Pub Date : 2022-07-15DOI: 10.1109/icaci55529.2022.9837754
Aman Kumar, Manish Khare, Saurabh Tiwari
The sentiments of developers play a major role in productivity, code quality, and satisfaction. The workload of the developers and their interest in a specific programming language affect the overall quality of the development process. Open source projects, where developers (or contributors) work based on their interest in contributing to the project apart of their routine work. In this paper, we are analysing the sentiments of the developers on GitHub while working on different open source projects. Our study mainly focuses on three aspects: (1) analysing the day of the week in which the comment was made by the developer, (2) emotions of the developer throughout the course of a project, and (3) emotions with different programming languages. The analysis was done by looking into the developer comments on issues, pull requests, and comments for the repository. Our results show that projects developed on Monday’s tend to more negative emotion. Additionally, comments written in issues have higher negative polarity in their sentimental content, and projects developed in Java and Python have more positive comments as compared to C and C++.
{"title":"Sentiment Analysis of Developers’ Comments on GitHub Repository: A Study","authors":"Aman Kumar, Manish Khare, Saurabh Tiwari","doi":"10.1109/icaci55529.2022.9837754","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837754","url":null,"abstract":"The sentiments of developers play a major role in productivity, code quality, and satisfaction. The workload of the developers and their interest in a specific programming language affect the overall quality of the development process. Open source projects, where developers (or contributors) work based on their interest in contributing to the project apart of their routine work. In this paper, we are analysing the sentiments of the developers on GitHub while working on different open source projects. Our study mainly focuses on three aspects: (1) analysing the day of the week in which the comment was made by the developer, (2) emotions of the developer throughout the course of a project, and (3) emotions with different programming languages. The analysis was done by looking into the developer comments on issues, pull requests, and comments for the repository. Our results show that projects developed on Monday’s tend to more negative emotion. Additionally, comments written in issues have higher negative polarity in their sentimental content, and projects developed in Java and Python have more positive comments as compared to C and C++.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115340799","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 : 2022-07-15DOI: 10.1109/icaci55529.2022.9837594
Na Wu, Zongwu Ke, Lei Feng
The prediction of time series data is very difficult. For example, the price of stocks belongs to time series. Small fluctuations in society, politics, economy and culture may affect the stocks in the stock market. In the stock market, it is very important for people to have a general judgment on stocks. Therefore, the study of stocks has practical significance. This experiment confirms that the results are affected by the data set and statesize. Statesize is predicted by the closing price of several days.On the premise that the appropriate size of statesize makes the final profit the highest, and on the premise that improved algorithm of Q value based on DQN adds regularization (DDQN), it is proved that under different data sets, adding Long Short-Term Memory (LSTM) and full connection layer are better than only full connection layer. DQN is composed of neural network and Q-learning. Q-learning is a basic algorithm in reinforcement learning. And it is proved that DDQN algorithm is better than DQN on the premise that the appropriate statesize makes the final profit the highest, and on the premise of adding regularization and LSTM. Finally, it is also proved that under certain preconditions, the combination of LSTM and DDQN is better than only DQN and full connection layer. The only indicator of this experiment is the total profit. At the same time, this paper uses the closing price to predict.
{"title":"Stock Price Forecast Based on LSTM and DDQN","authors":"Na Wu, Zongwu Ke, Lei Feng","doi":"10.1109/icaci55529.2022.9837594","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837594","url":null,"abstract":"The prediction of time series data is very difficult. For example, the price of stocks belongs to time series. Small fluctuations in society, politics, economy and culture may affect the stocks in the stock market. In the stock market, it is very important for people to have a general judgment on stocks. Therefore, the study of stocks has practical significance. This experiment confirms that the results are affected by the data set and statesize. Statesize is predicted by the closing price of several days.On the premise that the appropriate size of statesize makes the final profit the highest, and on the premise that improved algorithm of Q value based on DQN adds regularization (DDQN), it is proved that under different data sets, adding Long Short-Term Memory (LSTM) and full connection layer are better than only full connection layer. DQN is composed of neural network and Q-learning. Q-learning is a basic algorithm in reinforcement learning. And it is proved that DDQN algorithm is better than DQN on the premise that the appropriate statesize makes the final profit the highest, and on the premise of adding regularization and LSTM. Finally, it is also proved that under certain preconditions, the combination of LSTM and DDQN is better than only DQN and full connection layer. The only indicator of this experiment is the total profit. At the same time, this paper uses the closing price to predict.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124297178","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 : 2022-07-15DOI: 10.1109/icaci55529.2022.9837603
Changqing Long, Houping Dai, Guodong Zhang, Junhao Hu
This paper explores the finite-time synchronization issue of a class of delayed fuzzy neural networks (DFNNs) by constructing new Lyapunov functional. Under the novel adaptive controller, sufficient conditions are derived to assure the finite-time synchronization of the considered DFNNs. In addition, the fuzzy logics are taken into accounted in the proposed network model, which complements and extends some of the existing results where the fuzzy logics or time delays are not considered. In the end, the validity of the derived synchronization results are verified by simulation examples.
{"title":"New Results on Finite-Time Synchronization of Delayed Fuzzy Neural Networks","authors":"Changqing Long, Houping Dai, Guodong Zhang, Junhao Hu","doi":"10.1109/icaci55529.2022.9837603","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837603","url":null,"abstract":"This paper explores the finite-time synchronization issue of a class of delayed fuzzy neural networks (DFNNs) by constructing new Lyapunov functional. Under the novel adaptive controller, sufficient conditions are derived to assure the finite-time synchronization of the considered DFNNs. In addition, the fuzzy logics are taken into accounted in the proposed network model, which complements and extends some of the existing results where the fuzzy logics or time delays are not considered. In the end, the validity of the derived synchronization results are verified by simulation examples.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114699839","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 : 2022-07-15DOI: 10.1109/icaci55529.2022.9837518
Honggang Yang, Rui Fan, Jiejie Chen, Mengfei Xu
In view of the possibility that Recurrent Neural Network(RNN)’s stochastic gradient descent method will converge to the local optimum problem, two fractional stochastic gradient descent methods are proposed in this paper. The methods respectively use the fractional order substitution derivative part defined by Caputo and the fractional order substitution difference form defined by Riemann Liouville to improve the updating method of network parameters. Combining with the gradient descent characteristics, the influence of fractional order on the training results is discussed, and two adaptive order adjustment methods are proposed. Experiments on MNIST and FashionMNIST datasets show that the fractional stochastic gradient optimization algorithm can improve the classification accuracy and training speed of recurrent neural network.
{"title":"Recurrent Neural Networks with Fractional Order Gradient Method","authors":"Honggang Yang, Rui Fan, Jiejie Chen, Mengfei Xu","doi":"10.1109/icaci55529.2022.9837518","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837518","url":null,"abstract":"In view of the possibility that Recurrent Neural Network(RNN)’s stochastic gradient descent method will converge to the local optimum problem, two fractional stochastic gradient descent methods are proposed in this paper. The methods respectively use the fractional order substitution derivative part defined by Caputo and the fractional order substitution difference form defined by Riemann Liouville to improve the updating method of network parameters. Combining with the gradient descent characteristics, the influence of fractional order on the training results is discussed, and two adaptive order adjustment methods are proposed. Experiments on MNIST and FashionMNIST datasets show that the fractional stochastic gradient optimization algorithm can improve the classification accuracy and training speed of recurrent neural network.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123972228","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}
This paper addresses the bipartite synchronization of coupled neural networks with time-varying delay. By introducing an effective quantized controller, the bipartite synchronization of coupled neural networks with time-varying delay is realized and sufficient conditions for assuring the bipartite synchronization are derived in virtue of a Halanay inequality. Moreover, the bipartite synchronization of coupled neural networks without delay via quantized controller is also taken into account in corollary as a special case. In the end, a numerical example is provided to demonstrate the correctness of theoretical results.
{"title":"Asymptotic Bipartite Synchronization of Coupled Neural Networks Via Quantized Control","authors":"Ting Liu, Junhong Zhao, Peng Liu, Jian Yong, Shulong Fan, Junwei Sun","doi":"10.1109/icaci55529.2022.9837729","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837729","url":null,"abstract":"This paper addresses the bipartite synchronization of coupled neural networks with time-varying delay. By introducing an effective quantized controller, the bipartite synchronization of coupled neural networks with time-varying delay is realized and sufficient conditions for assuring the bipartite synchronization are derived in virtue of a Halanay inequality. Moreover, the bipartite synchronization of coupled neural networks without delay via quantized controller is also taken into account in corollary as a special case. In the end, a numerical example is provided to demonstrate the correctness of theoretical results.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127202207","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}
Session-based recommendation mainly solves the recommendation problem in the anonymous scene, which is a challenging task. In recent years, most methods based on graph neural network (GNN) have ignore the location information of neighboring items. So we propose a graph aggregation method that introduces relative location information to capture this information. Specifically, we use two methods to learn item embedding, the location graph aggregation method is mainly used to capture the location relationship information between neighbors, and common graph aggregation method is mainly used to capture higher-order relationship information between items. Finally, we construct a session recommendation model and demonstrate the effectiveness of the proposed method on three datasets.
{"title":"An Improved Graph Neural Network Method Using Relative Position Information for Session-based Recommendation","authors":"Shuai Zhang, Yujie Xiao, Mingze Li, Xiaowei Li, Benhui Chen","doi":"10.1109/icaci55529.2022.9837599","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837599","url":null,"abstract":"Session-based recommendation mainly solves the recommendation problem in the anonymous scene, which is a challenging task. In recent years, most methods based on graph neural network (GNN) have ignore the location information of neighboring items. So we propose a graph aggregation method that introduces relative location information to capture this information. Specifically, we use two methods to learn item embedding, the location graph aggregation method is mainly used to capture the location relationship information between neighbors, and common graph aggregation method is mainly used to capture higher-order relationship information between items. Finally, we construct a session recommendation model and demonstrate the effectiveness of the proposed method on three datasets.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131389474","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 : 2022-07-15DOI: 10.1109/icaci55529.2022.9837537
X. Li, Mingxin Kang
The rapid development of vehicle-to-everything (V2X) and intelligent control technologies brings new opportunities and challenges to the traditional automotive control architecture. More driving information about traffic scenarios and ambient events such as the road slope, the traffic light timing is possible to be obtained via V2X system. And then, those traffic information will be extracted by individual vehicle’s controller and be further utilized to design the optimal control strategy. Fuel economy performance and time losses for waiting for the traffic red light are the two main concerns by most drivers. In order to obtain a satisfactory fuel economy performance and lower traveling time loss, this paper investigates an eco-driving problem for road vehicles when assuming the information of the traffic light ahead is prior known. The optimization problem by balancing the fuel consumption and time loss is designed and meanwhile the time phase of the traffic light is also considered. The optimization problem is firstly solved with the dynamic programming (DP) algorithm. Preliminary simulations have been implemented and the simulation results demonstrate the potential ability in improvement of the fuel economy performance. Moreover, an equivalent problem is formulated under the switching control system framework, to guarantee the hard constraint of the red light. The equivalent problem provides an interesting topic for the open discussion.
{"title":"An Investigation on Vehicle Fuel Consumption Optimization Strategy Based on Scenario Information","authors":"X. Li, Mingxin Kang","doi":"10.1109/icaci55529.2022.9837537","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837537","url":null,"abstract":"The rapid development of vehicle-to-everything (V2X) and intelligent control technologies brings new opportunities and challenges to the traditional automotive control architecture. More driving information about traffic scenarios and ambient events such as the road slope, the traffic light timing is possible to be obtained via V2X system. And then, those traffic information will be extracted by individual vehicle’s controller and be further utilized to design the optimal control strategy. Fuel economy performance and time losses for waiting for the traffic red light are the two main concerns by most drivers. In order to obtain a satisfactory fuel economy performance and lower traveling time loss, this paper investigates an eco-driving problem for road vehicles when assuming the information of the traffic light ahead is prior known. The optimization problem by balancing the fuel consumption and time loss is designed and meanwhile the time phase of the traffic light is also considered. The optimization problem is firstly solved with the dynamic programming (DP) algorithm. Preliminary simulations have been implemented and the simulation results demonstrate the potential ability in improvement of the fuel economy performance. Moreover, an equivalent problem is formulated under the switching control system framework, to guarantee the hard constraint of the red light. The equivalent problem provides an interesting topic for the open discussion.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131511273","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 : 2022-07-15DOI: 10.1109/icaci55529.2022.9837654
Yaqian Hu, Leimin Wang, Xingxing Tan, Kan Zeng
In this paper, the finite-time synchronization (FTS) for inertial neural networks (INNs) is investigated based on periodically intermittent control. By utilizing the reduced order approach, INN system is transformed into two first-order systems. Then, proper periodically intermittent controllers are designed to obtain sufficient condition for FTS of INNs. An example is proposed to support the validity of the synchronization criterion.
{"title":"Finite-time Synchronization of Inertial Neural Networks via Periodically Intermittent Control","authors":"Yaqian Hu, Leimin Wang, Xingxing Tan, Kan Zeng","doi":"10.1109/icaci55529.2022.9837654","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837654","url":null,"abstract":"In this paper, the finite-time synchronization (FTS) for inertial neural networks (INNs) is investigated based on periodically intermittent control. By utilizing the reduced order approach, INN system is transformed into two first-order systems. Then, proper periodically intermittent controllers are designed to obtain sufficient condition for FTS of INNs. An example is proposed to support the validity of the synchronization criterion.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116909399","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 : 2022-07-15DOI: 10.1109/icaci55529.2022.9837580
Xinrui Jiang, Zhaorui Xin, Sitian Qin, Jiqiang Feng, Guocheng Li
This article discusses the problem of Nash equilibrium seeking for noncooperative game with equality constraints. In the problem, each player desires to maximize its nonsmooth payoff function which depends on both its own strategy and the strategy of other players. Besides, the game-player is subjected to private local equality constraints. We use a l1 penalty function to deal with the equality constraints and a Nash equilibrium seeking strategy is designed on the basis of differential inclusions and subgradient methods. And we show that the strategy of player is exponentially convergent to the Nash equilibrium with Lyapunov methods. Finally, a numerical example is presented to illustrate the validity of our theoretical results.
{"title":"Generalized Nash Equilibrium Seeking Strategy for Nonsmooth Noncooperative Game with Equality Constraints","authors":"Xinrui Jiang, Zhaorui Xin, Sitian Qin, Jiqiang Feng, Guocheng Li","doi":"10.1109/icaci55529.2022.9837580","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837580","url":null,"abstract":"This article discusses the problem of Nash equilibrium seeking for noncooperative game with equality constraints. In the problem, each player desires to maximize its nonsmooth payoff function which depends on both its own strategy and the strategy of other players. Besides, the game-player is subjected to private local equality constraints. We use a l1 penalty function to deal with the equality constraints and a Nash equilibrium seeking strategy is designed on the basis of differential inclusions and subgradient methods. And we show that the strategy of player is exponentially convergent to the Nash equilibrium with Lyapunov methods. Finally, a numerical example is presented to illustrate the validity of our theoretical results.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116689304","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}
The safety and reliability of the pantograph are critical and essential maintenance tasks in the railway transportation system. The majority of previous efforts proposed intelligent detection methods for achieving rapid and accurate inspection of the pantograph's health status. However, no research has been conducted on the automatic generation of pantograph health status reports, which is the primary reference basis for maintenance decisions. In this paper, in the light of the successful work of DenseCap, a pantograph image captioning model (PanCap for short) is proposed, which replaces VGG-16 with ResNet-50-FPN as the backbone to extract richer image features. In addition, Focal Loss and Transformer are used in PanCap to improve the description performance by addressing the problems of classification imbalance and dependent description. Evaluate the Visual Genome (VG) and pantograph image dataset, and the effectiveness of the proposed method is demonstrated by the experimental results.
{"title":"Automatic Pantograph Health Status Report Generation Based on Dense Captioning","authors":"Xinqiang Yin, Xiukun Wei, Zhaoxin Li, Dehua Wei, Qingfeng Tang","doi":"10.1109/icaci55529.2022.9837656","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837656","url":null,"abstract":"The safety and reliability of the pantograph are critical and essential maintenance tasks in the railway transportation system. The majority of previous efforts proposed intelligent detection methods for achieving rapid and accurate inspection of the pantograph's health status. However, no research has been conducted on the automatic generation of pantograph health status reports, which is the primary reference basis for maintenance decisions. In this paper, in the light of the successful work of DenseCap, a pantograph image captioning model (PanCap for short) is proposed, which replaces VGG-16 with ResNet-50-FPN as the backbone to extract richer image features. In addition, Focal Loss and Transformer are used in PanCap to improve the description performance by addressing the problems of classification imbalance and dependent description. Evaluate the Visual Genome (VG) and pantograph image dataset, and the effectiveness of the proposed method is demonstrated by the experimental results.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121151705","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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