Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.58
V. Vagin, O. Morosin
This paper contains a description of an argumentation system that uses a defeasible reasoning mechanism. The main idea and the key points are given. Also it contains main algorithms for detecting the conflicts and finding statuses of arguments. Solutions of some problems, which are not solvable in the classical logics, are presented.
{"title":"Modeling Defeasible Reasoning for Argumentation","authors":"V. Vagin, O. Morosin","doi":"10.1109/BRICS-CCI-CBIC.2013.58","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.58","url":null,"abstract":"This paper contains a description of an argumentation system that uses a defeasible reasoning mechanism. The main idea and the key points are given. Also it contains main algorithms for detecting the conflicts and finding statuses of arguments. Solutions of some problems, which are not solvable in the classical logics, are presented.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116688555","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.74
H. Alves, M. Valença
Understanding the influence of some factors on a particular phenomenon can be very relevant in many cases of decision-making. An example would be the identification of the level of influence that factors such as smoking, stress and lack of exercise have on the predisposition to heart disease. Knowing which of these inputs are relevant for a person to become a cardiac patient, it is possible to take some preventive measures. This article presents a new method to assist the not so simple task of feature selection, using the statistical function called curve of permanence. In this work we show parmanence curves applied on result data from the executions of some existing algorithms of feature selection, all of them based on Artificial Neural Networks (ANN). The objective of this study is to propose a technique that provides robustness to the process of determine the values of contributions of the inputs of an ANNs.
{"title":"Using Curves of Permanence to Study the Contribution of Input Variables in Artificial Neural Network Models: A New Proposed Methodology","authors":"H. Alves, M. Valença","doi":"10.1109/BRICS-CCI-CBIC.2013.74","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.74","url":null,"abstract":"Understanding the influence of some factors on a particular phenomenon can be very relevant in many cases of decision-making. An example would be the identification of the level of influence that factors such as smoking, stress and lack of exercise have on the predisposition to heart disease. Knowing which of these inputs are relevant for a person to become a cardiac patient, it is possible to take some preventive measures. This article presents a new method to assist the not so simple task of feature selection, using the statistical function called curve of permanence. In this work we show parmanence curves applied on result data from the executions of some existing algorithms of feature selection, all of them based on Artificial Neural Networks (ANN). The objective of this study is to propose a technique that provides robustness to the process of determine the values of contributions of the inputs of an ANNs.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126911248","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.59
Andre R. Da Cruz, F. Guimarães, R. Takahashi
This work presents four agents with different strategies to play a version of the 2-sided dominoes game, usually played in Minas Gerais state, Brazil. This incomplete information game must be played with two players and the goal is to discard all tiles first according to the rules. Each pair of agents was tested in a computational experiment, for 1,000,000 matches, in order to evaluate the individual effectiveness. In the first strategy, the agent uses random rules to select an adequate tile, the second agent observes the tiles already on the table and on its hand and selects one using a simple probability information computed in an amateur way, the third strategy also observes the tiles on the table and on the hand, and computes a probability information using the two end tiles of the table and the candidates opposite values in order to decide which one must be thrown, in the last strategy, the agent uses the third strategy and the Boltzmann exploration with a roulette wheel to select the tile. The results showed that the last strategy is the best and that even the random strategy is capable to win a significant number of matches.
{"title":"Comparing Strategies to Play a 2-Sided Dominoes Game","authors":"Andre R. Da Cruz, F. Guimarães, R. Takahashi","doi":"10.1109/BRICS-CCI-CBIC.2013.59","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.59","url":null,"abstract":"This work presents four agents with different strategies to play a version of the 2-sided dominoes game, usually played in Minas Gerais state, Brazil. This incomplete information game must be played with two players and the goal is to discard all tiles first according to the rules. Each pair of agents was tested in a computational experiment, for 1,000,000 matches, in order to evaluate the individual effectiveness. In the first strategy, the agent uses random rules to select an adequate tile, the second agent observes the tiles already on the table and on its hand and selects one using a simple probability information computed in an amateur way, the third strategy also observes the tiles on the table and on the hand, and computes a probability information using the two end tiles of the table and the candidates opposite values in order to decide which one must be thrown, in the last strategy, the agent uses the third strategy and the Boltzmann exploration with a roulette wheel to select the tile. The results showed that the last strategy is the best and that even the random strategy is capable to win a significant number of matches.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129042092","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.99
Marden B. Pasinato, Carlos E. Mello, Marie-Aude Aufaure, Geraldo Zimbrão
Context-Aware Recommender Systems (CARS) have emerged as a different way of providing more precise and interesting recommendations through the use of data about the context in which consumers buy goods and/or services. CARS consider not only the ratings given to items by consumers (users), but also the context attributes related to these ratings. Several algorithms and methods have been proposed in the literature in order to deal with context-aware ratings. Although there are lots of proposals and approaches working for this kind of recommendation, adequate and public datasets containing user's context-aware ratings about items are limited, and usually, even these are not large enough to evaluate the proposed CARS very well. One solution for this issue is to crawl this kind of data from e-commerce websites. However, it could be very time-expensive and also complicated due to problems regarding legal rights and privacy. In addition, crawled data from e-commerce websites may not be enough for a complete evaluation, being unable to simulate all possible users' behaviors and characteristics. In this article, we propose a methodology to generate a synthetic dataset for context-aware recommender systems, enabling researchers and developers to create their own dataset according to the characteristics in which they want to evaluate their algorithms and methods. Our methodology enables researchers to define the user's behavior of giving ratings based on the Probability Distribution Function (PDF) associated to their profiles.
{"title":"Generating Synthetic Data for Context-Aware Recommender Systems","authors":"Marden B. Pasinato, Carlos E. Mello, Marie-Aude Aufaure, Geraldo Zimbrão","doi":"10.1109/BRICS-CCI-CBIC.2013.99","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.99","url":null,"abstract":"Context-Aware Recommender Systems (CARS) have emerged as a different way of providing more precise and interesting recommendations through the use of data about the context in which consumers buy goods and/or services. CARS consider not only the ratings given to items by consumers (users), but also the context attributes related to these ratings. Several algorithms and methods have been proposed in the literature in order to deal with context-aware ratings. Although there are lots of proposals and approaches working for this kind of recommendation, adequate and public datasets containing user's context-aware ratings about items are limited, and usually, even these are not large enough to evaluate the proposed CARS very well. One solution for this issue is to crawl this kind of data from e-commerce websites. However, it could be very time-expensive and also complicated due to problems regarding legal rights and privacy. In addition, crawled data from e-commerce websites may not be enough for a complete evaluation, being unable to simulate all possible users' behaviors and characteristics. In this article, we propose a methodology to generate a synthetic dataset for context-aware recommender systems, enabling researchers and developers to create their own dataset according to the characteristics in which they want to evaluate their algorithms and methods. Our methodology enables researchers to define the user's behavior of giving ratings based on the Probability Distribution Function (PDF) associated to their profiles.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126859821","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.20
D. Ferrari, L. N. de Castro
Finding a good clustering solution for an unknown problem is a challenging task. Evolutionary algorithms have proved to be reliable methods to search for high quality solutions to complex problems. The present paper proposes a new set of genetic operators for the Fast Evolutionary Algorithm for Clustering (Fast-EAC) to improve the solution quality and computational efficiency. The new algorithm, called EAC-II, is compared with its original version in terms of quality of solutions and efficiency over several problems from the literature.
{"title":"New Genetic Operators for the Evolutionary Algorithm for Clustering","authors":"D. Ferrari, L. N. de Castro","doi":"10.1109/BRICS-CCI-CBIC.2013.20","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.20","url":null,"abstract":"Finding a good clustering solution for an unknown problem is a challenging task. Evolutionary algorithms have proved to be reliable methods to search for high quality solutions to complex problems. The present paper proposes a new set of genetic operators for the Fast Evolutionary Algorithm for Clustering (Fast-EAC) to improve the solution quality and computational efficiency. The new algorithm, called EAC-II, is compared with its original version in terms of quality of solutions and efficiency over several problems from the literature.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127730622","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.55
Michael Mitnovitsky, M. Shpitalni, M. Cohen
This paper examines a flexible flow shop problem that considers dynamic events, such as stochastic job arrivals, uncertain processing times, unexpected machine breakdowns and the possibility of processing flexibility. To achieve this goal, a new agent-based adaptive control system has been developed at the factory level, along with advanced decision-making strategies that provide responsive factories with adaptation and reconfiguration capabilities and advanced complementary scheduling abilities. The aim is to facilitate operational flexibility and increase productivity as well as offer strategic advantages such as analysis of factory development options by simulation. The feasibility of the proposed system is demonstrated by simulation under various experimental settings, among them shop utilization level, due date tightness and breakdown level.
{"title":"Operation and Control of Manufacturing Systems by Agents with Local Intelligence","authors":"Michael Mitnovitsky, M. Shpitalni, M. Cohen","doi":"10.1109/BRICS-CCI-CBIC.2013.55","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.55","url":null,"abstract":"This paper examines a flexible flow shop problem that considers dynamic events, such as stochastic job arrivals, uncertain processing times, unexpected machine breakdowns and the possibility of processing flexibility. To achieve this goal, a new agent-based adaptive control system has been developed at the factory level, along with advanced decision-making strategies that provide responsive factories with adaptation and reconfiguration capabilities and advanced complementary scheduling abilities. The aim is to facilitate operational flexibility and increase productivity as well as offer strategic advantages such as analysis of factory development options by simulation. The feasibility of the proposed system is demonstrated by simulation under various experimental settings, among them shop utilization level, due date tightness and breakdown level.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"548 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123244011","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.85
Bhekisipho Twala, Thembinkosi Nkonyana
Mapping and classification of human settlements from remotely sensed data has attracted a lot of attention in recent years. Real world data, however, often suffer from corruptions or noise but not always known. This is the heart of information-based remote sensing models. This paper investigates the impact of incomplete remotely sensed data in the evaluation of machine learning techniques (classifiers) for the task of predicting or classifying pixels into different land cover region types. Six classifiers are empirically evaluated by artificially simulating different missing data proportions, patterns and mechanisms using a multispectral image dataset. A 4-way repeated measures design is employed to analyse the data. The simulation results suggest classifiers as having their strengths and limitations in terms of dealing with the incomplete data problem with the artificial neural network classifier as substantially inferior and naïve Bayes classifier and support vector machines representing superior approaches.
{"title":"Extracting Supervised Learning Classifiers from Possibly Incomplete Remotely Sensed Data","authors":"Bhekisipho Twala, Thembinkosi Nkonyana","doi":"10.1109/BRICS-CCI-CBIC.2013.85","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.85","url":null,"abstract":"Mapping and classification of human settlements from remotely sensed data has attracted a lot of attention in recent years. Real world data, however, often suffer from corruptions or noise but not always known. This is the heart of information-based remote sensing models. This paper investigates the impact of incomplete remotely sensed data in the evaluation of machine learning techniques (classifiers) for the task of predicting or classifying pixels into different land cover region types. Six classifiers are empirically evaluated by artificially simulating different missing data proportions, patterns and mechanisms using a multispectral image dataset. A 4-way repeated measures design is employed to analyse the data. The simulation results suggest classifiers as having their strengths and limitations in terms of dealing with the incomplete data problem with the artificial neural network classifier as substantially inferior and naïve Bayes classifier and support vector machines representing superior approaches.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121999433","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.13
Leonardo Enzo Brito da Silva, J. A. F. Costa
This paper presents an automatic clustering system, built as a committee machine, which is used to cohesively partition the self-organizing map. In the proposed method, each expert from the committee machine analyzes the connections of the neuron grid based on a particular similarity matrix, and thus decides which ones should be pruned by gradually removing them and observing the intervals of stability. Those intervals are regarded as the ones in which the number of clusters found through connected components remain constant. The output of each expert is a connectivity matrix that effectively expresses which connections should remain as a binary true or false value. The final stage of the committee machine consists of combining the outputs of the experts, and through majority voting establish which connections should remain in the grid, and hence performing the segmentation of the map. The system was evaluated through its application to synthetic and real world data sets.
{"title":"Clustering the Self-Organizing Map Based on the Neurons' Associated Pattern Sets","authors":"Leonardo Enzo Brito da Silva, J. A. F. Costa","doi":"10.1109/BRICS-CCI-CBIC.2013.13","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.13","url":null,"abstract":"This paper presents an automatic clustering system, built as a committee machine, which is used to cohesively partition the self-organizing map. In the proposed method, each expert from the committee machine analyzes the connections of the neuron grid based on a particular similarity matrix, and thus decides which ones should be pruned by gradually removing them and observing the intervals of stability. Those intervals are regarded as the ones in which the number of clusters found through connected components remain constant. The output of each expert is a connectivity matrix that effectively expresses which connections should remain as a binary true or false value. The final stage of the committee machine consists of combining the outputs of the experts, and through majority voting establish which connections should remain in the grid, and hence performing the segmentation of the map. The system was evaluated through its application to synthetic and real world data sets.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122420401","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.23
Harold D. De Mello, A. V. Abs da Cruz, M. Vellasco
This paper presents a Copula-based Estimation of Distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and elitism operators during the optimization. We show that this approach improves the overall performance of the optimization when compared to other copula-based EDAs.
{"title":"Estimation of Distribution Algorithm Based on a Multivariate Extension of the Archimedean Copula","authors":"Harold D. De Mello, A. V. Abs da Cruz, M. Vellasco","doi":"10.1109/BRICS-CCI-CBIC.2013.23","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.23","url":null,"abstract":"This paper presents a Copula-based Estimation of Distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and elitism operators during the optimization. We show that this approach improves the overall performance of the optimization when compared to other copula-based EDAs.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127412056","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.111
Tobias Jordan, A. Presse, Paulo Cordeiro, F. Buarque, Marcelo Pita
This work presents the implementation of an agent based model concept to simulate a sample of the German society under a governmental social transfer system. Subsequently the behavior of the model is analyzed under changing conditions in order to proof that it can be used for the simulation of real societies under similar conditions. An important objective is to give evidence on economic interdependencies between individual behavior, governmental interaction and macroeconomic outcomes. The model is based on the economic concept of Homo Oeconomicus, while it widens some restrictions of this concept aiming to create a framework that resembles reality more closely. Our analysis provides evidence that the model works reasonably well and can serve as a basis for more detailed investigations.
{"title":"Computer Simulations of Small Societies Under Social Transfer Systems","authors":"Tobias Jordan, A. Presse, Paulo Cordeiro, F. Buarque, Marcelo Pita","doi":"10.1109/BRICS-CCI-CBIC.2013.111","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.111","url":null,"abstract":"This work presents the implementation of an agent based model concept to simulate a sample of the German society under a governmental social transfer system. Subsequently the behavior of the model is analyzed under changing conditions in order to proof that it can be used for the simulation of real societies under similar conditions. An important objective is to give evidence on economic interdependencies between individual behavior, governmental interaction and macroeconomic outcomes. The model is based on the economic concept of Homo Oeconomicus, while it widens some restrictions of this concept aiming to create a framework that resembles reality more closely. Our analysis provides evidence that the model works reasonably well and can serve as a basis for more detailed investigations.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114490062","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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