work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review
7172491899,10.26555/ijain.v12i2.2360,2005.11401,Toward effective Text-to-MongoDB query translation,International Journal of Advances in Intelligent Informatics,2026,article,llm,6,3026.0,3026.0,3367.337435490274,0,0.0,True,True,baseline,10758.097473034562,landmark,True
7154011589,10.55905/rdelosv19.n79-020,2005.14165,Modelos pré-treinados de LLM e questões éticas face aos direitos autorais,DELOS Desarrollo Local Sostenible,2026,article,llm,3,2847.0,2847.0,3168.1459612824883,0,0.0,True,True,baseline,10121.712989335558,landmark,True
4362515116,10.1007/s11704-026-60308-3,2303.18223,A Survey of Large Language Models,Frontiers of Computer Science,2026,article,llm,6,1549.0,1549.0,1723.7295728930715,1549,964.5692,True,True,post,5507.0366773729465,landmark,True
4288253152,10.5281/zenodo.12935200,,Multilingual Classifier for Automatic Dewey Decimal Classification trained on Open Access Linguistics Abstracts,Repositorio Institucional UPTC,2024,dissertation,llm,3,286.0,364.0,3102.1223140495867,364,,True,True,post,3102.1223140495867,landmark,True
4322718191,10.48550/arxiv.2302.13971,2302.13971,LLaMA: Open and Efficient Foundation Language Models,arXiv (Cornell University),2023,preprint,llm,4,2565.0,3629.0,1206.5430511141133,3837,,True,True,post,1206.5430511141133,landmark,True
4323655724,10.1016/j.lindif.2023.102274,,ChatGPT for good? On opportunities and challenges of large language models for education,Learning and Individual Differences,2023,article,llm,1,2213.0,4227.0,990.7064044933976,7222,1695.9108,True,True,post,990.7064044933976,landmark,True
4367369313,10.18260/jet.43.1.5,,"Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices",ASEE Journal of Engineering Technology,2026,article,llm,2,261.0,261.0,290.4411998225253,261,187.8562,True,True,post,927.9125712035759,landmark,True
4226278401,10.48550/arxiv.2203.02155,2203.02155,Discernment and Social Learning as a Companion Training Layer,arXiv (Cornell University),2022,preprint,llm,5,1547.0,3077.0,894.2065022118338,4202,,True,True,baseline,894.2065022118338,landmark,True
4396531244,10.22215/etd/2024-15938,,The Fusion of Multilingual Semantic Search and Large Language Models: A New Paradigm for Enhanced Topic Exploration and Contextual Search,,2024,dissertation,llm,6,20.0,20.0,778.8148148148148,20,,True,True,post,778.8148148148148,landmark,True
4221143046,10.48550/arxiv.2201.11903,2201.11903,"BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence",arXiv (Cornell University),2022,preprint,llm,3,962.0,2465.0,716.353275252574,4203,,True,True,baseline,716.353275252574,landmark,True
4327810158,10.4230/lipics.cosit.2024.11,2303.08774,GPT-4 Technical Report,arXiv (Cornell University),2023,preprint,llm,3,1261.0,1968.0,654.30606905279,2391,,True,True,post,654.30606905279,landmark,True
7135096569,10.1287/orsc.2025.21838,,Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality,Organization Science,2026,article,llm,1,183.0,183.0,203.64268033533384,183,650.2714,True,False,post,650.6053660163003,landmark,True
4414281281,10.1038/s41586-025-09422-z,,DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning,Nature,2025,article,llm,3,1099.0,1099.0,374.373593869335,1099,456.904,True,True,post,556.3784815078109,landmark,True
4389437528,10.61969/jai.1337500,,Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning,Journal of AI,2023,article,llm,2,1417.0,2361.0,553.3612067681362,2953,693.6856,True,True,post,553.3612067681362,landmark,True
4292779060,10.4230/lipics.giscience.2023.43,,"Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics",DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2023,article,llm,4,11174.0,13333.0,534.676590443066,13690,2570.1233,True,True,post,534.676590443066,landmark,True
4360836968,10.48550/arxiv.2303.12712,2303.12712,Sparks of Artificial General Intelligence: Early experiments with GPT-4,arXiv (Cornell University),2023,preprint,llm,2,1098.0,1420.0,472.11108641004154,1523,,True,True,post,472.11108641004154,landmark,True
4406800520,10.1007/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,462.72059335590455,landmark,True
4385223296,10.1007/978-3-031-37776-1_17,,"Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning",Sustainable development goals series,2023,book-chapter,llm,2,432.0,612.0,421.27957250425067,696,475.0832,False,True,post,421.27957250425067,landmark,True
4287689466,10.4230/oasics.slate.2025.1,,From Prediction to Precision: Leveraging LLMs for Equitable and Data-Driven Writing Placement in Developmental Education,OASIcs : OpenAccess Series in Informatics,2025,conference-paper,llm,3,722.0,722.0,548.6111682082906,722,151.0788,True,True,post,365.5188932198721,landmark,True
4366420437,10.3390/educsci13040410,,What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,Education Sciences,2023,article,llm,1,955.0,1541.0,361.17307057589915,1967,462.692,True,True,post,361.17307057589915,landmark,True
4317910584,10.37074/jalt.2023.6.1.9,,ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?,Journal of Applied Learning & Teaching,2023,article,llm,3,1057.0,1530.0,358.59493704161304,1771,416.3993,True,True,post,358.59493704161304,landmark,True
4313564799,10.2139/ssrn.4312418,,ChatGPT User Experience: Implications for Education,SSRN Electronic Journal,2023,preprint,llm,2,595.0,811.0,335.9058974941328,912,,False,True,post,335.9058974941328,landmark,True
4382618722,10.1016/j.fmre.2026.03.026,2306.16092,Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture,Fundamental Research,2026,article,llm,6,94.0,94.0,98.93209876543209,94,193.5725,True,True,post,334.1907344564603,landmark,True
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4392773210,10.1186/s41239-024-00448-3,,"Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education",International Journal of Educational Technology in Higher Education,2024,article,llm,1,576.0,1085.0,326.66642380265563,1085,415.2106,True,True,post,326.66642380265563,landmark,True
4387355843,10.1201/9781003616719-7,2310.01728,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,700.378454126837,landmark,True
4392947521,10.1038/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,186.93297525958593,landmark,True
7124890304,10.1016/j.jii.2026.101076,,Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy,Journal of Industrial Information Integration,2026,article,foundation_models,1,42.0,42.0,46.52414937759336,42,116.7549,False,False,post,149.31926433160993,landmark,True
4410551352,10.1038/s41586-025-09005-y,2405.13063,A foundation model for the Earth system,Nature,2025,article,foundation_models,3,249.0,249.0,69.74305555555556,249,109.0217,True,True,post,126.0584548639171,landmark,True
4322832366,10.1145/3773966.3778007,2303.00320,TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders,,2026,conference-paper,foundation_models,6,35.0,35.0,126.32971014492755,35,165.8809,True,True,post,124.4327202763416,landmark,True
7133233472,10.52202/075280-0441,2305.14314,QLoRA: Efficient Finetuning of Quantized LLMs,neural information processing systems,2023,conference-paper,foundation_models,7,287.0,532.0,122.68895117800083,1428,269.751,True,True,post,122.68895117800083,landmark,True
2990704537,,,SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems,neural information processing systems,2019,article,foundation_models,5,262.0,668.0,101.57304226845103,462,52.8392,True,False,baseline,101.57304226845103,landmark,True
7125271461,10.1039/d5dd00348b,,ChemBERTa-3: an open source training framework for chemical foundation models,Digital Discovery,2026,article,foundation_models,2,25.0,25.0,70.59021819073303,25,85.533,True,True,post,88.88051448310114,landmark,True
7119541039,10.1038/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,85.3252939037771,landmark,True
4393138529,10.48550/arxiv.2403.14608,2403.14608,Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey,arXiv (Cornell University),2024,preprint,foundation_models,6,83.0,95.0,82.87448253022023,95,,True,True,post,82.87448253022023,landmark,True
4390690043,10.1145/3800677,2401.02458,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,78.21485274512901,landmark,True
7128029160,10.1038/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,71.10441158648091,landmark,True
4402220065,10.1038/s41586-024-07894-z,,A pathology foundation model for cancer diagnosis and prognosis prediction,Nature,2024,article,foundation_models,2,275.0,562.0,69.80487182450983,562,83.6371,False,True,post,69.80487182450983,landmark,True
7118170538,10.3390/bdcc10010019,,Applications of Artificial Intelligence in Fisheries: From Data to Decisions,Big Data and Cognitive Computing,2026,article,foundation_models,8,17.0,17.0,17.85,17,28.0244,True,True,post,60.43874984850878,landmark,True
4411932262,10.1038/s41586-025-09215-4,,A foundation model to predict and capture human cognition,Nature,2025,article,foundation_models,1,110.0,110.0,26.933816382433506,110,60.8153,True,True,post,55.6884740362686,landmark,True
4411099958,10.1038/s41591-025-03747-y,2410.15038,A multimodal vision foundation model for clinical dermatology,Nature Medicine,2025,article,foundation_models,4,108.0,108.0,18.44502277163305,108,39.3828,True,True,post,54.67595632651827,landmark,True
4361229539,10.48550/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,53.528087966208936,landmark,True
4407135816,10.1148/radiol.240597,2411.18730,"Foundation Models in Radiology: What, How, Why, and Why Not",Radiology,2025,article,foundation_models,4,103.0,103.0,6.850784973366975,103,21.0891,True,True,post,52.14466205214242,landmark,True
4385948838,10.1038/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,49.064992972535784,landmark,True
4406643700,10.1016/j.jag.2025.104368,,Towards the next generation of Geospatial Artificial Intelligence,International Journal of Applied Earth Observation and Geoinformation,2025,article,foundation_models,2,91.0,91.0,19.115723270440252,91,404.3482,True,True,post,46.06955579364039,landmark,True
4406472599,10.1038/s42256-024-00961-0,2310.08184,Learning from models beyond fine-tuning,Nature Machine Intelligence,2025,article,foundation_models,4,86.0,86.0,29.29584083053941,86,31.2093,False,True,post,43.538261519264545,landmark,True
4393153153,10.1609/aaai.v38i12.29299,2312.15709,TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2024,conference-paper,foundation_models,5,68.0,113.0,41.75133936818769,113,44.645,True,True,post,41.75133936818769,landmark,True
4402727669,10.1109/cvpr52733.2024.01239,,mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration,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,foundation_models,5,136.0,184.0,38.61721404801364,184,40.1743,True,False,post,38.61721404801364,landmark,True
4401397240,10.1038/s41591-024-03185-2,2305.17100,A generalist vision–language foundation model for diverse biomedical tasks,Nature Medicine,2024,article,foundation_models,5,202.0,325.0,38.50242478427548,325,43.9301,False,True,post,38.50242478427548,landmark,True
4385285835,10.1101/2023.07.21.23292757,,Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling,medRxiv,2023,preprint,foundation_models,7,42.0,107.0,35.57456777878482,142,,True,True,post,35.57456777878482,landmark,True
4318718936,10.48550/arxiv.2301.12597,2301.12597,BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models,arXiv (Cornell University),2023,preprint,multimodal_ai,7,654.0,875.0,273.1441896618733,908,,True,True,post,273.1441896618733,landmark,True
4393148714,10.1609/aaai.v38i5.28226,2302.08453,T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models,Proceedings of the AAAI Conference on Artificial Intelligence,2024,conference-paper,multimodal_ai,7,683.0,923.0,193.71569872998148,923,194.9761,True,True,post,193.71569872998148,landmark,True
4396599905,10.1016/j.inffus.2026.104437,2404.18947,Multimodal fusion on low-quality data: A comprehensive survey,Information Fusion,2026,article,multimodal_ai,3,48.0,48.0,128.6576012223071,48,99.7217,True,True,post,170.6505878075542,landmark,True
4396952261,10.1007/978-3-031-53092-0_10,,Multimodal Deep Learning,Springer optimization and its applications,2024,book-chapter,multimodal_ai,3,188.0,189.0,147.22445489525438,189,23.3148,False,False,post,147.22445489525438,landmark,True
7133250524,10.52202/068431-1833,,LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models,neural information processing systems,2022,conference-paper,multimodal_ai,4,217.0,828.0,111.87473883318704,309,6.4706,False,False,baseline,111.87473883318704,landmark,True
4392947532,10.1038/s41591-024-02856-4,,A visual-language foundation model for computational pathology,Nature Medicine,2024,article,multimodal_ai,4,475.0,938.0,111.12392137738586,938,132.4965,False,True,post,111.12392137738586,landmark,True
7138474371,10.1609/aaai.v40i16.38386,,OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,4,31.0,31.0,159.42857142857142,30,608.3281,True,True,post,110.21183795904541,landmark,True
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7160627408,10.1109/csnt69054.2026.11502110,,Generative Artificial Intelligence for Cross-Domain Knowledge Synthesis and Intelligent Automation,IEEE International Conference on Communication Systems and Network Technologies (CSNT),2026,conference-paper,multimodal_ai,6,23.0,23.0,193.44642857142856,23,207.3469,True,False,post,81.77007332445305,landmark,True
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7143512276,10.1007/s11831-026-10560-4,,Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey,Archives of Computational Methods in Engineering,2026,article,multimodal_ai,5,21.0,21.0,23.368832169628472,21,75.865,True,True,post,74.65963216580496,landmark,True
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7138470740,10.1609/aaai.v40i18.38608,,Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,7,17.0,17.0,130.47598850731976,17,157.1634,True,True,post,60.43874984850878,landmark,True
7138363754,10.1609/aaai.v40i6.42404,,AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,6,17.0,17.0,130.47598850731976,17,157.1634,True,True,post,60.43874984850878,landmark,True
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3156636935,10.18653/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,204.20889496115336,landmark,True
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4392233542,10.3390/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,327.9259259259259,landmark,True
4311000453,10.48550/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,220.57287461123883,landmark,True
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4406417959,10.1109/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,142.25873821992252,landmark,True
2952218014,10.1109/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,126.90333200584095,landmark,True
2890964092,10.1109/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,108.08186643624245,landmark,True
3097777922,10.21437/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,107.00581075694473,landmark,True
2962866211,10.1109/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,106.2474690299835,landmark,True
1530164934,10.7275/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,105.97222222222223,landmark,True
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3167533889,10.48550/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,96.44297133944238,landmark,True
3169320628,10.1109/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,89.15495130101968,landmark,True
2975281968,,,"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,87.36363636363636,landmark,True
4372260310,10.1109/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,87.23607346034898,landmark,True
4372266552,10.1109/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,86.6908480012218,landmark,True
2900368655,10.1109/icassp.2019.8683855,1811.02508,SDR – Half-baked or Well Done?,IEEE International Conference on Acoustics Speech and Signal Processing,2019,conference-paper,speech_audio,1,151.0,313.0,85.71885565669702,1146,88.5281,True,True,baseline,85.71885565669702,landmark,True
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2808631503,10.21437/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,68.44152573756327,landmark,True
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2963188159,10.15607/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,129.2718583898181,landmark,True
4398796510,10.1109/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,127.98794085566564,landmark,True
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3003533476,10.1109/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,101.39190540115963,landmark,True
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4285585446,10.1016/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,245.39307870858832,landmark,True
2308543850,10.17615/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,195.9915254237288,landmark,True
4206484811,10.1109/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,192.52994302595485,landmark,True
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4392772072,10.30574/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,127.9568941162476,landmark,True
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4405586205,10.1016/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,124.94614366645354,landmark,True
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2171139698,10.22024/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,394.8918918918919,landmark,True
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4403110951,10.32657/10356/179951,,Semantics-aware human-computer interaction software testing,,2024,dissertation,software_engineering,6,1.0,1.0,304.3333333333333,1,,True,True,post,304.3333333333333,landmark,True
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3177813494,10.4230/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,275.3873466752744,landmark,True
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4391677389,10.17771/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,230.9189189189189,landmark,True
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2962772482,10.1145/3236009,,A survey of methods for explaining black box models,ISTI Open Portal,2019,article,software_engineering,1,719.0,1432.0,217.74340797667946,5022,232.193,True,True,baseline,217.74340797667946,landmark,True
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4381982883,10.3390/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,207.4225616221095,landmark,True
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2400988715,10.48550/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,78.21485274512901,landmark,True
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3207822760,10.33612/diss.168003240,,Computer programming skills: A cognitive perspective,,2021,dissertation,programming_languages,1,11.0,15.0,56.50485436893204,21,,True,True,baseline,56.50485436893204,landmark,True
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3146286581,,,Probabilistic Grammatical Evolution,Estudo Geral (Universidade de Coimbra),2021,dissertation,programming_languages,2,4.0,9.0,40.935164835164834,12,,True,True,baseline,40.935164835164834,landmark,True
7134194765,10.1111/tgis.70218,,A Debris‐Flow Hazard Assessment Framework Based on an Improved AHP – CRITIC Method: A Case Study of Gaoling Gully,Transactions in GIS,2026,article,programming_languages,2,11.0,11.0,15.074074074074074,11,77.5749,True,False,post,39.107426372564504,landmark,True
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4396905794,10.17760/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,38.94074074074074,landmark,True
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3188948186,10.48550/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,24.44561426312255,landmark,True
2962724414,10.1109/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,22.34837655707576,landmark,True
4408711372,10.1109/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,19.744095340131597,landmark,True
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2249134499,10.58088/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,19.080000000000002,landmark,True
3139485201,,,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,18.265060240963855,landmark,True
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3023977240,10.7488/era/309,,From constraint programming to heterogeneous parallelism,ERA,2020,dissertation,compilers,8,0.0,2.0,18.265060240963855,2,,True,True,baseline,18.265060240963855,landmark,True
3046781014,10.1007/978-3-030-47956-5_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,18.204255319148935,landmark,True
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3132279601,10.1007/978-3-030-62066-0_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,17.288455680779773,landmark,True
3103225940,,,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,16.862068965517242,landmark,True
4313894866,10.3390/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,16.423071260306244,landmark,True
4407942537,10.1145/3708493.3712691,,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,15.694024501130242,landmark,True
2787911489,10.1007/978-3-319-68394-2_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,14.319880418535128,landmark,True
2949967139,10.1109/cgo.2019.8661197,,Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code,IEEE/ACM International Symposium on Code Generation and Optimization (CGO),2019,conference-paper,compilers,6,26.0,53.0,14.250956889605158,182,15.9098,True,True,baseline,14.250956889605158,landmark,True
4385245566,10.4230/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,6621.879465206806,landmark,True
1498342204,10.1145/3788687,,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,1009.682644528029,landmark,True
3117137355,10.1145/3815481,,egg: Fast and Extensible Equality Saturation,Communications of the ACM,2026,article,formal_verification,3,132.0,132.0,146.88980220909326,132,0.0,True,False,post,469.28911647077405,landmark,True
1481397690,10.1007/978-3-319-10575-8_11,,Satisfiability Modulo Theories,Springer eBooks,2018,book-chapter,formal_verification,2,501.0,585.0,432.45569620253167,1122,250.357,False,False,baseline,432.45569620253167,landmark,True
2254118105,10.48550/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,213.31323475944274,landmark,True
4206807714,10.3233/faia336,,Handbook of Satisfiability,Frontiers in artificial intelligence and applications,2021,book,formal_verification,2,678.0,777.0,184.30546075085323,1038,93.9568,False,False,baseline,184.30546075085323,landmark,True
2540354435,10.48550/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,138.65360259363777,landmark,True
2921681895,10.18154/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,111.636815920398,landmark,True
4225922019,10.1007/978-3-030-99524-9_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,98.23784328546233,landmark,True
4318573502,10.5463/thesis.149,,Semantically informed methods in structural proof theory,,2022,dissertation,formal_verification,2,9.0,13.0,97.35745614035089,14,,True,True,baseline,97.35745614035089,landmark,True
2154148696,10.1007/978-3-319-77452-7_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,86.49113924050633,landmark,True
4297781990,10.4230/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,78.93772893772893,landmark,True
2789444712,10.1080/00207543.2018.1444806,,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,61.81756449008323,landmark,True
1586677474,,,Handbook of Satisfiability,,2021,article,formal_verification,2,374.0,375.0,60.365327611379016,486,1.7496,True,False,baseline,60.365327611379016,landmark,True
2486519520,10.48456/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,54.58021978021978,landmark,True
4289665848,10.4230/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,53.328308689860684,landmark,True
149631060,10.48550/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,53.32230215827339,landmark,True
2890660842,10.48550/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,52.040918390543304,landmark,True
1932627192,10.82444/warw.33484729,,Model checking quantum protocols,Warwick Research Archive Portal (University of Warwick),2026,dissertation,formal_verification,4,14.0,14.0,141.45283018867926,18,,True,True,post,49.77308811053664,landmark,True
4393942723,10.1007/978-3-031-57256-2_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,48.594819515239784,landmark,True
2957311447,10.1007/978-3-030-25540-4_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,46.39818783571157,landmark,True
2905489900,,,Machine Learning for SAT Solvers,UWSpace (University of Waterloo),2018,dissertation,formal_verification,3,3.0,5.0,44.97120921305182,9,,True,True,baseline,44.97120921305182,landmark,True
2900153411,10.1145/3290354,,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,44.06671585274556,landmark,True
6944541633,10.18154/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,43.15151515151515,landmark,True
3016155638,10.1109/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,42.08811748998665,landmark,True
4311415873,10.4230/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,1192.2396032310232,landmark,True
2402846924,10.1142/9789811205675_0004,,Particle Swarm Optimization,WORLD SCIENTIFIC eBooks,2019,book-chapter,algorithms,1,3563.0,3767.0,1171.7895003921208,6270,302.7094,False,False,baseline,1171.7895003921208,landmark,True
4294558607,10.4230/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,861.3021555600685,landmark,True
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4285092328,10.1145/3544585.3544600,,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,724.7350484463749,landmark,True
3148190347,10.1142/9789811273117_0001,,Introduction to Graph Theory,WORLD SCIENTIFIC eBooks,2023,book-chapter,algorithms,1,502.0,503.0,455.41427595628414,885,6.2728,False,False,post,455.41427595628414,landmark,True
7128526706,10.1007/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,323.52507271848816,landmark,True
2112641412,10.37236/11668,,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,288.61814892416174,landmark,True
3183881215,10.1007/978-1-4842-7077-6_3,,Introduction to Algorithms,Apress eBooks,2021,book-chapter,algorithms,1,276.0,434.0,279.4307771487391,877,222.2179,True,False,baseline,279.4307771487391,landmark,True
2003494253,10.1017/9781009260350.002,,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,275.60804020100505,landmark,True
7125913058,10.1109/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,259.5311022906553,landmark,True
1491280261,10.26190/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,209.69298245614038,landmark,True
3111526966,10.1201/9781003091479-1,,Introduction to Algorithms,Chapman & Hall/CRC eBooks,2020,book-chapter,algorithms,1,193.0,202.0,200.82966396292002,428,40.7298,True,False,baseline,200.82966396292002,landmark,True
3119051141,10.1016/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,176.3286814620167,landmark,True
4406080942,10.1007/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,167.06542210880582,landmark,True
2146101030,10.82444/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,163.5401466489061,landmark,True
4405348595,10.1142/9789811299223_0004,,Positive Definite Matrices,WORLD SCIENTIFIC eBooks,2024,book-chapter,algorithms,2,125.0,125.0,151.20861797162374,135,24.6654,True,True,post,151.20861797162374,landmark,True
7118096636,10.1016/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,149.31926433160993,landmark,True
4233225156,10.1007/978-3-030-34209-8_4,,Introduction to Algorithms,Undergraduate topics in computer science,2020,book-chapter,algorithms,1,141.0,149.0,148.13673232908457,330,38.8354,False,False,baseline,148.13673232908457,landmark,True
2891784792,10.48550/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,142.64098765432098,landmark,True
7155156112,10.1007/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,142.20882317296181,landmark,True
3139484821,10.1016/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,138.21931012158083,landmark,True
2121903617,10.1201/9781482268713-14,,Introduction to evolutionary algorithms,CRC Press eBooks,2018,book-chapter,algorithms,1,260.0,292.0,135.62163175992498,349,50.6462,True,False,baseline,135.62163175992498,landmark,True
1486707681,10.82444/warw.33642484,,Sequence distance embeddings,Warwick Research Archive Portal (University of Warwick),2026,dissertation,algorithms,2,38.0,38.0,1163.0179211469535,64,,True,True,post,135.09838201431373,landmark,True
2207864362,10.58088/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,134.92307692307693,landmark,True
4254202520,10.7551/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,227.2789500772002,landmark,True
1638203394,10.1007/978-3-030-11298-1,,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,122.10728346456693,landmark,True
2979043732,,,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,111.01694915254237,landmark,True
2957144896,10.3389/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,72.24848261060211,landmark,True
4241184911,10.1201/9780429494093-6,,Two-Dimensional Cellular Automata,CRC Press eBooks,2018,book-chapter,computational_complexity,2,86.0,93.0,68.7493670886076,307,25.2461,True,False,baseline,68.7493670886076,landmark,True
4394806711,10.1016/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,59.311783860943,landmark,True
4386113651,10.48550/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,57.664,landmark,True
1513303009,,,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,52.922228129373856,landmark,True
4406215947,10.3390/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,52.650920907017586,landmark,True
2905177801,,,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,51.149155722326455,landmark,True
2955897898,10.1109/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,49.00829497340763,landmark,True
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2808541536,,,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,44.30861723446894,landmark,True
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7122741622,10.3390/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,42.66264695188855,landmark,True
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4391177866,10.1007/978-3-031-42209-6_2,,Computational Complexity,Undergraduate topics in computer science,2024,book-chapter,computational_complexity,2,32.0,32.0,38.1794687724336,49,0.0,False,False,post,38.1794687724336,landmark,True
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3086066649,10.1108/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,32.031113421948945,landmark,True
7127964361,10.3390/systems14020179,,"Turning Knowledge into Innovation: The Systemic Role of Knowledge Management Capability, Intellectual Capital, and Knowledge Utilization",Systems,2026,article,computational_complexity,1,9.0,9.0,8.79467680608365,9,57.758,True,True,post,31.99698521391641,landmark,True
3122335070,,,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,30.68949343339587,landmark,True
2900179285,10.1145/3313276.3316369,1809.07115,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,30.27399489989691,landmark,True
4408441779,10.1109/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,29.86927243763498,landmark,True
2793823461,,,Endurable Transient Inconsistency in Byte-Addressable Persistent B+-Tree,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2018,dissertation,database_systems,3,17.0,42.0,377.7581573896353,89,,False,False,baseline,377.7581573896353,landmark,True
3080729669,,,Pengembangan Sistem Pemetaan Otomatis EntityRelationship Diagram Ke Dalam Database,,2019,dissertation,database_systems,2,2.0,15.0,320.60747663551405,62,,True,False,baseline,320.60747663551405,landmark,True
3204616723,,,Data Mining Concepts and Techniques Third Edition,Thammasat University Digital Collections,2021,article,database_systems,1,886.0,928.0,245.73952451819886,1176,66.8714,False,False,baseline,245.73952451819886,landmark,True
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3014294420,10.1017/9781108635592.003,,Introduction to Data Mining,Cambridge University Press eBooks,2019,book-chapter,database_systems,2,163.0,235.0,228.82157331393535,439,233.5555,True,False,baseline,228.82157331393535,landmark,True
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2963507231,10.1109/tkde.2017.2781227,,Untangling Blockchain: A Data Processing View of Blockchain Systems,IEEE Transactions on Knowledge and Data Engineering,2018,article,database_systems,5,201.0,405.0,214.72982183235243,1186,244.2498,True,True,baseline,214.72982183235243,landmark,True
2795038878,10.1145/3458723,1803.09010,Datasheets for datasets,Communications of the ACM,2021,article,database_systems,1,481.0,794.0,210.25558455544171,2104,205.3426,True,True,baseline,210.25558455544171,landmark,True
3032576892,,,Comparative study on Python web frameworks: Flask and Django,Theseus (Ammattikorkeakoulujen),2020,dissertation,database_systems,4,7.0,22.0,206.90719257540604,43,,True,True,baseline,206.90719257540604,landmark,True
3148181069,10.38094/jastt20165,,Classification Based on Decision Tree Algorithm for Machine Learning,Journal of Applied Science and Technology Trends,2021,article,database_systems,4,257.0,704.0,186.42308756553018,1871,279.1938,True,True,baseline,186.42308756553018,landmark,True
3037795158,10.1016/s2589-7500(20)30142-4,,Applications of digital technology in COVID-19 pandemic planning and response,The Lancet Digital Health,2020,article,database_systems,1,350.0,591.0,172.09443665792568,987,179.9177,True,True,baseline,172.09443665792568,landmark,True
4406085118,10.1145/3711118,2303.10158,Data-centric Artificial Intelligence: A Survey,ACM Computing Surveys,2025,article,database_systems,3,334.0,334.0,113.77687020232747,334,110.6889,True,True,post,169.09045752830647,landmark,True
4386450904,10.1145/3591366.3591378,,Linked Data - The Story So Far,ACM eBooks,2023,book-chapter,database_systems,2,285.0,301.0,166.41438539989264,319,42.7861,False,False,post,166.41438539989264,landmark,True
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4361200200,10.1201/9780429459016,,Spatial Data Science,Chapman & Hall/CRC eBooks,2023,book,database_systems,2,278.0,753.0,129.30007763975155,1250,222.9593,True,False,post,129.30007763975155,landmark,True
3202749896,10.3758/s13428-021-01694-3,,Data quality of platforms and panels for online behavioral research,Behavior Research Methods,2021,article,database_systems,1,164.0,469.0,121.87616674723884,1687,188.3384,False,True,baseline,121.87616674723884,landmark,True
4400494356,10.11606/t.82.2024.tde-05072024-092611,,Digital health research governance: from FAIR to RE-AIM,,2024,dissertation,database_systems,1,1.0,3.0,116.82222222222222,3,,True,True,post,116.82222222222222,landmark,True
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4362632858,10.21941/kcss/2023/2,,Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices,,2023,dissertation,database_systems,4,2.0,4.0,115.45945945945945,4,,True,True,post,115.45945945945945,landmark,True
3005599391,,,"Entangled Devices : An ethnographic study of students, mobile phones and capitalism",Työväentutkimus Vuosikirja,2020,dissertation,database_systems,1,11.0,12.0,112.8584686774942,15,,True,True,baseline,112.8584686774942,landmark,True
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4402762189,10.3390/electronics13183762,,Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis,Electronics,2024,article,database_systems,2,199.0,317.0,95.44078925847174,317,121.3104,True,True,post,95.44078925847174,landmark,True
4380791906,10.31979/etd.qrrp-3equ,,NoSQL Databases in Kubernetes,,2023,dissertation,database_systems,4,3.0,3.0,86.5945945945946,3,,True,True,post,86.5945945945946,landmark,True
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4394947112,10.1145/3805774,2404.10981,A Survey on Retrieval-Augmented Text Generation for Large Language Models,ACM Computing Surveys,2026,article,information_retrieval,5,70.0,70.0,77.8961072320949,70,184.2436,True,True,post,248.8654405526832,landmark,True
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4401857375,10.1145/3637528.3671470,2405.06211,A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models,ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),2024,conference-paper,information_retrieval,8,401.0,795.0,220.97973411345592,795,241.8493,True,True,post,220.97973411345592,landmark,True
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3097300053,10.1145/3535101,2011.02260,Graph Neural Networks in Recommender Systems: A Survey,ACM Computing Surveys,2022,article,information_retrieval,4,336.0,711.0,174.743146538718,1326,268.2685,True,True,baseline,174.743146538718,landmark,True
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4394867512,10.57784/1992/7835,,Privacy-enabled scalable recommender systems,HAL (Le Centre pour la Communication Scientifique Directe),2024,dissertation,information_retrieval,3,4.0,4.0,155.76296296296297,4,,True,True,post,155.76296296296297,landmark,True
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4255535209,10.1504/ijwgs.2018.10016848,,Blockchain challenges and opportunities: a survey,International Journal of Web and Grid Services,2018,article,distributed_computing,4,357.0,804.0,426.27846111904034,1582,497.7393,True,False,baseline,426.27846111904034,landmark,True
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3036601975,10.4230/oasics.nines.2026.13,2006.11477,In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication,arXiv (Cornell University),2020,preprint,distributed_computing,10,200.0,699.0,157.87121576165305,2368,,True,True,baseline,157.87121576165305,landmark,True
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3098508067,10.82308/18894,,Personal volunteer computing,,2020,dissertation,distributed_computing,4,17.0,18.0,154.15037593984962,25,,True,True,baseline,154.15037593984962,landmark,True
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2973502831,,,Performance Analysis of Proof-of-Elapsed-Time (PoET) Consensus in the Sawtooth Blockchain Framework,Scholars' Bank (University of Oregon),2019,dissertation,distributed_computing,5,3.0,14.0,140.31616982836496,28,,True,False,baseline,140.31616982836496,landmark,True
1572499759,10.56726/irjmets66464,,Microservices Architecture,International Research Journal of Modernization in Engineering Technology and Science,2025,article,distributed_computing,4,245.0,245.0,93.19077007930923,245,0.8816,True,True,post,124.03341944441644,landmark,True
2962850638,,,Logiikka ja vaativuus hajautetussa laskennassa,Aaltodoc (Aalto University),2019,dissertation,distributed_computing,4,14.0,22.0,123.65517241379311,38,,True,True,baseline,123.65517241379311,landmark,True
7123363719,10.1016/j.cnsns.2026.109676,,ADP-based dynamic event-triggered fault-tolerant control for nonlinear systems with actuator failures and full-state time-varying constraints,Communications in Nonlinear Science and Numerical Simulation,2026,article,distributed_computing,1,33.0,33.0,93.17908801176759,33,117.6079,False,False,post,117.32227911769351,landmark,True
3012404397,10.5626/ktcp.2020.26.3.135,1801.10228,Santa Claus Algorithm: Agent-Based Consensus Preprocessing Method for Scalability Improvement in Private Blockchain Environment,정보과학회 컴퓨팅의 실제 논문지,2020,article,distributed_computing,3,328.0,400.0,116.47677607981433,1,0.2325,True,True,baseline,116.47677607981433,landmark,True
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4386585104,10.1145/3569951.3597559,,ACCESS: Advancing Innovation,Practice and Experience in Advanced Research Computing,2023,conference-paper,distributed_computing,2,121.0,297.0,111.86639277215944,435,137.5418,True,True,post,111.86639277215944,landmark,True
2970155754,,,Learning Generalizable Device Placement Algorithms for Distributed Machine Learning,DSpace@MIT (Massachusetts Institute of Technology),2019,dissertation,distributed_computing,5,5.0,11.0,110.24841915085818,22,,True,False,baseline,110.24841915085818,landmark,True
110250037,10.7275/3278307,,The Use of Multimedia Material in Teaching Chinese as a Second Language and Pedagogical Implications,ScholarWorks@UMassAmherst (University of Massachusetts Amherst),2023,dissertation,cloud_computing,1,7.0,7.0,202.05405405405403,7,,True,True,post,202.05405405405403,landmark,True
4392909747,10.59646/ccp/156,,Cloud Computing: Principles And Paradigms,,2024,book,cloud_computing,2,504.0,504.0,155.20504731861197,517,14.8571,True,False,post,155.20504731861197,landmark,True
4386975643,10.1016/b978-0-323-91150-4.00006-9,,Cloud-based non-destructive characterization,Elsevier eBooks,2023,book-chapter,cloud_computing,2,19.0,35.0,141.43274853801168,37,154.3068,False,False,post,141.43274853801168,landmark,True
4229658977,10.1007/978-1-4842-4470-8_7,,Google Colaboratory,Apress eBooks,2019,book-chapter,cloud_computing,2,24.0,126.0,122.68731164917385,709,185.9225,True,False,baseline,122.68731164917385,landmark,True
7160954670,10.1109/icsft66733.2026.11506646,,Multi-Cloud Program Management For Large Scale Deployment,,2026,conference-paper,cloud_computing,4,26.0,26.0,245.48521739130436,26,297.1442,True,False,post,92.4357350624252,landmark,True
2784822088,10.2312/2632114,,A microservice architecture for the processing of large geospatial data in the Cloud,Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft),2018,dissertation,cloud_computing,3,3.0,8.0,81.83864915572232,15,,True,False,baseline,81.83864915572232,landmark,True
7118010419,10.37547/tajet/volume08issue01-02,,"Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices",The American Journal of Engineering And Technology,2026,review,cloud_computing,4,23.0,23.0,30.77027027027027,23,33.0364,True,True,post,81.77007332445305,landmark,True
4394946189,10.1145/3627703.3629553,2305.15778,Automatic Root Cause Analysis via Large Language Models for Cloud Incidents,,2024,conference-paper,cloud_computing,5,101.0,187.0,81.27074434424932,187,84.6778,True,True,post,81.27074434424932,landmark,True
4408156900,10.3390/electronics14051019,,Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities,Electronics,2025,review,cloud_computing,8,147.0,147.0,18.250462107208875,147,20.0516,True,True,post,74.42005166664985,landmark,True
4252792641,10.31979/etd.shqa-fdp6,,PRIORITIZED TASK SCHEDULING IN FOG COMPUTING,,2018,dissertation,cloud_computing,4,5.0,8.0,71.95393474088291,24,,True,False,baseline,71.95393474088291,landmark,True
4385584867,10.4018/978-1-6684-8634-4.ch009,,A Study on Smart Energy Management Framework Using Cloud Computing,"Practice, progress, and proficiency in sustainability",2023,book-chapter,cloud_computing,2,94.0,104.0,71.58999271314065,104,70.9894,True,False,post,71.58999271314065,landmark,True
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2784168960,,,Energy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacenters,Bilkent University Institutional Repository (Bilkent University),2018,dissertation,cloud_computing,4,3.0,6.0,61.37898686679174,8,,True,True,baseline,61.37898686679174,landmark,True
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3011330593,10.1007/s10586-020-03075-5,,A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments,Cluster Computing,2020,article,cloud_computing,3,109.0,189.0,55.03527669771227,355,59.7401,False,False,baseline,55.03527669771227,landmark,True
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2974429275,10.1109/tii.2019.2942190,,Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT,IEEE Transactions on Industrial Informatics,2019,article,edge_computing,5,79.0,275.0,112.25046733285639,1237,155.2674,True,False,baseline,112.25046733285639,landmark,True
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3201596224,,,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,308.9145728643216,landmark,True
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3041344359,,,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,265.4812030075188,landmark,True
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3015692940,10.14419/ijet.v7i4.21726,,Performance evaluation measurement of image steganography techniques with analysis of LSB based on variation image formats,International Journal of Engineering & Technology,2026,article,cybersecurity,2,41.0,41.0,109.89503437738733,41,0.0,True,True,post,145.7640437522859,landmark,True
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1598516026,10.55277/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,344.8563961944324,landmark,True
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1516024962,10.7275/409838,,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,146.84444444444443,landmark,True
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2910044495,,,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,116.57142857142857,landmark,True
2915352631,10.17487/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,110.30219435736677,landmark,True
4413785573,10.1109/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,89.10155845802977,landmark,True
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4214667959,10.1016/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,82.8248106660309,landmark,True
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2970408908,10.1007/978-3-030-63076-8_2,,Deep Leakage from Gradients,Lecture notes in computer science,2020,conference-paper,applied_cryptography,2,294.0,511.0,72.42380039311094,1489,84.6644,False,False,baseline,72.42380039311094,landmark,True
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4248175462,10.2139/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,1699.155537022696,landmark,True
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2898069736,10.1080/00207543.2018.1533261,,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,206.24666837724715,landmark,True
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3123868249,10.1093/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,150.21153446724054,landmark,True
2907683311,10.1109/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,139.59876301031593,landmark,True
2892520153,10.6028/nist.ir.8202,1906.11078,Blockchain technology overview,,2018,report,blockchain,5,103.0,273.0,137.2692548528491,1551,,True,True,baseline,137.2692548528491,landmark,True
3122636688,10.1093/rfs/hhz007,,Blockchain Disruption and Smart Contracts,Review of Financial Studies,2019,article,blockchain,1,189.0,332.0,135.5169278345757,1089,145.8951,True,False,baseline,135.5169278345757,landmark,True
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4402275599,10.38124/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,135.18269519575335,landmark,True
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2914735843,10.1109/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,128.1696245182433,landmark,True
2973816953,10.7551/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,128.09387022313413,landmark,True
3123722958,10.1002/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,123.53592218997065,landmark,True
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2273211146,10.17615/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,137.14285714285714,landmark,True
4406650295,10.1145/3714983.3714987,,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,118.46457204078958,landmark,True
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4386978266,10.7302/8543,,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,75.89999999999999,landmark,True
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2952332632,10.4230/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,73.13244814646819,landmark,True
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