Most cited computer science papers
- Verified
Introducing OCTAVE Allegro: Improving the Information Security Risk Assessment Process
Caralli, R. A., Stevens, J. F., Young, L. R., & Wilson, W. R. (2007)
Information and Cyber SecurityWeb Application Security Vulnerabilities
Cited by 294
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Generative AI for Software Practitioners
Ebert, C., & Louridas, P. (2023) · IEEE Software
Software Engineering ResearchSoftware Engineering Techniques and Practices
Cited by 293
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On Aliased Resizing and Surprising Subtleties in GAN Evaluation
Parmar, G., Zhang, R., & Zhu, J.-Y. (2021) · arXiv
Generative Adversarial Networks and Image SynthesisImage and Video Quality Assessment
Cited by 293
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Responsible AI—two frameworks for ethical design practice
Peters, D., Vold, K., Robinson, D., & Calvo, R. A. (2020) · IEEE Transactions on Technology and Society
Ethics and Social Impacts of AIFlooding and Environmental Impact
Cited by 293
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Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023) · arXiv
Ethics and Social Impacts of AIEmbodied and Extended Cognition
Cited by 293
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Quality-aware images
Wang, Z., Wu, G., Sheikh, H. R., Simoncelli, E. P., Yang, E.-H., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing
Advanced Steganography and Watermarking TechniquesImage and Video Quality Assessment
Cited by 293
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Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection
Kanjo, E., Younis, E. M. G., & Ang, C. S. (2019) · Information Fusion
Emotion and Mood RecognitionContext-Aware Activity Recognition Systems
Cited by 293
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Continuous Authentication for Voice Assistants
Feng, H., Fawaz, K., & Shin, K. G. (2017) · Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking
User Authentication and Security SystemsBiometric Identification and Security
Cited by 293
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A review of secure and privacy-preserving medical data sharing
Jin, H., Luo, Y., Li, P., & Mathew, J. (2019) · IEEE Access
Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning
Cited by 293
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Generative AI for economic research: use cases and implications for economists
Korinek, A. (2023) · Journal of Economic Literature
Artificial Intelligence ApplicationsComputational and Text Analysis Methods
Cited by 293
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Split menus: effectively using selection frequency to organize menus
Sears, A., & Shneiderman, B. (1994) · ACM Transactions on Computer-Human Interaction
Usability and User Interface DesignInteractive and Immersive Displays
Cited by 293
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Pervasive label errors in test sets destabilize machine learning benchmarks
Northcutt, C. G., Athalye, A., & Mueller, J. (2021) · arXiv
Machine Learning and Data ClassificationAdversarial Robustness in Machine Learning
Cited by 293
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Software Architecture Optimization Methods: A Systematic Literature Review
Aleti, A., Buhnova, B., Grunske, L., Koziolek, A., & Meedeniya, I. (2013) · IEEE Transactions on Software Engineering
Advanced Software Engineering MethodologiesModel-Driven Software Engineering Techniques
Cited by 293
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Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout
Tan, H., Yu, L., & Bansal, M. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Natural Language Processing TechniquesTopic Modeling
Cited by 293
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Developing iot applications in the fog: a distributed dataflow approach
Giang, N. K., Blackstock, M., Lea, R., & Leung, V. C. M. (2015) · 2015 5th International Conference on the Internet of Things (IOT)
IoT and Edge/Fog ComputingArduino and IoT Applications
Cited by 293
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Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering
Fan, C., Zhang, X., Zhang, S., Wang, W., Zhang, C., & Huang, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition
Cited by 293
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w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training
Chung, Y.-A., Zhang, Y., Han, W., Chiu, C.-C., Qin, J., Pang, R., & Wu, Y. (2021) · 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Speech Recognition and SynthesisSpeech and Audio Processing
Cited by 293
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Spreading activation models for trust propagation
Ziegler, C.-N., & Lausen, G. (2004) · IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004
Access Control and TrustSemantic Web and Ontologies
Cited by 293
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A Simple Method for Commonsense Reasoning
Trinh, T. H., & Le, Q. V. (2018) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 293
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A tutorial introduction to the minimum description length principle
Grunwald, P. (2004) · arXiv
Computability, Logic, AI AlgorithmsBayesian Modeling and Causal Inference
Cited by 293
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Efficient Toffoli gates using qudits
Ralph, T. C., Resch, K. J., & Gilchrist, A. (2007) · Physical Review A
Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography
Cited by 293
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A multi-modal open dataset for mental-disorder analysis
Cai, H., Yuan, Z., Gao, Y., Sun, S., Li, N., Tian, F., Xiao, H., Li, J., Yang, Z., Li, X., et al. (2022) · Scientific Data
Emotion and Mood RecognitionEEG and Brain-Computer Interfaces
Cited by 293
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Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Kendall, A., Badrinarayanan, V., & Cipolla, R. (2015) · arXiv
Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning
Cited by 292
- Verified
Are deep neural networks the best choice for modeling source code?
Hellendoorn, V. J., & Devanbu, P. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering
Software Engineering ResearchNatural Language Processing Techniques
Cited by 292
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