Check computer science papers: citations, claims and retractions

NuCypher KMS: Decentralized key management system

Egorov, M., Wilkison, M., & Nunez, D. (2017) · arXiv

Cryptography and Data SecurityBlockchain Technology Applications and Security

Cited by 29

Welfare Properties of Profit Maximizing Recommender Systems: Theory and Results from a Randomized Experiment

Zhang, X., Ferreira, P., de Matos, M. G., & Belo, R. (2021) · MIS Quarterly

Recommender Systems and TechniquesDigital Platforms and Economics

Cited by 29

Memristive chaotic system-based hybrid image encryption application with AES and RSA algorithms

Sahin, M. E. (2023) · Physica Scripta

Chaos-based Image/Signal EncryptionComputer Science and Engineering

Cited by 29

Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization

Stacker, L., Fei, J., Heidenreich, P., Bonarens, F., Rambach, J., Stricker, D., & Stiller, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 29

Inverse Scaling: When Bigger Isn't Better

McKenzie, I. R., Lyzhov, A., Pieler, M., Parrish, A., Mueller, A., Prabhu, A., McLean, E., Kirtland, A., Ross, A., Liu, A., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

Mitigating Artificial Intelligence Bias in Financial Systems: A Comparative Analysis of Debiasing Techniques

Oguntibeju, O. O. (2024) · Asian Journal of Research in Computer Science

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 29

Learning Autocompletion from Real-World Datasets

Aye, G. A., Kim, S., & Li, H. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 29

Yet another but more efficient black-box adversarial attack: tiling and evolution strategies

Meunier, L., Atif, J., & Teytaud, O. (2019) · arXiv

Adversarial Robustness in Machine LearningSoftware Testing and Debugging Techniques

Cited by 29

Labeling Network Intrusion Detection System (NIDS) Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models

Daniel, N., Kaiser, F. K., Giladi, S., Sharabi, S., Moyal, R., Shpolyansky, S., Murillo, A., Elyashar, A., & Puzis, R. (2025) · Big Data and Cognitive Computing

Network Security and Intrusion DetectionNetwork Packet Processing and Optimization

Cited by 29

Can Large-Scale Vocoded Spoofed Data Improve Speech Spoofing Countermeasure with a Self-Supervised Front End?

Wang, X., & Yamagishi, J. (2024) · ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisBiometric Identification and Security

Cited by 29

StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

Shen, Y., Tay, Y., Zheng, C., Bahri, D., Metzler, D., & Courville, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 29

TinyML in Africa: Opportunities and Challenges

Ooko, S. O., Muyonga Ogore, M., Nsenga, J., & Zennaro, M. (2021) · 2021 IEEE Globecom Workshops (GC Wkshps)

Internet of Things and AIIoT and Edge/Fog Computing

Cited by 29

A Mixed-Method Analysis of Text and Audio Search Interfaces with Varying Task Complexity

Vtyurina, A., Clarke, C. L. A., Law, E., Trippas, J. R., & Bota, H. (2020) · Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval

Information Retrieval and Search BehaviorUsability and User Interface Design

Cited by 29

Minimal random code learning: Getting bits back from compressed model parameters

Havasi, M., Peharz, R., & Hernández-Lobato, J. (2019) · Apollo - University of Cambridge Repository

Advanced Neural Network ApplicationsAdvanced Data Compression Techniques

Cited by 29

Scaling Neuroscience Research Using Federated Learning

Stripelis, D., Ambite, J. L., Lam, P., & Thompson, P. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 29

“Mathematical word problem generation from commonsense knowledge graph and equations,”

Liu, T., Fang, Q., Ding, W., Li, H., Wu, Z., & Liu, Z. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 29

Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach

Barnard, P., Macaluso, I., Marchetti, N., & DaSilva, L. A. (2022) · ICC 2022 - IEEE International Conference on Communications

Explainable Artificial Intelligence (XAI)Software-Defined Networks and 5G

Cited by 29

Anomaly Detection and Tracking Based on Mean–Reverting Processes with Unknown Parameters

Forti, N., Millefiori, L. M., Braca, P., & Willett, P. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Target Tracking and Data Fusion in Sensor NetworksAnomaly Detection Techniques and Applications

Cited by 29

EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents

Yang, R., Chen, H., Zhang, J., Zhao, M., Qian, C., Wang, K., Wang, Q., Koripella, T. V., Movahedi, M., Li, M., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 29

Directed Acyclic Graph Neural Network for Human Motion Prediction

Li, Q., Chalvatzaki, G., Peters, J., & Wang, Y. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA)

Human Motion and AnimationHuman Pose and Action Recognition

Cited by 29

Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning

Zhou, F., Mao, Y., Yu, L., Yang, Y., & Zhong, T. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Ethics and Social Impacts of AITopic Modeling

Cited by 29

Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit

D’Hondt, E., Ashby, T. J., Chakroun, I., Koninckx, T., & Wuyts, R. (2022) · Communications Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 29

Dice in the Black Box: User Experiences with an Inscrutable Algorithm

Springer, A., Hollis, V., & Whittaker, S. (2018) · arXiv

Innovative Human-Technology InteractionEthics and Social Impacts of AI

Cited by 29

Artificial Intelligence and Large Language Models in Advancing Cyber Threat Intelligence: A Systematic Literature Review

Alturkistani, H., & Chuprat, S. (2024) · Research Square

Network Security and Intrusion DetectionInformation and Cyber Security

Cited by 29