Special Issue "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: 1 December 2020.

Special Issue Editors

Prof. Dr. Phivos Mylonas
Website SciProfiles
Guest Editor
Department of Informatics, Ionion University, Kerkira, Greece
Interests: Knowledge-Assisted Multimedia Content Analysis; Multimedia Information Retrieval; Multimedia Content Personalization; User-adaptive Multimedia Content; User modeling; User profiling; Context Representation and Analysis; Human-computer Interaction
Special Issues and Collections in MDPI journals
Prof. Dr. Michael Dossis
Website
Guest Editor
Department of Informatics, University of Western Macedonia, 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
Website
Guest Editor
Department of Informatics, University of Piraeus, Greece
Interests: computer networks; communications; networking security; medical informatics and emergency response operations
Special Issues and Collections 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 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. Computation is an international peer-reviewed open access quarterly 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 1000 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 (2 papers)

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Research

Open AccessArticle
A Skyline-Based Decision Boundary Estimation Method for Binominal Classification in Big Data
Computation 2020, 8(3), 80; https://doi.org/10.3390/computation8030080 - 10 Sep 2020
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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Open AccessArticle
Algebraic Analysis of a Simplified Encryption Algorithm GOST R 34.12-2015
Computation 2020, 8(2), 51; https://doi.org/10.3390/computation8020051 - 28 May 2020
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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