Pub Date : 2024-01-24DOI: 10.1134/s036176882308008x
J. G. Hernández-Calderón, E. Benítez-Guerrero, J. R. Rojano-Cáceres, Carmen Mezura-Godoy
Abstract
This work seeks to contribute to the development of intelligent environments by presenting an approach oriented to the identification of On-Task and Off-Task behaviors in educational settings. This is accomplished by monitoring and analyzing the user-object interactions that users manifest while performing academic activities with a tangible-intangible hybrid system in a university intelligent environment configuration. With the proposal of a framework and the Orange Data Mining tool and the Neural Network, Random Forest, Naive Bayes, and Tree classification models, training and testing was carried out with the user-object interaction records of the 13 students (11 for training and two for testing) to identify representative sequences of behavior from user-object interaction records. The two models that had the best results, despite the small number of data, were the Neural Network and Naive Bayes. Although a more significant amount of data is necessary to perform a classification adequately, the process allowed exemplifying this process so that it can later be fully incorporated into an intelligent educational system.
{"title":"Mining User-Object Interaction Data for Student Modeling in Intelligent Learning Environments","authors":"J. G. Hernández-Calderón, E. Benítez-Guerrero, J. R. Rojano-Cáceres, Carmen Mezura-Godoy","doi":"10.1134/s036176882308008x","DOIUrl":"https://doi.org/10.1134/s036176882308008x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This work seeks to contribute to the development of intelligent environments by presenting an approach oriented to the identification of On-Task and Off-Task behaviors in educational settings. This is accomplished by monitoring and analyzing the user-object interactions that users manifest while performing academic activities with a tangible-intangible hybrid system in a university intelligent environment configuration. With the proposal of a framework and the Orange Data Mining tool and the Neural Network, Random Forest, Naive Bayes, and Tree classification models, training and testing was carried out with the user-object interaction records of the 13 students (11 for training and two for testing) to identify representative sequences of behavior from user-object interaction records. The two models that had the best results, despite the small number of data, were the Neural Network and Naive Bayes. Although a more significant amount of data is necessary to perform a classification adequately, the process allowed exemplifying this process so that it can later be fully incorporated into an intelligent educational system.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"121 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559548","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 : 2024-01-24DOI: 10.1134/s0361768823080091
Zazil Ibarra-Cuevas, Jose Nunez-Varela, Alberto Nunez-Varela, Francisco E. Martinez-Perez, Sandra E. Nava-Muñoz, Cesar A. Ramirez-Gamez, Hector G. Perez-Gonzalez
Abstract
Breast cancer is a serious threat to women’s health worldwide. Although the exact causes of this disease are still unknown, it is known that the incidence of breast cancer is associated with risk factors. Risk factors in cancer are any genetic, reproductive, hormonal, physical, biological, or lifestyle-related conditions that increase the likelihood of developing breast cancer. This research aims to identify the most relevant risk factors in patients with breast cancer in a dataset by following the Knowledge Discovery in Databases process. To determine the relevance of risk factors, this research implements two feature selection methods: the Chi-Squared test and Mutual Information; and seven classifiers are used to validate the results obtained. Our results show that the risk factors identified as the most relevant are related to the age of the patient, her menopausal status, whether she had undergone hormonal therapy, and her type of menopause.
{"title":"Determination of Relevant Risk Factors for Breast Cancer Using Feature Selection","authors":"Zazil Ibarra-Cuevas, Jose Nunez-Varela, Alberto Nunez-Varela, Francisco E. Martinez-Perez, Sandra E. Nava-Muñoz, Cesar A. Ramirez-Gamez, Hector G. Perez-Gonzalez","doi":"10.1134/s0361768823080091","DOIUrl":"https://doi.org/10.1134/s0361768823080091","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Breast cancer is a serious threat to women’s health worldwide. Although the exact causes of this disease are still unknown, it is known that the incidence of breast cancer is associated with risk factors. Risk factors in cancer are any genetic, reproductive, hormonal, physical, biological, or lifestyle-related conditions that increase the likelihood of developing breast cancer. This research aims to identify the most relevant risk factors in patients with breast cancer in a dataset by following the <i>Knowledge Discovery in Databases</i> process. To determine the relevance of risk factors, this research implements two feature selection methods: the <i>Chi-Squared test</i> and <i>Mutual Information</i>; and seven classifiers are used to validate the results obtained. Our results show that the risk factors identified as the most relevant are related to the age of the patient, her menopausal status, whether she had undergone hormonal therapy, and her type of menopause.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"18 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559570","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 : 2024-01-24DOI: 10.1134/s0361768823080261
L. V. Zhukova, I. M. Kovalchuk, A. A. Kochnev, V. R. Chugunov
Abstract
The widespread digitalization of the modern society and the development of information technology have increased the number of ways in which financial institutions and potential consumers of financial services can interact. At the same time, the advent of new financial products inevitably leads to a rise in threats, and the use of information technology facilitates the constant “improvement” of fraud schemes and unfair business practices, which negatively affect both the financial market as a whole and its individual participants such as financial institutions and their clients. With the development of the modern society, most financial transactions, including the fraudulent ones, have moved to the Internet. When services are provided remotely, it is more difficult to trace and prosecute the beneficiary. However, there are still ways to stop fraudulent activity, but they are associated with high costs of monitoring and analysis of huge amounts of unstructured information (BigData) available on the Internet, which takes a great amount of time and effort. A solution to illegal activity detection in financial markets is based on open data intelligence, machine learning, and systems analysis. This paper examines certain types of financial services provided on the Internet among which fraudulent activities are most common. In order to identify illegal financial services, some criteria are developed and grouped based on their contribution to the decision-making process. The main result of this study is the construction of a scale for a complex indicator, which is used to build a mathematical model based on the developed criteria and machine learning methods for determining the degree of illegality of online financial services.
{"title":"Building a Scale for Internet Fraud Detection Using Machine Learning","authors":"L. V. Zhukova, I. M. Kovalchuk, A. A. Kochnev, V. R. Chugunov","doi":"10.1134/s0361768823080261","DOIUrl":"https://doi.org/10.1134/s0361768823080261","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The widespread digitalization of the modern society and the development of information technology have increased the number of ways in which financial institutions and potential consumers of financial services can interact. At the same time, the advent of new financial products inevitably leads to a rise in threats, and the use of information technology facilitates the constant “improvement” of fraud schemes and unfair business practices, which negatively affect both the financial market as a whole and its individual participants such as financial institutions and their clients. With the development of the modern society, most financial transactions, including the fraudulent ones, have moved to the Internet. When services are provided remotely, it is more difficult to trace and prosecute the beneficiary. However, there are still ways to stop fraudulent activity, but they are associated with high costs of monitoring and analysis of huge amounts of unstructured information (BigData) available on the Internet, which takes a great amount of time and effort. A solution to illegal activity detection in financial markets is based on open data intelligence, machine learning, and systems analysis. This paper examines certain types of financial services provided on the Internet among which fraudulent activities are most common. In order to identify illegal financial services, some criteria are developed and grouped based on their contribution to the decision-making process. The main result of this study is the construction of a scale for a complex indicator, which is used to build a mathematical model based on the developed criteria and machine learning methods for determining the degree of illegality of online financial services.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"61 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881595","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 : 2024-01-24DOI: 10.1134/s0361768823080066
M. L. Córdoba-Tlaxcalteco, E. Benítez-Guerrero
Abstract
The field of human event recognition using visual data in smart environments has emerged as a fruitful and successful area of study, with extensive research and development efforts driving significant advancements. These advancements have not only provided valuable insights, but also led to practical applications in various domains. In this context, human actions, activities, interactions, and behaviors can all be considered as events of interest in smart environments. However, when it comes to smart classrooms, there is a lack of unified consensus on the definition of the term “human event.” This lack of agreement presents a significant challenge for educators, researchers, and developers, as it hampers their ability to precisely identify and classify the specific situations that are relevant within the educational context. The aim of this paper is to address this challenge by conducting a systematic literature review of relevant events in smart classrooms, with a focus on their applications in assistive technology. The review encompasses a comprehensive analysis of 227 published documents spanning from 2012 to 2022. It delves into key algorithms, methodologies, and applications of vision-based event recognition in smart environments. As the primary outcome, the review identifies the most significant events, classifying them according to single person behavior, or multiple-person interactions, or object-person interactions. It also examines their practical applications within the educational context. The paper concludes with a discussion on the relevance and practicality of vision-based human event recognition in smart classrooms, particularly in the post-COVID era.
{"title":"Human Event Recognition in Smart Classrooms Using Computer Vision: A Systematic Literature Review","authors":"M. L. Córdoba-Tlaxcalteco, E. Benítez-Guerrero","doi":"10.1134/s0361768823080066","DOIUrl":"https://doi.org/10.1134/s0361768823080066","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The field of human event recognition using visual data in smart environments has emerged as a fruitful and successful area of study, with extensive research and development efforts driving significant advancements. These advancements have not only provided valuable insights, but also led to practical applications in various domains. In this context, human actions, activities, interactions, and behaviors can all be considered as events of interest in smart environments. However, when it comes to smart classrooms, there is a lack of unified consensus on the definition of the term “human event.” This lack of agreement presents a significant challenge for educators, researchers, and developers, as it hampers their ability to precisely identify and classify the specific situations that are relevant within the educational context. The aim of this paper is to address this challenge by conducting a systematic literature review of relevant events in smart classrooms, with a focus on their applications in assistive technology. The review encompasses a comprehensive analysis of 227 published documents spanning from 2012 to 2022. It delves into key algorithms, methodologies, and applications of vision-based event recognition in smart environments. As the primary outcome, the review identifies the most significant events, classifying them according to single person behavior, or multiple-person interactions, or object-person interactions. It also examines their practical applications within the educational context. The paper concludes with a discussion on the relevance and practicality of vision-based human event recognition in smart classrooms, particularly in the post-COVID era.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"7 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559608","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 : 2024-01-24DOI: 10.1134/s0361768823080108
R. Juárez-Ramírez, C. X. Navarro, Samantha Jiménez, Alan Ramírez, Verónica Tapia-Ibarra, César Guerra-García, Hector G. Perez-Gonzalez, Carlos Fernández-y-Fernández
Abstract
Quantum computing is based on the principles of quantum mechanics, such as superposition, entanglement, measurement, and decoherence. The basic units of computation are qubits, which are abstract objects with a mathematical expression to implement the quantum mechanics principles. Alongside quantum hardware, software is a principal element for conducting quantum computing. The software consists of logic gates and quantum circuits that implement algorithms for the execution of quantum programs. Due to those characteristics, quantum computing is a paradigm that non-physics experts cannot understand. Under this new scheme for developing software, it is important to integrate a conceptual framework of the fundamentals on which quantum computing is based. In this paper, we present a kind of taxonomical view of the fundamental concepts of quantum computing and the derived concepts that integrate the emerging discipline of quantum software engineering. We performed a quasi-systematic mapping for conducting the systematic review because the objective of the review only intends to detect the fundamental concepts of quantum computing and quantum software. The results can help computer science students and professors as a starting point to address the study of this discipline.
{"title":"A Taxonomic View of the Fundamental Concepts of Quantum Computing–A Software Engineering Perspective","authors":"R. Juárez-Ramírez, C. X. Navarro, Samantha Jiménez, Alan Ramírez, Verónica Tapia-Ibarra, César Guerra-García, Hector G. Perez-Gonzalez, Carlos Fernández-y-Fernández","doi":"10.1134/s0361768823080108","DOIUrl":"https://doi.org/10.1134/s0361768823080108","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Quantum computing is based on the principles of quantum mechanics, such as superposition, entanglement, measurement, and decoherence. The basic units of computation are qubits, which are abstract objects with a mathematical expression to implement the quantum mechanics principles. Alongside quantum hardware, software is a principal element for conducting quantum computing. The software consists of logic gates and quantum circuits that implement algorithms for the execution of quantum programs. Due to those characteristics, quantum computing is a paradigm that non-physics experts cannot understand. Under this new scheme for developing software, it is important to integrate a conceptual framework of the fundamentals on which quantum computing is based. In this paper, we present a kind of taxonomical view of the fundamental concepts of quantum computing and the derived concepts that integrate the emerging discipline of quantum software engineering. We performed a quasi-systematic mapping for conducting the systematic review because the objective of the review only intends to detect the fundamental concepts of quantum computing and quantum software. The results can help computer science students and professors as a starting point to address the study of this discipline.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"10 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881598","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 : 2024-01-24DOI: 10.1134/s0361768823080236
F. Valdés-Souto, J. Valeriano-Assem, D. Torres-Robledo
Abstract
Any software development project needs to estimate non-functional requirements (NFR). Typically, software managers are forced to use expert judgment to estimate the NFR. Today, NFRs cannot be measured, as there is no standardized unit of measurement for them. Consequently, most estimation models focus on the functional user requirements (FUR) and do not consider the NFR in the estimation process because these terms are often subjective. The objective of this paper was to show how an NFR estimation model was created using fuzzy logic, and K-Nearest Neighbors classifier algorithm, aiming to consider the subjectivity embedded in NFR terms to solve a specific problem in a Mexican company. The proposed model was developed using a database with real projects from a Mexican company in the private sector. The results were beneficial and better than the initial model considering quality criteria like mean magnitude of relative error (MMRE), standard deviation of magnitude of relative error (SDMRE) and prediction level (Pred 25%). Additionally, the proposed approach allows the managers to identify quantitative elements related to NFR that could be used to interpret the data and build additional models.
{"title":"Improving a Model for NFR Estimation Using Band Classification and Selection with KNN","authors":"F. Valdés-Souto, J. Valeriano-Assem, D. Torres-Robledo","doi":"10.1134/s0361768823080236","DOIUrl":"https://doi.org/10.1134/s0361768823080236","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Any software development project needs to estimate non-functional requirements (NFR). Typically, software managers are forced to use expert judgment to estimate the NFR. Today, NFRs cannot be measured, as there is no standardized unit of measurement for them. Consequently, most estimation models focus on the functional user requirements (FUR) and do not consider the NFR in the estimation process because these terms are often subjective. The objective of this paper was to show how an NFR estimation model was created using fuzzy logic, and K-Nearest Neighbors classifier algorithm, aiming to consider the subjectivity embedded in NFR terms to solve a specific problem in a Mexican company. The proposed model was developed using a database with real projects from a Mexican company in the private sector. The results were beneficial and better than the initial model considering quality criteria like mean magnitude of relative error (MMRE), standard deviation of magnitude of relative error (SDMRE) and prediction level (Pred 25%). Additionally, the proposed approach allows the managers to identify quantitative elements related to NFR that could be used to interpret the data and build additional models.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"10 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881694","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 : 2024-01-24DOI: 10.1134/s0361768823080248
A. I. Varentsov, O. A. Imeev, A. V. Glazunov, E. V. Mortikov, V. M. Stepanenko
Abstract
This paper presents results of development of a numerical model of Lagrangian particle transport, as well as results of application of parallel computation methods to improve the efficiency of the software implementation of this model. The model is a software package that allows the transport and deposition of aerosol particles to be calculated taking into account properties of particles and the input data that describe atmospheric conditions and underlying surface geometry. The dynamic core, physical parameterizations, numerical implementation, and algorithm of the model are described. Results of successful verification of the model on analytical solutions are presented. Initially, the model was used for less computationally intensive problems. In this paper, given the need to use the model in more computationally intensive problems, we optimize the sequential software implementation of the model, as well as develop its software implementations that use parallel computing technologies (OpenMP, MPI, and CUDA). The results of testing different implementations of the model show that the optimization of the most computationally complex blocks in its sequential version can reduce the execution time by 27%. At the same time, the use of parallel computing technologies allows us to achieve acceleration by several orders of magnitude. The use of OpenMP in the dynamic block of the model provides almost 4-fold acceleration of this block; the use of MPI, almost 8-fold acceleration; and the use of CUDA, almost 16-fold acceleration (all other conditions being equal). We also give some recommendations on the choice of a parallel computing technology depending on the properties of a computing system.
{"title":"Numerical Simulation of Particulate Matter Transport in the Atmospheric Urban Boundary Layer Using the Lagrangian Approach: Physical Problems and Parallel Implementation","authors":"A. I. Varentsov, O. A. Imeev, A. V. Glazunov, E. V. Mortikov, V. M. Stepanenko","doi":"10.1134/s0361768823080248","DOIUrl":"https://doi.org/10.1134/s0361768823080248","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This paper presents results of development of a numerical model of Lagrangian particle transport, as well as results of application of parallel computation methods to improve the efficiency of the software implementation of this model. The model is a software package that allows the transport and deposition of aerosol particles to be calculated taking into account properties of particles and the input data that describe atmospheric conditions and underlying surface geometry. The dynamic core, physical parameterizations, numerical implementation, and algorithm of the model are described. Results of successful verification of the model on analytical solutions are presented. Initially, the model was used for less computationally intensive problems. In this paper, given the need to use the model in more computationally intensive problems, we optimize the sequential software implementation of the model, as well as develop its software implementations that use parallel computing technologies (OpenMP, MPI, and CUDA). The results of testing different implementations of the model show that the optimization of the most computationally complex blocks in its sequential version can reduce the execution time by 27%. At the same time, the use of parallel computing technologies allows us to achieve acceleration by several orders of magnitude. The use of OpenMP in the dynamic block of the model provides almost 4-fold acceleration of this block; the use of MPI, almost 8-fold acceleration; and the use of CUDA, almost 16-fold acceleration (all other conditions being equal). We also give some recommendations on the choice of a parallel computing technology depending on the properties of a computing system.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"18 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881697","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 : 2023-12-07DOI: 10.1134/s0361768823070046
D. M. Kiranov, M. A. Ryndin, I. S. Kozlov
Abstract
In this paper, we investigate the effectiveness of classical approaches to active learning in the problem of document segmentation with the aim of reducing the size of the training sample. A modified approach to selection of document images for labeling and subsequent model training is presented. The results of active learning are compared to those of transfer learning on fully labeled data. The paper also investigates how the problem domain of a training set, on which a model is initialized for transfer learning, affects the subsequent uptraining of the model.
{"title":"Active Learning and Transfer Learning for Document Segmentation","authors":"D. M. Kiranov, M. A. Ryndin, I. S. Kozlov","doi":"10.1134/s0361768823070046","DOIUrl":"https://doi.org/10.1134/s0361768823070046","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this paper, we investigate the effectiveness of classical approaches to active learning in the problem of document segmentation with the aim of reducing the size of the training sample. A modified approach to selection of document images for labeling and subsequent model training is presented. The results of active learning are compared to those of transfer learning on fully labeled data. The paper also investigates how the problem domain of a training set, on which a model is initialized for transfer learning, affects the subsequent uptraining of the model.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"67 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138553142","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 : 2023-12-07DOI: 10.1134/s0361768823070022
V. O. Afanasyev, S. A. Polyakov, A. E. Borodin, A. A. Belevantsev
Abstract
This paper describes a static analysis tool for finding defects, analyzing metrics and relations for programs written in the Kotlin language. The approach is implemented in the Svace static analyzer developed at the Ivannikov Institute for System Programming of the Russian Academy of Sciences. The paper focuses on the problems we faced during the implementation, the approaches we used to solve them, and the experimental results for the tool we built. The tool not only supports Kotlin but is also capable of analyzing mixed projects that use both Java and Kotlin. We hope that this paper will be useful to static analysis developers and language designers.
{"title":"Kotlin from the Point of View of Static Analysis Developer","authors":"V. O. Afanasyev, S. A. Polyakov, A. E. Borodin, A. A. Belevantsev","doi":"10.1134/s0361768823070022","DOIUrl":"https://doi.org/10.1134/s0361768823070022","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This paper describes a static analysis tool for finding defects, analyzing metrics and relations for programs written in the Kotlin language. The approach is implemented in the Svace static analyzer developed at the Ivannikov Institute for System Programming of the Russian Academy of Sciences. The paper focuses on the problems we faced during the implementation, the approaches we used to solve them, and the experimental results for the tool we built. The tool not only supports Kotlin but is also capable of analyzing mixed projects that use both Java and Kotlin. We hope that this paper will be useful to static analysis developers and language designers.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"26 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138553057","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 : 2023-12-07DOI: 10.1134/s036176882307006x
A. S. Sakhovskiy, E. V. Tutubalina
Abstract
Aggregating knowledge about drug, disease, and drug reaction entities across a broader range of domains and languages is critical for information extraction applications. In this work, we present a fine-grained evaluation intended to understand the efficiency of multilingual BERT-based models for biomedical named entity recognition (NER) and multi-label sentence classification. We investigate the role of transfer learning strategies between two English corpora and a novel annotated corpus of Russian reviews about drug therapy. In these corpora, labels for sentences indicate health-related issues or their absence. Sentences that belong to a certain class are additionally labeled at the entity level to identify fine-grained subtypes such as drug names, drug indications, and drug reactions. The evaluation results demonstrate that the BERT training on Russian and English raw reviews (5M in total) provides the best transfer capabilities for adverse drug reactions detection task on the Russian data. The macro F1 score of 74.85% in the NER task was achieved by our RuDR-BERT model. For the classification task, our EnRuDR-BERT model achieved the macro F1 score of 70%, gaining 8.64% over the score of a general-domain BERT model.
摘要 在更广泛的领域和语言中聚合有关药物、疾病和药物反应实体的知识对于信息提取应用至关重要。在这项工作中，我们提出了一项细粒度评估，旨在了解基于多语言 BERT 模型的生物医学命名实体识别（NER）和多标签句子分类的效率。我们研究了迁移学习策略在两个英语语料库和一个新的俄语药物治疗评论注释语料库之间的作用。在这些语料库中，句子的标签表示与健康相关的问题或不存在这些问题。属于某个类别的句子在实体层面上被额外标注，以识别细粒度的子类型，如药物名称、药物适应症和药物反应。评估结果表明，在俄语和英语原始评论（共 500 万条）上进行的 BERT 训练为俄语数据上的药物不良反应检测任务提供了最佳的转移能力。我们的 RuDR-BERT 模型在 NER 任务中取得了 74.85% 的宏观 F1 分数。在分类任务中，我们的 EnRuDR-BERT 模型取得了 70% 的宏观 F1 分数，比一般领域 BERT 模型的分数高出 8.64%。
{"title":"Cross-Lingual Transfer Learning in Drug-Related Information Extraction from User-Generated Texts","authors":"A. S. Sakhovskiy, E. V. Tutubalina","doi":"10.1134/s036176882307006x","DOIUrl":"https://doi.org/10.1134/s036176882307006x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Aggregating knowledge about drug, disease, and drug reaction entities across a broader range of domains and languages is critical for information extraction applications. In this work, we present a fine-grained evaluation intended to understand the efficiency of multilingual BERT-based models for biomedical named entity recognition (NER) and multi-label sentence classification. We investigate the role of transfer learning strategies between two English corpora and a novel annotated corpus of Russian reviews about drug therapy. In these corpora, labels for sentences indicate health-related issues or their absence. Sentences that belong to a certain class are additionally labeled at the entity level to identify fine-grained subtypes such as drug names, drug indications, and drug reactions. The evaluation results demonstrate that the BERT training on Russian and English raw reviews (5M in total) provides the best transfer capabilities for adverse drug reactions detection task on the Russian data. The macro F1 score of 74.85% in the NER task was achieved by our RuDR-BERT model. For the classification task, our EnRuDR-BERT model achieved the macro F1 score of 70%, gaining 8.64% over the score of a general-domain BERT model.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":"13 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138553053","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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