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Computers
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Selected Papers from ICCSA 2021
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Selected Papers from ICCSA 2021

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 44629

Special Issue Editor

Dr. Osvaldo Gervasi

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Guest Editor
Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy
Interests: parallel and distributed systems; grid computing; cloud computing; virtual reality and scientific visualization; implementation of algorithms for molecular studies; multimedia and internet computing; e-learning
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Special Issue Information

Dear Colleagues,

The 21st International Conference on Computational Science and Applications (ICCSA 2021) will be held on July 5–8, 2021, in collaboration with the University of Cagliari, Italy, hopefully in a blended form (some physical, the remaining online), depending on how the COVID-19 pandemic progresses. Computational science is a main pillar of most of the present research, industrial and commercial activities, and plays a unique role in exploiting information and communication technologies as innovative technologies. The ICCSA Conference offers a real opportunity to discuss new issues, tackle complex problems and find advanced enabling solutions able to shape new trends in computational science. For more information, see: http://www.iccsa.org/.

The authors of a number of selected high-quality full papers will be invited after the conference to submit revised and extended versions of their originally accepted conference papers to this Special Issue of Computers, published by MDPI, in open access format. The selection of these best papers will be based on their ratings in the conference review process, the quality of the presentation during the conference, and the expected impact on the research community. Each submission to this Special Issue should contain at least 50% new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases and a change of title, abstract and keywords. These extended submissions will undergo a peer-review process according to the journal’s rules of action. At least two technical committees will act as reviewers for each extended article submitted to this Special Issue; if needed, additional external reviewers will be invited to guarantee a high-quality reviewing process.

Prof. Dr. Osvaldo Gervasi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (11 papers)

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Research

20 pages, 1838 KiB  
Article
Combining Log Files and Monitoring Data to Detect Anomaly Patterns in a Data Center
by Laura Viola, Elisabetta Ronchieri and Claudia Cavallaro
Computers 2022, 11(8), 117; https://doi.org/10.3390/computers11080117 - 26 Jul 2022
Cited by 2 | Viewed by 3677
Abstract
Context—Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence and machine learning techniques to either log files or monitoring data: the former created by services at [...] Read more.
Context—Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence and machine learning techniques to either log files or monitoring data: the former created by services at run time, while the latter produced by specific sensors directly on the physical or virtual machine. Objectives—We propose a model that exploits information both in log files and monitoring data to identify patterns and detect anomalies over time both at the service level and at the machine level. Methods—The key idea is to construct a specific dictionary for each log file which helps to extract anomalous n-grams in the feature matrix. Several techniques of Natural Language Processing, such as wordclouds and Topic modeling, have been used to enrich such dictionary. A clustering algorithm was then applied to the feature matrix to identify and group the various types of anomalies. On the other side, time series anomaly detection technique has been applied to sensors data in order to combine problems found in the log files with problems stored in the monitoring data. Several services (i.e., log files) running on the same machine have been grouped together with the monitoring metrics. Results—We have tested our approach on a real data center equipped with log files and monitoring data that can characterize the behaviour of physical and virtual resources in production. The data have been provided by the National Institute for Nuclear Physics in Italy. We have observed a correspondence between anomalies in log files and monitoring data, e.g., a decrease in memory usage or an increase in machine load. The results are extremely promising. Conclusions—Important outcomes have emerged thanks to the integration between these two types of data. Our model requires to integrate site administrators’ expertise in order to consider all critical scenarios in the data center and understand results properly. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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21 pages, 649 KiB  
Article
Medical-Waste Chain: A Medical Waste Collection, Classification and Treatment Management by Blockchain Technology
by Hai Trieu Le, Khoi Le Quoc, The Anh Nguyen, Khoa Tran Dang, Hong Khanh Vo, Huong Hoang Luong, Hieu Le Van, Khiem Huynh Gia, Loc Van Cao Phu, Duy Nguyen Truong Quoc, Tran Huyen Nguyen, Ha Xuan Son and Nghia Duong-Trung
Computers 2022, 11(7), 113; https://doi.org/10.3390/computers11070113 - 9 Jul 2022
Cited by 38 | Viewed by 4823
Abstract
To prevent the spread of the COVID-19 pandemic, 2019 has seen unprecedented demand for medical equipment and supplies. However, the problem of waste treatment has not yet been given due attention, i.e., the traditional waste treatment process is done independently, and it is [...] Read more.
To prevent the spread of the COVID-19 pandemic, 2019 has seen unprecedented demand for medical equipment and supplies. However, the problem of waste treatment has not yet been given due attention, i.e., the traditional waste treatment process is done independently, and it is not easy to share the necessary information. Especially during the COVID-19 pandemic, the interaction between parties is minimized to limit infections. To evaluate the current system at medical centers, we also refer to the traditional waste treatment processes of four hospitals in Can Tho and Ho Chi Minh cities (Vietnam). Almost all hospitals are handled independently, lacking any interaction between the stakeholders. In this article, we propose a decentralized blockchain-based system for automating waste treatment processes for medical equipment and supplies after usage among the relevant parties, named Medical-Waste Chain. It consists of four components: medical equipment and supplies, waste centers, recycling plants, and sorting factories. Medical-Waste Chain integrates blockchain-based Hyperledger Fabric technology with decentralized storage of medical equipment and supply information, and securely shares related data with stakeholders. We present the system design, along with the interactions among the stakeholders, to ensure the minimization of medical waste generation. We evaluate the performance of the proposed solution using system-wide timing and latency analysis based on the Hyperledger Caliper engine. Our system is developed based on the hybrid-blockchain system, so it is fully scalable for both on-chain and off-chain-based extensions. Moreover, the participants do not need to pay any fees to use and upgrade the system. To encourage future use of Medical-Waste Chain, we also share a proof-of-concept on our Github repository. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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13 pages, 2919 KiB  
Article
Deploying Serious Games for Cognitive Rehabilitation
by Damiano Perri, Marco Simonetti and Osvaldo Gervasi
Computers 2022, 11(7), 103; https://doi.org/10.3390/computers11070103 - 23 Jun 2022
Cited by 3 | Viewed by 2975
Abstract
The telerehabilitation of patients with neurological lesions has recently assumed significant importance due to the COVID-19 pandemic, which has reduced the possibility of access to healthcare facilities by patients. Therefore, the possibility of exercise for these patients safely in their own homes has [...] Read more.
The telerehabilitation of patients with neurological lesions has recently assumed significant importance due to the COVID-19 pandemic, which has reduced the possibility of access to healthcare facilities by patients. Therefore, the possibility of exercise for these patients safely in their own homes has emerged as an essential need. Our efforts aim to provide an easy-to-implement and open-source methodology that provides doctors with a set of simple, low-cost tools to create and manage patient-adapted virtual reality telerehabilitation batteries of exercises. This is particularly important because many studies show that immediate action and appropriate, specific rehabilitation can guarantee satisfactory results. Appropriate therapy is based on crucial factors, such as the frequency, intensity, and specificity of the exercises. Our work’s most evident result is the definition of a methodology that allows the development of rehabilitation exercises with a limited effect in both economic and implementation terms, using software tools accessible to all. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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15 pages, 4055 KiB  
Article
Computer Vision-Based Inspection System for Worker Training in Build and Construction Industry
by M. Fikret Ercan and Ricky Ben Wang
Computers 2022, 11(6), 100; https://doi.org/10.3390/computers11060100 - 20 Jun 2022
Cited by 2 | Viewed by 2868
Abstract
Recently computer vision has been applied in various fields of engineering successfully ranging from manufacturing to autonomous cars. A key player in this development is the achievements of the latest object detection and classification architectures. In this study, we utilized computer vision and [...] Read more.
Recently computer vision has been applied in various fields of engineering successfully ranging from manufacturing to autonomous cars. A key player in this development is the achievements of the latest object detection and classification architectures. In this study, we utilized computer vision and the latest object detection techniques for an automated assessment system. It is developed to reduce the person-hours involved in worker training assessment. In our local building and construction industry, workers are required to be certificated for their technical skills in order to qualify working in this industry. For the qualification, they are required to go through a training and assessment process. During the assessment, trainees implement an assembly such as electrical wiring and wall-trunking by referring to technical drawings provided. Trainees’ work quality and correctness are then examined by a team of experts manually and visually, which is a time-consuming process. The system described in this paper aims to automate the assessment process to reduce the significant person-hours required during the assessment. We employed computer vision techniques to measure the dimensions, orientation, and position of the wall assembly produced hence speeding up the assessment process. A number of key parts and components are analyzed and their discrepancies from the technical drawing are reported as the assessment result. The performance of the developed system depends on the accurate detection of the wall assembly objects and their corner points. Corner points are used as reference points for the measurements, considering the shape of objects, in this particular application. However, conventional corner detection algorithms are founded upon pixel-based operations and they return many redundant or false corner points. In this study, we employed a hybrid approach using deep learning and conventional corner detection algorithms. Deep learning is employed to detect the whereabouts of objects as well as their reference corner points in the image. We then perform a search within these locations for potential corner points returned from the conventional corner detector algorithm. This approach resulted in highly accurate detection of reference points for measurements and evaluation of the assembly. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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21 pages, 1666 KiB  
Article
Building DeFi Applications Using Cross-Blockchain Interaction on the Wish Swap Platform
by Rita Tsepeleva and Vladimir Korkhov
Computers 2022, 11(6), 99; https://doi.org/10.3390/computers11060099 - 16 Jun 2022
Cited by 9 | Viewed by 4030
Abstract
Blockchain is a developing technology that can provide users with such advantages as decentralization, data security, and transparency of transactions. Blockchain has many applications, one of them is the decentralized finance (DeFi) industry. DeFi is a huge aggregator of various financial blockchain protocols. [...] Read more.
Blockchain is a developing technology that can provide users with such advantages as decentralization, data security, and transparency of transactions. Blockchain has many applications, one of them is the decentralized finance (DeFi) industry. DeFi is a huge aggregator of various financial blockchain protocols. At the moment, the total value locked in these protocols reaches USD 82 billion. Every day more and more new users come to DeFi with their investments. The concept of decentralized finance involves the creation of a single ecosystem of many blockchains that interact with each other. The problem of combining and interacting blockchains becomes crucial to enable DeFi. In this paper, we look at the essence of the DeFi industry, the possibilities of overcoming the problem of cross-blockchain interaction, present our approach to solving this problem with the Wish Swap platform, which, in particular, provides improved fault-tolerance for cross-chain interaction by using multiple backend nodes and multisignatures. We analyze the results of the proposed solution and demonstrate how a prototype pre-sale application can be created based on the proposed concept. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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10 pages, 424 KiB  
Article
Functional Data Analysis for Imaging Mean Function Estimation: Computing Times and Parameter Selection
by Juan A. Arias-López, Carmen Cadarso-Suárez and Pablo Aguiar-Fernández
Computers 2022, 11(6), 91; https://doi.org/10.3390/computers11060091 - 2 Jun 2022
Viewed by 2375
Abstract
In the field of medical imaging, one of the most extended research setups consists of the comparison between two groups of images, a pathological set against a control set, in order to search for statistically significant differences in brain activity. Functional Data Analysis [...] Read more.
In the field of medical imaging, one of the most extended research setups consists of the comparison between two groups of images, a pathological set against a control set, in order to search for statistically significant differences in brain activity. Functional Data Analysis (FDA), a relatively new field of statistics dealing with data expressed in the form of functions, uses methodologies which can be easily extended to the study of imaging data. Examples of this have been proposed in previous publications where the authors settle the mathematical groundwork and properties of the proposed estimators. The methodology herein tested allows for the estimation of mean functions and simultaneous confidence corridors (SCC), also known as simultaneous confidence bands, for imaging data and for the difference between two groups of images. FDA applied to medical imaging presents at least two advantages compared to previous methodologies: it avoids loss of information in complex data structures and avoids the multiple comparison problem arising from traditional pixel-to-pixel comparisons. Nonetheless, computing times for this technique have only been explored in reduced and simulated setups. In the present article, we apply this procedure to a practical case with data extracted from open neuroimaging databases; then, we measure computing times for the construction of Delaunay triangulations and for the computation of mean function and SCC for one-group and two-group approaches. The results suggest that the previous researcher has been too conservative in parameter selection and that computing times for this methodology are reasonable, confirming that this method should be further studied and applied to the field of medical imaging. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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13 pages, 4217 KiB  
Article
Traffic Request Generation through a Variational Auto Encoder Approach
by Stefano Chiesa and Sergio Taraglio
Computers 2022, 11(5), 71; https://doi.org/10.3390/computers11050071 - 29 Apr 2022
Cited by 1 | Viewed by 2127
Abstract
Traffic and transportation forecasting is a key issue in urban planning aimed to provide a greener and more sustainable environment to residents. Their privacy is a second key issue that requires synthetic travel data. A possible solution is offered by generative models. Here, [...] Read more.
Traffic and transportation forecasting is a key issue in urban planning aimed to provide a greener and more sustainable environment to residents. Their privacy is a second key issue that requires synthetic travel data. A possible solution is offered by generative models. Here, a variational autoencoder architecture has been trained on a floating car dataset in order to grasp the statistical features of the traffic demand in the city of Rome. The architecture is based on multilayer dense neural networks for encoding and decoding parts. A brief analysis of parameter influence is conducted. The generated trajectories are compared with those in the dataset. The resulting reconstructed synthetic data are employed to compute the traffic fluxes and geographic distribution of parked cars. Further work directions are provided. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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18 pages, 1398 KiB  
Article
IoTwins: Toward Implementation of Distributed Digital Twins in Industry 4.0 Settings
by Alessandro Costantini, Giuseppe Di Modica, Jean Christian Ahouangonou, Doina Cristina Duma, Barbara Martelli, Matteo Galletti, Marica Antonacci, Daniel Nehls, Paolo Bellavista, Cedric Delamarre and Daniele Cesini
Computers 2022, 11(5), 67; https://doi.org/10.3390/computers11050067 - 28 Apr 2022
Cited by 39 | Viewed by 4550
Abstract
While the digital twins paradigm has attracted the interest of several research communities over the past twenty years, it has also gained ground recently in industrial environments, where mature technologies such as cloud, edge and IoT promise to enable the cost-effective implementation of [...] Read more.
While the digital twins paradigm has attracted the interest of several research communities over the past twenty years, it has also gained ground recently in industrial environments, where mature technologies such as cloud, edge and IoT promise to enable the cost-effective implementation of digital twins. In the industrial manufacturing field, a digital model refers to a virtual representation of a physical product or process that integrates data taken from various sources, such as application program interface (API) data, historical data, embedded sensor data and open data, and that is capable of providing manufacturers with unprecedented insights into the product’s expected performance or the defects that may cause malfunctions. The EU-funded IoTwins project aims to build a solid platform that manufacturers can access to develop hybrid digital twins (DTs) of their assets, deploy them as close to the data origin as possible (on IoT gateway or on edge nodes) and take advantage of cloud-based resources to off-load intensive computational tasks such as, e.g., big data analytics and machine learning (ML) model training. In this paper, we present the main research goals of the IoTwins project and discuss its reference architecture, platform functionalities and building components. Finally, we discuss an industry-related use case that showcases how manufacturers can leverage the potential of the IoTwins platform to develop and execute distributed DTs for the the predictive-maintenance purpose. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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24 pages, 3167 KiB  
Article
Application Prospects of Blockchain Technology to Support the Development of Interport Communities
by Patrizia Serra, Gianfranco Fancello, Roberto Tonelli and Lodovica Marchesi
Computers 2022, 11(5), 60; https://doi.org/10.3390/computers11050060 - 21 Apr 2022
Cited by 7 | Viewed by 3923
Abstract
A key aspect for the efficiency and security of maritime transport is linked to the associated information flows. The optimal management of maritime transport requires the sharing of data in real-time between the various participating organizations. Moreover, as supply chains become increasingly integrated, [...] Read more.
A key aspect for the efficiency and security of maritime transport is linked to the associated information flows. The optimal management of maritime transport requires the sharing of data in real-time between the various participating organizations. Moreover, as supply chains become increasingly integrated, the connectivity of stakeholders must be ensured not only within the single port but also between ports. Blockchain could offer interesting opportunities in this regard and is believed to have a huge impact on the future of the digitization of the port and maritime industry. This document analyzes the state of art and practice of blockchain applications in the maritime industry and explores the application prospects and practical implications of blockchain for building an interport community. The paper uses SWOT analysis to address several research questions concerning the practical impacts and barriers related to the implementation of blockchain technology in port communities and develops a Proof of Concept (PoC) to concretely show how blockchain technology can be applied to roll-on roll-off transport and interport communities in real environments. In this regard, this study intends to contribute to the sector literature by providing a detailed framework that describes how to proceed to choose the correct blockchain scheme and implement the various management and operational aspects of an interport community by benefiting from the blockchain. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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15 pages, 475 KiB  
Article
Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection
by Diogo Ribeiro, Luís Miguel Matos, Guilherme Moreira, André Pilastri and Paulo Cortez
Computers 2022, 11(4), 54; https://doi.org/10.3390/computers11040054 - 8 Apr 2022
Cited by 13 | Viewed by 6083
Abstract
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a key industrial [...] Read more.
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a key industrial task. Since labeling is costly, requiring a manual effort, we focus on unsupervised detection approaches. In particular, we assume a computationally light low-dimensional problem formulation based on angle–torque pairs. Our work is focused on two unsupervised machine learning (ML) algorithms: isolation forest (IForest) and a deep learning autoencoder (AE). Several computational experiments were held by assuming distinct datasets and a realistic rolling window evaluation procedure. First, we compared the two ML algorithms with two other methods, a local outlier factor method and a supervised Random Forest, on older data related with two production days collected in November 2020. Since competitive results were obtained, during a second stage, we further compared the AE and IForest methods by adopting a more recent and larger dataset (from February to March 2021, totaling 26.9 million observations and related to three distinct assembled products). Both anomaly detection methods obtained an excellent quality class discrimination (higher than 90%) under a realistic rolling window with several training and testing updates. Turning to the computational effort, the AE is much lighter than the IForest for training (around 2.7 times faster) and inference (requiring 3.0 times less computation). This AE property is valuable within this industrial domain since it tends to generate big data. Finally, using the anomaly detection estimates, we developed an interactive visualization tool that provides explainable artificial intelligence (XAI) knowledge for the human operators, helping them to better identify the angle–torque regions associated with screw tightening failures. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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14 pages, 671 KiB  
Article
Football Match Line-Up Prediction Based on Physiological Variables: A Machine Learning Approach
by Alberto Cortez, António Trigo and Nuno Loureiro
Computers 2022, 11(3), 40; https://doi.org/10.3390/computers11030040 - 11 Mar 2022
Cited by 5 | Viewed by 5190
Abstract
One of the great challenges for football coaches is to choose the football line-up that gives more guarantees of success. Even though there are several dimensions to analyse the problem, such as the opposing team characteristics. The objective of this study is to [...] Read more.
One of the great challenges for football coaches is to choose the football line-up that gives more guarantees of success. Even though there are several dimensions to analyse the problem, such as the opposing team characteristics. The objective of this study is to identify, based on the players’ physiological variables collected using Global Positioning Systems (GPS), which players are the most suitable to be part of the starting team/line-up. The work was developed in two stages, first with the choice of the most important variables using the Recursive Feature Elimination algorithm, and then using logistic regression on these chosen variables. The logistic regression resulted in an index, called the line-up preparedness index, for the following player positions: Fullbacks, Central Midfielders and Wingers. For the other players’ positions, the model results were not satisfactory. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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