Project Details
AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems
Project Period: 1. 4. 2021 - 30. 9. 2024
Project Type: grant
Code: 8A21015, 101007350
Agency: ECSEL Joint Undertaking
Program: Společná technologická iniciativa ECSEL
Software engineering, operating systems, computer languages, Artificial intelligence, intelligent systems, multi agent systems
The project targets the development of a model-based framework to support teams during the automated continuous development of CPSs by means of integrated AI-augmented solutions. The overall AIDOaRT infrastructure will work with existing data sources, including traditional IT monitoring, log events, along with software models and measurements. The infrastructure is intended to operate within the DevOps process combining software development and information technology (IT) operations. Moreover, AI technological innovations have to ensure that systems are designed responsibly and contribute to our trust in their behaviour (i.e., requiring both accountability and explainability).
AIDOaRT aims to impact organizations where continuous deployment and operations management are standard operating procedures. DevOps teams may use the AIDOaRT framework to analyze event streams in real-time and historical data, extract meaningful insights from events for continuous improvement, drive faster deployments and better collaboration, and reduce downtime with proactive detection.
Hájková Gabriela, Mgr. (Děkanát FIT VUT)
Homoliak Ivan, Ing., Ph.D. (UITS FIT VUT)
Juříček Zdeněk, Jr. (Děkanát FIT VUT)
Kocman Radim, Ing., Ph.D. (CVT FIT VUT)
Kolář Martin, Ph.D. (UPGM FIT VUT)
Kula Michal, Ing., Ph.D. (UPGM FIT VUT)
Matýšek Michal, Ing. (UPGM FIT VUT)
Musil Petr, Ing., Ph.D. (UPGM FIT VUT)
Španěl Michal, Ing., Ph.D. (UPGM FIT VUT)
Zemčík Pavel, prof. Dr. Ing. (UPGM FIT VUT)
2024
- CHLUBNA Tomáš, MILET Tomáš and ZEMČÍK Pavel. Automatic 3D-Display-Friendly Scene Extraction from Video Sequences and Optimal Focusing Distance Identification. Multimedia Tools and Applications, vol. 83, no. 7, 2024, pp. 1-29. ISSN 1573-7721. Detail
- CHLUBNA Tomáš, MILET Tomáš and ZEMČÍK Pavel. How Capturing Camera Trajectory Distortion Affects User Experience on Looking Glass 3D Display. Multimedia Tools and Applications, vol. 2024, no. 83, pp. 20265-20287. ISSN 1573-7721. Detail
2023
- APAROVICH Maksim, KESIRAJU Santosh, DUFKOVÁ Aneta and SMRŽ Pavel. FIT BUT at SemEval-2023 Task 12: Sentiment Without Borders - Multilingual Domain Adaptation for Low-Resource Sentiment Classification. In: Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023). Toronto (online): Association for Computational Linguistics, 2023, pp. 1518-1524. ISBN 978-1-959429-99-9. Detail
- BAMBUŠEK Daniel, MATERNA Zdeněk, KAPINUS Michal, BERAN Vítězslav and SMRŽ Pavel. How Do I Get There? Overcoming Reachability Limitations of Constrained Industrial Environments in Augmented Reality Applications. In: 2023 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). Shanghai: Institute of Electrical and Electronics Engineers, 2023, pp. 115-122. ISBN 979-8-3503-4815-6. Detail
- CHLUBNA Tomáš, MILET Tomáš, ZEMČÍK Pavel and KULA Michal. Real-Time Light Field Video Focusing and GPU Accelerated Streaming. Journal of Signal Processing Systems, vol. 95, no. 6, 2023, pp. 703-719. ISSN 1939-8115. Detail
2021
- ALI Anas and SMRŽ Pavel. Camera auto-calibration for complex scenes. In: SPIE 11605. Rome: SPIE - the international society for optics and photonics, 2021, pp. 1-11. ISBN 978-1-5106-4041-2. Detail