In-loop filters have been widely utilized in latest video coding standards to improve the video coding efficiency by reducing compression artifacts. However, existing in-loop filters only utilize image local correlations, leading to limited performance improvement. In this paper, we explore a novel adaptive in-loop filter by means of the nonlocal similar content to improve the quality of reconstructed video frames. In our proposed filter, the input video frame is first divided into different image patch groups based on their similarity, and then a soft-thresholding method is applied to the singular values of matrices composed of image patches in every group. Since compression noise is highly correlated with image content, we propose a group-wise threshold estimation method based on image statistical characteristics, coding modes and quantization parameters. To ensure the filtering efficiency, slice level control flags are utilized and determined based on the distortion changes after filtering. The proposed in-loop filter is integrated into HM7.0, and experimental results show that it can significantly improve the performance of HEVC on top of the state-of-the-art in-loop filters.
{"title":"Nonlocal Adaptive In-Loop Filter via Content-Dependent Soft-Thresholding for HEVC","authors":"Xinfeng Zhang, Weisi Lin, Shiqi Wang, Siwei Ma","doi":"10.1109/ISM.2015.56","DOIUrl":"https://doi.org/10.1109/ISM.2015.56","url":null,"abstract":"In-loop filters have been widely utilized in latest video coding standards to improve the video coding efficiency by reducing compression artifacts. However, existing in-loop filters only utilize image local correlations, leading to limited performance improvement. In this paper, we explore a novel adaptive in-loop filter by means of the nonlocal similar content to improve the quality of reconstructed video frames. In our proposed filter, the input video frame is first divided into different image patch groups based on their similarity, and then a soft-thresholding method is applied to the singular values of matrices composed of image patches in every group. Since compression noise is highly correlated with image content, we propose a group-wise threshold estimation method based on image statistical characteristics, coding modes and quantization parameters. To ensure the filtering efficiency, slice level control flags are utilized and determined based on the distortion changes after filtering. The proposed in-loop filter is integrated into HM7.0, and experimental results show that it can significantly improve the performance of HEVC on top of the state-of-the-art in-loop filters.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134438213","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 this paper, an error resilient coding structure is proposed. Firstly an extended dual frame motion compensation (DFMC) coding structure is proposed, and then an end-to-end distortion model with error resilient filter is used for mode decision in Macroblock (MB) level rate distortion cost decision, finally the number of header bits packet in HQF is determined in frame level rate cost decision. Experimental results show that the proposed method can achieve better performance than previous schemes.
{"title":"Error Protection with Extended Dual Frame Motion Compensation","authors":"Da Liu, Li Wang, Chunyan Li, Y. Hao, Fang Yin","doi":"10.1109/ISM.2015.103","DOIUrl":"https://doi.org/10.1109/ISM.2015.103","url":null,"abstract":"In this paper, an error resilient coding structure is proposed. Firstly an extended dual frame motion compensation (DFMC) coding structure is proposed, and then an end-to-end distortion model with error resilient filter is used for mode decision in Macroblock (MB) level rate distortion cost decision, finally the number of header bits packet in HQF is determined in frame level rate cost decision. Experimental results show that the proposed method can achieve better performance than previous schemes.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115434985","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}
A recent trend in user-generated video is to broadcast live video from mobile phones. Mobile broadcasting platforms such as YouNow or Periscope understood this trend and attract multiple thousands of concurrent views per second. Amateur produced mobile live video often suffers from only a limited duration of the recordings. Usually, the live recordings do not cover entire events. To address this problem our approach tries to understand how to increase the recording duration by integrating features for influencing the user behavior including gamification.
{"title":"On Influencing Mobile Live Video Broadcasting Users","authors":"Stefan Wilk, Dimitri Wulffert, W. Effelsberg","doi":"10.1109/ISM.2015.110","DOIUrl":"https://doi.org/10.1109/ISM.2015.110","url":null,"abstract":"A recent trend in user-generated video is to broadcast live video from mobile phones. Mobile broadcasting platforms such as YouNow or Periscope understood this trend and attract multiple thousands of concurrent views per second. Amateur produced mobile live video often suffers from only a limited duration of the recordings. Usually, the live recordings do not cover entire events. To address this problem our approach tries to understand how to increase the recording duration by integrating features for influencing the user behavior including gamification.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114982875","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}
Adaptive video streaming is very important for delivering high-quality video content of MOOCs (Massive Online Open Courses) to online learners because they often have Internet connections with different levels of bandwidth. Although DASH (Dynamic adaptive streaming over HTTP) is widely accepted as a viable streaming technology to implement scalable Internet streaming using HTTP transport, few research efforts have been made to investigate how to apply this relatively new technology on improving the quality of experience (QoE) of MOOC video streaming. This paper proposes a DASH scheme for MOOC video streaming (MOOC-DASH) to improve QoE of DASH-based MOOC. This scheme consists of content-aware ROI-based video encoding for MOOCs video and bitrate selection algorithm to provide a high-quality and smooth video streaming service to online learners. Experimental results demonstrate that it can effectively reduce the bandwidth of the video content and improve QoE of MOOC Streaming.
自适应视频流对于向在线学习者提供高质量的mooc(大规模在线开放课程)视频内容非常重要，因为他们通常有不同带宽水平的互联网连接。尽管DASH (Dynamic adaptive streaming over HTTP)被广泛认为是一种可行的流媒体技术，可以使用HTTP传输实现可扩展的互联网流媒体，但很少有人研究如何将这种相对较新的技术应用于提高MOOC视频流媒体的体验质量(QoE)。为了提高基于DASH的MOOC视频流的QoE，本文提出了一种面向MOOC视频流的DASH方案(MOOC-DASH)。该方案由面向mooc视频的基于内容感知roi的视频编码和比特率选择算法组成，为在线学习者提供高质量、流畅的视频流服务。实验结果表明，该方法可以有效地降低视频内容的带宽，提高MOOC流媒体的QoE。
{"title":"MOOC-DASH: A DASH System for Delivering High-Quality MOOCs Videos","authors":"Yi Wang, Wenjun Wu, Yihua Lou","doi":"10.1109/ISM.2015.15","DOIUrl":"https://doi.org/10.1109/ISM.2015.15","url":null,"abstract":"Adaptive video streaming is very important for delivering high-quality video content of MOOCs (Massive Online Open Courses) to online learners because they often have Internet connections with different levels of bandwidth. Although DASH (Dynamic adaptive streaming over HTTP) is widely accepted as a viable streaming technology to implement scalable Internet streaming using HTTP transport, few research efforts have been made to investigate how to apply this relatively new technology on improving the quality of experience (QoE) of MOOC video streaming. This paper proposes a DASH scheme for MOOC video streaming (MOOC-DASH) to improve QoE of DASH-based MOOC. This scheme consists of content-aware ROI-based video encoding for MOOCs video and bitrate selection algorithm to provide a high-quality and smooth video streaming service to online learners. Experimental results demonstrate that it can effectively reduce the bandwidth of the video content and improve QoE of MOOC Streaming.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"386 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116328619","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}
Yuanyuan Wang, Yukiko Kawai, K. Sumiya, Y. Ishikawa
With the advance of video-on-demand (VOD) services such as Netfix, users are able to watch many kinds of videos anytime and anywhere. While watching a video, recently, users often search related information about it through the Web by using mobile PC. However, users cannot satisfactorily understand and enjoy it because the video keeps playing when they search about it. It is necessary to detect various questions of the video to supplement their related information about each scene for automatic search. However, only one video includes various topics of each scene, furthermore, viewers have different levels of knowledge. Therefore, we have developed a novel automatic video reinforcing system, called TV-Binder, it generates new video contents from one video stream related to viewers' interests and knowledge by adding other related contents (i.e., YouTube videos, images or maps) and by removing unnecessary original scenes, based on topics of each scene. As a result, viewers can satisfy and joyfully watch modified video contents without searching anything. At first, our system extract topics and detect their scenes of a video stream by using closed captions. The system then searches other necessary contents and determines unwanted original scenes based on popularity rating of each original scene and level of detail (LOD) controlling under time pressure. Through this, TV-Binder can automatically generate video contents are classified into four quadrants by two axes, one is digest and detailed videos, the other one is videos for experts with knowledge about particular topics and ordinary viewers without special knowledge. In this paper, we discuss our automatic video reinforcing system and an evaluation of its effectiveness.
{"title":"An Automatic Video Reinforcing System Based on Popularity Rating of Scenes and Level of Detail Controlling","authors":"Yuanyuan Wang, Yukiko Kawai, K. Sumiya, Y. Ishikawa","doi":"10.1109/ISM.2015.31","DOIUrl":"https://doi.org/10.1109/ISM.2015.31","url":null,"abstract":"With the advance of video-on-demand (VOD) services such as Netfix, users are able to watch many kinds of videos anytime and anywhere. While watching a video, recently, users often search related information about it through the Web by using mobile PC. However, users cannot satisfactorily understand and enjoy it because the video keeps playing when they search about it. It is necessary to detect various questions of the video to supplement their related information about each scene for automatic search. However, only one video includes various topics of each scene, furthermore, viewers have different levels of knowledge. Therefore, we have developed a novel automatic video reinforcing system, called TV-Binder, it generates new video contents from one video stream related to viewers' interests and knowledge by adding other related contents (i.e., YouTube videos, images or maps) and by removing unnecessary original scenes, based on topics of each scene. As a result, viewers can satisfy and joyfully watch modified video contents without searching anything. At first, our system extract topics and detect their scenes of a video stream by using closed captions. The system then searches other necessary contents and determines unwanted original scenes based on popularity rating of each original scene and level of detail (LOD) controlling under time pressure. Through this, TV-Binder can automatically generate video contents are classified into four quadrants by two axes, one is digest and detailed videos, the other one is videos for experts with knowledge about particular topics and ordinary viewers without special knowledge. In this paper, we discuss our automatic video reinforcing system and an evaluation of its effectiveness.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129747243","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 analysis of customer behavior from surveillance camera is one of the most important open topics for marketing. We develop a system to recognize different customer behaviors on the front of shelf: no interest, viewing, turning to shelf, touching, picking and returning to shelf and picking and putting into basket, which show customer's increasing interest to products. In the proposed system, head orientation, body orientation, and arm action, the multiple cues are integrated for the customer behavior recognition. The proposed system discretizes the head and body orientation of customer into 8 directions to estimate whether the customer is looking or turning to the merchandise shelf. Semi-Supervised Learning method is applied to optimize the training dataset and to generate an accurate classifier. As for the arm action recognition, a novel combined hand feature (CHF), which includes hand trajectory, tracking status and the relative position between hand and shopping basket, is proposed to describe different arm actions. The CHF is classified by Dynamic Bayesian Network (DBN) into different arm actions. A series of experiments demonstrate the effectiveness of the proposed methods and the performance to the developed system.
{"title":"Customer Behavior Recognition in Retail Store from Surveillance Camera","authors":"Jingwen Liu, Yanlei Gu, S. Kamijo","doi":"10.1109/ISM.2015.52","DOIUrl":"https://doi.org/10.1109/ISM.2015.52","url":null,"abstract":"The analysis of customer behavior from surveillance camera is one of the most important open topics for marketing. We develop a system to recognize different customer behaviors on the front of shelf: no interest, viewing, turning to shelf, touching, picking and returning to shelf and picking and putting into basket, which show customer's increasing interest to products. In the proposed system, head orientation, body orientation, and arm action, the multiple cues are integrated for the customer behavior recognition. The proposed system discretizes the head and body orientation of customer into 8 directions to estimate whether the customer is looking or turning to the merchandise shelf. Semi-Supervised Learning method is applied to optimize the training dataset and to generate an accurate classifier. As for the arm action recognition, a novel combined hand feature (CHF), which includes hand trajectory, tracking status and the relative position between hand and shopping basket, is proposed to describe different arm actions. The CHF is classified by Dynamic Bayesian Network (DBN) into different arm actions. A series of experiments demonstrate the effectiveness of the proposed methods and the performance to the developed system.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130050573","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}
Human action recognition is a hot research topic in image analysis and computer vision. In this paper, we propose Hybrid Centroid Canonical Correlation Analysis (HCCCA) and multi-set HCCCA for multimodal information analysis and fusion. Furthermore, we present a novel human action recognition framework by using multi-set HCCCA to fuse multimodal features, which include the hierarchal pyramid Depth Motion Map (DMM) for the depth images, the Histogram of Oriented Displacement (HOD) for the skeleton, and the statistical measurements for the accelerometer. The proposed framework was evaluated using two datasets MSR Action 3D dataset and UTD multimodal human action dataset. The experimental results demonstrated that the proposed framework can achieve a higher average accuracy compared to several existing methods.
{"title":"Human Action Recognition Using Hybrid Centroid Canonical Correlation Analysis","authors":"Nour El-Din El-Madany, Yifeng He, L. Guan","doi":"10.1109/ISM.2015.118","DOIUrl":"https://doi.org/10.1109/ISM.2015.118","url":null,"abstract":"Human action recognition is a hot research topic in image analysis and computer vision. In this paper, we propose Hybrid Centroid Canonical Correlation Analysis (HCCCA) and multi-set HCCCA for multimodal information analysis and fusion. Furthermore, we present a novel human action recognition framework by using multi-set HCCCA to fuse multimodal features, which include the hierarchal pyramid Depth Motion Map (DMM) for the depth images, the Histogram of Oriented Displacement (HOD) for the skeleton, and the statistical measurements for the accelerometer. The proposed framework was evaluated using two datasets MSR Action 3D dataset and UTD multimodal human action dataset. The experimental results demonstrated that the proposed framework can achieve a higher average accuracy compared to several existing methods.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128892190","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}
It is difficult to show a frontal face in video chatting because there is a gap between a display and a camera. We propose a method for real-time face reorientation by creating a 2.5-D face model from a single RGB-D camera and synthesizing the rotated face model with the original face image. Our method uses two kinds face models complementarily: a point cloud based model and a generic face model fitted to the user. We conducted subjective evaluation and confirmed the validity of our proposed system.
{"title":"Fast Face Model Reconstruction and Synthesis Using an RGB-D Camera and Its Subjective Evaluation","authors":"T. Yamasaki, I. Nakamura, K. Aizawa","doi":"10.1109/ISM.2015.107","DOIUrl":"https://doi.org/10.1109/ISM.2015.107","url":null,"abstract":"It is difficult to show a frontal face in video chatting because there is a gap between a display and a camera. We propose a method for real-time face reorientation by creating a 2.5-D face model from a single RGB-D camera and synthesizing the rotated face model with the original face image. Our method uses two kinds face models complementarily: a point cloud based model and a generic face model fitted to the user. We conducted subjective evaluation and confirmed the validity of our proposed system.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131964832","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}
Recent progress in web real-time communication (WebRTC) promotes multi-apps environment by creating islands of communication apps where users of one website or service cannot easily communicate with those of another. We describe the architecture and implementation of a multi-platform system to do user reachability in multiple communication services where users decide how they want to be reached on multiple apps, e.g., in an organization that has voice-over-IP, web conferencing and messaging from different vendors. Our architecture separates the user contacts from reachability apps, supports user and endpoint driven reachability policies, and has several independent and non-interoperable WebRTC-based apps for two-way and multi-party multimedia communication. Our flexible implementation can be used for enterprise or personal communications, or as a white-labeled app for consumers of a business.
{"title":"User Reachability in Multi-Apps Environment","authors":"Kundan Singh","doi":"10.1109/ISM.2015.91","DOIUrl":"https://doi.org/10.1109/ISM.2015.91","url":null,"abstract":"Recent progress in web real-time communication (WebRTC) promotes multi-apps environment by creating islands of communication apps where users of one website or service cannot easily communicate with those of another. We describe the architecture and implementation of a multi-platform system to do user reachability in multiple communication services where users decide how they want to be reached on multiple apps, e.g., in an organization that has voice-over-IP, web conferencing and messaging from different vendors. Our architecture separates the user contacts from reachability apps, supports user and endpoint driven reachability policies, and has several independent and non-interoperable WebRTC-based apps for two-way and multi-party multimedia communication. Our flexible implementation can be used for enterprise or personal communications, or as a white-labeled app for consumers of a business.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131587666","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}
Florian Schimanke, Sophie Ribbers, R. Mertens, O. Vornberger
Spaced repetition learning is an approach for choosing the most efficient intervals between rehearsing learning content. Typically used for tasks like learning vocabulary it also offers great potential for content selection in learning games. Learning games do, however differ from classic spaced repetition learning approaches in that content is not only accessed when indicated by a spaced repetition scheduling algorithm but also when the users simply want to play the game or when they decide to play the game multiple times in a row. In these cases, short term memory effects might mask learning effects in user performance, leading to faulty inputs to the calculation of spaced repetition interval lengths. This paper reviews current research literature on the interaction of short term and long term memory in order to determine how short term memory effects can be coped with in the context of spaced repetition based learning games.
{"title":"Implications of Short Term Memory Research for the Design of Spaced Repetition Based Mobile Learning Games","authors":"Florian Schimanke, Sophie Ribbers, R. Mertens, O. Vornberger","doi":"10.1109/ISM.2015.13","DOIUrl":"https://doi.org/10.1109/ISM.2015.13","url":null,"abstract":"Spaced repetition learning is an approach for choosing the most efficient intervals between rehearsing learning content. Typically used for tasks like learning vocabulary it also offers great potential for content selection in learning games. Learning games do, however differ from classic spaced repetition learning approaches in that content is not only accessed when indicated by a spaced repetition scheduling algorithm but also when the users simply want to play the game or when they decide to play the game multiple times in a row. In these cases, short term memory effects might mask learning effects in user performance, leading to faulty inputs to the calculation of spaced repetition interval lengths. This paper reviews current research literature on the interaction of short term and long term memory in order to determine how short term memory effects can be coped with in the context of spaced repetition based learning games.","PeriodicalId":250353,"journal":{"name":"2015 IEEE International Symposium on Multimedia (ISM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359317","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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