Pub Date : 2014-10-16DOI: 10.1109/NaBIC.2014.6921861
A. Madureira, Bruno Cunha, J. Pereira, I. Pereira, S. Gomes
User modeling and user adaptive interaction research areas are becoming crucial applied issues to understand and support users as they interact with technology. Modeling the decisions to be made and the constraints placed by market globalization in a way that can address the needs of all stakeholders has been a long time area of academic and industrial research, mainly for Planning, Scheduling, and Strategic decision making areas. Business analysts, developers, and organizations involved in all phases of the business value chain have requirements for applied business insight through modeling. In this paper, an architecture for user modeling on Intelligent and Adaptive Scheduling System is proposed.
{"title":"An architecture for user modeling on Intelligent and Adaptive Scheduling Systems","authors":"A. Madureira, Bruno Cunha, J. Pereira, I. Pereira, S. Gomes","doi":"10.1109/NaBIC.2014.6921861","DOIUrl":"https://doi.org/10.1109/NaBIC.2014.6921861","url":null,"abstract":"User modeling and user adaptive interaction research areas are becoming crucial applied issues to understand and support users as they interact with technology. Modeling the decisions to be made and the constraints placed by market globalization in a way that can address the needs of all stakeholders has been a long time area of academic and industrial research, mainly for Planning, Scheduling, and Strategic decision making areas. Business analysts, developers, and organizations involved in all phases of the business value chain have requirements for applied business insight through modeling. In this paper, an architecture for user modeling on Intelligent and Adaptive Scheduling System is proposed.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121188768","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 : 2014-10-16DOI: 10.1109/NaBIC.2014.6921850
Sergio Santander-Jiménez, M. A. Vega-Rodríguez
The inference of phylogenetic relationships represents one of the most challenging problems in bioinformatics. The increasing availability of biological data motivates the development of new algorithmic designs to conduct phylogenetic analyses on exponentially increasing search spaces. Bioinspired metaheuristics have arisen as a useful approach to address this problem, introducing different search strategies according to the way phylogenetic trees are represented and handled by the algorithm. In this work, we study the multiobjective and biological performance achieved by different Multiobjective Artificial Bee Colony implementations based on direct (tree-based) and indirect (distance-based) individual representations. Experiments on four real nucleotide data sets show meaningful differences in multiobjective performance between the analyzed approaches, obtaining significant biological results in comparison with other state-of-the-art phylogenetic methods.
{"title":"Performance analysis of Multiobjective Artificial Bee Colony implementations for phylogenetic reconstruction","authors":"Sergio Santander-Jiménez, M. A. Vega-Rodríguez","doi":"10.1109/NaBIC.2014.6921850","DOIUrl":"https://doi.org/10.1109/NaBIC.2014.6921850","url":null,"abstract":"The inference of phylogenetic relationships represents one of the most challenging problems in bioinformatics. The increasing availability of biological data motivates the development of new algorithmic designs to conduct phylogenetic analyses on exponentially increasing search spaces. Bioinspired metaheuristics have arisen as a useful approach to address this problem, introducing different search strategies according to the way phylogenetic trees are represented and handled by the algorithm. In this work, we study the multiobjective and biological performance achieved by different Multiobjective Artificial Bee Colony implementations based on direct (tree-based) and indirect (distance-based) individual representations. Experiments on four real nucleotide data sets show meaningful differences in multiobjective performance between the analyzed approaches, obtaining significant biological results in comparison with other state-of-the-art phylogenetic methods.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125144620","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 : 2014-10-16DOI: 10.1109/NaBIC.2014.6921872
Joana D. Dantas, L. Varela
In this paper are applied heuristics that try to find good solutions for a static single machine scheduling problem. In the considered problem different processing times and due dates are used and no preemption is allowed. The heuristics applied consider several performance measures, which intend to be customer and enterprise oriented. Customer oriented performance measures are mainly related to the accomplishment of due dates while enterprise-oriented ones typically consider other time-oriented measures, like the makespan. The heuristics used in this work are focused on Just-in-Time principles and on different costumer and enterprise performance measures, although preference is given to customer-oriented measures, namely the total number of tardy jobs and the maximum tardiness.
{"title":"Scheduling single-machine problem based on just-in-time principles","authors":"Joana D. Dantas, L. Varela","doi":"10.1109/NaBIC.2014.6921872","DOIUrl":"https://doi.org/10.1109/NaBIC.2014.6921872","url":null,"abstract":"In this paper are applied heuristics that try to find good solutions for a static single machine scheduling problem. In the considered problem different processing times and due dates are used and no preemption is allowed. The heuristics applied consider several performance measures, which intend to be customer and enterprise oriented. Customer oriented performance measures are mainly related to the accomplishment of due dates while enterprise-oriented ones typically consider other time-oriented measures, like the makespan. The heuristics used in this work are focused on Just-in-Time principles and on different costumer and enterprise performance measures, although preference is given to customer-oriented measures, namely the total number of tardy jobs and the maximum tardiness.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121797262","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 : 2014-10-16DOI: 10.1109/NaBIC.2014.6921869
A. Madureira, S. Gomes, Bruno Cunha, J. Pereira, J. M. Santos, I. Pereira
Current manufacturing scheduling has still difficulties to deal with real-world situations and, hence, human intervention is required to maintain real-time adaptation and optimization, to efficiently adapt to the inherent complex system dynamics. In this paper the prototype of an Adaptive Decision Support System for Interactive Scheduling with MetaCognition and User Modeling Experience (ADSyS) is proposed. A preliminary usability evaluation was streamlined to collect user's opinion about the system performance and interaction model.
{"title":"Prototype of an Adaptive Decision Support System for Interactive Scheduling with MetaCognition and User Modeling Experience","authors":"A. Madureira, S. Gomes, Bruno Cunha, J. Pereira, J. M. Santos, I. Pereira","doi":"10.1109/NaBIC.2014.6921869","DOIUrl":"https://doi.org/10.1109/NaBIC.2014.6921869","url":null,"abstract":"Current manufacturing scheduling has still difficulties to deal with real-world situations and, hence, human intervention is required to maintain real-time adaptation and optimization, to efficiently adapt to the inherent complex system dynamics. In this paper the prototype of an Adaptive Decision Support System for Interactive Scheduling with MetaCognition and User Modeling Experience (ADSyS) is proposed. A preliminary usability evaluation was streamlined to collect user's opinion about the system performance and interaction model.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121967532","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 : 2014-10-16DOI: 10.1109/NaBIC.2014.6921858
Emmanuel Dufourq, N. Pillay
Genetic programming (GP) has been applied to solve data classification problems numerous times in previous studies and the findings in the literature confirm that GP is able to perform well. In more recent studies, researchers have shown that using a team of classifiers can outperform a single classifier. These teams are referred to as ensembles. Previously, several different attempts at creating ensembles have been investigated; some more complex than others. In this study, four approaches have been proposed, in which the ensemble methods hybridize a genetic algorithm with a GP algorithm in different ways. The first three approaches made use of a generational GP model, while the fourth used a steady state GP model. The four approaches were tested on eight public data sets and the findings confirm that the proposed ensembles outperform the standard GP method, and additionally outperform other GP methods found in literature.
{"title":"Hybridizing evolutionary algorithms for creating classifier ensembles","authors":"Emmanuel Dufourq, N. Pillay","doi":"10.1109/NaBIC.2014.6921858","DOIUrl":"https://doi.org/10.1109/NaBIC.2014.6921858","url":null,"abstract":"Genetic programming (GP) has been applied to solve data classification problems numerous times in previous studies and the findings in the literature confirm that GP is able to perform well. In more recent studies, researchers have shown that using a team of classifiers can outperform a single classifier. These teams are referred to as ensembles. Previously, several different attempts at creating ensembles have been investigated; some more complex than others. In this study, four approaches have been proposed, in which the ensemble methods hybridize a genetic algorithm with a GP algorithm in different ways. The first three approaches made use of a generational GP model, while the fourth used a steady state GP model. The four approaches were tested on eight public data sets and the findings confirm that the proposed ensembles outperform the standard GP method, and additionally outperform other GP methods found in literature.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130118621","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 : 2014-10-16DOI: 10.1109/NaBIC.2014.6921859
Francisco Serdio, Alexandru-Ciprian Zavoianu, E. Lughofer, K. Pichler, T. Buchegger, Hajrudin Efendic
We demonstrate how Residual-Based Fault Detection can be improved by means of Genetic-Fuzzy Systems (GFSs). Thus, the performance of a pure Data-Driven Fault Detection System, which relies on system identification models, is improved using models created by Genetic-Fuzzy Systems. The evolutionary approach is used in the cases where a deterministic training of the fuzzy systems is not able to produce good results. As such, when the deterministic optimization algorithm is trapped in local optima, GFSs are used in order to improve (fine tune) the non-global solutions using built-in genetic operators that are able to help converged solutions escape from their locality. The results are presented by means of Fault Detection Curves (FDC) -inspired by Receiver Operating Characteristic (ROC) curves- and show how, even when considering a Fault Detection (FD) system with good detection capabilities, the introduction of new, genetically evolved, fuzzy models still produces an important improvement, reflected by higher Areas Under the Curve (AUC).
{"title":"Hybrid Genetic-Fuzzy Systems for improved performance in Residual-Based Fault Detection","authors":"Francisco Serdio, Alexandru-Ciprian Zavoianu, E. Lughofer, K. Pichler, T. Buchegger, Hajrudin Efendic","doi":"10.1109/NaBIC.2014.6921859","DOIUrl":"https://doi.org/10.1109/NaBIC.2014.6921859","url":null,"abstract":"We demonstrate how Residual-Based Fault Detection can be improved by means of Genetic-Fuzzy Systems (GFSs). Thus, the performance of a pure Data-Driven Fault Detection System, which relies on system identification models, is improved using models created by Genetic-Fuzzy Systems. The evolutionary approach is used in the cases where a deterministic training of the fuzzy systems is not able to produce good results. As such, when the deterministic optimization algorithm is trapped in local optima, GFSs are used in order to improve (fine tune) the non-global solutions using built-in genetic operators that are able to help converged solutions escape from their locality. The results are presented by means of Fault Detection Curves (FDC) -inspired by Receiver Operating Characteristic (ROC) curves- and show how, even when considering a Fault Detection (FD) system with good detection capabilities, the introduction of new, genetically evolved, fuzzy models still produces an important improvement, reflected by higher Areas Under the Curve (AUC).","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127884723","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 : 2014-10-16DOI: 10.1109/NABIC.2014.6921880
Sebastián Basterrech, E. Alba, V. Snás̃el
This article introduces a robust hybrid method for solving supervised learning tasks, which uses the Echo State Network (ESN) model and the Particle Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with the hidden-hidden weights fixed in the learning process. The recurrent part of the network stores the input information in internal states of the network. Another structure forms a free-memory method used as supervised learning tool. The setting procedure for initializing the recurrent structure of the ESN model can impact on the model performance. On the other hand, the PSO has been shown to be a successful technique for finding optimal points in complex spaces. Here, we present an approach to use the PSO for finding some initial hidden-hidden weights of the ESN model. We present empirical results that compare the canonical ESN model with this hybrid method on a wide range of benchmark problems.
{"title":"An experimental analysis of the Echo State Network initialization using the Particle Swarm Optimization","authors":"Sebastián Basterrech, E. Alba, V. Snás̃el","doi":"10.1109/NABIC.2014.6921880","DOIUrl":"https://doi.org/10.1109/NABIC.2014.6921880","url":null,"abstract":"This article introduces a robust hybrid method for solving supervised learning tasks, which uses the Echo State Network (ESN) model and the Particle Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with the hidden-hidden weights fixed in the learning process. The recurrent part of the network stores the input information in internal states of the network. Another structure forms a free-memory method used as supervised learning tool. The setting procedure for initializing the recurrent structure of the ESN model can impact on the model performance. On the other hand, the PSO has been shown to be a successful technique for finding optimal points in complex spaces. Here, we present an approach to use the PSO for finding some initial hidden-hidden weights of the ESN model. We present empirical results that compare the canonical ESN model with this hybrid method on a wide range of benchmark problems.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126614899","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 : 2014-10-16DOI: 10.1109/NABIC.2014.6921864
Varun Ojha, K. Jackowski, A. Abraham, V. Snás̃el
Predicting the dissolution rate of proteins plays a significant role in pharmaceutical/medical applications. The rate of dissolution of Poly Lactic-co-Glycolic Acid (PLGA) micro- and nanoparticles is influenced by several factors. Considering all factors leads to a dataset with three hundred features, making the prediction difficult and inaccurate. Our present study consists of three phases. Firstly, dimensionality reduction techniques are applied in order to simplify the task and eliminate irrelevant and redundant attributes. Subsequently, a heterogeneous pool of several classical regression algorithms is created and evaluated. Regression algorithms in the pool are independently trained to identify the problem at hand. Finally, we test several ensemble methods in order to elevate the accuracy of the prediction. The Evolutionary Weighted Ensemble method proposed in this paper offered the lowest RMSE and significantly outperformed competing classical algorithms and other ensemble techniques.
{"title":"Feature selection and ensemble of regression models for predicting the protein macromolecule dissolution profile","authors":"Varun Ojha, K. Jackowski, A. Abraham, V. Snás̃el","doi":"10.1109/NABIC.2014.6921864","DOIUrl":"https://doi.org/10.1109/NABIC.2014.6921864","url":null,"abstract":"Predicting the dissolution rate of proteins plays a significant role in pharmaceutical/medical applications. The rate of dissolution of Poly Lactic-co-Glycolic Acid (PLGA) micro- and nanoparticles is influenced by several factors. Considering all factors leads to a dataset with three hundred features, making the prediction difficult and inaccurate. Our present study consists of three phases. Firstly, dimensionality reduction techniques are applied in order to simplify the task and eliminate irrelevant and redundant attributes. Subsequently, a heterogeneous pool of several classical regression algorithms is created and evaluated. Regression algorithms in the pool are independently trained to identify the problem at hand. Finally, we test several ensemble methods in order to elevate the accuracy of the prediction. The Evolutionary Weighted Ensemble method proposed in this paper offered the lowest RMSE and significantly outperformed competing classical algorithms and other ensemble techniques.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116437102","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 : 2014-10-16DOI: 10.1109/NaBIC.2014.6921899
T. Krasinski, Sebastian Sakowski, T. Popławski
A major challenge in DNA computing area is to design autonomous and programmable biomolecular devices built on DNA. The significant achievement in the field of DNA nanodevices was a laboratory implementation of the 2-state biomolecular finite automaton based on one restriction enzyme FokI [3]. Although this practical implementation represents a proof of concept for autonomous computing with DNA molecules, it has a limited computational power. The restriction enzyme FokI enables construction an automata with at most 3-states. We propose to use several restriction enzymes (instead of one) which act autonomously in a test tube to construct more powerful finite state machines. It enables to build any finite nondeterministic automata or even push-down automata. The autonomous operation of the automaton is based on alternating cleavages of DNA molecules by several restriction enzymes. We illustrate this new idea by presenting a laboratory implementation of a particular case of finite automata. In this experiment two restriction endonucleases act autonomously on DNA in one test tube. This approach may be used (in the future) to build nanomachines, even push-down automata (made of DNA molecules) which may be applied in medicine, pharmacy or biotechnology.
{"title":"Towards an autonomous multistate biomolecular devices built on DNA","authors":"T. Krasinski, Sebastian Sakowski, T. Popławski","doi":"10.1109/NaBIC.2014.6921899","DOIUrl":"https://doi.org/10.1109/NaBIC.2014.6921899","url":null,"abstract":"A major challenge in DNA computing area is to design autonomous and programmable biomolecular devices built on DNA. The significant achievement in the field of DNA nanodevices was a laboratory implementation of the 2-state biomolecular finite automaton based on one restriction enzyme FokI [3]. Although this practical implementation represents a proof of concept for autonomous computing with DNA molecules, it has a limited computational power. The restriction enzyme FokI enables construction an automata with at most 3-states. We propose to use several restriction enzymes (instead of one) which act autonomously in a test tube to construct more powerful finite state machines. It enables to build any finite nondeterministic automata or even push-down automata. The autonomous operation of the automaton is based on alternating cleavages of DNA molecules by several restriction enzymes. We illustrate this new idea by presenting a laboratory implementation of a particular case of finite automata. In this experiment two restriction endonucleases act autonomously on DNA in one test tube. This approach may be used (in the future) to build nanomachines, even push-down automata (made of DNA molecules) which may be applied in medicine, pharmacy or biotechnology.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114989282","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 : 2014-10-16DOI: 10.1109/NaBIC.2014.6921892
A. J. Tallón-Ballesteros, José Cristóbal Riquelme Santos
This paper introduces two statistical outlier detection approaches by classes. Experiments on binary and multi-class classification problems reveal that the partial removal of outliers improves significantly one or two performance measures for C4.5 and 1-nearest neighbour classifiers. Also, a taxonomy of problems according to the amount of outliers is proposed.
{"title":"Deleting or keeping outliers for classifier training?","authors":"A. J. Tallón-Ballesteros, José Cristóbal Riquelme Santos","doi":"10.1109/NaBIC.2014.6921892","DOIUrl":"https://doi.org/10.1109/NaBIC.2014.6921892","url":null,"abstract":"This paper introduces two statistical outlier detection approaches by classes. Experiments on binary and multi-class classification problems reveal that the partial removal of outliers improves significantly one or two performance measures for C4.5 and 1-nearest neighbour classifiers. Also, a taxonomy of problems according to the amount of outliers is proposed.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123058312","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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