Check computer science papers: citations, claims and retractions

ESPBench: The Enterprise Stream Processing Benchmark

Hesse, G., Matthies, C., Perscheid, M., Uflacker, M., & Plattner, H. (2021) · Proceedings of the ACM/SPEC International Conference on Performance Engineering

Advanced Database Systems and QueriesParallel Computing and Optimization Techniques

Cited by 27

Analytical approximations of fractional delays: Lagrange interpolators and allpass filters

Tassart, S., & Depalle, P. (2002) · 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing

Digital Filter Design and ImplementationAdvanced Adaptive Filtering Techniques

Cited by 27

Using A Visual Programming Environment and Custom Robots to Learn C Programming and K-12 STEM Concepts

Krishnamoorthy, S. P., & Kapila, V. (2016) · Proceedings of the 6th Annual Conference on Creativity and Fabrication in Education

Teaching and Learning ProgrammingEducational Robotics and Engineering

Cited by 27

AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images

Yang, L., Duan, H., Teng, L., Zhu, Y., Liu, X., Hu, M., Min, X., Zhai, G., & Callet, P. L. (2024) · arXiv

Image and Video Quality AssessmentDigital Media Forensic Detection

Cited by 27

Conceptual processing is referenced to the experienced location of the self, not to the location of the physical body

Canzoneri, E., di Pellegrino, G., Herbelin, B., Blanke, O., & Serino, A. (2016) · Cognition

Virtual Reality Applications and ImpactsTactile and Sensory Interactions

Cited by 27

Finding Generalizable Evidence by Learning to Convince Q&A Models

Perez, E., Karamcheti, S., Fergus, R., Weston, J., Kiela, D., & Cho, K. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 27

ARchemist: Aiding Experimental Chemistry Education Using Augmented Reality Technology

Woźniak, M. P., Lewczuk, A., Adamkiewicz, K., Józiewicz, J., Malaya, M., & Ladonski, P. (2020) · Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

Augmented Reality ApplicationsVarious Chemistry Research Topics

Cited by 27

MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback

Yao, Z., Parashar, A., Zhou, H., Jang, W. S., Ouyang, F., Yang, Z., & Yu, H. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 27

Spectrum Efficient Communication for Heterogeneous IoT Networks

Gao, D., Wang, L., & Hu, B. (2022) · IEEE Transactions on Network Science and Engineering

Wireless Networks and ProtocolsEnergy Efficient Wireless Sensor Networks

Cited by 27

Multi-Parametric Fusion of 3D Power Doppler Ultrasound for Fetal Kidney Segmentation Using Fully Convolutional Neural Networks

Weerasinghe, N. H., Lovell, N. H., Welsh, A. W., & Stevenson, G. N. (2021) · IEEE Journal of Biomedical and Health Informatics

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 27

"I Hope This Is Helpful": Understanding Crowdworkers' Challenges and Motivations for an Image Description Task

Simons, R. N., Gurari, D., & Fleischmann, K. R. (2020) · Proceedings of the ACM on Human-Computer Interaction

Mobile Crowdsensing and CrowdsourcingMultimodal Machine Learning Applications

Cited by 27

Data money makers: An ethnographic analysis of a global cryptocurrency community

Caliskan, K. (2022) · The British Journal of Sociology

Blockchain Technology Applications and SecurityFinTech, Crowdfunding, Digital Finance

Cited by 27

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

Zheng, L., Chiang, W.-L., Sheng, Y., Li, T., Zhuang, S., Wu, Z., Zhuang, Y., Li, Z., Lin, Z., Xing, E. P., et al. (2023) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 27

Automated Placenta Segmentation with a Convolutional Neural Network Weighted by Acoustic Shadow Detection

Hu, R., Singla, R., Yan, R., Mayer, C., & Rohling, R. N. (2019) · 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 27

Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models

Bhatt, M., Chennabasappa, S., Nikolaidis, C., Wan, S., Evtimov, I., Gabi, D., Song, D., Ahmad, F., Aschermann, C., Fontana, L., et al. (2023) · arXiv

Adversarial Robustness in Machine LearningWeb Application Security Vulnerabilities

Cited by 27

Eliciting Latent Predictions from Transformers with the Tuned Lens

Belrose, N., Ostrovsky, I., McKinney, L., Furman, Z., Smith, L., Halawi, D., Biderman, S., & Steinhardt, J. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 27

Estimating the Personality of White-Box Language Models

Karra, S. R., Nguyen, S. T., & Tulabandhula, T. (2022) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 27

Evaluation of a Natural Language Processing Approach to Identify Social Determinants of Health in Electronic Health Records in a Diverse Community Cohort

Rouillard, C. J., Nasser, M. A., Hu, H., & Roblin, D. W. (2022) · Medical Care

Machine Learning in HealthcareMedical Coding and Health Information

Cited by 27

On a fast digital method of estimating the autocorrelation of a gaussian stationary process

Jacovitti, G., Neri, A., & Cusani, R. (1984) · IEEE Transactions on Acoustics, Speech, and Signal Processing

Advanced Adaptive Filtering TechniquesAdvanced Signal Processing Techniques

Cited by 27

Poisoning Federated Recommender Systems with Fake Users

Yin, M., Xu, Y., Fang, M., & Gong, N. Z. (2024) · Proceedings of the ACM Web Conference 2024

Privacy-Preserving Technologies in DataRecommender Systems and Techniques

Cited by 27

ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations

Müllner, P., Lex, E., Schedl, M., & Kowald, D. (2023) · ACM Transactions on Intelligent Systems and Technology

Privacy-Preserving Technologies in DataRecommender Systems and Techniques

Cited by 27

Security analysis of cyber-physical systems network infrastructure

Lavrova, D., Poltavtseva, M., & Shtyrkina, A. (2018) · 2018 IEEE Industrial Cyber-Physical Systems (ICPS)

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

Cited by 27

An Experience of Health Technology Assessment in New Models of Care for Subjects with Parkinson’s Disease by Means of a New Wearable Device

Giansanti, D., Maccioni, G., & Morelli, S. (2008) · Telemedicine and e-Health

Context-Aware Activity Recognition SystemsBalance, Gait, and Falls Prevention

Cited by 27

Model poisoning attacks against distributed machine learning systems

Tomsett, R., Chan, K. S., & Chakraborty, S. (2019) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 27