Check computer science papers: citations, claims and retractions

A Plan-Based Personality Model for Story Characters

Shirvani, A., & Ware, S. G. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

Artificial Intelligence in GamesNarrative Theory and Analysis

Cited by 9

Activity detection in uncontrolled free-living conditions using a single accelerometer

Lee, S. I., Ozsecen, M. Y., Della Toffola, L., Daneault, J.-F., Puiatti, A., Patel, S., & Bonato, P. (2015) · 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 9

Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML

van de Water, R., Schmidt, H., Elbers, P., Thoral, P., Arnrich, B., & Rockenschaub, P. (2023) · arXiv

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 9

A Novel Algorithm for Computer-Assisted Measurement of Cervical Length from Transvaginal Ultrasound Images

Wu, M., Fraser, R. F., & Chen, C. W. (2004) · IEEE Transactions on Information Technology in Biomedicine

Medical Image Segmentation TechniquesMedical Imaging and Analysis

Cited by 9

A review of two-factor authentication: suggested security effort moves to mandatory

Pomputius, A. F. (2018) · Medical Reference Services Quarterly

User Authentication and Security SystemsInformation and Cyber Security

Cited by 9

Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection

Yang, L., Zheng, Z., Chen, B., Zhao, Z., Lin, C., & Shen, C. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 9

LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis

Lee, J., Jeong, Y., Han, T., & Lee, T. (2025) · Applied Sciences

Network Security and Intrusion DetectionSoftware System Performance and Reliability

Cited by 9

In-context learning with retrieved demonstrations for language models: A survey

Luo, M., Xu, X., Liu, Y., Pasupat, P., & Kazemi, M. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 9

SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security

Liu, Z. (2023) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 9

FOCUS: object-centric world models for robotic manipulation

Ferraro, S., Mazzaglia, P., Verbelen, T., & Dhoedt, B. (2025) · Frontiers in Neurorobotics

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 9

Recent Advances in Object Detection Based on YOLO‐V4 and Faster RCNN: A Review

Das, A., Nandi, A., & Deb, I. (2024) · Mathematical Modeling for Computer Applications

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 9

EXCALIBUR: Encouraging and Evaluating Embodied Exploration

Zhu, H., Kapoor, R., Min, S. Y., Han, W., Li, J., Geng, K., Neubig, G., Bisk, Y., Kembhavi, A., & Weihs, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 9

Recalibrated Bandpass Filtering On Temporal Waveform For Audio Spoof Detection

Ren, Y., Liu, W., Liu, D., & Wang, L. (2021) · 2021 IEEE International Conference on Image Processing (ICIP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 9

What BERT Sees: Cross-Modal Transfer for Visual Question Generation

Scialom, T., Bordes, P., Dray, P.-A., Staiano, J., & Gallinari, P. (2020) · Proceedings of the 13th International Conference on Natural Language Generation

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 9

AgentBound: Securing Execution Boundaries of AI Agents

Bühler, C., Biagiola, M., Di Grazia, L., & Salvaneschi, G. (2026) · Proceedings of the ACM on Software Engineering

Access Control and TrustSecurity and Verification in Computing

Cited by 9

“Exercise difficulty prediction in online education systems,”

Fang, J., Zhao, W., & Jia, D. (2019) · 2019 International Conference on Data Mining Workshops (ICDMW)

Online Learning and AnalyticsMultimodal Machine Learning Applications

Cited by 9

Ubiquitous Distributed Deep Reinforcement Learning at the Edge: Analyzing Byzantine Agents in Discrete Action Spaces

Zhao, W., Queralta, J. P., Qingqing, L., & Westerlund, T. (2020) · Procedia Computer Science

Reinforcement Learning in RoboticsIoT and Edge/Fog Computing

Cited by 9

A Scaffolded GenAI Lab in Early Undergraduate CS: A Mixed-Methods, Multi-Course Evaluation

Dickey, E., Bejarano, A., Kuperus, R., & Fagundes, B. (2025) · arXiv

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 9

A Blockchain-Based Know Your Customer and Digital Identity Verification Framework for Cross-Border Financial Compliance

Omoegun, G. O., Fadayomi, O., Bello, A. D., Elebe, O., & Hammed, N. I. (2022) · International Journal of Multidisciplinary Research and Growth Evaluation

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 9

Language-Models-as-a-Service: Overview of a New Paradigm and its Challenges

La Malfa, E., Petrov, A., Frieder, S., Weinhuber, C., Burnell, R., Nazar, R., Cohn, A., Shadbolt, N., & Wooldridge, M. (2024) · Journal of Artificial Intelligence Research

Topic ModelingNatural Language Processing Techniques

Cited by 9

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Bai, Y., Lv, X., Zhang, J., Lyu, H., Tang, J., Huang, Z., Du, Z., Liu, X., Zeng, A., Hou, L., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

Refusal in Language Models Is Mediated by a Single Direction

Arditi, A., Obeso, O., Syed, A., Paleka, D., Panickssery, N., Gurnee, W., & Nanda, N. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 9

Hardware Penetration Testing Knocks Your SoCs Off

Fischer, M., Langer, F., Mono, J., Nasenberg, C., & Albartus, N. (2021) · IEEE Design & Test

Cryptographic Implementations and SecurityPhysical Unclonable Functions (PUFs) and Hardware Security

Cited by 9

A true random number generator with on-line testability

Bohl, E., Lewis, M., & Galkin, S. (2014) · 2014 19th IEEE European Test Symposium (ETS)

Physical Unclonable Functions (PUFs) and Hardware SecurityCryptographic Implementations and Security

Cited by 9