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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Luigi Gatto Giuseppe Fulvio Gaglio Agnese Augello Giuseppe Caggianese Luigi Gallo Marco La Cascia Engineering Department University of Palermo Palermo Italy Institute for high performance computing and networking (ICAR) National Research Council Naples Italy Institute for High Performance Computing and Networking (ICAR) National Research Council Palermo Italy

Metaverse aims at connecting the physical and digital world in a highly immersive virtual environment that can replicate society and social practices within it. It is populated not only by avatars corresponding to users but also by nonplayer characters (NPCs). These virtual agents should be able to perceive other agents and objects in the scene, reason and act preferably by exhibiting credible and acceptable social behavior. Relying on the Social Practice theory, this work proposes an architecture for the design and development of a virtual agent able to understand social practices in the Metaverse and act by respecting the social norms that the circumstances entail. The agent is equipped with modules for recognizing elements of the scene, knowledge and rules about a set of social practices. The scene information is exploited by a reasoning module that allows the agent to evaluate the current social practice, socially interpret the events, and activate the most appropriate behaviors. The paper describes the proposed architecture, its implementation, and the realization of an experimental virtual scenario.

关键词： Metaverse Avatars Cognition

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A blockchain-based smart contract system architecture for dependable health processes 5

A blockchain-based smart contract system architecture for de...

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5th Italian Conference on Cybersecurity, ITASEC 2021

作者： Ciampi, Mario Marangio, Fabrizio Schmid, Giovanni Sicuranza, Mario Institute for High Performance Computing and Networking National Research Council of Italy Naples Italy Department of Engineering University "Parthenope" Naples Italy

Healthcare is presumably one of the sectors which will have the higher prospects in the near future by adopting blockchain technologies. Indeed, blockchain technologies permit to keep track of the clinical consents, plans, and protocols related to a clinical trial or examination, so to get up-to-date and tamper-proof documentation which can be shared only among the patient and the healthcare personnel which was authorized for that clinical trial or examination. Moreover, smart contracts can be deployed and executed within various phases of the above processes in order to ensure their transparency and compliance to some guidelines and/or standards. This paper presents a novel blockchain and smart contract-based architecture, designed for allowing health professionals and decision-makers to be aware of both the tasks of a health process currently carried out for the care of a patient, and the possible deviations made with respect to the planned process. In this way, it is possible not only to register in an immutable way the health and audit data related to a specific health task, but also to analyse the reasons for which some health processes were not carried out as initially scheduled. © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Blockchain

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Bacteria Taxonomic Classification using Graph Neural Networks

Bacteria Taxonomic Classification using Graph Neural Network...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Domenico Amato Salvatore Calderaro Giosuè Lo Bosco Riccardo Rizzo Filippo Vella Dipartimento di Matematica e Informatica Università degli Studi di Palermo Palermo Italy National Research Council of Italy Institute for high performance computing and networking Palermo Italy

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Graph neural networks are effective and useful tools for problems that may be represented using graphs. The De Bruijn graph is a directed graph used to express overlaps between sequences of symbols in DNA sequence representation. In this paper, we present a method for sequence categorization using a De Bruijn sequence representation and a Convolutional Graph Neural Network (GCNN). We tested the methodology on a classification problem involving the 16S gene sequences. An analysis conducted on a dataset of 3000 16S sequences demonstrates results in comparison to state-of-the-art. The dataset utilized in the research and the source code can be accessed at https://***/Calder10/Bacteria-Taxonomic-Classification-using-GNN.

关键词： Databases Source coding Taxonomy Employment Symbols Directed graphs DNA

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Improving the learning performance by exploiting multimedia in eXtreme apprenticeship

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Multimedia Tools and Applications 2024年 1-25页

作者： Persia, Fabio D’Auria, Daniela Ge, Mouzhi Pilato, Giovanni Department of Information Engineering Computer Science and Mathematics University of L’Aquila Via Vetoio L’Aquila67100 Italy Faculty of Computer Science Free University of Bozen-Bolzano Piazza Domenicani 3 Bozen-Bolzano39100 Italy Faculty of European Campus Rottal-Inn Deggendorf Institute of Technology Dieter-Görlitz-Platz 1 Deggendorf94469 Germany Institute for High Performance Computing and Networking National Research Council of Italy Via Ugo La Malfa 153 Palermo90146 Italy

From prior studies on eXtreme Apprenticeship (XA), it can be seen that XA has emerged as an innovative and effective educational approach. The technology in computer science evolves rapidly and XA tackles the gap between University education and industry development. Additionally, distance learning by multimedia is more and more important, sometimes even becoming necessary. As a result, this paper intends to study how to integrate XA and multimedia to improve the learning performance in computer science, with particular reference to the programming courses. Since computer science is mostly driven by the new applications, XA and multimedia are considered to be effective and efficient to reduce the cognitive efforts by practical demonstration. The experimental results, based on the case study of the operating systems course at the Bachelor’s level, show that exploiting multimedia in XA can significantly improve the learning performance in terms of grade distributions, reliability, knowledge transfer and student satisfaction with respect to more traditional approaches based on frontal lectures. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distance education

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Assessment of MOSFET switching losses in an LLC converter by a calorimetric method

Assessment of MOSFET switching losses in an LLC converter by...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Alfio Scuto Giuseppe Sorrentino Marco Ventimiglia Gaetano Belverde Domenico Nardo Gianpaolo Vitale Giuseppe Lullo STMicroelectronics Catania Italy STMicroelectronics Munich Germany ICAR-CNR Institute for High Performance Computing and Networking Palermo Italy University of Palermo Palermo Italy

The present work aims to implement a calorimetric method to measure the power dissipated during MOSFET switching in a high efficiency LLC resonant converter. The power dissipated from switching can significantly impact the total power dissipated by the device and consequent efficiency, so correct quantification is fundamental for optimizing the devices and increasing the efficiency of the converters. In LLC converters, this type of dissipated power cannot be measured with standard methods such as measurement with oscilloscopes as some of the energy that is recovered and not dissipated. The calorimetric method requires reasonably accurate measurement of thermal resistance and the temperature of the die. To facilitate accurate temperature measurement, we used a device in an ACEPACK™ SMIT package module, which incorporates a complete half-bridge and an NTC thermistor for reading the temperature.

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Paving the Way for an Urban Intelligence OpenStack-Based Architecture

Paving the Way for an Urban Intelligence OpenStack-Based Arc...

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International Conference on Smart computing (SMARTCOMP)

作者： Giuseppe Tricomi Luca D'Agati Francesco Longo Giovanni Merlino Antonio Puliafito Stefano Silvestri CINI: National Interuniversity Consortium for Informatics Rome Italy Department of Engineering University of Messina Italy Institute for High Performance Computing and Networking of ICAR National Research Council (CNR) Naples Italy

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

This paper describes a novel architecture aiming to create an open-source template for implementing a platform enabling support for the deployment and integration of services and workflows that compose, manage, and continuously evolve a complex system: the Urban Intelligence. More specifically, the proposed Smart City IT architecture is an evolution of a concrete urban intelligence architecture meant mostly to support Data scientist activities and, obviously, the exploitation of Smart City services by citizens and users. The proposed solution, due to its open-source nature, is fully replicable and improves its ancestor by provisioning new characteristics: i) managing facilities of urban Cyber-Physical System via exploitation of a combination of Administrator/Data scientist workflows and IoT management platform, ii) the definition of workflow directly on the Edge (even through the exploitation of FaaS paradigm directly on IoTs), and iii) enhancement of Smart City infrastructure security via the reduction of external attack surface. The prototype of the proposed IT solution has been implemented leveraging open-source frameworks and technologies belonging to the OpenStack ecosystem.

关键词： Smart cities Distributed ledger Scalability Data security Ecosystems Prototypes Computer architecture

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Editorial

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Journal of Reliable Intelligent Environments 2021年第4期7卷 277-278页

作者： Augusto, Juan Carlos Coronato, Antonio Research Group on Development of Intelligent Environments Department of Computer Science Middlesex University London United Kingdom Institute for High Performance Computing and Networking National Research Council Naples Italy

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Integrating Different Data Modalities for the Classification of Alzheimer’s Disease Stages

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SN Computer Science 2023年第3期4卷 1-13页

作者： Maddalena, Lucia Granata, Ilaria Giordano, Maurizio Manzo, Mario Guarracino, Mario Rosario Institute for High-Performance Computing and Networking (ICAR) Consiglio Nazionale delle Ricerche (CNR) Via P. Castellino 111 Naples 80131 Italy Information Technology Services University of Naples “L’Orientale” Via Nuova Marina 59 Naples 80133 Italy Department of Economics and Law University of Cassino and Southern Lazio Campus Folcara Cassino 03043 Italy

Alzheimer’s disease (AD) is the most common form of dementia with physical, psychological, social, and economic impacts on patients, their carers, and society. Its early diagnosis allows clinicians to initiate the treatment as early as possible to arrest or slow down the disease progression more effectively. We consider the problem of classifying AD patients through a machine learning approach using different data modalities acquired by non-invasive techniques. We perform an extensive evaluation of a machine learning classification procedure using omics, imaging, and clinical features, extracted by the ANMerge dataset, taken alone or combined together. Experimental results suggest that integrating omics and imaging features leads to better performance than any of them taken separately. Moreover, clinical features consisting of just two cognitive test scores always lead to better performance than any of the other types of data or their combinations. Since these features are usually involved in the clinician diagnosis process, our results show how their adoption as classification features positively biases the results. © 2023, The Author(s).

关键词： Alzheimer’s disease Data integration Magnetic resonance imaging Omics imaging Transcriptomics

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Editorial

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Journal of Reliable Intelligent Environments 2021年第2期7卷 67-67页

作者： Coronato, Antonio Augusto, Juan Carlos Department of Computer Science Research Group on Development of Intelligent Environments Middlesex University London United Kingdom Institute for High Performance Computing and Networking National Research Council Naples Italy

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Self-Supervised Multi-LiDAR Object View Generation Using Single LiDAR 29

Self-Supervised Multi-LiDAR Object View Generation Using Sin...

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29th IEEE International Conference on Embedded and Real-Time computing Systems and Applications, RTCSA 2023

作者： Kuo, Yi-Hung Shih, Chi-Sheng Lin, Hsiang-Jui Hung, Shih-Hao Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350337860

3D object detection using point clouds has been widely used for self-driving vehicles, roadside vehicle detection, and tracking. However, the detection accuracy using single-LiDAR-scanned point clouds suffers from occlusion to have accurate bounding boxes. Several previous works proposed compensating for the point cloud to improve 3D detection. This work designed a self-labeling method that exploits the characteristics of multiple LiDAR-scanned point clouds collected on roadside units. This labeling method allows the point completion model to be trained without human-labeled bounding boxes. The point completion model uses the single-LiDAR-scanned point cloud as input and uses the extracted object points from the fused point cloud as the training target. The voxel IoU and recall of the point-completed point clouds increased by 17% and 22% compared to that of the raw point clouds. Additionally, the detection performance of DBSCAN, i.e., 3D AP@IoU=0.25 increased by 20% after applying the point completion algorithm. © 2023 IEEE.

关键词： Roadsides

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