Special Issue "Selected Papers from ICCSA 2020"

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

Deadline for manuscript submissions: closed (15 November 2020).

Special Issue Editor

Prof. Dr. Osvaldo Gervasi
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Perugia, 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 20th International Conference on Computational Science and Applications (ICCSA 2020) will be held on 1–4 July 2020 in Cagliari, Italy, in collaboration with the University of Cagliari, Italy. Computational Science is a main pillar in 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 full papers of high quality 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. The selection of these best papers will be based on their ratings in the conference review process, quality of presentation during the conference, and expected impact on the research community. Each submission to this Special Issue should contain at least 50% of 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 papers will be 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 1400 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.

Published Papers (6 papers)

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Research

Open AccessArticle
The Effect of a Phase Change on the Temperature Evolution during the Deposition Stage in Fused Filament Fabrication
Computers 2021, 10(2), 19; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020019 - 01 Feb 2021
Viewed by 450
Abstract
Additive Manufacturing Techniques such as Fused Filament Fabrication (FFF) produce 3D parts with complex geometries directly from a computer model without the need of using molds and tools, by gradually depositing material(s), usually in layers. Due to the rapid growth of these techniques, [...] Read more.
Additive Manufacturing Techniques such as Fused Filament Fabrication (FFF) produce 3D parts with complex geometries directly from a computer model without the need of using molds and tools, by gradually depositing material(s), usually in layers. Due to the rapid growth of these techniques, researchers have been increasingly interested in the availability of strategies, models or data that may assist process optimization. In fact, 3D printed parts often exhibit limited mechanical performance, which is usually the result of poor bonding between adjacent filaments. In turn, the latter is influenced by the temperature field history during deposition. This study aims at evaluating the influence of the phase change from the melt to the solid state undergone by semi-crystalline polymers such as Polylactic Acid (PLA), on the heat transfer during the deposition stage. The energy equation considering solidification is solved analytically and then inserted into a MatLab® code to model cooling in FFF. The deposition and cooling of simple geometries is studied first, in order to assess the differences in cooling of amorphous and semi-crystalline polymers. Acrylonitrile Butadiene Styrene (ABS) was taken as representing an amorphous material. Then, the deposition and cooling of a realistic 3D part is investigated, and the influence of the build orientation is discussed. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2020)
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Open AccessArticle
Anomalies Detection Using Isolation in Concept-Drifting Data Streams
Computers 2021, 10(1), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010013 - 19 Jan 2021
Viewed by 751
Abstract
Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based, etc. In this paper, we present a structured survey [...] Read more.
Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based, etc. In this paper, we present a structured survey of the existing anomaly detection methods for data streams with a deep view on Isolation Forest (iForest). We first provide an implementation of Isolation Forest Anomalies detection in Stream Data (IForestASD), a variant of iForest for data streams. This implementation is built on top of scikit-multiflow (River), which is an open source machine learning framework for data streams containing a single anomaly detection algorithm in data streams, called Streaming half-space trees. We performed experiments on different real and well known data sets in order to compare the performance of our implementation of IForestASD and half-space trees. Moreover, we extended the IForestASD algorithm to handle drifting data by proposing three algorithms that involve two main well known drift detection methods: ADWIN and KSWIN. ADWIN is an adaptive sliding window algorithm for detecting change in a data stream. KSWIN is a more recent method and it refers to the Kolmogorov–Smirnov Windowing method for concept drift detection. More precisely, we extended KSWIN to be able to deal with n-dimensional data streams. We validated and compared all of the proposed methods on both real and synthetic data sets. In particular, we evaluated the F1-score, the execution time, and the memory consumption. The experiments show that our extensions have lower resource consumption than the original version of IForestASD with a similar or better detection efficiency. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2020)
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Open AccessArticle
An Empirical Review of Automated Machine Learning
Computers 2021, 10(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010011 - 13 Jan 2021
Viewed by 779
Abstract
In recent years, Automated Machine Learning (AutoML) has become increasingly important in Computer Science due to the valuable potential it offers. This is testified by the high number of works published in the academic field and the significant efforts made in the industrial [...] Read more.
In recent years, Automated Machine Learning (AutoML) has become increasingly important in Computer Science due to the valuable potential it offers. This is testified by the high number of works published in the academic field and the significant efforts made in the industrial sector. However, some problems still need to be resolved. In this paper, we review some Machine Learning (ML) models and methods proposed in the literature to analyze their strengths and weaknesses. Then, we propose their use—alone or in combination with other approaches—to provide possible valid AutoML solutions. We analyze those solutions from a theoretical point of view and evaluate them empirically on three Atari games from the Arcade Learning Environment. Our goal is to identify what, we believe, could be some promising ways to create truly effective AutoML frameworks, therefore able to replace the human expert as much as possible, thereby making easier the process of applying ML approaches to typical problems of specific domains. We hope that the findings of our study will provide useful insights for future research work in AutoML. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2020)
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Open AccessArticle
Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition
Computers 2021, 10(1), 1; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010001 - 22 Dec 2020
Cited by 2 | Viewed by 685
Abstract
We developed an anthropomorphic multi-finger artificial hand for a fine-scale object grasping task, sensing the grasped object’s shape. The robotic hand was created using the 3D printer and has the servo bed for stand-alone finger movement. The data containing the robotic fingers’ angular [...] Read more.
We developed an anthropomorphic multi-finger artificial hand for a fine-scale object grasping task, sensing the grasped object’s shape. The robotic hand was created using the 3D printer and has the servo bed for stand-alone finger movement. The data containing the robotic fingers’ angular position are acquired using the Leap Motion device, and a hybrid Support Vector Machine (SVM) classifier is used for object shape identification. We trained the designed robotic hand on a few monotonous convex-shaped items similar to everyday objects (ball, cylinder, and rectangular box) using supervised learning techniques. We achieve the mean accuracy of object shape recognition of 94.4%. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2020)
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Open AccessArticle
Folding-BSD Algorithm for Binary Sequence Decomposition
Computers 2020, 9(4), 100; https://0-doi-org.brum.beds.ac.uk/10.3390/computers9040100 - 15 Dec 2020
Cited by 1 | Viewed by 605
Abstract
The Internet of Things (IoT) revolution leads to a range of critical services which rely on IoT devices. Nevertheless, they often lack proper security, becoming the gateway to attack the whole system. IoT security protocols often rely on stream ciphers, where pseudo-random number [...] Read more.
The Internet of Things (IoT) revolution leads to a range of critical services which rely on IoT devices. Nevertheless, they often lack proper security, becoming the gateway to attack the whole system. IoT security protocols often rely on stream ciphers, where pseudo-random number generators (PRNGs) are an essential part of them. In this article, a family of ciphers with strong characteristics that make them difficult to be analyzed by standard methods is described. In addition, we will discuss an innovative technique of sequence decomposition and present a novel algorithm to evaluate the strength of binary sequences, a key part of the IoT security stack. The density of the binomial sequences in the decomposition has been studied experimentally to compare the performance of the presented algorithm with previous works. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2020)
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Open AccessArticle
CogniSoft: A Platform for the Automation of Cognitive Assessment and Rehabilitation of Multiple Sclerosis
Computers 2020, 9(4), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/computers9040093 - 16 Nov 2020
Viewed by 873
Abstract
Cognitive disorders remain a major cause of disability in Multiple Sclerosis (MS). They lead to unemployment, the need for daily assistance, and a poor quality of life. The understanding of the origin, factors, processes, and consequences of cognitive disfunction is key to its [...] Read more.
Cognitive disorders remain a major cause of disability in Multiple Sclerosis (MS). They lead to unemployment, the need for daily assistance, and a poor quality of life. The understanding of the origin, factors, processes, and consequences of cognitive disfunction is key to its prevention, early diagnosis, and rehabilitation. The neuropsychological testing and continuous monitoring of cognitive status as part of the overall evaluation of patients with MS in parallel with clinical and paraclinical examinations are highly recommended. In order to improve health and disease understanding, a close linkage between fundamental, clinical, epidemiological, and socio-economic research is required. The effective sharing of data, standardized data processing, and the linkage of such data with large-scale cohort studies is a prerequisite for the translation of research findings into the clinical setting. In this context, this paper proposes a software platform for the cognitive assessment and rehabilitation of patients with MS called CogniSoft. The platform automates the Beck Depression Inventory (BDI-II) test and diagnostic tests for the evaluation of memory and executive functions based on the nature of Brief International Cognitive Assessment for MS (BICAMS), as well as implementing a set of games for cognitive rehabilitation based on BICAMS. The software architecture, core modules, and technologies used for their implementation are presented. Special attention is given to the development of cognitive tests for diagnostics and rehabilitation. Their automation enables better perception, avoids bias as a result of conducting the classic paper tests of various neurophysiologists, provides easy administration, and allows data collection in a uniform manner, which further enables analysis using statistical and machine learning algorithms. The CogniSoft platform is registered as medical software by the Bulgarian Drug Agency and it is currently deployed in the Neurological Clinic of the National Hospital of Cardiology in Sofia, Bulgaria. The first experiments prove the feasibility of the platform, showing that it saves time and financial resources while providing subjectivity in the interpretation of the cognitive test results. Full article
(This article belongs to the Special Issue Selected Papers from ICCSA 2020)
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