Pub Date : 2010-10-31DOI: 10.4156/JCIT.VOL5.ISSUE8.27
Wei Min
The theory of customer relationship management has been gradually penetrated into all levels of enterprise management. As a close business with customers, travel agency should focus on customer relationship management, which is very important. This paper attempts to combine the concept of customer relationship management and the processes of traditional travel agency marketing to construct the system architecture, considering the actual situation and specific requirements of a travel agency, combining with norms and standards of the management of travel agencies industry, as well as, using the current popular system architecture B/S (Browser/Sever) Mode. By extensively studying the theory of software engineering, database theory and object-oriented languages and Web programming, management information systems analysis and design process of travel agency marketing is discussed in detail based CRM.
{"title":"Construction of Marketing Management Information System of Travel Agency Based on Customer Relationship Management","authors":"Wei Min","doi":"10.4156/JCIT.VOL5.ISSUE8.27","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.27","url":null,"abstract":"The theory of customer relationship management has been gradually penetrated into all levels of enterprise management. As a close business with customers, travel agency should focus on customer relationship management, which is very important. This paper attempts to combine the concept of customer relationship management and the processes of traditional travel agency marketing to construct the system architecture, considering the actual situation and specific requirements of a travel agency, combining with norms and standards of the management of travel agencies industry, as well as, using the current popular system architecture B/S (Browser/Sever) Mode. By extensively studying the theory of software engineering, database theory and object-oriented languages and Web programming, management information systems analysis and design process of travel agency marketing is discussed in detail based CRM.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129349759","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 : 2010-10-31DOI: 10.4156/JCIT.VOL5.ISSUE8.19
Pei-yu Liu, Jing Zhao, Zhen-fang Zhu
Focusing on the uncertainty of classifying emails based-on email content and the incompleteness of email representation, the paper proposes a new representation using noncharacteristic information. The new approach refers to the whole email, contains feature items extracted from email content, and noncharacteristic items extracted from email header. In the expriment, we adopt Naive Bayes classifier to classify emails, classification results indicate that the new approach overcomes the shortcomings of original content-based filtering and improves the recall and the precision of spam filtering.
{"title":"Email Representation using Noncharacteristic Information and its Application","authors":"Pei-yu Liu, Jing Zhao, Zhen-fang Zhu","doi":"10.4156/JCIT.VOL5.ISSUE8.19","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.19","url":null,"abstract":"Focusing on the uncertainty of classifying emails based-on email content and the incompleteness of email representation, the paper proposes a new representation using noncharacteristic information. The new approach refers to the whole email, contains feature items extracted from email content, and noncharacteristic items extracted from email header. In the expriment, we adopt Naive Bayes classifier to classify emails, classification results indicate that the new approach overcomes the shortcomings of original content-based filtering and improves the recall and the precision of spam filtering.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128914026","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 : 2010-10-31DOI: 10.4156/JCIT.VOL5.ISSUE8.28
N. Dutta, I. S. Misra, Abhishek Majumder
IP based wireless mobile networks are of great importance now-a-days to users on the move. To provide connectivity to mobile users with same IP address despite of their change in location or point of attachment, the location information of mobile users must be kept up to date. Managing location information of such users basically needs information to be exchanged between different network entities. The transmission of such messages consumes bandwidth, incurred processing time and transmission cost. This cost is normally referred as signaling overhead caused by mobile users. The signaling overhead is mainly dependent on the frequency of location change caused by the movement of users and number of messages to be exchange to complete the location update process. Hierarchical arrangement of anchor agents can reduce such signaling overhead but degrades scalability in presence of large number of mobile users. The objective of this work is to analyze mathematically the location update frequency and cost in an N layered hierarchical architecture for IPv6 based network in wireless environment. The intention is to find optimal levels of hierarchy in terms of frequency and cost of location update to suggest a scalable IPv6 based mobile architecture. Result obtained from mathematical calculation shows that three levels architecture may provide optimal values for these two performance parameters. Keyword: HMIPv6, Mathematical Modeling, Performance Analysis, Signaling cost.
{"title":"Mathematical Analysis of Signaling Overhead in MIPv6 Based N-Layer Architecture","authors":"N. Dutta, I. S. Misra, Abhishek Majumder","doi":"10.4156/JCIT.VOL5.ISSUE8.28","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.28","url":null,"abstract":"IP based wireless mobile networks are of great importance now-a-days to users on the move. To provide connectivity to mobile users with same IP address despite of their change in location or point of attachment, the location information of mobile users must be kept up to date. Managing location information of such users basically needs information to be exchanged between different network entities. The transmission of such messages consumes bandwidth, incurred processing time and transmission cost. This cost is normally referred as signaling overhead caused by mobile users. The signaling overhead is mainly dependent on the frequency of location change caused by the movement of users and number of messages to be exchange to complete the location update process. Hierarchical arrangement of anchor agents can reduce such signaling overhead but degrades scalability in presence of large number of mobile users. The objective of this work is to analyze mathematically the location update frequency and cost in an N layered hierarchical architecture for IPv6 based network in wireless environment. The intention is to find optimal levels of hierarchy in terms of frequency and cost of location update to suggest a scalable IPv6 based mobile architecture. Result obtained from mathematical calculation shows that three levels architecture may provide optimal values for these two performance parameters. Keyword: HMIPv6, Mathematical Modeling, Performance Analysis, Signaling cost.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127475272","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 : 2010-10-31DOI: 10.4156/JCIT.VOL5.ISSUE8.14
T. Shabnam, M. Islam, M. Amin
Tanzilah Noor Shabnam, Md. Imdadul Islam, M. R. Amin 1, First Department of Electronics and Communications Engineering,East West University, 43 Mohakhali, Dhaka 1212, Bangladesh, tanzilah_nsu031@yahoo.com Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka 1342, Banglades, imdad@juniv.edu *3,Corresponding Department of Electronics and Communications Engineering,East West University, 43 Mohakhali, Dhaka 1212, Bangladesh, ramin@ewubd.edu doi: 10.4156/jcit.vol5.issue8.14
Imdadul Islam, M. R. Amin 1，东西大学第一电子与通信工程系，孟加拉国达卡1212莫哈卡里43号，tanzilah_nsu031@yahoo.com孟加拉国达卡1342萨瓦尔贾汉吉尔纳加尔大学计算机科学与工程系，imdad@juniv.edu *3，东西大学电子与通信工程系，孟加拉国达卡1212莫哈卡里43号，ramin@ewubd.edu doi:10.4156 / jcit.vol5.issue8.14
{"title":"Call Admission Control Strategy for System Throughput Maximization Using DOVE","authors":"T. Shabnam, M. Islam, M. Amin","doi":"10.4156/JCIT.VOL5.ISSUE8.14","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.14","url":null,"abstract":"Tanzilah Noor Shabnam, Md. Imdadul Islam, M. R. Amin 1, First Department of Electronics and Communications Engineering,East West University, 43 Mohakhali, Dhaka 1212, Bangladesh, tanzilah_nsu031@yahoo.com Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka 1342, Banglades, imdad@juniv.edu *3,Corresponding Department of Electronics and Communications Engineering,East West University, 43 Mohakhali, Dhaka 1212, Bangladesh, ramin@ewubd.edu doi: 10.4156/jcit.vol5.issue8.14","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130494062","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}
This paper proposed a complex ontology evolution based method of extracting data, and also completely designed an extraction system, which consists of four important components: Resolver, Extractor, Consolidator and the ontology construction components. The system gives priority to the construction of mini-ontology. When the user submits query keywords to the deep web query interface, the returned result will pass through the prior three components; after that, the final execution result will be returned to user in a unified form. This paper adopted an extraction method that is different from the general ontology extraction. More specifically, the ontology used in extraction here is dynamic evolution, which can adapt various data source better. Experimental results proved that this method could effectively extract the data in the query result pages.
{"title":"Heterogeneous Deep Web Data Extraction Using Ontology Evolution","authors":"Kerui Chen, Jinchao Zhao, Wanli Zuo, Fengling He, Yongheng Chen","doi":"10.4156/JCIT.VOL5.ISSUE8.23","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.23","url":null,"abstract":"This paper proposed a complex ontology evolution based method of extracting data, and also completely designed an extraction system, which consists of four important components: Resolver, Extractor, Consolidator and the ontology construction components. The system gives priority to the construction of mini-ontology. When the user submits query keywords to the deep web query interface, the returned result will pass through the prior three components; after that, the final execution result will be returned to user in a unified form. This paper adopted an extraction method that is different from the general ontology extraction. More specifically, the ontology used in extraction here is dynamic evolution, which can adapt various data source better. Experimental results proved that this method could effectively extract the data in the query result pages.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132790799","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 : 2010-10-31DOI: 10.4156/JCIT.VOL5.ISSUE8.4
Linkai Luo, Dengfeng Huang, Hong Peng, Qifeng Zhou, G. Shao, Fan Yang
Abstract To overcome the disadvantage of CV-ACC method that the high-density sample region may be close to the optimal hyper-plane, a parameter selection method for support vector machine (SVM) based on the decision value, named as CV-SNRMDV method, is proposed in this paper. SNRMDV is used as the criterion of cross-validation (CV) in our method, which is defined as the ratio between the difference of medians of decision values and the sum of the standard deviations from the medians. Compared with the traditional cross-validation accuracy (CV-ACC) method, CV-SNRMDV makes use of the information of sample distribution and decision value. Consequently CV-SNRMDV overcomes the disadvantage of CV-ACC. The experiments show our method obtains a better test accuracy on the simulated dataset, while the test accuracies on benchmark datasets are close to CV-ACC.
{"title":"A New Parameter Selection Method for Support Vector Machine Based on the Decision Value","authors":"Linkai Luo, Dengfeng Huang, Hong Peng, Qifeng Zhou, G. Shao, Fan Yang","doi":"10.4156/JCIT.VOL5.ISSUE8.4","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.4","url":null,"abstract":"Abstract To overcome the disadvantage of CV-ACC method that the high-density sample region may be close to the optimal hyper-plane, a parameter selection method for support vector machine (SVM) based on the decision value, named as CV-SNRMDV method, is proposed in this paper. SNRMDV is used as the criterion of cross-validation (CV) in our method, which is defined as the ratio between the difference of medians of decision values and the sum of the standard deviations from the medians. Compared with the traditional cross-validation accuracy (CV-ACC) method, CV-SNRMDV makes use of the information of sample distribution and decision value. Consequently CV-SNRMDV overcomes the disadvantage of CV-ACC. The experiments show our method obtains a better test accuracy on the simulated dataset, while the test accuracies on benchmark datasets are close to CV-ACC.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131671713","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 : 2010-10-31DOI: 10.4156/JCIT.VOL5.ISSUE8.9
Wei-Chih Hsu, Tsan-Ying Yu
Support Vector Machines (SVM) is a powerful classification technique in data mining and has been successfully applied to many real-world applications. Parameter selection of SVM will affect classification performance much during training process. However, parameter selection of SVM is usually identified by experience or grid search (GS). In this study, we use Taguchi method to make optimal approximation for the SVM-based E-mail Spam Filtering model. Six real-world mail data sets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the Taguchi method can find the effective model with high classification accuracy.
{"title":"E-mail Spam Filtering Based on Support Vector Machines with Taguchi Method for Parameter Selection","authors":"Wei-Chih Hsu, Tsan-Ying Yu","doi":"10.4156/JCIT.VOL5.ISSUE8.9","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.9","url":null,"abstract":"Support Vector Machines (SVM) is a powerful classification technique in data mining and has been successfully applied to many real-world applications. Parameter selection of SVM will affect classification performance much during training process. However, parameter selection of SVM is usually identified by experience or grid search (GS). In this study, we use Taguchi method to make optimal approximation for the SVM-based E-mail Spam Filtering model. Six real-world mail data sets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the Taguchi method can find the effective model with high classification accuracy.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126680603","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 : 2010-10-31DOI: 10.4156/JCIT.VOL5.ISSUE8.6
Shifei Ding, Li Xu, Chunyang Su, Hong Zhu
Artificial Neural Networks (ANNs), as a nonlinear and adaptive information processing systems, play an important role in machine learning, artificial intelligence, and data mining. But the performance of ANNs is sensitive to the number of neurons, and chieving a better network performance and simplifying the network topology are two competing objectives. While Genetic Algorithms (GAs) is a kind of random search algorithm which simulates the nature selection and evolution, which has the advantages of good global search abilities and learning the approximate optimal solution without the gradient information of the error functions. This paper makes a brief survey on ANNs optimization with GAs. Firstly, the basic principles of ANNs and GAs are introduced, by analyzing the advantages and disadvantages of GAs and ANNs, the superiority of using GAs to optimize ANNs is expressed. Secondly, we make a brief survey on the basic theories and algorithms of optimizing the network weights, optimizing the network architecture and optimizing the learning rules, and make a discussion on the latest research progresses. At last, we make a prospect on the development trend of the theory.
{"title":"Using Genetic Algorithms to Optimize Artificial Neural Networks","authors":"Shifei Ding, Li Xu, Chunyang Su, Hong Zhu","doi":"10.4156/JCIT.VOL5.ISSUE8.6","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.6","url":null,"abstract":"Artificial Neural Networks (ANNs), as a nonlinear and adaptive information processing systems, play an important role in machine learning, artificial intelligence, and data mining. But the performance of ANNs is sensitive to the number of neurons, and chieving a better network performance and simplifying the network topology are two competing objectives. While Genetic Algorithms (GAs) is a kind of random search algorithm which simulates the nature selection and evolution, which has the advantages of good global search abilities and learning the approximate optimal solution without the gradient information of the error functions. This paper makes a brief survey on ANNs optimization with GAs. Firstly, the basic principles of ANNs and GAs are introduced, by analyzing the advantages and disadvantages of GAs and ANNs, the superiority of using GAs to optimize ANNs is expressed. Secondly, we make a brief survey on the basic theories and algorithms of optimizing the network weights, optimizing the network architecture and optimizing the learning rules, and make a discussion on the latest research progresses. At last, we make a prospect on the development trend of the theory.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133315770","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 : 2010-10-31DOI: 10.4156/JCIT.VOL5.ISSUE8.21
Zhongzhi Li, Xue-gang Wang
Abstract A new data association method is presented for multiple target tracking. The proposed method is formulated using reverse prediction weighted neighbor to calculate the probability of candidate measurements from targets. The purpose of the proposed method is to eliminate the need to acquire prior knowledge such as detection probability and clutter density. The probability between targets and measurements are reflected in the reverse prediction residual norm.
{"title":"Multiple Target Tracking Using Reverse Prediction Weighted Neighbor Data Association","authors":"Zhongzhi Li, Xue-gang Wang","doi":"10.4156/JCIT.VOL5.ISSUE8.21","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.21","url":null,"abstract":"Abstract A new data association method is presented for multiple target tracking. The proposed method is formulated using reverse prediction weighted neighbor to calculate the probability of candidate measurements from targets. The purpose of the proposed method is to eliminate the need to acquire prior knowledge such as detection probability and clutter density. The probability between targets and measurements are reflected in the reverse prediction residual norm.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115857874","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 : 2010-10-31DOI: 10.4156/JCIT.VOL5.ISSUE8.5
S. Kotsiantis, D. Kanellopoulos, V. Tampakas
The problems of bankruptcy prediction and fraud detection have been extensively considered in the financial literature. The objective of this work is twofold. Firstly, we investigate the efficiency of multi-instance learning in bankruptcy prediction. For this reason, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 150 failed and solvent Greek firms in the recent period. It was found that multi-instance learning algorithms could enable experts to predict bankruptcies with satisfying accuracy. Secondly, we explore the effectiveness of multi-instance learning techniques in detecting firms that issue fraudulent financial statements (FFS). Therefore, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms. The results show that MIBoost algorithm with Decision Stump as base learner had the best accuracy in comparison with other multi-instance learners and single supervised machine learning techniques.
{"title":"Financial Application of Multi-Instance Learning: Two Greek Case Studies","authors":"S. Kotsiantis, D. Kanellopoulos, V. Tampakas","doi":"10.4156/JCIT.VOL5.ISSUE8.5","DOIUrl":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.5","url":null,"abstract":"The problems of bankruptcy prediction and fraud detection have been extensively considered in the financial literature. The objective of this work is twofold. Firstly, we investigate the efficiency of multi-instance learning in bankruptcy prediction. For this reason, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 150 failed and solvent Greek firms in the recent period. It was found that multi-instance learning algorithms could enable experts to predict bankruptcies with satisfying accuracy. Secondly, we explore the effectiveness of multi-instance learning techniques in detecting firms that issue fraudulent financial statements (FFS). Therefore, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms. The results show that MIBoost algorithm with Decision Stump as base learner had the best accuracy in comparison with other multi-instance learners and single supervised machine learning techniques.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128969802","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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