Check computer science papers: citations, claims and retractions

Deep Learning for IoT

Lin, T. (2020) · 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)

Adversarial Robustness in Machine LearningNetwork Security and Intrusion Detection

Cited by 59

Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires

Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering

Artificial Immune Systems ApplicationsT-cell and B-cell Immunology

Cited by 59

Automatic Assessment of Speech Intelligibility for Individuals with Aphasia

Le, D., Licata, K., Persad, C., & Provost, E. M. (2016) · IEEE/ACM Transactions on Audio, Speech, and Language Processing

Speech Recognition and SynthesisVoice and Speech Disorders

Cited by 59

Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants

Jeong, H., Kwak, S. S., Sohn, S., Lee, J. Y., Lee, Y. J., O’Brien, M. K., Park, Y., Avila, R., Kim, J.-T., Yoo, J.-Y., et al. (2021) · Proceedings of the National Academy of Sciences

Context-Aware Activity Recognition SystemsNon-Invasive Vital Sign Monitoring

Cited by 59

DigiNotar: Dissecting the First Dutch Digital Disaster

van der Meulen, N. (2013) · Journal of Strategic Security

Information and Cyber SecurityCybersecurity and Cyber Warfare Studies

Cited by 59

Representing Robot Task Plans as Robust Logical-Dynamical Systems

Paxton, C., Ratliff, N., Eppner, C., & Fox, D. (2019) · 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 59

Multi-task Learning for Multilingual Neural Machine Translation

Wang, Y., Zhai, C., & Hassan, H. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 59

On the Possibilities of AI-Generated Text Detection

Chakraborty, S., Bedi, A. S., Zhu, S., An, B., Manocha, D., & Huang, F. (2023) · arXiv

Authorship Attribution and ProfilingTopic Modeling

Cited by 59

How blockchain-timestamped protocols could improve the trustworthiness of medical science

Irving, G., & Holden, J. (2017) · F1000Research

Blockchain Technology Applications and SecurityAcademic integrity and plagiarism

Cited by 59

Secure and robust machine learning for healthcare: A survey

Qayyum, A., Qadir, J., Bilal, M., & Al-Fuqaha, A. (2021) · IEEE Reviews in Biomedical Engineering

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 59

Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction

Hou, M., Xu, C., Li, Z., Liu, Y., Liu, W., Chen, E., & Bian, J. (2022) · Proceedings of the ACM Web Conference 2022

Time Series Analysis and ForecastingForecasting Techniques and Applications

Cited by 59

Three-Dimensional Mean-Shift Edge Bundling for the Visualization of Functional Connectivity in the Brain

Bottger, J., Schafer, A., Lohmann, G., Villringer, A., & Margulies, D. S. (2014) · IEEE Transactions on Visualization and Computer Graphics

Data Visualization and AnalyticsFunctional Brain Connectivity Studies

Cited by 59

Control delay in Reinforcement Learning for real-time dynamic systems: A memoryless approach

Schuitema, E., Busoniu, L., Babuska, R., & Jonker, P. (2010) · 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 59

Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data

Pang, G., Cao, L., Chen, L., Lian, D., & Liu, H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Anomaly Detection Techniques and ApplicationsAdvanced Statistical Methods and Models

Cited by 59

Human Values in Software Engineering: Contrasting Case Studies of Practice

Hussain, W., Perera, H., Whittle, J., Nurwidyantoro, A., Hoda, R., Shams, R. A., & Oliver, G. (2022) · IEEE Transactions on Software Engineering

Software Engineering Techniques and PracticesEthics and Social Impacts of AI

Cited by 59

A Current Overview of the Use of Learning Analytics Dashboards

Masiello, I., Mohseni, Z. (., Palma, F., Nordmark, S., Augustsson, H., & Rundquist, R. (2024) · Education Sciences

Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Cited by 59

Recognizing the value of software: a software citation guide

Katz, D. S., Chue Hong, N. P., Clark, T., Muench, A., Stall, S., Bouquin, D., Cannon, M., Edmunds, S., Faez, T., Feeney, P., et al. (2021) · F1000Research

Research Data Management PracticesScientific Computing and Data Management

Cited by 59

FLOSS Participants' Perceptions About Gender and Inclusiveness: A Survey

Lee, A., & Carver, J. C. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

Open Source Software InnovationsGender and Technology in Education

Cited by 59

Adapting Language Models to Compress Contexts

Chevalier, A., Wettig, A., Ajith, A., & Chen, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 59

Race Recognition Using Deep Convolutional Neural Networks

Vo, T., Nguyen, T., & Le, C. T. (2018) · Symmetry

Face recognition and analysisAdvanced Neural Network Applications

Cited by 59

Private-Shared Disentangled Multimodal VAE for Learning of Latent Representations

Lee, M., & Pavlovic, V. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 59

Detecting insider threats through language change.

Taylor, P. J., Dando, C. J., Ormerod, T. C., Ball, L. J., Jenkins, M. C., Sandham, A., & Menacere, T. (2013) · Law and Human Behavior

Information and Cyber SecurityDeception detection and forensic psychology

Cited by 59

Divide et impera : subgoaling reduces the complexity of probabilistic inference and problem solving

Maisto, D., Donnarumma, F., & Pezzulo, G. (2015) · Journal of The Royal Society Interface

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 59

Quantum-dot cluster-state computing with encoded qubits

Weinstein, Y. S., Hellberg, C. S., & Levy, J. (2005) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum and electron transport phenomena

Cited by 59