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Authors = Alberto Marchisio ORCID = 0000-0002-0689-4776

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19 pages, 700 KiB  
Article
Driving International Collaboration Beyond Boundaries Through Hackathons: A Comparative Analysis of Four Hackathon Setups
by Alice Barana, Vasiliki Eirini Chatzea, Kelly Henao, Ania Maria Hildebrandt, Ilias Logothetis, Marina Marchisio Conte, Alexandros Papadakis, Alberto Rueda, Daniel Samoilovich, Georgios Triantafyllidis and Nikolas Vidakis
Information 2025, 16(6), 488; https://doi.org/10.3390/info16060488 - 12 Jun 2025
Viewed by 612
Abstract
Hackathon events have become increasingly popular in recent years as a modern tool for innovation in the education sector as they offer important learning advantages. Within the “INVITE” Erasmus+ project, four distinct hackathons were organized to bring together academic institutions, teachers, and students [...] Read more.
Hackathon events have become increasingly popular in recent years as a modern tool for innovation in the education sector as they offer important learning advantages. Within the “INVITE” Erasmus+ project, four distinct hackathons were organized to bring together academic institutions, teachers, and students in the design of innovative international virtual and blended collaborations. In addition, as part of the “INVITE” project, an Open Interactive Digital Ecosystem (digital platform) has been developed to facilitate hackathons organization and was tested within two of the events. This platform can enhance hosting action-training programs providing a shared open resources space for educators to contact peers and design projects. All four hackathons were held during 2024 and their duration and type (onsite, blended, hybrid, and online) varied significantly. However, all hackathon topics were related to sustainability, SDGs, and Green Agenda. In total, more than 220 participants enrolled in the four events, including students, researchers, and professors from different disciplines, age groups, and countries. All participants were provided with qualitative surveys to explore their satisfaction and experiences. The results compare different hackathon setups to reveal valuable insights regarding the optimal design for higher education hackathons. Full article
(This article belongs to the Special Issue Accessibility and Inclusion in Education: Enabling Digital Technologies)
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18 pages, 1469 KiB  
Article
A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks
by Farzad Nikfam, Raffaele Casaburi, Alberto Marchisio, Maurizio Martina and Muhammad Shafique
Information 2023, 14(10), 537; https://doi.org/10.3390/info14100537 - 1 Oct 2023
Cited by 3 | Viewed by 3349
Abstract
Machine learning (ML) is widely used today, especially through deep neural networks (DNNs); however, increasing computational load and resource requirements have led to cloud-based solutions. To address this problem, a new generation of networks has emerged called spiking neural networks (SNNs), which mimic [...] Read more.
Machine learning (ML) is widely used today, especially through deep neural networks (DNNs); however, increasing computational load and resource requirements have led to cloud-based solutions. To address this problem, a new generation of networks has emerged called spiking neural networks (SNNs), which mimic the behavior of the human brain to improve efficiency and reduce energy consumption. These networks often process large amounts of sensitive information, such as confidential data, and thus privacy issues arise. Homomorphic encryption (HE) offers a solution, allowing calculations to be performed on encrypted data without decrypting them. This research compares traditional DNNs and SNNs using the Brakerski/Fan-Vercauteren (BFV) encryption scheme. The LeNet-5 and AlexNet models, widely-used convolutional architectures, are used for both DNN and SNN models based on their respective architectures, and the networks are trained and compared using the FashionMNIST dataset. The results show that SNNs using HE achieve up to 40% higher accuracy than DNNs for low values of the plaintext modulus t, although their execution time is longer due to their time-coding nature with multiple time steps. Full article
(This article belongs to the Special Issue Security and Privacy for Artificial Intelligence: Opportunities and Challenges)
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22 pages, 557 KiB  
Review
An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks
by Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Muhammad Shafique, Guido Masera and Maurizio Martina
Future Internet 2020, 12(7), 113; https://doi.org/10.3390/fi12070113 - 7 Jul 2020
Cited by 154 | Viewed by 16423
Abstract
Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial Intelligence (AI) applications. Their ability to go beyond human precision has made these networks a milestone in the history of AI. However, while on the one hand they present [...] Read more.
Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial Intelligence (AI) applications. Their ability to go beyond human precision has made these networks a milestone in the history of AI. However, while on the one hand they present cutting edge performance, on the other hand they require enormous computing power. For this reason, numerous optimization techniques at the hardware and software level, and specialized architectures, have been developed to process these models with high performance and power/energy efficiency without affecting their accuracy. In the past, multiple surveys have been reported to provide an overview of different architectures and optimization techniques for efficient execution of Deep Learning (DL) algorithms. This work aims at providing an up-to-date survey, especially covering the prominent works from the last 3 years of the hardware architectures research for DNNs. In this paper, the reader will first understand what a hardware accelerator is, and what are its main components, followed by the latest techniques in the field of dataflow, reconfigurability, variable bit-width, and sparsity. Full article
(This article belongs to the Collection Featured Reviews of Future Internet Research)
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