Computer Science <p><img style="float: left; margin-right: 15px; margin-bottom: 5px;" src="" alt="" />The Computer Science Journal (ISSN: 1508-2806; e-ISSN: 2300-7036) is a quarterly published by the AGH University of Krakow Poland since 1999.<br />We publish original papers concerning theoretical and applied computer science problems. The main areas of interest of the journal are theoretical aspects of computer science, soft computing, HPC, cloud and distributed processing and simulation, multimedia systems and computer graphics, and natural language processing.</p> <p>Please note: we don't have any article processing charges, our journal is non-profit. The journal is indexed by Web of Science and SCOPUS.</p> <p> </p> <p> </p> <p><strong>Web of Science Impact Factor = 0.5</strong></p> <p><strong>Scopus Quartile = 3 (32nd - the highest percentile in SCOPUS)</strong></p> <!-- <p> </p> <p>Our journal is indexed in the following services: <a href="">Google Scholar</a>, <a href="">CrossRef metadata search</a>, <a href="">Directory of Open Access Journals</a>, <a href="">Open Archives Initiative</a>, <a href="">Digital Libraries Federation</a>, <a href="">BazTech</a>, <a href="">Index Copernicus</a>, <a href="">Ulrich's Periodicals Directory</a>, <a href="">EBSCOhost Applied Sciences</a>, <a href="">DBLP</a>, <a href="">ERIH PLUS</a>, <strong><a href="">SCOPUS</a> and Emerging Sources Citation Index - part of Clarivate Web of Science</strong>.</p> <p>This is an open access journal in accordance with the <a href="">BOAI</a>definition of open access. The content of the journal is freely available according to the <a href="">Creative Commons License Attribution 4.0 International (CC BY 4.0)</a></p> <p><a href=""><strong>SUBMISSION PAGE DIRECT LINK</strong></a></p> <p>Please note:</p> <ul> <li><strong>We do not apply any Article Processing Charges. Our journal is a fully non-profit endeavour.</strong></li> <li>The journal is Open Access (also free of charges).</li> <li>First Author of the accepted paper receives one complimentary hardcopy.</li> <li>We accept PDF or DOC/DOCX manuscripts for review.</li> <li>For final typesetting we strongly prefer Latex/Bibtex. If the authors of the accepted paper are unable to prepare the paper in Latex it will be translated to Latex - for the cost of likely significant delay in publishing. </li> <li>You may use our <a href="">Overleaf template</a> to prepare your paper.</li> <li>We review Survey papers - but only if the authors cite in their paper 3 recent papers by themselves, devoted to the area of the survey. We do not accept nor review surveys authored by non-experts.</li> <li>The paper submitted to our journal is expected to be 15-20 pages long.</li> <li>You are free to publish the early version of the paper in Arxiv, Research Gate and similar websites - but the paper should be updated with the final version, after the paper is accepted and published.</li> <li>We speed up the publication process by publishing early birds versions of the paper (with DOI).</li> <li>The submitted paper should follow typical guidelines for scientific publications - see for example this <a href="">Tutorial</a> by Jennifer Widom.</li> <li>We are using <a href="">IThenticate</a> to prevent (self)plagiarism.</li> <li>You can check our position at <a href=";tip=sid&amp;clean=0">Scimago Journal &amp; Country Rank</a>.</li> </ul> --> en-US (Computer Science Journal) (Computer Science Journal) Sun, 10 Mar 2024 19:52:12 +0100 OJS 60 A Survey on Syntactic Pattern Recognition Methods in Bioinformatics <p>Formal tools and models of syntactic pattern recognition which are used in bioinformatics are introduced and characterized in the paper. They include, among others: stochastic (string) grammars and automata, hidden Markov models, programmed grammars, attributed grammars, stochastic tree grammars, Tree Adjoining Grammars (TAGs), algebraic dynamic programming, NLC- and NCE-type graph grammars, and algebraic graph transformation systems. The survey of applications of these formal tools and models in bioinformatics is presented.</p> Mariusz Flasiński Copyright (c) 2024 Computer Science Sun, 10 Mar 2024 00:00:00 +0100 Using Deep Neural Networks to Improve the Precision of Fast-Sampled Particle Timing Detectors <p>Measurements from particle timing detectors are often affected by the time walk effect caused by statistical fluctuations in the charge deposited by passing particles. The constant fraction discriminator (CFD) algorithm is frequently used to mitigate this effect both in test setups and in running experiments, such as the CMS-PPS system at the CERN’s LHC. The CFD is simple and effective but does not leverage all voltage samples in a time series. Its performance could be enhanced with deep neural networks, which are commonly used for time series analysis, including computing the particle arrival time. We evaluated various neural network architectures using data acquired at the test beam facility in the DESY-II synchrotron, where a precise MCP (MicroChannel Plate) detector was installed in addition to PPS diamond timing detectors. MCP measurements were used as a reference to train the networks and compare the results with the standard CFD method. Ultimately, we improved the timing precision by 8% to 23%, depending on the detector's readout channel. The best results were obtained using a UNet-based model, which outperformed classical convolutional networks and the multilayer perceptron.</p> Mateusz Kocot, Krzysztof Misan, Leszek Grzanka, Valentina Avati, Edoardo Bossini, Nicola Minafra Copyright (c) 2024 Computer Science Sun, 10 Mar 2024 00:00:00 +0100 Machine Learning based Event Reconstruction for the MUonE Experiment <p>As currently operating high energy physics experiments produce a huge amount of data, new methods of fast and efficient event reconstruction are necessary to handle the immense load. Storing the unprocessed data is not feasible, forcing experiments to process the data online employing the algorithms of quality provided for the offline analysis, but within strict time constraints. In the MUonE experiment the machine learning based event reconstruction techniques are being implemented and tested in order to provide efficient online reduction of data and to maximize the statistical power of the final physics measurement.</p> Miłosz Zdybał, Marcin Kucharczyk, Marcin Wolter Copyright (c) 2024 Computer Science Sun, 10 Mar 2024 00:00:00 +0100 Generalizing Clustering Inferences with ML Augmentation of Ordinal Survey Data <p>In this paper, we attempt to generalize the ability to achieve quality inferences of survey data for a larger population through data augmentation and unification. Data augmentation techniques have proven effective in enhancing models' performance by expanding the dataset's size. We employ ML data augmentation, unification, and clustering techniques. First, we augment the \textit{limited} survey data size using data augmentation technique(s). Next, we carry out data unification, followed by clustering for inferencing. <br><br>We took two benchmark survey datasets to demonstrate the effectiveness of augmentation and unification. One is on features of students to be entrepreneurs, and the second is breast cancer survey data. We compare the results of the inference obtained from the raw survey data and the newly converted data. The results of this study indicate that the machine learning approach, data augmentation with the unification of data followed by clustering, can be beneficial for generalizing the inferences drawn from the survey data.</p> Bhupendera Kumar, Rajeev Kumar Copyright (c) 2024 Computer Science Sun, 10 Mar 2024 00:00:00 +0100 Efficient Selection Methods in Evolutionary Algorithms <p>Evolutionary algorithms mimic some elements of the theory of evolution. The survival of individuals and the possibility of producing offspring play a huge role in the process of natural evolution. This process is called a natural selection. <br />This mechanism is responsible for eliminating poor population members and gives the possibility of development for good ones. The evolutionary algorithm - an instance of evolution in the computer environment also requires a selection method, a computer version of natural selection. Widely used standard selection methods applied in evolutionary algorithms are usually derived from nature and prefer competition, randomness and some kind of ``fight'' among individuals. But computer environment is quite different from nature. Computer populations of individuals are usually small, they easily suffer from a premature convergence to local extremes. To avoid this drawback, computer selection methods must have different features than natural selection. In the computer selection methods randomness, fight and competition should be controlled or influenced to operate to the desired extent. Several new methods of individual selection are proposed in this work: several kinds of mixed selection, an interval selection and a taboo selection. Also advantages of passing them into the evolutionary algorithm are shown, using examples based on searching for the maximum α-clique problem and traditional TSP in comparison with traditionally considered as very efficient tournament selection, considered ineffective proportional (roulette) selection and similar classical methods.</p> Jarosław Tomasz Stańczak Copyright (c) 2024 Computer Science Sun, 10 Mar 2024 00:00:00 +0100 The Most Current Solutions using Virtual-Reality-Based Methods in Cardiac Surgery -- A Survey <p>There is a widespread belief that VR technologies can provide controlled, multi-sensory, interactive 3D stimulus environments that engage patients in interventions and measure, record and motivate required human performance. In order to investigate state-of-the-art and associated occupations we provided a careful review of 6 leading medical and technical bibliometric databases. Despite the apparent popularity of the topic of VR use in cardiac surgery, only 47 articles published between 2002 and 2022 met the inclusion criteria. Based on them VR-based solutions in cardiac surgery are useful both for medical specialists and for the patients themselves. The new lifestyle required from cardiac surgery patients is easier to implement thanks to VR-based educational and motivational tools. However, it is necessary to develop the above-mentioned tools and compare their effectiveness with Augmented Reality (AR). With the aforementioned reasons, interdisciplinary collaboration between scientists, clinicians and engineers is necessary.</p> Dariusz Mikolajewski, Anna Bryniarska, Piotr M. Wilczek, Maria Myslicka, Adam Sudol, Dominik Tenczynski, Michal Kostro, Dominika Rekawek, Rafal Tichy, Rafal Gasz, Mariusz Pelc, Jaroslaw Zygarlicki, Michal Koziol, Radek Martinek, Radana Kahankova Vilimkova, Dominik Vilimek, Aleksandra Kawala-Sterniuk Copyright (c) 2024 Computer Science Sun, 10 Mar 2024 00:00:00 +0100