Check computer science papers: citations, claims and retractions

Trustworthy AI Inference Systems: An Industry Research View

Cammarota, R., Schunter, M., Rajan, A., Boemer, F., Kiss, Á., Treiber, A., Weinert, C., Schneider, T., Stapf, E., Sadeghi, A.-R., et al. (2020) · arXiv

Security and Verification in ComputingPrivacy-Preserving Technologies in Data

Cited by 9

No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization

Mozes, M., & Kleinberg, B. (2021) · arXiv

Privacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

Cited by 8

Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties

Tung, H.-Y., Ding, M., Chen, Z., Bear, D., Gan, C., Tenenbaum, J. B., Yamins, D. L., Fan, J. E., & Smith, K. A. (2023) · arXiv

Advanced Vision and ImagingRobotics and Sensor-Based Localization

Cited by 8

Medical Imaging Enhancement with AI Models for Automatic Disease Detection and Classification Based on Medical Images

Deloitte Consulting, L. L. P., & Hampiholi, N. (2023) · International Journal of Engineering Applied Sciences and Technology

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 8

Improve Mathematical Reasoning in Language Models by Automated Process Supervision

Luo, L., Liu, Y., Liu, R., Phatale, S., Guo, M., Lara, H., Li, Y., Shu, L., Zhu, Y., Meng, L., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8

Deep reinforcement learning for multi-agent coordination

Aina, K. O., & Ha, S. (2026) · Artificial Life and Robotics

Reinforcement Learning in RoboticsModular Robots and Swarm Intelligence

Cited by 8

Time-Series Multi-Instance Learning for Weakly Supervised Industrial Fault Detection

Liu, C., He, S., Li, S., Shi, Z., & Meng, W. (2025) · IEEE Transactions on Industrial Informatics

Anomaly Detection Techniques and ApplicationsFault Detection and Control Systems

Cited by 8

Understanding R1-Zero-Like Training: A Critical Perspective

Liu, Z., Chen, C., Li, W., Qi, P., Pang, T., Du, C., Lee, W. S., & Lin, M. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8

Search Rank Fraud De-Anonymization in Online Systems

Rahman, M., Hernandez, N., Carbunar, B., & Chau, D. H. (2018) · Proceedings of the 29th on Hypertext and Social Media

Spam and Phishing DetectionCybercrime and Law Enforcement Studies

Cited by 8

A Taxonomy of Decentralized Identifier Methods for Practitioners

Hoops, F., Mühle, A., Matthes, F., & Meinel, C. (2023) · 2023 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS)

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 8

Detecting Scene-Plausible Perceptible Backdoors in Trained DNNs Without Access to the Training Set

Xiang, Z., Miller, D. J., Wang, H., & Kesidis, G. (2021) · Neural Computation

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 8

Private Prediction Sets

Angelopoulos, A. N., Bates, S., Zrnic, T., & Jordan, M. I. (2022) · Harvard Data Science Review

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 8

Demo abstract: riaps—a resilient information architecture platform for edge computing

Emfinger, W., Dubey, A., Volgyesi, P., Sallai, J., & Karsai, G. (2016) · 2016 IEEE/ACM Symposium on Edge Computing (SEC)

IoT and Edge/Fog ComputingDistributed and Parallel Computing Systems

Cited by 8

Effect handlers in Haskell, evidently

Xie, N., & Leijen, D. (2020) · Proceedings of the 13th ACM SIGPLAN International Symposium on Haskell

Logic, programming, and type systemsDistributed systems and fault tolerance

Cited by 8

NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails

Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., & Cohen, J. (2023) · arXiv

Speech and dialogue systemsEthics and Social Impacts of AI

Cited by 8

Decentralized and Privacy-Preserving Key Management Model

Soltani, R., Nguyen, U. T., & An, A. (2020) · 2020 International Symposium on Networks, Computers and Communications (ISNCC)

Blockchain Technology Applications and SecurityCryptography and Data Security

Cited by 8

CodeR: Issue Resolving with Multi-Agent and Task Graphs

Chen, D., Lin, S., Zeng, M., Zan, D., Wang, J.-G., Cheshkov, A., Sun, J., Yu, H., Dong, G., Aliev, A., et al. (2024) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 8

Quantum supremacy and high-dimensional integration

Arrazola, J. M., Rebentrost, P., & Weedbrook, C. (2017) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 8

Automatic emotion recognition in the wild using an ensemble of static and dynamic representations

Ghazi, M. M., & Ekenel, H. K. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction

Emotion and Mood RecognitionFace and Expression Recognition

Cited by 8

Unaware, Unfunded and Uneducated: A Systematic Review of SME Cybersecurity

Junior, C. R., Becker, I., & Johnson, S. (2023) · arXiv

Information and Cyber SecurityCybercrime and Law Enforcement Studies

Cited by 8

Multi-authority security framework for scalable EHR systems

Rezaeibagha, F., Mu, Y., Susilo, W., & Win, K. T. (2016) · International Journal of Medical Engineering and Informatics

Access Control and TrustCloud Data Security Solutions

Cited by 8

Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program

Druce, J., Niehaus, J., Moody, V., Jensen, D., & Littman, M. L. (2021) · arXiv

Explainable Artificial Intelligence (XAI)AI-based Problem Solving and Planning

Cited by 8

Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators

Khachatryan, L., Movsisyan, A., Tadevosyan, V., Henschel, R., Wang, Z., Navasardyan, S., & Shi, H. (2023) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 8

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

De, S., Smith, S. L., Fernando, A., Botev, A., Cristian-Muraru, G., Gu, A., Haroun, R., Berrada, L., Chen, Y., Srinivasan, S., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8