Project detail

AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems

Duration: 01.04.2021 — 31.03.2024

Funding resources

Ministerstvo školství, mládeže a tělovýchovy ČR - Společná technologická iniciativa ECSEL

- whole funder (2021-04-01 - 2024-03-31)
Evropská unie - Horizon 2020

- whole funder (2021-04-01 - 2024-03-31)

On the project

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.

Description in Czech
Projekt se zaměřuje na vývoj modelového rámce pro podporu týmů během automatizovaného nepřetržitého vývoje CPS pomocí integrovaných řešení rozšířených o AI. Celková infrastruktura AIDOaRT bude fungovat s existujícími zdroji dat, včetně tradičního monitorování IT, spolu se softwarovými modely a měřeními. Infrastruktura je určena k provozu v procesu DevOps, kombinujícím vývoj softwaru a operace informačních technologií (IT). Technologické inovace AI musí navíc zajistit, aby systémy byly navrženy odpovědně a přispěly k důvěře v jejich chování.

Keywords
Software engineering, operating systems, computer languages, Artificial intelligence, intelligent systems, multi agent systems

Key words in Czech
Softwarové inženýrství, operační systémy, počítačové jazyky, umělá inteligence, inteligentní systémy

Mark

101007350

Default language

English

People responsible

Hájková Gabriela, Mgr. - fellow researcher
Homoliak Ivan, doc. Ing., Ph.D. - fellow researcher
Juříček Zdeněk - fellow researcher
Kocman Radim, Ing., Ph.D. - fellow researcher
Kolář Martin, M.Sc., Ph.D. et Ph.D. - fellow researcher
Kula Michal, Ing., Ph.D. - fellow researcher
Matýšek Michal, Ing. - fellow researcher
Musil Petr, Ing., Ph.D. - fellow researcher
Španěl Michal, doc. Ing., Ph.D. - fellow researcher
Zemčík Pavel, prof. Dr. Ing., dr. h. c. - fellow researcher
Smrž Pavel, doc. RNDr., Ph.D. - principal person responsible

Units

Department of Computer Graphics and Multimedia
- co-beneficiary (2020-06-09 - 2024-03-31)
Nitte Meenakshi Institute of Technology
- co-beneficiary (2020-06-09 - 2024-03-31)
Mälardalen University
- beneficiary (2020-06-09 - 2024-03-31)

Results

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

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

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

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

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

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.; 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

Responsibility: Smrž Pavel, doc. RNDr., Ph.D.