Check computer science papers: citations, claims and retractions

LLM Multi-Agent Systems: Challenges and Open Problems

Han, S., Zhang, Q., Jin, W., & Xu, Z. (2024) · arXiv

Multi-Agent Systems and NegotiationDistributed Control Multi-Agent Systems

Cited by 33

Emotion assessment using Machine Learning and low-cost wearable devices

Laureanti, R., Bilucaglia, M., Zito, M., Circi, R., Fici, A., Rivetti, F., Valesi, R., Oldrini, C., Mainardi, L. T., & Russo, V. (2020) · 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Emotion and Mood RecognitionEEG and Brain-Computer Interfaces

Cited by 33

Initial Cybersecurity Framework in the New Capital City of Indonesia: Factors, Objectives, and Technology

Sensuse, D. I., Putro, P. A. W., Rachmawati, R., & Sunindyo, W. D. (2022) · Information

Information and Cyber SecuritySmart Cities and Technologies

Cited by 33

Learning to Predict Denotational Probabilities For Modeling Entailment

Lai, A., & Hockenmaier, J. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Topic ModelingNatural Language Processing Techniques

Cited by 33

AttriMIL: Revisiting attention-based multiple instance learning for whole-slide pathological image classification from a perspective of instance attributes

Cai, L., Huang, S., Zhang, Y., Lu, J., & Zhang, Y. (2025) · Medical Image Analysis

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 33

A Multivocal Review of MLOps Practices, Challenges and Open Issues

Eken, B., Pallewatta, S., Tran, N., Tosun, A., & Babar, M. A. (2026) · ACM Computing Surveys

Software Engineering Techniques and PracticesSoftware System Performance and Reliability

Cited by 33

Storage capacity of attractor neural networks with depressing synapses

Torres, J. J., Pantic, L., & Kappen, H. J. (2002) · Physical Review E

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 33

Text-To-Text Generative Adversarial Networks

Li, C., Su, Y., & Liu, W. (2018) · 2018 International Joint Conference on Neural Networks (IJCNN)

Topic ModelingGenerative Adversarial Networks and Image Synthesis

Cited by 33

Don't Use LLMs to Make Relevance Judgments

Soboroff, I. (2025) · Information Retrieval Research

Information Retrieval and Search BehaviorTopic Modeling

Cited by 33

Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-Reduction

Shelby, R., Rismani, S., & Rostamzadeh, N. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIArt, Technology, and Culture

Cited by 33

Cryptanalysis of an Image Encryption Algorithm Using DNA Coding and Chaos

Zhao, Y., Shi, Q., & Ding, Q. (2025) · Entropy

Chaos-based Image/Signal EncryptionComputer Science and Engineering

Cited by 33

Enabling Technologies for Ultra-Low Latency and High-Reliability Communication in 6G Networks

Mohammed, S. M., Al-Barrak, A., & Mahmood, N. T. (2024) · Ingénierie des systèmes d information

IoT and Edge/Fog ComputingVehicular Ad Hoc Networks (VANETs)

Cited by 33

Automatic Speech Recognition With Very Large Conversational Finnish and Estonian Vocabularies

Enarvi, S., Smit, P., Virpioja, S., & Kurimo, M. (2017) · IEEE/ACM Transactions on Audio, Speech, and Language Processing

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 33

Analysis of Quantum Approximate Optimization Algorithm under Realistic Noise in Superconducting Qubits

Alam, M., Ash-Saki, A., & Ghosh, S. (2019) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 33

Augmenting assessment with AI coding of online student discourse: A question of reliability

Misiejuk, K., Kaliisa, R., & Scianna, J. (2024) · Computers and Education: Artificial Intelligence

Online Learning and AnalyticsArtificial Intelligence in Education

Cited by 33

Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness

Chen, J., & Mueller, J. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 33

Research on ARM TrustZone

Li, W., Xia, Y., & Chen, H. (2019) · GetMobile: Mobile Computing and Communications

Security and Verification in ComputingDigital Rights Management and Security

Cited by 33

iCOP: Live forensics to reveal previously unknown criminal media on P2P networks

Peersman, C., Schulze, C., Rashid, A., Brennan, M., & Fischer, C. (2016) · Digital Investigation

Digital and Cyber ForensicsCybercrime and Law Enforcement Studies

Cited by 33

Feature Augmentation with Reinforcement Learning

Liu, J., Chai, C., Luo, Y., Lou, Y., Feng, J., & Tang, N. (2022) · 2022 IEEE 38th International Conference on Data Engineering (ICDE)

Machine Learning and Data ClassificationReinforcement Learning in Robotics

Cited by 33

Effective Long-Context Scaling of Foundation Models

Xiong, W., Liu, J., Molybog, I., Zhang, H., Bhargava, P., Hou, R., Martin, L., Rungta, R., Sankararaman, K. A., Oguz, B., et al. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 33

Will Admins Cope? Decentralized Moderation in the Fediverse

Anaobi, I. H., Raman, A., Castro, I., Zia, H. B., Ibosiola, D., & Tyson, G. (2023) · Proceedings of the ACM Web Conference 2023

Hate Speech and Cyberbullying DetectionMisinformation and Its Impacts

Cited by 33

Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models

Hase, P., Bansal, M., Kim, B., & Ghandeharioun, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 33

From Chest X-Rays to Radiology Reports: A Multimodal Machine Learning Approach

Singh, S., Karimi, S., Ho-Shon, K., & Hamey, L. (2019) · 2019 Digital Image Computing: Techniques and Applications (DICTA)

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 33

TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models

Gekhman, Z., Herzig, J., Aharoni, R., Elkind, C., & Szpektor, I. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 33