Check computer science papers: citations, claims and retractions

Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients

Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O., Haring, C., & Lukowicz, P. (2014) · Proceedings of the 5th Augmented Human International Conference

Context-Aware Activity Recognition SystemsMental Health Research Topics

Cited by 139

Steps Toward Robust Artificial Intelligence

Dietterich, T. G. (2017) · AI Magazine

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 139

FedMask: Joint Computation and Communication-Efficient Personalized Federated Learning via Heterogeneous Masking

Li, A., Sun, J., Zeng, X., Zhang, M., Li, H., & Chen, Y. (2021) · Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 139

Augmented reality in design education: Landscape architecture studies as ar experience

Kerr, J., & Lawson, G. (2020) · International Journal of Art & Design Education

Augmented Reality ApplicationsVirtual Reality Applications and Impacts

Cited by 139

Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning

Coffin Murray, M., & Pérez, J. (2015) · InSITE Conference

Intelligent Tutoring Systems and Adaptive LearningLearning Styles and Cognitive Differences

Cited by 139

Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture From Images “In the Wild”

Zuffi, S., Kanazawa, A., Berger-Wolf, T., & Black, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

3D Shape Modeling and AnalysisHuman Pose and Action Recognition

Cited by 139

Design and Discovery in Educational Assessment: Evidence-Centered Design, Psychometrics, and Educational Data Mining

Mislevy, R. J., Behrens, J. T., Dicerbo, K. E., & Levy, R. (2012) · Zenodo

Online Learning and AnalyticsPsychometric Methodologies and Testing

Cited by 139

Security Tradeoffs in Cyber Physical Systems: A Case Study Survey on Implantable Medical Devices

Altawy, R., & Youssef, A. M. (2016) · IEEE Access

Information and Cyber SecuritySmart Grid Security and Resilience

Cited by 139

Making the user more efficient: design for sustainable behaviour

Lockton, D., Harrison, D., & Stanton, N. (2008) · International Journal of Sustainable Engineering

Innovative Human-Technology InteractionSustainable Design and Development

Cited by 139

A Smartphone-Based Tool for Assessing Parkinsonian Hand Tremor

Kostikis, N., Hristu-Varsakelis, D., Arnaoutoglou, M., & Kotsavasiloglou, C. (2015) · IEEE Journal of Biomedical and Health Informatics

Context-Aware Activity Recognition SystemsArtificial Intelligence in Healthcare

Cited by 139

Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning

Achiam, J., & Sastry, S. (2017) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 139

Systems Approaches to Tackling Configuration Errors: A Survey

Xu, T., & Zhou, Y. (2015) · ACM Computing Surveys

Software System Performance and ReliabilityDistributed systems and fault tolerance

Cited by 139

Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images

Kalra, S., Tizhoosh, H. R., Choi, C., Shah, S., Diamandis, P., Campbell, C. J. V., & Pantanowitz, L. (2020) · Medical Image Analysis

Image Retrieval and Classification TechniquesAI in cancer detection

Cited by 139

OpenEI: An Open Framework for Edge Intelligence

Zhang, X., Wang, Y., Lu, S., Liu, L., xu, L., & Shi, W. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 139

Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare

Gupta, D., Kayode, O., Bhatt, S., Gupta, M., & Tosun, A. S. (2021) · 2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 139

Human on-line response to target expansion

Zhai, S., Conversy, S., Beaudouin-Lafon, M., & Guiard, Y. (2003) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Interactive and Immersive DisplaysMotor Control and Adaptation

Cited by 139

Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning

Coffin Murray, M., & Pérez, J. (2015) · Informing Science: The International Journal of an Emerging Transdiscipline

Intelligent Tutoring Systems and Adaptive LearningLearning Styles and Cognitive Differences

Cited by 139

Applications and Challenges in Video Surveillance via Drone: A Brief Survey

Dilshad, N., Hwang, J., Song, J., & Sung, N. (2020) · 2020 International Conference on Information and Communication Technology Convergence (ICTC)

Video Surveillance and Tracking MethodsUAV Applications and Optimization

Cited by 139

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

Gu, X., Sabrina, F., Fan, Z., & Sohail, S. (2023) · International Journal of Environmental Research and Public Health

Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security

Cited by 139

Identifying machine learning techniques for classification of target advertising

Choi, J.-A., & Lim, K. (2020) · ICT Express

Recommender Systems and TechniquesDigital Marketing and Social Media

Cited by 139

Airbert: In-domain Pretraining for Vision-and-Language Navigation

Guhur, P.-L., Tapaswi, M., Chen, S., Laptev, I., & Schmid, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 139

Application of Quantum Annealing to Nurse Scheduling Problem

Ikeda, K., Nakamura, Y., & Humble, T. S. (2019) · Scientific Reports

Quantum Computing Algorithms and ArchitectureScheduling and Timetabling Solutions

Cited by 139

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Chen, Y., Zhang, C., Ma, M., Liu, Y., Ding, R., Li, B., He, S., Rajmohan, S., Lin, Q., & Zhang, D. (2023) · Proceedings of the VLDB Endowment

Anomaly Detection Techniques and ApplicationsTime Series Analysis and Forecasting

Cited by 139

Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing

Han, S., Zhang, R. F., Shi, L., Richie, R., Liu, H., Tseng, A., Quan, W., Ryan, N., Brent, D., & Tsui, F. R. (2022) · Journal of Biomedical Informatics

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 139