Grant
Grant IT1676-22 GRUPOS SISI
Grupo de sistemas Inteligentes para sistemas Industriales
Of Regional scope. With a Public character. It has been granted under a regime of Competitive.
Researchers
Publications related to the project
Show by type2025
2024
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Towards Robust Defect Detection in Casting Using Contrastive Learning
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Towards a Probabilistic Fusion Approach for Robust Battery Prognostics
PHM Society European Conference
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From Past to Present: Human–Machine Interfaces Evolve Toward Adaptivity
Future Perspectives on Human-Computer Interaction Research
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Low delay network attributes randomization to proactively mitigate reconnaissance attacks in industrial control systems
Wireless Networks
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Gotham Testbed: A Reproducible IoT Testbed for Security Experiments and Dataset Generation
IEEE Transactions on Dependable and Secure Computing
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On the use of MiniCPS for conducting rigorous security experiments in Software-Defined Industrial Control Systems
Wireless Networks
2023
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Software-Defined Networking approaches for intrusion response in Industrial Control Systems: A survey
International Journal of Critical Infrastructure Protection
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Structured dataset of human-machine interactions enabling adaptive user interfaces
Scientific Data
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Towards a Probabilistic Error Correction Approach for Improved Drone Battery Health Assessment
Proceeding of the 33rd European Safety and Reliability Conference
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Federated Explainability for Network Anomaly Characterization
ACM International Conference Proceeding Series
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Exploring the transformation of user interactions to adaptive human-machine interfaces
ACM International Conference Proceeding Series
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Clustered federated learning architecture for network anomaly detection in large scale heterogeneous IoT networks
Computers and Security
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Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets
PeerJ Computer Science
2022
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Active Power Optimization of a Turning Process by Cutting Conditions Selection: A Q-Learning Approach
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA