Check computer science papers: citations, claims and retractions

Parameterized logarithmic framework for image enhancement

Panetta, K., Agaian, S., Zhou, Y., & Wharton, E. J. (2011) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)

Image Enhancement TechniquesAdvanced Image Processing Techniques

Cited by 143

Explaining the Explainer: A First Theoretical Analysis of LIME

Garreau, D., & von Luxburg, U. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 143

Large gradients via correlation in random parameterized quantum circuits

Volkoff, T., & Coles, P. J. (2021) · Quantum Science and Technology

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 143

Realizing the potential of blockchain technologies in genomics

Ozercan, H. I., Ileri, A. M., Ayday, E., & Alkan, C. (2018) · Genome Research

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 143

SupermarQ: A Scalable Quantum Benchmark Suite

Tomesh, T., Gokhale, P., Omole, V., Ravi, G. S., Smith, K. N., Viszlai, J., Wu, X.-C., Hardavellas, N., Martonosi, M. R., & Chong, F. T. (2022) · arXiv

Quantum Computing Algorithms and ArchitectureParallel Computing and Optimization Techniques

Cited by 143

SoK: Computer-Aided Cryptography

Barbosa, M., Barthe, G., Bhargavan, K., Blanchet, B., Cremers, C., Liao, K., & Parno, B. (2021) · 2021 IEEE Symposium on Security and Privacy (SP)

Cryptographic Implementations and SecurityCryptography and Data Security

Cited by 143

Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations

Sajjadi, M. S. M., Meyer, H., Pot, E., Bergmann, U., Greff, K., Radwan, N., Vora, S., Lucic, M., Duckworth, D., Dosovitskiy, A., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

3D Shape Modeling and AnalysisAdvanced Vision and Imaging

Cited by 143

Habitat 2.0: Training home assistants to rearrange their habitat

Szot, A., Clegg, A., Undersander, E., Wijmans, E., Zhao, Y., Turner, J., Maestre, N., Mukadam, M., Chaplot, D., Maksymets, O., et al. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 143

An empirical study of build maintenance effort

McIntosh, S., Adams, B., Nguyen, T. H. D., Kamei, Y., & Hassan, A. E. (2011) · Proceedings of the 33rd International Conference on Software Engineering

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 143

The Role of Self-Regulation in Programming Problem Solving Process and Success

Loksa, D., & Ko, A. J. (2016) · Proceedings of the 2016 ACM Conference on International Computing Education Research

Teaching and Learning ProgrammingInnovative Teaching and Learning Methods

Cited by 143

End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks

Wang, Y., Wang, L., Wang, H., & Li, P. (2019) · IEEE Access

Advanced Image Processing TechniquesAdvanced Neural Network Applications

Cited by 143

Self-Sovereign Identity in a Globalized World: Credentials-Based Identity Systems as a Driver for Economic Inclusion

Wang, F., & De Filippi, P. (2020) · Frontiers in Blockchain

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 143

Pleasant music as a countermeasure against visually induced motion sickness

Keshavarz, B., & Hecht, H. (2014) · Applied Ergonomics

Virtual Reality Applications and ImpactsNausea and vomiting management

Cited by 143

Clustered federated learning with adaptive local differential privacy on heterogeneous IoT data

He, Z., Wang, L., & Cai, Z. (2024) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataInternet Traffic Analysis and Secure E-voting

Cited by 143

What does the public think about artificial intelligence?—A criticality map to understand bias in the public perception of AI

Brauner, P., Hick, A., Philipsen, R., & Ziefle, M. (2023) · Frontiers in Computer Science

Ethics and Social Impacts of AIRisk Perception and Management

Cited by 143

A Comprehensive Study of Activity Recognition Using Accelerometers

Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · Informatics

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 143

Arbitrary quantum control of qubits in the presence of universal noise

Green, T. J., Sastrawan, J., Uys, H., & Biercuk, M. J. (2013) · New Journal of Physics

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 143

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Khan, W., Kumar, T., Zhang, C., Raj, K., Roy, A. M., & Luo, B. (2023) · Big Data and Cognitive Computing

Cloud Computing and Resource ManagementAdvanced Database Systems and Queries

Cited by 143

The Impact of Large Language Models on Programming Education and Student Learning Outcomes

Jošt, G., Taneski, V., & Karakatič, S. (2024) · Applied Sciences

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 143

Deepfakes Detection Techniques Using Deep Learning: A Survey

Almars, A. M. (2021) · Journal of Computer and Communications

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 143

Evaluating Verifiability in Generative Search Engines

Liu, N., Zhang, T., & Liang, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Information Retrieval and Search BehaviorMisinformation and Its Impacts

Cited by 143

Considerations for AI fairness for people with disabilities

Trewin, S., Basson, S., Muller, M., Branham, S., Treviranus, J., Gruen, D., Hebert, D., Lyckowski, N., & Manser, E. (2019) · AI Matters

Ethics and Social Impacts of AIDigital Accessibility for Disabilities

Cited by 143

Combining eye gaze input with a brain–computer interface for touchless human–computer interaction

Zander, T. O., Gaertner, M., Kothe, C., & Vilimek, R. (2010) · International Journal of Human–Computer Interaction

Gaze Tracking and Assistive TechnologyEEG and Brain-Computer Interfaces

Cited by 143

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Geiger, M., Spigler, S., d'Ascoli, S., Sagun, L., Baity-Jesi, M., Biroli, G., & Wyart, M. (2019) · Physical Review E

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 143