Check computer science papers: citations, claims and retractions

Data Access Committees

Cheah, P. Y., & Piasecki, J. (2020) · BMC Medical Ethics

Research Data Management PracticesEthics in Clinical Research

Cited by 59

Coordination among neural modules through a shared global workspace

Goyal, A., Didolkar, A., Lamb, A., Badola, K., Ke, N. R., Rahaman, N., Binas, J., Blundell, C., Mozer, M., & Bengio, Y. (2021) · arXiv

Neural Networks and ApplicationsPsychiatry, Mental Health, Neuroscience

Cited by 59

Integrated AI and Innovation Management: The Beginning of a Beautiful Friendship

Yams, N. B., Richardson, V., Shubina, G. E., Albrecht, S., & Gillblad, D. (2020) · Technology Innovation Management Review

Artificial Intelligence ApplicationsImpact of AI and Big Data on Business and Society

Cited by 59

Model metamers reveal divergent invariances between biological and artificial neural networks

Feather, J., Leclerc, G., Mądry, A., & McDermott, J. H. (2023) · Nature Neuroscience

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 59

Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

Portelas, R., Colas, C., Hofmann, K., & Oudeyer, P.-Y. (2019) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 59

Quantum computational quantification of protein–ligand interactions

Kirsopp, J. J. M., Di Paola, C., Manrique, D. Z., Krompiec, M., Greene‐Diniz, G., Guba, W., Meyder, A., Wolf, D., Strahm, M., & Muñoz Ramo, D. (2022) · International Journal of Quantum Chemistry

Quantum Computing Algorithms and ArchitectureComputational Drug Discovery Methods

Cited by 59

Blockchain Technology in Business Organizations: A Scoping Review

Li, Y., Marier-Bienvenue, T., Perron-Brault, A., Wang, X., & Paré, G. (2018) · Proceedings of the Annual Hawaii International Conference on System Sciences

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 59

Multi-Channel Man-in-the-Middle attacks against protected Wi-Fi networks: A state of the art review

Thankappan, M., Rifà-Pous, H., & Garrigues, C. (2022) · Expert Systems with Applications

Network Security and Intrusion DetectionWireless Networks and Protocols

Cited by 59

Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Hou, Y., Li, J., Fu, X., He, Z., Yan, A., Chen, X., & McAuley, J. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingRecommender Systems and Techniques

Cited by 59

Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation

Yariv, G., Gat, I., Benaim, S., Wolf, L., Schwartz, I., & Adi, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 59

Software defined IoT: Issues and challenges

Ninikrishna, T., Sarkar, S., Tengshe, R., Jha, M. K., Sharma, L., Daliya, V. K., & Routray, S. K. (2017) · 2017 International Conference on Computing Methodologies and Communication (ICCMC)

Software-Defined Networks and 5GIoT and Edge/Fog Computing

Cited by 59

Distributed e-health wide-world accounting ledger via blockchain

Casado-Vara, R., & Corchado, J. (2019) · Journal of Intelligent & Fuzzy Systems

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 59

Secure and Robust Machine Learning for Healthcare: A Survey

Qayyum, A., Qadir, J., Bilal, M., & Al-Fuqaha, A. (2020) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 59

Natural Language Generation enhances human decision-making with uncertain information

Gkatzia, D., Lemon, O., & Rieser, V. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Data Visualization and AnalyticsExplainable Artificial Intelligence (XAI)

Cited by 59

The “cyber security via determinism” paradigm for a quantum safe zero trust deterministic internet of things (IoT)

Szymanski, T. H. (2022) · IEEE Access

Software-Defined Networks and 5GNetwork Security and Intrusion Detection

Cited by 59

Naturalizing a Programming Language via Interactive Learning

Wang, S. I., Ginn, S., Liang, P., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 59

AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset

Cai, Z., Ghosh, S., Adatia, A. P., Hayat, M., Dhall, A., Gedeon, T., & Stefanov, K. (2023) · arXiv

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 59

A Comparison of the 3DES and AES Encryption Standards

Aleisa, N. (2015) · International Journal of Security and Its Applications

Cryptographic Implementations and SecurityComputer Science and Engineering

Cited by 59

Benchmarks for intuitive interaction with mobile devices

Naumann, A., & Hurtienne, J. (2010) · Proceedings of the 12th international conference on Human computer interaction with mobile devices and services

Usability and User Interface DesignInnovative Human-Technology Interaction

Cited by 59

Detecting anomalous deviations from standard maritime routes using the Ornstein-Uhlenbeck process

d'Afflisio, E., Braca, P., Millefiori, L. M., & Willett, P. (2018) · IEEE Transactions on Signal Processing

Anomaly Detection Techniques and ApplicationsMaritime Navigation and Safety

Cited by 59

Demystifying differentiable programming: shift/reset the penultimate backpropagator

Wang, F., Zheng, D., Decker, J., Wu, X., Essertel, G. M., & Rompf, T. (2019) · Proceedings of the ACM on Programming Languages

Logic, programming, and type systemsSoftware Testing and Debugging Techniques

Cited by 59

Correct-by-synthesis reinforcement learning with temporal logic constraints

Wen, M., Ehlers, R., & Topcu, U. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Formal Methods in VerificationReinforcement Learning in Robotics

Cited by 59

Large Language Models Understand and Can be Enhanced by Emotional Stimuli

Li, C., Wang, J., Zhang, Y., Zhu, K., Hou, W., Lian, J., Luo, F., Yang, Q., & Xie, X. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 59

Adaptive Dynamic Networks as Models for the Immune System and Autocatalytic Sets

FARMER, J. D., KAUFFMAN, S. A., PACKARD, N. H., & PERELSON, A. S. (1987) · Annals of the New York Academy of Sciences

Artificial Immune Systems ApplicationsComplex Systems and Dynamics

Cited by 59