Check computer science papers: citations, claims and retractions

Generating Together: Lessons Learned from Developing an Educational Visual Novel with AI Collaboration

Shields, S., Calderwood, A., Johnson-Bey, S., Wardrip-Fruin, N., Mateas, M., & Melcer, E. (2024) · 2024 IEEE Conference on Games (CoG)

Artificial Intelligence in GamesEducational Games and Gamification

Cited by 11

Amygdalar shape analysis method using surface contour aligning, spherical mapping, and probabilistic subregional segmentation

Kim, N., Kim, H. J., Hwang, J., Yoon, S. J., Cho, H. B., Renshaw, P. F., Lyoo, I. K., & Kim, J. E. (2011) · Neuroscience Letters

Medical Image Segmentation TechniquesMorphological variations and asymmetry

Cited by 11

Vibe coding: programming through conversation with artificial intelligence

Sarkar, A., & Drosos, I. (2025) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 11

QoE Driven Server Selection for VoD in the Cloud

Wang, C., Kim, H., & Morla, R. (2015) · 2015 IEEE 8th International Conference on Cloud Computing

Caching and Content DeliveryImage and Video Quality Assessment

Cited by 11

Toward Accountable and Explainable Artificial Intelligence Part Two: The Framework Implementation

Vice, J., & Khan, M. M. (2022) · IEEE Access

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 11

Student Query Trend Assessment with Semantical Annotation and Artificial Intelligent Multi-Agents

Malik, K. R., Mir, R. R., Farhan, M., Rafiq, T., & Aslam, M. (2017) · EURASIA Journal of Mathematics, Science and Technology Education

Semantic Web and OntologiesNatural Language Processing Techniques

Cited by 11

The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction

Baldassarre, G., Santucci, V. G., Cartoni, E., & Caligiore, D. (2017) · Behavioral and Brain Sciences

Psychiatry, Mental Health, NeuroscienceCognitive Computing and Networks

Cited by 11

Biometrics-based identifiers for digital identity management

Bhargav-Spantzel, A., Squicciarini, A., Bertino, E., Kong, X., & Zhang, W. (2010) · Proceedings of the 9th Symposium on Identity and Trust on the Internet

Biometric Identification and SecurityUser Authentication and Security Systems

Cited by 11

A Theoretical Framework for Estimating False Acceptance Rate of PRNU-Based Camera Identification

Saito, S., Tomioka, Y., & Kitazawa, H. (2017) · IEEE Transactions on Information Forensics and Security

Digital Media Forensic DetectionImage and Video Quality Assessment

Cited by 11

Using Conversational Agents To Support Learning By Teaching

Chhibber, N., & Law, E. (2019) · arXiv

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 11

Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs

Singh, A. K., & Strouse, D. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 11

Penggunaan Media Pembelajaran Berbasis Digital dalam Pembelajaran Sejarah untuk Meningkatkan Minat Belajar Peserta Didik

Putri, A. E. (2024) · JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah

Educational Technology and E-LearningEducation and Technology Integration

Cited by 11

AgentScope: A Flexible yet Robust Multi-Agent Platform

Gao, D., Li, Z., Pan, X., Kuang, W., Ma, Z., Qian, B., Wei, F., Zhang, W., Xie, Y., Chen, D., et al. (2024) · arXiv

Multi-Agent Systems and NegotiationArtificial Intelligence Applications

Cited by 11

Leveraging Special-Purpose Hardware for Local Search Heuristics

Liu, X., Ushijima-Mwesigwa, H., Mandal, A., Upadhyay, S., Safro, I., & Roy, A. (2019) · arXiv

Quantum Computing Algorithms and ArchitectureMetaheuristic Optimization Algorithms Research

Cited by 11

Large Language Models can Strategically Deceive their Users when Put Under Pressure

Scheurer, J., Balesni, M., & Hobbhahn, M. (2023) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 11

A predictive-coding network that is both discriminative and generative

Sun, W., & Orchard, J. (2020) · Neural Computation

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 11

Calibrating the Confidence of Large Language Models by Eliciting Fidelity

Zhang, M., Huang, M., Shi, R., Guo, L., Peng, C., Yan, P., Zhou, Y., & Qiu, X. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 11

A Performance Comparison of Containers and Unikernels for Reliable 5G Environments

Filipe, J. B., Meneses, F., Rehman, A. U., Corujo, D., & Aguiar, R. L. (2019) · 2019 15th International Conference on the Design of Reliable Communication Networks (DRCN)

Software-Defined Networks and 5GCloud Computing and Resource Management

Cited by 11

The risk of algorithmic injustice for interactive learning environments

Rospigliosi, P. A. (2021) · Interactive Learning Environments

Intelligent Tutoring Systems and Adaptive LearningEthics and Social Impacts of AI

Cited by 10

HunyuanVideo: A Systematic Framework For Large Video Generative Models

Kong, W., Tian, Q., Zhang, Z., Min, R., Dai, Z., Zhou, J., Xiong, J., Li, X., Wu, B., Zhang, J., et al. (2024) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 10

Enhancing locally adaptive smoothing of graph neural networks via Laplacian node disagreement

Wang, Y., Hu, L., Cao, X., Chang, Y., & Tsang, I. W. (2024) · IEEE Transactions on Knowledge and Data Engineering

Advanced Graph Neural NetworksGraph Theory and Algorithms

Cited by 10

IIoT-ARAS: IIoT/ICS Automated Risk Assessment System for Prediction and Prevention

Zahran, B., Hussaini, A., & Ali-Gombe, A. (2021) · Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy

Information and Cyber SecurityNetwork Security and Intrusion Detection

Cited by 10

Balancing of competitive two-player Game Levels with Reinforcement Learning

Rupp, F., Eberhardinger, M., & Eckert, K. (2023) · 2023 IEEE Conference on Games (CoG)

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 10

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10