With the increasing frequency of participation in social networking activities, tremendous value has been created by crowds. Thus some emerging industries come along with it to collect these values. At the same time, crowds require some compensation from the these project organizers for their privacy loss or cost of activities. This paper dedicate to exploit a users incentives system, it develops a game-theoretic model of crowdsourcing or crowdsensing services base on contests. The model consists of two parts: incentives and optimizing pricing. We start from the crowds' point of view, committed to dig out their equilibrium strategies. Based on this, a bonus pool and expected rewards are demonstrated for the organizer and crowds respectively.
{"title":"Optimal Crowds Contest Model for Crowdsourcing","authors":"Song Xu, Lei Liu, Li-zhen Cui, Yongqing Zheng","doi":"10.1145/3126973.3126982","DOIUrl":"https://doi.org/10.1145/3126973.3126982","url":null,"abstract":"With the increasing frequency of participation in social networking activities, tremendous value has been created by crowds. Thus some emerging industries come along with it to collect these values. At the same time, crowds require some compensation from the these project organizers for their privacy loss or cost of activities. This paper dedicate to exploit a users incentives system, it develops a game-theoretic model of crowdsourcing or crowdsensing services base on contests. The model consists of two parts: incentives and optimizing pricing. We start from the crowds' point of view, committed to dig out their equilibrium strategies. Based on this, a bonus pool and expected rewards are demonstrated for the organizer and crowds respectively.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125185170","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}
Dropout prediction research in MOOCs aims to predict whether students will drop out from the courses instead of completing them. Due to the high dropout rates in current MOOCs, this problem is of great importance. Current methods rely on features extracted by feature engineering, in which all features are extracted manually. This process is costly, time consuming, and not extensible to new datasets from different platforms or different courses with different characters. In this paper, we propose a model that can automatically extract features from the raw MOOC data. Our model is a deep neural network, which is a combination of Convolutional Neural Networks and Recurrent Neural Networks. Through extensive experiments on a public dataset, we show that the proposed model can achieve results comparable to feature engineering based methods.
{"title":"Deep Model for Dropout Prediction in MOOCs","authors":"Wei Wang, Han Yu, C. Miao","doi":"10.1145/3126973.3126990","DOIUrl":"https://doi.org/10.1145/3126973.3126990","url":null,"abstract":"Dropout prediction research in MOOCs aims to predict whether students will drop out from the courses instead of completing them. Due to the high dropout rates in current MOOCs, this problem is of great importance. Current methods rely on features extracted by feature engineering, in which all features are extracted manually. This process is costly, time consuming, and not extensible to new datasets from different platforms or different courses with different characters. In this paper, we propose a model that can automatically extract features from the raw MOOC data. Our model is a deep neural network, which is a combination of Convolutional Neural Networks and Recurrent Neural Networks. Through extensive experiments on a public dataset, we show that the proposed model can achieve results comparable to feature engineering based methods.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125661559","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}
Today, many applications depend on the projection on the population distribution in geographical regions, such as launching marketing campaigns and enhancing the public safety in certain densely-populated areas. Demographic and sociological researches have provided various ways of collecting people's trajectory data through offline means. However, collecting offline data consumes a lot of resources, and the data availability is usually limited. Fortunately, the wide spread of online social network (OSN) applications over mobile devices reflect many geographical information, where we could devise a light weight approach of conducting the study on the projection of the population distribution using the online data. In this paper, we propose a geo-homophily model in OSNs to help project the population distribution in a given division of geographical regions. We establish a three-layer theoretic framework: It first describes the relationship between the online message diffusion among friends in the OSN and the offline population distribution over a given division of regions via a Dirichlet process, and then projects the floating population across the regions. Evaluations over large-scale OSN datasets show that the proposed prediction models can characterize the process of the formation of the population distribution and the changes of the floating population over time with a high prediction accuracy.
{"title":"Population Distribution Projection by Modeling Geo Homophily in Online Social Networks","authors":"Yuanxing Zhang, Zhuqi Li, Kaigui Bian, Yichong Bai, Zhi Yang, Xiaoming Li","doi":"10.1145/3126973.3127000","DOIUrl":"https://doi.org/10.1145/3126973.3127000","url":null,"abstract":"Today, many applications depend on the projection on the population distribution in geographical regions, such as launching marketing campaigns and enhancing the public safety in certain densely-populated areas. Demographic and sociological researches have provided various ways of collecting people's trajectory data through offline means. However, collecting offline data consumes a lot of resources, and the data availability is usually limited. Fortunately, the wide spread of online social network (OSN) applications over mobile devices reflect many geographical information, where we could devise a light weight approach of conducting the study on the projection of the population distribution using the online data. In this paper, we propose a geo-homophily model in OSNs to help project the population distribution in a given division of geographical regions. We establish a three-layer theoretic framework: It first describes the relationship between the online message diffusion among friends in the OSN and the offline population distribution over a given division of regions via a Dirichlet process, and then projects the floating population across the regions. Evaluations over large-scale OSN datasets show that the proposed prediction models can characterize the process of the formation of the population distribution and the changes of the floating population over time with a high prediction accuracy.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121100257","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}
Evaluating the quality of workers is very important in crowdsourcing system and impactful methods are required in order to obtain the most appropriate quality. Previous work have introduced confidence intervals to estimate the quality of workers. However, we have found the size of the confidence interval is wide through analysis of experimental results, which leads to inaccurate worker error rates. In this paper, we propose an optimized algorithm of confidence interval to reduce the size of the confidence interval as narrow as possible and to estimate the quality of workers more precise. We verify our algorithm using the simulated data from our own crowdsourcing platform under realistic settings.
{"title":"Empirical Study on Assessment Algorithms with Confidence in Crowdsourcing","authors":"Yiming Cao, Lei Liu, Li-zhen Cui, Qingzhong Li","doi":"10.1145/3126973.3126994","DOIUrl":"https://doi.org/10.1145/3126973.3126994","url":null,"abstract":"Evaluating the quality of workers is very important in crowdsourcing system and impactful methods are required in order to obtain the most appropriate quality. Previous work have introduced confidence intervals to estimate the quality of workers. However, we have found the size of the confidence interval is wide through analysis of experimental results, which leads to inaccurate worker error rates. In this paper, we propose an optimized algorithm of confidence interval to reduce the size of the confidence interval as narrow as possible and to estimate the quality of workers more precise. We verify our algorithm using the simulated data from our own crowdsourcing platform under realistic settings.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132364630","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}
Xiang Gu, Y. Chai, Yi Liu, Jianping Shen, Yadong Huang, Yixuan Nan
The article1 aims to study the MCIN modeling method and design the MCIN-based architecture of smart agriculture (MCIN-ASA) which is different from current vertical architecture and involves production, management and commerce. Architecture is composed of three MCIN-ASA participants which are MCIN-ASA enterprises, individuals and commodity. In addition, architecture uses enterprises and individuals personalized portals as the carriers which are linked precisely with each other through a peer-to-peer network called six-degrees-of-separation block-chain. The authors want to establish a self-organization, open and ecological operational system which includes active, personalized consumption, direct, centralized distribution, distributed and smart production. The paper models three main MCIN-ASA participants, design the smart supply, demand and management functions, which shows the feasibility innovation and high efficiency of implementing MCIN on agriculture. The authors think that MCIN-ASA improves current agriculture greatly, and inspires a lot in production-marketing-combined electronic commerce.
{"title":"A MCIN-based Architecture of Smart Agriculture","authors":"Xiang Gu, Y. Chai, Yi Liu, Jianping Shen, Yadong Huang, Yixuan Nan","doi":"10.1145/3126973.3126999","DOIUrl":"https://doi.org/10.1145/3126973.3126999","url":null,"abstract":"The article1 aims to study the MCIN modeling method and design the MCIN-based architecture of smart agriculture (MCIN-ASA) which is different from current vertical architecture and involves production, management and commerce. Architecture is composed of three MCIN-ASA participants which are MCIN-ASA enterprises, individuals and commodity. In addition, architecture uses enterprises and individuals personalized portals as the carriers which are linked precisely with each other through a peer-to-peer network called six-degrees-of-separation block-chain. The authors want to establish a self-organization, open and ecological operational system which includes active, personalized consumption, direct, centralized distribution, distributed and smart production. The paper models three main MCIN-ASA participants, design the smart supply, demand and management functions, which shows the feasibility innovation and high efficiency of implementing MCIN on agriculture. The authors think that MCIN-ASA improves current agriculture greatly, and inspires a lot in production-marketing-combined electronic commerce.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126063192","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}
L. Meng, Nguyen Quy Hy, Xiaohai Tian, Zhiqi Shen, Chng Eng Siong, F. Guan, C. Miao, Cyril Leung
This paper presents an age-friendly system for improving the elderly's online shopping experience. Different from most related studies focusing on website design and content organization, we propose to integrate three assistive techniques to facilitate the elderly's browsing of products in E-commerce platforms, including the crowd-improved speech recognition, the multimodal search, and the personalized speech feedback. The first two techniques, namely, the crowd-improved speech recognition and the multimodal search, work together to allow the elderly search for desired products flexibly using either speech, an image, text, or any combination of them whichever are convenient for the elderly. The personalized speech feedback provides a speech summary of search result in a personalized voice. That is, the elderly are allowed to choose or even create their desired voices, and also can customize the voices in terms of pitch, speaking speed, and loudness. As a whole, the proposed system is expected to help and engage the elderly's E-commerce adoption. Testing on real-world E-commerce product datasets demonstrated the usability of the proposed system.
{"title":"Towards Age-friendly E-commerce Through Crowd-Improved Speech Recognition, Multimodal Search, and Personalized Speech Feedback","authors":"L. Meng, Nguyen Quy Hy, Xiaohai Tian, Zhiqi Shen, Chng Eng Siong, F. Guan, C. Miao, Cyril Leung","doi":"10.1145/3126973.3129306","DOIUrl":"https://doi.org/10.1145/3126973.3129306","url":null,"abstract":"This paper presents an age-friendly system for improving the elderly's online shopping experience. Different from most related studies focusing on website design and content organization, we propose to integrate three assistive techniques to facilitate the elderly's browsing of products in E-commerce platforms, including the crowd-improved speech recognition, the multimodal search, and the personalized speech feedback. The first two techniques, namely, the crowd-improved speech recognition and the multimodal search, work together to allow the elderly search for desired products flexibly using either speech, an image, text, or any combination of them whichever are convenient for the elderly. The personalized speech feedback provides a speech summary of search result in a personalized voice. That is, the elderly are allowed to choose or even create their desired voices, and also can customize the voices in terms of pitch, speaking speed, and loudness. As a whole, the proposed system is expected to help and engage the elderly's E-commerce adoption. Testing on real-world E-commerce product datasets demonstrated the usability of the proposed system.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124752625","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}
Telematics1 techniques enable insurers to capture the driving behavior of policyholders and correspondingly offer the personalized vehicle insurance rate, namely the usage-based insurance (UBI). A risky driving behavior scoring model for the personalized automobile insurance pricing was proposed based on telematics data. Firstly, three typical UBI pricing modes were analyzed. Drive behavior rate factors (DBRF) pricing mode was proposed based on mileage rate factors (MRF), in which insurance rate for each vehicle can be determined by the evaluation of individual driving behavior using OBD data. Then, on the basis of the analysis of influencing factors of safe driving, a driving behavior score model was established for DBRF by the improved EW-AHP (Entropy Weight- Analytic Hierarchy Process) Method. Finally, driving behavior scores of 100 drivers were computed by using the data collected from a 6-month field experiment. The results of three statistics analysis showed that the driving behavior score model could effectively reflect the risk level of driver's safe driving and provide a basis for the individual discount or surcharge that insurers offer to their policyholders.
{"title":"A Risky Driving Behavior Scoring Model for the Personalized Automobile Insurance Pricing","authors":"Zhishuo Liu, Qianhui Shen, Han Li, Jingmiao Ma","doi":"10.1145/3126973.3126978","DOIUrl":"https://doi.org/10.1145/3126973.3126978","url":null,"abstract":"Telematics1 techniques enable insurers to capture the driving behavior of policyholders and correspondingly offer the personalized vehicle insurance rate, namely the usage-based insurance (UBI). A risky driving behavior scoring model for the personalized automobile insurance pricing was proposed based on telematics data. Firstly, three typical UBI pricing modes were analyzed. Drive behavior rate factors (DBRF) pricing mode was proposed based on mileage rate factors (MRF), in which insurance rate for each vehicle can be determined by the evaluation of individual driving behavior using OBD data. Then, on the basis of the analysis of influencing factors of safe driving, a driving behavior score model was established for DBRF by the improved EW-AHP (Entropy Weight- Analytic Hierarchy Process) Method. Finally, driving behavior scores of 100 drivers were computed by using the data collected from a 6-month field experiment. The results of three statistics analysis showed that the driving behavior score model could effectively reflect the risk level of driver's safe driving and provide a basis for the individual discount or surcharge that insurers offer to their policyholders.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130862254","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}
Y. Liu, Usman Shittu Chitawa, G. Guo, Xingwei Wang, Zhenhua Tan, Shuang Wang
With the speed growth of financial technology (Fintech), modern electronic marketing has typically deployed the use of the World Wide Web. This has come with great challenges especially in decision making and in engaging the pre-tail for launching new products and services in an open environment susceptible to high risks and threats. A prodigious need to build a sellers reputation and trust between the seller and the buyer so as to diminish such risks and threats in online trading birthed the idea of reputation systems. The emergence of reputation systems has attracted a lot of researchers to propose rating aggregation methods such as simple mean and normal distribution based method. However, the existing methods cannot accurately produce reputation score in some cases. Hence, this paper proposes a new model aiming to producing even more accurate and effective reputation score. Our model uses the standard beta-distribution considering the received rating distribution, so as to generate the weights of each ratings and then derive the level weights of ratings. The final reputation score is the level weighted aggregation of the rating levels. The proposed model is innovative in the aspect that the ratings are not directly aggregated to the reputation score, but are treated as the samples in evaluating each respective rating levels. Through case studies, the model is demonstrated to achieve desired accuracy and effectiveness, and even performs better than the existing models.
{"title":"A Reputation Model for Aggregating Ratings based on Beta Distribution Function","authors":"Y. Liu, Usman Shittu Chitawa, G. Guo, Xingwei Wang, Zhenhua Tan, Shuang Wang","doi":"10.1145/3126973.3126992","DOIUrl":"https://doi.org/10.1145/3126973.3126992","url":null,"abstract":"With the speed growth of financial technology (Fintech), modern electronic marketing has typically deployed the use of the World Wide Web. This has come with great challenges especially in decision making and in engaging the pre-tail for launching new products and services in an open environment susceptible to high risks and threats. A prodigious need to build a sellers reputation and trust between the seller and the buyer so as to diminish such risks and threats in online trading birthed the idea of reputation systems. The emergence of reputation systems has attracted a lot of researchers to propose rating aggregation methods such as simple mean and normal distribution based method. However, the existing methods cannot accurately produce reputation score in some cases. Hence, this paper proposes a new model aiming to producing even more accurate and effective reputation score. Our model uses the standard beta-distribution considering the received rating distribution, so as to generate the weights of each ratings and then derive the level weights of ratings. The final reputation score is the level weighted aggregation of the rating levels. The proposed model is innovative in the aspect that the ratings are not directly aggregated to the reputation score, but are treated as the samples in evaluating each respective rating levels. Through case studies, the model is demonstrated to achieve desired accuracy and effectiveness, and even performs better than the existing models.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132749408","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}
Based on the auto-regressive model power spectrum analysis of sleep signal in time-frequency domain, it is found that each sleep stage has its own unique power spectrum in each frequency band. The change of sleep phase is accompanied with the change of sleep signal spectrum. In this paper, we firstly study the original RBF neural network for automatic sleep staging and then propose an improved classification algorithm in which the power spectrum of each sleep stage known as frequency domain features and five another time domain features are calculated as input parameters. The proposed classification algorithm is tested on ISRUC-Sleep data set. Experimental results demonstrate that classification algorithm based on the improved radial basis function network is effective in accuracy and efficiency.
{"title":"A sleep stage classification algorithm based on radial basis function networks","authors":"Zhihong Cui, Xiangwei Zheng","doi":"10.1145/3126973.3126976","DOIUrl":"https://doi.org/10.1145/3126973.3126976","url":null,"abstract":"Based on the auto-regressive model power spectrum analysis of sleep signal in time-frequency domain, it is found that each sleep stage has its own unique power spectrum in each frequency band. The change of sleep phase is accompanied with the change of sleep signal spectrum. In this paper, we firstly study the original RBF neural network for automatic sleep staging and then propose an improved classification algorithm in which the power spectrum of each sleep stage known as frequency domain features and five another time domain features are calculated as input parameters. The proposed classification algorithm is tested on ISRUC-Sleep data set. Experimental results demonstrate that classification algorithm based on the improved radial basis function network is effective in accuracy and efficiency.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123244156","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}
Nowadays, as many devices like mobile phones and smart watch/band are equipped with GPS-devices, a large volume of trajectory data is generated every day. With the availability of such trajectory data, many mining tasks have been proposed and investigated in the past decade. Since the raw trajectory data is usually very large, it is a big challenge to analyse and mine the raw data directly. In order to address this issue, a branch of research has been done to compress the trajectory data. This paper surveys recent research about trajectory compression. An overview of existing techniques for trajectory compression is provided.
{"title":"Compressing Trajectory for Trajectory Indexing","authors":"Kaiyu Feng, Zhiqi Shen","doi":"10.1145/3126973.3126979","DOIUrl":"https://doi.org/10.1145/3126973.3126979","url":null,"abstract":"Nowadays, as many devices like mobile phones and smart watch/band are equipped with GPS-devices, a large volume of trajectory data is generated every day. With the availability of such trajectory data, many mining tasks have been proposed and investigated in the past decade. Since the raw trajectory data is usually very large, it is a big challenge to analyse and mine the raw data directly. In order to address this issue, a branch of research has been done to compress the trajectory data. This paper surveys recent research about trajectory compression. An overview of existing techniques for trajectory compression is provided.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123271800","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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