Marcelo Bassani de Freitas, George D. C. Cavalcanti, R. Sabourin
Smart environments possess devices that collaborate to help the user non-intrusively. One possible aid smart environment offer is to anticipate user's tasks and perform them on his/her behalf or facilitate the action completion. In this paper, we propose a framework that predicts user's actions by learning his/her behavior when interacting with the smart environment. We prepare the datasets and train a predictor that is responsible to decide whether a target transducer value should be changed or not. Our solution achieves a significant improvement for all target transducers studied and most combinations of parameters yields better results than the base case.
{"title":"Transducer State Prediction System for Smart Environment Intelligent Control","authors":"Marcelo Bassani de Freitas, George D. C. Cavalcanti, R. Sabourin","doi":"10.1109/BRACIS.2015.32","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.32","url":null,"abstract":"Smart environments possess devices that collaborate to help the user non-intrusively. One possible aid smart environment offer is to anticipate user's tasks and perform them on his/her behalf or facilitate the action completion. In this paper, we propose a framework that predicts user's actions by learning his/her behavior when interacting with the smart environment. We prepare the datasets and train a predictor that is responsible to decide whether a target transducer value should be changed or not. Our solution achieves a significant improvement for all target transducers studied and most combinations of parameters yields better results than the base case.","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115455854","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}
D. V. D. Silva, R. Prudêncio, C. Ferraz, Alysson Bispo, T. Prota
In the last few years, cross-domain recommender systems emerged in order to improve and alleviate problems of single-domain recommender systems. Despite the great number of cross-domain recommender system approaches, there is a lack of studies concerned about the use of contextual features in cross domain recommender systems. The context-aware approach uses different contextual information (e.g., Location, time, and mood) in order to improve recommendations, where context can be treated as a bridge between different domains. In this paper, we investigate the adoption of two context-aware approaches in a cross-domain recommender system in order to improve its recommendation accuracy. For that, we describe the context aware cross-domain recommendation problem and the proposed context-aware algorithms. An experimental evaluation performed using a real dataset indicates that context-aware techniques can be a good approach in order to improve the cross-domain recommendation accuracy.
{"title":"Context-Aware Techniques for Cross-Domain Recommender Systems","authors":"D. V. D. Silva, R. Prudêncio, C. Ferraz, Alysson Bispo, T. Prota","doi":"10.1109/BRACIS.2015.42","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.42","url":null,"abstract":"In the last few years, cross-domain recommender systems emerged in order to improve and alleviate problems of single-domain recommender systems. Despite the great number of cross-domain recommender system approaches, there is a lack of studies concerned about the use of contextual features in cross domain recommender systems. The context-aware approach uses different contextual information (e.g., Location, time, and mood) in order to improve recommendations, where context can be treated as a bridge between different domains. In this paper, we investigate the adoption of two context-aware approaches in a cross-domain recommender system in order to improve its recommendation accuracy. For that, we describe the context aware cross-domain recommendation problem and the proposed context-aware algorithms. An experimental evaluation performed using a real dataset indicates that context-aware techniques can be a good approach in order to improve the cross-domain recommendation accuracy.","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128290490","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}
Michael Cruz, Hendrik T. Macedo, Adolfo P. Guimarães
Vehicle congestion is a serious concern in metropolitan areas. Some policies have been adopted in order to soften the problem: construction of alternative routes, encouragement for the use of bicycles, improvement on public transportation, among others. A practice that might help is carpooling. Carpooling consists in sharing private vehicle space among people with similar trajectories. Although there exist some software initiatives to facilitate the carpooling practice, none of them actually provides some key facilities such as searching for people with similar trajectories. The way in which such a trajectory is represented is also central. In the specific context of carpooling, the use of Points of Interest (POI) as a method for trajectory discretization is rather relevant. In this paper, we consider that and other assumptions to propose an innovative approach to generate trajectory clusters for carpooling purposes, based on Optics algorithm. We also propose a new similarity measure for trajectories. Two experiments have been performed in order to prove the feasibility of the approach. Furthermore, we compare our approach with K-means and Optics. Results have showed that the proposed approach has results similar for Davies-Boulding index (DBI).
{"title":"Grouping Similar Trajectories for Carpooling Purposes","authors":"Michael Cruz, Hendrik T. Macedo, Adolfo P. Guimarães","doi":"10.1109/BRACIS.2015.36","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.36","url":null,"abstract":"Vehicle congestion is a serious concern in metropolitan areas. Some policies have been adopted in order to soften the problem: construction of alternative routes, encouragement for the use of bicycles, improvement on public transportation, among others. A practice that might help is carpooling. Carpooling consists in sharing private vehicle space among people with similar trajectories. Although there exist some software initiatives to facilitate the carpooling practice, none of them actually provides some key facilities such as searching for people with similar trajectories. The way in which such a trajectory is represented is also central. In the specific context of carpooling, the use of Points of Interest (POI) as a method for trajectory discretization is rather relevant. In this paper, we consider that and other assumptions to propose an innovative approach to generate trajectory clusters for carpooling purposes, based on Optics algorithm. We also propose a new similarity measure for trajectories. Two experiments have been performed in order to prove the feasibility of the approach. Furthermore, we compare our approach with K-means and Optics. Results have showed that the proposed approach has results similar for Davies-Boulding index (DBI).","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128954257","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}
Many-Objective Optimization Problems (MaOPs) are problems that have more than three objectives to be optimized. Usually, the state-of-art of Multi-Objective Evolutionary algorithms scale poorly when the number of objective functions increases. To overcome this limitation, researches are investigating multi-swarm approaches. Besides, another newly strategy is the use of reference points to enhance the search of the algorithms. Based on those strategies, this work proposes a new multi-swarm algorithm, called Reference-Point Based Multi-Swarm Algorithm, R-Multi, which takes advantages of reference points to guide a multi-swarm search. The main idea is to use reference points to guide the search towards the Pareto front and to perform the communication between swarms allowing the necessary collaboration to have an effective exploration of the search space. Furthermore, this work presents a set of experiments that compare R-Multi to others multi-swarm algorithms and to MOEA/D-DRA. The algorithms are evaluated in several MaOPs observing both convergence and diversity. The results shows the validity of the proposed algorithm and stresses the good results of multi-swarm approaches in Many-Objective Optimization.
{"title":"Reference-Point Based Multi-swarm Algorithm for Many-Objective Problems","authors":"André Britto, A. Pozo","doi":"10.1109/BRACIS.2015.19","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.19","url":null,"abstract":"Many-Objective Optimization Problems (MaOPs) are problems that have more than three objectives to be optimized. Usually, the state-of-art of Multi-Objective Evolutionary algorithms scale poorly when the number of objective functions increases. To overcome this limitation, researches are investigating multi-swarm approaches. Besides, another newly strategy is the use of reference points to enhance the search of the algorithms. Based on those strategies, this work proposes a new multi-swarm algorithm, called Reference-Point Based Multi-Swarm Algorithm, R-Multi, which takes advantages of reference points to guide a multi-swarm search. The main idea is to use reference points to guide the search towards the Pareto front and to perform the communication between swarms allowing the necessary collaboration to have an effective exploration of the search space. Furthermore, this work presents a set of experiments that compare R-Multi to others multi-swarm algorithms and to MOEA/D-DRA. The algorithms are evaluated in several MaOPs observing both convergence and diversity. The results shows the validity of the proposed algorithm and stresses the good results of multi-swarm approaches in Many-Objective Optimization.","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127374232","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}
Multi-objective evolutionary algorithms (MOEAs) have been successfully applied for solving different software engineering problems. However, adapting and configuring these algorithms for a specific problem can demand significant effort from software engineers. Therefore, to help in this task, a hyper-heuristic, named HITO (Hyper-heuristic for the Integration and Test Order problem) was proposed to adaptively select search operators during the optimization process. HITO was successfully applied using NSGA-II for solving the integration and test order problem. HITO can use two hyper-heuristic selection methods: Choice Function and Multi-armed Bandit. However, a hypotheses behind this study is that HITO does not depend of NSGA-II and can be used with other MOEAs. To this aim, this paper presents results from evaluation experiments comparing the performance of HITO using two different MOEAs: NSGA-II and SPEA2. The results show that HITO is able to outperform both MOEAs.
多目标进化算法(moea)已成功地应用于解决各种软件工程问题。然而，针对特定问题调整和配置这些算法可能需要软件工程师付出巨大的努力。为此，提出了一种超启发式算法HITO (hyperheuristic for the Integration and Test Order problem)，用于优化过程中自适应地选择搜索算子。利用NSGA-II成功地应用HITO解决了集成和测试顺序问题。HITO可以使用两种超启发式选择方法:选择函数和多臂强盗。然而，本研究背后的假设是HITO不依赖于NSGA-II，可以与其他moea一起使用。为此，本文给出了使用NSGA-II和SPEA2两种不同的moea对HITO性能进行比较的评估实验结果。结果表明，HITO能够优于这两种moea。
{"title":"Evaluating a Multi-objective Hyper-Heuristic for the Integration and Test Order Problem","authors":"Giovani Guizzo, S. Vergilio, A. Pozo","doi":"10.1109/BRACIS.2015.11","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.11","url":null,"abstract":"Multi-objective evolutionary algorithms (MOEAs) have been successfully applied for solving different software engineering problems. However, adapting and configuring these algorithms for a specific problem can demand significant effort from software engineers. Therefore, to help in this task, a hyper-heuristic, named HITO (Hyper-heuristic for the Integration and Test Order problem) was proposed to adaptively select search operators during the optimization process. HITO was successfully applied using NSGA-II for solving the integration and test order problem. HITO can use two hyper-heuristic selection methods: Choice Function and Multi-armed Bandit. However, a hypotheses behind this study is that HITO does not depend of NSGA-II and can be used with other MOEAs. To this aim, this paper presents results from evaluation experiments comparing the performance of HITO using two different MOEAs: NSGA-II and SPEA2. The results show that HITO is able to outperform both MOEAs.","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132158486","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}
Francisco Caio M. Rodrigues, Lucas P. Queiroz, J. Gomes, Javam C. Machado
Graphics cards are complex electronic systems designed for high performance applications. Due to its processing power, graphics cards may operate at high temperatures, leading its components to a significant degradation level. This fact is even more present when any of the heat exchange components is not working properly. In such cases, graphics cards may operate in temperatures that are higher than the specified by the manufacturers. This work presents a methodology to detect over temperature events in graphics cards using regression models. The proposed approach was tested in real graphics cards from different manufacturers. The final model achieved promising results.
{"title":"Predicting Overtemperature Events in Graphics Cards Using Regression Models","authors":"Francisco Caio M. Rodrigues, Lucas P. Queiroz, J. Gomes, Javam C. Machado","doi":"10.1109/BRACIS.2015.38","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.38","url":null,"abstract":"Graphics cards are complex electronic systems designed for high performance applications. Due to its processing power, graphics cards may operate at high temperatures, leading its components to a significant degradation level. This fact is even more present when any of the heat exchange components is not working properly. In such cases, graphics cards may operate in temperatures that are higher than the specified by the manufacturers. This work presents a methodology to detect over temperature events in graphics cards using regression models. The proposed approach was tested in real graphics cards from different manufacturers. The final model achieved promising results.","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122236729","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}
Bayesian networks are probabilistic graphical models capable of modeling statistical uncertainty and are widely applied in many classification problems. Specifically, Naive Bayesian networks are largely used due to their simple, naive structure, while still producing precise results. Fuzzy systems, on the other hand, are a well known technique capable of dealing with linguistic vagueness by representing knowledge with simple and interpretable rules and membership functions. As traditional fuzzy systems are unable to model statistical uncertainty, Probabilistic Fuzzy Systems were developed in order to account for both kinds of uncertainties. In this work we propose the Probabilistic Fuzzy Naive Bayes classifier as a combination of both probabilistic fuzzy systems and naive bayesian networks, also capable of simultaneously modeling both kinds of uncertainties. The proposed model is firstly applied in a very simple classification problem in order to show its potential and advantage over traditional naive bayes classifiers, while maintaining their interpretability. For validation, experiments were done using benchmark classification data sets from the UCI machine learning repository and the results are then compared with other similar alternate methods.
{"title":"Probabilistic Fuzzy Naive Bayes","authors":"Gabriel Moura, M. Roisenberg","doi":"10.1109/BRACIS.2015.48","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.48","url":null,"abstract":"Bayesian networks are probabilistic graphical models capable of modeling statistical uncertainty and are widely applied in many classification problems. Specifically, Naive Bayesian networks are largely used due to their simple, naive structure, while still producing precise results. Fuzzy systems, on the other hand, are a well known technique capable of dealing with linguistic vagueness by representing knowledge with simple and interpretable rules and membership functions. As traditional fuzzy systems are unable to model statistical uncertainty, Probabilistic Fuzzy Systems were developed in order to account for both kinds of uncertainties. In this work we propose the Probabilistic Fuzzy Naive Bayes classifier as a combination of both probabilistic fuzzy systems and naive bayesian networks, also capable of simultaneously modeling both kinds of uncertainties. The proposed model is firstly applied in a very simple classification problem in order to show its potential and advantage over traditional naive bayes classifiers, while maintaining their interpretability. For validation, experiments were done using benchmark classification data sets from the UCI machine learning repository and the results are then compared with other similar alternate methods.","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127734099","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}
In this paper we investigate variations of Hidden Markov Models (HMM) as a viable tool for predicting the sentiment of soccer fans based on information regarding the result of matches. The models were constructed from data collected from a social network where fans of a soccer team periodically express feelings towards their team. Our claim is that the change in a fan's sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation performed between variations of the proposed models showed that a second order HMM, considering the match results and fan's gambling information, is the most accurate model.
{"title":"Using Markov Models to Learn the Sentiment of Soccer Fans from Bets and the Result of Matches","authors":"Rafael Bomfim, Vasco Furtado","doi":"10.1109/BRACIS.2015.60","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.60","url":null,"abstract":"In this paper we investigate variations of Hidden Markov Models (HMM) as a viable tool for predicting the sentiment of soccer fans based on information regarding the result of matches. The models were constructed from data collected from a social network where fans of a soccer team periodically express feelings towards their team. Our claim is that the change in a fan's sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation performed between variations of the proposed models showed that a second order HMM, considering the match results and fan's gambling information, is the most accurate model.","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128657771","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}
Learning concepts from data streams differs significantly from traditional batch learning, because in data streams the concepts to be learned may evolve over time. Incremental learning paradigm is a promising approach for learning in a data stream setting. However, in the presence of concept drifts, outdated concepts can cause misclassifications. Although several incremental Gaussian mixture models methods have been proposed in the literature, we notice that these algorithms lack an explicit policy to discard outdated concepts. In this paper, we propose a new incremental algorithm for data stream learning based on Gaussian Mixture Models. The proposed method is compared to various algorithms widely used in the literature, and the results show that it is competitive with them in various scenarios, overcoming them in some cases.
{"title":"IGMM-CD: A Gaussian Mixture Classification Algorithm for Data Streams with Concept Drifts","authors":"Luan Soares Oliveira, Gustavo E. A. P. A. Batista","doi":"10.1109/BRACIS.2015.61","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.61","url":null,"abstract":"Learning concepts from data streams differs significantly from traditional batch learning, because in data streams the concepts to be learned may evolve over time. Incremental learning paradigm is a promising approach for learning in a data stream setting. However, in the presence of concept drifts, outdated concepts can cause misclassifications. Although several incremental Gaussian mixture models methods have been proposed in the literature, we notice that these algorithms lack an explicit policy to discard outdated concepts. In this paper, we propose a new incremental algorithm for data stream learning based on Gaussian Mixture Models. The proposed method is compared to various algorithms widely used in the literature, and the results show that it is competitive with them in various scenarios, overcoming them in some cases.","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125970241","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}
Sampling and computation budgets are two of the key elements that determine the performance of a reinforcement learning algorithm. In essence, any reinforcement learning agent must sample the environment and perform some computation over the samples to decide its best action. Although very fundamental, the trade-off between sampling and computation is still not well understood. In this paper, we explore this trade-off in an actor-critic perspective. First, we propose a new RL algorithm, Dyna-MLAC, which uses model-based actor-critic updates (MLAC) within the Dyna framework. Then, we numerically indicate that the convergence time of Dyna-MLAC is smaller than pre-existing solutions, and that Dyna-MLAC allows to efficiently trade number of samples and computation time.
{"title":"Dyna-MLAC: Trading Computational and Sample Complexities in Actor-Critic Reinforcement Learning","authors":"Bruno Costa, W. Caarls, D. Menasché","doi":"10.1109/BRACIS.2015.62","DOIUrl":"https://doi.org/10.1109/BRACIS.2015.62","url":null,"abstract":"Sampling and computation budgets are two of the key elements that determine the performance of a reinforcement learning algorithm. In essence, any reinforcement learning agent must sample the environment and perform some computation over the samples to decide its best action. Although very fundamental, the trade-off between sampling and computation is still not well understood. In this paper, we explore this trade-off in an actor-critic perspective. First, we propose a new RL algorithm, Dyna-MLAC, which uses model-based actor-critic updates (MLAC) within the Dyna framework. Then, we numerically indicate that the convergence time of Dyna-MLAC is smaller than pre-existing solutions, and that Dyna-MLAC allows to efficiently trade number of samples and computation time.","PeriodicalId":416771,"journal":{"name":"2015 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114886005","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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