Check computer science papers: citations, claims and retractions

Automatically Measuring Question Authenticity in Real-World Classrooms

Kelly, S., Olney, A. M., Donnelly, P., Nystrand, M., & D’Mello, S. K. (2018) · Educational Researcher

Online Learning and AnalyticsComputational and Text Analysis Methods

Cited by 114

Edge Computing, IoT and Social Computing in Smart Energy Scenarios

Sittón-Candanedo, I., Alonso, R. S., García, Ó., Muñoz, L., & Rodríguez-González, S. (2019) · Sensors

IoT and Edge/Fog ComputingEnergy Efficiency in Computing

Cited by 114

The philosophical basis of algorithmic recourse

Venkatasubramanian, S., & Alfano, M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 114

SqueezeBERT: What can computer vision teach NLP about efficient neural networks?

Iandola, F., Shaw, A., Krishna, R., & Keutzer, K. (2020) · Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

Advanced Neural Network ApplicationsTopic Modeling

Cited by 114

Toward a Perspectivist Turn in Ground Truthing for Predictive Computing

Cabitza, F., Campagner, A., & Basile, V. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence

Mobile Crowdsensing and CrowdsourcingMachine Learning and Data Classification

Cited by 114

A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

Shidik, G. F., Noersasongko, E., Nugraha, A., Andono, P. N., Jumanto, J., & Kusuma, E. J. (2019) · IEEE Access

Video Surveillance and Tracking MethodsAdvanced Neural Network Applications

Cited by 114

Memory-aware curriculum federated learning for breast cancer classification

Jiménez-Sánchez, A., Tardy, M., González Ballester, M. A., Mateus, D., & Piella, G. (2023) · Computer Methods and Programs in Biomedicine

AI in cancer detectionPrivacy-Preserving Technologies in Data

Cited by 114

SpecInfer: Accelerating Large Language Model Serving with Tree-based Speculative Inference and Verification

Miao, X., Oliaro, G., Zhang, Z., Cheng, X., Wang, Z., Zhang, Z., Wong, R. Y. Y., Zhu, A., Yang, L., Shi, X., et al. (2024) · Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3

Natural Language Processing TechniquesTopic Modeling

Cited by 114

Addressing AI algorithmic bias in health care

Ratwani, R. M., Sutton, K., & Galarraga, J. E. (2024) · JAMA

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 114

Over-the-air Function Computation in Sensor Networks

Abari, O., Rahul, H., & Katabi, D. (2016) · arXiv

Distributed Sensor Networks and Detection AlgorithmsEnergy Efficient Wireless Sensor Networks

Cited by 114

Is ChatGPT the Ultimate Programming Assistant -- How far is it?

Tian, H., Lu, W., Li, T. O., Tang, X., Cheung, S.-C., Klein, J., & Bissyandé, T. F. (2023) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 114

Efficient quantum circuits for diagonal unitaries without ancillas

Welch, J., Greenbaum, D., Mostame, S., & Aspuru-Guzik, A. (2014) · New Journal of Physics

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 114

Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming

Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 114

Team-partitioned, opaque-transition reinforcement learning

Stone, P., & Veloso, M. (1999) · Proceedings of the third annual conference on Autonomous Agents

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 114

A Reliability Analysis of a Deep Neural Network

Bosio, A., Bernardi, P., Ruospo, A., & Sanchez, E. (2019) · 2019 IEEE Latin American Test Symposium (LATS)

Adversarial Robustness in Machine LearningVLSI and Analog Circuit Testing

Cited by 114

Large-Scale Distributed Dedicated- and Non-Dedicated Smart City Sensing Systems

Habibzadeh, H., Qin, Z., Soyata, T., & Kantarci, B. (2017) · IEEE Sensors Journal

Mobile Crowdsensing and CrowdsourcingSmart Cities and Technologies

Cited by 114

Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder

Du, M., Pentyala, S., Li, Y., & Hu, X. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 114

Assessing student perceptions and use of instructor versus AI ‐generated feedback

Er, E., Akçapınar, G., Bayazıt, A., Noroozi, O., & Banihashem, S. K. (2025) · British Journal of Educational Technology

Teaching and Learning ProgrammingStudent Assessment and Feedback

Cited by 114

Approaches, Challenges, and Applications for Deep Visual Odometry: Toward Complicated and Emerging Areas

Wang, K., Ma, S., Chen, J., Ren, F., & Lu, J. (2022) · IEEE Transactions on Cognitive and Developmental Systems

Robotics and Sensor-Based LocalizationAdvanced Vision and Imaging

Cited by 114

Benchmarking Advantage and D-Wave 2000Q quantum annealers with exact cover problems

Willsch, D., Willsch, M., Gonzalez Calaza, C. D., Jin, F., De Raedt, H., Svensson, M., & Michielsen, K. (2022) · Quantum Information Processing

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 114

PyQUBO: Python Library for Mapping Combinatorial Optimization Problems to QUBO Form

Zaman, M., Tanahashi, K., & Tanaka, S. (2021) · arXiv

Quantum Computing Algorithms and ArchitectureMetaheuristic Optimization Algorithms Research

Cited by 114

Advancing AI interpretability in medical imaging: a comparative analysis of pixel-level interpretability and grad-CAM models

Ennab, M., & Mcheick, H. (2025) · Machine Learning and Knowledge Extraction

Explainable Artificial Intelligence (XAI)COVID-19 diagnosis using AI

Cited by 114

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks

Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., & Hashimoto, T. (2023) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 114

An All-Digital Unified Physically Unclonable Function and True Random Number Generator Featuring Self-Calibrating Hierarchical Von Neumann Extraction in 14-nm Tri-gate CMOS

Satpathy, S. K., Mathew, S. K., Kumar, R., Suresh, V., Anders, M. A., Kaul, H., Agarwal, A., Hsu, S., Krishnamurthy, R. K., & De, V. (2019) · IEEE Journal of Solid-State Circuits

Physical Unclonable Functions (PUFs) and Hardware SecurityLow-power high-performance VLSI design

Cited by 114