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CORDIS - Risultati della ricerca dell’UE
CORDIS
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A network of excellence for distributed, trustworthy, efficient and scalable AI at the Edge

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

Final data management plan

Plan for management of data in the NoE and in the consortium - final version

Initial data management plan

Plan for management of data in the NoE and in the consortium - intial version

Seminars, workshop and events - Event Nr 1

Dissemination events, multiplier events

Roundtable on Edge AI functional and non-functional requirements

Expert workshop for requirements gathering and discussion

Workshops (co-organized with other pillars) - Workshop Nr 1

Two-day workshop for alignment with all pillars of the EU AI Lighthouse

Intermediate report and risk log - Report Nr 1

Updated report and log for risk prevention and mitigation (every 6 months)

dAIEDGE Communication, Dissemination and Exploitation plan

Plan of the project communication, dissemination and exploitation

Report on Edge AI functional and non-functional requirements

Comprehensive report on edge AI requirements

Call Announcement and Guide for Applicants - Call Nr 1

Open Call Package documents

Evolutive infrastructure inventory - Report Nr 1

Inventory of existing infrastructure for edge AU

Quality assurance plan

Measures for ensuring quality

Risk management plan

Plan and measures for risk prevention and mitigation

Distributed network design

Design and architecture of the distributed network of frameworks

Intermediate reports and risk log - Report Nr 2

Updated report and log for risk prevention and mitigation (every 6 months)

Reference document on European AI Lighthouse implementation

Plans and recommendations organisation of the EU AI Lighthouse

Evolutive infrastructure inventory - Report Nr 2

Inventory of existing infrastructure for edge AU

Report on Use Case definition, demonstration setup and validation plan

Complete and refined definition of the three use cases with evaluation plan

Pubblicazioni

Data in Brief

Autori: Jesus Ruiz-Santaquiteria; Juan D. Muñoz; Francisco J. Maigler; Oscar Deniz; Gloria Bueno
Pubblicato in: Data in Brief, 2023, ISSN 2352-3409
Editore: Elsevier BV
DOI: 10.1016/j.dib.2024.110030

WRIT: Web Request Integrity and Attestation Against Malicious Browser Extensions

Autori: Giorgos Vasiliadis, Apostolos Karampelas, Alexandros Shevtsov, Panagiotis Papadopoulos, Sotiris Ioannidis, Alexandros Kapravelos
Pubblicato in: IEEE Transactions on Dependable and Secure Computing, Numero 21, 2024, ISSN 1545-5971
Editore: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/TDSC.2023.3322516

A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses

Autori: Caelin Kaplan, Chuan Xu, Othmane Marfoq, Giovanni Neglia, Anderson Santana de Oliveira
Pubblicato in: Proceedings on Privacy Enhancing Technologies, Numero 2024, 2023, ISSN 2299-0984
Editore: Privacy Enhancing Technologies Symposium Advisory Board
DOI: 10.56553/popets-2024-0031

DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration

Autori: Perry Gibson, Jose Cano, Elliot Crowley, Amos Storkey, Michael O'Boyle
Pubblicato in: ACM Transactions on Architecture and Code Optimization, 2024, ISSN 1544-3566
Editore: Association for Computing Machinery (ACM)
DOI: 10.1145/3688609

Fast Transciphering Via Batched And Reconfigurable LUT Evaluation

Autori: Leonard Schild, Aysajan Abidin, Bart Preneel
Pubblicato in: IACR Transactions on Cryptographic Hardware and Embedded Systems, Numero 2024, 2024, ISSN 2569-2925
Editore: Universitatsbibliothek der Ruhr-Universitat Bochum
DOI: 10.46586/tches.v2024.i4.205-230

ShareBERT: Embeddings Are Capable of Learning Hidden Layers

Autori: Jia Cheng Hu, Roberto Cavicchioli, Giulia Berardinelli, Alessandro Capotondi
Pubblicato in: Proceedings of the AAAI Conference on Artificial Intelligence, Numero 38, 2024, ISSN 2374-3468
Editore: Association for the Advancement of Artificial Intelligence (AAAI)
DOI: 10.1609/aaai.v38i16.29781

Kraken: An Open-Source RISC-V SoC for Ultra-Low Power Multi-Modal Perception

Autori: Viviane Potocnik; Alfio Di Mauro; Christoph Leitner; Moritz Scherer; Georg Rutishauser; Lorenzo Lamberti; Luca Benini
Pubblicato in: 2024
Editore: www.researchsquare.com
DOI: 10.21203/rs.3.rs-4023416/v1

Leveraging AutoEncoders and chaos theory to improve adversarial example detection

Autori: Anibal Pedraza; Oscar Deniz; Harbinder Singh; Gloria Bueno
Pubblicato in: Neural Computing and Applications, 2024, ISBN 1826518275
Editore: Neural Computing and Applications (2024)
DOI: 10.1007/s00521-024-10141-1

Efficient Tiny Machine Learning for Human Activity Recognition on Low-Power Edge Devices

Autori: Vinamra Sharma, Danilo Pau, José Cano
Pubblicato in: 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI), 2024
Editore: IEEE
DOI: 10.1109/RTSI61910.2024.10761203

FedStale: leveraging Stale Updates in Federated Learning

Autori: Angelo Rodio, Giovanni Neglia
Pubblicato in: Frontiers in Artificial Intelligence and Applications, ECAI 2024, 2024
Editore: IOS Press
DOI: 10.3233/FAIA240849

Scheduling with Fully Compressible Tasks: Application to Deep Learning Inference with Neural Network Compression

Autori: Tiago Da Silva Barros, Frédéric Giroire, Ramon Aparicio-Pardo, Stéphane Pérennes, Emanuele Natale
Pubblicato in: 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2024
Editore: IEEE
DOI: 10.1109/CCGrid59990.2024.00045

Scheduling Machine Learning Compressible Inference Tasks with Limited Energy Budget

Autori: Tiago Da Silva Barros, Davide Ferre, Frederic Giroire, Ramon Aparicio-Pardo, Stephane Perennes
Pubblicato in: Proceedings of the 53rd International Conference on Parallel Processing, 2024
Editore: ACM
DOI: 10.1145/3673038.3673106

FedLoRa: IoT Spectrum Sensing Through Fast and Energy-Efficient Federated Learning in LoRa Networks

Autori: Fabio Busacca, Stefano Mangione, Giovanni Neglia, Ilenia Tinnirello, Sergio Palazzo, Francesco Restuccia
Pubblicato in: 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS), 2024
Editore: IEEE
DOI: 10.1109/MASS62177.2024.00047

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