Check computer science papers: citations, claims and retractions

The state of the art of agile kanban method: challenges and opportunities

Alaidaros, H., Omar, M., & Romli, R. (2021) · Independent Journal of Management & Production

Software Engineering Techniques and PracticesQuality and Supply Management

Cited by 33

Using collaborative, modified peer led team learning to improve student success and retention in intro cs

Biggers, M., Yilmaz, T., & Sweat, M. (2009) · Proceedings of the 40th ACM technical symposium on Computer science education

Teaching and Learning ProgrammingProblem and Project Based Learning

Cited by 33

Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art

Lovato, J., Zimmerman, J. W., Smith, I., Dodds, P., & Karson, J. L. (2024) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AILaw, AI, and Intellectual Property

Cited by 33

BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Huang, W., Liu, Y., Qin, H., Li, Y., Zhang, S., Liu, X., Magno, M., & Qi, X. (2024) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 33

The Emotional Roller Coaster of Responding to Requirements Changes in Software Engineering

Madampe, K., Hoda, R., & Grundy, J. (2023) · IEEE Transactions on Software Engineering

Software Engineering Techniques and PracticesSoftware Engineering Research

Cited by 33

Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data

Clark, A. M., Williams, A. J., & Ekins, S. (2015) · Journal of Cheminformatics

Research Data Management PracticesScientific Computing and Data Management

Cited by 33

Α Quantum Pattern Recognition Method for Improving Pairwise Sequence Alignment

Prousalis, K., & Konofaos, N. (2019) · Scientific Reports

Quantum Computing Algorithms and ArchitectureAlgorithms and Data Compression

Cited by 33

Understanding User Perceptions of Proactive Smart Speakers

Wei, J., Dingler, T., & Kostakos, V. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Innovative Human-Technology InteractionSpeech and dialogue systems

Cited by 33

Virtual Reality-Based Interventions for Patients with Paranoia: A Systematic Review

Monaghesh, E., Samad-Soltani, T., & Farhang, S. (2022) · Psychiatry Research

Virtual Reality Applications and ImpactsSchizophrenia research and treatment

Cited by 33

Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets

Caiafa, C. F., Solé-Casals, J., Marti-Puig, P., Zhe, S., & Tanaka, T. (2020) · Applied Sciences

Machine Learning and Data ClassificationNeural Networks and Applications

Cited by 33

Dialect prejudice predicts AI decisions about people's character, employability, and criminality

Hofmann, V., Kalluri, P. R., Jurafsky, D., & King, S. (2024) · arXiv

Ethics and Social Impacts of AILinguistic Variation and Morphology

Cited by 33

Efficient Exact Verification of Binarized Neural Networks

Jia, K., & Rinard, M. (2020) · arXiv

Adversarial Robustness in Machine LearningFormal Methods in Verification

Cited by 33

Facial Expression Recognition using Encoded Dynamic Features

Yang, P., Liu, Q., & Metaxas, D. N. (2007) · Multimedia and Expo, 2007 IEEE International Conference on

Face and Expression RecognitionEmotion and Mood Recognition

Cited by 33

DTATrans: Leveraging Dynamic Token-Based Quantization With Accuracy Compensation Mechanism for Efficient Transformer Architecture

Yang, T., Ma, F., Li, X., Liu, F., Zhao, Y., He, Z., & Jiang, L. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Advanced Neural Network ApplicationsLow-power high-performance VLSI design

Cited by 33

Detection of adversarial examples in text classification: Benchmark and baseline via robust density estimation

Yoo, K., Kim, J., Jang, J., & Kwak, N. (2022) · Findings of the Association for Computational Linguistics: ACL 2022

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 33

Cryptographic transistor for true random number generator with low power consumption

Kim, S.-I., You, H.-J., Kim, M.-S., An, U.-S., Kim, M.-S., Lee, D. H., Ryu, S.-T., & Choi, Y.-K. (2024) · Science Advances

Physical Unclonable Functions (PUFs) and Hardware SecurityAnalog and Mixed-Signal Circuit Design

Cited by 33

Roles and Utilization of Attention Heads in Transformer-based Neural Language Models

Jo, J.-Y., & Myaeng, S.-H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 33

Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?

Laugros, A., Caplier, A., & Ospici, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 33

A Trusted Approach for Decentralised and Privacy-Preserving Identity Management

Moreno, R. T., Garcia-Rodriguez, J., Bernabe, J. B., & Skarmeta, A. (2021) · IEEE Access

Blockchain Technology Applications and SecurityAccess Control and Trust

Cited by 33

Finding Friend and Foe in Multi-Agent Games

Serrino, J., Kleiman-Weiner, M., Parkes, D. C., & Tenenbaum, J. B. (2019) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 33

Development of an 360-degree virtual reality video-based immersive cycle training system for physical enhancement in older adults: a feasibility study: Development of immersive virtual cycle for older adults

Lee, N., Choi, W., & Lee, S. (2021) · BMC Geriatrics

Virtual Reality Applications and ImpactsStroke Rehabilitation and Recovery

Cited by 33

Technology evaluations are associated with psychological need satisfaction across different spheres of experience: an application of the METUX scales

Burnell, R., Peters, D., Ryan, R. M., & Calvo, R. A. (2023) · Frontiers in Psychology

Innovative Human-Technology InteractionTechnology Use by Older Adults

Cited by 33

Layer-Based Communication-Efficient Federated Learning with Privacy Preservation

LIAN, Z., WANG, W., HUANG, H., & SU, C. (2022) · IEICE Transactions on Information and Systems

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 33

BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search

White, C., Neiswanger, W., & Savani, Y. (2019) · arXiv

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 33