CORDIS provides links to public deliverables and publications of HORIZON projects.
Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .
Deliverables
[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 initial AI algorithm development (opens in new window)[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 (opens in new window)[T5.2-4] This report covers the initial demonstrator validation and evaluation of the demonstrators against the specified requirements and specifications.
Privacy-preserving methods (opens in new window)[T2.3] This deliverable will report the designed privacy-preserving methods for application of GDPR-compliant ML.
Life-cycle management framework for ML models (opens in new window)[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 (opens in new window)[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 (opens in new window)T613 Initial strategy for AITHENA based on an initial market and stakeholder analysis
Testing and evaluation methodology for AI-driven CCAM systems (opens in new window)[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.
Updated communication, dissemination and standardisation plan (opens in new window)Update of the communication, dissemination and standardisation plan
ML DevOps-oriented data life-cycle governance and provenance framework (opens in new window)[T2.2] This deliverable will report RTD activities related to ML DevOps tools for data governance and provenance.
Publications
Author(s):
A. Forrai (Siemens Industry Software Netherlands B.V.), V. Neelgundmath, K.K. Unni, I. Barosan (Eindhoven University of Technology)
Published in:
Proceedings: 2023 7th International Conference on System Reliability and Safety (ICSRS), 2023, ISSN 1272-4017
Publisher:
Zenodo
DOI:
10.5281/zenodo.12724017
Author(s):
Hassan Hotait (HAN – University of Applied Sciences), Alexandru Forrai (Siemens Industry Software Netherlands B.V.)
Published in:
Product solutions paper: 22nd Driving Simulation & Virtual Reality Conference, 2023, ISSN 1272-3883
Publisher:
Zenodo
DOI:
10.5281/zenodo.12723882
Author(s):
Paraskevas Karachatzis, Jan Ruh, Silviu S. Craciunas (TTTech Computertechnik AG, Vienna, Austria)
Published in:
RTNS '23: Proceedings of the 31st International Conference on Real-Time Networks and Systems, 2023, ISBN 9781450399838
Publisher:
ACM
DOI:
10.1145/3575757.3593660
Author(s):
Beemelmanns, Till; Zahr, Wassim; Eckstein, Lutz
Published in:
Machine Learning for Autonomous Driving Workshop 2023 (NeurIPS), 2023, ISSN 2331-8422
Publisher:
ML4AD/arXiv
DOI:
10.48550/arxiv.2312.14606
Author(s):
Georg Stettinger (Infineon Technologies AG); Patrick Weissensteiner (Virtual Vehicle Research GmbH); Siddartha Khastgir (International Manufacturing Centre, The University of Warwick)
Published in:
IEEE Access, Issue Volume: 12, 2024, ISSN 2169-3536
Publisher:
IEEE
DOI:
10.1109/ACCESS.2024.3364387
Author(s):
Itziar Urbieta, Andoni Mujika, Gonzalo Piérola, Eider Irigoyen, Marcos Nieto, Estibaliz Loyo, Naiara Aginako
Published in:
Multimedia Tools and Applications, Issue Volume 83, 2023, ISSN 2213-7793
Publisher:
Springer
DOI:
10.1007/s11042-023-16664-4
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