Pub Date : 1900-01-01DOI: 10.1109/SAMI.2017.7880322
Ádám Tóth, I. Vajda, Z. Vámossy
This paper features a software system called ∞Exarns (InfinityExams) which supports (primarily in higher education) paper-based examination and makes it easier, more comfortable and speeds up the whole process while keeping every single positive attribute of it but also reducing the number of negative aspects. The approach significantly differs from the ones used in the previous 10+ years which were implemented in such a way that they could not reproduce and replace the traditional paper-based examination model. The heart of the article relies on the most important element of the software which is the image processing flow.
{"title":"E-assessment using image processing in ∞Exams","authors":"Ádám Tóth, I. Vajda, Z. Vámossy","doi":"10.1109/SAMI.2017.7880322","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880322","url":null,"abstract":"This paper features a software system called ∞Exarns (InfinityExams) which supports (primarily in higher education) paper-based examination and makes it easier, more comfortable and speeds up the whole process while keeping every single positive attribute of it but also reducing the number of negative aspects. The approach significantly differs from the ones used in the previous 10+ years which were implemented in such a way that they could not reproduce and replace the traditional paper-based examination model. The heart of the article relies on the most important element of the software which is the image processing flow.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131336922","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 : 1900-01-01DOI: 10.1109/SAMI.2017.7880307
Cindy L. Bethel, Zachary Henkel, D. Eakin, D. May, Melinda Pilkinton
The objective of this research is to investigate the use of robots as intermediaries to gather sensitive information from children. The research is multidisciplinary in nature. The goals will be accomplished through the development of an integrated robotic framework that includes a novel architecture and an interactive user interface to gather information using methodologies recommended for forensic interviews with children. The Interactive Social Engagement Architecture (ISEA) is designed to integrate behavior-based robotics, human behavior models, cognitive architectures, and expert user input to increase social engagement between a human and system (e.g., robot, avatar, etc.). ISEA provides for the autonomous generation of robot behaviors for self-preservation and to convey social intelligence. The framework is designed to be modular and adaptable to different applications and domains; however for this project, the focus is on social engagement for information gathering. The interactive user interface provides interviewers with the ability to use a robot as an intermediary for gathering this information. The interface and framework have been iteratively improved through observations from user studies conducted to date with 186 children ages 8–12. This project compares the effectiveness of robot versus human interviewers to gather sensitive information from children using situations in which this would commonly occur — cases of child eyewitness memory and child reports of bullying. This research has the potential to transform how sensitive information is gathered as it relates to criminal investigations and proceedings.
{"title":"Moving toward an intelligent interactive social engagement framework for information gathering","authors":"Cindy L. Bethel, Zachary Henkel, D. Eakin, D. May, Melinda Pilkinton","doi":"10.1109/SAMI.2017.7880307","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880307","url":null,"abstract":"The objective of this research is to investigate the use of robots as intermediaries to gather sensitive information from children. The research is multidisciplinary in nature. The goals will be accomplished through the development of an integrated robotic framework that includes a novel architecture and an interactive user interface to gather information using methodologies recommended for forensic interviews with children. The Interactive Social Engagement Architecture (ISEA) is designed to integrate behavior-based robotics, human behavior models, cognitive architectures, and expert user input to increase social engagement between a human and system (e.g., robot, avatar, etc.). ISEA provides for the autonomous generation of robot behaviors for self-preservation and to convey social intelligence. The framework is designed to be modular and adaptable to different applications and domains; however for this project, the focus is on social engagement for information gathering. The interactive user interface provides interviewers with the ability to use a robot as an intermediary for gathering this information. The interface and framework have been iteratively improved through observations from user studies conducted to date with 186 children ages 8–12. This project compares the effectiveness of robot versus human interviewers to gather sensitive information from children using situations in which this would commonly occur — cases of child eyewitness memory and child reports of bullying. This research has the potential to transform how sensitive information is gathered as it relates to criminal investigations and proceedings.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"146 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120862612","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 : 1900-01-01DOI: 10.1109/SAMI.2017.7880315
A. Dineva, J. Tar, A. Várkonyi-Kóczy, V. Piuri
Up to now the fundamental tool of adaptive nonlinear control design is Lyapunov's 2nd or "Direct" Method. Recently the Sigmoid Generated Fixed Point Transformation (SGFPT) has been introduced for evading the application of the Lyapunov technique. This systematic method has been presented for the generation of whole families of Fixed Point Transformations and has been extended from Single Input Single Output (SISO) to Multiple Input Multiple Output (MIMO) systems. Few studies have been revealed that the original Robust Fixed Point Transformation (RFPT) can be successfully combined with some modification of the classical methods, such as the Modified Adaptive Inverse Dynamic Robot Controller (MAIDRC) and the Modified Adaptive Slotine-Li Robot Controller (MADSLRC). This paper presents that the SGFPT can also well coexist with the MAIDRC control design. Additionally, a novel, even more simplified tuning technique is proposed that also applies fixed point transformation-based tuning rule for parameter identification. The theoretical considerations are validated by numerical simulations made for a 2 Degree of Freedom (DoF) paradigm, in the adaptive control of two coupled mass-points with simultaneous parameter identification.
{"title":"Application of fixed point transformation to classical model identification using new tuning rule","authors":"A. Dineva, J. Tar, A. Várkonyi-Kóczy, V. Piuri","doi":"10.1109/SAMI.2017.7880315","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880315","url":null,"abstract":"Up to now the fundamental tool of adaptive nonlinear control design is Lyapunov's 2nd or \"Direct\" Method. Recently the Sigmoid Generated Fixed Point Transformation (SGFPT) has been introduced for evading the application of the Lyapunov technique. This systematic method has been presented for the generation of whole families of Fixed Point Transformations and has been extended from Single Input Single Output (SISO) to Multiple Input Multiple Output (MIMO) systems. Few studies have been revealed that the original Robust Fixed Point Transformation (RFPT) can be successfully combined with some modification of the classical methods, such as the Modified Adaptive Inverse Dynamic Robot Controller (MAIDRC) and the Modified Adaptive Slotine-Li Robot Controller (MADSLRC). This paper presents that the SGFPT can also well coexist with the MAIDRC control design. Additionally, a novel, even more simplified tuning technique is proposed that also applies fixed point transformation-based tuning rule for parameter identification. The theoretical considerations are validated by numerical simulations made for a 2 Degree of Freedom (DoF) paradigm, in the adaptive control of two coupled mass-points with simultaneous parameter identification.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116229228","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 : 1900-01-01DOI: 10.1109/SAMI.2017.7880356
Ivana Stromberger, N. Bačanin, M. Tuba
In this paper we applied the krill herd algorithm hybridized with the firefly algorithm to bound-constrained large-scale optimization problems. We tested basic krill herd algorithm and basic firefly algorithm on the standard set of benchmark functions. The results were acceptable. Then, we hybridized the krill herd algorithm with the firefly algorithm by applying firefly algorithm's search equation to the original krill herd algorithm implementation. We tested the robustness and effectiveness of our hybridized algorithm on the same large-scale numerical benchmarks with different dimensionality in order to make comparative analysis and to measure optimization enhancements of our approach. Testing results proved that our proposed hybridized implementation improved results almost uniformly and that it has significant potential when dealing with global optimization problems.
{"title":"Hybridized krill herd algorithm for large-scale optimization problems","authors":"Ivana Stromberger, N. Bačanin, M. Tuba","doi":"10.1109/SAMI.2017.7880356","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880356","url":null,"abstract":"In this paper we applied the krill herd algorithm hybridized with the firefly algorithm to bound-constrained large-scale optimization problems. We tested basic krill herd algorithm and basic firefly algorithm on the standard set of benchmark functions. The results were acceptable. Then, we hybridized the krill herd algorithm with the firefly algorithm by applying firefly algorithm's search equation to the original krill herd algorithm implementation. We tested the robustness and effectiveness of our hybridized algorithm on the same large-scale numerical benchmarks with different dimensionality in order to make comparative analysis and to measure optimization enhancements of our approach. Testing results proved that our proposed hybridized implementation improved results almost uniformly and that it has significant potential when dealing with global optimization problems.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122271509","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 : 1900-01-01DOI: 10.1109/SAMI.2017.7880327
M. Sarnovský, P. Butka, Andrea Huzvarova
The main objective of the work presented within this paper was to design and implement the system for twitter data analysis and visualization in R environment using the big data processing technologies. Our focus was to leverage existing big data processing frameworks with its storage and computational capabilities to support the analytical functions implemented in R language. We decided to build the backend on top of the Apache Hadoop framework including the Hadoop HDFS as a distributed filesystem and MapReduce as a distributed computation paradigm. RHadoop packages were then used to connect the R environment to the processing layer and to design and implement the analytical functions in a distributed manner. Visualizations were implemented on top of the solution as a RShiny application.
{"title":"Twitter data analysis and visualizations using the R language on top of the Hadoop platform","authors":"M. Sarnovský, P. Butka, Andrea Huzvarova","doi":"10.1109/SAMI.2017.7880327","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880327","url":null,"abstract":"The main objective of the work presented within this paper was to design and implement the system for twitter data analysis and visualization in R environment using the big data processing technologies. Our focus was to leverage existing big data processing frameworks with its storage and computational capabilities to support the analytical functions implemented in R language. We decided to build the backend on top of the Apache Hadoop framework including the Hadoop HDFS as a distributed filesystem and MapReduce as a distributed computation paradigm. RHadoop packages were then used to connect the R environment to the processing layer and to design and implement the analytical functions in a distributed manner. Visualizations were implemented on top of the solution as a RShiny application.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114998480","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 : 1900-01-01DOI: 10.1109/SAMI.2017.7880296
P. Lacko
Deep neural networks are intensively researched field of artificial intelligence. Big companies like Google, Microsoft, Baidu or Facebook are supporting research and development in this field. The recent victory over human player in the game of Go points to a huge potential of this approach. Machine learning approaches based on deep learning techniques bring significant gain over existing methods based on manually tuned features in different areas. In this paper we present the evolution of deep neural networks from first neuron models towards today's deep architectures.
{"title":"From perceptrons to deep neural networks","authors":"P. Lacko","doi":"10.1109/SAMI.2017.7880296","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880296","url":null,"abstract":"Deep neural networks are intensively researched field of artificial intelligence. Big companies like Google, Microsoft, Baidu or Facebook are supporting research and development in this field. The recent victory over human player in the game of Go points to a huge potential of this approach. Machine learning approaches based on deep learning techniques bring significant gain over existing methods based on manually tuned features in different areas. In this paper we present the evolution of deep neural networks from first neuron models towards today's deep architectures.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122699702","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 : 1900-01-01DOI: 10.1109/SAMI.2017.7880277
László Viktor Jánoky, P. Ekler
Participatory Computing is the concept of a group of computers contributing processing power and storage to form a larger, distributed computing system. Past experiences show that a system of this type can reach performance rivaling the fastest supercomputers in the world. In theory, any connected modern PC could participate and yet the prevalence of these systems is not comparable to their potential numbers. In this paper, we analyze the main barriers ahead the larger adaptation of participatory systems and propose solutions around them. Ultimately by applying these solutions we prove their workability by showing and evaluating a proof-of-concept system and measurements related to it. The proposed results can offer several advantages in the field of participatory computing where the new technologies are applied.
{"title":"The analysis of participatory computing in dynamic web environments","authors":"László Viktor Jánoky, P. Ekler","doi":"10.1109/SAMI.2017.7880277","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880277","url":null,"abstract":"Participatory Computing is the concept of a group of computers contributing processing power and storage to form a larger, distributed computing system. Past experiences show that a system of this type can reach performance rivaling the fastest supercomputers in the world. In theory, any connected modern PC could participate and yet the prevalence of these systems is not comparable to their potential numbers. In this paper, we analyze the main barriers ahead the larger adaptation of participatory systems and propose solutions around them. Ultimately by applying these solutions we prove their workability by showing and evaluating a proof-of-concept system and measurements related to it. The proposed results can offer several advantages in the field of participatory computing where the new technologies are applied.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128393152","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 : 1900-01-01DOI: 10.1109/SAMI.2017.7880332
Zsuzsanna Bede, B. Németh, P. Gáspár
The paper presents the modelling of mixed traffic flow, in which look-ahead controlled vehicles with a speed control are driven together with conventional vehicles. Since the speed profile of the lookahead control may differ from that of the conventional vehicle, the structure of the traffic flow changes. The paper analyses the impact of vehicles applying look-ahead control strategy on the traffic flow. The analysis is performed by using the VISSIM traffic simulation software. In this simulation a highway section using real topographic data is also built in. In the paper the results of the simulation-based analysis are also illustrated.
{"title":"Simulation-based analysis of mixed traffic flow using VISSIM environment","authors":"Zsuzsanna Bede, B. Németh, P. Gáspár","doi":"10.1109/SAMI.2017.7880332","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880332","url":null,"abstract":"The paper presents the modelling of mixed traffic flow, in which look-ahead controlled vehicles with a speed control are driven together with conventional vehicles. Since the speed profile of the lookahead control may differ from that of the conventional vehicle, the structure of the traffic flow changes. The paper analyses the impact of vehicles applying look-ahead control strategy on the traffic flow. The analysis is performed by using the VISSIM traffic simulation software. In this simulation a highway section using real topographic data is also built in. In the paper the results of the simulation-based analysis are also illustrated.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123780039","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 : 1900-01-01DOI: 10.1109/SAMI.2017.7880293
F. Babič, M. Vadovský, M. Muchová, Ján Paralič, L. Majnarić
Medical diagnostic is a complex process consisting of many input variables, which the general practitioner (GP) or specialist should take into account before confirm the expected diagnosis. In the case of electronic records, they have an opportunity to support this process within simple understandable results of the correctly applied suitable methods from machine learning or statistics. We used a small sample of patient's data from Croatia for experimental evaluation of this potential. We applied the methods as Welch's t-test, Pearson chi-square independence test, Youden's index, decision trees and simple K-Means. The cooperating medical expert evaluated the obtained results and confirmed the expected potential for daily medical practice.
{"title":"Simple understandable analysis of medical data to support the diagnostic process","authors":"F. Babič, M. Vadovský, M. Muchová, Ján Paralič, L. Majnarić","doi":"10.1109/SAMI.2017.7880293","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880293","url":null,"abstract":"Medical diagnostic is a complex process consisting of many input variables, which the general practitioner (GP) or specialist should take into account before confirm the expected diagnosis. In the case of electronic records, they have an opportunity to support this process within simple understandable results of the correctly applied suitable methods from machine learning or statistics. We used a small sample of patient's data from Croatia for experimental evaluation of this potential. We applied the methods as Welch's t-test, Pearson chi-square independence test, Youden's index, decision trees and simple K-Means. The cooperating medical expert evaluated the obtained results and confirmed the expected potential for daily medical practice.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128225042","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 : 1900-01-01DOI: 10.1109/SAMI.2017.7880302
T. Tompa, S. Kovács
This paper introduces a fuzzy knowledgebase reduction method with applying a clustering technique in the Fuzzy Rule Interpolation-based Q-learning (FRIQ-learning). The FRIQ-learning method stars with an empty knowledgebase, which is a fuzzy rule-base filled only with rules defining the boundaries of the problem space. Then the system builds the rule-base incrementally episode by episode, based on a properly defined reward function. The FRIQ-learning method is finished, when its terminating conditions become true. This case we get the final rule-base as a solution for the given problem. But the constructed final rule-base may contain redundant rules, which can be automatically omitted from the rule-base by reduction methods. The main goal of the paper is to introduce a new, clustering based reduction method, which is suitable for eliminating the unnecessary rules of the rule-base and hence decrease the size of the fuzzy knowledgebase. For demonstrating the benefits of the suggested clustering based fuzzy knowledgebase reduction method, application examples of the "cart pole" and the "mountain car" benchmarks are also discussed briefly in the paper.
{"title":"Clustering-based fuzzy knowledgebase reduction in the FRIQ-learning","authors":"T. Tompa, S. Kovács","doi":"10.1109/SAMI.2017.7880302","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880302","url":null,"abstract":"This paper introduces a fuzzy knowledgebase reduction method with applying a clustering technique in the Fuzzy Rule Interpolation-based Q-learning (FRIQ-learning). The FRIQ-learning method stars with an empty knowledgebase, which is a fuzzy rule-base filled only with rules defining the boundaries of the problem space. Then the system builds the rule-base incrementally episode by episode, based on a properly defined reward function. The FRIQ-learning method is finished, when its terminating conditions become true. This case we get the final rule-base as a solution for the given problem. But the constructed final rule-base may contain redundant rules, which can be automatically omitted from the rule-base by reduction methods. The main goal of the paper is to introduce a new, clustering based reduction method, which is suitable for eliminating the unnecessary rules of the rule-base and hence decrease the size of the fuzzy knowledgebase. For demonstrating the benefits of the suggested clustering based fuzzy knowledgebase reduction method, application examples of the \"cart pole\" and the \"mountain car\" benchmarks are also discussed briefly in the paper.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132753787","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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