Check computer science papers: citations, claims and retractions

Artificial intelligence and computer science in education: From kindergarten to university

Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016) · 2016 IEEE Frontiers in Education Conference (FIE)

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 437

Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D. (2019) · Journal of Biomedical Informatics

Machine Learning in HealthcarePrivacy-Preserving Technologies in Data

Cited by 437

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Madaio, M. A., Stark, L., Wortman Vaughan, J., & Wallach, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 436

We Are Data: Algorithms and the Making of Our Digital Selves

Cheney-Lippold, J. (2020) · NYU Press eBooks

Ethics and Social Impacts of AICybernetics and Technology in Society

Cited by 436

Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review

Shuvo, M. M. H., Islam, S. K., Cheng, J., & Morshed, B. I. (2023) · Proceedings of the IEEE

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 436

Grounded Copilot: How Programmers Interact with Code-Generating Models

Barke, S., James, M. B., & Polikarpova, N. (2023) · Proceedings of the ACM on Programming Languages

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 436

Investigating the influence of organizational factors on blockchain adoption: An innovation theory perspective

Clohessy, T., & Acton, T. (2019) · Industrial Management & Data Systems

Blockchain Technology Applications and SecurityTechnology Adoption and User Behaviour

Cited by 436

Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

Mills, J., Hu, J., & Min, G. (2020) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 436

Bayesian just-so stories in psychology and neuroscience.

Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin

Bayesian Modeling and Causal InferenceEmbodied and Extended Cognition

Cited by 436

LDP-Fed: federated learning with local differential privacy

Truex, S., Liu, L., Chow, K.-H., Gursoy, M. E., & Wei, W. (2020) · Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 435

Tracking and Kalman Filtering Made Easy

Brookner, E. (1998) · Wiley eBooks

Target Tracking and Data Fusion in Sensor NetworksControl Systems and Identification

Cited by 435

Review of Wearable Devices and Data Collection Considerations for Connected Health

Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors

Context-Aware Activity Recognition SystemsNon-Invasive Vital Sign Monitoring

Cited by 435

Recommendations for Good Scientific Practice and the Consumers of VR-Technology

Madary, M., & Metzinger, T. K. (2016) · Frontiers in Robotics and AI

Virtual Reality Applications and ImpactsEthics and Social Impacts of AI

Cited by 435

Transfer learning in hybrid classical-quantum neural networks

Mari, A., Bromley, T. R., Izaac, J., Schuld, M., & Killoran, N. (2020) · Quantum

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 435

A High-Throughput and Power-Efficient FPGA Implementation of YOLO CNN for Object Detection

Nguyen, D. T., Nguyen, T. N., Kim, H., & Lee, H.-J. (2019) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Advanced Neural Network ApplicationsEmbedded Systems Design Techniques

Cited by 435

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Liu, C.-F., Bennis, M., Debbah, M., & Poor, H. V. (2019) · IEEE Transactions on Communications

IoT and Edge/Fog ComputingAdvanced Wireless Network Optimization

Cited by 435

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms

Huang, S., & Ontañón, S. (2022) · The International FLAIRS Conference Proceedings

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 435

Thinking responsibly about responsible AI and ‘the dark side’ of AI

Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022) · European Journal of Information Systems

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 435

Dissecting Contextual Word Embeddings: Architecture and Representation

Peters, M., Neumann, M., Zettlemoyer, L., & Yih, W.-T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 434

Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light

Zhong, H.-S., Deng, Y.-H., Qin, J., Wang, H., Chen, M.-C., Peng, L.-C., Luo, Y.-H., Wu, D., Gong, S.-Q., Su, H., et al. (2021) · Physical Review Letters

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 434

Commonsense reasoning and commonsense knowledge in artificial intelligence

Davis, E., & Marcus, G. (2015) · Communications of the ACM

Logic, Reasoning, and KnowledgeAI-based Problem Solving and Planning

Cited by 434

Persuasive computers: perspectives and research directions

Fogg, B. (1998) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '98

Innovative Human-Technology InteractionSocial Robot Interaction and HRI

Cited by 434

FactSheets: Increasing trust in AI services through supplier's declarations of conformity

Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., et al. (2019) · IBM Journal of Research and Development

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 434

SUPERSEDED - CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit

Veaux, C., Yamagishi, J., & MacDonald, K. (2016) · University of Edinburgh. The Centre for Speech Technology Research (CSTR)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 434