The cloud has been leveraged for many applications across different industries. Despite its popularity, the cloud technologies are still immature. The security implications of cloud computing also dominate the research space. Many confidentiality-and integrity-based (C-I) security controls concerning data-at-rest and data-in-transit are focused on encryption. In the world where social-media platforms transparently gather data about user behaviors and user interests, the need for user privacy and data protection is of the utmost importance. However, how can a user know that his data is safe, that her data is secure, that his data's integrity is upheld; to be confident that her communications only reach the intended recipients? We propose: they can't. Many threats have been hypothesized in the shared-service arena, with many solutions formulated to avert those threats; however, we illustrate that many technologies and standards supporting C-I controls may be ineffective, not just against the adversarial actors, but also against trusted entities. Service providers and malicious insiders can intercept and decrypt network-and host-based data without any guest or user knowledge.
{"title":"Get Your Head Out of the Clouds: The Illusion of Confidentiality & Privacy","authors":"V. Urias, W. Stout, Caleb Loverro, B. V. Leeuwen","doi":"10.1109/SC2.2018.00015","DOIUrl":"https://doi.org/10.1109/SC2.2018.00015","url":null,"abstract":"The cloud has been leveraged for many applications across different industries. Despite its popularity, the cloud technologies are still immature. The security implications of cloud computing also dominate the research space. Many confidentiality-and integrity-based (C-I) security controls concerning data-at-rest and data-in-transit are focused on encryption. In the world where social-media platforms transparently gather data about user behaviors and user interests, the need for user privacy and data protection is of the utmost importance. However, how can a user know that his data is safe, that her data is secure, that his data's integrity is upheld; to be confident that her communications only reach the intended recipients? We propose: they can't. Many threats have been hypothesized in the shared-service arena, with many solutions formulated to avert those threats; however, we illustrate that many technologies and standards supporting C-I controls may be ineffective, not just against the adversarial actors, but also against trusted entities. Service providers and malicious insiders can intercept and decrypt network-and host-based data without any guest or user knowledge.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114257867","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}
{"title":"Message from the SC2 2018 General and Program Chairs","authors":"","doi":"10.1109/sc2.2018.00005","DOIUrl":"https://doi.org/10.1109/sc2.2018.00005","url":null,"abstract":"","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127874753","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}
Over the last couple of decades, the demand for storage in the Cloud has grown exponentially. Distributed Cloud storage and object storage for the increasing share of unstructured data, are in high focus in both academic and industrial research activities. At the same time, efficient storage and the corresponding costs are often contrasting parameters raising a trade-off problem for any proposed solution. To this aim, classifying the data in terms of access probability became a hot topic. This paper introduces Hera Object Storage, a storage system built on top of OpenStack Swift that aims at selecting the most appropriate storage tier for any object to be stored. The goal of the multi-tiering storage we propose is to be automated and seamless, guaranteeing the required storage performance at the lowest possible cost. The paper discusses the design challenges, the proposed algorithmic solutions to the scope and, based on a prototype implementation it presents a basic proof-of-concept validation.
{"title":"Hera Object Storage: A Seamless, Automated Multi-Tiering Solution on Top of OpenStack Swift","authors":"R. Hoppli, T. Bohnert, L. Militano","doi":"10.1109/SC2.2018.00011","DOIUrl":"https://doi.org/10.1109/SC2.2018.00011","url":null,"abstract":"Over the last couple of decades, the demand for storage in the Cloud has grown exponentially. Distributed Cloud storage and object storage for the increasing share of unstructured data, are in high focus in both academic and industrial research activities. At the same time, efficient storage and the corresponding costs are often contrasting parameters raising a trade-off problem for any proposed solution. To this aim, classifying the data in terms of access probability became a hot topic. This paper introduces Hera Object Storage, a storage system built on top of OpenStack Swift that aims at selecting the most appropriate storage tier for any object to be stored. The goal of the multi-tiering storage we propose is to be automated and seamless, guaranteeing the required storage performance at the lowest possible cost. The paper discusses the design challenges, the proposed algorithmic solutions to the scope and, based on a prototype implementation it presents a basic proof-of-concept validation.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121047974","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}
Ludwig Mittermeier, Florian Katenbrink, A. Seitz, Harald Mueller, B. Brügge
The data generated by Internet of Things (IoT) devices is constantly increasing. The use of cloud computing to process this data is associated with network congestion, has high latency and does not provide context awareness. Fog computing has been proposed as a solution to overcome these problems. The heterogeneous, distributed nature of fog and edge nodes requires a system that facilitates the development and deployment of applications and management of nodes in a cluster. Seamless computing is an extension of fog computing that respects mobility and heterogeneity of nodes. In industry scenarios where cost, energy or network latency optimization is preferred at runtime, static deployment is not sufficient. Production systems require availability, fault tolerance and extensibility, but not at the expense of usability. This research examines the requirements of a system for dynamic rescheduling of software components in a distributed, heterogeneous fog computing cluster at runtime. We propose Dynamic Scheduling for Seamless Computing (DYSCO) as a solution and present a concept implementation based on Kubernetes. We describe the configuration of Kubernetes for DYSCO, extend it with a monitoring tool and enhance the scheduler to enable dynamic components rescheduling. We evaluate the requirements of DYSCO with test cases for an industry-specific scenario in a fog computing cluster. It operates a safety-critical application that must immediately react to machine failures and an application that processes employee data for analytics. We show that DYSCO is able to reschedule software components at runtime, while ensuring technology independence, availability, fault tolerance and usability.
{"title":"Dynamic Scheduling for Seamless Computing","authors":"Ludwig Mittermeier, Florian Katenbrink, A. Seitz, Harald Mueller, B. Brügge","doi":"10.1109/SC2.2018.00013","DOIUrl":"https://doi.org/10.1109/SC2.2018.00013","url":null,"abstract":"The data generated by Internet of Things (IoT) devices is constantly increasing. The use of cloud computing to process this data is associated with network congestion, has high latency and does not provide context awareness. Fog computing has been proposed as a solution to overcome these problems. The heterogeneous, distributed nature of fog and edge nodes requires a system that facilitates the development and deployment of applications and management of nodes in a cluster. Seamless computing is an extension of fog computing that respects mobility and heterogeneity of nodes. In industry scenarios where cost, energy or network latency optimization is preferred at runtime, static deployment is not sufficient. Production systems require availability, fault tolerance and extensibility, but not at the expense of usability. This research examines the requirements of a system for dynamic rescheduling of software components in a distributed, heterogeneous fog computing cluster at runtime. We propose Dynamic Scheduling for Seamless Computing (DYSCO) as a solution and present a concept implementation based on Kubernetes. We describe the configuration of Kubernetes for DYSCO, extend it with a monitoring tool and enhance the scheduler to enable dynamic components rescheduling. We evaluate the requirements of DYSCO with test cases for an industry-specific scenario in a fog computing cluster. It operates a safety-critical application that must immediately react to machine failures and an application that processes employee data for analytics. We show that DYSCO is able to reschedule software components at runtime, while ensuring technology independence, availability, fault tolerance and usability.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128007284","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}
Semantic web technology has been used to help various software, including Intelligence Personal Assistant, by acquiring new data or understanding the knowledge through relations between data. However, it is hard to apply the current semantic web schemes such as RDFS reasoning to the real world data because of huge volume of data need to be processed. In this study, we design and enable RDFS reasoning with RETE algorithm on Apache Spark in parallel fashion. In addition, we apply rule sequence optimization ordering from existing studies to enhance the processing performance. From the empirical experiment results, we verified that the implementation of our design shows a strong scalability. However, the current naïve approach of using Spark provided distinct function to deduplicate data should be improved to yield a better processing performance. In future studies, we will study further to find new deduplication method.
{"title":"Enabling RETE Algorithm for RDFS Reasoning on Apache Spark","authors":"H. Ju, Sangyoon Oh","doi":"10.1109/SC2.2018.00028","DOIUrl":"https://doi.org/10.1109/SC2.2018.00028","url":null,"abstract":"Semantic web technology has been used to help various software, including Intelligence Personal Assistant, by acquiring new data or understanding the knowledge through relations between data. However, it is hard to apply the current semantic web schemes such as RDFS reasoning to the real world data because of huge volume of data need to be processed. In this study, we design and enable RDFS reasoning with RETE algorithm on Apache Spark in parallel fashion. In addition, we apply rule sequence optimization ordering from existing studies to enhance the processing performance. From the empirical experiment results, we verified that the implementation of our design shows a strong scalability. However, the current naïve approach of using Spark provided distinct function to deduplicate data should be improved to yield a better processing performance. In future studies, we will study further to find new deduplication method.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131354918","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 two practical Large Scale Multi-Objectives Scheduling (LSMOS) strategies, proposed for Cloud Computing environments. The goal is to address the problems of companies that manage a large cloud infrastructure with thousands of nodes, and would like to optimize the scheduling of several requests submitted online by users. In our context, requests submitted by users are configured according to multi-objectives criteria, as the number of used CPUs and the used memory size, to take an example. The novelty of our strategies is to select effectively, from a large set of nodes forming the Cloud Computing platform, a node that execute the user request such that this node has a good compromise among a large set of multi-objectives criteria. In this paper, first we show the limit, in terms of performance, of exact solutions. Second, we introduce approximate algorithms in order to deal with high dimensional problems in terms of nodes number and criteria number. The proposed two scheduling strategies are based on exact Kung multi-objectives decision algorithm and k-means clustering algorithm or LSH hashing (random projection based) algorithm. The experiments of our new strategies demonstrate the potential of our approach under different scenarios.
{"title":"Accelerating the Computation of Multi-Objectives Scheduling Solutions for Cloud Computing","authors":"C. Cérin, Tarek Menouer, M. Lebbah","doi":"10.1109/SC2.2018.00014","DOIUrl":"https://doi.org/10.1109/SC2.2018.00014","url":null,"abstract":"This paper presents two practical Large Scale Multi-Objectives Scheduling (LSMOS) strategies, proposed for Cloud Computing environments. The goal is to address the problems of companies that manage a large cloud infrastructure with thousands of nodes, and would like to optimize the scheduling of several requests submitted online by users. In our context, requests submitted by users are configured according to multi-objectives criteria, as the number of used CPUs and the used memory size, to take an example. The novelty of our strategies is to select effectively, from a large set of nodes forming the Cloud Computing platform, a node that execute the user request such that this node has a good compromise among a large set of multi-objectives criteria. In this paper, first we show the limit, in terms of performance, of exact solutions. Second, we introduce approximate algorithms in order to deal with high dimensional problems in terms of nodes number and criteria number. The proposed two scheduling strategies are based on exact Kung multi-objectives decision algorithm and k-means clustering algorithm or LSH hashing (random projection based) algorithm. The experiments of our new strategies demonstrate the potential of our approach under different scenarios.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123248056","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}
Cloud storage backends such as Amazon S3 are a potential storage solution to enterprises. However, to couple enterprises with these backends, at least two problems must be solved: first, how to make these semi-trusted backends as secure as on-premises storage; and second, how to selectively retrieve files as easy as on-premises storage. A security proxy can address both the problems by building a local index from keywords in files before encrypting and uploading files to these backends. But, if the local index is built in plaintext, file content is still vulnerable to local malicious staff. Searchable Encryption (SE) can get rid of this vulnerability by making index into ciphertext; however, its known constructions often require modifications to index database, and, to support wildcard queries, they are not efficient at all. In this paper, we present a security proxy that, based on our wildcard SE construction, can securely and efficiently couple enterprises with these backends. In particular, since our SE construction can work directly with existing database systems, it incurs only a little overhead, and when needed, permits the security proxy to run with constantly small storage footprint by readily out-sourcing all built indices to existing cloud databases.
{"title":"A Security Proxy to Cloud Storage Backends Based on an Efficient Wildcard Searchable Encryption","authors":"Shen-Ming Chung, Ming-Der Shieh, T. Chiueh","doi":"10.1109/SC2.2018.00026","DOIUrl":"https://doi.org/10.1109/SC2.2018.00026","url":null,"abstract":"Cloud storage backends such as Amazon S3 are a potential storage solution to enterprises. However, to couple enterprises with these backends, at least two problems must be solved: first, how to make these semi-trusted backends as secure as on-premises storage; and second, how to selectively retrieve files as easy as on-premises storage. A security proxy can address both the problems by building a local index from keywords in files before encrypting and uploading files to these backends. But, if the local index is built in plaintext, file content is still vulnerable to local malicious staff. Searchable Encryption (SE) can get rid of this vulnerability by making index into ciphertext; however, its known constructions often require modifications to index database, and, to support wildcard queries, they are not efficient at all. In this paper, we present a security proxy that, based on our wildcard SE construction, can securely and efficiently couple enterprises with these backends. In particular, since our SE construction can work directly with existing database systems, it incurs only a little overhead, and when needed, permits the security proxy to run with constantly small storage footprint by readily out-sourcing all built indices to existing cloud databases.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"23 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126225478","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}
{"title":"SC2 2018 Organizing Committee","authors":"","doi":"10.1109/sc2.2018.00006","DOIUrl":"https://doi.org/10.1109/sc2.2018.00006","url":null,"abstract":"","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121626175","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 MapReduce has emerged as an efficient platform for coping with big data. It achieves this goal by decoupling the data and then distributing the workloads to multiple reducers for processing in a fully parallel manner. Zipf's law asserts that, for many types of data studied in the physical and social sciences, the frequency of any event is inversely proportional to its rank in the frequency table, i.e. the key distribution is skewed. However, the hash function of MapReduce usually generates the unbalanced workloads to multiple reducers for the skewed data. The unbalanced workloads to multiple reducers lead to degrading the performance of MapReduce significantly, because the overall running time of a map-reduce cycle is determined by the longest running reducer. Thus, it is an important issue to develop a balanced partitioning algorithm which partitions the workloads evenly for all the reducers. This paper proposes a balanced partitioning mechanism with collapsed-condensed trie in MapReduce, which evenly distributes the workloads to the reducers. A collapsed-condensed trie is introduced for capturing the data statistics authentically, with which it requires a reasonable amount of memory usage and incurs a small running overhead. Then, we propose a quasi-optimal packing algorithm to assign sub-partitions to the reducers evenly, resulting in reducing the total execution time. The experiments using Inverted Indexing on the real-world datasets are conducted to evaluate the performance of our proposed partitioning mechanism.
{"title":"A Balanced Partitioning Mechanism Using Collapsed-Condensed Trie in MapReduce","authors":"Hsing-Lung Chen, Syu-Huan Chen","doi":"10.1109/SC2.2018.00020","DOIUrl":"https://doi.org/10.1109/SC2.2018.00020","url":null,"abstract":"The MapReduce has emerged as an efficient platform for coping with big data. It achieves this goal by decoupling the data and then distributing the workloads to multiple reducers for processing in a fully parallel manner. Zipf's law asserts that, for many types of data studied in the physical and social sciences, the frequency of any event is inversely proportional to its rank in the frequency table, i.e. the key distribution is skewed. However, the hash function of MapReduce usually generates the unbalanced workloads to multiple reducers for the skewed data. The unbalanced workloads to multiple reducers lead to degrading the performance of MapReduce significantly, because the overall running time of a map-reduce cycle is determined by the longest running reducer. Thus, it is an important issue to develop a balanced partitioning algorithm which partitions the workloads evenly for all the reducers. This paper proposes a balanced partitioning mechanism with collapsed-condensed trie in MapReduce, which evenly distributes the workloads to the reducers. A collapsed-condensed trie is introduced for capturing the data statistics authentically, with which it requires a reasonable amount of memory usage and incurs a small running overhead. Then, we propose a quasi-optimal packing algorithm to assign sub-partitions to the reducers evenly, resulting in reducing the total execution time. The experiments using Inverted Indexing on the real-world datasets are conducted to evaluate the performance of our proposed partitioning mechanism.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124523130","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}
With the exponential growth of data from ICT equipment and the continued development of low-cost storage technology, the scale and amount of data are continually increasing in many areas and moving throughout the cloud. However, most of them are infrequently accessed. Data temperature describes the frequency of data access: hot storage is dedicated to storing frequently accessed data, while cold storage is designed for infrequently accessed data. In this paper, we propose and implement an architecture of an automated tiered storage system that optimizes data allocation in data centers. Our proposed approach brings mutual benefits to both service providers and end users. Users do not need to consider which storage media they want to save, and access and service providers do not need to analyze data access or manually classify data. By successfully predicting infrequently accessed data and moving them to the cold storage, we obtain significant cost saving. While having the benefit of storage cost savings, we also ensure a quality of experience through the correctness of the predicted hot data. The operational strategy varies among cloud storage service providers, and as a result, we characterize different scenarios and provide customized optimal solutions.
{"title":"A Novel Automated Tiered Storage Architecture for Achieving Both Cost Saving and QoE","authors":"Ryo Irie, Shuuichirou Murata, Ying-Feng Hsu, Morito Matsuoka","doi":"10.1109/SC2.2018.00012","DOIUrl":"https://doi.org/10.1109/SC2.2018.00012","url":null,"abstract":"With the exponential growth of data from ICT equipment and the continued development of low-cost storage technology, the scale and amount of data are continually increasing in many areas and moving throughout the cloud. However, most of them are infrequently accessed. Data temperature describes the frequency of data access: hot storage is dedicated to storing frequently accessed data, while cold storage is designed for infrequently accessed data. In this paper, we propose and implement an architecture of an automated tiered storage system that optimizes data allocation in data centers. Our proposed approach brings mutual benefits to both service providers and end users. Users do not need to consider which storage media they want to save, and access and service providers do not need to analyze data access or manually classify data. By successfully predicting infrequently accessed data and moving them to the cold storage, we obtain significant cost saving. While having the benefit of storage cost savings, we also ensure a quality of experience through the correctness of the predicted hot data. The operational strategy varies among cloud storage service providers, and as a result, we characterize different scenarios and provide customized optimal solutions.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124557164","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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