CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.
I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .
Risultati finali
[T6.2-3] Report on the dissemination and standardisation activities carried out in tasks T6.2 and T6.3.
Methodology for explainable, trustworthy and human centric AI based system and function development (si apre in una nuova finestra)[T1.1-3] This deliverable will materialize the AITHENA methodology into a reference report to include definition of KPI related to trusted AI features (see GO-8).
Report on data sharing and integration with European data lakes, OpenData and OpenTool (si apre in una nuova finestra)[T6.4] Report on the plan and actions taken to integrate created data into data sharing initiatives at European level, including novel OpenData and OpenTool concepts.
Report on initial AI algorithm development (si apre in una nuova finestra)[T3.2-5] This report covers the initial AI algorithm developments including their initial feature validation according to the specified requirements and specifications.
Report on initial use case evaluation (si apre in una nuova finestra)[T5.2-4] This report covers the initial demonstrator validation and evaluation of the demonstrators against the specified requirements and specifications.
Privacy-preserving methods (si apre in una nuova finestra)[T2.3] This deliverable will report the designed privacy-preserving methods for application of GDPR-compliant ML.
Lessons learned, policy recommendations (si apre in una nuova finestra)[T6.5] Lessons learned, and derived policy recommendations for the exploitation of AI solutions in CCAM.
Life-cycle management framework for ML models (si apre in una nuova finestra)[T3.1] This report covers the designed AI-framework used for the development and life-cycle assessment of individual ML algorithms.
User group needs report and technical use case definition (si apre in una nuova finestra)[T1.4] This deliverable reports the action taken to identify user groups and gather from them requirements to detail the AITHENA use cases
Initial communication, dissemination and standardisation plan (si apre in una nuova finestra)T613 Initial strategy for AITHENA based on an initial market and stakeholder analysis
Report on final use case evaluation (si apre in una nuova finestra)[T5.2-4] This report covers the final demonstrator validation and evaluation of the demonstrators against the specified requirements and specifications. In addition, for each specific AI-driven approach in the use cases a dedicated AI lifecycle assessment is outlined.
Report on physical set-up, digital twin and hybrid testing approaches (si apre in una nuova finestra)[T4.2-4] This is the deliverable about activities T4.2-4, including report on physical set-up, digital twin and hybrid testing approaches.
Testing and evaluation methodology for AI-driven CCAM systems (si apre in una nuova finestra)[T5.1] This report outlines a joint testing and evaluation methodology for AI-driven CCAM systems to be applied in the corresponding use cases of task 5.2 to task 5.4.
Report on final AI algorithm development (si apre in una nuova finestra)[T3.2-5] This report covers the final AI algorithm developments including their final feature validation according to the specified requirements and specifications. In addition, all strength and weaknesses of the specific AI-driven approaches are outlined.
Updated communication, dissemination and standardisation plan (si apre in una nuova finestra)Update of the communication, dissemination and standardisation plan
ML DevOps-oriented data life-cycle governance and provenance framework (si apre in una nuova finestra)[T2.2] This deliverable will report RTD activities related to ML DevOps tools for data governance and provenance.
Pubblicazioni
Autori:
A. Forrai (Siemens Industry Software Netherlands B.V.), V. Neelgundmath, K.K. Unni, I. Barosan (Eindhoven University of Technology)
Pubblicato in:
Proceedings: 2023 7th International Conference on System Reliability and Safety (ICSRS), 2023, ISSN 1272-4017
Editore:
Zenodo
DOI:
10.5281/zenodo.12724017
Autori:
Hassan Hotait (HAN – University of Applied Sciences), Alexandru Forrai (Siemens Industry Software Netherlands B.V.)
Pubblicato in:
Product solutions paper: 22nd Driving Simulation & Virtual Reality Conference, 2023, ISSN 1272-3883
Editore:
Zenodo
DOI:
10.5281/zenodo.12723882
Autori:
Paraskevas Karachatzis, Jan Ruh, Silviu S. Craciunas (TTTech Computertechnik AG, Vienna, Austria)
Pubblicato in:
RTNS '23: Proceedings of the 31st International Conference on Real-Time Networks and Systems, 2023, ISBN 9781450399838
Editore:
ACM
DOI:
10.1145/3575757.3593660
Autori:
Beemelmanns, Till; Zahr, Wassim; Eckstein, Lutz
Pubblicato in:
Machine Learning for Autonomous Driving Workshop 2023 (NeurIPS), 2023, ISSN 2331-8422
Editore:
ML4AD/arXiv
DOI:
10.48550/arxiv.2312.14606
Autori:
Georg Stettinger (Infineon Technologies AG); Patrick Weissensteiner (Virtual Vehicle Research GmbH); Siddartha Khastgir (International Manufacturing Centre, The University of Warwick)
Pubblicato in:
IEEE Access, Numero Volume: 12, 2024, ISSN 2169-3536
Editore:
IEEE
DOI:
10.1109/ACCESS.2024.3364387
Autori:
Itziar Urbieta, Andoni Mujika, Gonzalo Piérola, Eider Irigoyen, Marcos Nieto, Estibaliz Loyo, Naiara Aginako
Pubblicato in:
Multimedia Tools and Applications, Numero Volume 83, 2023, ISSN 2213-7793
Editore:
Springer
DOI:
10.1007/s11042-023-16664-4
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