Check computer science papers: citations, claims and retractions

Learning Setting-Generalized Activity Models for Smart Spaces

Cook, D. (2012) · IEEE Intelligent Systems

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 445

Persuasive technology for health and wellness: State-of-the-art and emerging trends

Orji, R., & Moffatt, K. (2018) · Health Informatics Journal

Innovative Human-Technology InteractionMobile Health and mHealth Applications

Cited by 444

Privacy-Preserving Machine Learning: Threats and Solutions

Al-Rubaie, M., & Chang, J. M. (2019) · IEEE Security & Privacy

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 444

Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models

Bouschery, S. G., Blazevic, V., & Piller, F. T. (2023) · Journal of Product Innovation Management

Artificial Intelligence ApplicationsDesign Education and Practice

Cited by 444

Enabling the Internet of Things

Want, R., Schilit, B. N., & Jenson, S. (2015) · Computer

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 444

Learning by Tracking: Siamese CNN for Robust Target Association

Leal-Taixe, L., Canton-Ferrer, C., & Schindler, K. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Video Surveillance and Tracking MethodsAutonomous Vehicle Technology and Safety

Cited by 444

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022) · Mathematical Problems in Engineering

Online Learning and AnalyticsArtificial Intelligence in Education

Cited by 444

MoodScope: building a mood sensor from smartphone usage patterns

LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2013) · Proceeding of the 11th annual international conference on Mobile systems, applications, and services

Context-Aware Activity Recognition SystemsEmotion and Mood Recognition

Cited by 444

The Measure and Mismeasure of Fairness

Corbett-Davies, S., Gaebler, J. D., Nilforoshan, H., Shroff, R., & Goel, S. (2018) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 444

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

Mao, J., Gan, C., Kohli, P., Tenenbaum, J. B., & Wu, J. (2019) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 444

Iterative user-interface design

Nielsen, J. (1993) · Computer

Usability and User Interface DesignInnovative Human-Technology Interaction

Cited by 444

Gender Bias in Coreference Resolution

Rudinger, R., Naradowsky, J., Leonard, B., & Van Durme, B. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Natural Language Processing TechniquesGender Studies in Language

Cited by 443

Causal Intervention for Leveraging Popularity Bias in Recommendation

Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., & Zhang, Y. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 443

In defense of soft-assignment coding

Liu, L., Wang, L., & Liu, X. (2011) · 2011 International Conference on Computer Vision

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 443

Types of machine learning algorithms

Oladipupo, T. (2010) · New Advances in Machine Learning

Machine Learning and AlgorithmsData Mining and Machine Learning Applications

Cited by 443

Recurrent convolutional strategies for face manipulation detection in videos

Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., & Natarajan, P. (2019) · arXiv

Digital Media Forensic DetectionFace recognition and analysis

Cited by 443

Optimal quantum circuits for general two-qubit gates

Vatan, F., & Williams, C. (2004) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 443

Keeping the Resident in the Loop: Adapting the Smart Home to the User

Rashidi, P., & Cook, D. J. (2009) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans

Context-Aware Activity Recognition SystemsIoT-based Smart Home Systems

Cited by 443

Online Adaptation of Convolutional Neural Networks for Video Object Segmentation

Voigtlaender, P., & Leibe, B. (2017) · Procedings of the British Machine Vision Conference 2017

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 443

Secure audit logs to support computer forensics

Schneier, B., & Kelsey, J. (1999) · ACM Transactions on Information and System Security

Cryptography and Data SecuritySecurity and Verification in Computing

Cited by 443

Mixed-initiative interaction

Allen, J. E., Guinn, C. I., & Horvtz, E. (1999) · IEEE Intelligent Systems

AI-based Problem Solving and PlanningSpeech and dialogue systems

Cited by 442

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 442

AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise

Wyatt, J., Leach, A., Schmon, S. M., & Willcocks, C. G. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Anomaly Detection Techniques and ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 442

Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data

Osborne, J. (2013) · Sage eBooks

Data Quality and ManagementSurvey Methodology and Nonresponse

Cited by 442