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Proceedings of the 20th European Conference on Multi-Agent Systems, EUMAS 2023

作者： Sabatucci, Luca Augello, Agnese Caggianese, Giuseppe Gallo, Luigi Institute for High Performance Computing and Networking National Research Council of Italy Palermo Italy Institute for High Performance Computing and Networking National Research Council of Italy Naples Italy

ISBN: (纸本)9783031432637

Digital Twin is a promising paradigm to support the development of socio-technical systems for the digital transformation of society. For example, smart cities and healthcare applications gain advantages from this new paradigm. Currently, researchers are investigating methodologies that exploit Digital Twins as general-purpose abstractions for complex modelling and simulation. Taking inspiration from the Social Practice theory, this paper explores the idea of explicitly representing the physical and social context in socio-technical systems. To this aim, we introduce the concept of digital practice as an additional brick of a methodology for modelling and implementing socio-technical systems via digital twins and agents. We illustrate this preliminary idea by exploiting an assistance scenario for the elderly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Assisted living

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VITE I Conference: Contributes in the frame of a Human Augmentation Space 1

VITE I Conference: Contributes in the frame of a Human Augme...

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1st Various Innovative Technological Experiences, VITE I

作者： Augello, Agnese Caggianese, Giuseppe Gallo, Luigi Institute for High Performance Computing and Networking National Research Council of Italy Italy

Our contribution is to examine some of the works presented during the VITE I conference from a perspective of Human Augmentation (HA). In the paper, we provide a definition of HA framed by Enactivism theory, also schematizing our viewpoint in a three-dimensional space and into an architecture for designing and implementing HA systems. © SAIt 2023.

关键词： Augmented reality

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Digital Practices: Introducing Social Dimension in Digital Twins 2

Digital Practices: Introducing Social Dimension in Digital T...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Sabatucci, Luca Augello, Agnese Caggianese, Giuseppe Gallo, Luigi Institute for High Performance Computing and Networking National Research Council of Italy Italy

ISBN: (纸本)9798350300802

Digital Twin is one of the key components of society's digital transformation and a promising paradigm to support the development of cyber-physical systems. Currently, researchers are investigating methodologies that exploit Digital Twins as general-purpose abstractions for complex modelling and simulation. This paper explores the idea of explicitly representing the physical and social context in cyber-physical systems through dynamic digital entities. To this aim, we introduce the concept of Digital Practice as a shared (social) digital concept, highlighting how reality is not merely described by the state of physical objects (digital twins) but also by connections among them. We illustrate this concept by exploiting an assistance scenario for the elderly. © 2023 IEEE.

关键词： Ambient Assisted Living Digital Practices Digital Twins Social Practices Socio-Technical Systems

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Integrating Brain Networks and Multi-Modal Data for Early Detection of Alzheimer's Disease 32

Integrating Brain Networks and Multi-Modal Data for Early De...

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32nd Italian Symposium on Advanced Database Systems, SEBD 2024

作者： Comito, Carmela Pizzuti, Clara Sammarra, Marcello Socievole, Annalisa Institute for High Performance Computing and Networking Via P. Bucci 8-9/C Rende87036 Italy

Early diagnosis of Alzheimer's disease (AD) is crucial for providing timely treatment and care to patients. However, current diagnostic methods rely on clinical symptoms and biomarkers, which are often unreliable and invasive. Brain networks model the brain's structure and function in AD and other brain diseases. To fully capture their complexity, we need multi-modal models that combine different types of data, such as structural and functional connectivity, clinical and genetic information. This gives us a holistic view of the disease's many aspects. In this paper, we argue that brain networks and multi-modal data fusion can improve early diagnosis of AD by capturing the complex and heterogeneous nature of the disease. Using brain network modeling and multi-modal data fusion, we envisage a novel framework for detecting AD and its prodromal stages. The framework can simultaneously capture network properties from multi-modal as well as longitudinal datasets, which provide complementary information. © 2024 Copyright for this paper by its authors.

关键词： Data fusion

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Assessment of MOSFET switching Losses Based on Junction Temperature. Comparison in an LLC Converter between TO-247 and ACEPACK SMIT using MDmesh DM6 Technology

Assessment of MOSFET switching Losses Based on Junction Temp...

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2023 International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, PCIM Europe 2023

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

ISBN: (纸本)9783800760916

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 do this, two different methods have been used: the first measures the temperature on a TO-247 MOSFET with an IR thermal camera, the second uses an ACEPACK™ SMIT package module that incorporates a complete half-bridge and an NTC thermistor for reading the temperature. © VDE VERLAG GMBH · Berlin · Offenbach.

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AI-based Anomaly Detection of Consumer Device Behaviors

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IEEE Consumer Electronics Magazine 2025年

作者： Bergantin, Fulvio Forestiero, Agostino Macri, Davide Italian National Research Council Institute for High Performance Computing and Networking Italy

The faster proliferation of smart consumer devices has generated a remarkable volume of complex data, necessitating advanced anomaly detection mechanisms to identify security threats, operational system malfunctions and inefficiencies. The high dimensionality, heterogeneity, and continually changing conditions can lead to lower performance, mainly when methods are applied across different devices and environments. Furthermore, numerous advanced techniques are based on supervised learning, which necessitates extensive labeled anomaly data-a major limitation given the rarity and labeling complexity of IoT anomalies. These constraints underscore essential research gaps in the development of anomaly detection frameworks that are generalizable, scalable, and resilient across diverse IoT environments. This work aims to bridge these gaps by introducing an AI-driven anomaly detection framework, integrating deep autoencoders for feature representation and transfer learning to enhance adaptability. Autoencoders are trained to capture complex and intricate patterns of normal device behaviour, identifying anomalies through reconstruction error analysis. A transfer learning strategy is employed to tackle the issue of insufficient labelled anomaly data. This approach enables knowledge from data-rich source domains to be adapted to data-scarce target environments, thereby significantly enhancing the model's generalizability across diverse IoT settings. Experimental results prove the approach's scalability and adaptability, confirming its capacity to provide a tailored, high-performance security solution for smart consumer devices. These findings improve current security and privacy frameworks, enhancing the reliability and trustworthiness of IoT systems. © 2012 IEEE.

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Hyper-Parameter Optimization through Reinforcement Learning for Survival Prediction of Patients with Heart Failure

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Procedia Computer Science 2024年 239卷 1754-1761页

作者： Patrizia Ribino Claudia Di Napoli Giovanni Paragliola Luca Serino National Research Council (CNR) - Institute for High-Performance Computing and Networking (ICAR) - Italy

Cardiovascular diseases (CVDs) are a class of medical conditions characterized by impaired functionality of the heart and blood vessels, among which coronary heart disease, cerebrovascular diseases, and heart failure are prominent. These diseases account for approximately one-third of all deaths recorded worldwide. A significant challenge currently faced by clinicians is the early prediction of heart failure. The employment of Machine Learning techniques has emerged as a leading method in predicting patient survival who exhibit symptoms of heart failure. Nonetheless, achieving a significant level of accuracy remains a topic that demands further investigation. The present study introduces a novel approach that leverages Reinforcement Learning (RL) to enhance the performance of the artificial neural network (ANN) techniques for survival prediction by identifying the optimal configuration of model hyper-parameters (HPO). The proposed approach has been assessed using a conventional benchmark dataset comprising the medical records of 299 patients. The results were compared with recent ANN approaches working on the same dataset. The findings indicate that the proposed approaches enhance the performance of the ANN-based heart failure predictive model.

关键词： Self-Adaptive Systems Reinforcement Learning Survival Prediction Heart Failure

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An AI-Driven Architecture for Precision Agriculture: IoT, Machine Learning, and Digital Twin Integration for Sustainable Crop Protection

An AI-Driven Architecture for Precision Agriculture: IoT, Ma...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Costa, Gianni Forestiero, Agostino Gentile, Antonio Francesco MacRì, Davide Ortale, Riccardo Bernardi, Bruno Cerruto, Emanuele Institute for High Performance Computing and Networking CS Rende Italy Mediterranean University of Reggio Calabria Department of Agriculture RC Reggio Calabria Italy CT Catania Italy

ISBN: (纸本)9798350362480

General efficient crop protection in olive orchards is challenged by the need for precise agrochemical application minimizing environmental impact while ensuring effective spray coverage. Conventional approaches often lead to poor spray distribution, heavy off-target losses and a lack of real-time adaptability in variable field conditions. Such findings imply the need for an approach or system that can adjust application parameters dynamically to optimize both effectiveness and *** paper introduces an advanced AI-driven architecture designed to meet these challenges by integrating Internet of Things (IoT), machine learning, and digital twin technologies. The core of this approach is the development of a Digital Tree, a virtual model of olive trees that accurately simulates and predicts spray distribution and interactions with environmental variables. IoT sensors in the field collect real-time data on spray behavior and field conditions, which machine learning algorithms then process to refine application parameters dynamically. By enabling data-driven, adaptive decision-making, the digital twin supports optimal spray distribution, reduces off-target impact, and enhances environmental sustainability. This integrated solution offers a scalable and replicable methodology for smart agriculture, advancing precision and sustainability in complex agricultural environments. © 2024 IEEE.

关键词： Smart agriculture

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Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons 1st

Federated Learning for the Efficient Detection of Stegano...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Cassavia, Nunziato Caviglione, Luca Guarascio, Massimo Liguori, Angelica Surace, Giuseppe Zuppelli, Marco Institute for High Performance Computing and Networking Rende Italy Institute for Applied Mathematics and Information Technologies Genova Italy University of Calabria Rende Italy

ISBN: (纸本)9783031314681

An increasing number of threat actors takes advantage of information hiding techniques to prevent detection or to drop payloads containing attack routines. With the ubiquitous diffusion of mobile applications, high-resolution icons should be considered a very attractive carrier for cloaking malicious information via steganographic mechanisms. Despite machine learning approaches proven to be effective to detect hidden payloads, the mobile scenario could challenge their deployment in realistic use cases, for instance due to scalability constraints. Therefore, this paper introduces an approach based on federated learning able to prevent hazards characterizing production-quality scenarios, including different privacy regulations and lack of comprehensive datasets. Numerical results indicate that our approach achieves performances similar to those of centralized solutions. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep learning

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Personalization Goals for Run-Time Adaptation of Iot-Based Assistance Applications for the Elderly

SSRN

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SSRN 2024年

作者： Sabatucci, Luca Di Napoli, Claudia Institute for High Performance Computing and Networking National Research Council Italy

ContextThe increasing demand for Ambient Assisted Living (AAL) applications has led to the need for personalized assistive workflows that can adapt to individual users' *** primary objective is to define a novel methodology for modelling personalizable assistive workflows that can seamlessly integrate services provided by heterogeneous IoT devices and that can be adapted to the needs of each user according to run-time context-dependent *** propose an abstraction-refinement approach based on representing assistive tasks as workflows initially defined at a high level of abstraction that specifies their functional components. They are then specialised with contextual refinements referring to non-functional aspects and accounting for the needs of each specific user to obtain executable workflows. Our methodology enables designers to create two distinct yet overlapping layers: a general workflow defining the overall assistive objective and a specific personalization layer customizing the assistance for each *** evaluation demonstrates the effectiveness of our methodology in producing personalized assistive workflows that can adapt to changing conditions while maintaining the functional integrity of the underlying *** methodology for modelling personalizable workflows may be a flexible instrument for designers to conceive adaptable assistive applications that are automatically adapted to individual users' needs. The proposed personalization approach also takes advantage of the redundancy of IoT devices to provide a more personalized experience for the elderly who need assistance. © 2024, The Authors. All rights reserved.

关键词： Internet of things

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