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Selected Papers from the 25th International Conference on Information and...
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Selected Papers from the 25th International Conference on Information and Software Technologies (ICIST 2019)

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

Deadline for manuscript submissions: closed (20 February 2020) | Viewed by 22656

Special Issue Editors

Prof. Dr. Robertas Damaševičius

E-Mail Website
Guest Editor
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: disease diagnostics using artificial intelligence methods
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marcin Woźniak

E-Mail Website
Guest Editor
Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
Interests: computational intellgence; neural networks; image processing; expert systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ICIST Conference is hosted by the biggest technical university in the Baltic States—Kaunas University of Technology (Lithuania). ICIST 2019 connects researchers, engineers, developers, and practitioners from academia and industry, working in all major areas and interdisciplinary areas of information systems, business intelligence, software engineering, and information technology applications. The conference will feature original research and application papers on the theory, design, and implementation of modern information systems, software systems, artificial intelligence systems, and IT applications. In 2019, the conference will be held in Vilnius (Lithuania), on 10–12 October.

Selected papers that are presented at the conference are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. Submitted papers should be extended to the size of regular research or review articles with 50% extension of new results. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together on the Special Issue website. There are no page charges for this journal.

Please prepare and format your paper according to the Instructions for Authors. Use the LaTeX or Microsoft Word template file of the journal (both are available from the Instructions for Authors page). Manuscripts should be submitted online via our susy.mdpi.com editorial system.

Prof. Robertas Damaševičius
Dr. Marcin Woźniak
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. 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 (5 papers)

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24 pages, 25367 KiB  
Article
Indiscernibility Mask Key for Image Steganography
by Piotr Artiemjew and Aleksandra Kislak-Malinowska
Computers 2020, 9(2), 38; https://doi.org/10.3390/computers9020038 - 11 May 2020
Cited by 4 | Viewed by 4447
Abstract
Our concern in this paper is to explore the possibility of using rough inclusions for image steganography. We present our initial research using indiscernibility relation as a steganographic key for hiding information into the stego carrier by means of a fixed mask. The [...] Read more.
Our concern in this paper is to explore the possibility of using rough inclusions for image steganography. We present our initial research using indiscernibility relation as a steganographic key for hiding information into the stego carrier by means of a fixed mask. The information can be embedded into the stego-carrier in a semi-random way, whereas the reconstruction is performed in a deterministic way. The information shall be placed in selected bytes, which are indiscernible with the mask to a fixed degree. The bits indiscernible with other ratios (smaller or greater) form random gaps that lead to somehow unpredictable hiding of information presence. We assume that in our technique it can modify bits, the change of which does not cause a visual modification detectable by human sight, so we do not limit ourselves to the least significant bit. The only assumption is that we do not use the position when the mask we define uses it. For simplicity’s sake, in this work we present its operation, features, using the Least Significant Bit (LSB) method. In the experimental part, we have implemented our method in the context of hiding image into the image. The LSB technique in its simplest form is not resistant to stegoanalisys, so we used the well-known LSB matching method to mask the presence of our steganographic key usage. To verify the resistance to stegoanalisys we have conducted and discussed Chi-square and LSB enhancement test. The positive features of our method include its simplicity and speed, to decode a message we need to hide, or pass to another channel, a several-bit mask, degree of indiscernibility and size of the hidden file. We hope that our method will find application in the art of creating steganographic keys. Full article
(This article belongs to the Special Issue Selected Papers from the 25th International Conference on Information and Software Technologies (ICIST 2019))
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20 pages, 893 KiB  
Article
An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution
by Kiyoharu Tagawa
Computers 2020, 9(2), 32; https://doi.org/10.3390/computers9020032 - 16 Apr 2020
Cited by 3 | Viewed by 3398
Abstract
In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is [...] Read more.
In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is available nowadays due to advanced information technologies. Since the data set is too large to evaluate the probabilistic constraint of CCP, a new data reduction method called Weighted Stratified Sampling (WSS) is proposed to describe a relaxation problem of CCP. An adaptive Differential Evolution combined with a pruning technique is also proposed to solve the relaxation problem of CCP efficiently. The performance of WSS is compared with a well known method, Simple Random Sampling. Then, the proposed approach is applied to a real-world application, namely the flood control planning formulated as CCP. Full article
(This article belongs to the Special Issue Selected Papers from the 25th International Conference on Information and Software Technologies (ICIST 2019))
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21 pages, 376 KiB  
Article
Comparing Static and Dynamic Weighted Software Coupling Metrics
by Henning Schnoor and Wilhelm Hasselbring
Computers 2020, 9(2), 24; https://doi.org/10.3390/computers9020024 - 30 Mar 2020
Cited by 7 | Viewed by 6004
Abstract
Coupling metrics that count the number of inter-module connections in a software system are an established way to measure internal software quality with respect to modularity. In addition to static metrics, which are obtained from the source or compiled code of a program, [...] Read more.
Coupling metrics that count the number of inter-module connections in a software system are an established way to measure internal software quality with respect to modularity. In addition to static metrics, which are obtained from the source or compiled code of a program, dynamic metrics use runtime data gathered, e.g., by monitoring a system in production. Dynamic metrics have been used to improve the accuracy of static metrics for object-oriented software. We study weighted dynamic coupling that takes into account how often a connection (e.g., a method call) is executed during a system’s run. We investigate the correlation between dynamic weighted metrics and their static counterparts. To compare the different metrics, we use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses. Full article
(This article belongs to the Special Issue Selected Papers from the 25th International Conference on Information and Software Technologies (ICIST 2019))
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14 pages, 260 KiB  
Article
On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation
by Piotr Artiemjew and Krzysztof Krzysztof Ropiak
Computers 2020, 9(1), 13; https://doi.org/10.3390/computers9010013 - 15 Feb 2020
Cited by 1 | Viewed by 3655
Abstract
This paper is a continuation of works based on a previously developed new granulation method—homogeneous granulation. The most important new feature of this method compared to our previous ones is that there is no need to estimate optimal parameters. Approximation parameters are selected [...] Read more.
This paper is a continuation of works based on a previously developed new granulation method—homogeneous granulation. The most important new feature of this method compared to our previous ones is that there is no need to estimate optimal parameters. Approximation parameters are selected dynamically depending on the degree of homogeneity of decision classes. This makes the method fast and simple, which is an undoubted advantage despite the fact that it gives a slightly lower level of approximation to our other techniques. In this particular article, we are presenting its performance in the process of missing values absorption. We test selected strategies on synthetically damaged data from the UCI repository. The added value is to investigate the specific performance of our new granulation technique in absorbing missing values. The effectiveness of their absorption in the granulation process has been confirmed in our experiments. Full article
(This article belongs to the Special Issue Selected Papers from the 25th International Conference on Information and Software Technologies (ICIST 2019))
26 pages, 429 KiB  
Article
Modeling Bimodal Social Networks Subject to the Recommendation with the Cold Start User-Item Model
by Robert Albert Kłopotek
Computers 2020, 9(1), 11; https://doi.org/10.3390/computers9010011 - 12 Feb 2020
Cited by 2 | Viewed by 4112
Abstract
This paper describes the modeling of social networks subject to a recommendation. The Cold Start User-Item Model (CSUIM) of a bipartite graph is considered, which simulates bipartite graph growth based on several parameters. An algorithm is proposed to compute parameters of this model [...] Read more.
This paper describes the modeling of social networks subject to a recommendation. The Cold Start User-Item Model (CSUIM) of a bipartite graph is considered, which simulates bipartite graph growth based on several parameters. An algorithm is proposed to compute parameters of this model with desired properties. The primary desired property is that the generated graph has similar graph metrics. The next is a change in our graph growth process due to recommendations. The meaning of CSUI model parameters in the recommendation process is described. We make several simulations generating networks from the CSUI model to verify theoretical properties. Also, proposed methods are tested on real-life networks. We prove that the CSUIM model of bipartite graphs is very flexible and can be applied to many different problems. We also show that the parameters of this model can be easily obtained from an unknown bipartite graph. Full article
(This article belongs to the Special Issue Selected Papers from the 25th International Conference on Information and Software Technologies (ICIST 2019))
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