Pub Date : 2018-12-01DOI: 10.1186/s13173-018-0082-2
Anderson Bergamini de Neira, Igor Steinmacher, Igor Scaliante Wiese
Companies around the world use crowdsourcing platforms to complete simple tasks, collect product ideas, and launch advertising campaigns. Recently, crowdsourcing has also been used for software development to run tests, fix small defects, or perform small coding tasks. Among the pillars upholding the crowdsourcing business model are the platform participants, as they are responsible for accomplishing the requested tasks. Since successful crowdsourcing heavily relies on attracting and retaining participants, it is essential to understand how they behave. This exploratory study aims to understand a specific contributor profile: hyperspecialists. We analyzed developers’ participation on challenges in two ways. First, we analyzed the type of challenge that 664 Topcoder platform developers participated in during the first 18 months of their participation. Second, we focused on the profile of users who had more collaborations in the development challenges. After quantitative analysis, we observed that, in general, users who do not stop participating have behavioral traits that indicate hyper-specialization, since they participate in the majority of the same types of challenge. An interesting, though troubling, finding was the high dropout rate on the platform: 66% of participants discontinued their participation during the study period. The results also showed that hyperspecialization can be observed in terms of technologies required in the development challenges. We found that 60% of the 2,086 developers analyzed participated in at least 75% of challenges that required the same technology. We found hyperspecialists and non-specialists significantly differ in behavior and characteristics, including hyperspecialists’ lower winning rate when compared to non-specialists.
{"title":"Characterizing the hyperspecialists in the context of crowdsourcing software development","authors":"Anderson Bergamini de Neira, Igor Steinmacher, Igor Scaliante Wiese","doi":"10.1186/s13173-018-0082-2","DOIUrl":"https://doi.org/10.1186/s13173-018-0082-2","url":null,"abstract":"Companies around the world use crowdsourcing platforms to complete simple tasks, collect product ideas, and launch advertising campaigns. Recently, crowdsourcing has also been used for software development to run tests, fix small defects, or perform small coding tasks. Among the pillars upholding the crowdsourcing business model are the platform participants, as they are responsible for accomplishing the requested tasks. Since successful crowdsourcing heavily relies on attracting and retaining participants, it is essential to understand how they behave. This exploratory study aims to understand a specific contributor profile: hyperspecialists. We analyzed developers’ participation on challenges in two ways. First, we analyzed the type of challenge that 664 Topcoder platform developers participated in during the first 18 months of their participation. Second, we focused on the profile of users who had more collaborations in the development challenges. After quantitative analysis, we observed that, in general, users who do not stop participating have behavioral traits that indicate hyper-specialization, since they participate in the majority of the same types of challenge. An interesting, though troubling, finding was the high dropout rate on the platform: 66% of participants discontinued their participation during the study period. The results also showed that hyperspecialization can be observed in terms of technologies required in the development challenges. We found that 60% of the 2,086 developers analyzed participated in at least 75% of challenges that required the same technology. We found hyperspecialists and non-specialists significantly differ in behavior and characteristics, including hyperspecialists’ lower winning rate when compared to non-specialists.","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"36 4","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520078","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 : 2018-12-01DOI: 10.1186/s13173-018-0079-x
Luis Felipe Dias, Igor Steinmacher, Gustavo Pinto
{"title":"Who drives company-owned OSS projects: internal or external members?","authors":"Luis Felipe Dias, Igor Steinmacher, Gustavo Pinto","doi":"10.1186/s13173-018-0079-x","DOIUrl":"https://doi.org/10.1186/s13173-018-0079-x","url":null,"abstract":"","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13173-018-0079-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65831955","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 : 2018-11-28DOI: 10.1186/s13173-018-0080-4
Tiago de Melo, Altigran da Silva, Edleno S. de Moura
In this paper, we propose a method for enriching product catalogs, which traditionally include only objective data provided by manufacturers or retailers, with subjective information extracted from reviews written by customers. Our method was designed to associate opinions taken from reviews with the product attributes they refer to. This is done by matching aspect expression identified in opinions with attributes from the product, which we model here as aspect classes. To verify the effectiveness of our method, we executed an extensive experimental evaluation that revealed that customers frequently mention aspects related to product attributes in their reviews. The attributes often receive more mentions than the product itself. Our method consistently reached almost 0.7 of F 1 measure in the task of associating the opinion with the correct attribute (or with the product as a whole), across four product categories, in two different scenarios. These results significantly improved the results achieved by a representative baseline.
{"title":"An aspect-driven method for enriching product catalogs with user opinions","authors":"Tiago de Melo, Altigran da Silva, Edleno S. de Moura","doi":"10.1186/s13173-018-0080-4","DOIUrl":"https://doi.org/10.1186/s13173-018-0080-4","url":null,"abstract":"In this paper, we propose a method for enriching product catalogs, which traditionally include only objective data provided by manufacturers or retailers, with subjective information extracted from reviews written by customers. Our method was designed to associate opinions taken from reviews with the product attributes they refer to. This is done by matching aspect expression identified in opinions with attributes from the product, which we model here as aspect classes. To verify the effectiveness of our method, we executed an extensive experimental evaluation that revealed that customers frequently mention aspects related to product attributes in their reviews. The attributes often receive more mentions than the product itself. Our method consistently reached almost 0.7 of F 1 measure in the task of associating the opinion with the correct attribute (or with the product as a whole), across four product categories, in two different scenarios. These results significantly improved the results achieved by a representative baseline.","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"41 3","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520073","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 : 2018-11-07DOI: 10.1186/s13173-018-0077-z
Helton Douglas A. dos Santos, Marcelo Iury S. Oliveira, G. D. F. A. B. Lima, Karina Moura da Silva, Rayelle I. Vera Cruz S. Muniz, Bernadette Farias Lóscio
{"title":"Investigations into data published and consumed on the Web: a systematic mapping study","authors":"Helton Douglas A. dos Santos, Marcelo Iury S. Oliveira, G. D. F. A. B. Lima, Karina Moura da Silva, Rayelle I. Vera Cruz S. Muniz, Bernadette Farias Lóscio","doi":"10.1186/s13173-018-0077-z","DOIUrl":"https://doi.org/10.1186/s13173-018-0077-z","url":null,"abstract":"","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13173-018-0077-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65832406","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}
Custom user maps (also called map mashups) made on geoportals by novice users often lead to poor cartographic results, because cartographic expertise is not part of the mapmaking process. In order to integrate cartographic design functionality within a geoportal, we explored several strategies and design choices. These strategies aimed at integrating explanations about cartographic rules and functions within the mapmaking process. They are defined and implemented based on a review of human-centered design, usability best practices, and previous work on cartographic applications. Cartographic rules and functions were made part of a cartographic wizard, which was evaluated with the help of a usability study. The study results show that the overall user experience with the cartographic functions and the wizard workflow was positive, although implementing functionalities for a diverse target audience proved challenging. Additionally, the results show that offering different ways to access information is welcomed and that explanations pertaining directly to the specific user-generated map are both helpful and preferred. Finally, the results provide guidelines for user interaction design for cartographic functionality on geoportals and other online mapping platforms.
{"title":"Integrating Cartographic Knowledge Within a Geoportal: Interactions and Feedback in the User Interface","authors":"Nadia Panchaud, L. Hurni","doi":"10.14714/CP89.1402","DOIUrl":"https://doi.org/10.14714/CP89.1402","url":null,"abstract":"Custom user maps (also called map mashups) made on geoportals by novice users often lead to poor cartographic results, because cartographic expertise is not part of the mapmaking process. In order to integrate cartographic design functionality within a geoportal, we explored several strategies and design choices. These strategies aimed at integrating explanations about cartographic rules and functions within the mapmaking process. They are defined and implemented based on a review of human-centered design, usability best practices, and previous work on cartographic applications. Cartographic rules and functions were made part of a cartographic wizard, which was evaluated with the help of a usability study. The study results show that the overall user experience with the cartographic functions and the wizard workflow was positive, although implementing functionalities for a diverse target audience proved challenging. Additionally, the results show that offering different ways to access information is welcomed and that explanations pertaining directly to the specific user-generated map are both helpful and preferred. Finally, the results provide guidelines for user interaction design for cartographic functionality on geoportals and other online mapping platforms.","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"1 1","pages":"5-24"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43696081","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 : 2018-10-22DOI: 10.1186/s13173-018-0078-y
Willian Oizumi, Leonardo Sousa, Anderson Oliveira, Alessandro Garcia, Anne Benedicte Agbachi, Roberto Oliveira, Carlos Lucena
BackgroundDevelopers often have to locate design problems in the source code. Several types of design problems may manifest as code smells in the program. A code smell is a source code structure that may reveal a partial hint about the manifestation of a design problem. Recent studies suggest that developers should ignore smells occurring in isolation in a program location. Instead, they should focus on analyzing stinkier code, i.e., program locations—e.g., a class or a hierarchy—affected by multiple smells. There is evidence that the stinkier a program location is, the more likely it contains a design problem. However, there is no empirical evidence on whether developers can effectively identify a design problem in stinkier code. Developers may struggle to make an analysis of inter-related smells affecting the same program location. Besides that, the analysis of stinkier code may require proper tool support due to its analysis complexity. However, there is little knowledge on what are the requirements for a tool that helps developers in revealing stinkier program locations. As a result, developers may not be able to identify design problems due to tool issues.MethodTo address this matter, we aimed at achieving three goals. In the first case, we proposed Organic—a tool supporting the analysis of stinky code. In the second case, we applied a mixed-method approach to analyze if and how developers can effectively find design problems when reflecting upon stinky code—i.e., a program location affected by multiple smells. We conducted a study with 11 software professionals. Finally, in the third case, we aimed at understanding if Organic could be used by developers to identify design problems. To achieve this goal, we used a method from the Semiotic Engineering theory. This method enabled us to evaluate what are the tool issues that may hinder the identification of design problems in stinky code.ResultOur study revealed that only 36.36% of the developers found more design problems when explicitly reasoning about multiple smells as compared to single smells. Moreover, 63.63% of the developers reported much lesser false positives when using the first approach as compared to the latter. The second study, in its turn, showed that most developers may be unable to identify design problems in stinky code without proper tool support.ConclusionOur experiences, in particular the second study, helped us to refine the features of Organic for better supporting developers in reflecting upon stinkier code. For example, analyses of stinky code scattered in class hierarchies or packages is often difficult, time-consuming, and requires proper visualization support. Moreover, without effective support, it remains time-consuming to discard stinky program locations that do not represent design problems.
{"title":"On the identification of design problems in stinky code: experiences and tool support","authors":"Willian Oizumi, Leonardo Sousa, Anderson Oliveira, Alessandro Garcia, Anne Benedicte Agbachi, Roberto Oliveira, Carlos Lucena","doi":"10.1186/s13173-018-0078-y","DOIUrl":"https://doi.org/10.1186/s13173-018-0078-y","url":null,"abstract":"BackgroundDevelopers often have to locate design problems in the source code. Several types of design problems may manifest as code smells in the program. A code smell is a source code structure that may reveal a partial hint about the manifestation of a design problem. Recent studies suggest that developers should ignore smells occurring in isolation in a program location. Instead, they should focus on analyzing stinkier code, i.e., program locations—e.g., a class or a hierarchy—affected by multiple smells. There is evidence that the stinkier a program location is, the more likely it contains a design problem. However, there is no empirical evidence on whether developers can effectively identify a design problem in stinkier code. Developers may struggle to make an analysis of inter-related smells affecting the same program location. Besides that, the analysis of stinkier code may require proper tool support due to its analysis complexity. However, there is little knowledge on what are the requirements for a tool that helps developers in revealing stinkier program locations. As a result, developers may not be able to identify design problems due to tool issues.MethodTo address this matter, we aimed at achieving three goals. In the first case, we proposed Organic—a tool supporting the analysis of stinky code. In the second case, we applied a mixed-method approach to analyze if and how developers can effectively find design problems when reflecting upon stinky code—i.e., a program location affected by multiple smells. We conducted a study with 11 software professionals. Finally, in the third case, we aimed at understanding if Organic could be used by developers to identify design problems. To achieve this goal, we used a method from the Semiotic Engineering theory. This method enabled us to evaluate what are the tool issues that may hinder the identification of design problems in stinky code.ResultOur study revealed that only 36.36% of the developers found more design problems when explicitly reasoning about multiple smells as compared to single smells. Moreover, 63.63% of the developers reported much lesser false positives when using the first approach as compared to the latter. The second study, in its turn, showed that most developers may be unable to identify design problems in stinky code without proper tool support.ConclusionOur experiences, in particular the second study, helped us to refine the features of Organic for better supporting developers in reflecting upon stinkier code. For example, analyses of stinky code scattered in class hierarchies or packages is often difficult, time-consuming, and requires proper visualization support. Moreover, without effective support, it remains time-consuming to discard stinky program locations that do not represent design problems.","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"52 5","pages":"1-30"},"PeriodicalIF":0.0,"publicationDate":"2018-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520076","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 : 2018-10-12DOI: 10.1186/s13173-018-0076-0
André G. Maletzke, Denis M. dos Reis, Gustavo E. A. P. A. Batista
In the last years, learning from data streams has attracted the attention of researchers and practitioners due to its large number of applications. These applications have motivated the research community to propose a significant amount of methods to solve problems in diverse tasks, more prominently in classification, clustering, and anomaly detection. However, a relevant task known as quantification has remained mostly unexplored. The quantification goal is to provide an estimate of the class prevalence in an unlabeled set. Recently, we proposed the SQSI algorithm to quantify data streams with concept drifts. SQSI uses a statistical test to identify concept drifts and retrain the classifiers. However, the retraining involves requiring the labels for all newly arrived instances. In this paper, we extend SQSI algorithm by exploring instance selection techniques allied to semi-supervised learning. The idea is to request the classes of a smaller subset of recent examples. Our experiments demonstrate that SQSI’s extension significantly reduces the dependency on actual labels while maintaining or improving the quantification accuracy.
{"title":"Combining instance selection and self-training to improve data stream quantification","authors":"André G. Maletzke, Denis M. dos Reis, Gustavo E. A. P. A. Batista","doi":"10.1186/s13173-018-0076-0","DOIUrl":"https://doi.org/10.1186/s13173-018-0076-0","url":null,"abstract":"In the last years, learning from data streams has attracted the attention of researchers and practitioners due to its large number of applications. These applications have motivated the research community to propose a significant amount of methods to solve problems in diverse tasks, more prominently in classification, clustering, and anomaly detection. However, a relevant task known as quantification has remained mostly unexplored. The quantification goal is to provide an estimate of the class prevalence in an unlabeled set. Recently, we proposed the SQSI algorithm to quantify data streams with concept drifts. SQSI uses a statistical test to identify concept drifts and retrain the classifiers. However, the retraining involves requiring the labels for all newly arrived instances. In this paper, we extend SQSI algorithm by exploring instance selection techniques allied to semi-supervised learning. The idea is to request the classes of a smaller subset of recent examples. Our experiments demonstrate that SQSI’s extension significantly reduces the dependency on actual labels while maintaining or improving the quantification accuracy.","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"33 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2018-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520109","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 : 2018-10-01DOI: 10.1186/s13173-019-0091-9
Duong N. Nguyen, Aleksey Charapko, S. Kulkarni, M. Demirbas
{"title":"Using weaker consistency models with monitoring and recovery for improving performance of key-value stores","authors":"Duong N. Nguyen, Aleksey Charapko, S. Kulkarni, M. Demirbas","doi":"10.1186/s13173-019-0091-9","DOIUrl":"https://doi.org/10.1186/s13173-019-0091-9","url":null,"abstract":"","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13173-019-0091-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41533870","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 : 2018-09-21DOI: 10.1186/s13173-018-0075-1
Fernando Antônio Asevedo Nóbrega, Thiago Alexandre Salgueiro Pardo
{"title":"Update summarization: building from scratch for Portuguese and comparing to English","authors":"Fernando Antônio Asevedo Nóbrega, Thiago Alexandre Salgueiro Pardo","doi":"10.1186/s13173-018-0075-1","DOIUrl":"https://doi.org/10.1186/s13173-018-0075-1","url":null,"abstract":"","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72592060","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}
This paper presents a study of rates of dropout from Brazilian degree courses, based on data provided by the National Institute for Educational Studies and Research “Anísio Teixeira” (INEP) and a case study carried out at the University of Brasilia (UnB). Dropout was calculated by tracking the status of each student between 2010 and 2014 in the eight major areas according to the classification of the Organisation for Economic Co-operation and Development (OECD), for the major area of Science, Mathematics, and Computing, and for the area of Computing. Data were analyzed in order to check for potential evidence regarding the influence on dropout of factors such as algorithmic abstraction, number of applicants per place, or the gender of students. A survey was also performed using online questionnaires for circumvented students from the courses of Bachelor of Computer Science, Degree in Computing, and Computer Engineering between 2005 and 2015. This survey revealed the influence on dropout of several factors and particularly institutional and vocational factors; it is clear that difficulties in algorithmic abstraction and mathematical knowledge influence rates of dropout from computing courses.
{"title":"Influence of algorithmic abstraction and mathematical knowledge on rates of dropout from Computing degree courses","authors":"Raphael Magalhães Hoed, Marcelo Ladeira, Leticia Lopes Leite","doi":"10.1186/s13173-018-0074-2","DOIUrl":"https://doi.org/10.1186/s13173-018-0074-2","url":null,"abstract":"This paper presents a study of rates of dropout from Brazilian degree courses, based on data provided by the National Institute for Educational Studies and Research “Anísio Teixeira” (INEP) and a case study carried out at the University of Brasilia (UnB). Dropout was calculated by tracking the status of each student between 2010 and 2014 in the eight major areas according to the classification of the Organisation for Economic Co-operation and Development (OECD), for the major area of Science, Mathematics, and Computing, and for the area of Computing. Data were analyzed in order to check for potential evidence regarding the influence on dropout of factors such as algorithmic abstraction, number of applicants per place, or the gender of students. A survey was also performed using online questionnaires for circumvented students from the courses of Bachelor of Computer Science, Degree in Computing, and Computer Engineering between 2005 and 2015. This survey revealed the influence on dropout of several factors and particularly institutional and vocational factors; it is clear that difficulties in algorithmic abstraction and mathematical knowledge influence rates of dropout from computing courses.","PeriodicalId":39760,"journal":{"name":"Journal of the Brazilian Computer Society","volume":"7 1","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2018-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514951","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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