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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,True84226278401,10.48550/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,True94396531244,10.22215/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,True104221143046,10.48550/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,True114367369313,10.18260/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,True124389437528,10.61969/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,True134292779060,10.4230/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,True144414281281,10.1038/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,True154360836968,10.48550/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,True167135096569,10.1287/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,True174385223296,10.1007/978-3-031-37776-1_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,True184406800520,10.1007/s11432-024-4222-0,2309.07864,The rise and potential of large language model based agents: a survey,Science China Information Sciences,2025,article,llm,3,914.0,914.0,311.35347115247697,914,317.0864,False,True,post,theriseandpotentialoflargelanguagemodelbasedagentsasurvey,399.3920128401127,landmark,True194366420437,10.3390/educsci13040410,,What Is the Impact of ChatGPT on Education? 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Environments,2023,article,llm,3,941.0,1389.0,325.547952647582,1931,453.5275,True,True,post,whatifthedevilismyguardianangelchatgptasacasestudyofusingchatbotsineducation,325.547952647582,landmark,True254287689466,10.4230/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,True264320495408,10.1108/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,True274387355843,10.1201/9781003616719-7,2310.01728,TimeLLM: Time Series Forecasting by Reprogramming Large Language Models,CRC Press eBooks,2026,book-chapter,foundation_models,4,197.0,197.0,188.61702127659572,197,110.0789,True,True,post,timellmtimeseriesforecastingbyreprogramminglargelanguagemodels,499.48767120160613,landmark,True284392947521,10.1038/s41591-024-02857-3,,Towards a general-purpose foundation model for computational pathology,Nature Medicine,2024,article,foundation_models,2,770.0,1505.0,186.93297525958593,1505,223.3796,False,True,post,towardsageneralpurposefoundationmodelforcomputationalpathology,186.93297525958593,landmark,True297133233472,10.52202/075280-0441,2305.14314,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,True304410551352,10.1038/s41586-025-09005-y,2405.13063,A foundation model for the Earth 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framework for chemical foundation models,Digital Discovery,2026,article,foundation_models,2,25.0,25.0,70.59021819073303,25,85.533,True,True,post,chemberta3anopensourcetrainingframeworkforchemicalfoundationmodels,63.38676030477235,landmark,True377119541039,10.1038/s43247-025-03127-x,,On the foundations of Earth foundation models,Communications Earth & Environment,2026,article,foundation_models,3,24.0,24.0,26.707236765289682,24,83.0902,True,True,post,onthefoundationsofearthfoundationmodels,60.851289892581455,landmark,True384390690043,10.1145/3800677,2401.02458,Data-Centric Foundation Models in Computational Healthcare: A Survey,ACM Computing Surveys,2026,article,foundation_models,4,22.0,22.0,10.015503875968992,22,13.1625,True,True,post,datacentricfoundationmodelsincomputationalhealthcareasurvey,55.78034906819967,landmark,True394361229539,10.48550/arxiv.2303.16199,2303.16199,LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention,arXiv (Cornell University),2023,preprint,foundation_models,9,135.0,161.0,53.528087966208936,166,,True,True,post,llamaadapterefficientfinetuningoflanguagemodelswithzeroinitattention,53.528087966208936,landmark,True407128029160,10.1038/s41593-026-02202-6,,A generalizable foundation model for analysis of human brain MRI,Nature Neuroscience,2026,article,foundation_models,2,20.0,20.0,22.2560306377414,20,72.2524,True,True,post,ageneralizablefoundationmodelforanalysisofhumanbrainmri,50.70940824381788,landmark,True414385948838,10.1038/s41591-023-02504-3,,A visual–language foundation model for pathology image analysis using medical Twitter,Nature Medicine,2023,article,foundation_models,3,179.0,471.0,49.064992972535784,680,61.8263,False,False,post,avisuallanguagefoundationmodelforpathologyimageanalysisusingmedicaltwitter,49.064992972535784,landmark,True424411932262,10.1038/s41586-025-09215-4,,A foundation model to predict and capture human 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Sciences,2026,article,multimodal_ai,4,22.0,22.0,24.48163370151554,22,79.4776,True,True,post,semcapsentimentawaresemanticcaptioningformultimodalaspectbasedsentimentanalysis,55.78034906819967,landmark,True694399567248,10.1038/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,True707143512276,10.1007/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,True714410882873,10.1016/j.media.2025.103621,2411.03782,Navigating the landscape of multimodal AI in medicine: A scoping review on technical 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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,True774400228804,10.46569/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,True784383312437,10.1186/s41239-023-00408-3,2305.00280,A comprehensive AI policy education framework for university teaching and learning,International Journal of Educational Technology in Higher 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Multimedia,2026,article,generative_ai,3,66.0,66.0,176.9042016806723,66,205.676,True,False,post,structurepreservingfrequencyregularizedtextguidedoptimaltransportforunpairedrainstreaksandraindropsremoval,167.341047204599,landmark,True864398243313,10.1186/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,True877127274868,10.1002/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 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Intelligence,2024,article,generative_ai,1,207.0,394.0,118.623567721886,394,150.7769,True,True,post,theimpactofgenerativeaionhighereducationlearningandteachingastudyofeducatorsperspectives,118.623567721886,landmark,True904409269614,10.3390/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,True914395957952,10.54675/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,True924394863715,10.2139/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,True934401077562,10.1080/10447318.2024.2383033,,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,True947118187240,10.3390/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,True954407778399,10.1186/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,True964401917994,10.38124/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,True974411505840,10.1038/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,True982804078698,10.48550/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,True994321648656,10.1163/22134913-bja10078,2302.10913,From Paintbrush to Pixel: A Review of Deep Neural Networks in 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Sciences,2023,article,generative_ai,2,158.0,352.0,82.50027309715541,461,108.0946,True,True,post,exploringthepotentialimpactofartificialintelligenceaioninternationalstudentsinhighereducationgenerativeaichatbotsanalyticsandinternationalstudentsuccess,82.50027309715541,landmark,True1024406728221,10.1109/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,True1037161734976,10.1038/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,True1044225355126,10.1007/978-981-19-0493-6_1,,Introduction to Multi-agent Systems,Springer tracts in human-centered 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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,True1322973229164,10.32657/10356/90191,,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,True1332982316857,10.1038/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,True1347123365826,10.1109/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,True1352902907165,10.1126/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,True1367133226216,10.52202/068431-1787,2103.01955,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,True1373041807988,10.1007/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,True1382781726626,10.48550/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,True1392963923407,,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,True1407133481537,10.1016/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,True1412899138465,,,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,True1424391631327,10.48550/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,True1432895974005,,,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,True1444409147637,10.1038/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,True1452754517384,10.1609/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,True1462761873684,10.1609/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,True1477133208539,10.52202/075280-2338,,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,True1484327734444,10.5204/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,True1494383112908,10.1109/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,True1507122543576,10.1002/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,True1512742132707,10.26153/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,True1522962862931,10.4230/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,True15346659105,10.4230/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,True1544288283362,10.5281/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,True1551568555062,10.1145/3501714.3501732,,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,True1562100415632,10.48550/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,True1573208688400,10.1007/978-981-16-2102-4_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,True1583105488314,,,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,True1592945976633,10.1038/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,True1604400823820,10.58496/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,True1612999309192,10.1186/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,True1622999615587,10.1038/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,True1632439568532,10.1145/3233231,,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,True1643194464139,,,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,True1653041133507,10.1109/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,True16657706852,10.48550/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,True1672436219157,10.1137/16m1080173,1606.04838,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,True1683135028703,10.1007/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,True1692946444988,10.48550/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,True1703141797743,10.1109/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,True1713123554940,10.1007/978-3-030-57077-4_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,True1722889326414,10.21105/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,True1733021654819,10.1109/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,True1742995022099,10.1561/2200000083,1912.04977,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,True1752608595939,10.1007/978-3-030-05318-5_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,True1763157286395,10.4230/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,True1772964121744,10.4230/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,True1782963399829,10.57702/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,True1794322614756,10.4230/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,True1804382203079,10.1609/aaai.v37i9.26317,2205.13504,Are 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Intelligence,2026,article,deep_learning_architectures,1,102.0,102.0,112.98721991701245,102,283.5476,False,False,post,spatialchannelcollaborativemultiscalegraphinteractiondeeptransferlearningforunsupervisedrotatingmachineryfaultdiagnosis,258.6179820434712,landmark,True1913152893301,10.1016/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,True1924385763767,10.24963/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,True1937110333790,10.4230/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,True1947118302872,10.1007/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,True1952892035503,10.1007/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,True1962962776659,10.57702/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,True1972963285578,10.4230/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,True1983124006607,10.26190/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,True1994398159307,10.2139/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,True2002593744649,10.1016/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,True2012885195348,10.1016/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,True2022949117887,10.57702/o9raffed,,Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,arXiv (Cornell University),2024,preprint,computer_vision,3,14211.0,14214.0,5890.544305226324,14214,,True,True,post,batchnormalizationacceleratingdeepnetworktrainingbyreducinginternalcovariateshift,5890.544305226324,landmark,True2033118608800,10.57702/zp44cu3g,,Learning Multiple Layers of Features from Tiny Images,,2024,article,computer_vision,3,23325.0,23341.0,2765.184913506997,23395,559.7906,True,True,post,learningmultiplelayersoffeaturesfromtinyimages,2765.184913506997,landmark,True2044386076325,10.1109/cvpr52729.2023.00721,2207.02696,YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2023,conference-paper,computer_vision,5,6781.0,10586.0,1477.2917574868547,12390,1387.4265,True,True,baseline,yolov7trainablebagoffreebiessetsnewstateoftheartforrealtimeobjectdetectors,1477.2917574868547,landmark,True2053119786062,,,An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,International Conference on Learning Representations,2021,conference-paper,computer_vision,5,4071.0,9465.0,1181.820775699879,449,38.1179,True,False,baseline,animageisworth16x16wordstransformersforimagerecognitionatscale,1181.820775699879,landmark,True2063138516171,10.1109/iccv48922.2021.00986,2103.14030,Swin Transformer: Hierarchical Vision Transformer using Shifted Windows,IEEE/CVF International Conference on Computer Vision (ICCV),2021,conference-paper,computer_vision,6,3057.0,9391.0,1172.5809724878566,33262,1556.4216,True,True,baseline,swintransformerhierarchicalvisiontransformerusingshiftedwindows,1172.5809724878566,landmark,True2074402754006,10.1109/cvpr52733.2024.01605,,DETRs Beat YOLOs on Real-time Object Detection,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2024,conference-paper,computer_vision,4,2662.0,4773.0,1001.7389274520061,4548,991.256,True,False,post,detrsbeatyolosonrealtimeobjectdetection,1001.7389274520061,landmark,True2084403770406,10.1007/978-3-031-72751-1_1,2402.13616,YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,Lecture notes in computer science,2024,conference-paper,computer_vision,3,3072.0,4486.0,941.504468583637,4486,979.4658,False,True,post,yolov9learningwhatyouwanttolearnusingprogrammablegradientinformation,941.504468583637,landmark,True2094312933868,10.1109/cvpr52688.2022.01042,,High-Resolution Image Synthesis with Latent Diffusion Models,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,computer_vision,3,1866.0,6719.0,907.833780459159,16051,1184.1165,True,False,baseline,highresolutionimagesynthesiswithlatentdiffusionmodels,907.833780459159,landmark,True2104226002507,10.1109/cvpr52688.2022.01553,,Masked Autoencoders Are Scalable Vision Learners,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,computer_vision,4,1906.0,4278.0,849.9419256281733,7977,974.4316,True,True,baseline,maskedautoencodersarescalablevisionlearners,849.9419256281733,landmark,True2114399300671,10.1002/mp.17191,1912.01703,The effects of back‐projection variants in BPF‐like TOF PET reconstruction using CNN filtration – Based on simulated and clinical brain data,Medical Physics,2024,article,computer_vision,5,14860.0,14915.0,822.2465127032651,0,0.0,True,True,baseline,theeffectsofbackprojectionvariantsinbpfliketofpetreconstructionusingcnnfiltrationbasedonsimulatedandclinicalbraindata,822.2465127032651,landmark,True2127106270182,10.82419/203,,Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection,MBZUAI iRep,2022,dissertation,computer_vision,4,24.0,53.0,720.1328671328672,0,,True,True,baseline,bridgingthegapbetweenobjectandimagelevelrepresentationsforopenvocabularydetection,720.1328671328672,landmark,True2134415795831,10.52202/079017-3429,2405.14458,YOLOv10: Real-Time End-to-End Object Detection,neural information processing systems,2024,conference-paper,computer_vision,4,1157.0,2788.0,585.1347432927286,2788,608.7273,True,True,post,yolov10realtimeendtoendobjectdetection,585.1347432927286,landmark,True2143035524453,10.1109/cvpr42600.2020.00975,1911.05722,Momentum Contrast for Unsupervised Visual Representation Learning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2020,conference-paper,computer_vision,4,1903.0,3802.0,538.8557516528527,12652,650.908,True,True,baseline,momentumcontrastforunsupervisedvisualrepresentationlearning,538.8557516528527,landmark,True2154321232185,10.1007/978-3-031-25066-8_9,2105.05537,Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation,Lecture notes in computer science,2023,conference-paper,computer_vision,3,2204.0,3841.0,536.0171585591356,4853,526.4534,False,True,baseline,swinunetunetlikepuretransformerformedicalimagesegmentation,536.0171585591356,landmark,True2163005680577,10.48550/arxiv.2002.05709,2002.05709,A Simple Framework for Contrastive Learning of Visual Representations,arXiv (Cornell University),2020,preprint,computer_vision,5,1836.0,3360.0,523.1590380139643,6894,,True,True,baseline,asimpleframeworkforcontrastivelearningofvisualrepresentations,523.1590380139643,landmark,True2173182555022,,,Generative Adversarial Networks for Semantic Image Synthesis and Unconditional Synthesis with Limited Data,bonndoc (University of Bonn),2024,dissertation,computer_vision,3,45.0,47.0,515.6,47,,True,True,post,generativeadversarialnetworksforsemanticimagesynthesisandunconditionalsynthesiswithlimiteddata,515.6,landmark,True2183152382703,,,EVOLUTION OF YOLO ALGORITHM AND YOLOV5: THE STATE-OF-THE-ART OBJECT DETECTION ALGORITHM,,2021,dissertation,computer_vision,2,51.0,80.0,513.8375350140057,97,,False,False,baseline,evolutionofyoloalgorithmandyolov5thestateoftheartobjectdetectionalgorithm,513.8375350140057,landmark,True2194312443924,10.1109/cvpr52688.2022.01167,,A ConvNet for the 2020s,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,computer_vision,3,1653.0,3671.0,496.0050317109053,8300,604.8545,True,False,baseline,aconvnetforthe2020s,496.0050317109053,landmark,True2202335728318,10.57702/e7wwo0zk,,Reading digits in natural images with unsupervised feature learning,,2024,article,computer_vision,1,4147.0,4149.0,491.5278782460276,4149,55.5129,True,True,post,readingdigitsinnaturalimageswithunsupervisedfeaturelearning,491.5278782460276,landmark,True2214390873054,10.1109/iccv51070.2023.00355,,Adding Conditional Control to Text-to-Image Diffusion Models,IEEE/CVF International Conference on Computer Vision (ICCV),2023,conference-paper,computer_vision,6,1511.0,3376.0,471.1257295744967,4408,501.2109,True,False,post,addingconditionalcontroltotexttoimagediffusionmodels,471.1257295744967,landmark,True2222963163009,10.1109/cvpr.2018.00474,1801.04381,MobileNetV2: Inverted Residuals and Linear Bottlenecks,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2018,conference-paper,computer_vision,3,1242.0,3514.0,430.50535987748856,27247,513.8505,True,True,baseline,mobilenetv2invertedresidualsandlinearbottlenecks,430.50535987748856,landmark,True2234300424419,10.4230/lipics.giscience.2025.18,,U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping,Leibniz international proceedings in informatics,2025,conference-paper,computer_vision,3,977.0,977.0,144.34954954954955,977,1317.4998,True,True,post,uprithviintegratingafoundationmodelandunetforenhancedfloodinundationmapping,426.921221602615,landmark,True2244224035735,10.48550/arxiv.2204.06125,2204.06125,Hierarchical Text-Conditional Image Generation with CLIP Latents,arXiv (Cornell University),2022,preprint,computer_vision,5,904.0,1882.0,408.5254913442887,2203,,True,True,baseline,hierarchicaltextconditionalimagegenerationwithcliplatents,408.5254913442887,landmark,True2254200503948,10.7735/ksmte.2021.30.6.513,2004.10934,Comparative Analysis of Defect Detection Using YOLO of Deep Learning,Journal of the korean society of manufacturing technology engineers,2021,article,computer_vision,3,4221.0,6845.0,406.01302821116474,8,0.9035,True,True,baseline,comparativeanalysisofdefectdetectionusingyoloofdeeplearning,406.01302821116474,landmark,True2264398756379,10.1109/adics58448.2024.10533619,,YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness,,2024,conference-paper,computer_vision,2,1232.0,1876.0,393.72768235909564,1876,409.6027,True,False,post,yolov8anovelobjectdetectionalgorithmwithenhancedperformanceandrobustness,393.72768235909564,landmark,True2272626778328,10.65215/2q58a426,1706.03762,Attention Is All You Need,,2025,preprint,nlp,5,24486.0,24486.0,29646.72006980803,24487,,True,True,post,attentionisallyouneed,10699.68580569256,landmark,True2282963341956,10.18653/v1/n19-1423,,BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT),2019,conference-paper,nlp,3,9645.0,19296.0,2566.536785186562,31944,2211.5894,True,True,baseline,bertpretrainingofdeepbidirectionaltransformersforlanguageunderstanding,2566.536785186562,landmark,True2293098044990,,,Traduction Non Supervisée de Langages de Programmation,theses.fr (ABES),2022,dissertation,nlp,4,121.0,143.0,1240.408521303258,162,,True,True,baseline,traductionnonsupervisedelangagesdeprogrammation,1240.408521303258,landmark,True2302896457183,10.4230/lipics.cosit.2022.18,1810.04805,AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale,arXiv (Cornell University),2018,preprint,nlp,2,1630.0,5855.0,902.4576906598114,44705,,True,True,baseline,aiassistedpipelinefordynamicgenerationoftrustworthyhealthsupplementcontentatscale,902.4576906598114,landmark,True2312787560479,10.18653/v1/n18-1202,1802.05365,Deep Contextualized Word Representations,Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT),2018,conference-paper,nlp,2,2252.0,4791.0,741.8627823725466,11541,762.8608,True,True,baseline,deepcontextualizedwordrepresentations,741.8627823725466,landmark,True2324224308101,10.48550/arxiv.2204.02311,2204.02311,PaLM: Scaling Language Modeling with Pathways,arXiv (Cornell University),2022,preprint,nlp,7,950.0,1657.0,481.54051809067556,2051,,True,True,baseline,palmscalinglanguagemodelingwithpathways,481.54051809067556,landmark,True2334411403346,10.1145/3744746,2307.06435,A Comprehensive Overview of Large Language Models,ACM Transactions on Intelligent Systems and Technology,2025,article,nlp,5,1097.0,1097.0,373.692295245369,1097,382.0017,True,True,post,acomprehensiveoverviewoflargelanguagemodels,479.357809721667,landmark,True2342979736514,10.13025/17598,,Neural transfer learning for natural language processing,Arrow@dit (Dublin Institute of Technology),2019,dissertation,nlp,5,62.0,116.0,422.42293906810033,177,,True,True,baseline,neuraltransferlearningfornaturallanguageprocessing,422.42293906810033,landmark,True2354386506836,10.1515/9781474471459-050,,5.7 The WORDNET lexical database of English,Edinburgh University Press eBooks,2019,book-chapter,nlp,1,1138.0,1337.0,415.89661853577525,1865,230.3576,False,False,baseline,57thewordnetlexicaldatabaseofenglish,415.89661853577525,landmark,True2362982399380,10.18653/v1/2020.acl-main.703,1910.13461,"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",Annual Meeting of the Association for Computational Linguistics (ACL),2020,conference-paper,nlp,5,1454.0,2915.0,413.1416402072767,8244,528.5478,True,True,baseline,bartdenoisingsequencetosequencepretrainingfornaturallanguagegenerationtranslationandcomprehension,413.1416402072767,landmark,True2374391136507,10.1145/3641289,2307.03109,A Survey on Evaluation of Large Language Models,ACM Transactions on Intelligent Systems and Technology,2024,article,nlp,5,2160.0,3004.0,373.1207027772732,3004,440.36,True,True,post,asurveyonevaluationoflargelanguagemodels,373.1207027772732,landmark,True2382979826702,10.18653/v1/2020.emnlp-demos.6,,Transformers: State-of-the-Art Natural Language Processing,Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP),2020,conference-paper,nlp,3,965.0,2605.0,369.2054794991272,8292,488.3322,True,True,baseline,transformersstateoftheartnaturallanguageprocessing,369.2054794991272,landmark,True2394404534210,10.1145/3703155,2311.05232,"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions",ACM Transactions on Information Systems,2024,article,nlp,4,1138.0,2379.0,295.4907296628272,2379,354.0441,True,True,post,asurveyonhallucinationinlargelanguagemodelsprinciplestaxonomychallengesandopenquestions,295.4907296628272,landmark,True2407125799258,10.1109/tpami.2026.3657354,2312.12148,Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment,IEEE Transactions on Pattern Analysis and Machine Intelligence,2026,article,nlp,4,115.0,115.0,127.97217616701306,115,191.4688,True,True,post,parameterefficientfinetuningmethodsforpretrainedlanguagemodelsacriticalreviewandassessment,291.5790974019528,landmark,True2412983040767,10.18653/v1/2020.acl-main.747,1911.02116,Unsupervised Cross-lingual Representation Learning at Scale,Annual Meeting of the Association for Computational Linguistics (ACL),2020,conference-paper,nlp,3,1179.0,2047.0,290.1203902244581,5335,332.4086,True,True,baseline,unsupervisedcrosslingualrepresentationlearningatscale,290.1203902244581,landmark,True2422948223045,10.18653/v1/p19-1355,1906.02243,Energy and Policy Considerations for Deep Learning in NLP,Annual Meeting of the Association for Computational Linguistics (ACL),2019,conference-paper,nlp,4,368.0,863.0,257.5756075430014,3453,514.1231,True,True,baseline,energyandpolicyconsiderationsfordeeplearninginnlp,257.5756075430014,landmark,True2433044864684,,,Leveraging orthographic information to improve machine translation of under-resourced languages,,2020,dissertation,nlp,3,52.0,53.0,244.74101247248717,58,,False,False,baseline,leveragingorthographicinformationtoimprovemachinetranslationofunderresourcedlanguages,244.74101247248717,landmark,True2444404782964,10.18653/v1/2024.emnlp-main.64,,A Survey on In-context Learning,Conference on Empirical Methods in Natural Language Processing (EMNLP),2024,conference-paper,nlp,3,646.0,879.0,244.3285362084626,632,188.3078,True,True,post,asurveyonincontextlearning,244.3285362084626,landmark,True2454221142221,10.48550/arxiv.2203.05794,2203.05794,BERTopic: Neural topic modeling with a class-based TF-IDF procedure,arXiv (Cornell University),2022,preprint,nlp,3,416.0,812.0,235.9751965537891,1342,,True,True,baseline,bertopicneuraltopicmodelingwithaclassbasedtfidfprocedure,235.9751965537891,landmark,True2463169483174,10.18653/v1/2021.naacl-main.41,,mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer,Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT),2021,conference-paper,nlp,4,642.0,1458.0,231.16193233956648,1621,144.7472,True,True,baseline,mt5amassivelymultilingualpretrainedtexttotexttransformer,231.16193233956648,landmark,True2473196350727,10.4230/oasics.ldk.2021.34,,"HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case",DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2021,article,nlp,1,8042.0,12201.0,226.99110878661088,0,0.0,True,True,baseline,historiaehistoryofsocioculturaltransformationaslinguisticdatascienceahumanitiesusecase,226.99110878661088,landmark,True2484229005866,10.4230/oasics.slate.2022.19,2205.01068,Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs,arXiv (Cornell University),2022,preprint,nlp,5,473.0,740.0,215.05128749975853,879,,True,True,baseline,analysingofftheshelfoptionsforquestionansweringwithportuguesefaqs,215.05128749975853,landmark,True2492798812533,10.18653/v1/p18-1031,1801.06146,Universal Language Model Fine-tuning for Text Classification,Annual Meeting of the Association for Computational Linguistics (ACL),2018,conference-paper,nlp,6,617.0,1344.0,208.11178866806569,3882,191.3134,True,True,baseline,universallanguagemodelfinetuningfortextclassification,208.11178866806569,landmark,True2503156636935,10.18653/v1/2021.emnlp-main.552,2104.08821,SimCSE: Simple Contrastive Learning of Sentence Embeddings,Conference on Empirical Methods in Natural Language Processing (EMNLP),2021,conference-paper,nlp,5,480.0,1288.0,204.20889496115336,2686,230.7564,True,True,baseline,simcsesimplecontrastivelearningofsentenceembeddings,204.20889496115336,landmark,True2514402670856,10.18653/v1/2024.findings-acl.137,2402.03216,"M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation",,2024,conference-paper,nlp,6,315.0,700.0,194.5733507917222,700,212.9491,True,True,post,m3embeddingmultilingualitymultifunctionalitymultigranularitytextembeddingsthroughselfknowledgedistillation,194.5733507917222,landmark,True2522219249508,10.1007/978-3-032-27827-2_35,,Perceptual Signal Analysis and Eigenvalue-Weighted Metrics for Musical Audio Quality Assessment,Lecture notes in computer science,2026,conference-paper,speech_audio,2,844.0,844.0,3046.350724637681,845,0.0,False,True,post,perceptualsignalanalysisandeigenvalueweightedmetricsformusicalaudioqualityassessment,2139.9370278891147,landmark,True2531524333225,10.57702/jb3fvbn9,,Kaldi Speech Recognition Toolkit,Infoscience (Ecole Polytechnique Fédérale de Lausanne),2024,article,speech_audio,1,4597.0,4599.0,571.2323941653393,4609,5.5064,True,True,post,kaldispeechrecognitiontoolkit,571.2323941653393,landmark,True2544392233542,10.3390/s24051546,,A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids,Sensors,2024,dissertation,speech_audio,1,7.0,19.0,327.9259259259259,19,,True,True,post,areviewofmachinelearningapproachesforthepersonalizationofamplificationinhearingaids,327.9259259259259,landmark,True2554311000453,10.48550/arxiv.2212.04356,2212.04356,Robust Speech Recognition via Large-Scale Weak Supervision,arXiv (Cornell University),2022,preprint,speech_audio,5,337.0,759.0,220.57287461123883,1134,,True,True,post,robustspeechrecognitionvialargescaleweaksupervision,220.57287461123883,landmark,True2562936774411,10.21437/interspeech.2019-2680,1904.08779,SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition,Interspeech (USB),2019,conference-paper,speech_audio,5,465.0,1205.0,160.2755403270008,3499,180.2006,True,True,baseline,specaugmentasimpledataaugmentationmethodforautomaticspeechrecognition,160.2755403270008,landmark,True2572952218014,10.1109/taslp.2019.2915167,1809.07454,Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation,IEEE/ACM Transactions on Audio Speech and Language Processing,2019,article,speech_audio,2,273.0,598.0,126.90333200584095,2042,137.8473,True,True,baseline,convtasnetsurpassingidealtimefrequencymagnitudemaskingforspeechseparation,126.90333200584095,landmark,True2584406417959,10.1109/taslpro.2025.3530270,,Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers,IEEE Transactions on Audio Speech and Language Processing,2025,article,speech_audio,2,281.0,281.0,95.7224566672276,122,50.7671,True,False,post,neuralcodeclanguagemodelsarezeroshottexttospeechsynthesizers,122.78901051211342,landmark,True2592890964092,10.1109/icassp.2018.8461375,,X-Vectors: Robust DNN Embeddings for Speaker Recognition,IEEE International Conference on Acoustics Speech and Signal Processing,2018,conference-paper,speech_audio,4,274.0,698.0,108.08186643624245,2620,119.8439,True,False,baseline,xvectorsrobustdnnembeddingsforspeakerrecognition,108.08186643624245,landmark,True2603097777922,10.21437/interspeech.2020-3015,2005.08100,Conformer: Convolution-augmented Transformer for Speech Recognition,Interspeech (USB),2020,conference-paper,speech_audio,4,335.0,755.0,107.00581075694473,3070,145.6429,True,True,baseline,conformerconvolutionaugmentedtransformerforspeechrecognition,107.00581075694473,landmark,True2612962866211,10.1109/taslp.2018.2842159,1708.07524,Supervised Speech Separation Based on Deep Learning: An Overview,IEEE/ACM Transactions on Audio Speech and Language Processing,2018,article,speech_audio,3,206.0,429.0,106.2474690299835,1524,120.6683,True,True,baseline,supervisedspeechseparationbasedondeeplearninganoverview,106.2474690299835,landmark,True2621530164934,10.7275/a01b-fx07,,A Probabilistic Model of Hierarchical Music Analysis,Scholarworks (University of Massachusetts Amherst),2020,dissertation,speech_audio,1,15.0,15.0,105.97222222222223,36,,True,True,baseline,aprobabilisticmodelofhierarchicalmusicanalysis,105.97222222222223,landmark,True2632964243274,10.1109/icassp.2018.8461368,1712.05884,Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,IEEE International Conference on Acoustics Speech and Signal Processing,2018,conference-paper,speech_audio,3,299.0,662.0,102.50744352549069,2692,119.0117,True,True,baseline,naturalttssynthesisbyconditioningwavenetonmelspectrogrampredictions,102.50744352549069,landmark,True2643167533889,10.48550/arxiv.2106.04624,2106.04624,SpeechBrain: A General-Purpose Speech Toolkit,arXiv (Cornell University),2021,preprint,speech_audio,1,129.0,288.0,96.44297133944238,504,,True,True,baseline,speechbrainageneralpurposespeechtoolkit,96.44297133944238,landmark,True2653169320628,10.1109/taslp.2021.3122291,2106.07447,HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units,IEEE/ACM Transactions on Audio Speech and Language Processing,2021,article,speech_audio,5,222.0,765.0,89.15495130101968,2919,124.2084,True,True,baseline,hubertselfsupervisedspeechrepresentationlearningbymaskedpredictionofhiddenunits,89.15495130101968,landmark,True2662975281968,,,"Modeling Music : Studies of Music Transcription, Music Perception and Music Production",KTH Publication Database DiVA (KTH Royal Institute of Technology),2018,dissertation,speech_audio,6,1.0,13.0,87.36363636363636,15,,True,True,baseline,modelingmusicstudiesofmusictranscriptionmusicperceptionandmusicproduction,87.36363636363636,landmark,True2674372260310,10.1109/icassp49357.2023.10095969,2211.06687,Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation,IEEE International Conference on Acoustics Speech and Signal Processing,2023,conference-paper,speech_audio,5,124.0,320.0,87.23607346034898,439,97.1083,True,True,post,largescalecontrastivelanguageaudiopretrainingwithfeaturefusionandkeywordtocaptionaugmentation,87.23607346034898,landmark,True2684372266552,10.1109/icassp49357.2023.10095889,2206.04769,CLAP Learning Audio Concepts from Natural Language Supervision,IEEE International Conference on Acoustics Speech and Signal Processing,2023,conference-paper,speech_audio,4,134.0,318.0,86.6908480012218,438,96.4447,True,True,baseline,claplearningaudioconceptsfromnaturallanguagesupervision,86.6908480012218,landmark,True2693209984917,10.1109/jstsp.2022.3188113,2110.13900,WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing,IEEE Journal of Selected Topics in Signal Processing,2022,article,speech_audio,5,388.0,800.0,82.43168975381538,1897,117.1333,True,True,baseline,wavlmlargescaleselfsupervisedpretrainingforfullstackspeechprocessing,82.43168975381538,landmark,True2703196974791,10.21437/interspeech.2021-698,,AST: Audio Spectrogram Transformer,Interspeech (USB),2021,conference-paper,speech_audio,2,119.0,308.0,72.25665452507705,1086,83.5514,True,False,baseline,astaudiospectrogramtransformer,72.25665452507705,landmark,True2712775304348,10.1109/icassp.2018.8462105,1712.01769,State-of-the-Art Speech Recognition with Sequence-to-Sequence Models,IEEE International Conference on Acoustics Speech and Signal Processing,2018,conference-paper,speech_audio,6,240.0,455.0,70.45451178866807,1009,71.1573,True,True,baseline,stateoftheartspeechrecognitionwithsequencetosequencemodels,70.45451178866807,landmark,True2723015199127,10.1109/icassp40776.2020.9054266,,Dual-Path RNN: Efficient Long Sequence Modeling for Time-Domain Single-Channel Speech Separation,IEEE International Conference on Acoustics Speech and Signal Processing,2020,conference-paper,speech_audio,2,151.0,293.0,68.6972461420411,780,69.2189,True,False,baseline,dualpathrnnefficientlongsequencemodelingfortimedomainsinglechannelspeechseparation,68.6972461420411,landmark,True2732808631503,10.21437/interspeech.2018-1929,1806.05622,VoxCeleb2: Deep Speaker Recognition,Interspeech (USB),2018,conference-paper,speech_audio,4,149.0,442.0,68.44152573756327,2246,81.0403,True,True,baseline,voxceleb2deepspeakerrecognition,68.44152573756327,landmark,True2742972333964,10.21437/interspeech.2019-2008,,GELP: GAN-Excited Liner Prediction for Speech Synthesis from Mel-Spectrogram,Aaltodoc (Aalto University),2019,dissertation,speech_audio,3,8.0,17.0,61.90681003584229,34,,True,True,baseline,gelpganexcitedlinerpredictionforspeechsynthesisfrommelspectrogram,61.90681003584229,landmark,True2754221145109,10.48550/arxiv.2202.03555,2202.03555,"data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language",arXiv (Cornell University),2022,preprint,speech_audio,8,152.0,208.0,60.446848378310506,231,,True,True,baseline,data2vecageneralframeworkforselfsupervisedlearninginspeechvisionandlanguage,60.446848378310506,landmark,True2762898847420,10.1109/icassp.2019.8683143,1811.00002,Waveglow: A Flow-based Generative Network for Speech Synthesis,IEEE International Conference on Acoustics Speech and Signal Processing,2019,conference-paper,speech_audio,4,233.0,453.0,60.25296246317956,972,60.7013,True,True,baseline,waveglowaflowbasedgenerativenetworkforspeechsynthesis,60.25296246317956,landmark,True2777205708264,10.1146/annurev,,Relationship between educational robotics and executive functions in children with neurodevelopmental disorders,HAL (Le Centre pour la Communication Scientifique Directe),2026,conference-paper,robotics,1,117.0,117.0,897.9818032562595,123,157.1634,True,True,post,relationshipbetweeneducationalroboticsandexecutivefunctionsinchildrenwithneurodevelopmentaldisorders,296.6500382263346,landmark,True2784385473486,10.48550/arxiv.2307.15818,2307.15818,RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control,arXiv (Cornell University),2023,preprint,robotics,7,157.0,252.0,151.2,274,,True,True,post,rt2visionlanguageactionmodelstransferwebknowledgetoroboticcontrol,151.2,landmark,True2794293774946,10.1007/978-981-19-2980-9_32,,Employing Adaptive Learning and Intelligent Tutoring Robots for Virtual Classrooms and Smart Campuses: Reforming Education in the Age of Artificial Intelligence,Lecture notes in electrical engineering,2022,book-chapter,robotics,1,91.0,175.0,135.97268806899856,262,161.0471,False,False,baseline,employingadaptivelearningandintelligenttutoringrobotsforvirtualclassroomsandsmartcampusesreformingeducationintheageofartificialintelligence,135.97268806899856,landmark,True2802963188159,10.15607/rss.2018.xiv.019,1711.00199,PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes,,2018,conference-paper,robotics,4,201.0,451.0,129.2718583898181,2127,284.4153,True,True,baseline,posecnnaconvolutionalneuralnetworkfor6dobjectposeestimationinclutteredscenes,129.2718583898181,landmark,True2814383097638,10.1109/icra48891.2023.10160591,2209.07753,Code as Policies: Language Model Programs for Embodied Control,Proceedings - IEEE International Conference on Robotics and Automation/Proceedings,2023,conference-paper,robotics,3,237.0,511.0,125.95880564003319,722,324.1052,True,True,post,codeaspolicieslanguagemodelprogramsforembodiedcontrol,125.95880564003319,landmark,True2824385430679,10.15607/rss.2023.xix.025,2212.06817,RT-1: Robotics Transformer for Real-World Control at Scale,,2023,conference-paper,robotics,10,195.0,488.0,120.28942691259529,689,309.7205,True,True,post,rt1roboticstransformerforrealworldcontrolatscale,120.28942691259529,landmark,True2834414079054,10.15607/rss.2025.xxi.010,,π₀: A Vision-Language-Action Flow Model for General Robot Control,,2025,conference-paper,robotics,2,274.0,274.0,243.52116128619693,274,276.3475,True,True,post,avisionlanguageactionflowmodelforgeneralrobotcontrol,119.73020953850207,landmark,True2842990747716,10.1177/0278364919887447,,Learning dexterous in-hand manipulation,The International Journal of Robotics Research,2019,article,robotics,2,490.0,777.0,118.1470865906983,1585,89.792,True,True,baseline,learningdexterousinhandmanipulation,118.1470865906983,landmark,True2854383108457,10.1109/icra48891.2023.10161317,2209.11302,ProgPrompt: Generating Situated Robot Task Plans using Large Language Models,Proceedings - IEEE International Conference on Robotics and Automation/Proceedings,2023,conference-paper,robotics,7,245.0,492.0,113.46421800672258,583,109.6395,True,True,baseline,progpromptgeneratingsituatedrobottaskplansusinglargelanguagemodels,113.46421800672258,landmark,True2861581527321,10.7907/2e18-0e29,,Collision Detection and Avoidance in Computer Controlled Manipulators,CaltechTHESIS (California Institute of Technology),2018,dissertation,robotics,1,14.0,16.0,110.6320610687023,276,,True,True,baseline,collisiondetectionandavoidanceincomputercontrolledmanipulators,110.6320610687023,landmark,True2873043971245,10.1109/tro.2021.3075644,2007.11898,"ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM",IEEE Transactions on Robotics,2021,article,robotics,2,542.0,1312.0,104.21312513733956,4274,299.5553,True,True,baseline,orbslam3anaccurateopensourcelibraryforvisualvisualinertialandmultimapslam,104.21312513733956,landmark,True2887126053009,10.1016/j.inffus.2026.104193,2508.05342,Information-theoretic graph fusion with vision-language-action model for policy reasoning and dual robotic control,Information Fusion,2026,article,robotics,3,41.0,41.0,45.41643153526971,41,97.2957,False,True,post,informationtheoreticgraphfusionwithvisionlanguageactionmodelforpolicyreasoninganddualroboticcontrol,103.95428689982666,landmark,True2894323572061,10.48550/arxiv.2303.03378,2303.03378,PaLM-E: An Embodied Multimodal Language Model,arXiv (Cornell University),2023,preprint,robotics,7,263.0,330.0,103.01438010104935,346,,True,True,post,palmeanembodiedmultimodallanguagemodel,103.01438010104935,landmark,True2903003533476,10.1109/tits.2021.3054625,2002.00444,Deep Reinforcement Learning for Autonomous Driving: A Survey,IEEE Transactions on Intelligent Transportation Systems,2021,article,robotics,3,436.0,870.0,101.39190540115963,2313,122.2676,True,True,baseline,deepreinforcementlearningforautonomousdrivingasurvey,101.39190540115963,landmark,True2914385430674,10.15607/rss.2023.xix.016,2304.13705,Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware,,2023,conference-paper,robotics,6,86.0,395.0,97.36541727556381,649,291.7396,True,True,post,learningfinegrainedbimanualmanipulationwithlowcosthardware,97.36541727556381,landmark,True2927130402520,10.1109/tro.2026.3666139,2402.13255,How NeRFs and 3-D Gaussian Splatting Are Reshaping SLAM: A Survey,IEEE Transactions on Robotics,2026,article,robotics,3,38.0,38.0,49.59542857142858,38,48.5016,True,True,post,hownerfsand3dgaussiansplattingarereshapingslamasurvey,96.34787566325397,landmark,True2934398796510,10.1109/tnnls.2025.3650584,2405.14093,A Survey on Vision--Language--Action Models for Embodied AI,IEEE Transactions on Neural Networks and Learning Systems,2026,article,robotics,10,36.0,36.0,96.49320091673033,36,77.9076,True,True,post,asurveyonvisionlanguageactionmodelsforembodiedai,91.27693483887218,landmark,True2944385403811,10.15607/rss.2023.xix.026,,Diffusion Policy: Visuomotor Policy Learning via Action Diffusion,,2023,conference-paper,robotics,2,113.0,387.0,89.24929343211716,522,98.6756,False,True,post,diffusionpolicyvisuomotorpolicylearningviaactiondiffusion,89.24929343211716,landmark,True2952925329277,10.23919/ecc.2019.8796030,1903.11199,Control Barrier Functions: Theory and Applications,European Control Conference (ECC),2019,conference-paper,robotics,2,90.0,265.0,87.20226791818132,1936,124.0197,True,True,baseline,controlbarrierfunctionstheoryandapplications,87.20226791818132,landmark,True2964388761637,10.17760/d20592696,2308.13972,Towards autonomous multi-modal mobility morphobot (M4) robot: traversability estimation and 3D path planning,,2023,dissertation,robotics,4,1.0,4.0,86.35051546391752,4,,True,True,post,towardsautonomousmultimodalmobilitymorphobotm4robottraversabilityestimationand3dpathplanning,86.35051546391752,landmark,True2973129245057,10.1109/iros45743.2020.9341176,2007.00258,LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping,IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),2020,conference-paper,robotics,4,136.0,357.0,83.05667731629394,2199,295.3223,True,True,baseline,liosamtightlycoupledlidarinertialodometryviasmoothingandmapping,83.05667731629394,landmark,True2984390874280,10.1109/iccv51070.2023.00280,2212.04088,LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models,IEEE/CVF International Conference on Computer Vision (ICCV),2023,conference-paper,robotics,9,133.0,329.0,81.09676527508987,420,188.7991,True,True,post,llmplannerfewshotgroundedplanningforembodiedagentswithlargelanguagemodels,81.09676527508987,landmark,True2994406918931,10.1186/s44147-025-00582-3,,"UAV swarms: research, challenges, and future directions",Journal of Engineering and Applied Science,2025,article,robotics,1,182.0,182.0,69.22742920177257,182,80.227,True,True,post,uavswarmsresearchchallengesandfuturedirections,79.52882531389552,landmark,True3002758237641,10.1109/icra.2018.8460875,,Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping,Proceedings - IEEE International Conference on Robotics and Automation/Proceedings,2018,conference-paper,robotics,3,152.0,260.0,74.52479641098161,609,130.2781,True,True,baseline,usingsimulationanddomainadaptationtoimproveefficiencyofdeeproboticgrasping,74.52479641098161,landmark,True3017140452175,10.1109/tmech.2025.3579247,2501.02116,"Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning",IEEE/ASME Transactions on Mechatronics,2026,article,robotics,3,28.0,28.0,50.20033955857386,28,41.6958,True,True,post,humanoidlocomotionandmanipulationcurrentprogressandchallengesincontrolplanningandlearning,70.99317154134503,landmark,True3024399978538,10.3390/diagnostics14131333,,Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality?,Diagnostics,2024,dissertation,hci,3,35.0,60.0,658.2127659574469,60,,True,True,post,exploringaugmentedrealityintegrationindiagnosticimagingmythorreality,658.2127659574469,landmark,True3032518959197,10.25335/nw4q-va87,,Cybersickness prioritization and modeling,Michigan State University Libraries,2024,dissertation,hci,2,23.0,23.0,291.2096774193549,23,,False,True,post,cybersicknessprioritizationandmodeling,291.2096774193549,landmark,True3044387835442,10.1145/3586183.3606763,2304.03442,Generative Agents: Interactive Simulacra of Human Behavior,ACM Symposium on User Interface Software and Technology (UIST),2023,conference-paper,hci,6,466.0,1188.0,273.9745751869643,2048,387.1409,True,True,post,generativeagentsinteractivesimulacraofhumanbehavior,273.9745751869643,landmark,True3054404112583,10.1561/1100000095,2110.05352,"All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda",Foundations and Trends® in Human–Computer Interaction,2024,article,hci,7,1016.0,1085.0,265.27941176470586,1082,154.4433,True,True,baseline,alloneneedstoknowaboutmetaverseacompletesurveyontechnologicalsingularityvirtualecosystemandresearchagenda,265.27941176470586,landmark,True3064294891500,10.1109/comst.2022.3202047,2203.02662,"A Survey on Metaverse: Fundamentals, Security, and Privacy",IEEE Communications Surveys & Tutorials,2022,article,hci,7,568.0,1062.0,261.00875052618636,1575,347.0709,True,True,baseline,asurveyonmetaversefundamentalssecurityandprivacy,261.00875052618636,landmark,True3072910064571,10.1007/978-3-319-96906-0_6,,A Practical Guide to Usability Testing,Springer eBooks,2019,book-chapter,hci,1,301.0,354.0,258.125,659,109.4298,False,False,baseline,apracticalguidetousabilitytesting,258.125,landmark,True3082915602850,,,"Memes, Communities and Continuous Change: Chinese Internet Vernacular Explained",Data Archiving and Networked Services (DANS),2021,dissertation,hci,1,37.0,38.0,249.76068376068378,39,,False,True,baseline,memescommunitiesandcontinuouschangechineseinternetvernacularexplained,249.76068376068378,landmark,True3094414820664,10.21203/rs.3.rs-8148142/v1,2503.17473,How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study,Research Square,2026,preprint,hci,2,97.0,97.0,632.2017543859649,97,,False,True,post,howaiandhumanbehaviorsshapepsychosocialeffectsofextendedchatbotusealongitudinalrandomizedcontrolledstudy,245.94062998251673,landmark,True3104285585446,10.1016/j.ijinfomgt.2022.102542,,"Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy",International Journal of Information Management,2022,article,hci,2,787.0,1634.0,245.39307870858832,2732,283.5672,True,True,baseline,metaversebeyondthehypemultidisciplinaryperspectivesonemergingchallengesopportunitiesandagendaforresearchpracticeandpolicy,245.39307870858832,landmark,True3114409885395,10.1145/3706598.3713778,,The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers,CHI Conference on Human Factors in Computing Systems,2025,conference-paper,hci,3,502.0,502.0,162.5548189699133,502,530.8542,True,True,post,theimpactofgenerativeaioncriticalthinkingselfreportedreductionsincognitiveeffortandconfidenceeffectsfromasurveyofknowledgeworkers,219.35972696470085,landmark,True3122308543850,10.17615/cd1t-9y62,,Scene-motion- and latency-perception thresholds for head-mounted displays,Carolina Digital Repository (University of North Carolina at Chapel Hill),2019,dissertation,hci,2,25.0,26.0,195.9915254237288,30,,True,True,baseline,scenemotionandlatencyperceptionthresholdsforheadmounteddisplays,195.9915254237288,landmark,True3134206484811,10.1109/access.2021.3140175,,"A Metaverse: Taxonomy, Components, Applications, and Open Challenges",IEEE Access,2022,article,hci,1,791.0,1282.0,192.52994302595485,1824,197.5614,True,True,baseline,ametaversetaxonomycomponentsapplicationsandopenchallenges,192.52994302595485,landmark,True3144404301029,10.32604/cmc.2024.055575,,The Metaverse Review: Exploring the Boundless Ream of Digital Reality,"Computers, materials & continua/Computers, materials & continua (Print)",2024,dissertation,hci,2,11.0,15.0,189.9193548387097,15,,True,True,post,themetaversereviewexploringtheboundlessreamofdigitalreality,189.9193548387097,landmark,True3154402988539,10.3390/info15100596,2309.10892,Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education,Information,2024,article,hci,3,379.0,597.0,179.74180185270544,597,226.9308,True,True,post,artificialintelligenceenabledintelligentassistantforpersonalizedandadaptivelearninginhighereducation,179.74180185270544,landmark,True3164404826297,10.1080/10447318.2024.2431761,2303.13534,Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering,International Journal of Human-Computer Interaction,2024,article,hci,4,134.0,237.0,169.09230306674684,237,201.8338,True,True,post,promptingaiartaninvestigationintothecreativeskillofpromptengineering,169.09230306674684,landmark,True3174402128637,10.1080/0144929x.2024.2394886,,The promise and challenges of generative AI in education,Behaviour and Information Technology,2024,article,hci,3,193.0,515.0,155.05364816439413,515,197.0815,True,True,post,thepromiseandchallengesofgenerativeaiineducation,155.05364816439413,landmark,True3184366548330,10.1145/3544548.3581388,,Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts,CHI Conference on Human Factors in Computing Systems,2023,conference-paper,hci,4,330.0,657.0,151.5162423382454,902,170.5083,True,True,post,whyjohnnycantprompthownonaiexpertstryandfailtodesignllmprompts,151.5162423382454,landmark,True3194403637324,10.1038/s41562-024-02004-5,2408.12143,Promises and challenges of generative artificial intelligence for human learning,Nature Human Behaviour,2024,article,hci,2,213.0,483.0,145.41924672505314,483,184.453,False,True,post,promisesandchallengesofgenerativeartificialintelligenceforhumanlearning,145.41924672505314,landmark,True3204392772072,10.30574/gscarr.2024.18.2.0062,,AI in education: A review of personalized learning and educational technology,GSC Advanced Research and Reviews,2024,article,hci,4,285.0,425.0,127.9568941162476,425,162.6401,True,True,post,aiineducationareviewofpersonalizedlearningandeducationaltechnology,127.9568941162476,landmark,True3214392239564,10.1080/03075079.2024.2323593,,Human-AI collaboration patterns in AI-assisted academic writing,Studies in Higher Education,2024,article,hci,3,198.0,423.0,127.35474402628878,423,161.8747,True,True,post,humanaicollaborationpatternsinaiassistedacademicwriting,127.35474402628878,landmark,True3223102008235,10.17863/cam.56608,,"Addressing Injustices through MOOCs: A study among peri-urban, marginalised, South African youth",Apollo (University of Cambridge),2020,dissertation,hci,2,0.0,22.0,125.7948717948718,29,,True,True,baseline,addressinginjusticesthroughmoocsastudyamongperiurbanmarginalisedsouthafricanyouth,125.7948717948718,landmark,True3234405586205,10.1016/j.caeai.2024.100348,2405.11800,Generative AI in higher education: A global perspective of institutional adoption policies and guidelines,Computers and Education Artificial Intelligence,2024,article,hci,3,149.0,415.0,124.94614366645354,415,158.8133,True,True,post,generativeaiinhighereducationaglobalperspectiveofinstitutionaladoptionpoliciesandguidelines,124.94614366645354,landmark,True3243207138404,10.1145/3474085.3479238,2108.08985,Metaverse for Social Good,ACM International Conference on Multimedia (ACM MM),2021,conference-paper,hci,4,174.0,468.0,123.76423282498897,832,125.6356,True,True,baseline,metaverseforsocialgood,123.76423282498897,landmark,True3254408943584,10.3389/feduc.2025.1562391,,"Digital learning in the 21st century: trends, challenges, and innovations in technology integration",Frontiers in Education,2025,article,hci,2,274.0,274.0,237.88706394470083,274,299.3436,True,True,post,digitallearninginthe21stcenturytrendschallengesandinnovationsintechnologyintegration,119.73020953850207,landmark,True3264377235537,10.1109/jiot.2023.3278329,,"A Survey on the Metaverse: The State-of-the-Art, Technologies, Applications, and Challenges",IEEE Internet of Things Journal,2023,article,hci,1,393.0,633.0,119.44364807995791,716,119.1015,True,False,post,asurveyonthemetaversethestateofthearttechnologiesapplicationsandchallenges,119.44364807995791,landmark,True3272964015378,10.4230/lipics.ecoop.2025.6,,Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories,Leibniz international proceedings in informatics,2025,conference-paper,software_engineering,3,15245.0,15245.0,11583.902021240152,15246,2196.5447,True,True,post,detectingfunctionalityspecificvulnerabilitiesviaretrievingindividualfunctionalityequivalentapisinopensourcerepositories,6661.631548957897,landmark,True3284293846201,10.4230/oasics.saia.2024.1,,On Assessing ML Model Robustness: A Methodological Framework (Academic Track),OASIcs : OpenAccess Series in Informatics,2025,conference-paper,software_engineering,4,4701.0,4701.0,4178.076566446758,4702,904.6849,True,True,post,onassessingmlmodelrobustnessamethodologicalframeworkacademictrack,2054.203339563862,landmark,True3292095152777,10.7551/mitpress/12274.003.0035,,Managing the Development of Large Software Systems (1970),The MIT Press eBooks,2021,book-chapter,software_engineering,1,939.0,970.0,750.7662588538313,1778,102.1417,True,False,baseline,managingthedevelopmentoflargesoftwaresystems1970,750.7662588538313,landmark,True3302911537985,,,Microservices vs. Service-oriented architecture,Aaltodoc (Aalto University),2018,dissertation,software_engineering,2,50.0,67.0,685.3986866791745,93,,False,False,baseline,microservicesvsserviceorientedarchitecture,685.3986866791745,landmark,True3311486172410,10.1017/9781108974073.004,,Introduction to Software Testing,Cambridge University Press eBooks,2021,book-chapter,software_engineering,1,869.0,871.0,549.1255565449688,1056,136.0795,True,False,baseline,introductiontosoftwaretesting,549.1255565449688,landmark,True3327151510318,10.70675/416f6582z4576z444eza710z5e930d59bf97,,Unsupervised Translation of Programming Languages,,2022,dissertation,software_engineering,3,51.0,56.0,485.7543859649123,1,,True,False,baseline,unsupervisedtranslationofprogramminglanguages,485.7543859649123,landmark,True3334391974543,10.1109/tse.2024.3368208,2307.07221,"Software Testing With Large Language Models: Survey, Landscape, and Vision",IEEE Transactions on Software Engineering,2024,article,software_engineering,3,351.0,554.0,439.6107042253521,554,579.8344,True,True,post,softwaretestingwithlargelanguagemodelssurveylandscapeandvision,439.6107042253521,landmark,True3343185223169,,,An Effort Estimation Model for Agile Software Development,,2020,dissertation,software_engineering,1,36.0,45.0,423.2192575406032,60,,False,False,baseline,aneffortestimationmodelforagilesoftwaredevelopment,423.2192575406032,landmark,True3352171139698,10.22024/unikent/01.02.86353,,Test data generation: two evolutionary approaches to mutation testing,Kent Academic Repository (University of Kent),2021,dissertation,software_engineering,1,19.0,19.0,394.8918918918919,24,,True,True,baseline,testdatagenerationtwoevolutionaryapproachestomutationtesting,394.8918918918919,landmark,True3364402442491,10.5463/thesis.831,,From Smallest Software Particle to System Specification,,2024,dissertation,software_engineering,2,0.0,1.0,304.3333333333333,1,,True,True,post,fromsmallestsoftwareparticletosystemspecification,304.3333333333333,landmark,True3374408219312,10.17760/d20705205,,Leveraging type annotations for effective fuzzing of python programs,,2024,dissertation,software_engineering,3,1.0,1.0,304.3333333333333,1,,True,False,post,leveragingtypeannotationsforeffectivefuzzingofpythonprograms,304.3333333333333,landmark,True3384403110951,10.32657/10356/179951,,Semantics-aware human-computer interaction software testing,,2024,dissertation,software_engineering,6,1.0,1.0,304.3333333333333,1,,True,True,post,semanticsawarehumancomputerinteractionsoftwaretesting,304.3333333333333,landmark,True3393008797115,10.48550/arxiv.2002.12543,2002.12543,Metamorphic Testing: A New Approach for Generating Next Test Cases,arXiv (Cornell University),2020,preprint,software_engineering,1,246.0,292.0,286.17663817663816,448,,True,True,baseline,metamorphictestinganewapproachforgeneratingnexttestcases,286.17663817663816,landmark,True3403177813494,10.4230/lipics.esem.2026.34,2107.03374,A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages,arXiv (Cornell University),2021,preprint,software_engineering,6,127.0,678.0,275.3873466752744,1430,,True,True,baseline,adirectionawarestudyofllmbasedcodetranslationacrosseightprogramminglanguages,275.3873466752744,landmark,True3417131867455,10.1287/mnsc.2025.00535,,The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers,Management Science,2026,article,software_engineering,2,97.0,97.0,689.140622972096,25,228.8208,True,False,post,theeffectsofgenerativeaionhighskilledworkevidencefromthreefieldexperimentswithsoftwaredevelopers,245.94062998251673,landmark,True3427110046239,10.4230/oasics.icpec.2025.4,,Can Open Large Language Models Catch Vulnerabilities?,Repositório do ISCTE-IUL,2025,article,software_engineering,3,540.0,540.0,468.828520182987,1,1.0925,True,True,post,canopenlargelanguagemodelscatchvulnerabilities,235.96464653573398,landmark,True3434391677389,10.17771/pucrio.acad.65972,,IDENTIFYING CONCERNS WHEN SPECIFYING MACHINE LEARNING-ENABLED SYSTEMS: A PERSPECTIVE-BASED APPROACH,,2023,dissertation,software_engineering,1,7.0,8.0,230.9189189189189,3,,True,True,post,identifyingconcernswhenspecifyingmachinelearningenabledsystemsaperspectivebasedapproach,230.9189189189189,landmark,True3443007157104,10.1109/tse.2019.2962027,1906.10742,"Machine Learning Testing: Survey, Landscapes and Horizons",IEEE Transactions on Software Engineering,2020,article,software_engineering,4,245.0,385.0,227.48310158362304,888,240.8699,True,True,baseline,machinelearningtestingsurveylandscapesandhorizons,227.48310158362304,landmark,True34558732440,10.22024/unikent/01.02.86458,,An exploration of novice compilation behaviour in BlueJ,Kent Academic Repository (University of Kent),2021,dissertation,software_engineering,3,59.0,60.0,226.01941747572815,61,,True,True,baseline,anexplorationofnovicecompilationbehaviourinbluej,226.01941747572815,landmark,True3462725449579,10.1016/bs.adcom.2018.03.015,,Mutation Testing Advances: An Analysis and Survey,Advances in computers,2018,book-chapter,software_engineering,1,63.0,129.0,223.78585461689588,425,230.7729,False,True,baseline,mutationtestingadvancesananalysisandsurvey,223.78585461689588,landmark,True3474293212596,10.3847/1538-4357/ac7c74,,The Astropy Project : Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*,Repository of the Academy's Library (Library of the Hungarian Academy of Sciences),2022,article,software_engineering,3,704.0,2029.0,209.06737313811422,5022,296.1452,True,True,baseline,theastropyprojectsustainingandgrowingacommunityorientedopensourceprojectandthelatestmajorreleasev50ofthecorepackage,209.06737313811422,landmark,True3484381982883,10.3390/app13095783,,"ChatGPT for Education and Research: Opportunities, Threats, and Strategies",Applied Sciences,2023,article,software_engineering,3,547.0,885.0,207.4225616221095,1062,249.5576,True,True,post,chatgptforeducationandresearchopportunitiesthreatsandstrategies,207.4225616221095,landmark,True3492505871501,10.3139/9783446480537.002,,2 UML - Unified Modeling Language,Carl Hanser Verlag GmbH & Co. KG eBooks,2024,book-chapter,software_engineering,1,49.0,49.0,202.090395480226,186,0.0,True,True,post,2umlunifiedmodelinglanguage,202.090395480226,landmark,True3504281635063,10.33540/1197,,Evolution of Low-Code Platforms,,2022,dissertation,software_engineering,1,2.0,5.0,200.88235294117646,8,,True,True,baseline,evolutionoflowcodeplatforms,200.88235294117646,landmark,True3512100209048,,,Static And Dynamic Reverse Engineering Techniques for Java Software Systems,Tampere University Institutional Repository (Tampere University),2019,dissertation,software_engineering,3,20.0,20.0,200.45167118337852,79,,True,True,baseline,staticanddynamicreverseengineeringtechniquesforjavasoftwaresystems,200.45167118337852,landmark,True3522114444139,10.25946/26315761,,Innovation and Change in the Information Systems Curriculum of an Australian University:a Socio-Technical Perspective,Victoria University Research Repository (Victoria University),2024,dissertation,programming_languages,1,17.0,17.0,661.9925925925926,54,,True,True,post,innovationandchangeintheinformationsystemscurriculumofanaustralianuniversityasociotechnicalperspective,661.9925925925926,landmark,True3534396242417,10.1145/3649828,,Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach,Proceedings of the ACM on Programming Languages,2024,article,programming_languages,4,83.0,148.0,117.44112676056338,148,155.4628,True,True,post,enhancingstaticanalysisforpracticalbugdetectionanllmintegratedapproach,117.44112676056338,landmark,True3542327217190,10.26190/unsworks/23844,,Verification of programs in virtual memory using separation logic,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,programming_languages,3,13.0,13.0,97.35745614035089,13,,True,True,baseline,verificationofprogramsinvirtualmemoryusingseparationlogic,97.35745614035089,landmark,True3551844775953,10.82444/warw.33641182,,Metric domains for completeness,Warwick Research Archive Portal (University of Warwick),2026,dissertation,programming_languages,2,34.0,34.0,1040.594982078853,40,,True,True,post,metricdomainsforcompleteness,86.2059940144904,landmark,True3562491926874,10.26190/unsworks/17927,,Formal memory models for verifying C systems code,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,programming_languages,5,10.0,11.0,82.37938596491229,24,,True,True,baseline,formalmemorymodelsforverifyingcsystemscode,82.37938596491229,landmark,True3574205342885,10.1007/s13132-021-00854-2,,The Futures of Europe: Society 5.0 and Industry 5.0 as Driving Forces of Future Universities,Journal of the Knowledge Economy,2022,article,programming_languages,1,171.0,334.0,82.08749781143715,572,117.4739,True,True,baseline,thefuturesofeuropesociety50andindustry50asdrivingforcesoffutureuniversities,82.08749781143715,landmark,True3582189687801,10.7275/7408407.0,,General Program Synthesis from Examples Using Genetic Programming with Parent Selection Based on Random Lexicographic Orderings of Test Cases,Scholarworks (University of Massachusetts Amherst),2021,dissertation,programming_languages,3,18.0,18.0,81.87032967032967,18,,True,True,baseline,generalprogramsynthesisfromexamplesusinggeneticprogrammingwithparentselectionbasedonrandomlexicographicorderingsoftestcases,81.87032967032967,landmark,True3592796251079,10.1007/978-3-319-75844-2_6,,A Concise Introduction to Logic,Springer eBooks,2018,book-chapter,programming_languages,1,139.0,167.0,77.56442638324476,401,44.1726,False,False,baseline,aconciseintroductiontologic,77.56442638324476,landmark,True360159920301,10.48456/tr-346,,On intuitionistic linear logic,OpenGrey (Institut de l'Information Scientifique et Technique),2021,dissertation,programming_languages,2,16.0,16.0,72.77362637362637,39,,True,True,baseline,onintuitionisticlinearlogic,72.77362637362637,landmark,True3614389109635,10.5463/thesis.524,,A Unifying Theory for Graph Transformation,,2023,dissertation,programming_languages,3,2.0,2.0,69.63636363636364,2,,True,True,post,aunifyingtheoryforgraphtransformation,69.63636363636364,landmark,True362630549275,10.26190/unsworks/16534,,The clustered multikernel: an approach to formal verification of multiprocessor operating-system kernels,UNSWorks (UNSW Sydney),2022,dissertation,programming_languages,5,8.0,9.0,67.40131578947368,10,,True,True,baseline,theclusteredmultikernelanapproachtoformalverificationofmultiprocessoroperatingsystemkernels,67.40131578947368,landmark,True3634403223258,10.1145/3689736,2310.15991,WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models,Proceedings of the ACM on Programming Languages,2024,article,programming_languages,8,52.0,82.0,65.0687323943662,82,86.1348,True,True,post,whitefoxwhiteboxcompilerfuzzingempoweredbylargelanguagemodels,65.0687323943662,landmark,True3644313480275,10.1007/978-3-031-21762-3_3,,Algorithmics: The Spirit of Computing,SpringerBriefs in computer science,2023,book-chapter,programming_languages,2,64.0,64.0,57.94535519125683,90,30.3187,False,False,post,algorithmicsthespiritofcomputing,57.94535519125683,landmark,True3652790202156,10.14722/ndss.2018.23082,,ZEUS: Analyzing Safety of Smart Contracts,Network and Distributed System Security Symposium,2018,conference-paper,programming_languages,5,94.0,191.0,56.63186311787072,702,60.5919,True,True,baseline,zeusanalyzingsafetyofsmartcontracts,56.63186311787072,landmark,True3663207822760,10.33612/diss.168003240,,Computer programming skills: A cognitive perspective,,2021,dissertation,programming_languages,1,11.0,15.0,56.50485436893204,21,,True,True,baseline,computerprogrammingskillsacognitiveperspective,56.50485436893204,landmark,True3672400988715,10.48550/arxiv.2605.09524,2605.09524,Functional Stable Model Semantics and Answer Set Programming Modulo Theories,arXiv (Cornell University),2026,preprint,programming_languages,3,22.0,22.0,856.0701754385965,22,,True,True,post,functionalstablemodelsemanticsandanswersetprogrammingmodulotheories,55.78034906819967,landmark,True3684297611890,10.1017/9781108778756.017,,Subsystems of Second-Order Arithmetic,Cambridge University Press eBooks,2022,book-chapter,programming_languages,3,59.0,61.0,50.93184096224524,65,4.0902,True,False,baseline,subsystemsofsecondorderarithmetic,50.93184096224524,landmark,True3692915211782,10.1017/9781108654555.013,,Novice Programmers and Introductory Programming,Cambridge University Press eBooks,2019,book-chapter,programming_languages,1,11.0,45.0,45.68985849056604,175,59.7005,False,False,baseline,noviceprogrammersandintroductoryprogramming,45.68985849056604,landmark,True3704381516741,10.1007/978-3-031-22206-1_2,,The Python Programming Language,Springer eBooks,2023,book-chapter,programming_languages,2,70.0,82.0,45.335480407944175,96,25.4404,False,False,post,thepythonprogramminglanguage,45.335480407944175,landmark,True3713115704172,,,Lineage-driven Fault Injection for Actor-based Programs,KTH Publication Database DiVA (KTH Royal Institute of Technology),2019,dissertation,programming_languages,3,2.0,2.0,42.74766355140187,2,,True,True,baseline,lineagedrivenfaultinjectionforactorbasedprograms,42.74766355140187,landmark,True3722797025890,10.13025/17505,,Openness and praxis: A situated study of academic staff meaning-making and decision-making with respect to openness and use of open educational practices in higher education,Arrow@dit (Dublin Institute of Technology),2018,dissertation,programming_languages,1,3.0,6.0,38.953125,12,,True,True,baseline,opennessandpraxisasituatedstudyofacademicstaffmeaningmakinganddecisionmakingwithrespecttoopennessanduseofopeneducationalpracticesinhighereducation,38.953125,landmark,True3734396905794,10.17760/d20653005,,Predicting typeScript type annotations and definitions with machine learning,,2024,dissertation,programming_languages,3,0.0,1.0,38.94074074074074,1,,True,True,post,predictingtypescripttypeannotationsanddefinitionswithmachinelearning,38.94074074074074,landmark,True3744415797413,10.52202/079017-2000,2312.07104,SGLang: Efficient Execution of Structured Language Model Programs,neural information processing systems,2024,conference-paper,programming_languages,11,13.0,132.0,36.69097472072476,132,40.1561,True,True,post,sglangefficientexecutionofstructuredlanguagemodelprograms,36.69097472072476,landmark,True3754410362977,10.1038/s41586-025-08984-2,,Emergence of Calabi–Yau manifolds in high-precision black-hole scattering,Nature,2025,article,programming_languages,1,74.0,74.0,30.45267489711934,74,33.8684,True,True,post,emergenceofcalabiyaumanifoldsinhighprecisionblackholescattering,32.33589600674873,landmark,True3764391175874,10.11606/d.45.2023.tde-17012024-190825,,VCAT: an automatic assessment model for visual programming languages,,2023,dissertation,programming_languages,5,1.0,1.0,32.09090909090909,1,,True,True,post,vcatanautomaticassessmentmodelforvisualprogramminglanguages,32.09090909090909,landmark,True3773122286897,10.1109/cgo51591.2021.9370308,,MLIR: Scaling Compiler Infrastructure for Domain Specific Computation,IEEE/ACM International Symposium on Code Generation and Optimization (CGO),2021,conference-paper,compilers,4,74.0,155.0,42.480268473059475,541,52.9944,True,False,baseline,mlirscalingcompilerinfrastructurefordomainspecificcomputation,42.480268473059475,landmark,True3784399087350,10.1007/978-3-031-56586-1_67,,E-learning and the Future of Business Education: The Case of GCC,"Studies in systems, decision and control",2024,book-chapter,compilers,1,25.0,61.0,39.77033011272142,61,44.6583,False,False,post,elearningandthefutureofbusinesseducationthecaseofgcc,39.77033011272142,landmark,True3794398768423,10.1016/j.future.2024.05.034,2310.04963,LLM4VV: Developing LLM-driven testsuite for compiler validation,Future Generation Computer Systems,2024,article,compilers,5,36.0,48.0,38.08901408450704,48,50.4204,False,True,post,llm4vvdevelopingllmdriventestsuiteforcompilervalidation,38.08901408450704,landmark,True3804407983386,10.1016/j.energy.2025.135286,,Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning,Energy,2025,article,compilers,1,83.0,83.0,28.27389289459036,83,34.5383,False,False,post,paralleltimesnetbilstmmodelforultrashorttermphotovoltaicpowerforecastingusingstldecompositionandautotuning,36.268640115677634,landmark,True3812884622219,10.1039/c7np00064b,,"The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research",Natural Product Reports,2018,article,compilers,1,25.0,51.0,27.040051638148082,122,28.5226,True,True,baseline,thevalueofuniversallyavailablerawnmrdatafortransparencyreproducibilityandintegrityinnaturalproductresearch,27.040051638148082,landmark,True3823188948186,10.48550/arxiv.2108.05450,2108.05450,Speed Control of DC Motor Using Fuzzy PID Controller,arXiv (Cornell University),2021,preprint,compilers,2,55.0,73.0,24.44561426312255,87,,True,True,baseline,speedcontrolofdcmotorusingfuzzypidcontroller,24.44561426312255,landmark,True3834406882143,10.3389/fdgth.2025.1460167,,Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review,Frontiers in Digital Health,2025,review,compilers,2,54.0,54.0,4.153846153846154,54,8.4784,True,True,post,beyondthecurrentstateofjustintimeadaptiveinterventionsinmentalhealthaqualitativesystematicreview,23.5964646535734,landmark,True3842962724414,10.1109/jproc.2018.2817118,,Machine Learning in Compiler Optimization,Proceedings of the IEEE,2018,article,compilers,4,25.0,64.0,22.34837655707576,193,27.4802,True,True,baseline,machinelearningincompileroptimization,22.34837655707576,landmark,True3854392397123,10.1016/j.ijer.2024.102326,,Evolution to revolution: Critical exploration of educators’ perceptions of the impact of Artificial Intelligence (AI) on the teaching and learning process in the GCC region,International Journal of Educational Research,2024,article,compilers,1,37.0,64.0,19.268802878681992,64,24.4917,False,True,post,evolutiontorevolutioncriticalexplorationofeducatorsperceptionsoftheimpactofartificialintelligenceaiontheteachingandlearningprocessinthegccregion,19.268802878681992,landmark,True3862783614532,10.1145/3197978,1801.04405,A Survey on Compiler Autotuning using Machine Learning,ACM Computing Surveys,2018,article,compilers,5,21.0,55.0,19.205636103736982,233,25.7446,True,True,baseline,asurveyoncompilerautotuningusingmachinelearning,19.205636103736982,landmark,True3872206959660,10.58088/85wq-pw11,,Automatic selection of compiler optimizations using program characterization and machine learning,"Library, Museums and Press - UDSpace (University of Delaware)",2024,dissertation,compilers,5,1.0,1.0,19.080000000000002,1,,True,True,post,automaticselectionofcompileroptimizationsusingprogramcharacterizationandmachinelearning,19.080000000000002,landmark,True3882249134499,10.58088/vbbd-jy30,,Improving compiler optimizations using machine learning,"Library, Museums and Press - UDSpace (University of Delaware)",2024,dissertation,compilers,8,1.0,1.0,19.080000000000002,1,,True,True,post,improvingcompileroptimizationsusingmachinelearning,19.080000000000002,landmark,True3893214427835,,,Sparse tensor algebra compilation,DSpace@MIT (Massachusetts Institute of Technology),2020,dissertation,compilers,3,2.0,2.0,18.265060240963855,3,,True,True,baseline,sparsetensoralgebracompilation,18.265060240963855,landmark,True3903139485201,,,Towards automated construction of compiler optimizations,DSpace@MIT (Massachusetts Institute of Technology),2020,dissertation,compilers,5,2.0,2.0,18.265060240963855,3,,True,False,baseline,towardsautomatedconstructionofcompileroptimizations,18.265060240963855,landmark,True3913023977240,10.7488/era/309,,From constraint programming to heterogeneous parallelism,ERA,2020,dissertation,compilers,8,0.0,2.0,18.265060240963855,2,,True,True,baseline,fromconstraintprogrammingtoheterogeneousparallelism,18.265060240963855,landmark,True3923046781014,10.1007/978-3-030-47956-5_14,,ExaStencils: Advanced Multigrid Solver Generation,Lecture notes in computational science and engineering,2020,book-chapter,compilers,6,8.0,12.0,18.204255319148935,30,29.7428,True,True,baseline,exastencilsadvancedmultigridsolvergeneration,18.204255319148935,landmark,True3933132279601,10.1007/978-3-030-62066-0_12,,Coping with COVID-19: Higher Education in the GCC Countries,Springer proceedings in complexity,2021,conference-paper,compilers,1,23.0,37.0,17.288455680779773,43,37.049,False,False,baseline,copingwithcovid19highereducationinthegcccountries,17.288455680779773,landmark,True3944408711372,10.1109/tsg.2025.3553737,,A Progressive Polyhedral Approximation Method for Nonlinear PDE-Constrained Electricity-Water Nexus Dispatch,IEEE Transactions on Smart Grid,2025,article,compilers,3,39.0,39.0,8.943719676549865,39,8.8867,True,False,post,aprogressivepolyhedralapproximationmethodfornonlinearpdeconstrainedelectricitywaternexusdispatch,17.0418911386919,landmark,True3953103225940,,,An LLVM backend for the Open Modelica Compiler,KTH Publication Database DiVA (KTH Royal Institute of Technology),2019,dissertation,compilers,4,3.0,3.0,16.862068965517242,3,,True,True,baseline,anllvmbackendfortheopenmodelicacompiler,16.862068965517242,landmark,True3964313894866,10.3390/publications11010002,,"The Science of Literature Reviews: Searching, Identifying, Selecting, and Synthesising",Publications,2023,article,compilers,1,91.0,187.0,16.423071260306244,248,45.5306,True,True,post,thescienceofliteraturereviewssearchingidentifyingselectingandsynthesising,16.423071260306244,landmark,True3972787911489,10.1007/978-3-319-68394-2_11,,Automated Code Generation for Maximizing Performance of Detailed Chemistry Calculations in OpenFOAM,Springer eBooks,2018,book-chapter,compilers,4,5.0,10.0,14.319880418535128,37,25.2794,False,False,baseline,automatedcodegenerationformaximizingperformanceofdetailedchemistrycalculationsinopenfoam,14.319880418535128,landmark,True3983189874403,,,Efficient memory and GPU operations for Tiramisu compiler,,2019,dissertation,compilers,4,2.0,2.0,13.714285714285714,2,,False,False,baseline,efficientmemoryandgpuoperationsfortiramisucompiler,13.714285714285714,landmark,True3994407942537,10.1145/3708493.3712691,,LLM Compiler: Foundation Language Models for Compiler Optimization,ACM SIGPLAN International Conference on Compiler Construction (CC),2025,conference-paper,compilers,6,31.0,31.0,23.55532716683796,31,36.5894,True,True,post,llmcompilerfoundationlanguagemodelsforcompileroptimization,13.546118597421765,landmark,True4007127344046,10.1007/s44217-026-01188-4,,Artificial intelligence policy challenges and institutional readiness in Omani higher education,Discover Education,2026,article,compilers,3,5.0,5.0,35.52271252433485,5,45.7642,True,True,post,artificialintelligencepolicychallengesandinstitutionalreadinessinomanihighereducation,12.67735206095447,landmark,True4014385482927,10.1145/3596711.3596751,,Decoupling Algorithms from Schedules for Easy Optimization of Image Processing Pipelines,ACM eBooks,2023,book-chapter,compilers,5,7.0,7.0,12.636627906976743,7,3.981,True,False,post,decouplingalgorithmsfromschedulesforeasyoptimizationofimageprocessingpipelines,12.636627906976743,landmark,True4024385245566,10.4230/lipics.itp.2023.19,,Exploiting Generative AI to Scale up Intelligent Tutoring Systems,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2023,article,formal_verification,3,54921.0,72130.0,6621.879465206806,76658,4908.38,True,True,post,exploitinggenerativeaitoscaleupintelligenttutoringsystems,6621.879465206806,landmark,True4031498342204,10.1145/3788687,,An Introduction to Input/Output Automata,Formal Aspects of Computing,2026,article,formal_verification,2,284.0,284.0,801.9048786467272,1011,3.5639,True,True,post,anintroductiontoinputoutputautomata,720.0735970622139,landmark,True4042254118105,10.48550/arxiv.2608.14274,2608.14274,Solving QBF by Clause Selection,arXiv (Cornell University),2026,preprint,formal_verification,3,60.0,60.0,2151.793313069909,60,,True,True,post,solvingqbfbyclauseselection,152.12822473145363,landmark,True4052921681895,10.18154/rwth-2020-02348,1903.07993,Parameter synthesis in Markov models,RWTH Publications (RWTH Aachen),2020,dissertation,formal_verification,6,14.0,19.0,111.636815920398,26,,True,True,baseline,parametersynthesisinmarkovmodels,111.636815920398,landmark,True4062540354435,10.48550/arxiv.2605.09519,2605.09519,Weighted Rules under the Stable Model Semantics,arXiv (Cornell University),2026,preprint,formal_verification,2,39.0,39.0,1517.578947368421,39,,True,True,post,weightedrulesunderthestablemodelsemantics,98.88334607544486,landmark,True4074225922019,10.1007/978-3-030-99524-9_24,,cvc5: A Versatile and Industrial-Strength SMT Solver,Lecture notes in computer science,2022,conference-paper,formal_verification,4,111.0,222.0,98.23784328546233,468,135.8061,True,True,baseline,cvc5aversatileandindustrialstrengthsmtsolver,98.23784328546233,landmark,True4084318573502,10.5463/thesis.149,,Semantically informed methods in structural proof theory,,2022,dissertation,formal_verification,2,9.0,13.0,97.35745614035089,14,,True,True,baseline,semanticallyinformedmethodsinstructuralprooftheory,97.35745614035089,landmark,True4092154148696,10.1007/978-3-319-77452-7_9,,Supervisory Control of Timed Discrete-Event Systems,Communications and control engineering/Communications and control engineering series,2018,book-chapter,formal_verification,1,100.0,117.0,86.49113924050633,365,36.4666,False,False,baseline,supervisorycontroloftimeddiscreteeventsystems,86.49113924050633,landmark,True4104297781990,10.4230/oasics.dx.2024.10,,"Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems",HAL (Le Centre pour la Communication Scientifique Directe),2024,preprint,formal_verification,3,426.0,431.0,78.93772893772893,431,,True,True,post,leveraginganswersetprogrammingforcontinuousmonitoringfaultdetectionandexplanationofautomatedandautonomousdrivingsystems,78.93772893772893,landmark,True4112789444712,10.1080/00207543.2018.1444806,,Industry 4.0: state of the art and future trends,International Journal of Production Research,2018,article,formal_verification,4,298.0,734.0,61.81756449008323,3221,267.6956,True,False,baseline,industry40stateoftheartandfuturetrends,61.81756449008323,landmark,True4122486519520,10.48456/tr-792,,Machine learning and automated theorem proving,OpenGrey (Institut de l'Information Scientifique et Technique),2021,dissertation,formal_verification,4,12.0,12.0,54.58021978021978,14,,True,True,baseline,machinelearningandautomatedtheoremproving,54.58021978021978,landmark,True413149631060,10.48550/arxiv.2411.01592,2411.01592,Decidability Issues for Petri Nets -- a survey,arXiv (Cornell University),2024,preprint,formal_verification,3,33.0,33.0,53.32230215827339,72,,True,True,post,decidabilityissuesforpetrinetsasurvey,53.32230215827339,landmark,True4142890660842,10.48550/arxiv.1809.08098,1809.08098,Efficient Formal Safety Analysis of Neural Networks,arXiv (Cornell University),2018,preprint,formal_verification,6,41.0,98.0,52.040918390543304,167,,True,True,baseline,efficientformalsafetyanalysisofneuralnetworks,52.040918390543304,landmark,True4154393942723,10.1007/978-3-031-57256-2_15,,State of the Art in Software Verification and Witness Validation: SV-COMP 2024,Lecture notes in computer science,2024,conference-paper,formal_verification,4,66.0,78.0,48.594819515239784,78,53.289,True,True,post,stateoftheartinsoftwareverificationandwitnessvalidationsvcomp2024,48.594819515239784,landmark,True4162957311447,10.1007/978-3-030-25540-4_26,,The Marabou Framework for Verification and Analysis of Deep Neural Networks,Lecture notes in computer science,2019,conference-paper,formal_verification,4,55.0,141.0,46.39818783571157,498,53.6017,True,True,baseline,themarabouframeworkforverificationandanalysisofdeepneuralnetworks,46.39818783571157,landmark,True4172905489900,,,Machine Learning for SAT Solvers,UWSpace (University of Waterloo),2018,dissertation,formal_verification,3,3.0,5.0,44.97120921305182,9,,True,True,baseline,machinelearningforsatsolvers,44.97120921305182,landmark,True4182900153411,10.1145/3290354,,An abstract domain for certifying neural networks,Proceedings of the ACM on Programming Languages,2019,article,formal_verification,3,99.0,209.0,44.06671585274556,609,46.6555,True,True,baseline,anabstractdomainforcertifyingneuralnetworks,44.06671585274556,landmark,True4196944541633,10.18154/rwth-2023-09669,,Verification of multi-objective Markov models,RWTH Publications (RWTH Aachen),2023,dissertation,formal_verification,6,2.0,2.0,43.15151515151515,4,,True,True,post,verificationofmultiobjectivemarkovmodels,43.15151515151515,landmark,True4203016155638,10.1109/sp40000.2020.00024,,VerX: Safety Verification of Smart Contracts,Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy,2020,conference-paper,formal_verification,6,76.0,114.0,42.08811748998665,247,40.057,True,True,baseline,verxsafetyverificationofsmartcontracts,42.08811748998665,landmark,True4211639950690,10.14279/tuj.eceasst.24.334,,Checking Unsatisfiability for OCL Constraints,Technische Universität Berlin – Universitätsbibliothek,2024,article,formal_verification,3,53.0,53.0,42.056619718309854,58,2.1008,True,True,post,checkingunsatisfiabilityforoclconstraints,42.056619718309854,landmark,True4224384521943,10.1007/978-3-031-37703-7_18,,nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models,Lecture notes in computer science,2023,conference-paper,formal_verification,4,38.0,82.0,40.90918384733345,122,53.4819,True,True,post,nl2specinteractivelytranslatingunstructurednaturallanguagetotemporallogicswithlargelanguagemodels,40.90918384733345,landmark,True4232803355841,10.48550/arxiv.1805.08876,1805.08876,Soteria: Automated IoT Safety and Security Analysis,arXiv (Cornell University),2018,preprint,formal_verification,7,35.0,74.0,39.29620368265515,159,,True,True,baseline,soteriaautomatediotsafetyandsecurityanalysis,39.29620368265515,landmark,True4246891455501,10.3929/ethz-b-000553221,,"Efficient, Expressive, and Verified Temporal Query Evaluation",Repository for Publications and Research Data (ETH Zurich),2022,dissertation,formal_verification,5,4.0,5.0,38.535353535353536,5,,True,True,baseline,efficientexpressiveandverifiedtemporalqueryevaluation,38.535353535353536,landmark,True4252187038411,10.26190/unsworks/22938,,Modelling and programming embedded controllers with timed automata and synchronous languages,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,formal_verification,3,4.0,5.0,38.535353535353536,5,,True,True,baseline,modellingandprogrammingembeddedcontrollerswithtimedautomataandsynchronouslanguages,38.535353535353536,landmark,True4264289665848,10.4230/lipics.csl.2026.30,,Reward Interfaces with Best-Effort Implementations,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,formal_verification,4,15.0,15.0,42.354130914439814,15,0.0,True,True,post,rewardinterfaceswithbesteffortimplementations,38.03205618286341,landmark,True4274311415873,10.4230/lipics.wads.2025.45,,Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences,Leibniz international proceedings in informatics,2025,conference-paper,algorithms,3,2355.0,2355.0,1915.3516038917253,2354,1042.305,True,True,post,fastkdtreesforthekullbackleiblerdivergenceandotherdecomposablebregmandivergences,1029.0680418363954,landmark,True4284294558607,10.4230/lipics.sea.2024.20,,Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2024,article,algorithms,4,5110.0,5183.0,861.3021555600685,5183,320.7213,True,True,post,targetedbranchingforthemaximumindependentsetproblemusinggraphneuralnetworks,861.3021555600685,landmark,True4294285092328,10.1145/3544585.3544600,,A Note on Two Problems in Connexion with Graphs,ACM eBooks,2022,book-chapter,algorithms,1,631.0,868.0,724.7350484463749,1353,589.8043,True,False,baseline,anoteontwoproblemsinconnexionwithgraphs,724.7350484463749,landmark,True4302112641412,10.37236/11668,,A Dynamic Survey of Graph Labeling,The Electronic Journal of Combinatorics,2022,article,algorithms,1,1698.0,1780.0,288.61814892416174,2162,44.7232,True,True,post,adynamicsurveyofgraphlabeling,288.61814892416174,landmark,True4312003494253,10.1017/9781009260350.002,,Algorithms+Data Structures = Programs,Cambridge University Press eBooks,2024,book-chapter,algorithms,2,231.0,231.0,275.60804020100505,953,13.0275,True,False,post,algorithmsdatastructuresprograms,275.60804020100505,landmark,True4327128526706,10.1007/s10462-026-11504-x,,Glider snake optimizer (GSO): a nature-inspired metaheuristic algorithm for global and engineering optimization problems,Artificial Intelligence Review,2026,article,algorithms,5,91.0,91.0,101.26493940172338,91,328.7483,True,True,post,glidersnakeoptimizergsoanatureinspiredmetaheuristicalgorithmforglobalandengineeringoptimizationproblems,230.72780750937136,landmark,True4331491280261,10.26190/unsworks/6784,,Grammatical bias for evolutionary learning,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,algorithms,1,26.0,28.0,209.69298245614038,96,,True,True,baseline,grammaticalbiasforevolutionarylearning,209.69298245614038,landmark,True4347125913058,10.1109/tsmc.2026.3655483,,Multiresource-Constrained Flexible Job Shop Scheduling With Fixture Pallets and Setup Stations Under Pallet Automation Systems,IEEE Transactions on Systems Man and Cybernetics Systems,2026,article,algorithms,3,73.0,73.0,84.02038567493113,73,309.1219,True,True,post,multiresourceconstrainedflexiblejobshopschedulingwithfixturepalletsandsetupstationsunderpalletautomationsystems,185.08934008993526,landmark,True4353119051141,10.1016/j.cma.2020.113609,,The Arithmetic Optimization Algorithm,Computer Methods in Applied Mechanics and Engineering,2021,article,algorithms,2,900.0,1513.0,176.3286814620167,2928,183.3132,True,True,baseline,thearithmeticoptimizationalgorithm,176.3286814620167,landmark,True4364406080942,10.1007/s10462-024-11023-7,,Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems,Artificial Intelligence Review,2025,article,algorithms,3,330.0,330.0,112.41427295439541,330,137.3209,True,True,post,artificiallemmingalgorithmanovelbionicmetaheuristictechniqueforsolvingrealworldengineeringoptimizationproblems,144.200617327393,landmark,True4372891784792,10.48550/arxiv.2407.17712,2407.17712,Improving Online Algorithms via ML Predictions,arXiv (Cornell University),2024,preprint,algorithms,2,111.0,112.0,142.64098765432098,112,,True,True,post,improvingonlinealgorithmsviamlpredictions,142.64098765432098,landmark,True4383139484821,10.1016/j.cie.2021.107250,,Aquila Optimizer: A novel meta-heuristic optimization algorithm,Computers & Industrial Engineering,2021,article,algorithms,1,683.0,1186.0,138.21931012158083,2213,141.9398,False,False,baseline,aquilaoptimizeranovelmetaheuristicoptimizationalgorithm,138.21931012158083,landmark,True4392121903617,10.1201/9781482268713-14,,Introduction to evolutionary algorithms,CRC Press eBooks,2018,book-chapter,algorithms,1,260.0,292.0,135.62163175992498,349,50.6462,True,False,baseline,introductiontoevolutionaryalgorithms,135.62163175992498,landmark,True4402207864362,10.58088/v6j9-5f14,,Properties of some algebraically defined digraphs,"Library, Museums and Press - UDSpace (University of Delaware)",2024,dissertation,algorithms,1,6.0,6.0,134.92307692307693,6,,True,True,post,propertiesofsomealgebraicallydefineddigraphs,134.92307692307693,landmark,True4412125690626,,,Combinatorial optimization: networks and matroids,,2021,article,algorithms,2,738.0,779.0,125.39890722470469,3320,16.9958,True,True,baseline,combinatorialoptimizationnetworksandmatroids,125.39890722470469,landmark,True4422181620480,10.26190/unsworks/16444,,Robust solutions for constraint satisfaction and optimisation under uncertainty.,UNSWorks (UNSW Sydney),2022,dissertation,algorithms,2,16.0,16.0,120.8627450980392,17,,True,True,baseline,robustsolutionsforconstraintsatisfactionandoptimisationunderuncertainty,120.8627450980392,landmark,True4432117226423,10.1007/s43069-021-00101-z,,Worst-Case Analysis of a New Heuristic for the Travelling Salesman Problem,Operations Research Forum,2022,article,algorithms,2,683.0,742.0,120.31161039422922,1131,23.3118,True,True,baseline,worstcaseanalysisofanewheuristicforthetravellingsalesmanproblem,120.31161039422922,landmark,True4442146101030,10.82444/warw.33642076,,Communication complexity of document exchange,Symposium on Discrete Algorithms,2026,conference-paper,algorithms,3,46.0,46.0,353.0526747845123,122,9.2449,True,True,post,communicationcomplexityofdocumentexchange,116.63163896078113,landmark,True4452963224980,10.1109/tkde.2018.2807452,1709.07604,"A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications",IEEE Transactions on Knowledge and Data Engineering,2018,article,algorithms,4,296.0,609.0,114.94541813871597,2032,138.3391,True,True,baseline,acomprehensivesurveyofgraphembeddingproblemstechniquesandapplications,114.94541813871597,landmark,True4467118096636,10.1016/j.eswa.2026.131088,,A modified starfish optimization algorithm (M-SFOA) for global optimization problems and its application to heart disease risk prediction,Expert Systems with Applications,2026,article,algorithms,1,42.0,42.0,46.737664339256945,42,151.73,False,False,post,amodifiedstarfishoptimizationalgorithmmsfoaforglobaloptimizationproblemsanditsapplicationtoheartdiseaseriskprediction,106.48975731201755,landmark,True4472398690976,10.1609/aaai.v26i1.8120,,Online Task Assignment in Crowdsourcing Markets,Proceedings of the AAAI Conference on Artificial Intelligence,2021,conference-paper,algorithms,3,270.0,280.0,104.97509403273355,293,14.5296,True,True,baseline,onlinetaskassignmentincrowdsourcingmarkets,104.97509403273355,landmark,True4487155156112,10.1007/s10462-026-11529-2,,Gray langurs optimizer: a multi-group bio-inspired optimization algorithm,Artificial Intelligence Review,2026,article,algorithms,1,40.0,40.0,44.5120612754828,40,144.5047,True,True,post,graylangursoptimizeramultigroupbioinspiredoptimizationalgorithm,101.41881648763577,landmark,True4492560014408,10.48550/arxiv.2411.10949,2411.10949,Maximization of Approximately Submodular Functions,arXiv (Cornell University),2024,preprint,algorithms,2,62.0,62.0,100.18129496402878,62,,True,True,post,maximizationofapproximatelysubmodularfunctions,100.18129496402878,landmark,True4504366545219,10.1007/978-1-4471-7503-2_33,,Genetic Algorithms and Their Applications,Springer handbooks,2023,book-chapter,algorithms,1,104.0,181.0,100.06977992485238,232,122.5764,False,False,post,geneticalgorithmsandtheirapplications,100.06977992485238,landmark,True4514322738676,10.1201/9781003216001-5,,Introduction to Genetic Algorithm,Chapman & Hall/CRC eBooks,2023,book-chapter,algorithms,1,176.0,177.0,97.85829307568439,308,4.6255,True,False,post,introductiontogeneticalgorithm,97.85829307568439,landmark,True4524254202520,10.7551/mitpress/12274.003.0008,,"On Computable Numbers, with an Application to the Entscheidungsproblem (1936)",The MIT Press eBooks,2021,book-chapter,computational_complexity,1,312.0,353.0,227.2789500772002,521,95.7524,False,False,baseline,oncomputablenumberswithanapplicationtotheentscheidungsproblem1936,227.2789500772002,landmark,True4531638203394,10.1007/978-3-030-11298-1,,An Introduction to Kolmogorov Complexity and Its Applications,Texts in computer science,2019,book,computational_complexity,2,1326.0,1437.0,122.10728346456693,3681,45.0313,False,False,baseline,anintroductiontokolmogorovcomplexityanditsapplications,122.10728346456693,landmark,True4542979043732,,,ETH-tight algorithms for geometric network problems,TU/e Research Portal,2019,dissertation,computational_complexity,4,5.0,5.0,111.01694915254237,6,,True,True,baseline,ethtightalgorithmsforgeometricnetworkproblems,111.01694915254237,landmark,True4552957144896,10.3389/fpsyg.2019.01652,,Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model,Frontiers in Psychology,2019,article,computational_complexity,1,48.0,177.0,72.24848261060211,1039,114.0294,True,True,baseline,factorsdeterminingthebehavioralintentiontousemobilelearninganapplicationandextensionoftheutautmodel,72.24848261060211,landmark,True4564394806711,10.1016/j.heliyon.2024.e29523,,Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning,Heliyon,2024,article,computational_complexity,1,118.0,197.0,59.311783860943,197,74.6231,True,True,post,artificialintelligenceandsocialmediaonacademicperformanceandmentalwellbeingstudentperceptionsofpositiveimpactintheageofsmartlearning,59.311783860943,landmark,True4574386113651,10.48550/arxiv.2308.08708,2308.08708,Cheap Correlate or Discriminating Property? An Executable Validation Protocol for Consciousness- and Cognition-Theoretic Claims about AI,arXiv (Cornell University),2023,preprint,computational_complexity,1,89.0,170.0,57.664,193,,True,True,post,cheapcorrelateordiscriminatingpropertyanexecutablevalidationprotocolforconsciousnessandcognitiontheoreticclaimsaboutai,57.664,landmark,True4581513303009,,,Cellular Automata And Complexity: Collected Papers,CERN Document Server (European Organization for Nuclear Research),2019,article,computational_complexity,5,236.0,251.0,52.922228129373856,575,9.8222,True,True,baseline,cellularautomataandcomplexitycollectedpapers,52.922228129373856,landmark,True4592905177801,,,Optimal trees for prediction and prescription,DSpace@MIT (Massachusetts Institute of Technology),2018,dissertation,computational_complexity,1,2.0,5.0,51.149155722326455,27,,True,True,baseline,optimaltreesforpredictionandprescription,51.149155722326455,landmark,True4602955897898,10.1109/access.2019.2921522,,Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond,IEEE Access,2019,article,computational_complexity,2,240.0,596.0,49.00829497340763,2594,88.2939,True,True,baseline,wirelesscommunicationsandapplicationsabove100ghzopportunitiesandchallengesfor6gandbeyond,49.00829497340763,landmark,True4614377102915,10.1016/j.heliyon.2023.e16348,,"Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review",Heliyon,2023,article,computational_complexity,1,78.0,204.0,47.81265827221507,331,77.7811,True,True,post,challengesopportunitiesandprospectsofadoptingandusingsmartdigitaltechnologiesinlearningenvironmentsaniterativereview,47.81265827221507,landmark,True4623139085460,,,Reorienting machine learning education towards tinkerers and ML-engaged citizens,DSpace@MIT (Massachusetts Institute of Technology),2020,dissertation,computational_complexity,1,3.0,8.0,45.743589743589745,16,,True,False,baseline,reorientingmachinelearningeducationtowardstinkerersandmlengagedcitizens,45.743589743589745,landmark,True4634406215947,10.3390/systems13010043,,"Digital Transformation and Innovation Performance in Small- and Medium-Sized Enterprises: A Systems Perspective on the Interplay of Digital Adoption, Digital Drive, and Digital Culture",Systems,2025,article,computational_complexity,1,104.0,104.0,24.553686934023286,104,125.7829,True,True,post,digitaltransformationandinnovationperformanceinsmallandmediumsizedenterprisesasystemsperspectiveontheinterplayofdigitaladoptiondigitaldriveanddigitalculture,45.445043036511734,landmark,True4642808541536,,,Meet-continuity and locally compact sober dcpos,University of Birmingham Institutional Research Archive (University of Birmingham),2018,dissertation,computational_complexity,3,2.0,10.0,44.30861723446894,19,,True,True,baseline,meetcontinuityandlocallycompactsoberdcpos,44.30861723446894,landmark,True46573572444,10.4324/9781003213574,,Complexity Theory and the Social Sciences,Routledge eBooks,2022,book,computational_complexity,1,197.0,215.0,43.70967741935484,454,13.2502,True,False,baseline,complexitytheoryandthesocialsciences,43.70967741935484,landmark,True4663086066649,10.1108/aeds-04-2020-0063,,The role of digital learning toward students' satisfaction and university brand image at educational institutes of Pakistan: a post-effect of COVID-19,Asian Education and Development Studies,2020,article,computational_complexity,1,40.0,110.0,32.031113421948945,216,36.2625,True,False,baseline,theroleofdigitallearningtowardstudentssatisfactionanduniversitybrandimageateducationalinstitutesofpakistanaposteffectofcovid19,32.031113421948945,landmark,True4673122335070,,,Design and development of a mobile learning system for computer science education in Nigerian higher education context,UEF eRepo (University of Eastern Finland),2018,dissertation,computational_complexity,1,2.0,3.0,30.68949343339587,5,,True,True,baseline,designanddevelopmentofamobilelearningsystemforcomputerscienceeducationinnigerianhighereducationcontext,30.68949343339587,landmark,True4687133302269,10.1016/j.chbr.2026.100986,,AI-assisted learning tools and student learning outcomes: A cognitive load theory perspective,Computers in Human Behavior Reports,2026,article,computational_complexity,1,12.0,12.0,85.25451005840362,12,109.834,True,True,post,aiassistedlearningtoolsandstudentlearningoutcomesacognitiveloadtheoryperspective,30.425644946290728,landmark,True4697122741622,10.3390/metrology6010003,,"Recent Advances in Digital Fringe Projection Profilometry (2022–2025): Techniques, Applications, and Metrological Challenges—A Review",Metrology,2026,article,computational_complexity,1,12.0,12.0,32.16440030557678,12,37.3956,True,True,post,recentadvancesindigitalfringeprojectionprofilometry20222025techniquesapplicationsandmetrologicalchallengesareview,30.425644946290728,landmark,True4707160425037,10.1016/j.tcs.2026.116009,,Solving SAT by restricted spiking neural P systems with temporal division rules,Theoretical Computer Science,2026,article,computational_complexity,3,12.0,12.0,16.14637579169599,12,22.4653,False,False,post,solvingsatbyrestrictedspikingneuralpsystemswithtemporaldivisionrules,30.425644946290728,landmark,True4712900179285,10.1145/3313276.3316369,1809.07115,The Reachability Problem for Petri Nets Is Not Elementary,ACM SIGACT Symposium on Theory of Computing (STOC),2019,conference-paper,computational_complexity,4,56.0,92.0,30.27399489989691,145,23.6478,True,True,baseline,thereachabilityproblemforpetrinetsisnotelementary,30.27399489989691,landmark,True4724313549230,10.1109/acit57182.2022.9994193,,"The Influence of E-Iearning, M-learning, and D-learning on the Student Performance: Moderating Role of Institutional Support",International Arab Conference on Information Technology (ACIT),2022,conference-paper,computational_complexity,1,39.0,58.0,28.716532086697832,67,131.0986,True,False,baseline,theinfluenceofeiearningmlearninganddlearningonthestudentperformancemoderatingroleofinstitutionalsupport,28.716532086697832,landmark,True4734318219991,10.1108/itp-02-2021-0152,,Students' adoption of AI-based teacher-bots (T-bots) for learning in higher education,Information Technology and People,2023,article,computational_complexity,1,47.0,118.0,27.6563415496146,171,40.183,True,False,post,studentsadoptionofaibasedteacherbotstbotsforlearninginhighereducation,27.6563415496146,landmark,True4744408441779,10.1109/tem.2025.3551331,,Enhancing Creativity and Sustainable Competitive Advantage Through Data-Driven Decision-Making and Digital Leadership,IEEE Transactions on Engineering Management,2025,article,computational_complexity,1,59.0,59.0,19.444444444444446,59,84.1776,True,False,post,enhancingcreativityandsustainablecompetitiveadvantagethroughdatadrivendecisionmakinganddigitalleadership,25.781322491867233,landmark,True4753202325891,10.3390/s21196578,,"Electrochemical Impedance Spectroscopy (EIS): Principles, Construction, and Biosensing Applications",Sensors,2021,article,computational_complexity,1,85.0,323.0,25.03316097346723,1335,44.934,True,True,baseline,electrochemicalimpedancespectroscopyeisprinciplesconstructionandbiosensingapplications,25.03316097346723,landmark,True4762118110454,10.1017/s1755020325100920,,WHEN BI-INTERPRETABILITY IMPLIES SYNONYMY,The Review of Symbolic Logic,2025,article,computational_complexity,1,56.0,56.0,44.757894736842104,56,0.0,True,True,post,whenbiinterpretabilityimpliessynonymy,24.47040778889093,landmark,True4772793823461,,,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,True4783080729669,,,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,True4793204616723,,,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,True4803014294420,10.1017/9781108635592.003,,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,True4814324129606,10.1371/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,True4822963507231,10.1109/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,True4833032576892,,,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,True4843148181069,10.38094/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,True4853037795158,10.1016/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,True4864386450904,10.1145/3591366.3591378,,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,True4877161586974,10.5070/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,True4884406085118,10.1145/3711118,2303.10158,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,True4894408444355,10.1016/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,True4903202749896,10.3758/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,True4914400494356,10.11606/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,True4922593864460,10.1109/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,True4934362632858,10.21941/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,True4943005599391,,,"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,True4954402762189,10.3390/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,True4964380791906,10.31979/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,True4974401495975,10.1186/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,True4984408925021,10.3390/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,True4992126034719,10.1142/9789811201967_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,True5004319779432,10.1007/978-3-031-12382-5_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,True5014229641819,10.1017/9781108684163,,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,True5027118085507,10.1007/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,True5032274895364,10.1145/3591366.3591376,,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,True5044401857375,10.1145/3637528.3671470,2405.06211,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,True5052967467,10.26190/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,True5064394947112,10.1145/3805774,2404.10981,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,True5073097300053,10.1145/3535101,2011.02260,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,True5084288083766,10.1145/3285029,1707.07435,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,True5094395483946,10.1186/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,True5104394867512,10.57784/1992/7835,,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,True5112408538552,10.1609/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,True5124401834466,10.1007/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,True5134315977496,10.1145/3568022,2109.12843,"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,True5144394994587,10.1109/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,True5154400531953,10.1145/3626772.3657878,2309.07597,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,True5162945827670,10.1145/3331184.3331267,1905.08108,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,True5174387316367,10.1134/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,True5182914721378,10.1145/3308558.3313488,1902.07243,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,True5192963707260,10.1109/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,True5203094605801,10.1145/3404835.3462862,2010.10783,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,True5213092103025,10.1145/3564284,2010.03240,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,True5224323539384,10.5463/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,True5232604314403,10.1007/978-3-319-93417-4_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,True5243133849783,10.1109/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,True5252995202934,,,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,True5264400118952,10.1007/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,True5274381304672,10.1007/978-3-031-40744-4_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,True5282122465391,10.21276/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,True5294255535209,10.1504/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,True5301973501242,10.1145/3335772.3335934,,"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,True5312120510885,10.1145/3335772.3335936,,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,True5322902511784,10.5555/3291656.3291738,,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,True5333182706339,10.7717/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,True5344387321091,10.1145/3600006.3613165,2309.06180,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,True5352103863878,10.1145/3212998,,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,True5361502629466,10.1007/978-3-030-26253-2,,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,True5373036601975,10.4230/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,True5384393370257,10.57784/1992/7669,,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,True5392973502831,,,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,True5404300092207,10.4230/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,True5412962850638,,,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,True5427130586201,10.1109/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,True5433012404397,10.5626/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,True5442251066406,10.7916/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,True5452970155754,,,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,True5464220966162,10.1016/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,True5472926987861,10.1109/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,True5482794533297,10.1109/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,True5497123363719,10.1016/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,True5507118998287,10.1016/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,True5514391188276,10.1016/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,True552110250037,10.7275/3278307,,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,True5534392909747,10.59646/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,True5542784822088,10.2312/2632114,,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,True5554394946189,10.1145/3627703.3629553,2305.15778,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,True5564385584867,10.4018/978-1-6684-8634-4.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,True5574395047770,10.1007/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,True5582789491284,10.1016/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,True5597160954670,10.1109/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,True5604408156900,10.3390/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,True5612946627595,10.1007/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,True5622784168960,,,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,True5634364375730,10.4018/978-1-7998-8394-4.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,True5647118010419,10.37547/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,True5654293770403,10.1007/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,True5662974110037,10.1016/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,True5674386827074,10.4018/979-8-3693-1186-8.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,True5683011330593,10.1007/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,True5694390660438,10.1016/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,True5703042799967,10.1109/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,True5713123888896,10.1016/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,True5723008629209,10.1007/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,True5732980789769,10.1145/3362031,,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,True5747161556515,10.1109/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,True5757160823269,10.1109/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,True5763010543330,10.48550/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,True5774293584584,10.4230/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,True5784398201281,10.3390/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,True5792803371618,10.1016/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,True5802793346081,10.1109/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,True5813023617420,10.1109/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,True5822562507292,10.1201/9781003338628-7,,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,True5833017623177,10.1016/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,True5842908737503,10.3390/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,True5853023398824,10.1016/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,True5862947690458,10.1109/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,True5872885100636,10.3390/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,True5883006456063,10.1016/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,True5892974429275,10.1109/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,True5904386961654,10.3390/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,True5912912978522,10.1109/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,True5922950865323,10.1109/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,True5934313646859,10.1002/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,True5944401806686,10.1016/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,True5954320712473,10.1016/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,True5962983008824,10.1016/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,True5974408363154,10.3390/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,True5982918245750,10.1016/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,True5994407389811,10.3390/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,True6002921608598,10.1109/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,True6012951308194,,,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,True6022602956630,10.1201/9781003338604-2,,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,True6032762829140,10.6069/5wt6-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,True6042940715617,,,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,True6051500148551,10.48550/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,True6062772457693,10.1109/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,True6071823805418,10.17487/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,True6084292259483,10.1109/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,True6097123361055,10.1109/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,True6107124594522,10.1007/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,True6117134136722,10.1016/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,True6124415028496,10.1109/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,True6132962883549,10.1109/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,True614153405511,10.57709/1059389,,"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,True6154206510620,10.1007/978-3-030-87059-1_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,True6164322576964,10.1109/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,True6177127083419,10.1109/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,True6183104550706,10.1184/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,True6191710433637,10.17487/rfc9260,,Stream Control Transmission Protocol,,2022,report,computer_networks,1,645.0,659.0,119.70282413350449,1922,,True,False,baseline,streamcontroltransmissionprotocol,119.70282413350449,landmark,True6203194620979,10.1109/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,True6212545990795,10.17487/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,True6223015636663,10.1109/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,True6234396669794,10.4018/979-8-3693-1335-0.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,True6241150714886,,,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,True6254306827119,10.1007/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,True6263102359135,10.1007/978-3-030-72802-1_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,True6272905514290,,,An information security risk management gap analysis tool based on ISO/IEC 27005:2011 compliance for SMEs in Kenya,,2018,dissertation,cybersecurity,1,63.0,83.0,849.0759849906191,95,,False,False,baseline,aninformationsecurityriskmanagementgapanalysistoolbasedonisoiec270052011complianceforsmesinkenya,849.0759849906191,landmark,True6282460937040,10.1201/9781351251389-8,,Adversarial Examples in the Physical World,Chapman & Hall/CRC eBooks,2018,book-chapter,cybersecurity,4,805.0,1507.0,699.9376680212566,4388,809.1968,True,True,baseline,adversarialexamplesinthephysicalworld,699.9376680212566,landmark,True6294301785137,10.2139/ssrn.7475259,,The Illusion of Topological Compression: Why Persistent Homology Fails as a Real-Time Prompt-Injection Defense,SSRN Electronic Journal,2026,preprint,cybersecurity,2,252.0,252.0,9805.894736842105,252,,False,True,post,theillusionoftopologicalcompressionwhypersistenthomologyfailsasarealtimepromptinjectiondefense,638.9385438721052,landmark,True6307129030749,10.1145/3796519,,"Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions",ACM Transactions on Software Engineering and Methodology,2026,article,cybersecurity,4,178.0,178.0,198.07867267589847,149,498.5414,True,True,post,modelcontextprotocolmcplandscapesecuritythreatsandfutureresearchdirections,451.31373336997916,landmark,True6313188417193,,,Machine learning for offensive cyber operations,NORA - Norwegian Open Research Archives,2023,dissertation,cybersecurity,6,43.0,51.0,424.2320328542094,58,,True,True,post,machinelearningforoffensivecyberoperations,424.2320328542094,landmark,True6324289548059,10.4230/oasics.fmbc.2026.8,,Detecting Cross-Function Reentrancy from EVM Traces,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,cybersecurity,5,148.0,148.0,1051.4722907203115,148,9.1528,True,True,post,detectingcrossfunctionreentrancyfromevmtraces,375.24962100425233,landmark,True6333201596224,,,ELLIPTIC CURVE DIGITAL SIGNATURE ALGORITHM,NC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro),2021,dissertation,cybersecurity,2,27.0,28.0,308.9145728643216,35,,True,False,baseline,ellipticcurvedigitalsignaturealgorithm,308.9145728643216,landmark,True634652269645,10.57709/1059372,,A Design and Analysis of Graphical Password,Digital Archive @ GSU,2022,dissertation,cybersecurity,1,35.0,35.0,303.5964912280702,43,,True,True,baseline,adesignandanalysisofgraphicalpassword,303.5964912280702,landmark,True6352231944250,10.7551/mitpress/11636.003.0018,,Enigma: Decentralized Computation Platform with Guaranteed Privacy,The MIT Press eBooks,2018,book-chapter,cybersecurity,6,169.0,230.0,270.2898179020262,368,204.4551,True,True,baseline,enigmadecentralizedcomputationplatformwithguaranteedprivacy,270.2898179020262,landmark,True6363041344359,,,Machine Learning for anomaly detection in IoT networks :Malware analysis on the IoT-23 data set,,2020,dissertation,cybersecurity,4,8.0,31.0,265.4812030075188,64,,True,False,baseline,machinelearningforanomalydetectioniniotnetworksmalwareanalysisontheiot23dataset,265.4812030075188,landmark,True6374401666902,10.38124/ijisrt/ijisrt24aug395,,Cyberfraud in the Nigerian Banking Sector: The Techniques and Preventive Measures,International Journal of Innovative Science and Research Technology (IJISRT),2024,article,cybersecurity,1,739.0,739.0,222.49445823978112,739,282.4198,True,True,post,cyberfraudinthenigerianbankingsectorthetechniquesandpreventivemeasures,222.49445823978112,landmark,True6384353004773,10.1109/mitp.2023.3246667,,IEEE Symposium on Security and Privacy,IT Professional,2023,article,cybersecurity,2,671.0,853.0,199.92253679509537,1167,130.8885,False,True,post,ieeesymposiumonsecurityandprivacy,199.92253679509537,landmark,True6392805930283,10.1109/access.2018.2842685,2402.00687,A Review on the Use of Blockchain for the Internet of Things,IEEE Access,2018,article,cybersecurity,5,184.0,370.0,196.1729236493096,1078,221.3514,True,True,post,areviewontheuseofblockchainfortheinternetofthings,196.1729236493096,landmark,True6402624307925,10.1109/comst.2018.2842460,1706.00916,A Survey on Security and Privacy Issues of Bitcoin,IEEE Communications Surveys & Tutorials,2018,article,cybersecurity,6,207.0,367.0,194.58233237647738,1103,211.3082,True,True,pre,asurveyonsecurityandprivacyissuesofbitcoin,194.58233237647738,landmark,True6414392771733,,,Sécurisation des Algorithmes d'Apprentissage Automatique,theses.fr (ABES),2023,dissertation,cybersecurity,5,17.0,21.0,174.68377823408625,21,,True,True,post,scurisationdesalgorithmesdapprentissageautomatique,174.68377823408625,landmark,True6422789828921,10.5220/0006639801080116,,Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization,International Conference on Information Systems Security and Privacy (ICISSP),2018,conference-paper,cybersecurity,2,200.0,534.0,162.76665116819714,4757,217.8381,False,True,baseline,towardgeneratinganewintrusiondetectiondatasetandintrusiontrafficcharacterization,162.76665116819714,landmark,True6434406302454,10.1145/3712001,2402.00888,Security and Privacy Challenges of Large Language Models: A Survey,ACM Computing Surveys,2025,article,cybersecurity,5,357.0,357.0,121.61180437793685,357,141.8982,True,True,post,securityandprivacychallengesoflargelanguagemodelsasurvey,155.99884965417968,landmark,True6442309315845,10.58088/javg-d166,,Human-centric training and assessment for cyber situation awareness,"Library, Museums and Press - UDSpace (University of Delaware)",2024,dissertation,cybersecurity,4,4.0,4.0,155.76296296296297,4,,True,True,post,humancentrictrainingandassessmentforcybersituationawareness,155.76296296296297,landmark,True6452795085730,10.1109/mcc.2018.011791712,,Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy?,IEEE Cloud Computing,2018,article,cybersecurity,6,130.0,270.0,143.1532145549016,855,159.8872,True,False,baseline,blockchainapanaceaforhealthcarecloudbaseddatasecurityandprivacy,143.1532145549016,landmark,True6462984937325,,,Trusted Execution Environments in Cloud Computing,Aaltodoc (Aalto University),2021,dissertation,cybersecurity,5,22.0,31.0,140.99890109890111,42,,True,False,baseline,trustedexecutionenvironmentsincloudcomputing,140.99890109890111,landmark,True6472946416612,,,Inadvertently Making Cyber Criminals Rich: A Comprehensive Study of Cryptojacking Campaigns at Internet Scale,Research Repository (Delft University of Technology),2019,dissertation,cybersecurity,2,9.0,14.0,140.31616982836496,19,,True,True,baseline,inadvertentlymakingcybercriminalsrichacomprehensivestudyofcryptojackingcampaignsatinternetscale,140.31616982836496,landmark,True6481550000763,10.1184/r1/6621806,,Crying Wolf: An Empirical Study of SSL Warning Effectiveness,KiltHub Repository,2018,article,cybersecurity,3,245.0,262.0,138.91163782734898,331,29.7278,True,True,baseline,cryingwolfanempiricalstudyofsslwarningeffectiveness,138.91163782734898,landmark,True6492781723315,10.1109/sp.2019.00002,1801.01203,Spectre Attacks: Exploiting Speculative Execution,Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy,2019,conference-paper,cybersecurity,7,693.0,1001.0,133.14175590649612,1780,109.082,True,True,baseline,spectreattacksexploitingspeculativeexecution,133.14175590649612,landmark,True6504322207773,10.26686/wgtn.22180858,,"Automating Behavior-based Ransomware Analysis, Detection, and Classification Using Machine Learning",,2023,dissertation,cybersecurity,7,9.0,10.0,132.5,11,,True,True,post,automatingbehaviorbasedransomwareanalysisdetectionandclassificationusingmachinelearning,132.5,landmark,True6514392353733,10.1016/j.hcc.2024.100211,2312.02003,"A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly",High-Confidence Computing,2024,article,cybersecurity,7,728.0,1059.0,131.53622644511728,1059,157.601,True,True,post,asurveyonlargelanguagemodelllmsecurityandprivacythegoodthebadandtheugly,131.53622644511728,landmark,True6521598516026,10.55277/researchhub.8k9hq1lf.1,,Scantegrity II Municipal Election at Takoma Park: The First E2E Binding Governmental Election with Ballot Privacy,,2026,report,applied_cryptography,2,97.0,97.0,914.8011049723757,107,,True,True,post,scantegrityiimunicipalelectionattakomaparkthefirste2ebindinggovernmentalelectionwithballotprivacy,245.94062998251673,landmark,True6534293168393,10.1145/3549993.3550007,,New Directions in Cryptography,ACM eBooks,2022,book-chapter,applied_cryptography,1,316.0,402.0,215.4337868090236,668,135.2339,True,False,baseline,newdirectionsincryptography,215.4337868090236,landmark,True6541497727960,10.82444/warw.33628720,,FLINT : Fast library for number theory,Warwick Research Archive Portal (University of Warwick),2026,article,applied_cryptography,1,59.0,59.0,166.59291493012995,60,0.0,True,True,post,flintfastlibraryfornumbertheory,149.59275431926275,landmark,True6557134847443,10.1109/tce.2026.3672135,,"3D Non-Degenerate Hyperchaos: Design, Analysis, and Application in Image Encryption",IEEE Transactions on Consumer Electronics,2026,article,applied_cryptography,4,59.0,59.0,158.14163483575248,59,183.8619,True,False,post,3dnondegeneratehyperchaosdesignanalysisandapplicationinimageencryption,149.59275431926275,landmark,True6561516024962,10.7275/409838,,Energy Efficient Adaptive Reed-Solomon Decoding System,Scholarworks (University of Massachusetts Amherst),2023,dissertation,applied_cryptography,3,8.0,8.0,146.84444444444443,9,,True,True,post,energyefficientadaptivereedsolomondecodingsystem,146.84444444444443,landmark,True6577154469464,10.1145/3808699,,Cryptanalysis and Improvement of a Video Cryptosystem via Chaos and S-Box,ACM Transactions on Multimedia Computing Communications and Applications,2026,article,applied_cryptography,3,57.0,57.0,152.78090145148968,57,177.6293,True,True,post,cryptanalysisandimprovementofavideocryptosystemviachaosandsbox,144.52181349488094,landmark,True6584220942018,10.1201/9781003187165-10,,Public and private blockchains,CRC Press eBooks,2022,book-chapter,applied_cryptography,2,166.0,175.0,135.97268806899856,179,35.3352,True,False,baseline,publicandprivateblockchains,135.97268806899856,landmark,True6592910044495,,,Key Agreement with Physical Unclonable Functions and Biometric Identifiers,mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich),2019,dissertation,applied_cryptography,3,6.0,17.0,116.57142857142857,21,,False,False,baseline,keyagreementwithphysicalunclonablefunctionsandbiometricidentifiers,116.57142857142857,landmark,True6602915352631,10.17487/rfc8446,,The Transport Layer Security (TLS) Protocol Version 1.3,,2018,report,applied_cryptography,3,316.0,486.0,110.30219435736677,1851,,True,False,baseline,thetransportlayersecuritytlsprotocolversion13,110.30219435736677,landmark,True6614396670109,10.4018/979-8-3693-1335-0.ch012,,Advanced Digital Data Processing Using Cloud Cryptography,Advances in civil and industrial engineering book series,2024,book-chapter,applied_cryptography,4,133.0,133.0,86.71235909822867,133,97.3697,True,False,post,advanceddigitaldataprocessingusingcloudcryptography,86.71235909822867,landmark,True6624214667959,10.1016/j.bcra.2022.100067,,A survey on blockchain technology and its security,Blockchain Research and Applications,2022,article,applied_cryptography,4,164.0,337.0,82.8248106660309,571,120.3925,True,True,baseline,asurveyonblockchaintechnologyanditssecurity,82.8248106660309,landmark,True6634226075590,10.6028/nist.ir.8413,,Status report on the third round of the NIST Post-Quantum Cryptography Standardization process,,2022,report,applied_cryptography,3,144.0,242.0,79.32594644506003,395,,True,True,baseline,statusreportonthethirdroundofthenistpostquantumcryptographystandardizationprocess,79.32594644506003,landmark,True6644413785573,10.1109/tdsc.2025.3603570,,Encrypt a Story: A Video Segment Encryption Method Based on the Discrete Sinusoidal Memristive Rulkov Neuron,IEEE Transactions on Dependable and Secure Computing,2025,article,applied_cryptography,4,176.0,176.0,57.549674211696974,176,64.4002,True,False,post,encryptastoryavideosegmentencryptionmethodbasedonthediscretesinusoidalmemristiverulkovneuron,76.90699590794293,landmark,True6652910369831,10.1016/j.future.2019.01.018,,Blockchain based searchable encryption for electronic health record sharing,Future Generation Computer Systems,2019,article,applied_cryptography,5,84.0,167.0,68.16664743486187,482,87.1622,False,False,baseline,blockchainbasedsearchableencryptionforelectronichealthrecordsharing,68.16664743486187,landmark,True6664365934498,10.1007/978-3-031-30589-4_15,,An Efficient Key Recovery Attack on SIDH,Lecture notes in computer science,2023,conference-paper,applied_cryptography,1,145.0,223.0,67.25882266726688,281,73.5527,False,True,post,anefficientkeyrecoveryattackonsidh,67.25882266726688,landmark,True6672913851998,10.6028/nist.ir.8240,,Status report on the first round of the NIST post-quantum cryptography standardization process,,2019,report,applied_cryptography,2,70.0,153.0,65.49329758713137,363,,True,True,baseline,statusreportonthefirstroundofthenistpostquantumcryptographystandardizationprocess,65.49329758713137,landmark,True6683209866323,10.18063/wct.v0i0.407,,Separable Reversible Data Hiding in an Encrypted Image,Wireless Communication Technology,2018,article,applied_cryptography,3,323.0,372.0,65.312839313152,427,26.419,False,False,baseline,separablereversibledatahidinginanencryptedimage,65.312839313152,landmark,True6694406028284,10.1109/jiot.2025.3525623,,A Novel Dynamic Hill Cipher and Its Applications on Medical IoT,IEEE Internet of Things Journal,2025,article,applied_cryptography,4,147.0,147.0,48.06705743817872,147,53.7888,True,False,post,anoveldynamichillcipheranditsapplicationsonmedicaliot,64.2348204458387,landmark,True6703022665000,10.1016/s0140-6736(20)31034-5,,"COVID-19 immunity passports and vaccination certificates: scientific, equitable, and legal challenges",The Lancet,2020,article,applied_cryptography,1,158.0,218.0,63.47984296349881,291,61.3673,False,True,baseline,covid19immunitypassportsandvaccinationcertificatesscientificequitableandlegalchallenges,63.47984296349881,landmark,True6713156129242,10.1109/access.2021.3072849,,"A Survey on Blockchain Technology: Evolution, Architecture and Security",IEEE Access,2021,article,applied_cryptography,4,87.0,226.0,59.84604799688895,522,84.4336,True,True,baseline,asurveyonblockchaintechnologyevolutionarchitectureandsecurity,59.84604799688895,landmark,True67244936433,10.1145/3335741.3335755,,"How to play any mental game, or a completeness theorem for protocols with honest majority",ACM eBooks,2019,book-chapter,applied_cryptography,2,143.0,189.0,58.79166858882687,403,47.6938,True,False,baseline,howtoplayanymentalgameoracompletenesstheoremforprotocolswithhonestmajority,58.79166858882687,landmark,True6734317737920,10.3390/electronics12030546,,"Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends",Electronics,2023,article,applied_cryptography,7,136.0,241.0,56.48456197845015,282,66.2667,True,True,post,blockchaintechnologysecurityissueshealthcareapplicationschallengesandfuturetrends,56.48456197845015,landmark,True6742912977751,10.1109/tii.2019.2897133,,A Blockchain-Based Nonrepudiation Network Computing Service Scheme for Industrial IoT,IEEE Transactions on Industrial Informatics,2019,article,applied_cryptography,4,85.0,138.0,56.3293254252152,230,55.2964,True,False,baseline,ablockchainbasednonrepudiationnetworkcomputingserviceschemeforindustrialiot,56.3293254252152,landmark,True6757160693558,10.1007/s10473-026-0325-9,,Passivity-based synchronization of Markovian jump inertial neural networks via adaptive event-driven protocol and its application to image encryption,Acta Mathematica Scientia,2026,article,applied_cryptography,1,21.0,21.0,73.7245394876138,21,88.0004,False,False,post,passivitybasedsynchronizationofmarkovianjumpinertialneuralnetworksviaadaptiveeventdrivenprotocolanditsapplicationtoimageencryption,53.24487865600877,landmark,True6764254706618,10.6028/nist.ir.8309,,Status report on the second round of the NIST post-quantum cryptography standardization process,,2020,report,applied_cryptography,2,68.0,152.0,52.898263027295286,296,,True,True,baseline,statusreportonthesecondroundofthenistpostquantumcryptographystandardizationprocess,52.898263027295286,landmark,True6774248175462,10.2139/ssrn.3440802,,Bitcoin: A Peer-to-Peer Electronic Cash System,SSRN Electronic Journal,2019,preprint,blockchain,3,5543.0,7534.0,1699.155537022696,13922,,False,True,baseline,bitcoinapeertopeerelectroniccashsystem,1699.155537022696,landmark,True6782961501274,10.1007/978-3-030-17740-9_3,,Aleksander Berentsen Recommends “Bitcoin: A Peer-to-Peer Electronic Cash System” by Satoshi Nakamoto,Springer eBooks,2019,book-chapter,blockchain,2,144.0,226.0,220.05819391042294,259,176.7032,False,False,baseline,aleksanderberentsenrecommendsbitcoinapeertopeerelectroniccashsystembysatoshinakamoto,220.05819391042294,landmark,True6792898069736,10.1080/00207543.2018.1533261,,Blockchain technology and its relationships to sustainable supply chain management,International Journal of Production Research,2018,article,blockchain,2,100.0,389.0,206.24666837724715,4083,331.4245,True,False,baseline,blockchaintechnologyanditsrelationshipstosustainablesupplychainmanagement,206.24666837724715,landmark,True6802905867785,10.1109/access.2019.2896108,1805.02707,A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks,IEEE Access,2019,article,blockchain,4,279.0,493.0,201.23447416399344,995,178.3857,True,True,baseline,asurveyonconsensusmechanismsandminingstrategymanagementinblockchainnetworks,201.23447416399344,landmark,True6814320717855,10.1016/j.future.2023.02.008,,Blockchain for the metaverse: A Review,Future Generation Computer Systems,2023,article,blockchain,4,569.0,763.0,178.82871696911812,771,171.5415,True,True,baseline,blockchainforthemetaverseareview,178.82871696911812,landmark,True6823162358200,10.48550/arxiv.2105.07447,2105.07447,"Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges",arXiv (Cornell University),2021,preprint,blockchain,3,157.0,387.0,157.1901226597805,577,,True,True,baseline,nonfungibletokennftoverviewevaluationopportunitiesandchallenges,157.1901226597805,landmark,True6833123868249,10.1093/rfs/hhz015,,"Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?",Review of Financial Studies,2019,article,blockchain,2,246.0,368.0,150.21153446724054,1051,145.8951,True,True,baseline,sexdrugsandbitcoinhowmuchillegalactivityisfinancedthroughcryptocurrencies,150.21153446724054,landmark,True6842907683311,10.1109/access.2018.2890507,,Blockchain for AI: Review and Open Research Challenges,IEEE Access,2019,article,blockchain,1,200.0,342.0,139.59876301031593,997,153.0805,True,True,baseline,blockchainforaireviewandopenresearchchallenges,139.59876301031593,landmark,True6854400662300,10.38124/ijisrt/ijisrt24jul180,,Collaborative Networks: Integrating Blockchain for Enhanced Trust and Transparency,International Journal of Innovative Science and Research Technology (IJISRT),2024,article,blockchain,1,449.0,449.0,135.18269519575335,449,171.8245,True,True,post,collaborativenetworksintegratingblockchainforenhancedtrustandtransparency,135.18269519575335,landmark,True6864402275599,10.38124/ijisrt/ijisrt24aug1218,,Implementing Blockchain Technology for Optimized Supply Chain and Enhanced Sustainability,International Journal of Innovative Science and Research Technology (IJISRT),2024,article,blockchain,1,447.0,449.0,135.18269519575335,449,171.8245,True,True,post,implementingblockchaintechnologyforoptimizedsupplychainandenhancedsustainability,135.18269519575335,landmark,True6872606010969,10.1145/3183367,1704.03610,Blockchains for Business Process Management - Challenges and Opportunities,ACM Transactions on Management Information Systems,2018,article,blockchain,3,145.0,253.0,134.13986400885224,704,145.0233,True,True,baseline,blockchainsforbusinessprocessmanagementchallengesandopportunities,134.13986400885224,landmark,True6882914735843,10.1109/tsmc.2019.2895123,,"Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends",IEEE Transactions on Systems Man and Cybernetics Systems,2019,article,blockchain,2,136.0,314.0,128.1696245182433,1157,157.1419,True,False,baseline,blockchainenabledsmartcontractsarchitectureapplicationsandfuturetrends,128.1696245182433,landmark,True6892973816953,10.7551/mitpress/11645.003.0010,,How Blockchain Will Change Organizations,The MIT Press eBooks,2018,book-chapter,blockchain,1,55.0,109.0,128.09387022313413,375,138.4143,True,False,baseline,howblockchainwillchangeorganizations,128.09387022313413,landmark,True6903123722958,10.1002/isaf.1424,,Toward an ontology‐driven blockchain design for supply‐chain provenance,"Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management",2018,article,blockchain,2,122.0,233.0,123.53592218997065,651,132.5698,True,False,baseline,towardanontologydrivenblockchaindesignforsupplychainprovenance,123.53592218997065,landmark,True6912800028583,10.1016/bs.adcom.2018.03.006,,Blockchain Technology Use Cases in Healthcare,Advances in computers,2018,book-chapter,blockchain,1,42.0,100.0,117.51731213131572,434,154.6983,False,False,baseline,blockchaintechnologyusecasesinhealthcare,117.51731213131572,landmark,True6922784387076,10.1017/s1744137417000200,,Blockchains and the economic institutions of capitalism,Journal of Institutional Economics,2018,article,blockchain,4,143.0,220.0,116.64336000769761,577,110.4748,True,True,baseline,blockchainsandtheeconomicinstitutionsofcapitalism,116.64336000769761,landmark,True6934380356557,10.4018/978-1-6684-5422-0.ch012,,Blockchain Technology in Renovating Healthcare,Advances in healthcare information systems and administration book series,2023,book-chapter,blockchain,2,139.0,169.0,116.33373815885354,171,116.723,True,False,post,blockchaintechnologyinrenovatinghealthcare,116.33373815885354,landmark,True6942965761488,10.1016/j.tifs.2019.07.034,1908.07391,The rise of blockchain technology in agriculture and food supply chains,Trends in Food Science & Technology,2019,article,blockchain,2,102.0,285.0,116.33230250859661,1261,154.3302,True,True,baseline,theriseofblockchaintechnologyinagricultureandfoodsupplychains,116.33230250859661,landmark,True6956906013109,10.15495/epub_ubt_00007183,,Blockchain Adoption in Organizations : Technical Barriers and Potential Solutions,EPub Bayreuth (University of Bayreuth),2023,dissertation,blockchain,5,3.0,4.0,115.45945945945945,5,,True,True,post,blockchainadoptioninorganizationstechnicalbarriersandpotentialsolutions,115.45945945945945,landmark,True6964386476569,10.11606/d.55.2023.tde-06092023-102200,,Blockchain-based data governance for privacy-preserving in multi-stakeholder settings,,2023,dissertation,blockchain,3,3.0,4.0,115.45945945945945,4,,True,False,post,blockchainbaseddatagovernanceforprivacypreservinginmultistakeholdersettings,115.45945945945945,landmark,True6972803836845,10.1016/j.joule.2018.04.016,,Bitcoin's Growing Energy Problem,Joule,2018,article,blockchain,1,119.0,217.0,115.05276873486537,687,112.4835,False,True,baseline,bitcoinsgrowingenergyproblem,115.05276873486537,landmark,True6982927344969,10.2139/ssrn.6433340,,Global Blockchain Benchmarking Study,SSRN Electronic Journal,2026,preprint,blockchain,1,45.0,45.0,1480.9248554913295,45,,False,True,post,globalblockchainbenchmarkingstudy,114.09616854859023,landmark,True6992909202584,10.1109/tii.2019.2893433,,Privacy-Preserving Energy Trading Using Consortium Blockchain in Smart Grid,IEEE Transactions on Industrial Informatics,2019,article,blockchain,3,163.0,274.0,111.84228381528236,573,115.279,True,False,baseline,privacypreservingenergytradingusingconsortiumblockchaininsmartgrid,111.84228381528236,landmark,True7003163490558,10.15760/etd.7527,,Assessment of the Blockchain Technology Adoption for the Management of the Electronic Health Record Systems,,2021,dissertation,blockchain,3,4.0,10.0,110.32663316582915,20,,True,True,baseline,assessmentoftheblockchaintechnologyadoptionforthemanagementoftheelectronichealthrecordsystems,110.32663316582915,landmark,True7012783535300,10.3390/logistics2010002,,The Supply Chain Has No Clothes: Technology Adoption of Blockchain for Supply Chain Transparency,Logistics,2018,article,blockchain,2,82.0,208.0,110.28099491636864,951,138.9974,True,True,baseline,thesupplychainhasnoclothestechnologyadoptionofblockchainforsupplychaintransparency,110.28099491636864,landmark,True7023099878876,,,Array programming with NumPy,RePEc: Research Papers in Economics,2020,article,computer_architecture,4,2343.0,5512.0,689.4179496808111,17096,889.1394,True,True,baseline,arrayprogrammingwithnumpy,689.4179496808111,landmark,True7033046751266,10.1063/5.0014475,,Scalable molecular dynamics on CPU and GPU architectures with NAMD,The Journal of Chemical Physics,2020,article,computer_architecture,7,356.0,869.0,214.60681995758455,3144,304.3728,True,True,baseline,scalablemoleculardynamicsoncpuandgpuarchitectureswithnamd,214.60681995758455,landmark,True7044394998532,10.1145/3620665.3640366,,PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation,ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS),2024,conference-paper,computer_architecture,6,434.0,690.0,204.38576349024112,690,218.9123,True,False,post,pytorch2fastermachinelearningthroughdynamicpythonbytecodetransformationandgraphcompilation,204.38576349024112,landmark,True7054287393709,10.4230/lipics.ecrts.2026.17,,Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,computer_architecture,3,71.0,71.0,330.9365269461078,71,0.0,True,True,post,uncertaintyawareresourceallocationformultipathprogramswithinkernelpredictions,180.01839926555348,landmark,True7064297813615,10.4230/lipics.ecrts.2024.13,,DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2024,article,computer_architecture,5,674.0,707.0,157.60297582979868,708,51.0217,True,True,post,deeptrustrtconfidentialdeepneuralinferencemeetsrealtime,157.60297582979868,landmark,True7073111721177,10.1007/978-981-16-7487-7_7,,A Modern Primer on Processing in Memory,Computer architecture and design methodologies,2022,book-chapter,computer_architecture,2,90.0,125.0,139.4308943089431,168,112.4027,False,False,baseline,amodernprimeronprocessinginmemory,139.4308943089431,landmark,True7082273211146,10.17615/nyex-d314,,The multiprocessor real-time scheduling of general task systems,Carolina Digital Repository (University of North Carolina at Chapel Hill),2019,dissertation,computer_architecture,4,19.0,20.0,137.14285714285714,37,,True,True,baseline,themultiprocessorrealtimeschedulingofgeneraltasksystems,137.14285714285714,landmark,True7094286840061,10.7302/4622,,Algorithm-Architecture Co-Design for Domain-Specific Accelerators in Communication and Artificial Intelligence,Deep Blue (University of Michigan),2022,dissertation,computer_architecture,5,3.0,14.0,105.75490196078431,14,,True,True,baseline,algorithmarchitecturecodesignfordomainspecificacceleratorsincommunicationandartificialintelligence,105.75490196078431,landmark,True7103016932526,10.1063/5.0005082,,Q uantum ESPRESSO toward the exascale,The Journal of Chemical Physics,2020,article,computer_architecture,4,175.0,425.0,104.9573055028463,2032,159.393,True,True,baseline,quantumespressotowardtheexascale,104.9573055028463,landmark,True7114406650295,10.1145/3714983.3714987,,AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration,GetMobile Mobile Computing and Communications,2025,article,computer_architecture,9,234.0,234.0,76.51490775873349,234,83.4275,True,False,post,awqactivationawareweightquantizationforondevicellmcompressionandacceleration,102.2513468321514,landmark,True7124401211704,10.1109/isca59077.2024.00019,,Splitwise: Efficient Generative LLM Inference Using Phase Splitting,ACM/IEEE International Symposium on Computer Architecture (ISCA),2024,conference-paper,computer_architecture,6,125.0,292.0,86.49368541905856,286,90.7376,True,False,post,splitwiseefficientgenerativellminferenceusingphasesplitting,86.49368541905856,landmark,True7135673758,10.7275/4018988,,A Novel Reconfiguration Scheme in Quantum-Dot Cellular Automata for Energy Efficient Nanocomputing,ScholarWorks@UMassAmherst (University of Massachusetts Amherst),2023,dissertation,computer_architecture,4,4.0,4.0,86.3030303030303,4,,True,True,post,anovelreconfigurationschemeinquantumdotcellularautomataforenergyefficientnanocomputing,86.3030303030303,landmark,True7146922095860,10.1184/r1/14642340,,Practical Mechanisms for Reducing Processor–Memory Data Movement in Modern Workloads,Figshare,2021,dissertation,computer_architecture,9,9.0,11.0,81.43283582089553,11,,True,True,baseline,practicalmechanismsforreducingprocessormemorydatamovementinmodernworkloads,81.43283582089553,landmark,True7154386978266,10.7302/8543,,Accelerated Systems for Portable DNA Sequencing,Deep Blue (University of Michigan),2023,dissertation,computer_architecture,5,11.0,11.0,75.89999999999999,11,,True,True,post,acceleratedsystemsforportablednasequencing,75.89999999999999,landmark,True7164393145114,10.1109/mm.2024.3373763,2403.14123,AI and Memory Wall,IEEE Micro,2024,article,computer_architecture,8,201.0,336.0,74.90042415673601,336,105.8227,True,True,post,aiandmemorywall,74.90042415673601,landmark,True7171224547023,10.1201/9781315218823-12,,Model-Based Framework for Schedulability Analysis Using Uppaal 4.1,CRC Press eBooks,2018,book-chapter,computer_architecture,3,51.0,52.0,74.46337817638266,70,17.8443,True,True,baseline,modelbasedframeworkforschedulabilityanalysisusinguppaal41,74.46337817638266,landmark,True7182972087877,10.1145/3320060,,Demystifying Parallel and Distributed Deep Learning,ACM Computing Surveys,2019,article,computer_architecture,6,159.0,263.0,74.14530533429881,577,81.2716,True,False,baseline,demystifyingparallelanddistributeddeeplearning,74.14530533429881,landmark,True7192952332632,10.4230/oasics.asd.2020.1,,Towards a Reliable and Context-Based System Architecture for Autonomous Vehicles,OASIcs : OpenAccess Series in Informatics,2020,conference-paper,computer_architecture,3,395.0,516.0,73.13244814646819,797,52.3105,True,True,baseline,towardsareliableandcontextbasedsystemarchitectureforautonomousvehicles,73.13244814646819,landmark,True7202921153466,10.48550/arxiv.1903.05714,1903.05714,Basic Performance Measurements of the Intel Optane DC Persistent Memory Module,arXiv (Cornell University),2019,preprint,computer_architecture,6,108.0,175.0,71.88172043010752,191,,True,True,baseline,basicperformancemeasurementsoftheinteloptanedcpersistentmemorymodule,71.88172043010752,landmark,True7214403931219,10.17487/rfc9669,,BPF Instruction Set Architecture (ISA),,2024,report,computer_architecture,4,4.0,9.0,66.0,9,,False,False,post,bpfinstructionsetarchitectureisa,66.0,landmark,True7222931743911,10.1109/hpca.2019.00048,,Machine Learning at Facebook: Understanding Inference at the Edge,Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings,2019,conference-paper,computer_architecture,7,108.0,212.0,64.45974888151248,439,66.0809,True,False,baseline,machinelearningatfacebookunderstandinginferenceattheedge,64.45974888151248,landmark,True7231983572666,10.1016/c2011-0-06993-4,,The Art of Multiprocessor Programming,Elsevier eBooks,2020,book,computer_architecture,2,852.0,864.0,63.65562913907285,1093,24.414,False,False,baseline,theartofmultiprocessorprogramming,63.65562913907285,landmark,True72475685577,10.7275/2207079,,Electromagnetic Side-Channel Analysis for Hardware and Software Watermarking,Scholarworks (University of Massachusetts Amherst),2023,dissertation,computer_architecture,3,9.0,9.0,62.099999999999994,9,,True,True,post,electromagneticsidechannelanalysisforhardwareandsoftwarewatermarking,62.099999999999994,landmark,True7254410153295,10.1109/tcad.2025.3567878,,Bursting Firings in Memristive Hopfield Neural Network With Image Encryption and Hardware Implementation,IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2025,article,computer_architecture,4,142.0,142.0,32.564312668463614,142,32.3565,True,False,post,burstingfiringsinmemristivehopfieldneuralnetworkwithimageencryptionandhardwareimplementation,62.04996260754486,landmark,True7264380874786,10.1145/3579371.3589350,2304.01433,TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings,Proceedings - International Symposium on Computer Architecture/Proceedings,2023,conference-paper,computer_architecture,10,167.0,388.0,61.6547248352945,508,94.1928,True,True,post,tpuv4anopticallyreconfigurablesupercomputerformachinelearningwithhardwaresupportforembeddings,61.6547248352945,landmark,True7272156094048,10.1145/3501714.3501727,,Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach,ACM eBooks,2022,book-chapter,ai_accelerators,2,495.0,513.0,274.919235405545,727,46.4544,True,False,baseline,reverendbayesoninferenceenginesadistributedhierarchicalapproach,274.919235405545,landmark,True7283003967059,10.1038/s41586-020-03070-1,2002.00281,Parallel convolutional processing using an integrated photonic tensor core,Nature,2021,article,ai_accelerators,2,503.0,830.0,96.73020860110631,1874,105.2419,False,True,baseline,parallelconvolutionalprocessingusinganintegratedphotonictensorcore,96.73020860110631,landmark,True7297130682590,10.1016/j.vlsi.2026.102688,,Extreme multistability in discrete memristive neuron maps and implications for dual-field applications,Integration,2026,article,ai_accelerators,2,37.0,37.0,38.12608695652174,37,127.1161,False,False,post,extrememultistabilityindiscretememristiveneuronmapsandimplicationsfordualfieldapplications,93.81240525106308,landmark,True7304409283612,10.1038/s41586-025-08854-x,,Universal photonic artificial intelligence acceleration,Nature,2025,article,ai_accelerators,1,165.0,165.0,56.20713647719771,165,68.6604,False,False,post,universalphotonicartificialintelligenceacceleration,72.1003086636965,landmark,True7312783525259,10.1109/mm.2018.112130359,,Loihi: A Neuromorphic Manycore Processor with On-Chip Learning,IEEE Micro,2018,article,ai_accelerators,4,327.0,745.0,64.97395387432005,3897,115.1562,True,False,baseline,loihianeuromorphicmanycoreprocessorwithonchiplearning,64.97395387432005,landmark,True7327131231908,10.1109/tcyb.2026.3664460,,Reconfigurable Multiscroll Memristive Neural Network With Application to Telemedicine Privacy Protection,IEEE Transactions on Cybernetics,2026,article,ai_accelerators,5,25.0,25.0,67.00916730328495,25,77.9076,True,False,post,reconfigurablemultiscrollmemristiveneuralnetworkwithapplicationtotelemedicineprivacyprotection,63.38676030477235,landmark,True7337135102590,10.1109/tii.2026.3668794,,Biologically Plausible Memristive Decision-Making Circuit for Adaptive Control in Industrial Autonomous Navigation,IEEE Transactions on Industrial Informatics,2026,article,ai_accelerators,2,24.0,24.0,35.86027014438752,24,41.1527,True,False,post,biologicallyplausiblememristivedecisionmakingcircuitforadaptivecontrolinindustrialautonomousnavigation,60.851289892581455,landmark,True7347134974875,10.1109/icears67481.2026.11416637,,Neuromorphic Photonic Computing using Microring Resonator Arrays for Real-Time Edge AI Processing,International Conference on Electronics and Renewable Systems (ICEARS),2026,conference-paper,ai_accelerators,3,24.0,24.0,184.20139553974553,24,221.8777,False,False,post,neuromorphicphotoniccomputingusingmicroringresonatorarraysforrealtimeedgeaiprocessing,60.851289892581455,landmark,True7352944119451,10.1038/s41586-019-1157-8,2102.09360,All-optical spiking neurosynaptic networks with self-learning capabilities,Nature,2019,article,ai_accelerators,3,162.0,384.0,58.38929375910958,1529,75.8628,False,True,baseline,allopticalspikingneurosynapticnetworkswithselflearningcapabilities,58.38929375910958,landmark,True7367128511721,10.1109/tce.2026.3663359,,A Memristor-Based Neural Network Circuit With Classical Conditioning and Fear Generalization,IEEE Transactions on Consumer Electronics,2026,article,ai_accelerators,3,23.0,23.0,34.366092221704704,23,39.438,True,False,post,amemristorbasedneuralnetworkcircuitwithclassicalconditioningandfeargeneralization,58.315819480390566,landmark,True7374210357113,10.1038/s43588-021-00184-y,,Opportunities for neuromorphic computing algorithms and applications,Nature Computational Science,2022,article,ai_accelerators,2,224.0,537.0,42.17948197363679,1107,60.0929,False,True,baseline,opportunitiesforneuromorphiccomputingalgorithmsandapplications,42.17948197363679,landmark,True7384394727812,10.1126/science.adl1203,,Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence,Science,2024,article,ai_accelerators,4,198.0,308.0,38.256050750798984,308,45.8367,True,False,post,largescalephotonicchiplettaichiempowers160topswartificialgeneralintelligence,38.256050750798984,landmark,True7397141052859,10.1016/j.vlsi.2026.102731,,A dual-neuron second-order memristor-based Hopfield neural network with controllable extreme multistability and its application in image encryption,Integration,2026,article,ai_accelerators,4,15.0,15.0,40.20550038197097,15,46.7446,False,False,post,adualneuronsecondordermemristorbasedhopfieldneuralnetworkwithcontrollableextrememultistabilityanditsapplicationinimageencryption,38.03205618286341,landmark,True7407149961640,10.1016/j.neunet.2026.108953,,A discrete memristive cyclic Hopfield neural network with multi-cavity-like attractors and application in secure communication,Neural Networks,2026,article,ai_accelerators,3,14.0,14.0,14.426086956521738,14,48.098,False,False,post,adiscretememristivecyclichopfieldneuralnetworkwithmulticavitylikeattractorsandapplicationinsecurecommunication,35.49658577067252,landmark,True7414409363528,10.1186/s43593-025-00085-x,,Intelligent nanophotonics: when machine learning sheds light,eLight,2025,article,ai_accelerators,5,77.0,77.0,26.229997022692263,77,32.0415,True,True,post,intelligentnanophotonicswhenmachinelearningshedslight,33.64681070972503,landmark,True7427140216027,10.1016/j.neunet.2026.108887,,Discrete bidirectional memristive neural network-based hyperchaotic system and its FPGA implementation,Neural Networks,2026,article,ai_accelerators,3,13.0,13.0,13.395652173913042,13,44.6624,False,False,post,discretebidirectionalmemristiveneuralnetworkbasedhyperchaoticsystemanditsfpgaimplementation,32.96111535848162,landmark,True7434402694963,10.1038/s41377-024-01590-3,,Optical neural networks: progress and challenges,Light Science & Applications,2024,article,ai_accelerators,4,135.0,263.0,32.666692686558875,263,39.1398,True,True,post,opticalneuralnetworksprogressandchallenges,32.666692686558875,landmark,True7444210305137,10.1038/s41377-022-00717-8,,Photonic matrix multiplication lights up photonic accelerator and beyond,Light Science & Applications,2022,article,ai_accelerators,3,161.0,306.0,31.53012133083438,563,38.2097,True,True,baseline,photonicmatrixmultiplicationlightsupphotonicacceleratorandbeyond,31.53012133083438,landmark,True7454414574650,10.1016/j.chaos.2025.117292,,Field and delay effects on synchronization in a multilayer memristive neuronal network with a hub,Chaos Solitons & Fractals,2025,article,ai_accelerators,1,71.0,71.0,27.006304798493694,71,31.2974,False,False,post,fieldanddelayeffectsonsynchronizationinamultilayermemristiveneuronalnetworkwithahub,31.02498130377243,landmark,True7462799210008,10.48550/arxiv.1804.09848,1804.09848,Memristor Crossbars with 4.5 Terabits-per-Inch-Square Density and Two Nanometer Dimension,arXiv (Cornell University),2018,preprint,ai_accelerators,2,38.0,109.0,30.82828282828283,154,,True,True,baseline,memristorcrossbarswith45terabitsperinchsquaredensityandtwonanometerdimension,30.82828282828283,landmark,True7477130433466,10.1109/tcsi.2026.3663432,,A Second-Order Memristor Method to Construct Memristive Neural Networks With Multi-Butterfly and Multi-Scroll Dynamics,IEEE Transactions on Circuits and Systems I Regular Papers,2026,article,ai_accelerators,4,12.0,12.0,17.93013507219376,12,20.5764,True,False,post,asecondordermemristormethodtoconstructmemristiveneuralnetworkswithmultibutterflyandmultiscrolldynamics,30.425644946290728,landmark,True7487130516837,10.1142/s0218127426500914,,Gaussian-Function-Driven Chaotic Memristive Dynamics with Application to Image Encryption,International Journal of Bifurcation and Chaos,2026,article,ai_accelerators,3,12.0,12.0,32.16440030557678,12,37.3956,True,False,post,gaussianfunctiondrivenchaoticmemristivedynamicswithapplicationtoimageencryption,30.425644946290728,landmark,True7493121141908,10.1038/s41467-020-20719-7,,An optical neural chip for implementing complex-valued neural network,Nature Communications,2021,article,ai_accelerators,4,124.0,260.0,30.301029200346555,666,36.6979,True,True,baseline,anopticalneuralchipforimplementingcomplexvaluedneuralnetwork,30.301029200346555,landmark,True7504360605906,10.1109/isscc42615.2023.10067837,,"33.1 A 16nm 32Mb Embedded STT-MRAM with a 6ns Read-Access Time, a 1M-Cycle Write Endurance, 20-Year Retention at 150°C and MTJ-OTP Solutions for Magnetic Immunity",Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference,2023,conference-paper,ai_accelerators,2,27.0,53.0,29.22718446601942,69,119.8281,True,False,post,331a16nm32mbembeddedsttmramwitha6nsreadaccesstimea1mcyclewriteendurance20yearretentionat150candmtjotpsolutionsformagneticimmunity,29.22718446601942,landmark,True7514413052649,10.1016/j.vlsi.2025.102500,,An attractor-controllable memristive Hopfield neural network and its application on voice encryption,Integration,2025,article,ai_accelerators,4,66.0,66.0,14.175875486381322,66,28.5628,False,False,post,anattractorcontrollablememristivehopfieldneuralnetworkanditsapplicationonvoiceencryption,28.840123465478598,landmark,True7522916516729,10.5555/3323298.3323317,,SLM-DB: Single-Level Key-Value Store with Persistent Memory,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2019,dissertation,operating_systems,7,19.0,42.0,328.8494623655914,75,,True,True,baseline,slmdbsinglelevelkeyvaluestorewithpersistentmemory,328.8494623655914,landmark,True7534295093682,10.5281/zenodo.19901496,,fast16 as a Second-Order Subversion Attack,,2026,preprint,operating_systems,4,28.0,28.0,1089.5438596491229,28,,True,True,post,fast16asasecondordersubversionattack,70.99317154134503,landmark,True75425727798,10.48550/arxiv.1903.01955,1903.01955,The Lustre Storage Architecture,arXiv (Cornell University),2019,preprint,operating_systems,3,96.0,112.0,46.00430107526881,206,,True,True,baseline,thelustrestoragearchitecture,46.00430107526881,landmark,True7552930508541,10.4230/oasics.microservices.2020-2022.5,1902.03383,Cloud Programming Simplified: A Berkeley View on Serverless Computing,arXiv (Cornell University),2019,preprint,operating_systems,6,103.0,195.0,43.97867397390838,409,,True,True,baseline,cloudprogrammingsimplifiedaberkeleyviewonserverlesscomputing,43.97867397390838,landmark,True7564404401018,10.1145/3694715.3695964,2312.12456,PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU,ACM SIGOPS Symposium on Operating Systems Principles (SOSP),2024,conference-paper,operating_systems,10,64.0,133.0,39.39609644087256,133,42.1961,True,True,post,powerinferfastlargelanguagemodelservingwithaconsumergradegpu,39.39609644087256,landmark,True7573182301081,,,Zico: Efficient {GPU} Memory Sharing for Concurrent {DNN} Training,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2021,dissertation,operating_systems,5,0.0,5.0,37.01492537313433,8,,True,False,baseline,zicoefficientgpumemorysharingforconcurrentdnntraining,37.01492537313433,landmark,True7587130515337,10.1186/s12912-026-04420-6,,Artificial intelligence literacy and academic resilience in undergraduate nursing students: the mediating role of self-efficacy and artificial intelligence anxiety,BMC Nursing,2026,article,operating_systems,1,13.0,13.0,92.3590525632706,13,118.9868,True,True,post,artificialintelligenceliteracyandacademicresilienceinundergraduatenursingstudentsthemediatingroleofselfefficacyandartificialintelligenceanxiety,32.96111535848162,landmark,True7592975712713,10.1145/3341301.3359642,,A generic communication scheduler for distributed DNN training acceleration,,2019,conference-paper,operating_systems,8,53.0,119.0,31.997431506849313,321,32.6252,True,False,baseline,agenericcommunicationschedulerfordistributeddnntrainingacceleration,31.997431506849313,landmark,True7604384697207,10.22215/etd/2023-15552,,Methods and Techniques for Inter-cloud Data Transfers,,2023,dissertation,operating_systems,4,1.0,1.0,28.864864864864863,1,,True,True,post,methodsandtechniquesforinterclouddatatransfers,28.864864864864863,landmark,True7614394944658,10.1145/3627703.3629578,,"Orion: Interference-aware, Fine-grained GPU Sharing for ML Applications",,2024,conference-paper,operating_systems,7,56.0,97.0,28.73249138920781,97,30.7746,True,False,post,orioninterferenceawarefinegrainedgpusharingformlapplications,28.73249138920781,landmark,True7627162190207,10.1016/j.dss.2026.114696,,"AI-driven mental health therapy: A comparative study of emotional clarity and stigma reduction across journaling, human-guided therapy, and AI-guided therapy",Decision Support Systems,2026,article,operating_systems,1,10.0,10.0,5.225225225225225,10,63.1822,False,False,post,aidrivenmentalhealththerapyacomparativestudyofemotionalclarityandstigmareductionacrossjournalinghumanguidedtherapyandaiguidedtherapy,25.35470412190894,landmark,True7634295682935,,,CodaLab Competitions An Open Source Platform to Organize Scientific Challenges,HAL (Le Centre pour la Communication Scientifique Directe),2022,report,operating_systems,4,28.0,68.0,22.289935364727608,75,,True,True,baseline,codalabcompetitionsanopensourceplatformtoorganizescientificchallenges,22.289935364727608,landmark,True7643152809665,10.1145/3445814.3446701,,"Nightcore: efficient and scalable serverless computing for latency-sensitive, interactive microservices",,2021,conference-paper,operating_systems,5,33.0,78.0,21.88714698058747,215,30.1787,True,True,baseline,nightcoreefficientandscalableserverlesscomputingforlatencysensitiveinteractivemicroservices,21.88714698058747,landmark,True7653010663313,10.1145/3373376.3378515,,FlatStore: An Efficient Log-Structured Key-Value Storage Engine for Persistent Memory,International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS),2020,conference-paper,operating_systems,5,38.0,70.0,21.67711021009952,165,23.8709,True,False,baseline,flatstoreanefficientlogstructuredkeyvaluestorageengineforpersistentmemory,21.67711021009952,landmark,True7663092732687,,,PENGEMBANGAN SISTEM INFORMASI PENERIMAANMAHASISWA BARU DENGAN METODE BUSINESSPROCESS AUTOMATION (STUDI KASUS: INSTITUTTEKNOLOGI KALIMANTAN),,2019,dissertation,operating_systems,1,0.0,1.0,21.373831775700936,1,,True,False,baseline,pengembangansisteminformasipenerimaanmahasiswabarudenganmetodebusinessprocessautomationstudikasusinstitutteknologikalimantan,21.373831775700936,landmark,True7673138262672,10.5445/ir/1000097700,,SimuBoost: Scalable Parallelization of Functional System Simulation,Repository KITopen (Karlsruhe Institute of Technology),2019,dissertation,operating_systems,5,3.0,3.0,20.57142857142857,3,,True,True,baseline,simuboostscalableparallelizationoffunctionalsystemsimulation,20.57142857142857,landmark,True7684366587887,10.3390/su15086917,,Promising 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Circuits),2023,conference-paper,operating_systems,1,28.0,55.0,18.331575035365766,70,35.7909,True,False,post,qlcprogrammable3dferroelectricnandflashmemorybymemorywindowexpansionusingcellstackengineering,18.331575035365766,landmark,True7712807211499,,,Towards Efficient and Flexible Object Storage Using Resource and Functional Partitioning,VTechWorks (Virginia Tech),2018,dissertation,operating_systems,4,2.0,2.0,17.98848368522073,3,,True,False,baseline,towardsefficientandflexibleobjectstorageusingresourceandfunctionalpartitioning,17.98848368522073,landmark,True7724407217670,10.1145/3669940.3707256,2405.04437,vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention,ACM International Conference on Architectural Support for Programming Languages and Operating Systems 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Development,2024,article,operating_systems,1,38.0,53.0,15.956977383908523,53,20.2822,True,True,post,digitalfinancialinclusionfinancialstabilityandsustainabledevelopmentevidencefromaquantileonquantileregressionandwaveletcoherence,15.956977383908523,landmark,True7763181035463,,,{ZNS}: Avoiding the Block Interface Tax for Flash-based SSDs,USENIX Annual Technical Conference,2021,conference-paper,operating_systems,4,26.0,46.0,15.661939184411095,82,17.1134,False,False,baseline,znsavoidingtheblockinterfacetaxforflashbasedssds,15.661939184411095,landmark,True7772982169647,10.1038/s41586-019-1666-5,,Quantum supremacy using a programmable superconducting processor,Nature,2019,article,quantum_computing,4,941.0,2260.0,343.6453226447595,7695,402.3374,True,True,baseline,quantumsupremacyusingaprogrammablesuperconductingprocessor,343.6453226447595,landmark,True7782781738013,10.22331/q-2018-08-06-79,,Quantum Computing in the NISQ era and 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photons,Science,2020,article,quantum_computing,3,294.0,769.0,96.1833097432046,2267,120.9449,True,True,baseline,quantumcomputationaladvantageusingphotons,96.1833097432046,landmark,True7824213212652,10.1103/revmodphys.94.015004,,Noisy intermediate-scale quantum algorithms,Reviews of Modern Physics,2022,article,quantum_computing,2,509.0,908.0,93.55996787058045,1718,108.1665,True,True,baseline,noisyintermediatescalequantumalgorithms,93.55996787058045,landmark,True7834405183524,10.1038/s41586-024-08449-y,,Quantum error correction below the surface code threshold,Nature,2024,article,quantum_computing,3,394.0,751.0,93.2801756943183,751,111.7642,True,True,post,quantumerrorcorrectionbelowthesurfacecodethreshold,93.2801756943183,landmark,True7844386285839,10.3390/admsci13090196,,Managing the Strategic Transformation of Higher Education through Artificial Intelligence,Administrative Sciences,2023,article,quantum_computing,2,165.0,380.0,89.06279482079277,543,127.3637,True,True,post,managingthestrategictransformationofhighereducationthroughartificialintelligence,89.06279482079277,landmark,True7854407158326,10.1038/s41586-024-08404-x,,Distributed quantum computing across an optical network link,Nature,2025,article,quantum_computing,5,192.0,192.0,65.40466790073914,192,79.0635,True,True,post,distributedquantumcomputingacrossanopticalnetworklink,83.8985409904832,landmark,True7863156168805,10.15488/11476,,Ultra-low vibration closed-cycle cryogenic surface-electrode ion trap apparatus,Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover),2021,dissertation,quantum_computing,2,10.0,18.0,81.87032967032967,25,,True,True,baseline,ultralowvibrationclosedcyclecryogenicsurfaceelectrodeiontrapapparatus,81.87032967032967,landmark,True7874321610982,10.1038/s41586-022-05434-1,,Suppressing quantum errors by scaling a surface code logical qubit,Nature,2023,article,quantum_computing,1,462.0,879.0,80.69640995309555,1083,94.0878,True,True,post,suppressingquantumerrorsbyscalingasurfacecodelogicalqubit,80.69640995309555,landmark,True7884406693713,10.1038/s41586-024-08406-9,,Scaling and networking a modular photonic quantum computer,Nature,2025,article,quantum_computing,4,178.0,178.0,60.63557753297692,178,73.6539,True,True,post,scalingandnetworkingamodularphotonicquantumcomputer,77.78093904326046,landmark,True7892788945937,10.37686/qrl.v1i2.80,,Classification with Quantum Neural Networks on Near Term Processors,,2020,report,quantum_computing,1,188.0,222.0,77.25930521091811,332,,True,True,baseline,classificationwithquantumneuralnetworksonneartermprocessors,77.25930521091811,landmark,True7903174122836,10.1201/9781003358817-7,,Quantum Computing 40 Years Later,CRC Press 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Technology),2022,dissertation,quantum_computing,1,7.0,10.0,74.89035087719299,13,,True,True,baseline,timeoptimalholonomicquantumcomputation,74.89035087719299,landmark,True7944407091026,10.1016/s2589-7500(24)00267-x,,"Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study",The Lancet Digital Health,2025,article,quantum_computing,1,164.0,164.0,55.86648716521469,164,67.8282,True,True,post,screeningperformanceandcharacteristicsofbreastcancerdetectedinthemammographyscreeningwithartificialintelligencetrialmasaiarandomisedcontrolledparallelgroupnoninferioritysingleblindedscreeningaccuracystudy,71.66333709603774,landmark,True7952798434869,10.1038/s41586-019-0980-2,,Supervised learning with quantum-enhanced feature 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Warwick),2026,dissertation,quantum_computing,5,26.0,26.0,795.7491039426524,27,,True,True,post,formalverificationtechniquesusingquantumprocesscalculus,65.92223071696324,landmark,True7992482126025,10.1038/s41567-018-0124-x,,Characterizing quantum supremacy in near-term devices,Nature Physics,2018,article,quantum_computing,1,219.0,345.0,65.11686249237604,1156,65.4918,False,True,baseline,characterizingquantumsupremacyinneartermdevices,65.11686249237604,landmark,True8002950805899,10.1063/1.5089550,,A quantum engineer's guide to superconducting qubits,Applied Physics Reviews,2019,article,quantum_computing,1,162.0,416.0,63.255068239035374,2004,77.9349,True,True,baseline,aquantumengineersguidetosuperconductingqubits,63.255068239035374,landmark,True8013173899054,10.1103/physrevlett.127.180501,,Strong Quantum Computational Advantage Using a Superconducting Quantum Processor,Physical Review 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Journal,2018,article,scientific_computing,1,459.0,1025.0,193.46314218749401,7687,234.9501,True,True,baseline,theastropyprojectbuildinganopenscienceprojectandstatusofthev20corepackage,193.46314218749401,landmark,True8081494474111,10.1515/9783110666991-007,,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,True8092913606428,10.3758/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,True8103038025793,10.1016/j.cpc.2021.108033,,VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code,Computer Physics 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life,bioRxiv (Cold Spring Harbor Laboratory),2024,preprint,computational_biology,2,211.0,430.0,694.8057553956835,430,,True,True,post,chai1decodingthemolecularinteractionsoflife,694.8057553956835,landmark,True8293097145107,10.1093/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,pubchemin2021newdatacontentandimprovedwebinterfaces,383.79097718298823,landmark,True8304411463597,10.1101/2025.06.14.659707,,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,True8314416043407,10.1038/s41586-026-10644-y,2502.18864,Accelerating scientific discovery with 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Measurements,Leibniz international proceedings in informatics,2019,conference-paper,computational_biology,2,718.0,965.0,260.53544494720967,1141,577.848,True,True,baseline,jointlyembeddingmultiplesinglecellomicsmeasurements,260.53544494720967,landmark,True8353156458251,10.1093/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,interactivetreeoflifeitolv5anonlinetoolforphylogenetictreedisplayandannotation,249.05241916110123,landmark,True8364409229284,10.1093/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,True8374393936599,10.1093/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,True8383168436232,10.1021/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,True8392899070097,10.1093/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,pubchem2019updateimprovedaccesstochemicaldata,194.80273384490118,landmark,True8403011072970,10.1038/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,True8413211795435,10.1093/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,True8423098269892,10.17863/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,automaticchemicaldesignusingadatadrivencontinuousrepresentationofmolecules,178.86649066100819,landmark,True8434307425304,10.1093/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,True8444400581728,10.1021/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,anupdateonthenitrogenheterocyclecompositionsandpropertiesofusfdaapprovedpharmaceuticals20132023,176.8137166729525,landmark,True8454388522045,10.1371/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,True8464388615774,10.1093/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,True8474309506674,10.1093/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,True8484220831425,10.1093/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,True8497119529911,10.1126/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,True8504302307901,10.48550/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,True8512927961455,10.1093/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,True8524221151978,10.1145/3528223.3530127,2201.05989,Instant neural graphics primitives with a multiresolution hash encoding,ACM Transactions on Graphics,2022,article,computer_graphics,6,704.0,2033.0,763.1175545066307,4114,1035.1766,True,True,baseline,instantneuralgraphicsprimitiveswithamultiresolutionhashencoding,763.1175545066307,landmark,True8531483393136,10.82444/warw.33661294,,Advanced high dynamic range imaging : theory and practice,CERN Document Server (European Organization for Nuclear Research),2026,article,computer_graphics,2,214.0,214.0,573.5984721161192,216,3.1163,True,True,post,advancedhighdynamicrangeimagingtheoryandpractice,542.5906682088513,landmark,True8542910683382,,,Real-Time Simulation and Rendering of 3D Fluids,,2018,dissertation,computer_graphics,2,26.0,26.0,526.90625,62,,False,False,baseline,realtimesimulationandrenderingof3dfluids,526.90625,landmark,True8554385318467,10.1145/3592433,2308.04079,3D Gaussian Splatting for Real-Time Radiance Field Rendering,ACM Transactions on Graphics,2023,article,computer_graphics,2,1016.0,3644.0,395.070873513716,5862,825.8872,True,True,post,3dgaussiansplattingforrealtimeradiancefieldrendering,395.070873513716,landmark,True8563128261063,10.21105/joss.03021,,seaborn: statistical data visualization,The Journal of Open Source Software,2021,article,computer_graphics,3,1232.0,2401.0,279.8183504232003,7190,347.307,True,True,baseline,seabornstatisticaldatavisualization,279.8183504232003,landmark,True8571506965148,10.2312/14803,,Mastering the Information Age Solving Problems with Visual Analytics,University of Groningen research database (University of Groningen / Centre for Information Technology),2022,book-chapter,computer_graphics,2,513.0,517.0,220.7091108671789,552,15.8992,True,True,post,masteringtheinformationagesolvingproblemswithvisualanalytics,220.7091108671789,landmark,True8584231645407,10.2307/j.ctv65sx8p.8,,The Fastest Texture Mapping in Town:,University of Michigan Press eBooks,2018,book-chapter,computer_graphics,2,62.0,66.0,186.86419753086417,107,78.4444,False,False,baseline,thefastesttexturemappingintown,186.86419753086417,landmark,True8594390723081,10.1145/3807511,2401.03890,A Survey on 3D Gaussian Splatting,ACM Computing Surveys,2026,article,computer_graphics,3,67.0,67.0,121.09388038942977,67,72.919,True,True,post,asurveyon3dgaussiansplatting,169.8765176167899,landmark,True8603016007010,10.1111/cgf.14022,2004.03805,State of the Art on Neural Rendering,Computer Graphics Forum,2020,article,computer_graphics,3,129.0,213.0,133.21172638436482,430,143.0402,True,True,baseline,stateoftheartonneuralrendering,133.21172638436482,landmark,True8613163641770,10.2172/1782521,,Metric Type in the Target-matrix Mesh Optimization Paradigm,,2020,report,computer_graphics,3,2.0,6.0,112.49999999999999,9,,True,True,baseline,metrictypeinthetargetmatrixmeshoptimizationparadigm,112.49999999999999,landmark,True8624385289063,10.1145/3592426,2302.12249,MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes,ACM Transactions on Graphics,2023,article,computer_graphics,2,114.0,172.0,102.18535708938464,199,115.9383,True,True,post,merfmemoryefficientradiancefieldsforrealtimeviewsynthesisinunboundedscenes,102.18535708938464,landmark,True8637151814497,10.70675/339cd785z4790z4c97zb09dze298711dbccf,,Designing tools for 3D content authoring based on 3D sketching,,2023,dissertation,computer_graphics,2,0.0,2.0,98.75,2,,True,True,post,designingtoolsfor3dcontentauthoringbasedon3dsketching,98.75,landmark,True8644385482704,10.1145/3596711.3596766,,Photo tourism: exploring photo collections in 3D,ACM eBooks,2023,book-chapter,computer_graphics,2,184.0,195.0,93.00658376005852,222,25.2247,True,False,post,phototourismexploringphotocollectionsin3d,93.00658376005852,landmark,True8652478400128,10.26190/unsworks/9223,,Raster scan computer graphics and incremental computing systems,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,computer_graphics,3,1.0,1.0,89.2,3,,False,True,baseline,rasterscancomputergraphicsandincrementalcomputingsystems,89.2,landmark,True8663113347918,10.1109/iccv48922.2021.01245,2012.03918,NeRD: Neural Reflectance Decomposition from Image Collections,IEEE/CVF International Conference on Computer Vision (ICCV),2021,conference-paper,computer_graphics,2,90.0,203.0,84.667495854063,438,99.214,True,True,baseline,nerdneuralreflectancedecompositionfromimagecollections,84.667495854063,landmark,True8672978950353,10.33153/citrawira.v1i1.3299,,PERANCANGAN POP UP BOOK AND SOUND TEMBANG DOLANAN SEBAGAI MEDIA PENGENALAN ALAT MUSIK GAMELAN,Institutional Repository ISI Surakarta (Institut Seni Indonesia Surakarta),2020,dissertation,computer_graphics,1,1.0,3.0,81.7,4,,True,False,baseline,perancanganpopupbookandsoundtembangdolanansebagaimediapengenalanalatmusikgamelan,81.7,landmark,True8684402660132,10.1109/cvpr52733.2024.00813,2312.16812,Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2024,conference-paper,computer_graphics,2,110.0,208.0,77.27933949282485,208,83.0854,True,True,post,spacetimegaussianfeaturesplattingforrealtimedynamicviewsynthesis,77.27933949282485,landmark,True8692902812770,10.1145/3272127.3275109,,Differentiable Monte Carlo ray tracing through edge sampling,ACM Transactions on Graphics,2018,article,computer_graphics,5,38.0,105.0,75.6115107913669,428,97.9479,True,True,baseline,differentiablemontecarloraytracingthroughedgesampling,75.6115107913669,landmark,True8704396833408,10.1007/s10660-024-09855-0,,Integration of artificial intelligence technology and visual communication design in metaverse e-commerce and its potential opportunities,Electronic Commerce Research,2024,book-chapter,computer_graphics,2,10.0,17.0,73.82191780821917,17,84.1119,False,False,post,integrationofartificialintelligencetechnologyandvisualcommunicationdesigninmetaverseecommerceanditspotentialopportunities,73.82191780821917,landmark,True8712942551262,10.1145/3306346.3323008,1905.00824,Single image portrait relighting,ACM Transactions on Graphics,2019,article,computer_graphics,2,41.0,103.0,72.34294926183607,259,80.372,True,True,baseline,singleimageportraitrelighting,72.34294926183607,landmark,True8723135538239,10.1007/978-3-030-92540-6_7,2103.04183,HexDom: Polycube-Based Hexahedral-Dominant Mesh Generation,SEMA SIMAI Springer series,2022,book-chapter,computer_graphics,3,17.0,21.0,69.5427135678392,25,60.724,False,True,baseline,hexdompolycubebasedhexahedraldominantmeshgeneration,69.5427135678392,landmark,True8732972175553,10.1029/2019gc008515,,The Generic Mapping Tools Version 6,Geochemistry Geophysics Geosystems,2019,article,computer_graphics,3,99.0,447.0,67.9687872664635,3192,104.5271,True,True,baseline,thegenericmappingtoolsversion6,67.9687872664635,landmark,True8742202024431,10.1201/9781003059325-5,,Signed Distance Fields for Polygon Soup Meshes,A K Peters/CRC Press eBooks,2020,book-chapter,computer_graphics,3,34.0,35.0,67.15547703180212,39,32.903,True,False,baseline,signeddistancefieldsforpolygonsoupmeshes,67.15547703180212,landmark,True8752521773018,10.17615/3myy-vc96,,Efficient computation of discrete Voronoi diagram and homotopy-preserving simplified medial axis of a 3d polyhedron,Carolina Digital Repository (University of North Carolina at Chapel Hill),2019,dissertation,computer_graphics,4,3.0,3.0,66.61016949152543,3,,True,True,baseline,efficientcomputationofdiscretevoronoidiagramandhomotopypreservingsimplifiedmedialaxisofa3dpolyhedron,66.61016949152543,landmark,True8763045012476,10.70675/aa144e03zf76fz490bzab50ze72464fe07f4,,Kinetic data structures for the geometric modeling of urban environments,,2019,dissertation,computer_graphics,3,2.0,3.0,66.61016949152543,3,,True,True,baseline,kineticdatastructuresforthegeometricmodelingofurbanenvironments,66.61016949152543,landmark,True8774408302972,10.3390/educsci15030343,,The Impact of Artificial Intelligence (AI) on Students’ Academic Development,Education Sciences,2025,article,privacy_preserving_computation,1,469.0,469.0,407.18625178855723,469,511.2876,True,True,post,theimpactofartificialintelligenceaionstudentsacademicdevelopment,204.93966523196156,landmark,True8787127055292,10.3389/feduc.2025.1648661,,Artificial intelligence in higher education: a systematic review of its impact on student engagement and the mediating role of teaching methods,Frontiers in Education,2026,review,privacy_preserving_computation,1,54.0,54.0,102.8810408921933,54,112.4196,True,True,post,artificialintelligenceinhighereducationasystematicreviewofitsimpactonstudentengagementandthemediatingroleofteachingmethods,136.9154022583083,landmark,True8793036422000,10.1109/access.2020.3010226,2006.10306,A Survey of COVID-19 Contact Tracing Apps,IEEE Access,2020,article,privacy_preserving_computation,4,302.0,443.0,128.99802950839438,582,117.8531,True,True,baseline,asurveyofcovid19contacttracingapps,128.99802950839438,landmark,True8804302383326,,,Private Information Retrieval with Side Information,OakTrust (Texas A&M University Libraries),2018,dissertation,privacy_preserving_computation,5,24.0,28.0,124.06412825651303,28,,True,True,baseline,privateinformationretrievalwithsideinformation,124.06412825651303,landmark,True8813014084098,10.48550/arxiv.2003.11511,2003.11511,Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs,arXiv (Cornell University),2020,preprint,privacy_preserving_computation,2,247.0,307.0,112.68777120315582,350,,True,True,baseline,contacttracingmobileappsforcovid19privacyconsiderationsandrelatedtradeoffs,112.68777120315582,landmark,True8823013489453,10.1038/s41591-020-0832-5,,On the responsible use of digital data to tackle the COVID-19 pandemic,Nature Medicine,2020,article,privacy_preserving_computation,1,284.0,368.0,107.15863399342919,488,96.9324,False,True,baseline,ontheresponsibleuseofdigitaldatatotacklethecovid19pandemic,107.15863399342919,landmark,True8833021970994,10.2196/19857,,Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study,JMIR mhealth and uhealth,2020,article,privacy_preserving_computation,2,171.0,290.0,84.4456626578654,402,81.5906,True,True,baseline,acceptabilityofappbasedcontacttracingforcovid19crosscountrysurveystudy,84.4456626578654,landmark,True8847137155252,10.1145/3801079,,Differentially Private Federated Learning: A Systematic Review,ACM Computing Surveys,2026,review,privacy_preserving_computation,4,33.0,33.0,37.86734693877551,21,27.926,True,True,post,differentiallyprivatefederatedlearningasystematicreview,83.6705236022995,landmark,True8854407111324,10.3389/fpsyg.2025.1498132,,Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review,Frontiers in Psychology,2025,review,privacy_preserving_computation,1,190.0,190.0,29.307559598494354,190,32.5824,True,True,post,exploringtheeffectsofartificialintelligenceonstudentandacademicwellbeinginhighereducationaminireview,83.02459785516567,landmark,True8864406636601,10.1016/j.accinf.2025.100734,,Challenges and opportunities for artificial intelligence in auditing: Evidence from the field,International Journal of Accounting Information Systems,2025,article,privacy_preserving_computation,2,183.0,183.0,31.465784036766877,183,92.2647,True,True,post,challengesandopportunitiesforartificialintelligenceinauditingevidencefromthefield,79.9657968815543,landmark,True8874286110439,10.1515/9783839459249-009,,»Ohne die geht’s nicht« Zur Rolle von Apple und Google bei der Corona-Warn-App,transcript Verlag eBooks,2022,book-chapter,privacy_preserving_computation,1,100.0,100.0,77.6986788965706,100,67.9524,True,True,baseline,ohnediegehtsnichtzurrollevonappleundgooglebeidercoronawarnapp,77.6986788965706,landmark,True8884399027046,10.22024/unikent/01.02.102717,,Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System,theses.fr (ABES),2023,dissertation,privacy_preserving_computation,5,8.0,9.0,74.86447638603696,9,,True,True,post,secureandprivacypreservingfederatedlearningwithexplainableartificialintelligenceforsmarthealthcaresystem,74.86447638603696,landmark,True8893018102029,10.1609/aaai.v36i8.20825,2004.12088,SplitFed: When Federated Learning Meets Split Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2022,conference-paper,privacy_preserving_computation,7,202.0,373.0,74.10667093485475,720,85.2216,True,True,baseline,splitfedwhenfederatedlearningmeetssplitlearning,74.10667093485475,landmark,True8904414891672,10.1038/s41746-025-02280-z,2506.00660,"Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications",npj Digital Medicine,2026,article,privacy_preserving_computation,5,29.0,29.0,32.27124442472503,29,104.7659,True,True,post,differentialprivacyformedicaldeeplearningmethodstradeoffsanddeploymentimplications,73.52864195353592,landmark,True8914205766150,10.1787/8f394636-en,,Tracking and tracing COVID: Protecting privacy and data while using apps and biometrics,OECD policy responses to coronavirus (Covid-19),2020,report,privacy_preserving_computation,2,124.0,130.0,72.61058109280138,139,,False,False,baseline,trackingandtracingcovidprotectingprivacyanddatawhileusingappsandbiometrics,72.61058109280138,landmark,True8924412565294,10.1145/3749987,2209.02299,A Survey of Machine Unlearning,ACM Transactions on Intelligent Systems and Technology,2025,article,privacy_preserving_computation,7,164.0,164.0,55.86648716521469,164,47.4381,True,True,post,asurveyofmachineunlearning,71.66333709603774,landmark,True8932797770018,10.1016/j.future.2018.02.050,,A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment,Future Generation Computer Systems,2018,article,privacy_preserving_computation,3,55.0,132.0,69.98601600461856,192,60.259,False,False,baseline,atwostagelocalitysensitivehashingbasedapproachforprivacypreservingmobileservicerecommendationincrossplatformedgeenvironment,69.98601600461856,landmark,True8947135198685,10.1098/rsos.250959,,Breaking interprovincial data silos: how federated learning can unlock Canada's public health potential,Royal Society Open Science,2026,article,privacy_preserving_computation,3,27.0,27.0,30.045641360950892,27,46.964,True,True,post,breakinginterprovincialdatasiloshowfederatedlearningcanunlockcanadaspublichealthpotential,68.45770112915415,landmark,True8952799796316,10.1155/2018/3075849,,Privacy‐Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment,Wireless Communications and Mobile Computing,2018,article,privacy_preserving_computation,5,58.0,129.0,68.39542473178632,166,58.2504,True,True,baseline,privacyawaremultidimensionalmobileservicequalitypredictionandrecommendationindistributedfogenvironment,68.39542473178632,landmark,True8963016632787,10.1109/tifs.2020.2988575,1911.00222,Federated Learning With Differential Privacy: Algorithms and Performance Analysis,IEEE Transactions on Information Forensics and Security,2020,article,privacy_preserving_computation,8,206.0,490.0,61.28715445275716,2379,83.9132,True,True,baseline,federatedlearningwithdifferentialprivacyalgorithmsandperformanceanalysis,61.28715445275716,landmark,True8977155564432,10.1109/isbdas69350.2026.11484374,,Privacy-Preserving Anomaly Detection in Cloud Services using Hierarchical Federated Learning with Differential Privacy,,2026,conference-paper,privacy_preserving_computation,6,24.0,24.0,184.20139553974553,24,221.8777,True,True,post,privacypreservinganomalydetectionincloudservicesusinghierarchicalfederatedlearningwithdifferentialprivacy,60.851289892581455,landmark,True8984389365236,10.3390/educsci13121216,,Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review,Education Sciences,2023,article,privacy_preserving_computation,1,200.0,642.0,58.93867484628822,957,83.985,True,True,post,adaptivelearningusingartificialintelligenceinelearningaliteraturereview,58.93867484628822,landmark,True8993041977451,,,Analyzing adoption of contact tracing apps using UTAUT,,2020,dissertation,privacy_preserving_computation,1,1.0,6.0,56.4292343387471,7,,True,False,baseline,analyzingadoptionofcontacttracingappsusingutaut,56.4292343387471,landmark,True9007154488736,10.1109/iccsc67078.2026.11468534,,Predicting Mental Health Challenges Among University Students Using a Federated CNN-BiLSTM-Attention Framework,"International Conference on Computing, Sciences and Communications (ICCSC)",2026,conference-paper,privacy_preserving_computation,3,22.0,22.0,289.37333333333333,22,365.288,True,False,post,predictingmentalhealthchallengesamonguniversitystudentsusingafederatedcnnbilstmattentionframework,55.78034906819967,landmark,True9012790400697,10.1109/access.2018.2806881,,"A Survey on Big Data Market: Pricing, Trading and Protection",IEEE Access,2018,article,privacy_preserving_computation,5,60.0,105.0,55.670694549128406,356,61.4642,True,True,baseline,asurveyonbigdatamarketpricingtradingandprotection,55.670694549128406,landmark,True9027125492275,10.1016/j.ress.2026.112271,,Source-free domain adaptation for cross-domain remaining useful life prediction: A distributed federated learning perspective,Reliability Engineering & System Safety,2026,article,federated_learning,1,34.0,34.0,37.662406639004146,34,94.5159,False,False,post,sourcefreedomainadaptationforcrossdomainremainingusefullifepredictionadistributedfederatedlearningperspective,86.2059940144904,landmark,True9034382463479,10.1609/aaai.v37i9.26330,2212.01197,FedALA: Adaptive Local Aggregation for Personalized Federated Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2023,conference-paper,federated_learning,9,131.0,320.0,73.79786537022606,420,79.3941,True,True,post,fedalaadaptivelocalaggregationforpersonalizedfederatedlearning,73.79786537022606,landmark,True9044220990555,10.14711/thesis-991012994506603412,,Robust federated learning with attack-adaptive aggregation,,2021,dissertation,federated_learning,5,9.0,14.0,63.67692307692308,24,,True,False,baseline,robustfederatedlearningwithattackadaptiveaggregation,63.67692307692308,landmark,True9053133814152,10.1109/tnnls.2022.3160699,2103.00710,Towards Personalized Federated Learning,IEEE Transactions on Neural Networks and Learning Systems,2022,article,federated_learning,7,231.0,554.0,57.083945154517146,1182,74.5614,True,True,baseline,towardspersonalizedfederatedlearning,57.083945154517146,landmark,True9063159080474,,,Federated Learning for Internet of Things: A Comprehensive Survey,Deakin Research Online (Deakin University),2021,article,federated_learning,4,142.0,416.0,48.48164672055449,894,61.8395,True,True,baseline,federatedlearningforinternetofthingsacomprehensivesurvey,48.48164672055449,landmark,True9073100779497,10.1007/s41666-020-00082-4,1911.06270,Federated Learning for Healthcare Informatics,Journal of Healthcare Informatics Research,2020,article,federated_learning,2,140.0,343.0,42.90100811693001,1515,58.5309,False,True,baseline,federatedlearningforhealthcareinformatics,42.90100811693001,landmark,True9082995735216,,,Fair Resource Allocation in Federated Learning,International Conference on Learning Representations,2020,conference-paper,federated_learning,4,225.0,300.0,42.51886520143499,120,11.8933,True,False,baseline,fairresourceallocationinfederatedlearning,42.51886520143499,landmark,True9097159977201,10.1109/tmc.2026.3688489,,AsyncFed-SAR: Toward Communication-Efficient Asynchronous Federated Learning for SAR Image Target Recognition,IEEE Transactions on Mobile Computing,2026,article,federated_learning,3,16.0,16.0,17.80482451019312,16,57.8019,False,False,post,asyncfedsartowardcommunicationefficientasynchronousfederatedlearningforsarimagetargetrecognition,40.567526595054304,landmark,True9103045674654,10.1038/s41598-020-69250-1,,Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data,Scientific Reports,2020,article,federated_learning,4,122.0,323.0,40.39949160865421,1607,50.8594,True,True,baseline,federatedlearninginmedicinefacilitatingmultiinstitutionalcollaborationswithoutsharingpatientdata,40.39949160865421,landmark,True9113018464563,10.1109/mis.2020.2988604,1907.09173,FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare,IEEE Intelligent Systems,2020,article,federated_learning,7,141.0,309.0,38.648430052861144,999,49.6281,True,True,baseline,fedhealthafederatedtransferlearningframeworkforwearablehealthcare,38.648430052861144,landmark,True9124387105517,10.1145/3625558,2307.10616,Heterogeneous Federated Learning: State-of-the-art and Research Challenges,ACM Computing Surveys,2023,article,federated_learning,7,133.0,417.0,38.282597213243285,608,53.4131,True,True,post,heterogeneousfederatedlearningstateoftheartandresearchchallenges,38.282597213243285,landmark,True9134408476406,10.1016/j.jnca.2025.104165,,CBRFL: A framework for Committee-based Byzantine-Resilient Federated Learning,Journal of Network and Computer Applications,2025,article,federated_learning,4,83.0,83.0,28.27389289459036,83,34.5383,False,False,post,cbrflaframeworkforcommitteebasedbyzantineresilientfederatedlearning,36.268640115677634,landmark,True9144407318675,10.1016/j.aei.2025.103179,,Federated learning-empowered smart manufacturing and product lifecycle management: A review,Advanced Engineering Informatics,2025,article,federated_learning,2,82.0,82.0,27.933243582607346,82,34.1222,False,False,post,federatedlearningempoweredsmartmanufacturingandproductlifecyclemanagementareview,35.83166854801887,landmark,True9154393156671,10.1609/aaai.v38i15.29617,2401.03230,FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2024,conference-paper,federated_learning,8,66.0,121.0,33.633393493997694,121,36.8098,True,True,post,fedtgptrainableglobalprototypeswithadaptivemarginenhancedcontrastivelearningfordataandmodelheterogeneityinfederatedlearning,33.633393493997694,landmark,True9167118779462,10.1016/j.compeleceng.2025.110924,,Federated learning in healthcare: Recent progress and challenges,Computers & Electrical Engineering,2026,article,federated_learning,2,13.0,13.0,14.466419914531912,13,46.964,False,False,post,federatedlearninginhealthcarerecentprogressandchallenges,32.96111535848162,landmark,True9173196371845,10.1016/j.neucom.2021.07.098,2106.06843,Federated learning on non-IID data: A survey,Neurocomputing,2021,article,federated_learning,6,75.0,277.0,32.28225034036922,1043,48.3425,False,True,baseline,federatedlearningonnoniiddataasurvey,32.28225034036922,landmark,True9187133255133,10.3390/computers15030155,,"Federated Learning: A Survey of Core Challenges, Current Methods, and Opportunities",Computers,2026,article,federated_learning,5,12.0,12.0,13.353618382644841,12,43.3514,True,True,post,federatedlearningasurveyofcorechallengescurrentmethodsandopportunities,30.425644946290728,landmark,True9197104465905,10.71781/10873,,Re-weighted softmax cross-entropy to control forgetting in federated learning,Papyrus : Institutional Repository (Université de Montréal),2022,dissertation,federated_learning,3,1.0,4.0,29.956140350877195,0,,True,True,post,reweightedsoftmaxcrossentropytocontrolforgettinginfederatedlearning,29.956140350877195,landmark,True9204406269727,10.1016/j.eswa.2024.126354,,Federated learning framework based on trimmed mean aggregation rules,Expert Systems with Applications,2025,article,federated_learning,3,67.0,67.0,22.8235039028621,67,22.0546,False,True,post,federatedlearningframeworkbasedontrimmedmeanaggregationrules,29.277095033137364,landmark,True9214402716118,10.1109/cvpr52733.2024.01139,,FedAS: Bridging Inconsistency in Personalized Federated Learning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2024,conference-paper,federated_learning,5,56.0,104.0,28.90804068905587,104,31.6381,True,False,post,fedasbridginginconsistencyinpersonalizedfederatedlearning,28.90804068905587,landmark,True9224412162472,10.1038/s41746-025-01836-3,,A scoping review of the governance of federated learning in healthcare,npj Digital Medicine,2025,article,federated_learning,2,65.0,65.0,22.142205278896068,65,27.048,True,True,post,ascopingreviewofthegovernanceoffederatedlearninginhealthcare,28.403151897819832,landmark,True9234293155325,10.1016/j.ipm.2022.103061,,"Federated learning review: Fundamentals, enabling technologies, and future applications",Information Processing & Management,2022,article,federated_learning,3,116.0,274.0,28.232853740681765,550,37.8866,False,False,baseline,federatedlearningreviewfundamentalsenablingtechnologiesandfutureapplications,28.232853740681765,landmark,True9247121318593,10.1007/s10115-025-02673-2,,A comprehensive survey on federated learning for privacy preservation in digital healthcare applications,Knowledge and Information Systems,2026,article,federated_learning,3,11.0,11.0,12.24081685075777,11,39.7388,False,False,post,acomprehensivesurveyonfederatedlearningforprivacypreservationindigitalhealthcareapplications,27.890174534099835,landmark,True9254415878254,10.1109/jiot.2026.3669440,2510.15300,DFCA: Decentralized Federated Clustering Algorithm,IEEE Internet of Things Journal,2026,article,federated_learning,6,10.0,10.0,11.1280153188707,10,36.1262,True,True,post,dfcadecentralizedfederatedclusteringalgorithm,25.35470412190894,landmark,True9264312634912,10.1109/cvpr52688.2022.00985,2205.03993,Layer-wised Model Aggregation for Personalized Federated Learning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,federated_learning,6,48.0,126.0,25.033352648235116,240,30.554,True,True,baseline,layerwisedmodelaggregationforpersonalizedfederatedlearning,25.033352648235116,landmark,True9274406063062,10.1016/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,True9283084163594,10.26522/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,True9292963808661,10.4230/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,True9302981731882,10.1016/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,True9317130596601,10.1016/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,True9324406571986,10.1016/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,True9334330336443,10.48550/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,True9344399450035,10.1145/3630106.3658542,2311.16863,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,True9354318586239,10.1007/978-3-031-21448-6_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,True9364408314709,10.1038/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,True9374407243994,10.1080/08839514.2025.2463722,,"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,True9384360615722,10.3390/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,True9397133236347,10.1016/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,True9407128782475,10.1038/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,True9412670253439,10.1016/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,True9424402698709,10.1016/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,True9434366262984,10.1016/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,True9447128600296,10.1038/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,True9452969896603,10.1145/3457607,1908.09635,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,True9464409249263,10.1371/journal.pdig.0000810,2412.03576,Ethical challenges and evolving strategies in the integration of artificial 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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,True9504401588926,10.1787/15df715b-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,True9514410609100,10.1109/satml64287.2025.00010,2310.08419,Jailbreaking Black Box Large Language Models in Twenty Queries,IEEE Conference on Secure and Trustworthy Machine Learning 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Research,2020,article,computational_biology,4,246.0,2031.0,115.75029377203289,8015,303.9867,True,True,baseline,thestringdatabasein2021customizableproteinproteinnetworksandfunctionalcharacterizationofuseruploadedgenemeasurementsets,115.75029377203289,landmark,True10243159995035,10.1093/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,True10253095583226,10.1093/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,True10262995327724,10.1109/cvpr42600.2020.00813,1912.04958,Analyzing and Improving the Image Quality of StyleGAN,IEEE 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Center for Informatics),2022,article,nlp,3,1238.0,1430.0,147.34664543494497,1520,94.4336,True,True,baseline,questionansweringfortoxicologicalinformationextraction,147.34664543494497,landmark,True10724226069476,10.1109/cvpr52688.2022.00135,2112.01527,Masked-attention Mask Transformer for Universal Image Segmentation,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,computer_vision,3,497.0,1420.0,191.86247481053815,3362,243.9506,True,True,baseline,maskedattentionmasktransformerforuniversalimagesegmentation,191.86247481053815,landmark,True10734210598935,10.1016/j.procs.2022.01.135,,A Review of Yolo Algorithm Developments,Procedia Computer Science,2022,conference-paper,computer_vision,2,544.0,1406.0,189.9708729462089,2733,219.8066,True,True,baseline,areviewofyoloalgorithmdevelopments,189.9708729462089,landmark,True10743135367836,10.48550/arxiv.2103.00020,2103.00020,Learning Transferable Visual Models From Natural Language Supervision,arXiv (Cornell University),2021,preprint,computer_vision,6,625.0,1405.0,222.66089965397924,5217,,True,True,baseline,learningtransferablevisualmodelsfromnaturallanguagesupervision,222.66089965397924,landmark,True10753042011474,10.1109/cvprw50498.2020.00203,1911.11929,CSPNet: A New Backbone that can Enhance Learning Capability of CNN,IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),2020,conference-paper,computer_vision,3,534.0,1403.0,169.85871339984772,4922,202.034,True,True,baseline,cspnetanewbackbonethatcanenhancelearningcapabilityofcnn,169.85871339984772,landmark,True10762898402099,10.1093/nar/gky995,,The Pfam protein families database in 2019,Nucleic Acids 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