Pub Date : 2020-12-28DOI: 10.18523/2617-3808.2020.3.102-106
P. Stetsyuk, V. Lyashko, A. Suprun
{"title":"A Bfgs Method for the Problem of Building S-Shaped Curve","authors":"P. Stetsyuk, V. Lyashko, A. Suprun","doi":"10.18523/2617-3808.2020.3.102-106","DOIUrl":"https://doi.org/10.18523/2617-3808.2020.3.102-106","url":null,"abstract":"","PeriodicalId":433538,"journal":{"name":"NaUKMA Research Papers. Computer Science","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127321510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.18523/2617-3808.2020.3.97-101
David Gamayun, M. Korniichuk
{"title":"Methods of Working with Textures Using Computer Vision on Python","authors":"David Gamayun, M. Korniichuk","doi":"10.18523/2617-3808.2020.3.97-101","DOIUrl":"https://doi.org/10.18523/2617-3808.2020.3.97-101","url":null,"abstract":"","PeriodicalId":433538,"journal":{"name":"NaUKMA Research Papers. Computer Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128414101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.18523/2617-3808.2020.3.69-74
Kyrylo Gorokhovskyi, Oleksii Zhylenko, Oleh Franchuk
{"title":"Distributed System Technical Audit","authors":"Kyrylo Gorokhovskyi, Oleksii Zhylenko, Oleh Franchuk","doi":"10.18523/2617-3808.2020.3.69-74","DOIUrl":"https://doi.org/10.18523/2617-3808.2020.3.69-74","url":null,"abstract":"","PeriodicalId":433538,"journal":{"name":"NaUKMA Research Papers. Computer Science","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131971057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.18523/2617-3808.2020.3.149-153
D. Vasylenko, V. Kozopas, M. Korniichuk
{"title":"Implementation of the Contact Management System of Graduates of the Faculty of Informatics","authors":"D. Vasylenko, V. Kozopas, M. Korniichuk","doi":"10.18523/2617-3808.2020.3.149-153","DOIUrl":"https://doi.org/10.18523/2617-3808.2020.3.149-153","url":null,"abstract":"","PeriodicalId":433538,"journal":{"name":"NaUKMA Research Papers. Computer Science","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131118723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.18523/2617-3808.2020.3.75-82
Vladyslav Andronik, Olena Buchko
Accurate detection of shadows and removal in the image are complicated tasks, as it is difficult to understand whether darkening or gray is the cause of the shadow. This paper proposes an image shadow removal method based on generative adversarial networks. Our approach is trained in unsupervised fashion which means it does not depend on time-consuming data collection and data labeling. This together with training in a single end-to-end framework significantly raises its practical relevance. Taking the existing method for unsupervised image transfer between different domains, we have researched its applicability to the shadow removal problem. Two networks have been used. Тhe first network is used to add shadows in images and the second network for shadow removal. ISTD dataset has been used for evaluation clarity because it has ground truth shadow free images as well as shadow masks. For shadow removal we have used root mean squared error between generated and real shadow free images in LAB color space. Evaluation is divided into region and global where the former is applied to shadow regions while the latter to the whole images. Shadow detection is evaluated with the use of Intersection over Union, also known as the Jaccard index. It is computed between the generated and ground-truth binary shadow masks by dividing the area of overlap by the union of those two. We selected random 100 images for validation purposes. During the experiments multiple hypotheses have been tested. The majority of tests we conducted were about how to use an attention module and where to localize it. Our network produces better results compared to the existing approach in the field. Analysis showed that attention maps obtained from auxiliary classifier encourage the networks to concentrate on more distinctive regions between domains. However, generative adversarial networks demand more accurate and consistent architecture to solve the problem in a more efficient way.
{"title":"Image Shadow Removal Based on Generative Adversarial Networks","authors":"Vladyslav Andronik, Olena Buchko","doi":"10.18523/2617-3808.2020.3.75-82","DOIUrl":"https://doi.org/10.18523/2617-3808.2020.3.75-82","url":null,"abstract":"Accurate detection of shadows and removal in the image are complicated tasks, as it is difficult to understand whether darkening or gray is the cause of the shadow. This paper proposes an image shadow removal method based on generative adversarial networks. Our approach is trained in unsupervised fashion which means it does not depend on time-consuming data collection and data labeling. This together with training in a single end-to-end framework significantly raises its practical relevance. Taking the existing method for unsupervised image transfer between different domains, we have researched its applicability to the shadow removal problem. Two networks have been used. Тhe first network is used to add shadows in images and the second network for shadow removal. ISTD dataset has been used for evaluation clarity because it has ground truth shadow free images as well as shadow masks. For shadow removal we have used root mean squared error between generated and real shadow free images in LAB color space. Evaluation is divided into region and global where the former is applied to shadow regions while the latter to the whole images. Shadow detection is evaluated with the use of Intersection over Union, also known as the Jaccard index. It is computed between the generated and ground-truth binary shadow masks by dividing the area of overlap by the union of those two. We selected random 100 images for validation purposes. During the experiments multiple hypotheses have been tested. The majority of tests we conducted were about how to use an attention module and where to localize it. Our network produces better results compared to the existing approach in the field. Analysis showed that attention maps obtained from auxiliary classifier encourage the networks to concentrate on more distinctive regions between domains. However, generative adversarial networks demand more accurate and consistent architecture to solve the problem in a more efficient way.","PeriodicalId":433538,"journal":{"name":"NaUKMA Research Papers. Computer Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129767363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-16DOI: 10.18523/2617-3808.2018.14-20
P. Stetsyuk, V. Lyashko, Gabriela Mazyutynets
{"title":"Two-Stage Transportation Problem and Its AMPL-Realization","authors":"P. Stetsyuk, V. Lyashko, Gabriela Mazyutynets","doi":"10.18523/2617-3808.2018.14-20","DOIUrl":"https://doi.org/10.18523/2617-3808.2018.14-20","url":null,"abstract":"","PeriodicalId":433538,"journal":{"name":"NaUKMA Research Papers. Computer Science","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114157354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-16DOI: 10.18523/2617-3808.2018.58-64
A. Ivanov, Yevhenii Babichenko, Hlib Kanunnikov, Paul Karpus, Leonid Foiu-Khatskevych, Roman Kravchenko, Kyrylo Gorokhovskyi, Ievhen Nevmerzhitskyi
Blockchain as a technology is rapidly developing, finding more and more new entry points into everyday life. This is one of the elements of the technical Revolution 4.0, and it is used in the field of supply, maintenance of various types of registers, access to software products, combating DDOS attacks, distributed storage, fundraising for projects, IoT, etc. Nowadays, there are many blockchainplatforms in the world. They have one technological root but different applications. There are many prerequisites to the fact that in the future the number of new decentralized applications will increase. Therefore, it is important to develop a methodology for determining the optimal blockchainbased platform to solve a specific problem. As an example, consider the worldfamous platforms Ethereum, Nem, and Stellar. Each of them allows to develop decentralized applications, issue tokens, and execute transactions. At the same time, the key features of these blockchainbased platforms are not similar to one another. These very features will be considered in the article. Purpose. Identify the key parameters that characterize the blockchainbased platforms. This will provide an opportunity to present a complex blockchain technology in the form of a simple and understandable architecture. Based on these parameters and using the expertise of the article’s authors, we will be able to develop a methodology to be used to solve the problems of choosing the optimal blockchainbased platform for solving the problem of developing smart contracts and issuing tokens. Methods. Analysis of the complexity of using blockchainbased platforms. Implementation of token issuance, use of test and public networks, execution of transactions, analysis of the development team and the community, analysis of the user interface and the developer interface. Discussion. By developing a platform comparison methodology to determine optimal characteristics, we can take the development process to a new level. This will allow to quickly and effectively solve the tasks. Results. Creation of a methodology for comparison blockchainbased platforms.
{"title":"Technical Comparison Aspects of Leading Blockchain-Based Platforms on Key Characteristics","authors":"A. Ivanov, Yevhenii Babichenko, Hlib Kanunnikov, Paul Karpus, Leonid Foiu-Khatskevych, Roman Kravchenko, Kyrylo Gorokhovskyi, Ievhen Nevmerzhitskyi","doi":"10.18523/2617-3808.2018.58-64","DOIUrl":"https://doi.org/10.18523/2617-3808.2018.58-64","url":null,"abstract":"Blockchain as a technology is rapidly developing, finding more and more new entry points into everyday life. This is one of the elements of the technical Revolution 4.0, and it is used in the field of supply, maintenance of various types of registers, access to software products, combating DDOS attacks, distributed storage, fundraising for projects, IoT, etc. Nowadays, there are many blockchainplatforms in the world. They have one technological root but different applications. There are many prerequisites to the fact that in the future the number of new decentralized applications will increase. Therefore, it is important to develop a methodology for determining the optimal blockchainbased platform to solve a specific problem. As an example, consider the worldfamous platforms Ethereum, Nem, and Stellar. Each of them allows to develop decentralized applications, issue tokens, and execute transactions. At the same time, the key features of these blockchainbased platforms are not similar to one another. These very features will be considered in the article. Purpose. Identify the key parameters that characterize the blockchainbased platforms. This will provide an opportunity to present a complex blockchain technology in the form of a simple and understandable architecture. Based on these parameters and using the expertise of the article’s authors, we will be able to develop a methodology to be used to solve the problems of choosing the optimal blockchainbased platform for solving the problem of developing smart contracts and issuing tokens. Methods. Analysis of the complexity of using blockchainbased platforms. Implementation of token issuance, use of test and public networks, execution of transactions, analysis of the development team and the community, analysis of the user interface and the developer interface. Discussion. By developing a platform comparison methodology to determine optimal characteristics, we can take the development process to a new level. This will allow to quickly and effectively solve the tasks. Results. Creation of a methodology for comparison blockchainbased platforms.","PeriodicalId":433538,"journal":{"name":"NaUKMA Research Papers. Computer Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129819771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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