Check computer science papers: citations, claims and retractions

Quantum-inspired cascaded discrete-time quantum walks with induced chaotic dynamics and cryptographic applications

Abd El-Latif, A. A., Abd-El-Atty, B., Amin, M., & Iliyasu, A. M. (2020) · Scientific Reports

Chaos-based Image/Signal EncryptionQuantum Information and Cryptography

Cited by 155

GPT-3 and InstructGPT: technological dystopianism, utopianism, and “Contextual” perspectives in AI ethics and industry

Chan, A. (2023) · AI and Ethics

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 155

EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model

Ji, X., Zhou, H., Wang, K., Wu, Q., Wu, W., Xu, F., & Cao, X. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings

Face recognition and analysisGenerative Adversarial Networks and Image Synthesis

Cited by 155

Cultural Cognitive Style and Web Design: Beyond a Behavioral Inquiry into Computer-Mediated Communication

Faiola, A., & Matei, S. A. (2005) · Journal of Computer-Mediated Communication

Usability and User Interface DesignCultural Differences and Values

Cited by 155

Normality and actual causal strength

Icard, T. F., Kominsky, J. F., & Knobe, J. (2017) · Cognition

Bayesian Modeling and Causal InferenceLogic, Reasoning, and Knowledge

Cited by 155

How taking photos increases enjoyment of experiences

Diehl, K., Zauberman, G., & Barasch, A. (2016) · Journal of Personality and Social Psychology

Innovative Human-Technology InteractionFlow Experience in Various Fields

Cited by 155

Local interpretable model-agnostic explanations for classification of lymph node metastases

Palatnik de Sousa, I., Maria Bernardes Rebuzzi Vellasco, M., & Costa da Silva, E. (2019) · Sensors

Explainable Artificial Intelligence (XAI)AI in cancer detection

Cited by 155

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension

Yang, A., Wang, Q., Liu, J., Liu, K., Lyu, Y., Wu, H., She, Q., & Li, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 155

How Program History Can Improve Code Completion

Robbes, R., & Lanza, M. (2008) · 2008 23rd IEEE/ACM International Conference on Automated Software Engineering

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 155

Automatic Speech Recognition Errors Detection and Correction: A Review

Errattahi, R., El Hannani, A., & Ouahmane, H. (2018) · Procedia Computer Science

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 155

The Consciousness Prior

Bengio, Y. (2017) · arXiv

Topic ModelingPhilosophy and Theoretical Science

Cited by 155

Enhancing Student Motivation with use of Augmented Reality for Interactive Learning in Engineering Education

Kaur, D. P., Mantri, A., & Horan, B. (2020) · Procedia Computer Science

Augmented Reality ApplicationsMultimedia Learning Systems

Cited by 155

The Born supremacy: quantum advantage and training of an Ising Born machine

Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020) · npj Quantum Information

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 155

Deep fake detection and classification using error-level analysis and deep learning

Rafique, R., Gantassi, R., Amin, R., Frnda, J., Mustapha, A., & Alshehri, A. H. (2023) · Scientific Reports

Digital Media Forensic DetectionMisinformation and Its Impacts

Cited by 155

Physiolytics at the workplace: Affordances and constraints of wearables use from an employee's perspective

Mettler, T., & Wulf, J. (2019) · Information Systems Journal

Innovative Human-Technology InteractionInformation Systems Theories and Implementation

Cited by 155

Eliciting positive, negative and mixed emotional states: A film library for affective scientists

Samson, A. C., Kreibig, S. D., Soderstrom, B., Wade, A. A., & Gross, J. J. (2016) · Cognition and Emotion

Emotion and Mood RecognitionAesthetic Perception and Analysis

Cited by 155

Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination

Yin, Z., Wang, Y., Liu, L., Zhang, W., & Zhang, J. (2017) · Frontiers in Neurorobotics

Emotion and Mood RecognitionEEG and Brain-Computer Interfaces

Cited by 155

A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments

Goudarzi, M., Palaniswami, M., & Buyya, R. (2023) · IEEE Transactions on Mobile Computing

IoT and Edge/Fog ComputingCloud Computing and Resource Management

Cited by 155

The Effects of Operator Implementation Cost on Planfulness of Problem Solving and Learning

O'Hara, K. P., & Payne, S. J. (1998) · Cognitive Psychology

AI-based Problem Solving and PlanningVisual and Cognitive Learning Processes

Cited by 155

Investigating the Impact of Gender on Rank in Resume Search Engines

Chen, L., Ma, R., Hannák, A., & Wilson, C. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AINames, Identity, and Discrimination Research

Cited by 155

AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

Chen, P., Wu, L., & Wang, L. (2023) · Applied Sciences

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 155

Embodying Compassion: A Virtual Reality Paradigm for Overcoming Excessive Self-Criticism

Falconer, C. J., Slater, M., Rovira, A., King, J. A., Gilbert, P., Antley, A., & Brewin, C. R. (2014) · PLoS ONE

Virtual Reality Applications and ImpactsMindfulness and Compassion Interventions

Cited by 155

Visual SLAM for Handheld Monocular Endoscope

Grasa, O. G., Bernal, E., Casado, S., Gil, I., & Montiel, J. M. M. (2014) · IEEE Transactions on Medical Imaging

Robotics and Sensor-Based LocalizationAdvanced Vision and Imaging

Cited by 155

Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data

Cohen, I., Sebe, N., Gozman, F. G., Cirelo, M. C., & Huang, T. S. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.

Bayesian Modeling and Causal InferenceEmotion and Mood Recognition

Cited by 155