Trustworthy Efficient AI for Cloud-Edge Computing

Informazioni relative al progetto

MANOLO

ID dell’accordo di sovvenzione: 101135782

DOI

10.3030/101135782

Data della firma CE 23 Ottobre 2023

Data di avvio 1 Gennaio 2024

Data di completamento 31 Dicembre 2026

Finanziato da

• Digital, Industry and Space

Costo totale

€ 8 605 772,50

Contributo UE

€ 8 605 772,50

Coordinato da

UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Ireland

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

Documents, reports (10)

Data Inspection and Generation v.1

Report on the data infrastructure and preparation for training and testing

Data Management Plan v.2

Procedures set for data collection, processing, monitoring, dissemination, and ethics compliance

MANOLO Architecture, and Benchmarking framework v.2

High-level system architecture and benchmarking framework designs

Trustworthy Efficient AI for the Cloud-edge Deep Dive

Detailed mapping of Human-centred Needs and Requirements

Dissemination & Communication Plan & Activities v.2

Dissemination and Communication planning and outcomes.

MANOLO Architecture, and Benchmarking framework v.1

High-level system architecture and benchmarking framework designs

Use case designs and deployment plans

Detail plans for the preparation, deployment and assessment of the MANOLO use cases

Trustworthy Efficiency-Performance Assessment v.1

Report on the MANOLO assessment framework

Data Management Plan v.1

Procedures set for data collection, processing, monitoring, dissemination, and ethics compliance

Dissemination & Communication Plan & Activities v.1

Dissemination and Communication planning and outcomes.

Other (1)

Cloud-edge resources, infrastructure, and AI models mapping v.1

Report on requirements of the hyper distributed training model, and parameters abstracted from the cloud-edge system infrastructure

Pubblicazioni

Conference proceedings (11)

Virtual worlds, real risks: exploring user safety in the metaverse under the Digital Services Act

Autori: Noémie Krack, Jean De Meyere
Pubblicato in: International Congress Towards a Reponsible Development of the Metaverse, 2024
Editore: Zenodo
DOI: 10.5281/ZENODO.13944299

On the Reliability of Artificial Intelligence Systems

Autori: Konstantopoulos, Stasinos
Pubblicato in: Proceedings of the 13th Hellenic Conference on Artificial Intelligence, 2024
Editore: Association of Computing Machinery
DOI: 10.5281/ZENODO.7733680

Towards Smart Microfarming in an Urban Computing Continuum

Autori: Marla Grunewald, Mounir Bensalem, Jasenka Dizdarević, Admela Jukan
Pubblicato in: 2024 IEEE Latin-American Conference on Communications (LATINCOM), 2025, ISSN 2689-7563
Editore: IEEE
DOI: 10.1109/LATINCOM62985.2024.10770673

PETRA: Parallel End-to-end Training with Reversible Architectures

Autori: Stéphane Rivaud, Louis Fournier, Thomas Pumir, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
Pubblicato in: ICLR 2025 International Conference on Learning Representations, 2025
Editore: openreview.net
DOI: 10.48550/ARXIV.2406.02052

Human-Aware Design for Transferring Knowledge During Human-AI Co-Learning

Autori: Koutrintzes, Dimitrios; Spatharis, Christos; Dagioglou, Maria
Pubblicato in: 27th European Conference on Artificial Intelligence, 2024, ISSN 1613-0073
Editore: CEUR
DOI: 10.5281/ZENODO.14772017

Lecture Notes in Computer Science

Autori: Alexandros Nousias, Maria Dagioglou, Georgios Petasis
Pubblicato in: Lecture Notes in Computer Science, Value Engineering in Artificial Intelligence, 2025, ISSN 0302-9743
Editore: Springer Verlag
DOI: 10.1007/978-3-031-85463-7_15

Comparing Prior and Learned Time Representations in Transformer Models of Timeseries

Autori: Natalia Koliou; Tatiana Boura; Stasinos Konstantopoulos; Georgios Meramveliotakis; George Kosmadakis
Pubblicato in: Proceedings of the 13th Hellenic Conference on Artificial Intelligence, 2024
Editore: Association for Computing Machinery
DOI: 10.48550/ARXIV.2411.12476

Effective ML Model Versioning in Edge Networks

Autori: Fin Gentzen, Mounir Bensalem, Admela Jukan
Pubblicato in: 2025 International Wireless Communications and Mobile Computing (IWCMC), 2025
Editore: IEEE
DOI: 10.1109/IWCMC65282.2025.11059571

Growth strategies for arbitrary DAG neural architectures

Autori: Stella Douka, Manon Verbockhaven, Théo Rudkiewicz, Stéphane Rivaud, François P. Landes, Sylvain Chevallier, Guillaume Charpiat
Pubblicato in: ESANN 2025 - 33th European Symposium on Artificial Neural Networks European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2025
Editore: i6doc.com
DOI: 10.48550/ARXIV.2501.12690

$$ Xpression $$: A Unifying Metric to Optimize Compression and Explainability Robustness of AI Models

Autori: Eric Arazo, Hristo Stoev, Cristian Bosch, Andrés L. Suárez-Cetrulo, Ricardo Simón-Carbajo
Pubblicato in: Communications in Computer and Information Science, Explainable Artificial Intelligence, 2024, ISSN 1865-0937
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-63787-2_19

Explainable Artificial Intelligence

Pubblicato in: Communications in Computer and Information Science, 2024
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-63787-2

Other (1)

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts

Autori: spis, M., Cajas Ordónez, S. A., Suárez-Cetrulo, A. L., and Simón Carbajo, R.
Pubblicato in: arXiv e-prints, 2025
Editore: arXiv.org
DOI: 10.48550/ARXIV.2507.18464