Journal of Computer Science and Digital Technologies
The UNEC Journal of Computer Science and Digital Technologies (JCSDT) is a biannual, peer-reviewed, open-access publication launched by Azerbaijan State University of Economics (UNEC). JCSDT’s primary goal is to advance both theoretical foundations and practical applications in the rapidly evolving fields of computer science and digital technologies. It provides a global platform for researchers, practitioners, and policymakers to disseminate high-quality original research articles, comprehensive reviews, case studies, and short communications.
Azerbaijan State University of Economics (UNEC)
Evaluating VRChat Usability in Desktop Mode: A Student-Led Case Study on Social VR Platforms
Social virtual reality (Social VR) platforms such as VRChat have become prominent spaces for online interaction, yet academic research has focused predominantly on head-mounted display (HMD) experiences. This neglects the desktop mode, which accounts for a majority of VRChat’s users. This study investigates the usability of VRChat’s desktop mode from the perspective of undergraduate students, addressing three research questions: (1) How do students perceive its usability? (2) What challenges or limitations do they encounter? (3) What improvements could enhance user satisfaction? Eighteen students from Software, Computer, and Mechatronic Engineering participated in a structured usability test comprising ten core tasks (e.g., avatar creation, world navigation, friend addition, and object interaction). Data were collected through demographic questionnaires, post-test surveys, and detailed researcher observation notes. Quantitative results revealed high ratings for visual design and enjoyability but lower scores for learnability, security, and error handling. Qualitative findings indicated recurring barriers in navigation, avatar customization, friend adding, and object manipulation, compounded by performance issues such as low FPS and lengthy loading screens. Participants valued the platform’s creative and social potential, yet reported frustration with unclear menus, limited onboarding, and inconsistent interaction feedback. The results contribute to the underexplored domain of desktop-mode Social VR usability, complementing prior desktop VR research. Recommendations include improved in-app guidance, streamlined menu design, more accessible avatar customization, and optimization for non-VR hardware. These findings offer actionable insights for developers and inform the design of accessible, engaging Social VR experiences beyond HMD-centric paradigms.
Human–Computer Interaction & Digital Education Technologies
Copyright (c) 2025 Journal of Computer Science and Digital Technologies
Immersive Contact Geometry: A VR-Based Approach to Embodied Understanding of Legendrian and Transverse Knots
One of the main challenges to teaching advanced mathematics is understanding high dimensional geometric structures. Students' spatial understanding is limited by contact geometry's dependency on static 2D projections, which includes abstract concepts like Legendrian and transverse knots. The goal of this study is to present an immersive Virtual Reality (VR) environment that allows students to see and engage with three-dimensional contact manifolds and tight vs. overtwisted contact structures in them. To model contact planes and their relationship to Legendrian and transverse knots, a virtual reality learning environment was created by using Gravity Sketch. Participants in a pilot implementation explored knots in both standard and overtwisted contact structures in instructors supervision during a graduate summer school. Observations, informal interviews, and fieldnotes were collected to capture student engagement and conceptual understanding. According to participants, the VR experience allowed them to "see" tangency, cusps, and invariants for the first time, turning abstract concepts into real-world experiences. By using this method, teachers were able to spot misconceptions that were previously undetectable in 2D environments. Even if the sample is small, immersive virtual reality holds great promise for use as a precision pedagogical tool that facilitates embodied and conceptual learning in advanced mathematics, rather than just as a visualization tool. Formal pre/post assessments will be used in future research to measure conceptual advancements and spatial reasoning.
Virtual Reality, Contact Geometry, Legendrian and Transverse Knots, Mathematics Education, Spatial Visualization, Human-Computer Interaction
Mobile Application-Supported Instructional Design in Anatomy Education
Vocational anatomy education, especially in equine science, often places substantial cognitive demands on students because it combines spatially complex structures with unfamiliar Latin terminology. This study explores whether integrating an interactive three-dimensional mobile application into instruction can help manage cognitive load and improve learning outcomes. The intervention was developed through a deliberate alignment of multimedia learning principles with mobile human–computer interaction considerations. Using an iterative design-based research approach, a needs analysis with nine students first clarified the main sources of difficulty. Based on these findings, a two-week mobile application–supported implementation was conducted with a separate sample of eight students and evaluated through a mixed-methods design. Quantitative results showed a marked improvement from pre-test to post-test; the mean score increased from 36.38 to 66.75, and the difference was statistically significant (Z = -2.52, p = .008). Qualitative evidence complemented these results, indicating that three-dimensional visualizations supported comprehension, interactive engagement contributed to motivation, and the multisensory learning experience was perceived as helpful for retention. Overall, the findings suggest that a mobile application–supported instructional design grounded in cognitive and interaction-oriented design principles can offer a practical, accessible, and scalable pathway to strengthen learning outcomes in professional anatomy education.
Digital Transformation & Emerging Technologies
Mobile Learning, 3D Visualization, Equine Anatomy, Anatomy Education, Instructional Design.
A New Approach to Information Hierarchy Based on Data Spaces and Intelligent Digital Twins: Cyclic Layers Model (CLM)
The rapid evolution of the digital era exposes the limitations of traditional hierarchical information models, such as Ackoff’s DIKUW and Bellinger’s DIKW pyramid, which fail to fully address contemporary challenges in data management and ethical technology use. This study introduces the Cyclic Layers Model (CLM) as a unified, holistic alternative that consolidates fragmented knowledge management approaches into a single, dynamic framework. Drawing from bibliometric analysis of 1074 publications (1985–2025) across major databases, and narrowing to 535 relevant studies, the model restructures information flow through cyclic, interconnected layers that incorporate individual, organizational, and environmental factors. By integrating Data Spaces and Intelligent Digital Twins (IDTs), the model offers a comprehensive approach to addressing ethical AI deployment, data privacy, and sustainability issues. This framework not only advances theoretical under-standing in knowledge management but also provides practical pathways for responsible decision-making in sectors such as healthcare and smart cities. CLM thus sets the stage for future multidisciplinary research aimed at designing ethically and culturally aware intelligent systems.
DIKW pyramid, DIKUW, Data Spaces, Intelligent Digital Twins, Cyclic Layers Model
Cultural Technological Synergy in the Age of AI: A Conceptual Framework for Understanding Adaptive Modernization in Transitional Societies
This paper introduces the Cultural–Technological Synergy (CTS) framework, which rests on four established conceptual principles: (1) shared cultural values shape the behavior of individuals and institutions; (2) new technologies diffuse through social learning and demonstrable benefits; (3) durable systems depend on public legitimacy and consent; and (4) effective cross-institutional coordination is essential for scaling pilots into operational infrastructure. Drawing on these principles, CTS elucidates how cultural dynamics influence the capacity, incentives, and legitimacy required for artificial intelligence (AI) to evolve from experimental applications into essential public infrastructure. While recognizing economic, technological, infrastructural, and governance drivers, CTS adds cultural, societal, and psychological dimensions—operationalized through norms, values, identities, and risk perceptions—to be measured and compared on equal footing. CTS defines four interacting dimensions—heritage adaptability, cross-civilizational competence, innovation ethos, and strategic determination—that shape the progression from pilots and sectoral deployments to public infrastructure. These dimensions interface directly with the companion frameworks (Ibrahimov 2025): AIPI (AI as Public Infrastructure), which theorizes when AI attains infrastructural status, and ISI (Infrastructure Status Index), which operationalizes that status. In diagnostic use, CTS offers a lens for (i) evaluating cultural readiness, (ii) identifying bottlenecks, and (iii) supporting prioritization through analysis of how cultural factors condition capacity, incentives, and legitimacy in transitions to public infrastructure. Positioned at the meso-level, CTS specifies how cultural architectures enable or constrain institutional pathways across successive AIPI–ISI phases. The Azerbaijan case illustrates this logic—explaining ambition formation, legitimacy dynamics, and early coordination gains. The contribution is twofold: (1) CTS provides a meso-level analytical lens that foregrounds infrastructuralization dynamics and culture–institution interaction beyond simple adoption; and (2) it specifies scope conditions and testable propositions indicating when cultural factors matter more—or less—across AIPI–ISI consolidation phases.
Artificial Intelligence & Machine Learning
Adaptive Cross-Platform Threat Correlation Layer (ACTCL):A Cybersecurity Approach to Detecting Distributed Digital Violence on Social Media Platforms
Large-scale digital assaults and online harassment increasingly span multiple social media platforms, yet cross-platform threat correlation remains limited by privacy regulations, incompatible data formats, and the absence of a shared behavioural analysis layer. Current privacy-conscious machine-learning solutions focus on platform-specific anomaly detection rather than on correlating behaviour across decoupled environments. This paper introduces the Adaptive Cross-Platform Threat Correlation Layer (ACTCL), a lightweight cybersecurity framework that reframes digital violence as a privacy-aware behavioural correlation problem. ACTCL employs Anonymous Feature Hashing (AFH) to convert three transient behavioural features—temporal rhythm, activity concentration, and session duration—into irreversible eight-dimensional fingerprints that can be compared across systems without exposing raw data. Using a controlled synthetic dataset of 150 users, including 10 coordinated attackers, cosine similarity over hashed fingerprints reveals a degenerate similarity cluster for coordinated users (mean = 1.0000, SD = 0.0000) and a more dispersed distribution for normal users (mean = 0.9877, SD = 0.0224). These results show that organised malicious behaviour retains stable behavioural signatures even under strong anonymisation, enabling privacy-compliant cross-platform threat correlation. ACTCL offers a practical foundation for future multi-platform security cooperation, with applications in fraud detection, coordinated misinformation tracking, botnet analysis, and other distributed digital threats.
cross-platform security; digital violence detection; behavioural fingerprinting; privacy-preserving cybersecurity; federated threat correlation; social media security.
Transductive vs. Inductive Graph Learning for Illicit Bitcoin Transaction Detection
The Bitcoin transaction network represents a complex, high-dimensional system where the pseudonymous nature of flows facilitates ransomware extortion and money laundering activities. However, existing detection methodologies predominantly rely on local transaction features, failing to capture structural dependencies and topological obfuscation techniques used in ransomware and laundering. To address this limitation, this study proposed a hybrid Graph-Based Machine Learning (GBML) framework that integrates structural embeddings (Node2Vec) with ensemble classifiers and contrasts them against inductive GraphSAGE architectures using the Elliptic dataset. The analysis revealed that the Node2Vec-enhanced Random Forest model achieved an F1-score of 0.9277 and a ROC-AUC of 0.9956, substantially outperforming feature-only baselines. Furthermore, the inductive GraphSAGE model demonstrated remarkable robustness under a strict temporal split, achieving an F1-score of 0.8981 on future unseen transactions. This performance improvement is attributed to the encoding of neighborhood context and temporal dynamics, which exposes latent connections between illicit entities. Distinct from purely black-box deep learning approaches, this framework incorporates latent space visualization and permutation-based feature importance to ensure the forensic interpretability required for legal compliance. Consequently, the proposed method provides a robust solution for post-hoc forensic investigations in environments characterized by extreme class imbalance and evolving criminal patterns.
Usability Evaluation of Mobile Banking Applications Among Older Adults
This study investigates the experiences and challenges faced by older adults when using mobile banking applications. In today's context, a significant portion of financial transactions are conducted via mobile devices, which can pose considerable challenges, particularly for individuals with limited digital literacy. Therefore, this study is important in terms of facilitating older adults' access to banking services and promoting financial inclusion. Twelve elderly individuals enrolled in the “Internet Banking and Internet Shopping” course at Sinop University Third Age University participated in the study. Participants were given nine tasks to complete using the mobile applications of four different banks. These tasks included routine banking operations such as checking account balances, transferring money, paying bills, performing credit card transactions, and configuring security settings. Throughout the task completion process, the research team used observation forms. At the end of the study, the System Usability Scale was applied, followed by semi-structured interviews with the participants. The data obtained were analyzed using descriptive statistics for quantitative data and content analysis for qualitative data. The findings highlighted the key areas experienced by seniors when using mobile banking and identified their needs regarding mobile banking usage.
Analysis of Motor Learning and Depth Perception through Tangram Gameplay: An HCI Perspective
This study investigates the Tangram puzzle game as a frame work for examining depth perception and motor learning within the field of Human-Computer Interaction (HCI). Tangram, a traditional puzzle made up of geometric shapes, provides an engaging and adaptable platform to explore how interactive tasks foster cognitive and motor development. The research focuses on three objectives: first, to assess depth perception by analyzing how players manipulate two-dimensional (2D) shapes to construct perceptual representations of three dimensional (3D) structures, thereby enhancing spatial cognition; second, to demonstrate the suitability of Tangram as a lightweight and flexible experimental tool for systematically measuring user interaction; and third, to examine motor learning processes through the repeated manipulation of puzzle pieces, offering insights into the refinement of fine motor skills and hand–eye coordination. By embedding Tangram gameplay into a structured experimental design, this work contributes to HCI scholarship by highlighting the value of puzzle-based interaction in understanding cognitive processes, informing interface design, and advancing interactive systems that support learning, rehabilitation, and skill development.
The impact of LMS usage on cognitive load in a programming course:an evaluation using NASA-TLX
In the Learning Management System (LMS), the student's cognitive load affects learning. In this study, the cognitive load of students using LMS in object-oriented programming courses was examined. The research was carried out with 35 students who used LMS to learn a new topic related to the course. After completing the tasks given, the cognitive load levels of students were evaluated using the NASA-TLX scale. Findings were classified according to individual characteristics such as gender, LMS experience, and academic success. The results were interpreted as the presence of moderate cognitive load. It was also observed that cognitive load differed depending on gender, LMS experience, and academic success level. The study emphasises that individual differences should be considered in LMS design. In addition, the concept of cognitive load in digital education environments has become increasingly important for understanding the relationship between students and various influencing factors. It is suggested that cognitive load can be reduced in LMS with future improvements.
Digital Development Analysis based on cognitive modeling
The article examines the issue of digital development, which "obliges" the features of the development of modern society and the world economy. Global trends in economic processes are analyzed, as well as future technologies that can change our lives and affect industry. World experience shows that there is a direct connection between the level of informatization and the economic growth of regions. However, high investment rates and widespread implementation of informatization do not in themselves guarantee accelerated economic growth. According to estimates by the Organization for Economic Cooperation and Development, today, in the context of the information boom, regions may invest excessive funds in IT “either in an effort to compensate for a lack of qualifications or due to the absence of a clear market strategy for IT”. At present, the solution to this issue is everywhere faced with the problem of uncertainty, which practically excludes the possibility of using traditional methods of economic and mathematical modeling and decision-making. To take these relationships into account, a fuzzy cognitive map based on rules (Rule Based Fuzzy Cognitive Maps) and a cognitive analysis of the “state of digital development” is carried out. The map can be used for qualitative analysis and solution of a number of key problems related to the selection of priority areas of informatization, studying the dynamics of internal indicators of informatization, assessing the impact of informatization on the main indicators of economic growth of regions.
Approximate Solution for a Class of Optimal Control Problems in Distributed Parameter Systems
An approximate solution is developed using the direct method for a specific class of optimal control problems in systems governed by nonlinear boundary value problems of parabolic type. These problems are particularly significant in the modeling and optimization of dynamic processes distributed over space and time. The methodology is based on constructing finite-dimensional approximations of the original infinite-dimensional problem, allowing for a practical computational approach. By applying a priori estimate for the solutions of systems of linear ordinary differential equations, the convergence of the proposed direct method is rigorously proven. This convergence guarantees that the approximate solutions approach the exact solution of the original control problem in terms of minimizing the given functional. Furthermore, a constructive scheme for generating a minimizing sequence of controls is introduced, which depends on the chosen class of admissible controls. This scheme provides a systematic way to approach optimality in practical applications. As a practical illustration, the study presents an example related to determining the optimal technological regime for the operation of gas wells, which demonstrates the applicability of the proposed method to real-world engineering problems. The developed approach can serve as a valuable computational tool for solving similar optimal control problems in distributed parameter systems.
Working methodology for assessing digital transformation risks
The intensification of digital transformations increases risk situations and raises the issue of their assessment. Although these issues are the focus of researchers, there are still no working risk assessment tools that would gain the trust of users, especially representatives of micro and small businesses. To ensure competitiveness and overall business efficiency, it is advisable to create a working methodology for assessing the risks of digital transformation. The purpose of the study is to formulate a methodological approach designed to become a working tool for identifying the factors that determine the risk situation in the digital market and characterize their impact. For this purpose, the results of existing studies are commented on and a comparative description of methodological approaches to assessing the risks of digital transformation is given. The need for an urgent solution to the risk management problems associated with digital transformations was emphasized. Strategic measures to be taken to address the relevant problems were explained. The initial approach assumed a relatively easy-to-understand and implement methodological approach involving the creation of a bank of relevant risks for assessing cyber risks. From a methodological point of view, in the network implementation of multi-level business models, great importance is attached to the fact that digital business entities operating at the same level compete with each other and also enter into partnerships.
Is Zero Trust the Future of Cyber Defence? An Analysis of Principles, Adoption, and Effectiveness
Traditional network security models face increasing challenges due to evolving cyber threats and shifting IT environments. The Zero Trust model, which operates on the principle that no user, device, or system should be implicitly trusted regardless of network location, has gained significant attention in both research and practice. Despite its promise to address modern cybersecurity demands, widespread adoption of Zero Trust remains uneven, and questions persist regarding its practical benefits, implementation challenges, and long-term viability. This article aims to consolidate current knowledge on Zero Trust by examining its fundamental principles, architectural components, and enabling technologies. Additionally, we conducted a survey with IT professionals and academics to gather empirical insights on awareness, adoption levels, and perceived effectiveness of Zero Trust Security. Our analysis reveals that while Zero Trust is increasingly recognized for enhancing security posture and mitigating insider and advanced threats, obstacles such as lack of expertise and organizational resistance impede broader implementation. The findings highlight gaps in both academic research and practical guidance, underscoring the need for further study on deployment strategies, cost-benefit analyses, and user experience. This work provides a foundation for future research and practical efforts to advance Zero Trust as a robust cybersecurity framework.
Reverse Engineering Attacks on Android Applications: Techniques, Case Studies, and Defense Strategies
Reverse engineering remains a critical threat to the security of Android mobile applications due to the platform’s open-source nature and the accessibility of its application packages. This paper investigates the technical vulnerabilities that expose Android applications to reverse engineering, including the ease of decompiling APK files and extracting sensitive logic and data. It explores both static and dynamic analysis techniques, runtime manipulation, and code modification, which are commonly used by attackers to bypass security mechanisms or alter application behavior. Drawing on real-world case studies, the paper illustrates how these techniques have been exploited in practice, compromising application integrity and user privacy. In response, a range of defense strategies is evaluated, such as code obfuscation, string encryption, native code protection, root detection, and runtime integrity checks. The study also considers the legal and ethical implications of reverse engineering, emphasizing the importance of intellectual property protection and compliance with international regulations. The findings highlight the necessity of a multi-layered defense approach that integrates technical safeguards with legal awareness to effectively mitigate risks and enhance the resilience of Android applications.
The model context protocol: a standardization analysis for application integration
The Model Context Protocol (MCP), introduced by Anthropic, addresses critical standardization challenges in artificial intelligence application development by providing a unified framework for connecting Large Language Models to external resources and computational tools. This paper presents a comprehensive analysis of MCP's architecture, implementation patterns, and potential impact on the AI development ecosystem through both theoretical evaluation and empirical case study analysis. Through systematic evaluation of MCP's core components and detailed analysis of real-world implementations, we examine how this protocol addresses fragmentation in AI integration approaches. Our analysis reveals that MCP's client-server architecture and structured abstraction layer offer significant potential benefits for modularity, security, and developer productivity, while identifying key challenges in adoption and ecosystem maturity. This study provides a comprehensive academic analysis of MCP's standardization approach and its implications for the evolving AI development landscape.
Firmographica: Knowledge Graph and AI-based Framework for Short-Selling Risk Assessment
This study investigates how corporate ownership structures affect short selling in publicly traded banking firms by integrating knowledge graph methodologies with regression analysis. By combining graph-based centrality metrics with traditional indicators like firm size and ownership concentration, we systematically identify and rank the factors that influence short-selling activity. Our results indicate that firm size, the degree of ownership concentration, and PageRank centrality are consistently significant predictors of both the level and intensity of short-selling positions. Additionally, insider trading activity is shown to be a critical determinant of short-selling volatility, suggesting that internal market behaviors provide unique predictive value beyond standard financial metrics. The findings underscore the broader potential for graph analytics and machine learning approaches to enhance financial risk modeling and market surveillance. By providing a richer, network-driven perspective, this research contributes to a better understanding of market dynamics, supports the development of more robust governance frameworks, and informs both regulatory policy and investment strategies aimed at promoting transparency and stability in the financial sector.
Evaluating hybrid deep learning models for financial market trading
The prices of stocks are hard to predict due to the complex interplay of qualitative sentiment and quantitative indicators. This article analyzes 2 strategies for feature extraction which affect the quality of hybrid deep learning models. To conduct the experiment, the New York Times articles were web scraped, and the extracted sentiment data were combined with S&P500 historical data. Two hybrid models were developed: one using VADER sentiment analysis followed by a CNN to extract refined sentiment features before passing them to an LSTM, and another using FinBERT to generate contextual sentiment embeddings directly fed into an LSTM. Interestingly, the VADER-CNN-LSTM model achieved slightly better predictive performance, suggesting that lexicon-based methods, when combined with effective feature extraction layers like CNNs, can be competitive in small-data scenarios. Although FinBERT is designed to capture nuanced, domain-specific sentiment signals, its advantages may be underutilized without sufficient data volume. This highlights a key insight: the success of sentiment-enhanced forecasting models depends not only on model architecture, but also on the quantity and structure of the available data.
A Novel Fuzzy Inference System-Based Federated Learning Approach
Federated learning enables distributed training of machine learning models across a network of decentralised computational nodes, ensuring data privacy by retaining sensitive data on local devices. This paradigm supports the construction of high-performance models by leveraging non-i.i.d. and heterogeneous data dispersed across multiple clients, thereby removing the dependency on centralized data aggregation. Federated learning frameworks utilize a range of model aggregation algorithms—such as FedAvg (federated averaging), FedProx (proximal gradient methods), and FedOpt (adaptive federated optimization)—to achieve convergence by integrating locally updated model parameters into a global model, all while upholding data locality constraints. Fuzzy logic, characterized by its capacity to model vagueness and imprecision through fuzzy sets and linguistic variables, provides a robust mechanism for approximate reasoning in uncertain environments. This study proposes FedFIS, a novel fuzzy logic-based aggregation scheme embedded within the federated learning architecture. The FedFIS methodology circumvents the reliance on gradient-based optimization and computationally intensive mathematical formulations by leveraging fuzzy inference mechanisms, thus offering a computationally lightweight and privacy-preserving alternative for federated parameter aggregation.
Automated Apple Leaf Disease Classification Using Deep Convolutional Neural Networks: A Comparative Study on the Plant Village Dataset
The early and accurate identification of plant diseases play a vital role in ensuring agricultural productivity and food security. In this study, we investigate the effectiveness of state-of-the-art convolutional neural network (CNN) architectures for the automated classification of apple leaf diseases using the Plant Village Apple dataset. Five high-performance models DenseNet-264, EfficientNet-B4, EfficientNet-B5, Inception-V3, and MobileNet-V3-Large were fine-tuned on expertly labeled images. DenseNet-264 outperformed other models, achieving an accuracy of 98.32%, precision of 97.83%, recall of 98.21%, and an F1-score of 98.02%. Inception-V3 also demonstrated competitive results, while MobileNet-V3-Large offered a compelling balance between accuracy and computational efficiency, making it suitable for deployment on mobile and edge devices. The findings highlight the capability of deep learning to deliver fast, reliable, and objective diagnostics from ordinary field images, significantly reducing the need for manual inspection. This approach holds promises for enhancing disease management, safeguarding crop yield, and supporting precision agriculture.