Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.111
M. Leon, R. Laing
Efficient collaborative design during the early design stages in architecture is a condition for effective overall design and construction and visual and tactile interfaces can facilitate and support collaborative design leading to promoting communication and effective information exchange. The collaborative design process is captured nowadays within shared Building Information Models (BIMs) that bridge spatial, temporal and conceptual barriers, and the technological means for sharing that information are based on cloud technologies. Nevertheless, BIM models tend to be focused on detailed and construction design stages while the current paradigm on early stages conceptual design tends towards still using analogue means of communication. The focus of this paper is about bridging the conceptual design stage with the later detailed design BIM ones by using tactile and tangible interfaces and digital and cloud technologies. As a result, the information and initial ideas are effectively transferred between the different design stages. A specific case study is presented to illustrate the conceptual design process of a multidisciplinary focus group using both analogue means and tactile and tangible user interfaces (TUIs).
{"title":"Cloud and Computer Mediated Collaboration in the Early Architectural Design Stages: A Study of Early Design Stage Collaboration Related to BIM and the Cloud","authors":"M. Leon, R. Laing","doi":"10.1109/CloudCom.2013.111","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.111","url":null,"abstract":"Efficient collaborative design during the early design stages in architecture is a condition for effective overall design and construction and visual and tactile interfaces can facilitate and support collaborative design leading to promoting communication and effective information exchange. The collaborative design process is captured nowadays within shared Building Information Models (BIMs) that bridge spatial, temporal and conceptual barriers, and the technological means for sharing that information are based on cloud technologies. Nevertheless, BIM models tend to be focused on detailed and construction design stages while the current paradigm on early stages conceptual design tends towards still using analogue means of communication. The focus of this paper is about bridging the conceptual design stage with the later detailed design BIM ones by using tactile and tangible interfaces and digital and cloud technologies. As a result, the information and initial ideas are effectively transferred between the different design stages. A specific case study is presented to illustrate the conceptual design process of a multidisciplinary focus group using both analogue means and tactile and tangible user interfaces (TUIs).","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117118981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.40
Vasiliki Kalavri, V. Brundza, Vladimir Vlassov
Large-scale data processing frameworks, such as Hadoop MapReduce, are widely used to analyze enormous amounts of data. However, processing is often time-consuming, preventing interactive analysis. One way to decrease response time is partial job execution, where an approximate, early result becomes available to the user, prior to job completion. The Hadoop Online Prototype (HOP) uses online aggregation to provide early results, by partially executing jobs on subsets of the input, using a simplistic progress metric. Due to its sequential nature, values are not objectively represented in the input subset, often resulting in poor approximations or "data bias". In this paper, we propose a block sampling technique for large-scale data processing, which can be used for fast and accurate partial job execution. Our implementation of the technique on top of HOP uniformly samples HDFS blocks and uses in-memory shuffling to reduce data bias. Our prototype significantly improves the accuracy of HOP's early results, while only introducing minimal overhead. We evaluate our technique using real-world datasets and applications and demonstrate that our system outperforms HOP in terms of accuracy. In particular, when estimating the average temperature of the studied dataset, our system provides high accuracy (less than 20% absolute error) after processing only 10% of the input, while HOP needs to process 70% of the input to yield comparable results.
{"title":"Block Sampling: Efficient Accurate Online Aggregation in MapReduce","authors":"Vasiliki Kalavri, V. Brundza, Vladimir Vlassov","doi":"10.1109/CloudCom.2013.40","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.40","url":null,"abstract":"Large-scale data processing frameworks, such as Hadoop MapReduce, are widely used to analyze enormous amounts of data. However, processing is often time-consuming, preventing interactive analysis. One way to decrease response time is partial job execution, where an approximate, early result becomes available to the user, prior to job completion. The Hadoop Online Prototype (HOP) uses online aggregation to provide early results, by partially executing jobs on subsets of the input, using a simplistic progress metric. Due to its sequential nature, values are not objectively represented in the input subset, often resulting in poor approximations or \"data bias\". In this paper, we propose a block sampling technique for large-scale data processing, which can be used for fast and accurate partial job execution. Our implementation of the technique on top of HOP uniformly samples HDFS blocks and uses in-memory shuffling to reduce data bias. Our prototype significantly improves the accuracy of HOP's early results, while only introducing minimal overhead. We evaluate our technique using real-world datasets and applications and demonstrate that our system outperforms HOP in terms of accuracy. In particular, when estimating the average temperature of the studied dataset, our system provides high accuracy (less than 20% absolute error) after processing only 10% of the input, while HOP needs to process 70% of the input to yield comparable results.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116213813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.137
Albino Altomare, Eugenio Cesario, C. Comito, F. Marozzo, D. Talia
The increasing pervasiveness of mobile devices along with the use of technologies like GPS, Wifi networks, RFID, etc., allows for the collections of large amounts of movement data. This amount of information can be analyzed to extract descriptive and predictive models that can be profitable exploited to improve urban life. This paper presents an integrated Cloud based framework for efficiently managing and analyzing socio-environmental data in the urban context of cities. As case study, we introduce a parallel approach for discovering patterns and rules from trajectory data. Experimental evaluation shows that the trajectory pattern mining process can take advantage from a scalable execution environment offered by a Cloud architecture.
{"title":"Using Clouds for Smart City Applications","authors":"Albino Altomare, Eugenio Cesario, C. Comito, F. Marozzo, D. Talia","doi":"10.1109/CloudCom.2013.137","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.137","url":null,"abstract":"The increasing pervasiveness of mobile devices along with the use of technologies like GPS, Wifi networks, RFID, etc., allows for the collections of large amounts of movement data. This amount of information can be analyzed to extract descriptive and predictive models that can be profitable exploited to improve urban life. This paper presents an integrated Cloud based framework for efficiently managing and analyzing socio-environmental data in the urban context of cities. As case study, we introduce a parallel approach for discovering patterns and rules from trajectory data. Experimental evaluation shows that the trajectory pattern mining process can take advantage from a scalable execution environment offered by a Cloud architecture.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"471 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116577035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.81
Patrick Lubbecke, T. Anton, R. Lackes
This paper identifies specific risk factors of cross-border outsourcing to cloud environments and discusses methods of evaluation. Many approaches lack in comparability between costs and risks. Therefore, we present two evaluation approaches based on the capital value (costs) and Analytical Network Process (risks) to support the assessment of outsourcing decisions under special consideration of cross-border risk factors. Although the integration of the two approaches needs further research, we present an early attempt to combine these two methods.
{"title":"Cross-Border Risk Factors of Cloud Services: Risk Assessment of IS Outsourcing to Foreign Cloud Service Providers","authors":"Patrick Lubbecke, T. Anton, R. Lackes","doi":"10.1109/CloudCom.2013.81","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.81","url":null,"abstract":"This paper identifies specific risk factors of cross-border outsourcing to cloud environments and discusses methods of evaluation. Many approaches lack in comparability between costs and risks. Therefore, we present two evaluation approaches based on the capital value (costs) and Analytical Network Process (risks) to support the assessment of outsourcing decisions under special consideration of cross-border risk factors. Although the integration of the two approaches needs further research, we present an early attempt to combine these two methods.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126741852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.11
Alfred Bratterud, H. Haugerud
The smallest instance offered by Amazon EC2 comes with 615MB memory and a 7.9GB disk image. While small by today's standards, embedded web servers with memory footprints well under 100kB, indicate that there is much to be saved. In this work we investigate how large VM-populations the open Stack hyper visor can be made to sustain, by tuning it for scalability and minimizing virtual machine images. Request-driven Qemu images of 512 byte are written in assembly, and more than 110 000 such instances are successfully booted on a 48 core host, before memory is exhausted. Other factors are shown to dramatically improve scalability, to the point where 10 000 virtual machines consume no more than 2.06% of the hyper visor CPU.
{"title":"Maximizing Hypervisor Scalability Using Minimal Virtual Machines","authors":"Alfred Bratterud, H. Haugerud","doi":"10.1109/CloudCom.2013.11","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.11","url":null,"abstract":"The smallest instance offered by Amazon EC2 comes with 615MB memory and a 7.9GB disk image. While small by today's standards, embedded web servers with memory footprints well under 100kB, indicate that there is much to be saved. In this work we investigate how large VM-populations the open Stack hyper visor can be made to sustain, by tuning it for scalability and minimizing virtual machine images. Request-driven Qemu images of 512 byte are written in assembly, and more than 110 000 such instances are successfully booted on a 48 core host, before memory is exhausted. Other factors are shown to dramatically improve scalability, to the point where 10 000 virtual machines consume no more than 2.06% of the hyper visor CPU.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126072106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.23
Kenichi Kourai, H. Utsunomiya
Since Infrastructure-as-a-Service (IaaS) clouds contain many vulnerable virtual machines (VMs), intrusion detection systems (IDSes) should be run for all the VMs. IDS offloading is promising for this purpose because it allows IaaS providers to run IDSes in the outside of VMs without any cooperation of the users. However, offloaded IDSes cannot continue to monitor their target VM when the VM is migrated to another host. In this paper, we propose VMCoupler for enabling co-migration of offloaded IDSes and their target VM. Our approach is running offloaded IDSes in a special VM called a guard VM, which can monitor the internals of the target VM using VM introspection. VMCoupler can migrate a guard VM together with its target VM and restore the state of VM introspection at the destination. The migration processes of these two VMs are synchronized so that the target VM does not run without being monitored. We have confirmed that the overheads of kernel monitoring and co-migration were small.
{"title":"Synchronized Co-migration of Virtual Machines for IDS Offloading in Clouds","authors":"Kenichi Kourai, H. Utsunomiya","doi":"10.1109/CloudCom.2013.23","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.23","url":null,"abstract":"Since Infrastructure-as-a-Service (IaaS) clouds contain many vulnerable virtual machines (VMs), intrusion detection systems (IDSes) should be run for all the VMs. IDS offloading is promising for this purpose because it allows IaaS providers to run IDSes in the outside of VMs without any cooperation of the users. However, offloaded IDSes cannot continue to monitor their target VM when the VM is migrated to another host. In this paper, we propose VMCoupler for enabling co-migration of offloaded IDSes and their target VM. Our approach is running offloaded IDSes in a special VM called a guard VM, which can monitor the internals of the target VM using VM introspection. VMCoupler can migrate a guard VM together with its target VM and restore the state of VM introspection at the destination. The migration processes of these two VMs are synchronized so that the target VM does not run without being monitored. We have confirmed that the overheads of kernel monitoring and co-migration were small.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125980207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.76
V. Varadharajan, U. Tupakula
Cloud computing technologies are receiving a great deal of attention. Although there are several benefits with the cloud, the attackers can also use the cloud infrastructure for hosting malicious services and generating different types of attacks. In this paper we propose techniques for securing tenant transactions in the cloud.
{"title":"On the Security of Tenant Transactions in the Cloud","authors":"V. Varadharajan, U. Tupakula","doi":"10.1109/CloudCom.2013.76","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.76","url":null,"abstract":"Cloud computing technologies are receiving a great deal of attention. Although there are several benefits with the cloud, the attackers can also use the cloud infrastructure for hosting malicious services and generating different types of attacks. In this paper we propose techniques for securing tenant transactions in the cloud.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125984361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.88
Jiadong Wu, Bo Hong
The MapReduce programming model, along with its open-source implementation Hadoop has provided a cost effective solution for many data-intensive applications. Hadoop stores data distributively and exploits data locality by assigning tasks to where data is stored. In many cases, however, accessing remote data (rack-local and off-rack) is inevitable. In this paper we are evaluating the possibility of improving the remote data accessing performance by streaming data from multiple available replicas. The proposed design consists of a circular buffer, a slice reader and a enhanced Data Node. Such system is capable of adapting to both the static performance variance caused by network topology as well as dynamic variance caused by congestion. Extensive experiments show that mutil-source streaming can significantly improve the throughput of remote data access and accelerate the related map tasks by 10%-20%. In some imbalanced environment, the proposed system can even achieve as much as 4x speedup.
{"title":"Improving MapReduce Performance by Streaming Input Data from Multiple Replicas","authors":"Jiadong Wu, Bo Hong","doi":"10.1109/CloudCom.2013.88","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.88","url":null,"abstract":"The MapReduce programming model, along with its open-source implementation Hadoop has provided a cost effective solution for many data-intensive applications. Hadoop stores data distributively and exploits data locality by assigning tasks to where data is stored. In many cases, however, accessing remote data (rack-local and off-rack) is inevitable. In this paper we are evaluating the possibility of improving the remote data accessing performance by streaming data from multiple available replicas. The proposed design consists of a circular buffer, a slice reader and a enhanced Data Node. Such system is capable of adapting to both the static performance variance caused by network topology as well as dynamic variance caused by congestion. Extensive experiments show that mutil-source streaming can significantly improve the throughput of remote data access and accelerate the related map tasks by 10%-20%. In some imbalanced environment, the proposed system can even achieve as much as 4x speedup.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130733731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.33
V. Casola, Alessandra De Benedictis, M. Rak, G. Aversano, Umberto Villano
The integration of sensing infrastructures into the Cloud gives a number of advantages in providing sensor data as a service over the Internet. Many solutions are now available in the literature, and most of them focus on modeling sensor networks as part of the infrastructure to be offered as a service (IaaS), directly managed by means of the Cloud tools that provide resource virtualization. We propose a different approach: sensor networks are modeled as providers that offer their resources to a Cloud application that runs independently from Cloud providers. Being offered as a Service, any user can negotiate with the provider his desired requirements in terms of operational parameters and non-functional features (i.e. security, dependability, etc). In particular, we propose a SLA-based approach for the specification and management of usage term guarantees related to the access and configuration of private sensor networks. To this end, a Cloud Sensing Brokering Platform is designed to illustrate the innovative way to integrate Cloud and Sensor Networks.
{"title":"An SLA-Based Approach to Manage Sensor Networks as-a-Service","authors":"V. Casola, Alessandra De Benedictis, M. Rak, G. Aversano, Umberto Villano","doi":"10.1109/CloudCom.2013.33","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.33","url":null,"abstract":"The integration of sensing infrastructures into the Cloud gives a number of advantages in providing sensor data as a service over the Internet. Many solutions are now available in the literature, and most of them focus on modeling sensor networks as part of the infrastructure to be offered as a service (IaaS), directly managed by means of the Cloud tools that provide resource virtualization. We propose a different approach: sensor networks are modeled as providers that offer their resources to a Cloud application that runs independently from Cloud providers. Being offered as a Service, any user can negotiate with the provider his desired requirements in terms of operational parameters and non-functional features (i.e. security, dependability, etc). In particular, we propose a SLA-based approach for the specification and management of usage term guarantees related to the access and configuration of private sensor networks. To this end, a Cloud Sensing Brokering Platform is designed to illustrate the innovative way to integrate Cloud and Sensor Networks.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130452326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.57
Frank Dölitzscher, M. Knahl, C. Reich, N. Clarke
Security is still a major concern in Cloud computing, especially the detection of nefarious use or abuse of cloud instances. One reason for this, is the ever-growing complexity and dynamic of the underlying system design and architecture. To be able to detect misuse of cloud instances, this work presents an anomaly detection system for Infrastructure as a Service Clouds. It is based on Cloud customers' usage behaviour analysis. Neural networks are used to analyse and learn the normal usage behaviour of Cloud customers, to then detect anomalies which could originate from a cloud security incident caused by an overtaken virtual machine. It increases transparency for Cloud customers about the security of their Cloud instances and supports the Cloud provider to detect misuse of their infrastructure. A simulation environment and an anomaly detection prototype get presented. Experiments validate the effectiveness of the proposed system.
{"title":"Anomaly Detection in IaaS Clouds","authors":"Frank Dölitzscher, M. Knahl, C. Reich, N. Clarke","doi":"10.1109/CloudCom.2013.57","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.57","url":null,"abstract":"Security is still a major concern in Cloud computing, especially the detection of nefarious use or abuse of cloud instances. One reason for this, is the ever-growing complexity and dynamic of the underlying system design and architecture. To be able to detect misuse of cloud instances, this work presents an anomaly detection system for Infrastructure as a Service Clouds. It is based on Cloud customers' usage behaviour analysis. Neural networks are used to analyse and learn the normal usage behaviour of Cloud customers, to then detect anomalies which could originate from a cloud security incident caused by an overtaken virtual machine. It increases transparency for Cloud customers about the security of their Cloud instances and supports the Cloud provider to detect misuse of their infrastructure. A simulation environment and an anomaly detection prototype get presented. Experiments validate the effectiveness of the proposed system.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134235098","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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