Precise and timely predicting the amount of gas emitted from the coal is of great importance for coal mine safety. A novel artificial network model based on fuzzy-rough set for gas emission forecasting of coal mine is proposed in this paper. Some practices prove that this model can perform better than the tradition neural network.
{"title":"The Study of the Gas Emission Prediction Model Based on Fuzzy-rough Set Neural Network","authors":"Jing Hong, Yi Zhao","doi":"10.1109/ICMLC.2010.16","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.16","url":null,"abstract":"Precise and timely predicting the amount of gas emitted from the coal is of great importance for coal mine safety. A novel artificial network model based on fuzzy-rough set for gas emission forecasting of coal mine is proposed in this paper. Some practices prove that this model can perform better than the tradition neural network.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125177428","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 proposes Modified Ant Miner algorithm for intrusion detection. Ant Miner and its descendant have produced good result on many classification problems. Data mining technique is still relatively unexplored area for intrusion detection. In this paper, modification has been suggested in basic ant miner algorithm to improve accuracy and training time of algorithm. The KDD Cup 99 intrusion data set is used to evaluate our proposed algorithm and the result obtained from this experiment is compared with that of Support Vector Machine. It has been found that our proposed algorithm is more effective in case of DOS, U2R, and R2L type of attacks.
提出了一种改进的蚂蚁挖掘算法用于入侵检测。蚁矿机及其后续算法在许多分类问题上都取得了很好的结果。数据挖掘技术在入侵检测中仍然是一个相对未开发的领域。本文对基本蚂蚁挖掘算法进行了改进，提高了算法的准确率和训练时间。利用KDD Cup 99入侵数据集对该算法进行了验证，并将实验结果与支持向量机的结果进行了比较。实验结果表明，本文提出的算法在DOS、U2R和R2L类型的攻击中更为有效。
{"title":"Modified Ant Miner for Intrusion Detection","authors":"Deven Agravat, Urmi Vaishnav, P. Swadas","doi":"10.1109/ICMLC.2010.52","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.52","url":null,"abstract":"This paper proposes Modified Ant Miner algorithm for intrusion detection. Ant Miner and its descendant have produced good result on many classification problems. Data mining technique is still relatively unexplored area for intrusion detection. In this paper, modification has been suggested in basic ant miner algorithm to improve accuracy and training time of algorithm. The KDD Cup 99 intrusion data set is used to evaluate our proposed algorithm and the result obtained from this experiment is compared with that of Support Vector Machine. It has been found that our proposed algorithm is more effective in case of DOS, U2R, and R2L type of attacks.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114057384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In group-oriented applications like conferencing, chat groups and interactive gaming myriad messages are sent from one or more sources to multiple users. Multicasting is the optimum technique for such group oriented applications with effective network resource utilization. But maintaining security is a critical issue in group oriented protocols with frequent membership changes. Confidentiality can be achieved through changing the key material, known as rekeying every time a new member joins the group or existing member leaves from the group. Many techniques have been proposed earlier for this purpose. In centralized approach, a single key server is responsible to generate and distribute keys. In decentralized approach, a hierarchy of key managers distributes the keys. In distributed key-agreement protocol, the group members collectively generate and distribute a group key. This paper uses combination of both de-centralized and key agreement approaches to prevent a single point of failures and to improve the reliability as well as the performance of the system. This paper proposes new a technique(SGKP-1), using hybrid key trees, has certain advantages like secure channel establishment for the distribution of the key material, reducing the storage requirements and burden at each member, minimization of time requirement to become a new member of a group. The computational complexity further reduced using both the combination of public and private key crypto systems.
{"title":"Secure Group Key Distribution Using Hybrid Cryptosystem","authors":"V. Kumari, D. Nagaraju, K. Soumya, K. Raju","doi":"10.1109/ICMLC.2010.41","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.41","url":null,"abstract":"In group-oriented applications like conferencing, chat groups and interactive gaming myriad messages are sent from one or more sources to multiple users. Multicasting is the optimum technique for such group oriented applications with effective network resource utilization. But maintaining security is a critical issue in group oriented protocols with frequent membership changes. Confidentiality can be achieved through changing the key material, known as rekeying every time a new member joins the group or existing member leaves from the group. Many techniques have been proposed earlier for this purpose. In centralized approach, a single key server is responsible to generate and distribute keys. In decentralized approach, a hierarchy of key managers distributes the keys. In distributed key-agreement protocol, the group members collectively generate and distribute a group key. This paper uses combination of both de-centralized and key agreement approaches to prevent a single point of failures and to improve the reliability as well as the performance of the system. This paper proposes new a technique(SGKP-1), using hybrid key trees, has certain advantages like secure channel establishment for the distribution of the key material, reducing the storage requirements and burden at each member, minimization of time requirement to become a new member of a group. The computational complexity further reduced using both the combination of public and private key crypto systems.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127279141","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 presents a novel approach to achieve parallelism on multi-core systems out of the legacy software without recompilation. A profiler tool can be enhanced, from identifying the bottleneck areas, to analyzing the instruction set in bottleneck areas. As the instructions along with all data dependencies are available in the running program, heuristics can be applied to detect the candidates for instruction level parallelism. The serial regions can be regenerated into parallel regions for multiple cores using predefined OpenMP calls and instrument dynamically at runtime. We discuss the problems for parallelism 1) Identifying the parallel regions for parallelism from serial code 2) Detailed approach for generating code generation at runtime.
{"title":"Parallelism through dynamic instrumentation at runtime","authors":"Raj Yadav, Mankawal Deep Singh, Neha Mahajan","doi":"10.1109/ICMLC.2010.58","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.58","url":null,"abstract":"This paper presents a novel approach to achieve parallelism on multi-core systems out of the legacy software without recompilation. A profiler tool can be enhanced, from identifying the bottleneck areas, to analyzing the instruction set in bottleneck areas. As the instructions along with all data dependencies are available in the running program, heuristics can be applied to detect the candidates for instruction level parallelism. The serial regions can be regenerated into parallel regions for multiple cores using predefined OpenMP calls and instrument dynamically at runtime. We discuss the problems for parallelism 1) Identifying the parallel regions for parallelism from serial code 2) Detailed approach for generating code generation at runtime.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124550011","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}
Routing is the process of moving packets through an internetwork, such as the Internet. Routing consists of two separate but related tasks: i) Defining and selecting path in the network ii) Forwarding packets based upon the defined paths from a designated source node to a designated destination node. With the advance of wireless communication technology, small size and high performance computing and communication devices like commercial laptops and personal computers are increasingly used in convention centers, conferences and electronic classrooms. In wireless ad-hoc networks, a collection of nodes with wireless communications and networking capability communicate with each other without the aid of any centralized administrator. The nodes are powered by batteries with limited energy reservoir. It becomes difficult to recharge or replace the batteries of the nodes hence energy conservation is essential. An energy efficient routing protocol (EERP) balances node energy utilization to reduce energy consumption and increase the life of nodes thus increasing the network lifetime, reducing the routing delay and increasing the reliability of the packets reaching the destination. Wireless networks do not have any fixed communication infrastructure. For an active connection the end host as well as the intermediate nodes can be mobile. Therefore routes are subject to frequent disconnection. In such an environment it is important to minimize disruptions caused by changing topology for applications using voice and video. Power Aware Routing enables the nodes to detect misbehavior like deviation from regular routing and forwarding by observing the status of the node. By exploiting non-random behaviors for the mobility patterns that mobile user exhibit, state of network topology can be predicted and perform route reconstruction proactively in a timely manner. In this paper we propose an Energy Efficient- Power Aware routing algorithm where we have integrated energy efficiency with power awareness parameters for routing of packets.
{"title":"Study of Energy Efficient, Power Aware Routing Algorithm and Their Applications","authors":"A. A., G. Sakthidharan, Kanchan M. Miskin","doi":"10.1109/ICMLC.2010.44","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.44","url":null,"abstract":"Routing is the process of moving packets through an internetwork, such as the Internet. Routing consists of two separate but related tasks: i) Defining and selecting path in the network ii) Forwarding packets based upon the defined paths from a designated source node to a designated destination node. With the advance of wireless communication technology, small size and high performance computing and communication devices like commercial laptops and personal computers are increasingly used in convention centers, conferences and electronic classrooms. In wireless ad-hoc networks, a collection of nodes with wireless communications and networking capability communicate with each other without the aid of any centralized administrator. The nodes are powered by batteries with limited energy reservoir. It becomes difficult to recharge or replace the batteries of the nodes hence energy conservation is essential. An energy efficient routing protocol (EERP) balances node energy utilization to reduce energy consumption and increase the life of nodes thus increasing the network lifetime, reducing the routing delay and increasing the reliability of the packets reaching the destination. Wireless networks do not have any fixed communication infrastructure. For an active connection the end host as well as the intermediate nodes can be mobile. Therefore routes are subject to frequent disconnection. In such an environment it is important to minimize disruptions caused by changing topology for applications using voice and video. Power Aware Routing enables the nodes to detect misbehavior like deviation from regular routing and forwarding by observing the status of the node. By exploiting non-random behaviors for the mobility patterns that mobile user exhibit, state of network topology can be predicted and perform route reconstruction proactively in a timely manner. In this paper we propose an Energy Efficient- Power Aware routing algorithm where we have integrated energy efficiency with power awareness parameters for routing of packets.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121408764","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 presents an algorithm to automatically determine the number of clusters in a given input data set, under a mixture of Gaussians assumption. Our algorithm extends the Expectation- Maximization clustering approach by starting with a single cluster assumption for the data, and recursively splitting one of the clusters in order to find a tighter fit. An Information Criterion parameter is used to make a selection between the current and previous model after each split. We build this approach upon prior work done on both the K-Means and Expectation-Maximization algorithms. We also present a novel idea for intelligent cluster splitting which minimizes convergence time and substantially improves accuracy.
{"title":"Detecting the Number of Clusters during Expectation-Maximization Clustering Using Information Criterion","authors":"U. Gupta, Vinay Menon, Uday Babbar","doi":"10.1109/ICMLC.2010.47","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.47","url":null,"abstract":"This paper presents an algorithm to automatically determine the number of clusters in a given input data set, under a mixture of Gaussians assumption. Our algorithm extends the Expectation- Maximization clustering approach by starting with a single cluster assumption for the data, and recursively splitting one of the clusters in order to find a tighter fit. An Information Criterion parameter is used to make a selection between the current and previous model after each split. We build this approach upon prior work done on both the K-Means and Expectation-Maximization algorithms. We also present a novel idea for intelligent cluster splitting which minimizes convergence time and substantially improves accuracy.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115376700","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}
When the usages of electronic mail continue, unsolicited bulk email also continues to grow. These unsolicited bulk emails occupies server storage space and consumes large amount of network bandwidth. To overcome this serious problem, Anti-spam filters become a common component of internet security. Recently, Image spamming is a new kind of method of email spamming in which the text is embedded in image or picture files. Identifying and preventing spam is one of the top challenges in the internet world. Many approaches for identifying image spam have been established in literature. The artificial neural network is an effective classification method for solving feature extraction problems. In this paper we present an experimental system for the classification of image spam by considering statistical image feature histogram and mean value of an block of image. A comparative study of image classification based on color histogram and mean value is presented in this paper. The experimental result shows the performance of the proposed system and it achieves best results with minimum false positive.
{"title":"Statistical Feature Extraction for Classification of Image Spam Using Artificial Neural Networks","authors":"M. Soranamageswari, C. Meena","doi":"10.1109/ICMLC.2010.72","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.72","url":null,"abstract":"When the usages of electronic mail continue, unsolicited bulk email also continues to grow. These unsolicited bulk emails occupies server storage space and consumes large amount of network bandwidth. To overcome this serious problem, Anti-spam filters become a common component of internet security. Recently, Image spamming is a new kind of method of email spamming in which the text is embedded in image or picture files. Identifying and preventing spam is one of the top challenges in the internet world. Many approaches for identifying image spam have been established in literature. The artificial neural network is an effective classification method for solving feature extraction problems. In this paper we present an experimental system for the classification of image spam by considering statistical image feature histogram and mean value of an block of image. A comparative study of image classification based on color histogram and mean value is presented in this paper. The experimental result shows the performance of the proposed system and it achieves best results with minimum false positive.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114193626","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}
Road Extraction from satellite imagery is of fundamental importance in the context of spatial data capturing and updating for GIS applications. As fully automatic method for feature extraction is difficult due to the increasing complexity of objects. This paper proposes a semi-automatic road extraction methodology from high resolution satellite imagery using active contour model (Snakes). First the image is preprocessed using relaxed median filter. In the next step the user inputs initial seed points on the road to be extracted. Then the road segment is extracted using active contour model. The method is tested using high resolution satellite imagery and the results are presented in the paper.
{"title":"A Novel Approach Using Active Contour Model for Semi-Automatic Road Extraction from High Resolution Satellite Imagery","authors":"Anil P.N., S. Natarajan","doi":"10.1109/ICMLC.2010.36","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.36","url":null,"abstract":"Road Extraction from satellite imagery is of fundamental importance in the context of spatial data capturing and updating for GIS applications. As fully automatic method for feature extraction is difficult due to the increasing complexity of objects. This paper proposes a semi-automatic road extraction methodology from high resolution satellite imagery using active contour model (Snakes). First the image is preprocessed using relaxed median filter. In the next step the user inputs initial seed points on the road to be extracted. Then the road segment is extracted using active contour model. The method is tested using high resolution satellite imagery and the results are presented in the paper.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121492962","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}
Although there have been many recent studies of link prediction in co-authorship networks, few have tried to utilize the Semantic information hidden in abstracts of the research documents. We propose to build a link predictor in a co-authorship network where nodes represent researchers and links represent co-authorship. In this method, we use the structure of the constructed graph, and propose to add a semantic approach using abstract information, research titles and the event information to improve the accuracy of the predictor. Secondly, we make use of the fact that researchers tend to work in close knit communities. The knowledge of a pair of researchers lying in the same dense community can be used to improve the accuracy of our predictor further. Finally, we test out hypothesis on the DBLP database in a reasonable time by under-sampling and balancing the data set using decision trees and the SMOTE technique.
{"title":"Using Abstract Information and Community Alignment Information for Link Prediction","authors":"Mrinmaya Sachan, R. Ichise","doi":"10.1109/ICMLC.2010.25","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.25","url":null,"abstract":"Although there have been many recent studies of link prediction in co-authorship networks, few have tried to utilize the Semantic information hidden in abstracts of the research documents. We propose to build a link predictor in a co-authorship network where nodes represent researchers and links represent co-authorship. In this method, we use the structure of the constructed graph, and propose to add a semantic approach using abstract information, research titles and the event information to improve the accuracy of the predictor. Secondly, we make use of the fact that researchers tend to work in close knit communities. The knowledge of a pair of researchers lying in the same dense community can be used to improve the accuracy of our predictor further. Finally, we test out hypothesis on the DBLP database in a reasonable time by under-sampling and balancing the data set using decision trees and the SMOTE technique.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131395809","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}
M. Balajee, B. Suresh, M. Suneetha, V. Rani, G. Veerraju
This paper describes a new policy to schedule parallel jobs on Clusters that may be part of a Computational Grid. This algorithm proposed 3 Job Queues. In each Cluster, some number of resources is assigned to each of the Queue. The 1st Queue has some jobs which has low expected execution time(EET). The 2nd Queue has some jobs which has high expected execution time. The 3rd Queue has jobs which are part of Meta-Job from Computational Grid. In 1st there is no chance of starvation. But in 2nd Queue there is a chance of starvation. So this algorithm applied Aging technique to preempt the jobs which has low priority. And the 3rd Queue is fully dedicated to execute a part of Meta-Jobs only. So here we maintain multiple job Queues which are effectively separate jobs according to their projected execution time for Local Jobs and for part of Meta-Job. Here we preempt jobs by applying Aging Technique. Here we can avoid unnecessary traffic congestion in networks by comparing Expected Execution Time with Total Time for submitting job(s) and receiving result(s) from node(s).
{"title":"Premptive Job Scheduling with Priorities and Starvation cum Congestion Avoidance in Clusters","authors":"M. Balajee, B. Suresh, M. Suneetha, V. Rani, G. Veerraju","doi":"10.1109/ICMLC.2010.60","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.60","url":null,"abstract":"This paper describes a new policy to schedule parallel jobs on Clusters that may be part of a Computational Grid. This algorithm proposed 3 Job Queues. In each Cluster, some number of resources is assigned to each of the Queue. The 1st Queue has some jobs which has low expected execution time(EET). The 2nd Queue has some jobs which has high expected execution time. The 3rd Queue has jobs which are part of Meta-Job from Computational Grid. In 1st there is no chance of starvation. But in 2nd Queue there is a chance of starvation. So this algorithm applied Aging technique to preempt the jobs which has low priority. And the 3rd Queue is fully dedicated to execute a part of Meta-Jobs only. So here we maintain multiple job Queues which are effectively separate jobs according to their projected execution time for Local Jobs and for part of Meta-Job. Here we preempt jobs by applying Aging Technique. Here we can avoid unnecessary traffic congestion in networks by comparing Expected Execution Time with Total Time for submitting job(s) and receiving result(s) from node(s).","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128895041","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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