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Computation
Special Issues
Recent Advances in Computation Engineering
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Recent Advances in Computation Engineering

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 25942

Special Issue Editors

Dr. Phivos Mylonas

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 491 32 Corfu, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michael Dossis

E-Mail Website
Guest Editor
Department of Informatics, University of Western Macedonia, 52100 Kastoria, Greece
Interests: design automation; computer architecture; asic design; vhdl/verilog/system-c/system verilog/ada/prolog/c/c++/opencl/matlab; digital electronics; web site development; advanced and parallel architectures
Prof. Dr. Christos Douligeris

E-Mail Website
Guest Editor
Department of Informatics, University of Piraeus, 18534 Piraeus, Greece
Interests: computer networking; security; applications in the medical and health sectors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our era is clearly dominated by computer engineering, both in the form of our everyday business or personal life, as well as in the form of our everyday entertainment of infotainment actions. As this observation applies both to the research community, which is faced with enormous challenges, and the applied industry stakeholders, it is becoming evident that new approaches have to be introduced and invented in order to efficiently handle these computerized times.

The aim of the SEEDA-CECNSM conference series, and by association of the proposed Special Issue, is formed around four (4) main axes. The first axis focuses on design automation, whereas the second axis focuses on computer networks and communications. The third axis establishes a solid background for computer engineering tasks, and the fourth axis deals with the arising world of social media and related e-technologies. The ultimate task of all aforementioned topics is the facilitation of respective human actions associated to the associated computational tasks in order to constitute the life of involved individuals easier with respect to their everyday life.

This Special Issue aims to bring together interdisciplinary approaches that focus on the application of innovative, as well as existing computational engineering methodologies. Since typical computational data are typically dominated by medium, data or semantic heterogeneities and are dynamic in nature, computer science researchers are obliged and encouraged to develop new suitable algorithms, tools, and applications to efficiently tackle them, whereas existing ones need to be adapted to the individual special characteristics using traditional methodologies. Thus, the current Special Issue is fully open to all who want to contribute by submitting a relevant research manuscript.

In addition to the open call, selected papers which were presented at SEEDA-CECNSM 2020 are invited to be submitted as extended versions to this Special Issue of the journal Computation. The conference paper should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter and include a statement on what has been changed compared to the original conference paper. Each submission to this journal issue should contain at least 60% of new material, e.g., in the form of technical extensions, more in-depth evaluations or additional use cases.

All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in Open Access format in Computation and collected together on this Special Issue website.

Assoc. Prof. Dr. Phivos Mylonas
Prof. Dr. Michael Dossis
Prof. Dr. Christos Douligeris
Guest Editors

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. Computation 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.

Published Papers (7 papers)

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Research

15 pages, 502 KiB  
Article
Deep Learning for Fake News Detection in a Pairwise Textual Input Schema
by Despoina Mouratidis, Maria Nefeli Nikiforos and Katia Lida Kermanidis
Computation 2021, 9(2), 20; https://doi.org/10.3390/computation9020020 - 17 Feb 2021
Cited by 22 | Viewed by 5542
Abstract
In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake [...] Read more.
In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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11 pages, 3546 KiB  
Article
A Power Dissipation Monitoring Circuit for Intrusion Detection and Botnet Prevention on IoT Devices
by Dimitrios Myridakis, Paul Myridakis and Athanasios Kakarountas
Computation 2021, 9(2), 19; https://doi.org/10.3390/computation9020019 - 06 Feb 2021
Cited by 3 | Viewed by 2390
Abstract
Recently, there has been a sharp increase in the production of smart devices and related networks, and consequently the Internet of Things. One concern for these devices, which is constantly becoming more critical, is their protection against attacks due to their heterogeneity and [...] Read more.
Recently, there has been a sharp increase in the production of smart devices and related networks, and consequently the Internet of Things. One concern for these devices, which is constantly becoming more critical, is their protection against attacks due to their heterogeneity and the absence of international standards to achieve this goal. Thus, these devices are becoming vulnerable, with many of them not even showing any signs of malfunction or suspicious behavior. The aim of the present work is to introduce a circuit that is connected in series with the power supply of a smart device, specifically an IP camera, which allows analysis of its behavior. The detection circuit operates in real time (real-time detection), sampling the supply current of the device, processing the sampled values and finally indicating any detection of abnormal activities, based on a comparison to normal operation conditions. By utilizing techniques borrowed by simple power analysis side channel attack, it was possible to detect deviations from the expected operation of the IP camera, as they occurred due to intentional attacks, quarantining the monitored device from the rest of the network. The circuit is analyzed and a low-cost implementation (under 5US$) is illustrated. It achieved 100% success in the test results, showing excellent performance in intrusion detection. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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17 pages, 2362 KiB  
Article
ESTA: Educating Adolescents in Sustainable Travel Urban Behavior through Mobile Applications Using Motivational Features
by Maria Eftychia Angelaki, Theodoros Karvounidis and Christos Douligeris
Computation 2021, 9(2), 15; https://doi.org/10.3390/computation9020015 - 02 Feb 2021
Viewed by 2893
Abstract
This paper proposes the use of motivational features in mobile applications to support adolescents’ education in sustainable travel urban behavior, so that they become more mindful of their environmental impact. To this effect, existing persuasive strategies are adopted, implemented, and integrated into six [...] Read more.
This paper proposes the use of motivational features in mobile applications to support adolescents’ education in sustainable travel urban behavior, so that they become more mindful of their environmental impact. To this effect, existing persuasive strategies are adopted, implemented, and integrated into six simulated screens of a prospective mobile application named ESTA, designed for this purpose through a user-centered design process. These screens are then assessed by secondary education pupils, the outcome of which is analyzed and presented in detail. The analysis takes into consideration the possibility for the daily use of ESTA in order for the adolescents to foster an eco-friendly and healthy transit attitude and make more sustainable mobility choices that will follow them throughout their life. The potential effectiveness of ESTA is demonstrated via two use cases: the “Daily Commuting” case is addressed towards adolescents who want to move within their area of residence or neighborhood following their daily routine and activities, while the “Weekend Entertainment” is addressed towards adolescents who want to move using the available public transport modes, encouraging them to adopt greener weekend travel habits. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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34 pages, 38945 KiB  
Article
The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets
by Evangelos Maltezos, Athanasios Douklias, Aris Dadoukis, Fay Misichroni, Lazaros Karagiannidis, Markos Antonopoulos, Katerina Voulgary, Eleftherios Ouzounoglou and Angelos Amditis
Computation 2021, 9(2), 12; https://doi.org/10.3390/computation9020012 - 29 Jan 2021
Cited by 9 | Viewed by 4062
Abstract
Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) [...] Read more.
Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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25 pages, 1630 KiB  
Article
PPDM-TAN: A Privacy-Preserving Multi-Party Classifier
by Maria Eleni Skarkala, Manolis Maragoudakis, Stefanos Gritzalis and Lilian Mitrou
Computation 2021, 9(1), 6; https://doi.org/10.3390/computation9010006 - 16 Jan 2021
Cited by 4 | Viewed by 3148
Abstract
Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, process, and manage enormous amounts of data while [...] Read more.
Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, process, and manage enormous amounts of data while ensuring the preservation of private information. Cryptography, as shown by previous research, is the most accurate approach to acquiring knowledge while maintaining privacy. In this paper, we present an extension of a privacy-preserving data mining algorithm, thoroughly designed and developed for both horizontally and vertically partitioned databases, which contain either nominal or numeric attribute values. The proposed algorithm exploits the multi-candidate election schema to construct a privacy-preserving tree-augmented naive Bayesian classifier, a more robust variation of the classical naive Bayes classifier. The exploitation of the Paillier cryptosystem and the distinctive homomorphic primitive shows in the security analysis that privacy is ensured and the proposed algorithm provides strong defences against common attacks. Experiments deriving the benefits of real world databases demonstrate the preservation of private data while mining processes occur and the efficient handling of both database partition types. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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22 pages, 6039 KiB  
Article
A Skyline-Based Decision Boundary Estimation Method for Binominal Classification in Big Data
by Christos Kalyvas and Manolis Maragoudakis
Computation 2020, 8(3), 80; https://doi.org/10.3390/computation8030080 - 10 Sep 2020
Viewed by 3207
Abstract
One of the most common tasks nowadays in big data environments is the need to classify large amounts of data. There are numerous classification models designed to perform best in different environments and datasets, each with its advantages and disadvantages. However, when dealing [...] Read more.
One of the most common tasks nowadays in big data environments is the need to classify large amounts of data. There are numerous classification models designed to perform best in different environments and datasets, each with its advantages and disadvantages. However, when dealing with big data, their performance is significantly degraded because they are not designed—or even capable—of handling very large datasets. The current approach is based on a novel proposal of exploiting the dynamics of skyline queries to efficiently identify the decision boundary and classify big data. A comparison against the popular k-nearest neighbor (k-NN), support vector machines (SVM) and naïve Bayes classification algorithms shows that the proposed method is faster than the k-NN and the SVM. The novelty of this method is based on the fact that only a small number of computations are needed in order to make a prediction, while its full potential is revealed in very large datasets. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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20 pages, 2438 KiB  
Article
Algebraic Analysis of a Simplified Encryption Algorithm GOST R 34.12-2015
by Evgenia Ishchukova, Ekaterina Maro and Pavel Pristalov
Computation 2020, 8(2), 51; https://doi.org/10.3390/computation8020051 - 28 May 2020
Cited by 4 | Viewed by 3553
Abstract
In January 2016, a new standard for symmetric block encryption was established in the Russian Federation. The standard contains two encryption algorithms: Magma and Kuznyechik. In this paper we propose to consider the possibility of applying the algebraic analysis method to these ciphers. [...] Read more.
In January 2016, a new standard for symmetric block encryption was established in the Russian Federation. The standard contains two encryption algorithms: Magma and Kuznyechik. In this paper we propose to consider the possibility of applying the algebraic analysis method to these ciphers. To do this, we use the simplified algorithms Magma ⊕ and S-KN2. To solve sets of nonlinear Boolean equations, we choose two different approaches: a reduction and solving of the Boolean satisfiability problem (by using the CryptoMiniSat solver) and an extended linearization method (XL). In our research, we suggest using a security assessment approach that identifies the resistance of block ciphers to algebraic cryptanalysis. The algebraic analysis of an eight-round Magma (68 key bits were fixed) with the CryptoMiniSat solver demanded four known text pairs and took 3029.56 s to complete (the search took 416.31 s). The algebraic analysis of a five-round Magma cipher with weakened S-boxes required seven known text pairs and took 1135.61 s (the search took 3.36 s). The algebraic analysis of a five-round Magma cipher with disabled S-blocks (equivalent value substitution) led to getting only one solution for five known text pairs in 501.18 s (the search took 4.92 s). The complexity of the XL algebraic analysis of a four-round S-KN2 cipher with three text pairs was 236.33 s (took 1.191 Gb RAM). Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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