Applied Aspects of Information Technology
The publication focuses on the practical implementation of the latest information technologies in industry, energy, financial technologies, healthcare, sociology, education, project management, ecology, and human safety.
Intelligent computer-aided automation of operating state control for mining and metallurgical power complexes
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: Production rule, semantics, regression, monitoring, control, network configuration, heuristics, factor, automation, mode, stability, dispatching, instruction, dosage This paper addresses the synthesis of the informational framework for specialized expert systems designed to guarantee nominal power grid performance standards in post-contingency states through the application of heuristic situational dispatch control algorithms and automated decision support. The aim of this study is to substantiate a conceptual approach to the architecture of a knowledge base designed to support dispatch personnel decisions during power system parameter stabilization. The proposed paradigm involves generating a dataset utilizing a sensitivity matrix that correlates controlled state variables of the network with the magnitudes of dispatch control actions. The methodological framework of this research is based on the fundamental principles of power network theory, automatic control theory, artificial intelligence tools, mathematical statistics, and formal logic. Polynomial regression models were synthesized through multi-factor experimental design and the acquisition of a representative data sample. These mathematical relationships map the connection between the characteristics of the normal pre-contingency state, the network configuration, the nature of the disturbance, and the optimal magnitudes of control actions. The scientific novelty of this work lies in the development of a method for the comprehensive integration of heterogeneous information into a unified knowledge base. Specifically, formalized emergency restoration dispatch operational guidelines are integrated with calculated sensitivity matrix coefficients. The latter are determined via simulation modeling tied to the current network configuration and physical parameters of its equipment. Combining logico-semantic rules and numerical characteristics ensures high system performance sufficient for real-time operation. Results: the structural model of the knowledge base was optimized; the experimental design algorithm for calculating sensitivity matrix elements in regression models was adapted, along with the general algorithmic framework of the information and control system. The proposed heuristic control system facilitates solving a wide range of tasks: maintaining steady-state stability margins, optimizing active and reactive power flows, monitoring power grid parameters, and verifying the actions of operational and technological personnel. The practical value of this study lies in establishing a foundation for the deep intellectualization of automated dispatch control systems. Integrating the developed expert subsystem into active automated dispatch control system suites can significantly minimize decision-making time and enhance power supply reliability. An additional application of these models is their use as a baseline for training simulators intended for the professional development of dispatch and technological personnel.
Applied information technologies in energy engineering and automation
Applied information technology in energyengineering and automation
Copyright (c) 2026 Applied Aspects of Information Technology
Keystroke dynamics recognition using cluster-based prediction ellipses
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: Keystroke dynamics recognition, two-variate data, normalizing transformation, clustering, prediction ellipse, Box-Cox transformation Keystroke dynamics recognition is a biometric approach that enables user identification based on individual typing patterns. The relevance of the study is determined by the need to improve the accuracy of keystroke dynamics recognition under conditions of variability, heterogeneity, and deviation of data from a multivariate normal distribution. The object of this research is the keystroke dynamics recognition process, while the subject of the research is a mathematical model based on cluster-based prediction ellipses constructed in a two-variate feature space. The purpose of the article is to improve the accuracy of keystroke dynamics recognition by applying cluster-based prediction ellipses that take local data structures into account. The objectives of the study are to form cluster-based prediction ellipses constructed on local training samples and to compare the proposed approach with other one-class recognition methods. The methods of the study include Mardia’s test for assessing multivariate normality, the Box-Cox transformation for data normalization, k-means clustering, the elbow method and cluster size analysis, the construction of prediction ellipses based on the squared Mahalanobis distance, and robust covariance estimation based on the minimum covariance determinant. The scientific novelty consists in developing a cluster-based approach to constructing local prediction ellipses for keystroke dynamics recognition, which makes it possible to account for data heterogeneity and form more precise decision boundaries. The practical significance of the study lies in the possibility of improving the reliability of biometric user authentication systems without switching to more complex multiclass models. The results of the study demonstrate that the proposed approach significantly outperforms the one-class support vector machine and the prediction ellipse constructed for non-Gaussian data, while also providing better recognition of samples belonging to another class and recognition accuracy comparable to the prediction ellipse for normalized data. This improvement is achieved through adaptive modeling of local data distributions and more precise decision boundaries in the feature space. The conclusions confirm the effectiveness of clustering as a preprocessing stage for keystroke dynamics recognition based on cluster-based prediction ellipses and demonstrate the suitability of adaptive local modeling for heterogeneous keystroke dynamics data. Further research may focus on the application of alternative clustering methods, as well as on the use of advanced data normalization methods, such as the Johnson transformation. In addition, extending the feature space to a higher dimensionality by incorporating a larger number of keystroke dynamics characteristics may further improve recognition accuracy.
Assessment of material supply risks in make-to-order manufacturing based on the TabPFN neural network model
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: risk assessment, forecasting, make-to-order manufacturing, high-mix manufacturing, machine learning, tabular data, multiclass classification, TabPFN Relevance: Growing global instability and the vulnerability of supply chains lead to increasing risks in material procurement for industrial enterprises. The problem of forecasting and assessing material supply risks is particularly relevant in make-to-order manufacturing and high-mix, low-volume production. Such manufacturing systems, characterized by product diversity and low buffer inventories, are highly sensitive to delays in material delivery. Traditional risk management systems, designed for stable mass production, are often unable to react quickly enough to the dynamics of order-driven, high-mix environments. The purpose of this study is to improve risk assessment of production materials procurement through the use of machine learning for prediction of material delivery delays and development of an risk evaluation method on the base of such forecasts. To achieve this goal, the following tasks were solved: a model for forecasting delivery delays was developed and integrated supply risk index, based on probability of a delays and their impact introduced. The interpretability of the classifier model is also investigated, and an example of its integration into the resource planning system of the customer enterprise is provided. Methods: To obtain delay predictions, a multi-class classifier based on the state-of-the-art TabPFN model is used. The TabPFN model is based on a transformer neural network architecture adapted for tabular data analysis; a set of historical procurement data obtained from the customer's information systems was used for its training. Results: In the study, the TabPFN-based classifier model demonstrated a high degree of result reliability even when using default parameters, ordinal consistency of results, and a low tendency to underestimate supply delays, which indicates the obtained model's orientation toward risk minimization. The proposed risk assessment methodology allows for considering the impact of the forecasted supply delay on production depending on the current production program and warehouse inventories. Novelty: Combining multi-class delay classification based on business-defined thresholds with the state-of-the-art TabPFN model enables accurate delay forecasting even on limited datasets. Another novel aspect is the proposed approach to calculating a comprehensive risk index, which combines predicted delay probabilities with an evaluation of their impact on the production schedule. The combination of machine learning-enabled delay prediction and the proposed risk index allows for automated risk assessments on a near-real-time scale. Conclusions: The research demonstrates that the application of machine learning methods can enhance the accuracy and timeliness of supply risk assessment, enabling near-real-time monitoring and more informed decision-making regarding the provision of materials for production.
Systems analysis, applied information systems and technologies
Systems analysis, applied information systems and technology
Modelling security degradation of base Docker images using a rate-based metric
Section: Computer engineering and cybersecurity Categories: Computer systems and cybersecurity Keywords: Software ageing, technical debt, container security, Docker images, CI/CD governance, base image selection, vulnerability accumulation Topicality. Base Docker images are reused as long-lived software-supply-chain artifacts in pipelines. Unlike runtime-oriented software ageing, their degradation is primarily informational: the image may remain unchanged, while its scanner-observable security state changes as new vulnerabilities are disclosed and vulnerability databases evolve. Point-in-time scanner outputs identify known vulnerabilities at the scan date, but they do not show how quickly vulnerability burden has accumulated relative to image age, base operating-system family, and dependency footprint. This creates a scientific and technical contradiction: long-term base-image selection requires reproducible comparison of functionally suitable immutable artifacts, while existing indicators mainly describe current vulnerability state, static severity burden, package freshness, or short-term exploitability priority. Therefore, DevSecOps teams lack sufficient evidence for selecting candidates with a longer observable security horizon and lower expected security-maintenance burden. Purpose and objectives. The purpose of this study is to improve the scientific and practical grounding of long-term base Docker image selection in pipelines by formalizing observable security degradation as an age-normalized and base-family-contextualized accumulation process. To achieve this purpose, the study develops and empirically evaluates a layered comparative measurement model for identifying base-image candidates with longer observable security horizons. The objectives are to formalize severity-weighted vulnerability burden, normalize this burden by image age, contextualize degradation within base-image families, compare candidate variants at repository level, and evaluate whether the proposed metric provides information beyond raw vulnerability counts, static severity burden, vulnerability density, image age, and exploitability-oriented signals. Methods. The study uses a time-windowed dataset of Linux-based Docker images from popular Docker Hub repositories. Vulnerabilities are identified using a four-scanner ensemble consisting of Trivy, Grype, Docker Scout, and Snyk, deduplicated by vulnerability identifier, weighted by severity, and normalized by image age. The model is evaluated through family-level comparison, repository-level candidate comparison, and threshold-oriented security-horizon interpretation. Results. The proposed model links current vulnerability burden with age-normalized degradation intensity and interprets this signal within base-family, repository-level, and threshold-oriented contexts. Base operating-system families differ substantially. Repository-level comparisons show lower degradation intensity for reduced-footprint candidates than for higher-footprint candidates within same-ecosystem contexts, and lower degradation intensity for Alpine-based and Ubuntu-based candidates than for Debian-based candidates in repositories where direct comparison is available. Conclusions. The novelty of the study lies in formalizing base Docker image selection as a degradation-rate comparison problem. The proposed model differs from point-in-time vulnerability counts, static severity summaries, technical-lag measures, Exploit Prediction Scoring System, or Known Exploited Vulnerabilities, the proposed model provides an artifact-level signal for comparing candidate base images by the observed rate at which scanner-detected vulnerability burden accumulates over image age. The model is a reproducible comparative measurement procedure for long-term base-image selection. Practically, the model supports DevSecOps teams in selecting base-image candidates, prioritizing rebuilds, defining review policies, and governing pipelines by comparing images according to current vulnerability burden, observed degradation rate, base ecosystem, dependency-footprint context, and threshold-oriented security horizon.
A generalized method for constructing failure-behavior models of fault-tolerant multiprocessor systems with inter-subsystem redundancy
Section: Computer engineering and cybersecurity Categories: Computer systems and cybersecurity Keywords: Fault-tolerant multiprocessor systems, GL-models, inter-subsystem redundancy, reliability analysis, failure behavior, reserve resources Relevance. The evaluation of reliability characteristics of fault-tolerant multiprocessor systems is an important task, particularly during their analysis and design. GL-models of system failure behavior can be used for this purpose. For systems with inter-subsystem redundancy, such models should take into account the possibility of using the processors of a donor subsystem both when the subsystem remains operational and after it loses operability. Existing approaches do not provide a complete representation of these operating modes. Purpose. To develop a generalized method for constructing GL-models of the failure behavior of fault-tolerant multiprocessor systems with inter-subsystem redundancy. Objective. To formalize the principles of constructing GL-models of a donor subsystem taking into account different modes of processor utilization, to develop an algorithm implementing the proposed method, and to perform experimental verification of the correctness of the constructed models. Methods. The study employs the GL-model formalism, methods for constructing basic and auxiliary GL-models, the formation of extended state vectors, and the analysis of graph connectivity. Scientific novelty. A generalized method for constructing GL-models of fault-tolerant multiprocessor systems with inter-subsystem utilization of reserve processors is proposed. Unlike existing approaches, the proposed method takes into account the possibility of using the processors of a donor subsystem both when it remains operational and after it loses operability, when its operational processors may still be used to support other subsystems. Practical significance. The proposed method can be used for constructing GL-models of complex fault-tolerant multiprocessor systems and for the subsequent evaluation of their reliability characteristics. Results. A method and an algorithm for constructing GL-models of systems with inter-subsystem redundancy have been developed. The proposed approach is based on constructing auxiliary models that describe the sets of reserve and operational processors of a donor subsystem and on forming extended state vectors for constructing GL-models of recipient subsystems. Representative examples are considered for systems with limitations on the number of reserve resources and for systems employing sliding redundancy. Experimental studies confirmed the correctness of the constructed models. It is shown that the complexity of the edge-function expressions of the resulting GL-models is comparable to that of the corresponding basic models with equivalent fault-tolerance parameters. Conclusions. The proposed method provides a formal representation of inter-subsystem redundancy within GL-models and can be integrated with other approaches to constructing failure-behavior models of fault-tolerant multiprocessor systems. The experimental results confirm the correctness of the proposed method and demonstrate the feasibility of its application in the analysis and design of complex fault-tolerant multiprocessor systems.
Enhancing queue operation efficiency under peak server load in parallel architectures with chunking authentication
Section: Computer engineering and cybersecurity Categories: Computer systems and cybersecurity Keywords: Multi-server architecture, energy optimization, power-saving modes, heterogeneous workloads, dispatcher-deployer-executor (DDE) framework, task queuing, server state management Relevance. In modern computing systems, operation queues and multi-server architectures play a central role in managing resource-intensive, heterogeneous workloads, such as artificial intelligence training, audio/video conversion, and industrial equipment optimization via computational modeling. Traditional single-processor systems exhibit inherent bottlenecks, including sequential execution delays and inefficient resource utilization, whereas multi-server distributions enable parallel processing, albeit at elevated energy costs. Prior work introduced a parallelization method for dust particle flow modeling, yielding computational time reductions. however, escalating server rental expenses and environmental imperatives require energy-focused improvements without losing speed. Aim and objectives. This study modernizes the Dispatcher-Deployer-Executor architecture, emphasizing real-time energy optimization in multi-processor environments. A review of existing studies highlights problems with predictive methods, such as using machine learning to predict needs, and reactive methods, such as scaling based on processor use, especially for unpredictable and varied tasks. Energy conservation via idle server sleep modes remains underexplored in such contexts. The primary objective is to minimize average energy consumption over operational periods while preserving task throughput and latency constraints. Methods used. The study employs a combined theoretical and empirical approach. In the improved model and method, support for power-saving modes was added with the following states: Active with full power, Waiting with reduced power, Hibernation with minimal power and memory disk-buffering, and Maintenance with zero power for multiple servers. Total energy consumption is modeled to be minimized subject to execution time bounds. State transitions are governed by idle thresholds, with activation criteria incorporating task execution time, queue wait, and transition latency. The improvements include “wait-for-executor-release” logic, where queued tasks defer activation, and dual modes: disk-buffering for fault-tolerant, asynchronous execution versus direct submission to available executors. Security enhancements encompass chunked cryptographic message authentication, protected transport-layer communication, and dynamic mutual authentication with certificate pinning and ephemeral certificates. Implementation leverages Rust’s asynchronous framework, with energy metrics via hardware interfaces. Results. To exemplify queued operations, numerical simulations computed trajectories of dust particles in gas flows. Differential equations of particle motion that account for viscous friction, inertia, and gravity forces were integrated using the Runge-Kutta-Fehlberg method. The calculation results are used for optimization of pipes and channels of energy equipment. Experiments on a multi-server cluster simulated heterogeneous loads. Baseline full-active mode was compared to optimized states, demonstrating significant energy reductions while maintaining performance. Buffering mode enhanced fault tolerance; submission mode improved latency. Conclusions. In conclusion, the enhanced Dispatcher-Deployer-Executor framework advances sustainable distributed computing, balancing reliability, speed, and energy efficiency for heterogeneous tasks. Future extensions may incorporate predictive analytics for proactive scaling and edge-cloud hybridization.
dispatcher-deployer-executor (DDE) framework
Model for storing structured data in heterogeneous distributed programmable mobile systems
Section: Computer science and software engineering Categories: Digital control of technical and social systems, Computer science and software engineering Keywords: Modeling, mobile ad-hoc networks, heterogeneous systems, structured data, data replication, dynamic topology, edge and fog computing, data consistency The rapid development of mobile ad hoc networks and edge computing paradigms necessitates the design of resilient storage architectures capable of operating efficiently under stochastic topology changes. Existing decentralized storage approaches exhibit prohibitive signaling overhead and integrity degradation during critical link failures, making adaptive information management highly relevant. The purpose of the article is to ensure high availability and integrity of information in distributed programmable mobile systems in environments with dynamic network topologies and limited resources of mobile nodes. To achieve this, the following tasks were formulated: to propose a hybrid structured data storage model based on semantic standards; to develop an adaptive replication algorithm predicting device trajectories; and to experimentally evaluate the effectiveness of the developed solutions. The following methods were applied: graph theory for network topology modeling, the software-defined networking and software-defined storage paradigms for resource orchestration, and mathematical modeling techniques for calculating link expiration time. The scientific novelty of the work lies in the improvement of the structured data storage model through the integration of software-defined network paradigms and heuristic adaptive replication with competitive aging mechanisms, resolving the severe compromise between consistency and availability. The practical significance of the obtained results lies in their direct applicability to intelligent transportation systems, smart cities, and emergency response networks during disaster recovery. The most significant results demonstrate that the proposed model maintains a request delivery success rate of eighty-three point nine percent at movement speeds up to one hundred kilometers per hour while simultaneously reducing the average data lookup delay from two hundred and seven milliseconds to eighty-seven milliseconds. Conclusions. Developing a hybrid model based on intelligent orchestration and a two-level consistency policy successfully enables bounded inconsistency during network partitioning, enabling continuous application operability within isolated segments with a guaranteed delayed restoration of global semantic data integrity.
Computer science and software engineering
Digital control of technical and social systems
The taxonomy of knowledge graphvalidation approaches
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Knowledge graph, data validation, ontology reasoning, constraint languages, machine-learning-based error detection, Resource Description Framework database Relevance. Knowledge graphs encoded in Resource Description Framework have become a standard foundation for data integration, semantic search, and automated reasoning across domains ranging from biomedicine to enterprise knowledge management. As these graphs grow in scale and are maintained by distributed teams, it becomes increasingly important to ensure that their data follows the defined structural and semantic rules. Aim. This survey presents a systematic review of knowledge graph validation approaches published between 2011 and 2026, covering three principal families of methods and proposes a taxonomy that classifies existing papers within following three families: (i) ontology-based validation via Web Ontology Language reasoning, (ii) constraint-based validation using Shapes Constraint Language, Shape Expressions Language, and SPARQL Inferencing Notation (SPIN) language, and (iii) statistical and machine-learning-based error detection using knowledge graph embeddings and contrastive learning. The following objectives were conducted: to review the literature sources, to carry out comparative analysis across listed above methods, to suggest taxonomy of graph validation approaches, to discover gaps in research field. Scientific novelty: the survey introduces a single three-family taxonomy that characterizes each family by its underlying assumptions (Open World Assumption vs. Closed World Assumption), expressiveness, and computational cost, and it formally defines constraint drift, the progressive misalignment of Shapes Constraint Language shapes with their underlying ontology. Practical significance. The survey provides architectural design choices that resolve the tension between the Open World Assumption (OWA) of Web Ontology Language reasoning and the Closed World Assumption (CWA) of Shapes Constraint Language shapes across all three families, serving as a reference for practitioners and a roadmap for researchers. Results: a taxonomy of validation approaches is proposed and a comparative analysis of the three families is conducted. Constraint-based validation and Shapes Constraint Language in particular, is identified as the practical standard for Resource Description Framework data quality enforcement, with ontology-based reasoning complementing it for logical consistency and machine-learning methods extending it to distributional anomaly detection. Four open research gaps are identified: provenance-aware validation, incremental validation, validation under ontology evolution (including constraint drift, formally defined in subsection Gap 3), and hybrid constraint-plus-machine-learning approaches. Conclusions: no single family satisfies all validation requirements; ontology-based, constraint-based, and statistical/ML-based methods are complementary, and the OWA/CWA inconsistency remains a central design consideration across all three. Closing the identified gaps, in particular constraint drift and hybrid approaches, defines a roadmap for future work requiring coordinated effort across the standardisation, knowledge-graph-engineering, and machine-learning-on-graphs communities.
Research on the quality of automated program code generation using neural networks
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Large language models, automated code generation, software quality assessment, Angular framework, ISO/IEC 25010 quality framework, static code analysis, integrated quality score, software engineering benchmarks Relevance. Rapid advancements in large language models have significantly impacted software engineering, necessitating a rigorous evaluation of their capabilities in automated code generation. The aim of the study is to develop and validate a methodology for comprehensive, multi-dimensional quality assessment of program code generated by modern large language models in production-grade environments. Objectives. The study conducts a comparative analysis of the code generation quality produced by five leading neural networks – GPT-5, Claude Opus 4.1, Gemini 3.1 Pro, Grok 4, and DeepSeek-V3.1 – within the context of modern web development, evaluating their capacity to generate a production-ready, standalone Angular 19 component featuring complex drag-and-drop functionality, smooth animations, and mock Hypertext Transfer Protocol service integration. Methods. A two-level evaluation methodology was employed, combining automatic quantitative metrics – such as build correctness, TypeScript and ESLint error rates, cyclomatic complexity, bundle size, and security auditing – with a qualitative expert assessment of architectural integrity, maintainability, and documentation completeness. An Integrated Model Quality Assessment Metric was derived to rank the models based on weighted factors, prioritizing correctness (35%) and maintainability (30%) over performance (20%), documentation (10%), and security (5%). Scientific novelty. The proposed methodology integrates automated static analysis with structured expert evaluation into a single comparable metric grounded in the ISO/IEC 25010 quality framework, addressing the gap left by existing benchmarks that evaluate only functional correctness on isolated tasks. Practical significance. The findings provide crucial empirical data for selecting artificial intelligence tools in development workflows and demonstrate that production-oriented quality assessment requires multi-dimensional evaluation beyond syntactic correctness. Results. The empirical analysis revealed significant disparities across the tested architectures. Claude Opus 4.1 achieved the highest Integrated Quality Score (0.636), demonstrating superior code structure and documentation standards. Gemini 3.1 Pro followed closely (0.620), excelling in performance optimization and build stability. GPT-5 (0.447), while syntactically accurate, suffered from performance optimization issues, while DeepSeek-V3.1 (0.530) required substantial manual debugging. Grok 4 scored the lowest (0.240), with its reliance on outdated module-based architectures resulting in systemic deficiencies. Conclusions. While modern large language models are capable of generating valid code, substantial human oversight remains essential to ensure production readiness. The Integrated Quality Score proved effective in differentiating models whose surface-level syntactic performance appears similar.
Synthetic data generation from small real-world datasets for biological age estimation
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Synthetic data, biological age, generative models, machine learning, skeletal biomarkers, data privacy This article addresses the relevance of overcoming the shortage of high-quality clinical data required for training reliable machine learning models for biological age estimation. The collection of such data is expensive, time-consuming, and restricted by privacy regulations, while small sample sizes lead to overfitting. The aim of this research is to compare modern synthetic data generation methods using a limited set of skeletal system biomarkers to identify the optimal approach under small-sample conditions. The object of the study is the methodology of generating synthetic data for small tabular biomedical records applied to estimating biological age. The subject of the study encompasses statistical methods and generative machine learning models used to synthesize realistic clinical information. The objectives include selecting relevant biomarkers, implementing various data generation frameworks, assessing the quality of synthetic data, and evaluating privacy risks. The study evaluates the bootstrap resampling method, the synthetic minority oversampling technique, conditional tabular generative adversarial networks, tabular variational autoencoders, and the Gaussian copula approach. Validation relies on nonparametric statistical tests for distributional similarity, geometric proximity measurements, nearest neighbor privacy evaluations, and cross-validation testing. The results demonstrate that basic resampling fails to protect data privacy, while linear oversampling severely distorts chronological age distributions and lacks sample uniqueness. Deep generative adversarial networks exhibit mode collapse under small-sample conditions, shown by significant error discrepancies between training and testing scenarios. In contrast, the statistical copula approach provides the highest fidelity in maintaining complex multivariate correlations. Additionally, probabilistic autoencoders prove exceptionally capable of reproducing marginal distributions. Both of these successful methods effectively maintain privacy. The scientific novelty lies in the development of a comprehensive comparative framework for evaluating generative models under strict small-data constraints. The practical value of the study consists in providing clear recommendations for researchers: the Gaussian copula approach should be prioritized when preserving exact physiological relationships between features is critical, whereas tabular variational autoencoders are optimal when accurate population-level distributions are strictly required.
AI-assisted optimization of multi-agent system parameters: a model of iterative hyperparameter tuning in swarm intelligence algorithms
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: information technology, swarm intelligence, iterative tuning, multi–agent systems, hyperparameters, Swarm Chemistry, genetic algorithm, scan optimization The relevance of the study is driven by the necessity for rapid and safe analysis of territories affected by emergencies, which stimulates the search for innovative approaches in the field of autonomous systems, in particular the use of swarms of unmanned aerial vehicles for terrain scanning. The aim of the article is to present an intelligent multi-agent superstructure for the dynamic adjustment of existing swarm system parameters, based on a combination of Swarm Chemistry self-organization mechanisms and global optimization using an evolutionary algorithm. The task consists in the development of an iterative tuning technology that allows for real-time adaptation of the behaviour of individual agents and the entire formation in response to dynamic environmental changes without interfering with the base structure of the control algorithm. Among the methods employed is an architecture consisting of a set of specialized agents organized into four functional layers, performing continuous monitoring, sensitivity analysis, and mission hyperparameter adjustment, together with a combined methodology of a genetic algorithm and local optimization methods that take into account swarm heterogeneity and the individual scanning widths of each device. The scientific novelty of the work lies in the dynamic tuning system architecture that ensures the survivability and high performance of existing swarm solutions in complex conditions. The practical significance is determined by the significant potential for application in disaster zone monitoring, search for victims, and real-time damage assessment. The results of simulation demonstrated a significant reduction in mission execution time and maximization of the coverage area while simultaneously increasing the stability of swarm behaviour, even under conditions of partial loss of agents or the appearance of unpredictable obstacles. The conclusions point to further development of the model, which involves the integration of machine learning methods for an in-depth analysis of previous mission experience and the expansion of the system for operation in three-dimensional space.
Design of modified Duffing pendulum for trajectory generation
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: Duffing pendulum, chaotic systems, distance-based state variables, nonlinear observability, multi-dimensional attractors Relevance. The article proposes a new approach to the construction and analysis of chaotic dynamical systems based on distance-based state variables. In contrast to traditional state-space models, where the state of the system is described directly by coordinate variables, the proposed method represents the system using distances between the current state and a set of predefined reference (base) points. These base points can remain fixed in space or move along given trajectories. Such a geometric representation of the system state offers additional flexibility for modeling nonlinear dynamics and allows the construction of new types of chaotic behavior. Purpose and objectives. In this framework, the evolution of the system is described in terms of distances to selected base points, rather than through initial coordinates. This transformation generalizes traditional state-space descriptions and allows the developer to influence the structure of the dynamics by choosing the number, configuration, and movement of the reference points. As a result, the proposed formulation provides a convenient way to introduce additional nonlinear interactions into the system while maintaining a clear geometric interpretation. Methods. To demonstrate the proposed approach, the Duffing pendulum is used as a representative example. The classical Duffing oscillator is a well-known nonlinear system that can exhibit periodic, quasi-periodic, and chaotic oscillations. In this work, the Duffing dynamics is reformulated using distance-based variables defined with respect to several reference points. Two scenarios are considered: systems with fixed reference points and systems where the reference points move in time. In the latter case, the motion of the reference points can be periodic or chaotic, which further enriches the behavior of the system as a whole. Both continuous and discrete formulations of the model are developed. The continuous representation allows for the analysis of theoretical properties of the system, while the discrete version is well suited for numerical simulation and implementation on digital platforms and embedded devices. Results. Numerical experiments show that the modified systems reproduce the main qualitative features of the classical Duffing oscillator, as well as create new types of attractors. In particular, the models can generate unipolar and more complex multidimensional chaotic attractors. Conclusions. These results indicate that distance-based observability equations can serve as an effective tool for the design of new chaotic systems. The proposed approach can be useful in applications such as secure communication, parallel generation of chaotic signals, and the study of complex nonlinear dynamics.
Diabetic retinopathy diagnosis using deep neural networks
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: Diabetic retinopathy, fundus images, deep learning, adaptive attention, image recognition, uncertainty estimation Relevance: is driven by the high prevalence of diabetic retinopathy as one of the leading causes of vision loss worldwide and the limited availability of ophthalmological diagnostic resources. In medical applications, predictive uncertainty assessment is critically important, since erroneous yet confident decisions may lead to serious clinical consequences. Despite significant advances in deep learning-based DR diagnostic systems, insufficient attention has been paid to the systematic interaction between data preprocessing strategies, class imbalance handling, and predictive uncertainty estimation, all of which directly affect model reliability. Aim and objectives: the aim of this study is to develop and experimentally validate an automated DR stage classification framework that achieves high diagnostic performance while systematically evaluating predictive reliability and uncertainty. Methods: the proposed framework integrates computer vision and deep learning methods based on convolutional neural networks with transfer learning and incorporates an adaptive attention mechanism that dynamically regulate channel and spatial attention strength according to feature variability. The approach also incorporates two preprocessing pipelines, class-balancing techniques, and uncertainty estimation through stochastic inference without modifying the training procedure. A systematic experimental study was conducted to evaluate different combinations of preprocessing and balancing strategies under fixed architectural conditions. Model performance was evaluated using standard classification metrics and an original integral reliability indicator, which jointly accounts for classification quality, prediction confidence, and predictive uncertainty. Results: combining advanced preprocessing, targeted balancing strategies, adaptive attention mechanisms, and uncertainty estimation significantly improves both classification effectiveness and predictive reliability. Proposed configuration achieved the highest scores, while uncertainty-aware models exhibited lower predictive variance and higher confidence, particularly in challenging or ambiguous cases. Conclusions: the proposed framework provides a structured methodology for reliability-aware DR classification and contributes an integrated evaluation approach that enhances the practical applicability of deep learning systems in clinical decision support.
Adaptive and coordinated IT project management in dynamic environments: A multi-agent ai perspective
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: Multi-agent systems, adaptive project management, collective decision-making, agent coordination, risk management, artificial intelligence, dynamic role allocation, IT governance, decision support systems, project scheduling The increasing complexity and volatility of IT project environments expose the structural limitations of both plan-driven methodologies and human-centric Agile frameworks, which adapt at cadences incompatible with the tempo at which risks materialize and requirements shift in large-scale software development contexts. This paper develops an integrated theoretical and methodological basis for adaptive and coordinated IT project management using collectives of specialized AI agents, addressing three concurrent gaps in the literature: the absence of a unifying conceptual framework, the lack of formal agent coordination models for project management contexts, and the underdevelopment of collective decision-making and risk management methods for multi-agent governance systems. The proposed system is formally defined as a five-tuple comprising the dynamic project environment, a nine-agent collective organized across three functional layers, an adaptive policy set, and a human governance structure with explicit oversight constraints. Coordination relies on a conflict matrix and utility-based dynamic role reallocation; decisions are produced through weighted aggregation with confidence-driven dispatch; risk exposure is updated via multi-agent Bayesian assessment. Experimental validation against single-agent AI and human-only Agile baselines on a controlled six-sprint scenario demonstrates a 68% reduction in mean decision latency, a 2.4× improvement in risk detection lead time, a conflict resolution rate of 0.91, and an autonomous dispatch rate of 0.81 at final sprint, with a false escalation rate of 0.13. The results establish that distributed functional specialization and collective decision authority provide qualitative architectural advantages that monolithic AI systems and retrospective human governance cannot replicate, while preserving full human oversight accountability.
A method for operative sharing confidential images among a group of trusted users
Section: Computer engineering and cybersecurity Categories: Computer systems and cybersecurity Keywords: Chaotic Tent map, Vernam cipher, Diffie-Hellman protocol, Burmester-Desmedt protocol, Montgomery elliptic curve Encryption of high-resolution images plays a key role in ensuring their confidentiality and integrity. Given the rapid growth of traffic of such images in open communication channels, the development of new methods and means of their protection is becoming increasingly actual. The objective of this study is to develop and model a method for exchanging confidential images for a group of trusted users. In the process of the study, encryption methods with chaotic maps, cryptography on elliptic curves, and agreement group secret agreement with various protocols were used . One of the requirements for the method being developed is its operation on a time scale close to real. This is due to the rapid aging of information in images subject to analysis in the group. The study is based on the method of generating a pseudo-random sequence using a modified chaotic map Tent. A short key containing the parameters of this map is transmitted to trusted users who form the group. Then each user can expand the key into a pseudo-random sequence and encrypt/decrypt the image with the Vernam cipher . A group of trusted users is formed using the Diffie-Hellman protocol or Burmester-Desmedt on elliptic curves. The Weierstrass, Montgomery and Edwards curves were analyzed from the point of view of computational costs. The Montgomery curve was chosen as the most computationally efficient working elliptic curve. To transfer to a group of trusted users, the short key of Tent map is encrypted with a block cipher with a secret as the key. The developed method is modeled for the curve Montgomery Curve25519. Image security and confidentiality are ensured by five levels of protection. As a result, it was found that group formation according to the Burmester-Desmedt protocol ensures the operation of the method on a time scale close to real time and a high level of scalability.
Analytical modeling of OAM-based programmable hardware accelerators for data-invariant algorithms execution
Section: Computer engineering and cybersecurity Categories: Computer systems and cybersecurity Keywords: Programmable hardware accelerator, parallel architecture, ordered access memory, high-performance computing Relevance. The growing demand for high-performance computing in domains such as signal processing and scientific modeling necessitates efficient hardware accelerators capable of predictable and scalable execution of tasks. Algorithms with data-invariant structure, characterized by fixed execution schedules and static data dependencies, represent an important class of workloads that can benefit from specialized architectural solutions. Ordered Access Memory (OAM)-based programmable hardware accelerators offer deterministic and conflict-free parallel data access, making them a promising approach, however, systematic analytical methods for their performance and resource evaluation remain insufficiently developed. Aim and objectives. The aim of the scientific research highlighted in this paper is to develop a unified analytical framework for modeling and evaluation of OAM-based programmable hardware accelerators. The objectives include formalization of memory requirements, computational throughput, and execution time, as well as analysis of architectural trade-offs for different accelerator structures. Methods used. The study employs analytical modeling based on matrix representation of data, instructions, and indices, and derives formal expressions for basic system characteristics as functions of architectural parameters and algorithm properties. A unified execution time model is developed, incorporating preparation, computation, and output phases, with explicit consideration of memory access parallelism and overlapping operations. Comparative analysis is conducted for dual-memory and algorithm-adaptive memory structures. Results. The proposed framework provides models for estimating data, instruction, and index memory requirements, computational throughput, and execution time. It enables systematic exploration of the architectural parameter space and reveals trade-offs between memory organization, degree of parallelism, and performance. The analysis shows that adaptive memory structures reduce redundant data movement, leading to improved execution time and more efficient resource utilization compared to dual-memory designs. Conclusions. The novelty of the work lies in the development of a unified analytical modeling framework that jointly captures memory organization, computational structure, and execution dynamics of OAM-based accelerators. The practical value of the results consists in enabling early-stage, platform-independent evaluation and optimization of accelerator structures for data-invariant algorithms execution, supporting informed design decisions without the need for time-consuming simulation or prototyping.
Metrical loss functions for image segmentation based on convolutional neural networks
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Image segmentation, loss functions, convolutional neural networks, deep learning, metric, partition, computer vision Image segmentation remains a fundamental challenge in computer vision, with neural network training heavily dependent on appropriate loss functions. While common losses such as Dice are widely used, they lack rigorous mathematical foundations as proper distance metrics and do not fully capture the geometric structure of partitions. We introduce a weighted metric for comparing segmentation based on partition theory that satisfies all metric axioms, including the triangle inequality. The proposed metric compares partitions through symmetric difference and intersection operations, incorporating both spatial structure and semantic features via a weight function characterizing region properties, such as color, texture and other. We prove that the proposed functional forms a proper metric space on weighted partitions under specified conditions, with particular emphasis on establishing the triangle inequality. Experimental verification on synthetic segmentation tasks demonstrates feasibility, although practical implementation faces challenges, such as the need for differentiated segment extraction, which can be solved using the Straight-Through Estimator approximation. The triangle inequality property opens up opportunities for hierarchical approaches to segmentation and efficient partition search. This work bridges the gap between geometric clustering theory and deep learning-based segmentation, providing a theoretically grounded alternative to heuristic loss functions and also experimentally proves the possibility of using the proposed metric as a loss function when training convolutional neural networks.
Rational data sampling for surrogate modeling of the stress-strain state of plate elements
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Stress-strain state, surrogate modelling, regression analysis, machine learning, dataset, rational data sampling strategy, rapid diagnostics of engineering structures Background. Surrogate models based on machine learning are a promising alternative to computationally expensive finite element analysis for predicting the stress-strain state of structural elements. However, their efficiency critically depends on the quality of training data, while most existing approaches rely on abstract stochastic sampling methods that ignore the physical nature of engineering parameters. Aim. The aim of the study is to conduct a comparative analysis of the efficiency of stochastic versus physically-oriented data sampling strategies for the surrogate modelling of the stress-strain state of plate elements, with the focus shifted from increasing model complexity to developing an approach for rational dataset formation that integrates a priori engineering knowledge. Methods. Using the surrogate modelling of thin rigidly clamped plate deflection as an example, two fundamentally different sampling strategies are compared: the classical strategy, based on Sobol quasi-random sequences, and the proposed rational strategy. The latter is based on the real empirical properties of structural steels and accounts for the standardized plate thicknesses of sheet metal assortments. To evaluate and quantitatively compare the impact of the data structure on the precision of the prediction, an intelligent modelling procedure was developed. The following machine learning algorithms were used as test models for the regression task: K-Nearest Neighbours, Random Forest, XGBoost, Gaussian Process Regression, and Multilayer Perceptron. Results. It was found that switching to the rational strategy provides a consistent reduction in mean squared error across all evaluated models by three and a half to four times compared to the baseline strategy. Using the rational dataset increased the coefficient of determination by twenty-seven percent for k-nearest neighbors, twenty-three percent for random forest, and twenty-one percent for XGBoost. The highest precision was achieved by the models of gaussian process regression (with a coefficient of determination of ninety nine percent) and multilayer perceptron (with a coefficient of determination of ninety eight percent). It is proven that the low efficiency of traditional samples is due to the fact that more than fifty percent of the samples contain combinations of geometric dimensions and material properties that do not correspond to any real engineering standards. These unrealistic combinations create “information noise” during training. Conclusions. The proposed rational sampling approach serves as a basis for creating robust AI tools for the rapid diagnostics of engineering structures, providing high model generalisation capability with a smaller volume of input data.
rapid diagnostics of engineering structures
Development of a unified feature space for ensemble classification of polystructural heterogeneous small data
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Polystructural data, heterogeneous data, small data, ensemble classifiers, Gompertz function, audio classification Relevance. The paper addresses the problem of classification of polystructural heterogeneous small data, which include structured tabular information and unstructured audio signals. Objective. The study proposes a unified methodological framework for constructing an ensemble classification system based on a common feature space that ensures compatibility between different data types and machine learning models. Main Research Material. A unified feature space model is developed, incorporating a linear representation for structured data and a three-level representation for audio data, including tabular spectral features, spectrogram-based image representation, and temporal sequences of features. To improve the quality of input data, a Backward Feature Elimination procedure is applied to adapt feature subsets to the specifics of individual classifiers and the removal of non-informative features. The classification framework is based on a stacking ensemble architecture that combines multiple base models, including classical machine learning algorithms and deep learning models. Three aggregation strategies are considered: hard voting, soft voting, and soft voting with Gompertz fuzzy ranking, which enables nonlinear adjustment of classifier probabilities and improves robustness under uncertainty. Results. Experimental evaluation was conducted on five datasets from different domains, including healthcare, finance, audio signal analysis, and deepfake detection. The results demonstrate that the proposed approach consistently improves classification performance compared to individual models. The application of feature selection and the integration of ensemble methods provides significant gains for polystructural data. Conclusions. The proposed model offers a flexible and scalable solution for handling heterogeneous small data and can be effectively applied across multiple domains, providing improved generalization, robustness to noise, and adaptability to different data representations.
Sensor data preprocessing pipeline for grain temperature monitoring and forecasting datasets in drying systems
Section: Computer engineering and cybersecurity Categories: Computer systems and cybersecurity Keywords: grain temperature, sensor preprocessing, anomaly detection, feature engineering, grain drying, Isolation Forest Relevance. The quality of training data significantly affects the reliability of data-driven grain temperature forecasting systems. Sensor networks used in grain drying environments generate missing observations, measurement drift, and anomalous values caused by communication failures and sensor degradation. The absence of standardized preprocessing procedures reduces reproducibility and limits the practical applicability of predictive approaches. Purpose. The purpose of the study is to develop a methodology for sensor data collection and preprocessing intended for formation of datasets for grain temperature forecasting under continuous drying conditions. Objectives. The objectives include development of a multi-source monitoring architecture, justification of spatial sensor deployment, construction of a preprocessing pipeline for anomaly handling and missing value restoration, formation of feature vectors, and definition of a dataset partitioning strategy preserving temporal dependencies. Methods. The proposed methodology integrates digital temperature sensors, humidity sensors, carbon dioxide sensors based on non-dispersive infrared measurement, and meteorological data sources. Preprocessing consists of physical range validation, anomaly detection using the Isolation Forest algorithm, hierarchical missing value imputation, min-max normalization, cyclic temporal encoding, and chronological partitioning of datasets. Scientific novelty lies in the formalization of a topologic-spatiotemporal data validation framework that distinguishes local transducer degradation from collective thermal process transitions in non-stationary agricultural environments. This is achieved via a coupled multivariate Isolation Forest and neighbourhood graph co-occurrence heuristic that preserves high-frequency operational dynamics during continuous aeration adjustments. Practical significance. The methodology may be applied in monitoring systems for grain storage and drying installations and adapted to different silo geometries, sensor configurations, and environmental monitoring sources. Results. A monitoring architecture consisting of twelve temperature sensors arranged in three azimuthal rows and four depth levels was developed. The preprocessing procedure retained 92.5 percent of observations after anomaly filtering and missing value processing. The resulting dataset contained 34,984 usable epochs in the training partition and sixty-four features representing temperature history, neighbouring sensor values, meteorological variables, and cyclic temporal characteristics. Isolation Forest analysis identified 4.08 percent anomalous observations, of which 87.3 percent corresponded to isolated sensor faults. Conclusions. The developed methodology establishes a reproducible procedure for preparation of datasets intended for grain temperature forecasting tasks and reduces risks associated with temporal leakage, sensor faults, and inconsistent preprocessing of monitoring data. The proposed approach may serve as a basis for further development of predictive models for grain drying systems.
A system internals modeling and annotation language for large language model-driven software engineering
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: System modeling, software architecture, language models, token efficiency, code summarization, software engineering Rapid scaling of large language models has significantly disrupted traditional software engineering paradigms with unprecedented capabilities for code understanding, generation, and automated review. However, practical repository-level deployment is constrained by context limits and token cost. While retrieval-augmented generation is widely used to address this limit, it often splits a codebase into disconnected semantic chunks, omitting high-level structural dependencies. In contrast, alternative approaches that attempt to feed the entire repository structure into the context window typically rely on generic formats such as JSON. While widely recognized, these formats introduce redundant syntactic units that significantly influence the token budget, effectively reintroducing the bottleneck. This work introduces SiMAL (System internals Modeling and Annotation Language), a domain-specific language designed specifically for language-model-driven software engineering workflows, with the primary objective of providing a compact, human-readable, error-tolerant, yet highly structured representation of a software system, optimized for iterative interaction with language models. It combines both static and dynamic aspects of a software system, unifying architectural views, component and endpoint definitions, runtime deployment metadata, and other development artifacts into a single textual schema that can be converted to and from a normalized machine representation. This effort includes the definition of the language’s syntax and grammar, an open-source parser, and a visualizer that renders schemas into nested system diagrams. The proposed language is validated through a comprehensive protocol that assesses token efficiency alongside schema validity and semantic faithfulness. This includes deterministic structural checks (parsing integrity and annotation consistency), schema-to-code correspondence analysis, and an “LLM-as-a-judge” evaluation for repository coverage. The results indicate that prompt-efficient schema modeling reduces token overhead without systematically degrading structural usability or quality, making it a practical backbone for scalable autonomous software engineering.
Assessment of digital image quality for visual objects in Gamified Systems
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: visual perception, data informativeness, color difference, fractal dimension, integral quality indicator This paper devoted to the development and justification of a comprehensive approach to assessing the quality of digital images in gamification environments, taking into account the peculiarities of the perception of visual objects by users. The relevance of the study is due to the growing role of gamified visual interfaces in digital applications and the need for objective quality control of visual content, which directly affects the level of user engagement and motivation. The paper analyzes modern methods for assessing the informativeness and quality of digital images, in particular, approaches based on texture analysis, spatial structure, machine learning, and deep neural networks. A hybrid method is proposed that combines the evaluation of color differences according to the CIEDE2000 standard with fractal analysis of structural characteristics of images, which allows taking into account both color and geometric-textural properties of visual objects. The research methodology is based on the formulation and testing of hypotheses regarding the influence of image processing quality on the perception of objects in gamified environments, mathematical modeling of the integral quality indicator using weighting factors, as well as software implementation of the proposed approach using Python. Practical testing was carried out on a set of digital images of different levels of graphic complexity (simple, medium complexity, and complex), for which the CIEDE2000 indicators, fractal dimension, integral quality index, and MSE and SSIM metrics were calculated. The results obtained confirm that the combination of color and structural analysis provides a more complete and objective assessment of the quality of gamified images compared to the use of separate methods. The work shows that improving the quality of images is statistically associated with improving the perception of visual objects, and the use of a combined method allows for more correct differentiation of quality levels for images of different complexity. The proposed approach can be used for automated quality control of visual content in computer games, educational platforms and other gamified systems, and also creates a basis for further research in the direction of adaptive and intelligent assessment of visual data.
Models of semantic analysis of product descriptions for automatic determination of customs codes
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Customs classification, semantic analysis, machine learning, text processing, automation, harmonized system In today's global trade environment, accurate and fast classification of goods by Harmonized System (CUSTOMS/CUSTOMS) codes is crucial for the efficient functioning of customs processes. The increase in data volumes and the complexity of product descriptions create a need to implement intelligent methods for analyzing text information. The use of Natural Language Processing (NLP) technologies in combination with machine learning opens up new opportunities for automating the process of determining customs codes and reducing the human factor in customs classification. The aim of the study is to increase the accuracy of automated determination of Harmonized System codes by applying modern models of semantic analysis of text descriptions of goods. To achieve this goal, the following tasks were set: to analyze existing natural language processing models, to investigate their effectiveness for customs classification tasks, to form the optimal model architecture and to evaluate its advantages compared to traditional algorithms. The work uses semantic modeling and machine learning methods. An experimental approach was used to combine these models with classification algorithms, in particular, logistic regression, decision trees, and neural networks. The evaluation was carried out using accuracy, completeness, and measure indicators. The results of the study showed that the use of contextual embeddings, in particular the BERT model, provides a significant improvement in the accuracy of automated classification of goods compared to traditional statistical methods. The proposed generalized model, combining semantic analysis with machine learning, allows to increase the level of correct assignment of customs codes based on text descriptions, even in cases of ambiguous or incomplete data. The study confirmed the feasibility of integrating natural language processing technologies into customs classification systems. The scientific novelty lies in the development of a hybrid model that combines semantic text representations and classification algorithms, which increases the accuracy and efficiency of automated determination of customs codes. The practical significance of the work lies in the possibility of implementing the proposed approach in "smart customs" systems to optimize control processes and accelerate the clearance of goods.
An empirical evaluation of reasoning models for classifying information manipulation techniques
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Propaganda detection, artificial intelligence, reasoning models, large language models, information security, information space The rapid evolution of modern geopolitical conflicts has transformed information manipulation and propaganda techniques from a tool of persuasion into a sophisticated weapon of mass influence. As these operations become increasingly complex, relying on subtle psychological tactics rather than overt falsehoods, the development of advanced, automated detection mechanisms is a critical security challenge. This study aims to bridge the gap between theoretical capabilities and practical applications by empirically evaluating the performance of emerging reasoning models in the specific task of classifying information manipulation techniques. The primary objective is to benchmark these generative architectures against historical supervised baselines to determine if their internal chain-of-thought capabilities offer a tangible advantage over traditional pattern-matching approaches in identifying complex rhetorical strategies. The research methodology utilizes a standardized international benchmark dataset for propaganda detection (SemEval-2020 Task 11) to conduct a comparative analysis of frontier models without task-specific fine-tuning. The study employs an inference-only strategy, integrating role-playing, definition embedding, and structured reasoning instructions to simulate expert analysis. A key methodological contribution involves the systematic variation of the reasoning budget allocation during inference to measure the correlation between computational deliberation and classification accuracy. The investigation reveals a distinct semantic advantage where reasoning models significantly outperform previous supervised systems in detecting nuanced techniques that rely on cultural context, emotional weight, and indirect logic. However, the results also uncover a critical limitation where increased reasoning effort might degrade performance on structurally simple tasks, confirming the existence of an overthinking phenomenon in automated classification. The analysis further identifies a non-linear relationship between computational cost and performance, indicating that monolithic reasoning models often yield diminishing returns compared to lightweight architectures for high-volume processing. The paper concludes that while reasoning models represent a paradigm shift in semantic understanding, they are not yet a universal solution for all information manipulation types due to structural blind spots and economic inefficiencies. The study proposes moving away from single large models toward multi-agent systems. This proposed approach advocates for an adaptive system that assigns specialized tasks to a team of virtual experts, balancing precision with operational viability in the defense of the information space.
A fragment-based spatial-temporal video analysis method for detecting anomalous violent events
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: video analysis, spatio-temporal video processing, event detection, anomalous behavior detection, violent event detection, fragment-based representation, adaptive fragmentation, motion-based analysis, video anomaly detection In this work, we address the problem of violent event detection and classification in video streams under realistic computational constraints. Many safety-critical events, such as violent interactions or abnormal behavior, are characterized by short-term and spatially localized motion patterns, while the majority of video content remains static or irrelevant. Conventional deep learning approaches typically process full video frames or dense spatio-temporal representations, which leads to high computational cost and inefficient use of computational resources. We propose a fragment-based spatio-temporal video analysis method inspired by principles of video coding. Each video frame is divided into fragments, and motion activity is estimated using dense optical flow between consecutive frames. Only fragments exhibiting significant temporal changes are selected for further processing, while static regions are suppressed at an early stage. The fragment size is adaptively adjusted according to local motion intensity, allowing finer spatial resolution in dynamic regions and coarser representation in static areas. The selected fragments form a compact representation that is subsequently used for event classification via lightweight temporal aggregation. By reducing spatio-temporal redundancy prior to feature extraction, the proposed method significantly lowers computational complexity while preserving discriminative motion cues. The proposed method is evaluated on the UBI-Fights dataset, with additional training data augmentation using the Video Fight Detection (VFD2000) dataset. Experimental results demonstrate that the method achieves competitive performance with area under the receiver operating characteristic curve up to 0.72, area under the precision-recall curve up to 0.63, and binary F1-score up to 0.60, while maintaining efficient inference speed. These results indicate a favorable trade-off between accuracy and efficiency compared to dense frame-based baselines, making the method suitable for real-time and resource-constrained video analysis systems.
Improving the quality of mine detection by a mine-detecting drone by selecting an architecture for classifying objects in visible-light camera images
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Convolutional neural network, transformer, attention, weights, classification quality, tokenization The use of unmanned aerial vehicles (drones) is increasingly being used in humanitarian demining. Multiple sensors operating on different physical principles are used simultaneously to search for mines. This increases the probability of mine detection. To expedite the survey of the area, the initial processing of sensor signals is performed onboard the drone. Therefore, the requirement for sensor signal processing algorithms not only increases the probability of object detection but also improves the ratio of classification accuracy to the required computational effort. One of the sensors used is visible-light cameras. A large number of neural networks have been developed for classifying objects in images. However, a specific feature of their use for humanitarian demining is the lack of large datasets for training and testing. Therefore, the challenge arises of finding neural networks that offer a high ratio of classification accuracy to the required computational effort, while also being able to train on very small data sets. This paper compares convolutional neural networks and networks based on transformers. The networks were trained on a small dataset containing images of anti-tank mines, rocks, and various backgrounds. The study showed that convolutional neural networks train faster and are more resilient to image quality degradation. However, their potential is limited; increasing the number of layers does not significantly improve mine classification quality. Transformer-based neural networks offer greater flexibility and a wealth of options for architectural configuration, resulting in superior performance compared to convolutional networks. Although they are slower to train and potentially require an expanded training dataset, they are the preferred choice for implementing image processing on drones.
Architecture and implementation of a hybrid prototype system of a digital spectral twin of thin films
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: digital twin, spectroscopy, hybrid information system, thin-film structures, neural networks, parameter identification The relevance of this work is driven by the need to develop intelligent information systems for processing spectroscopic data and identifying material parameters in real time. The aim of the study is to develop a hybrid architecture prototype of a digital spectral twin for real-time identification of thin-film parameters. To achieve this aim, the tasks of integrating physical and neural network models, implementing an online self-adaptation mechanism, and evaluating the system’s performance on experimental data were addressed. A numerical scheme based on the transfer matrix method and physics-informed neural networks was implemented for modeling spectral responses and adaptive parameter estimation. The online self-adaptation mechanism, which continuously updates material parameters based on experimental spectral data under the presence of noise and slow drift, was investigated, along with the analysis of parametric identifiability using local spectral sensitivity. The results of the study is the development of an architecture that ensures stable convergence of thickness, refractive index, and absorption coefficient estimates over 100–200 iterations, and convergence of the overall spectrum up to 300 iterations. Relative errors below tree percent for thickness, hundredths of a percent for the refractive index, and tenths of a percent for the absorption coefficient were achieved. The model’s ability to track gradual parameter changes, maintain high predictive accuracy over long-term operation, and reduce spectral reconstruction errors was demonstrated. Robustness to noise and non-stationary conditions, as well as accurate reproduction of spectral maxima, relative intensities, and selective approximation, were confirmed. The developed hybrid architecture is an effective tool for intelligent spectroscopic monitoring and material identification. Further research will focus on extending the system to multilayer structures and industrial measurement applications.
A hybrid fuzzy-logic clustering framework for transparent credit risk assessment in microfinance lending
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: fuzzy logic, credit risk, credit scoring, risk modelling, creditworthiness, consumer lending, computer modeling Relevance. The assessment of credit risk for short-term unsecured loans in microfinance institutions is a critical problem for financial stability and portfolio management. This segment is characterised by unstable client behaviour and limited credit history, creating challenges for traditional statistical and machine learning scoring models. Transparency and explainability are increasingly important due to regulatory requirements and the need for adaptive methods capable of handling incomplete and noisy data. This study aims to develop a methodology for applying fuzzy logic to enhance the accuracy, stability, and transparency of automatic credit decisions in the microfinance sector. The main objectives include constructing an interpretable fuzzy model, defining fuzzy sets for key variables, developing a transparent rule base built on clustering and BadRate profiles, and validating internal and external consistency, monotonicity, and transparency. Methods. The study uses a dataset of 601,092 issued and closed loans of a Ukrainian microfinance institution in 2024. The proposed approach combines clustering-based segmentation with fuzzy rule-based credit risk assessment. Clustering was performed using the K-Means method, with the optimal number of segments determined by the Elbow method. Fuzzy sets were constructed using trapezoidal membership functions, and rules were formed based on cluster combinations and normalised BadRate coefficients. Aggregation and defuzzification were performed using the Mamdani method to obtain a numerical RiskScore. Validation included checks for monotonicity, rule consistency, and decision transparency at the observation level. Results. The model identifies stable behavioural segments reflecting different risk levels. The RiskScore demonstrates strong consistency with empirical BadRates distributions and forms three interpretable risk categories: Low, Medium, and High. The proposed fuzzy model effectively predicts credit risk under incomplete and unstable data. Conclusions. The scientific novelty of the study lies in the integration of clustering, BadRate profiles, and rule-based inference into a unified interpretable scoring framework for credit risk assessment in microfinance lending. The proposed approach has practical significance, providing a transparent and interpretable decision-support tool for credit risk evaluation, applicable to risk management, portfolio monitoring, loan loss provisioning, and regulatory-compliant scoring systems.
Specialized recurrent U-Net architecture for immunohistochemistry image segmentation
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: U-net, deep learning, segmentation, immunohistochemistry, software architecture, biomedical images Microobject segmentation in biomedical images is a complex and time-consuming process, and current efforts are aimed at qualitatively separating the studied objects from the background in the image. Immunohistochemical images are used for breast cancer analysis. One of the types of images is estrogen samples. The specificity of immunohistochemical images requires a more thorough approach to segmentation, because standard algorithms or classical neural network models do not allow achieving the required accuracy. This article presents a new convolutional network architecture based on a recurrent U-network for segmentation of immunohistochemical images. The proposed architecture is specifically designed to process the unique features of immunohistochemical images. The principles of recurrent convolutional neural networks were used to develop the model and train it using open datasets. The architecture proposed in this work is based on the configuration of neural network layers, which allows to obtain the best result for a given type of image. In addition, a pipeline of continuous integration of the program code and the model was implemented to ensure continuous retraining in a cloud environment, which significantly simplified the workflow for researchers and engineers. Additionally, a mechanism for processing medical images and providing automation of the big data processing process using computer vision and deep learning was proposed. The proposed method achieved higher segmentation accuracy. The architecture allows for more accurate cell segmentation on immunohistochemical images, which is a crucial step in the diagnostic pipeline. High-quality cell segmentation increases the reliability of subsequent analyses and parameter extraction, thereby contributing to increased diagnostic accuracy.
Estimating the number of lines of code of the latest releases of open-source applications using nonlinear regression models
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: regression model, estimation, lines of code, open-source application, normalizing transformation, Mardia criterion, outlier, Mahalanobis distance, test statistic, support vector regression, machine learning The problem of early estimating the number of lines of code of the latest releases of software, including open-source applications, is important because it directly affects the effort prediction for their development and subsequent improvement. The research aim is to build several regression models for early estimating the number of lines of code of the latest releases of open-source applications. The research object is the process of early estimating the number of lines of code of the latest releases of open-source applications. The research subject is the regression models for early estimating the number of lines of code of the latest releases of open-source applications. For early estimating the number of lines of code of the latest releases of open-source applications, the models, confidence, and prediction intervals of two nonlinear regressions with two predictors were constructed using the Box-Cox three-variate normalizing transformation and specialized techniques. These techniques, relying on multiple nonlinear regression analyses incorporating the use of multivariate normalizing transformations, account for the presence of outliers in multidimensional non-Gaussian data. In the paper, we built two nonlinear regression models for early estimating the number of lines of code of the latest releases of open-source applications that depend on two predictors: both the number of classes of their latest and first releases. The first model has good quality, but the second one can only be used for estimating the conditional mean, and it has poor quality for predicting the response as the dependent random variable. We have compared the quality of two constructed regression models and two linear support vector regressions. The quality of the above models is similar. An analysis has been carried out to compare the constructed models with nonlinear regression models that only depend on the number of classes for their current releases, which were built based on the Box-Cox bivariate transformation. Compared to such nonlinear regression models, the constructed models demonstrate a larger multiple coefficient of determination, a smaller value of the mean magnitude of relative error, a larger percentage of predictions that fall within 25 percent of the actual values, and narrower confidence and prediction intervals. The comparison results indicate better quality of the constructed models with two predictors. The prospects for further research may include the use of other data sets to construct the nonlinear regression models for early estimating the number of lines of code of the latest releases of open-source applications, for other restrictions on predictors.