Pub Date : 2014-12-01DOI: 10.1109/PDGC.2014.7030771
A. Sinha, Tapas Saini, S. Srikanth
Distributed computing is the method of running CPU intensive computations on multiple computers collectively in order to achieve a common objective. Common problems that can be solved on the distributed systems include climate/weather modeling, earthquake simulation, evolutionary computing problems and so on. These type of problems may involve billions or even trillions of computations. A single computer is not capable to finish these computations in short span of time, which is typically in days. Distributed computation helps to solve these problems in hours, which could take weeks to solve on a single computer. Distributed computing generally uses the existing resources of the organization. Traffic simulation is the process of simulating transportation systems through software on a virtual road network. Traffic simulation helps in analyzing city traffic at different time intervals of a single day. Common use cases could be analyzing city wide traffic, estimating traffic demand at a particular traffic junction and so on. This paper discusses about the approach to use distributed computing paradigm for optimizing the traffic simulations. Optimizing simulations involves running a number of traffic simulations followed by estimating the nearness of that simulation to the real available traffic data. This real data could be obtained by either manual counting at traffic junctions, or using the probes such as loop inductors, CCTV cameras etc. This distributed computing based approach works to find the best traffic simulation corresponding to the real data in hand, using evolutionary computing technique.
{"title":"Distributed computing approach to optimize road traffic simulation","authors":"A. Sinha, Tapas Saini, S. Srikanth","doi":"10.1109/PDGC.2014.7030771","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030771","url":null,"abstract":"Distributed computing is the method of running CPU intensive computations on multiple computers collectively in order to achieve a common objective. Common problems that can be solved on the distributed systems include climate/weather modeling, earthquake simulation, evolutionary computing problems and so on. These type of problems may involve billions or even trillions of computations. A single computer is not capable to finish these computations in short span of time, which is typically in days. Distributed computation helps to solve these problems in hours, which could take weeks to solve on a single computer. Distributed computing generally uses the existing resources of the organization. Traffic simulation is the process of simulating transportation systems through software on a virtual road network. Traffic simulation helps in analyzing city traffic at different time intervals of a single day. Common use cases could be analyzing city wide traffic, estimating traffic demand at a particular traffic junction and so on. This paper discusses about the approach to use distributed computing paradigm for optimizing the traffic simulations. Optimizing simulations involves running a number of traffic simulations followed by estimating the nearness of that simulation to the real available traffic data. This real data could be obtained by either manual counting at traffic junctions, or using the probes such as loop inductors, CCTV cameras etc. This distributed computing based approach works to find the best traffic simulation corresponding to the real data in hand, using evolutionary computing technique.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130350954","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 : 2014-12-01DOI: 10.1109/PDGC.2014.7030751
Himanshu Jindal, S. Saxena, Singara Singh
The Underwater Acoustic Sensor Networks (UWASN) consists of sensors that are deployed underwater for gathering information for the unexplored parts of oceans or rivers. UWASN consists of variable number of floating and anchored sensors, sink and vehicles that are deployed over an area to be explored. The characteristics of UWASN are mainly node mobility for floating, capacity for data collection and recording and autonomous vehicles which are battery operable. The communication is possible among underwater devices through optical waves, radio waves, electromagnetic and acoustics. Out of these, acoustics communication is best suited as it can carry digital information through underwater channel and can travel to longer distances. The communication can be classified in two parts: Single and multi hoping. But in underwater we use multi-hop communication for sending data from end nodes to sink nodes. The various challenges to UWASN are limited bandwidth, multipath fading, limited battery, limited data capacity and delay in propagation. Hence, in this paper we have focussed on various issues and challenges in underwater wireless sensor networks for acoustic communications.
{"title":"Challenges and issues in underwater acoustics sensor networks: A review","authors":"Himanshu Jindal, S. Saxena, Singara Singh","doi":"10.1109/PDGC.2014.7030751","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030751","url":null,"abstract":"The Underwater Acoustic Sensor Networks (UWASN) consists of sensors that are deployed underwater for gathering information for the unexplored parts of oceans or rivers. UWASN consists of variable number of floating and anchored sensors, sink and vehicles that are deployed over an area to be explored. The characteristics of UWASN are mainly node mobility for floating, capacity for data collection and recording and autonomous vehicles which are battery operable. The communication is possible among underwater devices through optical waves, radio waves, electromagnetic and acoustics. Out of these, acoustics communication is best suited as it can carry digital information through underwater channel and can travel to longer distances. The communication can be classified in two parts: Single and multi hoping. But in underwater we use multi-hop communication for sending data from end nodes to sink nodes. The various challenges to UWASN are limited bandwidth, multipath fading, limited battery, limited data capacity and delay in propagation. Hence, in this paper we have focussed on various issues and challenges in underwater wireless sensor networks for acoustic communications.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131192449","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 : 2014-12-01DOI: 10.1109/PDGC.2014.7030762
A. Ramya, E. Sivasankar
Sequential pattern mining and Document analysis is an important data mining problem in Big Data with broad applications. This paper investigates a specific framework for managing distributed processing in dataset pattern match and document analysis context. MapReduce programming model on a Hadoop cluster is highly scalable and works with commodity machines with integrated mechanisms for fault tolerance. In this paper, we propose a Knuth Morris Pratt based sequential pattern matching in distributed environment with the help of Hadoop Distributed File System as efficient mining of sequential patterns. It also investigates the feasibility of partitioning and clustering of text document datasets for document comparisons. It simplifies the search space and acquires a higher mining efficiency. Data mining tasks has been decomposed to many Map tasks and distributed to many Task trackers. The map tasks find the intermediate results and send to reduce task which consolidates the final result. Both theoretical analysis and experimental result with data as well as cluster of varying size shows the effectiveness of MapReduce model primarily based on time requirements.
{"title":"Distributed pattern matching and document analysis in big data using Hadoop MapReduce model","authors":"A. Ramya, E. Sivasankar","doi":"10.1109/PDGC.2014.7030762","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030762","url":null,"abstract":"Sequential pattern mining and Document analysis is an important data mining problem in Big Data with broad applications. This paper investigates a specific framework for managing distributed processing in dataset pattern match and document analysis context. MapReduce programming model on a Hadoop cluster is highly scalable and works with commodity machines with integrated mechanisms for fault tolerance. In this paper, we propose a Knuth Morris Pratt based sequential pattern matching in distributed environment with the help of Hadoop Distributed File System as efficient mining of sequential patterns. It also investigates the feasibility of partitioning and clustering of text document datasets for document comparisons. It simplifies the search space and acquires a higher mining efficiency. Data mining tasks has been decomposed to many Map tasks and distributed to many Task trackers. The map tasks find the intermediate results and send to reduce task which consolidates the final result. Both theoretical analysis and experimental result with data as well as cluster of varying size shows the effectiveness of MapReduce model primarily based on time requirements.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126847796","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 : 2014-12-01DOI: 10.1109/PDGC.2014.7030748
Ch. Sudhakar, Vatsal Rathod, T. Ramesh
Peer-to-peer model is one of the commonly used model for distributed computing. Some of the peers are having demand for certain resources some others may be having additional capacity of resources. Peers may have limitations on the number of concurrent connections (degree). In the present work allocation problem of peers having demand, capacity and degree is considered. The problem is to find an allocation of peers such that the number of peers allocated to a particular peer P should not exceed the degree of P and total demand of allocated peers should not exceed the capacity of P, while maximizing the overall throughput. Two versions namely Offline (when peers are known in advance) and Online (when peers can join and leave the network at any time) versions of the problem are considered. By introducing degree constraints the problem becomes NP-complete. Resource augmentation based three approaches are proposed to solve this problem. The performance (in terms of throughput) and the cost (in terms of disconnections and reconnections) of the proposed approaches is compared through a set of extensive simulations. The observed results are impressive.
{"title":"Heterogeneous resource allocation under degree constraints in peer-to-peer networks","authors":"Ch. Sudhakar, Vatsal Rathod, T. Ramesh","doi":"10.1109/PDGC.2014.7030748","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030748","url":null,"abstract":"Peer-to-peer model is one of the commonly used model for distributed computing. Some of the peers are having demand for certain resources some others may be having additional capacity of resources. Peers may have limitations on the number of concurrent connections (degree). In the present work allocation problem of peers having demand, capacity and degree is considered. The problem is to find an allocation of peers such that the number of peers allocated to a particular peer P should not exceed the degree of P and total demand of allocated peers should not exceed the capacity of P, while maximizing the overall throughput. Two versions namely Offline (when peers are known in advance) and Online (when peers can join and leave the network at any time) versions of the problem are considered. By introducing degree constraints the problem becomes NP-complete. Resource augmentation based three approaches are proposed to solve this problem. The performance (in terms of throughput) and the cost (in terms of disconnections and reconnections) of the proposed approaches is compared through a set of extensive simulations. The observed results are impressive.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126966217","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 : 2014-12-01DOI: 10.1109/PDGC.2014.7030728
P. Guleria, Niveditta Thakur, M. Sood
As competitive environment is prevailing among the academic institutions, challenge is to increase the quality of education through data mining. Student's performance is of great concern to the higher education. In this paper, we have applied data mining techniques by evaluating student's data using decision trees which is helpful in predicting the student's results. In this paper, we have calculated the Entropy of the attributes taken in Educational Data Set and the attribute having highest Information Gain is taken as the root node to split further. The results generated using Data Mining Techniques help faculty members to focus on students who are getting poor class results.
{"title":"Predicting student performance using decision tree classifiers and information gain","authors":"P. Guleria, Niveditta Thakur, M. Sood","doi":"10.1109/PDGC.2014.7030728","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030728","url":null,"abstract":"As competitive environment is prevailing among the academic institutions, challenge is to increase the quality of education through data mining. Student's performance is of great concern to the higher education. In this paper, we have applied data mining techniques by evaluating student's data using decision trees which is helpful in predicting the student's results. In this paper, we have calculated the Entropy of the attributes taken in Educational Data Set and the attribute having highest Information Gain is taken as the root node to split further. The results generated using Data Mining Techniques help faculty members to focus on students who are getting poor class results.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"117 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114089079","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 : 2014-12-01DOI: 10.1109/PDGC.2014.7030781
Parthu Balina, Kashish Garg, S. V. Rao
We studied the spectrum usage pattern in Waknaghat in the frequency bands ranging from 80 MHz to 500 MHz. The occupancy is quantified as the amount of spectrum detected above a certain received power threshold. The outcome of this study suggests that traditional methods of spectrum allocation (fixed channel allocation) is not efficient and that we may employ emerging spectrum sharing technology such as the cognitive radio technology for future wireless services. This study of spectrum survey is preliminary in its nature, of a very small range and future studies need to be performed for higher ranges (VHF and UHF bands) to determine potential secondary usage on those channels that have low or no active utilization.
{"title":"Spectrum occupancy measurements and analysis in a rural area setting","authors":"Parthu Balina, Kashish Garg, S. V. Rao","doi":"10.1109/PDGC.2014.7030781","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030781","url":null,"abstract":"We studied the spectrum usage pattern in Waknaghat in the frequency bands ranging from 80 MHz to 500 MHz. The occupancy is quantified as the amount of spectrum detected above a certain received power threshold. The outcome of this study suggests that traditional methods of spectrum allocation (fixed channel allocation) is not efficient and that we may employ emerging spectrum sharing technology such as the cognitive radio technology for future wireless services. This study of spectrum survey is preliminary in its nature, of a very small range and future studies need to be performed for higher ranges (VHF and UHF bands) to determine potential secondary usage on those channels that have low or no active utilization.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114287353","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 : 2014-12-01DOI: 10.1109/PDGC.2014.7030724
Shaify Kansal, Meenakshi Mittal
With rise in the use of internet in various fields like banking, military and government sector, the security and privacy of the data has been the main concern. Today, most of the data handling and electronic transactions are done on the internet. When the information is transferred from the sender to the receiver over the internet, it may be eavesdropped by an intruder and thus is a continuous threat to the secrecy or confidentiality of the data. The popular technique that protects the confidentiality of the data is cryptography which converts the plain text into unreadable form and then receiver applies reverse mechanism to decrypt the unreadable form of data to readable form. This mechanism is called as encryption-decryption process. Thus to secure the data over the internet, it is important to find out which algorithm performs better than the other algorithms. In this paper, the different symmetric encryption algorithms like DES, 3DES, AES have been analyzed with respect to different parameters and data types (like text files, image) on i7 processor.
{"title":"Performance evaluation of various symmetric encryption algorithms","authors":"Shaify Kansal, Meenakshi Mittal","doi":"10.1109/PDGC.2014.7030724","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030724","url":null,"abstract":"With rise in the use of internet in various fields like banking, military and government sector, the security and privacy of the data has been the main concern. Today, most of the data handling and electronic transactions are done on the internet. When the information is transferred from the sender to the receiver over the internet, it may be eavesdropped by an intruder and thus is a continuous threat to the secrecy or confidentiality of the data. The popular technique that protects the confidentiality of the data is cryptography which converts the plain text into unreadable form and then receiver applies reverse mechanism to decrypt the unreadable form of data to readable form. This mechanism is called as encryption-decryption process. Thus to secure the data over the internet, it is important to find out which algorithm performs better than the other algorithms. In this paper, the different symmetric encryption algorithms like DES, 3DES, AES have been analyzed with respect to different parameters and data types (like text files, image) on i7 processor.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126574855","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 the recent years, the problem of identifying suspicious behavior has gained importance and identifying this behavior using computational systems and autonomous algorithms is highly desirable in a tactical scenario. So far, the solutions have been primarily manual which elicit human observation of entities to discern the hostility of the situation. To cater to this problem statement, a number of fully automated and partially automated solutions exist. But, these solutions lack the capability of learning from experiences and work in conjunction with human supervision which is extremely prone to error. In this paper, a generalized methodology to predict the hostility of a given object based on its movement patterns is proposed which has the ability to learn and is based upon the mechanism of humans of “learning from experiences”. The methodology so proposed has been implemented in a computer simulation. The results show that the posited methodology has the potential to be applied in real world tactical scenarios.
{"title":"Hostile intent identification by movement pattern analysis: Using artificial neural networks","authors":"Souham Biswas, M. Nene","doi":"10.13140/2.1.4429.7281","DOIUrl":"https://doi.org/10.13140/2.1.4429.7281","url":null,"abstract":"In the recent years, the problem of identifying suspicious behavior has gained importance and identifying this behavior using computational systems and autonomous algorithms is highly desirable in a tactical scenario. So far, the solutions have been primarily manual which elicit human observation of entities to discern the hostility of the situation. To cater to this problem statement, a number of fully automated and partially automated solutions exist. But, these solutions lack the capability of learning from experiences and work in conjunction with human supervision which is extremely prone to error. In this paper, a generalized methodology to predict the hostility of a given object based on its movement patterns is proposed which has the ability to learn and is based upon the mechanism of humans of “learning from experiences”. The methodology so proposed has been implemented in a computer simulation. The results show that the posited methodology has the potential to be applied in real world tactical scenarios.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127507393","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 : 2014-12-01DOI: 10.1109/PDGC.2014.7030712
K. Govil, S. Gupta, A. Agarwal
A Mobile Ad hoc Network (MANET) is a wireless network where mobile nodes are connected to each other through infrastructure-less connections. Cluster head is a node within the cluster which acts as a leader node. It maintains the information having list of nodes, path of each and every nodes related to its corresponding cluster. Proposed algorithm forms cluster head which is for energy conservation by considering battery power and neighbor mobile node connectivity level.
{"title":"Cluster head selection technique for optimization of energy conservation in MANET","authors":"K. Govil, S. Gupta, A. Agarwal","doi":"10.1109/PDGC.2014.7030712","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030712","url":null,"abstract":"A Mobile Ad hoc Network (MANET) is a wireless network where mobile nodes are connected to each other through infrastructure-less connections. Cluster head is a node within the cluster which acts as a leader node. It maintains the information having list of nodes, path of each and every nodes related to its corresponding cluster. Proposed algorithm forms cluster head which is for energy conservation by considering battery power and neighbor mobile node connectivity level.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123985530","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 : 2014-12-01DOI: 10.1109/PDGC.2014.7030790
L. Sharma, H. Saini, Geetanjali Rathee, T. C. Panda
In the current era of the increase customers for cellular networks it is required to optimize the cellular network planning process. Cellular planning incorporates three major component named frequency planning, capacity expansion and coverage area optimization. As the number of cellular users are increasing day by day hence the coverage area should also be optimized in response to serve the more users. Therefore, in the present paper we have discussed one of the existing algorithms known as PSO Algorithm to optimize coverage area and further enhancement is suggested and evaluated in the proposed algorithm for coverage area optimization.
{"title":"Proposed optimized algorithm for coverage area with capacity calculation","authors":"L. Sharma, H. Saini, Geetanjali Rathee, T. C. Panda","doi":"10.1109/PDGC.2014.7030790","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030790","url":null,"abstract":"In the current era of the increase customers for cellular networks it is required to optimize the cellular network planning process. Cellular planning incorporates three major component named frequency planning, capacity expansion and coverage area optimization. As the number of cellular users are increasing day by day hence the coverage area should also be optimized in response to serve the more users. Therefore, in the present paper we have discussed one of the existing algorithms known as PSO Algorithm to optimize coverage area and further enhancement is suggested and evaluated in the proposed algorithm for coverage area optimization.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121328818","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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