16 міжнародна науково-практична конференція

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https://ekmair.ukma.edu.ua/handle/123456789/38241

У збірнику представлено тези доповідей науково-практичної конференції "Теоретичні та прикладні аспекти побудови програмних систем"(TAAPSD 2025), яка проходила в Києві з 24 по 25 листопада 2025 року.

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Accelerated Epidemic Simulation in Large-Scale Networks: Optimization of the Gillespie Algorithm and a Two-Layer Approach
(Національний університет "Києво-Могилянська академія", 2025) Kuryliak, Yulian; Emmerich, Michael
This study addresses the challenge of accelerating epidemic simulations in large-scale complex networks through algorithmic and structural optimization. Traditional Gillespie-based stochastic simulations accurately reproduce epidemic dynamics but become computationally prohibitive for networks exceeding tens of thousands of nodes. To overcome this limitation, we build upon such efficiency techniques as local rate updates and ordered event-selection structures, which reduce the computational complexity of each simulation step from O(n) to O(log(n)). Building on these principles, we propose a two-layer (micro–macro) modeling framework: the micro layer simulates intra-community dynamics, while the macro layer captures inter-community infections using hazard-integral rates derived from mobility data and the epidemic states of metanodes. This hierarchical approach enables scalable and parallelizable simulations that preserve stochastic accuracy while substantially reducing computational cost, allowing realistic modeling of epidemic spread across millions of agents and multiple cities.
Data Streaming Pipeline for the Quadcopter Flight Control Stack
(Національний університет "Києво-Могилянська академія", 2025) Zavaliy, Taras; Shakhovska, Nataliia; Yatsyshyn, Volodymyr
Modern UAV and UGV technologies are highly competitive dynamic field of applied research and development. Mainstream software stacks being used include PX4, ArduPilot, Betaflight, as well as ROS2 – a modular computing environment for processing sensor data streams, running perception and control algorithms, as well as software-in-the-loop simulations. In our research, ROS2 integration with Betaflight for on-board sensor data streaming is implemented and tested.
Design of an Intelligent Agent for Autonomous Microservice Optimization in Enterprise Systems
(Національний університет "Києво-Могилянська академія", 2025) Vanin, Danylo
This study introduces a conceptual framework for an intelligent agent designed to autonomously optimize microservice-based enterprise systems. Microservice architecture [3] serves as the basis for scalable and modular enterprise solutions, supporting agile DevOps practices and enabling independent deployment, updating, and management of services. While these features accelerate release cycles and improve maintainability, they also create persistent operational and architectural challenges [4, p. 633-634] that often necessitate manual intervention. These issues are especially significant in corporate settings, where requirements are dynamic and influenced by organizational, legal, and economic factors. In response, the software engineering field is increasingly exploring AI-driven approaches [1, p. 12]. Although machine learning has been utilized for anomaly detection [2], auto-scaling, and traffic routing, there remains a gap in the development of fully autonomous agents capable of intelligent architectural analysis and modification. The proposed agent addresses these challenges through a modular architecture that includes monitoring, decision-making, execution, and safety verification modules. The decision component employs reinforcement learning and formal quality models to balance objectives such as performance, cohesion, and latency, while maintaining adherence to system constraints. This design seeks to reduce architectural drift, improve service modularity, and support adaptive system behavior in real time. The agent is intended as a foundational element for intelligent DevOps and self-optimizing enterprise software systems.
Efficient Policy Learning via Knowledge Distillation for Robotic Manipulation
(Національний університет "Києво-Могилянська академія", 2025) Severhin, Oleksandr; Kuzmenko, Dmytro; Shvai, Nadiya
The work focuses on the computational intractability of large-scale Reinforcement Learning (RL) models for robotic manipulation. While world-like models like TD-MPC2 demonstrate high performance in various manipulative tasks, their immense parameter count (e.g., 317M) hinders training and deployment on resource-constrained hardware. This research investigates Knowledge Distillation (KD) with a loss function specifically described in [1] and [2] as a primary method for model compression. This involves training a lightweight "student" model to mimic the behavior of a large, pre-trained "teacher" model. Unlike in supervised learning, distilling knowledge in RL is uniquely complex; the objective is to transfer a dynamic, reward-driven policy, not a simple input-output function.
Information Education: Digital Knowledge and Online Language Learning
(Національний університет "Києво-Могилянська академія", 2025) Didmanidze, Ibraim; Didmanidze, T.
The presented paper outlines approaches to worldview researching language education in online environments and survey research that has been conducted to date. Is it worthwhile to use the Internet in Language Education? How does Language Education use change in on-line environments? What are the best ways to incorporate e-mail or the World Wide Web into the classroom? We can use several approaches to learn and research about such issues. One is to talk to fellow lecturers and professors. Another is to try practical things out in the classroom and see how they work. And another is to conduct and share scientific-theoretical research.
Information Practice Through Digital Ethics: Worldview Assessment of AI Reliability
(Національний університет "Києво-Могилянська академія", 2025) Bagrationi, I.; Bagratishvili, A.
The paper is devoted to the research and evaluation of ethical regularities of Artificial Intelligence as a certain digital existence in the context of worldview parameters. A critical review of the main provisions and principles surrounding the issue is given, finding and developing the progressive criteria in them and establishing a unique theoretical-worldview concept corresponding to modern requirements. The relationships fixed between the thinking of the fields of humanitarian and exact knowledge around the issue are shown in an original way; a kind of attempt is presented to understand and evaluate the system-conceptual model developed on the basis of moral criteria in the digital world of organized management in a new way.
Massively Parallel MorphoNAS Implementation
(Національний університет "Києво-Могилянська академія", 2025) Medvid, Serhii
Our GPU-accelerated MorphoNAS (Morphogenetic Neural Architecture Search) enhances the biologically motivated developmental model of the original MorphoNAS, from sequential developmental simulations to large scale parallel simulation. Like the original CPU-based MorphoNAS system, the GPU enhanced version models morphogenesis (neural development) through reaction-diffusion, progenitor differentiation and axon-guided wiring that grow recurrent controllers. Unlike the original CPU-based version, the GPU-based version has the ability to formulate growth and control in parallel execution of thousands of genomes. Through use of sparse, device-resident representations of developmental dynamics and recurrent rollouts, we produce behaviorally equivalent recurrent controllers with up to three orders of magnitude speedup. As a result, we have enabled the possibility of performing evolutionary search over populations of developmental programs, transforming MorphoNAS from a proof-of-concept model of artificial morphogenesis and adaptive architecture discovery to a scalable framework for those objectives.
Moore-Penrose Pseudoinverse Matrix
(Національний університет "Києво-Могилянська академія", 2025) Kravchuk, Oleg; Kriukova, Galyna
The Moore-Penrose pseudo-inverse is a foundational concept in modern numerical linear algebra, offering a principled approach to solving ill-posed and inconsistent systems arising in machine learning and other fields. This paper explores the pseudo-inverse from five distinct perspectives — axiomatic, variational, regularization, spectral, and algebraic graph theory — highlighting its theoretical depth and practical relevance across disciplines such as machine learning, signal processing, and network analysis.
PINN Modeling of Interfacial Gravity-Capillary Waves
(Національний університет "Києво-Могилянська академія", 2025) Avramenko, Olha; Sontikov, Maksym
This paper presents an automated computational framework for modeling hydrodynamic processes using physics-informed neural networks (PINNs). The modular system integrates all stages of numerical experimentation — from data generation and model training to validation and accuracy evaluation — ensuring reproducibility, flexibility, and scalability. The framework was verified on the classical problem of interfacial gravity–capillary waves between two incompressible fluids, using the analytical solution as a benchmark for numerical assessment. Computational experiments showed that increasing the number of training points from 400 to 1000 improved accuracy and convergence, with the Extended configuration achieving 98.86% accuracy and a MAPE of 1.14%, while Adaptive_LR remained stable. The results confirm the reliability and efficiency of the proposed PINN-based framework for solving complex hydrodynamic problems governed by nonlinear partial differential equations.
System and Approach for Automated Photo-Like Facial Image Generation for Use in Testing, Training or Evaluation
(Національний університет "Києво-Могилянська академія", 2025) Artiushenko, Bohdan
This research focuses on systems, apparatuses, and methods for automatically generating photo-like facial image items that may be used on an exam or test. The test may be used for evaluating a test-taker's proficiency in familiar faces recognition, training in facial recognition, prosopagnosia or other similar purpose or goal. A combination of generative artificial intelligence, image and face recognition and manual check is proposed and evaluated.

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Browsing 16 міжнародна науково-практична конференція by Subject "conference materials"

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