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IEEE Transactions on Technology and Society 2022年第4期3卷 323-332页

作者： Pilato, Giovanni Persia, Fabio Ge, Mouzhi D'Auria, Daniela Institute for High Performance Computing and Networking National Research Council of Italy ICAR-CNR Palermo90146 Italy University of l'Aquila Computer Science and Mathematics Department of Information Engineering L'Aquila67100 Italy Deggendorf Institute of Technology Faculty of European Campus Rottal-Inn Deggendorf94469 Germany Free University of Bozen-Bolzano Faculty of Computer Science Bolzano39100 Italy

Orienteering or itinerary planning applications aim to optimize travel routes exploiting user preference and other constraints, such as time budget or traffic conditions. For these algorithms, it is essential to explore the user preference to predict potential Points-Of-Interest (POI) or touristic routes. However, user preference has been significantly affected by the COVID-19, since health concern plays a key trade-off role now. For example, people may try to avoid crowdedness, even if there is a strong social desire. However, most orienteering applications just focus on user preferences, thus paying less attention to the variety of the data inputs, which has become an essential factor for the utility of the application in the COVID-19 era. Therefore, this paper proposes a social sensing system that considers the trade-off between user preference and various factors, such as crowdedness, fear of being infected, knowledge of the COVID-19, POI features, and desire for socialization. The experiments are conducted on profiling user interests with Doc2Vec and FastText based on the Yelp dataset. Furthermore, the proposed system is modular and can be efficiently adapted to different applications for COVID-aware itinerary planning. © 2020 IEEE.

关键词： COVID-19

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Sensing the Web for Induction of Association Rules and their Composition through Ensemble Techniques 10

Sensing the Web for Induction of Association Rules and their...

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10th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2019

作者： Augello, Agnese Infantino, Ignazio Pilato, Giovanni Vella, Filippo ICAR-CNR Institute for High Performance Computing and Networking National Research Council of Italy Palermo Italy

Starting from geophysical data collected from heterogeneous sources, such as meteorological stations and information gathered from the web, we seek unknown connections between the sampled values through the extraction of association rules. These rules imply the co-occurrence of two or more symbols in the same representation, and the rule confidence may vary according to the collected data. We propose, starting from traditional algorithms such as FP-Growth and Apriori, the creation of complex association rules through boosting of simpler ones. The composition enables the creation of rules that are robust and let emerge a larger number of interesting rules. © 2020 The Authors. Published by Elsevier B.V.

关键词： Big data

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Occupancy Prediction in Multi-Occupant IoT Environments Leveraging Federated Learning

Occupancy Prediction in Multi-Occupant IoT Environments Leve...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Irfanullah Khan Flavia C. Delicato Emilio Greco Massimo Guarascio Antonio Guerrieri Giandomenico Spezzano University of Calabria Rende (CS) Italy ICAR-CNR Institute for high performance computing and networking - National Research Council of Italy Rende (CS) Italy Instituto de Computacao Universidade Federal Fluminense Niteroi RJ Brazil

Energy efficiency and energy saving have become crucial issues in the face of increasing energy demand, the need for sustainable solutions, and concerns about climate change. Buildings, as significant contributors to energy consumption and greenhouse gas emissions, require effective measures for energy optimization that can also be reached by predicting the usage of the building spaces. This paper introduces a data-driven approach combining Internet of Things sensors, Machine Learning, Edge computing, and Federated Learning to predict multi-occupancy in buildings. The proposed approach is used on real data from the ICAR-CNR IoT Laboratory in order to extract insights into occupancy patterns within a multi-occupant environment. Finally, a comparative analysis conducted by varying Federated Learning configurations demonstrates the robustness of the solution.

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Augmenting Collaborative Practices: The Hololiver Case Study

Augmenting Collaborative Practices: The Hololiver Case Study

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

作者： Agnese Augello Giuseppe Caggianese Luigi Casoria Luigi Gallo Roberto Montalti Pietro Neroni Gianluca Rompianesi Roberto Troisi Institute for high performance computing and networking (ICAR) National Research Council Palermo Italy Institute for high performance computing and networking (ICAR) National Research Council Naples Italy Department of Public Health Division of Hepato-Biliary-Pancreatic Minimally Invasive and Robotic Surgery and Kidney Transplantation Federico II University Hospital Naples Italy Department of Clinical Medicine and Surgery Division of Hepato-Biliary-Pancreatic Minimally Invasive and Robotic Surgery and Kidney Transplantation Federico II University Hospital Naples Italy

Advances in Mixed Reality (MR) technologies are reshaping collaborative practices. The seamless integration of physical and virtual elements enhances the perception of the working environment, providing a more enriched collaborative task experience. While revealing intriguing potential across various sectors, wearing head-mounted displays (HMDs) can pose challenges in communication and in understanding others’ behaviours. This paper analyses the main elements of collaborative augmented practices through the case study of Hololiver, a MR system developed to assist surgeons in planning laparoscopic liver surgeries. The work discusses guidelines for designing interfaces to preserve awareness in MR interactions.

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Lower and upper estimates of the quantity of algebraic numbers

arXiv

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arXiv 2022年

作者： Sergeyev, Yaroslav D. University of Calabria Rende Italy Lobachevsky University Nizhni Novgorod Russia Institute of High Performance Computing and Networking The National Research Council of Italy Rende Italy

It is well known that the set of algebraic numbers (let us call it A) is countable. In this paper, instead of the usage of the classical terminology of cardinals proposed by Cantor, a recently introduced methodology using ①-based infinite numbers is applied to measure the set A (where the number ① is called grossone). Our interest to this methodology is explained by the fact that in certain cases where cardinals allow one to say only whether a set is countable or it has the cardinality of the continuum, the ①-based methodology can provide a more accurate measurement of infinite sets. In this article, lower and upper estimates of the number of elements of A are obtained. Both estimates are expressed in ①-based *** Codes 11R04, 11B99, 00A30, 05A99, 03A05, 00A35 Copyright © 2022, The Authors. All rights reserved.

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Some paradoxes of Infinity revisited

arXiv

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arXiv 2022年

In this article, some classical paradoxes of infinity such as Galileo's paradox, Hilbert's paradox of the Grand Hotel, Thomson's lamp paradox, and the rectangle paradox of Torricelli are considered. In addition, three paradoxes regarding divergent series and a new paradox dealing with multiplication of elements of an infinite set are also described. It is shown that the surprising counting system of an Amazonian tribe, Pirahã, working with only three numerals (one, two, many) can help us to change our perception of these paradoxes. A recently introduced methodology allowing one to work with finite, infinite, and infinitesimal numbers in a unique computational framework not only theoretically but also numerically is briefly described. This methodology is actively used nowadays in numerous applications in pure and applied mathematics and computer science as well as in teaching. It is shown in the article that this methodology also allows one to consider the paradoxes listed above in a new constructive *** Codes 00A30, 97F30, 40-08, 40A05, 03A05, 97E40, 97C30 Copyright © 2022, The Authors. All rights reserved.

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A view of computational models for image segmentation

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Annali dell'Universita di Ferrara 2022年第2期68卷 277-294页

作者： Antonelli, Laura De Simone, Valentina di Serafino, Daniela Institute for High Performance Computing and Networking (ICAR) CNR Via Pietro Castellino 111 Napoli 80131 Italy Department of Mathematics and Physics University of Campania “Luigi Vanvitelli” viale Abramo Lincoln 5 Caserta 81100 Italy Department of Mathematics and Applications “R. Caccioppoli” University of Naples Federico II Via Cintia Monte S. Angelo Napoli 80126 Italy

Image segmentation is a central topic in image processing and computer vision and a key issue in many applications, e.g., in medical imaging, microscopy, document analysis and remote sensing. According to the human perception, image segmentation is the process of dividing an image into non-overlapping regions. These regions, which may correspond, e.g., to different objects, are fundamental for the correct interpretation and classification of the scene represented by the image. The division into regions is not unique, but it depends on the application, i.e., it must be driven by the final goal of the segmentation and hence by the most significant features with respect to that goal. Thus, image segmentation can be regarded as a strongly ill-posed problem. A classical approach to deal with ill posedness consists in incorporating in the model a-priori information about the solution, e.g., in the form of penalty terms. In this work we provide a brief overview of basic computational models for image segmentation, focusing on edge-based and region-based variational models, as well as on statistical and machine-learning approaches. We also sketch numerical methods that are applied in computing solutions to these models. In our opinion, our view can help the readers identify suitable classes of methods for solving their specific problems. © 2022, The Author(s).

关键词： Ill-posed problems Image segmentation Machine-learning Numerical optimization

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Comparative Analysis of Ai Algorithms on Real Medical Data for Chronic Pain Detection

SSRN

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SSRN 2025年

作者： Macrì, Davide Comito, Carmela Forestiero, Agostino Metlichin, Elisabetta Giusti, Gian Domenico Ramacciati, Nicola Institute for High-Performance Computing and Networking National Research Council Italy Residenza Protetta"C.R.A. Casa Benedetta" Sigillo Perugia Italy Perugia Hospital Perugia Italy Department of Medicine and Surgery Perugia University Italy Department of Pharmacy Health Sciences and Nutritional Sciences University of Calabria Italy

Background and ObjectiveChronic pain is a pervasive healthcare challenge with profound implications for patient well-being, clinical decision-making, and resource allocation. Traditional methods of chronic pain detection often rely on subjective assessments and manual interpretation of clinical documentation, which can be time-consuming and prone to variability. Integrating Artificial Intelligence (AI) into healthcare offers promising opportunities to address these challenges, enabling more efficient, accurate, and standardized approaches to chronic pain *** study investigates the use of AI to detect chronic pain through the automated analysis of clinical notes written in Italian. Leveraging machine learning (ML) and natural language processing (NLP) techniques, the research aims to advance the understanding of chronic pain documentation patterns while demonstrating the potential for scalable, data-driven solutions in nursing and medical *** & MaterialsA dataset of 1,008 anonymized clinical notes, including 284 chronic and 724 non-chronic pain cases, was analyzed using advanced machine learning and text-processing techniques. The approach utilized term frequency-inverse document frequency (TF-IDF) representations to process the text data. Key metrics such as precision, sensitivity, and specificity were calculated to evaluate the model's performance. Statistical analyses compared word count and unique word usage between chronic and non-chronic pain *** AI-based approach achieved high accuracy, with precision (94\%), sensitivity (91\%), and specificity (93\%). Significant differences were identified between chronic and non-chronic pain notes in word count (73.91 vs. 119.86, p = 0.0021) and unique words (57.27 vs. 92.78, p = 0.0006). These findings highlight the ability of AI to distinguish chronic pain cases based on concise, keyword-rich clinical *** study highlights the effectiveness of artificial intelligence in a

关键词： Nursing

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Automatic Extraction of Interpretable Knowledge to Predict the Survival of Patients with Heart Failure

Automatic Extraction of Interpretable Knowledge to Predict t...

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IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Giovanna Sannino Giuseppe De Pietro Ivanoe De Falco Institute for High-Performance Computing and Networking (ICAR) National Research Council of Italy (CNR) Naples Italy

ISBN: (纸本)9781665439664

Cardiovascular diseases cause the death of almost 18 million people each year. Heart failure takes place when the heart does not pump a sufficient amount of blood to the body and is one of the most common causes of death. Healthcare technologies and Artificial Intelligence algorithms constitute promising solutions useful not only for the monitoring of people's health but also in order to tell which subjects are more prone to this problem, which is information of paramount relevance to save their lives. The goal of this paper is to understand the predictability of mortality of subjects suffering from left ventricular systolic dysfunction who previously experienced heart failures. To perform this important study, a publicly-available data set is considered that contains thirteen pieces of clinical, body, and lifestyle information about 299 subjects. In tackling this data set, not only do we wish to perform classification with reference to subjects' survival/death, but we also wish to automatically extract explainable knowledge about the reasons for the classification proposed. To this aim, we use DEREx, an Artificial Intelligence-based tool that relies on Evolutionary Algorithms and provides users with an easy-to-understand set of IF-THEN rules containing data set parameters. In this way, it performs the selection of the parameters that are the most relevant for the purpose of classification. We have run our experiments following a sound protocol established in the scientific literature for this data set. Our findings show that, apart from automatically obtaining easily interpretable knowledge, DEREx achieves better results in terms of widely-used quality indices as Matthews Correlation Coefficient, accuracy, and F score.

关键词： Heart Knowledge engineering Correlation coefficient Protocols Medical services Evolutionary computation Prediction algorithms

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Unsupervised Training Framework for 3D Point Cloud Object Detection Model 25

Unsupervised Training Framework for 3D Point Cloud Object De...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied computing

作者： Min-Jyun Wu Hsiang-Jui Lin Shih-Hao Hung Chi-Sheng Shih Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan High Performance and Scientific Computing (HPSC) Center National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Center of Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400706295

Point cloud data frames are critical, if not indispensable, for precise robot navigation and localization, but training the object detection models for them remains challenging. Many models require labeled 3D objects to train the model. However, the sparse and occluded 3D point cloud data make it difficult, if not impossible, to automate the labeling process. This work proposes a training framework to generate 3D labels on point clouds to tackle the aforementioned challenges. The proposed method takes advantage of the consecutive presence of the same object on different frames to automate the labeling process. The experimental results show that the unsupervised framework trains a robust model for 3D object detection. On the roadside data, the model archives 90.27% AP for scooters and 91.33% AP for cars. On nuScenes dataset, the framework demonstrates the detection precision doubles on IoU 0.25 and IoU 0.5 when the recalls remain similar, compared to the model trained by the MODEST framework.

关键词： computer systems organization

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