Факультет інформатики

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3D реконструкція сцени за відео з декількох камер
(2020) Томащук, Вадим; Крюкова, Галина
Робота складається з чотирьох розділів. В першому розглянуто базові поняття виявлення та опису особливостей об’єктів, які є основою для будь якого обраного підходу 3D реконструкції сцени, а також розглянуто найпопулярніші алгоритми для кращого сприйняття цього етапу в процесі розробки. В другому розділі описана теоретична база епіполярної геометрії, вирівнювання та триангуляції, а також згадано про алгоритми відстежування та оцінки позиції об’єкта. Третій розділ повністю присвячений поетапній розробці практичного застосування та частково описується необхідна теорія, така як побудова карт невідповідностей та глибини. В четвертому розділі звернено увагу на недоліки проведеної роботи, оцінено ефективність практичного застосування та простір для подальшого вдосконалення.
Adversarial robustness and attacks in Deep Learning Керівни
(2022) Кузьменко, Дмитро; Швай, Надія
The theoretical underpinnings for this field involve the notions of robustness and astuteness, local Lipschitzness, r-separability of datasets, robustness-accuracy tradeoff, and L-inf distance. This work will cover all the preliminaries, explain the choice of CIFAR-10 with L-inf metric space and eps=8/255 as a main dataset for the task, make use of already well-known attacks and defenses, introduce new ones, and try different ensembles on the 3 most robust models available on the benchmark – Adversarial Weight Perturbation, Augmentations and weight averaging, and Self-COnsistent Robust Error (SCORE-based model).
Control in Gordon-Newell Networks
(2021) Степанюк, Роман; Чорней, Руслан
As more and more companies are restructuring their business models to be more automized, the question of efficient usage of available capabilities arises. With the help of queuing networks, the only thing the customer has to do to get helpful information regarding the improvement of queueing processes in their company is to input the data that describes the system. As a result, they can be provided numbers, charts, and other data that will drastically improve the working process. By implying strategy improvement procedure on local decision-makers, optimal local strategy can be derived, improving the working process flow in the network.
Cемантична сегментація зображень з використанням Transformer архітектури
(2022) Іванюк-Скульський, Богдан; Швай, Надія
In this work we have presented a model that efficiently balances between local representations obtained by convolution blocks and a global representations obtained by transformer blocks. Proposed model outperforms, previously, standard decoder architecture DeepLabV3 by at least 1% Jaccard index with smaller number of parameters. In the best case this improvement is of 7%. As part of our future work we plan to experiment with (1) MS COCO dataset pretraining (2) hyperparameters search.
Development of a methodology for using microservice architecture in the construction of information systems
(2021) Zhylenko, Oleksii; Cherkasov, Dmytro
In this work will be defined what is microservice architecture, the most important quality attributes and system level requirements. We will gather guidelines grouped by quality attributes that should be used to reduce in future total cost of ownership system under develop. Created methodology will be used in synthetic Java project to demonstrate it on real example.
Development of CI / CD platform deployment automation module for group software development
(2021) Ivanov, O; Cherkasov, Dmytro
In the presented work we reviewed the main CI/CD principles and delivery workflow. We provided definition and benefits for each of part of the CI/CD. Latter we covered definition of CI and CD. Provided tools analysis and narrowed audience for expected module. We have chosen the platform base and picked up clouds for developing solution. After that we developed modules for automatics deployment into cloud as easiest for user as possible and created all necessary scripts with ability to integration in bigger system or launching out of the box. In the end test runs were done for every script and compared result and difficult of managing. Based on achieved results we provided the feedback.
Development of the system for plagiarism checking of Ukrainian texts
(2022) Bikchentaev, Mykola; Hlybovets, Andrii
So, the aim of this work is to review two machine learning models called BERT and Word2Vec, determine how can they be used in plagiarism detection, and develop an application where users can check texts for plagiarism.
Distributed system technical audit
(2020) Zhylenko, Oleksii; Hlybovets, Andrii
In this coursework will be defined what is distributed systems, review Monolithic, Microservice and serverless architecture. Also, we will deep dive into technical audit process, specify what aspects of system must be considered during audit. Then will iterate over checklists item in order to provide guidelines based on best practices in industry that helps to prepare for system audit.
Emitter Position Estimation Using Time Difference of Arrival
(2022) Musiiaka, O.; Hlybovets, Andrii
As a result of this work, the estimator convergence speed was improved by using a local linear transform. This new approach can be considered an adaptation of the Newton-Raphson algorithm with the Hessian matrix replaced with a statistically approximated value that guarantees the algorithm convergence and reduces the amount of computation. Performance testing of the optimization algorithms has shown that the proposed algorithm outperforms the steepest gradient descent 28.2 times and “momentum” modification 7.8 times on the CPU implementation.
Euclidean Algorithm for Sound Generation
(2021) Laiko, Artem; Horokhovskyi, Semen
This course work aims to research possible ways of Euclidean algorithm application and influence for sound generation process, as well as mathematical basis of sound and sound waves. The practice part applies obtained knowledge to develop a VST3 plug-in for sound wave morphing with the Euclidean algorithm application.
Evolutionary art generation using genetic algorithms
(2021) Moroz, Andrii; Horokhovskyi, Semen
This course work explains the concept of generative art and especially concentrates on evolutionary art. Evolutionary art is a part of generative art that uses genetic programming to produce art. Genetic programming for art generation works by creating an initial population, picks out ones that do not fit, and forms new ones combining images that went through selection. Also, this course work is going to explain how to generate images using genetic programming and show results from the developed program.
Impact of adversarial sparsity as an auxiliary metric in adversarial robustness
(2023) Кузьменко, Дмитро; Швай, Надія
The purpose of this research is to investigate adversarial sparsity in computer vision models and introduce a more efficient method for adversarial sparsity estimation. To fulfil this objective, the following tasks have been undertaken: To implement and evaluate an n-Ary search algorithm as an improvement over the conventional binary search method used in adversarial sparsity estimation. To benchmark and compare the performance of the proposed n-Ary search algorithm against the traditional binary search algorithm. To explore the implications of adversarial sparsity on the robustness of machine learning models.
Investigation of the relationship between software metrics measurements and its maintainability degree
(2020) Shapoval, Oleksandr; Hlybovets, Andrii
The goal of this thesis was to practically learn methods of empirical engineering software, algorithms for data collection and data analysis. Results include software measurement, analysis and selection of direct and indirect metrics for research and identification of dependencies between direct and indirect metrics. On the basis of received result were built dependencies between software metrics and software expertise properties. Metrics and properties selected by individual variation. Relationship between metric and expertise includes building direct relationships between the metric and expertise, indirect metrics and expertise. Additionally, was determined whether they have common trends of the relationship between those direct metrics and expert estimates, indirect metrics and expert estimates.
Modeling Distributed Generalized Suffix Trees For Quick Data Access
(2022) Діденко, Віра; Глибовець, Андрій
The aim of this work is to distribute generalized suffix tree construction, so the process is efficient in terms of time complexity and memory consumption. A distributed approach to constructing the suffix tree will allow working with large alphabets and very long strings that exceed the available memory capacity. In this work, an efficient and highly scalable algorithm for constructing generalized suffix trees on distributed parallel platforms was modeled. The experimental results proved that the modeled algorithm’s efficiency is no less than the before known Elastic Range algorithm (ERa) while out-performing ERa on specific data.
A multicriteria competitive Markov decision process
(2021) Левченко, Іларія; Чорней, Руслан
The course work is devoted to A multicriteria competitive Markov decision process; proposed software implementation of their solution. The work consists of an introduction, the main part that consists of six sections, a conclusion, a list of used sources and an appendix. Relevance. The modern-day world makes people face more and more complicated problems which require a solution and the price of mistake for them can be really high. Besides that, nowadays there is so much data that making a decision based on that intuitively and without analysis and math is not an option anymore. The multicriteria Markov decision process is much more similar to reallife than some other common games and decision models – choosing one of the available actions without knowing action chosen by the opponent as well as having vector reward rather than single reward are both much more common in a real application. However, solving such problems as they are is complicated. Therefore in this paper considered algorithm to transform them into linear programming problems, which have more well-known solution algorithms. The object of the study is a multicriteria Markov decision process. The subject of the study is an algorithm for solving the multicriteria competitive β-discounted Markov decision model. Purpose to study multicriteria competitive Markov decision games and algorithm to solve them. Theoretical research methods were used in the study; information from various scientific sources is analyzed, compared and summarized.
Object feature extraction for YOLO detectors
(2023) Абашкін, Олександр; Швай, Надія
The main goal of the research: To create an architecture that can surpass in quality and speed the solutions of that time such as the deformable part models (DPM) that were using the sliding window approach where the classifier is used for each evenly spaced location, and a the R-CNN that were using a network for generation potential bounding boxes and as a second stage applies a classifier on this regions.
Organization and control of continuous code delivery
(2020) Ivanov, Oleksii; Glybovets, Andrii
In this thesis will be defined what is: continuous integration (CI), continuous deployment and delivery (CD), branching strategies. Then will be covered common patterns and anti-patterns of implementation mentioned systems. The second part of the thesis will explore Continuous Integration with focus on it parts. Will be explained importance and ways of optimization of CI process. The third part will uncover Continuous Deployment process. Will be covered Infrastructure as a Code approach (IaC), release strategies for production, and zerodowntime deployments approaches. The finally: some example of zero-downtime implementation will be presented.
Recognizing gestures of the Ukrainian dаctylic аlphabet
(2023) Bikchentaev, Mykola; Hlybovets, Andrii
Research methods: analysis of scientific literature. Objectives of the study: 1. Study the concept of sign language and Ukrainian sign language in particular. 2. Review approaches to gesture recognition. 3. Build a model for recognizing gestures of the Ukrainian dactyl alphabet.
Researching semistructured problems of multicriteria optimization using the software system
(2020) Tryhub, Roman; Franchuk, Oleh
Object of research is optimal decision support process for semistructured (slightly formal) multicriteria optimization problems. Subject of research is mathematical model and method for solving semistructured problems of multicriteria optimization. Goal of research is to develop the optimal decision support system for solving semistructured problems of multicriteria optimization, which can be used by individual or collegial body who take responsible decisions. The next research problems are solved in this work: 1) to implement the process of solving the problem connected with finding an alternative which has the best (in total) criteria values as a software system; 2) to develop and implement the algorithm, which generates recommendations (“guidelines for actions”) for any of alternatives which lost so that the observance of them will guarantee the winning for this alternative. To achieve the goal, following research methods are used in this work: 1) the analytic hierarchy process (hereinafter AHP) – to formalize semistructured multicriteria optimization problem and to find an optimal solution; 2) mathematical programming methods – to formulate recommendations for alternatives which lost so that the observance of them will guarantee the winning; 3) verification test – to debug the software system and to develop the userfriendly interface oriented toward non-professional users.
Scaling SignalR WebSocket Real-Time Applications
(2021) Діденко, Віра; Глибовець, Андрій
Real-time applications depend on persistent connections in order to provide users with high frequency data updates from the application server. The idea behind persistent connections is that when a connection is established it is kept open, hence optimizing the data transfer process by saving time on establishing a new connection. As the number of continuous connections grows in a high-traffic application sustaining a high number of clients, eventually the server can run out of connection resources. In this research work the aim is to scale the persistent connections in order to limit the number of open connections that a single application server has to handle; therefore, designing real-time applications that can serve many clients in an efficient manner. This study introduces WebSocket scaling techniques, focusing on the Azure SignalR Service as the solution for scaling data-intensive applications.

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