Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.36
R. Moreira, N. Ebecken, A. S. Alves, F. França
This paper presents a method of tracking sea surface targets in video using the WiSARD weightless neural network. The tracking of objects in video is an important and challenging task in many applications. Difficulties can arise due to weather conditions, target trajectory and appearance, occlusions, lighting conditions and noise. Tracking is a high-level application and requires the object location frame by frame in real time. At each frame, a tracker based on detection by segmentation performs three main steps: detection, tracking and analysis of the object characteristics. These steps depend on the segmentation quality and the tracking performed by the WiSARD neural network depends on the image binarization quality. This paper proposes a fast hybrid binarization (thresholding and edge detection) in YcbCr color model and ways to configure a WiSARD neural network to improve efficiency when binarization errors occur.
{"title":"Tracking Targets in Sea Surface with the WiSARD Weightless Neural Network","authors":"R. Moreira, N. Ebecken, A. S. Alves, F. França","doi":"10.1109/BRICS-CCI-CBIC.2013.36","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.36","url":null,"abstract":"This paper presents a method of tracking sea surface targets in video using the WiSARD weightless neural network. The tracking of objects in video is an important and challenging task in many applications. Difficulties can arise due to weather conditions, target trajectory and appearance, occlusions, lighting conditions and noise. Tracking is a high-level application and requires the object location frame by frame in real time. At each frame, a tracker based on detection by segmentation performs three main steps: detection, tracking and analysis of the object characteristics. These steps depend on the segmentation quality and the tracking performed by the WiSARD neural network depends on the image binarization quality. This paper proposes a fast hybrid binarization (thresholding and edge detection) in YcbCr color model and ways to configure a WiSARD neural network to improve efficiency when binarization errors occur.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"30 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":"128004672","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.2013.34
C. Bastos-Filho, D. O. Nascimento
Fish School Search (FSS) is swarm-based optimizer that excels on multimodal search problems, but presents some drawbacks, such as the necessity to proper define the step used in some operators and the need to evaluate the fitness function twice per fish per iteration. This paper presents a simpler and enhanced version of the FSS, that features three advantages over the original FSS: high exploitation capability, just one fitness evaluation per fish per iteration and easy implementation. We name this novel version as FSS-II. Our proposal was compared to the FSS and the two most used PSO variations in terms velocity of convergence and robustness in six benchmark functions. FSS-II outperformed the other approaches in most of cases.
鱼群搜索(Fish School Search, FSS)是一种基于群体的优化器，它在多模态搜索问题上表现优异，但也存在一些缺点，例如在某些运算符中需要正确定义所使用的步骤，并且每次迭代需要对每条鱼评估两次适应度函数。本文提出了一种更简单和增强的FSS版本，与原始FSS相比，它具有三个优点:高开发能力，每次迭代只对每条鱼进行一次适应度评估，易于实现。我们将这个新版本命名为FSS-II。在六个基准函数的收敛速度和鲁棒性方面，我们的建议与FSS和两种最常用的PSO变量进行了比较。在大多数情况下，FSS-II优于其他方法。
{"title":"An Enhanced Fish School Search Algorithm","authors":"C. Bastos-Filho, D. O. Nascimento","doi":"10.1109/BRICS-CCI-CBIC.2013.34","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.34","url":null,"abstract":"Fish School Search (FSS) is swarm-based optimizer that excels on multimodal search problems, but presents some drawbacks, such as the necessity to proper define the step used in some operators and the need to evaluate the fitness function twice per fish per iteration. This paper presents a simpler and enhanced version of the FSS, that features three advantages over the original FSS: high exploitation capability, just one fitness evaluation per fish per iteration and easy implementation. We name this novel version as FSS-II. Our proposal was compared to the FSS and the two most used PSO variations in terms velocity of convergence and robustness in six benchmark functions. FSS-II outperformed the other approaches in most of cases.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"2018 38","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120971365","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.2013.72
F. Mota, V. Rosa, S. Botelho, Iverton Santos, G. Dimuro
Simulation of home use of electric energy is a very powerful tool for the purpose of studying, planning and managing at electric energy distribution companies. This paper presents a NetLogo-based multi-agent system for energy consumption simulation in residential areas. Several possible consumers profiles and household appliances with different powers are modeled and simulated using computational agents. Seven distinct profiles of possible behaviors of consumers and household appliances with different powers are modeled and simulated using computational agents.
{"title":"Simulating the Consumers' Energy Profiles Using Multiagent Systems","authors":"F. Mota, V. Rosa, S. Botelho, Iverton Santos, G. Dimuro","doi":"10.1109/BRICS-CCI-CBIC.2013.72","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.72","url":null,"abstract":"Simulation of home use of electric energy is a very powerful tool for the purpose of studying, planning and managing at electric energy distribution companies. This paper presents a NetLogo-based multi-agent system for energy consumption simulation in residential areas. Several possible consumers profiles and household appliances with different powers are modeled and simulated using computational agents. Seven distinct profiles of possible behaviors of consumers and household appliances with different powers are modeled and simulated using computational agents.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"43 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":"124039630","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.2013.106
Matsilele Mabaso, Bhekisipho Twala, D. Withey
Tracking of multiple bright particles (spots) in fluorescence microscopy image sequences is seen as a crucial step in understanding complex information in the cell. However, fluorescence microscopy generates high a volume of noisy image data that cannot be analysed efficiently by means of manual analysis. In this study we compare the performance of two computer-based tracking methods for tracking of bright particles in fluorescence microscopy image sequences. The methods under comparison are, Interacting Multiple Model filter and Feature Point Tracking. The performance of the methods is validated using synthetic but realistic image sequences and real images. The results from experiments show that the Interacting Multiple Model filter performed best, under the test conditions.
{"title":"Quantitative Comparison of Two Particle Tracking Methods in Fluorescence Microscopy Images","authors":"Matsilele Mabaso, Bhekisipho Twala, D. Withey","doi":"10.1109/BRICS-CCI-CBIC.2013.106","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.106","url":null,"abstract":"Tracking of multiple bright particles (spots) in fluorescence microscopy image sequences is seen as a crucial step in understanding complex information in the cell. However, fluorescence microscopy generates high a volume of noisy image data that cannot be analysed efficiently by means of manual analysis. In this study we compare the performance of two computer-based tracking methods for tracking of bright particles in fluorescence microscopy image sequences. The methods under comparison are, Interacting Multiple Model filter and Feature Point Tracking. The performance of the methods is validated using synthetic but realistic image sequences and real images. The results from experiments show that the Interacting Multiple Model filter performed best, under the test conditions.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"2 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":"132499264","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.2013.43
J. Baumgartner, A. G. Flesia, J. Gimenez, J. Pucheta
Image segmentation is one of the fundamental problems in computer vision. In this work, we present a new segmentation algorithm that is based on the theory of two-dimensional hidden Markov models (2D-HMM). Unlike most 2D-HMM approaches we do not apply the Viterbi Algorithm, instead we present a computationally efficient algorithm that propagates the state probabilities through the image. This approach can easily be extended to higher dimensions. We compare the proposed method with a 2D-HMM standard algorithm and Iterated Conditional Modes using real world images like a radiography or a satellite image as well as synthetic images. The experimental results show that our approach is highly capable of condensing image segments. This gives our algorithm a significant advantage over the standard algorithm when dealing with noisy images with few classes.
{"title":"A New Approach to Image Segmentation with Two-Dimensional Hidden Markov Models","authors":"J. Baumgartner, A. G. Flesia, J. Gimenez, J. Pucheta","doi":"10.1109/BRICS-CCI-CBIC.2013.43","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.43","url":null,"abstract":"Image segmentation is one of the fundamental problems in computer vision. In this work, we present a new segmentation algorithm that is based on the theory of two-dimensional hidden Markov models (2D-HMM). Unlike most 2D-HMM approaches we do not apply the Viterbi Algorithm, instead we present a computationally efficient algorithm that propagates the state probabilities through the image. This approach can easily be extended to higher dimensions. We compare the proposed method with a 2D-HMM standard algorithm and Iterated Conditional Modes using real world images like a radiography or a satellite image as well as synthetic images. The experimental results show that our approach is highly capable of condensing image segments. This gives our algorithm a significant advantage over the standard algorithm when dealing with noisy images with few classes.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"17 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":"123685896","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.2013.114
G. Farias, G. Dimuro, Glenda Dimuro, Esteban de Manuel Jerez
Piaget's theory of social exchanges has been used as the basis for the analysis of interactions in Multiagent Systems, allowing the modeling of interactions as services exchange processes between pairs of agents, followed by the evaluation of those services by the agents involved, producing the so-called social exchange values. The purpose of this work is to develop a BDI-Fuzzy agent model for the Jason platform, with abilities to assess qualitatively, subjectively the social exchanges values originated in the provision and in the receipt of non-economic services, based on Piaget's theory of social exchanges. An application to the simulation of exchange processes in a social organization, namely, the urban vegetable garden San Jerónimo (Seville, Spain) is presented.
{"title":"Exchanges of Services Based on Piaget's theory of Social Exchanges Using a BDI-Fuzzy Agent Model","authors":"G. Farias, G. Dimuro, Glenda Dimuro, Esteban de Manuel Jerez","doi":"10.1109/BRICS-CCI-CBIC.2013.114","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.114","url":null,"abstract":"Piaget's theory of social exchanges has been used as the basis for the analysis of interactions in Multiagent Systems, allowing the modeling of interactions as services exchange processes between pairs of agents, followed by the evaluation of those services by the agents involved, producing the so-called social exchange values. The purpose of this work is to develop a BDI-Fuzzy agent model for the Jason platform, with abilities to assess qualitatively, subjectively the social exchanges values originated in the provision and in the receipt of non-economic services, based on Piaget's theory of social exchanges. An application to the simulation of exchange processes in a social organization, namely, the urban vegetable garden San Jerónimo (Seville, Spain) is presented.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"4 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":"117153315","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.2013.44
P. S. D. M. Neto, Rosilda B. Souza, George D. C. Cavalcanti, T. Ferreira
Traditionally, the countries classification is performed based on several features, that are related to economic and social factors. However, the classification process is costly due to the difficulty of obtaining those features and the need for intervention of human expertise. In this paper, we propose an intelligent agent that classifies countries based on financial indices. The artificial agent calculates the probability density function (pdf) of the return series of financial indices. This pdf characterizes the fluctuation of a market. Based on the return series and pdf, the volatility and the B coefficient of the exponential function, that describe the behavior of world markets, are estimated. Then, the intelligent agent classifies the indices from developed and developing countries using a Self-Organizing Map (SOM) neural network. The results show that the proposed intelligent agent is an accurate, fast and cheap alternative to the classifications provided by traditional organizations.
{"title":"An Intelligent Agent to Classify Countries Based on Financial Indices","authors":"P. S. D. M. Neto, Rosilda B. Souza, George D. C. Cavalcanti, T. Ferreira","doi":"10.1109/BRICS-CCI-CBIC.2013.44","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.44","url":null,"abstract":"Traditionally, the countries classification is performed based on several features, that are related to economic and social factors. However, the classification process is costly due to the difficulty of obtaining those features and the need for intervention of human expertise. In this paper, we propose an intelligent agent that classifies countries based on financial indices. The artificial agent calculates the probability density function (pdf) of the return series of financial indices. This pdf characterizes the fluctuation of a market. Based on the return series and pdf, the volatility and the B coefficient of the exponential function, that describe the behavior of world markets, are estimated. Then, the intelligent agent classifies the indices from developed and developing countries using a Self-Organizing Map (SOM) neural network. The results show that the proposed intelligent agent is an accurate, fast and cheap alternative to the classifications provided by traditional organizations.","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":"128878093","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.2013.120
Thiago Dias, Wallace A. Pinheiro, R. Salles
This article presents the experience in the development of a semantic platform for decision support based on the DOODA cycle that aims to help dengue epidemics control. We discuss the formal representation of data as type of dengue, hydration tents and relief efforts classified in epidemiology, infrastructure and civil defense domains on an ontology that associated with a reasoner and a map engine, provides an overview of the epidemic and geolocalised actions that must be performed on each affected region. We conclude by presenting the results of simulations with data from the epidemic occurred in Rio de Janeiro in 2008.
{"title":"A Semantic Platform of Support Decision to Manage Dengue Epidemics","authors":"Thiago Dias, Wallace A. Pinheiro, R. Salles","doi":"10.1109/BRICS-CCI-CBIC.2013.120","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.120","url":null,"abstract":"This article presents the experience in the development of a semantic platform for decision support based on the DOODA cycle that aims to help dengue epidemics control. We discuss the formal representation of data as type of dengue, hydration tents and relief efforts classified in epidemiology, infrastructure and civil defense domains on an ontology that associated with a reasoner and a map engine, provides an overview of the epidemic and geolocalised actions that must be performed on each affected region. We conclude by presenting the results of simulations with data from the epidemic occurred in Rio de Janeiro in 2008.","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":"128374497","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.2013.89
Nestor T. M. Junior, Pablo V. A. Barros, Bruno José Torres Fernandes, B. Bezerra, Sergio M. M. Fernandes
Real-time recognition of dynamic gestures is a problem for most of the applications nowadays. The prediction approach can be used as a solution for this. This approach uses an incomplete gesture input and it tries to predict which gesture the given input represents. This paper presents the application of the dynamic gesture feature extraction technique called Convexity Local Contour Sequence (CLCS) as the extractor for the prediction task. Two predictor systems are used to achieve this task and results are compared and discussed in this paper.
{"title":"A Dynamic Gesture Prediction System Based on the CLCS Feature Extraction","authors":"Nestor T. M. Junior, Pablo V. A. Barros, Bruno José Torres Fernandes, B. Bezerra, Sergio M. M. Fernandes","doi":"10.1109/BRICS-CCI-CBIC.2013.89","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.89","url":null,"abstract":"Real-time recognition of dynamic gestures is a problem for most of the applications nowadays. The prediction approach can be used as a solution for this. This approach uses an incomplete gesture input and it tries to predict which gesture the given input represents. This paper presents the application of the dynamic gesture feature extraction technique called Convexity Local Contour Sequence (CLCS) as the extractor for the prediction task. Two predictor systems are used to achieve this task and results are compared and discussed in this paper.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"88 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":"122519040","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.2013.66
R. De Moraes Calazan, N. Nedjah, L. de Macedo Mourelle
Particle Swarm Optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. Compared to existing stochastic methods, PSO is very robust. Nevertheless, for real-world optimizations, it requires a high computational effort. In general, parallel implementations of PSO provide better performance. However, this depends heavily on the parallelization strategy engineered as well as the number and characteristics of the exploited processors. In this paper, we propose a cooperative strategy, which consists of subdividing an optimization problem into many simpler sub problems. Each of these sub-problems focuses on a distinct subset of the original problem dimensions. The optimization work for all the selected sub-problems is done in parallel. We map the work onto a GPU-based architecture. The performance of the strategy thus implemented is evaluated for four benchmark functions with high-dimension and different complexity and compared to that yielded by other parallelization strategies.
{"title":"A Cooperative Parallel Particle Swarm Optimization for High-Dimension Problems on GPUs","authors":"R. De Moraes Calazan, N. Nedjah, L. de Macedo Mourelle","doi":"10.1109/BRICS-CCI-CBIC.2013.66","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.66","url":null,"abstract":"Particle Swarm Optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. Compared to existing stochastic methods, PSO is very robust. Nevertheless, for real-world optimizations, it requires a high computational effort. In general, parallel implementations of PSO provide better performance. However, this depends heavily on the parallelization strategy engineered as well as the number and characteristics of the exploited processors. In this paper, we propose a cooperative strategy, which consists of subdividing an optimization problem into many simpler sub problems. Each of these sub-problems focuses on a distinct subset of the original problem dimensions. The optimization work for all the selected sub-problems is done in parallel. We map the work onto a GPU-based architecture. The performance of the strategy thus implemented is evaluated for four benchmark functions with high-dimension and different complexity and compared to that yielded by other parallelization strategies.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"6 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":"114889845","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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