Bridges are the hubs of transportation, so it is important to identify and locate bridges for satellite image interpretation. This paper proposes a new method of bridge extraction from high resolution remote sensing images. Firstly, river regions are segmented and road lineaments are extracted. Then, bridge region is represented as intersection of river with roads or roads with roads by using the recognition model proposed in this paper. Finally, a rule-based procedure is applied to verify candidate regions. The experiment results show that, not only bridges over river but also the overpasses can be extracted effectively. The method mainly uses structural information of lineaments on roads and the topological relations in bridge regions, and does not rely on accurate results of river segmentation, so it is robust for complex scenes.
{"title":"Extraction of Bridges from High Resolution Remote Sensing Image Based on Topology Modeling","authors":"Haitao Lu, Yong Deng, Zhijian Huang, Jinfang Zhang","doi":"10.1109/IC2E.2014.79","DOIUrl":"https://doi.org/10.1109/IC2E.2014.79","url":null,"abstract":"Bridges are the hubs of transportation, so it is important to identify and locate bridges for satellite image interpretation. This paper proposes a new method of bridge extraction from high resolution remote sensing images. Firstly, river regions are segmented and road lineaments are extracted. Then, bridge region is represented as intersection of river with roads or roads with roads by using the recognition model proposed in this paper. Finally, a rule-based procedure is applied to verify candidate regions. The experiment results show that, not only bridges over river but also the overpasses can be extracted effectively. The method mainly uses structural information of lineaments on roads and the topological relations in bridge regions, and does not rely on accurate results of river segmentation, so it is robust for complex scenes.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114704960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Shaw, B. Bordbar, John T. Saxon, K. Harrison, Chris I. Dalton
The Cloud attempts to provide its users with automatically scalable platforms to host many applications and operating systems. To allow for quick deployment, they are often homogenised to a few images, restricting the variations used within the Cloud. An exploitable vulnerability stored within an image means that each instance will suffer from it and as a result, an attacker can be sure of a high pay-off for their time. This makes the Cloud a prime target for malicious activities. There is a clear requirement to develop an automated and computationally-inexpensive method of discovering malicious behaviour as soon as it starts, such that remedial action can be adopted before substantial damage is caused. In this paper we propose the use of Mini-OS, a virtualised operating system that uses minimal resources on the Xen virtualisation platform, for analysing the memory space of other guest virtual machines. These detectors, which we call Forensic Virtual Machines (FVMs), are lightweight such that they are inherently computationally cheap to run. Such a small footprint allows the physical host to run numerous instances to find symptoms of malicious behaviour whilst potentially limiting attack vectors. We describe our experience of developing FVMs and how they can be used to complement existing methods to combat malware. We also evaluate them in terms of performance and the resources that they require.
{"title":"Forensic Virtual Machines: Dynamic Defence in the Cloud via Introspection","authors":"A. Shaw, B. Bordbar, John T. Saxon, K. Harrison, Chris I. Dalton","doi":"10.1109/IC2E.2014.59","DOIUrl":"https://doi.org/10.1109/IC2E.2014.59","url":null,"abstract":"The Cloud attempts to provide its users with automatically scalable platforms to host many applications and operating systems. To allow for quick deployment, they are often homogenised to a few images, restricting the variations used within the Cloud. An exploitable vulnerability stored within an image means that each instance will suffer from it and as a result, an attacker can be sure of a high pay-off for their time. This makes the Cloud a prime target for malicious activities. There is a clear requirement to develop an automated and computationally-inexpensive method of discovering malicious behaviour as soon as it starts, such that remedial action can be adopted before substantial damage is caused. In this paper we propose the use of Mini-OS, a virtualised operating system that uses minimal resources on the Xen virtualisation platform, for analysing the memory space of other guest virtual machines. These detectors, which we call Forensic Virtual Machines (FVMs), are lightweight such that they are inherently computationally cheap to run. Such a small footprint allows the physical host to run numerous instances to find symptoms of malicious behaviour whilst potentially limiting attack vectors. We describe our experience of developing FVMs and how they can be used to complement existing methods to combat malware. We also evaluate them in terms of performance and the resources that they require.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115422782","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. Stonebraker, Andrew Pavlo, Rebecca Taft, Michael L. Brodie
There is considerable interest in moving DBMS applications from inside enterprise data centers to the cloud, both to reduce cost and to increase flexibility and elasticity. Some of these applications are "green field" projects (i.e., new applications), others are existing legacy systems that must be migrated to the cloud. In another dimension, some are decision support applications while others are update-oriented. In this paper, we discuss the technical and political challenges that these various enterprise applications face when considering cloud deployment. In addition, a requirement for quality-of-service (QoS) guarantees will generate additional disruptive issues. In some circumstances, achieving good DBMS performance on current cloud architectures and future hardware technologies will be non-trivial. In summary, there is a difficult road ahead for enterprise database applications.
{"title":"Enterprise Database Applications and the Cloud: A Difficult Road Ahead","authors":"M. Stonebraker, Andrew Pavlo, Rebecca Taft, Michael L. Brodie","doi":"10.1109/IC2E.2014.97","DOIUrl":"https://doi.org/10.1109/IC2E.2014.97","url":null,"abstract":"There is considerable interest in moving DBMS applications from inside enterprise data centers to the cloud, both to reduce cost and to increase flexibility and elasticity. Some of these applications are \"green field\" projects (i.e., new applications), others are existing legacy systems that must be migrated to the cloud. In another dimension, some are decision support applications while others are update-oriented. In this paper, we discuss the technical and political challenges that these various enterprise applications face when considering cloud deployment. In addition, a requirement for quality-of-service (QoS) guarantees will generate additional disruptive issues. In some circumstances, achieving good DBMS performance on current cloud architectures and future hardware technologies will be non-trivial. In summary, there is a difficult road ahead for enterprise database applications.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123046367","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}
Modern enterprise data centers take advantage of virtual machine consolidation to allocate virtual machines to virtualized servers to increase energy efficiency. One key problem is to minimize the number of virtualized servers required while maintaining service quality. A promising approach is to exploit recurring load patterns exhibited by enterprise VMs for increased allocation efficiency. This paper shows that bin packing heuristics can deliver the same allocation quality as integer linear programs if calculation time is constrained. There were no significant differences between vector bin packing heuristics in simulations based on CPU load profiles obtained from enterprise data centers. We further show that consolidating in clusters of a few hundred virtual machines is sufficient as solution quality does not improve with larger clusters.
{"title":"Improving Enterprise VM Consolidation with High-Dimensional Load Profiles","authors":"A. Wolke, Carl Pfeiffer","doi":"10.1109/IC2E.2014.12","DOIUrl":"https://doi.org/10.1109/IC2E.2014.12","url":null,"abstract":"Modern enterprise data centers take advantage of virtual machine consolidation to allocate virtual machines to virtualized servers to increase energy efficiency. One key problem is to minimize the number of virtualized servers required while maintaining service quality. A promising approach is to exploit recurring load patterns exhibited by enterprise VMs for increased allocation efficiency. This paper shows that bin packing heuristics can deliver the same allocation quality as integer linear programs if calculation time is constrained. There were no significant differences between vector bin packing heuristics in simulations based on CPU load profiles obtained from enterprise data centers. We further show that consolidating in clusters of a few hundred virtual machines is sufficient as solution quality does not improve with larger clusters.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116427984","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}
Volunteer cloud federations (VCFs) are cloud federations where clouds may join and leave a federation without restrictions and may contribute resources to the federation without long term commitment. This makes it difficult to predict the long term availability of resources. Also, in IaaS VCFs, volunteers may collectively contribute a large number of heterogeneous virtual machine instances. In this paper, we focus on the problem of efficiently allocating this dynamic, heterogeneous capacity to a flow of incoming VM instantiation requests. We propose an approach, called stochastic least differential capacity (SLDC),that allows over-provisioning only when necessary. The approach uses historical information about recent instantiation requests to derive stochastic predictions regarding future demand. We implemented VCFSim, a VCF simulator that uses the proposed resource allocation solution. The results of the experimental evaluation show that the proposed approach is able to improve the success rate of VM instantiation requests by up to 38%compared to an approach that uses exact matching with no demand forecasting.
{"title":"A Stochastic Approach for Virtual Machine Placement in Volunteer Cloud Federations","authors":"A. Rezgui, S. Rezgui","doi":"10.1109/IC2E.2014.85","DOIUrl":"https://doi.org/10.1109/IC2E.2014.85","url":null,"abstract":"Volunteer cloud federations (VCFs) are cloud federations where clouds may join and leave a federation without restrictions and may contribute resources to the federation without long term commitment. This makes it difficult to predict the long term availability of resources. Also, in IaaS VCFs, volunteers may collectively contribute a large number of heterogeneous virtual machine instances. In this paper, we focus on the problem of efficiently allocating this dynamic, heterogeneous capacity to a flow of incoming VM instantiation requests. We propose an approach, called stochastic least differential capacity (SLDC),that allows over-provisioning only when necessary. The approach uses historical information about recent instantiation requests to derive stochastic predictions regarding future demand. We implemented VCFSim, a VCF simulator that uses the proposed resource allocation solution. The results of the experimental evaluation show that the proposed approach is able to improve the success rate of VM instantiation requests by up to 38%compared to an approach that uses exact matching with no demand forecasting.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124092346","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}
Bin Sun, Brian Hall, Hu Wang, Da Wei Zhang, Kai Ding
Cloud computing is a new paradigm for the delivery of IT services. It has enabled many promising opportunities for features that cannot be easily implemented in traditional IT environments, such as elastic scalability, self-service deployment, resiliency and recovery, and so forth. Benchmarking the cloud requires a well-defined set of cloud performance metrics that should be able to sensitively distinguish the capabilities of cloud systems that enable those features. One way of defining benchmark metrics is based on observations of the internal mechanisms in a cloud. For example, an elasticity evaluation may be based on measuring a resource provisioning interval in the cloud. However, a more meaningful evaluation should be based on user-centric metrics. In this article, we will introduce a set of performance metrics that can be directly measured, calculated and compared by the cloud users, including workload consumers and the users who deploy and manage the workload life cycles. We will also discuss ways to organize the user-centric metrics, with different emphasis, into a benchmark that represents different use cases.
{"title":"Benchmarking Private Cloud Performance with User-Centric Metrics","authors":"Bin Sun, Brian Hall, Hu Wang, Da Wei Zhang, Kai Ding","doi":"10.1109/IC2E.2014.74","DOIUrl":"https://doi.org/10.1109/IC2E.2014.74","url":null,"abstract":"Cloud computing is a new paradigm for the delivery of IT services. It has enabled many promising opportunities for features that cannot be easily implemented in traditional IT environments, such as elastic scalability, self-service deployment, resiliency and recovery, and so forth. Benchmarking the cloud requires a well-defined set of cloud performance metrics that should be able to sensitively distinguish the capabilities of cloud systems that enable those features. One way of defining benchmark metrics is based on observations of the internal mechanisms in a cloud. For example, an elasticity evaluation may be based on measuring a resource provisioning interval in the cloud. However, a more meaningful evaluation should be based on user-centric metrics. In this article, we will introduce a set of performance metrics that can be directly measured, calculated and compared by the cloud users, including workload consumers and the users who deploy and manage the workload life cycles. We will also discuss ways to organize the user-centric metrics, with different emphasis, into a benchmark that represents different use cases.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129125393","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}
To solve the increasingly prominent contradiction between the traditional damage control and the demand of high efficiency and reliability of ship system, a ship damage control system based on spatio-temporal database is presented and accomplished with cloud solution. A path planning algorithm based on Dijkstra is proposed to meet the dynamic road network as in the fire rescue scenario. The binary group is adopted to describe the weight of the path, and path network is pruning to reduce nodes accessed in advance. The simulation results show that the proposed algorithm improves the efficiency, capacity, intelligence and user experience, and provide efficient support for assistant decision-making.
{"title":"Ship Damage Control as a Service Based on Spatio-temporal Database","authors":"Yicheng Zheng, Yong Deng, Qingmeng Zhu","doi":"10.1109/IC2E.2014.89","DOIUrl":"https://doi.org/10.1109/IC2E.2014.89","url":null,"abstract":"To solve the increasingly prominent contradiction between the traditional damage control and the demand of high efficiency and reliability of ship system, a ship damage control system based on spatio-temporal database is presented and accomplished with cloud solution. A path planning algorithm based on Dijkstra is proposed to meet the dynamic road network as in the fire rescue scenario. The binary group is adopted to describe the weight of the path, and path network is pruning to reduce nodes accessed in advance. The simulation results show that the proposed algorithm improves the efficiency, capacity, intelligence and user experience, and provide efficient support for assistant decision-making.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132757798","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}
Hiroya Matsuba, M. Hiltunen, Kaustubh R. Joshi, R. Schlichting
Accurate and up-to-date knowledge of how a cloud tenant's VMs utilize the underlying cloud infrastructure is essential for many cloud management tasks including tenant onboarding, optimized VM placement, performance optimization, and debugging. Unfortunately, existing solutions such as instrumentation at the hypervisors or standard networking protocols such as LLDP only provide a partial picture of cloud tenant's application structures and how they stress the underlying infrastructure. In this paper, we consider whether it is possible to use sFlow, a standardized mechanism for packet header sampling available in most commodity network switches, to extract such information in an accurate and scalable manner. We overcome the challenges posed by the purely passive and highly sampled nature of sFlow data, and describe a tool, sFinder, that automatically and continuously extracts such information. Our evaluation using sampled sFlow data from a real private cloud show that sFinder is accurate and efficient.
{"title":"Discovering the Structure of Cloud Applications Using Sampled Packet Traces","authors":"Hiroya Matsuba, M. Hiltunen, Kaustubh R. Joshi, R. Schlichting","doi":"10.1109/IC2E.2014.45","DOIUrl":"https://doi.org/10.1109/IC2E.2014.45","url":null,"abstract":"Accurate and up-to-date knowledge of how a cloud tenant's VMs utilize the underlying cloud infrastructure is essential for many cloud management tasks including tenant onboarding, optimized VM placement, performance optimization, and debugging. Unfortunately, existing solutions such as instrumentation at the hypervisors or standard networking protocols such as LLDP only provide a partial picture of cloud tenant's application structures and how they stress the underlying infrastructure. In this paper, we consider whether it is possible to use sFlow, a standardized mechanism for packet header sampling available in most commodity network switches, to extract such information in an accurate and scalable manner. We overcome the challenges posed by the purely passive and highly sampled nature of sFlow data, and describe a tool, sFinder, that automatically and continuously extracts such information. Our evaluation using sampled sFlow data from a real private cloud show that sFinder is accurate and efficient.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128818383","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}
Hong Linh Truong, S. Dustdar, G. Copil, Alessio Gambi, W. Hummer, Duc-Hung Le, D. Moldovan
Platform-as-a-Service (PaaS) should support the design, deployment, execution, test and monitoring of native elastic systems constructed from elastic service units based on multi-dimensional elasticity requirements. In this paper, we discuss fundamental building blocks for enabling multi-dimensional elasticity programming of software-defined elastic systems. We describe CoMoT, a novel PaaS for elasticity in the cloud that is developed based on these fundamental building blocks.
{"title":"CoMoT -- A Platform-as-a-Service for Elasticity in the Cloud","authors":"Hong Linh Truong, S. Dustdar, G. Copil, Alessio Gambi, W. Hummer, Duc-Hung Le, D. Moldovan","doi":"10.1109/IC2E.2014.44","DOIUrl":"https://doi.org/10.1109/IC2E.2014.44","url":null,"abstract":"Platform-as-a-Service (PaaS) should support the design, deployment, execution, test and monitoring of native elastic systems constructed from elastic service units based on multi-dimensional elasticity requirements. In this paper, we discuss fundamental building blocks for enabling multi-dimensional elasticity programming of software-defined elastic systems. We describe CoMoT, a novel PaaS for elasticity in the cloud that is developed based on these fundamental building blocks.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114279756","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}
Khalil Blaiech, Omar Mounaouar, O. Cherkaoui, Ludovic Béliveau
Delivering high performance when several virtual nodes share the same physical resources requires finding the optimal resource allocation between them. In the context of Software Defined Network (SDN) and Network Virtualization, data plane requires the design of a new and more flexible flow packet processing. Virtual nodes involves several packet processing functions such as search operations in different data structures, processing the packets by modifying their respective contents and buffering them. Each packet processing requires a set of shared resources. If there is a conflict for a given resources, resource reassignment strategy is needed to ensure the continuity of the processing and solve resource congestion in accordance with the available hardware resources. In this paper, we propose a resource allocation strategy to share fairly the network processor resources. It is based on network calculus model and game theory algorithms. This strategy maps dynamically the suitable resources according to virtual nodes processing. In our implementation, we focus on packet processing tasks in regard to OpenFlow forwarding model within several processors to reassign resources.
{"title":"Runtime Resource Allocation Model over Network Processors","authors":"Khalil Blaiech, Omar Mounaouar, O. Cherkaoui, Ludovic Béliveau","doi":"10.1109/IC2E.2014.33","DOIUrl":"https://doi.org/10.1109/IC2E.2014.33","url":null,"abstract":"Delivering high performance when several virtual nodes share the same physical resources requires finding the optimal resource allocation between them. In the context of Software Defined Network (SDN) and Network Virtualization, data plane requires the design of a new and more flexible flow packet processing. Virtual nodes involves several packet processing functions such as search operations in different data structures, processing the packets by modifying their respective contents and buffering them. Each packet processing requires a set of shared resources. If there is a conflict for a given resources, resource reassignment strategy is needed to ensure the continuity of the processing and solve resource congestion in accordance with the available hardware resources. In this paper, we propose a resource allocation strategy to share fairly the network processor resources. It is based on network calculus model and game theory algorithms. This strategy maps dynamically the suitable resources according to virtual nodes processing. In our implementation, we focus on packet processing tasks in regard to OpenFlow forwarding model within several processors to reassign resources.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115715058","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：