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}
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.2013.159
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.2013.115
Carl M. Gustafson
In this paper, I build from scratch a basic agent-based macroeconomic model, featuring fifty representative agents whose decisions to consume and save depend on the current relative performance of the economy at-large. I run three different experiments in the framework: the first, on the effects of tax and spending "flexibility" on stabilizing output, the second, on the ability of spending stimulus to stabilize output, and the third, on redistributive measures across income groups and their effects on aggregate economic performance. I find that tax and spending flexibility accelerates the path back to stability after an initial imposed downturn, that spending stimulus does much the same, though with a greater initial "kick", and that redistribution in this model may take place and increase the welfare of lower-income agents without imposing a significant burden on overall performance.
{"title":"Three Fiscal Policy Experiments in an Agent-Based Macroeconomic Model","authors":"Carl M. Gustafson","doi":"10.1109/SocialCom.2013.115","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.115","url":null,"abstract":"In this paper, I build from scratch a basic agent-based macroeconomic model, featuring fifty representative agents whose decisions to consume and save depend on the current relative performance of the economy at-large. I run three different experiments in the framework: the first, on the effects of tax and spending \"flexibility\" on stabilizing output, the second, on the ability of spending stimulus to stabilize output, and the third, on redistributive measures across income groups and their effects on aggregate economic performance. I find that tax and spending flexibility accelerates the path back to stability after an initial imposed downturn, that spending stimulus does much the same, though with a greater initial \"kick\", and that redistribution in this model may take place and increase the welfare of lower-income agents without imposing a significant burden on overall performance.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"29 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":"132814718","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.2013.170
Michael S. Kim
A novel robust anomaly detection algorithm is applied to an image dataset using Apache Pig, Jython and GNU Octave. Each image in the set is transformed into a feature vector that represents color, edges, and texture numerically. Data is streamed using Pig through standard and user defined GNU Octave functions for feature transformation. Once the image set is transformed into the feature space, the dataset matrix (where the rows are distinct images, and the columns are features) is input into an original anomaly detection algorithm written by the author. This unsupervised outlier detection method scores outliers in linear time. The method is linear in the number of outliers but still suffers from the curse of dimensionality (in the feature space). The top scoring images are considered anomalies. Two experiments are conducted. The first experiment tests if top scoring images coincide with images which are marked as outliers in a prior image selection step. The second examines the scalability of the implementation in Pig using a larger data set. The results are analyzed quantitatively and qualitatively.
{"title":"Robust, Scalable Anomaly Detection for Large Collections of Images","authors":"Michael S. Kim","doi":"10.1109/SocialCom.2013.170","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.170","url":null,"abstract":"A novel robust anomaly detection algorithm is applied to an image dataset using Apache Pig, Jython and GNU Octave. Each image in the set is transformed into a feature vector that represents color, edges, and texture numerically. Data is streamed using Pig through standard and user defined GNU Octave functions for feature transformation. Once the image set is transformed into the feature space, the dataset matrix (where the rows are distinct images, and the columns are features) is input into an original anomaly detection algorithm written by the author. This unsupervised outlier detection method scores outliers in linear time. The method is linear in the number of outliers but still suffers from the curse of dimensionality (in the feature space). The top scoring images are considered anomalies. Two experiments are conducted. The first experiment tests if top scoring images coincide with images which are marked as outliers in a prior image selection step. The second examines the scalability of the implementation in Pig using a larger data set. The results are analyzed quantitatively and qualitatively.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"1 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":"130271455","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}
Emergency resources are often insufficient to satisfy fully the demands for professional help and supplies after a public disaster. Furthermore, in a mass casualty situation, the emphasis shifts from ensuring the best possible outcome for each individual patient to ensuring the best possible outcome for the greatest number of patients. Historically, various manual and electronic medical triage systems have been used both under civil and military conditions to determine the order and priority of emergency treatment, transport, and best possible destination for the patients [12][13][15][16][17][18]. Unfortunately, none of those solutions has proven flexible, accurate, scalable or unobtrusive enough to meet the public's expectations [7]. In this paper, we provide insights into the trends, innovations, and challenges of contemporary crowdsourced e-Health and medical informatics applications in the context of emergency preparedness and response. Additionally, we demonstrate a system, called CrowdHelp, for real-time patient assessment which uses mobile electronic triaging accomplished via crowdsourced information. With the use of our system, emergency management professionals receive most of the information they need for preparing themselves to provide timely and accurate treatments of their patients even before dispatching a response team to the event.
{"title":"Applications of Social Networks and Crowdsourcing for Disaster Management Improvement","authors":"Liliya I. Besaleva, A. Weaver","doi":"10.1109/MC.2016.133","DOIUrl":"https://doi.org/10.1109/MC.2016.133","url":null,"abstract":"Emergency resources are often insufficient to satisfy fully the demands for professional help and supplies after a public disaster. Furthermore, in a mass casualty situation, the emphasis shifts from ensuring the best possible outcome for each individual patient to ensuring the best possible outcome for the greatest number of patients. Historically, various manual and electronic medical triage systems have been used both under civil and military conditions to determine the order and priority of emergency treatment, transport, and best possible destination for the patients [12][13][15][16][17][18]. Unfortunately, none of those solutions has proven flexible, accurate, scalable or unobtrusive enough to meet the public's expectations [7]. In this paper, we provide insights into the trends, innovations, and challenges of contemporary crowdsourced e-Health and medical informatics applications in the context of emergency preparedness and response. Additionally, we demonstrate a system, called CrowdHelp, for real-time patient assessment which uses mobile electronic triaging accomplished via crowdsourced information. With the use of our system, emergency management professionals receive most of the information they need for preparing themselves to provide timely and accurate treatments of their patients even before dispatching a response team to the event.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"1209 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":"131516348","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.2013.97
Achim D. Brucker, Francesco Malmignati, M. Merabti, Q. Shi, Bo Zhou
Modern applications are inherently heterogeneous: they are built by composing loosely coupled services that are, usually, offered and operated by different service providers. While this approach increases the flexibility of the composed applications, it makes the implementation of security and trustworthiness requirements difficult. As the number of security requirements is increasing dramatically, there is a need for new approaches that integrate security requirements right from the beginning while composing service-based applications. In this paper, we present a framework for secure service composition using a model-based approach for specifying, building, and executing composed services. As a unique feature, this framework integrates security requirements as a first class citizen and, thus, avoids the ``security as an afterthought'' paradigm.
{"title":"A Framework for Secure Service Composition","authors":"Achim D. Brucker, Francesco Malmignati, M. Merabti, Q. Shi, Bo Zhou","doi":"10.1109/SocialCom.2013.97","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.97","url":null,"abstract":"Modern applications are inherently heterogeneous: they are built by composing loosely coupled services that are, usually, offered and operated by different service providers. While this approach increases the flexibility of the composed applications, it makes the implementation of security and trustworthiness requirements difficult. As the number of security requirements is increasing dramatically, there is a need for new approaches that integrate security requirements right from the beginning while composing service-based applications. In this paper, we present a framework for secure service composition using a model-based approach for specifying, building, and executing composed services. As a unique feature, this framework integrates security requirements as a first class citizen and, thus, avoids the ``security as an afterthought'' paradigm.","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":"131644532","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.2013.69
Michael J. Oehler, D. Phatak
Our contribution defines a conjunction operator for private stream searching. Private stream searching is a system of cryptographic methods that preserves the confidentiality of the search criteria and the result. The system uses an encrypted filter to conceal the search terms, processes a search without decrypting these terms, and saves the result to an encrypted buffer. Fundamentally, the system provides a private search capability based on a logical disjunction of search terms. Our conjunction operator broadens the search capability, and achieves this without significantly increasing the complexity of the private search system. The conjunction is processed as a bit wise summation of hashed keyword values to reference an encrypted entry in the filter. The method is best suited for a conjunction of fields from a record, does not impute a calculation of bilinear map, as required in prior research, and offers a practical utility that integrates into private stream searching. We demonstrate the practicality by including the conjunction operator into our domain specific language for private packet filtering.
{"title":"A Conjunction for Private Stream Searching","authors":"Michael J. Oehler, D. Phatak","doi":"10.1109/SocialCom.2013.69","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.69","url":null,"abstract":"Our contribution defines a conjunction operator for private stream searching. Private stream searching is a system of cryptographic methods that preserves the confidentiality of the search criteria and the result. The system uses an encrypted filter to conceal the search terms, processes a search without decrypting these terms, and saves the result to an encrypted buffer. Fundamentally, the system provides a private search capability based on a logical disjunction of search terms. Our conjunction operator broadens the search capability, and achieves this without significantly increasing the complexity of the private search system. The conjunction is processed as a bit wise summation of hashed keyword values to reference an encrypted entry in the filter. The method is best suited for a conjunction of fields from a record, does not impute a calculation of bilinear map, as required in prior research, and offers a practical utility that integrates into private stream searching. We demonstrate the practicality by including the conjunction operator into our domain specific language for private packet filtering.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"11 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":"131728012","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.2013.20
Akshay Patil, Golnaz Ghasemiesfeh, Roozbeh Ebrahimi, Jie Gao
In many eCommerce websites and consumer review websites, users can review products they purchased as well as the reviews others wrote. Users can also rate each other as trusted or untrusted relationships. By studying a data set from Epinions, we examine and quantify the correlation between trust/distrust relationships among the users and their ratings of the reviews. We discover that there is a strong alignment between the opinions of one's friends and his/her ratings. Our findings also suggest that there is a strong alignment between the collective opinion of a user's friends and the formation of his/her future relationships.
{"title":"Quantifying Social Influence in Epinions","authors":"Akshay Patil, Golnaz Ghasemiesfeh, Roozbeh Ebrahimi, Jie Gao","doi":"10.1109/SocialCom.2013.20","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.20","url":null,"abstract":"In many eCommerce websites and consumer review websites, users can review products they purchased as well as the reviews others wrote. Users can also rate each other as trusted or untrusted relationships. By studying a data set from Epinions, we examine and quantify the correlation between trust/distrust relationships among the users and their ratings of the reviews. We discover that there is a strong alignment between the opinions of one's friends and his/her ratings. Our findings also suggest that there is a strong alignment between the collective opinion of a user's friends and the formation of his/her future relationships.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"19 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":"134455846","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.2013.56
Ammar Hassan, A. Abbasi, D. Zeng
Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.
{"title":"Twitter Sentiment Analysis: A Bootstrap Ensemble Framework","authors":"Ammar Hassan, A. Abbasi, D. Zeng","doi":"10.1109/SocialCom.2013.56","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.56","url":null,"abstract":"Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"7 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":"129723841","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：