Кафедра "Програмна інженерія та інтелектуальні технології управління ім. А. В. Дабагяна"

Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/1665

Офіційний сайт кафедри http://web.kpi.kharkov.ua/asu

Від січня 2022 року кафедра має назву "Програмна інженерія та інтелектуальні технології управління ім. А. В. ДАБАГЯНА" (тоді ж, у січні 2022 року в окремий підрозділ виділилася кафедра "Інформаційні системи та технології"), попередні назви – "Програмна інженерія та інформаційні технології управління" (від 2015), "Автоматизовані системи управління" (від 1977); первісна назва – кафедра автоматичного управління рухом.

Кафедра автоматичного управління рухом заснована в 1964 році задля підготовки інженерів-дослідників у галузі автоматичного управління рухом з ініціативи професора Харківського політехнічного інституту Арега Вагаршаковича Дабагяна та генерального конструктора КБ "Електроприладобудування" Володимира Григоровича Сергєєва.

Кафедра входить до складу Навчально-наукового інституту комп'ютерних наук та інформаційних технологій Національного технічного університету "Харківський політехнічний інститут".

У складі науково-педагогічного колективу кафедри працюють: 4 доктора технічних наук; 24 кандидата наук: 22 – технічних, 1 – фізико-математичних, 1 – економічних, 1 – доктор філософії; 3 співробітників мають звання професора, 19 – доцента, 1 – старшого наукового співробітника.

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Collection and processing of a Medical Corpus in Ukrainian
(2020) Cherednichenko, Olga; Kanishcheva, Olga; Yakovleva, Olena; Arkatov, Denis
The text corpora are the basis of natural language studying. We describe the structure of a Ukrainian-language corpus (UKRMED), which contains a variety of medical text genres (Сlinical protocols, Blogs, and Wikipedia). The paper shows the process of collecting, creating and processing a corpus of medical data in Ukrainian. We represent our own framework for creating a text corpus. The medical domain and text simplification are chosen as corpus directions. The authors gave statistical characteristics of the corpus, an analysis of the morphological parts of speech is provided. Frequency lemmas for this medical corps are analyzed. The UKRMED corpus can be used for solving the task of natural language simplification.
Методичні вказівки щодо структури та змісту пояснювальних записок дипломних робіт магістра
(2023) Москаленко, Валентина Володимирівна; Копп, Андрій Михайлович; Чередніченко, Ольга Юріївна; Літвінова, Юлія Сергіївна
Методичні вказівки містять опис вимог до обов’язкової структури та мінімального змісту пояснювальних записок дипломних робіт магістра, студентів, які навчаються за спеціальністю 122 «Комп’ютерні науки» освітньої програми «Комп’ютерні науки та інтелектуальні системи».
Методичні вказівки щодо структури та змісту пояснювальних записок дипломних робіт бакалавра
(2023) Копп, Андрій Михайлович; Москаленко, Валентина Володимирівна; Чередніченко, Ольга Юріївна; Літвінова, Юлія Сергіївна
Методичні вказівки містять опис вимог до обов’язкової структури та мінімального змісту пояснювальних записок дипломних робіт бакалавра, студентів, які навчаються за спеціальністю 122 «Комп’ютерні науки» освітньої програми «Комп’ютерні науки та інтелектуальні системи».
Towards Classifying HTML-embedded Product Data Based On Machine Learning Approach
(2021) Matveiev, Oleksandr; Zubenko, Anastasiia; Yevtushenko, Dmitry; Cherednichenko, Olga
In this paper we explored machine learning approaches using descriptions and titles to classify footwear by brand. The provided data were taken from many different online stores. In particular, we have created a pipeline that automatically classifies product brands based on the provided data. The dataset is provided in JSON format and contains more than 40,000 rows. The categorization component was implemented using K-Nearest Neighbour (K-NN) and Support Vector Machine (SVM) algorithms. The results of the pipeline construction were evaluated basing on the classification report, especially the Precision weighted average value was considered during the calculation, which reached 79.0% for SVM and 72.0% for K-NN.
Multi-Agent Modeling of Project Management Processes in Distributed Teams
(2021) Cherednichenko, Olga; Matveiev, Oleksandr; Yanholenko, Olha; Maneva, Rositsa
Changes in the business environment, the innovative nature of projects, lack of necessary skills of project team members lead to increased uncertainty and inability to plan with a given degree of accuracy. Such projects use adaptive project and program management methodologies. In the field of information technology, the use of multi-agent systems is of particular interest. In the context of the use of multi-agent systems for the design of intelligent systems for various purposes, the development and study of a model and software implementation of a prototype of an agent platform are relevant. The aim of this work is to develop and research an agent platform that can be implemented in the work of the distributed team in order to improve the assignment of tasks. The paper presents the formal agent architecture as a basis of multi-agent model. The task assignment is a case study to implement and test multi-agent model prototype. The agent platform is developed based on Kotlin programming language. A prototype of the agent platform based on the FIPA specification allows to increase the productivity, scalability and interoperability of multiagent system.
Item Matching Based on Collection and Processing Customer Perception of Images
(2020) Cherednichenko, Olga; Vovk, Maryna; Ivashchenko, Oksana
The number of sellers and goods being sold on the e-marketplaces is growing, so the volume of data stored and processed by e-commerce information systems is increasing drastically. That is why the development of performance solutions is quite relevant. The given paper provides the approach of item matching based on the human perception of item images. The main goal of the study is to build a model for assessing the similarity of items. This paper provides a description of a software product for comparing product images collected on online trading platforms. The user evaluates the product visually. The developed software implements the crowdsourcing data collecting based on the comparator identification method. The use of this method involves an experiment in which the user is offered two images, by comparing which the determined binary reaction is obtained. The results show the perspective of the mobile client application as part of an item matching system that aims to optimize the search for products on the Internet.
A model for estimating the security level of mobile applications: a fuzzy logic approach
(2020) Yanholenko, Olha; Cherednichenko, Olga; Yakovleva, Olena; Arkatov, Denis
In this paper a model for solving the problem of estimating the security level of mobile applications was proposed. The estimation is performed based on a fuzzy inference system of the Mamdani type. The input criteria were defined as the most important security threats by applying the Analytic hierarchy process method. The pairwise comparison matrix was constructed from mobile security research on OWASP Top 10 Mobile Risks. The proposed methodology can be applied for any kind of mobile applications available for modern platforms, except specific cases when security analyst does not have a sufficient amount of information about the chosen application for performing the security level testing. Mobile security analysts can easily make further decisions about comprehensive mobile application security based on the results obtained with the help of the introduced model.
Developing the Key Attributes for Product Matching Based on the Item’s Image Tag Comparison
(2020) Cherednichenko, Olga; Yanholenko, Olha; Kanishcheva, Olga
With the constant growth of the number of products on e-marketplaces, buyers feel hard to find and choose items that would satisfy all their needs and expectations. Search and filtering algorithms of recommender systems, although are striving to help users, still fail quite often due to incomplete and inaccurate description of items. The given work suggests to combine analysis of both item description and item image in order to construct groups of similar items. Since a person can define whether two items are similar or not looking at two images and a brief description, it is suggested to form a set of similar items based on users’ judgments and then to extract the core of keywords for the specific type of products. Further, it is proposed to use the given core to evaluate the similarity of any new item added to the definite group. The case study deals with the building of the core of keywords for sneakers. The developed key attributes allow matching the items with a high precision, thus, proving the effectiveness of the method of the core construction.
Towards Structuring of Electronic Marketplaces Contents: Items Normalization Technology
(2020) Cherednichenko, Olga; Yanholenko, Olha; Vovk, Maryna; Sharonova, Nataliia
The E-commerce industry is going strong and is bringing a great profit to its stakeholders. However, there is probably no buyer of the e-marketplace who has not faced the issues connected with inappropriate search results or inadequate filtering and recommendation of irrelevant products. Modern search and collaborative filtering algorithms of e-commerce systems do work well with the input data of high quality but the reality is that often items’ description contains inaccuracies and incompleteness, which negatively affects the results. The given paper suggests the concept of e-marketplace items normalization which goal is to provide the unified and standardized patterns of items inside the system that can be used by search and filtering algorithms. Items normalization is implemented based on the algebra of predicates models specified in this work. The case study deals with constructing normalized models of knapsacks items from the online sports store. The developed models allowed to build 141 normalized item patterns with a unified set of attributes and their values.
Readability Evaluation for Ukrainian Medicine Corpus (UKRMED)
(2021) Cherednichenko, Olga; Kanishcheva, Olga
In our work, we decided to demonstrate how to work different readability formulas on our Ukrainian-language corpus (UKRMED) of medical texts. UKRMED contains three types of texts in the medical domain divided by their complexity: “Complex texts”, “Moderate texts”, and “Simple texts”. This research aims to (1) demonstrate the use of the most commonly used readability formulas on written health information in Ukrainian, (2) compare and contrast these different formulas to various texts (simple, complex, and moderate), (3) research different medical text features which will be used for text simplification and classification medical texts and (4) prepare recommendations for using these formulas to the evaluation of readability medical texts in Ukrainian.