Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.19
L. Antiqueira, Liang Zhao
Models of spiking neural networks have a great potential to become a crucial tool in the development of complex network theory. Of particular interest, these models can be used to better understand the important class of brain functional networks, which are frequently studied in the context of computational network analysis. A fundamental question is whether functional connectivity sampling via surface multichannel recordings is able to reproduce the main connectivity features of the underlying spatial neural network. In this work we address this problem through computational modeling using the integrate-and-fire spiking neuron model, which enabled us to relate neural connectivity and the respective mesoscopic dynamics. Functional samples were then compared to an idealized spatial neural network model in terms of established topological network measurements. Results show that some measurements (e.g., betweenness centrality) are able to fairly approximate functional and spatial networks. Therefore, under specific circumstances of sampling size and simulation approach, it is possible to say that functional networks are able to reproduce connectivity features of the underlying neural network.
{"title":"Structural Relationships between Spiking Neural Networks and Functional Samples","authors":"L. Antiqueira, Liang Zhao","doi":"10.1109/BRICS-CCI-CBIC.2013.19","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.19","url":null,"abstract":"Models of spiking neural networks have a great potential to become a crucial tool in the development of complex network theory. Of particular interest, these models can be used to better understand the important class of brain functional networks, which are frequently studied in the context of computational network analysis. A fundamental question is whether functional connectivity sampling via surface multichannel recordings is able to reproduce the main connectivity features of the underlying spatial neural network. In this work we address this problem through computational modeling using the integrate-and-fire spiking neuron model, which enabled us to relate neural connectivity and the respective mesoscopic dynamics. Functional samples were then compared to an idealized spatial neural network model in terms of established topological network measurements. Results show that some measurements (e.g., betweenness centrality) are able to fairly approximate functional and spatial networks. Therefore, under specific circumstances of sampling size and simulation approach, it is possible to say that functional networks are able to reproduce connectivity features of the underlying neural network.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132707034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.29
Filipe V. Nepomuceno, A. Engelbrecht
Heterogeneous particle swarm optimizers (HPSO) add multiple search behaviors to the swarm. This is done by allowing particles to utilize different update equations to each other. Dynamic and adaptive HPSO algorithms allow the particles to change their behaviors during the search. A number of factors come into play when dealing with the different behaviors, one of which is deciding when a particle should change its behavior. This paper presents a number of behavior changing schedules and strategies for HPSOs. The schedules are compared to each other using existing HPSO algorithms on the CEC 2013 benchmark functions for real-parameter optimization.
{"title":"Behavior Changing Schedules for Heterogeneous Particle Swarms","authors":"Filipe V. Nepomuceno, A. Engelbrecht","doi":"10.1109/BRICS-CCI-CBIC.2013.29","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.29","url":null,"abstract":"Heterogeneous particle swarm optimizers (HPSO) add multiple search behaviors to the swarm. This is done by allowing particles to utilize different update equations to each other. Dynamic and adaptive HPSO algorithms allow the particles to change their behaviors during the search. A number of factors come into play when dealing with the different behaviors, one of which is deciding when a particle should change its behavior. This paper presents a number of behavior changing schedules and strategies for HPSOs. The schedules are compared to each other using existing HPSO algorithms on the CEC 2013 benchmark functions for real-parameter optimization.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.62
R.B. Santos, E. O. Sousa, F.V. da Silva, S.L. da Cruz, A. Fileti
Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on acoustic method and on-line prediction of leak location using neural artificial networks. Audible noises generated by leakage were captured by a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1kHz, 5kHz and 9kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence, magnitude and location of a leak (outputs of the model). The results have shown the great potential of the technique and of the developed neural models. For all on-line tests, the neural model 1 (responsible for determining the occurrence and magnitude of the leak) showed 100% accuracy, except when the leakage occurred through a small orifice (1 mm), with leak located at 3 m from the microphone. In all cases where neural model 1 detected the leak, the neural model 2 (responsible determining the location) could accurately predict the exact location of the leak, except for an orifice of 3 mm, with leakage occurring at the inlet end of the pipeline, showing an error of approximately 1.2 m.
{"title":"Real-Time Monitoring of Gas Pipeline through Artificial Neural Networks","authors":"R.B. Santos, E. O. Sousa, F.V. da Silva, S.L. da Cruz, A. Fileti","doi":"10.1109/BRICS-CCI-CBIC.2013.62","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.62","url":null,"abstract":"Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on acoustic method and on-line prediction of leak location using neural artificial networks. Audible noises generated by leakage were captured by a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1kHz, 5kHz and 9kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence, magnitude and location of a leak (outputs of the model). The results have shown the great potential of the technique and of the developed neural models. For all on-line tests, the neural model 1 (responsible for determining the occurrence and magnitude of the leak) showed 100% accuracy, except when the leakage occurred through a small orifice (1 mm), with leak located at 3 m from the microphone. In all cases where neural model 1 detected the leak, the neural model 2 (responsible determining the location) could accurately predict the exact location of the leak, except for an orifice of 3 mm, with leakage occurring at the inlet end of the pipeline, showing an error of approximately 1.2 m.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132573592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.116
L. Pappalardo, F. Simini, S. Rinzivillo, D. Pedreschi, F. Giannotti
In the last years, the emergence of big data led scientists from diverse disciplines toward the study of the laws underlying human mobility. Although these recent discoveries have shed light on very interesting and fascinating aspects about people movements, they are generally focused on global and general mobility patterns. For this reason, they do not necessarily capture phenomena related to specific types of mobility, such as mobility by car, by public transportations means, by foot and so on. In this work, we aim to compare general human mobility with mobility expressed by a specific conveyance, trying to address the following question: What are the differences between general mobility and mobility by car? To answer this question, we present the results of an analysis performed on a big mobile phone dataset and on a GPS dataset storing information about car travels in Italy.
{"title":"Comparing General Mobility and Mobility by Car","authors":"L. Pappalardo, F. Simini, S. Rinzivillo, D. Pedreschi, F. Giannotti","doi":"10.1109/BRICS-CCI-CBIC.2013.116","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.116","url":null,"abstract":"In the last years, the emergence of big data led scientists from diverse disciplines toward the study of the laws underlying human mobility. Although these recent discoveries have shed light on very interesting and fascinating aspects about people movements, they are generally focused on global and general mobility patterns. For this reason, they do not necessarily capture phenomena related to specific types of mobility, such as mobility by car, by public transportations means, by foot and so on. In this work, we aim to compare general human mobility with mobility expressed by a specific conveyance, trying to address the following question: What are the differences between general mobility and mobility by car? To answer this question, we present the results of an analysis performed on a big mobile phone dataset and on a GPS dataset storing information about car travels in Italy.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"64 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116031607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.60
Eremeev Alexander Pavlovich, Fomina Marina Vladimirovna
Modeling of reasoning in intelligent systems on the example of intelligent decision support system of real time by means of integration of methods based on case-based reasoning (accumulated experience) and inductive notion formation in the presence of noisy data are considered.
{"title":"Modeling of Reasoning in Intelligent Systems by Means of Integration of Methods Based on Case-Based Reasoning and Inductive Notions Formation","authors":"Eremeev Alexander Pavlovich, Fomina Marina Vladimirovna","doi":"10.1109/BRICS-CCI-CBIC.2013.60","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.60","url":null,"abstract":"Modeling of reasoning in intelligent systems on the example of intelligent decision support system of real time by means of integration of methods based on case-based reasoning (accumulated experience) and inductive notion formation in the presence of noisy data are considered.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114510272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.113
Diego de Siqueira Braga, F. O. Alves, L. C. De S Menezes, Fernando Buarque de Lima Neto
Social simulation is a recently founded research area in which there are not that many tools. Net Logo, one popular representative tool is a modeling and simulation environment that provides a basic infrastructure for programming agent based simulations. Net Logo and other tools incur in the same problem that is sometimes agent characteristics and model features are defined together originating a deep impact on models maintenance. Aiming to study some characteristics in isolation from the rest of the simulation and simplifying social simulations implementation using agents this work questions some characteristics of tools for assisting Social Simulation.
{"title":"Tools for Social Simulation - What Is Missing?","authors":"Diego de Siqueira Braga, F. O. Alves, L. C. De S Menezes, Fernando Buarque de Lima Neto","doi":"10.1109/BRICS-CCI-CBIC.2013.113","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.113","url":null,"abstract":"Social simulation is a recently founded research area in which there are not that many tools. Net Logo, one popular representative tool is a modeling and simulation environment that provides a basic infrastructure for programming agent based simulations. Net Logo and other tools incur in the same problem that is sometimes agent characteristics and model features are defined together originating a deep impact on models maintenance. Aiming to study some characteristics in isolation from the rest of the simulation and simplifying social simulations implementation using agents this work questions some characteristics of tools for assisting Social Simulation.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131904284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.22
J. Mwaura, E. Keedwell, A. Engelbrecht
This paper utilises Evolved Linker Gene Expression Programming (EL-GEP), a new variant of Gene Expression Programming (GEP), to solve symbolic regression and sequence induction problems. The new technique was first proposed in [6] to evolve modularity in robotic behaviours. The technique extends the GEP algorithm by incorporating a new alphabetic set (linking set) from which genome linking functions are selected. Further, the EL-GEP algorithm allows the genetic operators to modify the linking functions during the evolution process, thus changing the length of the chromosome during a run. In the current work, EL-GEP has been utilised to solve both symbolic regression and sequence induction problems. The achieved results are compared with those derived from GEP. The results show that EL-GEP is a suitable method for solving optimisation problems.
{"title":"Evolved Linker Gene Expression Programming: A New Technique for Symbolic Regression","authors":"J. Mwaura, E. Keedwell, A. Engelbrecht","doi":"10.1109/BRICS-CCI-CBIC.2013.22","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.22","url":null,"abstract":"This paper utilises Evolved Linker Gene Expression Programming (EL-GEP), a new variant of Gene Expression Programming (GEP), to solve symbolic regression and sequence induction problems. The new technique was first proposed in [6] to evolve modularity in robotic behaviours. The technique extends the GEP algorithm by incorporating a new alphabetic set (linking set) from which genome linking functions are selected. Further, the EL-GEP algorithm allows the genetic operators to modify the linking functions during the evolution process, thus changing the length of the chromosome during a run. In the current work, EL-GEP has been utilised to solve both symbolic regression and sequence induction problems. The achieved results are compared with those derived from GEP. The results show that EL-GEP is a suitable method for solving optimisation problems.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"190 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133390332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.83
Juliano E. C. Cruz, E. H. Shiguemori, L. Guimarães
Automatic object recognition in digital satellite images is not a simple task due to several variations present in the capture process and object appearance and pose, consequently, different general purpose techniques have been proposed. In this paper, an approach with LBP boosted cascade classifier for automatic runway detection in high resolution satellite imagery is analyzed. Promising results are obtained with the methodology presented in this work, considering objects with variations of scale, rotation and images obtained by different sensors.
{"title":"Concrete and Asphalt Runway Detection in High Resolution Images Using LBP Cascade Classifier","authors":"Juliano E. C. Cruz, E. H. Shiguemori, L. Guimarães","doi":"10.1109/BRICS-CCI-CBIC.2013.83","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.83","url":null,"abstract":"Automatic object recognition in digital satellite images is not a simple task due to several variations present in the capture process and object appearance and pose, consequently, different general purpose techniques have been proposed. In this paper, an approach with LBP boosted cascade classifier for automatic runway detection in high resolution satellite imagery is analyzed. Promising results are obtained with the methodology presented in this work, considering objects with variations of scale, rotation and images obtained by different sensors.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128784136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.109
Agnaldo J. Rocha Reis, Luciana G. Castanheira, Ruben C. Barbosa
The power transformer is one of the most important equipment in an electric power system. If this equipment is out of order for some reason, the damage for both society and electric utilities are very significant. In this work, we present a comparative study of the application of Linear Networks, Multi-Layer Perceptrons - with three and four layers - and Radial Basis Functions Networks in the classification of incipient faults via Dissolved Gas Analysis (DGA) in power transformers. Besides, preprocessing techniques for databases have been discussed as well. The proposed procedures have been applied to real databases derived from chromatographic tests of power transformers. The results obtained by all techniques are compared and fully described.
{"title":"Enhancing Neural Networks-Based Classification of Incipient Faults in Power Transformers via Preprocessing","authors":"Agnaldo J. Rocha Reis, Luciana G. Castanheira, Ruben C. Barbosa","doi":"10.1109/BRICS-CCI-CBIC.2013.109","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.109","url":null,"abstract":"The power transformer is one of the most important equipment in an electric power system. If this equipment is out of order for some reason, the damage for both society and electric utilities are very significant. In this work, we present a comparative study of the application of Linear Networks, Multi-Layer Perceptrons - with three and four layers - and Radial Basis Functions Networks in the classification of incipient faults via Dissolved Gas Analysis (DGA) in power transformers. Besides, preprocessing techniques for databases have been discussed as well. The proposed procedures have been applied to real databases derived from chromatographic tests of power transformers. The results obtained by all techniques are compared and fully described.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129302361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.61
Seng-Beng Ho, Fiona Liausvia
In this paper we address the issues of how incrementally chunking learned action rules of increasing length and complexity can assist in solving problems of ever greater complexity. To this end, we employ a micro-world with simple objects and simplified physical behaviors. The agent first learns some basic elemental rules capturing the fundamental physical behaviors of the agent itself, the objects and their interactions. Then, some moderately complex problems such as going from a start state to a goal state that do not require too many steps are given to the system and the system uses a standard search process (e.g., A) to find solutions which do not require too much search time because the problems are relatively simple. The solutions are then remembered as "chunked" rules of taking a sequence of actions to achieve a certain goal. Later, when a more complex problem - one that requires many steps to solve - is encountered, the chunked rules discovered earlier can be used to greatly reduce the search space by providing chunked sub-steps. Problem solving for complex problems without the chunking process would be impossible, as the search space would be combinatorially large.
{"title":"Incremental Rule Chunking for Problem Solving","authors":"Seng-Beng Ho, Fiona Liausvia","doi":"10.1109/BRICS-CCI-CBIC.2013.61","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.61","url":null,"abstract":"In this paper we address the issues of how incrementally chunking learned action rules of increasing length and complexity can assist in solving problems of ever greater complexity. To this end, we employ a micro-world with simple objects and simplified physical behaviors. The agent first learns some basic elemental rules capturing the fundamental physical behaviors of the agent itself, the objects and their interactions. Then, some moderately complex problems such as going from a start state to a goal state that do not require too many steps are given to the system and the system uses a standard search process (e.g., A) to find solutions which do not require too much search time because the problems are relatively simple. The solutions are then remembered as \"chunked\" rules of taking a sequence of actions to achieve a certain goal. Later, when a more complex problem - one that requires many steps to solve - is encountered, the chunked rules discovered earlier can be used to greatly reduce the search space by providing chunked sub-steps. Problem solving for complex problems without the chunking process would be impossible, as the search space would be combinatorially large.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130463383","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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