Pub Date : 2023-12-07DOI: 10.1134/s0361768823070058
A. I. Perminov, D. Yu. Turdakov, O. V. Belyaeva
Abstract
This work is devoted to improving the quality of segmentation of images of various scientific papers and legal acts by neural network models by training them using modified loss functions that take into account special features of images of the appropriate subject domain. The analysis of existing loss functions is carried out, and new functions are proposed that work both with the coordinates of bounding boxes and use information about the pixels of the input image. To assess the quality, a neural network segmentation model with modified loss functions is trained, and a theoretical assessment is carried out using a simulation experiment showing the convergence rate and segmentation error. As a result of the study, rapidly converging loss functions are created that improve the quality of document image segmentation using additional information about the input data.
{"title":"Loss Function for Training Models of Segmentation of Document Images","authors":"A. I. Perminov, D. Yu. Turdakov, O. V. Belyaeva","doi":"10.1134/s0361768823070058","DOIUrl":"https://doi.org/10.1134/s0361768823070058","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This work is devoted to improving the quality of segmentation of images of various scientific papers and legal acts by neural network models by training them using modified loss functions that take into account special features of images of the appropriate subject domain. The analysis of existing loss functions is carried out, and new functions are proposed that work both with the coordinates of bounding boxes and use information about the pixels of the input image. To assess the quality, a neural network segmentation model with modified loss functions is trained, and a theoretical assessment is carried out using a simulation experiment showing the convergence rate and segmentation error. As a result of the study, rapidly converging loss functions are created that improve the quality of document image segmentation using additional information about the input data.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"33 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138553123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1134/s0361768823060026
S. S. Ablaev, F. S. Stonyakin, M. S. Alkousa, D. A. Pasechnyk
Abstract
This paper is devoted to some adaptive methods for variational inequalities with relatively smooth and relatively strongly monotone operators. Based on the recently proposed proximal version of the extragradient method for this class of problems, we study in detail the method with adaptively selected parameter values. The rate of convergence of this method is estimated. The result is generalized to the class of variational inequalities with relatively strongly monotone δ-generalized smooth operators. For the ridge regression problem and variational inequality associated with box-simplex games, numerical experiments are carried out to demonstrate the effectiveness of the proposed technique for adaptive parameter selection during the execution of the algorithm.
{"title":"Adaptive Methods for Variational Inequalities with Relatively Smooth and Reletively Strongly Monotone Operators","authors":"S. S. Ablaev, F. S. Stonyakin, M. S. Alkousa, D. A. Pasechnyk","doi":"10.1134/s0361768823060026","DOIUrl":"https://doi.org/10.1134/s0361768823060026","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This paper is devoted to some adaptive methods for variational inequalities with relatively smooth and relatively strongly monotone operators. Based on the recently proposed proximal version of the extragradient method for this class of problems, we study in detail the method with adaptively selected parameter values. The rate of convergence of this method is estimated. The result is generalized to the class of variational inequalities with relatively strongly monotone δ-generalized smooth operators. For the ridge regression problem and variational inequality associated with box-simplex games, numerical experiments are carried out to demonstrate the effectiveness of the proposed technique for adaptive parameter selection during the execution of the algorithm.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"20 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1134/s0361768823080029
Abstract
This paper shows quantitative research regarding knowledge, soft and hard skills, and experience acquired by students hired by a University Software Development Company (USDC). Additionally, suggestions regarding how to set up a USDC in an academic environment, facing real customers, are shown. There have been good and bad experiences, both will be presented in this paper. Furthermore, students' perceptions will be discussed. To identify students’ perceptions a questionnaire (survey) was applied. Its reliability was calculated through Cronbach’s alpha coefficient (α = 89). Additionally, the Pearson correlation coefficient was calculated (r) in order to identify questions that should be deleted to increase the questionnaire’s reliability. Outcomes could be useful when a software engineering faculty wishes to set up a USDC.
{"title":"Soft and Hard Skills Gained by Students through Real Projects Developed at a University Software Company","authors":"","doi":"10.1134/s0361768823080029","DOIUrl":"https://doi.org/10.1134/s0361768823080029","url":null,"abstract":"<span> <h3>Abstract</h3> <p>This paper shows quantitative research regarding knowledge, soft and hard skills, and experience acquired by students hired by a University Software Development Company (USDC). Additionally, suggestions regarding how to set up a USDC in an academic environment, facing real customers, are shown. There have been good and bad experiences, both will be presented in this paper. Furthermore, students' perceptions will be discussed. To identify students’ perceptions a questionnaire (survey) was applied. Its reliability was calculated through Cronbach’s alpha coefficient (α = 89). Additionally, the Pearson correlation coefficient was calculated (<em>r</em>) in order to identify questions that should be deleted to increase the questionnaire’s reliability. Outcomes could be useful when a software engineering faculty wishes to set up a USDC.</p> </span>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"2 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1134/s0361768823080121
Abstract
The article investigates the application of a neural network diagnosis model to histological images in order to detect plasma cells for chronic endometritis detection. A two-stage algorithm was developed for plasma cell detection. At the first stage, a CenterNet model was used to detect stromal and epithelial cells. The neural network was trained on an open dataset with histological images and further fine-tuned using an additional labeled dataset. A labeling protocol was used, and the coefficient of agreement between two experts was calculated, which turned out to be 0.81. At the second stage, using the developed algorithm based on computer vision methods, plasma cells were identified and their HSV color boundaries were calculated. For the two-stage algorithm the following quality metrics were obtained: precision = 0.70, recall = 0.43, f1-score = 0.53. The model then was modified to detect only plasma cells and trained on a dataset with histological images containing labeled plasma cells. The quality metrics of the modified detection model were obtained: precision = 0.73, recall = 0.89, f1-score = 0.8. As a result of the comparison, the modified detection model approach showed the best quality metrics. Automating the work of counting plasma cells will allow doctors to spend less time on routine activities.
{"title":"Artificial Intelligence (AI) Solution for Plasma Cells Detection","authors":"","doi":"10.1134/s0361768823080121","DOIUrl":"https://doi.org/10.1134/s0361768823080121","url":null,"abstract":"<span> <h3>Abstract</h3> <p>The article investigates the application of a neural network diagnosis model to histological images in order to detect plasma cells for chronic endometritis detection. A two-stage algorithm was developed for plasma cell detection. At the first stage, a CenterNet model was used to detect stromal and epithelial cells. The neural network was trained on an open dataset with histological images and further fine-tuned using an additional labeled dataset. A labeling protocol was used, and the coefficient of agreement between two experts was calculated, which turned out to be 0.81. At the second stage, using the developed algorithm based on computer vision methods, plasma cells were identified and their HSV color boundaries were calculated. For the two-stage algorithm the following quality metrics were obtained: precision = 0.70, recall = 0.43, f1-score = 0.53. The model then was modified to detect only plasma cells and trained on a dataset with histological images containing labeled plasma cells. The quality metrics of the modified detection model were obtained: precision = 0.73, recall = 0.89, f1-score = 0.8. As a result of the comparison, the modified detection model approach showed the best quality metrics. Automating the work of counting plasma cells will allow doctors to spend less time on routine activities.</p> </span>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"123 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1134/s0361768823070034
D. V. Efremov, V. V. Kopach, E. V. Kornykhin, V. V. Kuliamin, A. K. Petrenko, A. V. Khoroshilov, I. V. Shchepetkov
{"title":"Runtime Verification of Operating Systems Based on Abstract Models","authors":"D. V. Efremov, V. V. Kopach, E. V. Kornykhin, V. V. Kuliamin, A. K. Petrenko, A. V. Khoroshilov, I. V. Shchepetkov","doi":"10.1134/s0361768823070034","DOIUrl":"https://doi.org/10.1134/s0361768823070034","url":null,"abstract":"","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"68 1","pages":"559 - 565"},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138553058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1134/s0361768823060075
R. A. Vedernikov, A. V. Rogozin, A. V. Gasnikov
Abstract
In this paper, we consider a generalization of the decentralized Frank–Wolfe algorithm to time-varying networks, investigate the convergence properties of the algorithm, and carry out the corresponding numerical experiments. The time-varying network is modeled as a deterministic or stochastic sequence of graphs.
{"title":"Decentralized Conditional Gradient Method on Time-Varying Graphs","authors":"R. A. Vedernikov, A. V. Rogozin, A. V. Gasnikov","doi":"10.1134/s0361768823060075","DOIUrl":"https://doi.org/10.1134/s0361768823060075","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this paper, we consider a generalization of the decentralized Frank–Wolfe algorithm to time-varying networks, investigate the convergence properties of the algorithm, and carry out the corresponding numerical experiments. The time-varying network is modeled as a deterministic or stochastic sequence of graphs.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"81 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1134/s0361768823080145
Abstract
Modern Software Engineering thrives with innovative tools that aid developers in creating better software grounded on quality standards. Software bots are an emerging and exciting trend in this regard, supporting numerous software development activities. As an emerging trend, few studies describe and analyze different bots in software development. This research presents a systematic literature review covering the state of the art of applied and proposed bots for software development. Our study spans literature from 2003 to 2022, with 82 different bots applied in software development activities, covering 83 primary studies. We found four bot archetypes: chatbots which focus on direct communication with developers to aid them, analysis bots that display helpful information in different tasks, repair bots for resolving software defects, and development bots that combine aspects of other bot technologies to provide a service to the developer. The primary benefits of using bots are increasing software quality, providing useful information to developers, and saving time through the partial or total automation of development activities. However, drawbacks are reported, including limited effectiveness in task completion, high coupling to third-party technologies, and some prejudice from developers toward bots and their contributions. We discovered that including Bots in software development is a promising field of research in software engineering that has yet to be fully explored.
{"title":"Bots in Software Development: A Systematic Literature Review and Thematic Analysis","authors":"","doi":"10.1134/s0361768823080145","DOIUrl":"https://doi.org/10.1134/s0361768823080145","url":null,"abstract":"<span> <h3>Abstract</h3> <p>Modern Software Engineering thrives with innovative tools that aid developers in creating better software grounded on quality standards. Software bots are an emerging and exciting trend in this regard, supporting numerous software development activities. As an emerging trend, few studies describe and analyze different bots in software development. This research presents a systematic literature review covering the state of the art of applied and proposed bots for software development. Our study spans literature from 2003 to 2022, with 82 different bots applied in software development activities, covering 83 primary studies. We found four bot archetypes: chatbots which focus on direct communication with developers to aid them, analysis bots that display helpful information in different tasks, repair bots for resolving software defects, and development bots that combine aspects of other bot technologies to provide a service to the developer. The primary benefits of using bots are increasing software quality, providing useful information to developers, and saving time through the partial or total automation of development activities. However, drawbacks are reported, including limited effectiveness in task completion, high coupling to third-party technologies, and some prejudice from developers toward bots and their contributions. We discovered that including Bots in software development is a promising field of research in software engineering that has yet to be fully explored.</p> </span>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"58 22 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1134/s0361768823060063
S. I. Sadykov, A. V. Lobanov, A. M. Raigorodskii
Abstract
This paper focuses on solving a subclass of stochastic nonconvex-nonconcave black box optimization problems with a saddle point that satisfy the Polyak–Łojasiewicz (PL) condition. To solve this problem, we provide the first (to our best knowledge) gradient-free algorithm. The proposed approach is based on applying a gradient approximation (kernel approximation) to an oracle-biased stochastic gradient descent algorithm. We present theoretical estimates that guarantee its global linear rate of convergence to the desired accuracy. The theoretical results are checked on a model example by comparison with an algorithm using Gaussian approximation.
{"title":"Gradient-Free Algorithms for Solving Stochastic Saddle Optimization Problems with the Polyak–Łojasiewicz Condition","authors":"S. I. Sadykov, A. V. Lobanov, A. M. Raigorodskii","doi":"10.1134/s0361768823060063","DOIUrl":"https://doi.org/10.1134/s0361768823060063","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This paper focuses on solving a subclass of stochastic nonconvex-nonconcave black box optimization problems with a saddle point that satisfy the Polyak–Łojasiewicz (PL) condition. To solve this problem, we provide the first (to our best knowledge) gradient-free algorithm. The proposed approach is based on applying a gradient approximation (kernel approximation) to an oracle-biased stochastic gradient descent algorithm. We present theoretical estimates that guarantee its global linear rate of convergence to the desired accuracy. The theoretical results are checked on a model example by comparison with an algorithm using Gaussian approximation.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"34 11","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1134/s0361768823060038
G. V. Aivazian, F. S. Stonyakin, D. A. Pasechnyk, M. S. Alkousa, A. M. Raigorodsky, I. V. Baran
Abstract
In this paper, we investigate a variant of the Frank–Wolfe method for convex optimization problems with the adaptive selection of the step parameter corresponding to information about the smoothness of the objective function (the Lipschitz constant of the gradient). Theoretical estimates of the quality of the approximate solution provided by the method using adaptively selected parameters Lk are presented. For a class of problems on a convex feasible set with a convex objective function, the guaranteed convergence rate of the proposed method is sublinear. A special subclass of these problems (an objective function with the gradient dominance condition) is considered and the convergence rate of the method using adaptively selected parameters Lk is estimated. An important feature of the result obtained is the elaboration of the case where it is possible to guarantee, after the completion of the iteration, at least double reduction in the residual of the function. At the same time, the use of adaptively selected parameters in theoretical estimates makes the method applicable to both smooth and non-smooth problems, provided that the iteration termination criterion is met. For smooth problems, it can be proved that the theoretical estimates of the method are reliably optimal up to multiplication by a constant factor. Computational experiments are carried out and a comparison with two other algorithms is made to demonstrate the efficiency of the algorithm on a number of both smooth and non-smooth problems.
{"title":"Adaptive Variant of the Frank–Wolfe Algorithm for Convex Optimization Problems","authors":"G. V. Aivazian, F. S. Stonyakin, D. A. Pasechnyk, M. S. Alkousa, A. M. Raigorodsky, I. V. Baran","doi":"10.1134/s0361768823060038","DOIUrl":"https://doi.org/10.1134/s0361768823060038","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this paper, we investigate a variant of the Frank–Wolfe method for convex optimization problems with the adaptive selection of the step parameter corresponding to information about the smoothness of the objective function (the Lipschitz constant of the gradient). Theoretical estimates of the quality of the approximate solution provided by the method using adaptively selected parameters <i>L</i><sub><i>k</i></sub> are presented. For a class of problems on a convex feasible set with a convex objective function, the guaranteed convergence rate of the proposed method is sublinear. A special subclass of these problems (an objective function with the gradient dominance condition) is considered and the convergence rate of the method using adaptively selected parameters <i>L</i><sub><i>k</i></sub> is estimated. An important feature of the result obtained is the elaboration of the case where it is possible to guarantee, after the completion of the iteration, at least double reduction in the residual of the function. At the same time, the use of adaptively selected parameters in theoretical estimates makes the method applicable to both smooth and non-smooth problems, provided that the iteration termination criterion is met. For smooth problems, it can be proved that the theoretical estimates of the method are reliably optimal up to multiplication by a constant factor. Computational experiments are carried out and a comparison with two other algorithms is made to demonstrate the efficiency of the algorithm on a number of both smooth and non-smooth problems.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"24 3","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1134/s036176882306004x
N. A. Il’tyakov, M. A. Obozov, I. M. Dyshlevski, D. V. Yarmoshik, M. B. Kubentaeva, A. V. Gasnikov, E. V. Gasnikova
Abstract
The search for equilibrium in a two-stage traffic flow model reduces to the solution of a special nonsmooth convex optimization problem with two groups of different variables. For numerical solution of this problem, it is proposed to use the accelerated block-coordinate Nesterov–Stich method with a special choice of block probabilities at each iteration. Theoretical estimates of the complexity of this approach can appreciably improve the estimates of previously used approaches. However, in the general case they do not guarantee faster convergence. Numerical experiments with the proposed algorithms are carried out.
{"title":"On Accelerated Coordinate Descent Methods for Searching Equilibria in Two-Stage Transportation Equilibrium Traffic Flow Distribution Model","authors":"N. A. Il’tyakov, M. A. Obozov, I. M. Dyshlevski, D. V. Yarmoshik, M. B. Kubentaeva, A. V. Gasnikov, E. V. Gasnikova","doi":"10.1134/s036176882306004x","DOIUrl":"https://doi.org/10.1134/s036176882306004x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The search for equilibrium in a two-stage traffic flow model reduces to the solution of a special nonsmooth convex optimization problem with two groups of different variables. For numerical solution of this problem, it is proposed to use the accelerated block-coordinate Nesterov–Stich method with a special choice of block probabilities at each iteration. Theoretical estimates of the complexity of this approach can appreciably improve the estimates of previously used approaches. However, in the general case they do not guarantee faster convergence. Numerical experiments with the proposed algorithms are carried out.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"62 2","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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