Chen-Kun Tsung, H. Ho, Shengkai Chou, Janching Lin, Sing-Ling Lee
Modularity is widely-used objective function to detect communities and there are lots of algorithms based on modularity maximization. The leading eigenvector method is one of them where modularity is maximized by choosing the first eigenvector as partition result. To analyze in depth the information provided by other eigenvectors, modularity maximization could be transformed to vector partitioning problem. This paper proposes a method to find non-overlapping vertex vector sets so as to maximize the quadratic sum of norms of community vectors. We observe spatial distribution of the vertex vectors of networks and then discover two phenomenons. First, the vertex vectors belong to different communities are separated by an angle. Second, the node with a larger degree would correspond to a vertex vector with a larger norm. Based on these two phenomena, we design a heuristic community detection algorithm. When a network with weaker community structure, the over-partition problem is considered. The experiment results show that the proposed solution provides higher accuracy than other solutions.
{"title":"A Spectral Clustering Approach Based on Modularity Maximization for Community Detection Problem","authors":"Chen-Kun Tsung, H. Ho, Shengkai Chou, Janching Lin, Sing-Ling Lee","doi":"10.1109/ICS.2016.0012","DOIUrl":"https://doi.org/10.1109/ICS.2016.0012","url":null,"abstract":"Modularity is widely-used objective function to detect communities and there are lots of algorithms based on modularity maximization. The leading eigenvector method is one of them where modularity is maximized by choosing the first eigenvector as partition result. To analyze in depth the information provided by other eigenvectors, modularity maximization could be transformed to vector partitioning problem. This paper proposes a method to find non-overlapping vertex vector sets so as to maximize the quadratic sum of norms of community vectors. We observe spatial distribution of the vertex vectors of networks and then discover two phenomenons. First, the vertex vectors belong to different communities are separated by an angle. Second, the node with a larger degree would correspond to a vertex vector with a larger norm. Based on these two phenomena, we design a heuristic community detection algorithm. When a network with weaker community structure, the over-partition problem is considered. The experiment results show that the proposed solution provides higher accuracy than other solutions.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126094604","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}
While mobile applications make our lives more convenient, security concerns may arise when the mobile applications contain malicious code that would harm the mobile devices and their users financially and physically. In this article, we propose a malware detection framework to protect the mobile devices with the help of the cloud, where the cloud is equipped with the facilities for automatic analysis of large amount of new malware generated everyday, and the device is able to detect malicious intents of running software in real-time based on the knowledge of the analyzed malware. We evaluate the performance of our framework with Android-based systems as case studies. In particular, we study the impact of different system configurations on the time required for malware detection, including detecting algorithms (i.e., CNN and SVM), mobile processors (i.e., ARM CPU and NVIDIA GPU), and wireless networks (i.e., Wi-Fi and 3G for the communication between the device and the cloud). To the best of our knowledge, we are not aware of any other work studying performance impacts of the system configurations of the malware detection systems using the physical machines. As the widespread of malware, we believe that our empirical study is useful when designing antivirus software and can be applied to different application domains, such as automotive, and smart home.
{"title":"A Cloud-Assisted Malware Detection Framework for Mobile Devices","authors":"Shih-Hao Hung, Chia-Heng Tu, C. Yeh","doi":"10.1109/ICS.2016.0112","DOIUrl":"https://doi.org/10.1109/ICS.2016.0112","url":null,"abstract":"While mobile applications make our lives more convenient, security concerns may arise when the mobile applications contain malicious code that would harm the mobile devices and their users financially and physically. In this article, we propose a malware detection framework to protect the mobile devices with the help of the cloud, where the cloud is equipped with the facilities for automatic analysis of large amount of new malware generated everyday, and the device is able to detect malicious intents of running software in real-time based on the knowledge of the analyzed malware. We evaluate the performance of our framework with Android-based systems as case studies. In particular, we study the impact of different system configurations on the time required for malware detection, including detecting algorithms (i.e., CNN and SVM), mobile processors (i.e., ARM CPU and NVIDIA GPU), and wireless networks (i.e., Wi-Fi and 3G for the communication between the device and the cloud). To the best of our knowledge, we are not aware of any other work studying performance impacts of the system configurations of the malware detection systems using the physical machines. As the widespread of malware, we believe that our empirical study is useful when designing antivirus software and can be applied to different application domains, such as automotive, and smart home.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115451447","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}
Autonomous drones system has great potential in robotics research and it is used in industrial applications. One of the popular examples is Amazon Prime Air service which uses drones to delivery customers ordered products. Another example is under indoor environment such as transferring goods and products in warehouse. However, there is a research challenge when implementing - autonomous drones system in indoor environment in which wireless network interference could deteriorate-drones' control performance. The research aims to reduce the wireless network interference in the drones system for achieving a better performance in term of the controllability of drones' position. For instance, the settling time of drone to reach its set point position or speed of recovery back to set point position after a drone is disturbed in its position. The study shows that after changing the Wi-Fi channel of drones to different frequency, the network interference between drones can be reduced, thus the performance of positioning controllability can be improved.
{"title":"Investigating Wireless Network Interferences of Autonomous Drones with Camera Based Positioning Control System","authors":"K. Yap, K. Eu, Jun Ming Low","doi":"10.1109/ICS.2016.0081","DOIUrl":"https://doi.org/10.1109/ICS.2016.0081","url":null,"abstract":"Autonomous drones system has great potential in robotics research and it is used in industrial applications. One of the popular examples is Amazon Prime Air service which uses drones to delivery customers ordered products. Another example is under indoor environment such as transferring goods and products in warehouse. However, there is a research challenge when implementing - autonomous drones system in indoor environment in which wireless network interference could deteriorate-drones' control performance. The research aims to reduce the wireless network interference in the drones system for achieving a better performance in term of the controllability of drones' position. For instance, the settling time of drone to reach its set point position or speed of recovery back to set point position after a drone is disturbed in its position. The study shows that after changing the Wi-Fi channel of drones to different frequency, the network interference between drones can be reduced, thus the performance of positioning controllability can be improved.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130239041","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 Compatibility Test Suite (CTS) is provided by Google to help manufactures to ensure if their Android devices are in compliance with the Android compatibility standards. However, the CTS contains a huge number of test cases and it usually would take several hours to complete the CTS tests. This could seriously affect the development schedule of Android devices, especially when the CTS test is included in the daily system integration. To reduce the time to perform CTS tests and shorten the time-to-market of Android devices, this paper presents an approach for improving the CTS test efficiency. Particularly, the CTS test is decomposed into multiple tasks to be executed on different devices concurrently. In addition, the task scheduling and partitioning methods are considered in the approach. A cloud-based testing platform is developed to support the proposed approach. The experimental results show that the efficiency of CTS test can be much improved as the number of devices increases. Moreover, the results also indicate that the Longest Job First (LJF) scheduling and mixed partitioning methods can result in better test efficiency.
{"title":"A Concurrent Approach for Improving the Efficiency of Android CTS Testing","authors":"Chien-Hung Liu, Woei-Kae Chen, Shu-Ling Chen","doi":"10.1109/ICS.2016.0125","DOIUrl":"https://doi.org/10.1109/ICS.2016.0125","url":null,"abstract":"The Compatibility Test Suite (CTS) is provided by Google to help manufactures to ensure if their Android devices are in compliance with the Android compatibility standards. However, the CTS contains a huge number of test cases and it usually would take several hours to complete the CTS tests. This could seriously affect the development schedule of Android devices, especially when the CTS test is included in the daily system integration. To reduce the time to perform CTS tests and shorten the time-to-market of Android devices, this paper presents an approach for improving the CTS test efficiency. Particularly, the CTS test is decomposed into multiple tasks to be executed on different devices concurrently. In addition, the task scheduling and partitioning methods are considered in the approach. A cloud-based testing platform is developed to support the proposed approach. The experimental results show that the efficiency of CTS test can be much improved as the number of devices increases. Moreover, the results also indicate that the Longest Job First (LJF) scheduling and mixed partitioning methods can result in better test efficiency.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131053702","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}
Currently, traditional algorithm performs not well in terms of predicting the user's complaint in imbalanced IPTV dataset. To solve this problem, we combine status data from the set-top box with data of user's complaints and select the appropriate model to predict user's quality of experience (QoE). Concretely, we firstly perform data cleaning and select suitable attributes from the original dataset. Then, we apply random under-sampling and synthetic over-sampling to the preprocessed dataset. In order to get better performance, we improves the Synthetic Minority Over-sampling Technique (SMOTE) algorithm and combine it with K-means algorithm to generate a new dataset. After these procedures, we use the Naïve Bayes (NB) model in user's complaint dataset. Through the rigorous modeling and prediction, extensive experimental results show that this integrated algorithm performs better than the Borderline-SMOTE algorithm in predicting user's complaints.
{"title":"Improving User's Quality of Experience in Imbalanced Dataset","authors":"Tanghui Wang, Ruochen Huang, Xin Wei, Fang Zhou","doi":"10.1109/ICS.2016.0142","DOIUrl":"https://doi.org/10.1109/ICS.2016.0142","url":null,"abstract":"Currently, traditional algorithm performs not well in terms of predicting the user's complaint in imbalanced IPTV dataset. To solve this problem, we combine status data from the set-top box with data of user's complaints and select the appropriate model to predict user's quality of experience (QoE). Concretely, we firstly perform data cleaning and select suitable attributes from the original dataset. Then, we apply random under-sampling and synthetic over-sampling to the preprocessed dataset. In order to get better performance, we improves the Synthetic Minority Over-sampling Technique (SMOTE) algorithm and combine it with K-means algorithm to generate a new dataset. After these procedures, we use the Naïve Bayes (NB) model in user's complaint dataset. Through the rigorous modeling and prediction, extensive experimental results show that this integrated algorithm performs better than the Borderline-SMOTE algorithm in predicting user's complaints.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128229771","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}
Bo Zang, Ruochen Huang, Lei Wang, Jianxin Chen, Feng Tian, Xin Wei
K-nearest neighbor (KNN) is a popular classification algorithm with good scalability, which has been widely used in many fields. When dealing with imbalanced data, minority examples are given the same weight as majority examples in the existing KNN algorithm. In this paper, we pay more attention to the minority class than the majority class, and we increase the weight of minority class according to the local characteristic of minority class distribution. In addition, we compare the proposed algorithm with the existing Weighted Distance K-nearest neighbor (WDKNN). Experimental results show that our algorithm performs better than WDKNN in imbalanced data sets.
{"title":"An Improved KNN Algorithm Based on Minority Class Distribution for Imbalanced Dataset","authors":"Bo Zang, Ruochen Huang, Lei Wang, Jianxin Chen, Feng Tian, Xin Wei","doi":"10.1109/ICS.2016.0143","DOIUrl":"https://doi.org/10.1109/ICS.2016.0143","url":null,"abstract":"K-nearest neighbor (KNN) is a popular classification algorithm with good scalability, which has been widely used in many fields. When dealing with imbalanced data, minority examples are given the same weight as majority examples in the existing KNN algorithm. In this paper, we pay more attention to the minority class than the majority class, and we increase the weight of minority class according to the local characteristic of minority class distribution. In addition, we compare the proposed algorithm with the existing Weighted Distance K-nearest neighbor (WDKNN). Experimental results show that our algorithm performs better than WDKNN in imbalanced data sets.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125054499","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}
Alan Shenghan Tsai, Pin-Hsun Lin, Che-Ming Kuo, Hsuan-Jung Su
In recent years, machine-to-machine (M2M) networks are widely considered in wireless communication system. Machines typically have constrained power, and their processing and communication capabilities are limited. To avoid the transmission of redundant information to improve the data rate, compressive sensing is a promising tool to be considered. Compressive sensing (CS) is especially useful for avoiding the redundant information to be transmitted such that the amount of transmitted data can be reduced. A framework for two-tier architecture of a remote compressive sensing scheme for M2M networks is developed where a statistical model replaces the standard sparsity model of classical compressive sensing. We consider this framework with noisy channels and derive an minimum mean square error (MMSE) decoder. Furthermore, we provide a way to produce sensing matrices and compare the proposed sensing matrices with random ones.
{"title":"Remote Compressive Sensing for Noisy M2M Networks","authors":"Alan Shenghan Tsai, Pin-Hsun Lin, Che-Ming Kuo, Hsuan-Jung Su","doi":"10.1109/ICS.2016.0147","DOIUrl":"https://doi.org/10.1109/ICS.2016.0147","url":null,"abstract":"In recent years, machine-to-machine (M2M) networks are widely considered in wireless communication system. Machines typically have constrained power, and their processing and communication capabilities are limited. To avoid the transmission of redundant information to improve the data rate, compressive sensing is a promising tool to be considered. Compressive sensing (CS) is especially useful for avoiding the redundant information to be transmitted such that the amount of transmitted data can be reduced. A framework for two-tier architecture of a remote compressive sensing scheme for M2M networks is developed where a statistical model replaces the standard sparsity model of classical compressive sensing. We consider this framework with noisy channels and derive an minimum mean square error (MMSE) decoder. Furthermore, we provide a way to produce sensing matrices and compare the proposed sensing matrices with random ones.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121504524","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}
Shubhranshu Singh, Yen-Chang Chiu, Y. Tsai, Jen-Shun Yang
In the recent past there has been much research and development on cloud based computing. While cloud computing provides huge opportunities, it also imposes several challenges. One of the challenges that current data network operators and future 5G network are foreseeing is huge increase in data traffic. To fulfill such exponential data traffic growth, along with other user expectation, requires multiple innovative approaches and re-consideration of the current network design principals. Mobile Edge Fog computing, as proposed in this paper offers huge opportunities to future data network operators, as well as to equipment vendors. Due to its relatively newer concept, still much research is ongoing and detail architecture is still evolving. This paper proposes and discusses a novel Mobile Edge Computing design and architecture, along with real-time implementation details of the proposed solutions. It exploits many benefits of D2D by incorporating D2D functionalities into the proposed relay-gateways. The application running on top of the proposed network shows significant benefits e.g. in terms of delay as well as core-network signalling and data offloading.
{"title":"Mobile Edge Fog Computing in 5G Era: Architecture and Implementation","authors":"Shubhranshu Singh, Yen-Chang Chiu, Y. Tsai, Jen-Shun Yang","doi":"10.1109/ICS.2016.0151","DOIUrl":"https://doi.org/10.1109/ICS.2016.0151","url":null,"abstract":"In the recent past there has been much research and development on cloud based computing. While cloud computing provides huge opportunities, it also imposes several challenges. One of the challenges that current data network operators and future 5G network are foreseeing is huge increase in data traffic. To fulfill such exponential data traffic growth, along with other user expectation, requires multiple innovative approaches and re-consideration of the current network design principals. Mobile Edge Fog computing, as proposed in this paper offers huge opportunities to future data network operators, as well as to equipment vendors. Due to its relatively newer concept, still much research is ongoing and detail architecture is still evolving. This paper proposes and discusses a novel Mobile Edge Computing design and architecture, along with real-time implementation details of the proposed solutions. It exploits many benefits of D2D by incorporating D2D functionalities into the proposed relay-gateways. The application running on top of the proposed network shows significant benefits e.g. in terms of delay as well as core-network signalling and data offloading.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375272","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}
Wi-Fi Aware is a new technique which enables devices to communicate with each other in the same vicinity without the help of access points. In this approach, we leverage Wi-Fi Aware technique and take field trials on the power efficiency of Wi-Fi Aware communications. Based on the experiment results, we conclude that the Wi-Fi Aware devices are suggested to transmit data with the help of proximity nodes according to their link speed so as to save energy.
{"title":"Power-Efficient Communication Strategy for Wi-Fi Aware Technology","authors":"Lokesh Sharma, Shih-Lin Wu, Jia-ming Liang","doi":"10.1109/ICS.2016.0036","DOIUrl":"https://doi.org/10.1109/ICS.2016.0036","url":null,"abstract":"Wi-Fi Aware is a new technique which enables devices to communicate with each other in the same vicinity without the help of access points. In this approach, we leverage Wi-Fi Aware technique and take field trials on the power efficiency of Wi-Fi Aware communications. Based on the experiment results, we conclude that the Wi-Fi Aware devices are suggested to transmit data with the help of proximity nodes according to their link speed so as to save energy.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126365924","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}
A Wireless Sensor and Robot Network (WSRN) is composed of a set of sensors and robots. Compared with the traditional Wireless Sensor Network (WSN), the WSRN has added characteristics, such as the mobility of robots. The mobility of robots has been proven to be quite useful in prolonging the network lifetime of the monitoring environment. However, mobility is a major part of the energy consumption for robots. Thus, the mobility of robots requires intelligent control in order to efficiently complete appointed tasks. Compared with lightweight sensors, using robots to transmit heavy data (such as audio or video data) is more appropriate. Therefore, a communication path, which is composed of robots between the base station and the event point, should be maintained. In this paper, three movement schemes, called Greedy Back-up (GB), Stretched Back-up Path (SBP) and Back-up Path Regression (BPR) approaches, are proposed, aiming to construct the data delivery path while maximizing the network lifetime. Experimental results reveal that the proposed schemes achieve better performance than that of a related work in terms of network lifetime and average moving distance of robots.
{"title":"Data Collection for Robot Movement Mechanisms in Wireless Sensor and Robot Networks","authors":"Chao-Tsun Chang, Chih-Yung Chang, Chih-Yao Hsiao, Yu-Ting Chin","doi":"10.1109/ICS.2016.0094","DOIUrl":"https://doi.org/10.1109/ICS.2016.0094","url":null,"abstract":"A Wireless Sensor and Robot Network (WSRN) is composed of a set of sensors and robots. Compared with the traditional Wireless Sensor Network (WSN), the WSRN has added characteristics, such as the mobility of robots. The mobility of robots has been proven to be quite useful in prolonging the network lifetime of the monitoring environment. However, mobility is a major part of the energy consumption for robots. Thus, the mobility of robots requires intelligent control in order to efficiently complete appointed tasks. Compared with lightweight sensors, using robots to transmit heavy data (such as audio or video data) is more appropriate. Therefore, a communication path, which is composed of robots between the base station and the event point, should be maintained. In this paper, three movement schemes, called Greedy Back-up (GB), Stretched Back-up Path (SBP) and Back-up Path Regression (BPR) approaches, are proposed, aiming to construct the data delivery path while maximizing the network lifetime. Experimental results reveal that the proposed schemes achieve better performance than that of a related work in terms of network lifetime and average moving distance of robots.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127701208","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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