Pub Date : 2010-06-28DOI: 10.1109/ICCIS.2010.5518591
Jian-xin Xu, Xin Deng, Dongxu Ji
With the complete knowledge on the anatomical nerve connections of the nematode Caenorhabditis elegans (C. elegans), the chemotaxis behaviors including food attraction and toxin avoidance, are modeled using dynamic neural networks (DNN). This paper first uses artificial DNN, with 7 neurons, to model chemotaxis behaviors with single sensor neurons. Real time recurrent learning (RTRL) is carried out to train the DNN weights. Next, this paper split the single sensor neuron into the left and right pair (dual-sensor neuron), with the assumption that C. elegans can distinguish the input difference between left and right, and then the model is applied to learn to reproduce the chemotaxis behaviors. The simulation results conclude that DNN can well model the behaviors of C. elegans from sensory inputs to motor outputs both in single sensor and dual-sensor neuron networks.
{"title":"Study on C. elegans behaviors using recurrent neural network model","authors":"Jian-xin Xu, Xin Deng, Dongxu Ji","doi":"10.1109/ICCIS.2010.5518591","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518591","url":null,"abstract":"With the complete knowledge on the anatomical nerve connections of the nematode Caenorhabditis elegans (C. elegans), the chemotaxis behaviors including food attraction and toxin avoidance, are modeled using dynamic neural networks (DNN). This paper first uses artificial DNN, with 7 neurons, to model chemotaxis behaviors with single sensor neurons. Real time recurrent learning (RTRL) is carried out to train the DNN weights. Next, this paper split the single sensor neuron into the left and right pair (dual-sensor neuron), with the assumption that C. elegans can distinguish the input difference between left and right, and then the model is applied to learn to reproduce the chemotaxis behaviors. The simulation results conclude that DNN can well model the behaviors of C. elegans from sensory inputs to motor outputs both in single sensor and dual-sensor neuron networks.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131676255","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 : 2010-06-28DOI: 10.1109/ICCIS.2010.5518567
Javad Rahebi, Z. Elmi, Ali Farzam nia, Kamran Shayan
In this paper a new method for enhancement of digital image edge detection using ant colony optimization based on genetic algorithm has been used. In the proposed method first by the series of answers has been formed by artificial ants and then formed in a manner i.e. useful for genetic algorithm, then the answers played the role as initial population for genetic algorithm and the next population is made by genetic algorithm. Our method compared with Jing Tian method enjoys higher speed, less processing time and more answer's optimum. Also the proposed method has a better edge than other classical methods (such as sobel, etc).
{"title":"Digital image edge detection using an ant colony optimization based on genetic algorithm","authors":"Javad Rahebi, Z. Elmi, Ali Farzam nia, Kamran Shayan","doi":"10.1109/ICCIS.2010.5518567","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518567","url":null,"abstract":"In this paper a new method for enhancement of digital image edge detection using ant colony optimization based on genetic algorithm has been used. In the proposed method first by the series of answers has been formed by artificial ants and then formed in a manner i.e. useful for genetic algorithm, then the answers played the role as initial population for genetic algorithm and the next population is made by genetic algorithm. Our method compared with Jing Tian method enjoys higher speed, less processing time and more answer's optimum. Also the proposed method has a better edge than other classical methods (such as sobel, etc).","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124597057","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 : 2010-06-28DOI: 10.1109/ICCIS.2010.5518571
N. Chaudhari, Aruna Tiwari, Urjita Thakar, Jaya Thomas
We propose a semi supervised classifier for intrusion detection. In our approach, we classify the data entering the computer network. To achieve this, we start with two broad classes of data namely, malicious data and good data. We use Support vector machine based classifier with spherical decision boundaries to classify a chosen subset of malicious data taken as training samples. In the Intrusion Detection System (IDS) database, all data identified as malicious data according to our classifier is included as signature (of attack). Using our classifier for testing the out-of-sample data samples, we observe that the accuracy of the system is 72% for web log data.
{"title":"Semi-supervised classification for intrusion Detection System in networks","authors":"N. Chaudhari, Aruna Tiwari, Urjita Thakar, Jaya Thomas","doi":"10.1109/ICCIS.2010.5518571","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518571","url":null,"abstract":"We propose a semi supervised classifier for intrusion detection. In our approach, we classify the data entering the computer network. To achieve this, we start with two broad classes of data namely, malicious data and good data. We use Support vector machine based classifier with spherical decision boundaries to classify a chosen subset of malicious data taken as training samples. In the Intrusion Detection System (IDS) database, all data identified as malicious data according to our classifier is included as signature (of attack). Using our classifier for testing the out-of-sample data samples, we observe that the accuracy of the system is 72% for web log data.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134009098","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 : 2010-06-28DOI: 10.1109/ICCIS.2010.5518589
Yang Yang, Haibo Lin
Systems biology is an emerging multi-disciplinary area, which aims to understand the underneath regulatory mechanisms of the biomolecular interaction networks inside the cell through dynamical system approaches. The first challenge in systems biology is how to obtain an accurate and predictable computational model for the biomolecular networks under study. However, due to limited experimental data, it is unavoidable to have incomplete or even wrong models. Therefore, it is a critical task in systems biology to check the model's correctness, which is called model validation problem. This paper will focus on this issue, and propose a (un-)reachability analysis based model validation method. In particular, Petri net models are investigated, and the validation process is evaluated by the reachability of state equations. It is shown that the reachability can be checked by the existence of integer solutions of Diophantine equations. Two methods are proposed to solve the equations. The first one is by Smith normal form test, and the other is by integer programming. Two case studies are provided to demonstrate these two approaches. These tests can screen out the unreachable states and offer the hints to modify the model structure, which provides us more insights of the regulatory mechanism and helps biologists to generate hypotheses and design experiments.
{"title":"Reachability analysis based model validation in systems biology","authors":"Yang Yang, Haibo Lin","doi":"10.1109/ICCIS.2010.5518589","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518589","url":null,"abstract":"Systems biology is an emerging multi-disciplinary area, which aims to understand the underneath regulatory mechanisms of the biomolecular interaction networks inside the cell through dynamical system approaches. The first challenge in systems biology is how to obtain an accurate and predictable computational model for the biomolecular networks under study. However, due to limited experimental data, it is unavoidable to have incomplete or even wrong models. Therefore, it is a critical task in systems biology to check the model's correctness, which is called model validation problem. This paper will focus on this issue, and propose a (un-)reachability analysis based model validation method. In particular, Petri net models are investigated, and the validation process is evaluated by the reachability of state equations. It is shown that the reachability can be checked by the existence of integer solutions of Diophantine equations. Two methods are proposed to solve the equations. The first one is by Smith normal form test, and the other is by integer programming. Two case studies are provided to demonstrate these two approaches. These tests can screen out the unreachable states and offer the hints to modify the model structure, which provides us more insights of the regulatory mechanism and helps biologists to generate hypotheses and design experiments.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132652204","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 : 2010-06-28DOI: 10.1109/ICCIS.2010.5518563
Yu-Jie Li, Sun-Kyung Kang, Young-Un Kim, Sung-Tae Jung
This paper proposes a facial expression recognition system for the laughter therapy. The proposed system takes two steps: face detection and facial expression recognition. At the face detection stage, candidate facial areas are detected in real time from images taken by a camera in consideration of Haar-like features, followed by the application of a SVM(Support Vector Machine) classifier to detect face images in a more correct way. Next, histogram matching-based illumination normalization is used to mitigate the influence of lighting on the detected images. At the facial expression recognition stage, PCA (Principle Component Analysis) is used to capture features of the face, and real-time laugher recognition is made via a multi-layer perceptron artificial neural network. From the findings of this study, we conclude that the proposed method can improve facial expression recognition through illumination normalization based on histogram matching and by testing candidate facial images with a SVM.
{"title":"Development of a facial expression recognition system for the laughter therapy","authors":"Yu-Jie Li, Sun-Kyung Kang, Young-Un Kim, Sung-Tae Jung","doi":"10.1109/ICCIS.2010.5518563","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518563","url":null,"abstract":"This paper proposes a facial expression recognition system for the laughter therapy. The proposed system takes two steps: face detection and facial expression recognition. At the face detection stage, candidate facial areas are detected in real time from images taken by a camera in consideration of Haar-like features, followed by the application of a SVM(Support Vector Machine) classifier to detect face images in a more correct way. Next, histogram matching-based illumination normalization is used to mitigate the influence of lighting on the detected images. At the facial expression recognition stage, PCA (Principle Component Analysis) is used to capture features of the face, and real-time laugher recognition is made via a multi-layer perceptron artificial neural network. From the findings of this study, we conclude that the proposed method can improve facial expression recognition through illumination normalization based on histogram matching and by testing candidate facial images with a SVM.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131994986","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 : 2010-06-28DOI: 10.1109/ICCIS.2010.5518543
Haixian Zhang, Zhang Yi
Continuous attractors of recurrent neural networks (RNNs) have attracted extensive interests in recent years. It is often used to describe the encoding of continuous stimuli such as orientation, moving direction and spatial location of objects. This paper studies the dynamic shift mechanism of a class of continuous attractor neural networks. It shows that if the external input is a gaussian shape with its center varying along with time, by adding a slight shift to the weights, the symmetry of gaussian weight function is destroyed. Then, the activity profile will shift continuously without changing its shape, and the shift speed can be controlled accurately by a given constant. Simulations are employed to illustrate the theory.
{"title":"Dynamic shift mechanism of continuous attractors in a class of recurrent neural networks","authors":"Haixian Zhang, Zhang Yi","doi":"10.1109/ICCIS.2010.5518543","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518543","url":null,"abstract":"Continuous attractors of recurrent neural networks (RNNs) have attracted extensive interests in recent years. It is often used to describe the encoding of continuous stimuli such as orientation, moving direction and spatial location of objects. This paper studies the dynamic shift mechanism of a class of continuous attractor neural networks. It shows that if the external input is a gaussian shape with its center varying along with time, by adding a slight shift to the weights, the symmetry of gaussian weight function is destroyed. Then, the activity profile will shift continuously without changing its shape, and the shift speed can be controlled accurately by a given constant. Simulations are employed to illustrate the theory.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114541097","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 : 2010-06-28DOI: 10.1109/ICCIS.2010.5518566
R. Walia, R. Jarvis
Horizon detection is a pre-cursor to vision processing in air and water robotics. This paper makes three contributions to horizon detection. First, a theoretical framework for generating pseudo spectra images (PSI), from spectrum analysis of XYZ color-space is presented. Second, wavelengths in the visible spectrum are identified, at which the PSI has similar intensities for sky and clouds. Generating PSI at these wavelengths minimizes artifacts due to clouds in the sky, resulting in well defined horizon. Third, fitting ellipses are presented as an alternate to Hough Transform for horizon detection. Ellipses have lower computational complexity than Hough Transform and can accommodate curved edges as candidates for horizon.
{"title":"Horizon detection from pseudo spectra images of water scenes","authors":"R. Walia, R. Jarvis","doi":"10.1109/ICCIS.2010.5518566","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518566","url":null,"abstract":"Horizon detection is a pre-cursor to vision processing in air and water robotics. This paper makes three contributions to horizon detection. First, a theoretical framework for generating pseudo spectra images (PSI), from spectrum analysis of XYZ color-space is presented. Second, wavelengths in the visible spectrum are identified, at which the PSI has similar intensities for sky and clouds. Generating PSI at these wavelengths minimizes artifacts due to clouds in the sky, resulting in well defined horizon. Third, fitting ellipses are presented as an alternate to Hough Transform for horizon detection. Ellipses have lower computational complexity than Hough Transform and can accommodate curved edges as candidates for horizon.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116378090","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 : 2010-06-28DOI: 10.1109/ICCIS.2010.5518570
Farhad Nadi, Ahamad Tajudin Khader
Converging to suboptimal solutions in genetic algorithms prevents the search from reaching the global optima. Search space could have several suboptimal but one optimal solution. As the suboptimal solutions are within the search space, dividing the search space would bound them in different divisions. Thus, searching in each division separately would increase the probability of reaching the global optima. In other words, the optimal solution would be bounded in one of the divisions and then searching that division would result in finding the optimal solution. Although, the suboptimal solutions could be in the same division as optimal solution but the chance of finding the optimal solution in this case would be more compared to the cases that have no division. The proposed methodology divide the search space into partitions called regions. Individuals will be assigned to each region. The search continues while each set of individuals are focused in searching a region. Preliminary results shows a fair improvement in the performance and efficiency compared to genetic algorithm.
{"title":"Managing search in a partitioned Search space in GA","authors":"Farhad Nadi, Ahamad Tajudin Khader","doi":"10.1109/ICCIS.2010.5518570","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518570","url":null,"abstract":"Converging to suboptimal solutions in genetic algorithms prevents the search from reaching the global optima. Search space could have several suboptimal but one optimal solution. As the suboptimal solutions are within the search space, dividing the search space would bound them in different divisions. Thus, searching in each division separately would increase the probability of reaching the global optima. In other words, the optimal solution would be bounded in one of the divisions and then searching that division would result in finding the optimal solution. Although, the suboptimal solutions could be in the same division as optimal solution but the chance of finding the optimal solution in this case would be more compared to the cases that have no division. The proposed methodology divide the search space into partitions called regions. Individuals will be assigned to each region. The search continues while each set of individuals are focused in searching a region. Preliminary results shows a fair improvement in the performance and efficiency compared to genetic algorithm.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125208546","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 : 2010-06-28DOI: 10.1109/ICCIS.2010.5518556
M. Amjad, K. Ishaque, S. Abdullah, Z. Salam
This paper presents a control scheme that provides an efficient and a simple way to design a Fuzzy Logic Controller (FLC) for the autonomous underwater vehicle (AUV). The proposed method, known as the Single Input Fuzzy Logic Controller (SIFLC), condenses the conventional two-input FLC (CFLC) to a single input single output (SISO) controller. The SIFLC significantly reduces the rules and simplifies the tuning of control parameters. Practically, it can be easily implemented by a look-up table using a low cost microprocessor due to its piecewise linear control surface. To verify the effectiveness of the designed controller, the control algorithm is simulated using the Marine Systems Simulator (MSS) on the Matlab/Simulink® platform. The result clearly indicates that both the SIFLC and CFLC give almost identical response to the same input sets. However SIFLC requires very minimum tuning effort and its execution time is in the orders of two magnitudes less than CFLC.
{"title":"An alternative approach to design a Fuzzy Logic Controller for an autonomous underwater vehicle","authors":"M. Amjad, K. Ishaque, S. Abdullah, Z. Salam","doi":"10.1109/ICCIS.2010.5518556","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518556","url":null,"abstract":"This paper presents a control scheme that provides an efficient and a simple way to design a Fuzzy Logic Controller (FLC) for the autonomous underwater vehicle (AUV). The proposed method, known as the Single Input Fuzzy Logic Controller (SIFLC), condenses the conventional two-input FLC (CFLC) to a single input single output (SISO) controller. The SIFLC significantly reduces the rules and simplifies the tuning of control parameters. Practically, it can be easily implemented by a look-up table using a low cost microprocessor due to its piecewise linear control surface. To verify the effectiveness of the designed controller, the control algorithm is simulated using the Marine Systems Simulator (MSS) on the Matlab/Simulink® platform. The result clearly indicates that both the SIFLC and CFLC give almost identical response to the same input sets. However SIFLC requires very minimum tuning effort and its execution time is in the orders of two magnitudes less than CFLC.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129226512","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 : 2010-06-28DOI: 10.1109/ICCIS.2010.5518580
Shern Shiou Tan, Tomas Henrique Bode Maul, Neil Mennie, P. Mitchell
Luminophonics is a system that aims to maximize cross-modality conversion of information, specifically from the visual to auditory modalities, with the motivation to develop a better assistive technology for the visually impaired by using image sonification techniques. The project aims to research and develop generic and highly-configurable components concerned with different image processing techniques, attention mechanisms, orchestration approaches and psychological constraints. The swiping method that is introduced in this paper combines several techniques in order to explicitly convert the colour, size and position of objects. Preliminary tests suggest that the approach is valid and deserves further investigation.
{"title":"Swiping with luminophonics","authors":"Shern Shiou Tan, Tomas Henrique Bode Maul, Neil Mennie, P. Mitchell","doi":"10.1109/ICCIS.2010.5518580","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518580","url":null,"abstract":"Luminophonics is a system that aims to maximize cross-modality conversion of information, specifically from the visual to auditory modalities, with the motivation to develop a better assistive technology for the visually impaired by using image sonification techniques. The project aims to research and develop generic and highly-configurable components concerned with different image processing techniques, attention mechanisms, orchestration approaches and psychological constraints. The swiping method that is introduced in this paper combines several techniques in order to explicitly convert the colour, size and position of objects. Preliminary tests suggest that the approach is valid and deserves further investigation.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"PP 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126436080","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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