=Paper=
{{Paper
|id=Vol-3917/paper00
|storemode=property
|title=The evolving landscape of computer science and software engineering: Trends, challenges, and future directions
|pdfUrl=https://ceur-ws.org/Vol-3917/paper00.pdf
|volume=Vol-3917
|authors=Serhiy O. Semerikov,Andrii M. Striuk
|dblpUrl=https://dblp.org/rec/conf/cs-se-sw/X24
}}
==The evolving landscape of computer science and software engineering: Trends, challenges, and future directions==
https://ceur-ws.org/Vol-3917/paper00.pdf
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
The evolving landscape of computer science and software
engineering: Trends, challenges, and future directions
Serhiy O. Semerikov1,2,3,4,5 , Andrii M. Striuk4,1,5
1
Kryvyi Rih State Pedagogical University, 54 Universytetskyi Ave., Kryvyi Rih, 50086, Ukraine
2
Institute for Digitalisation of Education of the NAES of Ukraine, 9 M. Berlynskoho Str., Kyiv, 04060, Ukraine
3
Zhytomyr Polytechnic State University, 103 Chudnivsyka Str., Zhytomyr, 10005, Ukraine
4
Kryvyi Rih National University, 11 Vitalii Matusevych Str., Kryvyi Rih, 50027, Ukraine
5
Academy of Cognitive and Natural Sciences, 54 Gagarin Ave., Kryvyi Rih, 50086, Ukraine
Abstract
The 7th Workshop for Young Scientists in Computer Science & Software Engineering (CS&SE@SW 2024) brought
together researchers, practitioners, and experts to explore the latest advancements, trends, and challenges in the
rapidly evolving fields of computer science and software engineering. The workshop covered a wide range of
topics, including software engineering processes, theoretical computer science, computer systems, and cutting-
edge computer applications. The papers presented at the workshop showcase the innovative research being
conducted by young scientists, highlighting the potential for groundbreaking developments in areas such as
artificial intelligence, machine learning, data analytics, and human-computer interaction. This proceedings
volume provides a comprehensive overview of the research presented at the workshop, organized into four main
chapters: Software Engineering, Theoretical Computer Science, Computer Systems, and Computer Applications.
The Software Engineering chapter focuses on requirements, design, construction, testing, and methodologies,
emphasizing the importance of robust and efficient software development practices. The Theoretical Computer
Science chapter explores advancements in algorithms, data structures, theory of computation, and formal methods,
providing a foundation for future innovations. The Computer Systems chapter discusses developments in computer
architecture, performance, and databases, underlining the critical role of hardware and data management in
modern computing. Finally, the Computer Applications chapter showcases the practical applications of computer
science and software engineering, with a particular focus on graphics, visualization, human-computer interaction,
scientific computing, and artificial intelligence.
Keywords
computer science, software engineering, artificial intelligence, machine learning, human-computer interaction,
data analytics, algorithms, database systems, computer architecture, software development methodologies,
interdisciplinary research, scientific computing, visualization, formal methods, theory of computation
1. CS&SE@SW 2024: at a glance
Workshop for Young Scientists in Computer Science & Software Engineering (CS&SE@SW) is a peer-
reviewed workshop focusing on research advances applications of information technologies.
CS&SE@SW topics of interest since 2018 [1, 2, 3, 4, 5, 6] are:
1. Software engineering
• Software requirements [7, 8]
• Software design [7, 8, 9, 10, 11, 12]
• Software construction [12]
• Software testing [13]
• Software maintenance [7]
• Software engineering management [10]
CS&SE@SW 2024: 7th Workshop for Young Scientists in Computer Science & Software Engineering, December 27, 2024, Kryvyi
Rih, Ukraine
" semerikov@gmail.com (S. O. Semerikov); andrey.n.stryuk@gmail.com (A. M. Striuk)
~ https://acnsci.org/semerikov (S. O. Semerikov); http://mpz.knu.edu.ua/andrij-stryuk/ (A. M. Striuk)
0000-0003-0789-0272 (S. O. Semerikov); 0000-0001-9240-1976 (A. M. Striuk)
© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
1
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
• Software development process [14, 8, 9, 10, 11, 12]
• Software engineering models and methods [15]
• Software quality [7, 8]
• Software engineering professional practice [9]
2. Theoretical computer science
• Data structures and algorithms [16, 17, 18, 19]
• Theory of computation [19]
• Information and coding theory [20]
• Formal methods [20, 15]
3. Computer systems
• Computer architecture and computer engineering [21]
• Computer performance analysis [21]
• Databases [7]
4. Computer applications
• Computer graphics and visualization [20, 22, 14, 15, 23, 24, 25]
• Human-computer interaction [8, 13, 11, 12, 26]
• Scientific computing [20, 22, 21, 27]
• Artificial intelligence [22, 16, 28, 14, 17, 13, 10, 29, 27, 24, 25, 19, 30, 31, 32, 33, 34]
This volume represents the proceedings of the 7th
Workshop for Young Scientists in Computer Science
& Software Engineering (CS&SE@SW 2024), held
in Kryvyi Rih, Ukraine, on December 27, 2024. It
comprises 28 contributed papers that were carefully
peer-reviewed and selected from 64 submissions.
At least two program committee members reviewed
each submission. The papers included in this volume demonstrate the immense potential for ground-
breaking advancements and inspire further research in these dynamic and essential fields.
2. CS&SE@SW 2023 Program Committee
• Nadire Cavus, Near East University, Northern Cyprus [35, 36]
• Stuart Charters, Lincoln University, New Zealand [37, 38]
• Dragoş-Daniel Iordache, National Institute for Research and Development in Informatics - ICI
Bucuresti, Romania [39, 40]
• Orken Mamyrbayev, Institute of Information and Computational Technologies, Kazakhstan [41, 42]
• Bongkyo Moon, Quantum Informatics Research, Korea [43, 44]
• Michael O’Grady, University College Dublin, Ireland [45, 46]
• Grażyna Paliwoda-Pękosz, Krakow University of Economics, Poland [47, 48]
• Nagender Kumar Suryadevara, University of Hyderabad, India [49, 50]
• Tetiana Vakaliuk, Zhytomyr Polytecnic State University, Ukraine [51, 52]
• Nataliia Veretennikova, Lviv Polytechnic National University, Ukraine [53, 54]
• Alejandro Zunino, ISISTAN - UNCPBA & CONICET, Argentina [55, 56]
Additional reviewers:
• Roman Danel, Institute of Technology and Business in České Budějovice, Czechia [57, 58]
• Andriy Dudnik, Taras Shevchenko National University of Kyiv, Ukraine [59, 60]
• Emre Erturk, Eastern Institute of Technology, New Zealand [61, 62]
2
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
• Helena Fidlerová, Slovak University of Technology, Slovakia [63, 64]
• Oleksii Haluza, National Technical University “Kharkiv Polytechnic Institute”, Ukraine [65, 66]
• Pavlo Hryhoruk, Khmelnytskyi National University, Ukraine [67, 68]
• Oleksandr Kolgatin, Simon Kuznets Kharkiv National University of Economics, Ukraine [69, 70]
• Valerii Kontsedailo, Inner Circle, Netherlands [71, 72]
• Hennadiy Kravtsov, Kherson State University, Ukraine [73, 74]
• Vyacheslav Kryzhanivskyy, R&D Seco Tools AB, Sweden [75, 76]
• Andrey Kupin, Kryvyi Rih National University, Ukraine [77, 78]
• Nadiia Lobanchykova, PwC, Netherlands [79, 80]
• Mykhailo Medvediev, ADA University, Azerbaijan [81, 82]
• Vasyl Oleksiuk, Ternopil Volodymyr Hnatiuk National Pedagogical University, Ukraine [83, 84]
• Jaderick P. Pabico, University of the Philippines Los Baños, Philippines [85, 86]
• James B. Procter, University of Dundee, UK [87, 88]
• Oleg Pursky, Kyiv National University of Trade and Economics, Ukraine [89, 90]
• Serhiy Semerikov, Kryvyi Rih State Pedagogical University, Ukraine [91, 92]
• Etibar Seyidzade, Baku Engineering University, Azerbaijan [93, 94]
• Andrii Striuk, Kryvyi Rih National University, Ukraine [95, 96]
• Volodymyr Voytenko, Athabasca University, Canada [97, 98]
3. CS&SE@SW 2024 organizers
The 6th edition of the CS&SE@SW was coordinated by the Academy of Cognitive and Natural Sciences
(ACNS), a non-governmental organisation dedicated to nurturing the growth of researchers’ expertise in
the cognitive and natural sciences arena. ACNS’s mission encompasses enhancing research, safeguarding
rights and liberties, and catering to professional, scientific, social, and other interests.
ACNS is engaged in a spectrum of activities, including:
• Spearheading research initiatives within the cognitive and natural sciences domain and fostering
collaborative ties among researchers.
• Orchestrating conferences, workshops, training sessions, internships, and other platforms for
exchanging and disseminating knowledge in the realm of cognitive and natural sciences.
• Publishing conference proceedings, collections of scholarly works, and scientific journals (https:
//acnsci.org/cms/journals/):
– Educational Dimension (https://acnsci.org/ed)
– Educational Technology Quarterly (https://acnsci.org/etq)
– CTE Workshop Proceedings (https://acnsci.org/cte)
– Science Education Quarterly (https://acnsci.org/seq)
– Journal of Edge Computing (https://acnsci.org/jec)
Among ACNS’s prominent publications is the Diamond Open Access Science Education Quarterly
(SEQ), a peer-reviewed academic journal dedicated to advancing research and practice in science
education across all educational levels. The journal publishes original empirical studies [99, 100],
theoretical frameworks, literature reviews [101, 102], and innovative teaching methodologies [103, 104]
that contribute to the understanding and improvement of science teaching and learning.
3
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
4. CS&SE@SW 2024 articles overview
4.1. Software engineering
In their paper “Optimizing the process of ER diagram creation with PlantUML”, Kurotych and Bulatetska
[7] explore the capabilities of PlantUML, a popular open-source tool for generating diagrams based
on textual descriptions, in the context of creating Entity Relationship Diagrams (ERDs) for relational
databases. The authors identify several shortcomings in PlantUML’s basic functionality for ERD creation
and propose solutions to improve the quality and readability of the generated diagrams.
Figure 1: Excerpts from the paper presentation [7].
The paper describes techniques for enhancing the appearance of ERDs, such as highlighting primary
and foreign keys, removing unnecessary elements, and creating legends for user convenience. The
authors also introduce a plugin module to improve the structure and maintainability of the PlantUML
code (PUML) by organizing it into functions and procedures. This modular approach offers benefits
like standardized styles and reduced code duplication.
Furthermore, the paper presents Sqlant, a tool developed by the authors to automate the generation
of PUML code directly from a PostgreSQL database. Sqlant retrieves database schema information and
generates PUML code that can be used to visualize ERDs in the PlantUML environment. The integration
of PlantUML with automation tools like Sqlant is particularly beneficial in environments where database
structures undergo frequent changes.
Despite the limitations in PlantUML’s official documentation, the authors demonstrate its significant
potential for creating high-quality ERDs and streamlining the database modeling process. The proposed
approaches and tools contribute to the efficiency and effectiveness of development teams working with
relational databases.
In their paper “Design and evaluation of a personalized digital mathematics tutor for grade 6 learners”,
Shokaliuk and Kavetskyi [8] present the development and assessment of an adaptive mathematics
tool aimed at enhancing the learning experience and outcomes for sixth-grade students. The authors
highlight the limitations of traditional assessment methods in catering to the diverse needs of learners
and the potential of technology-enhanced solutions to address these challenges.
The proposed system leverages Python and CustomTkinter to create an engaging and intuitive user
interface that generates adaptive questions, provides immediate feedback, and tracks student progress
4
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 2: Excerpts from the paper presentation [8].
5
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
in real-time. The tool’s architecture consists of three main components: a test generator, a user interface,
and a student performance tracker. The test generator employs rule-based and probabilistic algorithms
to create questions tailored to the student’s ability level and target areas of weakness identified from
performance data.
To evaluate the effectiveness of the adaptive assessment tool, the authors conducted a quasi-experimental
study comparing the experimental group using the tool with a control group receiving traditional
instruction. The study assessed the impact on students’ problem-solving skills, attitudes towards
mathematics, and overall academic achievement. Data were collected through the tool’s log files and
semi-structured interviews, and analyzed using mixed methods, including data mining techniques and
thematic analysis.
The results demonstrate the tool’s positive impact on student learning and engagement. The system’s
adaptive feedback and personalized recommendations led to a 25% reduction in the average number
of attempts required to solve problems correctly. Students also reported increased enjoyment and
confidence in mathematics, with the experimental group showing significantly greater improvements
compared to the control group. The tool’s user-friendly interface, built using Python and CustomTkinter,
was well-received by students, with 85% finding it easy to use and 90% reporting it as motivating.
While the findings highlight the potential of adaptive assessment tools in mathematics education, the
authors acknowledge the study’s limitations, such as the small sample size and the lack of long-term
evaluation. They also outline future research directions, including the integration of machine learning
techniques to further enhance adaptability and the expansion of content coverage to higher grade levels
and more advanced mathematical topics.
In the paper “Methodology for implementing electronic audit projects (SAF–T UA) for large taxpayers
in Ukraine”, Chernukha et al. [9] delve into the intricacies of developing and implementing software
for generating the electronic audit file (SAF–T UA) in the context of large commercial enterprises in
Ukraine. The authors highlight the significance of this transition towards standardizing accounting
practices and aligning with European norms.
The study provides a comprehensive analysis of the challenges and considerations involved in the
SAF-T implementation process. It outlines the main problems, such as the lack of off-the-shelf software
solutions, the need for integration with existing accounting systems, resource allocation, staff training,
Figure 3: Excerpts from the paper presentation [9].
6
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
and compliance with audit conditions specific to Ukraine. The authors propose a general architecture
concept for the software development, emphasizing the importance of a dedicated project team and the
integration of data from various sources, including ERP and CRM systems.
To gain insights into the perceptions and expectations of key stakeholders, the researchers conducted
a survey involving officials from different functional areas within large enterprises. The findings
reveal a generally positive outlook towards the SAF-T implementation, with anticipated benefits such as
optimized accounting processes, reduced administrative costs, and increased transparency in interactions
with tax authorities. However, the survey also highlights challenges, such as the lack of familiarity with
tax authorities’ requirements and the need for extensive staff training.
The paper offers practical recommendations for the project team, covering aspects such as data
control, information security, archiving, and collaboration with fiscal authorities. The authors stress
the importance of involving specialists from various domains, including accounting, IT, merchandising,
and management, to ensure a comprehensive approach to the implementation process.
The study underscores the potential of SAF-T in simplifying tax control, enhancing transparency, and
facilitating Ukraine’s harmonization with European accounting practices. However, it also acknowledges
the technical, organizational, and methodological complexities that must be addressed for successful
implementation.
In the paper “Designing and evaluating an affordable Arduino-based lie detector prototype”, Pravyt-
skyi et al. [13] present the development and assessment of a low-cost lie detection system that combines
physiological sensors with machine learning techniques. The authors highlight the importance of lie
detection in various contexts and the limitations of existing methods, such as polygraphs and fMRI.
The proposed lie detector prototype utilizes an Arduino UNO development board and integrates
temperature, humidity, and pulse sensors to measure physiological responses. The system architecture
consists of three main components: an Arduino sketch for sensor data acquisition, a data collection
program for labeling and storing the data, and a machine learning model for classifying the data
sequences as truth or lies.
The machine learning component employs a long short-term memory (LSTM) neural network
implemented using the Keras library. The model is trained on overlapping sequences of sensor readings
to predict the probability of each sequence corresponding to a lie. The authors describe the data
preprocessing steps, model architecture, and training process in detail.
The lie detector prototype was evaluated on a set of 20 questions designed to elicit a mix of truthful
and deceptive responses. The results showed an accuracy of 55% in classifying true statements and 45%
in classifying lies, with an overall accuracy of 50%. While these results demonstrate the challenges in
developing an accurate lie detection system, they are comparable to the performance of average human
lie detectors and other machine learning approaches reported in the literature.
The authors discuss the limitations of the study, including the controlled laboratory setting, the
limited set of physiological measures, and the use of a single machine learning model for all participants.
They also highlight the ethical considerations surrounding lie detection technologies, such as reliability,
privacy, informed consent, and the potential for misuse or misinterpretation.
The paper concludes by emphasizing the need for further research and development to improve
the accuracy, reliability, and generalizability of affordable lie detection systems. The authors suggest
potential enhancements, such as incorporating additional sensors, developing personalized models, and
integrating behavioral measures. They also stress the importance of addressing the ethical challenges
and considering the appropriate role of lie detectors in various contexts.
In the paper “Development of the Student Simulator game: From concept to code”, Oleksiuk et al. [12]
present their experience in designing and developing an educational game application that simulates a
student’s journey through various computer science disciplines. The authors emphasize the relevance
of using games in the educational process to engage and motivate learners.
The study begins by analyzing different types of educational games employed in computer science
education, such as simulation games, puzzle-based learning games, and role-playing games. Through
a SWOT analysis, the authors justify the choice of simulators and combined gaming applications for
their project. They identify several basic requirements for the Student Simulator game, including a 3D
7
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 4: Excerpts from the paper presentation [13].
interface, multiple game locations, manipulation of object models, and player registration and rating.
The game development process is described in detail, following a project methodology that involves
students and faculty members. The authors create a matrix of game elements to map the main system
components to game features and design a comprehensive game model that combines all the game
locations and player actions.
After a comparative analysis, the authors select Godot as the game engine, Blender for creating
3D graphics, and Firebase for data storage and management. They provide insights into the decision-
making process, considering factors such as affordability, system requirements, team experience, and
tool capabilities.
The paper delves into the technical aspects of game development, including the implementation of
8
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 5: Excerpts from the paper presentation [12].
9
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
player movement, interaction systems, location management, and a virtual operating system called Pan-
daOS. The authors also discuss the integration of mini-games, such as the Bamboo+ visual programming
language and a test system using 3D tablet models.
User registration and authentication are handled using Firebase Authentication, while player data and
progress are stored in the Firestore database. The game also incorporates a rating system to encourage
healthy competition among players.
The authors reflect on the challenges and lessons learned during the development process, highlighting
the importance of teamwork, communication, and the use of project management tools like GitHub.
They also discuss the prospects and potential improvements for the Student Simulator game, such as
the integration of artificial intelligence for personalized learning and the implementation of multiplayer
and collaboration modes.
The paper concludes by emphasizing the modular structure of the Student Simulator game and its
potential for further expansion and improvement. The authors underscore the significance of involving
various specialists in the development process and the importance of promoting the game through a
dedicated website.
In their paper “Information system for generating recommendations for risk-oriented trading strate-
gies based on deep learning”, Rudnichenko et al. [14] present a comprehensive study on the development
and technical aspects of an information system that leverages deep learning models to generate rec-
ommendations for risk-oriented trading strategies on stock exchanges. The authors emphasize the
growing need for specialized tools to automate the analysis of alternatives, identify trends, and evaluate
trading strategies in the face of the increasing volume and complexity of financial data.
The study utilizes a dataset representing exchange trading information on Apple assets obtained from
the Yahoo Finance system. The authors develop a conceptual design for a software system comprising
three functionally independent modules and provide a formal schematization of these modules. They also
create a project of the system, including a diagram of the main components displaying the relationships
between the elements. The development process is carried out in the PyCharm environment, with a
well-organized structure of directories and files to manage the system software.
A graphical user interface with interactive widgets is implemented to facilitate data entry, processing,
and visualization. The authors conduct a thorough analysis of the developed modules, describing
the strategic recommendations they generate for making trading decisions. The obtained results are
interpreted, and their key features are identified. The paper concludes by outlining promising areas for
further research and possible ways to improve the system.
The study’s novelty lies in the adaptation, aggregation, and hybrid software implementation of
various approaches to forming recommendations for trading decisions within a single system built
on a modular architecture, as well as in the development and optimization of different deep learning
models with an assessment of their effectiveness. The proposed system has the potential to enhance
the accuracy and adaptability of trading decisions by integrating deep learning methods and providing
a comprehensive tool for data analysis and strategy evaluation.
In the paper “Modeling and simulating of Duffing pendulum in the moved coordinate system”,
Zemlianukhina et al. [15] propose a mathematical framework for designing novel discrete-time chaotic
systems based on existing ones. The authors’ approach involves applying coordinate transformations
to the domain where the initial system dynamic is defined, focusing on the shift of the 2D system
coordinate origin to define new system state variables that account for this shift.
The authors treat the resulting dynamical system as an interval system with piecewise linear interval
boundaries, enabling them to consider possible uncertainties caused by changes in system parameters
and the presence of nonlinear functions. This approach allows them to rewrite the system into a linear-
like form, simplifying the process of performing coordinate transformations compared to the initial
nonlinear systems. The study transforms the continuous-time system dynamic into a discrete-time
domain to facilitate its implementation in modern digital devices.
The discrete-time transformation enables the authors to define system dynamics using its previous
states to determine the piecewise constant factors in the system equations. The system equation is
designed to leverage information about previous system motions, its motion in the moved coordinate
10
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 6: Excerpts from the paper presentation [14].
11
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 7: Excerpts from the paper presentation [15].
system, and the motions of the considered moved coordinate system. To increase the complexity of
the system dynamic, the authors propose considering its perturbed motions as the difference between
motions in the moved and stationary coordinate systems.
The study demonstrates the application of the proposed approach by considering the Duffing pendu-
lum equations, a well-known chaotic system. The authors show that combining the motion equations of
the core system and the motions of the coordinate system’s origin can lead to the design of novel chaotic
systems with more complex dynamics. The systematic approach presented in this paper offers a solid
foundation for chaotic system design and has the potential to advance the field of secure communication
using chaotic signals.
In their paper “A web-based Kanban application for enhancing agile project management practices”,
Moiseienko et al. [10] present a comprehensive analysis of agile project management in the digital
era, focusing on a comparative study of popular tools and methodologies. The authors examine the
evolution of agile practices from their roots in software development to their application in diverse
contexts.
The study provides a detailed comparison of Scrum and Kanban methodologies, highlighting their
strengths, weaknesses, and suitability for different project types. The authors emphasize that the
choice between Scrum and Kanban depends on various factors, such as project size, complexity, team
composition, and organizational culture. They also discuss the potential benefits of hybrid approaches
that combine elements of both methodologies.
Additionally, the paper analyzes three prominent agile project management tools: Trello, Jira, and
Worksection. The authors evaluate their features, usability, and effectiveness in supporting agile
practices. They provide insights into the factors that organizations should consider when selecting
an agile project management tool, such as project complexity, team size, industry, and organizational
maturity.
To further contribute to the field, the authors present the development and user evaluation of Kards,
a web-based Kanban application designed to facilitate agile adoption. Kards aims to provide a simple
and accessible tool for individuals and teams to manage their projects using the Kanban methodology.
The application incorporates key features and functionalities that support the effective implementation
of Kanban principles, such as a visual Kanban board, task management, collaboration, and analytics.
The study’s findings highlight the potential of Kards to facilitate the adoption of agile project
12
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 8: Excerpts from the paper presentation [10].
management practices, particularly among teams and individuals new to Kanban. By providing a simple
and accessible tool, Kards can help organizations overcome some of the barriers to agile adoption and
realize the benefits of increased transparency, collaboration, and continuous improvement in their
project management efforts.
In their paper “An interactive online trainer for primary school computer science education: Design,
implementation, and theoretical foundations”, Zhdaniuk et al. [11] present an interactive online trainer
designed to address the challenges of introducing computational thinking and digital literacy skills
to young learners. The authors emphasize the importance of exposing students to computer science
concepts and skills from an early age to foster computational thinking, problem-solving abilities, and
digital literacy.
13
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 9: Excerpts from the paper presentation [11].
14
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
The study highlights the challenges of integrating computer science education into primary school
curricula, such as the lack of qualified teachers, shortage of age-appropriate learning resources, and
the need to make computer science concepts engaging, interactive, and accessible to children with
diverse learning styles and backgrounds. The authors propose interactive online trainers as a promising
solution to address these challenges, providing an engaging and accessible platform for students to
learn and practice computer science concepts at their own pace.
The interactive online trainer presented in this paper incorporates game-based learning, multimedia
elements, and self-regulated learning principles to promote student engagement, motivation, and
knowledge construction. The system features three main types of learning activities: image-text
matching, puzzle assembly, and multiple-choice quizzes, which are designed to progressively build
students’ understanding of computer science concepts.
The paper discusses the design principles, software architecture, and key features of the trainer, as
well as the theoretical foundations underpinning its design, including constructivist learning, game-
based learning, multimedia principles, and self-regulated learning. The authors also outline a plan for
evaluating the effectiveness of the trainer in terms of student learning outcomes, engagement, and
motivation using a mixed-methods, quasi-experimental research design.
The study’s findings suggest that the interactive online trainer has the potential to support the
integration of computer science education into primary school curricula and promote early exposure to
computational thinking and digital literacy skills. By providing a simple and accessible tool, the trainer
can help address the challenges of limited teacher expertise and access to age-appropriate learning
materials, thus promoting the widespread adoption of computer science education in primary schools.
4.2. Theoretical computer science
The paper “Overview of modern algorithms for world procedural generation in computer games” by
Laitaruk and Hryshanovych [16] provides a comprehensive survey of popular algorithms used for
procedurally generating game worlds. The authors emphasize the importance of procedural content
generation (PCG) in creating varied and immersive gaming experiences while optimizing development
resources.
The paper systematically examines several key algorithmic approaches:
• Graph grammars and rewriting systems for generating structured game elements like cities,
dungeons, and trees. The time complexity of these methods is analyzed in depth.
• Voronoi diagrams for partitioning game spaces into distinct regions, with a focus on the Fortune’s
algorithm and the impact of using different distance metrics like Manhattan, Euclidean, and
Minkowski.
• Gradient noises, particularly Perlin noise and fractional Brownian noise, for creating natural-
looking terrain, textures, and environmental effects. The usage of these techniques in games like
Minecraft is discussed.
• Cellular automata for generating cave-like structures, mazes, and simulating fluid dynamics, with
an analysis of neighborhood types and transition rules.
• Genetic algorithms for optimizing game world parameters based on desired gameplay features,
represented as a genotype-to-phenotype mapping.
The authors provide a comparative table summarizing the characteristics, use cases, and time com-
plexity of each method. They also discuss the combination of these techniques and the application of
physics-based simulation to enhance the realism and interactivity of the generated worlds.
The paper concludes by highlighting the trade-offs between computational complexity and the quality
and controllability of the generated content, as well as identifying promising areas for future research
in PCG for games.
The paper “Overview of small language models in practice” by Popov et al. [17] delves into the
emerging field of small language models (SLMs) and their practical applications. The authors highlight
15
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 10: Excerpts from the paper presentation [16].
the limitations of large language models (LLMs) in terms of computational resources, privacy concerns,
and generalization capabilities, which have motivated the development of SLMs.
The paper provides an in-depth analysis of the key features and advantages of SLMs, including their
resource efficiency, data privacy, and potential for fine-tuning to specific domains. The authors discuss
the main techniques for obtaining SLMs, such as pruning, knowledge distillation, and quantization,
along with their respective strengths and weaknesses.
The experimental evidence for SLM performance is critically examined, with a focus on recent
benchmarks and case studies. The authors note the challenges in comparing SLMs to LLMs due to
differences in model architectures, training data, and evaluation metrics. They also conduct a novel
question-answering experiment using a set of carefully designed sanity questions to assess the reliability
and common-sense reasoning capabilities of several state-of-the-art SLMs.
The paper addresses the terminological ambiguities surrounding AI and language models, proposing
refined definitions for terms like “SLM”, “local”, and “remote” models to facilitate clearer communication
within the research community.
Finally, the authors provide an overview of the current ecosystem of tools and platforms for managing
and deploying SLMs, highlighting their accessibility and potential for widespread adoption.
The paper concludes by emphasizing the promise of SLMs as a practical and efficient alternative to
LLMs in various applications, while also acknowledging the need for further research to fully understand
their capabilities and limitations.
The paper “Topic modelling of Ukrainian folk songs: A case study on Podillia region” by Petrovych
[18] explores the application of computational methods, particularly Latent Dirichlet Allocation (LDA),
to uncover thematic structures and motifs in the folk songs of the Podillia region in Ukraine. The authors
aim to bridge the gap between traditional folkloristic analysis and modern data-driven approaches.
The study utilizes a dataset of 2,762 folk songs, which undergoes preprocessing steps such as tok-
enization, lemmatization, and stopword removal. The author construct a document-term matrix and
apply LDA to identify the top 20 latent topics, each characterized by a set of keywords representing
distinct thematic clusters.
The results reveal recurrent themes in Podillia folk songs, including seasonal cycles, family rela-
tionships, social rituals, and emotional experiences. The author provide an in-depth interpretation of
each topic, discussing the cultural significance and narrative patterns associated with the identified
16
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 11: Excerpts from the paper presentation [17].
17
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 12: Excerpts from the paper presentation [18].
18
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
keywords.
The paper also addresses the challenges of adapting computational methods to Ukrainian folk song
corpora, such as dealing with colloquial language, regional dialects, and metaphorical expressions. The
author propose strategies for overcoming these obstacles, such as using language-specific preprocessing
tools and incorporating domain knowledge.
To assess the quality of the generated topics, the author employ coherence evaluation metrics and
compare their findings with traditional folkloristic classifications. She find that the computational
approach complements and enriches existing knowledge, providing new insights into the thematic
richness of Podillia folk songs.
The paper concludes by highlighting the potential of computational folkloristics in deepening our
understanding of cultural heritage and oral traditions, while also acknowledging the limitations and
areas for future research, such as refining methodologies and integrating hybrid approaches.
The paper “Bibliometric analysis and experimental assessment of chatbot training approaches”
presents a comprehensive analysis of chatbot training approaches through both bibliometric analysis and
experimental evaluation. The authors, Liashenko and Semerikov [19], make several key contributions:
1. Conduct an extensive bibliometric analysis of 549 publications from Scopus, identifying four key
research clusters:
• Natural language processing
• Application of NLP technologies in society
• Use of machine learning for NLP
• Chatbots in education and service sectors
2. Create and evaluate two novel datasets for chatbot training:
• A 1.9GB corpus from CEUR Workshop Proceedings (predominantly English)
• A 107MB corpus from Information Technologies and Learning Tools journal (predominantly
Ukrainian)
3. Provide a thorough examination of chatbot training approaches:
• Supervised learning (Seq2Seq and Transformer architectures)
• Reinforcement learning (including RLHF)
• Transfer learning methods
4. Present practical fine-tuning experiments:
• Fine-tune GPT-2-XL on the English corpus
• Fine-tune GPT2-uk on the Ukrainian corpus
• Demonstrate working implementations using transformers library
The methodological approach is rigorous, with clear documentation of the bibliometric analysis
process using VOSviewer and careful selection of models and evaluation metrics. The experimental
results validate the effectiveness of transfer learning for domain-specific chatbot development.
The paper’s main limitation is that it doesn’t provide quantitative evaluation metrics for the fine-
tuned models’ performance, though it does present a working prototype interface. However, this is
balanced by the comprehensive theoretical framework and practical implementation details provided.
The paper “Channel extractor for UAV PPM signals” by Smolij et al. [20] addresses the challenge
of efficiently transmitting control signals and data in unmanned aerial vehicle (UAV) communication
systems using pulse-position modulation (PPM). The authors propose a novel hardware solution for
extracting individual pulse-width modulation (PWM) channels from a single PPM signal line, enabling
multi-channel control of UAVs while minimizing wiring complexity.
The paper begins by providing a comprehensive background on UAV communication systems, signal
modulation techniques, and the advantages of PPM for transmitting multiple control signals over a
shared medium. The authors highlight the importance of reliable and responsive communication for
19
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 13: Excerpts from the paper presentation [19].
precise UAV control in various applications, such as reconnaissance, environmental monitoring, and
rescue operations.
The proposed PPM channel extractor system consists of three main components: a counter register,
a user-input channel register, and a compare circuit. The authors present a detailed schematic of the
circuit, implemented using inverters, XOR gates, JK flip-flops, and other logic elements. The operation
of the extractor is thoroughly explained, with a focus on the conversion of the PPM signal to individual
PWM channels based on user-defined channel indices.
The paper also analyses the DC component of the PPM signal and provides mathematical formulas for
its calculation. The authors discuss the relationship between pulse width, frequency, and the modulating
signal, as well as the power spectral density and signal-to-noise ratio of PPM.
The proposed system is simulated using the Micro-Cap software, and the results demonstrate the
successful extraction of PWM signals for single and multiple channels. The authors also observe and
discuss the presence of a minor "glitch" in the output signal, attributing it to the reset time of the
flip-flops and concluding that it does not adversely affect the control process.
The paper concludes by highlighting the flexibility, scalability, and robustness of the proposed PPM
20
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 14: Excerpts from the paper presentation [20].
channel extractor, as well as its potential for integration into existing UAV communication systems.
The authors suggest further improvements, such as eliminating the need for a separate synchronization
signal by incorporating an additional block for automatic circuit reset.
21
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
4.3. Computer systems
The paper “Development of an automated system for preparing mineral raw material samples for discrete
analysis” presents an innovative automated system for preparing mineral raw material samples for
analysis in the mining industry. The authors, Krapyvnyi et al. [21], make several notable contributions:
1. Develop a comprehensive automated sample preparation system integrating:
• Arduino-based embedded control with real-time data processing
• Hydraulic press with precision pressure control
• Custom firmware implementing PID control and state machine logic
• Modular hardware design for flexibility
2. Achieve significant performance improvements over manual methods:
• 65% reduction in processing time (70s vs manual methods)
• 50+ samples/hour throughput
• 0.53% RSD in bulk density measurements
• Equivalent analytical quality (65.4% vs 65.5% Fe content)
3. Create a robust control system demonstrating:
• <2% pressure overshoot
• ±0.1 bar steady-state error
• 500ms response time
• High repeatability across operating range
4. Validate system performance through:
• Extensive pressure control testing
• Bulk density consistency analysis
• Comparative studies with manual methods
• XRF analysis of prepared samples
Figure 15: Excerpts from the paper presentation [21].
22
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
The methodology is thorough, with detailed documentation of the hardware design, control algo-
rithms, and experimental validation. The results convincingly demonstrate the system’s advantages in
throughput, consistency and quality.
The main limitation is the focus on iron ore materials specifically, though the authors note the
system’s adaptability to other sample types. The modular design and flexible control software should
facilitate such extensions.
The paper “Methods of data analysis to study the effectiveness of scientific journal promotion” by
Korotun et al. [23] presents a comprehensive analysis of promotion strategies for a new scientific
journal focused on edge computing. The authors examine data collected from email invitations sent to
researchers worldwide to evaluate the effectiveness of the journal’s outreach efforts.
The study employs various statistical methods and machine learning techniques to analyze patterns
in researcher engagement. The methodology includes six key stages: data collection from ScienceDirect
publications, data cleaning, descriptive analysis, analytical analysis using regression and clustering,
interpretation of results, and formulation of recommendations.
The authors utilize the R programming language to perform their analysis, implementing linear
regression to model the relationship between emails sent and journal visits. They also apply k-means
clustering to segment countries into three distinct groups based on engagement levels:
Cluster 1: Countries showing low interest with minimal mailings and visits
Cluster 2: Countries with outlier behavior, showing either very high or very low engagement
relative to outreach efforts
Cluster 3: Countries with moderate engagement levels requiring further segmentation
Key findings include a strong positive correlation between email invitations and journal visits
(demonstrated through Spearman correlation), with some countries showing conversion rates exceeding
100%. The study provides valuable insights for journal promotion strategies, suggesting targeted
approaches based on regional response patterns.
The paper “Information systems development in accounting: A systematic network study” by
Horodyskyi et al. [27] presents a comprehensive scientometric analysis of research trends in accounting
information systems (AIS). The authors analyze 5,442 scholarly publications from the Web of Science
database, focusing on the intersection of information systems and accounting across multiple disciplines
including computer science, management, business finance, economics, and business.
The study employs sophisticated bibliometric analysis using the “bibliometrix” package in R to
identify key research trends, methodologies, and emerging themes. The authors’ findings reveal several
important developments in the field:
First, the conceptual structure analysis demonstrates two primary research clusters: one focusing
on technological implementation and performance measurement, and another examining financial
transparency and corporate governance. The research shows that while “information” serves as a
central bridging concept between these clusters, there is no dominant research paradigm, suggesting
the field is still evolving.
Second, the authors identify emerging research trends, particularly in blockchain technology, artificial
intelligence, and machine learning during 2020-2024. The study also highlights growing interest in big
data analytics and sustainable development as crucial future directions for AIS research.
Third, the geographical analysis reveals significant contributions from Ukraine, USA, and China, with
increasing participation from developing economies like Indonesia, Malaysia, and Brazil, indicating the
global relevance of AIS research.
4.4. Computer applications
The paper “A modified 3D-2D convolutional neural networks for robust mineral identification: Hyper-
spectral analysis in Djebel Meni (Northwestern Algeria)” by Attallah et al. [22] presents an innovative
23
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 16: Excerpts from the paper presentation [23].
24
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 17: Excerpts from the paper presentation [27].
25
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
approach to mineral classification using hyperspectral imaging data. The authors address the challeng-
ing task of identifying minerals in remote, geologically complex terrains through the integration of
deep learning techniques with hyperspectral remote sensing.
The research focuses on the Djebel Meni region in Northwestern Algeria, utilizing data from NASA’s
Hyperion EO-1 sensor to classify three key clay minerals: illite, kaolinite, and montmorillonite. The
authors propose a hybrid 3D-2D CNN architecture that effectively combines spatial and spectral feature
extraction capabilities. The methodology includes comprehensive preprocessing steps, including bad
bands removal, radiometric calibration, and atmospheric correction using the QUAC module.
A notable contribution is the detailed optimization of the CNN architecture, featuring four 3D
convolutional layers followed by three 2D convolutional layers. This hybrid approach enables the
network to capture both spectral dependencies and spatial features effectively. The model achieves
impressive results with an overall accuracy of 94.26% and a Kappa coefficient of 0.9401, outperforming
traditional methods like SAM and standalone 2D or 3D CNNs.
The experimental validation is thorough, utilizing a balanced dataset split across training (70%),
validation (10%), and testing (20%) sets. The authors implement various optimization techniques,
including batch normalization, L2 regularization, and dropout, to enhance model generalization. The
results are comprehensively evaluated using multiple metrics, including precision, recall, and F1-score
for each mineral class.
The paper makes significant contributions to the field of hyperspectral mineral mapping by:
• Introducing a novel hybrid CNN architecture specifically optimized for mineral classification
• Providing a comprehensive framework for preprocessing hyperspectral data
• Demonstrating superior classification performance compared to existing methods
• Establishing a reproducible methodology for mineral identification in complex geological settings
This research advances the application of deep learning in geological remote sensing and opens new
possibilities for automated mineral mapping in challenging terrains. The authors’ approach could be
particularly valuable for mineral exploration and geological surveys in remote or inaccessible regions.
The paper “Method of semantic features estimation for political propaganda techniques detection
using transformer neural networks” by Krak et al. [24] presents a novel method for detecting political
propaganda techniques using transformer neural networks enhanced with semantic feature analysis.
The authors address the critical challenge of identifying propaganda in media content, which has
become increasingly important in today’s information-rich society.
The proposed method introduces several key innovations:
• Integration of semantic features (text emotionality, bullying, fear, and hate speech) to improve
propaganda detection accuracy
• Modified transformer neural network architecture that processes both text data and numerical
semantic feature vectors
• Enhanced explainability of the model’s decisions through semantic feature analysis
The experimental results demonstrate significant improvements in detection accuracy for several
propaganda techniques. Notable gains were achieved for techniques such as “Red Herring” (9% im-
provement), “Whataboutism” (4% improvement), and “Thought Terminating Cliches” (3% improvement).
The method achieved an average accuracy of 89%, with maximum accuracy reaching 97% for certain
techniques.
The authors also emphasize the method’s contribution to Sustainable Development Goals (SDGs),
particularly SDG 4 (Quality Education) and SDG 16 (Peace, Justice and Strong Institutions), through its
potential to enhance media literacy and strengthen democratic institutions.
The work represents a significant advancement in automated propaganda detection, though there
remain opportunities for further optimization, particularly for techniques where accuracy decreased
or remained unchanged. Future research directions include expanding the set of detected semantic
features to improve detection accuracy for underperforming techniques.
26
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 18: Excerpts from the paper presentation [22].
27
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 19: Excerpts from the paper presentation [24].
The paper “Method for neural network cyberbullying detection in text content with visual analytic”
28
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
by Krak et al. [25] presents an innovative approach to detecting and interpreting cyberbullying in
text using neural networks and visual analytics. The authors recognize the growing significance of
cyberbullying detection systems, particularly given the increasing number of social media users and
decreasing age of users.
The proposed method combines a BERT-based neural network for multi-label classification of cy-
berbullying types with visual analytics to explain the model’s decisions. A key contribution is the
implementation of three complementary visualization approaches:
1. Color palette visualization – highlighting influential words with varying color intensities based
on their impact on classification
2. Local word importance diagrams – showing the contribution of individual words to specific
cyberbullying classifications
3. General word importance diagrams – presenting the overall significance of words across all
cyberbullying types
The authors trained and evaluated their model using the “Cyberbullying Classification” dataset,
achieving impressive performance metrics: Accuracy (0.956478), Precision (0.963677), Recall (0.956478),
and F1-Score (0.960019). These results demonstrate significant improvements over previous approaches,
with accuracy gains of 2.49-9.05% compared to similar studies.
A particular strength of the paper is its focus on explainability in AI decision-making, especially
crucial for sensitive applications like cyberbullying detection. The method integrates LIME (Local
Interpretable Model-agnostic Explanations) for generating interpretable visualizations that help users
understand why specific text segments are classified as cyberbullying.
The authors also connect their work to broader societal impacts, noting its alignment with multiple
UN Sustainable Development Goals (SDGs), including those related to well-being (SDG3), education
(SDG4), gender equality (SDG5), reducing inequalities (SDG10), and promoting justice (SDG16).
The paper concludes by suggesting future research directions, including adaptation for multiple lan-
guages, user studies to assess the impact of visual analytics on human decision-making, and exploration
of alternative interpretation methods.
The paper “Design and implementation of a mobile health application for physical activity tracking and
exercise motivation” by Stepanyuk et al. [28] presents a novel mHealth application aimed at promoting
physical activity and exercise adherence. The authors develop a modular system that incorporates
evidence-based strategies for behavior change, including real-time activity tracking, personalized
goal-setting, and motivational elements.
The paper’s architecture section details five key modules: core, tracking, planning, motivation, and
user interface, along with a synchronization component. The system employs the Model-View-Presenter
(MVP) architectural pattern to ensure modularity and extensibility. Notable implementation features
include sophisticated data privacy measures using SSL/TLS protocols and AES-256 encryption, along
with robust data anonymization techniques.
The authors conducted a preliminary evaluation with 2 participants over a 4-week period, measuring
daily step count, weekly active minutes, and goal achievement rates. While the study showed promising
results in terms of increased physical activity levels and user satisfaction, the extremely small sample
size (n=2) significantly limits the generalizability of the findings.
A key strength of the paper lies in its comprehensive technical documentation and thoughtful system
architecture. However, the evaluation section presents a major limitation due to its minimal participant
pool. The authors acknowledge this limitation and propose several directions for future work, including
larger-scale studies, integration of machine learning algorithms, and adaptation for specific populations
such as older adults or individuals with chronic conditions.
The paper “AI-agent-based system for fact-checking support using large language models” by Kuper-
shtein et al. [29] presents a timely solution for automated fact-checking using Large Language Models
(LLMs). Given the increasing prevalence of disinformation and its societal impact, the authors propose
an AI-based architecture to enhance the efficiency and accuracy of fact verification processes.
29
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 20: Excerpts from the paper presentation [25].
The paper provides a thorough analysis of disinformation’s impact, particularly focusing on Ukraine’s
experience with targeted misinformation campaigns. The authors present compelling statistics from
the European External Action Service’s report, showing that 21.3% of analyzed disinformation incidents
30
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 21: Excerpts from the paper presentation [28].
were directed against Ukraine during 2022-2023.
The core contribution is a comprehensive AI-agent-based system architecture comprising six main
components:
31
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 22: Excerpts from the paper presentation [29].
• User interface
• Request processing module
• Database with RAG implementation
• Web resources module
• Large Language Model integration
• Results analysis module
32
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
The experimental results demonstrate the system’s effectiveness, achieving 90% accuracy in fake news
detection. The authors conducted thorough testing across multiple scenarios, including verification of
claims about Ukrainian power outages and language policy changes. Notably, they openly discuss the
system’s limitations, such as occasional LLM “hallucinations” and the importance of human oversight.
A particular strength of the paper is its practical implementation using modern tools, including
OpenAI’s GPT-4o, Python libraries for web scraping, and Streamlit for the user interface. However, the
evaluation could benefit from a larger sample size of test cases and more rigorous comparative analysis
with existing fact-checking systems.
The paper “Advances in neural text generation: A systematic review (2022-2024)” by Slobodianiuk
and Semerikov [30] presents a comprehensive systematic review of recent developments in neural text
generation. The authors conducted this review to complement an earlier review covering 2015-2021,
focusing specifically on advances made between 2022 and 2024. Using the PRISMA methodology, they
analyzed 43 articles from the Scopus database to identify current trends, approaches, and methodologies
in neural text generation.
The review makes several significant contributions to the field. First, it identifies a clear shift towards
innovative model architectures, particularly Transformer-based models like GPT-2, GPT-3, and BERT,
while noting that traditional approaches like RNNs and LSTMs continue to serve specific applications.
Second, it documents the evolution of evaluation metrics, showing that while BLEU and ROUGE
remain standard, new metrics such as BERTScore have emerged to provide more nuanced assessment
of generated text quality.
A notable finding is the growing diversity in both datasets and applications. The authors observe
increased interest in unlabeled data and the expansion of text generation into specialized domains
such as medical text generation and table-to-text generation. The review also highlights an important
trend in language coverage – while English remains dominant, there is growing research interest in
low-resource languages, indicating a move towards more inclusive language technology development.
Methodologically, the study is robust, employing both automated analysis through large language
models (Claude 3 Sonnet and GPT-4) and human verification to ensure accurate data extraction. The
authors provide detailed comparisons with the previous review period, enabling readers to track the
evolution of the field over time.
The paper is particularly valuable for its systematic categorization of neural network architectures,
evaluation metrics, and applications in text generation. The authors present their findings through clear
tables and figures, making the information easily accessible to researchers and practitioners in the field.
One of the review’s strengths is its thorough examination of methodological trends, revealing that
while traditional approaches persist, innovative architectures – particularly those leveraging attention
mechanisms and transformer-based models – are becoming increasingly prevalent.
The paper concludes with important observations about future research directions, highlighting
open questions regarding quality assessment, domain adaptation, and ethical considerations in text
generation technologies. These insights make the review not just a summary of current work but also a
valuable resource for identifying promising future research directions.
The paper “Automating machine learning: A meta-synthesis of MLOps tools, frameworks and
architectures” by Hanchuk and Semerikov [31] presents a comprehensive meta-synthesis of MLOps
practices, tools, and frameworks. The authors address the growing need for effective operationalization
of machine learning models in production environments, noting that despite advances in ML algorithms,
deployment remains challenging.
The research employs a rigorous meta-synthesis methodology to analyze existing systematic reviews,
examining MLOps from multiple perspectives. The authors particularly focus on three key systematic
reviews from 2022-2023, supplemented with additional literature to provide a holistic view of the MLOps
landscape.
The manuscript makes several significant contributions:
1. Provides a detailed analysis of MLOps definitions, workflows, and core components
2. Identifies common frameworks and architectures facilitating MLOps implementation
33
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
3. Examines tools for creating ML pipelines and operationalizing models
4. Proposes a relationship diagram connecting MLOps principles, processes, and practices
A particularly valuable contribution is the authors’ systematic breakdown of MLOps practices
into key categories including continuous integration/delivery, model versioning, pipeline automation,
monitoring, and lifecycle management. They also address critical aspects such as data security, privacy,
and model explainability.
The manuscript concludes by identifying future research directions, including the need for detailed
implementation recommendations and new tools for automating ML model lifecycles. While compre-
hensive in scope, the work could benefit from more concrete case studies demonstrating the practical
application of the identified practices.
Figure 23: Excerpts from the paper presentation [30].
34
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
This paper makes a valuable contribution to the MLOps field by synthesizing existing knowledge
and providing a structured framework for understanding and implementing MLOps practices. It serves
as both a theoretical foundation and practical guide for organizations seeking to improve their machine
learning operations.
The paper “Research and development of a subtitle management system using artificial intelligence”
by Striuk and Hordiienko [32] presents an innovative AI-powered system for automating the generation
and management of video subtitles. The authors address the critical challenge of making video content
accessible to wider audiences, including individuals with hearing impairments and those who don’t
understand the spoken language, while noting that manual subtitle creation is time-consuming and
labor-intensive.
The proposed system leverages state-of-the-art automatic speech recognition (ASR) and machine
translation (MT) technologies to generate accurate, synchronized subtitles in multiple languages. The
system architecture consists of four main components: a speech recognition module utilizing advanced
acoustic and language models, a machine translation module employing encoder-decoder architecture
with attention mechanisms, a subtitle segmentation and formatting module, and a user-friendly interface
for managing the subtitle generation process.
The paper provides a comprehensive literature review covering key aspects of AI-based subtitle
generation, including speech recognition techniques, machine translation approaches, multimodal
methods, and evaluation methodologies. The authors analyze various approaches, from traditional
hidden Markov models to modern deep learning architectures like CNNs and RNNs, highlighting their
applications in different domains such as educational content and entertainment.
The paper concludes by discussing the implications of the proposed system for subtitle genera-
tion pipelines and identifying directions for future research, including expanding language coverage,
improving domain adaptation, and enhancing contextual understanding. The authors acknowledge
current limitations while emphasizing the system’s potential to significantly improve the efficiency and
accessibility of video content across different domains.
The paper “A comprehensive survey on reinforcement learning-based recommender systems: State-of-
the-art, challenges, and future perspectives” by Rossiiev et al. [33] presents an extensive overview of how
reinforcement learning (RL) is being applied to recommendation systems. The authors systematically
analyze the current state of research in this rapidly evolving field, examining both theoretical foundations
and practical applications.
The paper begins by highlighting the limitations of traditional recommendation approaches like
Figure 24: Excerpts from the paper presentation [31].
35
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 25: Excerpts from the paper presentation [32].
36
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 26: Excerpts from the paper presentation [33].
collaborative filtering and content-based methods, particularly their struggles with dynamic user
preferences and sparse feedback. The authors then present reinforcement learning as a promising
framework to address these challenges by formulating recommendation as a sequential decision-making
process.
The survey provides a thorough examination of how the recommendation problem can be modeled
using the Markov Decision Process (MDP) framework, detailing the construction of states, actions,
and rewards. It explores various RL approaches including model-free methods (Q-learning, SARSA),
model-based methods, policy gradient techniques (REINFORCE, Actor-Critic), and deep reinforcement
learning implementations (DQN, DDPG).
A significant contribution of this work is its analysis of how RL can be integrated with other
recommendation techniques. The authors discuss hybrid approaches combining RL with collaborative
filtering, content-based methods, knowledge graphs, and graph neural networks. This integration allows
systems to leverage the strengths of multiple approaches while mitigating their individual weaknesses.
The paper concludes by identifying key challenges and future research directions, including:
• The need for effective offline reinforcement learning methods
• Scalability and computational efficiency concerns
• Improving explainability and interpretability
• Ensuring robustness against adversarial attacks
• Developing better evaluation metrics and simulation environments
• Expanding real-world applications and case studies
The paper “Research and development of software for hydroacoustic signal analysis using machine
learning techniques” by Poliaiev et al. [34] presents a comprehensive software system for analyzing
hydroacoustic signals using machine learning techniques. The authors address the challenging problem
of underwater acoustic signal processing, which has important applications in navigation, marine
monitoring, and security systems.
The paper begins by establishing the complexity of hydroacoustic signal analysis, noting how
underwater acoustic propagation is affected by various environmental factors including depth, water
composition, and bottom topography. The authors highlight how recent advances in machine learning
have enabled more sophisticated approaches to processing these complex signals.
The proposed system incorporates multiple components: data acquisition, preprocessing, feature
extraction, and machine learning models for classification, regression, and clustering tasks. The
preprocessing pipeline includes denoising, normalization, segmentation, and handling of missing values.
The feature extraction process considers temporal, spectral, and statistical properties of the signals.
The machine learning methodology employs various models, including Support Vector Machines
(SVM), Random Forests, K-Nearest Neighbors (KNN), and Gaussian Mixture Models (GMM). The
37
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 27: Excerpts from the paper presentation [34].
experimental results demonstrate the effectiveness of these approaches, with SVM achieving 94%
accuracy in classification tasks and Support Vector Regression (SVR) showing superior performance in
regression tasks with an R-squared value of 0.65.
A notable contribution is the development of a user-friendly web interface that allows for interactive
signal analysis and visualization. The system’s modular architecture ensures scalability and ease of
integration with other applications through a RESTful API.
The authors validate their approach using real-world hydroacoustic data from government sources,
providing comprehensive evaluation metrics and analysis. The paper concludes by suggesting future
work in areas such as online learning, explainable AI, and distributed computing to further enhance the
system’s capabilities.
The paper “Investigating vulnerabilities of personal data on financial websites” by Fedorenko et al.
[26] explores the critical issue of personal data security on financial websites in the digital age. The
authors emphasize the severe consequences of data breaches, ranging from identity theft to long-term
reputational damage. They analyze the complex interplay of technological, human, and organizational
factors contributing to vulnerabilities.
The paper identifies common attack methods, such as SQL injection, cross-site scripting (XSS),
and phishing, which exploit weaknesses in web application security. It also highlights the increasing
sophistication of cybercriminals and the proliferation of hacking tools. The authors discuss recent
high-profile data breaches, like the Kyivstar incident in December 2023, to underscore the devastating
impact of successful attacks.
To address these challenges, the paper proposes a multi-pronged approach involving proactive
measures by organizations and vigilance by individual users. It outlines strategies like robust security
controls, regular auditing, strong password hygiene, and enabling two-factor authentication. The
authors conduct security assessments of two prominent Ukrainian financial websites, OLX.ua and
Privat24, providing practical insights into their security measures and areas for improvement.
The paper concludes by emphasizing the need for a comprehensive, multi-layered approach to
personal data protection on financial websites. It calls for future research to develop advanced vulnera-
bility detection tools, explore emerging technologies’ security challenges, and evaluate data protection
regulations’ effectiveness.
38
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Figure 28: Excerpts from the paper presentation [26].
5. CS&SE@SW 2024: Conclusion and outlook
The 7th Workshop for Young Scientists in Computer Science & Software Engineering (CS&SE@SW
2024) has once again showcased the impressive depth and breadth of research being conducted by
emerging talents in these rapidly evolving fields. The papers presented at this year’s workshop have
explored cutting-edge topics spanning software engineering, theoretical computer science, computer
systems, and computer applications.
In the field of software engineering, authors have proposed novel approaches for optimizing ER
diagram creation, developing personalized digital mathematics tutors, implementing electronic audit
projects, designing affordable lie detector prototypes, and creating engaging educational games. These
contributions demonstrate the potential for software engineering methodologies to address real-world
challenges and enhance user experiences across diverse domains.
Theoretical computer science papers have delved into modern algorithms for procedural content
generation, the practical applications of small language models, topic modeling of folk songs, and
advanced methods for chatbot training. These studies highlight the ongoing evolution of foundational
computer science concepts and their increasing relevance to contemporary problems.
In the realm of computer systems, researchers have developed innovative solutions for automat-
ing mineral sample preparation, analyzing scientific journal promotion strategies, and conducting
scientometric analyses of accounting information systems. These works underscore the critical role
of computer systems in enabling efficient data management, analysis, and decision-making across
39
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
industries.
Finally, the computer applications track has featured groundbreaking research on hyperspectral
mineral classification, propaganda detection using semantic features, cyberbullying identification
through visual analytics, mobile health apps for physical activity promotion, AI-based fact-checking
systems, and advanced subtitle generation techniques. These papers illustrate the immense potential
for computer applications to address societal challenges, improve public health, combat disinformation,
and enhance accessibility.
As we reflect on the success of CS&SE@SW 2024, it is evident that the workshop has provided a
valuable platform for young scientists to share their research, exchange ideas, and foster collaborations.
The high-quality submissions and thought-provoking discussions have not only advanced the state-of-
the-art in computer science and software engineering but also laid the foundation for future innovations.
Looking ahead, CS&SE@SW remains committed to nurturing the growth of emerging researchers
and facilitating the dissemination of cutting-edge knowledge. As the fields of computer science and
software engineering continue to evolve at an unprecedented pace, workshops like CS&SE@SW will
play an increasingly crucial role in shaping the future of these disciplines.
We eagerly anticipate the next edition of the workshop, to be held on December 26, 2025, in Kryvyi
Rih, Ukraine, where we will once again convene to explore the frontiers of computer science and
software engineering research.
Acknowledgments: We would like to express our sincere gratitude to all those who contributed to the success of this
workshop. First and foremost, we extend our appreciation to the authors for submitting their high-quality research and to the
program committee members and reviewers for their valuable time and expertise in evaluating the submissions.
We acknowledge the support of CEUR-WS.org, which hosts and publishes the workshop proceedings, thereby providing
an open-access platform for disseminating the presented research.
Finally, we extend our gratitude to Tetiana A. Vakaliuk for her generous support.
We hope that the discussions and collaborations initiated during this event will continue to inspire future research and
innovation in our field.
Declaration on Generative AI: During the preparation of this work, the authors used Claude 3 Opus and Claude 3.5
Sonnet in order to: Drafting content, Abstract drafting. After using these tools, the authors reviewed and edited the content
as needed and takes full responsibility for the publication’s content.
References
[1] A. E. Kiv, S. O. Semerikov, V. N. Soloviev, A. M. Striuk, First Student Workshop on Computer
Science & Software Engineering, CEUR Workshop Proceedings 2292 (2018) 1–10. URL: http:
//ceur-ws.org/Vol-2292/paper00.pdf.
[2] A. E. Kiv, S. O. Semerikov, V. N. Soloviev, A. M. Striuk, Second Student Workshop on Computer
Science & Software Engineering, CEUR Workshop Proceedings 2546 (2019) 1–20. URL: http:
//ceur-ws.org/Vol-2546/paper00.pdf.
[3] A. E. Kiv, S. O. Semerikov, V. N. Soloviev, A. M. Striuk, 3rd Workshop for Young Scientists in
Computer Science & Software Engineering, CEUR Workshop Proceedings 2832 (2020) 1–10. URL:
http://ceur-ws.org/Vol-2832/paper00.pdf.
[4] A. E. Kiv, S. O. Semerikov, V. N. Soloviev, A. M. Striuk, 4th Workshop for Young Scientists in
Computer Science & Software Engineering, CEUR Workshop Proceedings 3077 (2022) i–xxxv.
URL: https://ceur-ws.org/Vol-3077/intro.pdf.
[5] S. O. Semerikov, A. M. Striuk, Embracing Emerging Technologies: Insights from the 6th Workshop
for Young Scientists in Computer Science & Software Engineering, CEUR Workshop Proceedings
3662 (2024) 1–36. URL: https://ceur-ws.org/Vol-3662/paper00.pdf.
[6] S. O. Semerikov, A. M. Striuk, The evolving landscape of computer science and software engi-
neering: Trends, challenges, and future directions, CEUR Workshop Proceedings (2025) 1–46.
[7] A. O. Kurotych, L. V. Bulatetska, Optimizing the process of ER diagram creation with PlantUML,
CEUR Workshop Proceedings (2025) 47–57.
40
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
[8] S. V. Shokaliuk, A. O. Kavetskyi, Design and evaluation of a personalized digital mathematics
tutor for grade 6 learners, CEUR Workshop Proceedings (2025) 58–65.
[9] Y. O. Chernukha, O. V. Klochko, S. Kizim, A. Mozghalov, Methodology for implementing electronic
audit projects (SAF–T UA) for large taxpayers in Ukraine, CEUR Workshop Proceedings (2025)
66–79.
[10] N. Moiseienko, M. Moiseienko, D. Lubentsova, A web-based Kanban application for enhancing
agile project management practices, CEUR Workshop Proceedings (2025) 131–138.
[11] A. Zhdaniuk, O. Tarasova, M. Moiseienko, A. Stepanyuk, An interactive online trainer for primary
school computer science education: Design, implementation, and theoretical foundations, CEUR
Workshop Proceedings (2025) 139–151.
[12] V. P. Oleksiuk, D. Y. Dzhuha, P. P. Melnyk, D. V. Verbovetskyi, Development of the Student
Simulator game: From concept to code, CEUR Workshop Proceedings (2025) 89–109.
[13] S. V. Pravytskyi, P. V. Merzlykin, A. N. Stepanyuk, Designing and evaluating an affordable
Arduino-based lie detector prototype, CEUR Workshop Proceedings (2025) 80–88.
[14] N. Rudnichenko, V. Vychuzhanin, D. Shvedov, T. Otradskya, I. Petrov, Information system for
generating recommendations for risk-oriented trading strategies based on deep learning, CEUR
Workshop Proceedings (2025) 110–119.
[15] H. Zemlianukhina, R. Voliansky, N. Volianska, Modeling and simulating of Duffing pendulum in
the moved coordinate system, CEUR Workshop Proceedings (2025) 120–130.
[16] I. F. Laitaruk, T. O. Hryshanovych, Overview of modern algorithms for world procedural
generation in computer games, CEUR Workshop Proceedings (2025) 152–163.
[17] R. O. Popov, N. V. Karpenko, V. V. Gerasimov, Overview of small language models in practice,
CEUR Workshop Proceedings (2025) 164–182.
[18] O. B. Petrovych, Topic modelling of Ukrainian folk songs: A case study on Podillia region, CEUR
Workshop Proceedings (2025) 183–198.
[19] R. O. Liashenko, S. O. Semerikov, Bibliometric analysis and experimental assessment of chatbot
training approaches, CEUR Workshop Proceedings (2025) 199–225.
[20] V. M. Smolij, N. V. Smolij, O. Y. Kovalenko, M. Z. Shvydenko, Channel extractor for UAV PPM
signals, CEUR Workshop Proceedings (2025) 226–236.
[21] N. S. Krapyvnyi, A. A. Azaryan, O. V. Shvydkyi, D. V. Shvets, A. M. Hrytsenko, Development of
an automated system for preparing mineral raw material samples for discrete analysis, CEUR
Workshop Proceedings (2025) 237–244.
[22] Y. Attallah, E. Zigh, Z. Mehalli, A. Ali Pacha, A modified 3D-2D convolutional neural networks
for robust mineral identification: Hyperspectral analysis in Djebel Meni (Northwestern Algeria),
CEUR Workshop Proceedings (2025) 272–285.
[23] O. V. Korotun, T. A. Vakaliuk, T. M. Nikitchuk, M. O. Korotun, Methods of data analysis to study
the effectiveness of scientific journal promotion, CEUR Workshop Proceedings (2025) 245–259.
[24] I. Krak, M. Molchanova, V. Didur, O. Sobko, O. Mazurets, O. Barmak, Method of semantic features
estimation for political propaganda techniques detection using transformer neural networks,
CEUR Workshop Proceedings (2025) 286–297.
[25] I. Krak, O. Sobko, M. Molchanova, I. Tymofiiev, O. Mazurets, O. Barmak, Method for neural net-
work cyberbullying detection in text content with visual analytic, CEUR Workshop Proceedings
(2025) 298–309.
[26] O. H. Fedorenko, S. V. Velychko, Y. V. Kaidan, Investigating vulnerabilities of personal data on
financial websites, CEUR Workshop Proceedings (2025) 451–458.
[27] M. P. Horodyskyi, I. L. Hrabchuk, O. V. Bereznyi, O. S. Fedorova, I. M. Iefremov, Information
systems development in accounting: A systematic network study, CEUR Workshop Proceedings
(2025) 260–271.
[28] A. N. Stepanyuk, P. V. Merzlykin, Y. V. Zheludko, Design and implementation of a mobile health
application for physical activity tracking and exercise motivation, CEUR Workshop Proceedings
(2025) 310–320.
[29] L. Kupershtein, O. Zalepa, V. Sorokolit, S. Prokopenko, AI-agent-based system for fact-checking
41
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
support using large language models, CEUR Workshop Proceedings (2025) 321–331.
[30] A. V. Slobodianiuk, S. O. Semerikov, Advances in neural text generation: A systematic review
(2022-2024), CEUR Workshop Proceedings (2025) 332–361.
[31] D. O. Hanchuk, S. O. Semerikov, Automating machine learning: A meta-synthesis of MLOps
tools, frameworks and architectures, CEUR Workshop Proceedings (2025) 362–414.
[32] A. M. Striuk, V. V. Hordiienko, Research and development of a subtitle management system using
artificial intelligence, CEUR Workshop Proceedings (2025) 415–427.
[33] O. D. Rossiiev, N. N. Shapovalova, O. H. Rybalchenko, A. M. Striuk, A comprehensive survey
on reinforcement learning-based recommender systems: State-of-the-art, challenges, and future
perspectives, CEUR Workshop Proceedings (2025) 428–440.
[34] A. O. Poliaiev, N. N. Shapovalova, S. V. Bilashenko, A. M. Striuk, Research and development of
software for hydroacoustic signal analysis using machine learning techniques, CEUR Workshop
Proceedings (2025) 441–450.
[35] H. Bicen, N. Cavus, The most preferred social network sites by students, Procedia - Social and
Behavioral Sciences 2 (2010) 5864–5869. doi:10.1016/j.sbspro.2010.03.958.
[36] N. Cavus, Y. B. Mohammed, M. N. Yakubu, An Artificial Intelligence-Based Model for Prediction
of Parameters Affecting Sustainable Growth of Mobile Banking Apps, Sustainability 13 (2021)
6206. doi:10.3390/su13116206.
[37] D. Budgen, B. Kitchenham, S. Charters, S. Gibbs, A. Pohthong, J. Keung, P. Brereton, Lessons
from Conducting a Distributed Quasi-experiment, in: 2013 ACM / IEEE International Symposium
on Empirical Software Engineering and Measurement, 2013, pp. 143–152. doi:10.1109/ESEM.
2013.12.
[38] D. Ghimire, S. Charters, S. Gibbs, Scaling Agile Software Development Approach in Government
Organization in New Zealand, in: Proceedings of the 3rd International Conference on Software
Engineering and Information Management, ICSIM ’20, Association for Computing Machinery,
New York, NY, USA, 2020, p. 100–104. doi:10.1145/3378936.3378945.
[39] C. Pribeanu, D. D. Iordache, Evaluating the Motivational Value of an Augmented Reality System
for Learning Chemistry, in: A. Holzinger (Ed.), HCI and Usability for Education and Work,
volume 5298 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, Berlin, Heidelberg,
2008, pp. 31–42. doi:10.1007/978-3-540-89350-9_3.
[40] C. Pribeanu, D. D. Iordache, From Usability to User Experience: Evaluating the Educational
and Motivational Value of an Augmented Reality Learning Scenario, in: Affective, Interactive
and Cognitive Methods for E-Learning Design, IGI Global, 2010, p. 244–259. doi:10.4018/
978-1-60566-940-3.ch013.
[41] O. V. Dubolazov, A. G. Ushenko, Y. A. Ushenko, M. Y. Sakhnovskiy, P. M. Grygoryshyn,
N. Pavlyukovich, O. V. Pavlyukovich, V. T. Bachynskiy, S. V. Pavlov, V. D. Mishalov, Z. Omiotek,
O. Mamyrbaev, Laser Müller matrix diagnostics of changes in the optical anisotropy of biological
tissues, in: Information Technology in Medical Diagnostics II, CRC Press, 2019, p. 195–204.
doi:10.1201/9780429057618-24.
[42] A. Tolegenova, P. A. Kisała, A. Zhetpisbayeva, O. Mamyrbayev, B. Medetov, Experimental
determination of the characteristics of a transmission spectrum of tilted fiber Bragg gratings,
Metrology and Measurement Systems 26 (2019) 581–589. doi:10.24425/mms.2019.129585.
[43] H. Lee, B. Moon, A. H. Aghvami, Enhanced SIP for Reducing IMS Delay under WiFi-to-UMTS
Handover Scenario, in: 2008 The Second International Conference on Next Generation Mobile
Applications, Services, and Technologies, 2008, pp. 640–645. doi:10.1109/NGMAST.2008.63.
[44] J. Bae, B. Moon, Time synchronization with fast asynchronous diffusion in wireless sensor
network, in: 2009 International Conference on Cyber-Enabled Distributed Computing and
Knowledge Discovery, 2009, pp. 82–85. doi:10.1109/CYBERC.2009.5342158.
[45] J. Wan, C. A. Byrne, M. J. O’Grady, G. M. P. O’Hare, Managing Wandering Risk in People With
Dementia, IEEE Transactions on Human-Machine Systems 45 (2015) 819–823. doi:10.1109/
THMS.2015.2453421.
[46] M. O’Grady, D. Langton, F. Salinari, P. Daly, G. O’Hare, Service design for climate-smart agricul-
42
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
ture, Information Processing in Agriculture 8 (2021) 328–340. doi:10.1016/j.inpa.2020.07.
003.
[47] P. Lula, G. Paliwoda-Pundefinedkosz, An ontology-based cluster analysis framework, in: Pro-
ceedings of the First International Workshop on Ontology-Supported Business Intelligence, OBI
’08, Association for Computing Machinery, New York, NY, USA, 2008. doi:10.1145/1452567.
1452574.
[48] D. Dymek, M. Grabowski, G. Paliwoda-Pękosz, A proposition of an emerging technologies
expectations model: An example of student attitudes towards blockchain, Technological and
Economic Development of Economy 28 (2021) 101–130. doi:10.3846/tede.2021.15702.
[49] N. K. Suryadevara, S. Kelly, S. C. Mukhopadhyay, Ambient Assisted Living Environment To-
wards Internet of Things Using Multifarious Sensors Integrated with XBee Platform, in: S. C.
Mukhopadhyay (Ed.), Internet of Things: Challenges and Opportunities, volume 9 of Smart
Sensors, Measurement and Instrumentation, Springer International Publishing, Cham, 2014, pp.
217–231. doi:10.1007/978-3-319-04223-7_9.
[50] S. C. Mukhopadhyay, N. K. Suryadevara, A. Nag, Wearable Sensors and Systems in the IoT,
Sensors 21 (2021) 7880. doi:10.3390/s21237880.
[51] S. O. Semerikov, T. A. Vakaliuk, I. S. Mintii, V. A. Hamaniuk, V. N. Soloviev, O. V. Bondarenko, P. P.
Nechypurenko, S. V. Shokaliuk, N. V. Moiseienko, V. R. Ruban, Mask and Emotion: Computer
Vision in the Age of COVID-19, in: Digital Humanities Workshop, DHW 2021, Association for
Computing Machinery, New York, NY, USA, 2022, p. 103–124. doi:10.1145/3526242.3526263.
[52] I. S. Mintii, T. A. Vakaliuk, S. M. Ivanova, O. A. Chernysh, S. M. Hryshchenko, S. O. Semerikov,
Current state and prospects of distance learning development in Ukraine, CEUR Workshop
Proceedings 2898 (2021) 41–55.
[53] A. Bomba, N. Kunanets, M. Nazaruk, V. Pasichnyk, N. Veretennikova, Model of the Data Analysis
Process to Determine the Person’s Professional Inclinations and Abilities, in: Z. Hu, S. Petoukhov,
I. Dychka, M. He (Eds.), Advances in Computer Science for Engineering and Education II, volume
938 of Advances in Intelligent Systems and Computing, Springer International Publishing, Cham,
2020, pp. 482–492. doi:10.1007/978-3-030-16621-2_45.
[54] Y. Pankiv, N. Kunanets, O. Artemenko, N. Veretennikova, R. Nebesnyi, Project of an Intelligent
Recommender System for Parking Vehicles in Smart Cities, in: 2021 IEEE 16th International
Conference on Computer Sciences and Information Technologies (CSIT), volume 2, 2021, pp.
419–422. doi:10.1109/CSIT52700.2021.9648687.
[55] A. De Renzis, M. Garriga, A. Flores, A. Cechich, A. Zunino, Case-based Reasoning for Web
Service Discovery and Selection, Electronic Notes in Theoretical Computer Science 321 (2016)
89–112. doi:10.1016/j.entcs.2016.02.006, CLEI 2015, the XLI Latin American Computing
Conference.
[56] A. Zunino, M. Campo, Chronos: A multi-agent system for distributed automatic meeting
scheduling, Expert Systems with Applications 36 (2009) 7011–7018. doi:10.1016/j.eswa.
2008.08.024.
[57] L. Moravec, R. Danel, J. Chlopecký, Application of the Cyber Security Act in Havířovská
teplárenská společnost, a.s, in: R. Nemec, L. Chytilova (Eds.), SMSIS 2017 - Proceedings of the
12th International Conference on Strategic Management and its Support by Information Systems
2017, VSB-Technical University of Ostrava, 2017, pp. 425–433.
[58] N. Shakhovska, R. Holoshchuk, S. Fedushko, O. Kosar, R. Danel, M. Repka, The Sequential
Associative Rules Analysis of Patient’s Physical Characteristics, in: N. Shakhovska, S. Montenegro,
Y. Estève, S. Subbotin, N. Kryvinska, I. Izonin (Eds.), Proceedings of the 1st International Workshop
on Informatics & Data-Driven Medicine (IDDM 2018), Lviv, Ukraine, November 28-30, 2018,
volume 2255 of CEUR Workshop Proceedings, CEUR-WS.org, 2018, pp. 82–92. URL: https://ceur-ws.
org/Vol-2255/paper8.pdf.
[59] A. Dudnik, Y. Kravchenko, O. Trush, O. Leshchenko, N. Dakhno, V. Rakytskyi, Study of the
Features of Ensuring Quality Indicators in Multiservice Networks of the Wi-Fi Standard, in: 2021
IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT), 2021, pp.
43
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
93–98. doi:10.1109/ATIT54053.2021.9678691.
[60] L. Kuzmych, D. Ornatskyi, V. Kvasnikov, A. Kuzmych, A. Dudnik, S. Kuzmych, Development
of the Intelligent Instrument System for Measurement Parameters of the Stress - Strain State
of Complex Structures, in: 2022 IEEE 4th International Conference on Advanced Trends in
Information Theory (ATIT), 2022, pp. 120–124. doi:10.1109/ATIT58178.2022.10024222.
[61] E. Erturk, Using a Cloud Based Collaboration Technology in a Systems Analysis and Design
Course, International Journal of Emerging Technologies in Learning (iJET) 11 (2016) 33–37.
doi:10.3991/ijet.v11i01.4991.
[62] S. Day, E. Erturk, E-Learning objects in the cloud: SCORM compliance, creation and deployment
options, Knowledge Management and E-Learning 9 (2017) 449–467.
[63] J. Mesarosova, K. Martinovicova, H. Fidlerova, H. H. Chovanova, D. Babcanova, J. Samakova,
Improving the level of predictive maintenance maturity matrix in industrial enterprise, Acta
Logistica 9 (2022) 183–193. doi:10.22306/al.v9i2.292.
[64] H. Fidlerová, J. Prachař, P. Sakál, Application of Material Requirements Planning as Method for
Enhancement of Production Logistics in Industrial Company, Applied Mechanics and Materials
474 (2014) 49–54. doi:10.4028/www.scientific.net/AMM.474.49.
[65] A. F. Bardamid, A. I. Belyaeva, V. N. Bondarenko, J. W. Davis, A. A. Galuza, I. E. Garkusha, A. A.
Haasz, V. G. Konovalov, A. D. Kudlenko, M. Poon, I. V. Ryzhkov, S. I. Solodovchenko, A. F. Shtan,
V. S. Voitsenya, K. L. Yakimov, Ion fluence and energy effects on the optical properties of SS
mirrors bombarded by hydrogen ions, Physica Scripta T 103 (2002) 109–112.
[66] A. I. Belyaeva, A. A. Galuza, I. V. Kolenov, V. G. Konovalov, A. A. Savchenko, O. A. Skorik,
Effect of sputtering on the samples of ITER-grade tungsten preliminarily irradiated by tungsten
ions: Optical investigations, The Physics of Metals and Metallography 114 (2013) 703–713.
doi:10.1134/S0031918X13060033.
[67] P. Hryhoruk, N. Khrushch, S. Grygoruk, Model for Assessment of the Financial Security Level
of the Enterprise Based on the Desirability Scale, in: A. Kiv, S. Semerikov, V. N. Soloviev,
L. Kibalnyk, H. Danylchuk, A. Matviychuk (Eds.), Proceedings of the Selected Papers of the
8th International Conference on Monitoring, Modeling & Management of Emergent Economy,
M3E2-EEMLPEED 2019, Odessa, Ukraine, May 22-24, 2019, volume 2422 of CEUR Workshop
Proceedings, CEUR-WS.org, 2019, pp. 169–180. URL: https://ceur-ws.org/Vol-2422/paper14.pdf.
[68] P. Hryhoruk, N. Khrushch, S. Grygoruk, Environmental safety assessment: a regional dimension,
IOP Conference Series: Earth and Environmental Science 628 (2021) 012026. doi:10.1088/
1755-1315/628/1/012026.
[69] S. O. Semerikov, I. O. Teplytskyi, V. N. Soloviev, V. A. Hamaniuk, N. S. Ponomareva, O. H. Kolgatin,
L. S. Kolgatina, T. V. Byelyavtseva, S. M. Amelina, R. O. Tarasenko, Methodic quest: Reinventing
the system, Journal of Physics: Conference Series 1840 (2021) 012036. doi:10.1088/1742-6596/
1840/1/012036.
[70] V. N. Kukharenko, A. G. Kolgatin, The unsteady-state diffusion model of forming a cryoprecipitate,
Inzhenerno-Fizicheskii Zhurnal 61 (1991) 447–451.
[71] A. V. Riabko, T. A. Vakaliuk, O. V. Zaika, R. P. Kukharchuk, V. V. Kontsedailo, Chatbot algorithm
for solving physics problems, CEUR Workshop Proceedings 3553 (2023) 75–92.
[72] A. V. Riabko, T. A. Vakaliuk, O. V. Zaika, R. P. Kukharchuk, V. V. Kontsedailo, Cluster fault
tolerance model with migration of virtual machines, CEUR Workshop Proceedings 3374 (2023)
23–40.
[73] S. Papadakis, A. E. Kiv, H. M. Kravtsov, V. V. Osadchyi, M. V. Marienko, O. P. Pinchuk, M. P.
Shyshkina, O. M. Sokolyuk, I. S. Mintii, T. A. Vakaliuk, A. M. Striuk, S. O. Semerikov, Revolu-
tionizing education: using computer simulation and cloud-based smart technology to facilitate
successful open learning, CEUR Workshop Proceedings 3358 (2023) 1–18.
[74] S. Papadakis, A. E. Kiv, H. M. Kravtsov, V. V. Osadchyi, M. V. Marienko, O. P. Pinchuk, M. P.
Shyshkina, O. M. Sokolyuk, I. S. Mintii, T. A. Vakaliuk, L. E. Azarova, L. S. Kolgatina, S. M.
Amelina, N. P. Volkova, V. Y. Velychko, A. M. Striuk, S. O. Semerikov, Unlocking the power
of synergy: the joint force of cloud technologies and augmented reality in education, CEUR
44
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
Workshop Proceedings 3364 (2023) 1–23.
[75] V. Kryzhanivskyy, V. Bushlya, O. Gutnichenko, R. M’Saoubi, J.-E. Ståhl, Computational and
Experimental Inverse Problem Approach for Determination of Time Dependency of Heat Flux in
Metal Cutting, Procedia CIRP 58 (2017) 122–127. doi:10.1016/j.procir.2017.03.204, 16th
CIRP Conference on Modelling of Machining Operations (16th CIRP CMMO).
[76] P. Moskvin, V. Kryzhanivskyy, P. Lytvyn, L. Rashkovetskyi, Multifractal spectrums for volumes
of spatial forms on surface of ZnxCd1-xTe-Si (111) heterostructures and estimation of the fractal
surface energy, Journal of Crystal Growth 450 (2016) 28–33. doi:10.1016/j.jcrysgro.2016.
05.035.
[77] S. Semerikov, D. Zubov, A. Kupin, M. Kosei, V. Holiver, Models and Technologies for Autoscaling
Based on Machine Learning for Microservices Architecture, CEUR Workshop Proceedings 3664
(2024) 316–330.
[78] S. S. Korniienko, P. V. Zahorodko, A. M. Striuk, A. I. Kupin, S. O. Semerikov, A systematic review
of gamification in software engineering education, CEUR Workshop Proceedings 3844 (2024)
83–95.
[79] I. Pilkevych, O. Boychenko, N. Lobanchykova, T. Vakaliuk, S. Semerikov, Method of assessing
the influence of personnel competence on institutional information security, CEUR Workshop
Proceedings 2853 (2021) 266–275.
[80] N. M. Lobanchykova, T. A. Vakaliuk, V. P. Korbut, S. M. Lobanchykov, Y. B. Krasnov, Features of
designing systems for the formation of an internal microclimate of a high class of cleanliness
of operating rooms of medical institutions, IOP Conference Series: Earth and Environmental
Science 1415 (2024) 012124. doi:10.1088/1755-1315/1415/1/012124.
[81] A. V. Morozov, T. A. Vakaliuk, I. A. Tolstoy, Y. O. Kubrak, M. G. Medvediev, Digitalization of
thesis preparation life cycle: A case of Zhytomyr Polytechnic State University, CEUR Workshop
Proceedings 3553 (2023) 142–154.
[82] N. Lobanchykova, S. Kredentsar, I. Pilkevych, M. Medvediev, Information technology for mobile
perimeter security systems creation, Journal of Physics: Conference Series 1840 (2021) 012022.
doi:10.1088/1742-6596/1840/1/012022.
[83] V. P. Oleksiuk, J. A. Overko, O. M. Spirin, T. A. Vakaliuk, A secondary school’s experience of
a cloud-based learning environment deployment, CEUR Workshop Proceedings 3553 (2023)
93–109.
[84] V. P. Oleksiuk, O. R. Oleksiuk, T. A. Vakaliuk, A model of application and learning of cloud
technologies for future Computer Science teachers, CEUR Workshop Proceedings 3820 (2024)
82–101.
[85] I. Banicescu, R. L. Carino, J. P. Pabico, M. Balasubramaniam, Overhead analysis of a dynamic
load balancing library for cluster computing, in: 19th IEEE International Parallel and Distributed
Processing Symposium, 2005. doi:10.1109/IPDPS.2005.320.
[86] S. Dhandayuthapani, I. Banicescu, R. L. Carino, E. Hansen, J. R. Pabico, M. Rashid, Automatic
selection of loop scheduling algorithms using reinforcement learning, in: CLADE 2005. Pro-
ceedings Challenges of Large Applications in Distributed Environments, 2005., 2005, pp. 87–94.
doi:10.1109/CLADE.2005.1520907.
[87] H. Wright, K. Brodlie, J. Wood, J. Procter, Problem Solving Environments: Extending the Rôle
of Visualization Systems, in: A. Bode, T. Ludwig, W. Karl, R. Wismüller (Eds.), Euro-Par 2000
Parallel Processing, volume 1900 of Lecture Notes in Computer Science, Springer Berlin Heidelberg,
Berlin, Heidelberg, 2000, pp. 1323–1331. doi:10.1007/3-540-44520-X_185.
[88] S. A. MacGowan, F. Madeira, T. Britto-Borges, M. Warowny, A. Drozdetskiy, J. B. Procter, G. J.
Barton, The Dundee Resource for Sequence Analysis and Structure Prediction, Protein Science
29 (2020) 277–297. doi:10.1002/pro.3783.
[89] V. Derbentsev, N. Datsenko, V. Babenko, O. Pushko, O. Pursky, Forecasting Cryptocurrency Prices
Using Ensembles-Based Machine Learning Approach, in: 2020 IEEE International Conference
on Problems of Infocommunications. Science and Technology (PIC S&T), 2020, pp. 707–712.
doi:10.1109/PICST51311.2020.9468090.
45
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–46
[90] O. I. Purskiı̌, N. N. Zholonko, V. A. Konstantinov, Heat transfer in the orientationally disordered
phase of SF6, Low Temperature Physics 26 (2000) 278–281. doi:10.1063/1.593899.
[91] V. Tkachuk, Y. Yechkalo, S. Semerikov, M. Kislova, Y. Hladyr, Using Mobile ICT for Online
Learning During COVID-19 Lockdown, in: A. Bollin, V. Ermolayev, H. C. Mayr, M. Nikitchenko,
A. Spivakovsky, M. Tkachuk, V. Yakovyna, G. Zholtkevych (Eds.), Information and Communi-
cation Technologies in Education, Research, and Industrial Applications. ICTERI 2020, volume
1308 of Communications in Computer and Information Science, Springer International Publishing,
Cham, 2021, pp. 46–67. doi:10.1007/978-3-030-77592-6_3.
[92] D. S. Shepiliev, S. O. Semerikov, Y. V. Yechkalo, V. V. Tkachuk, O. M. Markova, Y. O. Modlo, I. S.
Mintii, M. M. Mintii, T. V. Selivanova, N. K. Maksyshko, T. A. Vakaliuk, V. V. Osadchyi, R. O.
Tarasenko, S. M. Amelina, A. E. Kiv, Development of career guidance quests using WebAR, Journal
of Physics: Conference Series 1840 (2021) 012028. doi:10.1088/1742-6596/1840/1/012028.
[93] A. Adamov, S. Mehdiyev, E. Seyidzade, Good practice of data modeling and database design for
UMIS. Course registration system implementation, in: 2014 IEEE 8th International Conference
on Application of Information and Communication Technologies (AICT), 2014, pp. 1–4. doi:10.
1109/ICAICT.2014.7035949.
[94] E. S. Vagif, A. A. Zahir, Developing of the creative abilities of the pupils by the using the project on
training method in the classes of the informatics in the general schools, in: 2011 5th International
Conference on Application of Information and Communication Technologies (AICT), 2011, pp.
1–5. doi:10.1109/ICAICT.2011.6110955.
[95] S. H. Lytvynova, S. O. Semerikov, A. M. Striuk, M. I. Striuk, L. S. Kolgatina, V. Y. Velychko, I. S.
Mintii, O. O. Kalinichenko, S. M. Tukalo, AREdu 2021 - Immersive technology today, CEUR
Workshop Proceedings 2898 (2021) 1–40.
[96] P. V. Zahorodko, S. O. Semerikov, V. N. Soloviev, A. M. Striuk, M. I. Striuk, H. M. Shalatska, Com-
parisons of performance between quantum-enhanced and classical machine learning algorithms
on the IBM Quantum Experience, Journal of Physics: Conference Series 1840 (2021) 012021.
doi:10.1088/1742-6596/1840/1/012021.
[97] F. Lin, A. Dewan, V. Voytenko, Open Interactive Algorithm Visualization, in: 2019 IEEE Canadian
Conference of Electrical and Computer Engineering (CCECE), 2019, pp. 1–4. doi:10.1109/
CCECE.2019.8861535.
[98] V. Voytenko, V. Vodichev, A. Kalinin, Comparative Analysis of Energy Performance of Induction
Single-Motor and Multi-Motor Traction Electric Drive, in: 2021 IEEE 2nd KhPI Week on Advanced
Technology (KhPIWeek), 2021, pp. 73–78. doi:10.1109/KhPIWeek53812.2021.9570063.
[99] D. A. Karnishyna, T. V. Selivanova, P. P. Nechypurenko, T. V. Starova, S. O. Semerikov, Enhancing
high school students’ understanding of molecular geometry with augmented reality, Science
Education Quarterly 1 (2024) 25–40. doi:10.55056/seq.818.
[100] S. L. Malchenko, From smartphones to stargazing: the impact of mobile-enhanced learning on
astronomy education, Science Education Quarterly 1 (2024) 1–7. doi:10.55056/seq.816.
[101] O. A. Konoval, A relativistic approach to teaching electrodynamics: Deriving Maxwell’s equations
from first principles, Science Education Quarterly 1 (2024) 41–102. doi:10.55056/seq.819.
[102] P. P. Nechypurenko, S. O. Semerikov, Implementing an integrated natural sciences course in
Ukrainian high schools: A nationwide experiment from 2018-2022, Science Education Quarterly
1 (2024) 8–13. doi:10.55056/seq.820.
[103] P. P. Nechypurenko, O. D. Kushnirova, The rebirth of home chemistry experiments: An inter-
national perspective and the Ukrainian context, Science Education Quarterly 1 (2024) 103–108.
doi:10.55056/seq.824.
[104] I. A. Teplitsky, Broadening didactic resource of physics dictation, Science Education Quarterly 1
(2024) 14–24. doi:10.55056/seq.817.
46