Herald of Advanced Information Technology
Development of a geoinformation method for assessing the accessibility of medical services in community infrastructure development projects
The article develops a geoinformation method that provides a solution to the scientific and applied problem of assessing the accessibility of medical services in community infrastructure development projects. Solving this problem is the basis for planning community infrastructure development during their post-war reconstruction. The relevance of the study is determined by the need to improve the effectiveness of management decisions regarding the assessment of medical service accessibility to community populations in conditions of spatial unevenness in the distribution of medical infrastructure facilities and limited resources. The work aims to develop a geoinformation method for assessing the accessibility of medical services for the population of communities, based on the use of open geospatial data from the OpenStreetMap framework, which takes into account the topology of the road network, the spatial distribution of medical facilities, the density and distribution of the population in the communities, and the time parameters of access to medical services. The proposed method is implemented on the basis of open data using OpenStreetMap tools, OSMnx and NetworkX libraries, which ensure flexibility, scalability and transparency of calculations. A distinctive feature of the method is the automated determination of areas of community access to medical services based on the construction of isochrones around medical infrastructure facilities, taking into account the type of facility and the acceptable time of arrival of community residents to them. The developed geoinformation method was tested for adequacy. It was found that deviations are within 1 minute or 0.3 km. The results of the experimental application of the method on the example of a typical territorial community showed its ability to identify ‘grey areas’, i.e. settlements that are not sufficiently covered by the network of medical institutions, and to assess the impact of changes in the structure of the medical network on the overall accessibility of medical services to the population. The practical value of the study lies in the creation of an algorithm that forms the basis for the development of a system to support management decisions on the strategic development of communities. In particular, in terms of assessing the population's access to medical services for planning community infrastructure development projects. The proposed method can be further expanded by integrating demographic forecasts, population mobility scenarios, and data on the state of road infrastructure, which opens up prospects for its use in a wider range of community infrastructure development project planning tasks.
Information technologies in energy systems engineering and manufacturing
Information technology in energy engineering and manufacturing
Copyright (c) 2025 Herald of Advanced Information Technology
Analysis of methods for supporting personalization in IT education
The increasing complexity of digital learning environments and the growing demand for individualized educational experiences underscore the importance of developing effective methods for personalizing practical tasks. The purpose of this study is to analyze existing approaches to the personalization of practical tasks in IT education and to identify their advantages, limitations, and potential for integration into digital learning ecosystems. The main objectives include the identification of technological solutions that enable automated generation and assessment of personalized practical tasks, and their effectiveness for supporting adaptive and scalable learning processes. The research methodology is based on a comparative analysis of scholarly sources, existing educational platforms, and practical implementations of personalized tasks. Special attention is given to the development of conceptual frameworks that enable personalization not only at the level of task complexity but also in terms of content variability, learning pathways, and assessment mechanisms. The review identifies the most effective strategies for achieving scalable personalization in digital education. The analysis also reveals the limitations of current solutions. The critical limitations are: insufficient support for comprehensive practical task parametrization, high technical barriers for instructors, incomplete learning management system integration, security vulnerabilities in local deployment scenarios, and limited automation in assessment processes. These constraints highlight the need for more sophisticated, user-friendly solutions. The findings provide a foundation for developing integrated information technologies that combine personalization methods with adaptive learning and artificial intelligence tools, supporting the evolution of digital learning ecosystems toward more effective, scalable, and integrity-focused educational approaches. The scientific novelty of the research lies in the systematic comparison of personalization methods and the identification of criteria for their effectiveness in IT education. The practical significance is determined by the possibility of integrating the proposed approaches into digital platforms, thereby improving the quality of training, optimizing the learning process, and ensuring the flexibility required by modern educational ecosystems.
Information model of remote sensing of soil
This paper presents a comprehensive information model and open architecture for unmanned aerial vehicle (UAV)-based remote sensing of soil. The development of remote sensing technologies using unmanned aerial vehicles (UAVs) and sensors of various measurement methods makes it possible to contactless detect various anomalies. The reliability of information after its processing is achieved by a combination of measurement methods, the correct choice of contactless measurement tools, and the use of machine learning methods for data processing and training on known anomalies. The modular UAV platform supports adaptive sensor integration and real-time data fusion for precision agriculture, environmental monitoring, and post-conflict applications. The information model defines the structure and flow of data between UAV sensors, onboard processors, and the ground control station, incorporating a digital twin for dynamic environmental modeling. A minimum set of necessary tools has been selected, based on the measurements and processing of which there will be enough information to identify anomalies in the soil with a high degree of reliability. The scope of application of the obtained results is quite extensive: from precision farming, including monitoring crop diseases and chemical non-contact soil analysis, to identifying foreign inclusions and anomalies in areas where military actions took place.
Information technologies and computer systems
Information technologyand computer systems
A model for keyword spotting in voice signal for specialized computer systems
Keyword spotting in voice signal is a crucial task for specialized, low-resource computer systems, such as ground drones, particularly when operating under challenging conditions with limited computational power and without reliable cloud access. This paper presents a novel, modular model for efficient keyword spotting that does not rely on deep neural networks. The model's core principle is the differential weighting of Mel-Frequency Cepstral Coefficients , prioritizing those coefficients most discriminative for phonetic content. The architecture incorporates robust signal conditioning, dynamic feature extraction (including delta and delta-delta derivatives), the transformation of acoustic features into compact string-based "fingerprints", and final classification using the Levenshtein distance. Experimental validation, conducted on a Ukrainian-language corpus of drone commands with lexicons of up to 200 words, demonstrated the model's high performance and scalability. The system achieved an F1-score of 0.92 under ideal conditions and showed significant resilience in noisy environments, maintaining an F1-score of 0.78 at a 5dB signal-to-noise ratio. Furthermore, the proposed system significantly outperformed a baseline version (using only basic Mel-Frequency Cepstral Coefficients without derivatives or normalization) by up to 33 percentage points in F1-score under challenging conditions. The study validates that this optimized classical Keyword Spotting approach provides an effective and fully autonomous solution for edge computing applications where resource efficiency and independence from cloud infrastructure are paramount, especially in critical scenarios like military operations.
Development and optimization of distributed high-performance systems with real-time data consistency
This article examines the theoretical and applied aspects of building distributed high-performance systems capable of processing large volumes of data with minimal latency while maintaining real-time consistency guarantees. The relevance of the research is determined by the rapid growth of data-intensive domains such as high-frequency trading, telemedicine, smart cities, the Internet of Things, and autonomous transport, where even millisecond delays can have critical consequences. The study focuses on the analysis of modern architectural models, including microservices, CQRS, Lambda and Kappa architectures, and event-driven systems with Apache Kafka. Methods of performance optimization are explored in detail, covering asynchronous request handling, caching, replication, and load balancing, with special attention to edge computing and 5G-based infrastructures. Particular emphasis is placed on theoretical and practical aspects of data consistency, including CAP-theorem (Consistency Availability Partition tolerance) trade-offs, consensus algorithms (Paxos, Raft), and CRDT (Conflict-free Replicated Data Type) structures. The results demonstrate that no universal architectural solution exists, but hybrid approaches combining strong and eventual consistency models can ensure reliability in mission-critical domains. The proposed analytical model for evaluating performance under different workload profiles enables the selection of optimal system configurations, balancing throughput, latency, and consistency requirements. The scientific novelty of the research lies in the integrated framework that unites architectural patterns, optimization techniques, and consensus mechanisms, as well as in the development of an analytical evaluation model that had not been sufficiently presented in previous studies. The practical significance is manifested in the formulation of recommendations for the design of fault-tolerant, scalable infrastructures for finance, telecommunications, healthcare, IoT, and smart cities.
AI-Generated video evaluation by fragment processing
Recent advances in generative AI have led to the development of techniques to generate visually realistic synthetic video. As a result, there is a growing demand for detectors capable of distinguishing between AI-generated videos. In this paper we propose a compact, fragment-based representation of video frames that enables robust spatial-temporal analysis and new approach to discrimination between real and synthetically generated footage. To achieve this goal, each frame is divided into fragments and a square matrix of size BxB is formed. Next, we compute the dominant singular value for every fragment, yielding a square S-map. This construction preserves local structure while normalizing geometry, so standard 2-D operators can be applied uniformly. We analyze spatial organization via 2-D Discrete Cosine Transform (DCT) energies and temporal change with robust thresholding to form a binary change mask. Then we apply Connected Component Labeling (CCL) on the binary change mask (4- or 8-connectivity) and compute the area of the Largest Connected Component (LCC). We derive an LCC time series that measures the spatial concentration of change. Empirically, synthetic videos exhibit higher rates of near-binary LCC toggling, longer plateaus, increased mass at rational steps, and fewer unique levels than real videos - signatures consistent with temporal quantization and procedural dynamics. The pipeline is lightweight (fragment-wise rank-1 SVD + CCL on a small grid), auditably interpretable, and suitable for batch screening and edge devices. It attains ROC-AUC ≈ 0.86 and TNR ≈ 0.94 on mixed-resolution datasets with further gains from per-granularity calibration.
Theoretical aspects of computer science, programming and data analysis
Theoretical aspects of computer science, programming anddata analysis
Uncertainty-aware multi-objective refactoring for code duplication
Code clones are recurring code fragments that may hinder software maintainability if not properly managed. While many clone detection tools exist, they often stop at identification and provide no clear guidance on whether a detected clone group should be refactored, how to do so, or in what order. This paper presents a machine learning–based method for recommending clone refactorings with prioritization and confidence estimation. The proposed approach represents code fragments using abstract syntax trees, program dependency graphs, and semantic embeddings from a pre-trained CodeBERT model. In addition, version control data is used to extract evolutionary features such as churn, age, and co-change patterns. A multi-class classifier predicts refactoring types, while open-set recognition techniques identify uncertain cases and flag them as unknown. Effort and benefit estimation models help prioritize suggestions based on a cost-effectiveness ratio. We evaluated the method on four open-source Java projects using a manually labeled dataset of 600 clone groups. The system achieves a macro-F1 score of zero point seven six on known refactoring types and an AUROC of zero point nine one for unknown detection. Prioritized recommendation quality reaches NDCG@3 of zero point eight nine, showing strong alignment with expert assessments. The results indicate that clone refactoring can be effectively supported through integrated code representation, uncertainty modeling, and prioritization. The approach transforms clone analysis from a passive task into an actionable process.
artificial intelligence in software engineering
Method for building ensemble classifiers of structured and unstructured data based on a unified approach
Effectively classifying heterogeneous data, including structured and unstructured data, is essential in diverse fields such as healthcare, finance, information security, and audio content analysis. This study aims to develop a unified approach for constructing ensemble classifiers capable of handling diverse data formats within a single framework, enhancing classification accuracy and robustness. The methodology integrates feature extraction and data preprocessing techniques, transforming heterogeneous datasets to a standardized numerical format suitable for ensemble learning. Eight base classifiers including K-nearest neighbors, support vector machines, random forest, extreme gradient boosting, logistic regression, multilayer perception, convolution neural networks and long short-term memory networks–were trained with optimized hyperparameters. The ensemble classification uses stacking with various aggregation types such as hard voting, soft voting, and soft voting using Gompertz fuzzy ranking to effectively combine model predictions while accounting for uncertainty and noise. Experimental evaluation across five datasets, covering medical diagnosis, credit risk, emotion recognition, music genres and deepfake detection–demonstrates consistent improvement in accuracy and F1-score metrics, with gains up to 8 percent compared to the best individual classifiers. The approach proves particularly effective for unstructured audio data, where temporal and spectral dependencies pose significant challenges. The results underscore the versatility the proposed unified ensemble methodology in addressing class imbalance and noise offering a scalable solution adaptable to various domains. This work contributes a comprehensive framework facilitating the development of robust classifiers for complex real-world data and paves the way for future research integrating heterogeneous data sources within cohesive predictive models.
Improving the performance of сlustering with wavelet function by using inequality constraints
Clustering methods based on gradient estimation are common in automated control and diagnostic systems, where reliable data processing is needed under noise and multimodality. Their use, however, is constrained by low robustness and high computational costs. Wavelet-based approaches are relevant because they enhance noise immunity and improve efficiency. The purpose of this work is to develop and study a clustering method that employs wavelet functions to introduce constraints and ensure stable performance under noisy conditions. The research included analysis of existing approaches, development of a wavelet-based method with inequality-type second-order constraints, creation of an algorithm for its implementation, and experimental evaluation. The proposed method relies on wavelet transforms with hyperbolic functions, which reduce the number of oracle calls, decrease computational stages, and accelerate convergence in classification and clustering problems. Experiments show that the method shortens the search time for the optimum by about one and a half to seven times at different signal-to-noise ratios, with a moderate increase in error of roughly five to fifteen percent for the De Jong test function. On synthetic datasets, the gain in computation time exceeded one point one compared with the baseline method. In a practical case of reliability assessment for resistors used in critical equipment, efficiency improved by nearly eight percent. Finally, the novelty lies in the clustering method with constraints-inequalities defined by wavelet processing. This can increase the computational speed in conditions of high noise levels, asymmetric objective functions, and small data samples.
On the computational complexity of cascade GL-models for fault-tolerant multiprocessor systems
The article addresses the problem of evaluating the computational complexity of basic cascade GL-models used for modeling the behavior of fault-tolerant multiprocessor systems in the flow of failures. The purpose of this work is to reduce the complexity of such models by optimal selection of their parameters. It has been demonstrated that a single system usually corresponds to an entire family of cascade GL-models, differing in cascade depth and parameters, with each having its own computational complexity. To simplify the process of modeling the system behavior under a flow of failures, it is advisable to choose the cascade GL-model configuration with the lowest complexity. However, additional constraints on the model, such as cascade depth limitations, must also be considered. This work applies an empirical-analytical research method. An analysis of computational complexity for cascade GL-models was conducted using specially developed software, which automated model construction for various combinations of parameters. Subsequently, a comparative analysis of the complexity of their edge function expressions was performed to identify dependencies on parameter values. Experimental studies were carried out for fault-tolerant multiprocessor systems with varying numbers of processors and different maximum allowable failure multiplicities (but not exceeding half of the total number of processors in the system). It was shown that cascade GL-models typically have significantly lower computational complexity compared to standard basic GL-models, especially for systems with a small maximum number of allowed failures. However, in cases where the allowed number of failures equals or exceeds half of the processor count, standard models may become less complex. Based on the conducted analysis, practical recommendations for selecting the parameters of cascade GL-models were formulated for the first time. In particular, the lowest complexity is achieved when the fault tolerance coefficient of the auxiliary model is minimized at each cascade level; however, this leads to a maximal cascade depth. If cascade depth is limited, the lowest complexity is achieved by evenly or nearly evenly distributing the fault-tolerance coefficients among auxiliary models. If an even distribution is impossible, it is advisable to place higher-value coefficients at deeper cascade levels. Experimental results demonstrate that the application of the proposed recommendations can significantly reduce the overall complexity of edge function expressions in the cascade GL-model compared to the basic GL-model, with the effectiveness of the approach increasing as the system size grows.
Internet of Energy based cellular structures for distributed Microgrids
In the face of evolving global energy challenges and the urgent need for resilient, decentralized, and digitized power systems, the concept of the Internet of Energy (IoE) emerges as a transformative framework for modern energy infrastructure. This paper explores the development and implementation of IoE-based cellular structures within distributed Microgrids, with a focus on Ukraine’s critical need for energy security and system restoration. Embracing the “3D” paradigm – Decarbonization, Decentralization, and Digitalization –the proposed architecture organizes energy systems into interconnected energy cells. These cells leverage distributed energy resources, demand-side management, and Energy Cloud 4.0 technologies to enable real-time energy balancing, market participation, and transactive energy operations. The paper presents a comprehensive structure for Microgrids based on IoE principles, detailing energy cell typologies, control algorithms for cost optimization, and strategies for grid flexibility and reliability. It highlights how cellular architectures can ensure autonomous operation, enhance resilience to outages, and foster integration with the European energy market. Through modeling and simulation, the paper validates the feasibility of this approach, demonstrating significant improvements in energy cost efficiency, stability, and sustainability. To organize the work and interaction of cells, 5 types of energy cells (generators, prosumers, passive consumers, electric vehicles, utility) have been identified that interact and have impacts on all participants of the Microgrid: economic impacts (changes in consumer electricity bills and revenues from energy sales), technical impacts (changes in the network scheme, approaches to network planning and operation) and social impacts (support, reliability of consumption, reduction of CO2 emissions). The technical, economic, and social benefits of transitioning to IoE-based cellular Microgrids are a foundational element in rebuilding and future-proofing Ukraine’s energy sector.
A multi-criteria scoring metric for evaluating deep learning models in bitcoin price forecasting
The increasing computational demands of deep learning have raised concerns about the environmental sustainability of artificial intelligence applications, particularly in high-frequency domains such as financial forecasting. This paper addresses the need for more holistic evaluation criteria by proposing a multi-criteria scoring metric for deep learning models used in Bitcoin price forecasting. The purpose of the study is to develop a performance metric that balances predictive accuracy with computational efficiency and environmental impact. The method involves combining traditional accuracy measures with training time, energy consumption, and carbon emissions into a unified performance score, calculated using a logistic scoring function. The metric was validated by applying it to forty-two configurations of Long Short-Term Memory models trained on historical Bitcoin price data. Each configuration was assessed for its forecasting accuracy, energy use and emissions (measured using a carbon-tracking tool). The results show that simpler Long Short-Term Memory models can offer competitive accuracy while significantly reducing training time and emissions. The highest-performing model achieved a balance of all criteria, while deeper architectures with marginal accuracy gains incurred disproportionate environmental costs. The study concludes that the proposed scoring metric offers a practical and scalable solution for selecting deep learning models under sustainability constraints, supporting more responsible Artificial Intelligence deployment in real-world settings.
Fuzzy models of wireless components sensor networks
The article addresses the problem of modeling wireless sensor network components operating under uncertainty and the influence of external factors. The relevance of the research stems from the growing need for reliable and autonomous monitoring and control systems in industrial environments, especially under conditions where wired communication is not feasible or consistent. Classical models with strictly defined input parameters fail to adequately reflect the dynamics and unpredictability of sensor node behavior and communication links, which limits the accuracy of performance assessment and complicates the design of robust systems. The aim of this study is to develop a formalized approach for describing wireless sensor network behavior under uncertainty using fuzzy logic and semantic representation. The proposed solution is based on a generalized modeling framework that utilizes the theory of fuzzy sets and supports both crisp and fuzzy input/output variables. Two universal formulations are introduced: one that models relationships solely between fuzzy variables, and another that links crisp parameters with fuzzy outputs. These models serve as a flexible basis for representing typical processes in wireless sensor networks, where parameters are often only partially known, qualitatively estimated, or described in linguistic terms. Variables are mapped into an orthogonal semantic space, allowing the use of membership functions for further reasoning and integration into ontological structures. Although detailed structural models of sensor nodes and communication channels are not included in this version and will be presented in a separate follow-up study, the current work lays the methodological groundwork for their development. The scientific novelty lies in the creation of a unified fuzzy modeling framework that supports both abstract and data-driven representations of uncertainty in wireless sensor networks. The practical significance is seen in the applicability of the proposed approach to designing adaptive, context-aware sensor networks capable of operating reliably in challenging and resource-constrained environments. The introduced models provide enhanced flexibility, semantic interpretability, and accuracy in representing real-world operational conditions.
A model for constructing neural network systems forrecognizing emotions of text fragments
Emotion Recognition in text is a crucial task in Natural Language Processing, particularly relevant given the exponential growth of textual data from social media and voice interfaces. However, developing effective emotion recognition systems for low-resource languages, such as Ukrainian, faces significant challenges, including linguistic informality, dialectal variations, and cultural specificities. This paper introduces a modular model (framework) for developing neural network-based tools for recognizing emotions in Ukrainian text fragments. The model encompasses a comprehensive data preprocessing pipeline, flexible architectural choices (including approaches based on Word to Vector, Long Short-Term Memory, and Transformers), and rigorous validation using standard metrics and interpretability methods. As part of an experimental study, two prototypes were implemented and compared: a lightweight classifier based on FastText and a more powerful classifier based on pretrained RoBERTa-base, both trained to recognize seven basic emotions. The results demonstrate that RoBERTa-base achieves high accuracy, significantly outperforming FastText and a baseline translation-based approach, yet it demands substantially more computational resources for inference. The study underscores the importance of creating Ukrainian-language corpora to enhance recognition capabilities and highlights the critical trade-off between accuracy and efficiency. It provides practical recommendations for model selection based on resource constraints and performance requirements for emotion analysis tasks in the Ukrainian language.
Analysis of existing approaches to automated refactoring of object-oriented software systems
Automated refactoring plays a crucial role in the maintenance and evolution of object-oriented software systems, where improving internal code structure directly impacts maintainability, scalability, and technical debt reduction. This paper presents an extended review of current approaches to automated refactoring, emphasizing methodological foundations, automation levels, the application of artificial intelligence, and practical integration into CI/CD workflows. We examine rule-based, graph-based, machine learning–based (CNNs, GNNs, LLMs), and history-aware (MSR) techniques, along with hybrid systems incorporating human-in-the-loop feedback. The taxonomy of refactoring types is aligned with established terminology – particularly Fowler’s classification – distinguishing structural, semantic (architectural), and behavioral transformations, all grounded in the principle of behavior preservation. Formal models are introduced to describe refactorings as graph transformations governed by preconditions and postconditions that ensure semantic equivalence between program versions. The paper provides a concrete example of a transformation generated by the DeepSmells tool, demonstrating the «before/after» change and explaining the rationale behind the AI-driven recommendation. The study also addresses the challenges of explainability and semantic drift, proposing mitigation strategies such as SHAP-based analysis, attention visualization in transformer architectures, integration with formal verification tools (e.g., SMT solvers, symbolic execution), and explainable AI recommendations. Special attention is given to the limitations of automated refactoring in dynamically typed languages (e.g., Python, JavaScript), where the lack of static type information reduces the effectiveness of traditional techniques. Generalization to multilingual systems is supported through models like CodeBERT, CodeT5, and PLBART, which operate over token-level, syntactic, and graph-based representations to enable language-agnostic refactoring. The paper also discusses real-world integration of automated refactoring into CI/CD environments, including the use of bots, refactoring-aware quality gates, and scheduled transformations applied at commit or merge time. Practical examples illustrate the verification of behavior preservation through regression testing or formal methods. This work targets software engineers, researchers, and tool developers engaged in intelligent software maintenance and automated quality assurance. By offering a consolidated classification, tool selection criteria, and practical scenarios, the paper delivers applied value for designing custom refactoring solutions or adopting existing technologies across diverse project constraints–ranging from safety-critical systems to large-scale continuous delivery pipelines.
Mykola A. Hodovychenko Mykola A. Hodovychenko
Adaptive super-resolution integration to enhance object detection on low-quality unmanned aerial vehicle imagery
The article addresses the problem of improving the accuracy of object detection in images captured by unmanned aerial vehicles under conditions of reduced spatial resolution and the presence of noise artifacts. The relevance of this research is driven by the practical need to maintain the reliability of computer vision systems in challenging field environments, where conventional detection algorithms tend to lose effectiveness. The aim of the study is to enhance the robustness of object detection in low-quality unmanned aerial vehicles imagery through the development of an adaptive preprocessing mechanism based on deep neural network–driven image super-resolution. The proposed approach involves the dynamic activation of the super-resolution module only in cases where image quality or detector confidence is insufficient. Within the framework of the study, a combination of the high-accuracy two-stage model Faster R-CNN and prior image upscaling using Real-ESRGAN is employed. An adaptive logic for triggering the image enhancement module is introduced, which is activated solely when the detector's confidence level falls below a defined threshold, thereby reducing computational overhead without compromising recognition performance. An experimental evaluation of the proposed method was conducted using unmanned aerial vehicles imagery degraded by various distortions, including blur, noise, and compression artifacts. The results demonstrate consistent improvements in detection accuracy across all tested image degradation types while maintaining acceptable processing time. The practical value of this research lies in its applicability to autonomous monitoring systems, search-and-rescue missions, and situational analysis tasks based on unmanned aerial vehicles video streams. The proposed approach opens up opportunities for further optimization by incorporating additional components, such as lightweight preliminary object filtering modules.
From classification to taxonomy: Automated structuring of vehicle repair names in multilingual corpora
This study introduces and rigorously validates a hybrid, five-stage Natural Language Processing pipeline that transforms unstructured, bilingual repair-order text into fully navigable, hierarchical action taxonomy – bridging the gap between flat keyword classification and business-grade knowledge organization. Addressing the limitations of both traditional and modern Natural Language Processing methods in technical, noisy, and domain-specific datasets, the proposed methodology integrates advanced lemmatization, manual core dictionary creation, semantic filtering, transformer-based classification, and embedding-driven clustering. Building on advanced Ukrainian lemmatization, dynamic semantic filtering, multilingual sentence embeddings, and density clustering, the pipeline systematically overcomes the noise, code-switching, and “long-tail” rarity that typify real-world automotive datasets. Tested on a corpus of over 4.3 million service records, the approach achieves over 92 % cluster coherence with minimal manual annotation. The resulting taxonomy unlocks four immediate industrial benefits: enterprise-wide repair analytics and benchmarking across branches and brands; intent-aware chatbots capable of precise service triage and automated quotation; inventory and workforce optimization through fine-grained job statistics; and a practical blueprint for industry-level standardization of repair nomenclature and data exchange. In sum, the work demonstrates that combining minimal expert input with modern embedding techniques and density clustering can automate taxonomy induction at industrial scale, setting a new benchmark for digital transformation initiatives that depend on accurate structuring of noisy technical language.
Organization of computations in clusters using transparent parallelizing principles
Methods of consructing of the systems identification and recognition requirements significant computational resources and therefore require usage of parallel systems, such as clusters or computers with multiple processors or processors with multiple cores. In this paper cluster computing organization principles based on transparent parallelizing are considered. Questions that arise while implementing this technology as a parallel calculations framework are described. Described technology has been implemented as a framework on Java programming language. Architecture of such framework is shown and functionality of its parts is described. The concept of a value ID and the concept of an unready value ID have been proposed to implement the proposed principles. The ID of a value is an ID that should be assigned to each value that is used as input or output parameter of procedure. These assignments are cluster-wide and are used to replace sending parameter value with sending its ID. The same values are often used in different calls in parallel programs, so using IDs allows the framework to save traffic. IDs of unready values are created each time during a procedure call and are assigned to the output parameters of the procedure. They are used to get the value of parameter in the moment of the first access. Also they are passed to the server as a part of information about an order. When the execution of an order is finished, value IDs are obtained for values of output parameters of the order and these IDs are assigned to the corresponding IDs of unready values. RMI technology has been used to implement communication between server and clients. Also JDBC has been used to implement storing of final and intermediate computations results to external database. In this paper is to propose method of execution time characteristics analysis for parallel applications that have been created using the technology of orders based transparent parallelizing. Its efficiency has been proven by solving the problem of determination of diagnostic value of formed features diagnostics on a cluster of 2, 3, 5 and 10 computers. The result of multiplication of execution time by number of processors has grown by not more than 1.13% when using 2, 3 or 5 computers instead of one, and by not more than 3.25% when using 10 computers instead of one during this experiment. The closest analogue of offered approach is T-system.
Moment of light pressure in the medium with resistance
The paper describes the simulation of fast rotational motion of a dynamically asymmetric satellite relative to the centre of mass under the influence of the joint effect of the moment of forces of light pressure and resistance. It is assumed that the surface of the spacecraft is a surface of revolution. The medium creates a weak resistance proportional to the angular velocity of the rigid body's own rotation relative to the centre of mass. Orbital motions with an arbitrary eccentricity are considered given. The mathematical model of satellite motion in this formulation is described by a rigid system of differential equations: the fast variables are Euler angles, and the slow variables are the modulus of the angular momentum vector, the kinetic energy, and the angles of orientation of the angular momentum vector in space. Averaging is performed over the Euler-Poinsot motion. The averaged system of equations of body motion allows numerical simulation of the satellite's motion relative to the centre of mass. The study is carried out in a dimensionless form for a multiparameter system of equations. For numerical calculation, an implicit third-order Adams method is used to integrate systems of differential equations. A personal computational package was developed for the constructed mathematical model of the satellite, as well as a library for calculating the complete elliptic integrals of the first and second kinds. Numerical calculation allows one to obtain the functions of modulating the modulus of the satellite kinetic moment vector, its orientation angles to the orbit, as well as the satellite kinetic energy values. The analysis of the influence of the parameters of the problem on the nature of the motion of the satellite relative to the centre of mass is carried out. A qualitative picture was obtained of the influence of the initial values ​​of the angles of orientation of the kinetic moment vector, the geometry of the masses, the eccentricity of the orbit, the characteristic numbers of disturbing moments on the hodograph character of the kinetic moment vector. The hodograph of the kinetic moment vector in three-dimensional space is simulated for various values of the system parameters. To construct three-dimensional objects on the scene, according to the carried out numerical calculations, we developed our own software using DirectX technology in C# language, simulating a virtual laboratory of a numerical experiment.
Algorithmization of the failed subschemes localization process
Approaches to the algorithmization of the process of localizing faulty subschemes of a wide class of electrical devices (in particular, electrical and electronic) are considered, appropriate algorithms are proposed, and the possibility of their application in practical applications is shown. In particular, it was noted that in modern conditions, with increasing requirements for the reliability of electrical devices and expanding the set of functions performed by them, not only the full feasibility of monitoring the operability of these devices at the production and operation stages should be considered, but also ensuring the potential possibility of diagnosing the developed devices (systems ) - at the design stage. At the same time, the observed complication of the methodological support of individual procedures and the whole diagnostic process as a whole is indicated, which, on the one hand, is connected with the need to carry out the most complete and comprehensive diagnosis, and on the other hand, determines a sharp increase in computing resources and labor costs for the implementation of a diagnostic experiment. One of the possible solutions to this contradiction is the formalization and subsequent algorithmization of diagnostic procedures, which ensures the automation of the latter and, as a result, reduces the time of diagnosis and improves its quality. The proposed algorithmic tools implement model-oriented methods for the localization of faulty subschemes of electrical devices (systems), in particular, the method of training and testing characteristics. A distinctive feature of these methods and the algorithmic support considered in the work is the use of models of the devices under study during a diagnostic experiment, which makes it possible to form and test functionally necessary (ideally, any) health hypotheses of the latter.
algorithmization of diagnostic procedures
Classifying mixed patterns of proteins in microscopic images with deep neural networks
Nowadays, accurate diagnosis of diseases, their treatment and prognosis is a very acute problem of modern medicine. By studying information about human proteins, you can identify differentially expressed proteins. These proteins are potentially interesting biomarkers that can be used for an accurate diagnosis, prognosis, or selection of individual treatments, especially for cancer. A surprising finding from this research is that we have relatively few proteins that are tissue specific. Almost half of all proteins are categorized as housekeeping proteins, expressed in all cells. Only 2,300 proteins in the human body have been identified as tissue enriched, meaning they have elevated expression levels in certain tissues. Thanks to advances in high-throughput microscopy, images are generated too quickly for manual evaluation. Consequently, the need for automating the analysis of biomedical images is as great as ever to speed up the understanding of human cells and diseases. Historically, the classification of proteins was limited to individual patterns in one or more cell types, but in order to fully understand the complexity of a human cell, models must classify mixed patterns according to a number of different human cells. The article formulates the problem of image classification in medical research. In this area, classification methods using deep convolutional neural networks are actively used. Presented article gives a brief overview of the various approaches and methods of similar research. As a dataset was taken “The Human Protein Atlas”, that presents a tissue-based map of the human proteome, completed in 2014 after 11 years of research. All protein expression profiling data is publicly accessible in an interactive database, enabling tissue-based exploration of the human proteome. It was done an analysis of the work and the methods that were used during the research. To solve this problem, the deep neural network model is proposed taking into account the characteristics of the domain and the sample under study. The neural network model is based on Inception-v3 architecture. Optimization procedure contains combination of several tweaks for fast convergence: stochastic gradient descent with warm restarts (learning rate schedule for exploring different local minima), progressive image resizing (training starts from small resolution and sequentially increases each cycle of SGDR). We propose new method for threshold selection for F1 measure. Developed model can be used to create an instrument integrated into the medical system of intellectual microscopy to determine the location of the protein from a high-performance image.
Volterra neural network construction in the nonlinear dynamic systems modeling problem
The features of using the theory of Volterra series and neural networks in problems of nonlinear dynamic systems modeling are considered. A comparative analysis of methods for constructing models of nonlinear dynamic systems based on the theory of Volterra series and neural networks is carried out; areas of effective application of each method are indicated. The problem statement is formulated, consisting in the creation of a mathematical apparatus for transforming models of nonlinear dynamic systems derived from the Volterra series apparatus into an artificial neural network of a certain structure. The three-layer structure of a direct signal propagation neural network has been substantiated and investigated for represent nonlinear dynamic systems. It is outlined a class of systems that can be efficiently approximated by this network. The dependence of the Volterra kernels coefficients and the weighting coefficients of the hidden layer of the three-layer forward-propagation neural network is established. An algorithm for constructing an artificial neural network based on the Volterra series is given. The results of computer simulation of nonlinear dynamic systems using the Volterra neural network and direct signal propagation neural network are presented. The analysis of experimental data confirms the effectiveness of using Volterra neural networks in problems of modeling nonlinear dynamic systems. Conclusions and recommendations on the effective use of Volterra neural networks for modeling nonlinear dynamic systems are made.
On the construction of a software architecture for nuclear systems on a crystal
The article discusses how to build software architecture for multi-core systems on a chip (SoC), based on asymmetric and symmetric multiprocessing, the hypervisor. Asymmetric multiprocessing is a port for several operating systems on physically separate processor cores. In symmetric multiprocessing in systems with core isolation, one OS is launched on several cores. OS SMP- system is ported without user intervention with a growing number of cores. Since all cores are managed by a single OS, message transfer between cores can occur at the L1 data cache level, providing faster communication with less jitter. Kernel isolation allows you to reserve a kernel for a hard real-time application, protecting it from the influence of other high-performance kernels, which for the software architecture allows you to select your operating system without creating low-level software when managing multiple operating systems. The hypervisor refers to a low-level software system. It manages several independent operating systems that are at a higher level. Developing multi-core systems-on-chip offerings focused on the embedded market are well suited for asymmetric multiprocessing configurations. This architecture is useful for developers who use the performance of a real-time operating system in combination with a diverse set of Linux kernel functions. The article discusses the software and hardware solutions contained in the XAPP1079 environment, which are required to run Linux on a single Zynq-7000 All Programmable system on a chip, and open source applications on the second core. Designing systems based on systems on a chip for high-performance and а real-time applications requires an optimal solution taking into account the factors: data transfer time; separation of the operating system. A system solution for high-performance and real-time applications using a symmetric multiprocessor processing architecture with kernel isolation provides low latency, jitter and real-time system operation, while maintaining software SoC scalability. Programmable logic integrated circuits containing multi-core subsystems have an efficient architecture with symmetric multiprocessing of data to ensure a compromise between the actual data transfer time and the low latency of their processing. The advantages of using symmetric multiprocessing manifest themselves if the load is distributed among several resources. In this case, the time required to complete the task is reduced. However, the performance gain brought about by a simple multiplication of the number of performers will not necessarily be linear. Some tasks should be performed only sequentially. Multi-core systems are able to process packages much more efficiently than single-core ones - but only if they are managed by optimized software. It is expedient to develop multi-core computing software, including an OS with support for symmetric and asymmetric multiprocessor data processing architectures, an embedded hypervisor, high-speed packet processing modules, and an exhaustive set of tools for the entire cycle of multi-core computing systems. The results of such development will find application in multiprocessor supercomputers and server applications, in terminal devices, access aggregators and basic devices - where the highest throughput is required.
Features of using diversifiers of Hackathon-contests in canvas-oriented approach to game design
The article suggests a canvas-oriented approach to the design of computer games in the creative process of problem-oriented execution of development stages: studying consumers as players (the outline of a business model, the outline of an empathy card); studying the aesthetics of the game based on player needs (the outline of psychotypes and aesthetics of players , the outline of player’s ability to manage information and actions); study of “Mechanics-Dynamics-Aesthetics” model properties (the outline of gamification model, the outline of game mechanics and dynamics); research and design of the game world (outline of the narrative, outline of the game world, outline of game levels, outline of ethical dilemmas). But canvas are not considered in the standard pre-defined development process but are considered in a special event-competition for rapid design in so-called Hackathon-competition, for example, in the Global Game Jam competition (GGJ) with a 48-hour game development cycle. A short period of time to design a game requires the same operational verification of the canvas of ethical dilemmas to reduce the risk of incorrect content of the game among beginner development teams. The analysis of the ethical dilemmas of the game was based on logic of the semiotic chain (sign - context - meaning - addressee), where each game object is a sign that in the specific context of the game narrative and player experience expresses meaning (meaning different connotations in terms of additional emotional, estimated, stylistic value of an object or phenomenon). The authors described GGJ-features in the form of diversifiers – optional restrictions that can be used by participants in their work so that their implementation in various variations can often create an innovative game. At the same time, it is noted that in research on design of computer games diversifiers are not given due attention as indicators of consumer requirements. Therefore, the purpose of the article was the inclusion of diversifiers in a canvas-oriented game design approach, which will take into account the effect of the short duration of the competition for the stability of the game design process. The prototypes of diversifiers in Hackathon-conditions of computer games design are presented. The structure of the diversifiers presented at two Hackathon contests (GGJ 2018 and GGJ 2019) was analyzed: five groups of diversifiers (sponsor, accessibility, art, sound, and design, narrative) with 64 diversifiers which were suggested to be grouped according to the “Mechanics-Dynamics-Aesthetics” model and copyrighted works on the design of the game. It is shown that the use of canvas structures the development process, reducing the time to generate design ideas in the process of inventing game mechanics and dynamics, when the canvas can become a roadmap for making creative decisions when designing a game. They can be a convenient basis for automating the process of designing a paper prototype of a game as a quest game process, provided that each outline represents a location with quest tasks, the variability of which depends on the capabilities of project team members. Diversifiers in the hackathon competition are important meta-components for the entire outline-oriented design of the game, optimizing the processes of creative and technical development not only from the point of view of competition, but also daily professional activity, creating a constant interaction of each stage of development with the requirements of the future game user.
Information technologies in socio-economic, organisational and technical systems
Information technology in socio-economic,organisational and technical systems
Improved project-based learning as the method of knowledge transfer between university and business
The universities should provide for students the possibilities for gathering the actual applied knowledge and soft skills to prepare them for labor market needs. There are two pillars of success. The first pillar is learner-focused pedagogy and active learning. Recent publications have demonstrated the vast potential of project-based learning (PBL) through information and communications technologies (ICT). The second pillar is close University-Business collaboration during the study process. In the paper, we proposed the modification of PBL – improved PBL, which involves the students in real problems solving. The real problem means that the problem is actual for Company just in time of the course period. Improved PBL provides a competent approach in the teaching activities. The advantages and difficulties of improved PBL implementation for different stakeholders are generalized. It is shown that improved PBL supported by ICT means is attractive for the companies within the framework of their collaboration with higher educational institutions. Authors describe two alternatives of ICT infrastructure – based on learning management system Moodle and extended by social media tools Google Docs and Telegram. As well, authors provide a short description of IPBL implementation process. The examples of two courses demonstrate the effectiveness of University-Business collaboration for attracting students to solving the real problems under changing market conditions. The improved PBL supported by ICT means helps students to understand the course material better as well as to adapt for the realization of their own educational and professional potential in the labor market. The actualization of teachers' knowledge and enhancement cooperation in the frame of the knowledge triangle “Students-University-Business” are additional gains of IPBL. Also, in conclusion, there are generalized the main challenges of IPBL implementation.
information and communication technologies
Information technology support for decision making geoinformation systems
The problems of creating GIS industry based on existing GIS and universal problem of increasing the efficiency of decision support systems, GIS industry by the alignment liability these models, methods and information technologies. The study identified the need to solve the following problems: analysis of problems creation of GIS industry, namely, administrative, territorial and municipal government, and in social projects for the study of tourism potential areas and conducting geomarketing, military applications, systems and environmental technogenic safety and it is shown that when creating information technology support decision-making it is necessary to consider the multi-purpose specifics of the subject field. Regarding the frequency of changes in incoming data, it is proposed to classify and catalog sectoral geographic information systems not only by territorial coverage and purpose, but also by the dynamics of the change in input data. At the same time we consider stationary (administrative-territorial geoinformation systems), quasi-stationary (tourist geoinformatics and systems) and dynamic (military geoinformation systems) sectoral geographic information systems. To unify the presentation location, with the required accuracy to obtain space-distribution data and attribute data completeness of industrial facilities heoinformation systems developed information model heoknowledge. To ensure the integrity of the proposed location bind them just attribute components using the asset ID. Further developed a conceptual model of decision support in the industry geographic information system that takes into account the information model location, hierarchy problems branch of geographic information system, incoming operational data, function, advantages and selection criteria person decides that allowed method of decision support for finding a rational solution to the problems of the second level. For the first time the support method decisions in the construction sector geographic information system that takes into account the proposed conceptual model that allow pouring automate the process of creating effective systems for distributed geospatial by industry purpose.
just at the trench and attribute and digital data heoknowledge
Architectural objects recognition technique in augmented reality technologies based on creating a specialized markers base
The paper proposes a method for recognizing architectural objects when creating augmented reality mobile applications based on building a database of specialized markers. The main method of augmented reality technology for the recognition of architectural objects was chosen - the technology based on special markers. The range of pattern recognition algorithms suitable for the task is highlighted. These are algorithms based on the selection of key points of images and their descriptors. The most important aim is the stable recognition of architectural objects upon mobile applications for augmented reality-type digital guide creation based on specialized markers. The scientific basis of the research is a systematic approach in the analysis of the considered markers recognition algorithms, machine learning for the development of a database of marker images and AO recognition are used. The technique consists of the following steps: processing images of architectural objects with the aim of identifying key points, obtaining descriptions of selected control points as descriptors, creating AR-metadata that correspond to architectural objects, organizing joint storage in the local database of descriptors and their corresponding metadata, visualizing the architectural object and AR metadata. To implement the stages of processing images of architectural objects and obtain descriptors of key points, algorithms for extracting key points on images, such as SIFT, MSER, SURF, RIFF, RF, are analyzed. It is shown that these algorithms are invariant to scaling, rotation, as well as resistant to changes in light, noise and viewing angle. A comprehensive use of them for processing architectural objects with the aim of obtaining descriptors of reference points was proposed. To ensure stable recognition of AO according to the developed methodology based on machine learning for processing architectural objects with the aim of obtaining descriptors of key points, it was proposed to create an additional module using an ordered stack. The launch sequence and the number of algorithms can be changed.
Software for automated design of network graphics of software systems reengineering
The subject of the work is the construction of a graphical network model of reengineering of the software system. Purpose of the paper is development of software for increasing the level of automation of designing network charts for the organization of production by reengineering software systems in the framework of project management. Network planning is one of the forms of graphical representation of the work content and the duration of implementation of strategic plans and long-term complexes of project, planning, organizational and other activities of the enterprise. Along with linear charts and table calculations, network planning methods are extensively used in the development of long-term plans and models for the creation of complex production systems and other objects of long-term use. The “Nodes-Activity” type of graphs is used in modern specialized packages of planning and operational management computer programs. The task before creating a software tool is the ability to work with all types of network charts with the possibilities of their comprehensive transformation. Methods. The article is based on methods of network planning for the PERT (Program Evaluation and Review Technique) methodology, the use of elements of graph theory and the Gantt chart method as an accounting method for project management. Simulation of the system software architecture is carried out within the UML (Unified Modeling Language) 2.5 methodology using the CASE toolkit Enterprise Architect 14. Project decisions proposed by the authors are the results of the article. The content of the design part is determined, firstly, by the specifics of the planning of software projects reengineering, and secondly, by the features of specific technical proposals for a project that is manageable. The architecture (project “Frame”) software for managing network planning of software project reengineering is designed in the article. Conclusions. The architecture is developed in the form of several structural and behavioral diagrams, namely: use case diagram, which provides an analyst with a detailed idea of the software field of application; sequence diagram that is designed to create a programmer's imagination on how to perform actions when working with a future program tool; statechart diagram that is required for a visual representation of those states in which the software can be at different times; class diagrams that are used to design the main form filling of the future software; component diagram that is designed to examine the composition of the components of the future software and indicate the sequence of compilation and assembly of individual modules. The numerical and temporal estimation of the planning parameters is based on the data obtained from the Gantt design charts.
Information technology of dynamic formation of content depending on the characteristics of the receiving -transmitting system
This paper presents an information technology for dynamic formation of content in conditions of limited resources. Due to the developed information technology, when connected to the site, the user will receive information in the form that his device can play. Information technology of dynamic formation of content consists of four stages: course formation, test formation, determining of the characteristics and display of information. The stage of course formation is based on a model of a system for receiving and transmitting information, namely, on the user's presentation and information resource. The presentation of the user includes the model of the receiving part of the system for receiving and transmitting information and the type of user in terms of access rights. The presentation of an information resource includes a basic content model and many registered users. In turn, the basic content model includes content that consists of multiple sections, and each section can be divided into multiple section elements. The test formation stage is based on the model of the system for receiving and transmitting information and contains an assessment of the representativeness of the test to ensure its quality, which is based on an analysis of the proportion of the number of questions covering different sections and educational objectives of the course. The steps of characterization and display of information are based on the method of dynamic formation of content. The main modules and users of information technology of dynamic formation of content are implemented using the information system "system of the dynamic determination of content". Approbation of information technology was performed at the Odessa Polytechnic University in disciplines that use elements of distance learning to ensure that all information on the course is viewed, regardless of the communication channel and students' devices. To assess the quality of the developed information technology, an experiment was conducted with the participation of students. As a result of the experiment, it was proved that the quality of the process of receiving and transmitting information increased by 1.55 times due to the use of the developed information technology.
Project manager job description as one of project management key success factors
The article discusses the practical aspects of profiling knowledge in project management, taking into account competence and experience for various categories of professionals involved in project management. Very often, project managers are forced to not only manage the project, but also train the project team members or help them develop their best skills to achieve the project goal. It is shown that success of project activities of organizations substantially depends on the professional personnel training and the proper formation of project participants' job descriptions. In addition to the formal responsibilities outlined in the basic project management methodologies the project manager plays an important role in the interaction between the project and the business area. Market pressure on project activities is associated with a rapid reduction in the life cycles of technologies and products, which significantly increases the responsibility of project managers for the ultimate fate of their project outputs. The employees’ knowledge, skills and competencies are the basis for the development and improvement of any organization. In this case, more and more situations arise when, under extremely short deadlines, it is necessary to make a decision on the project team formation and on the selection of the project manager. When executing projects, there is always a need to coordinate the work of managers, staff and project team. To do this, organizations develop the rules of interaction and managers’ responsibilities, as well as determine the managers’ responsibility for various types of work. The responsibilities of managers are fixed in the job descriptions, which serve as the basis for the formation of responsibility matrices. At the same time, the fulfillment of the type of work specified in the instruction must be supported by the performer’s certain competence. A general concept of forming job descriptions is proposed. Each instruction point is formed at the intersection of three coordinates: the performer, the work and the competence. A Markov model for changing the project manager’s states has been developed, which describes the system using homogeneous Markov chains with discrete states and time. Discrete states form a specific structure of communications and interaction of project processes, which take into account the system topology and contextual features of the processes of project activities.