Check computer science papers: citations, claims and retractions

Intent-driven network management with multi-agent LLMs: the confucius framework

Wang, Z., Lin, S., Yan, G., Ghorbani, S., Yu, M., Zhou, J., Hu, N., Baruah, L., Peters, S., Kamath, S., et al. (2025) · Proceedings of the ACM SIGCOMM 2025 Conference

Mobile Agent-Based Network ManagementSoftware-Defined Networks and 5G

Cited by 27

Activity Driven Weakly Supervised Object Detection

Yang, Z., Mahajan, D., Ghadiyaram, D., Nevatia, R., & Ramanathan, V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsHuman Pose and Action Recognition

Cited by 27

RudolfV: A Foundation Model by Pathologists for Pathologists

Dippel, J., Feulner, B., Winterhoff, T., Milbich, T., Tietz, S., Schallenberg, S., Dernbach, G., Kunft, A., Heinke, S., Eich, M.-L., et al. (2024) · arXiv

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 27

An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis

Elouataoui, W., El Mendili, S., & Gahi, Y. (2023) · Data

Data Quality and ManagementBig Data Technologies and Applications

Cited by 27

Combining Ensembles and Data Augmentation can Harm your Calibration

Wen, Y., Jerfel, G., Muller, R., Dusenberry, M. W., Snoek, J., Lakshminarayanan, B., & Tran, D. (2020) · arXiv

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 27

Cyber-Physical Inconsistency Vulnerability Identification for Safety Checks in Robotic Vehicles

Choi, H., Kate, S., Aafer, Y., Zhang, X., & Xu, D. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security

Software Testing and Debugging TechniquesSafety Systems Engineering in Autonomy

Cited by 27

Error mitigation with Clifford quantum-circuit data

Czarnik, P., Arrasmith, A., Coles, P. J., & Cincio, L. (2020) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 27

“Predicting the difficulty and response time of multiple choice questions using transfer learning,”

Xue, K., Yaneva, V., Runyon, C., & Baldwin, P. (2020) · Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

Online Learning and AnalyticsInnovations in Medical Education

Cited by 27

The role of artificial intelligence (AI) and machine learning in social work practice

Nuwasiima, M., Ahonon, M. P., & Kadiri, C. (2024) · World Journal of Advanced Research and Reviews

Ethics and Social Impacts of AISocial Work Education and Practice

Cited by 27

Calibrated Reliable Regression using Maximum Mean Discrepancy

Cui, P., Hu, W., & Zhu, J. (2020) · arXiv

Adversarial Robustness in Machine LearningGaussian Processes and Bayesian Inference

Cited by 27

A Comparison of Stream Processing Frameworks

Karakaya, Z., Yazici, A., & Alayyoub, M. (2017) · 2017 International Conference on Computer and Applications (ICCA)

Advanced Database Systems and QueriesCloud Computing and Resource Management

Cited by 27

An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach

Jamal, S., Wimmer, H., & Sarker, I. (2023) · Research Square

Spam and Phishing DetectionHate Speech and Cyberbullying Detection

Cited by 27

Chatgpt in developing economies

Kshetri, N. (2023) · IT Professional

Artificial Intelligence ApplicationsICT in Developing Communities

Cited by 27

Employing Transfer-Learning based CNN architectures to Enhance the Generalizability of Deepfake Detection

Suratkar, S., Johnson, E., Variyambat, K., Panchal, M., & Kazi, F. (2020) · 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

Digital Media Forensic DetectionMisinformation and Its Impacts

Cited by 27

Focus on quantum science and technology initiatives around the world

Thew, R., Jennewein, T., & Sasaki, M. (2020) · Quantum Science and Technology

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 27

Comparing record linkage software programs and algorithms using real-world data.

Karr, A. F., Taylor, M. T., West, S. L., Setoguchi, S., Kou, T. D., Gerhard, T., & Horton, D. B. (2019) · PLOS ONE

Data Quality and ManagementMedical Coding and Health Information

Cited by 27

A machine learning framework supporting prospective clinical decisions applied to risk prediction in oncology

Coombs, L., Orlando, A., Wang, X., Shaw, P., Rich, A. S., Lakhtakia, S., Titchener, K., Adamson, B., Miksad, R. A., & Mooney, K. (2022) · npj Digital Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 27

Do Androids Dream of Electric Copyright?: Comparative Analysis of Originality in Artificial Intelligence Generated Works

Guadamuz, A. (2021) · Artificial Intelligence and Intellectual Property

Law, AI, and Intellectual PropertyDigitalization, Law, and Regulation

Cited by 27

Recommendations to Create Programming Exercises to Overcome ChatGPT

Berrezueta-Guzman, J., & Krusche, S. (2023) · 2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T)

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 27

ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space

Yan, Y., Mascaro, E. V., & Lee, D. (2023) · 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids)

Human Motion and AnimationRobot Manipulation and Learning

Cited by 27

Privacy-Encoded Federated Learning Against Gradient-Based Data Reconstruction Attacks

Liu, H., Li, B., Gao, C., Xie, P., & Zhao, C. (2023) · IEEE Transactions on Information Forensics and Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 27

Recommendations for key-encapsulation mechanisms

Alagic, G., Barker, E., Chen, L., Moody, D., Robinson, A., Silberg, H., & Waller, N. (2025)

Cryptography and Data SecurityAdvanced Authentication Protocols Security

Cited by 27

Artificial intelligence in computer programming education: A systematic literature review

Manorat, P., Tuarob, S., & Pongpaichet, S. (2025) · Computers and Education: Artificial Intelligence

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 27

Recommendation Systems on E-Learning and Social Learning: A Systematic Review

Souabi, S., Retbi, A., Khalidi Idrissi, M. K. I., & Bennani, S. (2021) · Electronic Journal of e-Learning

Recommender Systems and TechniquesOnline Learning and Analytics

Cited by 27