Check computer science papers: citations, claims and retractions

Encode-Store-Retrieve: Augmenting Human Memory through Language-Encoded Egocentric Perception

Shen, J., Dudley, J., & Kristensson, P. O. (2023) · arXiv

Multimodal Machine Learning ApplicationsInformation Retrieval and Search Behavior

Cited by 10

Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens

Li, Z., Zhao, M., Das, D., ZHAO, H., Ma, Y., Liu, W., Beaudouin-Lafon, M., Wang, F., Ramakrishnan, IV, & Bi, X. (2022) · CHI Conference on Human Factors in Computing Systems

Interactive and Immersive DisplaysUsability and User Interface Design

Cited by 10

An Empirical Study of NetOps Capability of Pre-Trained Large Language Models

Miao, Y., Bai, Y., Chen, L., Li, D., Sun, H., Wang, X., Luo, Z., Ren, Y., Sun, D., Xu, X., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle

Dohmke, T., Iansiti, M., & Richards, G. (2023) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 10

A Lightweight Intrusion Detection System for Internet of Things: Clustering and Monte Carlo Cross-Entropy Approach

Almalawi, A. (2025) · Sensors

Network Security and Intrusion DetectionInternet of Things and AI

Cited by 10

Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

Shao, Y., Jiang, Y., Kanell, T. A., Xu, P., Khattab, O., & Lam, M. S. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

Deep Learning from Crowds

Rodrigues, F., & Pereira, F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Mobile Crowdsensing and CrowdsourcingMachine Learning and Data Classification

Cited by 10

“Try, Try, Try Again:” Sequence Analysis of User Interaction Data with a Voice User Interface

Myers, C. M., Laris Pardo, L. F., Acosta-Ruiz, A., Canossa, A., & Zhu, J. (2021) · CUI 2021 - 3rd Conference on Conversational User Interfaces

Speech and dialogue systemsUsability and User Interface Design

Cited by 10

RoomDesigner: Encoding Anchor-latents for Style-consistent and Shape-compatible Indoor Scene Generation

Zhao, Y., Zhao, Z., Li, J., Dong, S., & Gao, S. (2024) · 2024 International Conference on 3D Vision (3DV)

3D Shape Modeling and AnalysisGenerative Adversarial Networks and Image Synthesis

Cited by 10

Chain of Risks Evaluation (CORE): A framework for safer large language models in public mental health

Li, L., Kong, S., Zhao, H., Li, C., Teng, Y., & Wang, Y. (2025) · Psychiatry and Clinical Neurosciences

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 10

S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents

Gao, C., Lan, X., Lu, Z., Mao, J., Piao, J., Wang, H., Jin, D., & Li, Y. (2023) · arXiv

Multi-Agent Systems and NegotiationOpinion Dynamics and Social Influence

Cited by 10

Estimation of facial action intensities on 2d and 3D data

Bilge, M., Sankur, B., & Savran, A. (2011) · Zenodo

Face recognition and analysisEmotion and Mood Recognition

Cited by 10

Learning of Motor Control from Motor Babbling**This research is supported by CREST, JST.

Aoki, T., Nakamura, T., & Nagai, T. (2016) · IFAC-PapersOnLine

Reinforcement Learning in RoboticsMotor Control and Adaptation

Cited by 10

Internet of Things Pillow Detecting Sleeping Quality

Hsieh, Y.-Z. (2018) · 2018 1st International Cognitive Cities Conference (IC3)

Context-Aware Activity Recognition SystemsInternet of Things and AI

Cited by 10

IoT botnet detection using deep learning

Rabhi, S., Abbes, T., & Zarai, F. (2023) · 2023 International Wireless Communications and Mobile Computing (IWCMC)

Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-voting

Cited by 10

On the relevance of blockchain in identity management

Grüner, A., Mühle, A., & Meinel, C. (2018) · arXiv

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 10

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Kumar, S., Correa, C. G., Dasgupta, I., Marjieh, R., Hu, M. Y., Hawkins, R., Cohen, J. D., Daw, N., Narasimhan, K., & Griffiths, T. (2022) · Advances in Neural Information Processing Systems 35

Natural Language Processing TechniquesMachine Learning and Algorithms

Cited by 10

Demilitarized network to secure the data stored in industrial networks

Nuñez Alvarez, J. R., Zamora, Y. P., Pina, I. B., & Angarita, E. N. (2021) · International Journal of Electrical and Computer Engineering (IJECE)

Network Security and Intrusion DetectionCybersecurity and Information Systems

Cited by 10

Enhancing the Robustness of AI-Generated Text Detectors: A Survey

Liu, X., Li, Y., & Li, K. (2025) · Mathematics

Authorship Attribution and ProfilingAdversarial Robustness in Machine Learning

Cited by 10

A Low-Cost Tracking System for Running Race Applications Based on Bluetooth Low Energy Technology

Perez-Diaz-de-Cerio, D., Hernández-Solana, Á., Valdovinos, A., & Valenzuela, J. (2018) · Sensors

Bluetooth and Wireless Communication TechnologiesIndoor and Outdoor Localization Technologies

Cited by 10

Computational approaches in rigorous sociology: agent-based computational modeling and computational social science

Flache, A., Mäs, M., & Keijzer, M. A. (2022) · Handbook of Sociological Science

Multi-Agent Systems and NegotiationSimulation Techniques and Applications

Cited by 10

Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets

Sharafutdinov, K., Bhat, J. S., Fritsch, S. J., Nikulina, K., E. Samadi, M., Polzin, R., Mayer, H., Marx, G., Bickenbach, J., & Schuppert, A. (2022) · Frontiers in Big Data

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 10

Prediction-Oriented Bayesian Active Learning

Smith, F. B., Kirsch, A., Farquhar, S., Gal, Y., Foster, A., & Rainforth, T. (2023) · arXiv

Machine Learning and AlgorithmsGaussian Processes and Bayesian Inference

Cited by 10