{"title":"Blind no more: constant time non-random improving moves and exponentially powerful recombination","authors":"L. D. Whitley","doi":"10.1145/2598394.2605349","DOIUrl":"https://doi.org/10.1145/2598394.2605349","url":null,"abstract":"","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121847955","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}
When data is released or shared among different organizations, some sensitive or confidential information may be subject to be exposed by using data mining tools. Thus, a question arises: how can we protect sensitive knowledge while allowing other parties to extract the knowledge behind the shared data. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A sensitive rule can be hidden by adding items into the dataset to make the support of the antecedent part of the sensitive rule increase and accordingly the confidence of the sensitive rule decrease. The evolutionary multi-objective optimization (EMO) algorithm is utilized to find suitable transactions (or tuples) to be modified so as the side effects to be minimized. Experiments on real datasets demonstrated the effectiveness of the proposed method.
{"title":"Use EMO to protect sensitive knowledge in association rule mining by adding items","authors":"Peng Cheng, Jeng-Shyang Pan","doi":"10.1145/2598394.2598465","DOIUrl":"https://doi.org/10.1145/2598394.2598465","url":null,"abstract":"When data is released or shared among different organizations, some sensitive or confidential information may be subject to be exposed by using data mining tools. Thus, a question arises: how can we protect sensitive knowledge while allowing other parties to extract the knowledge behind the shared data. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A sensitive rule can be hidden by adding items into the dataset to make the support of the antecedent part of the sensitive rule increase and accordingly the confidence of the sensitive rule decrease. The evolutionary multi-objective optimization (EMO) algorithm is utilized to find suitable transactions (or tuples) to be modified so as the side effects to be minimized. Experiments on real datasets demonstrated the effectiveness of the proposed method.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114525295","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}
Mina Moradi Kordmahalleh, M. G. Sefidmazgi, A. Homaifar, Dukka Bahadur, A. Guiseppi-Elie
In nonlinear and chaotic time series prediction, constructing the mathematical model of the system dynamics is not an easy task. Partially connected Artificial Neural Network with Evolvable Topology (PANNET) is a new paradigm for prediction of chaotic time series without access to the dynamics and essential memory depth of the system. Evolvable topology of the PANNET provides flexibility in recognition of systems in contrast to fixed layered topology of the traditional ANNs. This evolvable topology guides the relationship between observation nodes and hidden nodes, where hidden nodes are extra nodes that play the role of memory or internal states of the system. In the proposed variable-length Genetic Algorithm (GA), internal neurons can be connected arbitrarily to any type of nodes. Besides, number of neurons, inputs and outputs for each neuron, origin and weight of each connection evolve in order to find the best configuration of the network.
{"title":"Time-series forecasting with evolvable partially connected artificial neural network","authors":"Mina Moradi Kordmahalleh, M. G. Sefidmazgi, A. Homaifar, Dukka Bahadur, A. Guiseppi-Elie","doi":"10.1145/2598394.2598435","DOIUrl":"https://doi.org/10.1145/2598394.2598435","url":null,"abstract":"In nonlinear and chaotic time series prediction, constructing the mathematical model of the system dynamics is not an easy task. Partially connected Artificial Neural Network with Evolvable Topology (PANNET) is a new paradigm for prediction of chaotic time series without access to the dynamics and essential memory depth of the system. Evolvable topology of the PANNET provides flexibility in recognition of systems in contrast to fixed layered topology of the traditional ANNs. This evolvable topology guides the relationship between observation nodes and hidden nodes, where hidden nodes are extra nodes that play the role of memory or internal states of the system. In the proposed variable-length Genetic Algorithm (GA), internal neurons can be connected arbitrarily to any type of nodes. Besides, number of neurons, inputs and outputs for each neuron, origin and weight of each connection evolve in order to find the best configuration of the network.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124103162","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}
Could decisions made during some search iterations use information discovered by other search iterations? Then store that information in tags: data that persist between search iterations.
{"title":"Tagging in metaheuristics","authors":"Ben Kovitz, J. Swan","doi":"10.1145/2598394.2609844","DOIUrl":"https://doi.org/10.1145/2598394.2609844","url":null,"abstract":"Could decisions made during some search iterations use information discovered by other search iterations? Then store that information in tags: data that persist between search iterations.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122363568","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}
R. Cerri, Rodrigo C. Barros, A. Freitas, A. Carvalho
Hierarchical Multi-Label Classification (HMC) is a complex classification problem where instances can be classified into many classes simultaneously, and these classes are organized in a hierarchical structure, having subclasses and superclasses. In this paper, we investigate the HMC problem of assign functions to proteins, being each function represented by a class (term) in the Gene Ontology (GO) taxonomy. It is a very difficult task, since the GO taxonomy has thousands of classes. We propose a Genetic Algorithm (GA) to generate HMC rules able to classify a given protein in a set of GO terms, respecting the hierarchical constraints imposed by the GO taxonomy. The proposed GA evolves rules with propositional and relational tests. Experiments using ten protein function datasets showed the potential of the method when compared to other literature methods.
{"title":"Evolving relational hierarchical classification rules for predicting gene ontology-based protein functions","authors":"R. Cerri, Rodrigo C. Barros, A. Freitas, A. Carvalho","doi":"10.1145/2598394.2611384","DOIUrl":"https://doi.org/10.1145/2598394.2611384","url":null,"abstract":"Hierarchical Multi-Label Classification (HMC) is a complex classification problem where instances can be classified into many classes simultaneously, and these classes are organized in a hierarchical structure, having subclasses and superclasses. In this paper, we investigate the HMC problem of assign functions to proteins, being each function represented by a class (term) in the Gene Ontology (GO) taxonomy. It is a very difficult task, since the GO taxonomy has thousands of classes. We propose a Genetic Algorithm (GA) to generate HMC rules able to classify a given protein in a set of GO terms, respecting the hierarchical constraints imposed by the GO taxonomy. The proposed GA evolves rules with propositional and relational tests. Experiments using ten protein function datasets showed the potential of the method when compared to other literature methods.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129740131","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}
We apply an evolutionary strategies (ES) algorithm to the problem of designing modulation schemes used in wireless communication systems. The ES is used to optimize the digital symbol to analog signal mapping, called a constellation. Typical human-designed constellations are compared to the constellations produced by our algorithms in a simulated radio environment with noise and multipath, in terms of bit error rate. We conclude that the algorithm, with diversity maintenance, find solutions that equal or outperform conventional ones in a given radio channel model, especially for those with higher number of symbols in the constellation (arity).
{"title":"Evolution of digital modulation schemes for radio systems","authors":"Ervin Teng, Derek Kozel, Bob Iannucci, J. Lohn","doi":"10.1145/2598394.2598449","DOIUrl":"https://doi.org/10.1145/2598394.2598449","url":null,"abstract":"We apply an evolutionary strategies (ES) algorithm to the problem of designing modulation schemes used in wireless communication systems. The ES is used to optimize the digital symbol to analog signal mapping, called a constellation. Typical human-designed constellations are compared to the constellations produced by our algorithms in a simulated radio environment with noise and multipath, in terms of bit error rate. We conclude that the algorithm, with diversity maintenance, find solutions that equal or outperform conventional ones in a given radio channel model, especially for those with higher number of symbols in the constellation (arity).","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129822379","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 the Multidimensional Knapsack Problem (MKP) there are items easily identifiable as highly (lowly) profitable and likely to be chosen (not chosen) to compose high-quality solutions. For all the other items, the Knapsack Core~(KC), the decision is harder. By focusing the search on the KC effective algorithms have been developed. However, the true KC is not available and most algorithms can only rely on items' efficiencies. Chu & Beasley Genetic Algorithm (CBGA), for example, uses efficiencies in a repair-operator which bias the search towards the KC. This paper shows that, as the search progresses, efficiencies lose their descriptive power and, consequently, CBGA's effectiveness decreases. As a result, CBGA rapidly finds its best solutions and stagnates. In order to circumvent this stagnation, extra information about the KC should be used to implement specific operators. Since there is a correlation between marginal probabilities in a population and efficiencies, we show that KCs can be estimated from the population during the search. By solving the estimated KCs with CPLEX, improvements were possible in many instances, evidencing CBGA's weakness to solve KCs and indicating a promising way to improve GAs for the MKP through the use of KC estimates.
{"title":"On the effectiveness of genetic algorithms for the multidimensional knapsack problem","authors":"J. P. Martins, Humberto J. Longo, A. Delbem","doi":"10.1145/2598394.2598477","DOIUrl":"https://doi.org/10.1145/2598394.2598477","url":null,"abstract":"In the Multidimensional Knapsack Problem (MKP) there are items easily identifiable as highly (lowly) profitable and likely to be chosen (not chosen) to compose high-quality solutions. For all the other items, the Knapsack Core~(KC), the decision is harder. By focusing the search on the KC effective algorithms have been developed. However, the true KC is not available and most algorithms can only rely on items' efficiencies. Chu & Beasley Genetic Algorithm (CBGA), for example, uses efficiencies in a repair-operator which bias the search towards the KC. This paper shows that, as the search progresses, efficiencies lose their descriptive power and, consequently, CBGA's effectiveness decreases. As a result, CBGA rapidly finds its best solutions and stagnates. In order to circumvent this stagnation, extra information about the KC should be used to implement specific operators. Since there is a correlation between marginal probabilities in a population and efficiencies, we show that KCs can be estimated from the population during the search. By solving the estimated KCs with CPLEX, improvements were possible in many instances, evidencing CBGA's weakness to solve KCs and indicating a promising way to improve GAs for the MKP through the use of KC estimates.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130186785","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}
There exit high variations among nano-devices in nano-electronic systems, owing to the extremely small size and the bottom-up self-assembly nanofabrication process. Therefore, it is important to develop logical function mapping techniques with the consideration of variation tolerance. In this paper, the variation tolerant logical mapping (VTLM) problem is treated as a multi-objective optimization problem (MOP), a hybridization of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a problem-specific local search is presented to solve the problem. The experiment results show that with the assistance of the problem-specific local search, the presented algorithm is effective, and can find better solutions than that without the local search.
{"title":"Hybridization of NSGA-II with greedy re-assignment for variation tolerant logic mapping on nano-scale crossbar architectures","authors":"Fugui Zhong, Bo Yuan, Bin Li","doi":"10.1145/2598394.2598430","DOIUrl":"https://doi.org/10.1145/2598394.2598430","url":null,"abstract":"There exit high variations among nano-devices in nano-electronic systems, owing to the extremely small size and the bottom-up self-assembly nanofabrication process. Therefore, it is important to develop logical function mapping techniques with the consideration of variation tolerance. In this paper, the variation tolerant logical mapping (VTLM) problem is treated as a multi-objective optimization problem (MOP), a hybridization of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a problem-specific local search is presented to solve the problem. The experiment results show that with the assistance of the problem-specific local search, the presented algorithm is effective, and can find better solutions than that without the local search.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128446635","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 optimization problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimized simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum corresponds to a set of so-called Pareto-optimal solutions for which no other solution has better function values in all objectives. Evolutionary Multiobjective Optimization (EMO) algorithms are widely used in practice for solving multiobjective optimization problems due to several reasons. As stochastic blackbox algorithms, EMO approaches allow to tackle problems with nonlinear, nondifferentiable, or noisy objective functions. As set-based algorithms, they allow to compute or approximate the full set of Pareto-optimal solutions in one algorithm run---opposed to classical solution-based techniques from the multicriteria decision making (MCDM) field. Using EMO approaches in practice has two other advantages: they allow to learn about a problem formulation, for example, by automatically revealing common design principles among (Pareto-optimal) solutions (innovization) and it has been shown that certain single-objective problems become easier to solve with randomized search heuristics if the problem is reformulated as a multiobjective one (multiobjectivization). This tutorial aims at giving a broad introduction to the EMO field and at presenting some of its recent research results in more detail. More specifically, we are going to (i) introduce the basic principles of EMO algorithms in comparison to classical solution-based approaches, (ii) show a few practical examples which motivate the use of EMO in terms of the mentioned innovization and multiobjectivization principles, and (iii) present a general overview of state-of-the-art algorithms and techniques. Moreover, we will present some of the most important research results in areas such as indicator-based EMO, preference articulation, and performance assessment. Though classified as introductory, this tutorial is intended for both novices and regular users of EMO. Those without any knowledge will learn about the foundations of multiobjective optimization and the basic working principles of state-of-the-art EMO algorithms. Open questions, presented throughout the tutorial, can serve for all participants as a starting point for future research and/or discussions during the conference.
{"title":"GECCO 2014 tutorial on evolutionary multiobjective optimization","authors":"D. Brockhoff","doi":"10.1145/2598394.2605339","DOIUrl":"https://doi.org/10.1145/2598394.2605339","url":null,"abstract":"Many optimization problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimized simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum corresponds to a set of so-called Pareto-optimal solutions for which no other solution has better function values in all objectives. Evolutionary Multiobjective Optimization (EMO) algorithms are widely used in practice for solving multiobjective optimization problems due to several reasons. As stochastic blackbox algorithms, EMO approaches allow to tackle problems with nonlinear, nondifferentiable, or noisy objective functions. As set-based algorithms, they allow to compute or approximate the full set of Pareto-optimal solutions in one algorithm run---opposed to classical solution-based techniques from the multicriteria decision making (MCDM) field. Using EMO approaches in practice has two other advantages: they allow to learn about a problem formulation, for example, by automatically revealing common design principles among (Pareto-optimal) solutions (innovization) and it has been shown that certain single-objective problems become easier to solve with randomized search heuristics if the problem is reformulated as a multiobjective one (multiobjectivization). This tutorial aims at giving a broad introduction to the EMO field and at presenting some of its recent research results in more detail. More specifically, we are going to (i) introduce the basic principles of EMO algorithms in comparison to classical solution-based approaches, (ii) show a few practical examples which motivate the use of EMO in terms of the mentioned innovization and multiobjectivization principles, and (iii) present a general overview of state-of-the-art algorithms and techniques. Moreover, we will present some of the most important research results in areas such as indicator-based EMO, preference articulation, and performance assessment. Though classified as introductory, this tutorial is intended for both novices and regular users of EMO. Those without any knowledge will learn about the foundations of multiobjective optimization and the basic working principles of state-of-the-art EMO algorithms. Open questions, presented throughout the tutorial, can serve for all participants as a starting point for future research and/or discussions during the conference.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126847017","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}
It is our great pleasure to welcome you to the GECCO'14 Student Workshop! The goal of the Student Workshop, organized as a joined event for graduate and undergraduate students, is to assist the students with their research in the field of Evolutionary Computation. Exceeding our expectations in both the number and quality of submitted papers, 14 peer-reviewed papers have finally been accepted for presentation at the workshop. They cover a wide range of subjects in evolutionary computation, presenting advances in theory as well as applications, e.g. robotics and the travelling salesman problem. The topics include particle swarm algorithms as well as flood evolution, reinforcement learning, parallelism, niching, and parameter tuning, and many more, all yielding interesting contributions to the field. During the workshop, the students will receive useful feedback on the quality of their work and presentation style. This will be assured by a question and answer period after each talk led by a mentor panel of established researchers. The students are encouraged to use this opportunity to get highly qualified feedback not only on the presented subject but also on future research directions. As it was good practice in the last years, the best contributions will receive a small award sponsored by GECCO. In addition, the contributing students are invited to present their work as a poster at the GECCO'14 Poster Session -- an excellent opportunity to network with industrial and academic members of the community. We hope that the variety of covered topics will catch the attention of a wide range of GECCO'14 attendees, who will learn about fresh research ideas and meet young researchers with related interests. Other students are encouraged to attend the workshop to learn from the work of their colleagues and broaden their (scientific) horizons.
{"title":"Session details: Workshop: student workshop","authors":"Tea Tušar, B. Naujoks","doi":"10.1145/3250287","DOIUrl":"https://doi.org/10.1145/3250287","url":null,"abstract":"It is our great pleasure to welcome you to the GECCO'14 Student Workshop! The goal of the Student Workshop, organized as a joined event for graduate and undergraduate students, is to assist the students with their research in the field of Evolutionary Computation. Exceeding our expectations in both the number and quality of submitted papers, 14 peer-reviewed papers have finally been accepted for presentation at the workshop. They cover a wide range of subjects in evolutionary computation, presenting advances in theory as well as applications, e.g. robotics and the travelling salesman problem. The topics include particle swarm algorithms as well as flood evolution, reinforcement learning, parallelism, niching, and parameter tuning, and many more, all yielding interesting contributions to the field. During the workshop, the students will receive useful feedback on the quality of their work and presentation style. This will be assured by a question and answer period after each talk led by a mentor panel of established researchers. The students are encouraged to use this opportunity to get highly qualified feedback not only on the presented subject but also on future research directions. As it was good practice in the last years, the best contributions will receive a small award sponsored by GECCO. In addition, the contributing students are invited to present their work as a poster at the GECCO'14 Poster Session -- an excellent opportunity to network with industrial and academic members of the community. We hope that the variety of covered topics will catch the attention of a wide range of GECCO'14 attendees, who will learn about fresh research ideas and meet young researchers with related interests. Other students are encouraged to attend the workshop to learn from the work of their colleagues and broaden their (scientific) horizons.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126932085","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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