Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.21
Nibir Bora, V. Zaytsev, Yu-Han Chang, R. Maheswaran
Social media generated by location-services-enabled cellular devices produce enormous amounts of location-based content. Spatiotemporal analysis of such data facilitate new ways of modeling human behavior and mobility patterns. In this paper, we use over 10 millions geo-tagged tweets from the city of Los Angeles as observations of human movement and apply them to understand the relationships of geographical regions, neighborhoods and gang territories. Using a graph based-representation of street gang territories as vertices and interactions between them as edges, we train a machine learning classifier to tell apart rival and non-rival links. We correctly identify 89% of the true rivalry network, which beats a standard baseline by about 30%. Looking at larger neighborhoods, we were able to show that distance traveled from home follows a power-law distribution, and the direction of displacement, i.e., the distribution of movement direction, can be used as a profile to identify physical (or geographic) barriers when it is not uniform. Finally, considering the temporal dimension of tweets, we detect events taking place around the city by identifying irregularities in tweeting patterns.
{"title":"Gang Networks, Neighborhoods and Holidays: Spatiotemporal Patterns in Social Media","authors":"Nibir Bora, V. Zaytsev, Yu-Han Chang, R. Maheswaran","doi":"10.1109/SocialCom.2013.21","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.21","url":null,"abstract":"Social media generated by location-services-enabled cellular devices produce enormous amounts of location-based content. Spatiotemporal analysis of such data facilitate new ways of modeling human behavior and mobility patterns. In this paper, we use over 10 millions geo-tagged tweets from the city of Los Angeles as observations of human movement and apply them to understand the relationships of geographical regions, neighborhoods and gang territories. Using a graph based-representation of street gang territories as vertices and interactions between them as edges, we train a machine learning classifier to tell apart rival and non-rival links. We correctly identify 89% of the true rivalry network, which beats a standard baseline by about 30%. Looking at larger neighborhoods, we were able to show that distance traveled from home follows a power-law distribution, and the direction of displacement, i.e., the distribution of movement direction, can be used as a profile to identify physical (or geographic) barriers when it is not uniform. Finally, considering the temporal dimension of tweets, we detect events taking place around the city by identifying irregularities in tweeting patterns.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125928517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SOCIALCOM.2013.142
Dennis G. Castleberry, Steven R. Brandt, F. Löffler
This paper details Inkling, a generalized executable paper system for generating hypermedia. Whereas a traditional paper has static content derived from the data, i.e. tables, charts, graphs, and animations, the executable paper dynamically generates these using an underlying code and editable input parameters specified in the paper itself. By use of a language which may be seamlessly incorporated into the paper text and made transparent to the reader or reviewer, the system allows for ease of both use and validation. Novel in our system is (1)generality, in that it provides a generic coupling between the paper-generating infrastructure and the backend science code, (2) a minimalist text-based human-readable input format which abstracts algorithms from the reader and reviewer, (3) out-of-order dependency-based execution, which allows the author to chain outputs to inputs, and (4) a scheme for building a database of author-contributed codes which may be easily shared, reused and referenced.
{"title":"Inkling: An Executable Paper System for Reviewing Scientific Applications","authors":"Dennis G. Castleberry, Steven R. Brandt, F. Löffler","doi":"10.1109/SOCIALCOM.2013.142","DOIUrl":"https://doi.org/10.1109/SOCIALCOM.2013.142","url":null,"abstract":"This paper details Inkling, a generalized executable paper system for generating hypermedia. Whereas a traditional paper has static content derived from the data, i.e. tables, charts, graphs, and animations, the executable paper dynamically generates these using an underlying code and editable input parameters specified in the paper itself. By use of a language which may be seamlessly incorporated into the paper text and made transparent to the reader or reviewer, the system allows for ease of both use and validation. Novel in our system is (1)generality, in that it provides a generic coupling between the paper-generating infrastructure and the backend science code, (2) a minimalist text-based human-readable input format which abstracts algorithms from the reader and reviewer, (3) out-of-order dependency-based execution, which allows the author to chain outputs to inputs, and (4) a scheme for building a database of author-contributed codes which may be easily shared, reused and referenced.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130465737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.65
Xing Xie, I. Ray, R. Adaikkalavan
Data Stream Management Systems (DSMSs) address the data processing needs of situational monitoring applications, where data must be collected on-the-fly and processed in real-time. Sensitive data in situational monitoring applications must be processed such that there is no leakage of confidential information. Towards this end, we design a DSMS that allows continuous queries to be executed on multilevel secure (MLS) data in an efficient and secure manner. We provide a prototype to demonstrate the feasibility of our ideas and present some experimental results that discuss the overhead and performance gain of our approach.
{"title":"On the Efficient Processing of Multilevel Secure Continuous Queries","authors":"Xing Xie, I. Ray, R. Adaikkalavan","doi":"10.1109/SocialCom.2013.65","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.65","url":null,"abstract":"Data Stream Management Systems (DSMSs) address the data processing needs of situational monitoring applications, where data must be collected on-the-fly and processed in real-time. Sensitive data in situational monitoring applications must be processed such that there is no leakage of confidential information. Towards this end, we design a DSMS that allows continuous queries to be executed on multilevel secure (MLS) data in an efficient and secure manner. We provide a prototype to demonstrate the feasibility of our ideas and present some experimental results that discuss the overhead and performance gain of our approach.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115252888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.60
Haifeng Liu, Zheng Hu, Dian Zhou, Hui Tian
User behavior analysis and prediction has been widely applied in personalized search, advertising precise delivery and other personalized services. It is a core problem how to evaluate the performance of prediction models or algorithms. The most used off-line experiment is a simple and convenient evaluation strategy. However, the existing assessment measures are most based on arithmetic average value theory, such as precision, recall, F measure, mean absolute error (MAE), root mean squared error (RMSE) etc. These approaches have two drawbacks. First, they cannot depict the prediction performance within a more fine-grained view and they only provide one average value to compare different algorithms' performances. Second, they are not reasonable if the evaluation results are not follow normal distribution. In this paper, according to analyze a mass of prediction evaluation results, we find that some performance evaluation results follow approximate power low distribution but not normal distribution. Therefore, the paper proposes a cumulative probability distribution model to evaluate the performance of prediction algorithms. The model first calculates the probability of each evaluation results. And then, it depicts the cumulative probability distribution function. Moreover, we further present an evaluation expectation value (EEV) to represent the overall performance of the prediction algorithms. Experiments on two real data sets show that the proposed model can provide deeper and more accurate assessment results.
{"title":"Cumulative Probability Distribution Model for Evaluating User Behavior Prediction Algorithms","authors":"Haifeng Liu, Zheng Hu, Dian Zhou, Hui Tian","doi":"10.1109/SocialCom.2013.60","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.60","url":null,"abstract":"User behavior analysis and prediction has been widely applied in personalized search, advertising precise delivery and other personalized services. It is a core problem how to evaluate the performance of prediction models or algorithms. The most used off-line experiment is a simple and convenient evaluation strategy. However, the existing assessment measures are most based on arithmetic average value theory, such as precision, recall, F measure, mean absolute error (MAE), root mean squared error (RMSE) etc. These approaches have two drawbacks. First, they cannot depict the prediction performance within a more fine-grained view and they only provide one average value to compare different algorithms' performances. Second, they are not reasonable if the evaluation results are not follow normal distribution. In this paper, according to analyze a mass of prediction evaluation results, we find that some performance evaluation results follow approximate power low distribution but not normal distribution. Therefore, the paper proposes a cumulative probability distribution model to evaluate the performance of prediction algorithms. The model first calculates the probability of each evaluation results. And then, it depicts the cumulative probability distribution function. Moreover, we further present an evaluation expectation value (EEV) to represent the overall performance of the prediction algorithms. Experiments on two real data sets show that the proposed model can provide deeper and more accurate assessment results.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"382 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122022543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.159
Naim Asaj, A. Held, M. Weber
Information technology is commonly used in automotive applications, and has introduced associated opportunities and threats. At the same time, the dissemination and use of certain privacy-sensitive data (i.e., identifying data) continues to increase, raising serious questions about privacy and anonymity. However, the effect of identifying data on privacy depends on various aspects, such as their basic structure. We propose that the preemptive assessment of privacy levels is a key factor for reliable privacy processes in vehicular development, extending the existing assessment during runtime. Thus, we identify a comprehensive and classified set of privacy indicators for identifiers, and explore the possible application of a single indicator by proposing privacy impact metrics that are based on entropy. We demonstrate the feasibility of our approach using a real dataset of vehicle identification numbers (VINs).
{"title":"An Integrative Approach for Measuring Privacy Impact of Identifiers in the Automotive Domain","authors":"Naim Asaj, A. Held, M. Weber","doi":"10.1109/SocialCom.2013.159","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.159","url":null,"abstract":"Information technology is commonly used in automotive applications, and has introduced associated opportunities and threats. At the same time, the dissemination and use of certain privacy-sensitive data (i.e., identifying data) continues to increase, raising serious questions about privacy and anonymity. However, the effect of identifying data on privacy depends on various aspects, such as their basic structure. We propose that the preemptive assessment of privacy levels is a key factor for reliable privacy processes in vehicular development, extending the existing assessment during runtime. Thus, we identify a comprehensive and classified set of privacy indicators for identifiers, and explore the possible application of a single indicator by proposing privacy impact metrics that are based on entropy. We demonstrate the feasibility of our approach using a real dataset of vehicle identification numbers (VINs).","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128022227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.156
Shaymaa Khater, Hicham G. Elmongui, D. Gračanin
Microblogs are specialized virtual social network web-based applications. Nowadays, following the microblogs is becoming more challenging as users can receive thousands of corpus updates every day. Going through all the corpuses updates is a time consuming process and affects the user's productivity in real life, especially for the users who have a lot of followees and thousands of tweets arriving at their timelines everyday. In this paper, we propose a personalized recommendation system that aims at giving the user a summary of all received corpuses. Considering the fact that the user interests changes over time, this summary should be based on the user's level of interest in the topic of the corpus at the time of reception. Our method considers three major elements: users's dynamic level of interest in a topic, user's social relationship such as the number of followers, their real geographical neighborhood, and other explicit features related to the publishers authority and the tweet's content.
{"title":"Personalized Microblogs Corpus Recommendation Based on Dynamic Users Interests","authors":"Shaymaa Khater, Hicham G. Elmongui, D. Gračanin","doi":"10.1109/SocialCom.2013.156","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.156","url":null,"abstract":"Microblogs are specialized virtual social network web-based applications. Nowadays, following the microblogs is becoming more challenging as users can receive thousands of corpus updates every day. Going through all the corpuses updates is a time consuming process and affects the user's productivity in real life, especially for the users who have a lot of followees and thousands of tweets arriving at their timelines everyday. In this paper, we propose a personalized recommendation system that aims at giving the user a summary of all received corpuses. Considering the fact that the user interests changes over time, this summary should be based on the user's level of interest in the topic of the corpus at the time of reception. Our method considers three major elements: users's dynamic level of interest in a topic, user's social relationship such as the number of followers, their real geographical neighborhood, and other explicit features related to the publishers authority and the tweet's content.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116515345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.29
Zahy Bnaya, Rami Puzis, Roni Stern, Ariel Felner
In many cases the best way to find a profile or a set of profiles matching some criteria in a social network is via targeted crawling. An important challenge in targeted crawling is to choose the next profile to explore. Existing heuristics for targeted crawling are usually tailored for specific search criterion and could lead to short-sighted crawling decisions. In this paper we propose and evaluate a generic approach for guiding a social network crawler that aims to provide a proper balance between exploration and exploitation based on the recently introduced variant of the Multi-Armed Bandit problem with volatile arms (VMAB). Our approach is general-purpose. In addition, it provides provable performance guarantees. Experimental results indicate that our approach compares favorably with the best existing heuristics on two different domains.
{"title":"Bandit Algorithms for Social Network Queries","authors":"Zahy Bnaya, Rami Puzis, Roni Stern, Ariel Felner","doi":"10.1109/SocialCom.2013.29","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.29","url":null,"abstract":"In many cases the best way to find a profile or a set of profiles matching some criteria in a social network is via targeted crawling. An important challenge in targeted crawling is to choose the next profile to explore. Existing heuristics for targeted crawling are usually tailored for specific search criterion and could lead to short-sighted crawling decisions. In this paper we propose and evaluate a generic approach for guiding a social network crawler that aims to provide a proper balance between exploration and exploitation based on the recently introduced variant of the Multi-Armed Bandit problem with volatile arms (VMAB). Our approach is general-purpose. In addition, it provides provable performance guarantees. Experimental results indicate that our approach compares favorably with the best existing heuristics on two different domains.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126238535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.106
Salim Jouili, Valentin Vansteenberghe
In recent years, more and more companies provide services that can not be anymore achieved efficiently using relational databases. As such, these companies are forced to use alternative database models such as XML databases, object-oriented databases, document-oriented databases and, more recently graph databases. Graph databases only exist for a few years. Although there have been some comparison attempts, they are mostly focused on certain aspects only. In this paper, we present a distributed graph database comparison framework and the results we obtained by comparing four important players in the graph databases market: Neo4j, Orient DB, Titan and DEX.
{"title":"An Empirical Comparison of Graph Databases","authors":"Salim Jouili, Valentin Vansteenberghe","doi":"10.1109/SocialCom.2013.106","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.106","url":null,"abstract":"In recent years, more and more companies provide services that can not be anymore achieved efficiently using relational databases. As such, these companies are forced to use alternative database models such as XML databases, object-oriented databases, document-oriented databases and, more recently graph databases. Graph databases only exist for a few years. Although there have been some comparison attempts, they are mostly focused on certain aspects only. In this paper, we present a distributed graph database comparison framework and the results we obtained by comparing four important players in the graph databases market: Neo4j, Orient DB, Titan and DEX.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114356947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.103
Xing Fang, J. Zhan, Nicholas Koceja
The increasing ease of data collection experience and the increasing availability of large data storage space lead to the existence of very large datasets that are commonly referred as "Big Data". Such data not only take over large amount of database storage, but also increase the difficulties for data analysis due to data diversity, which, also makes the datasets seemingly isolated with each other. In this paper, we present a solution to the problem that is to build up connections among the diverse datasets, based upon their similarities. Particularly, a concept of similarity graph along with a similarity graph generation algorithm were introduced. We then proposed a similarity graph reduction algorithm that reduces vertices of the graph for the purpose of graph simplification.
{"title":"Towards Network Reduction on Big Data","authors":"Xing Fang, J. Zhan, Nicholas Koceja","doi":"10.1109/SocialCom.2013.103","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.103","url":null,"abstract":"The increasing ease of data collection experience and the increasing availability of large data storage space lead to the existence of very large datasets that are commonly referred as \"Big Data\". Such data not only take over large amount of database storage, but also increase the difficulties for data analysis due to data diversity, which, also makes the datasets seemingly isolated with each other. In this paper, we present a solution to the problem that is to build up connections among the diverse datasets, based upon their similarities. Particularly, a concept of similarity graph along with a similarity graph generation algorithm were introduced. We then proposed a similarity graph reduction algorithm that reduces vertices of the graph for the purpose of graph simplification.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125577761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.91
A. Adriansyah, B. V. Dongen, Nicola Zannone
Modern IT systems have to deal with unpredictable situations and exceptions more and more often. In contrast, security mechanisms are usually very rigid. Functionality like break-the-glass is thus employed to allow users to bypass security mechanisms in case of emergencies. However, break-the-glass introduces a weak point in the system. In this paper, we present a flexible framework for controlling the use of break-the-glass using the notion of alignments. The framework measures to what extent a process execution diverges from the specification (i.e., using optimal alignments) and revokes the exceptional permissions granted to cope with the emergency when the severity of deviations cannot be tolerated. For the quantification of the severity of deviations, we extend alignment-based deviation analysis techniques by supporting the detection of high-level deviations such as activity replacements and swaps, hence providing a more accurate diagnosis of deviations than classical optimal alignments.
{"title":"Controlling Break-the-Glass through Alignment","authors":"A. Adriansyah, B. V. Dongen, Nicola Zannone","doi":"10.1109/SocialCom.2013.91","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.91","url":null,"abstract":"Modern IT systems have to deal with unpredictable situations and exceptions more and more often. In contrast, security mechanisms are usually very rigid. Functionality like break-the-glass is thus employed to allow users to bypass security mechanisms in case of emergencies. However, break-the-glass introduces a weak point in the system. In this paper, we present a flexible framework for controlling the use of break-the-glass using the notion of alignments. The framework measures to what extent a process execution diverges from the specification (i.e., using optimal alignments) and revokes the exceptional permissions granted to cope with the emergency when the severity of deviations cannot be tolerated. For the quantification of the severity of deviations, we extend alignment-based deviation analysis techniques by supporting the detection of high-level deviations such as activity replacements and swaps, hence providing a more accurate diagnosis of deviations than classical optimal alignments.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128056428","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：