Check computer science papers: citations, claims and retractions

Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation

Agarwal, S., Sundaresan, S., Mitra, S., Mahapatra, D., Gupta, A., Sharma, R., Kapu, N. J., Yu, T., & Saini, S. (2025) · Proceedings of the ACM on Management of Data

Topic ModelingParallel Computing and Optimization Techniques

Cited by 21

“Applying machine learning to improve curriculum design,”

Ball, R., Duhadway, L., Feuz, K., Jensen, J., Rague, B., & Weidman, D. (2019) · Proceedings of the 50th ACM Technical Symposium on Computer Science Education

Online Learning and AnalyticsInformation Systems Education and Curriculum Development

Cited by 21

One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study

Paolini, D., Dini, P., Soldaini, E., & Saponara, S. (2025) · Computers

Network Security and Intrusion DetectionAnomaly Detection Techniques and Applications

Cited by 21

Space Applications of Artificial Intelligence

Chien, S., & Morris, R. (2014) · AI Magazine

Artificial Intelligence ApplicationsSpace exploration and regulation

Cited by 21

ArgMed-Agents: Explainable Clinical Decision Reasoning with LLM Disscusion via Argumentation Schemes

Hong, S., Xiao, L., Zhang, X., & Chen, J. (2024) · 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 21

Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)

Thukral, M., Dhekane, S. G., Hiremath, S. K., Haresamudram, H., & Ploetz, T. (2024) · arXiv

Context-Aware Activity Recognition SystemsIoT-based Smart Home Systems

Cited by 21

Single Shot MC Dropout Approximation

Brach, K., Sick, B., & Dürr, O. (2020) · arXiv

Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine Learning

Cited by 21

Humanoid Agents: Platform for Simulating Human-like Generative Agents

Wang, Z., Chiu, Y. Y., & Chiu, Y. C. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Psychiatry, Mental Health, NeuroscienceMulti-Agent Systems and Negotiation

Cited by 21

Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning

Tennant, E., Hailes, S., & Musolesi, M. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

Reinforcement Learning in RoboticsEvolutionary Game Theory and Cooperation

Cited by 21

Resolving Code Review Comments with Machine Learning

Froemmgen, A., Austin, J., Choy, P., Ghelani, N., Kharatyan, L., Surita, G., Khrapko, E., Lamblin, P., Manzagol, P.-A., Revaj, M., et al. (2024) · Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 21

Vibrations in Steel Strips: Effects on Flatness Measurement and Filtering

Usamentiaga, R., Garcia, D. F., Molleda, J., Bulnes, F. G., & Bonet Brana, G. (2014) · IEEE Transactions on Industry Applications

Optical measurement and interference techniquesIndustrial Vision Systems and Defect Detection

Cited by 21

eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data

Peng, B., Ling, X., Chen, Z., Sun, H., & Ning, X. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 21

AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology

Nguyen, M. H., Chau, T. P., Nguyen, P. X., & Bui, N. D. Q. (2024) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 21

Towards Answer-unaware Conversational Question Generation

Nakanishi, M., Kobayashi, T., & Hayashi, Y. (2019) · Proceedings of the 2nd Workshop on Machine Reading for Question Answering

Topic ModelingSpeech and dialogue systems

Cited by 21

Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas

Kuo, Y.-L., Katz, B., & Barbu, A. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Reinforcement Learning in RoboticsLogic, Reasoning, and Knowledge

Cited by 21

SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents

Zhang, Q., Chen, Z., Pan, H., Caragea, C., Latecki, L. J., & Dragut, E. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 21

D-Score: Holistic Dialogue Evaluation Without Reference

Zhang, C., Lee, G., D'Haro, L. F., & Li, H. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 21

YaRN: Efficient Context Window Extension of Large Language Models

Peng, B., Quesnelle, J., Fan, H., & Shippole, E. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 21

Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved With Text

Zhu, W., Hessel, J., Awadalla, A., Gadre, S. Y., Dodge, J., Fang, A., Yu, Y., Schmidt, L., Wang, W. Y., & Choi, Y. (2023) · arXiv

Multimodal Machine Learning ApplicationsNatural Language Processing Techniques

Cited by 21

Can context monitoring improve QoE? A case study of video flash crowds in the internet of services

Hoßfeld, T., Skorin-Kapov, L., Haddad, Y., Pocta, P., Siris, V. A., Zgank, A., & Melvin, H. (2015) · 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)

Image and Video Quality AssessmentCaching and Content Delivery

Cited by 21

Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis

Kirk, J. R., Wray, R. E., Lindes, P., & Laird, J. E. (2023) · arXiv

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 21

Tactile Letters: A Tangible Tabletop with Texture Cues Supporting Alphabetic Learning for Dyslexic Children

Fan, M., & Antle, A. N. (2015) · Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction

Interactive and Immersive DisplaysTactile and Sensory Interactions

Cited by 21

Strengthening LLM Trust Boundaries: A Survey of Prompt Injection Attacks Surender Suresh Kumar Dr. M.L. Cummings Dr. Alexander Stimpson

Kumar, S. S., Cummings, M. L., & Stimpson, A. (2024) · 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 21

Efficient Deep Learning-Based Cyber-Attack Detection for Internet of Medical Things Devices

Judith, A., Kathrine, G. J. W., Silas, S., & J, A. (2023) · RAiSE-2023

Network Security and Intrusion DetectionInternet of Things and AI

Cited by 21