Check computer science papers: citations, claims and retractions

Big Data from the bottom up

Couldry, N., & Powell, A. (2014) · Big Data & Society

Ethics and Social Impacts of AIBig Data Technologies and Applications

Cited by 213

Atlas: Few-shot Learning with Retrieval Augmented Language Models

Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., & Grave, E. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 213

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

Ullah, A., Azeem, M., Ashraf, H., Alaboudi, A. A., Humayun, M., & Jhanjhi, N. (2021) · IEEE Access

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 213

Is Google enough? Comparison of an internet search engine with academic library resources

Brophy, J., & Bawden, D. (2005) · Aslib Proceedings

Information Retrieval and Search BehaviorLibrary Science and Information Literacy

Cited by 213

Complete Dictionary Recovery Over the Sphere I: Overview and the Geometric Picture

Sun, J., Qu, Q., & Wright, J. (2017) · IEEE Transactions on Information Theory

Sparse and Compressive Sensing TechniquesStatistical Methods and Inference

Cited by 213

Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting

Min, M., Wan, X., Xiao, L., Chen, Y., Xia, M., Wu, D., & Dai, H. (2019) · IEEE Internet of Things Journal

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 213

Identifying and discriminating between web and peer-to-peer traffic in the network core

Erman, J., Mahanti, A., Arlitt, M., & Williamson, C. (2007) · Proceedings of the 16th international conference on World Wide Web

Internet Traffic Analysis and Secure E-votingNetwork Packet Processing and Optimization

Cited by 213

BARGAIN-MATCH: A Game Theoretical Approach for Resource Allocation and Task Offloading in Vehicular Edge Computing Networks

Sun, Z., Sun, G., Liu, Y., Wang, J., & Cao, D. (2024) · IEEE Transactions on Mobile Computing

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

Cited by 213

Patterns for How Users Overcome Obstacles in Voice User Interfaces

Myers, C., Furqan, A., Nebolsky, J., Caro, K., & Zhu, J. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Speech and dialogue systemsUsability and User Interface Design

Cited by 213

Optimal quantitative cryptanalysis of permutation-only multimedia ciphers against plaintext attacks

Li, C., & Lo, K.-T. (2009) · arXiv

Chaos-based Image/Signal EncryptionCryptographic Implementations and Security

Cited by 213

Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement

Berk, R. A. (2021) · Annual Review of Criminology

Ethics and Social Impacts of AIArtificial Intelligence in Law

Cited by 213

Fifty Years of Artificial Reverberation

Valimaki, V., Parker, J. D., Savioja, L., Smith, J. O., & Abel, J. S. (2012) · IEEE Transactions on Audio, Speech, and Language Processing

Speech and Audio ProcessingMusic Technology and Sound Studies

Cited by 213

An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance

Chen, Z., Hu, W., Wang, J., Zhao, S., Amos, B., Wu, G., Ha, K., Elgazzar, K., Pillai, P., Klatzky, R., et al. (2017) · Proceedings of the Second ACM/IEEE Symposium on Edge Computing

IoT and Edge/Fog ComputingContext-Aware Activity Recognition Systems

Cited by 213

KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare

Ma, F., You, Q., Xiao, H., Chitta, R., Zhou, J., & Gao, J. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Machine Learning in HealthcareAdvanced Graph Neural Networks

Cited by 213

Understanding Insider Threat: A Framework for Characterising Attacks

Nurse, J. R. C., Buckley, O., Legg, P. A., Goldsmith, M., Creese, S., Wright, G. R. T., & Whitty, M. (2014) · 2014 IEEE Security and Privacy Workshops

Information and Cyber SecurityNetwork Security and Intrusion Detection

Cited by 212

Overfitting, underfitting and general model overconfidence and under-performance pitfalls and best practices in machine learning and AI

Aliferis, C., & Simon, G. (2024) · Health Informatics

Machine Learning and Data ClassificationArtificial Intelligence in Healthcare

Cited by 212

A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions

Wu, J., Yang, S., Zhan, R., Yuan, Y., Chao, L. S., & Wong, D. F. (2025) · Computational Linguistics

Authorship Attribution and ProfilingDigital Media Forensic Detection

Cited by 212

Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era

Chang, Z., Lei, L., Zhou, Z., Mao, S., & Ristaniemi, T. (2018) · IEEE Wireless Communications

Caching and Content DeliveryIoT and Edge/Fog Computing

Cited by 212

TorusE: Knowledge Graph Embedding on a Lie Group

Ebisu, T., & Ichise, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Graph Neural NetworksTopic Modeling

Cited by 212

Social Simulacra: Creating Populated Prototypes for Social Computing Systems

Park, J. S., Popowski, L., Cai, C., Morris, M. R., Liang, P., & Bernstein, M. S. (2022) · Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology

Innovative Human-Technology InteractionUsability and User Interface Design

Cited by 212

Ranking Visualizations of Correlation Using Weber's Law

Harrison, L., Yang, F., Franconeri, S., & Chang, R. (2014) · IEEE Transactions on Visualization and Computer Graphics

Data Visualization and AnalyticsComputer Graphics and Visualization Techniques

Cited by 212

Navigating the Ethical Challenges of Artificial Intelligence in Higher Education: An Analysis of Seven Global AI Ethics Policies

Slimi, Z., & Villarejo Carballido, B. (2023) · TEM Journal

Ethics and Social Impacts of AIArtificial Intelligence in Education

Cited by 212

The effect of sampling temperature on problem solving in large language models

Renze, M. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Topic ModelingNatural Language Processing Techniques

Cited by 212

Are GAN Generated Images Easy to Detect? A Critical Analysis of the State-Of-The-Art

Gragnaniello, D., Cozzolino, D., Marra, F., Poggi, G., & Verdoliva, L. (2021) · 2021 IEEE International Conference on Multimedia and Expo (ICME)

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 212