2021

Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/52264

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Human body modeling technologies for e-commerce systems
(Національний технічний університет "Харківський політехнічний інститут", 2021) Litvinov, Bogdan Ruslanovich; Bilova, Mariia Oleksiivna
Relevance of the research work is the analysis of the main features of 3D modeling for further implementation in e-commerce. Namely, the features of creating a human body 3D model with the ability to edit personal settings of individual parts of the body, as well as a basic set of clothes to provide a more realistic representation of the model. The features of the 3D model in general were considered in this article. The mathematical analysis of the 3D graphics rendering on the 2D monitor and the possibilities of control and editing of such models have been presented. The developed software product allows the user to create an anatomical three-dimensional model of the human body and then adjust it to his needs. The user can apply on created model variety of settings, namely more than 15 different views, with a full package of changes. It is possible to change the size, color of hair, eyebrows, eyes, face, body, legs. Also, the user is able to select the levels of skeletal frame views and additionally can select different backgrounds to provide a more realistic representation of the model in space. Additional functionality was implemented for more flexible configuration of the model’sface. The user can pre-determine points to select directions or sizes of different parts of the face using settings, displayed on the mouse or touchpad control. After adjustments, the user is able to manage the clothes that he had saved in the shopping cart from the online store, from which he later proceeded to the online fitting. After the fitting the user can test the creation of animations in 360 degrees of free movement. Finally, the user can go to the store to pay for the items he left in the shopping cart. Developed software allows improving main metrics of the on-line stores, which has a positive impact on increasing the growth of earnings.
Software testing results analysis for the requirements conformity using neural networks
(Національний технічний університет "Харківський політехнічний інститут", 2021) Shepeliev, Oleksandr Vadymovich; Bilova, Mariia Oleksiivna
The relevance of scientific work lies in the need to improve existing software designed to analyze the compliance of the results of software testing of the stated requirements. For the implementation of this goal, neural networks can be used by quality control specialists to m ake decisions about software quality, or project managers as an expert system, for one of the quality indicators for the customer. The article deals with software testing which is a process of validation and verification of compliance of the software application or business program with the technical requirements that guided its design and development, and work as expected, and identifies important errors or deficiencies classified by the severity of the program to be fixed. Existing systems do not provide for or have only partial integration of systems of work with the analysis of requirements, which should ensure the formation of expert assessment and provide an opportunity to justify the quality of the software product. Thus, a data processing model based on a fuzzy neural network was proposed. An approach to allow determining the compliance of the developed software with functional and non-functional requirements was proposed, taking into account how successfully or unsuccessfully implemented this or that requir ement. The ultimate goal of scientific work is the development of algorithmic software analysis of compliance of software testing results to stated requirements for support in the decisions taken. The following tasks are solved in scientific work: analysis of advantages and disadvantages of using existing systems when working with requirements; definition of general structure and classification of testing and requirements; characteristic main features of the use of neural networks; designing architecture, the module of research of conformity of results of testing software to the stated requirements.
Topic segmentation methods comparison on computer science texts
(Національний технічний університет "Харківський політехнічний інститут", 2021) Sokol, Volodymyr Yevhenovych; Krykun, Vitalii Oleksandrovich; Bilova, Mariia Oleksiivna; Perepelytsya, Ivan Dmytrovich; Pustovarov, Volodymyr Volodymyrovich
The demand for the creation of information systems that simplifies and accelerates work has greatly increased in the context of the rapid informatization of society and all its branches. It provokes the emergence of more and more companies involved in the development of software products and information systems in general. In order to ensure the systematization, processing and use of this knowledge, knowledge management systems are used. One of the main tasks of IT companies is continuous training of personnel. This requires export of the content from the company's knowledge management system to the learning management system. The main goal of the research is to choose an algorithm that allows solving the problem of marking up the text of articles close to those used in knowledge management systems of IT companies. To achieve this goal, it is necessary to compare various topic segmentation methods on a dataset with a computer science texts. Inspec is one such dataset used for keyword ext raction and in this research it has been adapted to the structure of the datasets used for the topic segmentation problem. The TextTi ling and TextSeg methods were used for comparison on some well-known data science metrics and specific metrics that relate to the topic segmentation problem. A new generalized metric was also introduced to compare the results for the topic segmentation problem. All software implementations of the algorithms were written in Python programming language and represent a set of interrelated functions. Results were obtained showing the advantages of the Text Seg method in comparison with TextTiling when compared using classical data science metrics and special metrics developed for the topic segmentation task. From all the metrics, including the introduced one it can be concluded that the TextSeg algorithm performs better than the TextTiling algorithm on the adapted Inspec test data set.