Computer Science and Mathematics

Abstract: The increasing complexity and volume of network traffic in modern 5G network environments pose significant challenges to maintain consistent Quality of Service (QoS) across diverse applications. QoS in 5G networks ensures efficient resource allocation, minimal latency, high throughput, and reliable connectivity. However, without accurate network traffic classification, QoS cannot be effectively optimized. This is because different applications have unique performance requirements. Traditional classification techniques such as port-based identification and Deep Packet Inspection (DPI) have become inadequate due to widespread encryption, port masquerading, and privacy concerns. This paper presents a supervised learning-based approach for network traffic classification specifically aimed at QoS optimization in 5G networks. A Random Forest classifier was implemented using flow level statistical features extracted from the MIRAGE-2019 dataset. The system includes an intelligent QoS policy mapping that categorizes applications according to their network requirements including priority levels, bandwidth needs, latency sensitivity, and jitter tolerance. Experimental results demonstrated 89.76% classification accuracy and 0.897 macro F1-score using Random Forest with RandomOverSampler, confirming the model’s effectiveness in accurately classifying encrypted traffic and supporting QoS optimization.

Abstract: Serious games and gamified interventions are increasingly being proposed as tools to reduce household food waste, yet the field lacks standardized methods to assess whether these interventions produce measurable behavioral and environmental impact. Despite the European Union (EU) Waste Framework Directive's call for comparable food waste monitoring, most studies report only knowledge gains or attitudinal shifts. This paper presents SG-FLW, a measurement framework that evaluates the evidence quality of interventions targeting food loss and waste across five dimensions: Baseline measurement, Knowledge attitudinal assessment, Direct behavioral change, Environmental conversion, and Persistence, each on a 0--3 ordinal scale. We demonstrate the framework through worked coding examples and apply it to thirty-five interventions (seven games, ten serious games, and eighteen gamification studies) drawn from two complementary reviews and targeted search. Results reveal a persistent complementary measurement gap: twenty-five interventions assess knowledge or attitudinal outcomes but rarely measure actual behavior, while the five achieving Moderate certainty through objective behavioral measurement (including waste weighing, audits, and consumption proxies) all neglect cognitive assessment. No study achieves environmental conversion, and only one includes post-intervention follow-up. SG-FLW provides a transparent, reproducible tool aligned with EU reporting standards that can be applied both retrospectively (to evaluate published studies) and prospectively (to guide measurement planning during intervention design). By making measurement gaps visible, the framework directs future research toward stronger evidence of impact.

Abstract: This paper presents a model for the organisation and management of metadata that enables the efficient compilation of specialised datasets from large and heterogeneous data collections. The model is built around three principal components: (i) an extensive metadata schema, organised into administrative, editorial, structural, descriptive, classificational, analytical and statistical categories, that describes the formal and content-related characteristics of data entries in accordance with the FAIR principles of findability, accessibility, interoperability and reusability; (ii) a graph-database representation in which metadata categories and their values are stored as nodes and relationships as directed, typed edges, capturing both hierarchical structures, such as taxonomies of domains, and non-hierarchical structures, such as cross-references between resources and links to external semantic resources; and (iii) a retrieval mechanism implemented through Cypher queries executed as graph traversals, supporting the extraction of thematically and application-oriented data subsets according to combinations of structural, descriptive and analytical criteria. The model is modality-independent by design and supports the incremental introduction of new categories and relations without migration of existing data. Its feasibility is demonstrated through a prototype for Bulgarian textual data, comprising a graph database of more than 200,000 documents and a web-based filtering interface, which together validate the suitability of the approach for the compilation of specialised datasets for the training and fine-tuning of large language models and other NLP applications.

Abstract: The Technology-Organization-Environment (TOE) framework is widely applied in organizational technology adoption research, yet its measurement practices remain fragmented. Across studies of EDI, cloud computing, blockchain, AI, and other contexts, researchers routinely rename, adapt, or recombine constructs without documenting how their operationalizations relate to prior work, producing a literature that is empirically rich but difficult to accumulate. This study addresses that problem by developing a measurement catalog of 14 reusable TOE constructs drawn from 45 empirical anchor studies. Using a targeted construct-selection approach, the study retained constructs that were peer-reviewed, tested at the firm level, statistically validated, and generalizable across technology domains. Related aliases were consolidated under canonical names through three documented rules based on shared theoretical mechanisms, item-level overlap, and functional equivalence. The catalog organizes constructs across the technological, organizational, and environmental contexts, provides core definitions with recommended measurement facets, and includes representative survey items with reported reliability coefficients. Beyond consolidation, this study identifies persistent gaps, including limited post-adoption measurement, weak readiness-capability differentiation, and underdeveloped governance constructs for emerging technologies. The catalog serves as a practical starting point for researchers designing TOE-based survey instruments and conceptual models, strengthening construct consistency while preserving the framework’s flexibility.

Abstract: Tabletop role-playing game (TTRPG) research increasingly produces complex multimodal and multimedia evidence, including speech, audiovisual recordings, character sheets, maps, platform traces, session notes, game-state data, and retrospective documentation. However, the field lacks a stable methodological infrastructure for organizing, comparing, retrieving, and reusing these heterogeneous forms of play-derived evidence. This article presents a scoping review of computational, multimodal, multimedia, and knowledge-organization approaches used to analyze TTRPG-derived data. The review followed PRISMA-informed transparency practices, used SPIDER to define the review scope, and synthesized 35 included sources through an extraction matrix, a research-question alignment matrix, and keyword co-occurrence analysis. The findings show that TTRPG research is distributed across publication ecosystems, data types, analytical purposes, and levels of formalization. Existing studies use qualitative interpretation, discourse and conversation analysis, player-experience evaluation, structured datasets, natural language processing, named entity recognition, semantic annotation, AI-assisted generation, and controlled vocabularies. However, these approaches remain weakly coordinated across disciplinary vocabularies, data models, and formalization practices. To address this gap, the article proposes a Polyvocal Semantic Infrastructure that responds to a double ontological problem in TTRPG research: the same object is named in multiple ways, and the same term is used for different objects across communities, systems, and disciplines. Operationalized through a semantic atlas and scope ladder, this infrastructure preserves interpretive plurality while supporting comparison, retrieval, provenance tracking, and future AI-assisted analysis of TTRPG-derived Multimedia Play Data.

Abstract: The rapid development of Artificial Intelligence (AI) and Machine Learning (ML) poses new challenges for high-performance system developers. The performance of such systems is often limited not by computational power, but by the efficiency of memory subsystem interaction. Cache behavior optimization becomes critically important, yet existing analysis tools fail to meet the demands of modern AI applications. They either provide only aggregated statistics or are characterized by a "semantic gap", presenting data in machine addresses rather than source code, which makes them ill-suited for analyzing the complex software systems typical of AI. This paper introduces CATS (C Annotated Trace-based Cache Simulator), a novel hybrid method and toolset for detailed cache efficiency analysis, designed to overcome these limitations. CATS combines dynamic tracing with static source code analysis to generate semantically annotated memory traces. This approach is particularly relevant for optimizing AI applications, as it allows precise identification of which data structures (e.g., weight matrices, tensors, or input vectors) are causing cache misses. For analyzing long-running tasks, such as training AI models, our method leverages AI techniques, specifically ML, for intelligent trace sampling, significantly reducing analysis time without sacrificing representativeness. The paper describes the methodology and architecture of CATS and presents experimental evaluation results. In the long term, the data collected by CATS can be used to train AI models capable of automatically providing developers with code refactoring recommendations to improve performance. Early CATS application identifies and resolves cache issues before final implementation, cutting performance optimization costs.

Abstract: Space-terrestrial integrated networks (STIN) are increasingly adopted to support time-sensitive missions such as collaborative monitoring and remote sensing, where user intents must be translated into actionable configurations across satellite and terrestrial segments. However, the pronounced heterogeneity in network capabilities, management vocabularies, and domain knowledge bases introduces severe semantic conflicts and knowledge discrepancies, making endogenous intent refinement unreliable and difficult to generalize. To address these limitations, this paper proposes a novel ontology-enhanced intent refinement framework that synergizes offline unified ontology construction with online real-time intent translation. Additionally, we develop an entity alignment model that autonomously identifies and resolves semantic conflicts by learning structural and attributive correspondences across heterogeneous knowledge graphs. Evaluation results based on various datasets and runtime comparisons demonstrate that the proposed framework achieves transferable and reusable endogenous intent governance under multi-vendor and cross-management domain conditions, ensuring end-to-end consistency and efficiency for automated orchestration in STIN-enabled remote sensing and monitoring applications.

Abstract: (1) Background. Food sovereignty and local sustainability are ensured by large agro-industrial holdings and small-scale farms; this is synergy forms a complementary model of the agrifood system. Maintaining this model’s balance requires the creation of a unified digital ecosystem that integrates all suppliers and consumers into production chains, thereby eliminating unnecessary intermediaries. (2) Methods. This study employs a comprehensive methodological framework, including systems analysis and mathematical modeling, to develop service algorithms. Object-oriented design and software engineering methods facilitated the development and implementation of a service-oriented architecture for the digital system. (3) Results. The study presents a multi-tier architecture featuring an integration bus based on a service-oriented approach. To implement direct supply-and-demand coupling strategies, the system integrates both internal services (microeconomic indicators) and external services (macroeconomic indicators). Additionally, a recommender system based on neural networks and mathematical models was developed to personalize consumer requests and manage product sales. (4) Conclusions. The software solution is consistent with the AgTech 4.0 concept and enables the creation of a seamless environment for interstate trade. The implementation of the system enhances the transparency of the “product footprint”, facilitates the redistribution of surpluses, and, consequently, contributes to price stabilization.

Abstract: To address the issues of large temperature fluctuations, poor spatial perception, and low control robustness in traditional residential air conditioners, this paper proposes an adaptive temperature control algorithm based on millimeter-wave radar and a Light Gradient Boosting Machine (LGBM). Given that the bed is the primary obstacle and heat source in a bedroom, we develop a bed localization method using point cloud clustering. This method accurately identifies the bed position through time - window filtering, outlier removal, and density clustering. An LGBM weak teacher model, trained on massive cloud data, takes the bed position, indoor temperature, and compressor parameters as inputs to optimize air direction and fan speed, thereby effectively suppressing steady-state fluctuations in the return air temperature. Experiments on 719 real - world devices demonstrate that the bed positioning accuracy reaches 83.6% under an error tolerance of 0.5 m, the average absolute temperature fluctuation is reduced to 0.21◦C, and the control accuracy of the air guide mechanism exceeds 0.98. The proposed method requires no hardware modification, offers strong generalizability and low deployment cost, significantly improves temperature stability and thermal comfort in bedroom environments, and provides a feasible technical solution for intelligent residential air conditioning control.

Abstract: This paper examines whether digital skill requirements in educational job postings are associated with higher advertised salaries in the labor markets of Almaty and Astana, Kazakhstan, and whether the observed salary gap reflects genuine skill valuation or employer heterogeneity. Using 794 vacancies collected via the HH.kz API, we construct an analytical sample of 755 observations after data cleaning. A seven-category dictionary-based skill extraction pipeline is implemented and its integral lexical consistency is evaluated against a TF-IDF+Logistic Regression baseline (5-fold CV: F1=0.755, AUC=0.928). Vacancies specifying at least one digital requirement carry a median advertised salary that is 18.2% higher than non-digital postings. OLS with HC3- robust standard errors and occupational and city controls yields a coefficient of 0.200 (SE = 0.039, p< 0.001, approximately +22.2%). Adding an employer-type proxy reduces this estimate to +15.2% (p < 0.001). In the Almaty subsample, the effect is no longer statistically significant (p = 0.075). Part of the observed premium reflects a measurement problem. Higher-paying employers are also more likely to specify digital requirements, inflating the estimated association. The design does not identify firm effects and supports only an associational interpretation. Quantile regression shows that the salary-digital gradient increases from +15.0% at Q25 to +44.4% at Q90. This pattern is consistent with labor market segmentation rather than a uniform skill premium.

Abstract: The article examines a crisis of academic integrity triggered by the emergence of a network of “predatory” (fake) websites mimicking the official Webometrics Ranking of World Universities (WUR). The author analyzes the origins of this phenomenon, linked to the temporary closure of the official portal and the migration of the Cybermetrics Lab’s data to new storage platforms. Three primary clone domains were identified that either falsify data or sell “position enhancement services” for a fee (up to €5,000 per month). The problem has escalated into an “information wildfire,” spreading across 29 countries. The highest number of cases involving the use of fraudulent data was recorded in Indonesia (50% of cases), Turkey, and Ukraine. The use of false rankings in official university reports and the media is classified as institutional fraud, misleading both prospective students and the state. It is emphasized that under new legislation (e.g., the 2026 Ukrainian Law “On Academic Integrity”), the publication of such data may lead to legal liability for university management. The author calls for the establishment of an international consortium of universities to support the official webometric audit and purge the digital space of fraudulent ranking systems, proposing a series of measures and a Comprehensive Program for Enhancing Integrity and Transparency in University Ranking Ecosystems.

Abstract: This study examines the perspectives of coffee industry stakeholders on blockchain traceability systems in Colombia and Switzerland. Adopting the Unified Theory of Acceptance and Use of Technology (UTAUT) as an interpretive framework, the study analyzes these perceptions through the constructs of performance expectancy, effort expectancy, social influence, and facilitating conditions. Using a quantitative cross-sectional design, we survey 360 participants, comprising 60 coffee supply chain companies and 300 consumers. Results reveal that 78.3% of stakeholders consider traceability essential, yet only 46.7% are familiar with blockchain. Stakeholders identify three primary benefits: improved transparency (91.7%), fraud prevention (88.3%), and enhanced security (86.7%). However, significant barriers persist: high implementation costs (95%), limited expertise (91.7%), and lack of awareness (93.3%). Geographic differences emerge: Colombian stakeholders prioritize cost reduction and fraud prevention, while Swiss participants focus on data management and privacy protection. Among consumers, 62.7% express interest in provenance information, 56.7% indicate willingness to pay for blockchain systems, and 59% show interest in tipping farmers. The study classifies benefits and drawbacks across nine dimensions, providing a comprehensive framework for understanding the multidimensional impacts of blockchain on coffee supply chains.

Abstract: Business Process Management (BPM) models represent routing behavior through control-flow constructs but do not provide a quantitative mechanism for evaluating uncertainty at decision points. This study introduces the Information Entropy Performance Indicator (IEPI) as a deterministic analytics artifact that maps BPMN 2.0 routing structures and externally specified probability assignments to uncertainty-based diagnostics. The IEPI engine takes as input a BPMN 2.0 process representation, a routing-probability map, and predefined viability thresholds, and computes (i) construct-level quantities based on normalized entropy and responsiveness, (ii) block-level propagated uncertainty measures using fixed composition rules, and (iii) a bounded process-level reporting index. The evaluation is conducted on analytically constructed BPMN scenarios with controlled routing configurations and fixed inputs, without reliance on statistical estimation or learning. Results show that construct-level classifications and process-level scores are well-defined and vary deterministically with threshold parameters and routing structure. Sensitivity analysis confirms consistent behavior under controlled parameter variation. The IEPI provides a reproducible analytical mapping from process structure to quantified uncertainty for evaluating routing behavior in BPMN-based models.

Abstract: Traditional European craft practices face dual pressures: the erosion of tacit knowledge held by aging practitioners, and the risk of cultural homogenization through uninformed digital adoption. This paper presents a comparative analysis of a structured design pilot conducted across five Representative Craft Instances (RCIs): glassblowing, tapestry, marble/silversmithing, porcelain, and woodcarving within the Horizon Europe CRAEFT project. Drawing on co-creative workshops, motion capture pipelines, physically based rendering (PBR), interactive simulation, and additive manufacturing, we analyze how context-specific digital tools performed as mediators rather than modernizers across heterogeneous craft domains. Cross-domain analysis reveals that digital tools achieve cultural legitimacy only when introduced through co-creative, practitioner-led cycles; that gesture and tacit knowledge are transferable via structured computational pipelines; and that methodological portability, not workflow replication, is the appropriate model for cross-context scaling. Implications are discussed for sustainable heritage policy, design education, and the development of craft-sensitive digital infrastructure in Europe.

Abstract: Traditional discriminative recommenders score and rank items indexed by single item IDs, whereas Semantic ID-based generative recommendation formulates recommendation as conditional generation of Semantic ID token sequences. This shift offers a unified view of retrieval and ranking and shows promising scaling properties, but the literature is fragmented across tokenization and quantization choices, model backbones, and training and decoding protocols, making systematic comparison difficult. To address this, we present the first survey that organizes the field, with four pivotal contributions. First, we introduce a unified five-stage reference pipeline: Representation Layer, Tokenization, Generative Backbone, Training, and Inference. This pipeline standardizes terminology and exposes shared structure. Second, grounded in this pipeline, we map existing methods into a fine-grained typology along semantic granularity, architectural coupling, and learning objectives. Third, based on this structured view, we provide a scaling-oriented perspective that connects component-level decisions to expressiveness, efficiency, and empirical performance, clarifying trade-offs. Finally, we synthesize open challenges and concrete directions that follow from the identified bottlenecks. To support reproducibility and controlled ablations across stages, we release UniGenRec (https://github.com/hupeiyu21/UniGenRec-A-universal-generative-recommendation-toolbox), an open-source modular toolbox implementing the proposed pipeline.

Abstract: This study analyses the application of games and gamification to increase student motivation and ensure an effective and sustainable learning process in the modern sustainable environment of digitalisation and e-learning. Recent changes have revealed that traditional teaching methods do not always sufficiently engage students, increasing the risk of decreased motivation to learn and poorer achievement. Gamification, which is based on the integration of game design elements – points, levels, badges, challenges – into learning activities, can help reduce these risks by promoting student autonomy, a sense of competence and social engagement. The study presents the application of a gamification system based on case analysis using an educational platform that allows the creation of educational games and the assessment of student engagement in real time. The results show that gamification methods significantly increase student activity and motivation to learn, while integrated game elements improve student’s motivation and learning experience. Based on the analysis, the paper presents conclusions and recommendations for the effective, high-quality, and sustainable organisation of the modern education process based on gamification.

Abstract: This paper presents the design, implementation, and evaluation of Shine Bright Like Silver, a mobile application developed for PIOP's Silversmithing Museum in Ioannina, Greece, to document, simulate, and present the sand casting and filigree silversmithing techniques. The proposed solution integrates semantic knowledge graphs, an interactive crafting simulation, and gamified learning elements to externalize and operationalize the implicit procedural knowledge involved in the craft. A user study was conducted with n=26 museum visitors to assess the system's effectiveness in terms of usability, learning outcomes, and early indicators of craft valorization. The findings suggest that combining semantic representation with interactive simulation, embedded within a museum context, offers a scalable and replicable framework for the digital preservation of heritage crafts. The work contributes to the broader discourse on technology-mediated craft education by illustrating how tacit knowledge can be systematically captured and transmitted through mobile digital environments, and by positioning visitor-facing tools within a broader reenactable preservation infrastructure.

Abstract: Public sector organizations face growing pressure to modernize service delivery through digitalization while ensuring transparency, interoperability, and citizen trust. Although blockchain technology offers promising capabilities for addressing these challenges, the absence of clear architectural guidelines for public sector contexts limits effective adoption. This study proposes BRA-PS, a Blockchain Reference Architecture for Public Sector Citizen-Centric Applications, developed from a realworld digitalization project in Quebec, Canada. The architecture organizes components into six layers (presentation, business, communication, smart contract, blockchain, and data) with cross-cutting concerns addressing governance, access control, security, and monitoring. A key design principle is the publicprivate workflow separation, which enables inter-organizational collaboration while preserving each organization’s operational autonomy and data confidentiality. We validated the architecture through a case study involving a vehicle registration process between two public agencies, supported by a proof-of-concept implementation using Hyperledger Fabric. An Architecture Tradeoff Analysis Method (ATAM) evaluation, conducted with a panel of five domain experts, identified six architectural risks, including IPFS confidentiality exposure and smart contract inflexibility, six non-risks, six sensitivity points, and six trade-offs across three key quality attributes: autonomy, collaboration, and functional suitability. The results confirm that BRA-PS effectively guides implementation decisions and stakeholder alignment. Practical recommendations derived from the evaluation provide actionable guidance for blockchain adoption in public sector services.

Abstract: This case study examines an encryption failure incident involving the exposure of sensitive personal data within a governmental information system environment. The analysis is based on the well-documented data breach that occurred within the U.S. Department of Veterans Affairs, in which a government employee stored a large dataset containing veterans’ personal information on a portable laptop device that lacked adequate encryption protection. Following the theft of the device from the employee’s residence, the personal records of approximately 26.5 million individuals were placed at risk of unauthorized exposure. Rather than interpreting the incident as an isolated technical failure, this study analyzes it through the Swiss cheese model, proposed by James Reason, showing that the breach resulted from the alignment of weaknesses across multiple layers of defence. These weaknesses included the absence of full-disk encryption, insufficient enforcement of data handling policies, weak access control procedures, inadequate oversight of sensitive data transfers outside controlled environments, and excessive reliance on individual user compliance. Based on this analysis, the study proposes corrective and preventive measures, including mandatory strong encryption for portable devices, formal cryptographic key management procedures, strengthened data access and handling controls, and enhanced monitoring and auditing mechanisms. These measures are intended to reinforce multiple defensive layers, improve the protection of sensitive information, and reduce the likelihood of similar incidents in operational environments handling confidential data.

Abstract: The European crafts ecosystem faces critical structural threats, declining practitioner numbers, weakening intergenerational transmission, limited digital literacy, and competition from industrial imitation. Existing online craft communities are narrowly material-specific and structurally illsuited to the cross-disciplinary dialogue required for systemic sector transformation. This paper presents the design, iterative development, and pilot evaluation of the Craeft Community, a multi-stakeholder Virtual Community of Prac-tice (VCoP) developed within the Horizon Europe CRAEFT project. Three research questions guided the study: how a multi-stakeholder VCoP should be structured to overcome disciplinary fragmentation; to what extent a stewarded digital forum can operationalize Situated Learning and Communities of Practice theory and what factors facilitate or inhib-it engagement and post-funding sustainability. Using design-based research, the platform evolved through four iterative phases, culminating in restructuring from a material-based architecture into five transversal thematic pillars, driven by survey evidence from 151 European craft professionals and systematic stakeholder feedback. The pilot phase yielded 86 registered members, 31 posts, and 27 interactions, with Transmission & Training as the most engaged pillar. Qualitative analysis reveals substantive cross-disciplinary discourse alongside a structural Effort-Engagement Gap, a persistent tension between forum partic-ipation demands and the gravitational pull of mainstream social media. The study demonstrates that a thematically organized, stewarded VCoP can meaningfully opera-tionalize apprenticeship-based learning in digital settings, advancing craft heritage preservation, economic resilience, and hybrid professional identity formation at the inter-section of craft and technology.