When generating layout schemes, both the material usage and practicality of the cutting process should be considered. This paper presents a two-section algorithm for generating guillotine-cutting schemes of rectangular blanks. It simplifies the cutting process by allowing only one size of blanks to appear in any rectangular block. The algorithm uses an implicit enumeration and a linear programming optimal cutting scheme to maximize the material usage. The algorithm was tested on some benchmark problems in the literature, and compared with the three types of layout scheme algorithm. The experimental results show that the algorithm is effective both in computation time and in material usage
{"title":"Optimal Two-Section Layouts for the Two-Dimensional Cutting Problem","authors":"Jun Ji, Dun-hua Huang, Feifei Xing, Yaodong Cui","doi":"10.3745/JIPS.01.0066","DOIUrl":"https://doi.org/10.3745/JIPS.01.0066","url":null,"abstract":"When generating layout schemes, both the material usage and practicality of the cutting process should be considered. This paper presents a two-section algorithm for generating guillotine-cutting schemes of rectangular blanks. It simplifies the cutting process by allowing only one size of blanks to appear in any rectangular block. The algorithm uses an implicit enumeration and a linear programming optimal cutting scheme to maximize the material usage. The algorithm was tested on some benchmark problems in the literature, and compared with the three types of layout scheme algorithm. The experimental results show that the algorithm is effective both in computation time and in material usage","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122626849","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 cloud has already become the core infrastructure of information systems, and government institutions are rapidly migrating information systems to the cloud. Government institutions in several countries use private clouds in their closed networks. However, because of the advantages of public clouds over private clouds, the demand for public clouds is increasing, and government institutions are expected to gradually switch to public clouds. When all data from government institutions are managed in the public cloud, the biggest concern for government institutions is the leakage of confidential data. The public-private-partnership (PPP) cloud provides a solution to this problem. PPP cloud is a form participation in a public cloud infrastructure and the building of a closed network data center. The PPP cloud prevents confidential data leakage and leverages the benefits of the public cloud to build a cloud quickly and easily maintain the cloud. In this paper, based on the case of the PPP cloud applied to the Korean government, the concept, architecture, operation model, and contract method of the PPP cloud are presented.
{"title":"Reference Architecture and Operation Model for PPP (Public-Private-Partnership) Cloud","authors":"Youngkon Lee, Ukhyun Lee","doi":"10.3745/JIPS.04.0212","DOIUrl":"https://doi.org/10.3745/JIPS.04.0212","url":null,"abstract":"The cloud has already become the core infrastructure of information systems, and government institutions are rapidly migrating information systems to the cloud. Government institutions in several countries use private clouds in their closed networks. However, because of the advantages of public clouds over private clouds, the demand for public clouds is increasing, and government institutions are expected to gradually switch to public clouds. When all data from government institutions are managed in the public cloud, the biggest concern for government institutions is the leakage of confidential data. The public-private-partnership (PPP) cloud provides a solution to this problem. PPP cloud is a form participation in a public cloud infrastructure and the building of a closed network data center. The PPP cloud prevents confidential data leakage and leverages the benefits of the public cloud to build a cloud quickly and easily maintain the cloud. In this paper, based on the case of the PPP cloud applied to the Korean government, the concept, architecture, operation model, and contract method of the PPP cloud are presented.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115776561","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}
Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance’s importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.
{"title":"Industrial Process Monitoring and Fault Diagnosis Based on Temporal Attention Augmented Deep Network","authors":"Ke Mu, Lin Luo, Qiao Wang, Fushun Mao","doi":"10.3745/JIPS.04.0211","DOIUrl":"https://doi.org/10.3745/JIPS.04.0211","url":null,"abstract":"Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance’s importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129407531","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}
In a personalized product recommendation system, when the amount of log data is large or sparse, the accuracy of model recommendation will be greatly affected. To solve this problem, a personalized product recommendation method using deep factorization machine (DeepFM) to analyze user behavior is proposed. Firstly, the K-means clustering algorithm is used to cluster the original log data from the perspective of similarity to reduce the data dimension. Then, through the DeepFM parameter sharing strategy, the relationship between low-and high-order feature combinations is learned from log data, and the click rate prediction model is constructed. Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back. The area under the curve (AUC) and Logloss of the proposed method are 0.8834 and 0.0253, respectively, on the Criteo dataset, and 0.7836 and 0.0348 on the KDD2012 Cup dataset, respectively. Compared with other newer recommendation methods, the proposed method can achieve better recommendation effect.
{"title":"Personalized Product Recommendation Method for Analyzing User Behavior Using DeepFM","authors":"Jianqiang Xu, Zhujiao Hu, Junzhong Zou","doi":"10.3745/JIPS.01.0069","DOIUrl":"https://doi.org/10.3745/JIPS.01.0069","url":null,"abstract":"In a personalized product recommendation system, when the amount of log data is large or sparse, the accuracy of model recommendation will be greatly affected. To solve this problem, a personalized product recommendation method using deep factorization machine (DeepFM) to analyze user behavior is proposed. Firstly, the K-means clustering algorithm is used to cluster the original log data from the perspective of similarity to reduce the data dimension. Then, through the DeepFM parameter sharing strategy, the relationship between low-and high-order feature combinations is learned from log data, and the click rate prediction model is constructed. Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back. The area under the curve (AUC) and Logloss of the proposed method are 0.8834 and 0.0253, respectively, on the Criteo dataset, and 0.7836 and 0.0348 on the KDD2012 Cup dataset, respectively. Compared with other newer recommendation methods, the proposed method can achieve better recommendation effect.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121408409","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}
Large amount of data is being generated in gaming servers due to the increase in the number of users and the variety of game services being provided. In particular, load balancing schemes for gaming servers are crucial consideration. The existing literature proposes algorithms that distribute loads in servers by mostly concentrating on load balancing and cooperative offloading. However, many proposed schemes impose heavy restrictions and assumptions, and such a limited service classification method is not enough to satisfy the wide range of service requirements. We propose a load balancing agent that combines the dynamic allocation programming method, a type of greedy algorithm, and proximal policy optimization, a reinforcement learning. Also, we compare performances of our proposed scheme and those of a scheme from previous literature, ProGreGA, by running a simulation.
{"title":"An Efficient Load Balancing Scheme for Gaming Server Using Proximal Policy Optimization Algorithm","authors":"Hye-young Kim","doi":"10.3745/JIPS.03.0158","DOIUrl":"https://doi.org/10.3745/JIPS.03.0158","url":null,"abstract":"Large amount of data is being generated in gaming servers due to the increase in the number of users and the variety of game services being provided. In particular, load balancing schemes for gaming servers are crucial consideration. The existing literature proposes algorithms that distribute loads in servers by mostly concentrating on load balancing and cooperative offloading. However, many proposed schemes impose heavy restrictions and assumptions, and such a limited service classification method is not enough to satisfy the wide range of service requirements. We propose a load balancing agent that combines the dynamic allocation programming method, a type of greedy algorithm, and proximal policy optimization, a reinforcement learning. Also, we compare performances of our proposed scheme and those of a scheme from previous literature, ProGreGA, by running a simulation.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123892177","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}
Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users’ deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users’ geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.
{"title":"POI Recommendation Method Based on Multi-Source Information Fusion Using Deep Learning in Location-Based Social Networks","authors":"Liqiang Sun","doi":"10.3745/JIPS.01.0068","DOIUrl":"https://doi.org/10.3745/JIPS.01.0068","url":null,"abstract":"Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users’ deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users’ geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127834565","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}
Smartphones are one of the most widely used mobile devices allowing users to communicate with each other. With the development of mobile apps, many companies now provide various services for their customers by studying interactive systems in the form of mobile messengers for business marketing and commercial promotion. Such interactive systems are called “chatbots.” In this paper, we propose a method of avoiding the redundant responses of chatbots, according to the utterances entered by the user. In addition, the redundant patterns of chatbot responses are classified into three categories for the first time. In order to verify the proposed method, a chatbot is implemented using Telegram, an open source messenger. By comparing the proposed method with an existent method for each pattern, it is confirmed that the proposed method significantly improves the redundancy avoidance rate. Furthermore, response performance and variation analysis of the proposed method are investigated in our experiment.
{"title":"Towards a Redundant Response Avoidance for Intelligent Chatbot","authors":"Hyuck-Moo Gwon, Yeong-Seok Seo","doi":"10.3745/JIPS.04.0213","DOIUrl":"https://doi.org/10.3745/JIPS.04.0213","url":null,"abstract":"Smartphones are one of the most widely used mobile devices allowing users to communicate with each other. With the development of mobile apps, many companies now provide various services for their customers by studying interactive systems in the form of mobile messengers for business marketing and commercial promotion. Such interactive systems are called “chatbots.” In this paper, we propose a method of avoiding the redundant responses of chatbots, according to the utterances entered by the user. In addition, the redundant patterns of chatbot responses are classified into three categories for the first time. In order to verify the proposed method, a chatbot is implemented using Telegram, an open source messenger. By comparing the proposed method with an existent method for each pattern, it is confirmed that the proposed method significantly improves the redundancy avoidance rate. Furthermore, response performance and variation analysis of the proposed method are investigated in our experiment.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"410 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124362171","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}
Aiming at the problem that complex external variables in natural scenes have a greater impact on facial expression recognition results, a facial expression recognition method based on two-stream convolutional neural network is proposed. The model introduces exponentially enhanced shared input weights before each level of convolution input, and uses soft attention mechanism modules on the space-time features of the combination of static and dynamic streams. This enables the network to autonomously find areas that are more relevant to the expression category and pay more attention to these areas. Through these means, the information of irrelevant interference areas is suppressed. In order to solve the problem of poor local robustness caused by lighting and expression changes, this paper also performs lighting preprocessing with the lighting preprocessing chain algorithm to eliminate most of the lighting effects. Experimental results on AFEW6.0 and Multi-PIE datasets show that the recognition rates of this method are 95.05% and 61.40%, respectively, which are better than other comparison methods.
{"title":"A Facial Expression Recognition Method Using Two-Stream Convolutional Networks in Natural Scenes","authors":"Lixing Zhao","doi":"10.3745/JIPS.01.0070","DOIUrl":"https://doi.org/10.3745/JIPS.01.0070","url":null,"abstract":"Aiming at the problem that complex external variables in natural scenes have a greater impact on facial expression recognition results, a facial expression recognition method based on two-stream convolutional neural network is proposed. The model introduces exponentially enhanced shared input weights before each level of convolution input, and uses soft attention mechanism modules on the space-time features of the combination of static and dynamic streams. This enables the network to autonomously find areas that are more relevant to the expression category and pay more attention to these areas. Through these means, the information of irrelevant interference areas is suppressed. In order to solve the problem of poor local robustness caused by lighting and expression changes, this paper also performs lighting preprocessing with the lighting preprocessing chain algorithm to eliminate most of the lighting effects. Experimental results on AFEW6.0 and Multi-PIE datasets show that the recognition rates of this method are 95.05% and 61.40%, respectively, which are better than other comparison methods.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125809205","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}
Automatically recognizing facial expressions in video sequences is a challenging task because there is little direct correlation between facial features and subjective emotions in video. To overcome the problem, a video facial expression recognition method using spatiotemporal recurrent neural network and feature fusion is proposed. Firstly, the video is preprocessed. Then, the double-layer cascade structure is used to detect a face in a video image. In addition, two deep convolutional neural networks are used to extract the time-domain and airspace facial features in the video. The spatial convolutional neural network is used to extract the spatial information features from each frame of the static expression images in the video. The temporal convolutional neural network is used to extract the dynamic information features from the optical flow information from multiple frames of expression images in the video. A multiplication fusion is performed with the spatiotemporal features learned by the two deep convolutional neural networks. Finally, the fused features are input to the support vector machine to realize the facial expression classification task. The experimental results on cNTERFACE, RML, and AFEW6.0 datasets show that the recognition rates obtained by the proposed method are as high as 88.67%, 70.32%, and 63.84%, respectively. Comparative experiments show that the proposed method obtains higher recognition accuracy than other recently reported methods.
{"title":"Video Expression Recognition Method Based on Spatiotemporal Recurrent Neural Network and Feature Fusion","authors":"Xuan Zhou","doi":"10.3745/JIPS.01.0067","DOIUrl":"https://doi.org/10.3745/JIPS.01.0067","url":null,"abstract":"Automatically recognizing facial expressions in video sequences is a challenging task because there is little direct correlation between facial features and subjective emotions in video. To overcome the problem, a video facial expression recognition method using spatiotemporal recurrent neural network and feature fusion is proposed. Firstly, the video is preprocessed. Then, the double-layer cascade structure is used to detect a face in a video image. In addition, two deep convolutional neural networks are used to extract the time-domain and airspace facial features in the video. The spatial convolutional neural network is used to extract the spatial information features from each frame of the static expression images in the video. The temporal convolutional neural network is used to extract the dynamic information features from the optical flow information from multiple frames of expression images in the video. A multiplication fusion is performed with the spatiotemporal features learned by the two deep convolutional neural networks. Finally, the fused features are input to the support vector machine to realize the facial expression classification task. The experimental results on cNTERFACE, RML, and AFEW6.0 datasets show that the recognition rates obtained by the proposed method are as high as 88.67%, 70.32%, and 63.84%, respectively. Comparative experiments show that the proposed method obtains higher recognition accuracy than other recently reported methods.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124512945","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}
Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create perchannel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.
{"title":"RAVIP: Real-Time AI Vision Platform for Heterogeneous Multi-Channel Video Stream","authors":"Jeong-Hun Lee, Kwang-il Hwang","doi":"10.3745/JIPS.02.0154","DOIUrl":"https://doi.org/10.3745/JIPS.02.0154","url":null,"abstract":"Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create perchannel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117295943","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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