Check computer science papers: citations, claims and retractions

Data mining techniques for software effort estimation: a comparative study

Dejaeger, K., Verbeke, W., Martens, D., & Baesens, B. (2012) · IEEE Transactions on Software Engineering

Software Engineering Techniques and PracticesSoftware Engineering Research

Cited by 224

Practical and provably secure three-factor authentication protocol based on extended chaotic-maps for mobile lightweight devices

Qiu, S., Wang, D., Xu, G., & Kumari, S. (2020) · IEEE Transactions on Dependable and Secure Computing

Advanced Authentication Protocols SecurityUser Authentication and Security Systems

Cited by 224

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

Zhao, S., Cui, J., Sheng, Y., Dong, Y., Liang, X., Chang, E. I., & Xu, Y. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisDigital Media Forensic Detection

Cited by 224

A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face

Lian, H., Lu, C., Li, S., Zhao, Y., Tang, C., & Zong, Y. (2023) · Entropy

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 224

Expression cloning

Noh, J.-Y., & Neumann, U. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques

Human Motion and Animation3D Shape Modeling and Analysis

Cited by 224

Geometric modeling in shape space

Kilian, M., Mitra, N. J., & Pottmann, H. (2007) · ACM SIGGRAPH 2007 papers

3D Shape Modeling and AnalysisComputational Geometry and Mesh Generation

Cited by 224

Subsampled Rényi Differential Privacy and Analytical Moments Accountant

Wang, Y.-X., Balle, B., & Kasiviswanathan, S. (2018) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 224

Aggregating Deep Convolutional Features for Image Retrieval

Babenko, A., & Lempitsky, V. (2015) · arXiv

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 224

Score-based Generative Modeling in Latent Space

Vahdat, A., Kreis, K., & Kautz, J. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 224

Building, Registrating, and Fusing Noisy Visual Maps

Ayache, N., & Faugeras, O. D. (1988) · The International Journal of Robotics Research

Robotics and Sensor-Based LocalizationAdvanced Vision and Imaging

Cited by 224

Federated-learning based privacy preservation and fraud-enabled blockchain IoMT system for healthcare

Lakhan, A., Mohammed, M. A., Nedoma, J., Martinek, R., Tiwari, P., Vidyarthi, A., Alkhayyat, A., & Wang, W. (2023) · IEEE Journal of Biomedical and Health Informatics

Blockchain Technology Applications and SecurityIoT and Edge/Fog Computing

Cited by 224

VQE method: a short survey and recent developments

Fedorov, D. A., Peng, B., Govind, N., & Alexeev, Y. (2022) · Materials Theory

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 224

Defining the undefinable: the black box problem in healthcare artificial intelligence

Wadden, J. J. (2022) · Journal of Medical Ethics

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 224

Bias in machine learning software: why? how? what to do?

Chakraborty, J., Majumder, S., & Menzies, T. (2021) · Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Ethics and Social Impacts of AIImbalanced Data Classification Techniques

Cited by 224

A survey on deep learning for software engineering

Yang, Y., Xia, X., Lo, D., & Grundy, J. (2022) · ACM Computing Surveys

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 223

Disentangling Adversarial Robustness and Generalization

Stutz, D., Hein, M., & Schiele, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningStochastic Gradient Optimization Techniques

Cited by 223

Decoherence can be useful in quantum walks

Kendon, V., & Tregenna, B. (2003) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum Mechanics and Applications

Cited by 223

Efficient Large Scale Language Modeling with Mixtures of Experts

Artetxe, M., Bhosale, S., Goyal, N., Mihaylov, T., Ott, M., Shleifer, S., Lin, X. V., Du, J., Iyer, S., Pasunuru, R., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 223

Privacy-preserving deep learning

Shokri, R., & Shmatikov, V. (2015) · 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 223

Mind the GAP: A balanced corpus of gendered ambiguous pronouns

Webster, K., Recasens, M., Axelrod, V., & Baldridge, J. (2018) · Transactions of the Association for Computational Linguistics

Natural Language Processing TechniquesTopic Modeling

Cited by 223

Privacy-Preserving Federated Learning Using Homomorphic Encryption

Park, J., & Lim, H. (2022) · Applied Sciences

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 223

It's who you know: graph mining using recursive structural features

Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., & Faloutsos, C. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Advanced Graph Neural NetworksComplex Network Analysis Techniques

Cited by 223

Internet of Things (IoT) security with blockchain technology: A state-of-the-art review

Ayub Khan, A., Laghari, A. A., Shaikh, Z. A., Dacko-Pikiewicz, Z., & Kot, S. (2022) · IEEE Access

Blockchain Technology Applications and SecurityIoT and Edge/Fog Computing

Cited by 223

Vision-language navigation with self-supervised auxiliary reasoning tasks

Zhu, F., Zhu, Y., Chang, X., & Liang, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 223