Check computer science papers: citations, claims and retractions

A Study on MITM (Man in the Middle) Vulnerability in Wireless Network Using 802.1X and EAP

Hwang, H., Jung, G., Sohn, K., & Park, S. (2008) · 2008 International Conference on Information Science and Security (ICISS 2008)

Advanced Authentication Protocols SecurityWireless Networks and Protocols

Cited by 69

Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners

Ren, A. Z., Dixit, A., Bodrova, A., Singh, S., Tu, S., Brown, N., Xu, P., Takayama, L., Xia, F., Varley, J., et al. (2023) · arXiv

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 69

A multi-level framework to identify HTTPS services

Shbair, W. M., Cholez, T., Francois, J., & Chrisment, I. (2016) · NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium

Internet Traffic Analysis and Secure E-votingWeb Application Security Vulnerabilities

Cited by 69

Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo

Havasi, M., Hernández-Lobato, J. M., & Murillo-Fuentes, J. J. (2018) · arXiv

Gaussian Processes and Bayesian InferenceMarkov Chains and Monte Carlo Methods

Cited by 69

Aligning Large Language Models with Human: A Survey

Wang, Y., Zhong, W., Li, L., Mi, F., Zeng, X., Huang, W., Shang, L., Jiang, X., & Liu, Q. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 69

Contrastive Learning Reduces Hallucination in Conversations

Sun, W., Shi, Z., Gao, S., Ren, P., De Rijke, M., & Ren, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence

Speech and dialogue systemsTopic Modeling

Cited by 69

Language models, like humans, show content effects on reasoning tasks

Lampinen, A. K., Dasgupta, I., Chan, S. C. Y., Sheahan, H. R., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2024) · PNAS Nexus

Logic, Reasoning, and KnowledgeTopic Modeling

Cited by 69

KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Hooper, C., Kim, S., Mohammadzadeh, H., Mahoney, M. W., Shao, Y. S., Keutzer, K., & Gholami, A. (2024) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 69

A research landscape of agentic AI and large language models: applications, challenges and future directions

Brohi, S., Mastoi, Q.-U.-A., Jhanjhi, N. Z., & Pillai, T. R. (2025) · Algorithms

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 69

QuGAN: A Quantum State Fidelity based Generative Adversarial Network

Stein, S. A., Baheri, B., Chen, D., Mao, Y., Guan, Q., Li, A., Fang, B., & Xu, S. (2021) · 2021 IEEE International Conference on Quantum Computing and Engineering (QCE)

Quantum Computing Algorithms and ArchitectureGenerative Adversarial Networks and Image Synthesis

Cited by 69

Improve software quality through practicing DevOps

Perera, P., Silva, R., & Perera, I. (2017) · 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer)

Software Engineering Techniques and PracticesSoftware System Performance and Reliability

Cited by 69

Deepfake Speech Detection Through Emotion Recognition: A Semantic Approach

Conti, E., Salvi, D., Borrelli, C., Hosler, B., Bestagini, P., Antonacci, F., Sarti, A., Stamm, M. C., & Tubaro, S. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisEmotion and Mood Recognition

Cited by 69

Making Language Models Better Reasoners with Step-Aware Verifier

Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., & Chen, W. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 69

Scoping the Cyber security body of knowledge

Rashid, A., Danezis, G., Chivers, H., Lupu, E., Martin, A., Lewis, M., & Peersman, C. (2018) · IEEE Security & Privacy

Information and Cyber SecurityInformation Systems Education and Curriculum Development

Cited by 69

Spotting segments displaying facial expression from image sequences using HMM

Otsuka, T., & Ohya, J. (2002) · Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition

Face recognition and analysisEmotion and Mood Recognition

Cited by 69

Racism, responsibility and autonomy in HCI: Testing perceptions of an AI agent

Hong, J.-W., & Williams, D. (2019) · Computers in Human Behavior

Ethics and Social Impacts of AISocial Robot Interaction and HRI

Cited by 69

KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Hooper, C., Kim, S., Mohammadzadeh, H., Mahoney, M., Shao, Y., Keutzer, K., & Gholami, A. (2024) · Advances in Neural Information Processing Systems 37

Advanced Neural Network ApplicationsTopic Modeling

Cited by 69

Stance Detection with Collaborative Role-Infused LLM-Based Agents

Lan, X., Gao, C., Jin, D., & Li, Y. (2024) · Proceedings of the International AAAI Conference on Web and Social Media

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 69

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection

Campanella, G., Kumar, N., Nanda, S., Singi, S., Fluder, E., Kwan, R., Muehlstedt, S., Pfarr, N., Schüffler, P. J., Häggström, I., et al. (2025) · Nature Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 69

Large Scale Terrain Generation from Tectonic Uplift and Fluvial Erosion

Cordonnier, G., Braun, J., Cani, M., Benes, B., Galin, É., Peytavie, A., & Guérin, É. (2016) · Computer Graphics Forum

Computer Graphics and Visualization Techniques3D Shape Modeling and Analysis

Cited by 69

Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation

Wang, G., Manhardt, F., Liu, X., Ji, X., & Tombari, F. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsRobot Manipulation and Learning

Cited by 69

Maximum Entropy RL (Provably) Solves Some Robust RL Problems

Eysenbach, B., & Levine, S. (2021) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 69

Risk Averse Robust Adversarial Reinforcement Learning

Pan, X., Seita, D., Gao, Y., & Canny, J. (2019) · 2019 International Conference on Robotics and Automation (ICRA)

Reinforcement Learning in RoboticsAdversarial Robustness in Machine Learning

Cited by 69

Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes

Du, X., Novoa-Laurentiev, J., Plasek, J. M., Chuang, Y.-W., Wang, L., Marshall, G. A., Mueller, S. K., Chang, F., Datta, S., Paek, H., et al. (2024) · eBioMedicine

Machine Learning in HealthcareDementia and Cognitive Impairment Research

Cited by 69