Check computer science papers: citations, claims and retractions

Why Machines will Never Rule the World: Artificial Intelligence without Fear

Landgrebe, J., & Smith, B. (2022) · Routledge eBooks

Psychiatry, Mental Health, NeuroscienceCybernetics and Technology in Society

Cited by 64

Validation of a Portable Device for Mapping Motor and Gait Disturbances in Parkinson’s Disease

Rodríguez-Molinero, A., Samà, A., Pérez-Martínez, D. A., Pérez López, C., Romagosa, J., Bayés, À., Sanz, P., Calopa, M., Gálvez-Barrón, C., de Mingo, E., et al. (2015) · JMIR mHealth and uHealth

Context-Aware Activity Recognition SystemsBalance, Gait, and Falls Prevention

Cited by 64

Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Guan, L., Valmeekam, K., Sreedharan, S., & Kambhampati, S. (2023) · arXiv

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 64

Artificial Intelligence for Biology

Hassoun, S., Jefferson, F., Shi, X., Stucky, B., Wang, J., & Rosa, E. (2022) · Integrative and Comparative Biology

Artificial Intelligence ApplicationsMachine Learning in Bioinformatics

Cited by 64

Quantum error correction of qudits beyond break-even

Brock, B. L., Singh, S., Eickbusch, A., Sivak, V. V., Ding, A. Z., Frunzio, L., Girvin, S. M., & Devoret, M. H. (2025) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 64

Food and Mood: Just-in-Time Support for Emotional Eating

Carroll, E. A., Czerwinski, M., Roseway, A., Kapoor, A., Johns, P., Rowan, K., & Schraefel, M. C. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

Innovative Human-Technology InteractionMobile Health and mHealth Applications

Cited by 64

Using sentiment analysis to predict opinion inversion in Tweets of political communication

Matalon, Y., Magdaci, O., Almozlino, A., & Yamin, D. (2021) · Scientific Reports

Sentiment Analysis and Opinion MiningMisinformation and Its Impacts

Cited by 64

Two-Layered Blockchain Architecture for Federated Learning Over the Mobile Edge Network

Feng, L., Yang, Z., Guo, S., Qiu, X., Li, W., & Yu, P. (2022) · IEEE Network

Blockchain Technology Applications and SecurityPrivacy-Preserving Technologies in Data

Cited by 64

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · arXiv

Multimodal Machine Learning ApplicationsExplainable Artificial Intelligence (XAI)

Cited by 64

LIRME: Locally Interpretable Ranking Model Explanation

Verma, M., & Ganguly, D. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Explainable Artificial Intelligence (XAI)Information Retrieval and Search Behavior

Cited by 64

Scaling Data Generation in Vision-and-Language Navigation

Wang, Z., Li, J., Hong, Y., Wang, Y., Wu, Q., Bansal, M., Gould, S., Tan, H., & Qiao, Y. (2023) · arXiv

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 64

Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module

Liu, Y., Yuan, Z., Mao, H., Liang, Z., Yang, W., Qiu, Y., Cheng, T., Li, X., Xu, H., & Gao, K. (2022) · Proceedings of the 2022 International Conference on Multimodal Interaction

Sentiment Analysis and Opinion MiningEmotion and Mood Recognition

Cited by 64

OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments

Xie, T., Zhang, D., Chen, J., Li, X., Zhao, S., Cao, R., Hua, T. J., Cheng, Z., Shin, D., Lei, F., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 64

Exploiting Prediction Error Inconsistencies through LSTM-based Classifiers to Detect Deepfake Videos

Amerini, I., & Caldelli, R. (2020) · Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security

Digital Media Forensic DetectionImage and Video Quality Assessment

Cited by 64

Utility of human-computer interactions: toward a science of preference measurement

Toomim, M., Kriplean, T., Pörtner, C., & Landay, J. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Usability and User Interface DesignMobile Crowdsensing and Crowdsourcing

Cited by 64

Ministral 3

Liu, A. H., Khandelwal, K., Subramanian, S., Jouault, V., Rastogi, A., Sadé, A., Jeffares, A., Jiang, A., Cahill, A., Gavaudan, A., et al. (2026) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 64

Desktop virtual environments: A study of navigation and age

Sayers, H. (2004) · Interacting with Computers

Usability and User Interface DesignInteractive and Immersive Displays

Cited by 64

Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning

Na, S., Niu, H., Lennox, B., & Arvin, F. (2022) · IEEE Transactions on Vehicular Technology

Reinforcement Learning in RoboticsAutonomous Vehicle Technology and Safety

Cited by 64

Generative AI in the Wild: Prospects, Challenges, and Strategies

Sun, Y., Jang, E., Ma, F., & Wang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Artificial Intelligence ApplicationsDigital Media and Visual Art

Cited by 64

A Fast Post-Training Pruning Framework for Transformers

Kwon, W., Kim, S., Mahoney, M. W., Hassoun, J., Keutzer, K., & Gholami, A. (2022) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 64

Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers

Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024) · Computers in Human Behavior Reports

Digital Media Forensic DetectionMisinformation and Its Impacts

Cited by 64

CaRB: A Crowdsourced Benchmark for Open IE

Bhardwaj, S., Aggarwal, S., & Mausam, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 64

Insider Threat Detection Based on Deep Belief Network Feature Representation

Lin, L., Zhong, S., Jia, C., & Chen, K. (2017) · 2017 International Conference on Green Informatics (ICGI)

Network Security and Intrusion DetectionInformation and Cyber Security

Cited by 64

ClueWeb22: 10 Billion Web Documents with Rich Information

Overwijk, A., Xiong, C., & Callan, J. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Information Retrieval and Search BehaviorWeb Data Mining and Analysis

Cited by 64