Check computer science papers: citations, claims and retractions

Can we trust the evaluation on ChatGPT?

Aiyappa, R., An, J., Kwak, H., & Ahn, Y.-Y. (2023) · Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 72

Cognitive Architectures for Language Agents

Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023) · arXiv

AI-based Problem Solving and PlanningPsychiatry, Mental Health, Neuroscience

Cited by 72

PSD computations using Welch's method. [Power Spectral Density (PSD)]

Solomon, Jr. (1991)

Digital Filter Design and ImplementationAdvanced Electrical Measurement Techniques

Cited by 72

Generative AI and Intellectual Property Rights

Smits, J., & Borghuis, T. (2022) · Information Technology and Law Series

Law, AI, and Intellectual PropertyIntellectual Property Rights and Media

Cited by 72

A novel bit level multiphase algorithm for image encryption based on PWLCM chaotic map

Hasheminejad, A., & Rostami, M. J. (2019) · Optik

Chaos-based Image/Signal EncryptionComputer Science and Engineering

Cited by 72

The “black box” at work

Ajunwa, I. (2020) · Big Data & Society

Ethics and Social Impacts of AILegal and Labor Studies

Cited by 72

Causal Reasoning from Meta-reinforcement Learning

Dasgupta, I., Wang, J., Chiappa, S., Mitrovic, J., Ortega, P., Raposo, D., Hughes, E., Battaglia, P., Botvinick, M., & Kurth-Nelson, Z. (2019) · arXiv

Reinforcement Learning in RoboticsBayesian Modeling and Causal Inference

Cited by 71

Can we trust AI not to further embed racial bias and prejudice?

Noor, P. (2020) · BMJ

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 71

The Fundamental Nature of the Log Loss Function

Vovk, V. (2015) · Lecture Notes in Computer Science

Computability, Logic, AI AlgorithmsAdvanced Bandit Algorithms Research

Cited by 71

Muffin: testing deep learning libraries via neural architecture fuzzing

Gu, J., Luo, X., Zhou, Y., & Wang, X. (2022) · Proceedings of the 44th International Conference on Software Engineering

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 71

Real-time Arm Skeleton Tracking and Gesture Inference Tolerant to Missing Wearable Sensors

Liu, Y., Li, Z., Liu, Z., & Wu, K. (2019) · Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

Hand Gesture Recognition SystemsContext-Aware Activity Recognition Systems

Cited by 71

ActivityGAN: generative adversarial networks for data augmentation in sensor-based human activity recognition

Li, X., Luo, J., & Younes, R. (2020) · Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

Context-Aware Activity Recognition SystemsGenerative Adversarial Networks and Image Synthesis

Cited by 71

HUMAN RIGHTS VS. ROBOT RIGHTS: Forecasts from Japan

Robertson, J. (2014) · Critical Asian Studies

Ethics and Social Impacts of AILiterary and Philosophical Studies

Cited by 71

Knowledge graphs: Introduction, history, and perspectives

Chaudhri, V. K., Baru, C., Chittar, N., Dong, X. L., Genesereth, M., Hendler, J., Kalyanpur, A., Lenat, D. B., Sequeda, J., Vrandečić, D., et al. (2022) · AI Magazine

Advanced Graph Neural NetworksSemantic Web and Ontologies

Cited by 71

Comprehensive analysis of big data variety landscape

Abawajy, J. (2015) · International Journal of Parallel, Emergent and Distributed Systems

Network Traffic and Congestion ControlBig Data Technologies and Applications

Cited by 71

Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining

Chang, M., D'Aniello, G., Gaeta, M., Orciuoli, F., Sampson, D., & Simonelli, C. (2020) · IEEE Access

Intelligent Tutoring Systems and Adaptive LearningSemantic Web and Ontologies

Cited by 71

How do we know how smart AI systems are?

Mitchell, M. (2023) · Science

Artificial Intelligence ApplicationsPsychiatry, Mental Health, Neuroscience

Cited by 71

Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review

Chotwanvirat, P., Prachansuwan, A., Sridonpai, P., & Kriengsinyos, W. (2024) · Journal of Medical Internet Research

Advanced Neural Network ApplicationsNutrition, Health and Food Behavior

Cited by 71

Research Stakeholders’ Views on Benefits and Challenges for Public Health Research Data Sharing in Kenya: The Importance of Trust and Social Relations

Jao, I., Kombe, F., Mwalukore, S., Bull, S., Parker, M., Kamuya, D., Molyneux, S., & Marsh, V. (2015) · PLOS ONE

Research Data Management PracticesData Analysis and Archiving

Cited by 71

Multi-view Models for Political Ideology Detection of News Articles

Kulkarni, V., Ye, J., Skiena, S., & Wang, W. Y. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Sentiment Analysis and Opinion MiningComputational and Text Analysis Methods

Cited by 71

Assessing the Capacity of Social Determinants of Health Data to Augment Predictive Models Identifying Patients in Need of Wraparound Social Services

Kasthurirathne, S. N., Vest, J. R., Menachemi, N., Halverson, P. K., & Grannis, S. J. (2018) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 71

CondenseNet V2: Sparse Feature Reactivation for Deep Networks

Yang, L., Jiang, H., Cai, R., Wang, Y., Song, S., Huang, G., & Tian, Q. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 71

Wearable accelerometry-based technology capable of assessing functional activities in neurological populations in community settings: a systematic review

Steins, D., Dawes, H., Esser, P., & Collett, J. (2014) · Journal of NeuroEngineering and Rehabilitation

Context-Aware Activity Recognition SystemsStroke Rehabilitation and Recovery

Cited by 71

Cryptography of medical images based on a combination between chaotic and neural network

Dridi, M., Hajjaji, M. A., Bouallegue, B., & Mtibaa, A. (2016) · IET Image Processing

Chaos-based Image/Signal EncryptionComputer Science and Engineering

Cited by 71