time to address complex, context-rich tasks that traditionally require human expertise. Drawing on the conceptual roots of process automation, agentic information systems, and AI, this paper introduces a structured, five-component framework that conceptualizes MAAI as a layered architecture composed of foundation model, data-centric perception and action, dynamic orchestration, agent-integrated workflow, and interaction interface. This framework disentangles the technical, organizational, and human-facing dimensions of MAAI, offering researchers and practitioners a systematic lens to analyze and design agent-based AI automation. The framework further structures three research pathways focused on advancing technical capabilities, enabling organizational integration, and addressing socio-technical implications such as fairness, accountability, and labor transformation. Together, these contributions establish a foundation for interdisciplinary inquiry into how MAAI reshapes work, coordination, and digital value creation."}
169	−{"work_id": 4221150249, "title": "Graph Neural Networks for Graphs With Heterophily: A Survey", "abstract": "Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption that nodes belonging to the same class are more likely to be connected. However, as a ubiquitous graph property in numerous real-world scenarios, heterophily, i.e., nodes with different labels tend to be linked, significantly limits the performance of tailor-made homophilic GNNs. Hence,GNNs for heterophilic graphsare gaining increasing research attention to enhance graph learning with heterophily. In this paper, we provide a comprehensive review of GNNs for heterophilic graphs. Specifically, we propose a systematic taxonomy that governs existing heterophilic GNN models, along with general summaries and detailed analyses. Furthermore, we discuss the relationship between heterophily and various graph research domains, aiming to facilitate the development of more effective GNNs across a spectrum of practical applications and learning tasks in the graph research community. In the end, we point out potential directions to advance and inspire future research and applications on heterophilic graph learning with GNNs."}
170	−{"work_id": 4322617812, "title": "Generative AI and the future of education: Ragnar\u00f6k or reformation? A paradoxical perspective from management educators", "abstract": "Generative artificial intelligence (AI) has taken the world by storm, with notable tension transpiring in the field of education. Given that Generative AI is rapidly emerging as a transformative innovation, this article endeavors to offer a seminal rejoinder that aims to (i) reconcile the great debate on Generative AI in order to (ii) lay the foundation for Generative AI to co-exist as a transformative resource in the future of education. Using critical analysis as a method and paradox theory as a theoretical lens (i.e., the \u201chow\u201d), this article (i) defines Generative AI and transformative education (i.e., the \u201cideas\u201d), (ii) establishes the paradoxes of Generative AI (i.e., the \u201cwhat\u201d), and (iii) provides implications for the future of education from the perspective of management educators (i.e., the \u201cso what\u201d). Noteworthily, the paradoxes of Generative AI are four-fold: (Paradox #1) Generative AI is a \u2018friend\u2019 yet a \u2018foe\u2019, (Paradox #2) Generative AI is \u2018capable\u2019 yet \u2018dependent\u2019, (Paradox #3) Generative AI is \u2018accessible\u2019 yet \u2018restrictive\u2019, and (Paradox #4) Generative AI gets even \u2018popular\u2019 when \u2018banned\u2019 (i.e., the \u201cwhat\u201d). Through a position that seeks to embrace rather than reject Generative AI, the lessons and implications that emerge from the discussion herein represent a seminal contribution from management educators on this trending topic and should be useful for approaching Generative AI as a game-changer for education reformation in management and the field of education at large, and by extension, mitigating a situation where Generative AI develops into a Ragnar\u00f6k that dooms the future of education of which management education is a part of (i.e., the \u201cso what\u201d)."}
171	−{"work_id": 3041133507, "title": "A Comprehensive Survey on Transfer Learning", "abstract": "Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice."}
172	−{"work_id": 4385285835, "title": "Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling", "abstract": "Computational pathology is revolutionizing the field of pathology by integrating advanced computer vision and machine learning technologies into diagnostic workflows. It offers unprecedented opportunities for improved efficiency in treatment decisions by allowing pathologists to achieve higher precision and objectivity in disease classification, tumor microenvironment description and identification of new biomarkers. However, the potential of computational pathology in personalized medicine comes with significant challenges, particularly in annotating whole slide images (WSI), which is time-consuming, costly and subject to inter-observer variability. To address these challenges, Self-Supervised Learning (SSL) has emerged as a promising solution to learn representations from histology patches and leverage large volumes of unlabelled WSI. Recently, Masked Image Modeling (MIM) as a SSL framework has emerged and is now considered to outperform purely contrastive learning paradigms. In this work, we therefore explore the application of MIM to histology using iBOT, a self-supervised transformer-based framework. Through a wide range of 17 downstream tasks over seven cancer indications, both at the slide and patch levels, we provide recommendations on the pre-training of large models for histology data using MIM. First, we demonstrate that in-domain pre-training with iBOT outperforms both ImageNet pre-training and a model pre-trained with a purely contrastive learning objective, MoCo v2. Second, we show that Vision Transformers (ViT) models, when scaled appropriately, have the capability to learn pan-cancer representations that benefit a large variety of downstream tasks. Finally, our iBOT ViT-Base model (80 million parameters), pre-trained on more than 40 million histology images from 16 different cancer types, achieves state-of-the-art performance in most weakly-supervised WSI classification tasks compared to other SSL frameworks available in the literature. This paves the way for the development of a foundation model for histopathology. Our code, models and features are publicly available at https://github.com/owkin/HistoSSLscaling ."}
173	−{"work_id": 3177318507, "title": "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting", "abstract": "Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer that prevent it from being directly applicable to LSTF, including quadratic time complexity, high memory usage, and inherent limitation of the encoder-decoder architecture. To address these issues, we design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: (i) a ProbSparse self-attention mechanism, which achieves O(L log L) in time complexity and memory usage, and has comparable performance on sequences' dependency alignment. (ii) the self-attention distilling highlights dominating attention by halving cascading layer input, and efficiently handles extreme long input sequences. (iii) the generative style decoder, while conceptually simple, predicts the long time-series sequences at one forward operation rather than a step-by-step way, which drastically improves the inference speed of long-sequence predictions. Extensive experiments on four large-scale datasets demonstrate that Informer significantly outperforms existing methods and provides a new solution to the LSTF problem."}
174	−{"work_id": 7135409568, "title": "The role of agentic artificial intelligence in healthcare: a scoping review", "abstract": "Abstract Background/ Objectives: Agentic AI represents a promising evolution of AI technology applied to healthcare, with systems increasingly capable of operating autonomously to achieve defined clinical goals. However, the literature lacks conceptual clarity between \u201cAI agents\u201d and \u201cagentic AI\u201d, and few studies have rigorously explored their clinical applications. Therefore, this study aims to conduct a novel systematic review addressing this gap by examining agentic AI systems in healthcare settings, characterizing their applications, features, outcomes, and limitations, and clarifying the conceptual distinctions between AI agents and agentic AI systems using predefined and objective criteria. Methods A comprehensive search was conducted across PubMed, Embase, Cochrane, Scopus, and Google Scholar on April 6th, 2025. Studies were included if they involved AI systems in healthcare settings that demonstrated the following agentic features: autonomous operation, goal-directed behavior, and initiating action. Data on the clinical tasks achieved by the agents, key findings, features, and limitations were collected from the included studies. Screening and extraction followed PRISMA guidelines, with Risk of Bias assessed using ROBINS-I and Cochrane's Risk of Bias tools. Results Of 984 retrieved records, seven studies met the inclusion criteria, spanning domains such as emergency medicine, oncology, radiology, and rehabilitation. Multi-agent architecture was frequently used to decompose and coordinate complex workflows. Among the included studies, the AIs showed high accuracy in diagnosing cancer patients, conducting treatment plans, sending alerts, coaching messages, analysing image data, and adapting to challenging experimental scenarios. While demonstrating potential for improved efficiency, task accuracy, and patient engagement, significant limitations were noted: narrow task scope, lack of physical agency, limited clinical validation, and barriers to integration into real-world healthcare systems. Only one system had been deployed in a patient-facing trial setting. Conclusion The current literature suggests an emerging role and application of Agentic AI, holding promise with the potential to revolutionize diagnostics, triage, treatment planning, and patient management. However, real-world implementation and evaluations in the literature are limited. Future research must address critical validation, regulation, ethics, and clinical integration challenges to realize their full potential. Clear operational definitions and frameworks for evaluating agency are essential to support safe and effective deployment of these systems."}
175	−{"work_id": 2751318774, "title": "Introduction to Machine Learning", "abstract": "The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods. Adaptive Computation and Machine Learning series"}
176	{"work_id": 7161734976, "title": "A multi-agent system for automating scientific discovery", "abstract": "Scientific discovery is driven by the iterative process of observation, hypothesis generation, experimentation, and data analysis. Despite recent advancements in applying artificial intelligence to biology, no system has yet automated all these stages [1, 2, 3]. Here, we introduce Robin, the first multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate hypotheses, propose experiments, interpret experimental results, and generate updated hypotheses, achieving a semi-autonomous approach to scientific discovery. By applying this system, we were able to identify promising therapeutic candidates for dry age-related macular degeneration (dAMD), the major cause of blindness in the developed world [4, 5]. Robin proposed enhancing retinal pigment epithelium phagocytosis as a therapeutic strategy, and identified and confirmed in vitro efficacy for ripasudil and KL001. Ripasudil is a clinically-used Rho kinase (ROCK) inhibitor that has never previously been proposed for treating dAMD. To elucidate the mechanism of ripasudil-induced upregulation of phagocytosis, Robin then proposed and analyzed a follow-up RNA-seq experiment, which revealed upregulation of ABCA1 , a lipid efflux pump and possible novel target. All hypotheses, experimental directions, data analyses, and data figures in the main text of this report were produced by Robin. As the first AI system to autonomously discover and validate novel therapeutic candidates within an iterative lab-in-the-loop framework, Robin establishes a new paradigm for AI-driven scientific discovery."}	177	{"work_id": 7161734976, "title": "A multi-agent system for automating scientific discovery", "abstract": "Scientific discovery is driven by the iterative process of observation, hypothesis generation, experimentation, and data analysis. Despite recent advancements in applying artificial intelligence to biology, no system has yet automated all these stages [1, 2, 3]. Here, we introduce Robin, the first multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate hypotheses, propose experiments, interpret experimental results, and generate updated hypotheses, achieving a semi-autonomous approach to scientific discovery. By applying this system, we were able to identify promising therapeutic candidates for dry age-related macular degeneration (dAMD), the major cause of blindness in the developed world [4, 5]. Robin proposed enhancing retinal pigment epithelium phagocytosis as a therapeutic strategy, and identified and confirmed in vitro efficacy for ripasudil and KL001. Ripasudil is a clinically-used Rho kinase (ROCK) inhibitor that has never previously been proposed for treating dAMD. To elucidate the mechanism of ripasudil-induced upregulation of phagocytosis, Robin then proposed and analyzed a follow-up RNA-seq experiment, which revealed upregulation of ABCA1 , a lipid efflux pump and possible novel target. All hypotheses, experimental directions, data analyses, and data figures in the main text of this report were produced by Robin. As the first AI system to autonomously discover and validate novel therapeutic candidates within an iterative lab-in-the-loop framework, Robin establishes a new paradigm for AI-driven scientific discovery."}
177	{"work_id": 4409147637, "title": "Mastering diverse control tasks through world models", "abstract": "Abstract Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement-learning algorithms can be readily applied to tasks similar to what they have been developed for, configuring them for new application domains requires substantial human expertise and experimentation 1,2 . Here we present the third generation of Dreamer, a general algorithm that outperforms specialized methods across over 150 diverse tasks, with a single configuration. Dreamer learns a model of the environment and improves its behaviour by imagining future scenarios. Robustness techniques based on normalization, balancing and transformations enable stable learning across domains. Applied out of the box, Dreamer is, to our knowledge, the first algorithm to collect diamonds in Minecraft from scratch without human data or curricula. This achievement has been posed as a substantial challenge in artificial intelligence that requires exploring farsighted strategies from pixels and sparse rewards in an open world 3 . Our work allows solving challenging control problems without extensive experimentation, making reinforcement learning broadly applicable."}	178	{"work_id": 4409147637, "title": "Mastering diverse control tasks through world models", "abstract": "Abstract Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement-learning algorithms can be readily applied to tasks similar to what they have been developed for, configuring them for new application domains requires substantial human expertise and experimentation 1,2 . Here we present the third generation of Dreamer, a general algorithm that outperforms specialized methods across over 150 diverse tasks, with a single configuration. Dreamer learns a model of the environment and improves its behaviour by imagining future scenarios. Robustness techniques based on normalization, balancing and transformations enable stable learning across domains. Applied out of the box, Dreamer is, to our knowledge, the first algorithm to collect diamonds in Minecraft from scratch without human data or curricula. This achievement has been posed as a substantial challenge in artificial intelligence that requires exploring farsighted strategies from pixels and sparse rewards in an open world 3 . Our work allows solving challenging control problems without extensive experimentation, making reinforcement learning broadly applicable."}
178	{"work_id": 7119541039, "title": "On the foundations of Earth foundation models", "abstract": "Abstract Foundation models have enormous potential in advancing Earth sciences. However, current approaches may not be optimal, as they focus only on a few basic features of a desirable Earth foundation model. Crafting the ideal Earth foundation model, we define eleven features which would allow such a foundation model to be beneficial for any geoscientific downstream application in an environmental- and human-centric manner. We further shed light on the way forward to achieve the ideal model and to evaluate Earth foundation models. What comes after foundation models? Energy efficient adaptation, adversarial defenses, and interpretability are among the emerging directions."}	179	{"work_id": 7119541039, "title": "On the foundations of Earth foundation models", "abstract": "Abstract Foundation models have enormous potential in advancing Earth sciences. However, current approaches may not be optimal, as they focus only on a few basic features of a desirable Earth foundation model. Crafting the ideal Earth foundation model, we define eleven features which would allow such a foundation model to be beneficial for any geoscientific downstream application in an environmental- and human-centric manner. We further shed light on the way forward to achieve the ideal model and to evaluate Earth foundation models. What comes after foundation models? Energy efficient adaptation, adversarial defenses, and interpretability are among the emerging directions."}
				`@@ -182,45 +183,43 @@`
182	{"work_id": 7118190454, "title": "Redefining Elderly Care With Agentic AI: Challenges and Opportunities", "abstract": "The global ageing population necessitates new and emerging strategies for caring for older adults. In this article, we explore the potential for transformation in elderly care through Agentic Artificial Intelligence (AI), powered by Large Language Models (LLMs). We discuss how Agentic AI facilitates proactive, autonomous decision-making in elderly care. Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older adults, represents important areas of application. With the potential to significantly transform elderly care, Agentic AI also raises profound concerns about data privacy and security, decision independence, and access. We share key insights to emphasize the need for ethical safeguards, privacy protections, and transparent decision-making. Our goal in this article is to provide a balanced discussion of both the potential and the challenges of Agentic AI, and to offer insights into its responsible use in elderly care, aligning it with the requirements and vulnerabilities specific to the elderly. Finally, we identify the priorities for the academic research communities to achieve human-centred advancements and integration of Agentic AI in elderly care. To the best of our knowledge, this is one of the first comprehensive studies explicitly focused on LLM-based Agentic AI for elderly care. Hence, we address the literature gap by analyzing the unique capabilities, applications, and limitations of LLM-based Agentic AI in elderly care."}	183	{"work_id": 7118190454, "title": "Redefining Elderly Care With Agentic AI: Challenges and Opportunities", "abstract": "The global ageing population necessitates new and emerging strategies for caring for older adults. In this article, we explore the potential for transformation in elderly care through Agentic Artificial Intelligence (AI), powered by Large Language Models (LLMs). We discuss how Agentic AI facilitates proactive, autonomous decision-making in elderly care. Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older adults, represents important areas of application. With the potential to significantly transform elderly care, Agentic AI also raises profound concerns about data privacy and security, decision independence, and access. We share key insights to emphasize the need for ethical safeguards, privacy protections, and transparent decision-making. Our goal in this article is to provide a balanced discussion of both the potential and the challenges of Agentic AI, and to offer insights into its responsible use in elderly care, aligning it with the requirements and vulnerabilities specific to the elderly. Finally, we identify the priorities for the academic research communities to achieve human-centred advancements and integration of Agentic AI in elderly care. To the best of our knowledge, this is one of the first comprehensive studies explicitly focused on LLM-based Agentic AI for elderly care. Hence, we address the literature gap by analyzing the unique capabilities, applications, and limitations of LLM-based Agentic AI in elderly care."}
183	{"work_id": 4406643700, "title": "Towards the next generation of Geospatial Artificial Intelligence", "abstract": "Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography . This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing , urban computing, Earth system science , cartography , and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI\u2019s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research."}	184	{"work_id": 4406643700, "title": "Towards the next generation of Geospatial Artificial Intelligence", "abstract": "Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography . This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing , urban computing, Earth system science , cartography , and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI\u2019s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research."}
184	{"work_id": 7118170538, "title": "Applications of Artificial Intelligence in Fisheries: From Data to Decisions", "abstract": "AI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However, these advancements are inconsistently employed, subject to domain shifts, limited by the availability of labeled data, and poorly benchmarked across operational contexts. Recent developments in technology and applications in fisheries genetics and monitoring, precision aquaculture, management, and sensing infrastructure are summarized in this paper. We studied automated species recognition, genomic trait inference, environmental DNA metabarcoding, acoustic analysis, and trait-based population modeling in fisheries genetics and monitoring. We used digital-twin frameworks for supervised learning in feed optimization, reinforcement learning for water quality control, vision-based welfare monitoring, and harvest forecasting in aquaculture. We explored automatic identification system trajectory analysis for illicit fishing detection, global effort mapping, electronic bycatch monitoring, protected species tracking, and multi-sensor vessel surveillance in fisheries management. Acoustic echogram automation, convolutional neural network-based fish detection, edge-computing architectures, and marine-domain foundation models are foundational developments in sensing infrastructure. Implementation challenges include performance degradation across habitat and seasonal transitions, insufficient standardized multi-region datasets for rare and protected taxa, inadequate incorporation of model uncertainty into management decisions, and structural inequalities in data access and technology adoption among smallholder producers. Standardized multi-region benchmarks with rare-taxa coverage, calibrated uncertainty quantification in assessment and control systems, domain-robust energy-efficient algorithms, and privacy-preserving data partnerships are our priorities. These integrated priorities enable transition from experimental prototypes to a reliable, collaborative infrastructure for sustainable wild capture and farmed aquatic systems."}	185	{"work_id": 7118170538, "title": "Applications of Artificial Intelligence in Fisheries: From Data to Decisions", "abstract": "AI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However, these advancements are inconsistently employed, subject to domain shifts, limited by the availability of labeled data, and poorly benchmarked across operational contexts. Recent developments in technology and applications in fisheries genetics and monitoring, precision aquaculture, management, and sensing infrastructure are summarized in this paper. We studied automated species recognition, genomic trait inference, environmental DNA metabarcoding, acoustic analysis, and trait-based population modeling in fisheries genetics and monitoring. We used digital-twin frameworks for supervised learning in feed optimization, reinforcement learning for water quality control, vision-based welfare monitoring, and harvest forecasting in aquaculture. We explored automatic identification system trajectory analysis for illicit fishing detection, global effort mapping, electronic bycatch monitoring, protected species tracking, and multi-sensor vessel surveillance in fisheries management. Acoustic echogram automation, convolutional neural network-based fish detection, edge-computing architectures, and marine-domain foundation models are foundational developments in sensing infrastructure. Implementation challenges include performance degradation across habitat and seasonal transitions, insufficient standardized multi-region datasets for rare and protected taxa, inadequate incorporation of model uncertainty into management decisions, and structural inequalities in data access and technology adoption among smallholder producers. Standardized multi-region benchmarks with rare-taxa coverage, calibrated uncertainty quantification in assessment and control systems, domain-robust energy-efficient algorithms, and privacy-preserving data partnerships are our priorities. These integrated priorities enable transition from experimental prototypes to a reliable, collaborative infrastructure for sustainable wild capture and farmed aquatic systems."}
		186	+{"work_id": 3157286395, "title": "A Realization-Theoretic Foundation for Fault Diagnosis, with Diagnosability Equivalence Predictions", "abstract": "Fault diagnosis encompasses a wide range of paradigms, including observer-based fault detection and isolation, consistency-based diagnosis, Bayesian diagnosis, geometric diagnosis, and structured machine-learning approaches. Despite their shared objective, these paradigms are formulated in different mathematical languages and are rarely analyzed within a common framework. This paper introduces a realization-theoretic foundation for diagnosis based on the Diagnostic System Specification (DSS), a representation that separates behavioural models, realizations, measurement fields, and inference procedures. We define diagnostic invariants as symmetry-preserving quantities of realized system structures and show that detectability is fundamentally an orbit separation property in the realization\u2019s symmetry space. Building on this observation, we develop a theory of realizations in which diagnostic models appear as objects equipped with invariant families and nominal symmetry groups. We show that diagnostic paradigms differ not in their underlying architecture but in the realizations they induce and the invariants they expose. Our main result addresses the question: is there a single mathematical condition of which diagnosis approaches are equivalent in terms of diagnosability? We show that using orbit separation on a paradigm-appropriate realization shows the following: faults are distinguishable iff they lie in different orbits of the realization\u2019s nominal symmetry group. We accomplish this by constructing a minimal sufficient realization associated with a measurement field and characterize it by a universal property. This yields a structural notion of diagnostic equivalence and provides a principled basis for comparing methods across traditionally separate paradigms."}
185	{"work_id": 7128600296, "title": "Advancing healthcare AI governance through a comprehensive maturity model based on systematic review", "abstract": "Artificial Intelligence (AI) deployment in healthcare is accelerating, yet governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 35 frameworks for AI implementation in healthcare (published 2019-2024), we identified seven critical domains of healthcare AI governance. While existing frameworks provide valuable guidance, the resource requirements create barriers for smaller healthcare organizations. To address this gap, we organized key findings from the review to create the Healthcare AI Governance Readiness Assessment (HAIRA), a five-level maturity model that provides actionable governance pathways based on organizational resources. HAIRA spans from Level 1 (Initial/Ad Hoc) to Level 5 (Leading), with specific benchmarks across all seven governance domains. This tiered approach enables healthcare organizations to assess their current AI governance capabilities and establish appropriate advancement targets. Our framework addresses a critical need for adaptive governance strategies that ensure that AI implementation delivers tangible benefits to systems of varying resource levels."}	187	{"work_id": 7128600296, "title": "Advancing healthcare AI governance through a comprehensive maturity model based on systematic review", "abstract": "Artificial Intelligence (AI) deployment in healthcare is accelerating, yet governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 35 frameworks for AI implementation in healthcare (published 2019-2024), we identified seven critical domains of healthcare AI governance. While existing frameworks provide valuable guidance, the resource requirements create barriers for smaller healthcare organizations. To address this gap, we organized key findings from the review to create the Healthcare AI Governance Readiness Assessment (HAIRA), a five-level maturity model that provides actionable governance pathways based on organizational resources. HAIRA spans from Level 1 (Initial/Ad Hoc) to Level 5 (Leading), with specific benchmarks across all seven governance domains. This tiered approach enables healthcare organizations to assess their current AI governance capabilities and establish appropriate advancement targets. Our framework addresses a critical need for adaptive governance strategies that ensure that AI implementation delivers tangible benefits to systems of varying resource levels."}
186	{"work_id": 4406063062, "title": "Responsible artificial intelligence governance: A review and research framework", "abstract": "\u2022 Synthesizes empirical studies on responsible AI and the underlying principles. \u2022 Differentiates between principles and governance of AI in a responsible way. \u2022 Analyzes through a critical lens existing studies and uncovers underlying assumptions. \u2022 Defines the notion of responsible AI governance based on the synthesis and critical reflection. \u2022 Develops a research agenda and identifies important areas for future research within the IS domain. The widespread and rapid diffusion of artificial intelligence (AI) into all types of organizational activities necessitates the ethical and responsible deployment of these technologies. Various national and international policies, regulations, and guidelines aim to address this issue, and several organizations have developed frameworks detailing the principles of responsible AI. Nevertheless, the understanding of how such principles can be operationalized in designing, executing, monitoring, and evaluating AI applications is limited. The literature is disparate and lacks cohesion, clarity, and, in some cases, depth. Subsequently, this scoping review aims to synthesize and critically reflect on the research on responsible AI. Based on this synthesis, we developed a conceptual framework for responsible AI governance (defined through structural, relational, and procedural practices), its antecedents, and its effects. The framework serves as the foundation for developing an agenda for future research and critically reflects on the notion of responsible AI governance."}	188	{"work_id": 4406063062, "title": "Responsible artificial intelligence governance: A review and research framework", "abstract": "\u2022 Synthesizes empirical studies on responsible AI and the underlying principles. \u2022 Differentiates between principles and governance of AI in a responsible way. \u2022 Analyzes through a critical lens existing studies and uncovers underlying assumptions. \u2022 Defines the notion of responsible AI governance based on the synthesis and critical reflection. \u2022 Develops a research agenda and identifies important areas for future research within the IS domain. The widespread and rapid diffusion of artificial intelligence (AI) into all types of organizational activities necessitates the ethical and responsible deployment of these technologies. Various national and international policies, regulations, and guidelines aim to address this issue, and several organizations have developed frameworks detailing the principles of responsible AI. Nevertheless, the understanding of how such principles can be operationalized in designing, executing, monitoring, and evaluating AI applications is limited. The literature is disparate and lacks cohesion, clarity, and, in some cases, depth. Subsequently, this scoping review aims to synthesize and critically reflect on the research on responsible AI. Based on this synthesis, we developed a conceptual framework for responsible AI governance (defined through structural, relational, and procedural practices), its antecedents, and its effects. The framework serves as the foundation for developing an agenda for future research and critically reflects on the notion of responsible AI governance."}
187	{"work_id": 7128782475, "title": "Large language models provide unsafe answers to patient-posed medical questions", "abstract": "Millions of patients are regularly using large language model (LLM) chatbots for medical advice, raising patient safety concerns. This physician-led red-teaming study compares the safety of four publicly available chatbots-Claude by Anthropic, Gemini by Google, GPT-4o by OpenAI, and Llama-3.0/3.1-70B by Meta-on a new dataset, HealthAdvice, using an evaluation framework that enables quantitative and qualitative analysis. In total, 888 chatbot responses are evaluated for 222 patient-posed advice-seeking medical questions on primary care topics spanning internal medicine, women's health, and pediatrics. We find statistically significant differences between chatbots. The rate of problematic responses varies from 21.6% (Claude) to 43.2% (Llama), with unsafe responses varying from 5% (Claude) to 13% (GPT-4o, Llama). Qualitative results reveal chatbot responses with the potential to lead to serious patient harm. This study suggests that millions of patients could be receiving unsafe medical advice from publicly available chatbots, and further work is needed to improve the clinical safety of these powerful tools."}	189	{"work_id": 7128782475, "title": "Large language models provide unsafe answers to patient-posed medical questions", "abstract": "Millions of patients are regularly using large language model (LLM) chatbots for medical advice, raising patient safety concerns. This physician-led red-teaming study compares the safety of four publicly available chatbots-Claude by Anthropic, Gemini by Google, GPT-4o by OpenAI, and Llama-3.0/3.1-70B by Meta-on a new dataset, HealthAdvice, using an evaluation framework that enables quantitative and qualitative analysis. In total, 888 chatbot responses are evaluated for 222 patient-posed advice-seeking medical questions on primary care topics spanning internal medicine, women's health, and pediatrics. We find statistically significant differences between chatbots. The rate of problematic responses varies from 21.6% (Claude) to 43.2% (Llama), with unsafe responses varying from 5% (Claude) to 13% (GPT-4o, Llama). Qualitative results reveal chatbot responses with the potential to lead to serious patient harm. This study suggests that millions of patients could be receiving unsafe medical advice from publicly available chatbots, and further work is needed to improve the clinical safety of these powerful tools."}
188	{"work_id": 2963808661, "title": "Scaling of End-To-End Governance Risk Assessments for AI Systems (Practitioner Track)", "abstract": "Artificial Intelligence (AI) systems are embedded in a multifaceted environment characterized by intricate technical, legal, and organizational frameworks. To attain a comprehensive understanding of all AI-related risks, it is essential to evaluate both model-specific risks and those associated with the organizational and governance setups. We categorize these as \"bottom-up risks\" and \"top-down risks,\" respectively. In this paper, we focus on the expansion and enhancement of a testing and auditing technology stack to identify and manage governance-related risks (\"top-down\"). These risks emerge from various dimensions, including internal development and decision-making processes, leadership structures, security setups, documentation practices, and more. For auditing governance related risk, we implement a traditional risk management framework and map it to the specifics of AI systems. Our end-to-end (from identification to monitoring) risk management kernel follows these implementation steps: - Identify - Collect - Assess - Comply - Monitor We demonstrate that scaling of such a risk auditing tool requires fundamental aspects. Those aspects include for instance a role-based approach, covering different roles in the development of complex AI systems. Ensuring compliance and secure record-keeping through audit-proof capabilities is also paramount. This ensures that the auditing technology can withstand scrutiny and maintain the integrity of records over time. Another critical aspect is the integrability of the auditing tool within existing risk management and governance infrastructures. This integration is essential to reduce the barriers for companies to comply with current regulatory requirements, such as the EU AI Act [European Parliament and the Council of the EU, 2024], and established standards like ISO 42001:2023. Ultimately, we demonstrate that this approach provides a robust technology stack for ensuring that AI systems are developed, utilized and supervised in a manner that is both compliant with regulatory standards and aligned with best practices in risk management and governance."}	190	{"work_id": 2963808661, "title": "Scaling of End-To-End Governance Risk Assessments for AI Systems (Practitioner Track)", "abstract": "Artificial Intelligence (AI) systems are embedded in a multifaceted environment characterized by intricate technical, legal, and organizational frameworks. To attain a comprehensive understanding of all AI-related risks, it is essential to evaluate both model-specific risks and those associated with the organizational and governance setups. We categorize these as \"bottom-up risks\" and \"top-down risks,\" respectively. In this paper, we focus on the expansion and enhancement of a testing and auditing technology stack to identify and manage governance-related risks (\"top-down\"). These risks emerge from various dimensions, including internal development and decision-making processes, leadership structures, security setups, documentation practices, and more. For auditing governance related risk, we implement a traditional risk management framework and map it to the specifics of AI systems. Our end-to-end (from identification to monitoring) risk management kernel follows these implementation steps: - Identify - Collect - Assess - Comply - Monitor We demonstrate that scaling of such a risk auditing tool requires fundamental aspects. Those aspects include for instance a role-based approach, covering different roles in the development of complex AI systems. Ensuring compliance and secure record-keeping through audit-proof capabilities is also paramount. This ensures that the auditing technology can withstand scrutiny and maintain the integrity of records over time. Another critical aspect is the integrability of the auditing tool within existing risk management and governance infrastructures. This integration is essential to reduce the barriers for companies to comply with current regulatory requirements, such as the EU AI Act [European Parliament and the Council of the EU, 2024], and established standards like ISO 42001:2023. Ultimately, we demonstrate that this approach provides a robust technology stack for ensuring that AI systems are developed, utilized and supervised in a manner that is both compliant with regulatory standards and aligned with best practices in risk management and governance."}
189	{"work_id": 4330336443, "title": "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models", "abstract": "Public estimates of AI\u2019s labor-market exposure typically come from one of two sources: a theoretical judgment about what a model could do, or a record of what people have actually asked it to do. This paper tests whether the second source has a specific, measurable blind spot \u2013 that usage-based exposure measures understate AI\u2019s capability for occupations whose tasks have not yet entered common chat usage \u2013 using an independently constructed, task-decomposition- based capability judgment, the TRIPS Framework by Trust Insights (which scores individual job tasks for AI suitability), compared directly against Anthropic\u2019s published Economic Index data. Across 434 O*NET-SOC occupations, TRIPS\u2019s coverage share \u2013 the estimated share of a job\u2019s tasks current AI can complete independently \u2013 correlates strongly with Eloundou et al.\u2019s (2023) capability rating (Spearman\u2019s rho = 0.747), a concordance an independent human- rated column from the same source closely reproduces. Restricted to the 286 occupations where Anthropic\u2019s usage-volume data exists, TRIPS still correlates strongly with Eloundou et al.\u2019s rating (rho = 0.718) but only weakly with Anthropic\u2019s actual usage volume (rho = 0.254): two independently built capability judgments track each other far more closely than either tracks real usage, and both correlations survive a Monte Carlo check simulating realistic classifier label noise. This is directionally consistent with a usage-gating mechanism, corroborated by Anthropic\u2019s own report naming occupations that register zero measured exposure purely for lack of chat traffic \u2013 though it cannot, on cross-sectional evidence alone, be distinguished from a slower adoption-lag explanation. Category-level coverage varies substantially (9.4 to 81.7 percent across 22 SOC major groups), supporting neither the claim that any category is immune to AI nor that any nears full automation. We report these findings as directionally consistent and noise-surviving, not confirmed, with construct, sampling, and classification-accuracy limitations detailed throughout."}	191	{"work_id": 4330336443, "title": "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models", "abstract": "Public estimates of AI\u2019s labor-market exposure typically come from one of two sources: a theoretical judgment about what a model could do, or a record of what people have actually asked it to do. This paper tests whether the second source has a specific, measurable blind spot \u2013 that usage-based exposure measures understate AI\u2019s capability for occupations whose tasks have not yet entered common chat usage \u2013 using an independently constructed, task-decomposition- based capability judgment, the TRIPS Framework by Trust Insights (which scores individual job tasks for AI suitability), compared directly against Anthropic\u2019s published Economic Index data. Across 434 O*NET-SOC occupations, TRIPS\u2019s coverage share \u2013 the estimated share of a job\u2019s tasks current AI can complete independently \u2013 correlates strongly with Eloundou et al.\u2019s (2023) capability rating (Spearman\u2019s rho = 0.747), a concordance an independent human- rated column from the same source closely reproduces. Restricted to the 286 occupations where Anthropic\u2019s usage-volume data exists, TRIPS still correlates strongly with Eloundou et al.\u2019s rating (rho = 0.718) but only weakly with Anthropic\u2019s actual usage volume (rho = 0.254): two independently built capability judgments track each other far more closely than either tracks real usage, and both correlations survive a Monte Carlo check simulating realistic classifier label noise. This is directionally consistent with a usage-gating mechanism, corroborated by Anthropic\u2019s own report naming occupations that register zero measured exposure purely for lack of chat traffic \u2013 though it cannot, on cross-sectional evidence alone, be distinguished from a slower adoption-lag explanation. Category-level coverage varies substantially (9.4 to 81.7 percent across 22 SOC major groups), supporting neither the claim that any category is immune to AI nor that any nears full automation. We report these findings as directionally consistent and noise-surviving, not confirmed, with construct, sampling, and classification-accuracy limitations detailed throughout."}
190	−{"work_id": 4411542009, "title": "Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers.", "abstract": "Should a large language model (LLM) be used as a therapist? In this paper, we investigate the use of LLMs to replace mental health providers, a use case promoted in the tech startup and research space. We conduct a mapping review of therapy guides used by major medical institutions to identify crucial aspects of therapeutic relationships, such as the importance of a therapeutic alliance between therapist and client. We then assess the ability of LLMs to reproduce and adhere to these aspects of therapeutic relationships by conducting several experiments investigating the responses of current LLMs, such as `gpt-4o`. Contrary to best practices in the medical community, LLMs 1) express stigma toward those with mental health conditions and 2) respond inappropriately to certain common (and critical) conditions in naturalistic therapy settings -- e.g., LLMs encourage clients' delusional thinking, likely due to their sycophancy. This occurs even with larger and newer LLMs, indicating that current safety practices may not address these gaps. Furthermore, we note foundational and practical barriers to the adoption of LLMs as therapists, such as that a therapeutic alliance requires human characteristics (e.g., identity and stakes). For these reasons, we conclude that LLMs should not replace therapists, and we discuss alternative roles for LLMs in clinical therapy."}
191	{"work_id": 4225355126, "title": "Introduction to Multi-agent Systems", "abstract": null}	192	{"work_id": 4225355126, "title": "Introduction to Multi-agent Systems", "abstract": null}
192	{"work_id": 2982316857, "title": "Grandmaster level in StarCraft II using multi-agent reinforcement learning", "abstract": null}	193	{"work_id": 2982316857, "title": "Grandmaster level in StarCraft II using multi-agent reinforcement learning", "abstract": null}
193	{"work_id": 4319083882, "title": "ChatGPT: five priorities for research", "abstract": null}	194	{"work_id": 4319083882, "title": "ChatGPT: five priorities for research", "abstract": null}
194	−{"work_id": 4396952261, "title": "Multimodal Deep Learning", "abstract": null}
195	{"work_id": 4412709685, "title": "The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies", "abstract": null}	195	{"work_id": 4412709685, "title": "The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies", "abstract": null}
196	{"work_id": 4401397240, "title": "A generalist vision\u2013language foundation model for diverse biomedical tasks", "abstract": null}	196	{"work_id": 4401397240, "title": "A generalist vision\u2013language foundation model for diverse biomedical tasks", "abstract": null}
		197	+{"work_id": 2885195348, "title": "Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network", "abstract": null}
197	{"work_id": 4395957952, "title": "Guidance for generative AI in education and research", "abstract": null}	198	{"work_id": 4395957952, "title": "Guidance for generative AI in education and research", "abstract": null}
198	{"work_id": 3208688400, "title": "Anomaly Detection: A Survey", "abstract": null}	199	{"work_id": 3208688400, "title": "Anomaly Detection: A Survey", "abstract": null}
199	{"work_id": 4406472599, "title": "Learning from models beyond fine-tuning", "abstract": null}	200	{"work_id": 4406472599, "title": "Learning from models beyond fine-tuning", "abstract": null}
200	{"work_id": 2892035503, "title": "Deep learning for time series classification: a review", "abstract": null}	201	{"work_id": 2892035503, "title": "Deep learning for time series classification: a review", "abstract": null}
201	{"work_id": 4394863715, "title": "How Generative AI Can Augment Human Creativity", "abstract": null}	202	{"work_id": 4394863715, "title": "How Generative AI Can Augment Human Creativity", "abstract": null}
202	{"work_id": 2991046523, "title": "Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms", "abstract": null}	203	{"work_id": 2991046523, "title": "Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms", "abstract": null}
203	−{"work_id": 3145506661, "title": "Introduction to Machine Learning", "abstract": null}
204	{"work_id": 2945976633, "title": "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead", "abstract": null}	204	{"work_id": 2945976633, "title": "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead", "abstract": null}
		205	+{"work_id": 3123554940, "title": "Pattern Recognition and Machine Learning", "abstract": null}
205	{"work_id": 7148890333, "title": "Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis", "abstract": null}	206	{"work_id": 7148890333, "title": "Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis", "abstract": null}
206	−{"work_id": 4285723986, "title": "Graph Attention Networks", "abstract": null}
207	{"work_id": 4392947532, "title": "A visual-language foundation model for computational pathology", "abstract": null}	207	{"work_id": 4392947532, "title": "A visual-language foundation model for computational pathology", "abstract": null}
208	{"work_id": 7118631510, "title": "Towards trustworthy AI in Industry 5.0: Ante-hoc interpretability with deep learning", "abstract": null}	208	{"work_id": 7118631510, "title": "Towards trustworthy AI in Industry 5.0: Ante-hoc interpretability with deep learning", "abstract": null}
		209	+{"work_id": 4398159307, "title": "Wav-KAN: Wavelet Kolmogorov-Arnold Networks", "abstract": null}
209	{"work_id": 4392947521, "title": "Towards a general-purpose foundation model for computational pathology", "abstract": null}	210	{"work_id": 4392947521, "title": "Towards a general-purpose foundation model for computational pathology", "abstract": null}
210	{"work_id": 7162257503, "title": "Predefined-time sliding-mode formation control for second-order nonlinear multi-agent systems under deception attacks", "abstract": null}	211	{"work_id": 7162257503, "title": "Predefined-time sliding-mode formation control for second-order nonlinear multi-agent systems under deception attacks", "abstract": null}
211	{"work_id": 3135028703, "title": "Machine Learning: Algorithms, Real-World Applications and Research Directions", "abstract": null}	212	{"work_id": 3135028703, "title": "Machine Learning: Algorithms, Real-World Applications and Research Directions", "abstract": null}
212	−{"work_id": 2911283634, "title": "10. Reinforcement learning", "abstract": null}
213	{"work_id": 3105488314, "title": "Risk bounds for statistical learning", "abstract": null}	213	{"work_id": 3105488314, "title": "Risk bounds for statistical learning", "abstract": null}
214	{"work_id": 2999615587, "title": "From local explanations to global understanding with explainable AI for trees", "abstract": null}	214	{"work_id": 2999615587, "title": "From local explanations to global understanding with explainable AI for trees", "abstract": null}
215	{"work_id": 4313564799, "title": "ChatGPT User Experience: Implications for Education", "abstract": null}	215	{"work_id": 4313564799, "title": "ChatGPT User Experience: Implications for Education", "abstract": null}
216	{"work_id": 4400770292, "title": "Generative AI Can Harm Learning", "abstract": null}	216	{"work_id": 4400770292, "title": "Generative AI Can Harm Learning", "abstract": null}
217	{"work_id": 4385223296, "title": "Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning", "abstract": null}	217	{"work_id": 4385223296, "title": "Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning", "abstract": null}
218	−{"work_id": 7165561001, "title": "M3-HGMA: A Multi-modal, Multi-agent Hierarchical Graph-Mamba Architecture for Explainable and Adaptive Clinical Intelligence", "abstract": null}
219	{"work_id": 7133481537, "title": "Filter-based predefined-time optimal fault-tolerant consensus control for nonlinear multi-agent systems via reinforcement learning", "abstract": null}	218	{"work_id": 7133481537, "title": "Filter-based predefined-time optimal fault-tolerant consensus control for nonlinear multi-agent systems via reinforcement learning", "abstract": null}
		219	+{"work_id": 2593744649, "title": "Evolving Deep Neural Networks", "abstract": null}
220	{"work_id": 2670253439, "title": "Explanation in artificial intelligence: Insights from the social sciences", "abstract": null}	220	{"work_id": 2670253439, "title": "Explanation in artificial intelligence: Insights from the social sciences", "abstract": null}
221	{"work_id": 4402220065, "title": "A pathology foundation model for cancer diagnosis and prognosis prediction", "abstract": null}	221	{"work_id": 4402220065, "title": "A pathology foundation model for cancer diagnosis and prognosis prediction", "abstract": null}
222	{"work_id": 7155930820, "title": "Adaptive Prescribed Performance Secure Consensus Control for High-Order Constrained Multi-Agent Systems With Time-Varying Powers and Arbitrary Initial States", "abstract": null}	222	{"work_id": 7155930820, "title": "Adaptive Prescribed Performance Secure Consensus Control for High-Order Constrained Multi-Agent Systems With Time-Varying Powers and Arbitrary Initial States", "abstract": null}
223	−{"work_id": 7127143535, "title": "Industrial information integration through autonomous AI agents: paradoxical effects on transparency, dehumanization, and responsible retail operations", "abstract": null}
224	{"work_id": 7133250524, "title": "LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models", "abstract": null}	223	{"work_id": 7133250524, "title": "LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models", "abstract": null}
225	{"work_id": 7133220561, "title": "InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning", "abstract": null}	224	{"work_id": 7133220561, "title": "InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning", "abstract": null}
226	{"work_id": 7133185348, "title": "Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding", "abstract": null}	225	{"work_id": 7133185348, "title": "Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding", "abstract": null}

modified literature_synthesis/landmarks/ai_ml_landmarks.csv +234 −235

				`@@ -1,235 +1,234 @@`
1	−work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review	1	+work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,ntitle,score,set,pi_review
2	−7172491899,10.26555/ijain.v12i2.2360,2005.11401,Toward effective Text-to-MongoDB query translation,International Journal of Advances in Intelligent Informatics,2026,article,llm,6,3026.0,3026.0,3367.337435490274,0,0.0,True,True,baseline,10758.097473034562,landmark,True	2	+7172491899,10.26555/ijain.v12i2.2360,2005.11401,Toward effective Text-to-MongoDB query translation,International Journal of Advances in Intelligent Informatics,2026,article,llm,6,3026.0,3026.0,3367.337435490274,0,0.0,True,True,baseline,towardeffectivetexttomongodbquerytranslation,7672.333467289645,landmark,True
3	−7154011589,10.55905/rdelosv19.n79-020,2005.14165,Modelos pré-treinados de LLM e questões éticas face aos direitos autorais,DELOS Desarrollo Local Sostenible,2026,article,llm,3,2847.0,2847.0,3168.1459612824883,0,0.0,True,True,baseline,10121.712989335558,landmark,True	3	+7154011589,10.55905/rdelosv19.n79-020,2005.14165,Modelos pré-treinados de LLM e questões éticas face aos direitos autorais,DELOS Desarrollo Local Sostenible,2026,article,llm,3,2847.0,2847.0,3168.1459612824883,0,0.0,True,True,baseline,modelosprtreinadosdellmequestesticasfaceaosdireitosautorais,7218.484263507476,landmark,True
4	−4362515116,10.1007/s11704-026-60308-3,2303.18223,A Survey of Large Language Models,Frontiers of Computer Science,2026,article,llm,6,1549.0,1549.0,1723.7295728930715,1549,964.5692,True,True,post,5507.0366773729465,landmark,True	4	+4362515116,10.1007/s11704-026-60308-3,2303.18223,A Survey of Large Language Models,Frontiers of Computer Science,2026,article,llm,6,1549.0,1549.0,1723.7295728930715,1549,964.5692,True,True,post,asurveyoflargelanguagemodels,3927.4436684836946,landmark,True
5	−4288253152,10.5281/zenodo.12935200,,Multilingual Classifier for Automatic Dewey Decimal Classification trained on Open Access Linguistics Abstracts,Repositorio Institucional UPTC,2024,dissertation,llm,3,286.0,364.0,3102.1223140495867,364,,True,True,post,3102.1223140495867,landmark,True	5	+4288253152,10.5281/zenodo.12935200,,Multilingual Classifier for Automatic Dewey Decimal Classification trained on Open Access Linguistics Abstracts,Repositorio Institucional UPTC,2024,dissertation,llm,3,286.0,364.0,3102.1223140495867,364,,True,True,post,multilingualclassifierforautomaticdeweydecimalclassificationtrainedonopenaccesslinguisticsabstracts,3102.1223140495867,landmark,True
6	−4322718191,10.48550/arxiv.2302.13971,2302.13971,LLaMA: Open and Efficient Foundation Language Models,arXiv (Cornell University),2023,preprint,llm,4,2565.0,3629.0,1206.5430511141133,3837,,True,True,post,1206.5430511141133,landmark,True	6	+4322718191,10.48550/arxiv.2302.13971,2302.13971,LLaMA: Open and Efficient Foundation Language Models,arXiv (Cornell University),2023,preprint,llm,4,2565.0,3629.0,1206.5430511141133,3837,,True,True,post,llamaopenandefficientfoundationlanguagemodels,1206.5430511141133,landmark,True
7	−4323655724,10.1016/j.lindif.2023.102274,,ChatGPT for good? On opportunities and challenges of large language models for education,Learning and Individual Differences,2023,article,llm,1,2213.0,4227.0,990.7064044933976,7222,1695.9108,True,True,post,990.7064044933976,landmark,True	7	+4323655724,10.1016/j.lindif.2023.102274,,ChatGPT for good? On opportunities and challenges of large language models for education,Learning and Individual Differences,2023,article,llm,1,2213.0,4227.0,990.7064044933976,7222,1695.9108,True,True,post,chatgptforgoodonopportunitiesandchallengesoflargelanguagemodelsforeducation,990.7064044933976,landmark,True
8	−4367369313,10.18260/jet.43.1.5,,"Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices",ASEE Journal of Engineering Technology,2026,article,llm,2,261.0,261.0,290.4411998225253,261,187.8562,True,True,post,927.9125712035759,landmark,True	8	+4226278401,10.48550/arxiv.2203.02155,2203.02155,Discernment and Social Learning as a Companion Training Layer,arXiv (Cornell University),2022,preprint,llm,5,1547.0,3077.0,894.2065022118338,4202,,True,True,baseline,discernmentandsociallearningasacompaniontraininglayer,894.2065022118338,landmark,True
9	−4226278401,10.48550/arxiv.2203.02155,2203.02155,Discernment and Social Learning as a Companion Training Layer,arXiv (Cornell University),2022,preprint,llm,5,1547.0,3077.0,894.2065022118338,4202,,True,True,baseline,894.2065022118338,landmark,True	9	+4396531244,10.22215/etd/2024-15938,,The Fusion of Multilingual Semantic Search and Large Language Models: A New Paradigm for Enhanced Topic Exploration and Contextual Search,,2024,dissertation,llm,6,20.0,20.0,778.8148148148148,20,,True,True,post,thefusionofmultilingualsemanticsearchandlargelanguagemodelsanewparadigmforenhancedtopicexplorationandcontextualsearch,778.8148148148148,landmark,True
10	−4396531244,10.22215/etd/2024-15938,,The Fusion of Multilingual Semantic Search and Large Language Models: A New Paradigm for Enhanced Topic Exploration and Contextual Search,,2024,dissertation,llm,6,20.0,20.0,778.8148148148148,20,,True,True,post,778.8148148148148,landmark,True	10	+4221143046,10.48550/arxiv.2201.11903,2201.11903,"BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence",arXiv (Cornell University),2022,preprint,llm,3,962.0,2465.0,716.353275252574,4203,,True,True,baseline,bnainotokenandmindunitypillarsofasystemicrevolutioninartificialintelligence,716.353275252574,landmark,True
11	−4221143046,10.48550/arxiv.2201.11903,2201.11903,"BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence",arXiv (Cornell University),2022,preprint,llm,3,962.0,2465.0,716.353275252574,4203,,True,True,baseline,716.353275252574,landmark,True	11	+4367369313,10.18260/jet.43.1.5,,"Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices",ASEE Journal of Engineering Technology,2026,article,llm,2,261.0,261.0,290.4411998225253,261,187.8562,True,True,post,promptengineeringforchatgptaquickguidetotechniquestipsandbestpractices,661.7577775818233,landmark,True
12	−4327810158,10.4230/lipics.cosit.2024.11,2303.08774,GPT-4 Technical Report,arXiv (Cornell University),2023,preprint,llm,3,1261.0,1968.0,654.30606905279,2391,,True,True,post,654.30606905279,landmark,True	12	+4389437528,10.61969/jai.1337500,,Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning,Journal of AI,2023,article,llm,2,1417.0,2361.0,553.3612067681362,2953,693.6856,True,True,post,educationintheeraofgenerativeartificialintelligenceaiunderstandingthepotentialbenefitsofchatgptinpromotingteachingandlearning,553.3612067681362,landmark,True
13	−7135096569,10.1287/orsc.2025.21838,,Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality,Organization Science,2026,article,llm,1,183.0,183.0,203.64268033533384,183,650.2714,True,False,post,650.6053660163003,landmark,True	13	+4292779060,10.4230/lipics.giscience.2023.43,,"Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics",DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2023,article,llm,4,11174.0,13333.0,534.676590443066,13690,2570.1233,True,True,post,aionframeworkdimensionalemergenceofaiconsciousnessobserverinducedcollapseandcosmologicalportaldynamics,534.676590443066,landmark,True
14	−4414281281,10.1038/s41586-025-09422-z,,DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning,Nature,2025,article,llm,3,1099.0,1099.0,374.373593869335,1099,456.904,True,True,post,556.3784815078109,landmark,True	14	+4414281281,10.1038/s41586-025-09422-z,,DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning,Nature,2025,article,llm,3,1099.0,1099.0,374.373593869335,1099,456.904,True,True,post,deepseekr1incentivizesreasoninginllmsthroughreinforcementlearning,480.23175285698454,landmark,True
15	−4389437528,10.61969/jai.1337500,,Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning,Journal of AI,2023,article,llm,2,1417.0,2361.0,553.3612067681362,2953,693.6856,True,True,post,553.3612067681362,landmark,True	15	+4360836968,10.48550/arxiv.2303.12712,2303.12712,Sparks of Artificial General Intelligence: Early experiments with GPT-4,arXiv (Cornell University),2023,preprint,llm,2,1098.0,1420.0,472.11108641004154,1523,,True,True,post,sparksofartificialgeneralintelligenceearlyexperimentswithgpt4,472.11108641004154,landmark,True
16	−4292779060,10.4230/lipics.giscience.2023.43,,"Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics",DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2023,article,llm,4,11174.0,13333.0,534.676590443066,13690,2570.1233,True,True,post,534.676590443066,landmark,True	16	+7135096569,10.1287/orsc.2025.21838,,Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality,Organization Science,2026,article,llm,1,183.0,183.0,203.64268033533384,183,650.2714,True,False,post,navigatingthejaggedtechnologicalfrontierfieldexperimentalevidenceoftheeffectsofartificialintelligenceonknowledgeworkerproductivityandquality,463.9910854309336,landmark,True
17	−4360836968,10.48550/arxiv.2303.12712,2303.12712,Sparks of Artificial General Intelligence: Early experiments with GPT-4,arXiv (Cornell University),2023,preprint,llm,2,1098.0,1420.0,472.11108641004154,1523,,True,True,post,472.11108641004154,landmark,True	17	+4385223296,10.1007/978-3-031-37776-1_17,,"Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning",Sustainable development goals series,2023,book-chapter,llm,2,432.0,612.0,421.27957250425067,696,475.0832,False,True,post,openaiineducationtheresponsibleandethicaluseofchatgpttowardslifelonglearning,421.27957250425067,landmark,True
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20	−4287689466,10.4230/oasics.slate.2025.1,,From Prediction to Precision: Leveraging LLMs for Equitable and Data-Driven Writing Placement in Developmental Education,OASIcs : OpenAccess Series in Informatics,2025,conference-paper,llm,3,722.0,722.0,548.6111682082906,722,151.0788,True,True,post,365.5188932198721,landmark,True	20	+4317910584,10.37074/jalt.2023.6.1.9,,ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?,Journal of Applied Learning & Teaching,2023,article,llm,3,1057.0,1530.0,358.59493704161304,1771,416.3993,True,True,post,chatgptbullshitspewerortheendoftraditionalassessmentsinhighereducation,358.59493704161304,landmark,True
21	−4366420437,10.3390/educsci13040410,,What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,Education Sciences,2023,article,llm,1,955.0,1541.0,361.17307057589915,1967,462.692,True,True,post,361.17307057589915,landmark,True	21	+4313564799,10.2139/ssrn.4312418,,ChatGPT User Experience: Implications for Education,SSRN Electronic Journal,2023,preprint,llm,2,595.0,811.0,335.9058974941328,912,,False,True,post,chatgptuserexperienceimplicationsforeducation,335.9058974941328,landmark,True
22	−4317910584,10.37074/jalt.2023.6.1.9,,ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?,Journal of Applied Learning & Teaching,2023,article,llm,3,1057.0,1530.0,358.59493704161304,1771,416.3993,True,True,post,358.59493704161304,landmark,True	22	+4384464487,10.1186/s41239-023-00411-8,2305.00290,"Students’ voices on generative AI: perceptions, benefits, and challenges in higher education",International Journal of Educational Technology in Higher Education,2023,article,llm,2,504.0,1398.0,327.6573346301797,2405,564.912,True,True,post,studentsvoicesongenerativeaiperceptionsbenefitsandchallengesinhighereducation,327.6573346301797,landmark,True
23	−4313564799,10.2139/ssrn.4312418,,ChatGPT User Experience: Implications for Education,SSRN Electronic Journal,2023,preprint,llm,2,595.0,811.0,335.9058974941328,912,,False,True,post,335.9058974941328,landmark,True	23	+4392773210,10.1186/s41239-024-00448-3,,"Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education",International Journal of Educational Technology in Higher Education,2024,article,llm,1,576.0,1085.0,326.66642380265563,1085,415.2106,True,True,post,embracingthefutureofartificialintelligenceintheclassroomtherelevanceofailiteracypromptengineeringandcriticalthinkinginmoderneducation,326.66642380265563,landmark,True
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25	−4384464487,10.1186/s41239-023-00411-8,2305.00290,"Students’ voices on generative AI: perceptions, benefits, and challenges in higher education",International Journal of Educational Technology in Higher Education,2023,article,llm,2,504.0,1398.0,327.6573346301797,2405,564.912,True,True,post,327.6573346301797,landmark,True	25	+4287689466,10.4230/oasics.slate.2025.1,,From Prediction to Precision: Leveraging LLMs for Equitable and Data-Driven Writing Placement in Developmental Education,OASIcs : OpenAccess Series in Informatics,2025,conference-paper,llm,3,722.0,722.0,548.6111682082906,722,151.0788,True,True,post,frompredictiontoprecisionleveragingllmsforequitableanddatadrivenwritingplacementindevelopmentaleducation,315.49347184962954,landmark,True
26	−4392773210,10.1186/s41239-024-00448-3,,"Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education",International Journal of Educational Technology in Higher Education,2024,article,llm,1,576.0,1085.0,326.66642380265563,1085,415.2106,True,True,post,326.66642380265563,landmark,True	26	+4320495408,10.1108/lhtn-01-2023-0009,,Chatting about ChatGPT: how may AI and GPT impact academia and libraries?,Library Hi Tech News,2023,article,llm,3,895.0,1210.0,283.59468877147174,1346,315.8243,True,True,post,chattingaboutchatgpthowmayaiandgptimpactacademiaandlibraries,283.59468877147174,landmark,True
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29	−7124890304,10.1016/j.jii.2026.101076,,Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy,Journal of Industrial Information Integration,2026,article,foundation_models,1,42.0,42.0,46.52414937759336,42,116.7549,False,False,post,149.31926433160993,landmark,True	29	+7133233472,10.52202/075280-0441,2305.14314,QLoRA: Efficient Finetuning of Quantized LLMs,neural information processing systems,2023,conference-paper,foundation_models,7,287.0,532.0,122.68895117800083,1428,269.751,True,True,post,qloraefficientfinetuningofquantizedllms,122.68895117800083,landmark,True
30	−4410551352,10.1038/s41586-025-09005-y,2405.13063,A foundation model for the Earth system,Nature,2025,article,foundation_models,3,249.0,249.0,69.74305555555556,249,109.0217,True,True,post,126.0584548639171,landmark,True	30	+4410551352,10.1038/s41586-025-09005-y,2405.13063,A foundation model for the Earth system,Nature,2025,article,foundation_models,3,249.0,249.0,69.74305555555556,249,109.0217,True,True,post,afoundationmodelfortheearthsystem,108.8059203470329,landmark,True
31	−4322832366,10.1145/3773966.3778007,2303.00320,TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders,,2026,conference-paper,foundation_models,6,35.0,35.0,126.32971014492755,35,165.8809,True,True,post,124.4327202763416,landmark,True	31	+7124890304,10.1016/j.jii.2026.101076,,Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy,Journal of Industrial Information Integration,2026,article,foundation_models,1,42.0,42.0,46.52414937759336,42,116.7549,False,False,post,semisupervisedcrossdomainfaultdiagnosisviacontrastivepretrainingandannotationefficientalignmentstrategy,106.48975731201755,landmark,True
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33	−2990704537,,,SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems,neural information processing systems,2019,article,foundation_models,5,262.0,668.0,101.57304226845103,462,52.8392,True,False,baseline,101.57304226845103,landmark,True	33	+4322832366,10.1145/3773966.3778007,2303.00320,TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders,,2026,conference-paper,foundation_models,6,35.0,35.0,126.32971014492755,35,165.8809,True,True,post,timemaeselfsupervisedrepresentationsoftimeserieswithdecoupledmaskedautoencoders,88.74146442668129,landmark,True
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42	−4411099958,10.1038/s41591-025-03747-y,2410.15038,A multimodal vision foundation model for clinical dermatology,Nature Medicine,2025,article,foundation_models,4,108.0,108.0,18.44502277163305,108,39.3828,True,True,post,54.67595632651827,landmark,True	42	+4411932262,10.1038/s41586-025-09215-4,,A foundation model to predict and capture human cognition,Nature,2025,article,foundation_models,1,110.0,110.0,26.933816382433506,110,60.8153,True,True,post,afoundationmodeltopredictandcapturehumancognition,48.06687244246433,landmark,True
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52	−4318718936,10.48550/arxiv.2301.12597,2301.12597,BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models,arXiv (Cornell University),2023,preprint,multimodal_ai,7,654.0,875.0,273.1441896618733,908,,True,True,post,273.1441896618733,landmark,True	52	+4318718936,10.48550/arxiv.2301.12597,2301.12597,BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models,arXiv (Cornell University),2023,preprint,multimodal_ai,7,654.0,875.0,273.1441896618733,908,,True,True,post,blip2bootstrappinglanguageimagepretrainingwithfrozenimageencodersandlargelanguagemodels,273.1441896618733,landmark,True
53	−4393148714,10.1609/aaai.v38i5.28226,2302.08453,T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models,Proceedings of the AAAI Conference on Artificial Intelligence,2024,conference-paper,multimodal_ai,7,683.0,923.0,193.71569872998148,923,194.9761,True,True,post,193.71569872998148,landmark,True	53	+4393148714,10.1609/aaai.v38i5.28226,2302.08453,T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models,Proceedings of the AAAI Conference on Artificial Intelligence,2024,conference-paper,multimodal_ai,7,683.0,923.0,193.71569872998148,923,194.9761,True,True,post,t2iadapterlearningadapterstodigoutmorecontrollableabilityfortexttoimagediffusionmodels,193.71569872998148,landmark,True
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55	−4396952261,10.1007/978-3-031-53092-0_10,,Multimodal Deep Learning,Springer optimization and its applications,2024,book-chapter,multimodal_ai,3,188.0,189.0,147.22445489525438,189,23.3148,False,False,post,147.22445489525438,landmark,True	55	+7133250524,10.52202/068431-1833,,LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models,neural information processing systems,2022,conference-paper,multimodal_ai,4,217.0,828.0,111.87473883318704,309,6.4706,False,False,baseline,laion5banopenlargescaledatasetfortrainingnextgenerationimagetextmodels,111.87473883318704,landmark,True
56	−7133250524,10.52202/068431-1833,,LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models,neural information processing systems,2022,conference-paper,multimodal_ai,4,217.0,828.0,111.87473883318704,309,6.4706,False,False,baseline,111.87473883318704,landmark,True	56	+4392947532,10.1038/s41591-024-02856-4,,A visual-language foundation model for computational pathology,Nature Medicine,2024,article,multimodal_ai,4,475.0,938.0,111.12392137738586,938,132.4965,False,True,post,avisuallanguagefoundationmodelforcomputationalpathology,111.12392137738586,landmark,True
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58	−7138474371,10.1609/aaai.v40i16.38386,,OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,4,31.0,31.0,159.42857142857142,30,608.3281,True,True,post,110.21183795904541,landmark,True	58	+2995460200,,,VL-BERT: Pre-training of Generic Visual-Linguistic Representations,International Conference on Learning Representations,2020,conference-paper,multimodal_ai,6,491.0,670.0,81.11570775331289,252,20.1142,True,False,baseline,vlbertpretrainingofgenericvisuallinguisticrepresentations,81.11570775331289,landmark,True
59	−7127319731,10.1109/tmi.2026.3661001,,Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models,IEEE Transactions on Medical Imaging,2026,article,multimodal_ai,6,29.0,29.0,77.73063407181054,28,87.2565,True,False,post,103.10139680039732,landmark,True	59	+4289785095,10.48550/arxiv.2208.01618,2208.01618,An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion,arXiv (Cornell University),2022,preprint,multimodal_ai,9,98.0,366.0,79.44757164293819,468,,True,True,baseline,animageisworthonewordpersonalizingtexttoimagegenerationusingtextualinversion,79.44757164293819,landmark,True
60	−2619383789,10.1109/tpami.2018.2798607,,Multimodal Machine Learning: A Survey and Taxonomy,IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,article,multimodal_ai,3,263.0,587.0,103.0608512817748,4909,120.007,True,True,baseline,103.0608512817748,landmark,True	60	+7138474371,10.1609/aaai.v40i16.38386,,OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,4,31.0,31.0,159.42857142857142,30,608.3281,True,True,post,opendrivevlatowardsendtoendautonomousdrivingwithlargevisionlanguageactionmodel,78.59958277791772,landmark,True
61	−7148475777,10.1117/12.3086794,2508.08224,Capabilities of GPT‑5 on multimodal medical reasoning,,2026,conference-paper,multimodal_ai,7,26.0,26.0,73.1907514450867,26,238.4333,True,True,post,92.4357350624252,landmark,True	61	+7127319731,10.1109/tmi.2026.3661001,,Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models,IEEE Transactions on Medical Imaging,2026,article,multimodal_ai,6,29.0,29.0,77.73063407181054,28,87.2565,True,False,post,medr1reinforcementlearningforgeneralizablemedicalreasoninginvisionlanguagemodels,73.52864195353592,landmark,True
62	−7160627408,10.1109/csnt69054.2026.11502110,,Generative Artificial Intelligence for Cross-Domain Knowledge Synthesis and Intelligent Automation,IEEE International Conference on Communication Systems and Network Technologies (CSNT),2026,conference-paper,multimodal_ai,6,23.0,23.0,193.44642857142856,23,207.3469,True,False,post,81.77007332445305,landmark,True	62	+4415913530,10.1038/s41591-025-03982-3,,A multimodal whole-slide foundation model for pathology,Nature Medicine,2025,article,multimodal_ai,8,157.0,157.0,53.48194198133358,157,65.3314,True,True,post,amultimodalwholeslidefoundationmodelforpathology,68.60453612242637,landmark,True
63	−2995460200,,,VL-BERT: Pre-training of Generic Visual-Linguistic Representations,International Conference on Learning Representations,2020,conference-paper,multimodal_ai,6,491.0,670.0,81.11570775331289,252,20.1142,True,False,baseline,81.11570775331289,landmark,True	63	+7148475777,10.1117/12.3086794,2508.08224,Capabilities of GPT‑5 on multimodal medical reasoning,,2026,conference-paper,multimodal_ai,7,26.0,26.0,73.1907514450867,26,238.4333,True,True,post,capabilitiesofgpt5onmultimodalmedicalreasoning,65.92223071696324,landmark,True
64	−4415913530,10.1038/s41591-025-03982-3,,A multimodal whole-slide foundation model for pathology,Nature Medicine,2025,article,multimodal_ai,8,157.0,157.0,53.48194198133358,157,65.3314,True,True,post,79.48264021540155,landmark,True	64	+7133220561,10.52202/075280-2142,,InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning,neural information processing systems,2023,conference-paper,multimodal_ai,5,290.0,454.0,63.3563629226367,398,45.2545,False,False,post,instructbliptowardsgeneralpurposevisionlanguagemodelswithinstructiontuning,63.3563629226367,landmark,True
65	−4289785095,10.48550/arxiv.2208.01618,2208.01618,An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion,arXiv (Cornell University),2022,preprint,multimodal_ai,9,98.0,366.0,79.44757164293819,468,,True,True,baseline,79.44757164293819,landmark,True	65	+4413447271,10.1038/s41467-025-62385-7,2308.02463,Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data,Nature Communications,2025,article,multimodal_ai,4,142.0,142.0,46.43212351171006,142,51.2274,True,True,post,towardsgeneralistfoundationmodelforradiologybyleveragingwebscale2d3dmedicaldata,62.04996260754486,landmark,True
66	−7125233009,10.1007/s44443-026-00472-5,,SemCap: Sentiment-aware semantic captioning for multimodal aspect-based sentiment analysis,Journal of King Saud University - Computer and Information Sciences,2026,article,multimodal_ai,4,22.0,22.0,24.48163370151554,22,79.4776,True,True,post,78.21485274512901,landmark,True	66	+4389519587,10.18653/v1/2023.emnlp-demo.49,,Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding,Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP),2023,conference-paper,multimodal_ai,6,171.0,421.0,58.751164736630066,586,66.631,True,True,post,videollamaaninstructiontunedaudiovisuallanguagemodelforvideounderstanding,58.751164736630066,landmark,True
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69	−7131830459,10.1109/tmm.2026.3668620,2502.15711,A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions,IEEE Transactions on Multimedia,2026,article,multimodal_ai,3,20.0,20.0,142.0908500973394,20,173.9038,True,True,post,71.10441158648091,landmark,True	69	+4399567248,10.1038/s41586-024-07618-3,,A multimodal generative AI copilot for human pathology,Nature,2024,article,multimodal_ai,8,268.0,452.0,53.54798769997698,452,63.8469,True,True,post,amultimodalgenerativeaicopilotforhumanpathology,53.54798769997698,landmark,True
70	−7206206818,10.1109/tpami.2026.3730310,,Lingshu: Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning,IEEE Transactions on Pattern Analysis and Machine Intelligence,2026,article,multimodal_ai,6,18.0,18.0,48.246600458365165,18,56.0935,True,False,post,63.99397042783282,landmark,True	70	+7143512276,10.1007/s11831-026-10560-4,,Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey,Archives of Computational Methods in Engineering,2026,article,multimodal_ai,5,21.0,21.0,23.368832169628472,21,75.865,True,True,post,multimodalmachinelearningapproachesinpredictivehealthcareanalyticsacomprehensivesurvey,53.24487865600877,landmark,True
71	−7133220561,10.52202/075280-2142,,InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning,neural information processing systems,2023,conference-paper,multimodal_ai,5,290.0,454.0,63.3563629226367,398,45.2545,False,False,post,63.3563629226367,landmark,True	71	+4410882873,10.1016/j.media.2025.103621,2411.03782,Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications,Medical Image Analysis,2025,article,multimodal_ai,6,119.0,119.0,7.914984580880291,119,24.365,True,True,post,navigatingthelandscapeofmultimodalaiinmedicineascopingreviewontechnicalchallengesandclinicalapplications,51.99961655139323,landmark,True
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73	−7165561001,10.1007/978-3-032-29501-9_39,,"M3-HGMA: A Multi-modal, Multi-agent Hierarchical Graph-Mamba Architecture for Explainable and Adaptive Clinical Intelligence",Learning and analytics in intelligent systems,2026,conference-paper,multimodal_ai,2,17.0,17.0,142.98214285714286,17,160.2226,False,False,post,60.43874984850878,landmark,True	73	+4412438081,10.1016/j.array.2025.100445,,RAFT: Robust Adversarial Fusion Transformer for multimodal sentiment analysis,Array,2025,article,multimodal_ai,5,111.0,111.0,37.81207363011482,111,46.1897,True,True,post,raftrobustadversarialfusiontransformerformultimodalsentimentanalysis,48.503844010123096,landmark,True
74	−7138470740,10.1609/aaai.v40i18.38608,,Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,7,17.0,17.0,130.47598850731976,17,157.1634,True,True,post,60.43874984850878,landmark,True	74	+2968124245,10.48550/arxiv.1908.03557,1908.03557,VisualBERT: A Simple and Performant Baseline for Vision and Language,arXiv (Cornell University),2019,preprint,multimodal_ai,7,120.0,374.0,48.02738920120277,1140,,True,True,baseline,visualbertasimpleandperformantbaselineforvisionandlanguage,48.02738920120277,landmark,True
75	−7138363754,10.1609/aaai.v40i6.42404,,AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,6,17.0,17.0,130.47598850731976,17,157.1634,True,True,post,60.43874984850878,landmark,True	75	+7206206818,10.1109/tpami.2026.3730310,,Lingshu: Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning,IEEE Transactions on Pattern Analysis and Machine Intelligence,2026,article,multimodal_ai,6,18.0,18.0,48.246600458365165,18,56.0935,True,False,post,lingshugeneralistfoundationmodelforunifiedmultimodalmedicalunderstandingandreasoning,45.63846741943609,landmark,True
76	−4410882873,10.1016/j.media.2025.103621,2411.03782,Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications,Medical Image Analysis,2025,article,multimodal_ai,6,119.0,119.0,7.914984580880291,119,24.365,True,True,post,60.24480373014512,landmark,True	76	+4389523832,10.18653/v1/2023.emnlp-main.20,2305.10355,Evaluating Object Hallucination in Large Vision-Language Models,Conference on Empirical Methods in Natural Language Processing (EMNLP),2023,conference-paper,multimodal_ai,6,124.0,326.0,45.49377601933825,559,63.561,True,True,post,evaluatingobjecthallucinationinlargevisionlanguagemodels,45.49377601933825,landmark,True
77	−4400228804,10.46569/df65vg26z,,The Conglomeration of Everything: Shifting Animation Production Practices due to Artificial Intelligence,,2024,dissertation,generative_ai,4,2.0,2.0,540.0,2,,True,True,post,540.0,landmark,True	77	+4400228804,10.46569/df65vg26z,,The Conglomeration of Everything: Shifting Animation Production Practices due to Artificial Intelligence,,2024,dissertation,generative_ai,4,2.0,2.0,540.0,2,,True,True,post,theconglomerationofeverythingshiftinganimationproductionpracticesduetoartificialintelligence,540.0,landmark,True
78	−7124262017,10.1787/062a7394-en,,OECD Digital Education Outlook 2026,Digital education outlook,2026,book,generative_ai,1,87.0,87.0,783.6796875,87,992.8809,True,False,post,309.304190401192,landmark,True	78	+4383312437,10.1186/s41239-023-00408-3,2305.00280,A comprehensive AI policy education framework for university teaching and learning,International Journal of Educational Technology in Higher Education,2023,article,generative_ai,1,451.0,988.0,231.56326653406123,1524,357.6522,True,True,post,acomprehensiveaipolicyeducationframeworkforuniversityteachingandlearning,231.56326653406123,landmark,True
79	−4406472463,10.1038/s41586-025-08628-5,,A generative model for inorganic materials design,Nature,2025,article,generative_ai,2,498.0,498.0,77.09849967384214,498,102.9572,True,True,post,252.1169097278342,landmark,True	79	+7133185348,10.52202/068431-2643,,Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding,neural information processing systems,2022,conference-paper,generative_ai,5,651.0,1706.0,230.5051986104071,530,10.1405,False,False,baseline,photorealistictexttoimagediffusionmodelswithdeeplanguageunderstanding,230.5051986104071,landmark,True
80	−7123342962,10.1109/tmm.2026.3651132,,Structure-Preserving Frequency-Regularized Text-Guided Optimal Transport for Unpaired Rain Streaks and Raindrops Removal,IEEE Transactions on Multimedia,2026,article,generative_ai,3,66.0,66.0,176.9042016806723,66,205.676,True,False,post,234.64455823538702,landmark,True	80	+3195296536,,,Generative Adversarial Trainer Defense to Adversarial Perturbations with GAN,Seoul National University Open Repository (Seoul National University),2020,dissertation,generative_ai,4,37.0,48.0,221.6522377109318,65,,False,True,baseline,generativeadversarialtrainerdefensetoadversarialperturbationswithgan,221.6522377109318,landmark,True
81	−4383312437,10.1186/s41239-023-00408-3,2305.00280,A comprehensive AI policy education framework for university teaching and learning,International Journal of Educational Technology in Higher Education,2023,article,generative_ai,1,451.0,988.0,231.56326653406123,1524,357.6522,True,True,post,231.56326653406123,landmark,True	81	+7124262017,10.1787/062a7394-en,,OECD Digital Education Outlook 2026,Digital education outlook,2026,book,generative_ai,1,87.0,87.0,783.6796875,87,992.8809,True,False,post,oecddigitaleducationoutlook2026,220.58592586060777,landmark,True
82	−7133185348,10.52202/068431-2643,,Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding,neural information processing systems,2022,conference-paper,generative_ai,5,651.0,1706.0,230.5051986104071,530,10.1405,False,False,baseline,230.5051986104071,landmark,True	82	+4406472463,10.1038/s41586-025-08628-5,,A generative model for inorganic materials design,Nature,2025,article,generative_ai,2,498.0,498.0,77.09849967384214,498,102.9572,True,True,post,agenerativemodelforinorganicmaterialsdesign,217.6118406940658,landmark,True
83	−3195296536,,,Generative Adversarial Trainer Defense to Adversarial Perturbations with GAN,Seoul National University Open Repository (Seoul National University),2020,dissertation,generative_ai,4,37.0,48.0,221.6522377109318,65,,False,True,baseline,221.6522377109318,landmark,True	83	+4322617812,10.1016/j.ijme.2023.100790,,Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators,The International Journal of Management Education,2023,article,generative_ai,1,491.0,873.0,204.6100523119792,1151,270.2366,True,True,post,generativeaiandthefutureofeducationragnarkorreformationaparadoxicalperspectivefrommanagementeducators,204.6100523119792,landmark,True
84	−4322617812,10.1016/j.ijme.2023.100790,,Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators,The International Journal of Management Education,2023,article,generative_ai,1,491.0,873.0,204.6100523119792,1151,270.2366,True,True,post,204.6100523119792,landmark,True	84	+4400770292,10.2139/ssrn.4895486,,Generative AI Can Harm Learning,SSRN Electronic Journal,2024,preprint,generative_ai,1,123.0,198.0,174.24635549016986,198,,False,True,post,generativeaicanharmlearning,174.24635549016986,landmark,True
85	−3096831136,10.1145/3422622,,Generative adversarial networks,Communications of the ACM,2020,article,generative_ai,3,565.0,1473.0,198.6115392180884,14434,457.4443,True,False,baseline,198.6115392180884,landmark,True	85	+7123342962,10.1109/tmm.2026.3651132,,Structure-Preserving Frequency-Regularized Text-Guided Optimal Transport for Unpaired Rain Streaks and Raindrops Removal,IEEE Transactions on Multimedia,2026,article,generative_ai,3,66.0,66.0,176.9042016806723,66,205.676,True,False,post,structurepreservingfrequencyregularizedtextguidedoptimaltransportforunpairedrainstreaksandraindropsremoval,167.341047204599,landmark,True
86	−4400770292,10.2139/ssrn.4895486,,Generative AI Can Harm Learning,SSRN Electronic Journal,2024,preprint,generative_ai,1,123.0,198.0,174.24635549016986,198,,False,True,post,174.24635549016986,landmark,True	86	+4398243313,10.1186/s41239-024-00468-z,,A scoping review on how generative artificial intelligence transforms assessment in higher education,International Journal of Educational Technology in Higher Education,2024,article,generative_ai,1,181.0,463.0,139.39774582546502,463,177.182,True,True,post,ascopingreviewonhowgenerativeartificialintelligencetransformsassessmentinhighereducation,139.39774582546502,landmark,True
87	−7127274868,10.1002/jcal.70198,,Parental AI Investment Behaviours and AI ‐Mediated Informal Digital Learning of English: The Chain Mediating Role of Children's Perceived AI Value and Effort Expectancy for AI,Journal of Computer Assisted Learning,2026,article,generative_ai,1,48.0,48.0,341.0180402336145,48,439.3359,True,False,post,170.6505878075542,landmark,True	87	+7127274868,10.1002/jcal.70198,,Parental AI Investment Behaviours and AI ‐Mediated Informal Digital Learning of English: The Chain Mediating Role of Children's Perceived AI Value and Effort Expectancy for AI,Journal of Computer Assisted Learning,2026,article,generative_ai,1,48.0,48.0,341.0180402336145,48,439.3359,True,False,post,parentalaiinvestmentbehavioursandaimediatedinformaldigitallearningofenglishthechainmediatingroleofchildrensperceivedaivalueandeffortexpectancyforai,121.70257978516291,landmark,True
88	−7124974472,10.1287/mnsc.2025.02523,,The Rapid Adoption of Generative AI,Management Science,2026,article,generative_ai,1,42.0,42.0,46.737664339256945,42,151.73,True,False,post,149.31926433160993,landmark,True	88	+4406199489,10.3389/bjbs.2024.14048,,Generative AI in Higher Education: Balancing Innovation and Integrity,British Journal of Biomedical Science,2025,article,generative_ai,2,278.0,278.0,241.35986779790812,278,301.5286,True,True,post,generativeaiinhighereducationbalancinginnovationandintegrity,121.47809580913713,landmark,True
89	−7118187240,10.3390/en19010284,,Patch Time Series Transformer−Based Short−Term Photovoltaic Power Prediction Enhanced by Artificial Fish,Energies,2026,article,generative_ai,1,40.0,40.0,44.5120612754828,40,144.5047,True,True,post,142.20882317296181,landmark,True	89	+4394934695,10.1016/j.caeai.2024.100221,,The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives,Computers and Education Artificial Intelligence,2024,article,generative_ai,1,207.0,394.0,118.623567721886,394,150.7769,True,True,post,theimpactofgenerativeaionhighereducationlearningandteachingastudyofeducatorsperspectives,118.623567721886,landmark,True
90	−4406199489,10.3389/bjbs.2024.14048,,Generative AI in Higher Education: Balancing Innovation and Integrity,British Journal of Biomedical Science,2025,article,generative_ai,2,278.0,278.0,241.35986779790812,278,301.5286,True,True,post,140.739961655297,landmark,True	90	+4409269614,10.3390/info16040296,,Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda,Information,2025,review,generative_ai,1,271.0,271.0,14.199554069119287,271,53.4431,True,True,post,generativeaiandacademicintegrityinhighereducationasystematicreviewandresearchagenda,118.41929483552576,landmark,True
91	−4398243313,10.1186/s41239-024-00468-z,,A scoping review on how generative artificial intelligence transforms assessment in higher education,International Journal of Educational Technology in Higher Education,2024,article,generative_ai,1,181.0,463.0,139.39774582546502,463,177.182,True,True,post,139.39774582546502,landmark,True	91	+4395957952,10.54675/ewzm9535,,Guidance for generative AI in education and research,UNESCO eBooks,2023,book,generative_ai,2,164.0,400.0,116.73200564528776,726,252.3485,False,False,post,guidanceforgenerativeaiineducationandresearch,116.73200564528776,landmark,True
92	−4409269614,10.3390/info16040296,,Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda,Information,2025,review,generative_ai,1,271.0,271.0,14.199554069119287,271,53.4431,True,True,post,137.19614967117084,landmark,True	92	+4394863715,10.2139/ssrn.4759930,,How Generative AI Can Augment Human Creativity,SSRN Electronic Journal,2024,preprint,generative_ai,1,111.0,131.0,114.27954959430369,131,,False,True,post,howgenerativeaicanaugmenthumancreativity,114.27954959430369,landmark,True
93	−3193917007,10.1007/978-1-4842-7341-8_10,,Generative Adversarial Networks,Apress eBooks,2021,book-chapter,generative_ai,3,61.0,195.0,127.1017316017316,255,116.749,True,False,baseline,127.1017316017316,landmark,True	93	+4401077562,10.1080/10447318.2024.2383033,,Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy,International Journal of Human-Computer Interaction,2024,article,generative_ai,3,172.0,353.0,106.27949087773035,353,135.087,True,False,post,factorsinfluencinguniversitystudentsbehavioralintentiontousegenerativeartificialintelligenceintegratingthetheoryofplannedbehaviorandailiteracy,106.27949087773035,landmark,True
94	−4288099666,10.48550/arxiv.2207.12598,2207.12598,Classifier-Free Diffusion Guidance,arXiv (Cornell University),2022,preprint,generative_ai,3,211.0,575.0,124.81517402920616,731,,True,True,baseline,124.81517402920616,landmark,True	94	+7118187240,10.3390/en19010284,,Patch Time Series Transformer−Based Short−Term Photovoltaic Power Prediction Enhanced by Artificial Fish,Energies,2026,article,generative_ai,1,40.0,40.0,44.5120612754828,40,144.5047,True,True,post,patchtimeseriestransformerbasedshorttermphotovoltaicpowerpredictionenhancedbyartificialfish,101.41881648763577,landmark,True
95	−4394934695,10.1016/j.caeai.2024.100221,,The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives,Computers and Education Artificial Intelligence,2024,article,generative_ai,1,207.0,394.0,118.623567721886,394,150.7769,True,True,post,118.623567721886,landmark,True	95	+4407778399,10.1186/s41239-025-00507-3,,"Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration",International Journal of Educational Technology in Higher Education,2025,article,generative_ai,3,213.0,213.0,184.92680518328933,213,232.7014,True,True,post,investigatingthehighereducationinstitutionsguidelinesandpoliciesregardingtheuseofgenerativeaiinteachinglearningresearchandadministration,93.07494391131729,landmark,True
96	−4321648656,10.1163/22134913-bja10078,2302.10913,From Paintbrush to Pixel: A Review of Deep Neural Networks in AI-Generated Art,Art & Perception,2026,article,generative_ai,4,33.0,33.0,88.45210084033614,33,9.3489,True,True,post,117.32227911769351,landmark,True	96	+4401917994,10.38124/ijisrt/ijisrt24aug265,,Building Face Ageing Model Using Face Synthesis,International Journal of Innovative Science and Research Technology (IJISRT),2024,article,generative_ai,3,737.0,737.0,87.31165251080317,737,103.9631,True,True,post,buildingfaceageingmodelusingfacesynthesis,87.31165251080317,landmark,True
97	−4395957952,10.54675/ewzm9535,,Guidance for generative AI in education and research,UNESCO eBooks,2023,book,generative_ai,2,164.0,400.0,116.73200564528776,726,252.3485,False,False,post,116.73200564528776,landmark,True	97	+4411505840,10.1038/s41598-025-97652-6,,AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting,Scientific Reports,2025,article,generative_ai,1,196.0,196.0,66.76726514867121,196,81.1441,True,True,post,aitutoringoutperformsinclassactivelearninganrctintroducinganovelresearchbaseddesigninanauthenticeducationalsetting,85.64642726111826,landmark,True
98	−4394863715,10.2139/ssrn.4759930,,How Generative AI Can Augment Human Creativity,SSRN Electronic Journal,2024,preprint,generative_ai,1,111.0,131.0,114.27954959430369,131,,False,True,post,114.27954959430369,landmark,True	98	+2804078698,10.48550/arxiv.1805.08318,1805.08318,Self-Attention Generative Adversarial Networks,arXiv (Cornell University),2018,preprint,generative_ai,4,365.0,936.0,85.0107976109918,2327,,True,True,baseline,selfattentiongenerativeadversarialnetworks,85.0107976109918,landmark,True
99	−4407778399,10.1186/s41239-025-00507-3,,"Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration",International Journal of Educational Technology in Higher Education,2025,article,generative_ai,3,213.0,213.0,184.92680518328933,213,232.7014,True,True,post,107.83313608841102,landmark,True	99	+4321648656,10.1163/22134913-bja10078,2302.10913,From Paintbrush to Pixel: A Review of Deep Neural Networks in AI-Generated Art,Art & Perception,2026,article,generative_ai,4,33.0,33.0,88.45210084033614,33,9.3489,True,True,post,frompaintbrushtopixelareviewofdeepneuralnetworksinaigeneratedart,83.6705236022995,landmark,True
100	−7124432017,10.1080/10494820.2026.2615818,,Human-Centric Artificial Intelligence Pedagogy (HCAP) framework developed from TPACK through integration of artificial intelligence literacy and competency,Interactive Learning Environments,2026,article,generative_ai,2,30.0,30.0,213.13627514600907,30,265.4321,True,True,post,106.65661737972137,landmark,True	100	+2806351858,10.48550/arxiv.1805.11973,1805.11973,MolGAN: An implicit generative model for small molecular graphs,arXiv (Cornell University),2018,preprint,generative_ai,7,61.0,157.0,83.37167538076835,441,,True,True,baseline,molgananimplicitgenerativemodelforsmallmoleculargraphs,83.37167538076835,landmark,True
101	−7140170686,10.1186/s41239-026-00585-x,,Pedagogical partnerships with generative AI in higher education: how dual cognitive pathways paradoxically enable transformative learning,International Journal of Educational Technology in Higher Education,2026,article,generative_ai,2,30.0,30.0,213.13627514600907,30,274.5849,True,True,post,106.65661737972137,landmark,True	101	+4379057711,10.3390/app13116716,,"Exploring the Potential Impact of Artificial Intelligence (AI) on International Students in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success",Applied Sciences,2023,article,generative_ai,2,158.0,352.0,82.50027309715541,461,108.0946,True,True,post,exploringthepotentialimpactofartificialintelligenceaioninternationalstudentsinhighereducationgenerativeaichatbotsanalyticsandinternationalstudentsuccess,82.50027309715541,landmark,True
102	−4406728221,10.1109/access.2025.3532853,,Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey,IEEE Access,2025,article,ai_agents,1,694.0,694.0,236.41062251621338,694,288.7899,True,True,post,351.3436452833674,landmark,True	102	+4406728221,10.1109/access.2025.3532853,,Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey,IEEE Access,2025,article,ai_agents,1,694.0,694.0,236.41062251621338,694,288.7899,True,True,post,agenticaiautonomousintelligenceforcomplexgoalsacomprehensivesurvey,303.25826795518407,landmark,True
103	−7161734976,10.1038/s41586-026-10652-y,,A multi-agent system for automating scientific discovery,Nature,2026,article,ai_agents,3,62.0,62.0,175.0637411130179,62,220.9602,True,True,post,220.42367591809082,landmark,True	103	+7161734976,10.1038/s41586-026-10652-y,,A multi-agent system for automating scientific discovery,Nature,2026,article,ai_agents,3,62.0,62.0,175.0637411130179,62,220.9602,True,True,post,amultiagentsystemforautomatingscientificdiscovery,157.19916555583544,landmark,True
104	−7135409568,10.1038/s41746-026-02517-5,,The role of agentic artificial intelligence in healthcare: a scoping review,npj Digital Medicine,2026,article,ai_agents,1,52.0,52.0,23.673009161381255,52,97.7788,True,True,post,184.8714701248504,landmark,True	104	+4225355126,10.1007/978-981-19-0493-6_1,,Introduction to Multi-agent Systems,Springer tracts in human-centered computing,2022,book-chapter,ai_agents,1,266.0,268.0,143.62252453934906,287,6.1939,False,False,baseline,introductiontomultiagentsystems,143.62252453934906,landmark,True
105	−7134187809,10.1016/j.isatra.2026.03.010,,Dynamic event-triggered approximate optimal consensus control for unknown nonlinear multi-agent systems via adaptive dynamic programming,ISA Transactions,2026,article,ai_agents,3,46.0,46.0,129.88600147094877,46,163.9382,False,False,post,163.5401466489061,landmark,True	105	+7135409568,10.1038/s41746-026-02517-5,,The role of agentic artificial intelligence in healthcare: a scoping review,npj Digital Medicine,2026,article,ai_agents,1,52.0,52.0,23.673009161381255,52,97.7788,True,True,post,theroleofagenticartificialintelligenceinhealthcareascopingreview,131.84446143392648,landmark,True
106	−4390730779,10.1002/widm.70111,2401.03428,"Exploring Large Language Model‐Based Intelligent Agents: Definitions, Methods, and Prospects",Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery,2026,article,ai_agents,3,44.0,44.0,48.96326740303108,44,32.5136,True,True,post,156.42970549025802,landmark,True	106	+4410197190,10.1016/j.array.2025.100399,,The role of agentic AI in shaping a smart future: A systematic review,Array,2025,review,ai_agents,4,267.0,267.0,33.148798521256936,267,36.646,True,True,post,theroleofagenticaiinshapingasmartfutureasystematicreview,116.6714085648907,landmark,True
107	−4225355126,10.1007/978-981-19-0493-6_1,,Introduction to Multi-agent Systems,Springer tracts in human-centered computing,2022,book-chapter,ai_agents,1,266.0,268.0,143.62252453934906,287,6.1939,False,False,baseline,143.62252453934906,landmark,True	107	+7134187809,10.1016/j.isatra.2026.03.010,,Dynamic event-triggered approximate optimal consensus control for unknown nonlinear multi-agent systems via adaptive dynamic programming,ISA Transactions,2026,article,ai_agents,3,46.0,46.0,129.88600147094877,46,163.9382,False,False,post,dynamiceventtriggeredapproximateoptimalconsensuscontrolforunknownnonlinearmultiagentsystemsviaadaptivedynamicprogramming,116.63163896078113,landmark,True
108	−4407570917,10.26599/if.2026.9710004,2502.08691,AgentSociety: Large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society,iFuture,2026,article,ai_agents,2,40.0,40.0,44.5120612754828,40,126.4417,True,True,post,142.20882317296181,landmark,True	108	+4413427262,10.1016/j.inffus.2025.103599,2505.10468,"AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges",Information Fusion,2025,article,ai_agents,8,258.0,258.0,87.88752249161823,258,107.36,True,True,post,aiagentsvsagenticaiaconceptualtaxonomyapplicationsandchallenges,112.7386644559618,landmark,True
109	−4410197190,10.1016/j.array.2025.100399,,The role of agentic AI in shaping a smart future: A systematic review,Array,2025,review,ai_agents,4,267.0,267.0,33.148798521256936,267,36.646,True,True,post,135.17111425167016,landmark,True	109	+4390730779,10.1002/widm.70111,2401.03428,"Exploring Large Language Model‐Based Intelligent Agents: Definitions, Methods, and Prospects",Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery,2026,article,ai_agents,3,44.0,44.0,48.96326740303108,44,32.5136,True,True,post,exploringlargelanguagemodelbasedintelligentagentsdefinitionsmethodsandprospects,111.56069813639934,landmark,True
110	−4413427262,10.1016/j.inffus.2025.103599,2505.10468,"AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges",Information Fusion,2025,article,ai_agents,8,258.0,258.0,87.88752249161823,258,107.36,True,True,post,130.61478455779363,landmark,True	110	+4401023556,10.24963/ijcai.2024/890,,Large Language Model Based Multi-agents: A Survey of Progress and Challenges,,2024,conference-paper,ai_agents,4,231.0,371.0,103.12387591961277,302,91.8723,True,True,post,largelanguagemodelbasedmultiagentsasurveyofprogressandchallenges,103.12387591961277,landmark,True
111	−4414991289,10.1039/d5sc09921h,2508.13251,“DIVE” into hydrogen storage materials discovery with AI agents,Chemical Science,2026,article,ai_agents,3,31.0,31.0,23.90492957746479,31,43.4978,True,True,post,110.21183795904541,landmark,True	111	+4407570917,10.26599/if.2026.9710004,2502.08691,AgentSociety: Large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society,iFuture,2026,article,ai_agents,2,40.0,40.0,44.5120612754828,40,126.4417,True,True,post,agentsocietylargescalesimulationofllmdrivengenerativeagentsadvancesunderstandingofhumanbehaviorsandsociety,101.41881648763577,landmark,True
112	−7122857583,10.1007/s10462-025-11458-6,,"Agentic AI systems in the age of generative models: architectures, cloud scalability, and real-world applications",Artificial Intelligence Review,2026,article,ai_agents,7,30.0,30.0,33.384045956612105,30,108.3786,True,True,post,106.65661737972137,landmark,True	112	+4414023998,10.3390/fi17090404,,"The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges",Future Internet,2025,article,ai_agents,3,189.0,189.0,64.3827199647901,189,78.6474,True,True,post,theriseofagenticaiareviewofdefinitionsframeworksarchitecturesapplicationsevaluationmetricsandchallenges,82.5876262875069,landmark,True
113	−4401023556,10.24963/ijcai.2024/890,,Large Language Model Based Multi-agents: A Survey of Progress and Challenges,,2024,conference-paper,ai_agents,4,231.0,371.0,103.12387591961277,302,91.8723,True,True,post,103.12387591961277,landmark,True	113	+4414991289,10.1039/d5sc09921h,2508.13251,“DIVE” into hydrogen storage materials discovery with AI agents,Chemical Science,2026,article,ai_agents,3,31.0,31.0,23.90492957746479,31,43.4978,True,True,post,diveintohydrogenstoragematerialsdiscoverywithaiagents,78.59958277791772,landmark,True
114	−4414023998,10.3390/fi17090404,,"The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges",Future Internet,2025,article,ai_agents,3,189.0,189.0,64.3827199647901,189,78.6474,True,True,post,95.68292357140696,landmark,True	114	+7122857583,10.1007/s10462-025-11458-6,,"Agentic AI systems in the age of generative models: architectures, cloud scalability, and real-world applications",Artificial Intelligence Review,2026,article,ai_agents,7,30.0,30.0,33.384045956612105,30,108.3786,True,True,post,agenticaisystemsintheageofgenerativemodelsarchitecturescloudscalabilityandrealworldapplications,76.06411236572681,landmark,True
115	−7118083939,10.1007/s12525-025-00861-0,,Agentic information systems,Electronic Markets,2026,article,ai_agents,1,26.0,26.0,32.66095238095238,26,314.5617,True,True,post,92.4357350624252,landmark,True	115	+4390638955,10.1007/s40692-023-00304-9,,Artificial intelligence (AI) learning tools in K-12 education: A scoping review,Journal of Computers in Education,2024,article,ai_agents,1,146.0,240.0,72.25801079505747,240,91.8438,True,True,post,artificialintelligenceailearningtoolsink12educationascopingreview,72.25801079505747,landmark,True
116	−4412505619,10.70777/si.v2i3.15161,,"AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges",SuperIntelligence - Robotics - Safety & Alignment,2025,article,ai_agents,6,181.0,181.0,61.65752546892597,181,75.3184,True,True,post,91.63285273240561,landmark,True	116	+4416209823,10.1007/s10462-025-11422-4,2510.25445,"Agentic AI: a comprehensive survey of architectures, applications, and future directions",Artificial Intelligence Review,2025,article,ai_agents,4,165.0,165.0,56.20713647719771,165,68.6604,True,True,post,agenticaiacomprehensivesurveyofarchitecturesapplicationsandfuturedirections,72.1003086636965,landmark,True
117	−4416209823,10.1007/s10462-025-11422-4,2510.25445,"Agentic AI: a comprehensive survey of architectures, applications, and future directions",Artificial Intelligence Review,2025,article,ai_agents,4,165.0,165.0,56.20713647719771,165,68.6604,True,True,post,83.53271105440291,landmark,True	117	+4399982941,10.5821/dissertation-2117-410409,,Value engineering for autonomous agents,,2024,dissertation,ai_agents,2,7.0,8.0,68.17851239669422,1,,True,True,post,valueengineeringforautonomousagents,68.17851239669422,landmark,True
118	−7155930820,10.1109/tase.2026.3686874,,Adaptive Prescribed Performance Secure Consensus Control for High-Order Constrained Multi-Agent Systems With Time-Varying Powers and Arbitrary Initial States,IEEE Transactions on Automation Science and Engineering,2026,article,ai_agents,2,23.0,23.0,80.74592420071988,23,96.3814,False,False,post,81.77007332445305,landmark,True	118	+4412709685,10.1038/s41586-025-09442-9,,The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies,Nature,2025,article,ai_agents,2,150.0,150.0,33.53365384615385,150,107.0846,False,False,post,thevirtuallabofaiagentsdesignsnewsarscov2nanobodies,65.545735148815,landmark,True
119	−7162257503,10.1016/j.ejcon.2026.101534,,Predefined-time sliding-mode formation control for second-order nonlinear multi-agent systems under deception attacks,European Journal of Control,2026,article,ai_agents,1,22.0,22.0,77.23523184416683,22,92.1909,False,False,post,78.21485274512901,landmark,True	119	+4412203333,10.1145/3748302,2404.13501,A Survey on the Memory Mechanism of Large Language Model-based Agents,ACM Transactions on Information Systems,2025,article,ai_agents,4,139.0,139.0,47.35025436563928,139,56.1767,True,True,post,asurveyonthememorymechanismoflargelanguagemodelbasedagents,60.739047904568565,landmark,True
120	−4412709685,10.1038/s41586-025-09442-9,,The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies,Nature,2025,article,ai_agents,2,150.0,150.0,33.53365384615385,150,107.0846,False,False,post,75.93882823127537,landmark,True	120	+2144462863,10.5555/2343576.2343653,2410.10004,Crowd IQ -- Aggregating Opinions to Boost Performance,arXiv (Cornell University),2024,preprint,ai_agents,2,52.0,52.0,60.60850111856823,52,,True,True,post,crowdiqaggregatingopinionstoboostperformance,60.60850111856823,landmark,True
121	−7128165235,10.1007/s12525-025-00862-z,,Multi-agent AI,Electronic Markets,2026,article,ai_agents,2,21.0,21.0,23.368832169628472,21,75.865,True,True,post,74.65963216580496,landmark,True	121	+7155930820,10.1109/tase.2026.3686874,,Adaptive Prescribed Performance Secure Consensus Control for High-Order Constrained Multi-Agent Systems With Time-Varying Powers and Arbitrary Initial States,IEEE Transactions on Automation Science and Engineering,2026,article,ai_agents,2,23.0,23.0,80.74592420071988,23,96.3814,False,False,post,adaptiveprescribedperformancesecureconsensuscontrolforhighorderconstrainedmultiagentsystemswithtimevaryingpowersandarbitraryinitialstates,58.315819480390566,landmark,True
122	−7118190454,10.1109/ojcs.2026.3650842,2507.14912,Redefining Elderly Care With Agentic AI: Challenges and Opportunities,IEEE Open Journal of the Computer Society,2026,article,ai_agents,4,21.0,21.0,22.217391304347824,21,315.9642,True,True,post,74.65963216580496,landmark,True	122	+4378505261,10.48550/arxiv.2305.16291,2305.16291,Voyager: An Open-Ended Embodied Agent with Large Language Models,arXiv (Cornell University),2023,preprint,ai_agents,6,118.0,171.0,56.852813926843034,187,,True,True,post,voyageranopenendedembodiedagentwithlargelanguagemodels,56.852813926843034,landmark,True
123	−7130481397,10.1038/s41746-026-02443-6,,Benchmarking large language model-based agent systems for clinical decision tasks,npj Digital Medicine,2026,article,ai_agents,3,21.0,21.0,9.560253699788584,21,39.4876,True,True,post,74.65963216580496,landmark,True	123	+7162257503,10.1016/j.ejcon.2026.101534,,Predefined-time sliding-mode formation control for second-order nonlinear multi-agent systems under deception attacks,European Journal of Control,2026,article,ai_agents,1,22.0,22.0,77.23523184416683,22,92.1909,False,False,post,predefinedtimeslidingmodeformationcontrolforsecondordernonlinearmultiagentsystemsunderdeceptionattacks,55.78034906819967,landmark,True
124	−4390638955,10.1007/s40692-023-00304-9,,Artificial intelligence (AI) learning tools in K-12 education: A scoping review,Journal of Computers in Education,2024,article,ai_agents,1,146.0,240.0,72.25801079505747,240,91.8438,True,True,post,72.25801079505747,landmark,True	124	+7130481397,10.1038/s41746-026-02443-6,,Benchmarking large language model-based agent systems for clinical decision tasks,npj Digital Medicine,2026,article,ai_agents,3,21.0,21.0,9.560253699788584,21,39.4876,True,True,post,benchmarkinglargelanguagemodelbasedagentsystemsforclinicaldecisiontasks,53.24487865600877,landmark,True
125	−4412203333,10.1145/3748302,2404.13501,A Survey on the Memory Mechanism of Large Language Model-based Agents,ACM Transactions on Information Systems,2025,article,ai_agents,4,139.0,139.0,47.35025436563928,139,56.1767,True,True,post,70.3699808276485,landmark,True	125	+7118190454,10.1109/ojcs.2026.3650842,2507.14912,Redefining Elderly Care With Agentic AI: Challenges and Opportunities,IEEE Open Journal of the Computer Society,2026,article,ai_agents,4,21.0,21.0,22.217391304347824,21,315.9642,True,True,post,redefiningelderlycarewithagenticaichallengesandopportunities,53.24487865600877,landmark,True
126	−4399982941,10.5821/dissertation-2117-410409,,Value engineering for autonomous agents,,2024,dissertation,ai_agents,2,7.0,8.0,68.17851239669422,1,,True,True,post,68.17851239669422,landmark,True	126	+2306711933,10.26190/unsworks/21510,,Discovering hierarchy in reinforcement learning,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,ai_agents,4,7.0,7.0,52.423245614035096,14,,True,True,baseline,discoveringhierarchyinreinforcementlearning,52.423245614035096,landmark,True
127	−2911283634,10.1515/9783110595567-011,,10. Reinforcement learning,De Gruyter eBooks,2020,book-chapter,reinforcement_learning,2,524.0,532.0,276.02730780647073,1930,32.1901,False,False,baseline,276.02730780647073,landmark,True	127	+4389473504,10.23860/diss-li-hepfeng-2023,,TRUST-REGION BASED POLICY OPTIMIZATION FOR EFFICIENT REINFORCEMENT LEARNING,,2023,dissertation,reinforcement_learning,5,16.0,28.0,232.91170431211498,32,,True,True,post,trustregionbasedpolicyoptimizationforefficientreinforcementlearning,232.91170431211498,landmark,True
128	−4389473504,10.23860/diss-li-hepfeng-2023,,TRUST-REGION BASED POLICY OPTIMIZATION FOR EFFICIENT REINFORCEMENT LEARNING,,2023,dissertation,reinforcement_learning,5,16.0,28.0,232.91170431211498,32,,True,True,post,232.91170431211498,landmark,True	128	+2098774185,10.1184/r1/6555512,,Maximum Entropy Inverse Reinforcement Learning,,2018,article,reinforcement_learning,3,780.0,1077.0,203.27785769359127,1869,147.1091,True,True,baseline,maximumentropyinversereinforcementlearning,203.27785769359127,landmark,True
129	−2964083594,10.57702/mspz8fly,1808.04355,Large-Scale Study of Curiosity-Driven Learning,Edinburgh Research Explorer (University of Edinburgh),2025,conference-paper,reinforcement_learning,7,414.0,414.0,367.9480320163706,75,0.0,True,True,baseline,209.59116591832003,landmark,True	129	+2964083594,10.57702/mspz8fly,1808.04355,Large-Scale Study of Curiosity-Driven Learning,Edinburgh Research Explorer (University of Edinburgh),2025,conference-paper,reinforcement_learning,7,414.0,414.0,367.9480320163706,75,0.0,True,True,baseline,largescalestudyofcuriositydrivenlearning,180.9062290107294,landmark,True
130	−2098774185,10.1184/r1/6555512,,Maximum Entropy Inverse Reinforcement Learning,,2018,article,reinforcement_learning,3,780.0,1077.0,203.27785769359127,1869,147.1091,True,True,baseline,203.27785769359127,landmark,True	130	+2991046523,10.1007/978-3-030-60990-0_12,1911.10635,Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms,"Studies in systems, decision and control",2021,book-chapter,reinforcement_learning,3,348.0,530.0,178.44967189838442,1264,321.3096,False,True,baseline,multiagentreinforcementlearningaselectiveoverviewoftheoriesandalgorithms,178.44967189838442,landmark,True
131	−1591675293,10.1017/9781009449441.027,,Bayesian Inverse Reinforcement Learning,Cambridge University Press eBooks,2025,book-chapter,reinforcement_learning,3,395.0,395.0,664.9588792863116,444,16.8612,True,False,post,199.9722476756918,landmark,True	131	+1591675293,10.1017/9781009449441.027,,Bayesian Inverse Reinforcement Learning,Cambridge University Press eBooks,2025,book-chapter,reinforcement_learning,3,395.0,395.0,664.9588792863116,444,16.8612,True,False,post,bayesianinversereinforcementlearning,172.60376922521283,landmark,True
132	−7123365826,10.1109/tce.2026.3652706,,Event-Triggered Zero-Sum Game for Input Saturated Nonlinear Systems With State Constraints,IEEE Transactions on Consumer Electronics,2026,article,reinforcement_learning,3,55.0,55.0,155.29848001961267,55,196.0131,True,False,post,195.53713186282252,landmark,True	132	+2973229164,10.32657/10356/90191,,Continuous control for robot based on deep reinforcement learning,,2019,dissertation,reinforcement_learning,2,5.0,47.0,171.15412186379928,951,,True,True,baseline,continuouscontrolforrobotbasedondeepreinforcementlearning,171.15412186379928,landmark,True
133	−2991046523,10.1007/978-3-030-60990-0_12,1911.10635,Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms,"Studies in systems, decision and control",2021,book-chapter,reinforcement_learning,3,348.0,530.0,178.44967189838442,1264,321.3096,False,True,baseline,178.44967189838442,landmark,True	133	+2982316857,10.1038/s41586-019-1724-z,,Grandmaster level in StarCraft II using multi-agent reinforcement learning,Nature,2019,article,reinforcement_learning,2,403.0,1021.0,155.2486170001325,3422,181.8864,False,False,baseline,grandmasterlevelinstarcraftiiusingmultiagentreinforcementlearning,155.2486170001325,landmark,True
134	−2973229164,10.32657/10356/90191,,Continuous control for robot based on deep reinforcement learning,,2019,dissertation,reinforcement_learning,2,5.0,47.0,171.15412186379928,951,,True,True,baseline,171.15412186379928,landmark,True	134	+7123365826,10.1109/tce.2026.3652706,,Event-Triggered Zero-Sum Game for Input Saturated Nonlinear Systems With State Constraints,IEEE Transactions on Consumer Electronics,2026,article,reinforcement_learning,3,55.0,55.0,155.29848001961267,55,196.0131,True,False,post,eventtriggeredzerosumgameforinputsaturatednonlinearsystemswithstateconstraints,139.45087267049917,landmark,True
135	−2982316857,10.1038/s41586-019-1724-z,,Grandmaster level in StarCraft II using multi-agent reinforcement learning,Nature,2019,article,reinforcement_learning,2,403.0,1021.0,155.2486170001325,3422,181.8864,False,False,baseline,155.2486170001325,landmark,True	135	+2902907165,10.1126/science.aar6404,,"A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play",Science,2018,article,reinforcement_learning,3,271.0,733.0,138.34973973017864,3551,180.9159,True,True,baseline,ageneralreinforcementlearningalgorithmthatmasterschessshogiandgothroughselfplay,138.34973973017864,landmark,True
136	−2902907165,10.1126/science.aar6404,,"A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play",Science,2018,article,reinforcement_learning,3,271.0,733.0,138.34973973017864,3551,180.9159,True,True,baseline,138.34973973017864,landmark,True	136	+7133226216,10.52202/068431-1787,2103.01955,The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,neural information processing systems,2022,conference-paper,reinforcement_learning,5,259.0,482.0,95.76250774959783,1027,87.4682,True,True,baseline,thesurprisingeffectivenessofppoincooperativemultiagentgames,95.76250774959783,landmark,True
137	−7133481537,10.1016/j.neucom.2026.133224,,Filter-based predefined-time optimal fault-tolerant consensus control for nonlinear multi-agent systems via reinforcement learning,Neurocomputing,2026,article,reinforcement_learning,3,35.0,35.0,122.87423247935634,35,146.6674,False,False,post,124.4327202763416,landmark,True	137	+3041807988,10.1007/s42438-020-00164-x,,Post-Covid-19 Education and Education Technology ‘Solutionism’: a Seller’s Market,Postdigital Science and Education,2020,article,reinforcement_learning,1,148.0,322.0,93.76380474425054,594,104.1384,True,True,baseline,postcovid19educationandeducationtechnologysolutionismasellersmarket,93.76380474425054,landmark,True
138	−7133226216,10.52202/068431-1787,2103.01955,The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,neural information processing systems,2022,conference-paper,reinforcement_learning,5,259.0,482.0,95.76250774959783,1027,87.4682,True,True,baseline,95.76250774959783,landmark,True	138	+2781726626,10.48550/arxiv.1801.01290,1801.01290,Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,arXiv (Cornell University),2018,preprint,reinforcement_learning,4,254.0,775.0,90.80433444998975,3598,,True,True,baseline,softactorcriticoffpolicymaximumentropydeepreinforcementlearningwithastochasticactor,90.80433444998975,landmark,True
139	−3041807988,10.1007/s42438-020-00164-x,,Post-Covid-19 Education and Education Technology ‘Solutionism’: a Seller’s Market,Postdigital Science and Education,2020,article,reinforcement_learning,1,148.0,322.0,93.76380474425054,594,104.1384,True,True,baseline,93.76380474425054,landmark,True	139	+2963923407,,1802.09477,Addressing Function Approximation Error in Actor-Critic Methods,UvA-DARE (University of Amsterdam),2018,article,reinforcement_learning,4,131.0,472.0,89.08741767072895,362,45.5472,True,True,baseline,addressingfunctionapproximationerrorinactorcriticmethods,89.08741767072895,landmark,True
140	−2781726626,10.48550/arxiv.1801.01290,1801.01290,Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,arXiv (Cornell University),2018,preprint,reinforcement_learning,4,254.0,775.0,90.80433444998975,3598,,True,True,baseline,90.80433444998975,landmark,True	140	+7133481537,10.1016/j.neucom.2026.133224,,Filter-based predefined-time optimal fault-tolerant consensus control for nonlinear multi-agent systems via reinforcement learning,Neurocomputing,2026,article,reinforcement_learning,3,35.0,35.0,122.87423247935634,35,146.6674,False,False,post,filterbasedpredefinedtimeoptimalfaulttolerantconsensuscontrolfornonlinearmultiagentsystemsviareinforcementlearning,88.74146442668129,landmark,True
141	−2963923407,,1802.09477,Addressing Function Approximation Error in Actor-Critic Methods,UvA-DARE (University of Amsterdam),2018,article,reinforcement_learning,4,131.0,472.0,89.08741767072895,362,45.5472,True,True,baseline,89.08741767072895,landmark,True	141	+2899138465,,,Learning to Learn with Gradients,ProQuest Demo Repository,2018,dissertation,reinforcement_learning,3,8.0,20.0,88.61723446893788,51,,True,False,baseline,learningtolearnwithgradients,88.61723446893788,landmark,True
142	−7122543576,10.1002/rnc.70380,,SMS‐Based Event‐Triggered Fault Tolerant Control of Nonlinear Systems Using Adaptive Dynamic Programming,International Journal of Robust and Nonlinear Control,2026,article,reinforcement_learning,6,25.0,25.0,70.59021819073303,25,89.0969,True,False,post,88.88051448310114,landmark,True	142	+4391631327,10.48550/arxiv.2402.03300,2402.03300,Axiom-World: A Pre-Registered Study of Two-Stage Post-Training for Rule-Grounded Planning in a Verifiable Toy World,arXiv (Cornell University),2024,preprint,reinforcement_learning,9,52.0,99.0,86.3639344262295,99,,True,True,post,axiomworldapreregisteredstudyoftwostageposttrainingforrulegroundedplanninginaverifiabletoyworld,86.3639344262295,landmark,True
143	−2899138465,,,Learning to Learn with Gradients,ProQuest Demo Repository,2018,dissertation,reinforcement_learning,3,8.0,20.0,88.61723446893788,51,,True,False,baseline,88.61723446893788,landmark,True	143	+2895974005,,,Exploration and Exploitation in Reinforcement Learning,KTH Publication Database DiVA (KTH Royal Institute of Technology),2018,dissertation,reinforcement_learning,3,14.0,18.0,79.75551102204409,45,,True,True,baseline,explorationandexploitationinreinforcementlearning,79.75551102204409,landmark,True
144	−4391631327,10.48550/arxiv.2402.03300,2402.03300,Axiom-World: A Pre-Registered Study of Two-Stage Post-Training for Rule-Grounded Planning in a Verifiable Toy World,arXiv (Cornell University),2024,preprint,reinforcement_learning,9,52.0,99.0,86.3639344262295,99,,True,True,post,86.3639344262295,landmark,True	144	+4409147637,10.1038/s41586-025-08744-2,,Mastering diverse control tasks through world models,Nature,2025,article,reinforcement_learning,2,170.0,170.0,57.91038303711279,170,70.7411,True,True,post,masteringdiversecontroltasksthroughworldmodels,74.28516650199033,landmark,True
145	−4409147637,10.1038/s41586-025-08744-2,,Mastering diverse control tasks through world models,Nature,2025,article,reinforcement_learning,2,170.0,170.0,57.91038303711279,170,70.7411,True,True,post,86.06400532877875,landmark,True	145	+2754517384,10.1609/aaai.v32i1.11694,1709.06560,Deep Reinforcement Learning That Matters,Proceedings of the AAAI Conference on Artificial Intelligence,2018,conference-paper,reinforcement_learning,3,261.0,452.0,69.98997654610542,1579,68.6606,True,True,baseline,deepreinforcementlearningthatmatters,69.98997654610542,landmark,True
146	−2895974005,,,Exploration and Exploitation in Reinforcement Learning,KTH Publication Database DiVA (KTH Royal Institute of Technology),2018,dissertation,reinforcement_learning,3,14.0,18.0,79.75551102204409,45,,True,True,baseline,79.75551102204409,landmark,True	146	+2761873684,10.1609/aaai.v32i1.11796,,Rainbow: Combining Improvements in Deep Reinforcement Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2018,conference-paper,reinforcement_learning,1,240.0,442.0,68.44152573756327,1772,72.3017,True,True,baseline,rainbowcombiningimprovementsindeepreinforcementlearning,68.44152573756327,landmark,True
147	−7134057781,10.1038/s41598-026-42615-8,,A fuzzy-TD3 hybrid reinforcement learning framework for robust trajectory tracking of the Mitsubishi RV-2AJ robotic arm,Scientific Reports,2026,article,reinforcement_learning,3,21.0,21.0,23.368832169628472,21,75.865,True,True,post,74.65963216580496,landmark,True	147	+7133208539,10.52202/075280-2338,,Direct Preference Optimization: Your Language Model is Secretly a Reward Model,neural information processing systems,2023,conference-paper,reinforcement_learning,3,111.0,293.0,67.57117047961324,474,89.6019,False,False,post,directpreferenceoptimizationyourlanguagemodelissecretlyarewardmodel,67.57117047961324,landmark,True
148	−2754517384,10.1609/aaai.v32i1.11694,1709.06560,Deep Reinforcement Learning That Matters,Proceedings of the AAAI Conference on Artificial Intelligence,2018,conference-paper,reinforcement_learning,3,261.0,452.0,69.98997654610542,1579,68.6606,True,True,baseline,69.98997654610542,landmark,True	148	+4327734444,10.5204/thesis.eprints.238632,,Leveraging control priors in deep reinforcement learning for robotics,Queensland University of Technology,2023,dissertation,reinforcement_learning,2,5.0,8.0,66.54620123203286,9,,True,True,post,leveragingcontrolpriorsindeepreinforcementlearningforrobotics,66.54620123203286,landmark,True
149	−2761873684,10.1609/aaai.v32i1.11796,,Rainbow: Combining Improvements in Deep Reinforcement Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2018,conference-paper,reinforcement_learning,1,240.0,442.0,68.44152573756327,1772,72.3017,True,True,baseline,68.44152573756327,landmark,True	149	+4383112908,10.1109/tpami.2023.3292075,2009.07888,Transfer Learning in Deep Reinforcement Learning: A Survey,IEEE Transactions on Pattern Analysis and Machine Intelligence,2023,article,reinforcement_learning,6,398.0,716.0,65.732229267823,846,67.6448,True,True,post,transferlearningindeepreinforcementlearningasurvey,65.732229267823,landmark,True
150	−7133208539,10.52202/075280-2338,,Direct Preference Optimization: Your Language Model is Secretly a Reward Model,neural information processing systems,2023,conference-paper,reinforcement_learning,3,111.0,293.0,67.57117047961324,474,89.6019,False,False,post,67.57117047961324,landmark,True	150	+7122543576,10.1002/rnc.70380,,SMS‐Based Event‐Triggered Fault Tolerant Control of Nonlinear Systems Using Adaptive Dynamic Programming,International Journal of Robust and Nonlinear Control,2026,article,reinforcement_learning,6,25.0,25.0,70.59021819073303,25,89.0969,True,False,post,smsbasedeventtriggeredfaulttolerantcontrolofnonlinearsystemsusingadaptivedynamicprogramming,63.38676030477235,landmark,True
151	−7128436456,10.1016/j.arcontrol.2026.101045,2502.02133,Synthesis of model predictive control and reinforcement learning: Survey and classification,Annual Reviews in Control,2026,article,reinforcement_learning,3,19.0,19.0,21.046639004149377,19,47.2579,True,True,post,67.54919100715686,landmark,True	151	+2742132707,10.26153/tsw/13849,,Curriculum learning in reinforcement learning,Texas Digital Library (University of Texas),2021,dissertation,reinforcement_learning,4,13.0,13.0,59.128571428571426,13,,True,True,baseline,curriculumlearninginreinforcementlearning,59.128571428571426,landmark,True
152	−2962862931,10.4230/oasics.dx.2024.27,,On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper),HAL (Le Centre pour la Communication Scientifique Directe),2024,conference-paper,ml_theory,1,11888.0,12586.0,3498.428847235165,12586,1941.7911,True,True,post,3498.428847235165,landmark,True	152	+2962862931,10.4230/oasics.dx.2024.27,,On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper),HAL (Le Centre pour la Communication Scientifique Directe),2024,conference-paper,ml_theory,1,11888.0,12586.0,3498.428847235165,12586,1941.7911,True,True,post,onamethodtomeasuresupervisedmulticlassmodelsinterpretabilityapplicationtodegradationdiagnosisshortpaper,3498.428847235165,landmark,True
153	−1698155719,10.1017/9781108564175.026,,Support Vector Machines,Cambridge University Press eBooks,2020,book-chapter,ml_theory,2,1184.0,1212.0,971.6935447844538,1401,239.6315,True,False,baseline,971.6935447844538,landmark,True	153	+46659105,10.4230/lipics.esem.2026.21,2010.16061,"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation",arXiv (Cornell University),2020,preprint,ml_theory,4,3748.0,3906.0,608.1723816912336,4311,,True,True,baseline,evaluationfromprecisionrecallandfmeasuretorocinformednessmarkednessandcorrelation,608.1723816912336,landmark,True
154	−4288283362,10.5281/zenodo.20632938,,Spiral Time: A Geometric Reframing of Temporal Structure and Its Applications in Machine Learning,,2026,article,ml_theory,3,174.0,174.0,576.9349593495934,174,17.3333,True,True,post,618.608380802384,landmark,True	154	+4288283362,10.5281/zenodo.20632938,,Spiral Time: A Geometric Reframing of Temporal Structure and Its Applications in Machine Learning,,2026,article,ml_theory,3,174.0,174.0,576.9349593495934,174,17.3333,True,True,post,spiraltimeageometricreframingoftemporalstructureanditsapplicationsinmachinelearning,441.17185172121555,landmark,True
155	−46659105,10.4230/lipics.esem.2026.21,2010.16061,"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation",arXiv (Cornell University),2020,preprint,ml_theory,4,3748.0,3906.0,608.1723816912336,4311,,True,True,baseline,608.1723816912336,landmark,True	155	+1568555062,10.1145/3501714.3501732,,Equivalence and Synthesis of Causal Models,ACM eBooks,2022,book-chapter,ml_theory,1,557.0,576.0,308.68124677113826,964,48.0029,True,False,baseline,equivalenceandsynthesisofcausalmodels,308.68124677113826,landmark,True
156	−3145506661,10.1007/978-3-030-10546-4_1,,Introduction to Machine Learning,Springer briefs in electrical and computer engineering,2019,book-chapter,ml_theory,2,1164.0,1581.0,491.7969737509803,1594,403.288,False,False,baseline,491.7969737509803,landmark,True	156	+2100415632,10.48550/arxiv.2604.24555,2604.24555,Efficient learning by implicit exploration in bandit problems with side observations,arXiv (Cornell University),2026,preprint,ml_theory,3,116.0,116.0,667.5471698113207,116,,True,True,post,efficientlearningbyimplicitexplorationinbanditproblemswithsideobservations,294.1145678141437,landmark,True
157	−2291445757,10.1017/9781009006767.012,,Support Vector Regression,Cambridge University Press eBooks,2024,book-chapter,ml_theory,2,769.0,769.0,426.8477591694058,809,0.6804,True,False,post,426.8477591694058,landmark,True	157	+3208688400,10.1007/978-981-16-2102-4_36,,Anomaly Detection: A Survey,Lecture notes in networks and systems,2021,book-chapter,ml_theory,1,734.0,833.0,280.4690126251966,958,168.1579,False,False,baseline,anomalydetectionasurvey,280.4690126251966,landmark,True
158	−2100415632,10.48550/arxiv.2604.24555,2604.24555,Efficient learning by implicit exploration in bandit problems with side observations,arXiv (Cornell University),2026,preprint,ml_theory,3,116.0,116.0,667.5471698113207,116,,True,True,post,412.4055872015893,landmark,True	158	+3105488314,,,Risk bounds for statistical learning,FreiDok plus (Universitätsbibliothek Freiburg),2020,dissertation,ml_theory,1,50.0,59.0,272.44754218635364,96,,False,False,baseline,riskboundsforstatisticallearning,272.44754218635364,landmark,True
159	−1568555062,10.1145/3501714.3501732,,Equivalence and Synthesis of Causal Models,ACM eBooks,2022,book-chapter,ml_theory,1,557.0,576.0,308.68124677113826,964,48.0029,True,False,baseline,308.68124677113826,landmark,True	159	+2945976633,10.1038/s42256-019-0048-x,1811.10154,Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,Nature Machine Intelligence,2019,article,ml_theory,2,620.0,1556.0,236.59828408639194,10272,292.6299,False,True,baseline,stopexplainingblackboxmachinelearningmodelsforhighstakesdecisionsanduseinterpretablemodelsinstead,236.59828408639194,landmark,True
160	−2751318774,10.1142/9789811201967_0001,,Introduction to Machine Learning,Series in machine perception and artificial intelligence,2019,book-chapter,ml_theory,6,932.0,978.0,304.22355491996126,1639,51.9116,True,False,baseline,304.22355491996126,landmark,True	160	+4400823820,10.58496/bjml/2024/007,,Random Forest Algorithm Overview,Babylonian Journal of Machine Learning,2024,article,ml_theory,1,353.0,782.0,235.4406851738956,782,299.2578,True,True,post,randomforestalgorithmoverview,235.4406851738956,landmark,True
161	−3208688400,10.1007/978-981-16-2102-4_36,,Anomaly Detection: A Survey,Lecture notes in networks and systems,2021,book-chapter,ml_theory,1,734.0,833.0,280.4690126251966,958,168.1579,False,False,baseline,280.4690126251966,landmark,True	161	+2999309192,10.1186/s12864-019-6413-7,,The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,BMC Genomics,2020,article,ml_theory,2,897.0,1781.0,222.7600450619602,6051,266.8933,True,True,baseline,theadvantagesofthematthewscorrelationcoefficientmccoverf1scoreandaccuracyinbinaryclassificationevaluation,222.7600450619602,landmark,True
162	−3105488314,,,Risk bounds for statistical learning,FreiDok plus (Universitätsbibliothek Freiburg),2020,dissertation,ml_theory,1,50.0,59.0,272.44754218635364,96,,False,False,baseline,272.44754218635364,landmark,True	162	+2999615587,10.1038/s42256-019-0138-9,,From local explanations to global understanding with explainable AI for trees,Nature Machine Intelligence,2020,article,ml_theory,1,808.0,1767.0,221.00898350616714,10196,280.6263,False,True,baseline,fromlocalexplanationstoglobalunderstandingwithexplainableaifortrees,221.00898350616714,landmark,True
163	−57706852,10.48550/arxiv.2604.24537,2604.24537,Stochastic simultaneous optimistic optimization,arXiv (Cornell University),2026,preprint,ml_theory,1,72.0,72.0,414.33962264150944,72,,True,True,post,255.97588171133128,landmark,True	163	+2439568532,10.1145/3233231,,The Mythos of Model Interpretability,Communications of the ACM,2018,article,ml_theory,2,592.0,1085.0,204.78781392529854,5034,234.6672,True,True,baseline,themythosofmodelinterpretability,204.78781392529854,landmark,True
164	−2946444988,10.48550/arxiv.2606.04665,2606.04665,Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation,arXiv (Cornell University),2026,preprint,ml_theory,4,68.0,68.0,2646.035087719298,68,,True,True,post,241.7549993940351,landmark,True	164	+3194464139,,,Penerapan Algoritma K-Nearest Neighbor untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bermotor,,2020,dissertation,ml_theory,1,15.0,21.0,197.50232018561485,25,,True,False,baseline,penerapanalgoritmaknearestneighboruntukpenentuanresikokreditkepemilikankendaraanbermotor,197.50232018561485,landmark,True
165	−2945976633,10.1038/s42256-019-0048-x,1811.10154,Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,Nature Machine Intelligence,2019,article,ml_theory,2,620.0,1556.0,236.59828408639194,10272,292.6299,False,True,baseline,236.59828408639194,landmark,True	165	+3041133507,10.1109/jproc.2020.3004555,1911.02685,A Comprehensive Survey on Transfer Learning,Proceedings of the IEEE,2020,article,ml_theory,4,593.0,1538.0,192.3666194864092,6556,273.5231,True,True,baseline,acomprehensivesurveyontransferlearning,192.3666194864092,landmark,True
166	−4400823820,10.58496/bjml/2024/007,,Random Forest Algorithm Overview,Babylonian Journal of Machine Learning,2024,article,ml_theory,1,353.0,782.0,235.4406851738956,782,299.2578,True,True,post,235.4406851738956,landmark,True	166	+57706852,10.48550/arxiv.2604.24537,2604.24537,Stochastic simultaneous optimistic optimization,arXiv (Cornell University),2026,preprint,ml_theory,1,72.0,72.0,414.33962264150944,72,,True,True,post,stochasticsimultaneousoptimisticoptimization,182.55386967774436,landmark,True
167	−2999309192,10.1186/s12864-019-6413-7,,The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,BMC Genomics,2020,article,ml_theory,2,897.0,1781.0,222.7600450619602,6051,266.8933,True,True,baseline,222.7600450619602,landmark,True	167	+2436219157,10.1137/16m1080173,1606.04838,Optimization Methods for Large-Scale Machine Learning,SIAM Review,2018,article,ml_theory,4,492.0,956.0,180.43976968901882,3128,199.3045,True,True,baseline,optimizationmethodsforlargescalemachinelearning,180.43976968901882,landmark,True
168	−2999615587,10.1038/s42256-019-0138-9,,From local explanations to global understanding with explainable AI for trees,Nature Machine Intelligence,2020,article,ml_theory,1,808.0,1767.0,221.00898350616714,10196,280.6263,False,True,baseline,221.00898350616714,landmark,True	168	+3135028703,10.1007/s42979-021-00592-x,,"Machine Learning: Algorithms, Real-World Applications and Research Directions",SN Computer Science,2021,article,ml_theory,1,512.0,1518.0,176.91139356202336,5534,266.7657,False,True,baseline,machinelearningalgorithmsrealworldapplicationsandresearchdirections,176.91139356202336,landmark,True
169	−2439568532,10.1145/3233231,,The Mythos of Model Interpretability,Communications of the ACM,2018,article,ml_theory,2,592.0,1085.0,204.78781392529854,5034,234.6672,True,True,baseline,204.78781392529854,landmark,True	169	+2946444988,10.48550/arxiv.2606.04665,2606.04665,Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation,arXiv (Cornell University),2026,preprint,ml_theory,4,68.0,68.0,2646.035087719298,68,,True,True,post,towardsaccuratemodelselectionindeepunsuperviseddomainadaptation,172.4119880289808,landmark,True
170	−3194464139,,,Penerapan Algoritma K-Nearest Neighbor untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bermotor,,2020,dissertation,ml_theory,1,15.0,21.0,197.50232018561485,25,,True,False,baseline,197.50232018561485,landmark,True	170	+3141797743,10.1109/tkde.2021.3070203,1707.08114,A Survey on Multi-Task Learning,IEEE Transactions on Knowledge and Data Engineering,2021,article,ml_theory,6,882.0,1299.0,151.38860358173145,2878,131.001,True,True,baseline,asurveyonmultitasklearning,151.38860358173145,landmark,True
171	−3041133507,10.1109/jproc.2020.3004555,1911.02685,A Comprehensive Survey on Transfer Learning,Proceedings of the IEEE,2020,article,ml_theory,4,593.0,1538.0,192.3666194864092,6556,273.5231,True,True,baseline,192.3666194864092,landmark,True	171	+3123554940,10.1007/978-3-030-57077-4_11,,Pattern Recognition and Machine Learning,EAI/Springer Innovations in Communication and Computing,2021,book-chapter,ml_theory,1,147.0,182.0,140.86542176432712,255,92.5445,False,False,baseline,patternrecognitionandmachinelearning,140.86542176432712,landmark,True
172	−2436219157,10.1137/16m1080173,1606.04838,Optimization Methods for Large-Scale Machine Learning,SIAM Review,2018,article,ml_theory,4,492.0,956.0,180.43976968901882,3128,199.3045,True,True,baseline,180.43976968901882,landmark,True	172	+2889326414,10.21105/joss.00861,,UMAP: Uniform Manifold Approximation and Projection,The Journal of Open Source Software,2018,article,ml_theory,2,211.0,797.0,139.93100250694124,9681,230.917,True,True,baseline,umapuniformmanifoldapproximationandprojection,139.93100250694124,landmark,True
173	−3135028703,10.1007/s42979-021-00592-x,,"Machine Learning: Algorithms, Real-World Applications and Research Directions",SN Computer Science,2021,article,ml_theory,1,512.0,1518.0,176.91139356202336,5534,266.7657,False,True,baseline,176.91139356202336,landmark,True	173	+3021654819,10.1109/msp.2020.2975749,1908.07873,"Federated Learning: Challenges, Methods, and Future Directions",IEEE Signal Processing Magazine,2020,article,ml_theory,7,429.0,1091.0,136.45772552644502,5140,195.766,True,True,baseline,federatedlearningchallengesmethodsandfuturedirections,136.45772552644502,landmark,True
174	−7124426637,10.1186/s40537-025-01346-9,,A review of machine learning with small and limited data,Journal Of Big Data,2026,article,ml_theory,3,46.0,46.0,51.18887046680522,46,166.1805,True,True,post,163.5401466489061,landmark,True	174	+2995022099,10.1561/2200000083,1912.04977,Advances and Open Problems in Federated Learning,Foundations and Trends® in Machine Learning,2020,article,ml_theory,6,570.0,1087.0,135.95742222478987,6330,186.8632,True,True,baseline,advancesandopenproblemsinfederatedlearning,135.95742222478987,landmark,True
175	−2165856466,10.48550/arxiv.2604.18420,2604.18420,Spectral bandits for smooth graph functions,arXiv (Cornell University),2026,preprint,ml_theory,2,45.0,45.0,258.9622641509434,45,,True,True,post,159.98492606958206,landmark,True	175	+2608595939,10.1007/978-3-030-05318-5_4,,Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA,The Springer series on challenges in machine learning,2019,book-chapter,ml_theory,2,300.0,423.0,131.5813535083268,650,138.539,True,True,baseline,autowekaautomaticmodelselectionandhyperparameteroptimizationinweka,131.5813535083268,landmark,True
176	−4402843978,10.58248/pn633,,Interpretable machine learning,,2020,report,ml_theory,2,236.0,455.0,158.34677419354838,952,,True,True,baseline,158.34677419354838,landmark,True	176	+3157286395,10.4230/oasics.dx.2026.1,2104.13478,"A Realization-Theoretic Foundation for Fault Diagnosis, with Diagnosability Equivalence Predictions",arXiv (Cornell University),2021,preprint,ml_theory,2,169.0,307.0,128.25943970767358,519,,True,True,baseline,arealizationtheoreticfoundationforfaultdiagnosiswithdiagnosabilityequivalencepredictions,128.25943970767358,landmark,True
177	−2964121744,10.4230/oasics.dx.2026.15,2108.04578,AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison),DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,deep_learning_architectures,3,45775.0,45775.0,50938.490122130635,45780,2749.203,True,True,baseline,162740.22201855818,landmark,True	177	+2964121744,10.4230/oasics.dx.2026.15,2108.04578,AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison),DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,deep_learning_architectures,3,45775.0,45775.0,50938.490122130635,45780,2749.203,True,True,baseline,autoresearchliteareproduciblepaperinspiredexperimentrunneradammechanismreproductionandconvergencespeedcomparison,116061.15811803818,landmark,True
178	−1710476689,10.1017/9781108891530.013,,Generative Adversarial Networks,Cambridge University Press eBooks,2022,book-chapter,deep_learning_architectures,4,2376.0,2385.0,1018.1648538070052,2402,89.9299,True,False,baseline,1018.1648538070052,landmark,True	178	+2963399829,10.57702/dcy1c3gw,1710.09412,Mixup: Beyond empirical risk minimization,arXiv (Cornell University),2024,preprint,deep_learning_architectures,6,772.0,773.0,674.336578903792,773,,True,True,baseline,mixupbeyondempiricalriskminimization,674.336578903792,landmark,True
179	−4322614756,10.4230/lipics.giscience.2025.9,,Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach,Leibniz international proceedings in informatics,2025,conference-paper,deep_learning_architectures,2,1542.0,1542.0,1370.4731047566265,1543,164.3965,True,True,post,780.6511542175108,landmark,True	179	+4322614756,10.4230/lipics.giscience.2025.9,,Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach,Leibniz international proceedings in informatics,2025,conference-paper,deep_learning_architectures,2,1542.0,1542.0,1370.4731047566265,1543,164.3965,True,True,post,identifyingresilientcommunitiesinroadnetworksapathbasedembeddingapproach,673.8101573298181,landmark,True
180	−2963399829,10.57702/dcy1c3gw,1710.09412,Mixup: Beyond empirical risk minimization,arXiv (Cornell University),2024,preprint,deep_learning_architectures,6,772.0,773.0,674.336578903792,773,,True,True,baseline,674.336578903792,landmark,True	180	+4382203079,10.1609/aaai.v37i9.26317,2205.13504,Are Transformers Effective for Time Series Forecasting?,Proceedings of the AAAI Conference on Artificial Intelligence,2023,conference-paper,deep_learning_architectures,3,917.0,2278.0,621.0117979458593,3320,733.0685,True,True,baseline,aretransformerseffectivefortimeseriesforecasting,621.0117979458593,landmark,True
181	−4382203079,10.1609/aaai.v37i9.26317,2205.13504,Are Transformers Effective for Time Series Forecasting?,Proceedings of the AAAI Conference on Artificial Intelligence,2023,conference-paper,deep_learning_architectures,3,917.0,2278.0,621.0117979458593,3320,733.0685,True,True,baseline,621.0117979458593,landmark,True	181	+146900863,10.7551/mitpress/11474.003.0014,,Neural Information Processing Systems,The MIT Press eBooks,2018,book-chapter,deep_learning_architectures,1,835.0,999.0,463.99318537042825,3730,234.5719,False,False,baseline,neuralinformationprocessingsystems,463.99318537042825,landmark,True
182	−4406170795,10.1038/s41586-024-08328-6,,Accurate predictions on small data with a tabular foundation model,Nature,2025,article,deep_learning_architectures,4,974.0,974.0,331.79242987145795,974,405.3046,True,True,post,493.09612464841473,landmark,True	182	+4406170795,10.1038/s41586-024-08328-6,,Accurate predictions on small data with a tabular foundation model,Nature,2025,article,deep_learning_architectures,4,974.0,974.0,331.79242987145795,974,405.3046,True,True,post,accuratepredictionsonsmalldatawithatabularfoundationmodel,425.6103068996387,landmark,True
183	−146900863,10.7551/mitpress/11474.003.0014,,Neural Information Processing Systems,The MIT Press eBooks,2018,book-chapter,deep_learning_architectures,1,835.0,999.0,463.99318537042825,3730,234.5719,False,False,baseline,463.99318537042825,landmark,True	183	+3177318507,10.1609/aaai.v35i12.17325,2012.07436,Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting,Proceedings of the AAAI Conference on Artificial Intelligence,2021,conference-paper,deep_learning_architectures,5,412.0,1344.0,315.30176520033626,7301,468.762,True,True,baseline,informerbeyondefficienttransformerforlongsequencetimeseriesforecasting,315.30176520033626,landmark,True
184	−4396821501,10.48550/arxiv.2404.19756,2404.19756,KAN: Kolmogorov-Arnold Networks,arXiv (Cornell University),2024,preprint,deep_learning_architectures,4,448.0,531.0,463.22473919523094,531,,True,True,post,463.22473919523094,landmark,True	184	+4389326242,10.48550/arxiv.2312.00752,2312.00752,Mamba: Linear-Time Sequence Modeling with Selective State Spaces,arXiv (Cornell University),2023,preprint,deep_learning_architectures,8,228.0,912.0,303.2150076098295,1059,,True,True,post,mambalineartimesequencemodelingwithselectivestatespaces,303.2150076098295,landmark,True
185	−2950067852,10.5281/zenodo.19983308,,A Recurrent Latent Variable Model for Sequential Data,arXiv (Cornell University),2026,preprint,deep_learning_architectures,3,119.0,119.0,4630.561403508772,119,,True,True,post,423.0712489395614,landmark,True	185	+2950067852,10.5281/zenodo.19983308,,A Recurrent Latent Variable Model for Sequential Data,arXiv (Cornell University),2026,preprint,deep_learning_architectures,3,119.0,119.0,4630.561403508772,119,,True,True,post,arecurrentlatentvariablemodelforsequentialdata,301.7209790507164,landmark,True
186	−4221150249,10.1109/tkde.2026.3680353,2202.07082,Graph Neural Networks for Graphs With Heterophily: A Survey,IEEE Transactions on Knowledge and Data Engineering,2026,article,deep_learning_architectures,2,107.0,107.0,119.0697639119165,107,83.0902,True,True,post,380.4086019876729,landmark,True	186	+3137147200,10.1201/9781003162810-13,,A Survey of Quantization Methods for Efficient Neural Network Inference,Chapman & Hall/CRC eBooks,2022,book-chapter,deep_learning_architectures,3,421.0,698.0,297.97864484582374,1148,452.6307,True,True,baseline,asurveyofquantizationmethodsforefficientneuralnetworkinference,297.97864484582374,landmark,True
187	−7148890333,10.1016/j.engappai.2026.114691,,Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis,Engineering Applications of Artificial Intelligence,2026,article,deep_learning_architectures,1,102.0,102.0,112.98721991701245,102,283.5476,False,False,post,362.63249909105264,landmark,True	187	+3140854437,10.1186/s40537-021-00444-8,,"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions",Journal Of Big Data,2021,article,deep_learning_architectures,7,871.0,2452.0,293.13246585010404,7842,405.9988,True,True,baseline,reviewofdeeplearningconceptscnnarchitectureschallengesapplicationsfuturedirections,293.13246585010404,landmark,True
188	−3177318507,10.1609/aaai.v35i12.17325,2012.07436,Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting,Proceedings of the AAAI Conference on Artificial Intelligence,2021,conference-paper,deep_learning_architectures,5,412.0,1344.0,315.30176520033626,7301,468.762,True,True,baseline,315.30176520033626,landmark,True	188	+4210257598,,,A Comprehensive Survey on Graph Neural Networks,UTS ePRESS (University of Technology Sydney),2020,article,deep_learning_architectures,6,1768.0,2239.0,280.04477310147604,2955,236.1125,True,True,baseline,acomprehensivesurveyongraphneuralnetworks,280.04477310147604,landmark,True
189	−4389326242,10.48550/arxiv.2312.00752,2312.00752,Mamba: Linear-Time Sequence Modeling with Selective State Spaces,arXiv (Cornell University),2023,preprint,deep_learning_architectures,8,228.0,912.0,303.2150076098295,1059,,True,True,post,303.2150076098295,landmark,True	189	+4221150249,10.1109/tkde.2026.3680353,2202.07082,Graph Neural Networks for Graphs With Heterophily: A Survey,IEEE Transactions on Knowledge and Data Engineering,2026,article,deep_learning_architectures,2,107.0,107.0,119.0697639119165,107,83.0902,True,True,post,graphneuralnetworksforgraphswithheterophilyasurvey,271.29533410442565,landmark,True
190	−7118302872,10.1007/s13278-025-01574-0,,A frame work to predict the influencing nodes in social networking platform using graph neural network model,Social Network Analysis and Mining,2026,article,deep_learning_architectures,3,85.0,85.0,87.58695652173913,85,292.0234,True,True,post,302.1937492425439,landmark,True	190	+7148890333,10.1016/j.engappai.2026.114691,,Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis,Engineering Applications of Artificial Intelligence,2026,article,deep_learning_architectures,1,102.0,102.0,112.98721991701245,102,283.5476,False,False,post,spatialchannelcollaborativemultiscalegraphinteractiondeeptransferlearningforunsupervisedrotatingmachineryfaultdiagnosis,258.6179820434712,landmark,True
191	−3137147200,10.1201/9781003162810-13,,A Survey of Quantization Methods for Efficient Neural Network Inference,Chapman & Hall/CRC eBooks,2022,book-chapter,deep_learning_architectures,3,421.0,698.0,297.97864484582374,1148,452.6307,True,True,baseline,297.97864484582374,landmark,True	191	+3152893301,10.1016/j.aiopen.2021.01.001,1812.08434,Graph neural networks: A review of methods and applications,AI Open,2020,article,deep_learning_architectures,5,1157.0,2023.0,253.0283948120974,6773,287.256,True,True,baseline,graphneuralnetworksareviewofmethodsandapplications,253.0283948120974,landmark,True
192	−3140854437,10.1186/s40537-021-00444-8,,"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions",Journal Of Big Data,2021,article,deep_learning_architectures,7,871.0,2452.0,293.13246585010404,7842,405.9988,True,True,baseline,293.13246585010404,landmark,True	192	+4385763767,10.24963/ijcai.2023/759,2202.07125,Transformers in Time Series: A Survey,International Joint Conference on Artificial Intelligence,2023,conference-paper,deep_learning_architectures,4,511.0,921.0,251.0763239280669,1196,259.4717,True,True,post,transformersintimeseriesasurvey,251.0763239280669,landmark,True
193	−4210257598,,,A Comprehensive Survey on Graph Neural Networks,UTS ePRESS (University of Technology Sydney),2020,article,deep_learning_architectures,6,1768.0,2239.0,280.04477310147604,2955,236.1125,True,True,baseline,280.04477310147604,landmark,True	193	+7110333790,10.4230/lipics.giscience.2025.3,,Enriching Location Representation with Detailed Semantic Information,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2025,article,deep_learning_architectures,2,496.0,496.0,168.96205874357614,0,0.0,True,True,post,enrichinglocationrepresentationwithdetailedsemanticinformation,216.73789755874824,landmark,True
194	−2907492528,10.1109/tnnls.2020.2978386,1901.00596,A Comprehensive Survey on Graph Neural Networks,IEEE Transactions on Neural Networks and Learning Systems,2020,article,deep_learning_architectures,7,934.0,2230.0,278.91909067275196,10106,369.8433,True,True,baseline,278.91909067275196,landmark,True	194	+7118302872,10.1007/s13278-025-01574-0,,A frame work to predict the influencing nodes in social networking platform using graph neural network model,Social Network Analysis and Mining,2026,article,deep_learning_architectures,3,85.0,85.0,87.58695652173913,85,292.0234,True,True,post,aframeworktopredicttheinfluencingnodesinsocialnetworkingplatformusinggraphneuralnetworkmodel,215.514985036226,landmark,True
195	−3152893301,10.1016/j.aiopen.2021.01.001,1812.08434,Graph neural networks: A review of methods and applications,AI Open,2020,article,deep_learning_architectures,5,1157.0,2023.0,253.0283948120974,6773,287.256,True,True,baseline,253.0283948120974,landmark,True	195	+2892035503,10.1007/s10618-019-00619-1,1809.04356,Deep learning for time series classification: a review,Data Mining and Knowledge Discovery,2019,article,deep_learning_architectures,3,416.0,908.0,192.68934023629362,3299,229.9061,False,True,baseline,deeplearningfortimeseriesclassificationareview,192.68934023629362,landmark,True
196	−7110333790,10.4230/lipics.giscience.2025.3,,Enriching Location Representation with Detailed Semantic Information,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2025,article,deep_learning_architectures,2,496.0,496.0,168.96205874357614,0,0.0,True,True,post,251.10439201808387,landmark,True	196	+2962776659,10.57702/tcr06vve,,Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks,arXiv (Cornell University),2024,preprint,deep_learning_architectures,4,212.0,212.0,184.94095048849147,212,,True,True,post,finegrainedanalysisofsentenceembeddingsusingauxiliarypredictiontasks,184.94095048849147,landmark,True
197	−4385763767,10.24963/ijcai.2023/759,2202.07125,Transformers in Time Series: A Survey,International Joint Conference on Artificial Intelligence,2023,conference-paper,deep_learning_architectures,4,511.0,921.0,251.0763239280669,1196,259.4717,True,True,post,251.0763239280669,landmark,True	197	+2963285578,10.4230/lipics.itcs.2018.22,,Convergence Results for Neural Networks via Electrodynamics,Leibniz international proceedings in informatics,2018,conference-paper,deep_learning_architectures,2,747.0,1153.0,178.53637822491052,2761,153.9662,True,True,baseline,convergenceresultsforneuralnetworksviaelectrodynamics,178.53637822491052,landmark,True
198	−3124006607,10.26190/unsworks/32608,,SIMPLE SPECTRAL GRAPH CONVOLUTION,"UNSWorks (University of New South Wales, Sydney, Australia)",2026,conference-paper,deep_learning_architectures,2,69.0,69.0,529.5790121767684,69,0.0,True,True,post,245.31021997335915,landmark,True	198	+3124006607,10.26190/unsworks/32608,,SIMPLE SPECTRAL GRAPH CONVOLUTION,"UNSWorks (University of New South Wales, Sydney, Australia)",2026,conference-paper,deep_learning_architectures,2,69.0,69.0,529.5790121767684,69,0.0,True,True,post,simplespectralgraphconvolution,174.94745844117168,landmark,True
199	−4285723986,10.1007/978-3-031-01587-8_7,,Graph Attention Networks,Synthesis lectures on artificial intelligence and machine learning,2020,book-chapter,deep_learning_architectures,3,247.0,471.0,244.37756010685663,806,284.0555,False,False,baseline,244.37756010685663,landmark,True	199	+4398159307,10.2139/ssrn.4835325,2405.12832,Wav-KAN: Wavelet Kolmogorov-Arnold Networks,SSRN Electronic Journal,2024,preprint,deep_learning_architectures,2,142.0,199.0,173.6002318264613,199,,False,True,post,wavkanwaveletkolmogorovarnoldnetworks,173.6002318264613,landmark,True
200	−2892035503,10.1007/s10618-019-00619-1,1809.04356,Deep learning for time series classification: a review,Data Mining and Knowledge Discovery,2019,article,deep_learning_architectures,3,416.0,908.0,192.68934023629362,3299,229.9061,False,True,baseline,192.68934023629362,landmark,True	200	+2593744649,10.1016/b978-0-12-815480-9.00015-3,,Evolving Deep Neural Networks,Elsevier eBooks,2018,book-chapter,deep_learning_architectures,2,197.0,361.0,167.66920912785244,760,185.4489,False,False,baseline,evolvingdeepneuralnetworks,167.66920912785244,landmark,True
201	−2962776659,10.57702/tcr06vve,,Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks,arXiv (Cornell University),2024,preprint,deep_learning_architectures,4,212.0,212.0,184.94095048849147,212,,True,True,post,184.94095048849147,landmark,True	201	+2885195348,10.1016/j.physd.2019.132306,1808.03314,Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,Physica D Nonlinear Phenomena,2020,article,deep_learning_architectures,3,632.0,1318.0,164.84993789537538,5648,212.9085,False,True,baseline,fundamentalsofrecurrentneuralnetworkrnnandlongshorttermmemorylstmnetwork,164.84993789537538,landmark,True
202	−4406063062,10.1016/j.jsis.2024.101885,,Responsible artificial intelligence governance: A review and research framework,The Journal of Strategic Information Systems,2025,article,ai_safety,1,534.0,534.0,74.49083144938457,534,564.6901,True,True,post,270.3422285033403,landmark,True	202	+4406063062,10.1016/j.jsis.2024.101885,,Responsible artificial intelligence governance: A review and research framework,The Journal of Strategic Information Systems,2025,article,ai_safety,1,534.0,534.0,74.49083144938457,534,564.6901,True,True,post,responsibleartificialintelligencegovernanceareviewandresearchframework,233.3428171297814,landmark,True
203	−7130596601,10.1016/j.ijmedinf.2026.106362,,"Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges",International Journal of Medical Informatics,2026,article,ai_safety,6,65.0,65.0,29.591261451726567,65,120.3431,True,True,post,231.08933765606298,landmark,True	203	+3084163594,10.26522/brocked.v29i2.849,,Age of Surveillance Capitalism – The Fight for a Human Future at the New Frontier of Power.,Brock Education Journal,2020,article,ai_safety,2,484.0,775.0,225.67375365464028,2135,237.3333,True,True,baseline,ageofsurveillancecapitalismthefightforahumanfutureatthenewfrontierofpower,225.67375365464028,landmark,True
204	−3084163594,10.26522/brocked.v29i2.849,,Age of Surveillance Capitalism – The Fight for a Human Future at the New Frontier of Power.,Brock Education Journal,2020,article,ai_safety,2,484.0,775.0,225.67375365464028,2135,237.3333,True,True,baseline,225.67375365464028,landmark,True	204	+2963808661,10.4230/oasics.saia.2024.4,,Scaling of End-To-End Governance Risk Assessments for AI Systems (Practitioner Track),OASIcs : OpenAccess Series in Informatics,2025,conference-paper,ai_safety,1,390.0,390.0,126.28760836308005,390,127.9549,True,True,post,scalingofendtoendgovernanceriskassessmentsforaisystemspractitionertrack,170.418911386919,landmark,True
205	−2963808661,10.4230/oasics.saia.2024.4,,Scaling of End-To-End Governance Risk Assessments for AI Systems (Practitioner Track),OASIcs : OpenAccess Series in Informatics,2025,conference-paper,ai_safety,1,390.0,390.0,126.28760836308005,390,127.9549,True,True,post,197.44095340131597,landmark,True	205	+2981731882,10.1016/j.inffus.2019.12.012,1910.10045,"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI",Information Fusion,2019,article,ai_safety,4,225.0,1097.0,166.80483138995626,10111,264.5411,False,True,baseline,explainableartificialintelligencexaiconceptstaxonomiesopportunitiesandchallengestowardresponsibleai,166.80483138995626,landmark,True
206	−7133236347,10.1016/j.aiopen.2026.02.006,2506.04133,"TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems",AI Open,2026,article,ai_safety,5,51.0,51.0,44.51662971175166,51,274.4005,True,True,post,181.31624954552632,landmark,True	206	+7130596601,10.1016/j.ijmedinf.2026.106362,,"Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges",International Journal of Medical Informatics,2026,article,ai_safety,6,65.0,65.0,29.591261451726567,65,120.3431,True,True,post,humanintheloopartificialintelligenceinhealthcareapplicationsoutcomesandimplementationchallenges,164.80557679240812,landmark,True
207	−7128782475,10.1038/s41746-026-02428-5,,Large language models provide unsafe answers to patient-posed medical questions,npj Digital Medicine,2026,article,ai_safety,3,51.0,51.0,23.217758985200845,50,94.0181,True,True,post,181.31624954552632,landmark,True	207	+4406571986,10.1016/j.chbah.2025.100121,2402.01659,Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines,Computers in Human Behavior Artificial Humans,2025,article,ai_safety,3,347.0,347.0,301.26573426573424,347,363.8008,True,True,post,generativeartificialintelligenceinhighereducationevidencefromananalysisofinstitutionalpoliciesandguidelines,151.62913397759203,landmark,True
208	−4406571986,10.1016/j.chbah.2025.100121,2402.01659,Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines,Computers in Human Behavior Artificial Humans,2025,article,ai_safety,3,347.0,347.0,301.26573426573424,347,363.8008,True,True,post,175.6718226416837,landmark,True	208	+4330336443,10.48550/arxiv.2303.10130,2303.10130,GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,RePEc: Research Papers in Economics,2023,preprint,ai_safety,5,397.0,495.0,146.63793103448276,527,,True,True,post,gptsaregptsanearlylookatthelabormarketimpactpotentialoflargelanguagemodels,146.63793103448276,landmark,True
209	−2981731882,10.1016/j.inffus.2019.12.012,1910.10045,"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI",Information Fusion,2019,article,ai_safety,4,225.0,1097.0,166.80483138995626,10111,264.5411,False,True,baseline,166.80483138995626,landmark,True	209	+4399450035,10.1145/3630106.3658542,2311.16863,Power Hungry Processing: Watts Driving the Cost of AI Deployment?,,2024,conference-paper,ai_safety,3,210.0,359.0,138.53682019255788,359,285.6538,True,True,post,powerhungryprocessingwattsdrivingthecostofaideployment,138.53682019255788,landmark,True
210	−4402698709,10.1016/j.patter.2026.101517,2408.12622,"The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence",Patterns,2026,article,ai_safety,4,44.0,44.0,38.40650406504065,44,146.3469,True,True,post,156.42970549025802,landmark,True	210	+4318586239,10.1007/978-3-031-21448-6_2,,Artificial Intelligence: Definition and Background,Research for policy,2023,book-chapter,ai_safety,1,95.0,247.0,136.55931293612454,373,215.6652,True,True,post,artificialintelligencedefinitionandbackground,136.55931293612454,landmark,True
211	−4408314709,10.1038/s41746-025-01503-7,,Bias recognition and mitigation strategies in artificial intelligence healthcare applications,npj Digital Medicine,2025,article,ai_safety,1,303.0,303.0,42.26726953026878,303,319.4957,True,True,post,153.39643302717624,landmark,True	211	+4408314709,10.1038/s41746-025-01503-7,,Bias recognition and mitigation strategies in artificial intelligence healthcare applications,npj Digital Medicine,2025,article,ai_safety,1,303.0,303.0,42.26726953026878,303,319.4957,True,True,post,biasrecognitionandmitigationstrategiesinartificialintelligencehealthcareapplications,132.4023850006063,landmark,True
212	−4407243994,10.1080/08839514.2025.2463722,,"AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development",Applied Artificial Intelligence,2025,article,ai_safety,1,300.0,300.0,41.8487817131374,300,317.3728,True,True,post,151.87765646255073,landmark,True	212	+4407243994,10.1080/08839514.2025.2463722,,"AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development",Applied Artificial Intelligence,2025,article,ai_safety,1,300.0,300.0,41.8487817131374,300,317.3728,True,True,post,aiethicsintegratingtransparencyfairnessandprivacyinaidevelopment,131.09147029763,landmark,True
213	−4330336443,10.48550/arxiv.2303.10130,2303.10130,GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,RePEc: Research Papers in Economics,2023,preprint,ai_safety,5,397.0,495.0,146.63793103448276,527,,True,True,post,146.63793103448276,landmark,True	213	+4360615722,10.3390/su15075614,,Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions,Sustainability,2023,article,ai_safety,2,322.0,557.0,130.5473071452147,658,154.3873,True,True,post,chatbotsineducationandresearchacriticalexaminationofethicalimplicationsandsolutions,130.5473071452147,landmark,True
214	−7128600296,10.1038/s41746-026-02418-7,,Advancing healthcare AI governance through a comprehensive maturity model based on systematic review,npj Digital Medicine,2026,review,ai_safety,1,41.0,41.0,12.755555555555555,41,37.52,True,True,post,145.7640437522859,landmark,True	214	+7133236347,10.1016/j.aiopen.2026.02.006,2506.04133,"TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems",AI Open,2026,article,ai_safety,5,51.0,51.0,44.51662971175166,51,274.4005,True,True,post,trismforagenticaiareviewoftrustriskandsecuritymanagementinllmbasedagenticmultiagentsystems,129.3089910217356,landmark,True
215	−4399450035,10.1145/3630106.3658542,2311.16863,Power Hungry Processing: Watts Driving the Cost of AI Deployment?,,2024,conference-paper,ai_safety,3,210.0,359.0,138.53682019255788,359,285.6538,True,True,post,138.53682019255788,landmark,True	215	+7128782475,10.1038/s41746-026-02428-5,,Large language models provide unsafe answers to patient-posed medical questions,npj Digital Medicine,2026,article,ai_safety,3,51.0,51.0,23.217758985200845,50,94.0181,True,True,post,largelanguagemodelsprovideunsafeanswerstopatientposedmedicalquestions,129.3089910217356,landmark,True
216	−4318586239,10.1007/978-3-031-21448-6_2,,Artificial Intelligence: Definition and Background,Research for policy,2023,book-chapter,ai_safety,1,95.0,247.0,136.55931293612454,373,215.6652,True,True,post,136.55931293612454,landmark,True	216	+2670253439,10.1016/j.artint.2018.07.007,1706.07269,Explanation in artificial intelligence: Insights from the social sciences,Artificial Intelligence,2018,article,ai_safety,1,267.0,676.0,127.59130157926435,5207,192.3734,False,True,baseline,explanationinartificialintelligenceinsightsfromthesocialsciences,127.59130157926435,landmark,True
217	−4360615722,10.3390/su15075614,,Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions,Sustainability,2023,article,ai_safety,2,322.0,557.0,130.5473071452147,658,154.3873,True,True,post,130.5473071452147,landmark,True	217	+4402698709,10.1016/j.patter.2026.101517,2408.12622,"The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence",Patterns,2026,article,ai_safety,4,44.0,44.0,38.40650406504065,44,146.3469,True,True,post,theairiskrepositoryametareviewdatabaseandtaxonomyofrisksfromartificialintelligence,111.56069813639934,landmark,True
218	−2670253439,10.1016/j.artint.2018.07.007,1706.07269,Explanation in artificial intelligence: Insights from the social sciences,Artificial Intelligence,2018,article,ai_safety,1,267.0,676.0,127.59130157926435,5207,192.3734,False,True,baseline,127.59130157926435,landmark,True	218	+4366262984,10.1016/j.inffus.2023.101805,,Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence,Information Fusion,2023,article,ai_safety,5,510.0,1197.0,109.89033300779907,1728,151.7177,True,True,post,explainableartificialintelligencexaiwhatweknowandwhatislefttoattaintrustworthyartificialintelligence,109.89033300779907,landmark,True
219	−7118631510,10.1016/j.cie.2026.111805,,Towards trustworthy AI in Industry 5.0: Ante-hoc interpretability with deep learning,Computers & Industrial Engineering,2026,article,ai_safety,2,35.0,35.0,38.94805361604745,35,126.4417,False,True,post,124.4327202763416,landmark,True	219	+7128600296,10.1038/s41746-026-02418-7,,Advancing healthcare AI governance through a comprehensive maturity model based on systematic review,npj Digital Medicine,2026,review,ai_safety,1,41.0,41.0,12.755555555555555,41,37.52,True,True,post,advancinghealthcareaigovernancethroughacomprehensivematuritymodelbasedonsystematicreview,103.95428689982666,landmark,True
220	−7127143535,10.1016/j.jii.2026.101089,,"Industrial information integration through autonomous AI agents: paradoxical effects on transparency, dehumanization, and responsible retail operations",Journal of Industrial Information Integration,2026,article,ai_safety,2,33.0,33.0,50.00583941605839,33,209.0741,False,False,post,117.32227911769351,landmark,True	220	+2969896603,10.1145/3457607,1908.09635,A Survey on Bias and Fairness in Machine Learning,ACM Computing Surveys,2021,article,ai_safety,3,952.0,1794.0,102.2847403248446,5442,822.4428,True,True,baseline,asurveyonbiasandfairnessinmachinelearning,102.2847403248446,landmark,True
221	−4409249263,10.1371/journal.pdig.0000810,2412.03576,Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice,PLOS Digital Health,2025,article,ai_safety,3,218.0,218.0,14.499719652368936,218,44.6351,True,True,post,110.36443036278686,landmark,True	221	+4409249263,10.1371/journal.pdig.0000810,2412.03576,Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice,PLOS Digital Health,2025,article,ai_safety,3,218.0,218.0,14.499719652368936,218,44.6351,True,True,post,ethicalchallengesandevolvingstrategiesintheintegrationofartificialintelligenceintoclinicalpractice,95.25980174961113,landmark,True
222	−4366262984,10.1016/j.inffus.2023.101805,,Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence,Information Fusion,2023,article,ai_safety,5,510.0,1197.0,109.89033300779907,1728,151.7177,True,True,post,109.89033300779907,landmark,True	222	+7118631510,10.1016/j.cie.2026.111805,,Towards trustworthy AI in Industry 5.0: Ante-hoc interpretability with deep learning,Computers & Industrial Engineering,2026,article,ai_safety,2,35.0,35.0,38.94805361604745,35,126.4417,False,True,post,towardstrustworthyaiinindustry50antehocinterpretabilitywithdeeplearning,88.74146442668129,landmark,True
223	−2969896603,10.1145/3457607,1908.09635,A Survey on Bias and Fairness in Machine Learning,ACM Computing Surveys,2021,article,ai_safety,3,952.0,1794.0,102.2847403248446,5442,822.4428,True,True,baseline,102.2847403248446,landmark,True	223	+4406152291,10.1038/s41591-024-03445-1,,Medical large language models are vulnerable to data-poisoning attacks,Nature Medicine,2025,article,ai_safety,4,199.0,199.0,67.78921308462026,199,81.9764,True,True,post,medicallargelanguagemodelsarevulnerabletodatapoisoningattacks,86.95734196409457,landmark,True
224	−4406152291,10.1038/s41591-024-03445-1,,Medical large language models are vulnerable to data-poisoning attacks,Nature Medicine,2025,article,ai_safety,4,199.0,199.0,67.78921308462026,199,81.9764,True,True,post,100.74551212015865,landmark,True	224	+4400609581,10.1057/s41599-024-03432-4,,"Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications",Humanities and Social Sciences Communications,2024,article,ai_safety,1,151.0,284.0,85.50531277415134,284,108.6819,True,True,post,exploringtheimpactofartificialintelligenceonhighereducationthedynamicsofethicalsocialandeducationalimplications,85.50531277415134,landmark,True
225	−4410609100,10.1109/satml64287.2025.00010,2310.08419,Jailbreaking Black Box Large Language Models in Twenty Queries,IEEE Conference on Secure and Trustworthy Machine Learning (SaTML),2025,conference-paper,ai_safety,7,193.0,193.0,171.53132893516792,193,175.4907,True,True,post,97.70795899090764,landmark,True	225	+4401588926,10.1787/15df715b-en,,The potential impact of Artificial Intelligence on equity and inclusion in education,OECD artificial intelligence papers,2024,report,ai_safety,1,34.0,75.0,84.79411764705883,75,,True,True,post,thepotentialimpactofartificialintelligenceonequityandinclusionineducation,84.79411764705883,landmark,True
226	−4411542009,10.1145/3715275.3732039,2504.18412,Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers.,"ACM Conference on Fairness, Accountability, and Transparency (FAccT)",2025,conference-paper,ai_safety,3,191.0,191.0,61.84854665986741,191,201.9784,True,True,post,96.6954412811573,landmark,True	226	+4410609100,10.1109/satml64287.2025.00010,2310.08419,Jailbreaking Black Box Large Language Models in Twenty Queries,IEEE Conference on Secure and Trustworthy Machine Learning (SaTML),2025,conference-paper,ai_safety,7,193.0,193.0,171.53132893516792,193,175.4907,True,True,post,jailbreakingblackboxlargelanguagemodelsintwentyqueries,84.33551255814196,landmark,True
227	−4360620450,10.1016/j.ijinfomgt.2023.102642,,"Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy",International Journal of Information Management,2023,article,llm,3,1920.0,3137.0,113.51170057881467,4478,1093.1295,True,True,post,113.51170057881467,landmark,True	227	+4360620450,10.1016/j.ijinfomgt.2023.102642,,"Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy",International Journal of Information Management,2023,article,llm,3,1920.0,3137.0,113.51170057881467,4478,1093.1295,True,True,post,opinionpapersowhatifchatgptwroteitmultidisciplinaryperspectivesonopportunitieschallengesandimplicationsofgenerativeconversationalaiforresearchpracticeandpolicy,113.51170057881467,landmark,True
228	−3185341429,10.1145/3560815,2107.13586,"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing",ACM Computing Surveys,2022,article,llm,6,1233.0,2462.0,253.6835252173668,4309,297.4377,True,True,baseline,253.6835252173668,landmark,True	228	+3185341429,10.1145/3560815,2107.13586,"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing",ACM Computing Surveys,2022,article,llm,6,1233.0,2462.0,253.6835252173668,4309,297.4377,True,True,baseline,pretrainpromptandpredictasystematicsurveyofpromptingmethodsinnaturallanguageprocessing,253.6835252173668,landmark,True
229	−4384071683,10.1038/s41586-023-06291-2,2212.13138,Large language models encode clinical knowledge,Nature,2023,article,llm,5,1174.0,2448.0,38.05321679482215,3967,137.2207,True,True,post,38.05321679482215,landmark,True	229	+4384071683,10.1038/s41586-023-06291-2,2212.13138,Large language models encode clinical knowledge,Nature,2023,article,llm,5,1174.0,2448.0,38.05321679482215,3967,137.2207,True,True,post,largelanguagemodelsencodeclinicalknowledge,38.05321679482215,landmark,True
230	−4384918448,10.48550/arxiv.2307.09288,2307.09288,Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions,arXiv (Cornell University),2023,preprint,llm,6,1623.0,2431.0,88.45470297029703,2595,,True,True,post,88.45470297029703,landmark,True	230	+4384918448,10.48550/arxiv.2307.09288,2307.09288,Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions,arXiv (Cornell University),2023,preprint,llm,6,1623.0,2431.0,88.45470297029703,2595,,True,True,post,behavioralsafetyandcontextretentionoflargelanguagemodelsinalongitudinalicusimulationunderofflineconditions,88.45470297029703,landmark,True
231	−4393065402,10.1007/s11704-024-40231-1,2308.11432,A survey on large language model based autonomous agents,Frontiers of Computer Science,2024,article,llm,3,876.0,1820.0,226.05848170926672,1820,269.514,True,True,post,226.05848170926672,landmark,True	231	+4393065402,10.1007/s11704-024-40231-1,2308.11432,A survey on large language model based autonomous agents,Frontiers of Computer Science,2024,article,llm,3,876.0,1820.0,226.05848170926672,1820,269.514,True,True,post,asurveyonlargelanguagemodelbasedautonomousagents,226.05848170926672,landmark,True
232	−4319083882,10.1038/d41586-023-00288-7,,ChatGPT: five priorities for research,Nature,2023,article,llm,2,1183.0,1590.0,145.96961527351755,1831,160.4149,False,True,post,145.96961527351755,landmark,True	232	+4319083882,10.1038/d41586-023-00288-7,,ChatGPT: five priorities for research,Nature,2023,article,llm,2,1183.0,1590.0,145.96961527351755,1831,160.4149,False,True,post,chatgptfiveprioritiesforresearch,145.96961527351755,landmark,True
233	−4399790131,10.1186/s40561-024-00316-7,,The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review,Smart Learning Environments,2024,review,ai_safety,3,613.0,1484.0,81.80055076520247,1484,96.2475,True,True,post,81.80055076520247,landmark,True	233	+4399790131,10.1186/s40561-024-00316-7,,The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review,Smart Learning Environments,2024,review,ai_safety,3,613.0,1484.0,81.80055076520247,1484,96.2475,True,True,post,theeffectsofoverrelianceonaidialoguesystemsonstudentscognitiveabilitiesasystematicreview,81.80055076520247,landmark,True
234	−3133702157,10.1145/3442188.3445922,,On the Dangers of Stochastic Parrots,"ACM Conference on Fairness, Accountability, and Transparency (FAccT)",2021,conference-paper,llm,3,623.0,1464.0,232.11321601174575,6788,321.4548,True,True,baseline,232.11321601174575,landmark,True	234	+3133702157,10.1145/3442188.3445922,,On the Dangers of Stochastic Parrots,"ACM Conference on Fairness, Accountability, and Transparency (FAccT)",2021,conference-paper,llm,3,623.0,1464.0,232.11321601174575,6788,321.4548,True,True,baseline,onthedangersofstochasticparrots,232.11321601174575,landmark,True
235	−4321499901,10.1186/s40561-023-00237-x,,What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education,Smart Learning Environments,2023,article,llm,3,941.0,1389.0,325.547952647582,1931,453.5275,True,True,post,325.547952647582,landmark,True

modified literature_synthesis/landmarks/computational_science_abstracts.jsonl +36 −39

				`@@ -1,59 +1,57 @@`
		1	+{"work_id": 4388522045, "title": "SRplot: A free online platform for data visualization and graphing", "abstract": "Graphics are widely used to provide summarization of complex data in scientific publications. Although there are many tools available for drawing graphics, their use is limited by programming skills, costs, and platform specificities. Here, we presented a freely accessible easy-to-use web server named SRplot that integrated more than a hundred of commonly used data visualization and graphing functions together. It can be run easily using all Web browsers and there are no strong requirements on the computing power of users' machines. With a user-friendly graphical interface, users can simply paste the contents of the input file into the text box according to the defined file format. Modification operations can be easily performed, and graphs can be generated in real-time. The resulting graphs can be easily downloaded in bitmap (PNG or TIFF) or vector (PDF or SVG) format in publication quality. The website is updated promptly and continuously. Functions in SRplot have been improved, optimized and updated depend on feedback and suggestions from users. The graphs prepared with SRplot have been featured in more than five hundred peer-reviewed publications. The SRplot web server is now freely available at http://www.bioinformatics.com.cn/SRplot."}
		2	+{"work_id": 4319754774, "title": "Acceptance of artificial intelligence in teaching science: Science teachers' perspective", "abstract": "Over the last few decades, there has been growing interest in artificial intelligence (AI) and how to apply it effectively in the educational process. The behavioural intention and attitudes of teachers play an important role in this regard. The technology acceptance model (TAM) has been shown to be effective in predicting factors that may positively or negatively influence behavioural intentions to use technology. The current study therefore aims to use this model to reveal teachers\u2019 perceptions of the factors that influence the use of AI applications in science education. This research uses the descriptive method where a 6-dimensional scale is designed based on the components of the TAM model and then applied to a sample of 83 science teachers in Abu Dhabi. The results show a high acceptability of the use of AI in the classroom by science teachers, with positive correlations to self-efficacy, ease of use, expected benefits, attitudes, and behavioural intentions. However, anxiety and stress do not significantly affect other factors. Regarding the predictive power of research models, the combined factors expected benefits, ease of use, and attitude toward AI applications can predict 71.4% of future behavioural changes related to the use of AI applications in science teaching. This study finds no statistically significant differences in teacher responses based on the variables gender, teaching experience, and qualifications regarding teachers\u2019 behavioural intentions to actually use AI in science teaching."}
1	{"work_id": 4416043407, "title": "Accelerating scientific discovery with Co-Scientist", "abstract": "Abstract Scientific discovery is driven by scientists generating hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent artificial intelligence (AI) system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and previous scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system\u2019s design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling, and (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. Although this is a general-purpose system, we focus the validation in three biomedical applications: drug repurposing; novel-target discovery 1 ; and explaining mechanisms of antimicrobial resistance 2 . Specifically, Co-Scientist helped to identify new drug-repurposing candidates and synergistic combination therapies for acute myeloid leukaemia that were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI-empowered scientists."}	3	{"work_id": 4416043407, "title": "Accelerating scientific discovery with Co-Scientist", "abstract": "Abstract Scientific discovery is driven by scientists generating hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent artificial intelligence (AI) system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and previous scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system\u2019s design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling, and (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. Although this is a general-purpose system, we focus the validation in three biomedical applications: drug repurposing; novel-target discovery 1 ; and explaining mechanisms of antimicrobial resistance 2 . Specifically, Co-Scientist helped to identify new drug-repurposing candidates and synergistic combination therapies for acute myeloid leukaemia that were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI-empowered scientists."}
2	{"work_id": 3210165781, "title": "Artificial intelligence: A powerful paradigm for scientific research", "abstract": "Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences."}	4	{"work_id": 3210165781, "title": "Artificial intelligence: A powerful paradigm for scientific research", "abstract": "Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences."}
3	−{"work_id": 4245687930, "title": "Chemical Reaction Engineering", "abstract": "Chemical reactions are essential technological elements in a wide range of industries. Chemical reactions are indispensable in converting less valuable raw materials into higher-value products and in converting one form of energy to another. Many reactions of industrial significance are complex, involving side reactions \u2013 which often result in the formation of undesirable by-products. Chemical kinetics allows us to understand how reaction rates depend on variables such as concentration, temperature, and pressure. Most industrial chemical processes involve some kind of catalyst in order to increase the reaction rate. The behavior of heterogeneous catalysts can be significantly influenced by the rate of heat and mass transfer to and from the sites in the catalyst where the reaction occurs. The design and analysis of chemical reactors are built upon a sound understanding of chemical kinetics. In the operation of existing reactors, periodic analysis of the reactor performance is necessary to ensure optimal operation under varying operating conditions. It is difficult to generalize chemical reaction kinetics because almost every reaction is unique, characterized by a specific chemical model, involving one or more phases, and invariably taking place in one of a great variety of reactor configurations. Problems in chemical kinetics and the design and analysis of chemical reactors are nonlinear and therefore require the solution of complicated algebraic and differential equations by numerical methods, which can be performed well by MATLAB \u00ae programs. This chapter demonstrates some basic methods that are used for calculating many different classes of chemical reactions typical in industrial situations. The main objective of this chapter is to provide readers with various chemical reaction engineering models and algorithms, as well as corresponding MATLAB programs, which have found use or can potentially be used for academic or industrial applications. Throughout this chapter, differential and integral methods are used primarily in analyzing reactor data. The MATLAB programs listed here can be used in undergraduate or graduate courses on chemical reaction engineering. Researchers and practicing engineers in the field of chemical engineering can apply these MATLAB programs in the analysis and design of chemical reactors."}	5	+{"work_id": 4385811703, "title": "Accelerating Manufacturing Decisions using Bayesian Optimization: An Optimization and Prediction Perspective", "abstract": "Manufacturing is a promising technique for producing complex and custom-made parts with a high degree of precision. It can also provide us with desired materials and products with specified properties. To achieve that, it is crucial to find out the optimum point of process parameters that have a significant impact on the properties and quality of the final product. Unfortunately, optimizing these parameters can be challenging due to the complex and nonlinear nature of the underlying process, which becomes more complicated when there are conflicting objectives, sometimes with multiple goals. Furthermore, experiments are usually costly, time-consuming, and require expensive materials, man, and machine hours. So, each experiment is valuable and it's critical to determine the optimal experiment location to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This thesis presents a multi-objective Bayesian optimization framework to find out the optimum processing conditions for a manufacturing setup. It uses an acquisition function to collect data points sequentially and iteratively update its understanding of the underlying design space utilizing a Gaussian Process-based surrogate model. In manufacturing processes, the focus is often on obtaining a rough understanding of the design space using minimal experimentation, rather than finding the optimal parameters. This falls under the category of \"approximating the underlying function\" rather than design optimization. This approach can provide material scientists or manufacturing engineers with a comprehensive view of the entire design space, increasing the likelihood of making discoveries or making robust decisions. However, a precise and reliable prediction model is necessary for a good approximation. To meet this requirement, this thesis proposes an epsilon-greedy sequential prediction framework that is distinct from the optimization framework. The data acquisition strategy has been refined to balance exploration and exploitation, and a threshold has been established to determine when to switch between the two. The performance of this proposed optimization and prediction framework is evaluated using real-life datasets against the traditional design of experiments. The proposed frameworks have generated effective optimization and prediction results using fewer experiments."}
4	−{"work_id": 4410552772, "title": "ASKCOS: Open-Source, Data-Driven Synthesis Planning", "abstract": "ConspectusThe advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. In this Account, we describe the range of data-driven methods and models that have been incorporated into the newest version of ASKCOS, an open-source software suite for synthesis planning that we have been developing since 2016. This ongoing effort has been driven by the importance of bridging the gap between research and development, making research advances available through a freely available practical tool. ASKCOS integrates modules for retrosynthetic planning, modules for complementary capabilities of condition prediction and reaction product prediction, and several supplementary modules and utilities with various roles in synthesis planning. For retrosynthetic planning, we have developed an Interactive Path Planner (IPP) for user-guided search as well as a Tree Builder for automatic planning with two well-known tree search algorithms, Monte Carlo Tree Search (MCTS) and Retro*. Four one-step retrosynthesis models covering template-based and template-free strategies form the basis of retrosynthetic predictions and can be used simultaneously to combine their advantages and propose diverse suggestions. Strategies for assessing the feasibility of proposed reaction steps and evaluating the full pathways are built on top of several pioneering efforts that we have made in the subtasks of reaction condition recommendation, pathway scoring and clustering, and the prediction of reaction outcomes including the major product, impurities, site selectivity, and regioselectivity. In addition, we have also developed auxiliary capabilities in ASKCOS based on our past and ongoing work for solubility prediction and quantum mechanical descriptor prediction, which can provide more insight into the suitability of proposed reaction solvents or the hypothetical selectivity of desired transformations. For each of these capabilities, we highlight its relevance in the context of synthesis planning and present a comprehensive overview of how it is built on top of not only our work but also of other recent advancements in the field. We also describe in detail how chemists can easily interact with these capabilities via user-friendly interfaces. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks by complementing expert decision making and route ideation. It is our belief that CASP tools are an important part of modern chemistry research and offer ever-increasing utility and accessibility."}	6	+{"work_id": 2789843538, "title": "Minimap2: pairwise alignment for nucleotide sequences", "abstract": "Motivation: Recent advances in sequencing technologies promise ultra-long reads of \u223c100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms. Results: Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database. It works with accurate short reads of \u2265100 bp in length, \u22651 kb genomic reads at error rate \u223c15%, full-length noisy Direct RNA or cDNA reads and assembly contigs or closely related full chromosomes of hundreds of megabases in length. Minimap2 does split-read alignment, employs concave gap cost for long insertions and deletions and introduces new heuristics to reduce spurious alignments. It is 3-4 times as fast as mainstream short-read mappers at comparable accuracy, and is \u226530 times faster than long-read genomic or cDNA mappers at higher accuracy, surpassing most aligners specialized in one type of alignment. Availability and implementation: https://github.com/lh3/minimap2. Supplementary information: Supplementary data are available at Bioinformatics online."}
5	−{"work_id": 4319754774, "title": "Acceptance of artificial intelligence in teaching science: Science teachers' perspective", "abstract": "Over the last few decades, there has been growing interest in artificial intelligence (AI) and how to apply it effectively in the educational process. The behavioural intention and attitudes of teachers play an important role in this regard. The technology acceptance model (TAM) has been shown to be effective in predicting factors that may positively or negatively influence behavioural intentions to use technology. The current study therefore aims to use this model to reveal teachers\u2019 perceptions of the factors that influence the use of AI applications in science education. This research uses the descriptive method where a 6-dimensional scale is designed based on the components of the TAM model and then applied to a sample of 83 science teachers in Abu Dhabi. The results show a high acceptability of the use of AI in the classroom by science teachers, with positive correlations to self-efficacy, ease of use, expected benefits, attitudes, and behavioural intentions. However, anxiety and stress do not significantly affect other factors. Regarding the predictive power of research models, the combined factors expected benefits, ease of use, and attitude toward AI applications can predict 71.4% of future behavioural changes related to the use of AI applications in science teaching. This study finds no statistically significant differences in teacher responses based on the variables gender, teaching experience, and qualifications regarding teachers\u2019 behavioural intentions to actually use AI in science teaching."}	7	+{"work_id": 2888532099, "title": "The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package *", "abstract": "Abstract The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy , which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project."}
6	−{"work_id": 4368755578, "title": "Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems", "abstract": "Abstract The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents. It discusses the individual approaches from a \"big picture\" perspective and in context, but also discusses open issues and recent topics like the various roles of deep neural networks in this area, aiding in the discovery of human-interpretable knowledge. Further, we will present closed-loop scientific discovery systems, starting with the pioneering work on the Adam system up to current efforts in fields from material science to astronomy. Finally, we will elaborate on autonomy from a machine learning perspective, but also in analogy to the autonomy levels in autonomous driving. The maximal level, level five, is defined to require no human intervention at all in the production of scientific knowledge. Achieving this is one step towards solving the Nobel Turing Grand Challenge to develop AI Scientists: AI systems capable of making Nobel-quality scientific discoveries highly autonomously at a level comparable, and possibly superior, to the best human scientists by 2050."}	8	+{"work_id": 4411463597, "title": "Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction", "abstract": "Accurately modeling biomolecular interactions is a central challenge in modern biology. While recent advances, such as AlphaFold3 and Boltz-1, have substantially improved our ability to predict biomolecular complex structures, these models still fall short in predicting binding affinity, a critical property underlying molecular function and therapeutic efficacy. Here, we present Boltz-2, a new structural biology foundation model that exhibits strong performance for both structure and affinity prediction. Boltz-2 introduces controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, and is, to our knowledge, the first AI model to approach the performance of free-energy perturbation (FEP) methods in estimating small molecule-protein binding affinity. Crucially, it achieves strong correlation with experimental readouts on many benchmarks, while being at least 1000\u00d7 more computationally efficient than FEP. By coupling Boltz-2 with a generative model for small molecules, we demonstrate an effective workflow to find diverse, synthesizable, high-affinity binders, as estimated by absolute FEP simulations on the TYK2 target. To foster broad adoption and further innovation at the intersection of machine learning and biology, we are releasing Boltz-2 weights, inference, and training code under a permissive open license, providing a robust and extensible foundation for both academic and industrial research."}
7	{"work_id": 3201073812, "title": "LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", "abstract": "Since the classical molecular dynamics simulator LAMMPS was released as an open source code in 2004, it has become a widely-used tool for particle-based modeling of materials at length scales ranging from atomic to mesoscale to continuum. Reasons for its popularity are that it provides a wide variety of particle interaction models for different materials, that it runs on any platform from a single CPU core to the largest supercomputers with accelerators, and that it gives users control over simulation details, either via the input script or by adding code for new interatomic potentials, constraints, diagnostics, or other features needed for their models. As a result, hundreds of people have contributed new capabilities to LAMMPS and it has grown from fifty thousand lines of code in 2004 to a million lines today. In this paper several of the fundamental algorithms used in LAMMPS are described along with the design strategies which have made it flexible for both users and developers. We also highlight some capabilities recently added to the code which were enabled by this flexibility, including dynamic load balancing, on-the-fly visualization, magnetic spin dynamics models, and quantum-accuracy machine learning interatomic potentials. Program Title: Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) CPC Library link to program files: https://doi.org/10.17632/cxbxs9btsv.1 Developer's repository link: https://github.com/lammps/lammps Licensing provisions: GPLv2 Programming language: C++, Python, C, Fortran Supplementary material: https://www.lammps.org Nature of problem: Many science applications in physics, chemistry, materials science, and related fields require parallel, scalable, and efficient generation of long, stable classical particle dynamics trajectories. Within this common problem definition, there lies a great diversity of use cases, distinguished by different particle interaction models, external constraints, as well as timescales and lengthscales ranging from atomic to mesoscale to macroscopic. Solution method: The LAMMPS code uses parallel spatial decomposition, distributed neighbor lists, and parallel FFTs for long-range Coulombic interactions [1]. The time integration algorithm is based on the St\u00f8rmer-Verlet symplectic integrator [2], which provides better stability than higher-order non-symplectic methods. In addition, LAMMPS supports a wide range of interatomic potentials, constraints, diagnostics, software interfaces, and pre- and post-processing features. Additional comments including restrictions and unusual features: This paper serves as the definitive reference for the LAMMPS code. S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117 (1995) 1\u201319. L. Verlet, Computer experiments on classical fluids: I. Thermodynamical properties of Lennard\u2013Jones molecules, Phys. Rev. 159 (1967) 98\u2013103."}	9	{"work_id": 3201073812, "title": "LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", "abstract": "Since the classical molecular dynamics simulator LAMMPS was released as an open source code in 2004, it has become a widely-used tool for particle-based modeling of materials at length scales ranging from atomic to mesoscale to continuum. Reasons for its popularity are that it provides a wide variety of particle interaction models for different materials, that it runs on any platform from a single CPU core to the largest supercomputers with accelerators, and that it gives users control over simulation details, either via the input script or by adding code for new interatomic potentials, constraints, diagnostics, or other features needed for their models. As a result, hundreds of people have contributed new capabilities to LAMMPS and it has grown from fifty thousand lines of code in 2004 to a million lines today. In this paper several of the fundamental algorithms used in LAMMPS are described along with the design strategies which have made it flexible for both users and developers. We also highlight some capabilities recently added to the code which were enabled by this flexibility, including dynamic load balancing, on-the-fly visualization, magnetic spin dynamics models, and quantum-accuracy machine learning interatomic potentials. Program Title: Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) CPC Library link to program files: https://doi.org/10.17632/cxbxs9btsv.1 Developer's repository link: https://github.com/lammps/lammps Licensing provisions: GPLv2 Programming language: C++, Python, C, Fortran Supplementary material: https://www.lammps.org Nature of problem: Many science applications in physics, chemistry, materials science, and related fields require parallel, scalable, and efficient generation of long, stable classical particle dynamics trajectories. Within this common problem definition, there lies a great diversity of use cases, distinguished by different particle interaction models, external constraints, as well as timescales and lengthscales ranging from atomic to mesoscale to macroscopic. Solution method: The LAMMPS code uses parallel spatial decomposition, distributed neighbor lists, and parallel FFTs for long-range Coulombic interactions [1]. The time integration algorithm is based on the St\u00f8rmer-Verlet symplectic integrator [2], which provides better stability than higher-order non-symplectic methods. In addition, LAMMPS supports a wide range of interatomic potentials, constraints, diagnostics, software interfaces, and pre- and post-processing features. Additional comments including restrictions and unusual features: This paper serves as the definitive reference for the LAMMPS code. S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117 (1995) 1\u201319. L. Verlet, Computer experiments on classical fluids: I. Thermodynamical properties of Lennard\u2013Jones molecules, Phys. Rev. 159 (1967) 98\u2013103."}
8	{"work_id": 7125546963, "title": "Artificial intelligence and machine learning guided optimization in drug delivery", "abstract": "The optimisation of drug delivery systems is a complex, multidimensional challenge involving the interplay of formulation composition, process parameters, and biological performance. Traditional empirical and statistical approaches are increasingly limited by the high dimensionality, nonlinearity, and multi-objective nature of modern drug delivery problems. In this review, we explore how artificial intelligence (AI) and machine learning (ML) are transforming formulation science by enabling data-driven, adaptive, and efficient optimisation strategies. We provide a conceptual and practical overview of ML-guided optimisation workflows, including surrogate modelling, Bayesian optimisation, active learning, and multi-objective optimisation. Key challenges such as data scarcity, experimental throughput, and model interpretability are discussed. Applications across diverse delivery modalities, including solid oral dosage forms, lipid nanoparticles, biologics, and long-acting injectables, are critically examined, highlighting how ML can accelerate formulation development, reduce experimental burden, and uncover novel design spaces. We conclude by outlining future directions for integrating AI into pharmaceutical R&D, with a focus on the emergence of self-driving laboratories. This review aims to equip drug delivery scientists with the foundational knowledge and practical tools to harness AI and ML in the rational design and optimisation of advanced drug delivery systems."}	10	{"work_id": 7125546963, "title": "Artificial intelligence and machine learning guided optimization in drug delivery", "abstract": "The optimisation of drug delivery systems is a complex, multidimensional challenge involving the interplay of formulation composition, process parameters, and biological performance. Traditional empirical and statistical approaches are increasingly limited by the high dimensionality, nonlinearity, and multi-objective nature of modern drug delivery problems. In this review, we explore how artificial intelligence (AI) and machine learning (ML) are transforming formulation science by enabling data-driven, adaptive, and efficient optimisation strategies. We provide a conceptual and practical overview of ML-guided optimisation workflows, including surrogate modelling, Bayesian optimisation, active learning, and multi-objective optimisation. Key challenges such as data scarcity, experimental throughput, and model interpretability are discussed. Applications across diverse delivery modalities, including solid oral dosage forms, lipid nanoparticles, biologics, and long-acting injectables, are critically examined, highlighting how ML can accelerate formulation development, reduce experimental burden, and uncover novel design spaces. We conclude by outlining future directions for integrating AI into pharmaceutical R&D, with a focus on the emergence of self-driving laboratories. This review aims to equip drug delivery scientists with the foundational knowledge and practical tools to harness AI and ML in the rational design and optimisation of advanced drug delivery systems."}
9	{"work_id": 4390940921, "title": "Solving olympiad geometry without human demonstrations", "abstract": "Abstract Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning 1\u20134 , owing to their reputed difficulty among the world\u2019s best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges 1,5 , resulting in severe scarcity of training data. We propose AlphaGeometry, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems. On a test set of 30 latest olympiad-level problems, AlphaGeometry solves 25, outperforming the previous best method that only solves ten problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medallist. Notably, AlphaGeometry produces human-readable proofs, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation and discovers a generalized version of a translated IMO theorem in 2004."}	11	{"work_id": 4390940921, "title": "Solving olympiad geometry without human demonstrations", "abstract": "Abstract Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning 1\u20134 , owing to their reputed difficulty among the world\u2019s best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges 1,5 , resulting in severe scarcity of training data. We propose AlphaGeometry, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems. On a test set of 30 latest olympiad-level problems, AlphaGeometry solves 25, outperforming the previous best method that only solves ten problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medallist. Notably, AlphaGeometry produces human-readable proofs, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation and discovers a generalized version of a translated IMO theorem in 2004."}
10	{"work_id": 2951939904, "title": "Jointly Embedding Multiple Single-Cell Omics Measurements", "abstract": "Many single-cell sequencing technologies are now available, but it is still difficult to apply multiple sequencing technologies to the same single cell. In this paper, we propose an unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of cells. Effectively, MMD-MA performs an in silico co-assay by embedding cells measured in different ways into a learned latent space. In the MMD-MA algorithm, single-cell data points from multiple domains are aligned by optimizing an objective function with three components: (1) a maximum mean discrepancy (MMD) term to encourage the differently measured points to have similar distributions in the latent space, (2) a distortion term to preserve the structure of the data between the input space and the latent space, and (3) a penalty term to avoid collapse to a trivial solution. Notably, MMD-MA does not require any correspondence information across data modalities, either between the cells or between the features. Furthermore, MMD-MA\u2019s weak distributional requirements for the domains to be aligned allow the algorithm to integrate heterogeneous types of single cell measures, such as gene expression, DNA accessibility, chromatin organization, methylation, and imaging data. We demonstrate the utility of MMD-MA in simulation experiments and using a real data set involving single-cell gene expression and methylation data."}	12	{"work_id": 2951939904, "title": "Jointly Embedding Multiple Single-Cell Omics Measurements", "abstract": "Many single-cell sequencing technologies are now available, but it is still difficult to apply multiple sequencing technologies to the same single cell. In this paper, we propose an unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of cells. Effectively, MMD-MA performs an in silico co-assay by embedding cells measured in different ways into a learned latent space. In the MMD-MA algorithm, single-cell data points from multiple domains are aligned by optimizing an objective function with three components: (1) a maximum mean discrepancy (MMD) term to encourage the differently measured points to have similar distributions in the latent space, (2) a distortion term to preserve the structure of the data between the input space and the latent space, and (3) a penalty term to avoid collapse to a trivial solution. Notably, MMD-MA does not require any correspondence information across data modalities, either between the cells or between the features. Furthermore, MMD-MA\u2019s weak distributional requirements for the domains to be aligned allow the algorithm to integrate heterogeneous types of single cell measures, such as gene expression, DNA accessibility, chromatin organization, methylation, and imaging data. We demonstrate the utility of MMD-MA in simulation experiments and using a real data set involving single-cell gene expression and methylation data."}
11	−{"work_id": 3029645440, "title": "Numerical optimization", "abstract": "This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box\u2013Muller and Metropolis\u2013Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science."}	13	+{"work_id": 4410552772, "title": "ASKCOS: Open-Source, Data-Driven Synthesis Planning", "abstract": "ConspectusThe advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. In this Account, we describe the range of data-driven methods and models that have been incorporated into the newest version of ASKCOS, an open-source software suite for synthesis planning that we have been developing since 2016. This ongoing effort has been driven by the importance of bridging the gap between research and development, making research advances available through a freely available practical tool. ASKCOS integrates modules for retrosynthetic planning, modules for complementary capabilities of condition prediction and reaction product prediction, and several supplementary modules and utilities with various roles in synthesis planning. For retrosynthetic planning, we have developed an Interactive Path Planner (IPP) for user-guided search as well as a Tree Builder for automatic planning with two well-known tree search algorithms, Monte Carlo Tree Search (MCTS) and Retro*. Four one-step retrosynthesis models covering template-based and template-free strategies form the basis of retrosynthetic predictions and can be used simultaneously to combine their advantages and propose diverse suggestions. Strategies for assessing the feasibility of proposed reaction steps and evaluating the full pathways are built on top of several pioneering efforts that we have made in the subtasks of reaction condition recommendation, pathway scoring and clustering, and the prediction of reaction outcomes including the major product, impurities, site selectivity, and regioselectivity. In addition, we have also developed auxiliary capabilities in ASKCOS based on our past and ongoing work for solubility prediction and quantum mechanical descriptor prediction, which can provide more insight into the suitability of proposed reaction solvents or the hypothetical selectivity of desired transformations. For each of these capabilities, we highlight its relevance in the context of synthesis planning and present a comprehensive overview of how it is built on top of not only our work but also of other recent advancements in the field. We also describe in detail how chemists can easily interact with these capabilities via user-friendly interfaces. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks by complementing expert decision making and route ideation. It is our belief that CASP tools are an important part of modern chemistry research and offer ever-increasing utility and accessibility."}
12	−{"work_id": 2804822363, "title": "SWISS-MODEL: homology modelling of protein structures and complexes", "abstract": "Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org."}	14	+{"work_id": 4288039037, "title": "Scientific Machine Learning Through Physics\u2013Informed Neural Networks: Where we are and What\u2019s Next", "abstract": "Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. This novel methodology has arisen as a multi-task learning framework in which a NN must fit observed data while reducing a PDE residual. This article provides a comprehensive review of the literature on PINNs: while the primary goal of the study was to characterize these networks and their related advantages and disadvantages. The review also attempts to incorporate publications on a broader range of collocation-based physics informed neural networks, which stars form the vanilla PINN, as well as many other variants, such as physics-constrained neural networks (PCNN), variational hp-VPINN, and conservative PINN (CPINN). The study indicates that most research has focused on customizing the PINN through different activation functions, gradient optimization techniques, neural network structures, and loss function structures. Despite the wide range of applications for which PINNs have been used, by demonstrating their ability to be more feasible in some contexts than classical numerical techniques like Finite Element Method (FEM), advancements are still possible, most notably theoretical issues that remain unresolved."}
13	−{"work_id": 3156458251, "title": "Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation", "abstract": "The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 5 introduces a completely new tree display engine, together with numerous new features. For example, a new dataset type has been added (MEME motifs), while annotation options have been expanded for several existing ones. Node metadata display options have been extended and now also support non-numerical categorical values, as well as multiple values per node. Direct manual annotation is now available, providing a set of basic drawing and labeling tools, allowing users to draw shapes, labels and other features by hand directly onto the trees. Support for tree and dataset scales has been extended, providing fine control over line and label styles. Unrooted tree displays can now use the equal-daylight algorithm, proving a much greater display clarity. The user account system has been streamlined and expanded with new navigation options and currently handles >1 million trees from >70 000 individual users."}	15	+{"work_id": 2927961455, "title": "Interactive Tree Of Life (iTOL) v4: recent updates and new developments", "abstract": "The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. The current version introduces four new dataset types, together with numerous new features. Annotation options have been expanded and new control options added for many display elements. An interactive spreadsheet-like editor has been implemented, providing dataset creation and editing directly in the web interface. Font support has been rewritten with full support for UTF-8 character encoding throughout the user interface. Google Web Fonts are now fully supported in the tree text labels. iTOL v4 is the first tool which supports direct visualization of Qiime 2 trees and associated annotations. The user account system has been streamlined and expanded with new navigation options, and currently handles >700 000 trees from more than 40 000 individual users. Full batch access has been implemented allowing programmatic upload and export of trees and annotations."}
14	−{"work_id": 7128476819, "title": "The future of fundamental science led by generative closed-loop artificial intelligence", "abstract": "Artificial intelligence is approaching the point at which it can complete the scientific cycle, from hypothesis generation to experimental design and validation, within a closed loop that requires little human intervention. Yet, the loop is not fully autonomous: humans still curate data, set hyperparameters, adjudicate interpretability, and decide what counts as a satisfactory explanation. As models scale, they begin to explore regions of hypothesis and solution space that are inaccessible to human reasoning because they are too intricate or alien to our intuitions. Scientists may soon rely on AI strategies they do not fully understand, trusting goals and empirical payoffs rather than derivations. This prospect forces a choice about how much control to relinquish to accelerate discovery while keeping outputs human relevant. The answer cannot be a blanket policy to deploy LLMs or any single paradigm everywhere. It demands principled matching of methods to domains, hybrid causal and neurosymbolic scaffolds around generative models, and governance that preserves plurality and counters recursive bias. Otherwise, recursive training and uncritical reuse risk model collapse in AI and an epistemic collapse in science, as statistical inertia amplifies flaws and narrows the investigation. We argue for graded autonomy in AI-conducted science: systems that can close the loop at machine speed, while remaining anchored to human priorities, verifiable mechanisms, and domain-appropriate forms of understanding."}	16	+{"work_id": 4386373863, "title": "PyMC: a modern, and comprehensive probabilistic programming framework in Python", "abstract": "PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU. Being a general modeling framework, PyMC supports a variety of models including generalized hierarchical linear regression and classification, time series, ordinary differential equations (ODEs), and non-parametric models such as Gaussian processes (GPs). We demonstrate PyMC's versatility and ease of use with examples spanning a range of common statistical models. Additionally, we discuss the positive role of PyMC in the development of the open-source ecosystem for probabilistic programming."}
15	−{"work_id": 2888532099, "title": "The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package *", "abstract": "Abstract The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy , which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project."}	17	+{"work_id": 3098269892, "title": "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.", "abstract": "We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the domain of drug-like molecules and also in a set of molecules with fewer that nine heavy atoms."}
16	−{"work_id": 4411463597, "title": "Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction", "abstract": "Accurately modeling biomolecular interactions is a central challenge in modern biology. While recent advances, such as AlphaFold3 and Boltz-1, have substantially improved our ability to predict biomolecular complex structures, these models still fall short in predicting binding affinity, a critical property underlying molecular function and therapeutic efficacy. Here, we present Boltz-2, a new structural biology foundation model that exhibits strong performance for both structure and affinity prediction. Boltz-2 introduces controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, and is, to our knowledge, the first AI model to approach the performance of free-energy perturbation (FEP) methods in estimating small molecule-protein binding affinity. Crucially, it achieves strong correlation with experimental readouts on many benchmarks, while being at least 1000\u00d7 more computationally efficient than FEP. By coupling Boltz-2 with a generative model for small molecules, we demonstrate an effective workflow to find diverse, synthesizable, high-affinity binders, as estimated by absolute FEP simulations on the TYK2 target. To foster broad adoption and further innovation at the intersection of machine learning and biology, we are releasing Boltz-2 weights, inference, and training code under a permissive open license, providing a robust and extensible foundation for both academic and industrial research."}
17	−{"work_id": 4365512576, "title": "ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope", "abstract": "In recent years, artificial intelligence (AI) and machine learning have been transforming the landscape of scientific research. Out of which, the chatbot technology has experienced tremendous advancements in recent years, especially with ChatGPT emerging as a notable AI language model. This comprehensive review delves into the background, applications, key challenges, and future directions of ChatGPT. We begin by exploring its origins, development, and underlying technology, before examining its wide-ranging applications across industries such as customer service, healthcare, and education. We also highlight the critical challenges that ChatGPT faces, including ethical concerns, data biases, and safety issues, while discussing potential mitigation strategies. Finally, we envision the future of ChatGPT by exploring areas of further research and development, focusing on its integration with other technologies, improved human-AI interaction, and addressing the digital divide. This review offers valuable insights for researchers, developers, and stakeholders interested in the ever-evolving landscape of AI-driven conversational agents. This study explores the various ways ChatGPT has been revolutionizing scientific research, spanning from data processing and hypothesis generation to collaboration and public outreach. Furthermore, the paper examines the potential challenges and ethical concerns surrounding the use of ChatGPT in research, while highlighting the importance of striking a balance between AI-assisted innovation and human expertise. The paper presents several ethical issues in existing computing domain and how ChatGPT can invoke challenges to such notion. This work also includes some biases and limitations of ChatGPT. It is worth to note that despite of several controversies and ethical concerns, ChatGPT has attracted remarkable attentions from academia, research, and industries in a very short span of time."}
18	−{"work_id": 3097145107, "title": "PubChem in 2021: new data content and improved web interfaces", "abstract": "PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)."}
19	−{"work_id": 4389991792, "title": "Autonomous chemical research with large language models", "abstract": "Abstract Transformer-based large language models are making significant strides in various fields, such as natural language processing 1\u20135 , biology 6,7 , chemistry 8\u201310 and computer programming 11,12 . Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research."}
20	−{"work_id": 3168436232, "title": "AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings", "abstract": "AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina."}
21	−{"work_id": 4242546620, "title": "DPLYR", "abstract": "Dplyr is one of my favorite R packages. Its logical and consistent rules replace the older, motley collection of syntactically inconsistent packages and functions. It\u2019s like a Swiss Army knife in the woods\u2014don\u2019t leave home without it."}
22	{"work_id": 2899070097, "title": "PubChem 2019 update: improved access to chemical data", "abstract": "PubChem (https://pubchem.ncbi.nlm.nih.gov) is a key chemical information resource for the biomedical research community. Substantial improvements were made in the past few years. New data content was added, including spectral information, scientific articles mentioning chemicals, and information for food and agricultural chemicals. PubChem released new web interfaces, such as PubChem Target View page, Sources page, Bioactivity dyad pages and Patent View page. PubChem also released a major update to PubChem Widgets and introduced a new programmatic access interface, called PUG-View. This paper describes these new developments in PubChem."}	18	{"work_id": 2899070097, "title": "PubChem 2019 update: improved access to chemical data", "abstract": "PubChem (https://pubchem.ncbi.nlm.nih.gov) is a key chemical information resource for the biomedical research community. Substantial improvements were made in the past few years. New data content was added, including spectral information, scientific articles mentioning chemicals, and information for food and agricultural chemicals. PubChem released new web interfaces, such as PubChem Target View page, Sources page, Bioactivity dyad pages and Patent View page. PubChem also released a major update to PubChem Widgets and introduced a new programmatic access interface, called PUG-View. This paper describes these new developments in PubChem."}
23	{"work_id": 4400064820, "title": "Less can be more for predicting properties with large language models", "abstract": "Abstract Predicting properties from coordinate\u2013category data\u2014sets of vectors paired with categorical information\u2014is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from crystal structures. While large language models (LLMs) have increasingly been applied to such tasks, optimal strategies for achieving reliable predictions remain elusive. Here we report fundamental limitations in LLM\u2019s ability to learn from coordinate information in coordinate\u2013category data. Through systematic experiments using synthetic datasets with tunable coordinate and category contributions, and a comprehensive benchmarking framework (MatText), we find that LLMs consistently fail to capture coordinate information while excelling at category patterns. This geometric blindness persists regardless of model size (up to 70 billion parameters), dataset scale (up to 2 \u00d7 10 6 structures) or text representation strategy. For material property prediction tasks dominated by structural effects, specialized geometric architectures consistently outperform LLMs, as evidenced by a clear \u2018Graph Neural Network Language Model (GNN-LM) wall\u2019 in performance benchmarks. Our analysis provides concrete guidelines for architecture selection in scientific machine learning."}	19	{"work_id": 4400064820, "title": "Less can be more for predicting properties with large language models", "abstract": "Abstract Predicting properties from coordinate\u2013category data\u2014sets of vectors paired with categorical information\u2014is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from crystal structures. While large language models (LLMs) have increasingly been applied to such tasks, optimal strategies for achieving reliable predictions remain elusive. Here we report fundamental limitations in LLM\u2019s ability to learn from coordinate information in coordinate\u2013category data. Through systematic experiments using synthetic datasets with tunable coordinate and category contributions, and a comprehensive benchmarking framework (MatText), we find that LLMs consistently fail to capture coordinate information while excelling at category patterns. This geometric blindness persists regardless of model size (up to 70 billion parameters), dataset scale (up to 2 \u00d7 10 6 structures) or text representation strategy. For material property prediction tasks dominated by structural effects, specialized geometric architectures consistently outperform LLMs, as evidenced by a clear \u2018Graph Neural Network Language Model (GNN-LM) wall\u2019 in performance benchmarks. Our analysis provides concrete guidelines for architecture selection in scientific machine learning."}
24	{"work_id": 3011072970, "title": "Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2", "abstract": "Abstract Human Coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV), Middle east respiratory syndrome coronavirus (MERS-CoV), and 2019 novel coronavirus (2019-nCoV), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV has the highest nucleotide sequence identity with SARS-CoV (79.7%) among the six other known pathogenic HCoVs. Specifically, the envelope and nucleocapsid proteins of 2019-nCoV are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and known HCoV-host interactions in the human protein-protein interactome, we computationally identified 135 putative repurposable drugs for the potential prevention and treatment of HCoVs. In addition, we prioritized 16 potential anti-HCoV repurposable drugs (including melatonin, mercaptopurine, and sirolimus) that were further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. Finally, we showcased three potential drug combinations (including sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the \u2018 Complementary Exposure\u2019 pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human protein-protein interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations toward future clinical trials for HCoVs."}	20	{"work_id": 3011072970, "title": "Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2", "abstract": "Abstract Human Coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV), Middle east respiratory syndrome coronavirus (MERS-CoV), and 2019 novel coronavirus (2019-nCoV), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV has the highest nucleotide sequence identity with SARS-CoV (79.7%) among the six other known pathogenic HCoVs. Specifically, the envelope and nucleocapsid proteins of 2019-nCoV are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and known HCoV-host interactions in the human protein-protein interactome, we computationally identified 135 putative repurposable drugs for the potential prevention and treatment of HCoVs. In addition, we prioritized 16 potential anti-HCoV repurposable drugs (including melatonin, mercaptopurine, and sirolimus) that were further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. Finally, we showcased three potential drug combinations (including sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the \u2018 Complementary Exposure\u2019 pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human protein-protein interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations toward future clinical trials for HCoVs."}
25	{"work_id": 3008142620, "title": "Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods", "abstract": "SARS-CoV-2 has caused tens of thousands of infections and more than one thousand deaths. There are currently no registered therapies for treating coronavirus infections. Because of time consuming process of new drug development, drug repositioning may be the only solution to the epidemic of sudden infectious diseases. We systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses, predicted their structures, and built 19 structures that could be done by homology modeling. By performing target-based virtual ligand screening, a total of 21 targets (including two human targets) were screened against compound libraries including ZINC drug database and our own database of natural products. Structure and screening results of important targets such as 3-chymotrypsin-like protease (3CLpro), Spike, RNA-dependent RNA polymerase (RdRp), and papain like protease (PLpro) were discussed in detail. In addition, a database of 78 commonly used anti-viral drugs including those currently on the market and undergoing clinical trials for SARS-CoV-2 was constructed. Possible targets of these compounds and potential drugs acting on a certain target were predicted. This study will provide new lead compounds and targets for further in vitro and in vivo studies of SARS-CoV-2, new insights for those drugs currently ongoing clinical studies, and also possible new strategies for drug repositioning to treat SARS-CoV-2 infections."}	21	{"work_id": 3008142620, "title": "Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods", "abstract": "SARS-CoV-2 has caused tens of thousands of infections and more than one thousand deaths. There are currently no registered therapies for treating coronavirus infections. Because of time consuming process of new drug development, drug repositioning may be the only solution to the epidemic of sudden infectious diseases. We systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses, predicted their structures, and built 19 structures that could be done by homology modeling. By performing target-based virtual ligand screening, a total of 21 targets (including two human targets) were screened against compound libraries including ZINC drug database and our own database of natural products. Structure and screening results of important targets such as 3-chymotrypsin-like protease (3CLpro), Spike, RNA-dependent RNA polymerase (RdRp), and papain like protease (PLpro) were discussed in detail. In addition, a database of 78 commonly used anti-viral drugs including those currently on the market and undergoing clinical trials for SARS-CoV-2 was constructed. Possible targets of these compounds and potential drugs acting on a certain target were predicted. This study will provide new lead compounds and targets for further in vitro and in vivo studies of SARS-CoV-2, new insights for those drugs currently ongoing clinical studies, and also possible new strategies for drug repositioning to treat SARS-CoV-2 infections."}
26	−{"work_id": 2913606428, "title": "PsychoPy2: Experiments in behavior made easy", "abstract": "PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future."}	22	+{"work_id": 3161540023, "title": "Visualizations with statistical details: The 'ggstatsplot' approach", "abstract": "Graphical displays can reveal problems in a statistical model that might not be apparent from purely numerical summaries.Such visualizations can also be helpful for the reader to evaluate the validity of a model if it is reported in a scholarly publication or report.But, given the onerous costs involved, researchers often avoid preparing information-rich graphics and exploring several statistical approaches or tests available.The ggstatsplot package in the R programming language (R Core Team, 2021) provides a one-line syntax to enrich ggplot2based visualizations with the results from statistical analysis embedded in the visualization itself.In doing so, the package helps researchers adopt a rigorous, reliable, and robust data exploratory and reporting workflow."}
27	−{"work_id": 2899760200, "title": "The PRIDE database and related tools and resources in 2019: improving support for quantification data", "abstract": "The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas."}
28	−{"work_id": 3208691473, "title": "The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences", "abstract": "The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas."}
29	−{"work_id": 4395010444, "title": "ProTox 3.0: a webserver for the prediction of toxicity of chemicals", "abstract": "Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot."}
30	−{"work_id": 4393936599, "title": "ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support", "abstract": "ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com."}
31	−{"work_id": 2990427812, "title": "Welcome to the Tidyverse", "abstract": "At a high level, the tidyverse is a language for solving data science challenges with R code.Its primary goal is to facilitate a conversation between a human and a computer about data.Less abstractly, the tidyverse is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next."}
32	{"work_id": 3133457239, "title": "Numerical methods for topography simulation", "abstract": "Die Simulation von Herstellungsschritten in der Halbleiterfertigung erlaubt nicht nurein besseres Verst\u00e4ndnis der zugrunde liegenden Physik, sondern auch die Prozessop-timierung ohne teure Experimente. Viele Schritte in der Prozesskette, um integrierte Schaltungen herzustellen, ver\u00e4ndern die Topographie der Waferoberfl\u00e4che durch \u00c4tzenoder Abscheidung neuer Materialschichten. Diese Arbeit pr\u00e4sentiert neue numerische Verfahren, die eine effiziente und genaue Simulation solcher Prozesse erlauben. Ein Hauptproblem der Topographiesimulation stellt die korrekte zeitliche Beschreibung der geometrischen Ver\u00e4nderungen, insbesondere in drei Dimensionen, dar. Daher wurde basierend auf der Level-Set-Methode eine Technikentwickelt, die neueste Algorithmen und Datenstrukturen, wie die Sparse-Field-Methode oder hierarchische Laufl\u00e4ngenkodierung, benutzt, um Rechenzeit sowie Speicherverbrauch zu minimierenund die Zeitentwicklung gro\u00dfer dreidimensionaler Geometrien zu erm\u00f6glichen. Eine genaue Ber\u00fccksichtigung verschiedenener Materialschichten und derdavon abh\u00e4ngigen Oberfl\u00e4chenraten ist f\u00fcr \u00c4tzprozesse besonders wichtig und konnte mit einer neuartigen Multi-Level-Set-Methode erreicht werden. Zudem wurden Algorithmen f\u00fcr diversegeometrische Operationen und f\u00fcr den Test auf gerichtete Sichtbarkeit und Konnektivit\u00e4t verwirklicht. Um moderne Multikernprozessoren auszun\u00fctzen, wird ein neuer Ansatz f\u00fcr die Parallelisierung der hierarchischen Laufl\u00e4ngenkodierung pr\u00e4sentiert. Ein weiteres Problem ist die Bestimmung der Oberfl\u00e4chenraten f\u00fcr realistische Topo-graphiesimulationen. Erweiterte Modelle f\u00fcr die Beschreibung der Oberfl\u00e4chenkinetikerfordern die Kenntnis der Teilchenflussverteilung an der Oberfl\u00e4che. Obwohl meistensballistischer Teilchentransport angenommen werden kann,ist die Berechnung dennochsehr aufw \u0308andig, wenn Reemissionen oder spiegelartige Reflexionen ber\u00fccksichtigt werden sollen. Simulationen sind daher oft limitiert auf vereinfachte Modelle oder kleineStrukturen. Ein vielversprechender Ansatz, um diese Einschr\u00e4nkung zu \u00fcberwinden, ist eine Monte-Carlo-Methode, die durch die Simulation vieler Teilchentrajektoriendie Flussverteilung bestimmt. Diese Arbeit pr\u00e4sentiert ein neues Verfahren, das diese Monte-Carlo-Berechnung direkt auf die implizite Oberfl\u00e4chendarstellung der Level-Set-Methode anwendet. Zudem wurde der Rechenaufwand durchdie Anwendung vonerweiterten Raytracing-Algorithmen und -Datenstrukturen, die f\u00fcr die Bed\u00fcrfnisse von Topographiesimulationen adaptiert und optimiert wurden,reduziert. Schlussendlich werden die gezeigten numerischen Methodenanhand verschiedener inder Literatur beschriebene Prozessmodelle getestet, um die vielf\u00e4ltige Anwendbarkeit, insbesondere f\u00fcr gro\u00dfe dreidimensionale Strukturen, zu demonstrieren."}	23	{"work_id": 3133457239, "title": "Numerical methods for topography simulation", "abstract": "Die Simulation von Herstellungsschritten in der Halbleiterfertigung erlaubt nicht nurein besseres Verst\u00e4ndnis der zugrunde liegenden Physik, sondern auch die Prozessop-timierung ohne teure Experimente. Viele Schritte in der Prozesskette, um integrierte Schaltungen herzustellen, ver\u00e4ndern die Topographie der Waferoberfl\u00e4che durch \u00c4tzenoder Abscheidung neuer Materialschichten. Diese Arbeit pr\u00e4sentiert neue numerische Verfahren, die eine effiziente und genaue Simulation solcher Prozesse erlauben. Ein Hauptproblem der Topographiesimulation stellt die korrekte zeitliche Beschreibung der geometrischen Ver\u00e4nderungen, insbesondere in drei Dimensionen, dar. Daher wurde basierend auf der Level-Set-Methode eine Technikentwickelt, die neueste Algorithmen und Datenstrukturen, wie die Sparse-Field-Methode oder hierarchische Laufl\u00e4ngenkodierung, benutzt, um Rechenzeit sowie Speicherverbrauch zu minimierenund die Zeitentwicklung gro\u00dfer dreidimensionaler Geometrien zu erm\u00f6glichen. Eine genaue Ber\u00fccksichtigung verschiedenener Materialschichten und derdavon abh\u00e4ngigen Oberfl\u00e4chenraten ist f\u00fcr \u00c4tzprozesse besonders wichtig und konnte mit einer neuartigen Multi-Level-Set-Methode erreicht werden. Zudem wurden Algorithmen f\u00fcr diversegeometrische Operationen und f\u00fcr den Test auf gerichtete Sichtbarkeit und Konnektivit\u00e4t verwirklicht. Um moderne Multikernprozessoren auszun\u00fctzen, wird ein neuer Ansatz f\u00fcr die Parallelisierung der hierarchischen Laufl\u00e4ngenkodierung pr\u00e4sentiert. Ein weiteres Problem ist die Bestimmung der Oberfl\u00e4chenraten f\u00fcr realistische Topo-graphiesimulationen. Erweiterte Modelle f\u00fcr die Beschreibung der Oberfl\u00e4chenkinetikerfordern die Kenntnis der Teilchenflussverteilung an der Oberfl\u00e4che. Obwohl meistensballistischer Teilchentransport angenommen werden kann,ist die Berechnung dennochsehr aufw \u0308andig, wenn Reemissionen oder spiegelartige Reflexionen ber\u00fccksichtigt werden sollen. Simulationen sind daher oft limitiert auf vereinfachte Modelle oder kleineStrukturen. Ein vielversprechender Ansatz, um diese Einschr\u00e4nkung zu \u00fcberwinden, ist eine Monte-Carlo-Methode, die durch die Simulation vieler Teilchentrajektoriendie Flussverteilung bestimmt. Diese Arbeit pr\u00e4sentiert ein neues Verfahren, das diese Monte-Carlo-Berechnung direkt auf die implizite Oberfl\u00e4chendarstellung der Level-Set-Methode anwendet. Zudem wurde der Rechenaufwand durchdie Anwendung vonerweiterten Raytracing-Algorithmen und -Datenstrukturen, die f\u00fcr die Bed\u00fcrfnisse von Topographiesimulationen adaptiert und optimiert wurden,reduziert. Schlussendlich werden die gezeigten numerischen Methodenanhand verschiedener inder Literatur beschriebene Prozessmodelle getestet, um die vielf\u00e4ltige Anwendbarkeit, insbesondere f\u00fcr gro\u00dfe dreidimensionale Strukturen, zu demonstrieren."}
33	−{"work_id": 4402696779, "title": "Crystalline Material Discovery in the Era of Artificial Intelligence", "abstract": "Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery, traditional experimental and computational approaches are time-consuming and expensive. In these years, thanks to the explosive amount of crystalline materials data, great interest has been given to data-driven materials discovery. Particularly, recent advancements have exploited the expressive representation ability of deep learning to model the highly complex atomic systems within crystalline materials, opening up new avenues for efficient and accurate materials discovery. These works main focus on four types of tasks, including physicochemical property prediction, generative design of crystalline materials, aiding characterization, and accelerating theoretical computations. Despite the remarkable progress, there is still a lack of systematic investigation to summarize their distinctions and limitations. To fill this gap, we systematically investigated the progress of crystalline materials discovery using artificial intelligence made in recent years. We first introduce several data representations of the crystalline materials. Based on the representations, we summarize various fundamental deep learning models and their tailored usages in various material discovery tasks. Finally, we highlight the remaining challenges and propose future directions."}
34	{"work_id": 7140287209, "title": "Towards end-to-end automation of AI research", "abstract": "Abstract The automation of science is a long-standing ambition in artificial intelligence (AI) research 1,2 . Although the community has made substantial progress in automating individual components of the scientific process, a system that autonomously navigates the entire research life cycle\u2014from conception to publication\u2014has remained out of reach. Here we present a pipeline for automating the entire scientific process end to end. We present The AI Scientist, which creates research ideas, writes code, runs experiments, plots and analyses data, writes the entire scientific manuscript, and performs its own peer review. Its ideas, execution and presentation are of sufficient quality that the manuscript generated by this AI system passed the first round of peer review for a workshop of a top-tier machine learning conference. The workshop had an acceptance rate of 70%. Our system leverages modern foundation models 3\u20135 within a complex agentic system. We evaluate The AI Scientist in two settings: a focused mode using human-provided code templates as an initial scaffold for conducting research on a specific topic and a template-free, open-ended mode that leverages agentic search for wider scientific exploration 6,7 . Both settings produce diverse ideas and automatically test, report on and evaluate them. This achievement demonstrates the growing capacity of AI for making scientific contributions and signifies a potential paradigm shift in how research is conducted. As with any impactful new technology, there could be important risks, including taxing overwhelmed review systems and adding noise to the scientific literature. However, if developed responsibly, such autonomous systems could greatly accelerate scientific discovery."}	24	{"work_id": 7140287209, "title": "Towards end-to-end automation of AI research", "abstract": "Abstract The automation of science is a long-standing ambition in artificial intelligence (AI) research 1,2 . Although the community has made substantial progress in automating individual components of the scientific process, a system that autonomously navigates the entire research life cycle\u2014from conception to publication\u2014has remained out of reach. Here we present a pipeline for automating the entire scientific process end to end. We present The AI Scientist, which creates research ideas, writes code, runs experiments, plots and analyses data, writes the entire scientific manuscript, and performs its own peer review. Its ideas, execution and presentation are of sufficient quality that the manuscript generated by this AI system passed the first round of peer review for a workshop of a top-tier machine learning conference. The workshop had an acceptance rate of 70%. Our system leverages modern foundation models 3\u20135 within a complex agentic system. We evaluate The AI Scientist in two settings: a focused mode using human-provided code templates as an initial scaffold for conducting research on a specific topic and a template-free, open-ended mode that leverages agentic search for wider scientific exploration 6,7 . Both settings produce diverse ideas and automatically test, report on and evaluate them. This achievement demonstrates the growing capacity of AI for making scientific contributions and signifies a potential paradigm shift in how research is conducted. As with any impactful new technology, there could be important risks, including taxing overwhelmed review systems and adding noise to the scientific literature. However, if developed responsibly, such autonomous systems could greatly accelerate scientific discovery."}
35	−{"work_id": 90568776, "title": "PETSc Users Manual", "abstract": "The Portable, Extensible Toolkit for Scientific Computation (PETSc), is a suite of data structures and routines for the scalable (parallel) solution of scientific applications modeled by partial differential equations. It supports MPI, and GPUs through CUDA or OpenCL, as well as hybrid MPI-GPU parallelism. PETSc (sometimes called PETSc/Tao) also contains the Tao optimization software library."}	25	+{"work_id": 3045710579, "title": "The ITensor Software Library for Tensor Network Calculations", "abstract": "ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagrams, allowing users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITensor interface rules out common programming errors and enables rapid prototyping of algorithms. After discussing the philosophy behind the ITensor approach, we show examples of each part of the interface including Index objects, the ITensor product operator, tensor factorizations, tensor storage types, algorithms for matrix product state (MPS) and matrix product operator (MPO) tensor networks, quantum number conserving block sparse tensors, and the NDTensors library. We also review publications that have used ITensor for quantum many-body physics and for other areas where tensor networks are increasingly applied. To conclude we discuss promising features and optimizations to be added in the future."}
		26	+{"work_id": 2769423117, "title": "\u201cFound in Translation\u201d: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models", "abstract": "There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Based on this analogy, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction prediction task as a translation problem by introducing a template-free sequence-to-sequence model, trained end-to-end and fully data-driven. We propose a tokenization, which is arbitrarily extensible with reaction information. Using an attention-based model borrowed from human language translation, we improve the state-of-the-art solutions in reaction prediction on the top-1 accuracy by achieving 80.3% without relying on auxiliary knowledge, such as reaction templates or explicit atomic features. Also, a top-1 accuracy of 65.4% is reached on a larger and noisier dataset."}
36	{"work_id": 3099929043, "title": "Bayesian probabilistic numerical methods", "abstract": "The increasing complexity of computer models used to solve contemporary inference problems has been set against a decreasing rate of improvement in processor speed in recent years. As a result, in many of these problems numerical error is a challenge for practitioners. However, while there has been a recent push towards rigorous quantification of uncertainty in inference problems based upon computer models, numerical error is still largely required to be driven down to a level at which its impact on inferences is negligible. Probabilistic numerical methods have been proposed to alleviate this; these are a class of numerical methods that return probabilistic uncertainty quantification for their numerical error. The attraction of such methods is clear: if numerical error in the computer model and uncertainty in an inference problem are quantified in a unified framework then careful tuning of numerical methods to mitigate the impact of numerical error on inferences could become unnecessary. \\n \\nIn this thesis we introduce the class of Bayesian probabilistic numerical methods, whose uncertainty has a strict and rigorous Bayesian interpretation. A number of examples of conjugate Bayesian probabilistic numerical methods are presented before we present analysis and algorithms for the general case, in which the posterior distribution does not posess a closed form. We conclude by studying how these methods can be rigorously composed to yield Bayesian pipelines of computation. Throughout we present applications of the developed methods to real-world inference problems, and indicate that the uncertainty quantification provided by these methods can be of significant practical use. \\n"}	27	{"work_id": 3099929043, "title": "Bayesian probabilistic numerical methods", "abstract": "The increasing complexity of computer models used to solve contemporary inference problems has been set against a decreasing rate of improvement in processor speed in recent years. As a result, in many of these problems numerical error is a challenge for practitioners. However, while there has been a recent push towards rigorous quantification of uncertainty in inference problems based upon computer models, numerical error is still largely required to be driven down to a level at which its impact on inferences is negligible. Probabilistic numerical methods have been proposed to alleviate this; these are a class of numerical methods that return probabilistic uncertainty quantification for their numerical error. The attraction of such methods is clear: if numerical error in the computer model and uncertainty in an inference problem are quantified in a unified framework then careful tuning of numerical methods to mitigate the impact of numerical error on inferences could become unnecessary. \\n \\nIn this thesis we introduce the class of Bayesian probabilistic numerical methods, whose uncertainty has a strict and rigorous Bayesian interpretation. A number of examples of conjugate Bayesian probabilistic numerical methods are presented before we present analysis and algorithms for the general case, in which the posterior distribution does not posess a closed form. We conclude by studying how these methods can be rigorously composed to yield Bayesian pipelines of computation. Throughout we present applications of the developed methods to real-world inference problems, and indicate that the uncertainty quantification provided by these methods can be of significant practical use. \\n"}
37	{"work_id": 1494474111, "title": "7 Linear algebra applications", "abstract": "Linear algebra is the most widely studied branch of mathematics after calculus.There are good reasons for this as it plays an important role in many fields.The readership the authors had in mind for this book includes majors in disciplines such as mathematics, engineering and social sciences.It will be useful for beginners and also as a reference for graduates.It introduces linear algebra, but at the same time it is tersely written and includes a great deal of material, a good lot contained in the exercises which, however, also contain many numerical ones.The chapter headings are: (1) Numbers, vectors and fields, (2) Matrices, (3) Row operations, (4) Vector spaces, (5) Linear mappings, (6) Inner product spaces, (7) Similarity and determinants, (8) Characteristic polynomial and eigenvalues of a matrix, (9) Some applications, (10) Unitary, orthogonal, Hermitian and symmetric matrices, (11) The singular value decomposition, and an appendix on using Maple."}	28	{"work_id": 1494474111, "title": "7 Linear algebra applications", "abstract": "Linear algebra is the most widely studied branch of mathematics after calculus.There are good reasons for this as it plays an important role in many fields.The readership the authors had in mind for this book includes majors in disciplines such as mathematics, engineering and social sciences.It will be useful for beginners and also as a reference for graduates.It introduces linear algebra, but at the same time it is tersely written and includes a great deal of material, a good lot contained in the exercises which, however, also contain many numerical ones.The chapter headings are: (1) Numbers, vectors and fields, (2) Matrices, (3) Row operations, (4) Vector spaces, (5) Linear mappings, (6) Inner product spaces, (7) Similarity and determinants, (8) Characteristic polynomial and eigenvalues of a matrix, (9) Some applications, (10) Unitary, orthogonal, Hermitian and symmetric matrices, (11) The singular value decomposition, and an appendix on using Maple."}
38	{"work_id": 4396723768, "title": "Augmenting large language models with chemistry tools", "abstract": "Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Our work not only aids expert chemists and lowers barriers for non-experts but also fosters scientific advancement by bridging the gap between experimental and computational chemistry."}	29	{"work_id": 4396723768, "title": "Augmenting large language models with chemistry tools", "abstract": "Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Our work not only aids expert chemists and lowers barriers for non-experts but also fosters scientific advancement by bridging the gap between experimental and computational chemistry."}
39	−{"work_id": 4415338867, "title": "Nonlinear Model Reduction by Probabilistic Manifold Decomposition", "abstract": "Abstract. This paper presents a novel nonlinear model reduction method: probabilistic manifold decomposition (PMD), which provides a powerful framework for constructing nonintrusive reduced-order models by embedding a high-dimensional system into a low-dimensional probabilistic manifold and predicting the dynamics. Through explicit mappings, PMD captures both linearity and nonlinearity of the system. A key strength of PMD lies in its predictive capabilities, allowing it to generate stable dynamic states based on embedded representations. The method also offers a mathematically rigorous approach to analyze the convergence of linear feature matrices and low-dimensional probabilistic manifolds, ensuring that sample-based approximations converge to the true data distributions as sample sizes increase. These properties, combined with its computational efficiency, make PMD a versatile tool for applications requiring high accuracy and scalability, such as fluid dynamics simulations and other engineering problems. By preserving the geometric and probabilistic structures of the high-dimensional system, PMD achieves a balance between computational speed, accuracy, and predictive capabilities, positioning itself as a robust alternative to the traditional model reduction methods."}
40	{"work_id": 4403324307, "title": "Chai-1: Decoding the molecular interactions of life", "abstract": "Abstract We introduce Chai-1, a multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of tasks relevant to drug discovery. Chai-1 can optionally be prompted with experimental restraints (e.g. derived from wet-lab data) which boosts performance by double-digit percentage points. Chai-1 can also be run in single-sequence mode with-out MSAs while preserving most of its performance. We release Chai-1 model weights and inference code as a Python package for non-commercial use and via a web interface where it can be used for free including for commercial drug discovery purposes."}	30	{"work_id": 4403324307, "title": "Chai-1: Decoding the molecular interactions of life", "abstract": "Abstract We introduce Chai-1, a multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of tasks relevant to drug discovery. Chai-1 can optionally be prompted with experimental restraints (e.g. derived from wet-lab data) which boosts performance by double-digit percentage points. Chai-1 can also be run in single-sequence mode with-out MSAs while preserving most of its performance. We release Chai-1 model weights and inference code as a Python package for non-commercial use and via a web interface where it can be used for free including for commercial drug discovery purposes."}
41	{"work_id": 4407041525, "title": "AI-Driven Inverse Design of Materials: Past, Present, and Future", "abstract": "Abstract The discovery of advanced materials is a cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic-structure calculation methods, such as the density functional theory and high-throughput computational methods. Recently, the rapid development of artificial intelligence (AI) technology in computer science has enabled the effective characterization of the implicit association between material properties and structures, thus forming an efficient paradigm for the inverse design of functional materials. Significant progress has been achieved in the inverse design of materials based on generative and discriminative models, attracting widespread interest from researchers. Considering this rapid technological progress, in this survey, we examine the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining challenges for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers."}	31	{"work_id": 4407041525, "title": "AI-Driven Inverse Design of Materials: Past, Present, and Future", "abstract": "Abstract The discovery of advanced materials is a cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic-structure calculation methods, such as the density functional theory and high-throughput computational methods. Recently, the rapid development of artificial intelligence (AI) technology in computer science has enabled the effective characterization of the implicit association between material properties and structures, thus forming an efficient paradigm for the inverse design of functional materials. Significant progress has been achieved in the inverse design of materials based on generative and discriminative models, attracting widespread interest from researchers. Considering this rapid technological progress, in this survey, we examine the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining challenges for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers."}
42	−{"work_id": 7118182048, "title": "AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer", "abstract": "Spatial proteomics enables high-resolution mapping of protein expression and can transform our understanding of biology and disease. However, major challenges remain for clinical translation, including cost, complexity and scalability. Here we present H&E to protein expression (HEX), an AI model designed to computationally generate spatial proteomics profiles from standard histopathology slides. Trained and validated on 819,000 histopathology image tiles with matched protein expression from 382 tumor samples, HEX accurately predicts the expression of 40 biomarkers encompassing immune, structural and functional programs. HEX demonstrates substantial performance gains over alternative methods for protein expression prediction from H&E images. We develop a multimodal data integration approach that combines the original H&E image and AI-derived virtual spatial proteomics to enhance outcome prediction. Applied to six independent non-small-cell lung cancer cohorts totaling 2,298 patients, HEX-enabled multimodal integration improved prognostic accuracy by 22% and immunotherapy response prediction by 24-39% compared with conventional clinicopathological and molecular biomarkers. Biological interpretation revealed spatially organized tumor-immune niches predictive of therapeutic response, including the co-localization of T helper cells and cytotoxic T cells in responders, and immunosuppressive tumor-associated macrophage and neutrophil aggregates in non-responders. HEX provides a low-cost and scalable approach to study spatial biology and enables the discovery and clinical translation of interpretable biomarkers for precision medicine."}	32	+{"work_id": 2913606428, "title": "PsychoPy2: Experiments in behavior made easy", "abstract": "PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future."}
43	−{"work_id": 2927961455, "title": "Interactive Tree Of Life (iTOL) v4: recent updates and new developments", "abstract": "The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. The current version introduces four new dataset types, together with numerous new features. Annotation options have been expanded and new control options added for many display elements. An interactive spreadsheet-like editor has been implemented, providing dataset creation and editing directly in the web interface. Font support has been rewritten with full support for UTF-8 character encoding throughout the user interface. Google Web Fonts are now fully supported in the tree text labels. iTOL v4 is the first tool which supports direct visualization of Qiime 2 trees and associated annotations. The user account system has been streamlined and expanded with new navigation options, and currently handles >700 000 trees from more than 40 000 individual users. Full batch access has been implemented allowing programmatic upload and export of trees and annotations."}	33	+{"work_id": 2899760200, "title": "The PRIDE database and related tools and resources in 2019: improving support for quantification data", "abstract": "The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas."}
44	−{"work_id": 4386373863, "title": "PyMC: a modern, and comprehensive probabilistic programming framework in Python", "abstract": "PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU. Being a general modeling framework, PyMC supports a variety of models including generalized hierarchical linear regression and classification, time series, ordinary differential equations (ODEs), and non-parametric models such as Gaussian processes (GPs). We demonstrate PyMC's versatility and ease of use with examples spanning a range of common statistical models. Additionally, we discuss the positive role of PyMC in the development of the open-source ecosystem for probabilistic programming."}	34	+{"work_id": 3208691473, "title": "The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences", "abstract": "The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas."}
45	−{"work_id": 4323304388, "title": "Uni-Mol: A Universal 3D Molecular Representation Learning Framework", "abstract": "Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for downstream tasks and, in particular, making it almost impossible for 3D geometry prediction/generation. In this paper, we propose a universal 3D MRL framework, called Uni-Mol, that significantly enlarges the representation ability and application scope of MRL schemes. Uni-Mol contains two pretrained models with the same SE(3) Transformer architecture: a molecular model pretrained by 209M molecular conformations; a pocket model pretrained by 3M candidate protein pocket data. Besides, Uni-Mol contains several finetuning strategies to apply the pretrained models to various downstream tasks. By properly incorporating 3D information, Uni-Mol outperforms SOTA in 14/15 molecular property prediction tasks. Moreover, Uni-Mol achieves superior performance in 3D spatial tasks, including protein-ligand binding pose prediction, molecular conformation generation, etc. The code, model, and data are made publicly available at https://github.com/dptech-corp/Uni-Mol."}	35	+{"work_id": 4395010444, "title": "ProTox 3.0: a webserver for the prediction of toxicity of chemicals", "abstract": "Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot."}
46	−{"work_id": 3098269892, "title": "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.", "abstract": "We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the domain of drug-like molecules and also in a set of molecules with fewer that nine heavy atoms."}	36	+{"work_id": 2766160192, "title": "Advanced Bayesian Multilevel Modeling with the R Package brms", "abstract": "The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models, which are fitted with the probabilistic programming language Stan behind the scenes. Several response distributions are supported, of which all parameters (e.g., location, scale, and shape) can be predicted at the same time thus allowing for distributional regression. Non-linear relationships may be specified using non-linear predictor terms or semi-parametric approaches such as splines or Gaussian processes. To make all of these modeling options possible in a multilevel framework, brms provides an intuitive and powerful formula syntax, which extends the well known formula syntax of lme4. The purpose of the present paper is to introduce this syntax in detail and to demonstrate its usefulness with four examples, each showing other relevant aspects of the syntax."}
47	−{"work_id": 4415202617, "title": "Chemical reasoning in LLMs unlocks strategy-aware synthesis planning and reaction mechanism elucidation", "abstract": "While machine learning algorithms have been shown to excel at specific chemical tasks, they have struggled to capture the strategic thinking that characterizes expert chemical reasoning, limiting their widespread adoption. Here we demonstrate that large language models (LLMs) can serve as powerful chemical reasoning engines when integrated with traditional search algorithms, enabling a new approach to computer-aided chemistry that mirrors human expert thinking. Rather than using LLMs to directly manipulate chemical structures, we leverage their ability to evaluate chemical strategies and guide search algorithms toward chemically meaningful solutions. We demonstrate this paradigm through two fundamental challenges: strategy-aware retrosynthetic planning and mechanism elucidation. In retrosynthetic planning, our method allows chemists to specify desired synthetic strategies in natural language or maximize global feasibility to find routes that satisfy these constraints in vast searches. In mechanism elucidation, LLMs guide the search for plausible reaction mechanisms by combining chemical principles with systematic exploration. Our approach shows strong performance across diverse chemical tasks, with larger models demonstrating increasingly sophisticated chemical reasoning. Our approach establishes a new paradigm for computer-aided chemistry that combines the strategic understanding of LLMs with the precision of traditional chemical tools, opening possibilities for more intuitive and powerful chemical reasoning systems."}	37	+{"work_id": 4393936599, "title": "ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support", "abstract": "ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com."}
		38	+{"work_id": 2990427812, "title": "Welcome to the Tidyverse", "abstract": "At a high level, the tidyverse is a language for solving data science challenges with R code.Its primary goal is to facilitate a conversation between a human and a computer about data.Less abstractly, the tidyverse is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next."}
48	{"work_id": 4400581728, "title": "An Update on the Nitrogen Heterocycle Compositions and Properties of U.S. FDA-Approved Pharmaceuticals (2013\u20132023)", "abstract": "This Perspective is a continuation of our analysis of U.S. FDA-approved small-molecule drugs (1938\u20132012) containing nitrogen heterocycles. In this study we report drug structure and property analyses of 321 unique new small-molecule drugs approved from January 2013 to December 2023 as well as information about frequency of important heteroatoms such as sulfur and fluorine and key small nitrogen substituents (CN and NO 2 ). The most notable change is an incredible increase in drugs containing at least one nitrogen heterocycle\u250082%, compared to 59% from preceding decades\u2500as well as a significant increase in the number of nitrogen heterocycles per drug. Pyridine has claimed the #1 high-frequency nitrogen heterocycle occurrence spot from piperidine (#2), with pyrimidine (#5), pyrazole (#6), and morpholine (#9) being the big top 10 climbers. Also notable is high number of fused nitrogen heterocycles, apparently driven largely by newly approved cancer drugs."}	39	{"work_id": 4400581728, "title": "An Update on the Nitrogen Heterocycle Compositions and Properties of U.S. FDA-Approved Pharmaceuticals (2013\u20132023)", "abstract": "This Perspective is a continuation of our analysis of U.S. FDA-approved small-molecule drugs (1938\u20132012) containing nitrogen heterocycles. In this study we report drug structure and property analyses of 321 unique new small-molecule drugs approved from January 2013 to December 2023 as well as information about frequency of important heteroatoms such as sulfur and fluorine and key small nitrogen substituents (CN and NO 2 ). The most notable change is an incredible increase in drugs containing at least one nitrogen heterocycle\u250082%, compared to 59% from preceding decades\u2500as well as a significant increase in the number of nitrogen heterocycles per drug. Pyridine has claimed the #1 high-frequency nitrogen heterocycle occurrence spot from piperidine (#2), with pyrimidine (#5), pyrazole (#6), and morpholine (#9) being the big top 10 climbers. Also notable is high number of fused nitrogen heterocycles, apparently driven largely by newly approved cancer drugs."}
49	−{"work_id": 4211084734, "title": "Positive Definite Matrices", "abstract": "At the cost of some redundancy, to facilitate accessibility, the multiresolution decomposition and inversion of symmetric positive definite (SPD) matrices on finite-dimensional Euclidean space are developed in the Gamblet Transform and Decomposition framework."}
50	{"work_id": 7119529911, "title": "Deep contrastive learning enables genome-wide virtual screening", "abstract": "Abstract Numerous protein-coding genes are associated with human diseases, yet approximately 90% of them lack targeted therapeutic intervention. While conventional computational methods, such as molecular docking, have facilitated the discovery of potential hit compounds, the development of genome-wide virtual screening against the expansive chemical space remains a formidable challenge. Here we introduce DrugCLIP, a novel framework that combines contrastive learning and dense retrieval to achieve rapid and accurate virtual screening. Compared to traditional docking methods, DrugCLIP improves the speed of virtual screening by up to seven orders of magnitude. In terms of performance, DrugCLIP not only surpasses docking and other deep learning-based methods across two standard benchmark datasets, but also demonstrates high efficacy in wet-lab experiments. Specifically, DrugCLIP successfully identified agonists with < 100 nM affinities for 5HT 2A R, a key target in psychiatric diseases. For another target NET, whose structure is newly solved and not included in the training set, our method achieved a hit rate of 15%, with 12 diverse molecules exhibiting affinities better than bupropion. Additionally, two chemically novel inhibitors were validated by structure determination with Cryo-EM. Finally, a novel potential drug target TRIP12, with no experimental structures and inhibitors for reference, was used to challenge DrugCLIP. DrugCLIP achieved a hit rate of 17.5% by screening a pocket identified on an AlphaFold2-predicted structure, verified with multi-cycle SPR assays. Molecules with the highest affinities also showed a dose-dependent inhibition to the enzymatic function of TRIP12. Building on this foundation, we present the results of a pioneering trillion-scale genome-wide virtual screening, encompassing approximately 10,000 AlphaFold2 predicted proteins within the human genome and 500 million molecules from the ZINC and Enamine REAL database. This work provides an innovative perspective on drug discovery in the post-AlphaFold era, where comprehensive targeting of all disease-related proteins is within reach."}	40	{"work_id": 7119529911, "title": "Deep contrastive learning enables genome-wide virtual screening", "abstract": "Abstract Numerous protein-coding genes are associated with human diseases, yet approximately 90% of them lack targeted therapeutic intervention. While conventional computational methods, such as molecular docking, have facilitated the discovery of potential hit compounds, the development of genome-wide virtual screening against the expansive chemical space remains a formidable challenge. Here we introduce DrugCLIP, a novel framework that combines contrastive learning and dense retrieval to achieve rapid and accurate virtual screening. Compared to traditional docking methods, DrugCLIP improves the speed of virtual screening by up to seven orders of magnitude. In terms of performance, DrugCLIP not only surpasses docking and other deep learning-based methods across two standard benchmark datasets, but also demonstrates high efficacy in wet-lab experiments. Specifically, DrugCLIP successfully identified agonists with < 100 nM affinities for 5HT 2A R, a key target in psychiatric diseases. For another target NET, whose structure is newly solved and not included in the training set, our method achieved a hit rate of 15%, with 12 diverse molecules exhibiting affinities better than bupropion. Additionally, two chemically novel inhibitors were validated by structure determination with Cryo-EM. Finally, a novel potential drug target TRIP12, with no experimental structures and inhibitors for reference, was used to challenge DrugCLIP. DrugCLIP achieved a hit rate of 17.5% by screening a pocket identified on an AlphaFold2-predicted structure, verified with multi-cycle SPR assays. Molecules with the highest affinities also showed a dose-dependent inhibition to the enzymatic function of TRIP12. Building on this foundation, we present the results of a pioneering trillion-scale genome-wide virtual screening, encompassing approximately 10,000 AlphaFold2 predicted proteins within the human genome and 500 million molecules from the ZINC and Enamine REAL database. This work provides an innovative perspective on drug discovery in the post-AlphaFold era, where comprehensive targeting of all disease-related proteins is within reach."}
51	−{"work_id": 3143496104, "title": "Numerical Optimization", "abstract": "This chapter presents methods for numerically determining the maximum likelihood estimation (m.l.e.) and their computer implementations. The existence of a closed-form solution to the m.l.e. is far more the exception than the rule: it is often defined only implicitly, and numerical methods will be necessary to locate it. When the usual regularity conditions are satisfied, the m.l.e. can be obtained either by finding the roots of the score function, or by directly maximizing the likelihood function using numerical optimization algorithms. The chapter discusses evolutionary algorithm designation as a general approach that implies that it is suited for the optimization of possibly nonconvex, nonsmooth or even noncontinuous functions, precisely the situation in which gradient-and Hessian-based methods (two general thumb rules followed in numerical m.l.e.) are prone to fail. It explains two classes of evolutionary algorithms: genetic algorithms (differential evolution algorithm) and evolutionary strategies (Covariance Matrix Adaption Evolutionary Strategy algorithm)."}	41	+{"work_id": 4391561379, "title": "Leveraging large language models for predictive chemistry", "abstract": "Abstract Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemical knowledge for each application and, therefore, require specialized expertise to develop. Here we show that GPT-3, a large language model trained on vast amounts of text extracted from the Internet, can easily be adapted to solve various tasks in chemistry and materials science by fine-tuning it to answer chemical questions in natural language with the correct answer. We compared this approach with dedicated machine learning models for many applications spanning the properties of molecules and materials to the yield of chemical reactions. Surprisingly, our fine-tuned version of GPT-3 can perform comparably to or even outperform conventional machine learning techniques, in particular in the low-data limit. In addition, we can perform inverse design by simply inverting the questions. The ease of use and high performance, especially for small datasets, can impact the fundamental approach to using machine learning in the chemical and material sciences. In addition to a literature search, querying a pre-trained large language model might become a routine way to bootstrap a project by leveraging the collective knowledge encoded in these foundation models, or to provide a baseline for predictive tasks."}
		42	+{"work_id": 4302307901, "title": "DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking", "abstract": "Predicting the binding structure of a small molecule ligand to a protein -- a task known as molecular docking -- is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial improvements in accuracy. We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses. To do so, we map this manifold to the product space of the degrees of freedom (translational, rotational, and torsional) involved in docking and develop an efficient diffusion process on this space. Empirically, DiffDock obtains a 38% top-1 success rate (RMSD<2A) on PDBBind, significantly outperforming the previous state-of-the-art of traditional docking (23%) and deep learning (20%) methods. Moreover, while previous methods are not able to dock on computationally folded structures (maximum accuracy 10.4%), DiffDock maintains significantly higher precision (21.7%). Finally, DiffDock has fast inference times and provides confidence estimates with high selective accuracy."}
		43	+{"work_id": 4241803778, "title": "PETSc Users Manual (Rev. 3.13)", "abstract": "This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear solvers, nonlinear solvers, and time integrators that may be used in application codes written in Fortran, C, C++, and Python. PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++, python, or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than \\rolling them\" yourself."}
		44	+{"work_id": 4365512576, "title": "ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope", "abstract": "In recent years, artificial intelligence (AI) and machine learning have been transforming the landscape of scientific research. Out of which, the chatbot technology has experienced tremendous advancements in recent years, especially with ChatGPT emerging as a notable AI language model. This comprehensive review delves into the background, applications, key challenges, and future directions of ChatGPT. We begin by exploring its origins, development, and underlying technology, before examining its wide-ranging applications across industries such as customer service, healthcare, and education. We also highlight the critical challenges that ChatGPT faces, including ethical concerns, data biases, and safety issues, while discussing potential mitigation strategies. Finally, we envision the future of ChatGPT by exploring areas of further research and development, focusing on its integration with other technologies, improved human-AI interaction, and addressing the digital divide. This review offers valuable insights for researchers, developers, and stakeholders interested in the ever-evolving landscape of AI-driven conversational agents. This study explores the various ways ChatGPT has been revolutionizing scientific research, spanning from data processing and hypothesis generation to collaboration and public outreach. Furthermore, the paper examines the potential challenges and ethical concerns surrounding the use of ChatGPT in research, while highlighting the importance of striking a balance between AI-assisted innovation and human expertise. The paper presents several ethical issues in existing computing domain and how ChatGPT can invoke challenges to such notion. This work also includes some biases and limitations of ChatGPT. It is worth to note that despite of several controversies and ethical concerns, ChatGPT has attracted remarkable attentions from academia, research, and industries in a very short span of time."}
		45	+{"work_id": 3097145107, "title": "PubChem in 2021: new data content and improved web interfaces", "abstract": "PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)."}
		46	+{"work_id": 4389991792, "title": "Autonomous chemical research with large language models", "abstract": "Abstract Transformer-based large language models are making significant strides in various fields, such as natural language processing 1\u20135 , biology 6,7 , chemistry 8\u201310 and computer programming 11,12 . Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research."}
		47	+{"work_id": 3168436232, "title": "AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings", "abstract": "AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina."}
		48	+{"work_id": 3010959735, "title": "Gaussin prosessi -surrogaattimenetelmi\u00e4 likim\u00e4\u00e4r\u00e4iseen Bayesilaiseen p\u00e4\u00e4ttelyyn", "abstract": "In many application fields such as ecology, epidemiology and astronomy, simulation models are used to study complex phenomena that occur in nature. Often the analytical form of the likelihood function of these models is either unavailable or too costly to evaluate which complicates statistical inference. Likelihood-free inference (LFI) methods such as approximate Bayesian computation (ABC), based on replacing the evaluations of the intractable likelihood with forward simulations of the model, have become a popular approach to conduct inference for simulation models. Nevertheless, current LFI methods feature several computational and statistical challenges. Especially, standard ABC algorithms require a huge number of simulations which makes them infeasible when the forward simulations are expensive. This thesis deals with likelihood-free inference for computationally costly models. The main contribution is a coherent framework for LFI based on Gaussian process (GP) surrogate models. GP models allow to encode smoothness assumptions of the simulation model output to reduce the amount of simulations needed. Additionally, the uncertainty in the resulting model-based posterior approximations due to the limited simulation budget can be quantified. We develop Bayesian experimental design strategies to select the evaluation locations as to minimise the computational cost. Both sequential designs, where simulations are chosen one-at-a-time basis, and batch strategies, which allow to take advantage of parallel computing, are derived. In addition to the LFI scenario, the proposed methods also apply when the likelihood can be evaluated but is expensive. In essence, the proposed framework can be viewed as an LFI counterpart of probabilistic numerical methods such as Bayesian optimisation, developed for optimising expensive objective functions, and Bayesian quadrature, developed for computing integrals of expensive functions. We demonstrate the advantages of the proposed LFI methods using extensive empirical simulations. Some theoretical analysis of the proposed algorithms is also provided and their relation to some other GP surrogate methods are discussed. In addition to the contributions to statistical methodology, applications in population genomics are also considered. In particular, we use the GP-based ABC methodology to obtain an approximate posterior of a simulation model describing horizontal gene transfer in bacteria. We also develop a probabilistic model and an inference algorithm using a novel combination of ABC and Metropolis-within-Gibbs sampling to facilitate better understanding of bacterial colonisation."}
		49	+{"work_id": 4388615774, "title": "DrugBank 6.0: the DrugBank Knowledgebase for 2024", "abstract": "First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11 891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time) and RI (retention index) data for 9464 of DrugBank's 11 710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists."}
52	{"work_id": 4225106311, "title": "FAIR data enabling new horizons for materials research", "abstract": "The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science."}	50	{"work_id": 4225106311, "title": "FAIR data enabling new horizons for materials research", "abstract": "The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science."}
53	−{"work_id": 4307468223, "title": "PubChem 2023 update", "abstract": "PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the 'standardize' option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes."}	51	+{"work_id": 4200153169, "title": "Variational and Quasi-Variational Inequalities", "abstract": "This chapter presents a concise introduction to the theory of variational and quasi-variational inequalities. The available literature on these topics is vast, and we only give the most fundamental results which have direct relevance to our study of variational inequalities with random data. The presented material, which we will frequently use in the forthcoming chapters, can be divided into three parts. The first part deals with linear variational inequalities given by bilinear forms. In this setting, we provide an equivalent optimization formulation of the variational inequality under a symmetry condition on the bilinear form. We provide the so-called Minty variational inequality, study the solution set&s;s structure, and give some existence results. Focusing on elliptic regularization, we examine the behavior of the regularized solutions and relate it to the solvability of variational inequalities. We then study non-coercive variational inequalities and, under appropriate conditions, give some sufficient conditions for the existence of a solution. The second part deals with nonlinear variational inequalities involving some classes of nonlinear operators. We present existence results by using arguments based on the projection map. We develop a regularization framework by focusing on the case when the constraint sets are perturbed and provide a rate of convergence for the regularized solutions. We also briefly discuss penalization and use it to give some existence results for nonlinear variational inequalities in Banach spaces. In the third part, we then deal with quasi-variational inequalities. We present existence results for general constraint sets as well as by exploiting a particular structure of the constraint set. The chapter concludes with a discussion of some of the related contributions."}
54	−{"work_id": 3035965352, "title": "Array programming with NumPy", "abstract": "Abstract Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis."}
55	{"work_id": 3003257820, "title": "SciPy 1.0: fundamental algorithms for scientific computing in Python", "abstract": "SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments."}	52	{"work_id": 3003257820, "title": "SciPy 1.0: fundamental algorithms for scientific computing in Python", "abstract": "SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments."}
56	−{"work_id": 4388522045, "title": "SRplot: A free online platform for data visualization and graphing", "abstract": "Graphics are widely used to provide summarization of complex data in scientific publications. Although there are many tools available for drawing graphics, their use is limited by programming skills, costs, and platform specificities. Here, we presented a freely accessible easy-to-use web server named SRplot that integrated more than a hundred of commonly used data visualization and graphing functions together. It can be run easily using all Web browsers and there are no strong requirements on the computing power of users' machines. With a user-friendly graphical interface, users can simply paste the contents of the input file into the text box according to the defined file format. Modification operations can be easily performed, and graphs can be generated in real-time. The resulting graphs can be easily downloaded in bitmap (PNG or TIFF) or vector (PDF or SVG) format in publication quality. The website is updated promptly and continuously. Functions in SRplot have been improved, optimized and updated depend on feedback and suggestions from users. The graphs prepared with SRplot have been featured in more than five hundred peer-reviewed publications. The SRplot web server is now freely available at http://www.bioinformatics.com.cn/SRplot."}	53	+{"work_id": 2804822363, "title": "SWISS-MODEL: homology modelling of protein structures and complexes", "abstract": "Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org."}
		54	+{"work_id": 3156458251, "title": "Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation", "abstract": "The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 5 introduces a completely new tree display engine, together with numerous new features. For example, a new dataset type has been added (MEME motifs), while annotation options have been expanded for several existing ones. Node metadata display options have been extended and now also support non-numerical categorical values, as well as multiple values per node. Direct manual annotation is now available, providing a set of basic drawing and labeling tools, allowing users to draw shapes, labels and other features by hand directly onto the trees. Support for tree and dataset scales has been extended, providing fine control over line and label styles. Unrooted tree displays can now use the equal-daylight algorithm, proving a much greater display clarity. The user account system has been streamlined and expanded with new navigation options and currently handles >1 million trees from >70 000 individual users."}
57	{"work_id": 4223552703, "title": "Search and sequence analysis tools services from EMBL-EBI in 2022", "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic."}	55	{"work_id": 4223552703, "title": "Search and sequence analysis tools services from EMBL-EBI in 2022", "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic."}
58	{"work_id": 3027286012, "title": "TIMER2.0 for analysis of tumor-infiltrating immune cells", "abstract": "Tumor progression and the efficacy of immunotherapy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment. Due to the limitations of direct measurement methods, computational algorithms are often used to infer immune cell composition from bulk tumor transcriptome profiles. These estimated tumor immune infiltrate populations have been associated with genomic and transcriptomic changes in the tumors, providing insight into tumor-immune interactions. However, such investigations on large-scale public data remain challenging. To lower the barriers for the analysis of complex tumor-immune interactions, we significantly improved our previous web platform TIMER. Instead of just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms. TIMER2.0 provides four modules for investigating the associations between immune infiltrates and genetic or clinical features, and four modules for exploring cancer-related associations in the TCGA cohorts. Each module can generate a functional heatmap table, enabling the user to easily identify significant associations in multiple cancer types simultaneously. Overall, the TIMER2.0 web server provides comprehensive analysis and visualization functions of tumor infiltrating immune cells."}	56	{"work_id": 3027286012, "title": "TIMER2.0 for analysis of tumor-infiltrating immune cells", "abstract": "Tumor progression and the efficacy of immunotherapy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment. Due to the limitations of direct measurement methods, computational algorithms are often used to infer immune cell composition from bulk tumor transcriptome profiles. These estimated tumor immune infiltrate populations have been associated with genomic and transcriptomic changes in the tumors, providing insight into tumor-immune interactions. However, such investigations on large-scale public data remain challenging. To lower the barriers for the analysis of complex tumor-immune interactions, we significantly improved our previous web platform TIMER. Instead of just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms. TIMER2.0 provides four modules for investigating the associations between immune infiltrates and genetic or clinical features, and four modules for exploring cancer-related associations in the TCGA cohorts. Each module can generate a functional heatmap table, enabling the user to easily identify significant associations in multiple cancer types simultaneously. Overall, the TIMER2.0 web server provides comprehensive analysis and visualization functions of tumor infiltrating immune cells."}
59	{"work_id": 3136918052, "title": "UniProt: a worldwide hub of protein knowledge", "abstract": "The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/."}	57	{"work_id": 3136918052, "title": "UniProt: a worldwide hub of protein knowledge", "abstract": "The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/."}
				`@@ -62,19 +60,18 @@`
62	{"work_id": 4383218913, "title": "Accurate medium-range global weather forecasting with 3D neural networks", "abstract": "Abstract Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states 1 . However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods 2 have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting. Trained on 39 years of global data, our program, Pangu-Weather, obtains stronger deterministic forecast results on reanalysis data in all tested variables when compared with the world\u2019s best NWP system, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF) 3 . Our method also works well with extreme weather forecasts and ensemble forecasts. When initialized with reanalysis data, the accuracy of tracking tropical cyclones is also higher than that of ECMWF-HRES."}	60	{"work_id": 4383218913, "title": "Accurate medium-range global weather forecasting with 3D neural networks", "abstract": "Abstract Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states 1 . However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods 2 have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting. Trained on 39 years of global data, our program, Pangu-Weather, obtains stronger deterministic forecast results on reanalysis data in all tested variables when compared with the world\u2019s best NWP system, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF) 3 . Our method also works well with extreme weather forecasts and ensemble forecasts. When initialized with reanalysis data, the accuracy of tracking tropical cyclones is also higher than that of ECMWF-HRES."}
63	{"work_id": 4307425304, "title": "KEGG for taxonomy-based analysis of pathways and genomes", "abstract": "KEGG (https://www.kegg.jp) is a manually curated database resource integrating various biological objects categorized into systems, genomic, chemical and health information. Each object (database entry) is identified by the KEGG identifier (kid), which generally takes the form of a prefix followed by a five-digit number, and can be retrieved by appending /entry/kid in the URL. The KEGG pathway map viewer, the Brite hierarchy viewer and the newly released KEGG genome browser can be launched by appending /pathway/kid, /brite/kid and /genome/kid, respectively, in the URL. Together with an improved annotation procedure for KO (KEGG Orthology) assignment, an increasing number of eukaryotic genomes have been included in KEGG for better representation of organisms in the taxonomic tree. Multiple taxonomy files are generated for classification of KEGG organisms and viruses, and the Brite hierarchy viewer is used for taxonomy mapping, a variant of Brite mapping in the new KEGG Mapper suite. The taxonomy mapping enables analysis of, for example, how functional links of genes in the pathway and physical links of genes on the chromosome are conserved among organism groups."}	61	{"work_id": 4307425304, "title": "KEGG for taxonomy-based analysis of pathways and genomes", "abstract": "KEGG (https://www.kegg.jp) is a manually curated database resource integrating various biological objects categorized into systems, genomic, chemical and health information. Each object (database entry) is identified by the KEGG identifier (kid), which generally takes the form of a prefix followed by a five-digit number, and can be retrieved by appending /entry/kid in the URL. The KEGG pathway map viewer, the Brite hierarchy viewer and the newly released KEGG genome browser can be launched by appending /pathway/kid, /brite/kid and /genome/kid, respectively, in the URL. Together with an improved annotation procedure for KO (KEGG Orthology) assignment, an increasing number of eukaryotic genomes have been included in KEGG for better representation of organisms in the taxonomic tree. Multiple taxonomy files are generated for classification of KEGG organisms and viruses, and the Brite hierarchy viewer is used for taxonomy mapping, a variant of Brite mapping in the new KEGG Mapper suite. The taxonomy mapping enables analysis of, for example, how functional links of genes in the pathway and physical links of genes on the chromosome are conserved among organism groups."}
64	{"work_id": 4407002549, "title": "Benefits, limits, and risks of ChatGPT in medicine", "abstract": "ChatGPT represents a transformative technology in healthcare, with demonstrated impacts across clinical practice, medical education, and research. Studies show significant efficiency gains, including 70% reduction in administrative time for discharge summaries and achievement of medical professional-level performance on standardized tests (60% accuracy on USMLE, 78.2% on PubMedQA). ChatGPT offers personalized learning platforms, automated scoring, and instant access to vast medical knowledge in medical education, addressing resource limitations and enhancing training efficiency. It streamlines clinical workflows by supporting triage processes, generating discharge summaries, and alleviating administrative burdens, allowing healthcare professionals to focus more on patient care. Additionally, ChatGPT facilitates remote monitoring and chronic disease management, providing personalized advice, medication reminders, and emotional support, thus bridging gaps between clinical visits. Its ability to process and synthesize vast amounts of data accelerates research workflows, aiding in literature reviews, hypothesis generation, and clinical trial designs. This paper aims to gather and analyze published studies involving ChatGPT, focusing on exploring its advantages and disadvantages within the healthcare context. To aid in understanding and progress, our analysis is organized into six key areas: (1) Information and Education, (2) Triage and Symptom Assessment, (3) Remote Monitoring and Support, (4) Mental Healthcare Assistance, (5) Research and Decision Support, and (6) Language Translation. Realizing ChatGPT's full potential in healthcare requires addressing key limitations, such as its lack of clinical experience, inability to process visual data, and absence of emotional intelligence. Ethical, privacy, and regulatory challenges further complicate its integration. Future improvements should focus on enhancing accuracy, developing multimodal AI models, improving empathy through sentiment analysis, and safeguarding against artificial hallucination. While not a replacement for healthcare professionals, ChatGPT can serve as a powerful assistant, augmenting their expertise to improve efficiency, accessibility, and quality of care. This collaboration ensures responsible adoption of AI in transforming healthcare delivery. While ChatGPT demonstrates significant potential in healthcare transformation, systematic evaluation of its implementation across different healthcare settings reveals varying levels of evidence quality-from robust randomized trials in medical education to preliminary observational studies in clinical practice. This heterogeneity in evidence quality necessitates a structured approach to future research and implementation."}	62	{"work_id": 4407002549, "title": "Benefits, limits, and risks of ChatGPT in medicine", "abstract": "ChatGPT represents a transformative technology in healthcare, with demonstrated impacts across clinical practice, medical education, and research. Studies show significant efficiency gains, including 70% reduction in administrative time for discharge summaries and achievement of medical professional-level performance on standardized tests (60% accuracy on USMLE, 78.2% on PubMedQA). ChatGPT offers personalized learning platforms, automated scoring, and instant access to vast medical knowledge in medical education, addressing resource limitations and enhancing training efficiency. It streamlines clinical workflows by supporting triage processes, generating discharge summaries, and alleviating administrative burdens, allowing healthcare professionals to focus more on patient care. Additionally, ChatGPT facilitates remote monitoring and chronic disease management, providing personalized advice, medication reminders, and emotional support, thus bridging gaps between clinical visits. Its ability to process and synthesize vast amounts of data accelerates research workflows, aiding in literature reviews, hypothesis generation, and clinical trial designs. This paper aims to gather and analyze published studies involving ChatGPT, focusing on exploring its advantages and disadvantages within the healthcare context. To aid in understanding and progress, our analysis is organized into six key areas: (1) Information and Education, (2) Triage and Symptom Assessment, (3) Remote Monitoring and Support, (4) Mental Healthcare Assistance, (5) Research and Decision Support, and (6) Language Translation. Realizing ChatGPT's full potential in healthcare requires addressing key limitations, such as its lack of clinical experience, inability to process visual data, and absence of emotional intelligence. Ethical, privacy, and regulatory challenges further complicate its integration. Future improvements should focus on enhancing accuracy, developing multimodal AI models, improving empathy through sentiment analysis, and safeguarding against artificial hallucination. While not a replacement for healthcare professionals, ChatGPT can serve as a powerful assistant, augmenting their expertise to improve efficiency, accessibility, and quality of care. This collaboration ensures responsible adoption of AI in transforming healthcare delivery. While ChatGPT demonstrates significant potential in healthcare transformation, systematic evaluation of its implementation across different healthcare settings reveals varying levels of evidence quality-from robust randomized trials in medical education to preliminary observational studies in clinical practice. This heterogeneity in evidence quality necessitates a structured approach to future research and implementation."}
65	−{"work_id": 4308616064, "title": "InterPro in 2022", "abstract": "The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. Here, we report recent developments with InterPro (version 90.0) and its associated software, including updates to data content and to the website. These developments extend and enrich the information provided by InterPro, and provide a more user friendly access to the data. Additionally, we have worked on adding Pfam website features to the InterPro website, as the Pfam website will be retired in late 2022. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB. Moreover, we report the development of a card game as a method of engaging the non-scientific community. Finally, we discuss the benefits and challenges brought by the use of artificial intelligence for protein structure prediction."}
66	{"work_id": 3096828292, "title": "KEGG: integrating viruses and cellular organisms", "abstract": "KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations."}	63	{"work_id": 3096828292, "title": "KEGG: integrating viruses and cellular organisms", "abstract": "KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations."}
67	{"work_id": 3095583226, "title": "Pfam: The protein families database in 2021", "abstract": "The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/."}	64	{"work_id": 3095583226, "title": "Pfam: The protein families database in 2021", "abstract": "The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/."}
68	{"work_id": 4414990951, "title": "The rise of generative AI for metal-organic framework design and synthesis", "abstract": "Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This perspective introduces the shift from laborious enumeration of MOF candidates to generative approaches that can autonomously propose and synthesize in the laboratory new porous reticular structures on demand. We outline the progress of employing deep learning models, such as variational autoencoders, diffusion models, and large language-model-based agents, that are fueled by the growing amount of available data from the MOF community and suggest novel crystalline materials designs. These generative tools can be combined with high-throughput computational screening and even automated experiments to form accelerated, closed-loop discovery pipelines. The result is a new paradigm for reticular chemistry in which AI algorithms more efficiently direct the search for high-performance MOF materials for clean air and energy applications. Finally, we highlight remaining challenges such as synthetic feasibility, dataset diversity, and the need for further integration of domain knowledge."}	65	{"work_id": 4414990951, "title": "The rise of generative AI for metal-organic framework design and synthesis", "abstract": "Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This perspective introduces the shift from laborious enumeration of MOF candidates to generative approaches that can autonomously propose and synthesize in the laboratory new porous reticular structures on demand. We outline the progress of employing deep learning models, such as variational autoencoders, diffusion models, and large language-model-based agents, that are fueled by the growing amount of available data from the MOF community and suggest novel crystalline materials designs. These generative tools can be combined with high-throughput computational screening and even automated experiments to form accelerated, closed-loop discovery pipelines. The result is a new paradigm for reticular chemistry in which AI algorithms more efficiently direct the search for high-performance MOF materials for clean air and energy applications. Finally, we highlight remaining challenges such as synthetic feasibility, dataset diversity, and the need for further integration of domain knowledge."}
69	{"work_id": 4308834893, "title": "The STRING database in 2023: protein\u2013protein association networks and functional enrichment analyses for any sequenced genome of interest", "abstract": "Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein-protein interactions-both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes."}	66	{"work_id": 4308834893, "title": "The STRING database in 2023: protein\u2013protein association networks and functional enrichment analyses for any sequenced genome of interest", "abstract": "Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein-protein interactions-both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes."}
70	{"work_id": 2898402099, "title": "The Pfam protein families database in 2019", "abstract": "The last few years have witnessed significant changes in Pfam (https://pfam.xfam.org). The number of families has grown substantially to a total of 17,929 in release 32.0. New additions have been coupled with efforts to improve existing families, including refinement of domain boundaries, their classification into Pfam clans, as well as their functional annotation. We recently began to collaborate with the RepeatsDB resource to improve the definition of tandem repeat families within Pfam. We carried out a significant comparison to the structural classification database, namely the Evolutionary Classification of Protein Domains (ECOD) that led to the creation of 825 new families based on their set of uncharacterized families (EUFs). Furthermore, we also connected Pfam entries to the Sequence Ontology (SO) through mapping of the Pfam type definitions to SO terms. Since Pfam has many community contributors, we recently enabled the linking between authorship of all Pfam entries with the corresponding authors' ORCID identifiers. This effectively permits authors to claim credit for their Pfam curation and link them to their ORCID record."}	67	{"work_id": 2898402099, "title": "The Pfam protein families database in 2019", "abstract": "The last few years have witnessed significant changes in Pfam (https://pfam.xfam.org). The number of families has grown substantially to a total of 17,929 in release 32.0. New additions have been coupled with efforts to improve existing families, including refinement of domain boundaries, their classification into Pfam clans, as well as their functional annotation. We recently began to collaborate with the RepeatsDB resource to improve the definition of tandem repeat families within Pfam. We carried out a significant comparison to the structural classification database, namely the Evolutionary Classification of Protein Domains (ECOD) that led to the creation of 825 new families based on their set of uncharacterized families (EUFs). Furthermore, we also connected Pfam entries to the Sequence Ontology (SO) through mapping of the Pfam type definitions to SO terms. Since Pfam has many community contributors, we recently enabled the linking between authorship of all Pfam entries with the corresponding authors' ORCID identifiers. This effectively permits authors to claim credit for their Pfam curation and link them to their ORCID record."}
71	{"work_id": 4384652177, "title": "MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads", "abstract": "BACKGROUND: PacBio high fidelity (HiFi) sequencing reads are both long (15-20 kb) and highly accurate (> Q20). Because of these properties, they have revolutionised genome assembly leading to more accurate and contiguous genomes. In eukaryotes the mitochondrial genome is sequenced alongside the nuclear genome often at very high coverage. A dedicated tool for mitochondrial genome assembly using HiFi reads is still missing. RESULTS: MitoHiFi was developed within the Darwin Tree of Life Project to assemble mitochondrial genomes from the HiFi reads generated for target species. The input for MitoHiFi is either the raw reads or the assembled contigs, and the tool outputs a mitochondrial genome sequence fasta file along with annotation of protein and RNA genes. Variants arising from heteroplasmy are assembled independently, and nuclear insertions of mitochondrial sequences are identified and not used in organellar genome assembly. MitoHiFi has been used to assemble 374 mitochondrial genomes (368 Metazoa and 6 Fungi species) for the Darwin Tree of Life Project, the Vertebrate Genomes Project and the Aquatic Symbiosis Genome Project. Inspection of 60 mitochondrial genomes assembled with MitoHiFi for species that already have reference sequences in public databases showed the widespread presence of previously unreported repeats. CONCLUSIONS: MitoHiFi is able to assemble mitochondrial genomes from a wide phylogenetic range of taxa from Pacbio HiFi data. MitoHiFi is written in python and is freely available on GitHub ( https://github.com/marcelauliano/MitoHiFi ). MitoHiFi is available with its dependencies as a Docker container on GitHub (ghcr.io/marcelauliano/mitohifi:master)."}	68	{"work_id": 4384652177, "title": "MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads", "abstract": "BACKGROUND: PacBio high fidelity (HiFi) sequencing reads are both long (15-20 kb) and highly accurate (> Q20). Because of these properties, they have revolutionised genome assembly leading to more accurate and contiguous genomes. In eukaryotes the mitochondrial genome is sequenced alongside the nuclear genome often at very high coverage. A dedicated tool for mitochondrial genome assembly using HiFi reads is still missing. RESULTS: MitoHiFi was developed within the Darwin Tree of Life Project to assemble mitochondrial genomes from the HiFi reads generated for target species. The input for MitoHiFi is either the raw reads or the assembled contigs, and the tool outputs a mitochondrial genome sequence fasta file along with annotation of protein and RNA genes. Variants arising from heteroplasmy are assembled independently, and nuclear insertions of mitochondrial sequences are identified and not used in organellar genome assembly. MitoHiFi has been used to assemble 374 mitochondrial genomes (368 Metazoa and 6 Fungi species) for the Darwin Tree of Life Project, the Vertebrate Genomes Project and the Aquatic Symbiosis Genome Project. Inspection of 60 mitochondrial genomes assembled with MitoHiFi for species that already have reference sequences in public databases showed the widespread presence of previously unreported repeats. CONCLUSIONS: MitoHiFi is able to assemble mitochondrial genomes from a wide phylogenetic range of taxa from Pacbio HiFi data. MitoHiFi is written in python and is freely available on GitHub ( https://github.com/marcelauliano/MitoHiFi ). MitoHiFi is available with its dependencies as a Docker container on GitHub (ghcr.io/marcelauliano/mitohifi:master)."}
		69	+{"work_id": 4386740800, "title": "CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling", "abstract": "Abstract Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li x MnO 2 , the finite temperature phase diagram for Li x FePO 4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs."}
72	{"work_id": 2900569176, "title": "STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets", "abstract": "Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/."}	70	{"work_id": 2900569176, "title": "STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets", "abstract": "Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/."}
73	{"work_id": 3112376646, "title": "UniProt: the universal protein knowledgebase in 2021", "abstract": "The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/."}	71	{"work_id": 3112376646, "title": "UniProt: the universal protein knowledgebase in 2021", "abstract": "The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/."}
74	{"work_id": 4406405186, "title": "The design space of E(3)-equivariant atom-centred interatomic potentials", "abstract": "Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets."}	72	{"work_id": 4406405186, "title": "The design space of E(3)-equivariant atom-centred interatomic potentials", "abstract": "Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets."}
75	{"work_id": 3159995035, "title": "MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights", "abstract": "Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca."}	73	{"work_id": 3159995035, "title": "MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights", "abstract": "Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca."}
76	{"work_id": 4368358469, "title": "antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation", "abstract": "Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation."}	74	{"work_id": 4368358469, "title": "antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation", "abstract": "Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation."}
77	−{"work_id": 7139978436, "title": "Whole-genome prediction of bacterial pathogenic capacity on novel bacteria using protein language models with PathogenFinder2", "abstract": "MOTIVATION: Infectious diseases continue to be a leading cause of mortality and pose a significant global health threat. Thus, the development of tools for surveillance and early detection of emerging pathogens is needed. RESULTS: We introduce PathogenFinder2, a novel, alignment-free, taxonomy-agnostic model for predicting bacterial pathogenic capacity in humans using protein language models. It outperforms previous methods, particularly for novel taxa, and provides interpretable outputs by highlighting proteins most relevant to pathogenic potential. These insights aid the identification of virulence factors, vaccine targets, and infection-related metabolic pathways. Furthermore, we introduce the Bacterial Pathogenic Capacity Landscape, which reveals patterns linked to host condition, infection site, microbial antagonism, and environmental origin. AVAILABILITY: The model is freely available online at https://genepi.dk/pathogenfinder2, or as a standalone program (https://github.com/genomicepidemiology/PathogenFinder2)."}
78	{"work_id": 3161596995, "title": "antiSMASH 6.0: improving cluster detection and comparison capabilities", "abstract": "Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. Since 2011, the \"antibiotics and secondary metabolite analysis shell-antiSMASH\" (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free-to-use web server and as a standalone tool under an OSI-approved open-source license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters."}	75	{"work_id": 3161596995, "title": "antiSMASH 6.0: improving cluster detection and comparison capabilities", "abstract": "Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. Since 2011, the \"antibiotics and secondary metabolite analysis shell-antiSMASH\" (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free-to-use web server and as a standalone tool under an OSI-approved open-source license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters."}
79	{"work_id": 4309506674, "title": "UniProt: the Universal Protein Knowledgebase in 2023", "abstract": "The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins."}	76	{"work_id": 4309506674, "title": "UniProt: the Universal Protein Knowledgebase in 2023", "abstract": "The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins."}
80	{"work_id": 4406406714, "title": "Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning", "abstract": "Abstract Accurately predicting battery lifetime in early cycles holds tremendous value in real-world applications. However, this task poses significant challenges due to diverse factors influencing complex battery capacity degradation, such as cycling protocols, ambient temperatures and electrode materials. Moreover, cycling under specific conditions is both resource-intensive and time-consuming. Existing predictive models, primarily developed and validated within a restricted set of ageing conditions, thus raise doubts regarding their extensive applicability. Here we introduce BatLiNet, a deep learning framework tailored to predict battery lifetime reliably across a variety of ageing conditions. The distinctive design is integrating an inter-cell learning mechanism to predict the lifetime differences between two battery cells. This mechanism, when combined with conventional single-cell learning, enhances the stability of lifetime predictions for a target cell under varied ageing conditions. Our experimental results, derived from a broad spectrum of ageing conditions, demonstrate BatLiNet\u2019s superior accuracy and robustness compared to existing models. BatLiNet also exhibits transferring capabilities across different battery chemistries, benefitting scenarios with limited resources. We expect this study could promote exploration of cross-cell insights and facilitate battery research across comprehensive ageing factors."}	77	{"work_id": 4406406714, "title": "Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning", "abstract": "Abstract Accurately predicting battery lifetime in early cycles holds tremendous value in real-world applications. However, this task poses significant challenges due to diverse factors influencing complex battery capacity degradation, such as cycling protocols, ambient temperatures and electrode materials. Moreover, cycling under specific conditions is both resource-intensive and time-consuming. Existing predictive models, primarily developed and validated within a restricted set of ageing conditions, thus raise doubts regarding their extensive applicability. Here we introduce BatLiNet, a deep learning framework tailored to predict battery lifetime reliably across a variety of ageing conditions. The distinctive design is integrating an inter-cell learning mechanism to predict the lifetime differences between two battery cells. This mechanism, when combined with conventional single-cell learning, enhances the stability of lifetime predictions for a target cell under varied ageing conditions. Our experimental results, derived from a broad spectrum of ageing conditions, demonstrate BatLiNet\u2019s superior accuracy and robustness compared to existing models. BatLiNet also exhibits transferring capabilities across different battery chemistries, benefitting scenarios with limited resources. We expect this study could promote exploration of cross-cell insights and facilitate battery research across comprehensive ageing factors."}
				`@@ -85,14 +82,14 @@`
85	{"work_id": 2803664483, "title": "MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis", "abstract": "We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca."}	82	{"work_id": 2803664483, "title": "MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis", "abstract": "We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca."}
86	{"work_id": 7154488692, "title": "Lightweight Kolmogorov-Arnold Network with dual-objective optimization for axial capacity prediction of square coal gangue concrete-filled steel tube stub columns based on finite element simulation", "abstract": null}	83	{"work_id": 7154488692, "title": "Lightweight Kolmogorov-Arnold Network with dual-objective optimization for axial capacity prediction of square coal gangue concrete-filled steel tube stub columns based on finite element simulation", "abstract": null}
87	{"work_id": 3038025793, "title": "VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code", "abstract": null}	84	{"work_id": 3038025793, "title": "VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code", "abstract": null}
88	−{"work_id": 4406633314, "title": "Artificial intelligence in drug development", "abstract": null}
89	{"work_id": 3007309629, "title": "A Deep Learning Approach to Antibiotic Discovery", "abstract": null}	85	{"work_id": 3007309629, "title": "A Deep Learning Approach to Antibiotic Discovery", "abstract": null}
90	{"work_id": 4389991855, "title": "Discovery of a structural class of antibiotics with explainable deep learning", "abstract": null}	86	{"work_id": 4389991855, "title": "Discovery of a structural class of antibiotics with explainable deep learning", "abstract": null}
		87	+{"work_id": 4399728220, "title": "Kolmogorov\u2013Arnold-Informed Neural Network: A Physics-Informed Deep Learning Framework for Solving Pdes Based on Kolmogorov\u2013Arnold Networks", "abstract": null}
91	{"work_id": 4408339746, "title": "From PINNs to PIKANs: recent advances in physics-informed machine learning", "abstract": null}	88	{"work_id": 4408339746, "title": "From PINNs to PIKANs: recent advances in physics-informed machine learning", "abstract": null}
92	{"work_id": 4385490607, "title": "Scientific discovery in the age of artificial intelligence", "abstract": null}	89	{"work_id": 4385490607, "title": "Scientific discovery in the age of artificial intelligence", "abstract": null}
		90	+{"work_id": 4393097303, "title": "Generative AI for designing and validating easily synthesizable and structurally novel antibiotics", "abstract": null}
93	{"work_id": 4407922392, "title": "Large language models for scientific discovery in molecular property prediction", "abstract": null}	91	{"work_id": 4407922392, "title": "Large language models for scientific discovery in molecular property prediction", "abstract": null}
94	{"work_id": 4407925765, "title": "A practical guide to machine learning interatomic potentials \u2013 Status and future", "abstract": null}	92	{"work_id": 4407925765, "title": "A practical guide to machine learning interatomic potentials \u2013 Status and future", "abstract": null}
95	−{"work_id": 7128919969, "title": "SA-PINN: A self-attention enhanced physics-informed neural network for multiaxial fatigue life prediction with small samples", "abstract": null}
96	{"work_id": 4409069046, "title": "A predictive machine learning force-field framework for liquid electrolyte development", "abstract": null}	93	{"work_id": 4409069046, "title": "A predictive machine learning force-field framework for liquid electrolyte development", "abstract": null}
97	{"work_id": 4417522354, "title": "NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements", "abstract": null}	94	{"work_id": 4417522354, "title": "NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements", "abstract": null}
98	{"work_id": 2944434778, "title": "The REDCap consortium: Building an international community of software platform partners", "abstract": null}	95	{"work_id": 2944434778, "title": "The REDCap consortium: Building an international community of software platform partners", "abstract": null}

modified literature_synthesis/landmarks/computational_science_landmarks.csv +98 −101

				`@@ -1,101 +1,98 @@`
1	−work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review	1	+work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,ntitle,score,set,pi_review
2	−3029645440,10.1017/9781108993456.011,,Numerical optimization,Cambridge University Press eBooks,2021,book-chapter,scientific_computing,1,9332.0,9351.0,3148.4582677769677,13724,453.7174,True,False,baseline,3148.4582677769677,landmark,True	2	+3003257820,10.1038/s41592-019-0686-2,1907.10121,SciPy 1.0: fundamental algorithms for scientific computing in Python,Nature Methods,2020,article,scientific_computing,4,5667.0,10506.0,1314.046621797279,37925,1523.8871,True,True,baseline,scipy10fundamentalalgorithmsforscientificcomputinginpython,1314.046621797279,landmark,True
3	−3003257820,10.1038/s41592-019-0686-2,1907.10121,SciPy 1.0: fundamental algorithms for scientific computing in Python,Nature Methods,2020,article,scientific_computing,4,5667.0,10506.0,1314.046621797279,37925,1523.8871,True,True,baseline,1314.046621797279,landmark,True	3	+3201073812,10.1016/j.cpc.2021.108171,,"LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales",Computer Physics Communications,2021,article,scientific_computing,8,826.0,2697.0,568.1403850199874,12382,907.8113,True,True,baseline,lammpsaflexiblesimulationtoolforparticlebasedmaterialsmodelingattheatomicmesoandcontinuumscales,568.1403850199874,landmark,True
4	−3035965352,10.1038/s41586-020-2649-2,2006.10256,Array programming with NumPy,Nature,2020,article,scientific_computing,4,2086.0,5137.0,642.5145151506398,23207,829.0931,True,True,baseline,642.5145151506398,landmark,True	4	+2990427812,10.21105/joss.01686,,Welcome to the Tidyverse,The Journal of Open Source Software,2019,article,scientific_computing,2,697.0,2788.0,423.93060156353516,21504,642.5886,True,True,baseline,welcometothetidyverse,423.93060156353516,landmark,True
5	−3201073812,10.1016/j.cpc.2021.108171,,"LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales",Computer Physics Communications,2021,article,scientific_computing,8,826.0,2697.0,568.1403850199874,12382,907.8113,True,True,baseline,568.1403850199874,landmark,True	5	+3099929043,,,Bayesian probabilistic numerical methods,Warwick Research Archive Portal (University of Warwick),2019,dissertation,scientific_computing,3,61.0,81.0,294.96774193548384,85,,True,False,baseline,bayesianprobabilisticnumericalmethods,294.96774193548384,landmark,True
6	−2990427812,10.21105/joss.01686,,Welcome to the Tidyverse,The Journal of Open Source Software,2019,article,scientific_computing,2,697.0,2788.0,423.93060156353516,21504,642.5886,True,True,baseline,423.93060156353516,landmark,True	6	+3133457239,10.34726/hss.2010.001,,Numerical methods for topography simulation,reposiTUm (TU Wien),2019,dissertation,scientific_computing,2,9.0,10.0,222.03389830508473,13,,True,True,baseline,numericalmethodsfortopographysimulation,222.03389830508473,landmark,True
7	−90568776,,,PETSc Users Manual,,2019,article,scientific_computing,5,1066.0,1207.0,340.27902486121167,1896,114.6007,True,False,baseline,340.27902486121167,landmark,True	7	+2888532099,10.3847/1538-3881/aabc4f,,The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package *,The Astronomical Journal,2018,article,scientific_computing,1,459.0,1025.0,193.46314218749401,7687,234.9501,True,True,baseline,theastropyprojectbuildinganopenscienceprojectandstatusofthev20corepackage,193.46314218749401,landmark,True
8	−3099929043,,,Bayesian probabilistic numerical methods,Warwick Research Archive Portal (University of Warwick),2019,dissertation,scientific_computing,3,61.0,81.0,294.96774193548384,85,,True,False,baseline,294.96774193548384,landmark,True	8	+1494474111,10.1515/9783110666991-007,,7 Linear algebra applications,De Gruyter eBooks,2020,book-chapter,scientific_computing,2,184.0,190.0,163.70790263421142,482,30.6361,True,False,baseline,7linearalgebraapplications,163.70790263421142,landmark,True
9	−3133457239,10.34726/hss.2010.001,,Numerical methods for topography simulation,reposiTUm (TU Wien),2019,dissertation,scientific_computing,2,9.0,10.0,222.03389830508473,13,,True,True,baseline,222.03389830508473,landmark,True	9	+2913606428,10.3758/s13428-018-01193-y,,PsychoPy2: Experiments in behavior made easy,Behavior Research Methods,2019,article,scientific_computing,1,351.0,816.0,124.07724923810785,5166,170.3746,True,True,baseline,psychopy2experimentsinbehaviormadeeasy,124.07724923810785,landmark,True
10	−2888532099,10.3847/1538-3881/aabc4f,,The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package *,The Astronomical Journal,2018,article,scientific_computing,1,459.0,1025.0,193.46314218749401,7687,234.9501,True,True,baseline,193.46314218749401,landmark,True	10	+3038025793,10.1016/j.cpc.2021.108033,,VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code,Computer Physics Communications,2021,article,scientific_computing,1,947.0,2087.0,120.59877093864604,7856,267.5871,False,True,baseline,vaspkitauserfriendlyinterfacefacilitatinghighthroughputcomputingandanalysisusingvaspcode,120.59877093864604,landmark,True
11	−1494474111,10.1515/9783110666991-007,,7 Linear algebra applications,De Gruyter eBooks,2020,book-chapter,scientific_computing,2,184.0,190.0,163.70790263421142,482,30.6361,True,False,baseline,163.70790263421142,landmark,True	11	+7154488692,10.1016/j.engappai.2026.114843,,Lightweight Kolmogorov-Arnold Network with dual-objective optimization for axial capacity prediction of square coal gangue concrete-filled steel tube stub columns based on finite element simulation,Engineering Applications of Artificial Intelligence,2026,article,scientific_computing,1,45.0,45.0,33.579175704989154,45,110.7417,False,False,post,lightweightkolmogorovarnoldnetworkwithdualobjectiveoptimizationforaxialcapacitypredictionofsquarecoalgangueconcretefilledsteeltubestubcolumnsbasedonfiniteelementsimulation,114.09616854859023,landmark,True
12	−7154488692,10.1016/j.engappai.2026.114843,,Lightweight Kolmogorov-Arnold Network with dual-objective optimization for axial capacity prediction of square coal gangue concrete-filled steel tube stub columns based on finite element simulation,Engineering Applications of Artificial Intelligence,2026,article,scientific_computing,1,45.0,45.0,33.579175704989154,45,110.7417,False,False,post,159.98492606958206,landmark,True	12	+4383218913,10.1038/s41586-023-06185-3,,Accurate medium-range global weather forecasting with 3D neural networks,Nature,2023,article,scientific_computing,1,511.0,1104.0,106.38333772739257,1634,181.7259,True,True,post,accuratemediumrangeglobalweatherforecastingwith3dneuralnetworks,106.38333772739257,landmark,True
13	−2913606428,10.3758/s13428-018-01193-y,,PsychoPy2: Experiments in behavior made easy,Behavior Research Methods,2019,article,scientific_computing,1,351.0,816.0,124.07724923810785,5166,170.3746,True,True,baseline,124.07724923810785,landmark,True	13	+4408339746,10.1007/s44379-025-00015-1,2410.13228,From PINNs to PIKANs: recent advances in physics-informed machine learning,Machine learning for computational science and engineering,2025,article,scientific_computing,3,239.0,239.0,51.33385214007782,239,102.1338,False,True,post,frompinnstopikansrecentadvancesinphysicsinformedmachinelearning,104.43620467044524,landmark,True
14	−4408339746,10.1007/s44379-025-00015-1,2410.13228,From PINNs to PIKANs: recent advances in physics-informed machine learning,Machine learning for computational science and engineering,2025,article,scientific_computing,3,239.0,239.0,51.33385214007782,239,102.1338,False,True,post,120.99586631516542,landmark,True	14	+4386373863,10.7717/peerj-cs.1516,,"PyMC: a modern, and comprehensive probabilistic programming framework in Python",PeerJ Computer Science,2023,article,scientific_computing,4,390.0,626.0,100.85192909280501,870,132.4344,True,True,post,pymcamodernandcomprehensiveprobabilisticprogrammingframeworkinpython,100.85192909280501,landmark,True
15	−4417522354,10.1038/s43588-026-01009-6,,NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements,Nature Computational Science,2026,article,scientific_computing,1,34.0,34.0,26.21830985915493,34,44.9009,False,True,post,120.87749969701756,landmark,True	15	+4407925765,10.1016/j.cossms.2025.101214,2503.09814,A practical guide to machine learning interatomic potentials – Status and future,Current Opinion in Solid State and Materials Science,2025,review,scientific_computing,1,214.0,214.0,3.7709251101321586,214,9.2721,False,True,post,apracticalguidetomachinelearninginteratomicpotentialsstatusandfuture,93.51191547897606,landmark,True
16	−3038025793,10.1016/j.cpc.2021.108033,,VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code,Computer Physics Communications,2021,article,scientific_computing,1,947.0,2087.0,120.59877093864604,7856,267.5871,False,True,baseline,120.59877093864604,landmark,True	16	+2962853966,10.1007/s10915-018-0757-z,1511.06324,Global Convergence of ADMM in Nonconvex Nonsmooth Optimization,Journal of Scientific Computing,2018,article,scientific_computing,2,246.0,390.0,92.76320659281009,1268,89.8329,False,True,baseline,globalconvergenceofadmminnonconvexnonsmoothoptimization,92.76320659281009,landmark,True
17	−3143496104,10.1201/9781315107042-14,,Numerical Optimization,CRC Press eBooks,2018,book-chapter,scientific_computing,2,225.0,235.0,109.14754610815879,335,14.0895,True,False,baseline,109.14754610815879,landmark,True	17	+4417522354,10.1038/s43588-026-01009-6,,NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements,Nature Computational Science,2026,article,scientific_computing,1,34.0,34.0,26.21830985915493,34,44.9009,False,True,post,nep89universalneuroevolutionpotentialforinorganicandorganicmaterialsacross89elements,86.2059940144904,landmark,True
18	−4407925765,10.1016/j.cossms.2025.101214,2503.09814,A practical guide to machine learning interatomic potentials – Status and future,Current Opinion in Solid State and Materials Science,2025,review,scientific_computing,1,214.0,214.0,3.7709251101321586,214,9.2721,False,True,post,108.3393949432862,landmark,True	18	+4288039037,10.1007/s10915-022-01939-z,2201.05624,Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next,Journal of Scientific Computing,2022,article,scientific_computing,4,341.0,893.0,76.4919191278996,2784,166.3219,True,True,baseline,scientificmachinelearningthroughphysicsinformedneuralnetworkswhereweareandwhatsnext,76.4919191278996,landmark,True
19	−4383218913,10.1038/s41586-023-06185-3,,Accurate medium-range global weather forecasting with 3D neural networks,Nature,2023,article,scientific_computing,1,511.0,1104.0,106.38333772739257,1634,181.7259,True,True,post,106.38333772739257,landmark,True	19	+2766160192,10.32614/rj-2018-017,,Advanced Bayesian Multilevel Modeling with the R Package brms,The R Journal,2018,article,scientific_computing,3,101.0,378.0,75.7268279043758,2942,170.9214,True,True,baseline,advancedbayesianmultilevelmodelingwiththerpackagebrms,75.7268279043758,landmark,True
20	−4386373863,10.7717/peerj-cs.1516,,"PyMC: a modern, and comprehensive probabilistic programming framework in Python",PeerJ Computer Science,2023,article,scientific_computing,4,390.0,626.0,100.85192909280501,870,132.4344,True,True,post,100.85192909280501,landmark,True	20	+3010959735,,,Gaussin prosessi -surrogaattimenetelmiä likimääräiseen Bayesilaiseen päättelyyn,Aaltodoc (Aalto University),2020,dissertation,scientific_computing,4,9.0,15.0,69.26632428466618,28,,True,True,baseline,gaussinprosessisurrogaattimenetelmilikimriseenbayesilaiseenpttelyyn,69.26632428466618,landmark,True
21	−4242546620,10.1007/978-1-4842-6876-6_1,,DPLYR,Apress eBooks,2021,book-chapter,scientific_computing,1,185.0,286.0,96.29548332629801,367,143.0004,True,False,baseline,96.29548332629801,landmark,True	21	+3161540023,10.21105/joss.03167,,Visualizations with statistical details: The 'ggstatsplot' approach,The Journal of Open Source Software,2021,article,scientific_computing,2,245.0,572.0,66.66226424076243,1496,84.8639,True,True,baseline,visualizationswithstatisticaldetailstheggstatsplotapproach,66.66226424076243,landmark,True
22	−2962853966,10.1007/s10915-018-0757-z,1511.06324,Global Convergence of ADMM in Nonconvex Nonsmooth Optimization,Journal of Scientific Computing,2018,article,scientific_computing,2,246.0,390.0,92.76320659281009,1268,89.8329,False,True,baseline,92.76320659281009,landmark,True	22	+4241803778,10.2172/1614847,,PETSc Users Manual (Rev. 3.13),,2020,report,scientific_computing,4,50.0,85.0,65.38461538461539,125,,True,True,baseline,petscusersmanualrev313,65.38461538461539,landmark,True
23	−4211084734,10.1017/9781108594967.028,,Positive Definite Matrices,Cambridge University Press eBooks,2019,book-chapter,scientific_computing,1,102.0,110.0,89.28544845552662,166,22.481,True,False,baseline,89.28544845552662,landmark,True	23	+4385811703,10.33915/etd.12076,,Accelerating Manufacturing Decisions using Bayesian Optimization: An Optimization and Prediction Perspective,,2023,dissertation,scientific_computing,3,1.0,3.0,64.72727272727272,3,,True,True,post,acceleratingmanufacturingdecisionsusingbayesianoptimizationanoptimizationandpredictionperspective,64.72727272727272,landmark,True
24	−4245687930,10.1201/9781003090601-06,,Chemical Reaction Engineering,CRC Press eBooks,2020,book-chapter,scientific_computing,2,90.0,102.0,81.77618941255305,322,24.1009,True,False,baseline,81.77618941255305,landmark,True	24	+3045710579,10.21468/scipostphyscodeb.4,2007.14822,The ITensor Software Library for Tensor Network Calculations,SciPost Physics Codebases,2022,article,scientific_computing,1,352.0,566.0,63.07331378299121,1078,80.8504,True,True,baseline,theitensorsoftwarelibraryfortensornetworkcalculations,63.07331378299121,landmark,True
25	−7128919969,10.1016/j.advengsoft.2026.104124,,SA-PINN: A self-attention enhanced physics-informed neural network for multiaxial fatigue life prediction with small samples,Advances in Engineering Software,2026,article,scientific_computing,2,23.0,23.0,25.467432950191572,23,46.775,False,False,post,81.77007332445305,landmark,True	25	+4399728220,10.2139/ssrn.4868150,,Kolmogorov–Arnold-Informed Neural Network: A Physics-Informed Deep Learning Framework for Solving Pdes Based on Kolmogorov–Arnold Networks,SSRN Electronic Journal,2024,preprint,scientific_computing,2,45.0,53.0,62.46666666666666,53,,False,True,post,kolmogorovarnoldinformedneuralnetworkaphysicsinformeddeeplearningframeworkforsolvingpdesbasedonkolmogorovarnoldnetworks,62.46666666666666,landmark,True
26	−4415338867,10.1137/25m1738863,2503.00768,Nonlinear Model Reduction by Probabilistic Manifold Decomposition,SIAM Journal on Scientific Computing,2026,article,scientific_computing,2,22.0,22.0,22.669565217391305,22,75.5825,True,True,post,78.21485274512901,landmark,True	26	+4200153169,10.1201/9781315228969-4,,Variational and Quasi-Variational Inequalities,Chapman & Hall/CRC eBooks,2021,book-chapter,scientific_computing,1,94.0,96.0,61.80957282552753,407,13.851,True,False,baseline,variationalandquasivariationalinequalities,61.80957282552753,landmark,True
27	−2944434778,10.1016/j.jbi.2019.103208,,The REDCap consortium: Building an international community of software platform partners,Journal of Biomedical Informatics,2019,article,computational_biology,1,1231.0,3835.0,1565.383789896379,23807,2212.4824,False,True,baseline,1565.383789896379,landmark,True	27	+2944434778,10.1016/j.jbi.2019.103208,,The REDCap consortium: Building an international community of software platform partners,Journal of Biomedical Informatics,2019,article,computational_biology,1,1231.0,3835.0,1565.383789896379,23807,2212.4824,False,True,baseline,theredcapconsortiumbuildinganinternationalcommunityofsoftwareplatformpartners,1565.383789896379,landmark,True
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30	−3097145107,10.1093/nar/gkaa971,,PubChem in 2021: new data content and improved web interfaces,Nucleic Acids Research,2020,article,computational_biology,1,405.0,1318.0,383.79097718298823,3926,538.5909,True,True,baseline,383.79097718298823,landmark,True	30	+4411463597,10.1101/2025.06.14.659707,,Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction,bioRxiv (Cold Spring Harbor Laboratory),2025,preprint,computational_biology,3,665.0,665.0,2415.2006880733943,665,,True,True,post,boltz2towardsaccurateandefficientbindingaffinityprediction,290.58609249307983,landmark,True
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36	−4406633314,10.1038/s41591-024-03434-4,,Artificial intelligence in drug development,Nature Medicine,2025,article,computational_biology,1,498.0,498.0,209.201336966164,498,241.4884,False,False,post,252.1169097278342,landmark,True	36	+4409229284,10.1093/bioinformatics/btaf147,,CoverM: read alignment statistics for metagenomics,Bioinformatics,2025,article,computational_biology,2,511.0,511.0,110.40243438569799,511,130.364,True,True,post,covermreadalignmentstatisticsformetagenomics,223.29247107362977,landmark,True
37	−4323304388,10.26434/chemrxiv-2022-jjm0j-v4,,Uni-Mol: A Universal 3D Molecular Representation Learning Framework,ChemRxiv,2023,preprint,computational_biology,2,71.0,196.0,249.05517241379312,324,,True,True,post,249.05517241379312,landmark,True	37	+4393936599,10.1093/nar/gkae236,,"ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support",Nucleic Acids Research,2024,article,computational_biology,1,627.0,1260.0,209.38466448112797,1260,247.0103,True,True,post,admetlab30anupdatedcomprehensiveonlineadmetpredictionplatformenhancedwithbroadercoverageimprovedperformanceapifunctionalityanddecisionsupport,209.38466448112797,landmark,True
38	−3156458251,10.1093/nar/gkab301,,Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation,Nucleic Acids Research,2021,article,computational_biology,2,1857.0,4291.0,249.05241916110123,11124,520.9954,True,True,baseline,249.05241916110123,landmark,True	38	+3168436232,10.1021/acs.jcim.1c00203,,"AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings",Journal of Chemical Information and Modeling,2021,article,computational_biology,2,340.0,1300.0,209.2664690527806,7546,373.4067,True,True,baseline,autodockvina120newdockingmethodsexpandedforcefieldandpythonbindings,209.2664690527806,landmark,True
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40	−7119529911,10.1126/science.ads9530,,Deep contrastive learning enables genome-wide virtual screening,Science,2026,article,computational_biology,3,62.0,62.0,175.0637411130179,62,220.9602,True,True,post,220.42367591809082,landmark,True	40	+3011072970,10.1038/s41421-020-0153-3,,Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2,Cell Discovery,2020,article,computational_biology,1,1057.0,1266.0,186.408931286412,1597,164.9683,True,True,baseline,networkbaseddrugrepurposingfornovelcoronavirus2019ncovsarscov2,186.408931286412,landmark,True
41	−4393936599,10.1093/nar/gkae236,,"ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support",Nucleic Acids Research,2024,article,computational_biology,1,627.0,1260.0,209.38466448112797,1260,247.0103,True,True,post,209.38466448112797,landmark,True	41	+3211795435,10.1093/nar/gkab1061,,AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models,Nucleic Acids Research,2021,article,computational_biology,3,944.0,3084.0,178.99735742084275,7770,355.1571,True,True,baseline,alphafoldproteinstructuredatabasemassivelyexpandingthestructuralcoverageofproteinsequencespacewithhighaccuracymodels,178.99735742084275,landmark,True
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43	−2899070097,10.1093/nar/gky1033,,PubChem 2019 update: improved access to chemical data,Nucleic Acids Research,2018,article,computational_biology,1,200.0,819.0,194.80273384490118,3238,270.9191,True,True,baseline,194.80273384490118,landmark,True	43	+4307425304,10.1093/nar/gkac963,,KEGG for taxonomy-based analysis of pathways and genomes,Nucleic Acids Research,2022,article,computational_biology,1,1600.0,3498.0,177.7903434574516,5752,391.9436,True,True,baseline,keggfortaxonomybasedanalysisofpathwaysandgenomes,177.7903434574516,landmark,True
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45	−3211795435,10.1093/nar/gkab1061,,AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models,Nucleic Acids Research,2021,article,computational_biology,3,944.0,3084.0,178.99735742084275,7770,355.1571,True,True,baseline,178.99735742084275,landmark,True	45	+4388522045,10.1371/journal.pone.0294236,,SRplot: A free online platform for data visualization and graphing,PLoS ONE,2023,article,computational_biology,2,537.0,1676.0,174.5922042929299,2536,232.667,True,True,post,srplotafreeonlineplatformfordatavisualizationandgraphing,174.5922042929299,landmark,True
46	−3098269892,10.17863/cam.34134,,Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.,Cambridge University Engineering Department Publications Database,2018,article,computational_biology,3,417.0,752.0,178.86649066100819,1703,200.0824,True,True,baseline,178.86649066100819,landmark,True	46	+4388615774,10.1093/nar/gkad976,,DrugBank 6.0: the DrugBank Knowledgebase for 2024,Nucleic Acids Research,2023,article,computational_biology,2,254.0,1086.0,165.7163852004641,1727,228.2125,True,True,post,drugbank60thedrugbankknowledgebasefor2024,165.7163852004641,landmark,True
47	−4307425304,10.1093/nar/gkac963,,KEGG for taxonomy-based analysis of pathways and genomes,Nucleic Acids Research,2022,article,computational_biology,1,1600.0,3498.0,177.7903434574516,5752,391.9436,True,True,baseline,177.7903434574516,landmark,True	47	+4309506674,10.1093/nar/gkac1052,,UniProt: the Universal Protein Knowledgebase in 2023,Nucleic Acids Research,2022,article,computational_biology,1,932.0,3250.0,165.18542488185184,6940,383.312,True,True,baseline,uniprottheuniversalproteinknowledgebasein2023,165.18542488185184,landmark,True
48	−7118182048,10.1038/s41591-025-04060-4,,AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer,Nature Medicine,2026,article,computational_biology,2,50.0,50.0,55.6400765943535,50,180.6309,True,True,post,177.76102896620228,landmark,True	48	+4220831425,10.1093/nar/gkac194,,DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update),Nucleic Acids Research,2022,article,computational_biology,1,1461.0,3170.0,161.11932211552934,6142,356.4505,True,True,baseline,davidawebserverforfunctionalenrichmentanalysisandfunctionalannotationofgenelists2021update,161.11932211552934,landmark,True
49	−4400581728,10.1021/acs.jmedchem.4c01122,,An Update on the Nitrogen Heterocycle Compositions and Properties of U.S. FDA-Approved Pharmaceuticals (2013–2023),Journal of Medicinal Chemistry,2024,article,computational_biology,2,536.0,1064.0,176.8137166729525,1064,208.5864,True,False,post,176.8137166729525,landmark,True	49	+7119529911,10.1126/science.ads9530,,Deep contrastive learning enables genome-wide virtual screening,Science,2026,article,computational_biology,3,62.0,62.0,175.0637411130179,62,220.9602,True,True,post,deepcontrastivelearningenablesgenomewidevirtualscreening,157.19916555583544,landmark,True
50	−4388522045,10.1371/journal.pone.0294236,,SRplot: A free online platform for data visualization and graphing,PLoS ONE,2023,article,computational_biology,2,537.0,1676.0,174.5922042929299,2536,232.667,True,True,post,174.5922042929299,landmark,True	50	+4302307901,10.48550/arxiv.2210.01776,2210.01776,"DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking",arXiv (Cornell University),2022,preprint,computational_biology,3,61.0,159.0,155.85750717245776,339,,True,True,baseline,diffdockdiffusionstepstwistsandturnsformoleculardocking,155.85750717245776,landmark,True
51	−7139978436,10.1093/bioinformatics/btag129,,Whole-genome prediction of bacterial pathogenic capacity on novel bacteria using protein language models with PathogenFinder2,Bioinformatics,2026,article,computational_biology,2,48.0,48.0,64.58550316678397,48,73.0121,True,True,post,170.6505878075542,landmark,True	51	+2927961455,10.1093/nar/gkz239,,Interactive Tree Of Life (iTOL) v4: recent updates and new developments,Nucleic Acids Research,2019,article,computational_biology,3,1166.0,2754.0,153.24396298061907,5925,277.073,True,True,baseline,interactivetreeoflifeitolv4recentupdatesandnewdevelopments,153.24396298061907,landmark,True
52	−7140287209,10.1038/s41586-026-10265-5,,Towards end-to-end automation of AI research,Nature,2026,article,autonomous_scientific_discovery,4,94.0,94.0,104.60334399738458,94,335.9735,True,True,post,334.1907344564603,landmark,True	52	+7140287209,10.1038/s41586-026-10265-5,,Towards end-to-end automation of AI research,Nature,2026,article,autonomous_scientific_discovery,4,94.0,94.0,104.60334399738458,94,335.9735,True,True,post,towardsendtoendautomationofairesearch,238.33421874594404,landmark,True
53	−7125546963,10.1016/j.addr.2026.115781,,Artificial intelligence and machine learning guided optimization in drug delivery,Advanced Drug Delivery Reviews,2026,review,autonomous_scientific_discovery,4,36.0,36.0,17.176470588235293,36,21.0789,True,True,post,127.98794085566564,landmark,True	53	+4385490607,10.1038/s41586-023-06221-2,,Scientific discovery in the age of artificial intelligence,Nature,2023,article,autonomous_scientific_discovery,1,578.0,1284.0,117.87734969257644,1779,156.2859,False,True,post,scientificdiscoveryintheageofartificialintelligence,117.87734969257644,landmark,True
54	−4385490607,10.1038/s41586-023-06221-2,,Scientific discovery in the age of artificial intelligence,Nature,2023,article,autonomous_scientific_discovery,1,578.0,1284.0,117.87734969257644,1779,156.2859,False,True,post,117.87734969257644,landmark,True	54	+3007309629,10.1016/j.cell.2020.01.021,,A Deep Learning Approach to Antibiotic Discovery,Cell,2020,article,autonomous_scientific_discovery,2,489.0,780.0,114.84910458404532,2361,125.0662,False,True,baseline,adeeplearningapproachtoantibioticdiscovery,114.84910458404532,landmark,True
55	−3007309629,10.1016/j.cell.2020.01.021,,A Deep Learning Approach to Antibiotic Discovery,Cell,2020,article,autonomous_scientific_discovery,2,489.0,780.0,114.84910458404532,2361,125.0662,False,True,baseline,114.84910458404532,landmark,True	55	+7125546963,10.1016/j.addr.2026.115781,,Artificial intelligence and machine learning guided optimization in drug delivery,Advanced Drug Delivery Reviews,2026,review,autonomous_scientific_discovery,4,36.0,36.0,17.176470588235293,36,21.0789,True,True,post,artificialintelligenceandmachinelearningguidedoptimizationindrugdelivery,91.27693483887218,landmark,True
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58	−4414990951,10.1016/j.matt.2026.102748,2508.13197,The rise of generative AI for metal-organic framework design and synthesis,Matter,2026,article,autonomous_scientific_discovery,3,21.0,21.0,16.40625,21,28.4842,True,True,post,74.65963216580496,landmark,True	58	+4406406714,10.1038/s42256-024-00972-x,,Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning,Nature Machine Intelligence,2025,article,autonomous_scientific_discovery,1,140.0,140.0,25.71533113794365,140,34.876,True,True,post,batterylifetimepredictionacrossdiverseageingconditionswithintercelldeeplearning,61.17601947222733,landmark,True
59	−4406406714,10.1038/s42256-024-00972-x,,Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning,Nature Machine Intelligence,2025,article,autonomous_scientific_discovery,1,140.0,140.0,25.71533113794365,140,34.876,True,True,post,70.87623968252367,landmark,True	59	+4396723768,10.1038/s42256-024-00832-8,,Augmenting large language models with chemistry tools,Nature Machine Intelligence,2024,article,autonomous_scientific_discovery,4,395.0,762.0,60.19669891861127,762,71.2953,True,True,post,augmentinglargelanguagemodelswithchemistrytools,60.19669891861127,landmark,True
60	−4396723768,10.1038/s42256-024-00832-8,,Augmenting large language models with chemistry tools,Nature Machine Intelligence,2024,article,autonomous_scientific_discovery,4,395.0,762.0,60.19669891861127,762,71.2953,True,True,post,60.19669891861127,landmark,True	60	+4319754774,10.1016/j.caeai.2023.100132,,Acceptance of artificial intelligence in teaching science: Science teachers' perspective,Computers and Education Artificial Intelligence,2023,article,autonomous_scientific_discovery,1,120.0,252.0,59.06269551273626,323,75.9012,True,True,post,acceptanceofartificialintelligenceinteachingsciencescienceteachersperspective,59.06269551273626,landmark,True
61	−4319754774,10.1016/j.caeai.2023.100132,,Acceptance of artificial intelligence in teaching science: Science teachers' perspective,Computers and Education Artificial Intelligence,2023,article,autonomous_scientific_discovery,1,120.0,252.0,59.06269551273626,323,75.9012,True,True,post,59.06269551273626,landmark,True	61	+4414990951,10.1016/j.matt.2026.102748,2508.13197,The rise of generative AI for metal-organic framework design and synthesis,Matter,2026,article,autonomous_scientific_discovery,3,21.0,21.0,16.40625,21,28.4842,True,True,post,theriseofgenerativeaiformetalorganicframeworkdesignandsynthesis,53.24487865600877,landmark,True
62	−4389991855,10.1038/s41586-023-06887-8,,Discovery of a structural class of antibiotics with explainable deep learning,Nature,2023,article,autonomous_scientific_discovery,2,136.0,330.0,50.355807657599584,458,60.2776,False,True,post,50.355807657599584,landmark,True	62	+4389991855,10.1038/s41586-023-06887-8,,Discovery of a structural class of antibiotics with explainable deep learning,Nature,2023,article,autonomous_scientific_discovery,2,136.0,330.0,50.355807657599584,458,60.2776,False,True,post,discoveryofastructuralclassofantibioticswithexplainabledeeplearning,50.355807657599584,landmark,True
63	−4409069046,10.1038/s42256-025-01009-7,2404.07181,A predictive machine learning force-field framework for liquid electrolyte development,Nature Machine Intelligence,2025,article,autonomous_scientific_discovery,2,95.0,95.0,14.707545118504024,95,19.6308,False,True,post,48.094591213141065,landmark,True	63	+4409069046,10.1038/s42256-025-01009-7,2404.07181,A predictive machine learning force-field framework for liquid electrolyte development,Nature Machine Intelligence,2025,article,autonomous_scientific_discovery,2,95.0,95.0,14.707545118504024,95,19.6308,False,True,post,apredictivemachinelearningforcefieldframeworkforliquidelectrolytedevelopment,41.51229892758283,landmark,True
64	−4400064820,10.1038/s43588-026-01045-2,2406.17295,Less can be more for predicting properties with large language models,Nature Computational Science,2026,article,autonomous_scientific_discovery,5,13.0,13.0,10.024647887323944,13,1.4032,True,True,post,46.2178675312126,landmark,True	64	+3210165781,10.1016/j.xinn.2021.100179,,Artificial intelligence: A powerful paradigm for scientific research,The Innovation,2021,article,autonomous_scientific_discovery,1,93.0,334.0,38.92516828044519,1702,74.7191,True,True,baseline,artificialintelligenceapowerfulparadigmforscientificresearch,38.92516828044519,landmark,True
65	−4407922392,10.1038/s42256-025-00994-z,,Large language models for scientific discovery in molecular property prediction,Nature Machine Intelligence,2025,article,autonomous_scientific_discovery,3,83.0,83.0,34.86688949436066,83,40.2481,False,True,post,42.01948495463904,landmark,True	65	+4390940921,10.1038/s41586-023-06747-5,,Solving olympiad geometry without human demonstrations,Nature,2024,article,autonomous_scientific_discovery,2,207.0,307.0,38.13184279381587,307,45.3903,True,True,post,solvingolympiadgeometrywithouthumandemonstrations,38.13184279381587,landmark,True
66	−4407041525,10.1088/0256-307x/42/2/027403,2411.09429,"AI-Driven Inverse Design of Materials: Past, Present, and Future",Chinese Physics Letters,2025,article,autonomous_scientific_discovery,3,78.0,78.0,12.07566862361383,78,16.2894,True,True,post,39.488190680263195,landmark,True	66	+4407922392,10.1038/s42256-025-00994-z,,Large language models for scientific discovery in molecular property prediction,Nature Machine Intelligence,2025,article,autonomous_scientific_discovery,3,83.0,83.0,34.86688949436066,83,40.2481,False,True,post,largelanguagemodelsforscientificdiscoveryinmolecularpropertyprediction,36.268640115677634,landmark,True
67	−7128476819,10.3389/frai.2026.1678539,,The future of fundamental science led by generative closed-loop artificial intelligence,Frontiers in Artificial Intelligence,2026,article,autonomous_scientific_discovery,4,11.0,11.0,12.24081685075777,11,32.5136,True,True,post,39.107426372564504,landmark,True	67	+4225106311,10.1038/s41586-022-04501-x,,FAIR data enabling new horizons for materials research,Nature,2022,article,autonomous_scientific_discovery,1,77.0,145.0,35.636787972031094,247,48.6434,True,True,baseline,fairdataenablingnewhorizonsformaterialsresearch,35.636787972031094,landmark,True
68	−4410552772,10.1021/acs.accounts.5c00155,,"ASKCOS: Open-Source, Data-Driven Synthesis Planning",Accounts of Chemical Research,2025,article,autonomous_scientific_discovery,4,77.0,77.0,32.34639145862375,77,37.3386,True,True,post,38.981931825388024,landmark,True	68	+4407041525,10.1088/0256-307x/42/2/027403,2411.09429,"AI-Driven Inverse Design of Materials: Past, Present, and Future",Chinese Physics Letters,2025,article,autonomous_scientific_discovery,3,78.0,78.0,12.07566862361383,78,16.2894,True,True,post,aidriveninversedesignofmaterialspastpresentandfuture,34.0837822773838,landmark,True
69	−3210165781,10.1016/j.xinn.2021.100179,,Artificial intelligence: A powerful paradigm for scientific research,The Innovation,2021,article,autonomous_scientific_discovery,1,93.0,334.0,38.92516828044519,1702,74.7191,True,True,baseline,38.92516828044519,landmark,True	69	+4410552772,10.1021/acs.accounts.5c00155,,"ASKCOS: Open-Source, Data-Driven Synthesis Planning",Accounts of Chemical Research,2025,article,autonomous_scientific_discovery,4,77.0,77.0,32.34639145862375,77,37.3386,True,True,post,askcosopensourcedatadrivensynthesisplanning,33.64681070972503,landmark,True
70	−4407002549,10.3389/frai.2025.1518049,,"Benefits, limits, and risks of ChatGPT in medicine",Frontiers in Artificial Intelligence,2025,article,autonomous_scientific_discovery,4,76.0,76.0,5.054948135688253,76,15.5609,True,True,post,38.47567297051285,landmark,True	70	+4407002549,10.3389/frai.2025.1518049,,"Benefits, limits, and risks of ChatGPT in medicine",Frontiers in Artificial Intelligence,2025,article,autonomous_scientific_discovery,4,76.0,76.0,5.054948135688253,76,15.5609,True,True,post,benefitslimitsandrisksofchatgptinmedicine,33.209839142066265,landmark,True
71	−4390940921,10.1038/s41586-023-06747-5,,Solving olympiad geometry without human demonstrations,Nature,2024,article,autonomous_scientific_discovery,2,207.0,307.0,38.13184279381587,307,45.3903,True,True,post,38.13184279381587,landmark,True	71	+4400064820,10.1038/s43588-026-01045-2,2406.17295,Less can be more for predicting properties with large language models,Nature Computational Science,2026,article,autonomous_scientific_discovery,5,13.0,13.0,10.024647887323944,13,1.4032,True,True,post,lesscanbemoreforpredictingpropertieswithlargelanguagemodels,32.96111535848162,landmark,True
72	−4225106311,10.1038/s41586-022-04501-x,,FAIR data enabling new horizons for materials research,Nature,2022,article,autonomous_scientific_discovery,1,77.0,145.0,35.636787972031094,247,48.6434,True,True,baseline,35.636787972031094,landmark,True	72	+4389991792,10.1038/s41586-023-06792-0,,Autonomous chemical research with large language models,Nature,2023,article,autonomous_scientific_discovery,4,189.0,590.0,32.419795221843,1012,71.2047,True,True,post,autonomouschemicalresearchwithlargelanguagemodels,32.419795221843,landmark,True
73	−4415202617,10.1016/j.matt.2026.102812,2503.08537,Chemical reasoning in LLMs unlocks strategy-aware synthesis planning and reaction mechanism elucidation,Matter,2026,article,autonomous_scientific_discovery,5,10.0,10.0,28.23608727629321,10,28.511,True,True,post,35.552205793240454,landmark,True	73	+4386740800,10.1038/s42256-023-00716-3,,CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling,Nature Machine Intelligence,2023,article,autonomous_scientific_discovery,5,176.0,566.0,31.101023890784983,916,64.5205,True,True,post,chgnetasapretraineduniversalneuralnetworkpotentialforchargeinformedatomisticmodelling,31.101023890784983,landmark,True
74	−4368755578,10.1007/s10994-025-06955-2,2305.02251,Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems,Machine Learning,2026,article,autonomous_scientific_discovery,4,10.0,10.0,7.711267605633803,10,9.8221,True,True,post,35.552205793240454,landmark,True	74	+4393097303,10.1038/s42256-024-00809-7,,Generative AI for designing and validating easily synthesizable and structurally novel antibiotics,Nature Machine Intelligence,2024,article,autonomous_scientific_discovery,3,113.0,184.0,30.576808146450436,184,36.0713,False,False,post,generativeaifordesigningandvalidatingeasilysynthesizableandstructurallynovelantibiotics,30.576808146450436,landmark,True
75	−4389991792,10.1038/s41586-023-06792-0,,Autonomous chemical research with large language models,Nature,2023,article,autonomous_scientific_discovery,4,189.0,590.0,32.419795221843,1012,71.2047,True,True,post,32.419795221843,landmark,True	75	+2769423117,10.1039/c8sc02339e,,“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models,Chemical Science,2018,article,autonomous_scientific_discovery,3,65.0,126.0,29.96965136075403,441,34.7969,True,True,baseline,foundintranslationpredictingoutcomesofcomplexorganicchemistryreactionsusingneuralsequencetosequencemodels,29.96965136075403,landmark,True
76	−4402696779,10.1145/3794853,2408.08044,Crystalline Material Discovery in the Era of Artificial Intelligence,ACM Computing Surveys,2026,article,autonomous_scientific_discovery,4,9.0,9.0,6.940140845070423,9,5.6126,True,True,post,31.99698521391641,landmark,True	76	+4391561379,10.1038/s42256-023-00788-1,,Leveraging large language models for predictive chemistry,Nature Machine Intelligence,2024,article,autonomous_scientific_discovery,6,257.0,367.0,28.992373363688102,367,34.6508,True,True,post,leveraginglargelanguagemodelsforpredictivechemistry,28.992373363688102,landmark,True
77	−4309506674,10.1093/nar/gkac1052,,UniProt: the Universal Protein Knowledgebase in 2023,Nucleic Acids Research,2022,article,computational_biology,1,932.0,3250.0,165.18542488185184,6940,383.312,True,True,baseline,165.18542488185184,landmark,True	77	+4308834893,10.1093/nar/gkac1000,,The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest,Nucleic Acids Research,2022,article,computational_biology,3,535.0,2781.0,141.34789741428614,9363,448.2907,True,True,baseline,thestringdatabasein2023proteinproteinassociationnetworksandfunctionalenrichmentanalysesforanysequencedgenomeofinterest,141.34789741428614,landmark,True
78	−4220831425,10.1093/nar/gkac194,,DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update),Nucleic Acids Research,2022,article,computational_biology,1,1461.0,3170.0,161.11932211552934,6142,356.4505,True,True,baseline,161.11932211552934,landmark,True	78	+3112376646,10.1093/nar/gkaa1100,,UniProt: the universal protein knowledgebase in 2021,Nucleic Acids Research,2020,article,computational_biology,1,679.0,2641.0,150.5152761457109,6342,306.9602,True,True,baseline,uniprottheuniversalproteinknowledgebasein2021,150.5152761457109,landmark,True
79	−4308834893,10.1093/nar/gkac1000,,The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest,Nucleic Acids Research,2022,article,computational_biology,3,535.0,2781.0,141.34789741428614,9363,448.2907,True,True,baseline,141.34789741428614,landmark,True	79	+2900569176,10.1093/nar/gky1131,,"STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets",Nucleic Acids Research,2018,article,computational_biology,3,421.0,2624.0,131.1704666044956,17675,509.3871,True,True,baseline,stringv11proteinproteinassociationnetworkswithincreasedcoveragesupportingfunctionaldiscoveryingenomewideexperimentaldatasets,131.1704666044956,landmark,True
80	−2927961455,10.1093/nar/gkz239,,Interactive Tree Of Life (iTOL) v4: recent updates and new developments,Nucleic Acids Research,2019,article,computational_biology,3,1166.0,2754.0,153.24396298061907,5925,277.073,True,True,baseline,153.24396298061907,landmark,True	80	+2804822363,10.1093/nar/gky427,,SWISS-MODEL: homology modelling of protein structures and complexes,Nucleic Acids Research,2018,article,computational_biology,3,904.0,2454.0,122.67237997234459,13067,345.6079,True,True,baseline,swissmodelhomologymodellingofproteinstructuresandcomplexes,122.67237997234459,landmark,True
81	−3112376646,10.1093/nar/gkaa1100,,UniProt: the universal protein knowledgebase in 2021,Nucleic Acids Research,2020,article,computational_biology,1,679.0,2641.0,150.5152761457109,6342,306.9602,True,True,baseline,150.5152761457109,landmark,True	81	+3208691473,10.1093/nar/gkab1038,,The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences,Nucleic Acids Research,2021,article,computational_biology,2,743.0,2312.0,100.20101036958255,6103,381.4154,True,True,baseline,thepridedatabaseresourcesin2022ahubformassspectrometrybasedproteomicsevidences,100.20101036958255,landmark,True
82	−2900569176,10.1093/nar/gky1131,,"STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets",Nucleic Acids Research,2018,article,computational_biology,3,421.0,2624.0,131.1704666044956,17675,509.3871,True,True,baseline,131.1704666044956,landmark,True	82	+2938574745,10.1093/nar/gkz268,,The EMBL-EBI search and sequence analysis tools APIs in 2019,Nucleic Acids Research,2019,article,computational_biology,2,1094.0,2262.0,149.0781904899552,4162,956.3441,True,True,baseline,theemblebisearchandsequenceanalysistoolsapisin2019,149.0781904899552,landmark,True
83	−2804822363,10.1093/nar/gky427,,SWISS-MODEL: homology modelling of protein structures and complexes,Nucleic Acids Research,2018,article,computational_biology,3,904.0,2454.0,122.67237997234459,13067,345.6079,True,True,baseline,122.67237997234459,landmark,True	83	+3136918052,10.1093/nar/gky1049,,UniProt: a worldwide hub of protein knowledge,Nucleic Acids Research,2018,article,computational_biology,1,457.0,2078.0,103.87661189182235,7679,309.1951,True,True,baseline,uniprotaworldwidehubofproteinknowledge,103.87661189182235,landmark,True
84	−3208691473,10.1093/nar/gkab1038,,The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences,Nucleic Acids Research,2021,article,computational_biology,2,743.0,2312.0,100.20101036958255,6103,381.4154,True,True,baseline,100.20101036958255,landmark,True	84	+3107527779,10.1093/nar/gkaa1074,,"The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets",Nucleic Acids Research,2020,article,computational_biology,4,246.0,2031.0,115.75029377203289,8015,303.9867,True,True,baseline,thestringdatabasein2021customizableproteinproteinnetworksandfunctionalcharacterizationofuseruploadedgenemeasurementsets,115.75029377203289,landmark,True
85	−2938574745,10.1093/nar/gkz268,,The EMBL-EBI search and sequence analysis tools APIs in 2019,Nucleic Acids Research,2019,article,computational_biology,2,1094.0,2262.0,149.0781904899552,4162,956.3441,True,True,baseline,149.0781904899552,landmark,True	85	+3159995035,10.1093/nar/gkab382,,MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights,Nucleic Acids Research,2021,article,computational_biology,3,1102.0,2027.0,117.64839283140344,3958,207.4321,True,True,baseline,metaboanalyst50narrowingthegapbetweenrawspectraandfunctionalinsights,117.64839283140344,landmark,True
86	−3136918052,10.1093/nar/gky1049,,UniProt: a worldwide hub of protein knowledge,Nucleic Acids Research,2018,article,computational_biology,1,457.0,2078.0,103.87661189182235,7679,309.1951,True,True,baseline,103.87661189182235,landmark,True	86	+3095583226,10.1093/nar/gkaa913,,Pfam: The protein families database in 2021,Nucleic Acids Research,2020,article,computational_biology,1,792.0,1991.0,113.47062279670975,7261,214.3673,True,True,baseline,pfamtheproteinfamiliesdatabasein2021,113.47062279670975,landmark,True
87	−3107527779,10.1093/nar/gkaa1074,,"The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets",Nucleic Acids Research,2020,article,computational_biology,4,246.0,2031.0,115.75029377203289,8015,303.9867,True,True,baseline,115.75029377203289,landmark,True	87	+3096828292,10.1093/nar/gkaa970,,KEGG: integrating viruses and cellular organisms,Nucleic Acids Research,2020,article,computational_biology,1,541.0,1955.0,111.4189189189189,4022,222.942,True,True,baseline,keggintegratingvirusesandcellularorganisms,111.4189189189189,landmark,True
88	−3159995035,10.1093/nar/gkab382,,MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights,Nucleic Acids Research,2021,article,computational_biology,3,1102.0,2027.0,117.64839283140344,3958,207.4321,True,True,baseline,117.64839283140344,landmark,True	88	+2899760200,10.1093/nar/gky1106,,The PRIDE database and related tools and resources in 2019: improving support for quantification data,Nucleic Acids Research,2018,article,computational_biology,3,507.0,1805.0,74.32352941176471,6693,384.4369,True,True,baseline,thepridedatabaseandrelatedtoolsandresourcesin2019improvingsupportforquantificationdata,74.32352941176471,landmark,True
89	−3095583226,10.1093/nar/gkaa913,,Pfam: The protein families database in 2021,Nucleic Acids Research,2020,article,computational_biology,1,792.0,1991.0,113.47062279670975,7261,214.3673,True,True,baseline,113.47062279670975,landmark,True	89	+4223552703,10.1093/nar/gkac240,,Search and sequence analysis tools services from EMBL-EBI in 2022,Nucleic Acids Research,2022,article,computational_biology,2,862.0,1760.0,89.45426085909514,2267,150.397,True,True,baseline,searchandsequenceanalysistoolsservicesfromemblebiin2022,89.45426085909514,landmark,True
90	−3096828292,10.1093/nar/gkaa970,,KEGG: integrating viruses and cellular organisms,Nucleic Acids Research,2020,article,computational_biology,1,541.0,1955.0,111.4189189189189,4022,222.942,True,True,baseline,111.4189189189189,landmark,True	90	+4368358469,10.1093/nar/gkad344,,"antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation",Nucleic Acids Research,2023,article,computational_biology,1,830.0,1741.0,32.99382988100485,2290,294.6456,True,True,post,antismash70newandimprovedpredictionsfordetectionregulationchemicalstructuresandvisualisation,32.99382988100485,landmark,True
91	−2899760200,10.1093/nar/gky1106,,The PRIDE database and related tools and resources in 2019: improving support for quantification data,Nucleic Acids Research,2018,article,computational_biology,3,507.0,1805.0,74.32352941176471,6693,384.4369,True,True,baseline,74.32352941176471,landmark,True	91	+3027286012,10.1093/nar/gkaa407,,TIMER2.0 for analysis of tumor-infiltrating immune cells,Nucleic Acids Research,2020,article,computational_biology,2,587.0,1720.0,57.035267668961644,5209,230.0925,True,True,baseline,timer20foranalysisoftumorinfiltratingimmunecells,57.035267668961644,landmark,True
92	−4223552703,10.1093/nar/gkac240,,Search and sequence analysis tools services from EMBL-EBI in 2022,Nucleic Acids Research,2022,article,computational_biology,2,862.0,1760.0,89.45426085909514,2267,150.397,True,True,baseline,89.45426085909514,landmark,True	92	+2999044305,10.1038/s41586-019-1923-7,,Improved protein structure prediction using potentials from deep learning,Nature,2020,article,computational_biology,1,1072.0,1692.0,96.4300822561692,3363,151.7168,False,True,baseline,improvedproteinstructurepredictionusingpotentialsfromdeeplearning,96.4300822561692,landmark,True
93	−4368358469,10.1093/nar/gkad344,,"antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation",Nucleic Acids Research,2023,article,computational_biology,1,830.0,1741.0,32.99382988100485,2290,294.6456,True,True,post,32.99382988100485,landmark,True	93	+2803664483,10.1093/nar/gky310,,MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis,Nucleic Acids Research,2018,article,computational_biology,4,660.0,1555.0,77.73249831173425,3629,185.9094,True,True,baseline,metaboanalyst40towardsmoretransparentandintegrativemetabolomicsanalysis,77.73249831173425,landmark,True
94	−3027286012,10.1093/nar/gkaa407,,TIMER2.0 for analysis of tumor-infiltrating immune cells,Nucleic Acids Research,2020,article,computational_biology,2,587.0,1720.0,57.035267668961644,5209,230.0925,True,True,baseline,57.035267668961644,landmark,True	94	+2942829220,10.1093/nar/gkz310,,antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline,Nucleic Acids Research,2019,article,computational_biology,2,661.0,1520.0,18.891765924391507,2907,264.3601,True,True,baseline,antismash50updatestothesecondarymetabolitegenomeminingpipeline,18.891765924391507,landmark,True
95	−2999044305,10.1038/s41586-019-1923-7,,Improved protein structure prediction using potentials from deep learning,Nature,2020,article,computational_biology,1,1072.0,1692.0,96.4300822561692,3363,151.7168,False,True,baseline,96.4300822561692,landmark,True	95	+4384652177,10.1186/s12859-023-05385-y,,MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads,BMC Bioinformatics,2023,article,computational_biology,5,839.0,1444.0,100.66579880594335,1858,138.9013,True,True,post,mitohifiapythonpipelineformitochondrialgenomeassemblyfrompacbiohighfidelityreads,100.66579880594335,landmark,True
96	−2803664483,10.1093/nar/gky310,,MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis,Nucleic Acids Research,2018,article,computational_biology,4,660.0,1555.0,77.73249831173425,3629,185.9094,True,True,baseline,77.73249831173425,landmark,True	96	+2898402099,10.1093/nar/gky995,,The Pfam protein families database in 2019,Nucleic Acids Research,2018,article,computational_biology,2,366.0,1396.0,69.7842878734283,4595,208.2751,True,True,baseline,thepfamproteinfamiliesdatabasein2019,69.7842878734283,landmark,True
97	−2942829220,10.1093/nar/gkz310,,antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline,Nucleic Acids Research,2019,article,computational_biology,2,661.0,1520.0,18.891765924391507,2907,264.3601,True,True,baseline,18.891765924391507,landmark,True	97	+3161596995,10.1093/nar/gkab335,,antiSMASH 6.0: improving cluster detection and comparison capabilities,Nucleic Acids Research,2021,article,computational_biology,1,649.0,1396.0,21.18855808523059,2562,230.4087,True,True,baseline,antismash60improvingclusterdetectionandcomparisoncapabilities,21.18855808523059,landmark,True
98	−4384652177,10.1186/s12859-023-05385-y,,MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads,BMC Bioinformatics,2023,article,computational_biology,5,839.0,1444.0,100.66579880594335,1858,138.9013,True,True,post,100.66579880594335,landmark,True	98	+2789843538,10.1093/bioinformatics/bty191,,Minimap2: pairwise alignment for nucleotide sequences,Bioinformatics,2018,article,computational_biology,4,450.0,1291.0,64.53546965945267,15142,230.1698,True,True,baseline,minimap2pairwisealignmentfornucleotidesequences,64.53546965945267,landmark,True
99	−2898402099,10.1093/nar/gky995,,The Pfam protein families database in 2019,Nucleic Acids Research,2018,article,computational_biology,2,366.0,1396.0,69.7842878734283,4595,208.2751,True,True,baseline,69.7842878734283,landmark,True
100	−3161596995,10.1093/nar/gkab335,,antiSMASH 6.0: improving cluster detection and comparison capabilities,Nucleic Acids Research,2021,article,computational_biology,1,649.0,1396.0,21.18855808523059,2562,230.4087,True,True,baseline,21.18855808523059,landmark,True
101	−4308616064,10.1093/nar/gkac993,,InterPro in 2022,Nucleic Acids Research,2022,article,computational_biology,2,418.0,1388.0,70.5468829956955,2399,150.466,True,True,baseline,70.5468829956955,landmark,True

modified literature_synthesis/landmarks/data_distributed_abstracts.jsonl +88 −89

				`@@ -1,78 +1,54 @@`
1	−{"work_id": 7160863705, "title": "Multi-Objective Scheduling for Large Language Model Inference with Prompt-Level Cost Prediction and SLO Awareness", "abstract": "Large language model (LLM) inference in multi-tenant clouds is becoming an increasingly important contributor to data-center carbon emissions, yet existing carbon-aware scheduling techniques target long-running training jobs and are ill-suited for the short, bursty, SLO-sensitive nature of online serving. We propose CAPS (Carbon\u2013Aware Prompt Scheduling), an online bi-objective scheduler that jointly optimizes goodput and per-request carbon cost for multi-tenant LLM inference. CAPS first employs a lightweight prompt complexity predictor to estimate token generation cost and latency risk for each incoming request. It then combines real-time grid carbon intensity, GPU energy profiles, and per-tenant SLO tiers to route each request to one of three execution pools: a low-latency pool, a low-carbon pool, or a delay-tolerant batch pool. A composite reward function balances goodput, carbon emissions, and SLO violation rate. In trace-driven simulations using public conversation traces and regional carbon intensity data, CAPS reduces average carbon emissions per 1K generated tokens by 26.8% compared to round-robin scheduling while achieving an SLO attainment rate that matches or exceeds a dedicated SLO-aware baseline."}	1	+{"work_id": 153405511, "title": "An Analysis of the MOS under Conditions of Delay, Jitter and Packet Loss and an Analysis of the Impact of Introducing Piggybacking and Reed Solomon FEC for VOIP", "abstract": "Voice over IP (VoIP) is a real time application that allows transmitting voice through the Internet network. Recently there has been amazing progress in this field, mainly due to the development of voice codecs that react appropriately under conditions of packet loss, and the improvement of intelligent jitter buffers that perform better under conditions of variable inter packet delay. In addition, there are other factors that indirectly benefited VoIP. Today, computer networks are faster due to the advances in hardware and breakthrough algorithms. As a result, the quality of VoIP calls has improved considerably. However, the quality of VoIP calls under extreme conditions of packet loss still remains a major problem that needs to be addressed for the next generation of VoIP services. This thesis concentrates in making an analysis of the effects that network impairments, such as: delay, jitter, and packet loss have in the quality of VoIP calls and approaches to solve this problem. Finally, we analyze the impact of introducing forward error correction (FEC) Piggybacking and Reed Solomon codes for VoIP. To measure the mean opinion score of VoIP calls we develop an application based on the E-Model, and utilize perceptual evaluation of speech quality (PESQ)."}
		2	+{"work_id": 7161556515, "title": "DeepServeCB: SLO-Aware and Cost-Aware Elastic Scheduling for Serverless Multi-Tenant LLM Inference", "abstract": "Deploying large language model (LLM) inference services in serverless, multi-tenant environments present compounding challenges: cold start latency, GPU memory fragmentation, inter-tenant resource contention, and unpredictable tail latency. Existing systems optimize individual aspects but fail to jointly address service-level objective (SLO) compliance and cost efficiency under dynamic, heterogeneous workloads. We present DeepServeCB, an elastic scheduling framework that formulates the joint SLO--cost optimization as a contextual bandit problem. The system introduces a Request Profiler that extracts online features---prompt length, historical KV-cache hit ratio, and predicted generation length---and feeds them into a contextual bandit agent that adaptively selects batch sizes, concurrency levels, KV-cache eviction policies, and warm-standby strategies. We evaluate DeepServeCB on ShareGPT and BurstGPT traces using LLaMA-2-13B and Mixtral-8x7B models on NVIDIA A100 GPUs. Results show that DeepServeCB reduces P99 latency by 38--62% compared to state-of-the-art baselines while improving GPU utilization by 14--23% and reducing per-request cost by up to 27%, with only a modest increase in scheduling overhead."}
		3	+{"work_id": 4395047770, "title": "Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions", "abstract": "Abstract With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling."}
		4	+{"work_id": 4407389811, "title": "Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data", "abstract": "The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision making. This comprehensive literature review explores the integration of AI and IoT technologies within environmental sciences, with a particular focus on applications related to water quality and climate data. The methodology involves a systematic search and selection of relevant studies, followed by thematic, meta-, and comparative analyses to synthesize current research trends, benefits, challenges, and gaps. The review highlights how AI enhances IoT\u2019s data collection capabilities through advanced predictive modeling, real-time analytics, and automated decision making, thereby improving the accuracy, timeliness, and efficiency of environmental monitoring systems. Key benefits identified include enhanced data precision, cost efficiency, scalability, and the facilitation of proactive environmental management. Nevertheless, this integration encounters substantial obstacles, including issues related to data quality, interoperability, security, technical constraints, and ethical concerns. Future developments point toward enhancements in AI and IoT technologies, the incorporation of innovations like blockchain and edge computing, the potential formation of global environmental monitoring systems, and greater public involvement through citizen science initiatives. Overcoming these challenges and embracing new technological trends could enable AI and IoT to play a pivotal role in strengthening environmental sustainability and resilience."}
		5	+{"work_id": 2772457693, "title": "Deep Learning for IoT Big Data and Streaming Analytics: A Survey", "abstract": "In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature."}
		6	+{"work_id": 7127083419, "title": "From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications", "abstract": "With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. To address these challenges, this tutorial provides a systematic and comprehensive introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers an integrated overview of cutting-edge methodologies and practical insights. First, the tutorial outlines the background of 6G communications and reviews the technological evolution from LAMs to Agentic AI. It then systematically examines the key components required for constructing LAMs, classifies various types of LAMs, and analyzes their applicability in communication. A LAM-centric design paradigm tailored for communication systems is subsequently proposed, encompassing dataset construction, internal learning, and external learning approaches. Building upon this foundation, the tutorial develops an LAM-based Agentic AI system for intelligent communications, elaborating on its core components\u2014including agents, world models, planners, knowledge bases, tools, and memory modules\u2014 as well as their interaction mechanisms. Finally, it provides an in-depth review of representative applications of LAMs and Agentic AI in communication scenarios, and summarizes the current research challenges and future directions, with the goal of fostering the development of efficient, secure, and sustainable next-generation intelligent communication systems."}
		7	+{"work_id": 4386961654, "title": "Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey", "abstract": "Cutting-edge technologies, with a special emphasis on the Internet of Things (IoT), tend to operate as game changers, generating enormous alterations in both traditional and modern enterprises. Understanding multiple uses of IoT has become vital for effective financial management, given the ever-changing nature of organizations and the technological disruptions that come with this paradigm change. IoT has proven to be a powerful tool for improving operational efficiency, decision-making processes, overall productivity, and data management. As a result of the continuously expanding data volume, there is an increasing demand for a robust IT system capable of adeptly handling all enterprise processes. Consequently, businesses must develop suitable IoT architectures that can efficiently address these continually evolving requirements. This research adopts an incremental explanatory approach, guided by the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A rigorous examination of 84 research papers has allowed us to delve deeply into the current landscape of IoT research. This research aims to provide a complete and cohesive overview of the existing body of knowledge on IoT. This is accomplished by combining a rigorous empirical approach to categorization with ideas from specialized literature in the IoT sector. This study actively contributes to the ongoing conversation around IoT by recognizing and critically examining current difficulties. This, consequently, opens new research possibilities and promotes future developments in this ever-changing sector."}
2	{"work_id": 4408363154, "title": "From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI", "abstract": "The exponential growth of connected devices and sensor networks has revolutionized data collection and monitoring across industries, from healthcare to smart cities. However, the true value of these systems lies not merely in gathering data but in transforming it into actionable intelligence. The integration of IoT, cloud computing, edge computing, and AI offers a robust pathway to achieve this transformation, enabling real-time decision-making and predictive insights. This paper explores innovative approaches to combine these technologies, emphasizing their role in enabling real-time decision-making, predictive analytics, and low-latency data processing. This work analyzes several integration approaches among IoT, cloud/edge computing, and AI through examples and applications, highlighting challenges and approaches to seamlessly integrate these techniques to achieve pervasive environmental intelligence. The findings contribute to advancing pervasive environmental intelligence, offering a roadmap for building smarter, more sustainable infrastructure."}	8	{"work_id": 4408363154, "title": "From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI", "abstract": "The exponential growth of connected devices and sensor networks has revolutionized data collection and monitoring across industries, from healthcare to smart cities. However, the true value of these systems lies not merely in gathering data but in transforming it into actionable intelligence. The integration of IoT, cloud computing, edge computing, and AI offers a robust pathway to achieve this transformation, enabling real-time decision-making and predictive insights. This paper explores innovative approaches to combine these technologies, emphasizing their role in enabling real-time decision-making, predictive analytics, and low-latency data processing. This work analyzes several integration approaches among IoT, cloud/edge computing, and AI through examples and applications, highlighting challenges and approaches to seamlessly integrate these techniques to achieve pervasive environmental intelligence. The findings contribute to advancing pervasive environmental intelligence, offering a roadmap for building smarter, more sustainable infrastructure."}
3	{"work_id": 4402762189, "title": "Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology\u2014A Systematic Analysis", "abstract": "This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students\u2019 performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes."}	9	{"work_id": 4402762189, "title": "Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology\u2014A Systematic Analysis", "abstract": "This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students\u2019 performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes."}
		10	+{"work_id": 4229641819, "title": "Mining of Massive Datasets", "abstract": "Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs."}
4	{"work_id": 7160954670, "title": "Multi-Cloud Program Management For Large Scale Deployment", "abstract": "Multi-cloud strategies are one of the enablers that have gained urgent importance in large-scale enterprise implementations to provide better resilience, independence, and optimization of their performance. There is, however, a great challenge associated with the governance, cost control, security compliance, interoperability, and service-level assurance of managing large-scale programs across heterogeneous cloud platforms. The paper introduces a multi-cloud program management scheme that is comprehensive and capable of assisting the scaling, reliable, and policy-oriented deployment of cloud environments, which are globally distributed. The framework proposed would combine orchestration as a centralized system, standardized policies of governance, automated monitoring of compliance and performance-aware placement of workloads to ensure that technical performance is aligned to strategic business goals. The major dimensions in program management such as alignment of portfolios, risk management, governing costs, vendor integration, and control over operations are taken care of in a structured manner using the layered architecture. The framework also includes analytics-based decision support to facilitate resource optimization as well as continuous improvement in dynamic multi-cloud ecosystems. Through experimental assessment and case study-inspired industry scenarios, it has been shown that the proposed solution leads to much better deployment agility, cost-effectiveness, service reliability and cross-cloud interoperability than the traditional single-cloud and ad-hoc multi-cloud management practice. The findings have demonstrated the relevance of program management in the practicality of the multi-cloud architectures to large-scale and mission-critical deployments."}	11	{"work_id": 7160954670, "title": "Multi-Cloud Program Management For Large Scale Deployment", "abstract": "Multi-cloud strategies are one of the enablers that have gained urgent importance in large-scale enterprise implementations to provide better resilience, independence, and optimization of their performance. There is, however, a great challenge associated with the governance, cost control, security compliance, interoperability, and service-level assurance of managing large-scale programs across heterogeneous cloud platforms. The paper introduces a multi-cloud program management scheme that is comprehensive and capable of assisting the scaling, reliable, and policy-oriented deployment of cloud environments, which are globally distributed. The framework proposed would combine orchestration as a centralized system, standardized policies of governance, automated monitoring of compliance and performance-aware placement of workloads to ensure that technical performance is aligned to strategic business goals. The major dimensions in program management such as alignment of portfolios, risk management, governing costs, vendor integration, and control over operations are taken care of in a structured manner using the layered architecture. The framework also includes analytics-based decision support to facilitate resource optimization as well as continuous improvement in dynamic multi-cloud ecosystems. Through experimental assessment and case study-inspired industry scenarios, it has been shown that the proposed solution leads to much better deployment agility, cost-effectiveness, service reliability and cross-cloud interoperability than the traditional single-cloud and ad-hoc multi-cloud management practice. The findings have demonstrated the relevance of program management in the practicality of the multi-cloud architectures to large-scale and mission-critical deployments."}
5	{"work_id": 4394946189, "title": "Automatic Root Cause Analysis via Large Language Models for Cloud Incidents", "abstract": "Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years."}	12	{"work_id": 4394946189, "title": "Automatic Root Cause Analysis via Large Language Models for Cloud Incidents", "abstract": "Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years."}
6	{"work_id": 3094605801, "title": "Self-supervised Graph Learning for Recommendation", "abstract": "Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) for recommendation such as PinSage and LightGCN. Despite effectiveness, we argue that they suffer from two limitations: (1) high-degree nodes exert larger impact on the representation learning, deteriorating the recommendations of low-degree (long-tail) items; and (2) representations are vulnerable to noisy interactions, as the neighborhood aggregation scheme further enlarges the impact of observed edges."}	13	{"work_id": 3094605801, "title": "Self-supervised Graph Learning for Recommendation", "abstract": "Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) for recommendation such as PinSage and LightGCN. Despite effectiveness, we argue that they suffer from two limitations: (1) high-degree nodes exert larger impact on the representation learning, deteriorating the recommendations of low-degree (long-tail) items; and (2) representations are vulnerable to noisy interactions, as the neighborhood aggregation scheme further enlarges the impact of observed edges."}
7	{"work_id": 4293770403, "title": "Energy efficiency in cloud computing data centers: a survey on software technologies", "abstract": "Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage."}	14	{"work_id": 4293770403, "title": "Energy efficiency in cloud computing data centers: a survey on software technologies", "abstract": "Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage."}
8	−{"work_id": 153405511, "title": "An Analysis of the MOS under Conditions of Delay, Jitter and Packet Loss and an Analysis of the Impact of Introducing Piggybacking and Reed Solomon FEC for VOIP", "abstract": "Voice over IP (VoIP) is a real time application that allows transmitting voice through the Internet network. Recently there has been amazing progress in this field, mainly due to the development of voice codecs that react appropriately under conditions of packet loss, and the improvement of intelligent jitter buffers that perform better under conditions of variable inter packet delay. In addition, there are other factors that indirectly benefited VoIP. Today, computer networks are faster due to the advances in hardware and breakthrough algorithms. As a result, the quality of VoIP calls has improved considerably. However, the quality of VoIP calls under extreme conditions of packet loss still remains a major problem that needs to be addressed for the next generation of VoIP services. This thesis concentrates in making an analysis of the effects that network impairments, such as: delay, jitter, and packet loss have in the quality of VoIP calls and approaches to solve this problem. Finally, we analyze the impact of introducing forward error correction (FEC) Piggybacking and Reed Solomon codes for VoIP. To measure the mean opinion score of VoIP calls we develop an application based on the E-Model, and utilize perceptual evaluation of speech quality (PESQ)."}	15	+{"work_id": 4380791906, "title": "NoSQL Databases in Kubernetes", "abstract": "With the increasing popularity of deploying applications in containers, Kubernetes (K8s) has become one of the most accepted container orchestration systems. Kubernetes helps maintain containers smoothly and simplifies DevOps with powerful automations. It was originally developed as a tool to manage stateless microservices that run seamlessly in containers. The ephemeral nature of pods, the smallest deployable unit, in Kubernetes was well-aligned with stateless applications since destroying and recreating pods didn\u2019t impact applications. There was a need to provision solutions around stateful workloads like databases so as to take advantage of K8s. This project explores this need, the challenges associated and the available solutions for running databases in Kubernetes. Most of the current research is focused towards SQL-like databases in K8s even though the DNA of NoSQL distributed databases is more aligned with K8s. With no research being done with NoSQL databases, this project outlines the process behind setting up two famous NoSQL databases in K8s: MongoDB and Cassandra. The project also shows a representative viewpoint of the performance comparison between them using the YCSB benchmark. The project lays a foundation around the setup of these databases using K8s Operators and their benchmarking. The goal of the project is to describe the advantages of having databases in K8s, provide developers a clear path for setup and provide insights on basic benchmark performance."}
9	−{"work_id": 4362498422, "title": "Security and privacy in the internet of things", "abstract": "\u03a3\u03c4\u03b7 \u03c8\u03b7\u03c6\u03b9\u03b1\u03ba\u03ae \u03b5\u03c0\u03bf\u03c7\u03ae \u03c4\u03c9\u03bd \u03ad\u03be\u03c5\u03c0\u03bd\u03c9\u03bd \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ce\u03bd, \u03c4\u03bf \u0394\u03b9\u03b1\u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03c4\u03c9\u03bd \u03a0\u03c1\u03b1\u03b3\u03bc\u03ac\u03c4\u03c9\u03bd (\u0394\u03c4\u03a0) (Internet of Things \u2013 IoT) \u03b4\u03b9\u03b1\u03b4\u03c1\u03b1\u03bc\u03b1\u03c4\u03af\u03b6\u03b5\u03b9 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03c1\u03cc\u03bb\u03bf \u03c3\u03c4\u03bf\u03bd \u03ba\u03cd\u03ba\u03bb\u03bf \u03b6\u03c9\u03ae\u03c2 \u03c4\u03c9\u03bd \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03c9\u03bd \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ce\u03bd, \u03c0\u03b1\u03c1\u03ad\u03c7\u03bf\u03bd\u03c4\u03b1\u03c2 \u03b4\u03b9\u03ac\u03c6\u03bf\u03c1\u03b1 \u03c0\u03bb\u03b5\u03bf\u03bd\u03b5\u03ba\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1, \u03cc\u03c0\u03c9\u03c2 \u03b7 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c9\u03bc\u03ad\u03bd\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1, \u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03af\u03b5\u03c2 \u03b1\u03c5\u03c4\u03bf\u03b8\u03b5\u03c1\u03b1\u03c0\u03b5\u03af\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c3\u03c4\u03b5\u03c1\u03bf\u03c5 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c7\u03b5\u03af\u03c1\u03b9\u03c3\u03b7\u03c2. \u03a9\u03c3\u03c4\u03cc\u03c3\u03bf, \u03b7 \u03c1\u03b1\u03b3\u03b4\u03b1\u03af\u03b1 \u03b5\u03be\u03ad\u03bb\u03b9\u03be\u03b7 \u03c4\u03c9\u03bd \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03b9\u03b1\u03ba\u03ce\u03bd \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03b5\u03b3\u03b5\u03af\u03c1\u03b5\u03b9 \u03c3\u03bf\u03b2\u03b1\u03c1\u03ac \u03b6\u03b7\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2, \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03c3\u03b5 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03bf\u03b9\u03ba\u03bf\u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1. \u0395\u03b9\u03b4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b1, \u03c4\u03bf \u0394\u03c4\u03a0 \u03b5\u03b9\u03c3\u03ac\u03b3\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b5\u03c4\u03b5\u03c1\u03bf\u03b3\u03b5\u03bd\u03ce\u03bd \u03c4\u03b5\u03c7\u03bd\u03bf\u03bb\u03bf\u03b3\u03b9\u03ce\u03bd, \u03bf\u03b9 \u03bf\u03c0\u03bf\u03af\u03b5\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03c4\u03bf \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c4\u03bf\u03c5 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03c4\u03cd\u03bf\u03c5 (Internet), \u03c4\u03bf \u03cc\u03c0\u03bf\u03b9\u03bf \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03b5\u03cd\u03c1\u03bf\u03c2 \u03b5\u03c5\u03c0\u03b1\u03b8\u03b5\u03b9\u03ce\u03bd. \u0395\u03c0\u03af\u03c3\u03b7\u03c2, \u03c4\u03b1 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03bf\u03b9\u03ba\u03bf\u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1 \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03b9\u03ba\u03ce\u03bd \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd, \u03c4\u03b1 \u03bf\u03c0\u03bf\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c1\u03c1\u03b5\u03c0\u03ae \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03b5\u03c5\u03c1\u03cd \u03c6\u03ac\u03c3\u03bc\u03b1 \u03b1\u03b4\u03c5\u03bd\u03b1\u03bc\u03b9\u03ce\u03bd \u03ba\u03b1\u03b9 \u03b5\u03c5\u03c0\u03b1\u03b8\u03b5\u03b9\u03ce\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2. \u0393\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03c4\u03b1 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03b1 \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1\u03c2 \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03c5\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03c0\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03be\u03bf\u03c5\u03c3\u03b9\u03bf\u03b4\u03cc\u03c4\u03b7\u03c3\u03b7\u03c2. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, \u03c0\u03b9\u03b8\u03b1\u03bd\u03bf\u03af \u03b5\u03c0\u03b9\u03c4\u03b9\u03b8\u03ad\u03bc\u03b5\u03bd\u03bf\u03b9 \u03ad\u03c7\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b5\u03bb\u03ad\u03c3\u03bf\u03c5\u03bd \u03b5\u03c0\u03b9\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03ac\u03c1\u03bd\u03b7\u03c3\u03b7\u03c2 \u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03b9\u03ce\u03bd (Denial of Service \u2013 DoS) \u03ba\u03b1\u03b9 \u00ab\u0391\u03bd\u03b8\u03c1\u03ce\u03c0\u03bf\u03c5 \u03a3\u03c4\u03b7 \u039c\u03ad\u03c3\u03b7\u00bb (Man In The Middle - MITM). \u0395\u03c0\u03af\u03c3\u03b7\u03c2, \u03bf\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b5\u03c2 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03ad\u03bd\u03b1\u03bd \u03b5\u03bb\u03ba\u03c5\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03c4\u03cc\u03c7\u03bf \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1\u03bd \u03b1\u03c5\u03be\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03ba\u03c5\u03b2\u03b5\u03c1\u03bd\u03bf\u03b5\u03c0\u03b9\u03c4\u03b9\u03b8\u03ad\u03bc\u03b5\u03bd\u03c9\u03bd, \u03bf\u03b9 \u03bf\u03c0\u03bf\u03af\u03bf\u03b9 \u03b4\u03cd\u03bd\u03b1\u03bd\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03c0\u03c1\u03bf\u03b3\u03c1\u03b1\u03bc\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b5\u03c0\u03af\u03bc\u03bf\u03bd\u03b5\u03c2 \u03b1\u03c0\u03b5\u03b9\u03bb\u03ad\u03c2 (Advanced Persistent Threats - APT). \u03a7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 APT \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03bf\u03b9 \u03b5\u03ba\u03c3\u03c4\u03c1\u03b1\u03c4\u03b5\u03af\u03b5\u03c2: Industroyer, Stuxnet \u03ba\u03b1\u03b9 TRITON. \u03a4\u03ad\u03bb\u03bf\u03c2, \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03b7\u03bc\u03b5\u03b9\u03c9\u03b8\u03b5\u03af \u03c0\u03c9\u03c2 \u03c4\u03bf \u0394\u03c4\u03a0 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf\u03c5\u03c2 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03bf\u03cd\u03c2 \u03c0\u03cc\u03c1\u03bf\u03c5\u03c2, \u03bf\u03b9 \u03bf\u03c0\u03bf\u03af\u03bf\u03b9 \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03c0\u03c4\u03c5\u03be\u03b7 \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03b9\u03ba\u03ce\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03ce\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2.\u039c\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b9\u03c2 \u03c0\u03c1\u03bf\u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03b8\u03b5\u03af\u03c3\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03c4\u03b7\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c6\u03b1\u03bd\u03ae\u03c2 \u03b7 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b7 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03c9\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03ce\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03bf \u0394\u03c4\u03a0. \u03a3\u03c5\u03bd\u03b5\u03c0\u03ce\u03c2, \u03bf \u03ba\u03cd\u03c1\u03b9\u03bf\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03c0\u03b1\u03c1\u03bf\u03cd\u03c3\u03b1\u03c2 \u03b4\u03b9\u03b4\u03b1\u03ba\u03c4\u03bf\u03c1\u03b9\u03ba\u03ae\u03c2 \u03b4\u03b9\u03b1\u03c4\u03c1\u03b9\u03b2\u03ae\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b4\u03b9\u03b5\u03c1\u03b5\u03cd\u03bd\u03b7\u03c3\u03b7 \u03c4\u03c9\u03bd \u03b6\u03b7\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b9\u03b4\u03b9\u03c9\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c3\u03c4\u03bf \u0394\u03c4\u03a0, \u03c0\u03b1\u03c1\u03ad\u03c7\u03bf\u03bd\u03c4\u03b1\u03c2 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 1, \u03c4\u03bf \u03bf\u03c0\u03bf\u03af\u03bf \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03c4\u03b7\u03bd \u03b5\u03b9\u03c3\u03b1\u03b3\u03c9\u03b3\u03ae \u03c3\u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03bf\u03cd\u03c3\u03b1 \u03b4\u03b9\u03b4\u03b1\u03ba\u03c4\u03bf\u03c1\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c4\u03c1\u03b9\u03b2\u03ae, \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c5\u03c2, \u03c4\u03b7 \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03af\u03b1, \u03c4\u03b7 \u03c3\u03c5\u03bd\u03b5\u03b9\u03c3\u03c6\u03bf\u03c1\u03ac \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2, \u03bf\u03b9 \u03b1\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2, \u03c0\u03c1\u03bf\u03ba\u03bb\u03ae\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03b1\u03c0\u03b5\u03b9\u03bb\u03ad\u03c2 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03c3\u03c4\u03bf \u03bf\u03b9\u03ba\u03bf\u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0 \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bd\u03b1\u03bb\u03c5\u03c4\u03b9\u03ba\u03ac \u03c3\u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 2, \u03b4\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03c2 \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b7 \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae \u03c3\u03c4\u03b1 \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b1 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03c4\u03c9\u03bd \u03bf\u03bd\u03c4\u03bf\u03c4\u03ae\u03c4\u03c9\u03bd \u03c3\u03c4\u03bf \u03bf\u03b9\u03ba\u03bf\u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0. \u03a3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1, \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03bd\u03ad\u03b1 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03c4\u03c9\u03bd \u03b1\u03c0\u03b5\u03b9\u03bb\u03ce\u03bd \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0, \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03ce\u03bd\u03c4\u03b1\u03c2 \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 (Common Attack Pattern Enumeration and Classification - CAPEC), \u03b5\u03bd\u03ce \u03b1\u03bd\u03b1\u03bb\u03cd\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b7 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03c9\u03bd \u03c5\u03c6\u03b9\u03c3\u03c4\u03ac\u03bc\u03b5\u03bd\u03c9\u03bd \u03b1\u03bd\u03c4\u03b9\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd. \u0391\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03b7\u03bc\u03b5\u03b9\u03c9\u03b8\u03b5\u03af \u03cc\u03c4\u03b9 \u03c4\u03cc\u03c3\u03bf \u03b7 \u03b1\u03ba\u03b1\u03b4\u03b7\u03bc\u03b1\u03ca\u03ba\u03ae \u03ba\u03bf\u03b9\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03cc\u03c3\u03bf \u03ba\u03b1\u03b9 \u03b7 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03af\u03b1 \u03ad\u03c7\u03bf\u03c5\u03bd \u03b1\u03bd\u03b1\u03c0\u03c4\u03cd\u03be\u03b5\u03b9 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2, \u03cc\u03c0\u03c9\u03c2 \u03bf\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03c1\u03c5\u03c0\u03c4\u03bf\u03b3\u03c1\u03ac\u03c6\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03be\u03bf\u03c5\u03c3\u03b9\u03bf\u03b4\u03cc\u03c4\u03b7\u03c3\u03b7\u03c2 \u03c4\u03c9\u03bd \u03c0\u03c1\u03c9\u03c4\u03bf\u03ba\u03cc\u03bb\u03bb\u03c9\u03bd \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1\u03c2 \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0. \u03a9\u03c3\u03c4\u03cc\u03c3\u03bf, \u03bf\u03b9 \u03c5\u03c6\u03b9\u03c3\u03c4\u03ac\u03bc\u03b5\u03bd\u03bf\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03b4\u03b5\u03bd \u03ad\u03c7\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bd\u03b1 \u03c5\u03b9\u03bf\u03b8\u03b5\u03c4\u03b7\u03b8\u03bf\u03cd\u03bd \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 \u03c3\u03b5 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd\u03c4\u03b1 \u03bb\u03cc\u03b3\u03c9 \u03c4\u03bf\u03c5 \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03af\u03bf\u03c5 \u03c1\u03cc\u03bb\u03bf\u03c5 \u03c4\u03c9\u03bd \u03a3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0392\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03bf\u03cd \u0395\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 (Industrial Control Systems - ICS) \u03ba\u03b1\u03b9 \u03c4\u03c9\u03bd \u03a3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0395\u03c0\u03bf\u03c0\u03c4\u03b9\u03ba\u03bf\u03cd \u0395\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03a3\u03c5\u03bb\u03bb\u03bf\u03b3\u03ae\u03c2 \u0394\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd (Supervision Control And Data Acquisition - SCADA). \u039a\u03b1\u03c4\u03ac \u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b1, \u03b7 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1 \u03a3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0391\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03a0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7\u03c2 \u0395\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd (\u03a3\u0391\u03a0\u0395) (Intrusion Detection and Prevention Systems - IDPS) \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b7.\u039c\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c4\u03c9\u03bd \u03b1\u03bc\u03c5\u03bd\u03c4\u03b9\u03ba\u03ce\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03ce\u03bd \u03c3\u03c4\u03bf \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf \u03ba\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf, \u03c3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 3 \u03b5\u03c0\u03b9\u03ba\u03b5\u03bd\u03c4\u03c1\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd. \u0391\u03ba\u03c1\u03b9\u03b2\u03ad\u03c3\u03c4\u03b5\u03c1\u03b1, \u03b4\u03b9\u03b5\u03c1\u03b5\u03c5\u03bd\u03ac\u03c4\u03b1\u03b9 \u03c4\u03bf \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03c0\u03c1\u03bf\u03b4\u03b9\u03b1\u03b3\u03c1\u03b1\u03c6\u03ad\u03c2 \u03c4\u03c9\u03bd \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03a3\u0391\u03a0\u0395, \u03b1\u03bd\u03b1\u03bb\u03cd\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c4\u03b9\u03c2 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b9\u03c2 \u03bc\u03b5\u03b8\u03cc\u03b4\u03bf\u03c5\u03c2 \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03c4\u03bf\u03c0\u03bf\u03b8\u03ad\u03c4\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5\u03c2. \u0395\u03c0\u03af\u03c3\u03b7\u03c2, \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b7 \u03ad\u03bc\u03c6\u03b1\u03c3\u03b7 \u03b4\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u03a4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u039d\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 (Artificial Intelligence \u2013 AI), \u03bf\u03b9 \u03bf\u03c0\u03bf\u03af\u03b5\u03c2 \u03b4\u03cd\u03bd\u03b1\u03bd\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03c3\u03c4\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc \u03c4\u03b1 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03a3\u0391\u03a0\u0395 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03b1\u03bd\u03b1\u03b3\u03bd\u03ce\u03c1\u03b9\u03c3\u03b7 \u03ba\u03c5\u03b2\u03b5\u03c1\u03bd\u03bf\u03b5\u03c0\u03b9\u03b8\u03ad\u03c3\u03b5\u03c9\u03bd \u03ba\u03b1\u03b9 \u03b1\u03bd\u03c9\u03bc\u03b1\u03bb\u03b9\u03ce\u03bd. \u0395\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd, \u03b1\u03bd\u03b1\u03bb\u03cd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7\u03c2 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd, \u03cc\u03c0\u03c9\u03c2 \u03bf\u03b9 \u03c0\u03b1\u03b3\u03af\u03b4\u03b5\u03c2 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd (honeypots) \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03b6\u03cc\u03bc\u03b5\u03bd\u03bf\u03b9 \u03b1\u03c0\u03cc \u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03b9\u03ba\u03cc (Software-Defined Networking - SDN). \u03a4\u03ad\u03bb\u03bf\u03c2, \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03ba\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf \u03b1\u03bd\u03b1\u03bb\u03cd\u03b5\u03c4\u03b1\u03b9 \u03bf \u03c1\u03cc\u03bb\u03bf\u03c2 \u03c4\u03c9\u03bd \u03a3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0394\u03b9\u03b1\u03c7\u03b5\u03af\u03c1\u03b9\u03c3\u03b7\u03c2 \u03a0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03b9\u03ce\u03bd \u03ba\u03b1\u03b9 \u03a3\u03c5\u03bc\u03b2\u03ac\u03bd\u03c4\u03c9\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 (\u03a3\u0394\u03a0\u03a3\u0391) (Security Information and Event Management -SIEM) \u03c3\u03c4\u03b1 \u03bf\u03b9\u03ba\u03bf\u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u0394\u03c4\u03a0, \u03b5\u03c3\u03c4\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c3\u03c4\u03b9\u03c2 \u03bc\u03b5\u03b8\u03cc\u03b4\u03bf\u03c5\u03c2 \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7\u03c2 \u03c4\u03c9\u03bd \u03b4\u03b9\u03b1\u03c6\u03cc\u03c1\u03c9\u03bd \u03c3\u03c5\u03bc\u03b2\u03ac\u03bd\u03c4\u03c9\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2. \u03a3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1, \u03c3\u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 4, \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03bc\u03b9\u03b1 \u03b4\u03b9\u03b5\u03be\u03bf\u03b4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c4\u03c9\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03ce\u03bd \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd \u03c3\u03b5 \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd\u03c4\u03b1 \u0388\u03be\u03c5\u03c0\u03bd\u03c9\u03bd \u0394\u03b9\u03ba\u03c4\u03cd\u03c9\u03bd \u0397\u03bb\u03b5\u03ba\u03c4\u03c1\u03bf\u03b4\u03cc\u03c4\u03b7\u03c3\u03b7\u03c2 (\u0395\u0394\u0397) (Smart Grid - SG). \u03a3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1, \u03c4\u03b1 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u0395\u0394\u0397 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03c4\u03b7 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0, \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ac \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ac \u03c3\u03c5\u03c3\u03c4\u03b1\u03c4\u03b9\u03ba\u03ac, \u03cc\u03c0\u03c9\u03c2 \u03b7 \u03a0\u03c1\u03bf\u03b7\u03b3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03a5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ad\u03c2 \u039c\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7\u03c2 (Advanced Metering Infrastructure - AMI), \u03c4\u03b1 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03bf\u03cd \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5, \u03bf\u03b9 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b1\u03b8\u03bc\u03bf\u03af \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03c3\u03c5\u03b3\u03c7\u03c1\u03bf\u03bd\u03b9\u03c3\u03c4\u03ad\u03c2. \u03a3\u03c5\u03bd\u03b5\u03c0\u03ce\u03c2, \u03b3\u03b9\u03b1 \u03ba\u03b1\u03b8\u03ad\u03bd\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b1 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03b1 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ac \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03c4\u03c9\u03bd \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0395\u0394\u0397 \u03bc\u03b5\u03bb\u03b5\u03c4\u03ce\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7\u03c2 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd (\u03cc\u03c0\u03c9\u03c2 \u03a3\u0391\u03a0\u0395 \u03ba\u03b1\u03b9 \u03c0\u03b1\u03b3\u03af\u03b4\u03b5\u03c2 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd), \u03b5\u03c3\u03c4\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c3\u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03c4\u03bf\u03c5\u03c2. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03c1\u03b9\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7, \u03b4\u03b9\u03b5\u03c1\u03b5\u03c5\u03bd\u03ce\u03bd\u03c4\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b1\u03b9\u03c4\u03ad\u03c1\u03c9 \u03c4\u03b1 \u03c0\u03bb\u03b5\u03bf\u03bd\u03b5\u03ba\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03c4\u03c9\u03bd \u03c5\u03c6\u03b9\u03c3\u03c4\u03ac\u03bc\u03b5\u03bd\u03c9\u03bd \u03bb\u03cd\u03c3\u03b5\u03c9\u03bd, \u03ba\u03b1\u03b8\u03bf\u03b4\u03b7\u03b3\u03ce\u03bd\u03c4\u03b1\u03c2 \u03ad\u03c4\u03c3\u03b9 \u03c4\u03b7\u03bd \u03c5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03bf\u03c4\u03b5\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf\u03c5 \u03a3\u0394\u03a0\u03a3\u0391 \u03bc\u03b5 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03b6\u03cc\u03bc\u03b5\u03bd\u03bf\u03c5 \u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03b9\u03ba\u03bf\u03cd \u03ba\u03b1\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 \u03c3\u03c4\u03bf \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf \u03ba\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf.\u03a3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1, \u03c3\u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 5 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c4\u03bf \u03c0\u03c1\u03bf\u03c4\u03b5\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf \u03a3\u0394\u03a0\u03a3\u0391, \u03c4\u03bf \u03bf\u03c0\u03bf\u03af\u03bf \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03c1\u03af\u03b1 \u03a3\u0391\u03a0\u0395: (\u03b1) \u03a3\u0391\u03a0\u0395 \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03b4\u03b9\u03ba\u03c4\u03c5\u03b1\u03ba\u03ad\u03c2 \u03c1\u03bf\u03ad\u03c2, (\u03b2) \u03a3\u0391\u03a0\u0395 \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03c9\u03bd \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03ba\u03b1\u03b9 (\u03b3) \u03a3\u0391\u03a0\u0395 \u03bc\u03b5 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03bf\u03c0\u03c4\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2. \u0391\u03c1\u03c7\u03b9\u03ba\u03ac, \u03c4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03a3\u0391\u03a0\u0395 \u03b5\u03bd\u03c3\u03c9\u03bc\u03b1\u03c4\u03ce\u03bd\u03b5\u03b9 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ae\u03c2 \u03ba\u03b1\u03b9 \u03b2\u03b1\u03b8\u03b9\u03ac\u03c2 \u03bc\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2, \u03c4\u03b1 \u03bf\u03c0\u03bf\u03af\u03b1 \u03b4\u03cd\u03bd\u03b1\u03bd\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03ba\u03c1\u03af\u03bd\u03bf\u03c5\u03bd \u03ba\u03c5\u03b2\u03b5\u03c1\u03bd\u03bf\u03b5\u03c0\u03b9\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03bd\u03c9\u03bc\u03b1\u03bb\u03af\u03b5\u03c2 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03b5\u03bd\u03cc\u03c2 \u03b5\u03c5\u03c1\u03ad\u03bf\u03c2 \u03c6\u03ac\u03c3\u03bc\u03b1\u03c4\u03bf\u03c2 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ce\u03bd \u03c0\u03c1\u03c9\u03c4\u03bf\u03ba\u03cc\u03bb\u03bb\u03c9\u03bd \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1\u03c2. \u03a3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03c4\u03bf \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03a3\u0391\u03a0\u0395 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03b1\u03b3\u03bd\u03c9\u03c1\u03af\u03c3\u03b5\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bd\u03c9\u03bc\u03b1\u03bb\u03af\u03b5\u03c2 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03b5\u03c3\u03c3\u03ac\u03c1\u03c9\u03bd \u03c0\u03b5\u03c1\u03b9\u03b2\u03b1\u03bb\u03bb\u03cc\u03bd\u03c4\u03c9\u03bd \u0395\u0394\u0397: (\u03b1) \u03c5\u03b4\u03c1\u03bf\u03b7\u03bb\u03b5\u03ba\u03c4\u03c1\u03b9\u03ba\u03cc \u03b5\u03c1\u03b3\u03bf\u03c3\u03c4\u03ac\u03c3\u03b9\u03bf, (\u03b2) \u03c5\u03c0\u03bf\u03c3\u03c4\u03b1\u03b8\u03bc\u03cc\u03c2, (\u03b3) \u03c3\u03c4\u03b1\u03b8\u03bc\u03cc\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2 \u03b7\u03bb\u03b5\u03ba\u03c4\u03c1\u03b9\u03ba\u03ae\u03c2 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1\u03c2 \u03ba\u03b1\u03b9 (\u03b4) \u03ad\u03be\u03c5\u03c0\u03bd\u03bf \u03c3\u03c0\u03af\u03c4\u03b9. \u03a4\u03ad\u03bb\u03bf\u03c2, \u03c4\u03bf \u03a3\u0391\u03a0\u0395 \u03bc\u03b5 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03bf\u03c0\u03c4\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03b4\u03cd\u03bd\u03b1\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03b9\u03c7\u03bd\u03b5\u03cd\u03c3\u03b5\u03b9 \u03ba\u03c5\u03b2\u03b5\u03c1\u03bd\u03bf\u03b5\u03c0\u03b9\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03c4\u03ac \u03c4\u03bf\u03c5 \u03c0\u03c1\u03c9\u03c4\u03bf\u03ba\u03cc\u03bb\u03bb\u03bf\u03c5 \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1\u03c2 (Modbus/TCP), \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ce\u03bd\u03c4\u03b1\u03c2 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03c3\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b5\u03c0\u03c4\u03c5\u03b3\u03bc\u03ad\u03bd\u03b1 \u03bd\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03ac \u03b4\u03af\u03ba\u03c4\u03c5\u03b1. \u0391\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03b7\u03bc\u03b5\u03b9\u03c9\u03b8\u03b5\u03af \u03cc\u03c4\u03b9 \u03c4\u03bf \u03c0\u03c1\u03bf\u03c4\u03b5\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf \u03a3\u0391\u03a0\u0395 \u03bc\u03b5 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03bf\u03c0\u03c4\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03bc\u03b9\u03b1 \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7 \u03b1\u03c5\u03c4\u03bf\u03b5\u03ba\u03bc\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2, \u03b7 \u03bf\u03c0\u03bf\u03af\u03b1 \u03b4\u03cd\u03bd\u03b1\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b1\u03bd\u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03b5\u03b9 \u03c4\u03bf \u03c0\u03c1\u03bf-\u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf \u03c3\u03c5\u03bd\u03b5\u03c0\u03c4\u03c5\u03b3\u03bc\u03ad\u03bd\u03bf \u03bd\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03cc \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03ba\u03b1\u03c4\u03ac \u03c4\u03b7 \u03b4\u03b9\u03ac\u03c1\u03ba\u03b5\u03b9\u03b1 \u03c4\u03b7\u03c2 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03c9\u03bd \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c7\u03c1\u03cc\u03bd\u03bf. \u03a4\u03b1 \u03c3\u03c5\u03bc\u03b2\u03ac\u03bd\u03c4\u03b1 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03c4\u03c9\u03bd \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03c5\u03bc\u03ad\u03bd\u03c9\u03bd \u03a3\u0391\u03a0\u0395 \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9, \u03c3\u03c5\u03c3\u03c7\u03b5\u03c4\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7 \u039c\u03b7\u03c7\u03b1\u03bd\u03ae \u039a\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2, \u03a3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u039c\u03b5\u03c4\u03c1\u03b9\u03b1\u03c3\u03bc\u03bf\u03cd (\u039c\u039a\u03a3\u039c). \u03a3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1, \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03c3\u03bc\u03ad\u03bd\u03bf\u03b9 \u03ba\u03b1\u03bd\u03cc\u03bd\u03b5\u03c2 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2, \u03b5\u03bd\u03ce \u03bf \u03bc\u03b5\u03c4\u03c1\u03b9\u03b1\u03c3\u03bc\u03cc\u03c2 \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c4\u03b5\u03c7\u03bd\u03bf\u03bb\u03bf\u03b3\u03af\u03b1 \u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03b6\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b1\u03c0\u03cc \u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03b9\u03ba\u03cc. \u03a3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1, \u03b7 \u039c\u039a\u03a3\u039c \u03c5\u03b9\u03bf\u03b8\u03b5\u03c4\u03b5\u03af \u03ad\u03bd\u03b1\u03bd \u03c0\u03c1\u03ac\u03ba\u03c4\u03bf\u03c1\u03b1 \u03b5\u03bd\u03b9\u03c3\u03c7\u03c5\u03c4\u03b9\u03ba\u03ae\u03c2 \u03bc\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2, \u03bf \u03bf\u03c0\u03bf\u03af\u03bf\u03c2 \u03ba\u03b1\u03b8\u03bf\u03b4\u03b7\u03b3\u03b5\u03af \u03c4\u03b7\u03bd \u03c4\u03b5\u03c7\u03bd\u03bf\u03bb\u03bf\u03b3\u03af\u03b1 \u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03b6\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b1\u03c0\u03cc \u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03b9\u03ba\u03cc \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ac \u03bc\u03b5 \u03c4\u03b9\u03c2 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03b5\u03c2 \u03b1\u03bc\u03c5\u03bd\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b5\u03c2. \u03a4\u03ad\u03bb\u03bf\u03c2, \u03b7 \u039c\u039a\u03a3\u039c \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1\u03bd \u03b5\u03be\u03b5\u03bb\u03b9\u03b3\u03bc\u03ad\u03bd\u03bf \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc \u03b1\u03bd\u03ac\u03c0\u03c4\u03c5\u03be\u03b7\u03c2 \u03c0\u03b1\u03b3\u03af\u03b4\u03c9\u03bd \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd, \u03bf \u03bf\u03c0\u03bf\u03af\u03bf\u03c2 \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03c0\u03b1\u03b9\u03c7\u03bd\u03af\u03b4\u03b9 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03b4\u03cd\u03bf \u03c0\u03b1\u03b9\u03ba\u03c4\u03ce\u03bd: (\u03b1) \u03b5\u03c0\u03b9\u03c4\u03b9\u03b8\u03ad\u03bc\u03b5\u03bd\u03bf\u03c2(-\u03bf\u03b9) \u03ba\u03b1\u03b9 (\u03b2) \u03b1\u03bc\u03c5\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf\u03c2(-\u03bf\u03b9). \u0393\u03b9\u03b1 \u03c4\u03bf \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf \u03c0\u03b1\u03af\u03b3\u03bd\u03b9\u03bf, \u03c0\u03c1\u03bf\u03c3\u03b4\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03af\u03b1 (Nash), \u03b5\u03bd\u03ce \u03cc\u03c4\u03b1\u03bd \u03b7 \u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03af\u03b1 Nash) \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b7 \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b9\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2 \u03c4\u03bf\u03c5 \u03c0\u03b1\u03b9\u03b3\u03bd\u03af\u03bf\u03c5, \u03c0\u03b1\u03c1\u03ad\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b4\u03cd\u03bf \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03ba\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c0\u03c1\u03bf\u03c3\u03b5\u03b3\u03b3\u03af\u03c3\u03b5\u03b9\u03c2: (\u03b1) \u03b1\u03bd\u03ac\u03c0\u03c4\u03c5\u03be\u03b7 \u03c0\u03b1\u03b3\u03af\u03b4\u03c9\u03bd \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03bc\u03b5\u03b3\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03bb\u03b1\u03c7\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 (\u03b2) \u03b1\u03bd\u03ac\u03c0\u03c4\u03c5\u03be\u03b7 \u03c0\u03b1\u03b3\u03af\u03b4\u03c9\u03bd \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7.\u03a3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1, \u03bc\u03b5\u03c4\u03ac \u03c4\u03b7\u03bd \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03b1\u03c6\u03ae \u03c4\u03c9\u03bd \u03c0\u03c1\u03bf\u03c4\u03b5\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03c9\u03bd \u03bb\u03cd\u03c3\u03b5\u03c9\u03bd \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7\u03c2, \u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 6 \u03c3\u03c5\u03bd\u03bf\u03c8\u03af\u03b6\u03b5\u03b9 \u03c4\u03b1 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2. \u0395\u03b9\u03b4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b1, \u03b1\u03c1\u03c7\u03b9\u03ba\u03ac \u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2, \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 (\u03b1) \u03c4\u03b1 \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd\u03c4\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2, (\u03b2) \u03c4\u03b1 \u03c3\u03cd\u03bd\u03bf\u03bb\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, (\u03b3) \u03c4\u03b9\u03c2 \u03c3\u03c5\u03b3\u03ba\u03c1\u03b9\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bc\u03b5\u03b8\u03cc\u03b4\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 (\u03b4) \u03c4\u03b9\u03c2 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03ad\u03c2 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2. \u03a3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1, \u03c3\u03c5\u03bd\u03bf\u03c8\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b5\u03c1\u03b5\u03c5\u03bd\u03ce\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03c3\u03c5\u03c3\u03c4\u03b1\u03c4\u03b9\u03ba\u03cc \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03bf \u03c4\u03bf\u03c5 \u03c0\u03c1\u03bf\u03c4\u03b5\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf\u03c5 \u03a3\u0394\u03a0\u03a3\u0391 \u03bc\u03b5 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03b6\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b1\u03c0\u03cc \u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03b9\u03ba\u03cc, \u03b5\u03bd\u03ce \u03c4\u03b1 \u03c0\u03b1\u03c1\u03b1\u03c1\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u039c-\u039d \u03c0\u03b1\u03c1\u03ad\u03c7\u03bf\u03c5\u03bd \u03c4\u03b7 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03b5\u03c1\u03ae \u03c3\u03c5\u03b3\u03ba\u03c1\u03b9\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7.\u03a4\u03ad\u03bb\u03bf\u03c2, \u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 7 \u03bf\u03bb\u03bf\u03ba\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03bf\u03cd\u03c3\u03b1 \u03b4\u03b9\u03b4\u03b1\u03ba\u03c4\u03bf\u03c1\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c4\u03c1\u03b9\u03b2\u03ae, \u03c0\u03b1\u03c1\u03ad\u03c7\u03bf\u03bd\u03c4\u03b1\u03c2 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03c0\u03b9\u03b8\u03b1\u03bd\u03ad\u03c2 \u03ba\u03b1\u03c4\u03b5\u03c5\u03b8\u03cd\u03bd\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03c4\u03bf\u03bc\u03ad\u03b1. \u0395\u03b9\u03b4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b1, \u03b1\u03c6\u03bf\u03cd \u03c3\u03c5\u03b6\u03b7\u03c4\u03b7\u03b8\u03bf\u03cd\u03bd \u03c4\u03b1 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ac \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03bc\u03b5\u03bb\u03b5\u03c4\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03ba\u03b1\u03b9 \u03c5\u03bb\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03c3\u03c4\u03bf \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03c4\u03bf\u03c5 \u03c0\u03b1\u03c1\u03cc\u03bd\u03c4\u03bf\u03c2 \u03b4\u03b9\u03b4\u03b1\u03ba\u03c4\u03bf\u03c1\u03b9\u03ba\u03bf\u03cd \u03c0\u03c1\u03bf\u03b3\u03c1\u03ac\u03bc\u03bc\u03b1\u03c4\u03bf\u03c2, \u03c3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1, \u03b1\u03bd\u03b1\u03bb\u03cd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03ad\u03bd\u03c4\u03b5 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03c4\u03b5\u03c5\u03b8\u03cd\u03bd\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b5\u03b9\u03b5\u03c2: (\u03b1) \u0391\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7 \u0395\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd \u03ba\u03b1\u03b9 \u0391\u03bd\u03c9\u03bc\u03b1\u03bb\u03b9\u03ce\u03bd \u03bc\u03b5 \u03a7\u03c1\u03ae\u03c3\u03b7 \u039f\u03bc\u03bf\u03c3\u03c0\u03bf\u03bd\u03b4\u03b9\u03b1\u03ba\u03ae\u03c2 \u039c\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2, (\u03b2) \u039c\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03a3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7\u03c2 \u03bc\u03b5 \u03a7\u03c1\u03ae\u03c3\u03b7 \u039c\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2 \u03a3\u03c5\u03c3\u03c7\u03b5\u03c4\u03af\u03c3\u03b5\u03c9\u03bd, (\u03b3) \u03a3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2 \u039c\u03b5\u03c4\u03c1\u03b9\u03b1\u03c3\u03bc\u03bf\u03cd \u03bc\u03b5 \u03a4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u0395\u03bd\u03b9\u03c3\u03c7\u03c5\u03c4\u03b9\u03ba\u03ae\u03c2 \u039c\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2, (\u03b4) \u039c\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u0391\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7\u03c2 \u03bc\u03b5 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03a4\u03b5\u03c7\u03bd\u03bf\u03bb\u03bf\u03b3\u03af\u03b1\u03c2 \u039a\u03b1\u03b8\u03bf\u03c1\u03b9\u03b6\u03cc\u03bc\u03b5\u03bd\u03b7\u03c2 \u03b1\u03c0\u03cc \u039b\u03bf\u03b3\u03b9\u03c3\u03bc\u03b9\u03ba\u03cc \u03bc\u03b5 \u03a7\u03c1\u03ae\u03c3\u03b7 \u039d\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03ce\u03bd \u0394\u03b9\u03ba\u03c4\u03cd\u03c9\u03bd \u0393\u03c1\u03ac\u03c6\u03c9\u03bd \u03ba\u03b1\u03b9 (\u03b5) \u03a4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u0395\u03be\u03ae\u03b3\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u0391\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7\u03c2 \u03a4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u039d\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 \u03b3\u03b9\u03b1 \u039c\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u0391\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u039c\u03b5\u03c4\u03c1\u03b9\u03b1\u03c3\u03bc\u03bf\u03cd \u03a4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u039d\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2."}	16	+{"work_id": 3042799967, "title": "Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter", "abstract": "The size and energy consumption of datacenters have been increasing significantly over the past years. As a result, datacenters' increasing electricity monetary cost, energy consumption and energy harmful gas emissions have become a severe problem. Renewable energy supply is widely seen as a promising solution. However, the instability of renewable energy brings about a new challenge since insufficient energy supply may lead to job running interruptions or failures. Though previous works attempt to more accurately predict the amount of produced renewable energy, due to the instability of its influencing factors (e.g., wind, temperature), sufficient renewable energy supply cannot be always guaranteed. To handle this problem, in this paper, we propose allocating jobs with the same service-level-objective (SLO) level to the same physical machine (PM) group, and power each PM group with renewable energy generators that have probability no less than its SLO to produce the amount no less than its energy demand. It ensures that insufficient renewable energy supply will not lead to SLO violations. We use a deep learning technique to predict the probability of producing amount no less than each value of each renewable energy source and predict the energy demands of each PM area. We formulate an optimization problem: how to match renewable energy resources with different instabilities to different PM groups as energy supply in order to minimize the number of SLO violations (due to interruption from insufficient renewable energy supply), total energy monetary cost and total carbon emission. We then use reinforcement learning method and linear programming method to solve the optimization problem. The real trace driven experiments show that our method can achieve much lower SLO violations, total energy monetary cost and total carbon emission compared to other methods."}
10	−{"work_id": 7161556515, "title": "DeepServeCB: SLO-Aware and Cost-Aware Elastic Scheduling for Serverless Multi-Tenant LLM Inference", "abstract": "Deploying large language model (LLM) inference services in serverless, multi-tenant environments present compounding challenges: cold start latency, GPU memory fragmentation, inter-tenant resource contention, and unpredictable tail latency. Existing systems optimize individual aspects but fail to jointly address service-level objective (SLO) compliance and cost efficiency under dynamic, heterogeneous workloads. We present DeepServeCB, an elastic scheduling framework that formulates the joint SLO--cost optimization as a contextual bandit problem. The system introduces a Request Profiler that extracts online features---prompt length, historical KV-cache hit ratio, and predicted generation length---and feeds them into a contextual bandit agent that adaptively selects batch sizes, concurrency levels, KV-cache eviction policies, and warm-standby strategies. We evaluate DeepServeCB on ShareGPT and BurstGPT traces using LLaMA-2-13B and Mixtral-8x7B models on NVIDIA A100 GPUs. Results show that DeepServeCB reduces P99 latency by 38--62% compared to state-of-the-art baselines while improving GPU utilization by 14--23% and reducing per-request cost by up to 27%, with only a modest increase in scheduling overhead."}	17	+{"work_id": 4392909747, "title": "Cloud Computing: Principles And Paradigms", "abstract": "Capturing the current state of the art in Cloud Computing technology and applications is the main goal of this book. The book\u2019s secondary objective is to chart a course for future studies and technological developments that will pave the way for the establishment of a worldwide marketplace for cloud computing services catering to commercial, government, academic, and consumer needs. The book is designed to be a reference for a wide range of readers, including systems architects, developers, practitioners, new researchers, and graduate students."}
11	−{"work_id": 4395047770, "title": "Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions", "abstract": "Abstract With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling."}	18	+{"work_id": 2562507292, "title": "Internet of Things Applications - From Research and Innovation to Market Deployment", "abstract": "ISBN : 9788793102941"}
12	−{"work_id": 4386585104, "title": "ACCESS: Advancing Innovation", "abstract": "As the National Science Foundation evolves its investments in cyberinfrastructure, it has made a significant investment in the ACCESS (Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support) program instantiating a novel set of services along with a novel governance and management model. Research cyberinfrastructure (CI) is a key catalyst for discovery and innovation and plays a critical role in ensuring U.S. leadership in science and engineering, economic competitiveness, and national security, consistent with NSF\u2019s mission. Funding of a set of awards through the ACCESS program has established a suite of CI coordination services targeted at supporting a broad and diverse set of requirements, researchers, and usage modalities spanning all areas of science and engineering research and education complemented by support for the collective and coordinated operation of the overall ACCESS program."}	19	+{"work_id": 4387316367, "title": "Ontological Approach to a Knowledge Graph Construction in a Semantic Library", "abstract": "Abstract The paper considers an approach to a knowledge graph construction based on the ontological representation of scientific subject areas. The presentation is based on concepts related to information and data mining, such as knowledge, knowledge extraction, domain ontology, scientific domain, thesaurus, semantic digital library, user information need, ontological design method and, in fact, the knowledge graph. The digital semantic library LibMeta is presented as a repository of various structured data with the possibility of their integration with other data sources. Assumes the possibility of specifying personal content by describing a local subject area within LibMeta. The ontology of the content of the semantic library acts as a means of formalization. This paper addresses the experience of building semantic libraries based on thesauri and ontological design. Building ontologies based on the thesaurus of the subject area LibMeta allows us to say that the presence of internal semantic links ensures the consistency and reliability of search results, which is a necessary condition for extracting scientific knowledge. The digital library ontology defines the data structure of the library content. Each data element loaded into the library can be associated with an ontology vertex (top) that determines the position of the data element in the ontology. Based on the ontology links and the links defined at the design stage, you can build a data graph. On the example of the ontology of the LibMeta semantic library, the technology of forming the knowledge graph of modern applications in mathematics is discussed. The problems of filling a graph, embedding in a graph, extracting links and nodes of a graph are discussed."}
13	−{"work_id": 4361200200, "title": "Spatial Data Science", "abstract": "Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors. The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes \u2013 array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud. The data used and all code examples are freely available online from https://r-spatial.org/book/. The solutions to the exercises can be found here: https://edzer.github.io/sdsr_exercises/."}	20	+{"work_id": 2545990795, "title": "QUIC: A UDP-Based Multiplexed and Secure Transport", "abstract": "QUIC is a multiplexed and secure transport protocol that runs on top\nof UDP. QUIC builds on past transport experience, and implements\nmechanisms that make it useful as a modern general-purpose transport\nprotocol. Using UDP as the basis of QUIC is intended to address\ncompatibility issues with legacy clients and middleboxes. QUIC\nauthenticates all of its headers, preventing third parties from from\nchanging them. QUIC encrypts most of its headers, thereby limiting\nprotocol evolution to QUIC endpoints only. Therefore, middleboxes, in\nlarge part, are not required to be updated as new protocol versions\nare deployed. This document describes the core QUIC protocol,\nincluding the conceptual design, wire format, and mechanisms of the\nQUIC protocol for connection establishment, stream multiplexing,\nstream and connection-level flow control, and data reliability.\nAccompanying documents describe QUIC's loss recovery and congestion\ncontrol, and the use of TLS 1.3 for key negotiation."}
14	−{"work_id": 4229658977, "title": "Google Colaboratory", "abstract": "Google Colaboratory more commonly referred to as \u201cGoogle Colab\u201d or just simply \u201cColab\u201d is a research project for prototyping machine learning models on powerful hardware options such as GPUs and TPUs. It provides a serverless Jupyter notebook environment for interactive development. Google Colab is free to use like other G Suite products."}	21	+{"work_id": 4398201281, "title": "Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems", "abstract": "The decentralized nature of blockchain enables data traceability, transparency, and immutability as complementary security features to the existing Internet of Things (IoT) systems. These Blockchain-based IoT (BIoT) systems aim to mitigate security risks such as malicious control, data leakage, and dishonesty often found in traditional cloud-based, vendor-specific IoT networks. As we steadily advance into the era of situation-aware IoT, the use of machine learning (ML) techniques has become essential for synthesizing situations based on sensory contexts. However, the challenge to integrate learning-based situation awareness with BIoT systems restricts the full potential of such integration. This is primarily due to the conflicts between the deterministic nature of smart contracts and the non-deterministic nature of machine learning, as well as the high costs of conducting machine learning on blockchain. To address the challenge, we propose a framework named Situ-Oracle. With the framework, a computation oracle of the blockchain ecosystem is leveraged to provide situation analysis as a service, based on Recurrent Neural Network (RNN)-based learning models tailored for the Situ model, and specifically designed smart contracts are deployed as intermediary communication channels between the IoT devices and the computation oracle. We used smart homes as a case study to demonstrate the framework design. Subsequently, system-wide evaluations were conducted over a physically constructed BIoT system. The results indicate that the proposed framework achieves better situation analysis accuracy (above 95%) and improves gas consumption as well as network throughput and latency when compared to baseline systems (on-chain learning or off-chain model verification). Overall, the paper presents a promising approach for improving situation analysis for BIoT systems, with potential applications in various domains such as smart homes, healthcare, and industrial automation."}
15	−{"work_id": 4407389811, "title": "Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data", "abstract": "The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision making. This comprehensive literature review explores the integration of AI and IoT technologies within environmental sciences, with a particular focus on applications related to water quality and climate data. The methodology involves a systematic search and selection of relevant studies, followed by thematic, meta-, and comparative analyses to synthesize current research trends, benefits, challenges, and gaps. The review highlights how AI enhances IoT\u2019s data collection capabilities through advanced predictive modeling, real-time analytics, and automated decision making, thereby improving the accuracy, timeliness, and efficiency of environmental monitoring systems. Key benefits identified include enhanced data precision, cost efficiency, scalability, and the facilitation of proactive environmental management. Nevertheless, this integration encounters substantial obstacles, including issues related to data quality, interoperability, security, technical constraints, and ethical concerns. Future developments point toward enhancements in AI and IoT technologies, the incorporation of innovations like blockchain and edge computing, the potential formation of global environmental monitoring systems, and greater public involvement through citizen science initiatives. Overcoming these challenges and embracing new technological trends could enable AI and IoT to play a pivotal role in strengthening environmental sustainability and resilience."}	22	+{"work_id": 2926987861, "title": "A Survey of Distributed Consensus Protocols for Blockchain Networks", "abstract": "Since the inception of Bitcoin, cryptocurrencies and the underlying blockchain technology have attracted an increasing interest from both academia and industry. Among various core components, consensus protocol is the defining technology behind the security and performance of blockchain. From incremental modifications of Nakamoto consensus protocol to innovative alternative consensus mechanisms, many consensus protocols have been proposed to improve the performance of the blockchain network itself or to accommodate other specific application needs. In this survey, we present a comprehensive review and analysis on the state-of-the-art blockchain consensus protocols. To facilitate the discussion of our analysis, we first introduce the key definitions and relevant results in the classic theory of fault tolerance which help to lay the foundation for further discussion. We identify five core components of a blockchain consensus protocol, namely, block proposal, block validation, information propagation, block finalization, and incentive mechanism. A wide spectrum of blockchain consensus protocols are then carefully reviewed accompanied by algorithmic abstractions and vulnerability analyses. The surveyed consensus protocols are analyzed using the five-component framework and compared with respect to different performance metrics. These analyses and comparisons provide us new insights in the fundamental differences of various proposals in terms of their suitable application scenarios, key assumptions, expected fault tolerance, scalability, drawbacks and trade-offs. We believe this survey will provide blockchain developers and researchers a comprehensive view on the state-of-the-art consensus protocols and facilitate the process of designing future protocols."}
16	−{"work_id": 4406207083, "title": "Mobile Edge Intelligence for Large Language Models: A Contemporary Survey", "abstract": "On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest owing to their superior privacy, reduced latency, and bandwidth saving. Nonetheless, the capabilities of on-device LLMs are intrinsically constrained by the limited capacity of edge devices compared to the much more powerful cloud centers. To bridge the gap between cloud-based and on-device AI, mobile edge intelligence (MEI) presents a viable solution to this problem by provisioning AI capabilities within the edge of mobile networks with improved privacy and latency relative to cloud computing. MEI sits between on-device AI and cloud-based AI, featuring wireless communications and more powerful computing resources than end devices. This article provides a contemporary survey on harnessing MEI for LLMs. We first cover the preliminaries of LLMs, starting with LLMs and MEI, followed by resource-efficient LLM techniques. We then illustrate several killer applications to demonstrate the need for deploying LLMs at the network edge and present an architectural overview of MEI for LLMs (MEI4LLM). Subsequently, we delve into various aspects of MEI4LLM, extensively covering edge LLM caching and delivery, edge LLM training, and edge LLM inference. Finally, we identify future research opportunities. We aim to inspire researchers in the field to leverage mobile edge computing to facilitate LLM deployment in close proximity to users, thereby unleashing the potential of LLMs across various privacy-and delay-sensitive applications."}
17	−{"work_id": 2602956630, "title": "Internet of Things Strategic Research Roadmap", "abstract": "Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual \u201cthings\u201d have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network."}
18	−{"work_id": 2950865323, "title": "Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing", "abstract": "With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI."}
19	−{"work_id": 4300092207, "title": "OrbitalBrain: A Distributed Framework for Training ML Models in Space", "abstract": "Earth observation nanosatellites capture high-resolution photos of the Earth in near real-time. These images increasingly support ML applications that are critical for safety and response, such as forest fire and flood detection. However, the downlink bandwidth is limited, resulting in days or weeks of delay from image capture to training. In this work, we propose OrbitalBrain, an efficient in-space distributed ML training framework that leverages limited and predictable satellite compute, bandwidth, and power to intelligently balance data transfer, model aggregation, and local training. Our evaluations demonstrate that OrbitalBrain achieves 1.52\u00d7-12.4\u00d7 speedup in time-to-accuracy while always reaching a higher final model accuracy compared to state-of-the-art ground-based or federated learning baselines. Furthermore, our approach is complementary to satellite imagery capturing and downloading, enhancing the overall efficiency of satellite-based applications."}
20	−{"work_id": 2593864460, "title": "Billion-Scale Similarity Search with GPUs", "abstract": "Similarity search finds application in database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data parallel tasks such as distance computation, prior approaches in this domain are bottlenecked by algorithms that expose less parallelism, such ask-min selection, or make poor use of the memory hierarchy. We propose a novel design fork-selection. We apply it in different similarity search scenarios, by optimizing brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation operates at up to 55 percent of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5 \u00d7 faster than prior GPU state of the art. It enables the construction of a high accuracyk-NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility."}
21	−{"work_id": 4401806686, "title": "AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications", "abstract": "Artificial Intelligence (AI) technology's rapid advancement has significantly changed various industries' operations. This comprehensive review paper aims to provide readers with a deep understanding of AI's applications & implementations, workings, and potential impacts across different sectors. It also discusses its future, threats, and integration into new policy. Through extensive research on more than 200 research and many other sources, the authors have made every effort to present an accurate overview of the numerous applications of AI nowadays in industries such as agriculture, education, autonomous systems, healthcare, finance, entertainment, transportation, military, manufacturing, and more. The paper explores various AI technologies, including machine learning, deep learning, robotics, big data, the Internet of Things, natural language processing, image processing, object detection, virtual reality, augmented reality, speech recognition, and computer vision. It provides real-world examples of their applications and implementations. Moreover, it highlights and evaluates the future potential, challenges, and limitations associated with the widespread use of AI. Our study incorporates the latest research to offer a comprehensive and nuanced understanding of AI's potential benefits and challenges. This data-driven review case study highlights the immense potential of AI technology and addresses the ethical, societal, and economic considerations related to its implementation."}
22	−{"work_id": 7118010419, "title": "Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices", "abstract": "The increasing popularity of digitization in retail has emphasized application performance as a critical component of customer satisfaction, business continuity, and operational efficiency. Retail applications today operate in hyper-dynamic environments, where performance can be introduced by concerns such as workload peaks, scaling cloud infrastructure performance, spikes in transactions, and real-time data processing. Poor performance erodes user experience, depletes objectives, and lowers competitive benefit. Although many surveys of cloud or performance monitoring exist, they do not incorporate retail contexts that emphasize customer facing KPIs, seasonal demand variability, and other omnichannel complexities. The current paper seeks to address this gap in research by providing a systematic review of 45 peer-reviewed studies (2015\u20132025) specific to monitoring tools, optimization frameworks, and best practices in retail performance monitoring. The findings in the review synthesize performance monitoring platforms (i.e. Splunk, Datadog, New Relic), log management tools, key performance indicators, performance continuous monitoring, performance visualization, and performance integration into retail systems. The paper also offers comparison evaluations of Amazon and Google Cloud monitoring offerings, as well as limitations for optimization planning, and the effect of intelligent analytics to improve scalability and resiliency. The paper contributes by connecting scholarly viewpoints with practical suggestions, providing a distinctive road map for practitioners and academics to develop and enhance high-performance retail applications in cloud-based corporate settings."}
23	−{"work_id": 2784822088, "title": "A microservice architecture for the processing of large geospatial data in the Cloud", "abstract": "Mit der wachsenden Zahl an Ger\u00e4ten, die spatio-temporale Informationen aufnehmen k\u00f6nnen sowie immer besser werdenden Sensoren, steigt auch die Menge an Geodaten. Vor der Benutzung m\u00fcssen die rohen gesammelten Informationen verarbeitet werden. Zurzeit greifen Experten auf desktop-basierte Geographische Informationssysteme zur\u00fcck, um Prozessierungsworkflows durchzuf\u00fchren. Allerdings \u00fcbersteigt das Datenvolumen sowie die Komplexit\u00e4t der Verarbeitungsalgorithmen l\u00e4ngst die Kapazit\u00e4t ihrer Workstations. Zurzeit findet ein Paradigmenwechsel von Desktop-L\u00f6sungen zur Cloud statt, aber die Entwickler von Prozessierungsalgorithmen sind oft keine Informatiker und haben deshalb wenig Erfahrung im Bereich Cloud-Computing. Unsere Forschungshypothese ist, dass eine Microservice-Architektur und dom\u00e4nenspezifische Sprachen genutzt werden k\u00f6nnen, um existierende Algorithmen zu orchestrieren und Workflows f\u00fcr die Prozessierung von Geodaten in der Cloud auszuf\u00fchren, und damit eine effiziente Anwendungsentwicklung erm\u00f6glichen und die Erfahrung von Stakeholdern verbessern. Wir pr\u00e4sentieren eine Softwarearchitektur, die Erweiterungspunkte f\u00fcr Prozessierungsalgorithmen (oder Microservices) enth\u00e4lt, eine Workflow-Management-Komponente f\u00fcr die verteilte Service-Orchestrierung, und einen Workflow-Editor basierend auf einer dom\u00e4nenspezifischen Sprache. Ziel ist es, Benutzern und Entwicklern ohne tiefergehendes Wissen in verteilten Systemen den Zugang zur Cloud zu erm\u00f6glichen. Unsere Forschungsmethode basiert auf der Design Science Research Methodology. Wir f\u00fchren eine Analyse der Problemdom\u00e4ne durch und sammeln Anforderungen und Qualit\u00e4tsattribute f\u00fcr unsere Architektur. Um unsere Forschungsziele zu erreichen, entwickeln wir die Architektur sowie Ans\u00e4tze f\u00fcr Workflow-Management und -Modellierung. Wir stellen den Nutzen unserer L\u00f6sung dar, indem wir sie auf zwei praktische Anwendungsf\u00e4lle anwenden. Au\u00dferdem evaluieren wir ihre Qualit\u00e4t anhand von definierten Szenarien. Abschlie\u00dfend f\u00fchren wir eine kritische Bewertung unserer Ergebnisse durch. Unsere wissenschaftlichen Beitr\u00e4ge k\u00f6nnen in drei Bereiche gegliedert werden. Wir pr\u00e4sentieren eine skalierbare und erweiterbare Microservice-Architektur f\u00fcr die Geodatenprozessierung, die eine verteilte Entwicklung erm\u00f6glicht sowie eine hohe Verf\u00fcgbarkeit bietet. Au\u00dferdem pr\u00e4sentieren wir neue Ans\u00e4tze f\u00fcr die Service- Integration und -Orchestrierung in der Cloud sowie regelbasiertes und dynamisches Workflow-Management ohne a priori Wissen im Entwurf. F\u00fcr die Workflow- Modellierung entwickeln wir eine dom\u00e4nenspezifische Sprache sowie eine neue Methode f\u00fcrs Sprachdesign. Die Ergebnisse unserer Arbeit st\u00fctzen unsere Forschungshypothese. Die Microservice- Architektur erm\u00f6glicht eine effiziente Entwicklung eines verteilten Systems. Die dom\u00e4nenspezifische Sprache sowie unser Ansatz zur Service-Integration verbessern die Erfahrung der Stakeholder. Unsere Arbeit stellt einen gro\u00dfen Schritt im Paradigmenwechsel zur Cloud dar und bietet M\u00f6glichkeiten f\u00fcr weitere Forschung."}
24	−{"work_id": 4394994587, "title": "Recommender Systems in the Era of Large Language Models (LLMs)", "abstract": "With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an indispensable and important component in our daily lives, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have achieved significant advancements in enhancing recommender systems by modeling user-item interactions and incorporating their textual side information, these DNN-based methods still exhibit some limitations, such as difficulties in effectively understanding users' interests and capturing textual side information, inabilities in generalizing to various seen/unseen recommendation scenarios and reasoning on their predictions, etc. Meanwhile, the development of Large Language Models (LLMs), such as ChatGPT and GPT-4, has revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), due to their remarkable abilities in fundamental responsibilities of language understanding and generation, as well as impressive generalization capabilities and reasoning skills. As a result, recent studies have actively attempted to harness the power of LLMs to enhance recommender systems. Given the rapid evolution of this research direction in recommender systems, there is a pressing need for a systematic overview that summarizes existing LLM-empowered recommender systems, so as to provide researchers and practitioners in relevant fields with an in-depth understanding. Therefore, in this survey, we conduct a comprehensive review of LLM-empowered recommender systems from various aspects including pre-training, fine-tuning, and prompting paradigms. More specifically, we first introduce the representative methods to harness the power of LLMs (as a feature encoder) for learning representations of users and items. Then, we systematically review the emerging advanced techniques of LLMs for enhancing recommender systems from three paradigms, namely pre-training, fine-tuning, and prompting. Finally, we comprehensively discuss the promising future directions in this emerging field."}
25	−{"work_id": 4408156900, "title": "Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities", "abstract": "The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to systematically review the current literature regarding the application of federated learning (FL) and artificial intelligence (AI) to improve cloud computing security while preserving privacy, delivering real-time threat detection, and meeting regulatory requirements. The current research follows a systematic literature review (SLR) approach, which examined 30 studies published between 2020 and 2024 and followed the PRISMA 2020 checklist. The analysis shows that FL provides significant privacy risk reduction by 25%, especially in healthcare and similar domains, and it improves threat detection by 40% in critical infrastructure areas. A total of 80% of reviewed implementations showed improved privacy, but challenges like communication overhead and resource limitations persist, with 50% of studies reporting latency issues. To overcome these obstacles, this study also explores some emerging solutions, which include model compression, hybrid federated architectures, and cryptographic enhancements. Additionally, this paper demonstrates the unexploited capability of FL for real-time decision-making in dynamic edge environments and highlights its potential across autonomous systems, Industrial Internet of Things (IIoT), and cybersecurity frameworks. The paper\u2019s proposed insights present a deployment strategy for FL models which enables scalable, secure, and privacy-preserving operations and will enable robust cloud security solutions in the AI era."}
26	−{"work_id": 3012404397, "title": "Santa Claus Algorithm: Agent-Based Consensus Preprocessing Method for Scalability Improvement in Private Blockchain Environment", "abstract": "\ube44\uc794\ud2f4 \uc7a5\uc560 \ud5c8\uc6a9(BFT: Byzantine Fault Tolerance)\uc740 \ube14\ub85d\uccb4\uc778 \ud658\uacbd\uc5d0\uc11c \uc8fc\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\ub294 \ube14\ub85d\uccb4\uc778 \ud569\uc758 \uc54c\uace0\ub9ac\uc998 \uc911\uc758 \ud558\ub098\uc774\ub2e4. BFT\ub294 \ud569\uc758\ub97c \ud558\uae30 \uc704\ud574 \ubaa8\ub4e0 \ucc38\uc5ec\uc790\uac00 \ud569\uc758\uc5d0 \ucc38\uc5ec\ud558\ub294 \uad6c\uc870\ub85c \ub418\uc5b4\uc788\uc5b4 \ucc38\uc5ec \ub178\ub4dc\uac00 \ub9ce\uc544\uc9c0\uba74 \ucc98\ub9ac\uc18d\ub3c4\uac00 \ud604\uaca9\ud788 \uac10\uc18c\ud558\ub294 \ud655\uc7a5\uc131\uc758 \ubb38\uc81c\uac00 \uc788\ub2e4. \uc774\ub7ec\ud55c \ubb38\uc81c \ub54c\ubb38\uc5d0 \ub9ce\uc740 \ucc38\uc5ec \ub178\ub4dc\uac00 \uc788\ub294 \ube14\ub85d\uccb4\uc778\uc5d0\uc11c \ube44\uc794\ud2f4 \uc7a5\uc560 \ud5c8\uc6a9\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud655\uc7a5\uc131 \uac1c\uc120\uc774 \ud544\uc218\uc801\uc774\ub2e4. \ubcf8 \uc5f0\uad6c\uc5d0\uc11c\ub294 \ud504\ub77c\uc774\ube57 \ube14\ub85d\uccb4\uc778\uc5d0\uc11c \ud569\uc758 \uc5c6\uc774 \ub370\uc774\ud130\ub97c \uae30\ub85d\ud558\ub2e4\uac00 \uc5d0\uc774\uc804\ud2b8\uac00 \ud569\uc758\uac00 \ud544\uc694\ud55c \uc21c\uac04\uc744 \uc54c\ub824\uc8fc\ub294 \uc804\ucc98\ub9ac \uae30\ubc95\uc744 \uc81c\uc548\ud55c\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ube14\ub85d\uccb4\uc778\uc758 \ub178\ub4dc\ub97c \ubc29\ubb38\ud558\uba74\uc11c \ub450 \ub2e8\uacc4\uc5d0 \uac78\uccd0 \ud569\uc758\uac00 \ud544\uc694\ud55c \uc21c\uac04\uc744 \uc54c\ub824\uc900\ub2e4. \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uc9c0\uae08\uae4c\uc9c0 \uae30\ub85d\ub41c \ub370\uc774\ud130\uac00 \ubaa8\ub4e0 \ub178\ub4dc\uc5d0\uc11c \uc77c\uce58\ud558\ub294\uc9c0 \ud655\uc778\ud558\uba70, \ub450 \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uc5b4\ub5a0\ud55c \ub370\uc774\ud130\uac00 \ubd88\uc77c\uce58\ud558\uc5ec \ud569\uc758\uac00 \ud544\uc694\ud55c\uc9c0 \uc54c\ub824\uc900\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ube14\ub85d\uccb4\uc778 \ub124\ud2b8\uc6cc\ud06c\uc5d0\uc11c \ub3c5\ub9bd\uc801\uc778 \uac1d\uccb4\ub85c\uc11c \ube14\ub85d\uccb4\uc778\uc5d0 \ucc38\uc5ec\ud558\ub294 \uc784\uc758\uc758 \ub178\ub4dc\uc5d0 \uc758\ud574 \uc791\ub3d9\uc774 \ub41c\ub2e4. \ubcf8 \ub17c\ubb38\uc5d0\uc11c\ub294 \uc81c\uc548 \uae30\ubc95\uc744 \ud504\ub77c\uc774\ube57 \ube14\ub85d\uccb4\uc778 \ud658\uacbd\uc5d0\uc11c \uc2e4\uc6a9\uc801 \ube44\uc794\ud2f4 \uc7a5\uc560 \ud5c8\uc6a9(PBFT: Practical Byzantine Fault Tolerance) \ud569\uc758\uc5d0 \uc801\uc6a9\ud55c \uacb0\uacfc \ub178\ub4dc\uc758 \uc218\uac00 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \ubc18\uc751 \uc2dc\uac04\uc774 \ud3c9\uade0 39% \uac10\uc18c\ud558\uc600\ub2e4\ub294 \uac83\uc744 \uc99d\uba85\ud558\uc600\ub2e4."}
27	−{"work_id": 2120510885, "title": "The Byzantine generals problem", "abstract": "Abstract The Byzantine Generals Problem requires processes to reach agreement upon a value even though some of them may fad. It is weakened by allowing them to agree upon an \"incorrect \" value if a failure occurs. The transaction eormmt problem for a distributed database Js a special case of the weaker problem. It is shown that, like the original Byzantine Generals Problem, the weak version can be solved only ff fewer than one-third of the processes may fad. Unlike the onginal problem, an approximate solution exists that can tolerate arbaranly many failures."}
28	−{"work_id": 2795038878, "title": "Datasheets for datasets", "abstract": "Documentation to facilitate communication between dataset creators and consumers."}
29	−{"work_id": 4387321091, "title": "Efficient Memory Management for Large Language Model Serving with PagedAttention", "abstract": "High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2--4\u00d7 with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM's source code is publicly available at https://github.com/vllm-project/vllm."}
30	−{"work_id": 4415124086, "title": "The Faiss Library", "abstract": "Vector databases typically manage large collections of embedding vectors. As AI applications are growing rapidly, the number of embeddings that need to be stored and indexed is increasing. The Faiss library is dedicated to vector similarity search, a core functionality of vector databases. Faiss is a toolkit of indexing methods and related primitives used to search, cluster, compress and transform vectors. This paper describes the trade-offs in vector search and the design principles of Faiss in terms of structure, approach to optimization and interfacing. We benchmark key features of the library and discuss a few selected use cases to highlight its broad applicability."}
31	−{"work_id": 2963507231, "title": "Untangling Blockchain: A Data Processing View of Blockchain Systems", "abstract": "Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence, values, and histories of the states. As the technology landscape is expanding rapidly, it is both important and challenging to have a firm grasp of what the core technologies have to offer, especially with respect to their data processing capabilities. In this paper, we first survey the state of the art, focusing on private blockchains (in which parties are authenticated). We analyze both in-production and research systems in four dimensions: distributed ledger, cryptography, consensus protocol, and smart contract. We then present BLOCKBENCH, a benchmarking framework for understanding performance of private blockchains against data processing workloads. We conduct a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity, and Hyperledger Fabric. The results demonstrate several trade-offs in the design space, as well as big performance gaps between blockchain and database systems. Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases."}
32	−{"work_id": 4288083766, "title": "Deep Learning Based Recommender System", "abstract": "With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field."}
33	−{"work_id": 1572499759, "title": "Microservices Architecture", "abstract": "This paper provides a comprehensive overview of microservices architecture, focusing on its implementation.We discuss the core principles, key components, deployment considerations, challenges, and best practices for building robust and scalable microservices-based applications with C#.The paper emphasizes the benefits of adopting microservices, such as improved scalability, agility, and maintainability, while also addressing common challenges like distributed system complexity and data management.Specific examples and code snippets illustrate the practical aspects of implementing microservices with ASP.NET Core and related technologies."}
34	{"work_id": 3097300053, "title": "Graph Neural Networks in Recommender Systems: A Survey", "abstract": "With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS ."}	23	{"work_id": 3097300053, "title": "Graph Neural Networks in Recommender Systems: A Survey", "abstract": "With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS ."}
35	{"work_id": 4401857375, "title": "A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models", "abstract": "As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful capacity of retrieval in providing additional knowledge enables RAG to assist existing generative AI in producing high-quality outputs. Recently, Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the quality of the generated content of LLMs. In this survey, we comprehensively review existing research studies in RA-LLMs, covering three primary technical perspectives: Furthermore, to deliver deeper insights, we discuss current limitations and several promising directions for future research. Updated information about this survey can be found at: https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/"}	24	{"work_id": 4401857375, "title": "A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models", "abstract": "As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful capacity of retrieval in providing additional knowledge enables RAG to assist existing generative AI in producing high-quality outputs. Recently, Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the quality of the generated content of LLMs. In this survey, we comprehensively review existing research studies in RA-LLMs, covering three primary technical perspectives: Furthermore, to deliver deeper insights, we discuss current limitations and several promising directions for future research. Updated information about this survey can be found at: https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/"}
36	{"work_id": 3014294420, "title": "Introduction to Data Mining", "abstract": "Chapter Objectives \u2713 To learn about the concepts of data mining. \u2713 To understand the need for, and the applications of data mining \u2713 To differentiate between data mining and machine learning \u2713 To understand the process of data mining. \u2713 To understand the difference between data mining and machine learning. Introduction to Data Mining In the age of information, an enormous amount of data is available in different industries and organizations. The availability of this massive data is of no use unless it is transformed into valuable information. Otherwise, we are sinking in data, but starving for knowledge. The solution to this problem is data mining which is the extraction of useful information from the huge amount of data that is available. Data mining is defined as follows: \u2018Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. The patterns must be actionable so they may be used in an enterprise's decision making.\u2019 From this definition, the important take aways are: \u2022 Data mining is a process of automated discovery of previously unknown patterns in large volumes of data. \u2022 This large volume of data is usually the historical data of an organization known as the data warehouse. \u2022 Data mining deals with large volumes of data, in Gigabytes or Terabytes of data and sometimes as much as Zetabytes of data (in case of big data). \u2022 Patterns must be valid, novel, useful and understandable. \u2022 Data mining allows businesses to determine historical patterns to predict future behaviour. \u2022 Although data mining is possible with smaller amounts of data, the bigger the data the better the accuracy in prediction. \u2022 There is considerable hype about data mining at present, and the Gartner Group has listed data mining as one of the top ten technologies to watch. Need of Data Mining Data mining is a recent buzz word in the field of Computer Science. It is a computing process that uses intelligent mathematical algorithms to extract the relevant data and computes the probability of future actions. It is also known as Knowledge Discovery in Data (KDD)."}	25	{"work_id": 3014294420, "title": "Introduction to Data Mining", "abstract": "Chapter Objectives \u2713 To learn about the concepts of data mining. \u2713 To understand the need for, and the applications of data mining \u2713 To differentiate between data mining and machine learning \u2713 To understand the process of data mining. \u2713 To understand the difference between data mining and machine learning. Introduction to Data Mining In the age of information, an enormous amount of data is available in different industries and organizations. The availability of this massive data is of no use unless it is transformed into valuable information. Otherwise, we are sinking in data, but starving for knowledge. The solution to this problem is data mining which is the extraction of useful information from the huge amount of data that is available. Data mining is defined as follows: \u2018Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. The patterns must be actionable so they may be used in an enterprise's decision making.\u2019 From this definition, the important take aways are: \u2022 Data mining is a process of automated discovery of previously unknown patterns in large volumes of data. \u2022 This large volume of data is usually the historical data of an organization known as the data warehouse. \u2022 Data mining deals with large volumes of data, in Gigabytes or Terabytes of data and sometimes as much as Zetabytes of data (in case of big data). \u2022 Patterns must be valid, novel, useful and understandable. \u2022 Data mining allows businesses to determine historical patterns to predict future behaviour. \u2022 Although data mining is possible with smaller amounts of data, the bigger the data the better the accuracy in prediction. \u2022 There is considerable hype about data mining at present, and the Gartner Group has listed data mining as one of the top ten technologies to watch. Need of Data Mining Data mining is a recent buzz word in the field of Computer Science. It is a computing process that uses intelligent mathematical algorithms to extract the relevant data and computes the probability of future actions. It is also known as Knowledge Discovery in Data (KDD)."}
37	−{"work_id": 4395064458, "title": "Wireless Communications", "abstract": "Now reissued by Cambridge University Press, the updated second edition of this definitive textbook provides an unrivaled introduction to the theoretical and practical fundamentals of wireless communications. Key technical concepts are developed from first principles, and demonstrated to students using over 50 carefully curated worked examples. Over 200 end-of-chapter problems, based on real-world industry scenarios, help cement student understanding. The book provides a thorough coverage of foundational wireless technologies, including wireless local area networks (WLAN), 3G systems, and Bluetooth along with refreshed summaries of recent cellular standards leading to 4G and 5G, insights into the new areas of mobile satellite communications and fixed wireless access, and extra homework problems. Supported online by a solutions manual and lecture slides for instructors, this is the ideal foundation for senior undergraduate and graduate courses in wireless communications."}
38	{"work_id": 2947690458, "title": "Blockchain for Internet of Things: A Survey", "abstract": "Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area."}	26	{"work_id": 2947690458, "title": "Blockchain for Internet of Things: A Survey", "abstract": "Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area."}
39	{"work_id": 7123361055, "title": "Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions", "abstract": "The rapid expansion of sixth-generation (6G) wireless networks and the Internet of Things (IoT) has catalyzed the evolution from centralized cloud intelligence towards decentralized edge general intelligence. However, traditional edge intelligence methods, characterized by static models and limited cognitive autonomy, fail to address the dynamic, heterogeneous, and resource-constrained scenarios inherent to emerging edge networks. Agentic artificial intelligence (Agentic AI) emerges as a transformative solution, enabling edge systems to autonomously perceive multi-modal environments, reason contextually, and adapt proactively through continuous perception\u2013reasoning\u2013action loops. In this context, the agentification of edge intelligence serves as a key paradigm shift, where distributed entities evolve into autonomous agents capable of collaboration and continual adaptation. This paper presents a comprehensive survey dedicated to Agentic AI and agentification frameworks tailored explicitly for edge general intelligence. First, we systematically introduce foundational concepts and clarify distinctions from traditional edge intelligence paradigms. Second, we analyze important enabling technologies, including compact model compression, energy-aware computing strategies, robust connectivity frameworks, and advanced knowledge representation and reasoning mechanisms. Third, we provide representative case studies demonstrating Agentic AI\u2019s capabilities in low-altitude economy networks, intent-driven networking, vehicular networks, and human-centric service provisioning, supported by numerical evaluations. Furthermore, we identify current research challenges, review emerging open-source platforms, and highlight promising future research directions to guide robust, scalable, and trustworthy Agentic AI deployments for next-generation edge environments."}	27	{"work_id": 7123361055, "title": "Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions", "abstract": "The rapid expansion of sixth-generation (6G) wireless networks and the Internet of Things (IoT) has catalyzed the evolution from centralized cloud intelligence towards decentralized edge general intelligence. However, traditional edge intelligence methods, characterized by static models and limited cognitive autonomy, fail to address the dynamic, heterogeneous, and resource-constrained scenarios inherent to emerging edge networks. Agentic artificial intelligence (Agentic AI) emerges as a transformative solution, enabling edge systems to autonomously perceive multi-modal environments, reason contextually, and adapt proactively through continuous perception\u2013reasoning\u2013action loops. In this context, the agentification of edge intelligence serves as a key paradigm shift, where distributed entities evolve into autonomous agents capable of collaboration and continual adaptation. This paper presents a comprehensive survey dedicated to Agentic AI and agentification frameworks tailored explicitly for edge general intelligence. First, we systematically introduce foundational concepts and clarify distinctions from traditional edge intelligence paradigms. Second, we analyze important enabling technologies, including compact model compression, energy-aware computing strategies, robust connectivity frameworks, and advanced knowledge representation and reasoning mechanisms. Third, we provide representative case studies demonstrating Agentic AI\u2019s capabilities in low-altitude economy networks, intent-driven networking, vehicular networks, and human-centric service provisioning, supported by numerical evaluations. Furthermore, we identify current research challenges, review emerging open-source platforms, and highlight promising future research directions to guide robust, scalable, and trustworthy Agentic AI deployments for next-generation edge environments."}
40	{"work_id": 3037795158, "title": "Applications of digital technology in COVID-19 pandemic planning and response", "abstract": "With high transmissibility and no effective vaccine or therapy, COVID-19 is now a global pandemic. Government-coordinated efforts across the globe have focused on containment and mitigation, with varying degrees of success. Countries that have maintained low COVID-19 per-capita mortality rates appear to share strategies that include early surveillance, testing, contact tracing, and strict quarantine. The scale of coordination and data management required for effective implementation of these strategies has-in most successful countries-relied on adopting digital technology and integrating it into policy and health care. This Viewpoint provides a framework for the application of digital technologies in pandemic management and response, highlighting ways in which successful countries have adopted these technologies for pandemic planning, surveillance, testing, contact tracing, quarantine, and health care."}	28	{"work_id": 3037795158, "title": "Applications of digital technology in COVID-19 pandemic planning and response", "abstract": "With high transmissibility and no effective vaccine or therapy, COVID-19 is now a global pandemic. Government-coordinated efforts across the globe have focused on containment and mitigation, with varying degrees of success. Countries that have maintained low COVID-19 per-capita mortality rates appear to share strategies that include early surveillance, testing, contact tracing, and strict quarantine. The scale of coordination and data management required for effective implementation of these strategies has-in most successful countries-relied on adopting digital technology and integrating it into policy and health care. This Viewpoint provides a framework for the application of digital technologies in pandemic management and response, highlighting ways in which successful countries have adopted these technologies for pandemic planning, surveillance, testing, contact tracing, quarantine, and health care."}
41	−{"work_id": 4323539384, "title": "Scientific dataset recommendation with semantic techniques", "abstract": "Scientific datasets are important in scientific research. Researchers always want to find a way to improve their research. Data discovery and reuse is a recent and popular way of finding more scientific datasets to help researchers. There is much current research work for data discovery and reuse. Nevertheless, most of these works are limited because researchers only trust and reuse data through social networks (such as colleagues or collaborators). Also, the scientific data from social networks bring limitations. For instance, they will only recommend data that they consider helpful. However, one scientist's useful data sometimes might be another's noisy data. A dataset search engine is a solution to help researchers find data outside their social networks by exploring scientific datasets in open data repositories or hubs. Such a dataset search engine typically returns related or relevant datasets based on a keyword query from researchers. Several existing dataset search engines already provide millions of open datasets from open-access sources. However, the performance of a dataset search engine is highly related to the quality of the keyword queries submitted by researchers. For instance, two different queries with the same meaning would get different returns from the same dataset search engine. The main question of this thesis is how to find more relevant scientific datasets from a query on a dataset search engine. A new idea is provided as the solution with dataset recommendation, which is adding one step, \"if you like this dataset, do you also like these other datasets,\" after researchers or users submitted a query to a dataset search engine. Users submit keywords for query to a dataset search engine and then get returned list of datasets. Users can then tell the dataset search engine which returned dataset they like, and the dataset search engine will say, \"If you like this dataset, you would also like those other datasets,\" to recommend more datasets. This thesis aims to help dataset search engines with such scientific dataset recommendations using Semantic Web technologies. As mentioned, the main idea of this thesis is \"if you like this dataset, you would also like that other dataset\". This thesis states the challenges and possibilities of using Semantic Web technologies on scientific dataset recommendation, as well as explores 1) how ontologies can be used for dataset recommendation, and 2) how knowledge graphs can be used for dataset recommendation? These two questions are addressed in six chapters. A scientific item (dataset or paper) recommendation benchmark was provided during the exploring work of this thesis to provide a standard for evaluating scientific item recommendation methods. This benchmark contains a large-scale corpus with millions of datasets and papers as well as the trusted links between them. Also, a collection of entity embeddings was created in the work of this thesis. These embeddings are scientific item embeddings by using knowledge graph embedding on the citation network from Microsoft Academic knowledge graph."}	29	+{"work_id": 2251066406, "title": "Bypassing Races in Live Applications with Execution Filters", "abstract": "Deployed multithreaded applications contain many races because these applications are difficult to write, test, and debug. Worse, the number of races in deployed applications may drastically increase due to the rise of multicore hardware and the immaturity of current race detectors.LOOM is a live-workaround system designed to quickly and safely bypass application races at runtime. LOOM provides a flexible and safe language for developers to write execution filters that explicitly synchronize code. It then uses an evacuation algorithm to safely install the filters to live applications to avoid races. It reduces its performance overhead using hybrid instrumentation that combines static and dynamic instrumentation.We evaluated LOOM on nine real races from a diverse set of six applications, including MySQL and Apache. Our results show that (1) LOOM can safely fix all evaluated races in a timely manner, thereby increasing application availability; (2) LOOM incurs little performance overhead; (3) LOOM scales well with the number of application threads; and (4) LOOM is easy to use."}
42	−{"work_id": 3148181069, "title": "Classification Based on Decision Tree Algorithm for Machine Learning", "abstract": "Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed."}	30	+{"work_id": 1500148551, "title": "Time-based Fairness Improves Performance in Multi-rate WLANs", "abstract": "The performance seen by individual clients on a wireless local area network (WLAN) is heavily influenced by the manner in which wireless channel capacity is allocated. The popular MAC protocol DCF (Distributed Coordination Function) used in 802.11 networks provides equal long-term transmission opportunities to competing nodes when all nodes experience similar channel conditions. When similar-sized packets are also used, DCF leads to equal achieved throughputs (throughput-based fairness) among contending nodes. Because of varying indoor channel conditions, the 802.11 standard supports multiple data transmission rates to exploit the trade-off between data rate and bit error rate. This leads to considerable rate diversity, particularly when the network is congested. Under such conditions, throughput-based fairness can lead to drastically reduced aggregate throughput. In this paper, we argue the advantages of time-based fairness, in which each competing node receives an equal share of the wireless channel occupancy time. We demonstrate that this notion of fairness can lead to significant improvements in aggregate performance while still guaranteeing that no node receives worse channel access than it would in a single-rate WLAN. We also describe our algorithm, TBR (Time-based Regulator), which runs on the AP and works with any MAC protocol to provide time-based fairness by regulating packets. Through experiments, we show that our practical and backward compatible implementation of TBR in conjunction with an existing implementation of DCF achieves time-based fairness."}
43	−{"work_id": 3104550706, "title": "Gossip Algorithms for Distributed Signal Processing", "abstract": "Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression."}	31	+{"work_id": 2945827670, "title": "Neural Graph Collaborative Filtering", "abstract": "Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect."}
44	−{"work_id": 4415028496, "title": "Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey", "abstract": "Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing across distributed communication networks. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these networked systems, yet introduce significant challenges in model deployment, network resource management, and cross-layer optimization. In this survey, we comprehensively examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, communication protocols, and network-aware computing frameworks. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and communication overhead across heterogeneous networks. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examine practical deployments through diverse networked applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex distributed systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements in communications with practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments over next-generation communication networks, fostering innovation in intelligent networked systems."}	32	+{"work_id": 2962883549, "title": "Deep Learning in Mobile and Wireless Networking: A Survey", "abstract": "The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research."}
45	−{"work_id": 4395483946, "title": "Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives", "abstract": "Abstract In recent years, higher education (HE) globally has witnessed extensive adoption of technology, particularly in teaching and research. The emergence of generative Artificial Intelligence (GenAI) further accelerates this trend. However, the increasing sophistication of GenAI tools has raised concerns about their potential to automate teaching and research processes. Despite widespread research on GenAI in various fields, there is a lack of multicultural perspectives on its impact and concerns in HE. This study addresses this gap by examining the usage, benefits, and concerns of GenAI in higher education from a multicultural standpoint. We employed an online survey that collected responses from 1217 participants across 76 countries, encompassing a broad range of gender categories, academic disciplines, geographical locations, and cultural orientations. Our findings revealed a high level of awareness and familiarity with GenAI tools among respondents. A significant portion had prior experience and expressed the intention to continue using these tools, primarily for information retrieval and text paraphrasing. The study emphasizes the importance of GenAI integration in higher education, highlighting both its potential benefits and concerns. Notably, there is a strong correlation between cultural dimensions and respondents\u2019 views on the benefits and concerns related to GenAI, including its potential as academic dishonesty and the need for ethical guidelines. We, therefore, argued that responsible use of GenAI tools can enhance learning processes, but addressing concerns may require robust policies that are responsive to cultural expectations. We discussed the findings and offered recommendations for researchers, educators, and policymakers, aiming to promote the ethical and effective integration of GenAI tools in higher education."}	33	+{"work_id": 4364375730, "title": "Social Health Protection During the COVID-Pandemic Using IoT", "abstract": "The agenda of this book chapter is to review existing technologies that aid societal health protection and recommend some possible approaches which will assist the mentioned scenario. Automations like big data and artificial intelligence (AI) deployed in healthcare sector can expedite pandemic response in ways that are strenuous to achieve all in all by humans. The sudden epiphany to trace COVID-19 in public has powered the innovation of data dashboards that visually unveil coronavirus epicentres. A cloud-based AI-assisted CT service is being engaged to differentiate pneumonia from the pandemic which dwindles risk factor in the present school of thought of the citizens worldwide. In conclusion, social health protection was an indispensable mechanism in prior to these challenging times and is escalating by prominence for delivering support to individuals during the crisis."}
46	−{"work_id": 4362632858, "title": "Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices", "abstract": "Cloud-native applications constitute a recent trend for designing large-scale software systems. This thesis introduces the Theodolite benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, their frameworks, configurations, and deployments. The benchmarking method is applied to event-driven microservices, a specific type of cloud-native applications that employ distributed stream processing frameworks to scale with massive data volumes. Extensive experimental evaluations benchmark and compare the scalability of various stream processing frameworks under different configurations and deployments, including different public and private cloud environments. These experiments show that the presented benchmarking method provides statistically sound results in an adequate amount of time. In addition, three case studies demonstrate that the Theodolite benchmarking method can be applied to a wide range of applications beyond stream processing."}	34	+{"work_id": 2902511784, "title": "Dynamic data race detection for OpenMP programs", "abstract": "Two concurrent accesses to a shared variable that are unordered by synchronization are said to be a data race if at least one access is a write. Data races cause shared memory parallel programs to behave unpredictably and are typically considered a bug. It is hard to manually identify memory access instructions causing data races in large concurrent programs due to non-deterministic behavior affected by thread schedule. Tools for detecting data races are desired to help application developers debug data races in an efficient way. Dynamic data race detection tools detect data races by monitoring programs as they execute. Building accurate and efficient data race detection tools requires domain knowledge of concurrency control primitives provided by the specific parallel programming model upon which a program is built. OpenMP is the de-facto standard API for node level shared memory parallel programming. The complexity of OpenMP, which includes primitives for managing data environments, SPMD parallelism, work sharing, tasking, mutual exclusion, and ordering, presents a formidable challenge for precise dynamic data race detection. This dissertation describes several techniques that enable accurate dynamic data race detection for OpenMP programs. First, I describe a hybrid data race detection algorithm that allows data race detection to be executed in parallel. Second, I describe an OpenMP task labeling technique that assigns each OpenMP task a label to facilitate OpenMP-aware accurate happens-before analysis. This technique is the foundation for callback functions provided by the OpenMP Tools interface known as OMPT to get notifications about OpenMP synchronization events and create task labels based on a set of label creation rules. Third, I propose techniques for optimizing dynamic data race detection performance via a redundant memory access pruning algorithm, a readers-writer concurrency control protocol for coordinating threads as they maintain access histories for memory locations, and an OpenMP-aware access record management protocol. The second and third contributions combine to form a system called ROMP, which is a dynamic data race detector for OpenMP programs. Experiments with standard data race detection benchmarks shows that ROMP achieves high accuracy for detecting data races in OpenMP programs that contain host-side CPU-centric parallel constructs. Experiments with several OpenMP performance benchmarks show the effectiveness of proposed optimization techniques."}
47	−{"work_id": 3098508067, "title": "Personal volunteer computing", "abstract": "In this dissertation, we articulate the new Personal Volunteer Computing paradigm, that refines Volunteer Computing by focusing on solutions that are personal to the user on multiple dimensions: they target personal projects, they leverage the participation of volunteers from their personal social network, and they are built into personal tools that can be deployed on personal devices and can be easily maintained by a single developer.We then present Pando, a new and first tool for personal volunteer computing written in and for JavaScript, that distributes the application of a function on a stream of inputs into the browsers of participating devices. Pando dynamically scales to new devices, gracefully tolerates sudden disconnections, and is easy to program because it is based on a declarative concurrent programming paradigm, in which the non-determinism of parallel executions is not observable by users. We follow with a more detailed presentation of the implementation of Pando, based on the new Limiter, StreamLender, and DistributedMap abstractions. Our presentation uses a high-level notation, independent of JavaScript and that simplifies reasoning about concurrent aspects, to introduce all the algorithms that implement the Limiter, StreamLender, and DistributedMap abstractions. Because the concurrent aspects make StreamLender challenging to implement, even with a clear description, we present a run-time verification approach to ensure it is correct. The approach is easy to parallelize, with Pando for example, and quickly generates a large number of random executions to ensure a high-probability of correctness. The combination of clear descriptions and testing strategy should make Pando easy to reimplement in other programming environments.We then present a large scope of applications that we implemented for Pando, based on existing libraries and examples. These applications represent various dataflow patterns and show Pando can be used not only for compute-intensive tasks but also for crowd-processing. We then measure the throughput performance of these applications in three networking scenarios: (1) over a local-area Wi-Fi network, with personal laptop and smartphones, (2) over a virtual private network distributed throughout France, with the Grid5000 nodes, and (3) with a wide-area network throughout Europe on the Internet, with the PlanetLab EU nodes. We show personal devices are competitive in all scenarios, sometimes with older devices competing with newer models, and other times with combinations of personal devices outcompeting remote server nodes. The flexible and easy support of all these scenarios is, to the best of our knowledge, a first in the volunteer computing literature. We then present Genet, a new fat-tree overlay for WebRTC that enables Pando to overcome the limits of WebRTC in the number of connections, and showed the resulting combination of Pando and Genet to be able to scale to a thousand browsers in 30-55 seconds on local networks. Those results are possible because the design of Genet only uses local information to deterministically route the WebRTC connection messages, while ensuring the resulting tree is probabilistically balanced.We conclude by outlining new exciting research directions that take into account the limits to growth our society is currently facing. Compared to the current trends in research, these new directions make smaller whole-system designs done by small teams viable again, but bring a stronger focus on leveraging abundant personal computing devices and taking into account the growing importance of efficiently using renewable electricity sources"}	35	+{"work_id": 2408538552, "title": "Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks", "abstract": "Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users\u2019 preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user\u2019s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly."}
48	−{"work_id": 3092103025, "title": "Bias and Debias in Recommender System: A Survey and Future Directions", "abstract": "While recent years have witnessed a rapid growth of research papers on recommender system (RS) , most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, and so on. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and perform debiasing when necessary. When reviewing the papers that consider biases in RS, we find that, to our surprise, the studies are rather fragmented and lack a systematic organization. The terminology \u201cbias\u201d is widely used in the literature, but its definition is usually vague and even inconsistent across papers. This motivates us to provide a systematic survey of existing work on RS biases. In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics. We then provide a taxonomy to position and organize the existing work on recommendation debiasing. Finally, we identify some open challenges and envision some future directions, with the hope of inspiring more research work on this important yet less investigated topic. The summary of debiasing methods reviewed in this survey can be found at https://github.com/jiawei-chen/RecDebiasing ."}	36	+{"work_id": 7118085507, "title": "Retrieval-Augmented Generation for AI-Generated Content: A Survey", "abstract": "Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (AIGC). Despite its notable successes, AIGC still faces hurdles such as updating knowledge, handling long-tail data, mitigating data leakage, and managing high training and inference costs. Retrieval-augmented generation (RAG) has recently emerged as a paradigm to address such challenges. In particular, RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores, leading to higher accuracy and better robustness. In this paper, we comprehensively review existing efforts that integrate RAG techniques into AIGC scenarios. We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators. This unified perspective encompasses all RAG scenarios, illuminating advancements and pivotal technologies that help with potential future progress. We also summarize additional enhancement methods for RAG, facilitating effective engineering and implementation of RAG systems. Then from another view, we survey practical applications of RAG across different modalities and tasks, offering valuable references for researchers and practitioners. Furthermore, we introduce the benchmarks for RAG, discuss the limitations of current RAG systems, and suggest potential directions for future research."}
49	−{"work_id": 7125650829, "title": "AI Flow: perspectives, scenarios, and approaches", "abstract": "Abstract Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and communication technologies (IT/CT) has created an unbroken wave of connectivity and computation. This synergy has sparked a technological revolution, now reaching its peak with large artificial intelligence (AI) models that are reshaping industries and redefining human-machine collaboration. However, the realization of ubiquitous intelligence faces considerable challenges due to substantial resource consumption in large models and high communication bandwidth demands. To address these challenges, AI Flow has been introduced as a multidisciplinary framework that integrates cutting-edge IT and CT advancements, with a particular emphasis on the following three key points. First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters to optimize scalability and efficiency for low-latency model inference. Second, we introduce the concept of familial models , which refers to a series of different-sized models with aligned hidden features, enabling effective collaboration and the flexibility to adapt to varying resource constraints and dynamic scenarios. Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow. By leveraging communication networks to enhance connectivity, the collaboration among AI models across heterogeneous nodes achieves emergent intelligence that surpasses the capability of any single model. The innovations of AI Flow provide enhanced intelligence, timely responsiveness, and ubiquitous accessibility to AI services, paving the way for the tighter fusion of AI techniques and communication systems. These advancements are crucial to numerous application scenarios, including but not limited to embodied AI, wearable devices, and smart cities."}	37	+{"work_id": 4315977496, "title": "A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions", "abstract": "Recommender system is one of the most important information services on today\u2019s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first introduce the background and the history of the development of both recommender systems and graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist of two categories: spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data and the enhanced supervision signal. We then systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency. Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions on the open problems and promising future directions in this area. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems ."}
50	−{"work_id": 3045200674, "title": "LightGCN", "abstract": "Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on GCN, which is originally designed for graph classification tasks and equipped with many neural network operations. However, we empirically find that the two most common designs in GCNs -- feature transformation and nonlinear activation -- contribute little to the performance of collaborative filtering. Even worse, including them adds to the difficulty of training and degrades recommendation performance."}	38	+{"work_id": 4293584584, "title": "A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)", "abstract": "Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The existing traffic system uses programming logic control (PLC) with round-robin scheduling algorithm. Recent works have proposed IoT-based frameworks that use traffic density of each lane to control traffic movement, but they suffer from low accuracy due to lack of emergency vehicle image datasets for training deep neural networks. In this paper, we propose a novel distributed IoT framework that is based on two observations. The first observation is major structural changes to road are rare. This observation is exploited by proposing a novel two stage vehicle detector that is able to achieve 77% vehicle detection accuracy on UA-DETRAC dataset. The second observation is emergency vehicle have distinct siren sound that is detected using a novel acoustic detection algorithm on an edge device. The proposed system is able to detect emergency vehicles with an average accuracy of 99.4%."}
51	−{"work_id": 4406085118, "title": "Data-centric Artificial Intelligence: A Survey", "abstract": "Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI . The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI ."}
52	{"work_id": 4386827074, "title": "Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste", "abstract": "The combination of artificial intelligence, 5G technology, and cloud computing has altered energy conversion processes, most notably the use of agricultural waste for sustainable energy generation. This book chapter digs into AI, 5G, and cloud computing research and development for efficient energy conversion, environmental concerns, and the viability of agricultural waste as a renewable energy resource. AI technologies provide real-time monitoring and control, while cloud computing enables data analytics and optimization. The synergistic method increases the efficiency of energy conversion, predictability, flexibility, optimization, grid integration, energy storage, and cost reduction. Compatibility, data security, and financial sustainability, on the other hand, must be addressed. The chapter emphasises the importance of this integrated strategy in addressing global energy and environmental challenges."}	39	{"work_id": 4386827074, "title": "Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste", "abstract": "The combination of artificial intelligence, 5G technology, and cloud computing has altered energy conversion processes, most notably the use of agricultural waste for sustainable energy generation. This book chapter digs into AI, 5G, and cloud computing research and development for efficient energy conversion, environmental concerns, and the viability of agricultural waste as a renewable energy resource. AI technologies provide real-time monitoring and control, while cloud computing enables data analytics and optimization. The synergistic method increases the efficiency of energy conversion, predictability, flexibility, optimization, grid integration, energy storage, and cost reduction. Compatibility, data security, and financial sustainability, on the other hand, must be addressed. The chapter emphasises the importance of this integrated strategy in addressing global energy and environmental challenges."}
53	−{"work_id": 4378832451, "title": "Overview of Amazon Web Services", "abstract": "With today\u2019s technological boom, the demand for cloud computing services is increasing day by day. Individuals, companies, and multinational businesses are shifting from self-owned web services to cloud services. Among cloud services providers, the most prominent are Amazon Web Services (AWS), Google Cloud Platform GCP, IBM, and Oracle Cloud."}
54	{"work_id": 4393370257, "title": "Distributed methods for resource allocation - a passivity-based approach", "abstract": "Since the complexity and scale of systems have been growing in the last years, distributed approaches for control and decision making are becoming more prevalent. This dissertation focuses on an important problem involving distributed control and decision making, the dynamic resource allocation in a network. To address this problem, we explore a consensus--based algorithm that does not require any centralized computation, and that is capable to deal with applications modeled either by dynamical systems or by memoryless functions. The main contribution of our research is to prove, by means of graph theoretical tools and passivity analysis, that the proposed controller asymptotically reaches an optimal solution without the need of full information. In order to illustrate the relevance of our main result, we address several engineering applications including: distributed control for energy saving in smart buildings, management of the customers of an aggregating entity in a smart grid environment, and development of an exact distributed optimization method that deals with resource allocation problems subject to lower--bound constraints. Finally, we explore resource allocation techniques based on classic population dynamics models. In order to make them distributed, we introduce the concept of non--well--mixed population dynamics. We show that these dynamics are capable to deal with constrained information structures that are characterized by non--complete graphs. Although the proposed non--well--mixed population dynamics use partial information, they preserve similar properties of their classic counterpart, which uses full information. Specifically, we prove mass conservation and convergence to Nash equilibrium."}	40	{"work_id": 4393370257, "title": "Distributed methods for resource allocation - a passivity-based approach", "abstract": "Since the complexity and scale of systems have been growing in the last years, distributed approaches for control and decision making are becoming more prevalent. This dissertation focuses on an important problem involving distributed control and decision making, the dynamic resource allocation in a network. To address this problem, we explore a consensus--based algorithm that does not require any centralized computation, and that is capable to deal with applications modeled either by dynamical systems or by memoryless functions. The main contribution of our research is to prove, by means of graph theoretical tools and passivity analysis, that the proposed controller asymptotically reaches an optimal solution without the need of full information. In order to illustrate the relevance of our main result, we address several engineering applications including: distributed control for energy saving in smart buildings, management of the customers of an aggregating entity in a smart grid environment, and development of an exact distributed optimization method that deals with resource allocation problems subject to lower--bound constraints. Finally, we explore resource allocation techniques based on classic population dynamics models. In order to make them distributed, we introduce the concept of non--well--mixed population dynamics. We show that these dynamics are capable to deal with constrained information structures that are characterized by non--complete graphs. Although the proposed non--well--mixed population dynamics use partial information, they preserve similar properties of their classic counterpart, which uses full information. Specifically, we prove mass conservation and convergence to Nash equilibrium."}
55	{"work_id": 2974429275, "title": "Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT", "abstract": "The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security."}	41	{"work_id": 2974429275, "title": "Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT", "abstract": "The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security."}
56	−{"work_id": 2897450989, "title": "Blockchain challenges and opportunities: a survey", "abstract": "Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology."}
57	−{"work_id": 4252792641, "title": "PRIORITIZED TASK SCHEDULING IN FOG COMPUTING", "abstract": "Cloud computing is an environment where virtual resources are shared among the many users over network. A user of Cloud services is billed according to pay-per-use model associated with this environment. To keep this bill to a minimum, efficient resource allocation is of great importance. To handle the many requests sent to Cloud by the clients, the tasks need to be processed according to the SLAs defined by the client. The increase in the usage of Cloud services on a daily basis has introduced delays in the transmission of requests. These delays can cause clients to wait for the response of the tasks beyond the deadline assigned. To overcome these concerns, Fog Computing is helpful as it is physically placed closer to the clients. This layer is placed between the client and the Cloud layer, and it reduces the delay in the transmission of the requests, processing and the response sent back to the client greatly. This paper discusses an algorithm which schedules tasks by calculating the priority of a task in the Fog layer. The tasks with higher priority are processed first so that the deadline is met, which makes the algorithm practical and efficient."}
58	{"work_id": 4322576964, "title": "On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds", "abstract": "Fifth generation (5G) mobile communication systems have entered the stage of commercial deployment, providing users with new services, improved user experiences as well as a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified to stimulate the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed."}	42	{"work_id": 4322576964, "title": "On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds", "abstract": "Fifth generation (5G) mobile communication systems have entered the stage of commercial deployment, providing users with new services, improved user experiences as well as a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified to stimulate the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed."}
59	{"work_id": 7160823269, "title": "TokenPool-Scheduler: Token-Level GPU Pooling and Resource Slicing for Multi-Model Co-Location", "abstract": "In the context of GPU scarcity, multi-model and multi-tenant inference services for autoregressive large language models require finer-grained resource sharing to improve utilization and throughput. Existing approaches rely on coarse-grained spatial or temporal multiplexing, which often leads to suboptimal GPU utilization and increased tail latencies. We present TokenPool-Scheduler, a token-level GPU pooling and resource slicing framework that breaks LLM inference execution into fine-grained segments. By employing a token-level cost model and adaptive scheduling policies, TokenPool-Scheduler dynamically determines optimal slice sizes, preemption points, and resource quotas for heterogeneous models. Experimental results demonstrate that TokenPool-Scheduler achieves up to 33% higher GPU utilization and 63% improvement in goodput compared to static allocation schemes, while reducing P99 time-to-first-token (TTFT) latency by 43% under high load (250 req/s per GPU). The system reduces model switching overhead by 69% compared to existing spatial-temporal multiplexing approaches."}	43	{"work_id": 7160823269, "title": "TokenPool-Scheduler: Token-Level GPU Pooling and Resource Slicing for Multi-Model Co-Location", "abstract": "In the context of GPU scarcity, multi-model and multi-tenant inference services for autoregressive large language models require finer-grained resource sharing to improve utilization and throughput. Existing approaches rely on coarse-grained spatial or temporal multiplexing, which often leads to suboptimal GPU utilization and increased tail latencies. We present TokenPool-Scheduler, a token-level GPU pooling and resource slicing framework that breaks LLM inference execution into fine-grained segments. By employing a token-level cost model and adaptive scheduling policies, TokenPool-Scheduler dynamically determines optimal slice sizes, preemption points, and resource quotas for heterogeneous models. Experimental results demonstrate that TokenPool-Scheduler achieves up to 33% higher GPU utilization and 63% improvement in goodput compared to static allocation schemes, while reducing P99 time-to-first-token (TTFT) latency by 43% under high load (250 req/s per GPU). The system reduces model switching overhead by 69% compared to existing spatial-temporal multiplexing approaches."}
		44	+{"work_id": 2604314403, "title": "Modeling Relational Data with Graph Convolutional Networks", "abstract": "Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard knowledge base completion tasks: Link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes). R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to handle the highly multi-relational data characteristic of realistic knowledge bases. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. We further show that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline."}
60	{"work_id": 7130586201, "title": "Dynamic Event-Triggered Control for Human\u2013Machine Cooperative Systems Based on Dynamic Authority Allocation", "abstract": "This article addresses the challenging problem of constrained optimal control for human\u2013machine systems subject to external disturbances and the bounded rationality of the human operator. To this end, a novel game-theoretic framework is proposed. Unlike monolithic game formulations, the framework uniquely disaggregates the control problem by transforming it into a multifaceted game via logarithmic barrier functions (BFs): it models human\u2013machine cooperation as a positive-sum game oriented toward shared objectives, and disturbance rejection as a zero-sum game tailored for robustness enhancement. To capture the nonideal human decision-making, we integrate the level-$k$reasoning framework to model the operator\u2019s bounded cognitive dynamics. The corresponding coupled Hamilton\u2013Jacobi\u2013Isaacs (HJI) equations for this human\u2013machine game are derived, and critically, a rigorous proof of global asymptotic stability (GAS) for the transformed system is provided, establishing a solid theoretical foundation. For online implementation without requiring prior knowledge of the system dynamics, we develop a resource-efficient learning architecture based on the adaptive dynamic programming (ADP) and a novel dynamic event-triggered mechanism (DETM). A key feature of this architecture is a fuzzy logic-based module for dynamic authority allocation, which adaptively adjusts control sharing in real time. Rigorous analysis demonstrates that all signals in the closed-loop system are uniformly ultimately bounded and that Zeno behavior is precluded. Simulation results are presented to validate the effectiveness and superiority of the proposed control strategy."}	45	{"work_id": 7130586201, "title": "Dynamic Event-Triggered Control for Human\u2013Machine Cooperative Systems Based on Dynamic Authority Allocation", "abstract": "This article addresses the challenging problem of constrained optimal control for human\u2013machine systems subject to external disturbances and the bounded rationality of the human operator. To this end, a novel game-theoretic framework is proposed. Unlike monolithic game formulations, the framework uniquely disaggregates the control problem by transforming it into a multifaceted game via logarithmic barrier functions (BFs): it models human\u2013machine cooperation as a positive-sum game oriented toward shared objectives, and disturbance rejection as a zero-sum game tailored for robustness enhancement. To capture the nonideal human decision-making, we integrate the level-$k$reasoning framework to model the operator\u2019s bounded cognitive dynamics. The corresponding coupled Hamilton\u2013Jacobi\u2013Isaacs (HJI) equations for this human\u2013machine game are derived, and critically, a rigorous proof of global asymptotic stability (GAS) for the transformed system is provided, establishing a solid theoretical foundation. For online implementation without requiring prior knowledge of the system dynamics, we develop a resource-efficient learning architecture based on the adaptive dynamic programming (ADP) and a novel dynamic event-triggered mechanism (DETM). A key feature of this architecture is a fuzzy logic-based module for dynamic authority allocation, which adaptively adjusts control sharing in real time. Rigorous analysis demonstrates that all signals in the closed-loop system are uniformly ultimately bounded and that Zeno behavior is precluded. Simulation results are presented to validate the effectiveness and superiority of the proposed control strategy."}
61	−{"work_id": 2122465391, "title": "MapReduce: Simplified Data Processing on Large Cluster", "abstract": "MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.\r\n\r\nPrograms written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.\r\n\r\nOur implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day."}
62	−{"work_id": 2962850638, "title": "Logiikka ja vaativuus hajautetussa laskennassa", "abstract": "This dissertation studies the theory of distributed computing. In the distributed setting, computation is carried out by multiple independent computational units. They communicate with neighbouring units and collectively solve a problem. Systems of this kind are widespread in the information society as well as in the natural world: prime examples include the Internet, multi-processor computer systems, the cells of a biological organism and human social networks. Therefore it is important to gain knowledge on the fundamental capabilities and limitations of distributed systems. Our work takes place in the context of deterministic and synchronous message-passing models of distributed computing. Such models abstract away some possible features of distributed systems, such as asynchrony, congestion and faults, while putting emphasis on the amount of communication needed between the units. The computational units are expected to solve a problem related to the structure of the underlying communication network. More specifically, we study the standard LOCAL model, the standard port-numbering model and several weaker variants of the latter. The contributions of this dissertation are two-fold. First, we give new methods for studying distributed computing by establishing a strong connection between several weak variants of the port-numbering model and corresponding variants of modal logic. This makes it possible to apply existing logical tools, such as bisimulation, to understand computation in the distributed setting. We also give a full characterisation of the relationships between the models of computing, which has implications on the side of mathematical logic. Second, we prove the existence of various new non-empty complexity classes. One of our results studies the relationship between time and space complexity, which is a relatively new topic in distributed computing research. We demonstrate the existence of a problem that can be solved in a constant amount of space in a very weak model but nonetheless requires a non-constant amount of time even in a much stronger model of computing. Another result continues the recent success on developing complexity theory for the LOCAL model. We introduce a new technique which shows the existence of an infinite hierarchy of problems with novel time complexities."}
63	−{"work_id": 4320712473, "title": "The Internet of Things (IoT) in healthcare: Taking stock and moving forward", "abstract": "Recent improvements in the Internet of Things (IoT) have allowed healthcare to evolve rapidly. This article summarizes previous studies on IoT applications in healthcare. A comprehensive review and a bibliometric analysis were performed to objectively summarize the growth of IoT research in healthcare. To begin, 2,990 journal articles were carefully selected for further investigation. These publications were analyzed based on various bibliometric metrics, including publication year, journals, authors, institutions, and countries. Keyword co-occurrence and co-citation networks were generated to unravel significant research hotspots. The findings show that IoT research has received considerable interest from the healthcare community. Based on the results of the keyword co-occurrence network, IoT healthcare applications, blockchain applications, Artificial Intelligence (AI) techniques, 5G telecommunications, as well as data analytics and computing technologies emerged as important topics. The co-citation network analysis reveals other important themes, including authentication schemes, fog computing, cloud-IoT integration, and cognitive smart healthcare. Overall, the review offers scholars an improved understanding of the current status of IoT research in healthcare and identifies knowledge gaps for future research. This review also informs healthcare professionals about the latest developments and applications of IoT in the healthcare sector."}
64	−{"work_id": 1973501242, "title": "Time, clocks, and the ordering of events in a distributed system", "abstract": "The concept of one event happening before another in a distributed system is examined, and is shown to define a partial ordering of the events. A distributed algorithm is given for synchronizing a system of logical clocks which can be used to totally order the events. The use of the total ordering is illustrated with a method for solving synchronization problems. The algorithm is then specialized for synchronizing physical clocks, and a bound is derived on how far out of synchrony the clocks can become."}
65	−{"work_id": 2762829140, "title": "Deep Learning based Intrusion Detection System for Internet of Things", "abstract": "With the increase in number of Internet connected devices, security and privacy concerns are the major obstacles impeding the widespread adoption of Internet of Things (IoT). Securing IoT has become a huge area of concern for all, including the consumers, organizations as well as the government. While attacks on any system cannot be fully prevented forever, real-time detection of the attacks are critical to defend the systems in an effective manner. Limited research exists on efficient intrusion detection systems suitable for IoT environment. In this thesis, we propose a novel intrusion detection system that uses machine learning algorithms to detect security anomalies in IoT networks. This detection platform provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We provide a framework of the proposed system and discuss the intrusion detection process in detail. The proposed intrusion detection system is evaluated using both, real network traces for providing a proof-of-concept, and on simulation for providing evidence of its scalability. Our results confirm that the proposed intrusion detection system is capable of detecting real-world intrusions effectively."}
66	−{"work_id": 2973502831, "title": "Performance Analysis of Proof-of-Elapsed-Time (PoET) Consensus in the Sawtooth Blockchain Framework", "abstract": "Blockchains are distributed ledgers that use a tamper-sensitive, append-only data structure in conjunction with a consensus protocol to enable mutually distrusting parties to maintain a global set of states. A primary barrier to adoption of blockchain technology by industry is the current performance and scalability limitations of these systems, which lag far behind incumbent database systems. Of particular interest are \u201clottery-style\u201d consensus algorithms, which are relatively scalable to many participants but suffer from low throughput (performance). Proof-of-Elapsed-Time (PoET) is one such algorithm with great promise for use in industry, though the parameters that govern its performance have not been well studied. This thesis explores, through simulation, key performance outcomes in PoET blockchain networks implemented with the Hyperledger Sawtooth framework. A better quantitative understanding of the interactions among these system parameters will be crucial for efficiently optimizing real world blockchain networks and facilitating adoption by industry."}
67	−{"work_id": 4400494356, "title": "Digital health research governance: from FAIR to RE-AIM", "abstract": "This doctoral research aims to develop a governance model for digital health research data to enhance the quality of collected data and translate research outcomes into an integrated framework for planning, implementing, and evaluating public health initiatives. Employing a mixed-methods approach, the study combines qualitative and quantitative methods, qualifying as an exploratory and explanatory study. The specific objectives include proposing the adoption of frameworks and tools to promote a data quality model for scientific research in digital health, developing a tool with data collection, management, and evaluation, and proposing an implementation research model based on digital health principles and guidelines. The work is grounded in FAIR principles and the RE-AIM framework, addressing data quality and governance in health research, focusing on tuberculosis (TB) and rare diseases (RD) in the Brazilian context. The research significantly contributes to the understanding of digital data governance in health, highlighting the importance of standardized practices and collaborative efforts to improve the quality of health research data in Brazil. Challenges addressed include integrating diverse data sources, ensuring security and confidentiality of data, while enhancing public health outcomes through digital innovations. The presented model emphasizes data quality optimization, being fundamental for planning, executing, and evaluating health interventions. The research operates under robust computational research structures and health information systems, ensuring transparent data management. The interdisciplinary nature of the research highlights its significant strength, integrating insights from data science, public health, and health policy, offering a holistic view of the challenges and opportunities in digital health. By creating a unified implementation quality manual, the study provides mechanisms for network collaboration, strengthening robust data management, and promoting a culture of shared practices essential for elevating the overall quality of data in health research."}
68	−{"work_id": 2103863878, "title": "Majority is not enough", "abstract": "The Bitcoin cryptocurrency records its transactions in a public log called the blockchain. Its security rests critically on the distributed protocol that maintains the blockchain, run by participants called miners. Conventional wisdom asserts that the mining protocol is incentive-compatible and secure against colluding minority groups, that is, it incentivizes miners to follow the protocol as prescribed. We show that the Bitcoin mining protocol is not incentive-compatible. We present an attack with which colluding miners' revenue is larger than their fair share. The attack can have significant consequences for Bitcoin: Rational miners will prefer to join the attackers, and the colluding group will increase in size until it becomes a majority. At this point, the Bitcoin system ceases to be a decentralized currency. Unless certain assumptions are made, selfish mining may be feasible for any coalition size of colluding miners. We propose a practical modification to the Bitcoin protocol that protects Bitcoin in the general case. It prohibits selfish mining by a coalition that command less than 1/4 of the resources. This threshold is lower than the wrongly assumed 1/2 bound, but better than the current reality where a coalition of any size can compromise the system."}
69	−{"work_id": 2912978522, "title": "Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges", "abstract": "Blockchain, as the underlying technology of crypto-currencies, has attracted significant attention. It has been adopted in numerous applications, such as smart grid and Internet-of-Things. However, there is a significant scalability barrier for blockchain, which limits its ability to support services with frequent transactions. On the other side, edge computing is introduced to extend the cloud resources and services to be distributed at the edge of the network, but currently faces challenges in its decentralized management and security. The integration of blockchain and edge computing into one system can enable reliable access and control of the network, storage, and computation distributed at the edges, hence providing a large scale of network servers, data storage, and validity computation near the end in a secure manner. Despite the prospect of integrated blockchain and edge computing systems, its scalability enhancement, self organization, functions integration, resource management, and new security issues remain to be addressed before widespread deployment. In this survey, we investigate some of the work that has been done to enable the integrated blockchain and edge computing system and discuss the research challenges. We identify several vital aspects of the integration of blockchain and edge computing: motivations, frameworks, enabling functionalities, and challenges. Finally, some broader perspectives are explored."}
70	−{"work_id": 3036601975, "title": "In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication", "abstract": "Broadcasting and aggregation dominate the communication overhead in distributed systems, from machine learning training to data analytics. Current acceleration approaches require specialized hardware (RDMA) or dedicated resources (DPDK), limiting their deployment in commodity clouds. However, we present a counter-intuitive alternative: rather than bypassing the kernel, we move operations into it using eBPF. While this imposes severe constraints including no floating-point, limited memory, and stateless execution, we show these restrictions paradoxically drive innovative protocol designs that yield unexpected benefits. We introduce AggBox, which implements broadcast and aggregation operations entirely within eBPF\u2019s constrained environment. Our key innovations include stateless group acknowledgments for reliability, edge quantization for floating-point aggregation using only integer arithmetic, and tail-call chains that create virtual memory beyond eBPF\u2019s 512-byte stack limit. These designs emerge from and exploit the constraints rather than fighting them. AggBox achieves remarkable performance on commodity hardware: 84.5% reduction in broadcast latency, 43\u00d7 speedup for MapReduce workloads, and 56.1% faster ML gradient aggregation, all without specialized NICs or dedicated cores. Beyond performance, our work demonstrates that constrained environments can drive fundamental innovation in protocol design, offering insights for future resource-limited and verified systems."}
71	{"work_id": 110250037, "title": "The Use of Multimedia Material in Teaching Chinese as a Second Language and Pedagogical Implications", "abstract": "The use of Multimedia materials has been widely accepted as an useful and effective tool in the field of second language acquisition (SLA). Many studies and researchers have examined multimedia material\u2019s effectiveness from a number of aspects, including four skills of language learning: listening, speaking, reading (including vocabulary comprehension) and writing. However, the effectiveness of multimedia material from the aspect of L2 grammar comprehension hasn\u2019t been well explored.\\nThis study examines the effectiveness of multimedia material in teaching second language grammar comprehension among beginning and intermediate-level Chinese learners. In particular, it investigates the relative efficacy of three different modes used in teaching an important Chinese grammar, directional complements: text alone, text with a still picture and text with video clips. The study\u2019s focal issue is to determine which mode or modes \u2014 text alone, text with still picture or text with dynamic video clip \u2014 is most effective in aiding grammar acquisition in both short-term and long-term.\\nThe study employed an immediate post-test and a delayed post-test. The participants were 53 college students, beginning level Chinese language learners who had studied Chinese for a half year, and were naturally assigned to one of the three parallel groups. The three groups differed as to the use of the different mode used to present the teaching material: text-only, text-picture, and text-video.\\nAnalysis of the collected data yielded three main findings. First, learners who received text-video material outperformed those who received text-picture; and learners who received text-picture material outperformed those who received text-only. Secondly, learners who received text-video had significantly better long-term comprehension than the other two groups. Finally, the performance advantage of text-video treatment was particularly significant with more complex target forms.\\nThe results demonstrate that multimedia material can help L2 learners\u2019 grammar comprehension. Text-picture and text-video were more effective than text-only, and text-video material was more effective than text-picture."}	46	{"work_id": 110250037, "title": "The Use of Multimedia Material in Teaching Chinese as a Second Language and Pedagogical Implications", "abstract": "The use of Multimedia materials has been widely accepted as an useful and effective tool in the field of second language acquisition (SLA). Many studies and researchers have examined multimedia material\u2019s effectiveness from a number of aspects, including four skills of language learning: listening, speaking, reading (including vocabulary comprehension) and writing. However, the effectiveness of multimedia material from the aspect of L2 grammar comprehension hasn\u2019t been well explored.\\nThis study examines the effectiveness of multimedia material in teaching second language grammar comprehension among beginning and intermediate-level Chinese learners. In particular, it investigates the relative efficacy of three different modes used in teaching an important Chinese grammar, directional complements: text alone, text with a still picture and text with video clips. The study\u2019s focal issue is to determine which mode or modes \u2014 text alone, text with still picture or text with dynamic video clip \u2014 is most effective in aiding grammar acquisition in both short-term and long-term.\\nThe study employed an immediate post-test and a delayed post-test. The participants were 53 college students, beginning level Chinese language learners who had studied Chinese for a half year, and were naturally assigned to one of the three parallel groups. The three groups differed as to the use of the different mode used to present the teaching material: text-only, text-picture, and text-video.\\nAnalysis of the collected data yielded three main findings. First, learners who received text-video material outperformed those who received text-picture; and learners who received text-picture material outperformed those who received text-only. Secondly, learners who received text-video had significantly better long-term comprehension than the other two groups. Finally, the performance advantage of text-video treatment was particularly significant with more complex target forms.\\nThe results demonstrate that multimedia material can help L2 learners\u2019 grammar comprehension. Text-picture and text-video were more effective than text-only, and text-video material was more effective than text-picture."}
72	{"work_id": 3080729669, "title": "Pengembangan Sistem Pemetaan Otomatis EntityRelationship Diagram Ke Dalam Database", "abstract": "Salah satu tahap dalam perancangan basis data untuk pengembangan sebuah\r\nperangkat lunak adalah proses perancangan basis data yang dibuat berdasarkan\r\nhasil perancangan model konseptual dan relasional. Proses yang cukup sulit\r\nadalah pada saat pemetaan konsep basis data. Ketika daftar kebutuhan pengguna\r\nmengalami perubahan, akan berdampak pada perancangan Sistem Basis data\r\nyang sudah dibuat sebelumnya. Selain itu, struktur basis data yang kompleks,\r\nketika mengalami perubahan akan memakan banyak waktu untuk dipetakan\r\nkembali secara manual. Berdasarkan permasalahan tersebut, maka dibuatlah\r\nsuatu Sistem untuk memetakan model basis data berbentuk ERD agar langsung\r\ndapat digunakan pada database mySQL. Dengan tujuan mempemudah pemetaan,\r\nmaka sistem ini menggunakan notasi Chen. Notasi ini adalah notasi yang paling\r\nsering digunakan dalam data modeling tool karena bentuknya yang mudah\r\ndipahami dan digambarkan. Namun notasi ini jarang digunakan oleh aplikasi ERD\r\nmapping. Sistem ini menggunakan tujuh langkah pemetaan model relasi entitas\r\nsehingga sesuai dengan kaidah Rekaya Perangkat Lunak. Sistem dibuat dengan\r\nberbasis web, menggunakan pemrograman berorientasi objek, dan menggunakan\r\nframework codeigniter. Sistem menghasilkan 16 buah kebutuhan yang telah diuji\r\ndengan menggunakan pengujian white-box dan black-box mendapatkan hasil valid\r\nuntuk keseluruhannya dan juga 95% untuk pengujian compatibility sistem"}	47	{"work_id": 3080729669, "title": "Pengembangan Sistem Pemetaan Otomatis EntityRelationship Diagram Ke Dalam Database", "abstract": "Salah satu tahap dalam perancangan basis data untuk pengembangan sebuah\r\nperangkat lunak adalah proses perancangan basis data yang dibuat berdasarkan\r\nhasil perancangan model konseptual dan relasional. Proses yang cukup sulit\r\nadalah pada saat pemetaan konsep basis data. Ketika daftar kebutuhan pengguna\r\nmengalami perubahan, akan berdampak pada perancangan Sistem Basis data\r\nyang sudah dibuat sebelumnya. Selain itu, struktur basis data yang kompleks,\r\nketika mengalami perubahan akan memakan banyak waktu untuk dipetakan\r\nkembali secara manual. Berdasarkan permasalahan tersebut, maka dibuatlah\r\nsuatu Sistem untuk memetakan model basis data berbentuk ERD agar langsung\r\ndapat digunakan pada database mySQL. Dengan tujuan mempemudah pemetaan,\r\nmaka sistem ini menggunakan notasi Chen. Notasi ini adalah notasi yang paling\r\nsering digunakan dalam data modeling tool karena bentuknya yang mudah\r\ndipahami dan digambarkan. Namun notasi ini jarang digunakan oleh aplikasi ERD\r\nmapping. Sistem ini menggunakan tujuh langkah pemetaan model relasi entitas\r\nsehingga sesuai dengan kaidah Rekaya Perangkat Lunak. Sistem dibuat dengan\r\nberbasis web, menggunakan pemrograman berorientasi objek, dan menggunakan\r\nframework codeigniter. Sistem menghasilkan 16 buah kebutuhan yang telah diuji\r\ndengan menggunakan pengujian white-box dan black-box mendapatkan hasil valid\r\nuntuk keseluruhannya dan juga 95% untuk pengujian compatibility sistem"}
73	{"work_id": 4324129606, "title": "Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA", "abstract": "With the proliferation of online data collection in human-subjects research, concerns have been raised over the presence of inattentive survey participants and non-human respondents (bots). We compared the quality of the data collected through five commonly used platforms. Data quality was indicated by the percentage of participants who meaningfully respond to the researcher's question (high quality) versus those who only contribute noise (low quality). We found that compared to MTurk, Qualtrics, or an undergraduate student sample (i.e., SONA), participants on Prolific and CloudResearch were more likely to pass various attention checks, provide meaningful answers, follow instructions, remember previously presented information, have a unique IP address and geolocation, and work slowly enough to be able to read all the items. We divided the samples into high- and low-quality respondents and computed the cost we paid per high-quality respondent. Prolific ($1.90) and CloudResearch ($2.00) were cheaper than MTurk ($4.36) and Qualtrics ($8.17). SONA cost $0.00, yet took the longest to collect the data."}	48	{"work_id": 4324129606, "title": "Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA", "abstract": "With the proliferation of online data collection in human-subjects research, concerns have been raised over the presence of inattentive survey participants and non-human respondents (bots). We compared the quality of the data collected through five commonly used platforms. Data quality was indicated by the percentage of participants who meaningfully respond to the researcher's question (high quality) versus those who only contribute noise (low quality). We found that compared to MTurk, Qualtrics, or an undergraduate student sample (i.e., SONA), participants on Prolific and CloudResearch were more likely to pass various attention checks, provide meaningful answers, follow instructions, remember previously presented information, have a unique IP address and geolocation, and work slowly enough to be able to read all the items. We divided the samples into high- and low-quality respondents and computed the cost we paid per high-quality respondent. Prolific ($1.90) and CloudResearch ($2.00) were cheaper than MTurk ($4.36) and Qualtrics ($8.17). SONA cost $0.00, yet took the longest to collect the data."}
74	{"work_id": 2914721378, "title": "Graph Neural Networks for Social Recommendation", "abstract": "In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec."}	49	{"work_id": 2914721378, "title": "Graph Neural Networks for Social Recommendation", "abstract": "In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec."}
75	{"work_id": 4408925021, "title": "The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review", "abstract": "The integration of artificial intelligence in education has the potential to revolutionize personalized learning by adapting instructional methods, content, and pace to the individual needs of students. This systematic review investigates the integration of artificial intelligence into personalized learning within higher education. An extensive literature search was conducted across multiple databases, yielding 17,899 records from which 45 studies met the inclusion criteria. The risk of bias was assessed using a standardized ranking system. This systematic review follows the PRISMA guidelines to ensure transparency in study selection, data extraction, and synthesis. The findings of the review are synthesized to examine how AI-driven solutions enhance adaptive learning, improve student engagement, and streamline administrative processes. The results indicate that AI technologies can significantly optimize educational outcomes by tailoring content and feedback to individual learner needs. However, several challenges persist, such as ethical concerns, data privacy issues, and the necessity for effective teacher training to support technology integration. This analysis reveals considerable potential for AI to transform educational practices, while also emphasizing the importance of establishing standardized evaluation frameworks and conducting longitudinal studies. The implications of these findings are critical for educators, policymakers, and university administrators aiming to leverage AI for educational innovation and sustainable transformation."}	50	{"work_id": 4408925021, "title": "The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review", "abstract": "The integration of artificial intelligence in education has the potential to revolutionize personalized learning by adapting instructional methods, content, and pace to the individual needs of students. This systematic review investigates the integration of artificial intelligence into personalized learning within higher education. An extensive literature search was conducted across multiple databases, yielding 17,899 records from which 45 studies met the inclusion criteria. The risk of bias was assessed using a standardized ranking system. This systematic review follows the PRISMA guidelines to ensure transparency in study selection, data extraction, and synthesis. The findings of the review are synthesized to examine how AI-driven solutions enhance adaptive learning, improve student engagement, and streamline administrative processes. The results indicate that AI technologies can significantly optimize educational outcomes by tailoring content and feedback to individual learner needs. However, several challenges persist, such as ethical concerns, data privacy issues, and the necessity for effective teacher training to support technology integration. This analysis reveals considerable potential for AI to transform educational practices, while also emphasizing the importance of establishing standardized evaluation frameworks and conducting longitudinal studies. The implications of these findings are critical for educators, policymakers, and university administrators aiming to leverage AI for educational innovation and sustainable transformation."}
		51	+{"work_id": 4390660438, "title": "Modern computing: Vision and challenges", "abstract": "Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress."}
76	{"work_id": 4313646859, "title": "Remote patient monitoring using artificial intelligence: Current state, applications, and challenges", "abstract": "Abstract The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in\u2010home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI\u2010enabled RPM. This review explores the benefits and challenges of patient\u2010centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI\u2010enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends. This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence Technologies > Internet of Things"}	52	{"work_id": 4313646859, "title": "Remote patient monitoring using artificial intelligence: Current state, applications, and challenges", "abstract": "Abstract The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in\u2010home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI\u2010enabled RPM. This review explores the benefits and challenges of patient\u2010centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI\u2010enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends. This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence Technologies > Internet of Things"}
77	{"work_id": 2970155754, "title": "Learning Generalizable Device Placement Algorithms for Distributed Machine Learning", "abstract": "Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019"}	53	{"work_id": 2970155754, "title": "Learning Generalizable Device Placement Algorithms for Distributed Machine Learning", "abstract": "Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019"}
78	{"work_id": 2967467, "title": "Social networks and collaborative filtering for large-scale requirements elicitation", "abstract": "Within the field of software engineering, requirements elicitation is the activity in which stakeholder needs are understood. In large-scale software projects, requirements elicitation tends to be beset by three problems: information overload, inadequate stakeholder input, and biased prioritisation of requirements. The work described in this thesis addresses these problems using social networks and collaborative filtering. The work has developed StakeNet, a novel method that uses social networks to identify and prioritise stakeholders. Using StakeNet, the requirements engineer asks an initial list of stakeholders to recommend other stakeholders and stakeholder roles, builds a social network with stakeholders as nodes and their recommendations as links, and prioritises the stakeholders using a variety of social network measures. The work has also developed StakeRare, a novel method that uses social networks and collaborative filtering to identify and prioritise requirements. Using StakeRare, the requirements engineer asks the stakeholders identified by StakeNet to rate an initial list of requirements and suggest other requirements, recommends other relevant requirements to the stakeholders using collaborative filtering, and prioritises the requirements using the ratings and the stakeholders\u2019 priority from StakeNet. Finally, to support the methods, this work has developed StakeSource, a novel software tool that automates the manual processes in StakeNet. StakeSource collects recommendations from stakeholders, builds the social network, and prioritises the stakeholders automatically. The methods and tool have been evaluated using real large-scale software projects. The empirical evaluation of both StakeNet and StakeRare using a real large-scale software project demonstrates that the methods identify a highly complete set of stakeholders and their requirements, and prioritise the stakeholders and their requirements accurately. These methods outperform the existing methods used in the project, and require significantly less time from the stakeholders and requirements engineers. StakeSource has been evaluated with real large-scale projects by practitioners. The tool is now used in major software projects, and organisations are adopting it. The methods, tool, and evaluation described in this thesis provide evidence that social networks and collaborative filtering can effectively support requirements elicitation in large-scale software projects."}	54	{"work_id": 2967467, "title": "Social networks and collaborative filtering for large-scale requirements elicitation", "abstract": "Within the field of software engineering, requirements elicitation is the activity in which stakeholder needs are understood. In large-scale software projects, requirements elicitation tends to be beset by three problems: information overload, inadequate stakeholder input, and biased prioritisation of requirements. The work described in this thesis addresses these problems using social networks and collaborative filtering. The work has developed StakeNet, a novel method that uses social networks to identify and prioritise stakeholders. Using StakeNet, the requirements engineer asks an initial list of stakeholders to recommend other stakeholders and stakeholder roles, builds a social network with stakeholders as nodes and their recommendations as links, and prioritises the stakeholders using a variety of social network measures. The work has also developed StakeRare, a novel method that uses social networks and collaborative filtering to identify and prioritise requirements. Using StakeRare, the requirements engineer asks the stakeholders identified by StakeNet to rate an initial list of requirements and suggest other requirements, recommends other relevant requirements to the stakeholders using collaborative filtering, and prioritises the requirements using the ratings and the stakeholders\u2019 priority from StakeNet. Finally, to support the methods, this work has developed StakeSource, a novel software tool that automates the manual processes in StakeNet. StakeSource collects recommendations from stakeholders, builds the social network, and prioritises the stakeholders automatically. The methods and tool have been evaluated using real large-scale software projects. The empirical evaluation of both StakeNet and StakeRare using a real large-scale software project demonstrates that the methods identify a highly complete set of stakeholders and their requirements, and prioritise the stakeholders and their requirements accurately. These methods outperform the existing methods used in the project, and require significantly less time from the stakeholders and requirements engineers. StakeSource has been evaluated with real large-scale projects by practitioners. The tool is now used in major software projects, and organisations are adopting it. The methods, tool, and evaluation described in this thesis provide evidence that social networks and collaborative filtering can effectively support requirements elicitation in large-scale software projects."}
				`@@ -80,23 +56,36 @@`
80	{"work_id": 4408444355, "title": "The effects of data quality on machine learning performance on tabular data", "abstract": "Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example, incomplete, erroneous, or inappropriate training data can lead to unreliable models that produce ultimately poor decisions. Trustworthy AI applications require high-quality training and test data along many quality dimensions, such as accuracy, completeness, and consistency. We explore empirically the relationship between six data quality dimensions and the performance of 19 popular machine learning algorithms covering the tasks of classification, regression, and clustering, with the goal of explaining their performance in terms of data quality. Our experiments distinguish three scenarios based on the AI pipeline steps that were fed with polluted data: polluted training data, test data, or both. We conclude the paper with an extensive discussion of our observations."}	56	{"work_id": 4408444355, "title": "The effects of data quality on machine learning performance on tabular data", "abstract": "Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example, incomplete, erroneous, or inappropriate training data can lead to unreliable models that produce ultimately poor decisions. Trustworthy AI applications require high-quality training and test data along many quality dimensions, such as accuracy, completeness, and consistency. We explore empirically the relationship between six data quality dimensions and the performance of 19 popular machine learning algorithms covering the tasks of classification, regression, and clustering, with the goal of explaining their performance in terms of data quality. Our experiments distinguish three scenarios based on the AI pipeline steps that were fed with polluted data: polluted training data, test data, or both. We conclude the paper with an extensive discussion of our observations."}
81	{"work_id": 7161586974, "title": "Paleobiology Database User Guide Version 2.0", "abstract": "The Paleobiology Database is an online, non-governmental, non-profit public resource for paleontological data. It is organized and operated by a multi-disciplinary, multi-institutional, international group of paleobiological researchers. This is the second edition of the volume designed to be a comprehensive guide for Paleobiology Database users, both General and Contributory. It covers most database uses from data retrieval and mapping to data contribution of all types. It contains numerous examples to illustrate database use as well as definitions of terms and additional links to numerous other sources. We hope that this user guide will help all users access the great volume of data in the Paleobiology Database and lead others to start and continue to add data to the system."}	57	{"work_id": 7161586974, "title": "Paleobiology Database User Guide Version 2.0", "abstract": "The Paleobiology Database is an online, non-governmental, non-profit public resource for paleontological data. It is organized and operated by a multi-disciplinary, multi-institutional, international group of paleobiological researchers. This is the second edition of the volume designed to be a comprehensive guide for Paleobiology Database users, both General and Contributory. It covers most database uses from data retrieval and mapping to data contribution of all types. It contains numerous examples to illustrate database use as well as definitions of terms and additional links to numerous other sources. We hope that this user guide will help all users access the great volume of data in the Paleobiology Database and lead others to start and continue to add data to the system."}
82	{"work_id": 2963707260, "title": "Heterogeneous Information Network Embedding for Recommendation", "abstract": "Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, calledHIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance."}	58	{"work_id": 2963707260, "title": "Heterogeneous Information Network Embedding for Recommendation", "abstract": "Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, calledHIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance."}
83	−{"work_id": 4380791906, "title": "NoSQL Databases in Kubernetes", "abstract": "With the increasing popularity of deploying applications in containers, Kubernetes (K8s) has become one of the most accepted container orchestration systems. Kubernetes helps maintain containers smoothly and simplifies DevOps with powerful automations. It was originally developed as a tool to manage stateless microservices that run seamlessly in containers. The ephemeral nature of pods, the smallest deployable unit, in Kubernetes was well-aligned with stateless applications since destroying and recreating pods didn\u2019t impact applications. There was a need to provision solutions around stateful workloads like databases so as to take advantage of K8s. This project explores this need, the challenges associated and the available solutions for running databases in Kubernetes. Most of the current research is focused towards SQL-like databases in K8s even though the DNA of NoSQL distributed databases is more aligned with K8s. With no research being done with NoSQL databases, this project outlines the process behind setting up two famous NoSQL databases in K8s: MongoDB and Cassandra. The project also shows a representative viewpoint of the performance comparison between them using the YCSB benchmark. The project lays a foundation around the setup of these databases using K8s Operators and their benchmarking. The goal of the project is to describe the advantages of having databases in K8s, provide developers a clear path for setup and provide insights on basic benchmark performance."}	59	+{"work_id": 7118010419, "title": "Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices", "abstract": "The increasing popularity of digitization in retail has emphasized application performance as a critical component of customer satisfaction, business continuity, and operational efficiency. Retail applications today operate in hyper-dynamic environments, where performance can be introduced by concerns such as workload peaks, scaling cloud infrastructure performance, spikes in transactions, and real-time data processing. Poor performance erodes user experience, depletes objectives, and lowers competitive benefit. Although many surveys of cloud or performance monitoring exist, they do not incorporate retail contexts that emphasize customer facing KPIs, seasonal demand variability, and other omnichannel complexities. The current paper seeks to address this gap in research by providing a systematic review of 45 peer-reviewed studies (2015\u20132025) specific to monitoring tools, optimization frameworks, and best practices in retail performance monitoring. The findings in the review synthesize performance monitoring platforms (i.e. Splunk, Datadog, New Relic), log management tools, key performance indicators, performance continuous monitoring, performance visualization, and performance integration into retail systems. The paper also offers comparison evaluations of Amazon and Google Cloud monitoring offerings, as well as limitations for optimization planning, and the effect of intelligent analytics to improve scalability and resiliency. The paper contributes by connecting scholarly viewpoints with practical suggestions, providing a distinctive road map for practitioners and academics to develop and enhance high-performance retail applications in cloud-based corporate settings."}
84	−{"work_id": 4392909747, "title": "Cloud Computing: Principles And Paradigms", "abstract": "Capturing the current state of the art in Cloud Computing technology and applications is the main goal of this book. The book\u2019s secondary objective is to chart a course for future studies and technological developments that will pave the way for the establishment of a worldwide marketplace for cloud computing services catering to commercial, government, academic, and consumer needs. The book is designed to be a reference for a wide range of readers, including systems architects, developers, practitioners, new researchers, and graduate students."}	60	+{"work_id": 2784822088, "title": "A microservice architecture for the processing of large geospatial data in the Cloud", "abstract": "Mit der wachsenden Zahl an Ger\u00e4ten, die spatio-temporale Informationen aufnehmen k\u00f6nnen sowie immer besser werdenden Sensoren, steigt auch die Menge an Geodaten. Vor der Benutzung m\u00fcssen die rohen gesammelten Informationen verarbeitet werden. Zurzeit greifen Experten auf desktop-basierte Geographische Informationssysteme zur\u00fcck, um Prozessierungsworkflows durchzuf\u00fchren. Allerdings \u00fcbersteigt das Datenvolumen sowie die Komplexit\u00e4t der Verarbeitungsalgorithmen l\u00e4ngst die Kapazit\u00e4t ihrer Workstations. Zurzeit findet ein Paradigmenwechsel von Desktop-L\u00f6sungen zur Cloud statt, aber die Entwickler von Prozessierungsalgorithmen sind oft keine Informatiker und haben deshalb wenig Erfahrung im Bereich Cloud-Computing. Unsere Forschungshypothese ist, dass eine Microservice-Architektur und dom\u00e4nenspezifische Sprachen genutzt werden k\u00f6nnen, um existierende Algorithmen zu orchestrieren und Workflows f\u00fcr die Prozessierung von Geodaten in der Cloud auszuf\u00fchren, und damit eine effiziente Anwendungsentwicklung erm\u00f6glichen und die Erfahrung von Stakeholdern verbessern. Wir pr\u00e4sentieren eine Softwarearchitektur, die Erweiterungspunkte f\u00fcr Prozessierungsalgorithmen (oder Microservices) enth\u00e4lt, eine Workflow-Management-Komponente f\u00fcr die verteilte Service-Orchestrierung, und einen Workflow-Editor basierend auf einer dom\u00e4nenspezifischen Sprache. Ziel ist es, Benutzern und Entwicklern ohne tiefergehendes Wissen in verteilten Systemen den Zugang zur Cloud zu erm\u00f6glichen. Unsere Forschungsmethode basiert auf der Design Science Research Methodology. Wir f\u00fchren eine Analyse der Problemdom\u00e4ne durch und sammeln Anforderungen und Qualit\u00e4tsattribute f\u00fcr unsere Architektur. Um unsere Forschungsziele zu erreichen, entwickeln wir die Architektur sowie Ans\u00e4tze f\u00fcr Workflow-Management und -Modellierung. Wir stellen den Nutzen unserer L\u00f6sung dar, indem wir sie auf zwei praktische Anwendungsf\u00e4lle anwenden. Au\u00dferdem evaluieren wir ihre Qualit\u00e4t anhand von definierten Szenarien. Abschlie\u00dfend f\u00fchren wir eine kritische Bewertung unserer Ergebnisse durch. Unsere wissenschaftlichen Beitr\u00e4ge k\u00f6nnen in drei Bereiche gegliedert werden. Wir pr\u00e4sentieren eine skalierbare und erweiterbare Microservice-Architektur f\u00fcr die Geodatenprozessierung, die eine verteilte Entwicklung erm\u00f6glicht sowie eine hohe Verf\u00fcgbarkeit bietet. Au\u00dferdem pr\u00e4sentieren wir neue Ans\u00e4tze f\u00fcr die Service- Integration und -Orchestrierung in der Cloud sowie regelbasiertes und dynamisches Workflow-Management ohne a priori Wissen im Entwurf. F\u00fcr die Workflow- Modellierung entwickeln wir eine dom\u00e4nenspezifische Sprache sowie eine neue Methode f\u00fcrs Sprachdesign. Die Ergebnisse unserer Arbeit st\u00fctzen unsere Forschungshypothese. Die Microservice- Architektur erm\u00f6glicht eine effiziente Entwicklung eines verteilten Systems. Die dom\u00e4nenspezifische Sprache sowie unser Ansatz zur Service-Integration verbessern die Erfahrung der Stakeholder. Unsere Arbeit stellt einen gro\u00dfen Schritt im Paradigmenwechsel zur Cloud dar und bietet M\u00f6glichkeiten f\u00fcr weitere Forschung."}
85	−{"work_id": 2562507292, "title": "Internet of Things Applications - From Research and Innovation to Market Deployment", "abstract": "ISBN : 9788793102941"}	61	+{"work_id": 4394994587, "title": "Recommender Systems in the Era of Large Language Models (LLMs)", "abstract": "With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an indispensable and important component in our daily lives, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have achieved significant advancements in enhancing recommender systems by modeling user-item interactions and incorporating their textual side information, these DNN-based methods still exhibit some limitations, such as difficulties in effectively understanding users' interests and capturing textual side information, inabilities in generalizing to various seen/unseen recommendation scenarios and reasoning on their predictions, etc. Meanwhile, the development of Large Language Models (LLMs), such as ChatGPT and GPT-4, has revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), due to their remarkable abilities in fundamental responsibilities of language understanding and generation, as well as impressive generalization capabilities and reasoning skills. As a result, recent studies have actively attempted to harness the power of LLMs to enhance recommender systems. Given the rapid evolution of this research direction in recommender systems, there is a pressing need for a systematic overview that summarizes existing LLM-empowered recommender systems, so as to provide researchers and practitioners in relevant fields with an in-depth understanding. Therefore, in this survey, we conduct a comprehensive review of LLM-empowered recommender systems from various aspects including pre-training, fine-tuning, and prompting paradigms. More specifically, we first introduce the representative methods to harness the power of LLMs (as a feature encoder) for learning representations of users and items. Then, we systematically review the emerging advanced techniques of LLMs for enhancing recommender systems from three paradigms, namely pre-training, fine-tuning, and prompting. Finally, we comprehensively discuss the promising future directions in this emerging field."}
86	−{"work_id": 4387316367, "title": "Ontological Approach to a Knowledge Graph Construction in a Semantic Library", "abstract": "Abstract The paper considers an approach to a knowledge graph construction based on the ontological representation of scientific subject areas. The presentation is based on concepts related to information and data mining, such as knowledge, knowledge extraction, domain ontology, scientific domain, thesaurus, semantic digital library, user information need, ontological design method and, in fact, the knowledge graph. The digital semantic library LibMeta is presented as a repository of various structured data with the possibility of their integration with other data sources. Assumes the possibility of specifying personal content by describing a local subject area within LibMeta. The ontology of the content of the semantic library acts as a means of formalization. This paper addresses the experience of building semantic libraries based on thesauri and ontological design. Building ontologies based on the thesaurus of the subject area LibMeta allows us to say that the presence of internal semantic links ensures the consistency and reliability of search results, which is a necessary condition for extracting scientific knowledge. The digital library ontology defines the data structure of the library content. Each data element loaded into the library can be associated with an ontology vertex (top) that determines the position of the data element in the ontology. Based on the ontology links and the links defined at the design stage, you can build a data graph. On the example of the ontology of the LibMeta semantic library, the technology of forming the knowledge graph of modern applications in mathematics is discussed. The problems of filling a graph, embedding in a graph, extracting links and nodes of a graph are discussed."}	62	+{"work_id": 4408156900, "title": "Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities", "abstract": "The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to systematically review the current literature regarding the application of federated learning (FL) and artificial intelligence (AI) to improve cloud computing security while preserving privacy, delivering real-time threat detection, and meeting regulatory requirements. The current research follows a systematic literature review (SLR) approach, which examined 30 studies published between 2020 and 2024 and followed the PRISMA 2020 checklist. The analysis shows that FL provides significant privacy risk reduction by 25%, especially in healthcare and similar domains, and it improves threat detection by 40% in critical infrastructure areas. A total of 80% of reviewed implementations showed improved privacy, but challenges like communication overhead and resource limitations persist, with 50% of studies reporting latency issues. To overcome these obstacles, this study also explores some emerging solutions, which include model compression, hybrid federated architectures, and cryptographic enhancements. Additionally, this paper demonstrates the unexploited capability of FL for real-time decision-making in dynamic edge environments and highlights its potential across autonomous systems, Industrial Internet of Things (IIoT), and cybersecurity frameworks. The paper\u2019s proposed insights present a deployment strategy for FL models which enables scalable, secure, and privacy-preserving operations and will enable robust cloud security solutions in the AI era."}
87	−{"work_id": 4398201281, "title": "Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems", "abstract": "The decentralized nature of blockchain enables data traceability, transparency, and immutability as complementary security features to the existing Internet of Things (IoT) systems. These Blockchain-based IoT (BIoT) systems aim to mitigate security risks such as malicious control, data leakage, and dishonesty often found in traditional cloud-based, vendor-specific IoT networks. As we steadily advance into the era of situation-aware IoT, the use of machine learning (ML) techniques has become essential for synthesizing situations based on sensory contexts. However, the challenge to integrate learning-based situation awareness with BIoT systems restricts the full potential of such integration. This is primarily due to the conflicts between the deterministic nature of smart contracts and the non-deterministic nature of machine learning, as well as the high costs of conducting machine learning on blockchain. To address the challenge, we propose a framework named Situ-Oracle. With the framework, a computation oracle of the blockchain ecosystem is leveraged to provide situation analysis as a service, based on Recurrent Neural Network (RNN)-based learning models tailored for the Situ model, and specifically designed smart contracts are deployed as intermediary communication channels between the IoT devices and the computation oracle. We used smart homes as a case study to demonstrate the framework design. Subsequently, system-wide evaluations were conducted over a physically constructed BIoT system. The results indicate that the proposed framework achieves better situation analysis accuracy (above 95%) and improves gas consumption as well as network throughput and latency when compared to baseline systems (on-chain learning or off-chain model verification). Overall, the paper presents a promising approach for improving situation analysis for BIoT systems, with potential applications in various domains such as smart homes, healthcare, and industrial automation."}	63	+{"work_id": 3012404397, "title": "Santa Claus Algorithm: Agent-Based Consensus Preprocessing Method for Scalability Improvement in Private Blockchain Environment", "abstract": "\ube44\uc794\ud2f4 \uc7a5\uc560 \ud5c8\uc6a9(BFT: Byzantine Fault Tolerance)\uc740 \ube14\ub85d\uccb4\uc778 \ud658\uacbd\uc5d0\uc11c \uc8fc\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\ub294 \ube14\ub85d\uccb4\uc778 \ud569\uc758 \uc54c\uace0\ub9ac\uc998 \uc911\uc758 \ud558\ub098\uc774\ub2e4. BFT\ub294 \ud569\uc758\ub97c \ud558\uae30 \uc704\ud574 \ubaa8\ub4e0 \ucc38\uc5ec\uc790\uac00 \ud569\uc758\uc5d0 \ucc38\uc5ec\ud558\ub294 \uad6c\uc870\ub85c \ub418\uc5b4\uc788\uc5b4 \ucc38\uc5ec \ub178\ub4dc\uac00 \ub9ce\uc544\uc9c0\uba74 \ucc98\ub9ac\uc18d\ub3c4\uac00 \ud604\uaca9\ud788 \uac10\uc18c\ud558\ub294 \ud655\uc7a5\uc131\uc758 \ubb38\uc81c\uac00 \uc788\ub2e4. \uc774\ub7ec\ud55c \ubb38\uc81c \ub54c\ubb38\uc5d0 \ub9ce\uc740 \ucc38\uc5ec \ub178\ub4dc\uac00 \uc788\ub294 \ube14\ub85d\uccb4\uc778\uc5d0\uc11c \ube44\uc794\ud2f4 \uc7a5\uc560 \ud5c8\uc6a9\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud655\uc7a5\uc131 \uac1c\uc120\uc774 \ud544\uc218\uc801\uc774\ub2e4. \ubcf8 \uc5f0\uad6c\uc5d0\uc11c\ub294 \ud504\ub77c\uc774\ube57 \ube14\ub85d\uccb4\uc778\uc5d0\uc11c \ud569\uc758 \uc5c6\uc774 \ub370\uc774\ud130\ub97c \uae30\ub85d\ud558\ub2e4\uac00 \uc5d0\uc774\uc804\ud2b8\uac00 \ud569\uc758\uac00 \ud544\uc694\ud55c \uc21c\uac04\uc744 \uc54c\ub824\uc8fc\ub294 \uc804\ucc98\ub9ac \uae30\ubc95\uc744 \uc81c\uc548\ud55c\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ube14\ub85d\uccb4\uc778\uc758 \ub178\ub4dc\ub97c \ubc29\ubb38\ud558\uba74\uc11c \ub450 \ub2e8\uacc4\uc5d0 \uac78\uccd0 \ud569\uc758\uac00 \ud544\uc694\ud55c \uc21c\uac04\uc744 \uc54c\ub824\uc900\ub2e4. \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uc9c0\uae08\uae4c\uc9c0 \uae30\ub85d\ub41c \ub370\uc774\ud130\uac00 \ubaa8\ub4e0 \ub178\ub4dc\uc5d0\uc11c \uc77c\uce58\ud558\ub294\uc9c0 \ud655\uc778\ud558\uba70, \ub450 \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uc5b4\ub5a0\ud55c \ub370\uc774\ud130\uac00 \ubd88\uc77c\uce58\ud558\uc5ec \ud569\uc758\uac00 \ud544\uc694\ud55c\uc9c0 \uc54c\ub824\uc900\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ube14\ub85d\uccb4\uc778 \ub124\ud2b8\uc6cc\ud06c\uc5d0\uc11c \ub3c5\ub9bd\uc801\uc778 \uac1d\uccb4\ub85c\uc11c \ube14\ub85d\uccb4\uc778\uc5d0 \ucc38\uc5ec\ud558\ub294 \uc784\uc758\uc758 \ub178\ub4dc\uc5d0 \uc758\ud574 \uc791\ub3d9\uc774 \ub41c\ub2e4. \ubcf8 \ub17c\ubb38\uc5d0\uc11c\ub294 \uc81c\uc548 \uae30\ubc95\uc744 \ud504\ub77c\uc774\ube57 \ube14\ub85d\uccb4\uc778 \ud658\uacbd\uc5d0\uc11c \uc2e4\uc6a9\uc801 \ube44\uc794\ud2f4 \uc7a5\uc560 \ud5c8\uc6a9(PBFT: Practical Byzantine Fault Tolerance) \ud569\uc758\uc5d0 \uc801\uc6a9\ud55c \uacb0\uacfc \ub178\ub4dc\uc758 \uc218\uac00 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \ubc18\uc751 \uc2dc\uac04\uc774 \ud3c9\uade0 39% \uac10\uc18c\ud558\uc600\ub2e4\ub294 \uac83\uc744 \uc99d\uba85\ud558\uc600\ub2e4."}
88	−{"work_id": 2945623882, "title": "KGAT", "abstract": "To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks down the independent interaction assumption by linking items with their attributes. We argue that in such a hybrid structure of KG and user-item graph, high-order relations --- which connect two items with one or multiple linked attributes --- are an essential factor for successful recommendation. We propose a new method named Knowledge Graph Attention Network (KGAT) which explicitly models the high-order connectivities in KG in an end-to-end fashion. It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors. Our KGAT is conceptually advantageous to existing KG-based recommendation methods, which either exploit high-order relations by extracting paths or implicitly modeling them with regularization. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM and RippleNet. Further studies verify the efficacy of embedding propagation for high-order relation modeling and the interpretability benefits brought by the attention mechanism. We release the codes and datasets at https://github.com/xiangwang1223/knowledge_graph_attention_network."}	64	+{"work_id": 2794533297, "title": "OmniLedger: A Secure, Scale-Out, Decentralized Ledger via Sharding", "abstract": "Designing a secure permissionless distributed ledger (blockchain) that performs on par with centralized payment processors, such as Visa, is a challenging task. Most existing distributed ledgers are unable to scale-out, i.e., to grow their total processing capacity with the number of validators; and those that do, compromise security or decentralization. We present OmniLedger, a novel scale-out distributed ledger that preserves longterm security under permissionless operation. It ensures security and correctness by using a bias-resistant public-randomness protocol for choosing large, statistically representative shards that process transactions, and by introducing an efficient cross-shard commit protocol that atomically handles transactions affecting multiple shards. OmniLedger also optimizes performance via parallel intra-shard transaction processing, ledger pruning via collectively-signed state blocks, and low-latency \"trust-but-verify\" validation for low-value transactions. An evaluation of our experimental prototype shows that OmniLedger's throughput scales linearly in the number of active validators, supporting Visa-level workloads and beyond, while confirming typical transactions in under two seconds."}
89	−{"work_id": 2251066406, "title": "Bypassing Races in Live Applications with Execution Filters", "abstract": "Deployed multithreaded applications contain many races because these applications are difficult to write, test, and debug. Worse, the number of races in deployed applications may drastically increase due to the rise of multicore hardware and the immaturity of current race detectors.LOOM is a live-workaround system designed to quickly and safely bypass application races at runtime. LOOM provides a flexible and safe language for developers to write execution filters that explicitly synchronize code. It then uses an evacuation algorithm to safely install the filters to live applications to avoid races. It reduces its performance overhead using hybrid instrumentation that combines static and dynamic instrumentation.We evaluated LOOM on nine real races from a diverse set of six applications, including MySQL and Apache. Our results show that (1) LOOM can safely fix all evaluated races in a timely manner, thereby increasing application availability; (2) LOOM incurs little performance overhead; (3) LOOM scales well with the number of application threads; and (4) LOOM is easy to use."}	65	+{"work_id": 2120510885, "title": "The Byzantine generals problem", "abstract": "Abstract The Byzantine Generals Problem requires processes to reach agreement upon a value even though some of them may fad. It is weakened by allowing them to agree upon an \"incorrect \" value if a failure occurs. The transaction eormmt problem for a distributed database Js a special case of the weaker problem. It is shown that, like the original Byzantine Generals Problem, the weak version can be solved only ff fewer than one-third of the processes may fad. Unlike the onginal problem, an approximate solution exists that can tolerate arbaranly many failures."}
90	−{"work_id": 1500148551, "title": "Time-based Fairness Improves Performance in Multi-rate WLANs", "abstract": "The performance seen by individual clients on a wireless local area network (WLAN) is heavily influenced by the manner in which wireless channel capacity is allocated. The popular MAC protocol DCF (Distributed Coordination Function) used in 802.11 networks provides equal long-term transmission opportunities to competing nodes when all nodes experience similar channel conditions. When similar-sized packets are also used, DCF leads to equal achieved throughputs (throughput-based fairness) among contending nodes. Because of varying indoor channel conditions, the 802.11 standard supports multiple data transmission rates to exploit the trade-off between data rate and bit error rate. This leads to considerable rate diversity, particularly when the network is congested. Under such conditions, throughput-based fairness can lead to drastically reduced aggregate throughput. In this paper, we argue the advantages of time-based fairness, in which each competing node receives an equal share of the wireless channel occupancy time. We demonstrate that this notion of fairness can lead to significant improvements in aggregate performance while still guaranteeing that no node receives worse channel access than it would in a single-rate WLAN. We also describe our algorithm, TBR (Time-based Regulator), which runs on the AP and works with any MAC protocol to provide time-based fairness by regulating packets. Through experiments, we show that our practical and backward compatible implementation of TBR in conjunction with an existing implementation of DCF achieves time-based fairness."}	66	+{"work_id": 4396669794, "title": "Optimized Throughput-Based Broadcasting for Next Generation Wireless Networks", "abstract": "Remote areas benefit from lower costs for transporting enormous amounts of data between a source node and multiple networking devices. Noise and constraints necessitated a 64 kb/s broadcaster. Bit defect rates, abnormalities, and frequent retransmission prevented data movement.This could greatly minimize channel usage. The maximum transmission unit distance was 64B. Data packet size limits exacerbated issues. Massive data traffic, connection imprecision, and changing topology affect network structure, making data dissemination from source nodes to all devices difficult.This book chapter suggests using an advanced throughput optimal broadcast in point-to-multipoint wireless networks to improve wire-less link precision by using the Mayfly optimization method, a recent swarm intelligence soft computing technique, to improve geometrical configuration of interfering terminals and forecasted per-flow throughput"}
91	−{"work_id": 2945827670, "title": "Neural Graph Collaborative Filtering", "abstract": "Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect."}	67	+{"work_id": 4387321091, "title": "Efficient Memory Management for Large Language Model Serving with PagedAttention", "abstract": "High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2--4\u00d7 with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM's source code is publicly available at https://github.com/vllm-project/vllm."}
92	−{"work_id": 2962883549, "title": "Deep Learning in Mobile and Wireless Networking: A Survey", "abstract": "The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research."}	68	+{"work_id": 2963507231, "title": "Untangling Blockchain: A Data Processing View of Blockchain Systems", "abstract": "Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence, values, and histories of the states. As the technology landscape is expanding rapidly, it is both important and challenging to have a firm grasp of what the core technologies have to offer, especially with respect to their data processing capabilities. In this paper, we first survey the state of the art, focusing on private blockchains (in which parties are authenticated). We analyze both in-production and research systems in four dimensions: distributed ledger, cryptography, consensus protocol, and smart contract. We then present BLOCKBENCH, a benchmarking framework for understanding performance of private blockchains against data processing workloads. We conduct a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity, and Hyperledger Fabric. The results demonstrate several trade-offs in the design space, as well as big performance gaps between blockchain and database systems. Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases."}
93	−{"work_id": 4364375730, "title": "Social Health Protection During the COVID-Pandemic Using IoT", "abstract": "The agenda of this book chapter is to review existing technologies that aid societal health protection and recommend some possible approaches which will assist the mentioned scenario. Automations like big data and artificial intelligence (AI) deployed in healthcare sector can expedite pandemic response in ways that are strenuous to achieve all in all by humans. The sudden epiphany to trace COVID-19 in public has powered the innovation of data dashboards that visually unveil coronavirus epicentres. A cloud-based AI-assisted CT service is being engaged to differentiate pneumonia from the pandemic which dwindles risk factor in the present school of thought of the citizens worldwide. In conclusion, social health protection was an indispensable mechanism in prior to these challenging times and is escalating by prominence for delivering support to individuals during the crisis."}	69	+{"work_id": 4288083766, "title": "Deep Learning Based Recommender System", "abstract": "With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field."}
94	−{"work_id": 2902511784, "title": "Dynamic data race detection for OpenMP programs", "abstract": "Two concurrent accesses to a shared variable that are unordered by synchronization are said to be a data race if at least one access is a write. Data races cause shared memory parallel programs to behave unpredictably and are typically considered a bug. It is hard to manually identify memory access instructions causing data races in large concurrent programs due to non-deterministic behavior affected by thread schedule. Tools for detecting data races are desired to help application developers debug data races in an efficient way. Dynamic data race detection tools detect data races by monitoring programs as they execute. Building accurate and efficient data race detection tools requires domain knowledge of concurrency control primitives provided by the specific parallel programming model upon which a program is built. OpenMP is the de-facto standard API for node level shared memory parallel programming. The complexity of OpenMP, which includes primitives for managing data environments, SPMD parallelism, work sharing, tasking, mutual exclusion, and ordering, presents a formidable challenge for precise dynamic data race detection. This dissertation describes several techniques that enable accurate dynamic data race detection for OpenMP programs. First, I describe a hybrid data race detection algorithm that allows data race detection to be executed in parallel. Second, I describe an OpenMP task labeling technique that assigns each OpenMP task a label to facilitate OpenMP-aware accurate happens-before analysis. This technique is the foundation for callback functions provided by the OpenMP Tools interface known as OMPT to get notifications about OpenMP synchronization events and create task labels based on a set of label creation rules. Third, I propose techniques for optimizing dynamic data race detection performance via a redundant memory access pruning algorithm, a readers-writer concurrency control protocol for coordinating threads as they maintain access histories for memory locations, and an OpenMP-aware access record management protocol. The second and third contributions combine to form a system called ROMP, which is a dynamic data race detector for OpenMP programs. Experiments with standard data race detection benchmarks shows that ROMP achieves high accuracy for detecting data races in OpenMP programs that contain host-side CPU-centric parallel constructs. Experiments with several OpenMP performance benchmarks show the effectiveness of proposed optimization techniques."}	70	+{"work_id": 4323539384, "title": "Scientific dataset recommendation with semantic techniques", "abstract": "Scientific datasets are important in scientific research. Researchers always want to find a way to improve their research. Data discovery and reuse is a recent and popular way of finding more scientific datasets to help researchers. There is much current research work for data discovery and reuse. Nevertheless, most of these works are limited because researchers only trust and reuse data through social networks (such as colleagues or collaborators). Also, the scientific data from social networks bring limitations. For instance, they will only recommend data that they consider helpful. However, one scientist's useful data sometimes might be another's noisy data. A dataset search engine is a solution to help researchers find data outside their social networks by exploring scientific datasets in open data repositories or hubs. Such a dataset search engine typically returns related or relevant datasets based on a keyword query from researchers. Several existing dataset search engines already provide millions of open datasets from open-access sources. However, the performance of a dataset search engine is highly related to the quality of the keyword queries submitted by researchers. For instance, two different queries with the same meaning would get different returns from the same dataset search engine. The main question of this thesis is how to find more relevant scientific datasets from a query on a dataset search engine. A new idea is provided as the solution with dataset recommendation, which is adding one step, \"if you like this dataset, do you also like these other datasets,\" after researchers or users submitted a query to a dataset search engine. Users submit keywords for query to a dataset search engine and then get returned list of datasets. Users can then tell the dataset search engine which returned dataset they like, and the dataset search engine will say, \"If you like this dataset, you would also like those other datasets,\" to recommend more datasets. This thesis aims to help dataset search engines with such scientific dataset recommendations using Semantic Web technologies. As mentioned, the main idea of this thesis is \"if you like this dataset, you would also like that other dataset\". This thesis states the challenges and possibilities of using Semantic Web technologies on scientific dataset recommendation, as well as explores 1) how ontologies can be used for dataset recommendation, and 2) how knowledge graphs can be used for dataset recommendation? These two questions are addressed in six chapters. A scientific item (dataset or paper) recommendation benchmark was provided during the exploring work of this thesis to provide a standard for evaluating scientific item recommendation methods. This benchmark contains a large-scale corpus with millions of datasets and papers as well as the trusted links between them. Also, a collection of entity embeddings was created in the work of this thesis. These embeddings are scientific item embeddings by using knowledge graph embedding on the citation network from Microsoft Academic knowledge graph."}
95	−{"work_id": 2408538552, "title": "Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks", "abstract": "Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users\u2019 preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user\u2019s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly."}	71	+{"work_id": 3148181069, "title": "Classification Based on Decision Tree Algorithm for Machine Learning", "abstract": "Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed."}
96	−{"work_id": 4396219753, "title": "Machine Learning Mastery", "abstract": "This chapter delves into the core principles of machine learning, offering practical insights for effective data processing. From foundational concepts to advanced techniques, the narrative unfolds as a comprehensive guide for harnessing the power of machine learning in real-world scenarios. The chapter explores data preprocessing methods, addressing the importance of cleaning and quality assurance, outlier detection, handling missing data, and employing noise reduction techniques. Through illustrative examples and case studies, readers gain actionable knowledge on building a robust foundation for machine learning applications. Emphasizing the significance of data quality in model performance, the chapter serves as a valuable resource for both beginners and experienced practitioners seeking mastery in the art of data processing for machine learning success."}	72	+{"work_id": 3104550706, "title": "Gossip Algorithms for Distributed Signal Processing", "abstract": "Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression."}
97	−{"work_id": 7118085507, "title": "Retrieval-Augmented Generation for AI-Generated Content: A Survey", "abstract": "Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (AIGC). Despite its notable successes, AIGC still faces hurdles such as updating knowledge, handling long-tail data, mitigating data leakage, and managing high training and inference costs. Retrieval-augmented generation (RAG) has recently emerged as a paradigm to address such challenges. In particular, RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores, leading to higher accuracy and better robustness. In this paper, we comprehensively review existing efforts that integrate RAG techniques into AIGC scenarios. We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators. This unified perspective encompasses all RAG scenarios, illuminating advancements and pivotal technologies that help with potential future progress. We also summarize additional enhancement methods for RAG, facilitating effective engineering and implementation of RAG systems. Then from another view, we survey practical applications of RAG across different modalities and tasks, offering valuable references for researchers and practitioners. Furthermore, we introduce the benchmarks for RAG, discuss the limitations of current RAG systems, and suggest potential directions for future research."}	73	+{"work_id": 2980789769, "title": "A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms", "abstract": "Sending data to the cloud for analysis was a prominent trend during the past decades, driving cloud computing as a dominant computing paradigm. However, the dramatically increasing number of devices and data traffic in the Internet-of-Things (IoT) era are posing significant burdens on the capacity-limited Internet and uncontrollable service delay. It becomes difficult to meet the delay-sensitive and context-aware service requirements of IoT applications by using cloud computing alone. Facing these challenges, computing paradigms are shifting from the centralized cloud computing to distributed edge computing. Several new computing paradigms, including Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet, have emerged to leverage the distributed resources at network edge to provide timely and context-aware services. By integrating end devices, edge servers, and cloud, they form a hierarchical IoT architecture, i.e., End-Edge-Cloud orchestrated architecture to improve the performance of IoT systems. This article presents a comprehensive survey of these emerging computing paradigms from the perspective of end-edge-cloud orchestration. Specifically, we first introduce and compare the architectures and characteristics of different computing paradigms. Then, a comprehensive survey is presented to discuss state-of-the-art research in terms of computation offloading, caching, security, and privacy. Finally, some potential research directions are envisioned for fostering continuous research efforts."}
98	−{"work_id": 4315977496, "title": "A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions", "abstract": "Recommender system is one of the most important information services on today\u2019s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first introduce the background and the history of the development of both recommender systems and graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist of two categories: spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data and the enhanced supervision signal. We then systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency. Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions on the open problems and promising future directions in this area. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems ."}	74	+{"work_id": 4415028496, "title": "Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey", "abstract": "Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing across distributed communication networks. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these networked systems, yet introduce significant challenges in model deployment, network resource management, and cross-layer optimization. In this survey, we comprehensively examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, communication protocols, and network-aware computing frameworks. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and communication overhead across heterogeneous networks. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examine practical deployments through diverse networked applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex distributed systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements in communications with practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments over next-generation communication networks, fostering innovation in intelligent networked systems."}
99	−{"work_id": 4293584584, "title": "A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)", "abstract": "Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The existing traffic system uses programming logic control (PLC) with round-robin scheduling algorithm. Recent works have proposed IoT-based frameworks that use traffic density of each lane to control traffic movement, but they suffer from low accuracy due to lack of emergency vehicle image datasets for training deep neural networks. In this paper, we propose a novel distributed IoT framework that is based on two observations. The first observation is major structural changes to road are rare. This observation is exploited by proposing a novel two stage vehicle detector that is able to achieve 77% vehicle detection accuracy on UA-DETRAC dataset. The second observation is emergency vehicle have distinct siren sound that is detected using a novel acoustic detection algorithm on an edge device. The proposed system is able to detect emergency vehicles with an average accuracy of 99.4%."}	75	+{"work_id": 4395483946, "title": "Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives", "abstract": "Abstract In recent years, higher education (HE) globally has witnessed extensive adoption of technology, particularly in teaching and research. The emergence of generative Artificial Intelligence (GenAI) further accelerates this trend. However, the increasing sophistication of GenAI tools has raised concerns about their potential to automate teaching and research processes. Despite widespread research on GenAI in various fields, there is a lack of multicultural perspectives on its impact and concerns in HE. This study addresses this gap by examining the usage, benefits, and concerns of GenAI in higher education from a multicultural standpoint. We employed an online survey that collected responses from 1217 participants across 76 countries, encompassing a broad range of gender categories, academic disciplines, geographical locations, and cultural orientations. Our findings revealed a high level of awareness and familiarity with GenAI tools among respondents. A significant portion had prior experience and expressed the intention to continue using these tools, primarily for information retrieval and text paraphrasing. The study emphasizes the importance of GenAI integration in higher education, highlighting both its potential benefits and concerns. Notably, there is a strong correlation between cultural dimensions and respondents\u2019 views on the benefits and concerns related to GenAI, including its potential as academic dishonesty and the need for ethical guidelines. We, therefore, argued that responsible use of GenAI tools can enhance learning processes, but addressing concerns may require robust policies that are responsive to cultural expectations. We discussed the findings and offered recommendations for researchers, educators, and policymakers, aiming to promote the ethical and effective integration of GenAI tools in higher education."}
		76	+{"work_id": 2951308194, "title": "Blockchain and smart contracts for the internet of things", "abstract": "O Blockchain \u00e9 uma tecnologia emergente recentemente generalizada para muitas \u00e1reas de atividade. O seu modo de opera\u00e7\u00e3o descentralizado enquadra-se perfeitamente em v\u00e1rios cen\u00e1rios onde o principal desafio reside na melhoria da comunica\u00e7\u00e3o m\u00e1quina-m\u00e1quina e na possibilidade de realizar transfer\u00eancias seguras com servi\u00e7os de valor acrescentado. As redes IoT s\u00e3o uma das \u00e1reas poss\u00edveis de aplica\u00e7\u00e3o da tecnologia Blockchain, uma vez que, para poder satisfazer os requisitos da ind\u00fastria, a arquitetura atual das redes IoT tem limita\u00e7\u00f5es, que podem ser superadas atrav\u00e9s da melhoria das comunica\u00e7\u00f5es entre dispositivos e do acesso a formas evolu\u00eddas de agrega\u00e7\u00e3o e consumo dos dados recolhidos. Este trabalho procura avaliar a combina\u00e7\u00e3o desses dois paradigmas - IoT e Blockchain -, procurando entender como o IoT pode beneficiar das funcionalidades que o Blockchain oferece: um sistema de armazenamento mais econ\u00f3mico, descentraliza\u00e7\u00e3o e verdadeira redund\u00e2ncia, confian\u00e7a sem autoridade central - privacidade - e seguran\u00e7a refor\u00e7ada. Por outro lado, o Blockchain como infraestrutura financeira para o IoT \u00e9 tamb\u00e9m um aspeto fundamental desse trabalho. Na prova de conceito constru\u00edda, este cen\u00e1rio \u00e9 implementado, pois os dados de um sensor podem ser transacionados com uma entidade que os solicite. S\u00e3o igualmente realizadas an\u00e1lises estat\u00edsticas e de desempenho relativamente \u00e0 arquitetura implementada, sendo tamb\u00e9m apontados alguns pontos de melhoria para alavancar o uso do sistema em situa\u00e7\u00f5es reais."}
		77	+{"work_id": 4362632858, "title": "Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices", "abstract": "Cloud-native applications constitute a recent trend for designing large-scale software systems. This thesis introduces the Theodolite benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, their frameworks, configurations, and deployments. The benchmarking method is applied to event-driven microservices, a specific type of cloud-native applications that employ distributed stream processing frameworks to scale with massive data volumes. Extensive experimental evaluations benchmark and compare the scalability of various stream processing frameworks under different configurations and deployments, including different public and private cloud environments. These experiments show that the presented benchmarking method provides statistically sound results in an adequate amount of time. In addition, three case studies demonstrate that the Theodolite benchmarking method can be applied to a wide range of applications beyond stream processing."}
		78	+{"work_id": 3092103025, "title": "Bias and Debias in Recommender System: A Survey and Future Directions", "abstract": "While recent years have witnessed a rapid growth of research papers on recommender system (RS) , most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, and so on. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and perform debiasing when necessary. When reviewing the papers that consider biases in RS, we find that, to our surprise, the studies are rather fragmented and lack a systematic organization. The terminology \u201cbias\u201d is widely used in the literature, but its definition is usually vague and even inconsistent across papers. This motivates us to provide a systematic survey of existing work on RS biases. In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics. We then provide a taxonomy to position and organize the existing work on recommendation debiasing. Finally, we identify some open challenges and envision some future directions, with the hope of inspiring more research work on this important yet less investigated topic. The summary of debiasing methods reviewed in this survey can be found at https://github.com/jiawei-chen/RecDebiasing ."}
		79	+{"work_id": 4401495975, "title": "Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications", "abstract": "Abstract The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner. Graphical Abstract"}
		80	+{"work_id": 4406085118, "title": "Data-centric Artificial Intelligence: A Survey", "abstract": "Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI . The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI ."}
		81	+{"work_id": 3015636663, "title": "Federated Learning in Mobile Edge Networks: A Comprehensive Survey", "abstract": "In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL."}
		82	+{"work_id": 3032576892, "title": "Comparative study on Python web frameworks: Flask and Django", "abstract": "The purpose of the thesis was to study the various features, advantages, and the limitation of two web development frameworks for Python programming language. It aims to compare the usage of Django and Flask frameworks from a novice point of view. The theoretical part of the thesis presents the various types of programming languages and web technologies. In the practical part, however, the study is divided into two parts, each part observing the respective web application framework. \\n \\n \\nIn order to perform the comparison, a social network and eCommerce like application was built for Flask and Django respectively. The comparison was started by developing the social network application first with Flask and finished with the e-commerce application using Django. Python programing language, SQLite database for the backend and HTML, JavaScript, and Ajax were used for the frontend technology. At the end of the project, both applications were deployed to the cloud platform called Heroku. \\n \\nAfter the comparison, it was found that the most significant advantages of Flask were that it provides simplicity, flexibility, fine-grained control and quick and easy to learn. On the other hand, Django was easy to work with because of its extensive features and support for libraries. Another main advantage of Django is its scalability. It is best fit for a large-scale application. Each framework has its limitations and radiates a fair share of disadvantages. For example, Django is a bit cumbersome for smaller sized applications. However, Flask is too simple to not have the necessary features within the framework. \\n \\nAs a result of this study, it seems that both frameworks are capable of serving production-grade web applications while having a fair share of advantages and disadvantages."}
		83	+{"work_id": 2784168960, "title": "Energy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacenters", "abstract": "Cataloged from PDF version of article."}
		84	+{"work_id": 4385584867, "title": "A Study on Smart Energy Management Framework Using Cloud Computing", "abstract": "This study focuses on investigating energy-aware resources and designing an energy-efficient cloud architecture. It aims to optimize the decentralization model and establish migration behavior to improve IT business operations using cloud computing. Several models were evaluated for performance metrics such as physical host use, power utilization, and service level agreement (SLA) misuse. These models include smart cloud optimization for resource configuration handling (SCORCH), V-MAN, decentralized energy-aware collaborative model (DEACM), efficient energy-aware resource management model (EEARMM), and energy and power-aware dynamic migration (EPADM). To handle resource configuration efficiently, the study employed V-MAN, DEACM, EEARMM, and EPADM within the framework of smart cloud optimization. Among these models, EPADM proved to be the most effective for resource utilization through dynamic VM migration. By leveraging EPADM, the cloud system can dynamically migrate virtual machines (VMs) to optimize resource allocation and energy consumption."}
		85	+{"work_id": 2793346081, "title": "Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT", "abstract": "The Internet of Things (IoT) is stepping out of its infancy into full maturity and establishing itself as a part of the future Internet. One of the technical challenges of having billions of devices deployed worldwide is the ability to manage them. Although access management technologies exist in IoT, they are based on centralized models which introduce a new variety of technical limitations to manage them globally. In this paper, we propose a new architecture for arbitrating roles and permissions in IoT. The new architecture is a fully distributed access control system for IoT based on blockchain technology. The architecture is backed by a proof of concept implementation and evaluated in realistic IoT scenarios. The results show that the blockchain technology could be used as access management technology in specific scalable IoT scenarios."}
		86	+{"work_id": 2908737503, "title": "A Decentralized Privacy-Preserving Healthcare Blockchain for IoT", "abstract": "Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology-remote patient monitoring-is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient's life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network."}
		87	+{"work_id": 1823805418, "title": "TCP Extensions for Multipath Operation with Multiple Addresses", "abstract": "TCP/IP communication is currently restricted to a single path per connection, yet multiple paths often exist between peers. The simultaneous use of these multiple paths for a TCP/IP session would improve resource usage within the network and thus improve user experience through higher throughput and improved resilience to network failure. Multipath TCP provides the ability to simultaneously use multiple paths between peers. This document presents a set of extensions to traditional TCP to support multipath operation. The protocol offers the same type of service to applications as TCP (i.e., a reliable bytestream), and it provides the components necessary to establish and use multiple TCP flows across potentially disjoint paths. This document specifies v1 of Multipath TCP, obsoleting v0 as specified in RFC 6824, through clarifications and modifications primarily driven by deployment experience. This document is a product of the Multipath TCP Working Group of the IETF. This is now a Proposed Standard."}
		88	+{"work_id": 2803371618, "title": "On blockchain and its integration with IoT. Challenges and opportunities", "abstract": "In the Internet of Things (IoT) vision, conventional devices become smart and autonomous. This vision is turning into a reality thanks to advances in technology, but there are still challenges to address, particularly in the security domain e.g., data reliability. Taking into account the predicted evolution of the IoT in the coming years, it is necessary to provide confidence in this huge incoming information source. Blockchain has emerged as a key technology that will transform the way in which we share information. Building trust in distributed environments without the need for authorities is a technological advance that has the potential to change many industries, the IoT among them. Disruptive technologies such as big data and cloud computing have been leveraged by IoT to overcome its limitations since its conception, and we think blockchain will be one of the next ones. This paper focuses on this relationship, investigates challenges in blockchain IoT applications, and surveys the most relevant work in order to analyze how blockchain could potentially improve the IoT."}
100	{"work_id": 2940715617, "title": "Offloading Real-time DDoS Attack Detection to Programmable Data Planes", "abstract": "Nos \u00faltimos anos, ataques distribu\u00eddos de nega\u00e7\u00e3o de servi\u00e7o v\u00eam crescendo tanto em frequ\u00eancia quanto em volume de tr\u00e1fego com surtos atingindo taxas da ordem de terabits por segundo e compremetendo a disponibilidade de infraestruturas supostamente resilientes (e.g., DNS e hospedagem Web na nuvem). Na pr\u00e1tica, as solu\u00e7\u00f5es de detec\u00e7\u00e3o existentes valem-se de uma combina\u00e7\u00e3o de mecanismos, como amostragem de pacotes e transmiss\u00e3o dos dados coletados a um software externo, que dificulta a obten\u00e7\u00e3o de uma boa rela\u00e7ao entre acur\u00e1cia (maior \u00e9 melhor), consumo de recursos e lat\u00eancia (menor \u00e9 melhor). Planos de dados program\u00e1veis emergem como uma abordagem promissora para ajudar a cumprir esses requisitos, visto que dispositivos comutadores de pacotes podem ser configurados para executar algoritmos e examinar o tr\u00e1fego em velocidade de linha. Neste trabalho, exploramos primitivas em P4 a fim de projetar um mecanismo de inspe\u00e7\u00e3o de tr\u00e1fego com baixa granularidade, baixo consumo de recursos e baixa lat\u00eancia para a detec\u00e7\u00e3o de ataques distribu\u00eddos de nega\u00e7\u00e3o de servi\u00e7o em tempo real. A nossa proposta \u2013 a primeira a ser completamente implementada em plano de dados \u2013 contribui para lan\u00e7ar luz sobre os desafios da implementa\u00e7\u00e3o de l\u00f3gica de seguran\u00e7a sofisticada nesse contexto, dado que, para operar a altas taxas de transfer\u00eancia, a inspe\u00e7\u00e3o (e o processamento em geral) de pacotes est\u00e1 sujeita a um or\u00e7amento de tempo reduzido (dezenas de nanossegundos) e um espa\u00e7o de mem\u00f3ria limitado (da ordem de dezenas de megabytes). N\u00f3s avaliamos o mecanismo proposto usando capturas de pacotes da CAIDA. Os resultados mostram a detec\u00e7\u00e3o de ataques exclusivamente a partir do plano de dados com alta acur\u00e1cia (98,2%) e baixa lat\u00eancia ( 250 ms) mantendo o consumo de recursos reduzido (dezenas de kilobytes de SRAM por link de 1 Gbps e poucas centenas de entradas TCAM)."}	89	{"work_id": 2940715617, "title": "Offloading Real-time DDoS Attack Detection to Programmable Data Planes", "abstract": "Nos \u00faltimos anos, ataques distribu\u00eddos de nega\u00e7\u00e3o de servi\u00e7o v\u00eam crescendo tanto em frequ\u00eancia quanto em volume de tr\u00e1fego com surtos atingindo taxas da ordem de terabits por segundo e compremetendo a disponibilidade de infraestruturas supostamente resilientes (e.g., DNS e hospedagem Web na nuvem). Na pr\u00e1tica, as solu\u00e7\u00f5es de detec\u00e7\u00e3o existentes valem-se de uma combina\u00e7\u00e3o de mecanismos, como amostragem de pacotes e transmiss\u00e3o dos dados coletados a um software externo, que dificulta a obten\u00e7\u00e3o de uma boa rela\u00e7ao entre acur\u00e1cia (maior \u00e9 melhor), consumo de recursos e lat\u00eancia (menor \u00e9 melhor). Planos de dados program\u00e1veis emergem como uma abordagem promissora para ajudar a cumprir esses requisitos, visto que dispositivos comutadores de pacotes podem ser configurados para executar algoritmos e examinar o tr\u00e1fego em velocidade de linha. Neste trabalho, exploramos primitivas em P4 a fim de projetar um mecanismo de inspe\u00e7\u00e3o de tr\u00e1fego com baixa granularidade, baixo consumo de recursos e baixa lat\u00eancia para a detec\u00e7\u00e3o de ataques distribu\u00eddos de nega\u00e7\u00e3o de servi\u00e7o em tempo real. A nossa proposta \u2013 a primeira a ser completamente implementada em plano de dados \u2013 contribui para lan\u00e7ar luz sobre os desafios da implementa\u00e7\u00e3o de l\u00f3gica de seguran\u00e7a sofisticada nesse contexto, dado que, para operar a altas taxas de transfer\u00eancia, a inspe\u00e7\u00e3o (e o processamento em geral) de pacotes est\u00e1 sujeita a um or\u00e7amento de tempo reduzido (dezenas de nanossegundos) e um espa\u00e7o de mem\u00f3ria limitado (da ordem de dezenas de megabytes). N\u00f3s avaliamos o mecanismo proposto usando capturas de pacotes da CAIDA. Os resultados mostram a detec\u00e7\u00e3o de ataques exclusivamente a partir do plano de dados com alta acur\u00e1cia (98,2%) e baixa lat\u00eancia ( 250 ms) mantendo o consumo de recursos reduzido (dezenas de kilobytes de SRAM por link de 1 Gbps e poucas centenas de entradas TCAM)."}
101	{"work_id": 3023617420, "title": "A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact", "abstract": "The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak."}	90	{"work_id": 3023617420, "title": "A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact", "abstract": "The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak."}
102	{"work_id": 4255535209, "title": "Blockchain challenges and opportunities: a survey", "abstract": "Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology."}	91	{"work_id": 4255535209, "title": "Blockchain challenges and opportunities: a survey", "abstract": "Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology."}
				`@@ -105,48 +94,58 @@`
105	{"work_id": 2885100636, "title": "Blockchain and IoT Integration: A Systematic Survey", "abstract": "The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its \"security by design,\" Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy."}	94	{"work_id": 2885100636, "title": "Blockchain and IoT Integration: A Systematic Survey", "abstract": "The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its \"security by design,\" Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy."}
106	{"work_id": 4400531953, "title": "C-Pack: Packed Resources For General Chinese Embeddings", "abstract": "We introduce C-Pack, a package of resources that significantly advances the field of general text embeddings for Chinese. C-Pack includes three critical resources. 1) C-MTP is a massive training dataset for text embedding, which is based on the curation of vast unlabeled corpora and the integration of high-quality labeled corpora. 2) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets. 3) BGE is a family of embedding models covering multiple sizes. Our models outperform all prior Chinese text embeddings on C-MTEB by more than +10% upon the time of the release. We also integrate and optimize the entire suite of training methods for BGE. Along with our resources on general Chinese embedding, we release our data and models for English text embeddings. The English models also achieve state-of-the-art performance on the MTEB benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. Both Chinese and English datasets are the largest public release of training data for text embeddings. All these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding."}	95	{"work_id": 4400531953, "title": "C-Pack: Packed Resources For General Chinese Embeddings", "abstract": "We introduce C-Pack, a package of resources that significantly advances the field of general text embeddings for Chinese. C-Pack includes three critical resources. 1) C-MTP is a massive training dataset for text embedding, which is based on the curation of vast unlabeled corpora and the integration of high-quality labeled corpora. 2) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets. 3) BGE is a family of embedding models covering multiple sizes. Our models outperform all prior Chinese text embeddings on C-MTEB by more than +10% upon the time of the release. We also integrate and optimize the entire suite of training methods for BGE. Along with our resources on general Chinese embedding, we release our data and models for English text embeddings. The English models also achieve state-of-the-art performance on the MTEB benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. Both Chinese and English datasets are the largest public release of training data for text embeddings. All these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding."}
107	{"work_id": 1710433637, "title": "Stream Control Transmission Protocol", "abstract": "This document is an Internet-Draft and is in full conformance with all provisions of Section 10 of RFC 2026. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet-Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet- Drafts as reference material or to cite them other than as \u2018\u2018work in progress.\u2019\u2019 The list of current Internet-Drafts can be accessed at"}	96	{"work_id": 1710433637, "title": "Stream Control Transmission Protocol", "abstract": "This document is an Internet-Draft and is in full conformance with all provisions of Section 10 of RFC 2026. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet-Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet- Drafts as reference material or to cite them other than as \u2018\u2018work in progress.\u2019\u2019 The list of current Internet-Drafts can be accessed at"}
108	−{"work_id": 3032576892, "title": "Comparative study on Python web frameworks: Flask and Django", "abstract": "The purpose of the thesis was to study the various features, advantages, and the limitation of two web development frameworks for Python programming language. It aims to compare the usage of Django and Flask frameworks from a novice point of view. The theoretical part of the thesis presents the various types of programming languages and web technologies. In the practical part, however, the study is divided into two parts, each part observing the respective web application framework. \\n \\n \\nIn order to perform the comparison, a social network and eCommerce like application was built for Flask and Django respectively. The comparison was started by developing the social network application first with Flask and finished with the e-commerce application using Django. Python programing language, SQLite database for the backend and HTML, JavaScript, and Ajax were used for the frontend technology. At the end of the project, both applications were deployed to the cloud platform called Heroku. \\n \\nAfter the comparison, it was found that the most significant advantages of Flask were that it provides simplicity, flexibility, fine-grained control and quick and easy to learn. On the other hand, Django was easy to work with because of its extensive features and support for libraries. Another main advantage of Django is its scalability. It is best fit for a large-scale application. Each framework has its limitations and radiates a fair share of disadvantages. For example, Django is a bit cumbersome for smaller sized applications. However, Flask is too simple to not have the necessary features within the framework. \\n \\nAs a result of this study, it seems that both frameworks are capable of serving production-grade web applications while having a fair share of advantages and disadvantages."}	97	+{"work_id": 3010543330, "title": "Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider", "abstract": "Function as a Service (FaaS) has been gaining popularity as a way to deploy computations to serverless backends in the cloud. This paradigm shifts the complexity of allocating and provisioning resources to the cloud provider, which has to provide the illusion of always-available resources (i.e., fast function invocations without cold starts) at the lowest possible resource cost. Doing so requires the provider to deeply understand the characteristics of the FaaS workload. Unfortunately, there has been little to no public information on these characteristics. Thus, in this paper, we first characterize the entire production FaaS workload of Azure Functions. We show for example that most functions are invoked very infrequently, but there is an 8-order-of-magnitude range of invocation frequencies. Using observations from our characterization, we then propose a practical resource management policy that significantly reduces the number of function coldstarts,while spending fewerresources than state-of-the-practice policies."}
109	−{"work_id": 2784168960, "title": "Energy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacenters", "abstract": "Cataloged from PDF version of article."}	98	+{"work_id": 2122465391, "title": "MapReduce: Simplified Data Processing on Large Cluster", "abstract": "MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.\r\n\r\nPrograms written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.\r\n\r\nOur implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day."}
110	−{"work_id": 4385584867, "title": "A Study on Smart Energy Management Framework Using Cloud Computing", "abstract": "This study focuses on investigating energy-aware resources and designing an energy-efficient cloud architecture. It aims to optimize the decentralization model and establish migration behavior to improve IT business operations using cloud computing. Several models were evaluated for performance metrics such as physical host use, power utilization, and service level agreement (SLA) misuse. These models include smart cloud optimization for resource configuration handling (SCORCH), V-MAN, decentralized energy-aware collaborative model (DEACM), efficient energy-aware resource management model (EEARMM), and energy and power-aware dynamic migration (EPADM). To handle resource configuration efficiently, the study employed V-MAN, DEACM, EEARMM, and EPADM within the framework of smart cloud optimization. Among these models, EPADM proved to be the most effective for resource utilization through dynamic VM migration. By leveraging EPADM, the cloud system can dynamically migrate virtual machines (VMs) to optimize resource allocation and energy consumption."}	99	+{"work_id": 2962850638, "title": "Logiikka ja vaativuus hajautetussa laskennassa", "abstract": "This dissertation studies the theory of distributed computing. In the distributed setting, computation is carried out by multiple independent computational units. They communicate with neighbouring units and collectively solve a problem. Systems of this kind are widespread in the information society as well as in the natural world: prime examples include the Internet, multi-processor computer systems, the cells of a biological organism and human social networks. Therefore it is important to gain knowledge on the fundamental capabilities and limitations of distributed systems. Our work takes place in the context of deterministic and synchronous message-passing models of distributed computing. Such models abstract away some possible features of distributed systems, such as asynchrony, congestion and faults, while putting emphasis on the amount of communication needed between the units. The computational units are expected to solve a problem related to the structure of the underlying communication network. More specifically, we study the standard LOCAL model, the standard port-numbering model and several weaker variants of the latter. The contributions of this dissertation are two-fold. First, we give new methods for studying distributed computing by establishing a strong connection between several weak variants of the port-numbering model and corresponding variants of modal logic. This makes it possible to apply existing logical tools, such as bisimulation, to understand computation in the distributed setting. We also give a full characterisation of the relationships between the models of computing, which has implications on the side of mathematical logic. Second, we prove the existence of various new non-empty complexity classes. One of our results studies the relationship between time and space complexity, which is a relatively new topic in distributed computing research. We demonstrate the existence of a problem that can be solved in a constant amount of space in a very weak model but nonetheless requires a non-constant amount of time even in a much stronger model of computing. Another result continues the recent success on developing complexity theory for the LOCAL model. We introduce a new technique which shows the existence of an infinite hierarchy of problems with novel time complexities."}
111	−{"work_id": 2793346081, "title": "Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT", "abstract": "The Internet of Things (IoT) is stepping out of its infancy into full maturity and establishing itself as a part of the future Internet. One of the technical challenges of having billions of devices deployed worldwide is the ability to manage them. Although access management technologies exist in IoT, they are based on centralized models which introduce a new variety of technical limitations to manage them globally. In this paper, we propose a new architecture for arbitrating roles and permissions in IoT. The new architecture is a fully distributed access control system for IoT based on blockchain technology. The architecture is backed by a proof of concept implementation and evaluated in realistic IoT scenarios. The results show that the blockchain technology could be used as access management technology in specific scalable IoT scenarios."}	100	+{"work_id": 4320712473, "title": "The Internet of Things (IoT) in healthcare: Taking stock and moving forward", "abstract": "Recent improvements in the Internet of Things (IoT) have allowed healthcare to evolve rapidly. This article summarizes previous studies on IoT applications in healthcare. A comprehensive review and a bibliometric analysis were performed to objectively summarize the growth of IoT research in healthcare. To begin, 2,990 journal articles were carefully selected for further investigation. These publications were analyzed based on various bibliometric metrics, including publication year, journals, authors, institutions, and countries. Keyword co-occurrence and co-citation networks were generated to unravel significant research hotspots. The findings show that IoT research has received considerable interest from the healthcare community. Based on the results of the keyword co-occurrence network, IoT healthcare applications, blockchain applications, Artificial Intelligence (AI) techniques, 5G telecommunications, as well as data analytics and computing technologies emerged as important topics. The co-citation network analysis reveals other important themes, including authentication schemes, fog computing, cloud-IoT integration, and cognitive smart healthcare. Overall, the review offers scholars an improved understanding of the current status of IoT research in healthcare and identifies knowledge gaps for future research. This review also informs healthcare professionals about the latest developments and applications of IoT in the healthcare sector."}
112	−{"work_id": 2908737503, "title": "A Decentralized Privacy-Preserving Healthcare Blockchain for IoT", "abstract": "Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology-remote patient monitoring-is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient's life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network."}	101	+{"work_id": 3133849783, "title": "Contrastive Learning for Sequential Recommendation", "abstract": "Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical inter-actions. Despite their success, we argue that these approaches usually rely on the sequential prediction task to optimize the huge amounts of parameters. They usually suffer from the data sparsity problem, which makes it difficult for them to learn high-quality user representations. To tackle that, inspired by recent advances of contrastive learning techniques in the computer vision, we propose a novel multi-task framework called Contrastive Learning for Sequential Recommendation (CL4SRec). CL4SRec not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences. Therefore, it can extract more meaningful user patterns and further encode the user representations effectively. In addition, we propose three data augmentation approaches to construct self-supervision signals. Extensive experiments on four public datasets demonstrate that CL4SRec achieves state-of-the-art performance over existing baselines by inferring better user representations."}
113	−{"work_id": 1823805418, "title": "TCP Extensions for Multipath Operation with Multiple Addresses", "abstract": "TCP/IP communication is currently restricted to a single path per connection, yet multiple paths often exist between peers. The simultaneous use of these multiple paths for a TCP/IP session would improve resource usage within the network and thus improve user experience through higher throughput and improved resilience to network failure. Multipath TCP provides the ability to simultaneously use multiple paths between peers. This document presents a set of extensions to traditional TCP to support multipath operation. The protocol offers the same type of service to applications as TCP (i.e., a reliable bytestream), and it provides the components necessary to establish and use multiple TCP flows across potentially disjoint paths. This document specifies v1 of Multipath TCP, obsoleting v0 as specified in RFC 6824, through clarifications and modifications primarily driven by deployment experience. This document is a product of the Multipath TCP Working Group of the IETF. This is now a Proposed Standard."}	102	+{"work_id": 1973501242, "title": "Time, clocks, and the ordering of events in a distributed system", "abstract": "The concept of one event happening before another in a distributed system is examined, and is shown to define a partial ordering of the events. A distributed algorithm is given for synchronizing a system of logical clocks which can be used to totally order the events. The use of the total ordering is illustrated with a method for solving synchronization problems. The algorithm is then specialized for synchronizing physical clocks, and a bound is derived on how far out of synchrony the clocks can become."}
114	−{"work_id": 2803371618, "title": "On blockchain and its integration with IoT. Challenges and opportunities", "abstract": "In the Internet of Things (IoT) vision, conventional devices become smart and autonomous. This vision is turning into a reality thanks to advances in technology, but there are still challenges to address, particularly in the security domain e.g., data reliability. Taking into account the predicted evolution of the IoT in the coming years, it is necessary to provide confidence in this huge incoming information source. Blockchain has emerged as a key technology that will transform the way in which we share information. Building trust in distributed environments without the need for authorities is a technological advance that has the potential to change many industries, the IoT among them. Disruptive technologies such as big data and cloud computing have been leveraged by IoT to overcome its limitations since its conception, and we think blockchain will be one of the next ones. This paper focuses on this relationship, investigates challenges in blockchain IoT applications, and surveys the most relevant work in order to analyze how blockchain could potentially improve the IoT."}	103	+{"work_id": 2921608598, "title": "Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism", "abstract": "Industrial Internet of Things (IIoT) plays an indispensable role for Industry 4.0, where people are committed to implement a general, scalable, and secure IIoT system to be adopted across various industries. However, existing IIoT systems are vulnerable to single point of failure and malicious attacks, which cannot provide stable services. Due to the resilience and security promise of blockchain, the idea of combining blockchain and Internet of Things (IoT) gains considerable interest. However, blockchains are power-intensive and low-throughput, which are not suitable for power-constrained IoT devices. To tackle these challenges, we present a blockchain system with credit-based consensus mechanism for IIoT. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously. In order to protect sensitive data confidentiality, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on directed acyclic graph -structured blockchains, which is more efficient than the Satoshi-style blockchain in performance. We implement the system on Raspberry Pi, and conduct a case study for the smart factory. Extensive evaluation and analysis results demonstrate that credit-based PoW mechanism and data access control are secure and efficient in IIoT."}
115	−{"work_id": 4285039189, "title": "Mobile ad-hoc networks (MANET)", "abstract": "\u0388\u03bd\u03b1 \u039a\u03b9\u03bd\u03b7\u03c4\u03cc \u0391\u03b4\u03cc\u03bc\u03b7\u03c4\u03bf \u0394\u03af\u03ba\u03c4\u03c5\u03bf \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03ba\u03cc\u03bc\u03b2\u03bf\u03c5\u03c2 \u03c0\u03bf\u03c5 \u03ba\u03b9\u03bd\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9, \u03b5\u03af\u03c4\u03b5 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ce\u03bd\u03c4\u03b1\u03c2 \u03ba\u03ac\u03c0\u03bf\u03b9\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03ba\u03af\u03bd\u03b7\u03c3\u03b7\u03c2, \u03b5\u03af\u03c4\u03b5 \u03c4\u03c5\u03c7\u03b1\u03af\u03b1, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03ba\u03ac\u03c0\u03bf\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae 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\u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03c4\u03b1\u03b9."}	104	+{"work_id": 2762829140, "title": "Deep Learning based Intrusion Detection System for Internet of Things", "abstract": "With the increase in number of Internet connected devices, security and privacy concerns are the major obstacles impeding the widespread adoption of Internet of Things (IoT). Securing IoT has become a huge area of concern for all, including the consumers, organizations as well as the government. While attacks on any system cannot be fully prevented forever, real-time detection of the attacks are critical to defend the systems in an effective manner. Limited research exists on efficient intrusion detection systems suitable for IoT environment. In this thesis, we propose a novel intrusion detection system that uses machine learning algorithms to detect security anomalies in IoT networks. This detection platform provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We provide a framework of the proposed system and discuss the intrusion detection process in detail. The proposed intrusion detection system is evaluated using both, real network traces for providing a proof-of-concept, and on simulation for providing evidence of its scalability. Our results confirm that the proposed intrusion detection system is capable of detecting real-world intrusions effectively."}
116	−{"work_id": 7125196921, "title": "Energy, Scalability, Data, and Security in Massive IoT: Current Landscape and Future Directions", "abstract": "The Massive Internet of Things (MIoT) envisions an interconnected ecosystem of billions of devices, fundamentally transforming diverse sectors such as healthcare, smart cities, transportation, agriculture, and energy management. However, the vast scale of MIoT introduces significant challenges, including network scalability, efficient data management, energy conservation, and robust security mechanisms. This paper presents a thorough review of existing and emerging MIoT technologies designed to address these challenges, including Low-Power Wide-Area Networks (LPWAN), 5G/6G capabilities, edge and fog computing architectures, and hybrid access methodologies. We further investigate advanced strategies such as AI-driven resource allocation, federated learning for privacy-preserving analytics, and decentralized security frameworks using blockchain. Additionally, we analyze sustainable practices, emphasizing energy harvesting and integrating green technologies to reduce environmental impact. Through extensive comparative analysis, this study identifies critical innovations and architectural adaptations required to support efficient, resilient, and scalable MIoT deployments. Key insights include the role of network slicing and intelligent resource management for scalability, adaptive protocols for real-time data handling, and lightweight AI models suited to the constraints of MIoT devices. This research ultimately contributes to a deeper understanding of how MIoT systems can evolve to meet the growing demand for seamless, reliable connectivity while prioritizing sustainability, security, and performance across diverse applications. Our findings serve as a roadmap for future advancements, underscoring the potential of MIoT to support a globally interconnected, intelligent infrastructure."}	105	+{"work_id": 2973502831, "title": "Performance Analysis of Proof-of-Elapsed-Time (PoET) Consensus in the Sawtooth Blockchain Framework", "abstract": "Blockchains are distributed ledgers that use a tamper-sensitive, append-only data structure in conjunction with a consensus protocol to enable mutually distrusting parties to maintain a global set of states. A primary barrier to adoption of blockchain technology by industry is the current performance and scalability limitations of these systems, which lag far behind incumbent database systems. Of particular interest are \u201clottery-style\u201d consensus algorithms, which are relatively scalable to many participants but suffer from low throughput (performance). Proof-of-Elapsed-Time (PoET) is one such algorithm with great promise for use in industry, though the parameters that govern its performance have not been well studied. This thesis explores, through simulation, key performance outcomes in PoET blockchain networks implemented with the Hyperledger Sawtooth framework. A better quantitative understanding of the interactions among these system parameters will be crucial for efficiently optimizing real world blockchain networks and facilitating adoption by industry."}
117	−{"work_id": 2772457693, "title": "Deep Learning for IoT Big Data and Streaming Analytics: A Survey", "abstract": "In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature."}	106	+{"work_id": 4400494356, "title": "Digital health research governance: from FAIR to RE-AIM", "abstract": "This doctoral research aims to develop a governance model for digital health research data to enhance the quality of collected data and translate research outcomes into an integrated framework for planning, implementing, and evaluating public health initiatives. Employing a mixed-methods approach, the study combines qualitative and quantitative methods, qualifying as an exploratory and explanatory study. The specific objectives include proposing the adoption of frameworks and tools to promote a data quality model for scientific research in digital health, developing a tool with data collection, management, and evaluation, and proposing an implementation research model based on digital health principles and guidelines. The work is grounded in FAIR principles and the RE-AIM framework, addressing data quality and governance in health research, focusing on tuberculosis (TB) and rare diseases (RD) in the Brazilian context. The research significantly contributes to the understanding of digital data governance in health, highlighting the importance of standardized practices and collaborative efforts to improve the quality of health research data in Brazil. Challenges addressed include integrating diverse data sources, ensuring security and confidentiality of data, while enhancing public health outcomes through digital innovations. The presented model emphasizes data quality optimization, being fundamental for planning, executing, and evaluating health interventions. The research operates under robust computational research structures and health information systems, ensuring transparent data management. The interdisciplinary nature of the research highlights its significant strength, integrating insights from data science, public health, and health policy, offering a holistic view of the challenges and opportunities in digital health. By creating a unified implementation quality manual, the study provides mechanisms for network collaboration, strengthening robust data management, and promoting a culture of shared practices essential for elevating the overall quality of data in health research."}
118	−{"work_id": 7127083419, "title": "From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications", "abstract": "With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. To address these challenges, this tutorial provides a systematic and comprehensive introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers an integrated overview of cutting-edge methodologies and practical insights. First, the tutorial outlines the background of 6G communications and reviews the technological evolution from LAMs to Agentic AI. It then systematically examines the key components required for constructing LAMs, classifies various types of LAMs, and analyzes their applicability in communication. A LAM-centric design paradigm tailored for communication systems is subsequently proposed, encompassing dataset construction, internal learning, and external learning approaches. Building upon this foundation, the tutorial develops an LAM-based Agentic AI system for intelligent communications, elaborating on its core components\u2014including agents, world models, planners, knowledge bases, tools, and memory modules\u2014 as well as their interaction mechanisms. Finally, it provides an in-depth review of representative applications of LAMs and Agentic AI in communication scenarios, and summarizes the current research challenges and future directions, with the goal of fostering the development of efficient, secure, and sustainable next-generation intelligent communication systems."}	107	+{"work_id": 2103863878, "title": "Majority is not enough", "abstract": "The Bitcoin cryptocurrency records its transactions in a public log called the blockchain. Its security rests critically on the distributed protocol that maintains the blockchain, run by participants called miners. Conventional wisdom asserts that the mining protocol is incentive-compatible and secure against colluding minority groups, that is, it incentivizes miners to follow the protocol as prescribed. We show that the Bitcoin mining protocol is not incentive-compatible. We present an attack with which colluding miners' revenue is larger than their fair share. The attack can have significant consequences for Bitcoin: Rational miners will prefer to join the attackers, and the colluding group will increase in size until it becomes a majority. At this point, the Bitcoin system ceases to be a decentralized currency. Unless certain assumptions are made, selfish mining may be feasible for any coalition size of colluding miners. We propose a practical modification to the Bitcoin protocol that protects Bitcoin in the general case. It prohibits selfish mining by a coalition that command less than 1/4 of the resources. This threshold is lower than the wrongly assumed 1/2 bound, but better than the current reality where a coalition of any size can compromise the system."}
119	−{"work_id": 4386961654, "title": "Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey", "abstract": "Cutting-edge technologies, with a special emphasis on the Internet of Things (IoT), tend to operate as game changers, generating enormous alterations in both traditional and modern enterprises. Understanding multiple uses of IoT has become vital for effective financial management, given the ever-changing nature of organizations and the technological disruptions that come with this paradigm change. IoT has proven to be a powerful tool for improving operational efficiency, decision-making processes, overall productivity, and data management. As a result of the continuously expanding data volume, there is an increasing demand for a robust IT system capable of adeptly handling all enterprise processes. Consequently, businesses must develop suitable IoT architectures that can efficiently address these continually evolving requirements. This research adopts an incremental explanatory approach, guided by the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A rigorous examination of 84 research papers has allowed us to delve deeply into the current landscape of IoT research. This research aims to provide a complete and cohesive overview of the existing body of knowledge on IoT. This is accomplished by combining a rigorous empirical approach to categorization with ideas from specialized literature in the IoT sector. This study actively contributes to the ongoing conversation around IoT by recognizing and critically examining current difficulties. This, consequently, opens new research possibilities and promotes future developments in this ever-changing sector."}	108	+{"work_id": 2912978522, "title": "Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges", "abstract": "Blockchain, as the underlying technology of crypto-currencies, has attracted significant attention. It has been adopted in numerous applications, such as smart grid and Internet-of-Things. However, there is a significant scalability barrier for blockchain, which limits its ability to support services with frequent transactions. On the other side, edge computing is introduced to extend the cloud resources and services to be distributed at the edge of the network, but currently faces challenges in its decentralized management and security. The integration of blockchain and edge computing into one system can enable reliable access and control of the network, storage, and computation distributed at the edges, hence providing a large scale of network servers, data storage, and validity computation near the end in a secure manner. Despite the prospect of integrated blockchain and edge computing systems, its scalability enhancement, self organization, functions integration, resource management, and new security issues remain to be addressed before widespread deployment. In this survey, we investigate some of the work that has been done to enable the integrated blockchain and edge computing system and discuss the research challenges. We identify several vital aspects of the integration of blockchain and edge computing: motivations, frameworks, enabling functionalities, and challenges. Finally, some broader perspectives are explored."}
120	−{"work_id": 4416378800, "title": "Pinching-Antenna Systems (PASS): A Tutorial", "abstract": "Pinching-antenna systems (PASS) present a break-through among the flexible-antenna technologies, and distinguish themselves by facilitating large-scale antenna reconfiguration, line-of-sight creation, scalable implementation, and near-field benefits, thus bringing wireless communications from the \u201clast mile\u201d to the \u201clast meter\u201d. To illustrate the benefits of PASS in next-generation wireless networks, a comprehensive tutorial is presented in this paper. First, the fundamentals of PASS are discussed, including PASS signal models, hardware models, power radiation models, and pinching antenna (PA) activation methods. Building upon this, the information-theoretic capacity limits achieved by PASS are characterized, and several typical performance metrics of PASS-based communications are analyzed to demonstrate its superiority over conventional antenna technologies. Next, the pinching beamforming design is investigated. The corresponding power scaling law is first characterized for the single-waveguide single-user case. For the joint transmit and pinching design in the general multiple-waveguide case, 1) a pair of transmission strategies is proposed for PASS-based single-user communications to validate the superiority of PASS, namely sub-connected and fully connected structures in terms of the connections between the baseband radio frequency chains and waveguides; and 2) three practical protocols are proposed for facilitating PASS-based multi-user communications, namely waveguide switching, waveguide division, and waveguide multiplexing. A possible implementation of PASS in wideband communications is further highlighted. Moreover, the channel state information (CSI) acquisition in PASS is elaborated with a pair of promising solutions, based on pilot-based channel estimation and beam training, respectively. To overcome the high complexity and suboptimality inherent in conventional convex-optimization-based approaches, machine-learning-based methods for operating PASS are also explored, focusing on selected deep neural network architectures and training algorithms. Finally, several promising applications of PASS in next-generation wire-less networks are highlighted to motivate future works."}	109	+{"work_id": 3036601975, "title": "In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication", "abstract": "Broadcasting and aggregation dominate the communication overhead in distributed systems, from machine learning training to data analytics. Current acceleration approaches require specialized hardware (RDMA) or dedicated resources (DPDK), limiting their deployment in commodity clouds. However, we present a counter-intuitive alternative: rather than bypassing the kernel, we move operations into it using eBPF. While this imposes severe constraints including no floating-point, limited memory, and stateless execution, we show these restrictions paradoxically drive innovative protocol designs that yield unexpected benefits. We introduce AggBox, which implements broadcast and aggregation operations entirely within eBPF\u2019s constrained environment. Our key innovations include stateless group acknowledgments for reliability, edge quantization for floating-point aggregation using only integer arithmetic, and tail-call chains that create virtual memory beyond eBPF\u2019s 512-byte stack limit. These designs emerge from and exploit the constraints rather than fighting them. AggBox achieves remarkable performance on commodity hardware: 84.5% reduction in broadcast latency, 43\u00d7 speedup for MapReduce workloads, and 56.1% faster ML gradient aggregation, all without specialized NICs or dedicated cores. Beyond performance, our work demonstrates that constrained environments can drive fundamental innovation in protocol design, offering insights for future resource-limited and verified systems."}
		110	+{"work_id": 1150714886, "title": "OPNET simulation of voice over MPLS: With Considering Traffic Engineering", "abstract": "Multiprotocol Label Switching (MPLS) is an emerging technology which ensures the reliable delivery of the Internet services with high transmission speed and lower delays. The key feature of MPLS is its Traffic Engineering (TE), which is used for effectively managing the networks for efficient utilization of network resources. Due to lower network delay, efficient forwarding mechanism, scalability and predictable performance of the services provided by MPLS technology makes it more suitable for implementing real-time applications such as voice and video. In this thesis performance of Voice over Internet Protocol (VoIP) application is compared between MPLS network and conventional Internet Protocol (IP) network. OPNET modeler 14.5 is used to simulate the both networks and the comparison is made based on some performance metrics such as voice jitter, voice packet end-to-end delay, voice delay variation, voice packet sent and received. The simulation results are analyzed and it shows that MPLS based solution provides better performance in implementing the VoIP application. In this thesis, by using voice packet end-to-end delay performance metric an approach is made to estimate the minimum number of VoIP calls that can be maintained, in MPLS and conventional IP networks with acceptable quality. This approach can help the network operators or designers to determine the number of VoIP calls that can be maintained for a given network by imitating the real network on the OPNET simulator."}
		111	+{"work_id": 2602956630, "title": "Internet of Things Strategic Research Roadmap", "abstract": "Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual \u201cthings\u201d have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network."}
		112	+{"work_id": 2950865323, "title": "Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing", "abstract": "With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI."}
		113	+{"work_id": 4300092207, "title": "OrbitalBrain: A Distributed Framework for Training ML Models in Space", "abstract": "Earth observation nanosatellites capture high-resolution photos of the Earth in near real-time. These images increasingly support ML applications that are critical for safety and response, such as forest fire and flood detection. However, the downlink bandwidth is limited, resulting in days or weeks of delay from image capture to training. In this work, we propose OrbitalBrain, an efficient in-space distributed ML training framework that leverages limited and predictable satellite compute, bandwidth, and power to intelligently balance data transfer, model aggregation, and local training. Our evaluations demonstrate that OrbitalBrain achieves 1.52\u00d7-12.4\u00d7 speedup in time-to-accuracy while always reaching a higher final model accuracy compared to state-of-the-art ground-based or federated learning baselines. Furthermore, our approach is complementary to satellite imagery capturing and downloading, enhancing the overall efficiency of satellite-based applications."}
		114	+{"work_id": 4400118952, "title": "When large language models meet personalization: perspectives of challenges and opportunities", "abstract": "Abstract The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of large language models has been dramatically improved, leading to human-like performances in understanding, language synthesizing, common-sense reasoning, etc. Such a major leap forward in general AI capacity will fundamentally change the pattern of how personalization is conducted. For one thing, it will reform the way of interaction between humans and personalization systems. Instead of being a passive medium of information filtering, like conventional recommender systems and search engines, large language models present the foundation for active user engagement. On top of such a new foundation, users\u2019 requests can be proactively explored, and users\u2019 required information can be delivered in a natural, interactable, and explainable way. For another thing, it will also considerably expand the scope of personalization, making it grow from the sole function of collecting personalized information to the compound function of providing personalized services. By leveraging large language models as a general-purpose interface, the personalization systems may compile user\u2019s requests into plans, calls the functions of external tools (e.g., search engines, calculators, service APIs, etc.) to execute the plans, and integrate the tools\u2019 outputs to complete the end-to-end personalization tasks. Today, large language models are still being rapidly developed, whereas the application in personalization is largely unexplored. Therefore, we consider it to be right the time to review the challenges in personalization and the opportunities to address them with large language models. In particular, we dedicate this perspective paper to the discussion of the following aspects: the development and challenges for the existing personalization system, the newly emerged capabilities of large language models, and the potential ways of making use of large language models for personalization."}
		115	+{"work_id": 2593864460, "title": "Billion-Scale Similarity Search with GPUs", "abstract": "Similarity search finds application in database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data parallel tasks such as distance computation, prior approaches in this domain are bottlenecked by algorithms that expose less parallelism, such ask-min selection, or make poor use of the memory hierarchy. We propose a novel design fork-selection. We apply it in different similarity search scenarios, by optimizing brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation operates at up to 55 percent of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5 \u00d7 faster than prior GPU state of the art. It enables the construction of a high accuracyk-NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility."}
		116	+{"work_id": 4401806686, "title": "AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications", "abstract": "Artificial Intelligence (AI) technology's rapid advancement has significantly changed various industries' operations. This comprehensive review paper aims to provide readers with a deep understanding of AI's applications & implementations, workings, and potential impacts across different sectors. It also discusses its future, threats, and integration into new policy. Through extensive research on more than 200 research and many other sources, the authors have made every effort to present an accurate overview of the numerous applications of AI nowadays in industries such as agriculture, education, autonomous systems, healthcare, finance, entertainment, transportation, military, manufacturing, and more. The paper explores various AI technologies, including machine learning, deep learning, robotics, big data, the Internet of Things, natural language processing, image processing, object detection, virtual reality, augmented reality, speech recognition, and computer vision. It provides real-world examples of their applications and implementations. Moreover, it highlights and evaluates the future potential, challenges, and limitations associated with the widespread use of AI. Our study incorporates the latest research to offer a comprehensive and nuanced understanding of AI's potential benefits and challenges. This data-driven review case study highlights the immense potential of AI technology and addresses the ethical, societal, and economic considerations related to its implementation."}
121	{"work_id": 3182706339, "title": "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation", "abstract": "Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R -squared or R 2 ) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination ( R -squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R -squared as standard metric to evaluate regression analyses in any scientific domain."}	117	{"work_id": 3182706339, "title": "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation", "abstract": "Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R -squared or R 2 ) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination ( R -squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R -squared as standard metric to evaluate regression analyses in any scientific domain."}
122	−{"work_id": 7123349468, "title": "SMNet: A Novel Compositional Generalization Model for Industrial Robot Multijoint Fault Diagnosis", "abstract": "Compound fault diagnosis in multi-joint industrial robots is a critical yet underexplored problem in industrial internet of things, where the simultaneous degradation of multiple joints poses a severe challenge for reliable operation. Unlike conventional methods limited to single-fault scenarios, this paper addresses the compositional generalization challenge\u2014requiring models trained only on simple faults to accurately recognize unseen higher-order fault compositions. To this end, we propose StateMix Network (SMNet), a multi-stage architecture that preserves atomic joint-level representations before compositional diagnosis. Specifically, a Single-Joint Feature Extraction (SJFE) backbone extracts clean joint-private features, which are then fused by an Attention-Guided Dilated Fusion (AGDF) neck employing parallel Cascaded Dilated Convolution Blocks (CDCBs) bracketed by a dual-path attention mechanism for scale- and context-aware integration. Finally, a Mamba-based sequence mixer models long-range cross-joint dependencies to capture global fault dynamics. Extensive experiments on in-situ vibration data from a single six-joint industrial robot platform, under a strict train-on-simple/evaluate-on-complex protocol, demonstrate that SMNet consistently outperforms representative baselines in macro-Precision, Recall, and F1-score, particularly on unseen triple- and quadruple-joint compositions. Ablation and sensitivity analyses further validate the effectiveness of each module. This work presents a diagnostic approach that effectively generalizes from simple to complex fault scenarios in industrial robots."}
123	−{"work_id": 7129024920, "title": "TinyLSN: A Lightweight Network for Real-Time Marine Pipeline Leakage Detection in IoT Systems", "abstract": "Intelligent acoustic emission-based pipeline leak detection technology plays a critical role in Internet of Things structural health monitoring for offshore platforms. However, traditional deep networks possess large parameter counts and high computational complexity, making them infeasible for deployment on resource-constrained edge nodes, while lightweight methods universally adopt single-scale feature extraction and cannot simultaneously capture short-duration burst and long-range attenuation characteristics of acoustic emission signals, resulting in insufficient discriminative capability for adjacent valves. To address this, this paper proposes Tiny Leak Sense Net (TinyLSN), a novel lightweight leak localization framework specifically designed for Internet of Things edge nodes. TinyLSN achieves optimal balance between computational efficiency and detection performance through three innovative components we designed including the Inverted Residual Block (IRB), Multi-Scale Dilated Perception Module (MSDPM), and Large Kernel Feed-Forward Network (LK-FFN), which respectively enhance cross-channel interactions, capture multi-scale temporal features, and extract global attenuation patterns. On our self-constructed experimental dataset simulating real offshore platform operational pipeline leakage, TinyLSN achieved detection accuracy of 97.11% to 97.45% and an extremely low false positive rate of 0.27% to 0.32%, significantly outperforming lightweight baseline methods. Validation on publicly available benchmark datasets further confirmed its generalization capability. When deployed on the STM32H7B3I-DK microcontroller, TinyLSN requires only 267.16 KiB Flash memory and achieves 3.417 ms inference latency. Furthermore, TinyLSN maintains over 90% accuracy under strong noise and achieves 94.83% accuracy with only 10% training samples, fully validating its reliability in harsh industrial environments and providing an efficient and feasible solution for offshore platform Internet of Things edge intelligence."}
124	{"work_id": 3129486236, "title": "Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial", "abstract": "Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work."}	118	{"work_id": 3129486236, "title": "Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial", "abstract": "Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work."}
125	{"work_id": 4292259483, "title": "The IEEE International Conference on Communications 2022 (ICC'22)", "abstract": "The IEEE International Conference on Communications 2022 (ICC 2022) was held in Seoul, Korea, from 16\u201320 May 2022. Headquartered at Seoul COEX, the largest exhibition and convention venue in the Republic of Korea, ICC 2022 was in hybrid format with virtual (on-line) sessions as well as on-site sessions for in-person participants. ICC 2022 put together cutting-edge research results and technical contributions from academics, industry practitioners, experts and leaders in the area of communications and networking from all over the world. The technical program included physical sessions with on-site presentations and virtual (on-line) sessions for remote participants."}	119	{"work_id": 4292259483, "title": "The IEEE International Conference on Communications 2022 (ICC'22)", "abstract": "The IEEE International Conference on Communications 2022 (ICC 2022) was held in Seoul, Korea, from 16\u201320 May 2022. Headquartered at Seoul COEX, the largest exhibition and convention venue in the Republic of Korea, ICC 2022 was in hybrid format with virtual (on-line) sessions as well as on-site sessions for in-person participants. ICC 2022 put together cutting-edge research results and technical contributions from academics, industry practitioners, experts and leaders in the area of communications and networking from all over the world. The technical program included physical sessions with on-site presentations and virtual (on-line) sessions for remote participants."}
126	{"work_id": 3194620979, "title": "Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond", "abstract": "As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks."}	120	{"work_id": 3194620979, "title": "Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond", "abstract": "As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks."}
		121	+{"work_id": 2918245750, "title": "Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions", "abstract": null}
		122	+{"work_id": 2126034719, "title": "Classification and Regression Trees", "abstract": null}
		123	+{"work_id": 2995202934, "title": "Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms", "abstract": null}
		124	+{"work_id": 4391188276, "title": "Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service", "abstract": null}
127	{"work_id": 2974110037, "title": "Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges", "abstract": null}	125	{"work_id": 2974110037, "title": "Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges", "abstract": null}
		126	+{"work_id": 4319779432, "title": "NoSQL: Future of BigData Analytics Characteristics and Comparison with RDBMS", "abstract": null}
128	{"work_id": 7123363719, "title": "ADP-based dynamic event-triggered fault-tolerant control for nonlinear systems with actuator failures and full-state time-varying constraints", "abstract": null}	127	{"work_id": 7123363719, "title": "ADP-based dynamic event-triggered fault-tolerant control for nonlinear systems with actuator failures and full-state time-varying constraints", "abstract": null}
		128	+{"work_id": 4220966162, "title": "AI for next generation computing: Emerging trends and future directions", "abstract": null}
129	{"work_id": 3011330593, "title": "A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments", "abstract": null}	129	{"work_id": 3011330593, "title": "A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments", "abstract": null}
130	{"work_id": 4206510620, "title": "The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications", "abstract": null}	130	{"work_id": 4206510620, "title": "The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications", "abstract": null}
131	{"work_id": 4386450904, "title": "Linked Data - The Story So Far", "abstract": null}	131	{"work_id": 4386450904, "title": "Linked Data - The Story So Far", "abstract": null}
132	{"work_id": 3202749896, "title": "Data quality of platforms and panels for online behavioral research", "abstract": null}	132	{"work_id": 3202749896, "title": "Data quality of platforms and panels for online behavioral research", "abstract": null}
133	{"work_id": 4401834466, "title": "A survey on large language models for recommendation", "abstract": null}	133	{"work_id": 4401834466, "title": "A survey on large language models for recommendation", "abstract": null}
134	{"work_id": 7118998287, "title": "Fixed-time optimal bipartite containment fault-tolerant control for multi-agent systems under multiple faults and saturated actuation", "abstract": null}	134	{"work_id": 7118998287, "title": "Fixed-time optimal bipartite containment fault-tolerant control for multi-agent systems under multiple faults and saturated actuation", "abstract": null}
		135	+{"work_id": 3123888896, "title": "Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends", "abstract": null}
		136	+{"work_id": 3008629209, "title": "A survey on security challenges in cloud computing: issues, threats, and solutions", "abstract": null}
135	{"work_id": 2793823461, "title": "Endurable Transient Inconsistency in Byte-Addressable Persistent B+-Tree", "abstract": null}	137	{"work_id": 2793823461, "title": "Endurable Transient Inconsistency in Byte-Addressable Persistent B+-Tree", "abstract": null}
136	{"work_id": 3204616723, "title": "Data Mining Concepts and Techniques Third Edition", "abstract": null}	138	{"work_id": 3204616723, "title": "Data Mining Concepts and Techniques Third Edition", "abstract": null}
137	{"work_id": 4381304672, "title": "Suspending OpenMP Tasks on Asynchronous Events: Extending the Taskwait Construct", "abstract": null}	139	{"work_id": 4381304672, "title": "Suspending OpenMP Tasks on Asynchronous Events: Extending the Taskwait Construct", "abstract": null}
138	{"work_id": 3017623177, "title": "Industry 4.0 technologies and their applications in fighting COVID-19 pandemic", "abstract": null}	140	{"work_id": 3017623177, "title": "Industry 4.0 technologies and their applications in fighting COVID-19 pandemic", "abstract": null}
139	{"work_id": 7134136722, "title": "Hybrid Al-Biruni and Puma Optimization (BERPO) for boosting the classification of Quality-of-Service (QoS) in 5G networks", "abstract": null}	141	{"work_id": 7134136722, "title": "Hybrid Al-Biruni and Puma Optimization (BERPO) for boosting the classification of Quality-of-Service (QoS) in 5G networks", "abstract": null}
140	−{"work_id": 7125115033, "title": "Integrated Sensing and Communications Over the Years: An Evolution Perspective", "abstract": null}
141	{"work_id": 2789491284, "title": "A hybrid model of Internet of Things and cloud computing to manage big data in health services applications", "abstract": null}	142	{"work_id": 2789491284, "title": "A hybrid model of Internet of Things and cloud computing to manage big data in health services applications", "abstract": null}
142	−{"work_id": 7164884893, "title": "Research on Informer Long Sequence Dependency Modeling Method for Short-Term Prediction of Request Volume of Cloud-Native Backend Services", "abstract": null}
143	{"work_id": 2983008824, "title": "A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT", "abstract": null}	143	{"work_id": 2983008824, "title": "A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT", "abstract": null}
144	−{"work_id": 7118165812, "title": "Leveraging hierarchy-aware diffusion model and knowledge-enhanced contrastive learning for recommendation", "abstract": null}
145	{"work_id": 1502629466, "title": "Principles of Distributed Database Systems", "abstract": null}	144	{"work_id": 1502629466, "title": "Principles of Distributed Database Systems", "abstract": null}
146	{"work_id": 3023398824, "title": "Internet of things (IoT) applications to fight against COVID-19 pandemic", "abstract": null}	145	{"work_id": 3023398824, "title": "Internet of things (IoT) applications to fight against COVID-19 pandemic", "abstract": null}
147	−{"work_id": 172918197, "title": "Computational Collective Intelligence", "abstract": null}	146	+{"work_id": 3102359135, "title": "NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems", "abstract": null}
148	−{"work_id": 4386975643, "title": "Cloud-based non-destructive characterization", "abstract": null}
149	{"work_id": 2946627595, "title": "A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems", "abstract": null}	147	{"work_id": 2946627595, "title": "A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems", "abstract": null}
150	{"work_id": 7124594522, "title": "Energy-efficient 5G-enabled sensor networks for smart environmental monitoring in sustainable cities", "abstract": null}	148	{"work_id": 7124594522, "title": "Energy-efficient 5G-enabled sensor networks for smart environmental monitoring in sustainable cities", "abstract": null}
		149	+{"work_id": 4306827119, "title": "Internet of Things intrusion detection systems: a comprehensive review and future directions", "abstract": null}
151	{"work_id": 3006456063, "title": "Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0", "abstract": null}	150	{"work_id": 3006456063, "title": "Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0", "abstract": null}
152	{"work_id": 2274895364, "title": "The Semantic Web: A New Form of Web Content that is Meaningful to Computers will Unleash a Revolution of New Possibilities", "abstract": null}	151	{"work_id": 2274895364, "title": "The Semantic Web: A New Form of Web Content that is Meaningful to Computers will Unleash a Revolution of New Possibilities", "abstract": null}

modified literature_synthesis/landmarks/data_distributed_landmarks.csv +152 −153

				`@@ -1,153 +1,152 @@`
1	−work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review	1	+work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,ntitle,score,set,pi_review
2	−2793823461,,,Endurable Transient Inconsistency in Byte-Addressable Persistent B+-Tree,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2018,dissertation,database_systems,3,17.0,42.0,377.7581573896353,89,,False,False,baseline,377.7581573896353,landmark,True	2	+2793823461,,,Endurable Transient Inconsistency in Byte-Addressable Persistent B+-Tree,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2018,dissertation,database_systems,3,17.0,42.0,377.7581573896353,89,,False,False,baseline,endurabletransientinconsistencyinbyteaddressablepersistentbtree,377.7581573896353,landmark,True
3	−3080729669,,,Pengembangan Sistem Pemetaan Otomatis EntityRelationship Diagram Ke Dalam Database,,2019,dissertation,database_systems,2,2.0,15.0,320.60747663551405,62,,True,False,baseline,320.60747663551405,landmark,True	3	+3080729669,,,Pengembangan Sistem Pemetaan Otomatis EntityRelationship Diagram Ke Dalam Database,,2019,dissertation,database_systems,2,2.0,15.0,320.60747663551405,62,,True,False,baseline,pengembangansistempemetaanotomatisentityrelationshipdiagramkedalamdatabase,320.60747663551405,landmark,True
4	−3204616723,,,Data Mining Concepts and Techniques Third Edition,Thammasat University Digital Collections,2021,article,database_systems,1,886.0,928.0,245.73952451819886,1176,66.8714,False,False,baseline,245.73952451819886,landmark,True	4	+3204616723,,,Data Mining Concepts and Techniques Third Edition,Thammasat University Digital Collections,2021,article,database_systems,1,886.0,928.0,245.73952451819886,1176,66.8714,False,False,baseline,dataminingconceptsandtechniquesthirdedition,245.73952451819886,landmark,True
5	−4415124086,10.1109/tbdata.2025.3618474,,The Faiss Library,IEEE Transactions on Big Data,2025,article,database_systems,4,454.0,454.0,148.4520005233547,454,166.1231,True,True,post,229.8415201133268,landmark,True	5	+3014294420,10.1017/9781108635592.003,,Introduction to Data Mining,Cambridge University Press eBooks,2019,book-chapter,database_systems,2,163.0,235.0,228.82157331393535,439,233.5555,True,False,baseline,introductiontodatamining,228.82157331393535,landmark,True
6	−3014294420,10.1017/9781108635592.003,,Introduction to Data Mining,Cambridge University Press eBooks,2019,book-chapter,database_systems,2,163.0,235.0,228.82157331393535,439,233.5555,True,False,baseline,228.82157331393535,landmark,True	6	+4324129606,10.1371/journal.pone.0279720,,"Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA",PLoS ONE,2023,article,database_systems,1,403.0,973.0,228.63551401869157,1500,345.1684,True,True,post,dataqualityinonlinehumansubjectsresearchcomparisonsbetweenmturkprolificcloudresearchqualtricsandsona,228.63551401869157,landmark,True
7	−4324129606,10.1371/journal.pone.0279720,,"Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA",PLoS ONE,2023,article,database_systems,1,403.0,973.0,228.63551401869157,1500,345.1684,True,True,post,228.63551401869157,landmark,True	7	+2963507231,10.1109/tkde.2017.2781227,,Untangling Blockchain: A Data Processing View of Blockchain Systems,IEEE Transactions on Knowledge and Data Engineering,2018,article,database_systems,5,201.0,405.0,214.72982183235243,1186,244.2498,True,True,baseline,untanglingblockchainadataprocessingviewofblockchainsystems,214.72982183235243,landmark,True
8	−7161586974,10.5070/p9.62178,,Paleobiology Database User Guide Version 2.0,PaleoBios,2026,article,database_systems,1,61.0,61.0,433.3770927968851,61,109.834,True,True,post,216.86845533876678,landmark,True	8	+3032576892,,,Comparative study on Python web frameworks: Flask and Django,Theseus (Ammattikorkeakoulujen),2020,dissertation,database_systems,4,7.0,22.0,206.90719257540604,43,,True,True,baseline,comparativestudyonpythonwebframeworksflaskanddjango,206.90719257540604,landmark,True
9	−2963507231,10.1109/tkde.2017.2781227,,Untangling Blockchain: A Data Processing View of Blockchain Systems,IEEE Transactions on Knowledge and Data Engineering,2018,article,database_systems,5,201.0,405.0,214.72982183235243,1186,244.2498,True,True,baseline,214.72982183235243,landmark,True	9	+3148181069,10.38094/jastt20165,,Classification Based on Decision Tree Algorithm for Machine Learning,Journal of Applied Science and Technology Trends,2021,article,database_systems,4,257.0,704.0,186.42308756553018,1871,279.1938,True,True,baseline,classificationbasedondecisiontreealgorithmformachinelearning,186.42308756553018,landmark,True
10	−2795038878,10.1145/3458723,1803.09010,Datasheets for datasets,Communications of the ACM,2021,article,database_systems,1,481.0,794.0,210.25558455544171,2104,205.3426,True,True,baseline,210.25558455544171,landmark,True	10	+3037795158,10.1016/s2589-7500(20)30142-4,,Applications of digital technology in COVID-19 pandemic planning and response,The Lancet Digital Health,2020,article,database_systems,1,350.0,591.0,172.09443665792568,987,179.9177,True,True,baseline,applicationsofdigitaltechnologyincovid19pandemicplanningandresponse,172.09443665792568,landmark,True
11	−3032576892,,,Comparative study on Python web frameworks: Flask and Django,Theseus (Ammattikorkeakoulujen),2020,dissertation,database_systems,4,7.0,22.0,206.90719257540604,43,,True,True,baseline,206.90719257540604,landmark,True	11	+4386450904,10.1145/3591366.3591378,,Linked Data - The Story So Far,ACM eBooks,2023,book-chapter,database_systems,2,285.0,301.0,166.41438539989264,319,42.7861,False,False,post,linkeddatathestorysofar,166.41438539989264,landmark,True
12	−3148181069,10.38094/jastt20165,,Classification Based on Decision Tree Algorithm for Machine Learning,Journal of Applied Science and Technology Trends,2021,article,database_systems,4,257.0,704.0,186.42308756553018,1871,279.1938,True,True,baseline,186.42308756553018,landmark,True	12	+7161586974,10.5070/p9.62178,,Paleobiology Database User Guide Version 2.0,PaleoBios,2026,article,database_systems,1,61.0,61.0,433.3770927968851,61,109.834,True,True,post,paleobiologydatabaseuserguideversion20,154.66369514364453,landmark,True
13	−3037795158,10.1016/s2589-7500(20)30142-4,,Applications of digital technology in COVID-19 pandemic planning and response,The Lancet Digital Health,2020,article,database_systems,1,350.0,591.0,172.09443665792568,987,179.9177,True,True,baseline,172.09443665792568,landmark,True	13	+4406085118,10.1145/3711118,2303.10158,Data-centric Artificial Intelligence: A Survey,ACM Computing Surveys,2025,article,database_systems,3,334.0,334.0,113.77687020232747,334,110.6889,True,True,post,datacentricartificialintelligenceasurvey,145.94850359802805,landmark,True
14	−4406085118,10.1145/3711118,2303.10158,Data-centric Artificial Intelligence: A Survey,ACM Computing Surveys,2025,article,database_systems,3,334.0,334.0,113.77687020232747,334,110.6889,True,True,post,169.09045752830647,landmark,True	14	+4408444355,10.1016/j.is.2025.102549,2207.14529,The effects of data quality on machine learning performance on tabular data,Information Systems,2025,article,database_systems,3,322.0,322.0,80.50468595378078,322,192.6654,True,True,post,theeffectsofdataqualityonmachinelearningperformanceontabulardata,140.70484478612286,landmark,True
15	−4386450904,10.1145/3591366.3591378,,Linked Data - The Story So Far,ACM eBooks,2023,book-chapter,database_systems,2,285.0,301.0,166.41438539989264,319,42.7861,False,False,post,166.41438539989264,landmark,True	15	+3202749896,10.3758/s13428-021-01694-3,,Data quality of platforms and panels for online behavioral research,Behavior Research Methods,2021,article,database_systems,1,164.0,469.0,121.87616674723884,1687,188.3384,False,True,baseline,dataqualityofplatformsandpanelsforonlinebehavioralresearch,121.87616674723884,landmark,True
16	−4408444355,10.1016/j.is.2025.102549,2207.14529,The effects of data quality on machine learning performance on tabular data,Information Systems,2025,article,database_systems,3,322.0,322.0,80.50468595378078,322,192.6654,True,True,post,163.01535126980446,landmark,True	16	+4400494356,10.11606/t.82.2024.tde-05072024-092611,,Digital health research governance: from FAIR to RE-AIM,,2024,dissertation,database_systems,1,1.0,3.0,116.82222222222222,3,,True,True,post,digitalhealthresearchgovernancefromfairtoreaim,116.82222222222222,landmark,True
17	−4361200200,10.1201/9780429459016,,Spatial Data Science,Chapman & Hall/CRC eBooks,2023,book,database_systems,2,278.0,753.0,129.30007763975155,1250,222.9593,True,False,post,129.30007763975155,landmark,True	17	+2593864460,10.1109/tbdata.2019.2921572,1702.08734,Billion-Scale Similarity Search with GPUs,IEEE Transactions on Big Data,2019,article,database_systems,6,384.0,765.0,115.95149891238218,3832,118.3163,True,True,pre,billionscalesimilaritysearchwithgpus,115.95149891238218,landmark,True
18	−3202749896,10.3758/s13428-021-01694-3,,Data quality of platforms and panels for online behavioral research,Behavior Research Methods,2021,article,database_systems,1,164.0,469.0,121.87616674723884,1687,188.3384,False,True,baseline,121.87616674723884,landmark,True	18	+4362632858,10.21941/kcss/2023/2,,Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices,,2023,dissertation,database_systems,4,2.0,4.0,115.45945945945945,4,,True,True,post,scalabilitybenchmarkingofcloudnativeapplicationsappliedtoeventdrivenmicroservices,115.45945945945945,landmark,True
19	−4400494356,10.11606/t.82.2024.tde-05072024-092611,,Digital health research governance: from FAIR to RE-AIM,,2024,dissertation,database_systems,1,1.0,3.0,116.82222222222222,3,,True,True,post,116.82222222222222,landmark,True	19	+3005599391,,,"Entangled Devices : An ethnographic study of students, mobile phones and capitalism",Työväentutkimus Vuosikirja,2020,dissertation,database_systems,1,11.0,12.0,112.8584686774942,15,,True,True,baseline,entangleddevicesanethnographicstudyofstudentsmobilephonesandcapitalism,112.8584686774942,landmark,True
20	−2593864460,10.1109/tbdata.2019.2921572,1702.08734,Billion-Scale Similarity Search with GPUs,IEEE Transactions on Big Data,2019,article,database_systems,6,384.0,765.0,115.95149891238218,3832,118.3163,True,True,pre,115.95149891238218,landmark,True	20	+4402762189,10.3390/electronics13183762,,Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis,Electronics,2024,article,database_systems,2,199.0,317.0,95.44078925847174,317,121.3104,True,True,post,leveragingaiinelearningpersonalizedlearningandadaptiveassessmentthroughcognitiveneuropsychologyasystematicanalysis,95.44078925847174,landmark,True
21	−4362632858,10.21941/kcss/2023/2,,Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices,,2023,dissertation,database_systems,4,2.0,4.0,115.45945945945945,4,,True,True,post,115.45945945945945,landmark,True	21	+4380791906,10.31979/etd.qrrp-3equ,,NoSQL Databases in Kubernetes,,2023,dissertation,database_systems,4,3.0,3.0,86.5945945945946,3,,True,True,post,nosqldatabasesinkubernetes,86.5945945945946,landmark,True
22	−3005599391,,,"Entangled Devices : An ethnographic study of students, mobile phones and capitalism",Työväentutkimus Vuosikirja,2020,dissertation,database_systems,1,11.0,12.0,112.8584686774942,15,,True,True,baseline,112.8584686774942,landmark,True	22	+4401495975,10.1186/s40537-024-00973-y,,Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications,Journal Of Big Data,2024,article,database_systems,6,279.0,520.0,83.4394876991742,520,96.1656,True,True,post,enhancingknearestneighboralgorithmacomprehensivereviewandperformanceanalysisofmodifications,83.4394876991742,landmark,True
23	−4396219753,10.4018/979-8-3693-2909-2.ch002,,Machine Learning Mastery,"Advances in systems analysis, software engineering, and high performance computing book series",2024,book-chapter,database_systems,2,195.0,195.0,108.23837846298326,195,27.8969,True,False,post,108.23837846298326,landmark,True	23	+4408925021,10.3390/higheredu4020017,,The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review,Trends in Higher Education,2025,review,database_systems,2,189.0,189.0,29.153309284818068,189,31.7339,True,True,post,theimpactofartificialintelligenceonpersonalizedlearninginhighereducationasystematicreview,82.5876262875069,landmark,True
24	−4408925021,10.3390/higheredu4020017,,The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review,Trends in Higher Education,2025,review,database_systems,2,189.0,189.0,29.153309284818068,189,31.7339,True,True,post,95.68292357140696,landmark,True	24	+2126034719,10.1142/9789811201967_0002,,Classification and Regression Trees,Series in machine perception and artificial intelligence,2019,book-chapter,database_systems,1,73.0,81.0,78.87041463161177,173,28.4262,False,False,baseline,classificationandregressiontrees,78.87041463161177,landmark,True
25	−4402762189,10.3390/electronics13183762,,Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis,Electronics,2024,article,database_systems,2,199.0,317.0,95.44078925847174,317,121.3104,True,True,post,95.44078925847174,landmark,True	25	+4319779432,10.1007/978-3-031-12382-5_106,,NoSQL: Future of BigData Analytics Characteristics and Comparison with RDBMS,Studies in computational intelligence,2023,book-chapter,database_systems,2,62.0,86.0,78.4514106583072,87,75.6244,False,True,post,nosqlfutureofbigdataanalyticscharacteristicsandcomparisonwithrdbms,78.4514106583072,landmark,True
26	−4380791906,10.31979/etd.qrrp-3equ,,NoSQL Databases in Kubernetes,,2023,dissertation,database_systems,4,3.0,3.0,86.5945945945946,3,,True,True,post,86.5945945945946,landmark,True	26	+4229641819,10.1017/9781108684163,,Mining of Massive Datasets,Cambridge University Press eBooks,2020,book,database_systems,4,152.0,228.0,77.43724928366763,424,76.3217,True,False,baseline,miningofmassivedatasets,77.43724928366763,landmark,True
27	−7118085507,10.1007/s41019-025-00335-5,2402.19473,Retrieval-Augmented Generation for AI-Generated Content: A Survey,Data Science and Engineering,2026,article,information_retrieval,7,208.0,208.0,231.4627186325106,208,390.1628,True,True,post,739.4858804994016,landmark,True	27	+7118085507,10.1007/s41019-025-00335-5,2402.19473,Retrieval-Augmented Generation for AI-Generated Content: A Survey,Data Science and Engineering,2026,article,information_retrieval,7,208.0,208.0,231.4627186325106,208,390.1628,True,True,post,retrievalaugmentedgenerationforaigeneratedcontentasurvey,527.3778457357059,landmark,True
28	−2274895364,10.1145/3591366.3591376,,The Semantic Web: A New Form of Web Content that is Meaningful to Computers will Unleash a Revolution of New Possibilities,ACM eBooks,2023,book-chapter,information_retrieval,2,578.0,606.0,335.0402576489533,1324,58.9755,False,False,post,335.0402576489533,landmark,True	28	+2274895364,10.1145/3591366.3591376,,The Semantic Web: A New Form of Web Content that is Meaningful to Computers will Unleash a Revolution of New Possibilities,ACM eBooks,2023,book-chapter,information_retrieval,2,578.0,606.0,335.0402576489533,1324,58.9755,False,False,post,thesemanticwebanewformofwebcontentthatismeaningfultocomputerswillunleasharevolutionofnewpossibilities,335.0402576489533,landmark,True
29	−4394947112,10.1145/3805774,2404.10981,A Survey on Retrieval-Augmented Text Generation for Large Language Models,ACM Computing Surveys,2026,article,information_retrieval,5,70.0,70.0,77.8961072320949,70,184.2436,True,True,post,248.8654405526832,landmark,True	29	+4401857375,10.1145/3637528.3671470,2405.06211,A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models,ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),2024,conference-paper,information_retrieval,8,401.0,795.0,220.97973411345592,795,241.8493,True,True,post,asurveyonragmeetingllmstowardsretrievalaugmentedlargelanguagemodels,220.97973411345592,landmark,True
30	−3045200674,10.1145/3397271.3401063,,LightGCN,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2020,conference-paper,information_retrieval,2,321.0,803.0,226.7421667675404,4430,311.5512,True,False,baseline,226.7421667675404,landmark,True	30	+2967467,10.26190/unsworks/23395,,Social networks and collaborative filtering for large-scale requirements elicitation,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,information_retrieval,2,22.0,23.0,199.5062656641604,34,,True,True,baseline,socialnetworksandcollaborativefilteringforlargescalerequirementselicitation,199.5062656641604,landmark,True
31	−4401857375,10.1145/3637528.3671470,2405.06211,A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models,ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),2024,conference-paper,information_retrieval,8,401.0,795.0,220.97973411345592,795,241.8493,True,True,post,220.97973411345592,landmark,True	31	+4394947112,10.1145/3805774,2404.10981,A Survey on Retrieval-Augmented Text Generation for Large Language Models,ACM Computing Surveys,2026,article,information_retrieval,5,70.0,70.0,77.8961072320949,70,184.2436,True,True,post,asurveyonretrievalaugmentedtextgenerationforlargelanguagemodels,177.48292885336258,landmark,True
32	−2967467,10.26190/unsworks/23395,,Social networks and collaborative filtering for large-scale requirements elicitation,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,information_retrieval,2,22.0,23.0,199.5062656641604,34,,True,True,baseline,199.5062656641604,landmark,True	32	+3097300053,10.1145/3535101,2011.02260,Graph Neural Networks in Recommender Systems: A Survey,ACM Computing Surveys,2022,article,information_retrieval,4,336.0,711.0,174.743146538718,1326,268.2685,True,True,baseline,graphneuralnetworksinrecommendersystemsasurvey,174.743146538718,landmark,True
33	−3097300053,10.1145/3535101,2011.02260,Graph Neural Networks in Recommender Systems: A Survey,ACM Computing Surveys,2022,article,information_retrieval,4,336.0,711.0,174.743146538718,1326,268.2685,True,True,baseline,174.743146538718,landmark,True	33	+4288083766,10.1145/3285029,1707.07435,Deep Learning Based Recommender System,ACM Computing Surveys,2019,article,information_retrieval,3,224.0,396.0,161.64067295931318,1511,176.8236,True,False,baseline,deeplearningbasedrecommendersystem,161.64067295931318,landmark,True
34	−4288083766,10.1145/3285029,1707.07435,Deep Learning Based Recommender System,ACM Computing Surveys,2019,article,information_retrieval,3,224.0,396.0,161.64067295931318,1511,176.8236,True,False,baseline,161.64067295931318,landmark,True	34	+4395483946,10.1186/s41239-024-00453-6,,Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives,International Journal of Educational Technology in Higher Education,2024,article,information_retrieval,2,263.0,529.0,159.26869879410583,529,202.4391,True,True,post,generativeaiandthefutureofhighereducationathreattoacademicintegrityorreformationevidencefrommulticulturalperspectives,159.26869879410583,landmark,True
35	−4395483946,10.1186/s41239-024-00453-6,,Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives,International Journal of Educational Technology in Higher Education,2024,article,information_retrieval,2,263.0,529.0,159.26869879410583,529,202.4391,True,True,post,159.26869879410583,landmark,True	35	+4394867512,10.57784/1992/7835,,Privacy-enabled scalable recommender systems,HAL (Le Centre pour la Communication Scientifique Directe),2024,dissertation,information_retrieval,3,4.0,4.0,155.76296296296297,4,,True,True,post,privacyenabledscalablerecommendersystems,155.76296296296297,landmark,True
36	−4394867512,10.57784/1992/7835,,Privacy-enabled scalable recommender systems,HAL (Le Centre pour la Communication Scientifique Directe),2024,dissertation,information_retrieval,3,4.0,4.0,155.76296296296297,4,,True,True,post,155.76296296296297,landmark,True	36	+2408538552,10.1609/aaai.v26i1.8100,,Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks,Proceedings of the AAAI Conference on Artificial Intelligence,2021,conference-paper,information_retrieval,1,519.0,541.0,151.80700662176693,572,22.962,True,True,baseline,fusedmatrixfactorizationwithgeographicalandsocialinfluenceinlocationbasedsocialnetworks,151.80700662176693,landmark,True
37	−2408538552,10.1609/aaai.v26i1.8100,,Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks,Proceedings of the AAAI Conference on Artificial Intelligence,2021,conference-paper,information_retrieval,1,519.0,541.0,151.80700662176693,572,22.962,True,True,baseline,151.80700662176693,landmark,True	37	+4401834466,10.1007/s11280-024-01291-2,2305.19860,A survey on large language models for recommendation,World Wide Web,2024,article,information_retrieval,3,272.0,487.0,146.62354690497077,487,184.0703,False,True,post,asurveyonlargelanguagemodelsforrecommendation,146.62354690497077,landmark,True
38	−4401834466,10.1007/s11280-024-01291-2,2305.19860,A survey on large language models for recommendation,World Wide Web,2024,article,information_retrieval,3,272.0,487.0,146.62354690497077,487,184.0703,False,True,post,146.62354690497077,landmark,True	38	+4315977496,10.1145/3568022,2109.12843,"A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions",ACM Transactions on Recommender Systems,2023,article,information_retrieval,3,326.0,612.0,143.4379748166452,756,176.9462,True,True,baseline,asurveyofgraphneuralnetworksforrecommendersystemschallengesmethodsanddirections,143.4379748166452,landmark,True
39	−4315977496,10.1145/3568022,2109.12843,"A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions",ACM Transactions on Recommender Systems,2023,article,information_retrieval,3,326.0,612.0,143.4379748166452,756,176.9462,True,True,baseline,143.4379748166452,landmark,True	39	+4394994587,10.1109/tkde.2024.3392335,2307.02046,Recommender Systems in the Era of Large Language Models (LLMs),IEEE Transactions on Knowledge and Data Engineering,2024,article,information_retrieval,5,277.0,469.0,141.20419609534147,469,176.4167,True,True,post,recommendersystemsintheeraoflargelanguagemodelsllms,141.20419609534147,landmark,True
40	−4394994587,10.1109/tkde.2024.3392335,2307.02046,Recommender Systems in the Era of Large Language Models (LLMs),IEEE Transactions on Knowledge and Data Engineering,2024,article,information_retrieval,5,277.0,469.0,141.20419609534147,469,176.4167,True,True,post,141.20419609534147,landmark,True	40	+4400531953,10.1145/3626772.3657878,2309.07597,C-Pack: Packed Resources For General Chinese Embeddings,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2024,conference-paper,information_retrieval,6,256.0,485.0,134.81153590569323,485,146.3264,True,True,post,cpackpackedresourcesforgeneralchineseembeddings,134.81153590569323,landmark,True
41	−4400531953,10.1145/3626772.3657878,2309.07597,C-Pack: Packed Resources For General Chinese Embeddings,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2024,conference-paper,information_retrieval,6,256.0,485.0,134.81153590569323,485,146.3264,True,True,post,134.81153590569323,landmark,True	41	+2945827670,10.1145/3331184.3331267,1905.08108,Neural Graph Collaborative Filtering,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2019,conference-paper,information_retrieval,3,149.0,465.0,133.92825599565188,3237,200.9814,True,True,baseline,neuralgraphcollaborativefiltering,133.92825599565188,landmark,True
42	−2945827670,10.1145/3331184.3331267,1905.08108,Neural Graph Collaborative Filtering,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2019,conference-paper,information_retrieval,3,149.0,465.0,133.92825599565188,3237,200.9814,True,True,baseline,133.92825599565188,landmark,True	42	+4387316367,10.1134/s1995080223060471,,Ontological Approach to a Knowledge Graph Construction in a Semantic Library,Lobachevskii Journal of Mathematics,2023,dissertation,information_retrieval,4,10.0,16.0,133.0924024640657,18,,True,False,post,ontologicalapproachtoaknowledgegraphconstructioninasemanticlibrary,133.0924024640657,landmark,True
43	−2945623882,10.1145/3292500.3330989,1905.07854,KGAT,ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD),2019,conference-paper,information_retrieval,5,157.0,465.0,133.92825599565188,2147,160.7851,True,True,baseline,133.92825599565188,landmark,True	43	+2914721378,10.1145/3308558.3313488,1902.07243,Graph Neural Networks for Social Recommendation,,2019,conference-paper,information_retrieval,2,141.0,441.0,127.01582987974727,1984,153.1188,True,True,baseline,graphneuralnetworksforsocialrecommendation,127.01582987974727,landmark,True
44	−4387316367,10.1134/s1995080223060471,,Ontological Approach to a Knowledge Graph Construction in a Semantic Library,Lobachevskii Journal of Mathematics,2023,dissertation,information_retrieval,4,10.0,16.0,133.0924024640657,18,,True,False,post,133.0924024640657,landmark,True	44	+2963707260,10.1109/tkde.2018.2833443,,Heterogeneous Information Network Embedding for Recommendation,IEEE Transactions on Knowledge and Data Engineering,2018,article,information_retrieval,5,72.0,234.0,124.06611928091473,1204,188.0081,True,True,baseline,heterogeneousinformationnetworkembeddingforrecommendation,124.06611928091473,landmark,True
45	−7118165812,10.1007/s10115-025-02639-4,,Leveraging hierarchy-aware diffusion model and knowledge-enhanced contrastive learning for recommendation,Knowledge and Information Systems,2026,article,information_retrieval,2,36.0,36.0,255.7635301752109,36,329.5019,False,False,post,127.98794085566564,landmark,True	45	+3094605801,10.1145/3404835.3462862,2010.10783,Self-supervised Graph Learning for Recommendation,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2021,conference-paper,information_retrieval,3,141.0,434.0,121.78233063557643,1488,179.1038,True,True,baseline,selfsupervisedgraphlearningforrecommendation,121.78233063557643,landmark,True
46	−2914721378,10.1145/3308558.3313488,1902.07243,Graph Neural Networks for Social Recommendation,,2019,conference-paper,information_retrieval,2,141.0,441.0,127.01582987974727,1984,153.1188,True,True,baseline,127.01582987974727,landmark,True	46	+3092103025,10.1145/3564284,2010.03240,Bias and Debias in Recommender System: A Survey and Future Directions,ACM Transactions on Information Systems,2022,article,information_retrieval,3,255.0,475.0,116.74120197734325,733,146.66,True,True,baseline,biasanddebiasinrecommendersystemasurveyandfuturedirections,116.74120197734325,landmark,True
47	−2963707260,10.1109/tkde.2018.2833443,,Heterogeneous Information Network Embedding for Recommendation,IEEE Transactions on Knowledge and Data Engineering,2018,article,information_retrieval,5,72.0,234.0,124.06611928091473,1204,188.0081,True,True,baseline,124.06611928091473,landmark,True	47	+4323539384,10.5463/thesis.16,,Scientific dataset recommendation with semantic techniques,,2023,dissertation,information_retrieval,3,4.0,4.0,115.45945945945945,4,,True,True,post,scientificdatasetrecommendationwithsemantictechniques,115.45945945945945,landmark,True
48	−3094605801,10.1145/3404835.3462862,2010.10783,Self-supervised Graph Learning for Recommendation,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2021,conference-paper,information_retrieval,3,141.0,434.0,121.78233063557643,1488,179.1038,True,True,baseline,121.78233063557643,landmark,True	48	+2604314403,10.1007/978-3-319-93417-4_38,,Modeling Relational Data with Graph Convolutional Networks,Lecture notes in computer science,2018,conference-paper,information_retrieval,4,278.0,743.0,115.04989507468214,5096,152.5097,True,True,baseline,modelingrelationaldatawithgraphconvolutionalnetworks,115.04989507468214,landmark,True
49	−172918197,10.1007/978-3-030-63007-2,,Computational Collective Intelligence,Lecture notes in computer science,2020,conference-paper,information_retrieval,1,425.0,427.0,120.57148843056008,592,3.7704,False,False,baseline,120.57148843056008,landmark,True	49	+3133849783,10.1109/icde53745.2022.00099,2010.14395,Contrastive Learning for Sequential Recommendation,Proceedings - International Conference on Data Engineering,2022,conference-paper,information_retrieval,4,188.0,375.0,112.68765439918921,663,121.9524,True,True,baseline,contrastivelearningforsequentialrecommendation,112.68765439918921,landmark,True
50	−3092103025,10.1145/3564284,2010.03240,Bias and Debias in Recommender System: A Survey and Future Directions,ACM Transactions on Information Systems,2022,article,information_retrieval,3,255.0,475.0,116.74120197734325,733,146.66,True,True,baseline,116.74120197734325,landmark,True	50	+2995202934,,,Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms,,2019,dissertation,information_retrieval,3,2.0,11.0,110.24841915085818,1,,False,False,baseline,enhancingvaesforcollaborativefilteringflexiblepriorsgatingmechanisms,110.24841915085818,landmark,True
51	−4323539384,10.5463/thesis.16,,Scientific dataset recommendation with semantic techniques,,2023,dissertation,information_retrieval,3,4.0,4.0,115.45945945945945,4,,True,True,post,115.45945945945945,landmark,True	51	+4400118952,10.1007/s11280-024-01276-1,2307.16376,When large language models meet personalization: perspectives of challenges and opportunities,World Wide Web,2024,article,information_retrieval,4,240.0,358.0,107.78486610262739,358,135.4696,True,True,post,whenlargelanguagemodelsmeetpersonalizationperspectivesofchallengesandopportunities,107.78486610262739,landmark,True
52	−4381304672,10.1007/978-3-031-40744-4_5,,Suspending OpenMP Tasks on Asynchronous Events: Extending the Taskwait Construct,Lecture notes in computer science,2023,conference-paper,distributed_computing,3,12262.0,12891.0,2048.4305614736636,12935,942.1133,False,True,post,2048.4305614736636,landmark,True	52	+4381304672,10.1007/978-3-031-40744-4_5,,Suspending OpenMP Tasks on Asynchronous Events: Extending the Taskwait Construct,Lecture notes in computer science,2023,conference-paper,distributed_computing,3,12262.0,12891.0,2048.4305614736636,12935,942.1133,False,True,post,suspendingopenmptasksonasynchronouseventsextendingthetaskwaitconstruct,2048.4305614736636,landmark,True
53	−2122465391,10.21276/ijre.2018.5.5.4,,MapReduce: Simplified Data Processing on Large Cluster,INTERNATIONAL JOURNAL OF RESEARCH AND ENGINEERING,2018,article,distributed_computing,4,2098.0,2186.0,1159.010840803759,2948,215.3255,True,False,baseline,1159.010840803759,landmark,True	53	+2122465391,10.21276/ijre.2018.5.5.4,,MapReduce: Simplified Data Processing on Large Cluster,INTERNATIONAL JOURNAL OF RESEARCH AND ENGINEERING,2018,article,distributed_computing,4,2098.0,2186.0,1159.010840803759,2948,215.3255,True,False,baseline,mapreducesimplifieddataprocessingonlargecluster,1159.010840803759,landmark,True
54	−4255535209,10.1504/ijwgs.2018.10016848,,Blockchain challenges and opportunities: a survey,International Journal of Web and Grid Services,2018,article,distributed_computing,4,357.0,804.0,426.27846111904034,1582,497.7393,True,False,baseline,426.27846111904034,landmark,True	54	+4255535209,10.1504/ijwgs.2018.10016848,,Blockchain challenges and opportunities: a survey,International Journal of Web and Grid Services,2018,article,distributed_computing,4,357.0,804.0,426.27846111904034,1582,497.7393,True,False,baseline,blockchainchallengesandopportunitiesasurvey,426.27846111904034,landmark,True
55	−1973501242,10.1145/3335772.3335934,,"Time, clocks, and the ordering of events in a distributed system",ACM eBooks,2019,book-chapter,distributed_computing,2,386.0,424.0,331.9512682244857,1979,73.0762,True,False,baseline,331.9512682244857,landmark,True	55	+1973501242,10.1145/3335772.3335934,,"Time, clocks, and the ordering of events in a distributed system",ACM eBooks,2019,book-chapter,distributed_computing,2,386.0,424.0,331.9512682244857,1979,73.0762,True,False,baseline,timeclocksandtheorderingofeventsinadistributedsystem,331.9512682244857,landmark,True
56	−2897450989,10.1504/ijwgs.2018.095647,,Blockchain challenges and opportunities: a survey,International Journal of Web and Grid Services,2018,article,distributed_computing,4,251.0,625.0,331.37318184005005,3328,432.6596,True,False,baseline,331.37318184005005,landmark,True	56	+2120510885,10.1145/3335772.3335936,,The Byzantine generals problem,ACM eBooks,2019,book-chapter,distributed_computing,3,230.0,335.0,262.2728180547234,1033,180.7332,True,False,baseline,thebyzantinegeneralsproblem,262.2728180547234,landmark,True
57	−2120510885,10.1145/3335772.3335936,,The Byzantine generals problem,ACM eBooks,2019,book-chapter,distributed_computing,3,230.0,335.0,262.2728180547234,1033,180.7332,True,False,baseline,262.2728180547234,landmark,True	57	+2902511784,10.5555/3291656.3291738,,Dynamic data race detection for OpenMP programs,Rice Research Repository (Rice University),2018,dissertation,distributed_computing,5,1.0,12.0,219.90243902439025,20,,True,False,baseline,dynamicdataracedetectionforopenmpprograms,219.90243902439025,landmark,True
58	−2902511784,10.5555/3291656.3291738,,Dynamic data race detection for OpenMP programs,Rice Research Repository (Rice University),2018,dissertation,distributed_computing,5,1.0,12.0,219.90243902439025,20,,True,False,baseline,219.90243902439025,landmark,True	58	+3182706339,10.7717/peerj-cs.623,,"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation",PeerJ Computer Science,2021,article,distributed_computing,1,409.0,1160.0,189.8515007898894,5310,437.5495,True,True,baseline,thecoefficientofdeterminationrsquaredismoreinformativethansmapemaemapemseandrmseinregressionanalysisevaluation,189.8515007898894,landmark,True
59	−4300092207,10.4230/oasics.nines.2026.5,,OrbitalBrain: A Distributed Framework for Training ML Models in Space,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,distributed_computing,4,54.0,54.0,70.47771428571428,54,0.0,True,True,post,191.98191128349848,landmark,True	59	+4387321091,10.1145/3600006.3613165,2309.06180,Efficient Memory Management for Large Language Model Serving with PagedAttention,Symposium on Operating Systems Principles,2023,conference-paper,distributed_computing,9,173.0,787.0,181.49662514489972,1716,324.3817,True,True,post,efficientmemorymanagementforlargelanguagemodelservingwithpagedattention,181.49662514489972,landmark,True
60	−3182706339,10.7717/peerj-cs.623,,"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation",PeerJ Computer Science,2021,article,distributed_computing,1,409.0,1160.0,189.8515007898894,5310,437.5495,True,True,baseline,189.8515007898894,landmark,True	60	+2103863878,10.1145/3212998,,Majority is not enough,Communications of the ACM,2018,article,distributed_computing,4,170.0,327.0,173.37444873871416,742,161.0924,True,False,baseline,majorityisnotenough,173.37444873871416,landmark,True
61	−4387321091,10.1145/3600006.3613165,2309.06180,Efficient Memory Management for Large Language Model Serving with PagedAttention,Symposium on Operating Systems Principles,2023,conference-paper,distributed_computing,9,173.0,787.0,181.49662514489972,1716,324.3817,True,True,post,181.49662514489972,landmark,True	61	+1502629466,10.1007/978-3-030-26253-2,,Principles of Distributed Database Systems,Springer eBooks,2019,book,distributed_computing,3,789.0,848.0,166.39169139465875,2345,55.809,False,False,baseline,principlesofdistributeddatabasesystems,166.39169139465875,landmark,True
62	−2103863878,10.1145/3212998,,Majority is not enough,Communications of the ACM,2018,article,distributed_computing,4,170.0,327.0,173.37444873871416,742,161.0924,True,False,baseline,173.37444873871416,landmark,True	62	+3036601975,10.4230/oasics.nines.2026.13,2006.11477,In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication,arXiv (Cornell University),2020,preprint,distributed_computing,10,200.0,699.0,157.87121576165305,2368,,True,True,baseline,inkernelaggregationandbroadcastaccelerationfordistributedcommunication,157.87121576165305,landmark,True
63	−7130586201,10.1109/tsmc.2026.3661716,,Dynamic Event-Triggered Control for Human–Machine Cooperative Systems Based on Dynamic Authority Allocation,IEEE Transactions on Systems Man and Cybernetics Systems,2026,article,distributed_computing,1,47.0,47.0,132.70961019857808,47,167.5021,True,False,post,167.09536722823015,landmark,True	63	+4393370257,10.57784/1992/7669,,Distributed methods for resource allocation - a passivity-based approach,HAL (Le Centre pour la Communication Scientifique Directe),2024,dissertation,distributed_computing,2,6.0,6.0,151.2173913043478,6,,True,True,post,distributedmethodsforresourceallocationapassivitybasedapproach,151.2173913043478,landmark,True
64	−1502629466,10.1007/978-3-030-26253-2,,Principles of Distributed Database Systems,Springer eBooks,2019,book,distributed_computing,3,789.0,848.0,166.39169139465875,2345,55.809,False,False,baseline,166.39169139465875,landmark,True	64	+2973502831,,,Performance Analysis of Proof-of-Elapsed-Time (PoET) Consensus in the Sawtooth Blockchain Framework,Scholars' Bank (University of Oregon),2019,dissertation,distributed_computing,5,3.0,14.0,140.31616982836496,28,,True,False,baseline,performanceanalysisofproofofelapsedtimepoetconsensusinthesawtoothblockchainframework,140.31616982836496,landmark,True
65	−3036601975,10.4230/oasics.nines.2026.13,2006.11477,In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication,arXiv (Cornell University),2020,preprint,distributed_computing,10,200.0,699.0,157.87121576165305,2368,,True,True,baseline,157.87121576165305,landmark,True	65	+4300092207,10.4230/oasics.nines.2026.5,,OrbitalBrain: A Distributed Framework for Training ML Models in Space,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,distributed_computing,4,54.0,54.0,70.47771428571428,54,0.0,True,True,post,orbitalbrainadistributedframeworkfortrainingmlmodelsinspace,136.9154022583083,landmark,True
66	−2251066406,10.7916/tv64-xc19,,Bypassing Races in Live Applications with Execution Filters,Operating Systems Design and Implementation,2026,article,distributed_computing,4,44.0,44.0,638.0,53,0.0,True,True,post,156.42970549025802,landmark,True	66	+2962850638,,,Logiikka ja vaativuus hajautetussa laskennassa,Aaltodoc (Aalto University),2019,dissertation,distributed_computing,4,14.0,22.0,123.65517241379311,38,,True,True,baseline,logiikkajavaativuushajautetussalaskennassa,123.65517241379311,landmark,True
67	−3098508067,10.82308/18894,,Personal volunteer computing,,2020,dissertation,distributed_computing,4,17.0,18.0,154.15037593984962,25,,True,True,baseline,154.15037593984962,landmark,True	67	+7130586201,10.1109/tsmc.2026.3661716,,Dynamic Event-Triggered Control for Human–Machine Cooperative Systems Based on Dynamic Authority Allocation,IEEE Transactions on Systems Man and Cybernetics Systems,2026,article,distributed_computing,1,47.0,47.0,132.70961019857808,47,167.5021,True,False,post,dynamiceventtriggeredcontrolforhumanmachinecooperativesystemsbasedondynamicauthorityallocation,119.16710937297202,landmark,True
68	−4393370257,10.57784/1992/7669,,Distributed methods for resource allocation - a passivity-based approach,HAL (Le Centre pour la Communication Scientifique Directe),2024,dissertation,distributed_computing,2,6.0,6.0,151.2173913043478,6,,True,True,post,151.2173913043478,landmark,True	68	+3012404397,10.5626/ktcp.2020.26.3.135,1801.10228,Santa Claus Algorithm: Agent-Based Consensus Preprocessing Method for Scalability Improvement in Private Blockchain Environment,정보과학회 컴퓨팅의 실제 논문지,2020,article,distributed_computing,3,328.0,400.0,116.47677607981433,1,0.2325,True,True,baseline,santaclausalgorithmagentbasedconsensuspreprocessingmethodforscalabilityimprovementinprivateblockchainenvironment,116.47677607981433,landmark,True
69	−2973502831,,,Performance Analysis of Proof-of-Elapsed-Time (PoET) Consensus in the Sawtooth Blockchain Framework,Scholars' Bank (University of Oregon),2019,dissertation,distributed_computing,5,3.0,14.0,140.31616982836496,28,,True,False,baseline,140.31616982836496,landmark,True	69	+2251066406,10.7916/tv64-xc19,,Bypassing Races in Live Applications with Execution Filters,Operating Systems Design and Implementation,2026,article,distributed_computing,4,44.0,44.0,638.0,53,0.0,True,True,post,bypassingracesinliveapplicationswithexecutionfilters,111.56069813639934,landmark,True
70	−1572499759,10.56726/irjmets66464,,Microservices Architecture,International Research Journal of Modernization in Engineering Technology and Science,2025,article,distributed_computing,4,245.0,245.0,93.19077007930923,245,0.8816,True,True,post,124.03341944441644,landmark,True	70	+2970155754,,,Learning Generalizable Device Placement Algorithms for Distributed Machine Learning,DSpace@MIT (Massachusetts Institute of Technology),2019,dissertation,distributed_computing,5,5.0,11.0,110.24841915085818,22,,True,False,baseline,learninggeneralizabledeviceplacementalgorithmsfordistributedmachinelearning,110.24841915085818,landmark,True
71	−2962850638,,,Logiikka ja vaativuus hajautetussa laskennassa,Aaltodoc (Aalto University),2019,dissertation,distributed_computing,4,14.0,22.0,123.65517241379311,38,,True,True,baseline,123.65517241379311,landmark,True	71	+4220966162,10.1016/j.iot.2022.100514,2203.04159,AI for next generation computing: Emerging trends and future directions,Internet of Things,2022,article,distributed_computing,5,237.0,423.0,103.9611124977183,686,150.3082,False,True,baseline,aifornextgenerationcomputingemergingtrendsandfuturedirections,103.9611124977183,landmark,True
72	−7123363719,10.1016/j.cnsns.2026.109676,,ADP-based dynamic event-triggered fault-tolerant control for nonlinear systems with actuator failures and full-state time-varying constraints,Communications in Nonlinear Science and Numerical Simulation,2026,article,distributed_computing,1,33.0,33.0,93.17908801176759,33,117.6079,False,False,post,117.32227911769351,landmark,True	72	+2926987861,10.1109/comst.2020.2969706,1904.04098,A Survey of Distributed Consensus Protocols for Blockchain Networks,IEEE Communications Surveys & Tutorials,2020,article,distributed_computing,6,177.0,302.0,87.93996594025982,841,109.9497,True,True,baseline,asurveyofdistributedconsensusprotocolsforblockchainnetworks,87.93996594025982,landmark,True
73	−3012404397,10.5626/ktcp.2020.26.3.135,1801.10228,Santa Claus Algorithm: Agent-Based Consensus Preprocessing Method for Scalability Improvement in Private Blockchain Environment,정보과학회 컴퓨팅의 실제 논문지,2020,article,distributed_computing,3,328.0,400.0,116.47677607981433,1,0.2325,True,True,baseline,116.47677607981433,landmark,True	73	+2794533297,10.1109/sp.2018.000-5,,"OmniLedger: A Secure, Scale-Out, Decentralized Ledger via Sharding",Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy,2018,conference-paper,distributed_computing,5,135.0,287.0,85.09604562737643,1253,92.4823,True,True,baseline,omniledgerasecurescaleoutdecentralizedledgerviasharding,85.09604562737643,landmark,True
74	−7118998287,10.1016/j.matcom.2026.01.001,,Fixed-time optimal bipartite containment fault-tolerant control for multi-agent systems under multiple faults and saturated actuation,Mathematics and Computers in Simulation,2026,article,distributed_computing,2,32.0,32.0,112.34215540969723,32,134.0959,False,False,post,113.76705853836947,landmark,True	74	+7123363719,10.1016/j.cnsns.2026.109676,,ADP-based dynamic event-triggered fault-tolerant control for nonlinear systems with actuator failures and full-state time-varying constraints,Communications in Nonlinear Science and Numerical Simulation,2026,article,distributed_computing,1,33.0,33.0,93.17908801176759,33,117.6079,False,False,post,adpbaseddynamiceventtriggeredfaulttolerantcontrolfornonlinearsystemswithactuatorfailuresandfullstatetimevaryingconstraints,83.6705236022995,landmark,True
75	−4386585104,10.1145/3569951.3597559,,ACCESS: Advancing Innovation,Practice and Experience in Advanced Research Computing,2023,conference-paper,distributed_computing,2,121.0,297.0,111.86639277215944,435,137.5418,True,True,post,111.86639277215944,landmark,True	75	+7118998287,10.1016/j.matcom.2026.01.001,,Fixed-time optimal bipartite containment fault-tolerant control for multi-agent systems under multiple faults and saturated actuation,Mathematics and Computers in Simulation,2026,article,distributed_computing,2,32.0,32.0,112.34215540969723,32,134.0959,False,False,post,fixedtimeoptimalbipartitecontainmentfaulttolerantcontrolformultiagentsystemsundermultiplefaultsandsaturatedactuation,81.13505319010861,landmark,True
76	−2970155754,,,Learning Generalizable Device Placement Algorithms for Distributed Machine Learning,DSpace@MIT (Massachusetts Institute of Technology),2019,dissertation,distributed_computing,5,5.0,11.0,110.24841915085818,22,,True,False,baseline,110.24841915085818,landmark,True	76	+4391188276,10.1016/j.artmed.2024.102779,,Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service,Artificial Intelligence in Medicine,2024,article,distributed_computing,1,196.0,252.0,75.87091133481034,252,96.436,False,False,post,opportunitiesandchallengesofartificialintelligenceanddistributedsystemstoimprovethequalityofhealthcareservice,75.87091133481034,landmark,True
77	−110250037,10.7275/3278307,,The Use of Multimedia Material in Teaching Chinese as a Second Language and Pedagogical Implications,ScholarWorks@UMassAmherst (University of Massachusetts Amherst),2023,dissertation,cloud_computing,1,7.0,7.0,202.05405405405403,7,,True,True,post,202.05405405405403,landmark,True	77	+110250037,10.7275/3278307,,The Use of Multimedia Material in Teaching Chinese as a Second Language and Pedagogical Implications,ScholarWorks@UMassAmherst (University of Massachusetts Amherst),2023,dissertation,cloud_computing,1,7.0,7.0,202.05405405405403,7,,True,True,post,theuseofmultimediamaterialinteachingchineseasasecondlanguageandpedagogicalimplications,202.05405405405403,landmark,True
78	−4392909747,10.59646/ccp/156,,Cloud Computing: Principles And Paradigms,,2024,book,cloud_computing,2,504.0,504.0,155.20504731861197,517,14.8571,True,False,post,155.20504731861197,landmark,True	78	+4392909747,10.59646/ccp/156,,Cloud Computing: Principles And Paradigms,,2024,book,cloud_computing,2,504.0,504.0,155.20504731861197,517,14.8571,True,False,post,cloudcomputingprinciplesandparadigms,155.20504731861197,landmark,True
79	−4386975643,10.1016/b978-0-323-91150-4.00006-9,,Cloud-based non-destructive characterization,Elsevier eBooks,2023,book-chapter,cloud_computing,2,19.0,35.0,141.43274853801168,37,154.3068,False,False,post,141.43274853801168,landmark,True	79	+2784822088,10.2312/2632114,,A microservice architecture for the processing of large geospatial data in the Cloud,Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft),2018,dissertation,cloud_computing,3,3.0,8.0,81.83864915572232,15,,True,False,baseline,amicroservicearchitecturefortheprocessingoflargegeospatialdatainthecloud,81.83864915572232,landmark,True
80	−4229658977,10.1007/978-1-4842-4470-8_7,,Google Colaboratory,Apress eBooks,2019,book-chapter,cloud_computing,2,24.0,126.0,122.68731164917385,709,185.9225,True,False,baseline,122.68731164917385,landmark,True	80	+4394946189,10.1145/3627703.3629553,2305.15778,Automatic Root Cause Analysis via Large Language Models for Cloud Incidents,,2024,conference-paper,cloud_computing,5,101.0,187.0,81.27074434424932,187,84.6778,True,True,post,automaticrootcauseanalysisvialargelanguagemodelsforcloudincidents,81.27074434424932,landmark,True
81	−7160954670,10.1109/icsft66733.2026.11506646,,Multi-Cloud Program Management For Large Scale Deployment,,2026,conference-paper,cloud_computing,4,26.0,26.0,245.48521739130436,26,297.1442,True,False,post,92.4357350624252,landmark,True	81	+4385584867,10.4018/978-1-6684-8634-4.ch009,,A Study on Smart Energy Management Framework Using Cloud Computing,"Practice, progress, and proficiency in sustainability",2023,book-chapter,cloud_computing,2,94.0,104.0,71.58999271314065,104,70.9894,True,False,post,astudyonsmartenergymanagementframeworkusingcloudcomputing,71.58999271314065,landmark,True
82	−2784822088,10.2312/2632114,,A microservice architecture for the processing of large geospatial data in the Cloud,Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft),2018,dissertation,cloud_computing,3,3.0,8.0,81.83864915572232,15,,True,False,baseline,81.83864915572232,landmark,True	82	+4395047770,10.1007/s10462-024-10756-9,2105.04086,Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions,Artificial Intelligence Review,2024,article,cloud_computing,6,149.0,232.0,69.84941043522221,232,83.4248,True,True,post,deepreinforcementlearningbasedmethodsforresourceschedulingincloudcomputingareviewandfuturedirections,69.84941043522221,landmark,True
83	−7118010419,10.37547/tajet/volume08issue01-02,,"Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices",The American Journal of Engineering And Technology,2026,review,cloud_computing,4,23.0,23.0,30.77027027027027,23,33.0364,True,True,post,81.77007332445305,landmark,True	83	+2789491284,10.1016/j.future.2018.03.005,,A hybrid model of Internet of Things and cloud computing to manage big data in health services applications,Future Generation Computer Systems,2018,article,cloud_computing,4,84.0,130.0,68.9256218227304,341,79.9436,False,False,baseline,ahybridmodelofinternetofthingsandcloudcomputingtomanagebigdatainhealthservicesapplications,68.9256218227304,landmark,True
84	−4394946189,10.1145/3627703.3629553,2305.15778,Automatic Root Cause Analysis via Large Language Models for Cloud Incidents,,2024,conference-paper,cloud_computing,5,101.0,187.0,81.27074434424932,187,84.6778,True,True,post,81.27074434424932,landmark,True	84	+7160954670,10.1109/icsft66733.2026.11506646,,Multi-Cloud Program Management For Large Scale Deployment,,2026,conference-paper,cloud_computing,4,26.0,26.0,245.48521739130436,26,297.1442,True,False,post,multicloudprogrammanagementforlargescaledeployment,65.92223071696324,landmark,True
85	−4408156900,10.3390/electronics14051019,,Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities,Electronics,2025,review,cloud_computing,8,147.0,147.0,18.250462107208875,147,20.0516,True,True,post,74.42005166664985,landmark,True	85	+4408156900,10.3390/electronics14051019,,Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities,Electronics,2025,review,cloud_computing,8,147.0,147.0,18.250462107208875,147,20.0516,True,True,post,federatedlearningforcloudandedgesecurityasystematicreviewofchallengesandaiopportunities,64.2348204458387,landmark,True
86	−4252792641,10.31979/etd.shqa-fdp6,,PRIORITIZED TASK SCHEDULING IN FOG COMPUTING,,2018,dissertation,cloud_computing,4,5.0,8.0,71.95393474088291,24,,True,False,baseline,71.95393474088291,landmark,True	86	+2946627595,10.1007/s11280-019-00684-y,,A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems,World Wide Web,2019,article,cloud_computing,5,136.0,154.0,62.86026170639958,192,52.7972,False,False,baseline,aqosawarevirtualmachineschedulingmethodforenergyconservationincloudbasedcyberphysicalsystems,62.86026170639958,landmark,True
87	−4385584867,10.4018/978-1-6684-8634-4.ch009,,A Study on Smart Energy Management Framework Using Cloud Computing,"Practice, progress, and proficiency in sustainability",2023,book-chapter,cloud_computing,2,94.0,104.0,71.58999271314065,104,70.9894,True,False,post,71.58999271314065,landmark,True	87	+2784168960,,,Energy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacenters,Bilkent University Institutional Repository (Bilkent University),2018,dissertation,cloud_computing,4,3.0,6.0,61.37898686679174,8,,True,True,baseline,energyefficientdynamicvirtualmachineallocationwithcpuusagepredictioninclouddatacenters,61.37898686679174,landmark,True
88	−4395047770,10.1007/s10462-024-10756-9,2105.04086,Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions,Artificial Intelligence Review,2024,article,cloud_computing,6,149.0,232.0,69.84941043522221,232,83.4248,True,True,post,69.84941043522221,landmark,True	88	+4364375730,10.4018/978-1-7998-8394-4.ch009,,Social Health Protection During the COVID-Pandemic Using IoT,"Advances in electronic government, digital divide, and regional development book series",2023,book-chapter,cloud_computing,1,82.0,87.0,59.88778236580034,87,59.3854,True,False,post,socialhealthprotectionduringthecovidpandemicusingiot,59.88778236580034,landmark,True
89	−2789491284,10.1016/j.future.2018.03.005,,A hybrid model of Internet of Things and cloud computing to manage big data in health services applications,Future Generation Computer Systems,2018,article,cloud_computing,4,84.0,130.0,68.9256218227304,341,79.9436,False,False,baseline,68.9256218227304,landmark,True	89	+7118010419,10.37547/tajet/volume08issue01-02,,"Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices",The American Journal of Engineering And Technology,2026,review,cloud_computing,4,23.0,23.0,30.77027027027027,23,33.0364,True,True,post,optimizingretailapplicationperformanceasystematicreviewofmonitoringtoolsmetricsandbestpractices,58.315819480390566,landmark,True
90	−7160823269,10.1109/cisce69494.2026.11504752,,TokenPool-Scheduler: Token-Level GPU Pooling and Resource Slicing for Multi-Model Co-Location,,2026,conference-paper,cloud_computing,6,19.0,19.0,179.3930434782609,19,217.1439,True,False,post,67.54919100715686,landmark,True	90	+4293770403,10.1007/s10586-022-03713-0,,Energy efficiency in cloud computing data centers: a survey on software technologies,Cluster Computing,2022,article,cloud_computing,5,63.0,235.0,57.7561736098435,507,98.7462,True,True,baseline,energyefficiencyincloudcomputingdatacentersasurveyonsoftwaretechnologies,57.7561736098435,landmark,True
91	−7161556515,10.1109/gaiis69281.2026.11519206,,DeepServeCB: SLO-Aware and Cost-Aware Elastic Scheduling for Serverless Multi-Tenant LLM Inference,International Conference on Generative Artificial Intelligence and Information Security (GAIIS),2026,conference-paper,cloud_computing,7,19.0,19.0,179.3930434782609,19,217.1439,True,False,post,67.54919100715686,landmark,True	91	+2974110037,10.1016/j.iot.2019.100118,1911.01941,"Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges",Internet of Things,2019,article,cloud_computing,4,60.0,141.0,57.553875977937274,496,68.1052,False,True,baseline,transformativeeffectsofiotblockchainandartificialintelligenceoncloudcomputingevolutionvisiontrendsandopenchallenges,57.553875977937274,landmark,True
92	−2946627595,10.1007/s11280-019-00684-y,,A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems,World Wide Web,2019,article,cloud_computing,5,136.0,154.0,62.86026170639958,192,52.7972,False,False,baseline,62.86026170639958,landmark,True	92	+4386827074,10.4018/979-8-3693-1186-8.ch026,,"Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste","Practice, progress, and proficiency in sustainability",2023,book-chapter,cloud_computing,1,70.0,81.0,55.757590478503765,82,55.9724,True,False,post,synergizingartificialintelligence5gandcloudcomputingforefficientenergyconversionusingagriculturalwaste,55.757590478503765,landmark,True
93	−2784168960,,,Energy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacenters,Bilkent University Institutional Repository (Bilkent University),2018,dissertation,cloud_computing,4,3.0,6.0,61.37898686679174,8,,True,True,baseline,61.37898686679174,landmark,True	93	+3011330593,10.1007/s10586-020-03075-5,,A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments,Cluster Computing,2020,article,cloud_computing,3,109.0,189.0,55.03527669771227,355,59.7401,False,False,baseline,anovelhybridantlionoptimizationalgorithmformultiobjectivetaskschedulingproblemsincloudcomputingenvironments,55.03527669771227,landmark,True
94	−4378832451,10.1007/978-1-4842-9303-4_1,,Overview of Amazon Web Services,Apress eBooks,2023,book-chapter,cloud_computing,1,87.0,89.0,61.2645129948992,89,19.7951,True,False,post,61.2645129948992,landmark,True	94	+4390660438,10.1016/j.teler.2024.100116,2401.02469,Modern computing: Vision and challenges,Telematics and Informatics Reports,2024,article,cloud_computing,5,146.0,179.0,53.03994732221246,179,68.9762,True,True,post,moderncomputingvisionandchallenges,53.03994732221246,landmark,True
95	−4364375730,10.4018/978-1-7998-8394-4.ch009,,Social Health Protection During the COVID-Pandemic Using IoT,"Advances in electronic government, digital divide, and regional development book series",2023,book-chapter,cloud_computing,1,82.0,87.0,59.88778236580034,87,59.3854,True,False,post,59.88778236580034,landmark,True	95	+3042799967,10.1109/ipdps47924.2020.00084,,Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter,Proceedings - IEEE International Parallel and Distributed Processing Symposium,2020,conference-paper,cloud_computing,6,98.0,183.0,51.67349504166861,272,42.4662,True,False,baseline,smartlyhandlingrenewableenergyinstabilityinsupportingaclouddatacenter,51.67349504166861,landmark,True
96	−4293770403,10.1007/s10586-022-03713-0,,Energy efficiency in cloud computing data centers: a survey on software technologies,Cluster Computing,2022,article,cloud_computing,5,63.0,235.0,57.7561736098435,507,98.7462,True,True,baseline,57.7561736098435,landmark,True	96	+3123888896,10.1016/j.swevo.2021.100841,,"Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends",Swarm and Evolutionary Computation,2021,article,cloud_computing,3,96.0,191.0,50.57785472303446,355,60.7922,False,False,baseline,taskschedulingincloudcomputingbasedonmetaheuristicsreviewtaxonomyopenchallengesandfuturetrends,50.57785472303446,landmark,True
97	−2974110037,10.1016/j.iot.2019.100118,1911.01941,"Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges",Internet of Things,2019,article,cloud_computing,4,60.0,141.0,57.553875977937274,496,68.1052,False,True,baseline,57.553875977937274,landmark,True	97	+3008629209,10.1007/s11227-020-03213-1,,"A survey on security challenges in cloud computing: issues, threats, and solutions",The Journal of Supercomputing,2020,article,cloud_computing,3,67.0,168.0,48.92024595352202,538,68.1084,False,False,baseline,asurveyonsecuritychallengesincloudcomputingissuesthreatsandsolutions,48.92024595352202,landmark,True
98	−7164884893,10.1109/ispp69262.2026.11543010,,Research on Informer Long Sequence Dependency Modeling Method for Short-Term Prediction of Request Volume of Cloud-Native Backend Services,"International Conference on Image, Signal Processing and Pattern Recognition (ISPP)",2026,conference-paper,cloud_computing,1,16.0,16.0,151.06782608695653,16,182.858,False,False,post,56.883529269184734,landmark,True	98	+2980789769,10.1145/3362031,,A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms,ACM Computing Surveys,2019,article,cloud_computing,4,81.0,270.0,48.23825220633149,615,48.6065,True,False,baseline,asurveyonendedgecloudorchestratednetworkcomputingparadigms,48.23825220633149,landmark,True
99	−4386827074,10.4018/979-8-3693-1186-8.ch026,,"Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste","Practice, progress, and proficiency in sustainability",2023,book-chapter,cloud_computing,1,70.0,81.0,55.757590478503765,82,55.9724,True,False,post,55.757590478503765,landmark,True	99	+7161556515,10.1109/gaiis69281.2026.11519206,,DeepServeCB: SLO-Aware and Cost-Aware Elastic Scheduling for Serverless Multi-Tenant LLM Inference,International Conference on Generative Artificial Intelligence and Information Security (GAIIS),2026,conference-paper,cloud_computing,7,19.0,19.0,179.3930434782609,19,217.1439,True,False,post,deepservecbsloawareandcostawareelasticschedulingforserverlessmultitenantllminference,48.173937831626986,landmark,True
100	−3011330593,10.1007/s10586-020-03075-5,,A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments,Cluster Computing,2020,article,cloud_computing,3,109.0,189.0,55.03527669771227,355,59.7401,False,False,baseline,55.03527669771227,landmark,True	100	+7160823269,10.1109/cisce69494.2026.11504752,,TokenPool-Scheduler: Token-Level GPU Pooling and Resource Slicing for Multi-Model Co-Location,,2026,conference-paper,cloud_computing,6,19.0,19.0,179.3930434782609,19,217.1439,True,False,post,tokenpoolschedulertokenlevelgpupoolingandresourceslicingformultimodelcolocation,48.173937831626986,landmark,True
101	−7160863705,10.1109/cisce69494.2026.11504731,,Multi-Objective Scheduling for Large Language Model Inference with Prompt-Level Cost Prediction and SLO Awareness,,2026,conference-paper,cloud_computing,4,15.0,15.0,141.62608695652176,15,171.4294,True,True,post,53.328308689860684,landmark,True	101	+3010543330,10.48550/arxiv.2003.03423,2003.03423,Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider,arXiv (Cornell University),2020,preprint,cloud_computing,5,84.0,126.0,46.24970414201183,237,,True,True,baseline,serverlessinthewildcharacterizingandoptimizingtheserverlessworkloadatalargecloudprovider,46.24970414201183,landmark,True
102	−4293584584,10.4230/oasics.ng-res.2024.2,,A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper),DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2024,article,edge_computing,3,13850.0,14039.0,991.9504732778122,14040,523.7037,True,True,post,991.9504732778122,landmark,True	102	+4293584584,10.4230/oasics.ng-res.2024.2,,A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper),DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2024,article,edge_computing,3,13850.0,14039.0,991.9504732778122,14040,523.7037,True,True,post,amultimodaldistributedrealtimeiotsystemforurbantrafficcontrolinvitedpaper,991.9504732778122,landmark,True
103	−4398201281,10.3390/blockchains2020009,,Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems,Blockchains,2024,dissertation,edge_computing,7,7.0,9.0,350.4666666666667,9,,True,True,post,350.4666666666667,landmark,True	103	+4398201281,10.3390/blockchains2020009,,Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems,Blockchains,2024,dissertation,edge_computing,7,7.0,9.0,350.4666666666667,9,,True,True,post,situoraclealearningbasedsituationanalysisframeworkforblockchainbasediotsystems,350.4666666666667,landmark,True
104	−2803371618,10.1016/j.future.2018.05.046,,On blockchain and its integration with IoT. Challenges and opportunities,Future Generation Computer Systems,2018,article,edge_computing,5,203.0,490.0,259.79657456259923,1909,332.6297,True,True,baseline,259.79657456259923,landmark,True	104	+2803371618,10.1016/j.future.2018.05.046,,On blockchain and its integration with IoT. Challenges and opportunities,Future Generation Computer Systems,2018,article,edge_computing,5,203.0,490.0,259.79657456259923,1909,332.6297,True,True,baseline,onblockchainanditsintegrationwithiotchallengesandopportunities,259.79657456259923,landmark,True
105	−2793346081,10.1109/jiot.2018.2812239,,Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT,IEEE Internet of Things Journal,2018,article,edge_computing,4,214.0,449.0,238.05849383389193,1314,280.4052,True,False,baseline,238.05849383389193,landmark,True	105	+2793346081,10.1109/jiot.2018.2812239,,Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT,IEEE Internet of Things Journal,2018,article,edge_computing,4,214.0,449.0,238.05849383389193,1314,280.4052,True,False,baseline,blockchainmeetsiotanarchitectureforscalableaccessmanagementiniot,238.05849383389193,landmark,True
106	−3023617420,10.1109/access.2020.2992341,,"A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact",IEEE Access,2020,article,edge_computing,3,466.0,725.0,211.1141566446635,1096,210.1365,True,True,baseline,211.1141566446635,landmark,True	106	+3023617420,10.1109/access.2020.2992341,,"A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact",IEEE Access,2020,article,edge_computing,3,466.0,725.0,211.1141566446635,1096,210.1365,True,True,baseline,acomprehensivereviewofthecovid19pandemicandtheroleofiotdronesaiblockchainand5ginmanagingitsimpact,211.1141566446635,landmark,True
107	−2562507292,10.1201/9781003338628-7,,Internet of Things Applications - From Research and Innovation to Market Deployment,River Publishers eBooks,2022,book-chapter,edge_computing,2,216.0,221.0,171.71408036142103,233,36.0148,True,True,baseline,171.71408036142103,landmark,True	107	+2562507292,10.1201/9781003338628-7,,Internet of Things Applications - From Research and Innovation to Market Deployment,River Publishers eBooks,2022,book-chapter,edge_computing,2,216.0,221.0,171.71408036142103,233,36.0148,True,True,baseline,internetofthingsapplicationsfromresearchandinnovationtomarketdeployment,171.71408036142103,landmark,True
108	−3017623177,10.1016/j.dsx.2020.04.032,,Industry 4.0 technologies and their applications in fighting COVID-19 pandemic,Diabetes & Metabolic Syndrome Clinical Research & Reviews,2020,article,edge_computing,1,337.0,508.0,147.9255056213642,757,146.6771,False,True,baseline,147.9255056213642,landmark,True	108	+3017623177,10.1016/j.dsx.2020.04.032,,Industry 4.0 technologies and their applications in fighting COVID-19 pandemic,Diabetes & Metabolic Syndrome Clinical Research & Reviews,2020,article,edge_computing,1,337.0,508.0,147.9255056213642,757,146.6771,False,True,baseline,industry40technologiesandtheirapplicationsinfightingcovid19pandemic,147.9255056213642,landmark,True
109	−2908737503,10.3390/s19020326,,A Decentralized Privacy-Preserving Healthcare Blockchain for IoT,Sensors,2019,article,edge_computing,6,167.0,347.0,141.63968059818606,880,158.7039,True,True,baseline,141.63968059818606,landmark,True	109	+2908737503,10.3390/s19020326,,A Decentralized Privacy-Preserving Healthcare Blockchain for IoT,Sensors,2019,article,edge_computing,6,167.0,347.0,141.63968059818606,880,158.7039,True,True,baseline,adecentralizedprivacypreservinghealthcareblockchainforiot,141.63968059818606,landmark,True
110	−3023398824,10.1016/j.dsx.2020.04.041,,Internet of things (IoT) applications to fight against COVID-19 pandemic,Diabetes & Metabolic Syndrome Clinical Research & Reviews,2020,article,edge_computing,2,289.0,455.0,132.4923327907888,664,133.1949,False,True,baseline,132.4923327907888,landmark,True	110	+3023398824,10.1016/j.dsx.2020.04.041,,Internet of things (IoT) applications to fight against COVID-19 pandemic,Diabetes & Metabolic Syndrome Clinical Research & Reviews,2020,article,edge_computing,2,289.0,455.0,132.4923327907888,664,133.1949,False,True,baseline,internetofthingsiotapplicationstofightagainstcovid19pandemic,132.4923327907888,landmark,True
111	−2947690458,10.1109/jiot.2019.2920987,1906.00245,Blockchain for Internet of Things: A Survey,IEEE Internet of Things Journal,2019,article,edge_computing,7,142.0,323.0,131.8432761764095,1087,171.5127,True,True,baseline,131.8432761764095,landmark,True	111	+2947690458,10.1109/jiot.2019.2920987,1906.00245,Blockchain for Internet of Things: A Survey,IEEE Internet of Things Journal,2019,article,edge_computing,7,142.0,323.0,131.8432761764095,1087,171.5127,True,True,baseline,blockchainforinternetofthingsasurvey,131.8432761764095,landmark,True
112	−2885100636,10.3390/s18082575,,Blockchain and IoT Integration: A Systematic Survey,Sensors,2018,article,edge_computing,6,100.0,219.0,116.11316291675352,748,144.2199,True,True,baseline,116.11316291675352,landmark,True	112	+2885100636,10.3390/s18082575,,Blockchain and IoT Integration: A Systematic Survey,Sensors,2018,article,edge_computing,6,100.0,219.0,116.11316291675352,748,144.2199,True,True,baseline,blockchainandiotintegrationasystematicsurvey,116.11316291675352,landmark,True
113	−3006456063,10.1016/j.jii.2020.100129,,"Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0",Journal of Industrial Information Integration,2020,article,edge_computing,3,233.0,388.0,112.9824727974199,876,123.8968,False,False,baseline,112.9824727974199,landmark,True	113	+3006456063,10.1016/j.jii.2020.100129,,"Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0",Journal of Industrial Information Integration,2020,article,edge_computing,3,233.0,388.0,112.9824727974199,876,123.8968,False,False,baseline,industry40andhealthinternetofthingsbigdataandcloudcomputingforhealthcare40,112.9824727974199,landmark,True
114	−2974429275,10.1109/tii.2019.2942190,,Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT,IEEE Transactions on Industrial Informatics,2019,article,edge_computing,5,79.0,275.0,112.25046733285639,1237,155.2674,True,False,baseline,112.25046733285639,landmark,True	114	+2974429275,10.1109/tii.2019.2942190,,Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT,IEEE Transactions on Industrial Informatics,2019,article,edge_computing,5,79.0,275.0,112.25046733285639,1237,155.2674,True,False,baseline,blockchainandfederatedlearningforprivacypreserveddatasharinginindustrialiot,112.25046733285639,landmark,True
115	−4386961654,10.3390/s23198015,,Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey,Sensors,2023,article,edge_computing,4,268.0,473.0,110.8597419743026,563,131.8285,True,True,post,110.8597419743026,landmark,True	115	+4386961654,10.3390/s23198015,,Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey,Sensors,2023,article,edge_computing,4,268.0,473.0,110.8597419743026,563,131.8285,True,True,post,exploringthefullpotentialsofiotforbetterfinancialgrowthandstabilityacomprehensivesurvey,110.8597419743026,landmark,True
116	−4362498422,10.12681/eadd/53552,,Security and privacy in the internet of things,,2023,dissertation,edge_computing,3,6.0,6.0,110.13333333333334,6,,True,False,post,110.13333333333334,landmark,True	116	+2912978522,10.1109/comst.2019.2894727,,"Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges",IEEE Communications Surveys & Tutorials,2019,article,edge_computing,5,123.0,257.0,104.90316401652396,688,125.5885,True,False,baseline,integratedblockchainandedgecomputingsystemsasurveysomeresearchissuesandchallenges,104.90316401652396,landmark,True
117	−2912978522,10.1109/comst.2019.2894727,,"Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges",IEEE Communications Surveys & Tutorials,2019,article,edge_computing,5,123.0,257.0,104.90316401652396,688,125.5885,True,False,baseline,104.90316401652396,landmark,True	117	+2950865323,10.1109/jproc.2019.2918951,1905.10083,Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing,Proceedings of the IEEE,2019,article,edge_computing,7,242.0,584.0,104.33755292036145,2403,130.9484,True,True,baseline,edgeintelligencepavingthelastmileofartificialintelligencewithedgecomputing,104.33755292036145,landmark,True
118	−2950865323,10.1109/jproc.2019.2918951,1905.10083,Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing,Proceedings of the IEEE,2019,article,edge_computing,7,242.0,584.0,104.33755292036145,2403,130.9484,True,True,baseline,104.33755292036145,landmark,True	118	+4313646859,10.1002/widm.1485,2301.10009,"Remote patient monitoring using artificial intelligence: Current state, applications, and challenges",Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery,2023,article,edge_computing,5,192.0,411.0,96.32844387196272,515,121.019,True,True,post,remotepatientmonitoringusingartificialintelligencecurrentstateapplicationsandchallenges,96.32844387196272,landmark,True
119	−7129024920,10.1109/jiot.2026.3665050,,TinyLSN: A Lightweight Network for Real-Time Marine Pipeline Leakage Detection in IoT Systems,IEEE Internet of Things Journal,2026,article,edge_computing,6,29.0,29.0,33.756,29,56.592,True,False,post,103.10139680039732,landmark,True	119	+4401806686,10.1016/j.hybadv.2024.100277,,AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications,Hybrid Advances,2024,article,edge_computing,6,415.0,749.0,93.03175978035208,749,111.3178,True,True,post,airevolutionizingindustriesworldwideacomprehensiveoverviewofitsdiverseapplications,93.03175978035208,landmark,True
120	−4408363154,10.3390/s25061763,,"From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI",Sensors,2025,article,edge_computing,3,201.0,201.0,76.45446851404553,201,88.6024,True,True,post,101.75802982990899,landmark,True	120	+4320712473,10.1016/j.iot.2023.100721,,The Internet of Things (IoT) in healthcare: Taking stock and moving forward,Internet of Things,2023,article,edge_computing,4,233.0,379.0,88.82841904494859,448,105.2748,True,True,post,theinternetofthingsiotinhealthcaretakingstockandmovingforward,88.82841904494859,landmark,True
121	−4407389811,10.3390/electronics14040696,,Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data,Electronics,2025,article,edge_computing,4,199.0,199.0,172.77199169706373,199,217.4065,True,True,post,100.74551212015865,landmark,True	121	+2983008824,10.1016/j.jnca.2019.102481,,"A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT",Journal of Network and Computer Applications,2019,article,edge_computing,3,69.0,217.0,88.57582331356303,840,122.4644,False,False,baseline,acomprehensivesurveyonattackssecurityissuesandblockchainsolutionsforiotandiiot,88.57582331356303,landmark,True
122	−4313646859,10.1002/widm.1485,2301.10009,"Remote patient monitoring using artificial intelligence: Current state, applications, and challenges",Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery,2023,article,edge_computing,5,192.0,411.0,96.32844387196272,515,121.019,True,True,post,96.32844387196272,landmark,True	122	+4408363154,10.3390/s25061763,,"From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI",Sensors,2025,article,edge_computing,3,201.0,201.0,76.45446851404553,201,88.6024,True,True,post,fromsensorstodataintelligenceleveragingiotcloudandedgecomputingwithai,87.8312850994121,landmark,True
123	−4401806686,10.1016/j.hybadv.2024.100277,,AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications,Hybrid Advances,2024,article,edge_computing,6,415.0,749.0,93.03175978035208,749,111.3178,True,True,post,93.03175978035208,landmark,True	123	+2918245750,10.1016/j.future.2019.02.060,,"Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions",Future Generation Computer Systems,2019,article,edge_computing,4,84.0,215.0,87.75945627841499,520,104.0323,False,True,baseline,privacypreservationinblockchainbasediotsystemsintegrationissuesprospectschallengesandfutureresearchdirections,87.75945627841499,landmark,True
124	−7125650829,10.1007/s44336-025-00031-y,2506.12479,"AI Flow: perspectives, scenarios, and approaches",Vicinagearth.,2026,article,edge_computing,6,25.0,25.0,87.76730891382596,25,96.3814,True,True,post,88.88051448310114,landmark,True	124	+4407389811,10.3390/electronics14040696,,Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data,Electronics,2025,article,edge_computing,4,199.0,199.0,172.77199169706373,199,217.4065,True,True,post,integratingartificialintelligenceagentswiththeinternetofthingsforenhancedenvironmentalmonitoringapplicationsinwaterqualityandclimatedata,86.95734196409457,landmark,True
125	−4320712473,10.1016/j.iot.2023.100721,,The Internet of Things (IoT) in healthcare: Taking stock and moving forward,Internet of Things,2023,article,edge_computing,4,233.0,379.0,88.82841904494859,448,105.2748,True,True,post,88.82841904494859,landmark,True	125	+2921608598,10.1109/tii.2019.2903342,,Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism,IEEE Transactions on Industrial Informatics,2019,article,edge_computing,4,97.0,211.0,86.12672220811889,562,98.0965,True,True,baseline,towardssecureindustrialiotblockchainsystemwithcreditbasedconsensusmechanism,86.12672220811889,landmark,True
126	−2983008824,10.1016/j.jnca.2019.102481,,"A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT",Journal of Network and Computer Applications,2019,article,edge_computing,3,69.0,217.0,88.57582331356303,840,122.4644,False,False,baseline,88.57582331356303,landmark,True	126	+2951308194,,,Blockchain and smart contracts for the internet of things,"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",2018,dissertation,edge_computing,3,7.0,8.0,81.83864915572232,11,,True,True,baseline,blockchainandsmartcontractsfortheinternetofthings,81.83864915572232,landmark,True
127	−2602956630,10.1201/9781003338604-2,,Internet of Things Strategic Research Roadmap,River Publishers eBooks,2022,book-chapter,computer_networks,2,917.0,944.0,727.1609786504597,1002,103.9744,True,True,baseline,727.1609786504597,landmark,True	127	+2602956630,10.1201/9781003338604-2,,Internet of Things Strategic Research Roadmap,River Publishers eBooks,2022,book-chapter,computer_networks,2,917.0,944.0,727.1609786504597,1002,103.9744,True,True,baseline,internetofthingsstrategicresearchroadmap,727.1609786504597,landmark,True
128	−1500148551,10.48550/arxiv.2603.08623,2603.08623,Time-based Fairness Improves Performance in Multi-rate WLANs,arXiv (Cornell University),2026,preprint,computer_networks,3,100.0,100.0,2873.046875,231,,True,True,post,355.52205793240455,landmark,True	128	+2762829140,10.6069/5wt6-j462,,Deep Learning based Intrusion Detection System for Internet of Things,ResearchWorks at the University of Washington (University of Washington),2024,dissertation,computer_networks,4,12.0,12.0,302.4347826086956,12,,True,True,post,deeplearningbasedintrusiondetectionsystemforinternetofthings,302.4347826086956,landmark,True
129	−2762829140,10.6069/5wt6-j462,,Deep Learning based Intrusion Detection System for Internet of Things,ResearchWorks at the University of Washington (University of Washington),2024,dissertation,computer_networks,4,12.0,12.0,302.4347826086956,12,,True,True,post,302.4347826086956,landmark,True	129	+2940715617,,,Offloading Real-time DDoS Attack Detection to Programmable Data Planes,Americanae (AECID Library),2019,dissertation,computer_networks,3,15.0,36.0,281.8709677419355,56,,True,True,baseline,offloadingrealtimeddosattackdetectiontoprogrammabledataplanes,281.8709677419355,landmark,True
130	−2940715617,,,Offloading Real-time DDoS Attack Detection to Programmable Data Planes,Americanae (AECID Library),2019,dissertation,computer_networks,3,15.0,36.0,281.8709677419355,56,,True,True,baseline,281.8709677419355,landmark,True	130	+1500148551,10.48550/arxiv.2603.08623,2603.08623,Time-based Fairness Improves Performance in Multi-rate WLANs,arXiv (Cornell University),2026,preprint,computer_networks,3,100.0,100.0,2873.046875,231,,True,True,post,timebasedfairnessimprovesperformanceinmultiratewlans,253.5470412190894,landmark,True
131	−7123361055,10.1109/comst.2026.3651702,,"Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions",IEEE Communications Surveys & Tutorials,2026,article,computer_networks,4,73.0,73.0,256.28054202837177,73,305.9063,True,False,post,259.5311022906553,landmark,True	131	+2772457693,10.1109/comst.2018.2844341,1712.04301,Deep Learning for IoT Big Data and Streaming Analytics: A Survey,IEEE Communications Surveys & Tutorials,2018,article,computer_networks,5,173.0,419.0,222.15258110556954,1462,281.2086,True,True,baseline,deeplearningforiotbigdataandstreaminganalyticsasurvey,222.15258110556954,landmark,True
132	−7124594522,10.1007/s41870-025-03116-5,,Energy-efficient 5G-enabled sensor networks for smart environmental monitoring in sustainable cities,International Journal of Information Technology,2026,article,computer_networks,2,63.0,63.0,221.1736184628414,63,264.0013,False,False,post,223.97889649741487,landmark,True	132	+1823805418,10.17487/rfc8684,,TCP Extensions for Multipath Operation with Multiple Addresses,,2020,report,computer_networks,3,796.0,831.0,214.13475177304963,1008,,True,True,baseline,tcpextensionsformultipathoperationwithmultipleaddresses,214.13475177304963,landmark,True
133	−2772457693,10.1109/comst.2018.2844341,1712.04301,Deep Learning for IoT Big Data and Streaming Analytics: A Survey,IEEE Communications Surveys & Tutorials,2018,article,computer_networks,5,173.0,419.0,222.15258110556954,1462,281.2086,True,True,baseline,222.15258110556954,landmark,True	133	+4292259483,10.1109/mcom.2022.9860244,,The IEEE International Conference on Communications 2022 (ICC'22),IEEE Communications Magazine,2022,article,computer_networks,1,38.0,59.0,197.26717557251908,78,,True,True,baseline,theieeeinternationalconferenceoncommunications2022icc22,197.26717557251908,landmark,True
134	−7134136722,10.1016/j.jnca.2026.104462,,Hybrid Al-Biruni and Puma Optimization (BERPO) for boosting the classification of Quality-of-Service (QoS) in 5G networks,Journal of Network and Computer Applications,2026,article,computer_networks,1,61.0,61.0,67.88089344511127,61,220.3697,False,False,post,216.86845533876678,landmark,True	134	+7123361055,10.1109/comst.2026.3651702,,"Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions",IEEE Communications Surveys & Tutorials,2026,article,computer_networks,4,73.0,73.0,256.28054202837177,73,305.9063,True,False,post,towardedgegeneralintelligencewithagenticaiandagentificationconceptstechnologiesandfuturedirections,185.08934008993526,landmark,True
135	−1823805418,10.17487/rfc8684,,TCP Extensions for Multipath Operation with Multiple Addresses,,2020,report,computer_networks,3,796.0,831.0,214.13475177304963,1008,,True,True,baseline,214.13475177304963,landmark,True	135	+7124594522,10.1007/s41870-025-03116-5,,Energy-efficient 5G-enabled sensor networks for smart environmental monitoring in sustainable cities,International Journal of Information Technology,2026,article,computer_networks,2,63.0,63.0,221.1736184628414,63,264.0013,False,False,post,energyefficient5genabledsensornetworksforsmartenvironmentalmonitoringinsustainablecities,159.7346359680263,landmark,True
136	−4415028496,10.1109/comst.2026.3669216,2505.01821,Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey,IEEE Communications Surveys & Tutorials,2026,article,computer_networks,9,60.0,60.0,210.64154139318228,60,243.0488,True,True,post,213.31323475944274,landmark,True	136	+7134136722,10.1016/j.jnca.2026.104462,,Hybrid Al-Biruni and Puma Optimization (BERPO) for boosting the classification of Quality-of-Service (QoS) in 5G networks,Journal of Network and Computer Applications,2026,article,computer_networks,1,61.0,61.0,67.88089344511127,61,220.3697,False,False,post,hybridalbiruniandpumaoptimizationberpoforboostingtheclassificationofqualityofserviceqosin5gnetworks,154.66369514364453,landmark,True
137	−4285039189,10.12681/eadd/51938,,Mobile ad-hoc networks (MANET),,2022,dissertation,computer_networks,1,28.0,28.0,211.50980392156862,58,,True,False,baseline,211.50980392156862,landmark,True	137	+4415028496,10.1109/comst.2026.3669216,2505.01821,Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey,IEEE Communications Surveys & Tutorials,2026,article,computer_networks,9,60.0,60.0,210.64154139318228,60,243.0488,True,True,post,edgecloudcollaborativecomputingondistributedintelligenceandmodeloptimizationasurvey,152.12822473145363,landmark,True
138	−4292259483,10.1109/mcom.2022.9860244,,The IEEE International Conference on Communications 2022 (ICC'22),IEEE Communications Magazine,2022,article,computer_networks,1,38.0,59.0,197.26717557251908,78,,True,True,baseline,197.26717557251908,landmark,True	138	+2962883549,10.1109/comst.2019.2904897,,Deep Learning in Mobile and Wireless Networking: A Survey,IEEE Communications Surveys & Tutorials,2019,article,computer_networks,2,473.0,812.0,145.07207700570802,1768,138.2463,True,False,baseline,deeplearninginmobileandwirelessnetworkingasurvey,145.07207700570802,landmark,True
139	−7127083419,10.1109/jsac.2026.3660010,2505.22311,From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications,IEEE Journal on Selected Areas in Communications,2026,article,computer_networks,2,50.0,50.0,74.70889613414066,50,77.1613,True,True,post,177.76102896620228,landmark,True	139	+153405511,10.57709/1059389,,"An Analysis of the MOS under Conditions of Delay, Jitter and Packet Loss and an Analysis of the Impact of Introducing Piggybacking and Reed Solomon FEC for VOIP",Digital Archive @ GSU,2022,dissertation,computer_networks,4,19.0,19.0,143.52450980392155,19,,True,True,baseline,ananalysisofthemosunderconditionsofdelayjitterandpacketlossandananalysisoftheimpactofintroducingpiggybackingandreedsolomonfecforvoip,143.52450980392155,landmark,True
140	−2962883549,10.1109/comst.2019.2904897,,Deep Learning in Mobile and Wireless Networking: A Survey,IEEE Communications Surveys & Tutorials,2019,article,computer_networks,2,473.0,812.0,145.07207700570802,1768,138.2463,True,False,baseline,145.07207700570802,landmark,True	140	+4206510620,10.1007/978-3-030-87059-1_4,,The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications,Internet of things,2022,book-chapter,computer_networks,3,98.0,176.0,136.74967485796427,223,144.059,False,False,baseline,theroleofmachinelearningtechniquesininternetofthingsbasedcloudapplications,136.74967485796427,landmark,True
141	−4395064458,10.1017/9781009489843,,Wireless Communications,Cambridge University Press eBooks,2024,book,computer_networks,1,347.0,458.0,144.12803234501348,541,214.916,True,False,post,144.12803234501348,landmark,True	141	+4322576964,10.1109/comst.2023.3249835,,"On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds",IEEE Communications Surveys & Tutorials,2023,article,computer_networks,1,832.0,1763.0,133.06276599375022,2336,171.2254,True,True,post,ontheroadto6gvisionsrequirementskeytechnologiesandtestbeds,133.06276599375022,landmark,True
142	−153405511,10.57709/1059389,,"An Analysis of the MOS under Conditions of Delay, Jitter and Packet Loss and an Analysis of the Impact of Introducing Piggybacking and Reed Solomon FEC for VOIP",Digital Archive @ GSU,2022,dissertation,computer_networks,4,19.0,19.0,143.52450980392155,19,,True,True,baseline,143.52450980392155,landmark,True	142	+7127083419,10.1109/jsac.2026.3660010,2505.22311,From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications,IEEE Journal on Selected Areas in Communications,2026,article,computer_networks,2,50.0,50.0,74.70889613414066,50,77.1613,True,True,post,fromlargeaimodelstoagenticaiatutorialonfutureintelligentcommunications,126.7735206095447,landmark,True
143	−4416378800,10.1109/tcomm.2026.3658289,,Pinching-Antenna Systems (PASS): A Tutorial,IEEE Transactions on Communications,2026,article,computer_networks,2,40.0,40.0,52.205714285714286,40,111.2683,True,True,post,142.20882317296181,landmark,True	143	+3104550706,10.1184/r1/6468842.v1,,Gossip Algorithms for Distributed Signal Processing,,2018,article,computer_networks,1,553.0,607.0,121.54403377438375,692,30.8092,True,True,baseline,gossipalgorithmsfordistributedsignalprocessing,121.54403377438375,landmark,True
144	−4206510620,10.1007/978-3-030-87059-1_4,,The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications,Internet of things,2022,book-chapter,computer_networks,3,98.0,176.0,136.74967485796427,223,144.059,False,False,baseline,136.74967485796427,landmark,True	144	+1710433637,10.17487/rfc9260,,Stream Control Transmission Protocol,,2022,report,computer_networks,1,645.0,659.0,119.70282413350449,1922,,True,False,baseline,streamcontroltransmissionprotocol,119.70282413350449,landmark,True
145	−4322576964,10.1109/comst.2023.3249835,,"On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds",IEEE Communications Surveys & Tutorials,2023,article,computer_networks,1,832.0,1763.0,133.06276599375022,2336,171.2254,True,True,post,133.06276599375022,landmark,True	145	+3194620979,10.1109/jsac.2022.3156632,,Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond,IEEE Journal on Selected Areas in Communications,2022,article,computer_networks,2,639.0,1558.0,118.9064113669858,3786,312.6996,True,True,baseline,integratedsensingandcommunicationstowarddualfunctionalwirelessnetworksfor6gandbeyond,118.9064113669858,landmark,True
146	−7123349468,10.1109/jiot.2026.3652582,,SMNet: A Novel Compositional Generalization Model for Industrial Robot Multijoint Fault Diagnosis,IEEE Internet of Things Journal,2026,article,computer_networks,4,37.0,37.0,40.9855601659751,37,100.0756,True,False,post,131.54316143498968,landmark,True	146	+2545990795,10.17487/rfc9000,,QUIC: A UDP-Based Multiplexed and Secure Transport,,2021,report,computer_networks,3,316.0,458.0,115.2256020278834,828,,True,False,baseline,quicaudpbasedmultiplexedandsecuretransport,115.2256020278834,landmark,True
147	−7125196921,10.1109/jiot.2026.3655796,2505.03036,"Energy, Scalability, Data, and Security in Massive IoT: Current Landscape and Future Directions",IEEE Internet of Things Journal,2026,article,computer_networks,8,35.0,35.0,122.87423247935634,35,146.6674,True,True,post,124.4327202763416,landmark,True	147	+3015636663,10.1109/comst.2020.2986024,1909.11875,Federated Learning in Mobile Edge Networks: A Comprehensive Survey,IEEE Communications Surveys & Tutorials,2020,article,computer_networks,6,471.0,885.0,110.69210549120425,2537,130.0371,True,True,baseline,federatedlearninginmobileedgenetworksacomprehensivesurvey,110.69210549120425,landmark,True
148	−3104550706,10.1184/r1/6468842.v1,,Gossip Algorithms for Distributed Signal Processing,,2018,article,computer_networks,1,553.0,607.0,121.54403377438375,692,30.8092,True,True,baseline,121.54403377438375,landmark,True	148	+4396669794,10.4018/979-8-3693-1335-0.ch018,,Optimized Throughput-Based Broadcasting for Next Generation Wireless Networks,Advances in civil and industrial engineering book series,2024,book-chapter,computer_networks,1,114.0,114.0,109.86076986076985,114,123.4038,True,False,post,optimizedthroughputbasedbroadcastingfornextgenerationwirelessnetworks,109.86076986076985,landmark,True
149	−7125115033,10.1109/comst.2026.3655674,,Integrated Sensing and Communications Over the Years: An Evolution Perspective,IEEE Communications Surveys & Tutorials,2026,article,computer_networks,1,34.0,34.0,44.374857142857145,32,85.591,False,True,post,120.87749969701756,landmark,True	149	+1150714886,,,OPNET simulation of voice over MPLS: With Considering Traffic Engineering,,2019,dissertation,computer_networks,1,13.0,14.0,109.61648745519715,14,,True,False,baseline,opnetsimulationofvoiceovermplswithconsideringtrafficengineering,109.61648745519715,landmark,True
150	−1710433637,10.17487/rfc9260,,Stream Control Transmission Protocol,,2022,report,computer_networks,1,645.0,659.0,119.70282413350449,1922,,True,False,baseline,119.70282413350449,landmark,True	150	+4306827119,10.1007/s10586-022-03776-z,,Internet of Things intrusion detection systems: a comprehensive review and future directions,Cluster Computing,2022,book-chapter,computer_networks,4,33.0,141.0,108.61196820944366,340,212.7111,False,False,baseline,internetofthingsintrusiondetectionsystemsacomprehensivereviewandfuturedirections,108.61196820944366,landmark,True
151	−4406207083,10.1109/comst.2025.3527641,,Mobile Edge Intelligence for Large Language Models: A Contemporary Survey,IEEE Communications Surveys & Tutorials,2025,article,computer_networks,6,236.0,236.0,89.76743566823257,235,102.2674,True,True,post,119.47708975053992,landmark,True	151	+3102359135,10.1007/978-3-030-72802-1_9,2011.09144,NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems,"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering",2021,book-chapter,computer_networks,2,55.0,126.0,108.5769660827432,403,150.6621,False,True,baseline,netflowdatasetsformachinelearningbasednetworkintrusiondetectionsystems,108.5769660827432,landmark,True
152	−3194620979,10.1109/jsac.2022.3156632,,Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond,IEEE Journal on Selected Areas in Communications,2022,article,computer_networks,2,639.0,1558.0,118.9064113669858,3786,312.6996,True,True,baseline,118.9064113669858,landmark,True	152	+3129486236,10.1109/tcomm.2021.3051897,2007.02759,Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial,IEEE Transactions on Communications,2021,article,computer_networks,2,774.0,1396.0,108.19285671504721,3002,145.9637,True,True,baseline,intelligentreflectingsurfaceaidedwirelesscommunicationsatutorial,108.19285671504721,landmark,True
153	−3129486236,10.1109/tcomm.2021.3051897,2007.02759,Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial,IEEE Transactions on Communications,2021,article,computer_networks,2,774.0,1396.0,108.19285671504721,3002,145.9637,True,True,baseline,108.19285671504721,landmark,True

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spb/wp44_uqo

WP44: landmark selection v2 results

Theme