With the booming development of social media in recent years, researchers have begun to pay more attention to extracting personal profiles from information. Keyword extraction plays an important role in extracting personal profiles. However, most of the previous studies are only valid for ordinary text, but not ideal for social media short text. In this paper, we propose an improved method for keyword extraction based on Word2vec and Textrank to solve the unique problem of social media short text. Our approach uses the Word2vec to capture the semantic features between words in selected text, and meanwhile naturally fuses the word frequency, semantic relation and directional relation into Textrank to extract keywords. We conduct the experiments on the three datasets. The experimental results show the superior performance of our method in keyword extraction.
{"title":"Keyword Extraction for Social Media Short Text","authors":"Dexin Zhao, Nana Du, Zhi Chang, Yukun Li","doi":"10.1109/WISA.2017.12","DOIUrl":"https://doi.org/10.1109/WISA.2017.12","url":null,"abstract":"With the booming development of social media in recent years, researchers have begun to pay more attention to extracting personal profiles from information. Keyword extraction plays an important role in extracting personal profiles. However, most of the previous studies are only valid for ordinary text, but not ideal for social media short text. In this paper, we propose an improved method for keyword extraction based on Word2vec and Textrank to solve the unique problem of social media short text. Our approach uses the Word2vec to capture the semantic features between words in selected text, and meanwhile naturally fuses the word frequency, semantic relation and directional relation into Textrank to extract keywords. We conduct the experiments on the three datasets. The experimental results show the superior performance of our method in keyword extraction.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115719589","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}
Shuo Feng, Qian Wang, Derong Shen, Yue Kou, Tiezheng Nie, Ge Yu
Nowadays, people prefer to take part in multiple social networks to enjoy different kinds of services. Consequently, a significant task is to identify users across networks. Most state-of-the-art works on this issue exploit user local structure features (e.g., friend, follow and followed). In this paper, we first proposes the notion of user global view features, which represent the location of users in the network. Then, we present an iterative two-stage algorithm (GAUI) using Global view features with user Attribute features to solve User Identification. In GAUI, we iteratively update pairwise similarity and predict new matching users. Certainly, we present a community based core anchor link filter strategy to reduce the computation cost, and present a stable matching based mapping strategy to improve the accuracy. At last, the experiments conducted on two real-world aligned networks demonstrate that our method has better performance on precision and recall.
{"title":"User Identification across Social Networks Based on Global View Features","authors":"Shuo Feng, Qian Wang, Derong Shen, Yue Kou, Tiezheng Nie, Ge Yu","doi":"10.1109/WISA.2017.36","DOIUrl":"https://doi.org/10.1109/WISA.2017.36","url":null,"abstract":"Nowadays, people prefer to take part in multiple social networks to enjoy different kinds of services. Consequently, a significant task is to identify users across networks. Most state-of-the-art works on this issue exploit user local structure features (e.g., friend, follow and followed). In this paper, we first proposes the notion of user global view features, which represent the location of users in the network. Then, we present an iterative two-stage algorithm (GAUI) using Global view features with user Attribute features to solve User Identification. In GAUI, we iteratively update pairwise similarity and predict new matching users. Certainly, we present a community based core anchor link filter strategy to reduce the computation cost, and present a stable matching based mapping strategy to improve the accuracy. At last, the experiments conducted on two real-world aligned networks demonstrate that our method has better performance on precision and recall.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130284725","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}
Yi Feng, Jidong Ge, Yemao Zhou, Chuanyi Li, Zhongjin Li, Xiaoyu Zhou, B. Luo
This paper presents a method of the association statistics between the cause of action and the statute. According to the close relationship between the cause of action and the statute in the written judgment, this paper puts forward the statistical analysis of the cause of action and the statute. The method mainly includes the pretreatment of semi-structured written judgments, reading information of the cause of action and the statute from structured documents, standardizing statutes, depositing in the database, generating EXCEL form of the association statistics from the cause of action to the statue and generating TXT form of the association statistics from the statue to the cause of action. In the process of reasoning and assessment, we can achieve the prediction of statutes and narrow the size of the cause of action.
{"title":"A Method of the Association Statistics between the Cause of Action and the Statutes","authors":"Yi Feng, Jidong Ge, Yemao Zhou, Chuanyi Li, Zhongjin Li, Xiaoyu Zhou, B. Luo","doi":"10.1109/WISA.2017.3","DOIUrl":"https://doi.org/10.1109/WISA.2017.3","url":null,"abstract":"This paper presents a method of the association statistics between the cause of action and the statute. According to the close relationship between the cause of action and the statute in the written judgment, this paper puts forward the statistical analysis of the cause of action and the statute. The method mainly includes the pretreatment of semi-structured written judgments, reading information of the cause of action and the statute from structured documents, standardizing statutes, depositing in the database, generating EXCEL form of the association statistics from the cause of action to the statue and generating TXT form of the association statistics from the statue to the cause of action. In the process of reasoning and assessment, we can achieve the prediction of statutes and narrow the size of the cause of action.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122269146","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}
With the continuous increasing of biomedical data, how to effectively use these large-scale data sets has become an urgent problem. It is also an essential issue to make benefit to users by consuming these biomedical data on the Semantic Web in a reasonable way. We present a visualization approach based on a tree-like layered interactive user interface, realize the queries of the relationships between targets, compounds, and diseases, and show the width of the path between the two biological entities according to their correlations. Furthermore, we design an iterative query method, which can find not only direct results of the input entity, but also extended results with some similarities of the input entity. Thus, the potential relationships among the extended results can be further investigated by biomedical scientists. Therefore, we have developed a user-friendly visualization system that can leverage the rich sets of the linked biomedical data.
{"title":"Visualization of Linked Biomedical Data Using Cluster Chart","authors":"Yiran Shan, Xin Wang","doi":"10.1109/WISA.2017.43","DOIUrl":"https://doi.org/10.1109/WISA.2017.43","url":null,"abstract":"With the continuous increasing of biomedical data, how to effectively use these large-scale data sets has become an urgent problem. It is also an essential issue to make benefit to users by consuming these biomedical data on the Semantic Web in a reasonable way. We present a visualization approach based on a tree-like layered interactive user interface, realize the queries of the relationships between targets, compounds, and diseases, and show the width of the path between the two biological entities according to their correlations. Furthermore, we design an iterative query method, which can find not only direct results of the input entity, but also extended results with some similarities of the input entity. Thus, the potential relationships among the extended results can be further investigated by biomedical scientists. Therefore, we have developed a user-friendly visualization system that can leverage the rich sets of the linked biomedical data.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"18 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131775000","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 boom in Linked Open Data (LOD) has recently stimulated the research of a new generation of recommender systems—LOD-enabled recommender systems, in which the similarity measure for LOD is one of the core issues. The partitioned information content (PIC)-based semantic similarity (PICSS) is a newly developed symmetric similarity measure for LOD. However, recent studies have shown that asymmetric similarity measures are more effective than symmetric similarity measures in solving recommendation problems. In this paper we develop an asymmetric item-item similarity measure for LOD—the asymmetric PIC-based semantic similarity measure (APICSS), which applies our proposed two notions: the proportion of common PIC between two resources in the PIC of a resource and the PIC difference between two resources, on the basis of the notion of PIC. Experimental evaluation with the item-based collaborative filtering method on the MovieLens 100k dataset, the DBpedia 2016-04 release, and the DBpedia-MovieLens 100k dataset shows that our APICSS measure outperforms the PICSS measure in terms of both Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The average RMSE accuracy has an increase of 1.58% and the maximum RMSE accuracy has an increase of 2.07%, compared to PICSS. The average MAE accuracy has an increase of 1.63% and the maximum MAE accuracy has an increase of 2.19%, compared to PICSS.
{"title":"Asymmetric Item-Item Similarity Measure for Linked Open Data Enabled Collaborative Filtering","authors":"Chengwang Mao, Zhuoming Xu, Xiuli Wang","doi":"10.1109/WISA.2017.23","DOIUrl":"https://doi.org/10.1109/WISA.2017.23","url":null,"abstract":"The boom in Linked Open Data (LOD) has recently stimulated the research of a new generation of recommender systems—LOD-enabled recommender systems, in which the similarity measure for LOD is one of the core issues. The partitioned information content (PIC)-based semantic similarity (PICSS) is a newly developed symmetric similarity measure for LOD. However, recent studies have shown that asymmetric similarity measures are more effective than symmetric similarity measures in solving recommendation problems. In this paper we develop an asymmetric item-item similarity measure for LOD—the asymmetric PIC-based semantic similarity measure (APICSS), which applies our proposed two notions: the proportion of common PIC between two resources in the PIC of a resource and the PIC difference between two resources, on the basis of the notion of PIC. Experimental evaluation with the item-based collaborative filtering method on the MovieLens 100k dataset, the DBpedia 2016-04 release, and the DBpedia-MovieLens 100k dataset shows that our APICSS measure outperforms the PICSS measure in terms of both Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The average RMSE accuracy has an increase of 1.58% and the maximum RMSE accuracy has an increase of 2.07%, compared to PICSS. The average MAE accuracy has an increase of 1.63% and the maximum MAE accuracy has an increase of 2.19%, compared to PICSS.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127401522","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}
To detect anomalies according to system log is a hot topic recently. For the harmonic monitoring system of the power grid, the common practice of anomaly detection is to conduct machine learning. The learning model is trained with the historical anomaly data, and used for online detection. The premise of this method is to predefine a set of indicators as the input features of the machine learning model. However, existing methods rely mainly on business experience to extract such indicators, which limits the scope of the indicators used for data analysis, but also limits the accuracy of power quality perturbation analysis. In this paper, we propose an algorithm for power quality disturbance detection which investigates the correlation among the harmonic monitoring indicators, and extract the frequently concurrent abnormal indicators as the features to locate power quality disturbance detection. With the verification of the historical disturbance records, we prove that our algorithm can effectively detect the power quality disturbing events.
{"title":"Extracting Log Patterns Based on Association Analysis for Power Quality Disturbance Detection","authors":"D. Feng, Tongxun Wang, Chen Liu, Shen Su","doi":"10.1109/WISA.2017.15","DOIUrl":"https://doi.org/10.1109/WISA.2017.15","url":null,"abstract":"To detect anomalies according to system log is a hot topic recently. For the harmonic monitoring system of the power grid, the common practice of anomaly detection is to conduct machine learning. The learning model is trained with the historical anomaly data, and used for online detection. The premise of this method is to predefine a set of indicators as the input features of the machine learning model. However, existing methods rely mainly on business experience to extract such indicators, which limits the scope of the indicators used for data analysis, but also limits the accuracy of power quality perturbation analysis. In this paper, we propose an algorithm for power quality disturbance detection which investigates the correlation among the harmonic monitoring indicators, and extract the frequently concurrent abnormal indicators as the features to locate power quality disturbance detection. With the verification of the historical disturbance records, we prove that our algorithm can effectively detect the power quality disturbing events.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123181471","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}
Web table augmentation enables users to augment attributes based on key column and other known information. For table augmentation, most of systems return a single result which could not meet the users' needs of selection and validation. Furthermore, previous works only consider the entity-attribute binary tables with the first column corresponding to the entity name and the second to an attribute to be extended. When a table has multiple columns to be extended, the result table consolidated by binary tables will suffer from entity inconsistency. In this paper, we present a framework called TAT to build Top-k consistent results for web table augmentation. While ensuring the consistency of entities, TAT provides as diverse results as possible. We design two algorithms, exclusive and iterative algorithm, for web table augmentation that return Top-k results based on different requirements from users. The experiments show that TAT could return Top-k consistent results without loss of precision or coverage.
{"title":"Building Top-k Consistent Results for Web Table Augmentation","authors":"Fei Qi, Xiaoyu Wu, Ning Wang","doi":"10.1109/WISA.2017.30","DOIUrl":"https://doi.org/10.1109/WISA.2017.30","url":null,"abstract":"Web table augmentation enables users to augment attributes based on key column and other known information. For table augmentation, most of systems return a single result which could not meet the users' needs of selection and validation. Furthermore, previous works only consider the entity-attribute binary tables with the first column corresponding to the entity name and the second to an attribute to be extended. When a table has multiple columns to be extended, the result table consolidated by binary tables will suffer from entity inconsistency. In this paper, we present a framework called TAT to build Top-k consistent results for web table augmentation. While ensuring the consistency of entities, TAT provides as diverse results as possible. We design two algorithms, exclusive and iterative algorithm, for web table augmentation that return Top-k results based on different requirements from users. The experiments show that TAT could return Top-k consistent results without loss of precision or coverage.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"2016 27","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120968046","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}
Collaborative filtering algorithm is one of the most widely used algorithms in recommender systems and has demonstrated promising results. But it relies too much on similarity to find the nearest neighbors. Whatever, the trust between users is also an import factor needed to be considered. This paper proposed a collaborative filtering algorithm that combined the user similarity and trust to obtain a more appropriate nearest neighbors set. Users not only have same interests as their nearest neighbors, but also have higher level of acceptance in the items recom-mended by their nearest neighbors. Extensive experiments based on Film Trust and MovieLens datasets have shown that the approach has major potential in improving the accuracy of recommended item.
{"title":"A Collaborative Filtering Algorithm Based on User Similarity and Trust","authors":"Qingzhou Wu, Mengxing Huang, Yangzi Mu","doi":"10.1109/WISA.2017.21","DOIUrl":"https://doi.org/10.1109/WISA.2017.21","url":null,"abstract":"Collaborative filtering algorithm is one of the most widely used algorithms in recommender systems and has demonstrated promising results. But it relies too much on similarity to find the nearest neighbors. Whatever, the trust between users is also an import factor needed to be considered. This paper proposed a collaborative filtering algorithm that combined the user similarity and trust to obtain a more appropriate nearest neighbors set. Users not only have same interests as their nearest neighbors, but also have higher level of acceptance in the items recom-mended by their nearest neighbors. Extensive experiments based on Film Trust and MovieLens datasets have shown that the approach has major potential in improving the accuracy of recommended item.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116511230","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}
He Liu, Fupeng Huang, Han Li, Weiwei Liu, Tongxun Wang
Since a low-quality data may influence the effectiveness and reliability of applications, data quality is required to be guaranteed. Data quality assessment is considered as the foundation of the promotion of data quality, so it is essential to access the data quality before any other data related activities. In the electric power industry, more and more electric power data is continuously accumulated, and many electric power applications have been developed based on these data. In China, the power grid has many special characteristic, traditional big data assessment frameworks cannot be directly applied. Therefore, a big data framework for electric power data quality assessment is proposed. Based on big data techniques, the framework can accumulate both the real-time data and the history data, provide an integrated computation environment for electric power big data assessment, and support the storage of different types of data.
{"title":"A Big Data Framework for Electric Power Data Quality Assessment","authors":"He Liu, Fupeng Huang, Han Li, Weiwei Liu, Tongxun Wang","doi":"10.1109/WISA.2017.29","DOIUrl":"https://doi.org/10.1109/WISA.2017.29","url":null,"abstract":"Since a low-quality data may influence the effectiveness and reliability of applications, data quality is required to be guaranteed. Data quality assessment is considered as the foundation of the promotion of data quality, so it is essential to access the data quality before any other data related activities. In the electric power industry, more and more electric power data is continuously accumulated, and many electric power applications have been developed based on these data. In China, the power grid has many special characteristic, traditional big data assessment frameworks cannot be directly applied. Therefore, a big data framework for electric power data quality assessment is proposed. Based on big data techniques, the framework can accumulate both the real-time data and the history data, provide an integrated computation environment for electric power big data assessment, and support the storage of different types of data.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128072081","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 view of the fact that the QoS-based service selection method in the current service selection method is less concerned with the personality attribute characteristics of the service requesters and the service selection method based on collaborative filtering, the service providers', Based on the characteristics of the personality of the service requester, this paper describes the user correlation by defining the similarity and domain relevance of the user, and the calculation method of the recommended credibility is given by using the credible measurement theory. Using the analytic hierarchy process (AHP) to determine the weight of each correlation factor, this paper proposes a credible service selection model based on collaborative filtering service selection trust model (SSTM). The simulation results show that the model can effectively improve the efficiency of service selection and resist the attack of malicious feedback. There are two major innovations as following: Firstly, to make a introduction of user relevance to reflect the degree of close between two users (service requester) under the network environment; to apply the user’s personality attribute characteristics to the service provider reputation value during the prediction, to improve the accuracy of service selection by reducing the size of service providers. Secondly, combining user relevance and recommendation credibility organically, using AHP to determine the weight of relevant factors in the service selection index system so that we can make the reputation of the predicted service provider more reliable and effectively resist the malicious user feedback.
{"title":"A Trusted Service Selection Method Based on User's Personality Feature and Service Recommendation","authors":"Weijin Jiang, Jiahui Chen, Qijie Feng","doi":"10.1109/WISA.2017.55","DOIUrl":"https://doi.org/10.1109/WISA.2017.55","url":null,"abstract":"In view of the fact that the QoS-based service selection method in the current service selection method is less concerned with the personality attribute characteristics of the service requesters and the service selection method based on collaborative filtering, the service providers', Based on the characteristics of the personality of the service requester, this paper describes the user correlation by defining the similarity and domain relevance of the user, and the calculation method of the recommended credibility is given by using the credible measurement theory. Using the analytic hierarchy process (AHP) to determine the weight of each correlation factor, this paper proposes a credible service selection model based on collaborative filtering service selection trust model (SSTM). The simulation results show that the model can effectively improve the efficiency of service selection and resist the attack of malicious feedback. There are two major innovations as following: Firstly, to make a introduction of user relevance to reflect the degree of close between two users (service requester) under the network environment; to apply the user’s personality attribute characteristics to the service provider reputation value during the prediction, to improve the accuracy of service selection by reducing the size of service providers. Secondly, combining user relevance and recommendation credibility organically, using AHP to determine the weight of relevant factors in the service selection index system so that we can make the reputation of the predicted service provider more reliable and effectively resist the malicious user feedback.","PeriodicalId":204706,"journal":{"name":"2017 14th Web Information Systems and Applications Conference (WISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133151514","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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