ElAr :: Перегляд Кафедра штучного інтелекту (ШІ) за Назва

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(НПП ЧП «Технологический Центр», 2014) Terziyan, V.; Shevchenko, O.; Golovianko, M.

Статья посвящена задачам, связанным с самоуправляемыми и эволюционирующими массивами знаний. Мы предполагаем, что граф самоуправляемых знаний представляет из себя гибрид из явных декларативных знаний о себе и явных процедурных знаний. Предложено расширение к традиционной RDF модели, описывающей граф знаний в соответствии со стандартами Semantic Web. Предложена концепция Executable Knowledge и Knowledge Computing, основанная на добавлении исполняемых свойств к традиционно используемым (data type и object type) типам свойств в рамках RDF модели. Статья также представляет пилотную реализацию Executable Knowledge, как модуля для Protege.

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Autonomous Agent's Behavior Model Based on Automata Theory

(ХНУРЕ, 2018) Філатов, В. О.; Семенець, В. В.; Filatov, V. O.; Semenets, V. V.

This paper is devoted to a problem of management the corporate system's information space. The management is carried out on the technology of the program agents. The approach to construction the program agent's model on a basis of frame structure is considered. The frame is fixed as universal extension system. For the decision of concrete tasks the functional modules - slots can be added in it. Base structure for model of behavior of the program agents in information environment is the model based on the finite-state machine. The offered models can be used for development the systems for administration of information resources in the allocated computing systems.

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CLIPTraVeLGAN for Semantically Robust Unpaired Image Translation

(2023) Bodyanskiy, Y.; Ryabova, N.; Lavrynenko, R.

In this paper a novel approach for semantically robust unpaired image translation is presented. CLIPTraVeLGAN replaces the Siamese network in TraVeLGAN with a contrastively pretrained language-image model (CLIP) with frozen weights. This approach significantly simplifies the model selection and training process of TraVeLGAN, making it more robust and easier to use.

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Data mining in relational systems

(ХНУРЕ, 2020) Філатов, В. О.; Семенець, В. В.; Золотухін, О. В.

The subject of the research is methods of relational database mining. The purpose of the research is to develop scientificallygrounded models for supporting intelligent technologies for integrating and managing information resources of distributed computing systems. Explore the features of the operational specification of the relational data model. To develop a method for evaluating a relational data model and a procedure for constructing functional associative rules when solving problems of mining relational databases. In accordance with the set research goal, the presented article considers the following tasks: analysis of existing methods and technologies for data mining. Research of methods for representing intelligent models by means of relational systems. Development of technology for evaluating the relational data model for building functional association rules in the tasks of mining relational databases. Development of design tools and maintenance of applied data mining tasks; development of applied problems of data mining. Results: The analysis of existing methods and technologies for data mining is carried out. The features of the structural specification of a relational database, the formation of association rules for building a decision support system are investigated. Information technology has been developed, a methodology for the design of information and analytical systems, based on the relational data model, for solving practical problems of mining, practical recommendations have been developed for the use of a relational data model for building functional association rules in problems of mining relational databases, conclusion: the main source of knowledge for database operation can be a relational database. In this regard, the study of data properties is an urgent task in the construction of systems of association rules. On the one hand, associative rules are close to logical models, which makes it possible to organize efficient inference procedures on them, and on the other hand, they more clearly reflect knowledge than classical models. They do not have the strict limitations typical of logical calculus, which makes it possible to change the interpretation of product elements. The search for association rules is far from a trivial task, as it might seem at first glance. One of the problems is the algorithmic complexity of finding frequently occurring itemsets, since as the number of items grows, the number of potential itemsets grows exponentially.

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Face recognition problems for artificial intelligence in case with social medias

(ХНУРЕ, 2020) Логвінова, К. В.

Nowadays, a certain kind of experience in the era of information technology and information technology, the problem of creating “smart” technology is especially urgent. On the “smart” technologies on the current day, you can rozumіti cars with programs of any complexity, all kinds of pralny machines, especially the robbery mode before the robots, which can make pictures and cherubs by co-workers.

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Good practices of Industry 4.0 in Ukraine

(2022) Golovianko, M.; Gryshko, S.; Titova, L.; Filatov, V.

There are examples and good practices of using AI and developing Industry 5.0 all over Europe. This desk research is made by the team from Kharkiv National University of Radio Electronics within Erasmus+ JoInME Project “JoInt Multidisciplinary training program on Entrepreneurship in the field of artificial intelligence for industry 5.0” to identify the winning practices from Ukrainian startups or companies working in the Industry 4.0/5.0 and using AI.

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Kernel principal component analysis in data stream mining tasks

(2016) Bodyanskiy, Ye. V.; Deineko, A. O.; Eze, F. M.; Shalamov, M. O.

Currently, self-learning systems of computational intelligence [1, 2] and, above all , artificial neural networks (ANN ), that tune their parameters without a teacher on the basis of the self-learning paradigm [3], are widely used in solving various problems of Data Mining, Exploratory Data Analysis etc. Among these tasks, most frequently encountered in the Text Mining, Web Mining, Medical Data Mining, it be can mentioned the problem of compression of large data sets, for whose solution principal component analysis (PCA) is widely used, which consists in the orthogonal projection of input data vectors from the original n-dimensional space in the m- dimensional space of reduced dimensionality

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Multiagent Approach for Managing the Information Space of Corporate Systems

(ХНУРЕ, 2018) Філатов, В. О.; Семенець, В. В.; Filatov, V. O.; Semenets, V. V.

The subject matter of the study is the integration and management of information resources of distributed computing corporate systems within the common information space. The goal of the study is to develop scientifically based models for supporting intelligent technologies for the integration and management of information resources of distributed computing systems. In accordance with the goal of the study, the article deals with the following tasks: to develop a formal-logical model of the information space, to study and formalize agent-oriented tasks, to select the model of a software agent, to substantiate the information technology aimed at effective information management in corporate systems.

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Named Entity Recognition Problem for Long Entities in English Texts

(2021) Shatalov, O.; Ryabova, N.

This paper is related to the problem of natural language processing (NLP), namely the named entity recognition (NER). This paper reveals the features of named entities recognition in English texts using deep learning (DL). The peculiarity of the study was the rather long length of the presented named entities: many of them could include a rather large number of words. The main problem was the amount of text that had to be recognized as a single entity. The results of the research are described here show the effectiveness of using deep neural network architectures for the task of recognizing long named entities in texts in English.

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Ontology-based International Degree Recognition

(IEEE Computer Society, 2005) Terziyan, V.; Kaykova, O.; Vitko, O.; Titova, L.

Bologna process and “eurointegration” needs the development of new opened educational standards (there exist substantial differences between Ukrainian and EU standards in higher education ) none (even the best) of the European universities can provide students with the optimal set of courses of the best experts Educational Ontologies. Semantic Web is not only a technology as many used to name it; Semantic Web is not only an environment as many naming it now; Semantic Web it is a new context within which one should rethink and re- interpret his existing businesses, resources, services, technologies, processes, environments, products etc. to raise them to totally new level of performance. Management and Internationalization of Education are specific fields, in which advances of Semantic Web approach can bring essential effect.

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Personalized Adaptation of Learning Environments

(2019) Filatov, V.; Yerokhin, A.; Zolotukhin, O.; Kudryavtseva, M.

This work is devoted to the development of personalized training systems. A major problem in learning environmens is applying the same approach to all students: teaching materials, time for their mastering, and a training program that is designed in the same way for everyone. Although, each student is individual has his own skills, ability to assimilate the material, his preferences and other. Recently, recommendation systems, of which the system of personalized learning is a part, have become widespread in the learning environment. On the one hand, this shift is due to mathematical approaches, such as machine learning and data mining, that are used in such systems while, on the other hand, the requirements of technological standards "validated" by the World Wide Web Consortium (W3C). According to this symbiosis of mathematical methods and advanced technologies, it is possible to implement a system that has several advantages: identifying current skill levels, building individual learning trajectories, tracking progress, and recommending relevant learning material. The conducted research demonstrates how to make learning environment more adaptive to the users according to their knowledge base, behavior, preferences, and abilities. In this research, a model of a learning ecosystem based on the knowledge and skills annotations is presented. This model is a general model of the all life learning. Second, this thesis focuses on the creation of tools for personalized assessment, recommendation, and advising.

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Reengineering relational database on analysis functional dependent attribute

(Proceedings of the X Intern. Scient. and Techn. Conf. «Computer Science & Information Technologies» (CSIT’2015), 14-17 sept. 2015. – Lviv, Ukraine. – P. 85-88., 2015) Filatov, V. O.; Radchenko, V.

The task of re-engineering information system which is based uses a relational database. An approach to the definition of functionally dependent attributes of the database in step reengineering and modified synthesis algorithm logic of a relational database.

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Relational vs non-relational databases

(«European Scientific Platform», 2022) Sliusarenko T.; Filatov V.; Слюсаренко Т.; Філатов В.

In this paper the difference between relational and non-relational databases are given.

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Report on TRUST project impact on national level

(2015) Golovianko, M.

Impact of TRUST project results on national level is reported

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Semantic Portal as a Tool for Structural Reform of the Ukrainian Educational System

(2014) Головянко, М. В.; Терзіян, В. Я.; Шевченко, О. Ю.

Education is recognized as a fundamental enabler of human development. The adoption of information and communications technologies (ICTs) by education (especially in developing countries) contributes to educational system reforms, in addition to the traditional advantages, such as social openness and accessibility. Yet the academic community has not studied sufficiently the challenging context in which ICTs are used as instruments for the reform of inefficient, and sometimes even corrupted, educational systems rather than just as means for smarter classrooms, remote access, or content management. The object of this study is Ukrainian higher education (HE) and its quality assurance (QA) system, which appears to be neither flexible nor transparent. We offer an ICT solution to provide the needed transparency. We show that such transparency is urgently needed for the structural reform of this system, in order to empower people and recover their trust and respect toward HE. The solution is based on ontology-driven portal as a digital ecosystem for national QA. We show how the content of the portal can be managed and verified based on users' social activity and reputation management, and how the quality evaluations of different players can be made with personalized procedures and quality indicators.

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Semantic segmentation of deforestation on satellite images in the kharkiv region

(ХНУРЕ, 2020) Маврова, К. Г.

Deforestation is a crucial problem nowadays. It can cause climate change, soil erosion, flooding, and increase greenhouse gases in the atmosphere. And such activity as illegal forest logging is one of the main reasons for deforestation. The process of finding these clearcuts consists of downloading images from satellite, preprocessing the bands, image analysis and labeling by a person. So there is a lot of manual routine work that can be automated using Deep Learning.

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ShortTermScientificMission Report

(2016) Golovianko, M.

Reort on the STSM devoted to study existing search techniques and develop the new, optimized ones for query answering in Big Data under Open World Assumption.

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Synthesis of Semantic Model of Subject Area at Integration of Relational Databases

(2019) Filatov, V.; Semenets, V.; Zolotukhin, O.

The article considers the problem of synthesizing a semantic model of the subject area at integration of heterogeneous information resources. In this case, emphasis is placed on ensuring the universality of the means of description, without regard to artificial limitations on the data typification and categorization. Two types of logical existence rules are introduced: functional and structural ones, which allow analyzing not only explicitly defined, but also logically deducible information objects, that is, determining the boundary of the subject area. Information about all possible information objects of the subject area makes it possible to determine the area of intersection of the integrable data semantics.

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The Set of Manuals for Accounting Credits for Courses and Projects

(Kharkov State Technical University of Radio Electronics, 2000) Філатов, В. О.; Захарченко, В. Ф.; Лесная, Н. С.; Filatov, V. O.; Zaharchenko, V.; Lesna, N.

The present guide comes out of work within the bounds of TEMPUS UM_CP-20560-1999 project on investigating of International European Higher Educational System, based on EUROPEAN CREDIT TRANSFER SYSTEM (ECTS). The authors of this guide made it their aim: - Ukrainian user’s, from decane to student level, acquaintance with the International European Higher Educational System based on ECTS in view of multi-stage studying in the educational institutions of the European Union; - providing the reader the information about main concepts of educational process management by the example of Kharkov State Technical University of Radio Electronics, and its differences from the European Educational System; - elaboration of the requirements for informational and methodical-organizational support of the higher educational institution faculty on introduction the ECTS model into the educational process (by the example of Computer Science faculty); - acquaintance of the Ukrainian user with particular features of the ECTS Information Package.

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Towards Russian Text Generation Problem Using OpenAI’s GPT-2

(CEUR Workshop Proceedings, 2021) Shatalov, O.; Ryabova, N. V.

This work is devoted to Natural Language Generation (NLG) problem. The modern approaches in this area based on deep neural networks are considered. The most famous and promising deep neural network architectures that are related to this problem are considered, in particular, the most popular free software solutions for NLG based on Transformers architecture with pre-trained deep neural network models GPT-2 and BERT. The main problem is that the main part of already existing solutions is devoted to the English language. But there are few models that are able to generate text in Russian. Moreover, the text they generate often belongs to a general topic and not about a specific subject area. The object of the study is the generation of a contextually coherent narrow-profile text in Russian. Within the framework of the study, a model was trained for generating coherent articles of a given subject area in Russian, as well as a software application for interacting with it.

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