Pub Date : 2014-08-11DOI: 10.1109/INISTA.2014.6873623
Héctor D. Menéndez, David Camacho
Image Segmentation is one of the most challenging problems in Computer Vision. This process consists in dividing an image in different parts which share a common property, for example, identify a concrete object within a photo. Different approaches have been developed over the last years. This work is focused on Unsupervised Data Mining methodologies, specially on Graph Clustering methods, and their application to previous problems. These techniques blindly divide the image into different parts according to a criterion. This work applies a Multi-Objective Genetic Algorithm in order to perform good clustering results comparing to classical and modern clustering algorithms. The algorithm is analysed and compared against different clustering methods, using a precision and recall evaluation, and the Berkeley Image Database to carry out the experimental evaluation.
{"title":"A Multi-Objective Graph-based Genetic Algorithm for image segmentation","authors":"Héctor D. Menéndez, David Camacho","doi":"10.1109/INISTA.2014.6873623","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873623","url":null,"abstract":"Image Segmentation is one of the most challenging problems in Computer Vision. This process consists in dividing an image in different parts which share a common property, for example, identify a concrete object within a photo. Different approaches have been developed over the last years. This work is focused on Unsupervised Data Mining methodologies, specially on Graph Clustering methods, and their application to previous problems. These techniques blindly divide the image into different parts according to a criterion. This work applies a Multi-Objective Genetic Algorithm in order to perform good clustering results comparing to classical and modern clustering algorithms. The algorithm is analysed and compared against different clustering methods, using a precision and recall evaluation, and the Berkeley Image Database to carry out the experimental evaluation.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129942904","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 : 2014-06-23DOI: 10.1109/INISTA.2014.6873604
Thunchira Thongmee, Hiroto Suzuki, T. Ohno, U. Silparcha
This paper proposes the Blockwise Strong Relationship (BSR) method that calculates the degree of relationship between any pair of stocks based on only their prices. Our method deploys the data transformation adapted from the symbolic aggregation approximation (SAX) and the distance measure using dynamic time warping (DTW). We propose that the time series data should be processed in blocks of some appropriate size rather than the whole series at once. The experiment was done using IMI Energy indices. The result shows that our method can accurately draw the strongest related pair of stocks out of those that all look related on the surface.
{"title":"Finding Strong Relationships of stock prices using blockwise symbolic representation with dynamic time warping","authors":"Thunchira Thongmee, Hiroto Suzuki, T. Ohno, U. Silparcha","doi":"10.1109/INISTA.2014.6873604","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873604","url":null,"abstract":"This paper proposes the Blockwise Strong Relationship (BSR) method that calculates the degree of relationship between any pair of stocks based on only their prices. Our method deploys the data transformation adapted from the symbolic aggregation approximation (SAX) and the distance measure using dynamic time warping (DTW). We propose that the time series data should be processed in blocks of some appropriate size rather than the whole series at once. The experiment was done using IMI Energy indices. The result shows that our method can accurately draw the strongest related pair of stocks out of those that all look related on the surface.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121846079","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 : 2014-06-23DOI: 10.1109/INISTA.2014.6873630
Muhammad Afzal, Maqbool Hussain, W. A. Khan, Taqdir Ali, Sungyoung Lee, B. Kang
Healthcare domain is continuously growing with new knowledge emerged at different levels of clinical interest. At the same time, there is an increasing interest in the use of clinical decision support systems (CDSSs) to increase the healthcare quality and efficiency. Majorly the existing CDSSs are not designed to adapt scientific research in a well-established and automatic manner. Clinicians and researchers access the online resources on frequent basis for unmet questions during the course of patient care. They usually follow a dis-integrated approach to search for their required information from resources of their interest. Additionally, there is lack of defined mechanism to integrate the relevant knowledge for future use. To overcome the disintegrated and non-automatic approach, we introduce the concept of KnowledgeButton; a comprehensive model for evidence adaption from online credible knowledge sources in a well-defined and established manner. It saves the time of clinicians spend unnecessary in searching research evidence using disintegrated and manual mechanism. In this paper, we provide architecture design, workflows, and scenarios complemented with primary results. It covers walk-through from search query generation to evaluation of search results.
{"title":"KnowledgeButton: An evidence adaptive tool for CDSS and clinical research","authors":"Muhammad Afzal, Maqbool Hussain, W. A. Khan, Taqdir Ali, Sungyoung Lee, B. Kang","doi":"10.1109/INISTA.2014.6873630","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873630","url":null,"abstract":"Healthcare domain is continuously growing with new knowledge emerged at different levels of clinical interest. At the same time, there is an increasing interest in the use of clinical decision support systems (CDSSs) to increase the healthcare quality and efficiency. Majorly the existing CDSSs are not designed to adapt scientific research in a well-established and automatic manner. Clinicians and researchers access the online resources on frequent basis for unmet questions during the course of patient care. They usually follow a dis-integrated approach to search for their required information from resources of their interest. Additionally, there is lack of defined mechanism to integrate the relevant knowledge for future use. To overcome the disintegrated and non-automatic approach, we introduce the concept of KnowledgeButton; a comprehensive model for evidence adaption from online credible knowledge sources in a well-defined and established manner. It saves the time of clinicians spend unnecessary in searching research evidence using disintegrated and manual mechanism. In this paper, we provide architecture design, workflows, and scenarios complemented with primary results. It covers walk-through from search query generation to evaluation of search results.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129109291","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 : 2014-06-23DOI: 10.1109/INISTA.2014.6873645
E. O. Tartan, H. Erdem, A. Berkol
Efficient elevator group control is an important issue for vertical transportation in high-rise buildings. From the engineering design perspective, regulation of average waiting time and journey time while considering energy consumption is an optimization problem. Alternatively to the conventional algorithms for scheduling and dispatching cars to hall calls, intelligent systems based methods have drawn much attention in the last years. This study aims to improve the elevator group control system's performance by applying genetic algorithm based optimization algorithms considering two systems. Firstly, average passenger waiting time is optimized in the conventional elevator systems in which a hall call is submitted by indicating the travel direction. Secondly, a recent development in elevator industry is considered and it is assumed that instead of direction indicators there are destination button panels at floors that allow passengers to specify their destinations. In this case optimization of average waiting time, journey time and car trip time is investigated. Two proposed algorithms have been applied considering preload conditions in a building with 20 floors and 4 cars. The simulation results have been compared with a previous study and conventional duplex algorithm.
{"title":"Optimization of waiting and journey time in group elevator system using genetic algorithm","authors":"E. O. Tartan, H. Erdem, A. Berkol","doi":"10.1109/INISTA.2014.6873645","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873645","url":null,"abstract":"Efficient elevator group control is an important issue for vertical transportation in high-rise buildings. From the engineering design perspective, regulation of average waiting time and journey time while considering energy consumption is an optimization problem. Alternatively to the conventional algorithms for scheduling and dispatching cars to hall calls, intelligent systems based methods have drawn much attention in the last years. This study aims to improve the elevator group control system's performance by applying genetic algorithm based optimization algorithms considering two systems. Firstly, average passenger waiting time is optimized in the conventional elevator systems in which a hall call is submitted by indicating the travel direction. Secondly, a recent development in elevator industry is considered and it is assumed that instead of direction indicators there are destination button panels at floors that allow passengers to specify their destinations. In this case optimization of average waiting time, journey time and car trip time is investigated. Two proposed algorithms have been applied considering preload conditions in a building with 20 floors and 4 cars. The simulation results have been compared with a previous study and conventional duplex algorithm.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114377806","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 : 2014-06-23DOI: 10.1109/INISTA.2014.6873647
Jian Yu, Ping Guo, A. Luo
Deconvolution is known as an ill-posed problem. In order to solve such a problem, a regularization method is needed to constrain the solution space and find a plausible and stable solution. In practice, it is very computation intensive when using cross-validation method to select the regularization parameter. In this paper, we present an adaptive regularization method to find the optimal regularization parameter value and represent the trade-off between model fitness of the data and the smoothness of the extracted signal. Spectral signal extraction experimental results demonstrate that the time complexity the proposed method is much lower than the one without adaptive regularization and is convenient for users also. And quantitative performance analysis show that the proposed intelligent approach performs better than that of current deconvolution extraction method and other extraction method used in the Large Area Multi-Objects Fiber Spectroscopy Telescope spectral signal processing pipeline.
{"title":"Adaptive regularization deconvolution extraction algorithm for spectral signal processing","authors":"Jian Yu, Ping Guo, A. Luo","doi":"10.1109/INISTA.2014.6873647","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873647","url":null,"abstract":"Deconvolution is known as an ill-posed problem. In order to solve such a problem, a regularization method is needed to constrain the solution space and find a plausible and stable solution. In practice, it is very computation intensive when using cross-validation method to select the regularization parameter. In this paper, we present an adaptive regularization method to find the optimal regularization parameter value and represent the trade-off between model fitness of the data and the smoothness of the extracted signal. Spectral signal extraction experimental results demonstrate that the time complexity the proposed method is much lower than the one without adaptive regularization and is convenient for users also. And quantitative performance analysis show that the proposed intelligent approach performs better than that of current deconvolution extraction method and other extraction method used in the Large Area Multi-Objects Fiber Spectroscopy Telescope spectral signal processing pipeline.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130771655","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 : 2014-06-23DOI: 10.1109/INISTA.2014.6873597
F. Bisio, P. Gastaldo, R. Zunino, S. Decherchi
Inductive bias represents an important factor in learning theory, as it can shape the generalization properties of a learning machine. This paper shows that biased regularization can be used as inductive bias to effectively tackle the semi-supervised classification problem. Thus, semi-supervised learning is formalized as a supervised learning problem biased by an unsupervised reference solution. The proposed framework has been tested on a malware-detection problem. Experimental results confirmed the effectiveness of the semi-supervised methodology presented in this paper.
{"title":"Semi-supervised machine learning approach for unknown malicious software detection","authors":"F. Bisio, P. Gastaldo, R. Zunino, S. Decherchi","doi":"10.1109/INISTA.2014.6873597","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873597","url":null,"abstract":"Inductive bias represents an important factor in learning theory, as it can shape the generalization properties of a learning machine. This paper shows that biased regularization can be used as inductive bias to effectively tackle the semi-supervised classification problem. Thus, semi-supervised learning is formalized as a supervised learning problem biased by an unsupervised reference solution. The proposed framework has been tested on a malware-detection problem. Experimental results confirmed the effectiveness of the semi-supervised methodology presented in this paper.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131169489","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 : 2014-06-23DOI: 10.1109/INISTA.2014.6873608
Zeynep Banu Ozger, B. Diri, Canan Girgin
Question identification is a field Natural Language Processing and also Information Extraction. The aim of work is detecting Turkish tweets which are including question expressions. The application contains three stages: applying some pre-processing steps to data set for cleaning unnecessary data like Retweet, determining candidate tweets via a rule-based method and extracting tweets which are really include questions using Conditional Random Fields. For this purpose one million tweets were collected and labeled. Tweets are ungrammatical data type. According to results; the model developed has been largely successful on tweets. Additionally, it is a first study about identifying questions on Turkish tweets.
{"title":"Question identification on Turkish tweets","authors":"Zeynep Banu Ozger, B. Diri, Canan Girgin","doi":"10.1109/INISTA.2014.6873608","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873608","url":null,"abstract":"Question identification is a field Natural Language Processing and also Information Extraction. The aim of work is detecting Turkish tweets which are including question expressions. The application contains three stages: applying some pre-processing steps to data set for cleaning unnecessary data like Retweet, determining candidate tweets via a rule-based method and extracting tweets which are really include questions using Conditional Random Fields. For this purpose one million tweets were collected and labeled. Tweets are ungrammatical data type. According to results; the model developed has been largely successful on tweets. Additionally, it is a first study about identifying questions on Turkish tweets.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122365330","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 : 2014-06-23DOI: 10.1109/INISTA.2014.6873620
Y. Todorov, M. Terziyska
This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear system dynamics. The presented approach assumes a state-space representation in order to obtain a more compact form of the model, without statement of a great number of parameters needed to represent a nonlinear behavior. To increase the flexibility of the network, simple Takagi-Sugeno inferences are used to estimate the current system states, by a set of a multiple local linear state estimators. Afterwards, the output of the network is defined, as function of the current and estimated system parameters. A simple learning algorithm based on two step Gradient descent procedure to adjust the network parameters, is applied. The potentials of the proposed modeling network are demonstrated by simulation experiments to model an oscillating pendulum and a nonlinear drying plant.
{"title":"State-space fuzzy-neural network for modeling of nonlinear dynamics","authors":"Y. Todorov, M. Terziyska","doi":"10.1109/INISTA.2014.6873620","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873620","url":null,"abstract":"This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear system dynamics. The presented approach assumes a state-space representation in order to obtain a more compact form of the model, without statement of a great number of parameters needed to represent a nonlinear behavior. To increase the flexibility of the network, simple Takagi-Sugeno inferences are used to estimate the current system states, by a set of a multiple local linear state estimators. Afterwards, the output of the network is defined, as function of the current and estimated system parameters. A simple learning algorithm based on two step Gradient descent procedure to adjust the network parameters, is applied. The potentials of the proposed modeling network are demonstrated by simulation experiments to model an oscillating pendulum and a nonlinear drying plant.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121129477","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 : 2014-06-23DOI: 10.1109/INISTA.2014.6873589
Xinyi Le, Jun Wang, Zheng Yan
A neurodynamic optimization approach is proposed for robust pole assignment problem of second-order control systems via output feedback. With a suitable robustness measure serving as the objective function, the robust pole assignment problem is formulated as a quasi-convex optimization problem with linear constraints. Next, the problem further is reformulated as a convex feasibility problem. Two coupled recurrent neural networks are applied for solving the optimization problem with guaranteed optimality and exact pole assignment. Simulation results are included to substantiate the effectiveness of the proposed approach.
{"title":"Neurodynamics-based robust pole assignment for synthesizing second-order control systems via output feedback based on a convex feasibility problem reformulation","authors":"Xinyi Le, Jun Wang, Zheng Yan","doi":"10.1109/INISTA.2014.6873589","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873589","url":null,"abstract":"A neurodynamic optimization approach is proposed for robust pole assignment problem of second-order control systems via output feedback. With a suitable robustness measure serving as the objective function, the robust pole assignment problem is formulated as a quasi-convex optimization problem with linear constraints. Next, the problem further is reformulated as a convex feasibility problem. Two coupled recurrent neural networks are applied for solving the optimization problem with guaranteed optimality and exact pole assignment. Simulation results are included to substantiate the effectiveness of the proposed approach.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127934727","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 : 2014-06-23DOI: 10.1109/INISTA.2014.6873612
Michael During, P. Pascheka
Autonomous agents plan their paths through known and unknown environments to reach their goals. When multiple autonomous agents share the same area, conflict situations may occur that need to be solved. We present a decentralized decision making algorithm to solve conflicts among autonomous agents. It is based on two main ideas: First, we introduce an innovative operationalization of cooperative behavior which allows to determine whether a behavior is cooperative by computing the total utility and comparing it to a reference utility. Second, we use motion primitives as a representation of available maneuvers obeying individual and environmental restrictions. The decentralized decision making algorithm is based on communication among the autonomous agents to find an optimal maneuver combination. Simulations show that our algorithm is applicable to different highway traffic scenarios of two automated vehicles. We use a mean-square acceleration as an individual cost function and show that our intelligent controller leads to cooperative solutions.
{"title":"Cooperative decentralized decision making for conflict resolution among autonomous agents","authors":"Michael During, P. Pascheka","doi":"10.1109/INISTA.2014.6873612","DOIUrl":"https://doi.org/10.1109/INISTA.2014.6873612","url":null,"abstract":"Autonomous agents plan their paths through known and unknown environments to reach their goals. When multiple autonomous agents share the same area, conflict situations may occur that need to be solved. We present a decentralized decision making algorithm to solve conflicts among autonomous agents. It is based on two main ideas: First, we introduce an innovative operationalization of cooperative behavior which allows to determine whether a behavior is cooperative by computing the total utility and comparing it to a reference utility. Second, we use motion primitives as a representation of available maneuvers obeying individual and environmental restrictions. The decentralized decision making algorithm is based on communication among the autonomous agents to find an optimal maneuver combination. Simulations show that our algorithm is applicable to different highway traffic scenarios of two automated vehicles. We use a mean-square acceleration as an individual cost function and show that our intelligent controller leads to cooperative solutions.","PeriodicalId":339652,"journal":{"name":"2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134122199","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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