Check computer science papers: citations, claims and retractions

Differentially Private Fair Learning

Jagielski, M., Kearns, M., Mao, J., Oprea, A., Roth, A., Sharifi-Malvajerdi, S., & Ullman, J. (2018) · arXiv

Privacy-Preserving Technologies in DataEthics and Social Impacts of AI

Cited by 62

Developing a Simple and Cost-Effective Markerless Augmented Reality Tool for Chemistry Education

Abdinejad, M., Ferrag, C., Qorbani, H. S., & Dalili, S. (2021) · Journal of Chemical Education

Augmented Reality ApplicationsVarious Chemistry Research Topics

Cited by 62

Automated Meningioma Segmentation in Multiparametric MRI: Comparable Effectiveness of a Deep Learning Model and Manual Segmentation

Laukamp, K. R., Pennig, L., Thiele, F., Reimer, R., Görtz, L., Shakirin, G., Zopfs, D., Timmer, M., Perkuhn, M., & Borggrefe, J. (2021) · Clinical Neuroradiology

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 62

An intrusion detection method based on machine learning and state observer for train-ground communication systems

Gao, B., Bu, B., Zhang, W., & Li, X. (2022) · IEEE Transactions on Intelligent Transportation Systems

Network Security and Intrusion DetectionSmart Grid Security and Resilience

Cited by 62

A review of Methods for Bias Correction in Medical Images

Song, S., Zheng, Y., & He, Y. (2017) · Biomedical Engineering Review

Medical Image Segmentation TechniquesImage Enhancement Techniques

Cited by 62

Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images

Kim, B., Kim, K. C., Park, Y., Kwon, J.-Y., Jang, J., & Seo, J. K. (2018) · Physiological Measurement

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 62

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

Lakkaraju, H., Slack, D., Chen, Y., Tan, C., & Singh, S. (2022) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 62

A research agenda for computational empowerment for emerging technology education

Smith, R. C., Schaper, M.-M., Tamashiro, M. A., Van Mechelen, M., Petersen, M. G., & Iversen, O. S. (2023) · International Journal of Child-Computer Interaction

Teaching and Learning ProgrammingEducation and Technology Integration

Cited by 62

Variational Bayesian Unlearning

Nguyen, Q. P., Low, B. K. H., & Jaillet, P. (2020) · arXiv

Privacy-Preserving Technologies in DataGaussian Processes and Bayesian Inference

Cited by 62

Generative Transformer for Accurate and Reliable Salient Object Detection

Mao, Y., Zhang, J., Wan, Z., Dai, Y., Li, A., Lv, Y., Tian, X., Fan, D.-P., & Barnes, N. (2021) · arXiv

Visual Attention and Saliency DetectionAdvanced Neural Network Applications

Cited by 62

A Survey on Cloudlets, Mobile Edge, and Fog Computing

Muniswamaiah, M., Agerwala, T., & Tappert, C. C. (2021) · 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom)

IoT and Edge/Fog ComputingCaching and Content Delivery

Cited by 62

Development of computer programming attitude scale for university students

Cetin, I., & Ozden, M. Y. (2015) · Computer Applications in Engineering Education

Teaching and Learning ProgrammingGender and Technology in Education

Cited by 62

BETTI NUMBERS OF GAUSSIAN FIELDS

Park, C., Pranav, P., Chingangbam, P., Van De Weygaert, R., Jones, B., Vegter, G., Kim, I., Hidding, J., & Hellwing, W. A. (2013) · Journal of The Korean Astronomical Society

Topological and Geometric Data AnalysisCosmology and Gravitation Theories

Cited by 62

Blockchain and Artificial Intelligence

Marwala, T., & Xing, B. (2018) · arXiv

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 62

Missing the missing values: The ugly duckling of fairness in machine learning

Fernando, M., Cèsar, F., David, N., & José, H. (2021) · International Journal of Intelligent Systems

Ethics and Social Impacts of AIPrivacy-Preserving Technologies in Data

Cited by 62

All-for-one and one-for-all?: a multi-level analysis of communication patterns and individual performance in geographically distributed software development

Ehrlich, K., & Cataldo, M. (2012) · Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work

Software Engineering Techniques and PracticesTeam Dynamics and Performance

Cited by 62

Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives

Aquino, Y. S. J., Carter, S. M., Houssami, N., Braunack-Mayer, A., Win, K. T., Degeling, C., Wang, L., & Rogers, W. A. (2025) · Journal of Medical Ethics

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 62

Automated classification of self-grooming in mice using open-source software

van den Boom, B. J. G., Pavlidi, P., Wolf, C. J. H., Mooij, A. H., & Willuhn, I. (2017) · Journal of Neuroscience Methods

Human Pose and Action RecognitionCell Image Analysis Techniques

Cited by 62

ATISS: Autoregressive Transformers for Indoor Scene Synthesis

Paschalidou, D., Kar, A., Shugrina, M., Kreis, K., Geiger, A., & Fidler, S. (2021) · arXiv

3D Shape Modeling and AnalysisGenerative Adversarial Networks and Image Synthesis

Cited by 62

The Inadequacy of Reinforcement Learning From Human Feedback—Radicalizing Large Language Models via Semantic Vulnerabilities

McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., & Halgamuge, M. N. (2024) · IEEE Transactions on Cognitive and Developmental Systems

Ethics and Social Impacts of AITopic Modeling

Cited by 62

Using Virtual Reality Environments to Augment Cognitive Behavioral Therapy for Fears and Phobias in Autistic Adults

Maskey, M., Rodgers, J., Ingham, B., Freeston, M., Evans, G., Labus, M., & Parr, J. R. (2019) · Autism in Adulthood

Virtual Reality Applications and ImpactsAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Cited by 62

Learning Visible Connectivity Dynamics for Cloth Smoothing

Lin, X., Wang, Y., Huang, Z., & Held, D. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 62

SycEval: Evaluating LLM Sycophancy

Fanous, A., Goldberg, J., Agarwal, A., Lin, J., Zhou, A., Xu, S., Bikia, V., Daneshjou, R., & Koyejo, S. (2025) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AITopic Modeling

Cited by 62

Semantic and structural image segmentation for prosthetic vision

Sanchez-Garcia, M., Martinez-Cantin, R., & Guerrero, J. J. (2020) · PLOS ONE

Advanced Neural Network ApplicationsNeuroscience and Neural Engineering

Cited by 62