Check computer science papers: citations, claims and retractions

Supporting skill integration in an intelligent tutoring system for code tracing

Huang, Y., Brusilovsky, P., Guerra, J., Koedinger, K., & Schunn, C. (2023) · Journal of Computer Assisted Learning

Intelligent Tutoring Systems and Adaptive LearningTeaching and Learning Programming

Cited by 32

Building towards Automated Cyberbullying Detection: A Comparative Analysis

Al-Harigy, L. M., Al-Nuaim, H. A., Moradpoor, N., & Tan, Z. (2022) · Computational Intelligence and Neuroscience

Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion Mining

Cited by 32

Exploring the Impact of AI Value Alignment in Collaborative Ideation: Effects on Perception, Ownership, and Output

Guo, A., Pataranutaporn, P., & Maes, P. (2024) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionDesign Education and Practice

Cited by 32

Commentary: Mouse Genetic Nomenclature: Standardization of Strain, Gene, and Protein Symbols

Sundberg, J. P., & Schofield, P. N. (2010) · Veterinary Pathology

Research Data Management PracticesAnimal Genetics and Reproduction

Cited by 32

DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation

Frigo, O., Martin-Gaffe, L., & Wacongne, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Advanced Neural Network ApplicationsAdvanced Image Fusion Techniques

Cited by 32

Artificial intelligence is breaking patent law

George, A., & Walsh, T. (2022) · Nature

Law, AI, and Intellectual PropertyIntellectual Property Law

Cited by 32

“Integrating an adjusted conversational agent into a mobile-assisted language learning application.”

Troussas, C., Krouska, A., & Virvou, M. (2017) · 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)

Intelligent Tutoring Systems and Adaptive LearningMobile Learning in Education

Cited by 32

A Comparison of Immediate and Scheduled Feedback in Introductory Programming Projects

Leinonen, J., Denny, P., & Whalley, J. (2022) · Proceedings of the 53rd ACM Technical Symposium on Computer Science Education

Teaching and Learning ProgrammingStudent Assessment and Feedback

Cited by 32

Text Mining an Automatic Short Answer Grading (ASAG), Comparison of Three Methods of Cosine Similarity, Jaccard Similarity and Dice's Coefficient

wahyuningsih, T. (2021) · Journal of Applied Data Sciences

Educational Technology and AssessmentOnline Learning and Analytics

Cited by 32

CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers

Zhao, D., Zhou, J.-T., & Li, K. (2025) · IEEE Transactions on Sustainable Computing

Cloud Computing and Resource ManagementEnergy Efficiency in Computing

Cited by 32

Duplicate Detection in Documents and WebPages Using Improved Longest Common Subsequence and Documents Syntactical Structures

Elhadi, M., & Al-Tobi, A. (2009) · 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology

Data Quality and ManagementSoftware Engineering Research

Cited by 32

Sch-net: a deep learning architecture for automatic detection of schizophrenia

Fu, J., Yang, S., He, F., He, L., Li, Y., Zhang, J., & Xiong, X. (2021) · BioMedical Engineering OnLine

Speech Recognition and SynthesisSchizophrenia research and treatment

Cited by 31

Commentary: AI psychosis is not a new threat: Lessons from media-induced delusions

Carlbring, P., & Andersson, G. (2025) · Internet Interventions

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 31

An Architecture-Tracking Approach to Evaluate a Modular and Extensible Flight Software for CubeSat Nanosatellites

Gonzalez, C. E., Rojas, C. J., Bergel, A., & Diaz, M. A. (2019) · IEEE Access

Advanced Software Engineering MethodologiesSpacecraft Design and Technology

Cited by 31

FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models

Jing, L., Li, R., Chen, Y., & Du, X. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 31

Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Dai, D., Sun, Y., Dong, L., Hao, Y., Ma, S., Sui, Z., & Wei, F. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 31

Regulatory Frameworks for Clinical Trial Data Sharing: Scoping Review

Gudi, N., Kamath, P., Chakraborty, T., Jacob, A. G., Parsekar, S. S., Sarbadhikari, S. N., & John, O. (2022) · Journal of Medical Internet Research

Research Data Management PracticesEthics in Clinical Research

Cited by 31

MaskCAE: Masked Convolutional AutoEncoder via Sensor Data Reconstruction for Self-Supervised Human Activity Recognition

Cheng, D., Zhang, L., Qin, L., Wang, S., Wu, H., & Song, A. (2024) · IEEE Journal of Biomedical and Health Informatics

Context-Aware Activity Recognition SystemsTime Series Analysis and Forecasting

Cited by 31

What Makes a Well-Documented Notebook? A Case Study of Data Scientists’ Documentation Practices in Kaggle

Wang, A. Y., Wang, D., Drozdal, J., Liu, X., Park, S., Oney, S., & Brooks, C. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

Software Engineering ResearchScientific Computing and Data Management

Cited by 31

Evaluating model drift in machine learning algorithms

Nelson, K., Corbin, G., Anania, M., Kovacs, M., Tobias, J., & Blowers, M. (2015) · 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)

Adversarial Robustness in Machine LearningNetwork Security and Intrusion Detection

Cited by 31

The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels

Stone, A., Ramirez, O., Konolige, K., & Jonschkowski, R. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 31

Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence

Chen, H.-T., Zhang, M., & Choi, E. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 31

Hand tracking and visualization in a virtual reality simulation

Cameron, C. R., DiValentin, L. W., Manaktala, R., McElhaney, A. C., Nostrand, C. H., Quinlan, O. J., Sharpe, L. N., Slagle, A. C., Wood, C. D., Zheng, Y. Y., et al. (2011) · 2011 IEEE Systems and Information Engineering Design Symposium

Hand Gesture Recognition SystemsVirtual Reality Applications and Impacts

Cited by 31

Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers

Peng, K., Mathur, A., & Narayanan, A. (2021) · arXiv

Ethics and Social Impacts of AIFace recognition and analysis

Cited by 31