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Enriching Metaverse with Memories Through Generative AI: A Case Study

Enriching Metaverse with Memories Through Generative AI: A C...

Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Maria Rausa Salvatore Gaglio Agnese Augello Giuseppe Caggianese Silvia Franchini Luigi Gallo Luca Sabatucci University of Palermo Institute for high performance computing and networking (ICAR) National Research Council (CNR) Italy

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

The paper introduces MetaMemory, an approach to generate 3D models from either textual descriptions or photographs of objects, offering dual input modes for enhanced representation. MetaMemory's architecture is discussed presenting the tools employed in extracting the object from the image, generating the 3D mesh from texts or images, and visualizing the object reconstruction in an immersive scenario. Afterwards, a case study in which we experienced reconstructing memories of ancient crafts is examined together with the achieved results, by highlighting current limitations and potential applications.

关键词： Solid modeling Three-dimensional displays Metaverse Extended reality Neural engineering Metrology Data models Artificial intelligence Image reconstruction

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Dynamic edge server placement in mobile edge computing using modified red deer optimization algorithm and Markov game theory

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Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 12297-12315页

作者： Asghari, Ali Vahdani, Alireza Azgomi, Hossein Forestiero, Agostino Department of Computer Engineering Shafagh Institute of Higher Education Tonekabon Iran Department of Computer Engineering Islamic Azad University Rasht Branch Rasht Iran Institute for High-Performance Computing and Networking National Research Council of Italy c/o University of Calabria CS Rende Italy

Today, the development of intelligent mobile communication equipment, online applications such as online games, e-commerce, e-learning, and multimedia applications, and also the advent of the fifth generation of mobile communication networks have drawn mobile cloud services providers' special attention to this area. Cloud resources are brought to the edge of the network and adjacent to cloud users to increase the quality of the service provided. Proper placement of cloud resources reduces the resource access delay and balances the workload. Numerous studies have been conducted in this field. In most studies, it is assumed that the candidate location of the resources is fixed. The resources' area is also considered flat. Since the requested services of cloud users vary at different times of the day and in different geographical areas, the resources do not have the same efficiency during different times of the same day. For example, some administrative and business areas are active during the day, and at other times the demand decreases sharply. Thus, the mobility of cloud servers can be considered a suitable solution. In the proposed method, a two-stage static (SSP-RM) and dynamic (DSP-RM) model of cloud resource placement using Red deer (RD) and Markov game (MG) algorithms is introduced. Some cloud resources will be portable according to temporal and spatial requirements so that they can be transferred to different geographical areas according to different demands to increase their efficiency and distribute a more balanced workload on them while reducing latency. In the proposed method, by clustering the cloud resource deployment area and using the RD algorithm, the complexity of the cloud resource placement problem is reduced, and by using the Markov game model, the global convergence of resource deployment is obtained. Finally, dynamic cloud resource placement is performed using the Q-Learning (QL) algorithm. The results of experiments show that the proposed algor

关键词： Computer games

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A Study on Consumer-Grade EEG Headsets in BCI Applications

A Study on Consumer-Grade EEG Headsets in BCI Applications

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Pervasive and Intelligent computing (PICom), IEEE Conference on

作者： Luigi Rizzo Paolo Zicari Franco Cicirelli Antonio Guerrieri Massimo Micieli Andrea Vinci ICAR-CNR - Institute for high performance computing and networking - National Research Council of Italy Rende CS Italy

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

This paper emphasizes the importance of wearable electroencephalogram (EEG) headsets with electrodes and their potential applications, particularly in the specialization of Brain-Computer Interfaces (BCIs). We aim to contribute to the field of BCIs by providing a short overview of recent literature to identify some usage scenarios for consumer-grade EEG headsets (non-medical editions). The paper highlights how each scenario requires a specific set of appropriately positioned electrodes. Moreover, it will show different BCI settings that comprehend signal preprocessing of brain cerebral waves, feature extraction, control signals, and classification algorithms. The paper will also discuss several key insights into the practical use of consumer-grade low-cost devices. The proposed study aims to be a helpful starting point for researchers interested in working on consumer-grade EEG headsets for BCI by providing an understanding of the current state-of-the-art limits and challenges and giving hints about what setting are suitable for specific application scenarios.

关键词： Headphones Electrodes Electric potential Feature extraction Electroencephalography Brain-computer interfaces Classification algorithms

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Serious Games as Emerging E-Health Interventions: Design of an Exergame for the SMILER Project 13th

Serious Games as Emerging E-Health Interventions: Design of...

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13th International Conference on Games and Learning Alliance, GALA 2024

作者： Augello, Agnese Caggianese, Giuseppe Gaglio, Giuseppe Fulvio Gallo, Luigi Malizia, Velia La Grutta, Stefania Tancredi, Giancarlo Turchetta, Attilio Vacca, Rosa Anna Scala, Iris Institute for High Performance Computing and Networking CNR Palermo Italy Institute for High Performance Computing and Networking CNR Naples Italy University of Palermo Palermo Italy Department of Information Science and Technology Pegaso University Naples Italy Institute of Translational Pharmacology CNR Palermo Italy Pediatric Department Sapienza University of Rome Rome Italy Sport Medicine Unit Bambino Gesù Children’s Hospital Roma Italy Institute of Biomembranes Bioenergetics and Molecular Biotechnologies CNR Bari Italy Department of Maternal and Child Health Federico II University Napoli Italy

ISBN: (纸本)9783031782688

This paper outlines the design and implementation of an exergame developed under the strategic Italian CNR project SMILER (Serious gaMes in chILdren with nEurological or Respiratory disorders). The project aims at investigating how Serious Games (SG) can enhance the health conditions of young people with chronic diseases, specifically targeting two categories: individuals with Down Syndrome and those with Asthma. The SG employs a narrative strategy within an immersive, interactive setup. The child’s movements are captured and translated into actions performed by an in-game avatar, who is central to the story. Additionally, a wristband continuously monitors the child’s physiological data, ensuring they remain within the aerobic zone. In this document, the game’s architectural modules and design decisions are described, along with introducing the experimental plan to explore the game’s impact on transcriptome profiling (CTRA) gene patterns and neuropsychological outcomes through an upcoming pilot study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Game design

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Evolutionary and Quantum computing for Community Detection: A Survey

Evolutionary and Quantum Computing for Community Detection: ...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Clara Pizzuti National Research Council of Italy (CNR) Institute for High Performance Computing and Networking (ICAR) Rende CS Italy

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Rising quantum technologies undertake to provide new strategies for solving hard combinatorial problems. Community detection is a fundamental issue in network studies with applications in various complex systems. A review of methods proposed in the last years in the context of quantum optimization and quantum-inspired metaheuristics to detect communities in networks is presented. Quantum optimization is introduced by recalling the main available computation models and the most popular hybridization approaches. The advantages and limitations of methods based on quantum computing and evolutionary computing are discussed.

关键词： Surveys Quantum computing Reviews Computational modeling Metaheuristics Complex systems

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AI Approach for Enhanced Thalassemia Diagnosis Using Blood Smear Images

AI Approach for Enhanced Thalassemia Diagnosis Using Blood S...

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

作者： Mazzuca, Daniela Bergantin, Fulvio MacRì, Davide Zinno, Francesco Forestiero, Agostino Immunohaematology Section Annunziata Hospital Via F. Migliori CS 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

This paper aims to propose an approach leveraging Artificial Intelligence (AI) to diagnose thalassemia through medical imaging. The idea is to employ a U-net neural network architecture for precise erythrocyte morphology detection and classification in thalassemia diagnosis. This accomplishment was realized by developing and assessing a supervised semantic segmentation model of blood smear images, coupled with the deployment of various data engineering techniques. This methodology enables new applications in tailored medical interventions and contributes to the evolution of AI within precision healthcare, establishing new benchmarks in personalized treatment planning and disease management. © 2024 The Authors.

关键词： Network architecture

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Deep Learning Based End-to-End Specular Reflection Removal for Medical Endoscopic Images 23

Deep Learning Based End-to-End Specular Reflection Removal f...

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2023 Research in Adaptive and Convergent Systems, RACS 2023

作者： Shih, Chi-Sheng Liao, Yu-Cheng Tan, Ching-Ting Graduate Institute of Networking and Multimedia Department of Computer Science and Information Engineering High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Department of Otolaryngology National Taiwan University Hospital Taipei Taiwan

ISBN: (纸本)9798400702280

Endoscopic images usually have many overexposed regions due to strong and focused light sources and, consequently, physicians need to change the camera angle for a clear view from time to time. This work targets removing the specular reflections from the endoscopy images but maintaining the structural and semantic texture in the images. Unlike the state-of-the-art image inpainting methods, the proposed method treats the specular reflection removal problem as an image-to-image translation task by skipping the specular detection step. The technique uses Dense Multi-scale Fusion Network (DMFN), which aims for small size and clear images, as the base and revises the network to support the large size and noisy endoscopy images. Moreover, the method can recover structure from large overexposed regions. Experimental results show that under different glare ratios and granularity settings, it is more effective than the Partial Convolution Model when recovering the corrupted pixels: the mean average error of pixel values decreases 13.2%, and the PSNR value increases 2.62dB. Qualitative analysis shows that even in the worst case when the proposed model generates output images with MAE up to 4.30, its recoveries still possess good visual qualities. The proposed model can process a 480 × 768 image in 16.2ms on a computer equipped with 36 CPU cores and an RTX-3090 GPU. It usually takes at least 20 minutes for a human to recover such images. © 2023 ACM.

关键词： Endoscopy

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Real-Time 3D Posture Tracking for Surgeons in Pediatric Minimally Invasive Surgery 17

Real-Time 3D Posture Tracking for Surgeons in Pediatric Mini...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Neroni, Pietro Caggianese, Giuseppe Esposito, Ciro Gallo, Luigi Institute for High Performance Computing and Networking National Research Council of Italy Via Pietro Castellino 111 Naples80131 Italy Azienda Ospedaliera Universitaria Federico II Via Sergio Pansini 5 Naples80131 Italy Italy

ISBN: (纸本)9789897587467

Minimally invasive pediatric surgery presents ergonomic challenges that significantly increase the risk of musculoskeletal disorders (MSDs) among surgeons due to prolonged periods of suboptimal posture. This study introduces a real-time posture monitoring and correction system designed to address this issue. The system utilizes depth camera technology, interactive feedback mechanisms, advanced skeletal tracking, and ergonomic assessment algorithms to continuously monitor and evaluate surgeons’ posture. Through rapid data processing, the system provides real-time feedback, enabling immediate posture adjustments during surgical procedures. It delivers non-intrusive alerts to inform medical staff when incorrect postures are detected, thereby promoting ergonomic well-being and reducing the incidence of MSDs. Designed for seamless integration into the perioperative environment, the system meets strict requirements for privacy, sterility, and operational efficiency. Beyond its application in surgical practice, the system can also enhance surgical education and training by providing real-time feedback, enabling personalized learning pathways, and gamified simulation exercises. It provides detailed analyses of trainee performance, enabling instructors to deliver targeted feedback and develop adaptive training strategies based on detected posture deviations. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Transplantation (surgical)

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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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Machine learning for the definition of landslide alert models: a case study in Campania region, Italy

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Discover Artificial Intelligence 2022年第1期2卷 1-11页

作者： Pota, Marco Pecoraro, Gaetano Rianna, Guido Reder, Alfredo Calvello, Michele Esposito, Massimo Institute for High Performance Computing and Networking National Research Council of Italy Naples Italy Department of Civil Engineering University of Salerno Fisciano Italy Division Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici Caserta Italy

Landslide early warning systems at regional scale are typically based on correlations between landslides that occurred in the past and rainfall monitoring data in order to identify trigger rainfall thresholds. Recently, the availability of large datasets of atmospheric measurements allows including additional variables, increasing the reliability of the models. However, conducting the analyses with traditional techniques can be quite complex and time-consuming. The purpose of this preliminary study is to demonstrate that machine learning techniques can be used to analyze monitoring data in order to select the most relevant variables for the triggering of shallow rainfall-induced landslides at regional scale. The models developed herein were tested in one of the alert zones defined by civil protection for the management of geo-hydrological risk in Campania region, Italy. Two data sources were used in the analysis. The atmospheric variables are derived from the ERA5-Land atmospheric reanalysis. The data on landslide events are retrieved from "FraneItalia", a georeferenced catalog of landslides occurred in Italy developed by consulting online sources from 2010 onwards. The models developed were calibrated and validated in order to define combinations of rainfall variables and soil water content for the prediction of the occurrence of landslides. Finally, the performance of the models was assessed using statistical indicators derived from contingency matrices. © The Author(s) 2022.

关键词： Fuzzy logic

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