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 – 31. 3. 2024
Project Type: grant
Code: 8A21015, 101007350
Agency: Evropská unie
Program: Horizon 2020
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. (DFIT)
Homoliak Ivan, Ing., Ph.D. (DITS)
Juříček Zdeněk (DFIT-FO)
Kocman Radim, Ing., Ph.D. (CVT)
Kolář Martin, M.Sc., Ph.D. et Ph.D.
Kula Michal, Ing., Ph.D. (DCGM)
Matýšek Michal, Ing. (DCGM)
Musil Petr, Ing., Ph.D. (DCGM)
Španěl Michal, Ing., Ph.D. (DCGM)
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
2024
- CHLUBNA, T.; MILET, T.; ZEMČÍK, P. Automatic 3D-Display-Friendly Scene Extraction from Video Sequences and Optimal Focusing Distance Identification. MULTIMEDIA TOOLS AND APPLICATIONS, 2024, vol. 83, no. 7,
p. 1-29. ISSN: 1573-7721. Detail - CHLUBNA, T.; MILET, T.; ZEMČÍK, P. How Capturing Camera Trajectory Distortion Affects User Experience on Looking Glass 3D Display. MULTIMEDIA TOOLS AND APPLICATIONS, 2024, vol. 2024, no. 83,
p. 20265-20287. ISSN: 1573-7721. Detail - CHLUBNA, T.; MILET, T.; ZEMČÍK, P. Lightweight All-Focused Light Field Rendering. COMPUTER VISION AND IMAGE UNDERSTANDING, 2024, vol. 244, no. 7,
p. 7-8. ISSN: 1077-3142. Detail - CHLUBNA, T.; ZEMČÍK, P.; MILET, T. Efficient Random-Access GPU Video Decoding for Light-Field Rendering. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2024, vol. 2024, no. 102,
p. 1-14. ISSN: 1047-3203. Detail
2023
- APAROVICH, M.; KESIRAJU, S.; DUFKOVÁ, A.; SMRŽ, P. 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.
p. 1518-1524. ISBN: 978-1-959429-99-9. Detail - BAMBUŠEK, D.; MATERNA, Z.; KAPINUS, M.; BERAN, V.; SMRŽ, P. 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.
p. 115-122. ISBN: 979-8-3503-4815-6. Detail - CHLUBNA, T.; MILET, T.; ZEMČÍK, P.; KULA, M. Real-Time Light Field Video Focusing and GPU Accelerated Streaming. Journal of Signal Processing Systems for Signal Image and Video Technology, 2023, vol. 95, no. 6,
p. 703-719. ISSN: 1939-8115. Detail
2021
- ALI, A.; SMRŽ, P. Camera auto-calibration for complex scenes. In SPIE 11605. Rome: SPIE - the international society for optics and photonics, 2021.
p. 1-11. ISBN: 978-1-5106-4041-2. Detail