https://repository.hneu.edu.ua/handle/123456789/172 2026-05-02T12:38:45Z https://repository.hneu.edu.ua/handle/123456789/39692 Назва: Сценарне огнітивне моделювання впливу загроз на інформаційну безпеку об’єктів критичної інфраструктури в умовах запровадження правового режиму воєнного стану Автори: Тютюник В. В.; Тютюник О. О. Короткий огляд (реферат): Мета роботи: озроблення та дослідження когнітивної моделі сценаріїв впливу загроз на ІБ ОКІ в умовах воєнного стану з метою підтримки прийняття управлінських рішень. 2026-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/39645 Назва: How to Integrate Internet Marketing into a Cloud Storage System: Developing a Microservice for Customer Acquisition Автори: Vilkhivska O. V.; Brynza N. O.; Bulakh M. I. Короткий огляд (реферат): The article addresses the pressing issue of enhancing the efficiency of promoting cloud data storage services amid intense competition in the SaaS and IaaS markets. The article analyzes current trends in cloud technology development and the critical role of internet marketing as a key tool for customer acquisition and retention in IT companies. Based on a theoretical review, market dynamics analysis, and business process modeling, the study substantiates the need to integrate digital marketing tools directly into the information system of a cloud storage provider. The “Best-of-Breed” strategy is proposed and substantiated. This approach involves targeted integration of best-in-class specialized services, such as SendPulse for email campaign automation, Google Ads for contextual advertising, and Ahrefs for SEO monitoring, rather than expensive, risky all-in-one platforms like HubSpot or Salesforce Marketing Cloud. The architecture was designed, and a microservice called “Integration Module” (acting as an API Gateway) was implemented. This ensures unified, asynchronous, and secure data exchange between the company’s core information system and external marketing platforms. The microservice is built in Python using the modern asynchronous FastAPI framework, Pydantic for input data validation, SQLAlchemy with PostgreSQL for storing on-boarding events and logs, and pytest for comprehensive unit and integration testing. Emphasis is placed on automating on-boarding and customer retention: trigger-based email sequences are launched automatically upon account status changes (e.g., the start of a trial period, transition to a paid plan, approaching the end of promotional offers), significantly improving conversion rates and customer lifetime value (LTV). The full software development lifecycle is presented: requirements analysis, business process modeling using BPMN 2.0 notation, architecture design with UML component diagrams, implementation of key endpoints, testing (including negative scenarios and validation checks), containerization with Docker, and successful production deployment. The results demonstrate that this solution optimizes marketing costs, accelerates response to user behavior, enables personalized communication, and makes the information system more flexible and scalable. The developed microservice serves as a universal tool adaptable to IT companies of any size, offering subscription-based digital services. This also provides a solid foundation for future functional expansion (integration with chatbots, behavioral analytics, referral systems, etc.). The work holds both theoretical significance and high practical value for advancing digital marketing within cloud infrastructure. 2026-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/39444 Назва: A systematic approach to comparative analysis of software testing methods Автори: Brynza N. Короткий огляд (реферат): The paper presents a systematic approach to the comparative analysis of software testing methods, including manual, automated, and intelligent testing. The relevance of software quality assurance and the complexity of selecting an appropriate testing method depending on project conditions are emphasized. A methodology based on criteria such as cost, defect detection accuracy, and execution time is proposed. The study outlines key stages, including data collection, modeling, and experimental validation using modern tools and machine learning technologies. 2026-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/39401 Назва: Small Language Models for perplexity-based text classification Автори: Gorokhovatskyi O. Короткий огляд (реферат): The research explores the use of Small Language Models (SLMs), specifically those with 1B parameters or fewer, to classify text as either AI-generated or human-written in the Ukrainian language. By leveraging perplexity – a measure of a model's uncertainty – as a key feature, the study evaluates the performance of models like Gemma 3 and Llama 3.2. The authors further propose a classification method using Convolutional Neural Networks (CNN) trained on token-level probability vectors, achieving accuracy rates up to 0.8559. 2026-01-01T00:00:00Z