Andrii Pukach

The globalized IT market relies heavily on international teams, but cross-team collaboration suffers from operational delays caused by time-zone gaps and cognitive, perception-based incompatibilities. This study develops a human-oriented information technology (IT) for the unified transfer of software maintenance requests within international product and outsourcing enterprises. The technology’s methodological foundation shifts the problematic of subjective perception in human–software interaction (HSI) from an irrational domain into a measurable, rational plane. Empirical validation was conducted using a dataset of 25 subjects/agents across 7 teams, mapping 12 product-domain factors to synthesize personalized multi-factor portraits/profiles. This technology successfully neutralizes cognitive barriers and minimizes request stagnation caused by redundant clarifying queries. Under strict constraints of initial blind asynchronous transmission, the developed IT achieved a statistically significant optimization of baseline corporate KPIs: the Service Level Agreement Compliance Rate (SLA CR) advanced by 3.1% to 80.4% (p < 0.001), the Customer Satisfaction Score (CSAT) increased by 2.5% to 75.3% (p < 0.01), and the Mean Time to Resolution (MTTR) was compressed by 0.9 h to 29.2 h (p = 0.0157), ensuring predictability and efficiency in software lifecycle maintenance.

This paper addresses the critical problem of user identification within the framework of human-computer interaction (HCI). A novel identification method is proposed based on users' "polyfactor portraits." These portraits effectively reflect how individuals subjectively perceive specific HCI objects. The primary objective of this approach is to model the irrational components of human behavior, thereby improving system automation and intellectualization. Structurally, the developed method integrates a specialized identification algorithm with a core supporting mathematical model. To validate the efficiency of the proposed approach, a comprehensive practical experiment was conducted. This experiment involved the identification of technical support team members based on the dominant features of their interaction with a specific supported software product. The experimental results confirm the applicability of the method. Finally, the paper outlines future research directions aimed at potential improvements, technical enhancements, and broader practical applications of this identification method in real-world systems.

The article presents the architecture, model, and functional capabilities of a system for automated generation, translation, and publication of content based on large language models. The relevance of the development is determined by the needs of businesses, educational platforms, and socio-political projects for the rapid creation of multilingual materials, as well as by the necessity to counteract disinformation campaigns. The proposed solution is distinguished by its versatility and ease of deployment, even in resource-constrained environments, surpassing existing analogues that typically cover only individual stages of content management. Future development of the system envisions the integration of monitoring, automatic scaling, and the implementation of multi-level role management.

This study develops a specialized method for the synthesis of intelligent platforms for cross-referral transition between cognitive basis systems (CBSs) in the field of HCI object perception subjectivization, within the context of global scientific and applied efforts to increase the intelligence level of interaction between humans and software and/or hardware products (SHPs). The proposed method comprises a conceptual model, a mathematical model, and a specialized algorithm. Practical implementation was conducted using R (within an appropriate IDE) and Python. The method was approbated by solving a relevant applied problem: synthesizing a cross-referral transition platform between a specialized CBS for HCI object perception subjectivization and an existing personality classification system based on 16 Jungian sociotypes. The results indicated a 29.5% baseline correspondence rate for pairs of dominant perception impact factors regarding their exclusive alignment with specific Jungian sociotypes. Furthermore, the accuracy rate of the built-in multilayer perceptron (MLP) artificial neural network (ANN) for dominant factor pairs reached approximately 83.3% ± 2.95%, while introducing a third dominant factor raised the accuracy threshold to a potential 96.7% within the evaluated scenario. In addition, the paper outlines prospects for further research into the potential of the proposed method for identifying and documenting cross-referral relationships between fundamental constituents of various conventional and alternative CBSs.

This work is devoted to the investigation of an actual scientific and applied problem in the identification of exit points for early-exit DNNs based on the analysis of neural chains, which is one of the complex tasks related to the scientific and applied problems of DNN optimization, including, in particular, those based on the existing early-exit concept. The obtained computational complexity of the developed method is not limited by the latter itself, but instead, it mainly depends on the chosen algorithm for analyzing the occurrences of particular substrings (i.e., trimmed neural chains) into a defined list of strings (i.e., full neural chains). For example, in the framework of the conducted research, the Python operator “in” has been used (for this purpose), which uses an in-built optimized algorithm based on the combination of the Boyer–Moore and Horspool algorithms with a linear scalability, and computational complexity that approaches the arithmetic product of the total number of strings (i.e., full neural chains) in the array by the average length of the string in the same array. The performed practical approbation of the developed method gave positive results in decreasing the overall time for obtaining the final result of the considered DNN, as well as significantly decreasing the following timing parameters of the considered DNN: the minimal time to obtain the final result (reduced by more than 5 times); the average time to obtain the final result (reduced by ~1.4 times); and the total time spent processing all 22,500 modeling cases in total (reduced by ~1.39 times). In terms of the main positive aspects and advantages of the developed method, we could highlight its maximal versatility (in terms of the studied DNNs, their architectural and/or structural features, application areas, and input data representation, as well as further software implementation of the proposed method), together with its maximal simplicity of representation and understanding, which ensures the possibility of working with this method even for novice and inexperienced researchers and users who have only basic knowledge of DNNs. In addition, the main results and conclusions of the conducted research are given, and the prospects for further research are considered.

The scientific and applied problem of positioning the support objects (of software products’ comprehensive support) in relation to the relevant impact factors (within the existing polysubject environments of complex support) is considered in the context of a more global scientific and applied problem of automation and intellectualization of balancing the environments of software products’ comprehensive support. The object of the study is the process of multifactor positioning of the support object (of software products’ comprehensive support). The subject of the research are methods and means of mathematical, heuristic and computer modeling. The aim of the research is to develop a multifactor positioning model of the support object of software products' comprehensive support. In order to achieve the set goal, the necessary research tasks, presented below in the text, have been solved. In particular, a comprehensive analysis has been carried out for the problematic(s) of such fundamental components as: comprehensive support, object of support, subjects of support, impact factors, and support environment. Thereby, a relation(s) has been established between each of these fundamental components in order to take into account their relevance in the context of the researched pro- blem/issue(s). At the final stage, a multifactor positioning model of the object of software products' comprehensive support has been developed, that provides the possibility of solving the initial stage of the problem of balancing environments (of software products’ comprehensive support), namely the positioning problem. The developed model acts as a basic positioning mechanism using a polar and/or Cartesian coordinate system, providing possibilities for further modelling and research into possible path(s) and way(s) for further potential development, adaptation, refinement(s), and improvement(s). As a practical approbation of the developed model, the relevant practical applied task of calculating the tendency of the displacement dynamics of the support object relative to the given specific impact factor (within the existing support environment) has been solved and presented in scope of this research.

This paper addresses the challenge of achieving effective load distribution in information systems operating under dynamic and unpredictable traffic conditions. The study examines traditional static load balancing algorithms, such as Round Robin and Least Connections, and highlights their limitations in adaptive environments. To overcome these constraints, a node rating–based approach is proposed, which dynamically evaluates server states by considering CPU utilization, memory load, and average response time. A comparative simulation of the proposed method against the Least Connections algo- rithm was conducted using a representative request dataset. The results show that the rating-based me- thod mitigates peak loads, enhances system stability, and ensures more efficient resource utilization. Furthermore, the approach demonstrates scalability and suitability for deployment in modern cloud infrastructures.

An information-cognitive concept of a predicting method for obtaining specialized human–computer interaction (HCI) objects’ perception subjectivization results, based on impact factors analysis, with the use of multilayer perceptron (MP) artificial neural networks (ANNs), has been developed. The main purpose of the developed method is to increase the level of intellectualization and automation of research into relevant processes of HCI objects’ perception subjectivization, especially in the context of software products’ comprehensive support processes. The method is based on the developed conceptual models and the developed mathematical model, as well as a specialized developed algorithm. Results prediction is carried out on the basis of a preliminary in-depth analysis of a set of unique direct chains (UDCs) of neurons of the relevant encapsulated MP ANN, built on the basis of researching the results of isolated influences of each of the previously declared impact factors and further comparing the present direct chain (of each separate investigated modeling case) with UDCs from the aforementioned sets. As an example of practical approbation of the developed method, the appropriate practically applied problem of identifying a member of the support team, whose multifactor portrait is as close as possible to the corresponding multifactor portrait of a given client’s user, has been resolved.

У статті розглядається проблему автоматизованої генерації описів хмарної інфраструктури на основі вхідних запитів, сформульованих природною мовою. Традиційні підходи до реалізації подібних задач базуються на використанні засобів інфраструктури як коду (Infrastructure-as-Code, IaC), зокрема мови Terraform, яка вимагає дотримання точного синтаксису, володіння спеціалізованими знаннями та розуміння великої кількості параметрів низького рівня. Унаслідок цього, навіть створення відносно простої інфраструктури є складним і схильним до помилок процесом, особливо для користувачів без глибокої технічної підготовки. У межах дослідження запропоновано метод, який поєднує можливості великих мовних моделей (ВММ) із проміжною високорівневою мовою опису, що дозволяє зменшити обсяг коду, підвищити читабельність та зменшити кількість помилок при генерації інфраструктури. Побудовано формальну модель процесу, яка охоплює формулювання запиту від користувача природною мовою, генерацію структурованого опису та його трансляцію в код Terraform. Для оцінювання ефективності запропонованого методу проведено порівняльний експеримент, що включає три тестові сценарії зростаючої складності: від розгортання одного приватного сервера до масштабованого набору публічних серверів за балансувальником навантаження. Кожен сценарій реалізовано двома способами — шляхом прямої генерації коду Terraform та через проміжну мову. Порівняння здійснювалося за такими критеріями, як кількість синтаксичних і семантичних помилок, довжина згенерованого коду та кількість визначених ресурсів. Результати експерименту свідчать, що використання проміжної мови суттєво зменшує ймовірність синтаксичних і семантичних помилок, скорочує обсяг опису та підвищує надійність і зрозумілість кінцевих конфігурацій. Крім того, високий рівень абстракції опису полегшує процес навчання для мовних моделей, дозволяючи їм генерувати більш коректний код з меншою кількістю інструкцій. Запропонований метод є актуальним у контексті спрощення DevOps-процесів, тестування хмарної інфраструктури та інтеграції генеративних моделей у середовище розробки. Перспективи подальших досліджень включають донавчання моделей на доменно-специфічних наборах даних та розширення проміжної мови новими типами хмарних ресурсів.

This research is devoted for resolution of scientific and applied problem of consideration (taking into account) the perception subjectivization of the object of human-machine interaction – by the subjects of this interaction, in digital twin models of human-machine interaction subjects. In order to solve the declared scientific and applied problem, a specialized perception subjectivization component has been developed for digital twin models of human-machine interaction subjects, which makes it possible to increase the potential and obtain further development of these models by expanding their conceptual purpose and functional capabilities, exploring the processes of not only human-machine interaction itself, but also, in addition, the issues of the subjective perception of human-machine interaction objects by the subjects of this interaction. In turn, the development and implementation of this component provides new opportunities in researching and solving relevant issues of the global scientific and applied problems of automation and intellectualization of human-machine interaction processes, as well as comprehensive support processes of software products, which is one of the relevant components of this interaction. A conceptual model of the declared component has been developed, which fully reflects the key principles of its structure and integrative properties and mechanisms. A basic mathematical model of the perception subjectivization component for digital twin models of human-machine interaction subjects has been developed, as well as a corresponding specialized algorithm for synthesizing this component within various and potential existing human-machine interaction models. Approbation of the developed component has been carried out on the example of solving the relevant practical task of identifying the most compatible subjects among the number of existing ones within the framework of the researched human-machine interaction. An analysis of potential research directions was conducted regarding the possibilities of further development(s) and improvement(s) of both the proposed component and human-machine interaction models as a whole, in the context of research area(s) of the scientific and applied problems of automation and intellectualization of the human-machine interaction processes, including software products’ comprehensive support as one of the relevant components of this interaction.