Special Issue "Symmetry in Software Engineering and E-learning"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer Science and Symmetry/Asymmetry".

Deadline for manuscript submissions: 31 August 2022 | Viewed by 1521

Special Issue Editor

Prof. Dr. Roman Tsarev
E-Mail Website
Guest Editor
Department of Information Technology, International Academy of Science and Technologies, 159A, Pokrovsky Blvd., 143582 Moscow, Russia
Interests: software engineering; N-version programming; optimization; E-learning

Special Issue Information

Dear colleagues,

There is a lot of work involved in applying symmetry and asymmetry to computer science and e-learning problems. We are faced with symmetry in data structures, sequences, and time series. We meet symmetry in algorithms of different classes and problems from different fields. Neural networks, bio-inspired and evolutionary algorithms, N-version software, encryption algorithms, blockchain, and software and hardware systems for education and e-learning are often characterized by symmetry or, on the contrary, asymmetry. Symmetry is an exceptional characteristic that has widely been deployed in diverse research fields of software engineering and e-learning.

This Special Issue is intended to present recent progress and developments in the fields of software engineering and e-learning. Interested researchers are invited to submit their work in the form of original research papers or review articles. We welcome original contributions to a variety of emerging areas, including (though not limited to) software engineering, software dependability, object-oriented technology,  programming languages, artificial intelligence, machine leaning, data mining, neural networks, genetic and evolutionary algorithms, human–computer interaction, virtual reality and computer graphics, computer animation, visual and multimedia computing,  render optimization, software economics and metrics, cost modeling and analysis, forecasting and decision support, econometrics, blockchain, Internet and information systems, web-based applications, e-learning, distance education, massive open online courses, and learning management systems.

Prof. Dr. Roman Tsarev
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. Symmetry 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.

Keywords

  • software engineering
  • dependability
  • machine leaning
  • artificial intelligence
  • neural network
  • bio-inspired algorithm
  • statistical learning
  • mathematical logic
  • render optimization
  • blockchain
  • econometrics
  • forecasting
  • e-learning
  • massive open online course
  • learning management system

Published Papers (2 papers)

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Research

Article
Research and Development of a Unified Methodology for Assessing the Resource Efficiency of International Digital Platform Promotion for E-Learning
Symmetry 2022, 14(3), 497; https://doi.org/10.3390/sym14030497 - 28 Feb 2022
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Abstract
Ideas related to the systematization of educational technologies are of great interest. This is due, among other things, to the principles of symmetry. On the basis of their application, the synthesis of well-established facts and principles in international education will be realized. Practical [...] Read more.
Ideas related to the systematization of educational technologies are of great interest. This is due, among other things, to the principles of symmetry. On the basis of their application, the synthesis of well-established facts and principles in international education will be realized. Practical needs that are directed towards the future will also be realized. This paper is devoted to the study and creation of a methodology that makes it possible to evaluate the resource efficiency of an interethnic digital platform. The developed methodology of resource allocation for the promotion of digital educational programs is unified and can be applied in any region or country. Considering the specific legislative acts on the financial support of higher education and the need for professionals in certain areas, this method allows you to give a qualitative assessment and determine the necessary resources for the development and promotion of an interethnic digital educational platform, identifying the most appropriate areas of education depending on the region or country. Full article
(This article belongs to the Special Issue Symmetry in Software Engineering and E-learning)
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Article
Execution Time Prediction for Cypher Queries in the Neo4j Database Using a Learning Approach
Symmetry 2022, 14(1), 55; https://doi.org/10.3390/sym14010055 - 01 Jan 2022
Viewed by 409
Abstract
With database management systems becoming complex, predicting the execution time of graph queries before they are executed is one of the challenges for query scheduling, workload management, resource allocation, and progress monitoring. Through the comparison of query performance prediction methods, existing research works [...] Read more.
With database management systems becoming complex, predicting the execution time of graph queries before they are executed is one of the challenges for query scheduling, workload management, resource allocation, and progress monitoring. Through the comparison of query performance prediction methods, existing research works have solved such problems in traditional SQL queries, but they cannot be directly applied in Cypher queries on the Neo4j database. Additionally, most query performance prediction methods focus on measuring the relationship between correlation coefficients and retrieval performance. Inspired by machine-learning methods and graph query optimization technologies, we used the RBF neural network as a prediction model to train and predict the execution time of Cypher queries. Meanwhile, the corresponding query pattern features, graph data features, and query plan features were fused together and then used to train our prediction models. Furthermore, we also deployed a monitor node and designed a Cypher query benchmark for the database clusters to obtain the query plan information and native data store. The experimental results of four benchmarks showed that the average mean relative error of the RBF model reached 16.5% in the Northwind dataset, 12% in the FIFA2021 dataset, and 16.25% in the CORD-19 dataset. This experiment proves the effectiveness of our proposed approach on three real-world datasets. Full article
(This article belongs to the Special Issue Symmetry in Software Engineering and E-learning)
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