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

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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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Design of an Air Pollution Monitoring System Based on a Low-Cost Sensor Node

Design of an Air Pollution Monitoring System Based on a Low-...

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2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Guerron, Rocio-Alejandra Carni, Domenico-Luca Lanuzza, Marco D'Amore, Francesco Bencardino, Mariantonia Lamonaca, Francesco Taco, Ramiro University of Calabria Department of Computer Engineering Modeling Electronics and Systems Rende Italy Institute for High Performance Computing and Networking Italian National Research Council Rende Italy Institute of Atmospheric Pollution Research Italian National Research Council Rende Italy

ISBN: (纸本)9798350385014

Air quality influences the life of the living beings that inhabit planet Earth. high levels of air pollutants in ambient air are in fact able to affect human health and also ecosystem integrity. Their monitoring allows for regulating the most appropriate air quality indices and verifying whether they are below the established harmful levels. However, this is a task that requires many physical and economic resources. For this reason, the research activity herein presented proposes a low-cost system, which is able to qualitatively evaluate the meteorological conditions and the main pollutants that affect a certain area. Through this proposal, the behavior of Alphasense's electrochemical gas low-cost sensors is studied. Their response to the concentrations of the main air pollutants regulated by the relevant European Directive (2008/50/CE), such as Carbon Monoxide, Nitrogen Dioxide, Ozone, and Sulfur Dioxide, is characterized and analyzed under various environmental conditions of Temperature and Relative Humidity and as a function of the electrical current signal obtained by the chemistry of the sensor. © 2024 IEEE.

关键词： Air quality

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Proteins Transcription Factor Prediction Using Graph Neural Networks 19th

Proteins Transcription Factor Prediction Using Graph Neural ...

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19th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2024

作者： Amato, Domenico Calderaro, Salvatore Lo Bosco, Giosué Vella, Filippo Rizzo, Riccardo Department of Mathematics and Computer Science University of Palermo Palermo 90123 Italy Institute for High-Performance Computing and Networking National Research Council of Italy Palermo 90146 Italy

ISBN: (纸本)9783031897030

A transcription factor is a regulatory protein that modulates the transcriptional velocity of genetic information from DNA to mRNA. This protein orchestrates the activation and deactivation of specific genes, thereby regulating their expression to coincide precisely with requisite phases and quantities throughout the cellular lifecycle. This paper introduces an innovative deep-learning methodology designed to ascertain the presence of transcription factors within protein sequences. This method, through a graph-based representation of the sequence, employs a Graph Neural Network for protein classification. We evaluated this novel approach across four datasets derived from the Swiss-Prot database;we achieved promising results that surpass those obtained through traditional fixed-length encodings and conventional machine-learning techniques and are comparable with state-of-the-art deep models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph Neural Networks Proteins Transcription Factor

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Transformers for Interpretable Classification of Histopathological Images 18th

Transformers for Interpretable Classification of Histopath...

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18th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2023

作者： Amato, Domenico Calderaro, Salvatore Lo Bosco, Giosué Marino, Giuseppe Vella, Filippo Rizzo, Riccardo Department of Mathematics and Computer Science University of Palermo Palermo 90123 Italy Institute for High-Performance Computing and Networking National Research Council of Italy Palermo 90146 Italy

ISBN: (纸本)9783031907135

Recent studies have shown how machine learning techniques can help and support the analysis of expert physicians’ digitalized biopsy images. In particular, these techniques can improve the process related to some repetitive tasks, provide information, and speed up physician decision-making. Classifiers based on neural networks, particularly those that use Vision Transformers, have proven to be state-of-the-art in several classification tasks. In this paper, we apply vision transformers to classify histological images provided in the BreakHis and GasHisSDB datasets, which collect images from breast and gastric biopsies, respectively. The transformer architecture allows classification performances close to the state-of-the-art, also providing an explainable mechanism based on attention maps that localize and highlight the image regions relevant to the classifier’s decision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Breast Cancer Classification Gastric Cancer Interpretability Vision Transformer

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Enhancing Thrombophilia Risk Prediction Through AI-Based Methodologies

Enhancing Thrombophilia Risk Prediction Through AI-Based Met...

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20th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2024

作者： Mazzuca, Daniela Zinno, Francesco Forestiero, Agostino Immunohaematology Section Annunziata Hospital Cosenza Italy Department of Clinical and Experimental Medicine University Magna Græcia of Catanzaro Italy Institute for High-Performance Computing and Networking National Research Council CS Rende Italy

ISBN: (纸本)9781643685182

Thrombophilia, a predisposition to thrombosis, poses significant diagnostic challenges due to its multi-factorial nature, encompassing genetic and acquired factors. Current diagnostic paradigms, primarily relying on a combination of clinical assessment and targeted laboratory tests, often fail to capture the complex interplay of factors contributing to thrombophilia risk. This paper proposes an innovative artificial intelligence (AI)-based methodology aimed to enhance the prediction of thrombophilia risk. The designed multidimensional risk assessment model integrates and elaborates through AI a comprehensive collection of patient data types, including genetic markers, clinical parameters, patient history, and lifestyle factors, in order to obtain advanced and personalized explainable diagnoses. © 2024 The Authors.

关键词： Artificial intelligence

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