In previous work we've shown that reflection supports the integration of components and of self-optimization within a complex system. In this paper, we discuss some early work on how some of these capabilities could support a similar integration and conflict resolution among the members of a system of systems (SoS). We start with a brief overview of the Wrappings approach to reflection, the notion of a web of reflection, and the CARS test bed where we are developing our concepts. We then introduce some early work on a new reflective service, the Brain Patch which helps to integrate a system into a System of Systems (SoS) by being both a domain specific expert on the reason for the formation of the SoS and its goals, situation, operating rules, etc. And by also continually observing and building a model of the system assigned to it.
{"title":"Early Work on the Brain Patch, a Reflective Service for System of Systems Integration","authors":"K. Bellman, C. Landauer","doi":"10.1109/ICAC.2015.73","DOIUrl":"https://doi.org/10.1109/ICAC.2015.73","url":null,"abstract":"In previous work we've shown that reflection supports the integration of components and of self-optimization within a complex system. In this paper, we discuss some early work on how some of these capabilities could support a similar integration and conflict resolution among the members of a system of systems (SoS). We start with a brief overview of the Wrappings approach to reflection, the notion of a web of reflection, and the CARS test bed where we are developing our concepts. We then introduce some early work on a new reflective service, the Brain Patch which helps to integrate a system into a System of Systems (SoS) by being both a domain specific expert on the reason for the formation of the SoS and its goals, situation, operating rules, etc. And by also continually observing and building a model of the system assigned to it.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"118 1","pages":"249-254"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86900296","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}
Zhimin Gao, Nicholas DeSalvo, P. D. Khoa, Seung-Hun Kim, Lei Xu, W. Ro, Rakesh M. Verma, W. Shi
Big data is a hot topic and has found various applications in different areas such as scientific research, financial analysis, and market studies. The development of cloud computing technology provides an adequate platform for big data applications. No matter public or private, the outsourcing and sharing characteristics of the computation model make security a big concern for big data processing in the cloud. Most existing works focus on protection of data privacy but integrity protection of the processing procedure receives little attention, which may lead the big data application user to wrong conclusions and cause serious consequences. To address this challenge, we design an integrity protection solution for big data processing in cloud environments using reputation based redundancy computation. The implementation and experiment results show that the solution only adds limited cost to achieve integrity protection and is practical for real world applications.
{"title":"Integrity Protection for Big Data Processing with Dynamic Redundancy Computation","authors":"Zhimin Gao, Nicholas DeSalvo, P. D. Khoa, Seung-Hun Kim, Lei Xu, W. Ro, Rakesh M. Verma, W. Shi","doi":"10.1109/ICAC.2015.34","DOIUrl":"https://doi.org/10.1109/ICAC.2015.34","url":null,"abstract":"Big data is a hot topic and has found various applications in different areas such as scientific research, financial analysis, and market studies. The development of cloud computing technology provides an adequate platform for big data applications. No matter public or private, the outsourcing and sharing characteristics of the computation model make security a big concern for big data processing in the cloud. Most existing works focus on protection of data privacy but integrity protection of the processing procedure receives little attention, which may lead the big data application user to wrong conclusions and cause serious consequences. To address this challenge, we design an integrity protection solution for big data processing in cloud environments using reputation based redundancy computation. The implementation and experiment results show that the solution only adds limited cost to achieve integrity protection and is practical for real world applications.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"7 1","pages":"159-160"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86992018","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}
Ingo Mauser, Christian Hirsch, Sebastian Kochanneck, H. Schmeck
An unprecedented rise of renewable and distributed energy resources imposes unprecedented challenges in terms of complexity to power grids. Multitudes of devices are not only connected to the electricity grid but need appropriate information and communication technologies for proving their services. These devices ask for novel control mechanisms on different levels and regional scales. In this paper, we show how concepts from Organic Computing may support the controlled self-organization of the future smart grid. We propose a generic hierarchical architecture as a framework for various energy management systems. This architecture is able to reflect the physical power grid structure as well as the interdependencies of its stakeholders, user objectives, subsystems, and devices. It enables adaptive responses to changing objectives as well as disturbances in the system. Various simulations of systems based on the proposed architecture show the applicability of the proposed architecture to domains of energy management in smart grids.
{"title":"Organic Architecture for Energy Management and Smart Grids","authors":"Ingo Mauser, Christian Hirsch, Sebastian Kochanneck, H. Schmeck","doi":"10.1109/ICAC.2015.10","DOIUrl":"https://doi.org/10.1109/ICAC.2015.10","url":null,"abstract":"An unprecedented rise of renewable and distributed energy resources imposes unprecedented challenges in terms of complexity to power grids. Multitudes of devices are not only connected to the electricity grid but need appropriate information and communication technologies for proving their services. These devices ask for novel control mechanisms on different levels and regional scales. In this paper, we show how concepts from Organic Computing may support the controlled self-organization of the future smart grid. We propose a generic hierarchical architecture as a framework for various energy management systems. This architecture is able to reflect the physical power grid structure as well as the interdependencies of its stakeholders, user objectives, subsystems, and devices. It enables adaptive responses to changing objectives as well as disturbances in the system. Various simulations of systems based on the proposed architecture show the applicability of the proposed architecture to domains of energy management in smart grids.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"48 6 1","pages":"101-108"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90696251","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}
S. Sicari, A. Rizzardi, A. Coen-Porisini, L. Grieco, T. Monteil
Machine-to-Machine (M2M) paradigm is one of the main concern of Internet of Things (IoT). Its scope is to interconnect billions of heterogeneous devices able to interact in various application domains. Since M2M suffers from a high vertical fragmentation of current M2M markets and lacks of standards, the European Telecommunications Standards Institute (ETSI) released a set of specifications for a common M2M service platform. An ETSI-compliant M2M service platform has been proposed in the context of the open source OM2M project. However such a platform currently only marginally addresses security and privacy issues, which are fundamental requirements for its large-scale adoption. Therefore, an extension of the OM2M platform is proposed, defining a new policy enforcement plug in, which aims to manage the access to the resources provided by the platform itself and to handle any violation attempts of the policies.
{"title":"Secure OM2M Service Platform","authors":"S. Sicari, A. Rizzardi, A. Coen-Porisini, L. Grieco, T. Monteil","doi":"10.1109/ICAC.2015.59","DOIUrl":"https://doi.org/10.1109/ICAC.2015.59","url":null,"abstract":"Machine-to-Machine (M2M) paradigm is one of the main concern of Internet of Things (IoT). Its scope is to interconnect billions of heterogeneous devices able to interact in various application domains. Since M2M suffers from a high vertical fragmentation of current M2M markets and lacks of standards, the European Telecommunications Standards Institute (ETSI) released a set of specifications for a common M2M service platform. An ETSI-compliant M2M service platform has been proposed in the context of the open source OM2M project. However such a platform currently only marginally addresses security and privacy issues, which are fundamental requirements for its large-scale adoption. Therefore, an extension of the OM2M platform is proposed, defining a new policy enforcement plug in, which aims to manage the access to the resources provided by the platform itself and to handle any violation attempts of the policies.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"1 1","pages":"313-318"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90260535","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 pervasive computing environments, wireless sensor networks (WSNs) play an important role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time. However, sensor data is often faulty. Faults are not so exceptional and in most deployments tend to occur frequently. Therefore, the capability of self-healing is important to ensure higher levels of reliability and availability. We design a framework which provides self-healing capabilities, enabling a flexible choice of components for detection, classification, and correction of faults at runtime. Within our framework, a variety of fault detection and classification algorithms can be applied, depending on the characteristics of the sensor data types as well as the topology of the sensor networks. A set of mechanisms for each and every step of the self-healing framework, covering detection, classification, and correction of faults are proposed. To validate the applicability, we illustrate a case study where our solution is implemented as an adaptation engine and integrated seamlessly into the ClouT system. The engine processes data coming from physical sensors deployed in Santander, Spain, providing corrected sensor data to other smart city applications developed in the ClouT project.
{"title":"A Self-Healing Framework for Online Sensor Data","authors":"T. Nguyen, Marco Aiello, Takuro Yonezawa, K. Tei","doi":"10.1109/ICAC.2015.61","DOIUrl":"https://doi.org/10.1109/ICAC.2015.61","url":null,"abstract":"In pervasive computing environments, wireless sensor networks (WSNs) play an important role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time. However, sensor data is often faulty. Faults are not so exceptional and in most deployments tend to occur frequently. Therefore, the capability of self-healing is important to ensure higher levels of reliability and availability. We design a framework which provides self-healing capabilities, enabling a flexible choice of components for detection, classification, and correction of faults at runtime. Within our framework, a variety of fault detection and classification algorithms can be applied, depending on the characteristics of the sensor data types as well as the topology of the sensor networks. A set of mechanisms for each and every step of the self-healing framework, covering detection, classification, and correction of faults are proposed. To validate the applicability, we illustrate a case study where our solution is implemented as an adaptation engine and integrated seamlessly into the ClouT system. The engine processes data coming from physical sensors deployed in Santander, Spain, providing corrected sensor data to other smart city applications developed in the ClouT project.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"13 1","pages":"295-300"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90283318","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}
Stephen Lee, Rahul Urgaonkar, R. Sitaraman, P. Shenoy
Content delivery networks (CDNs) employ hundreds of data centers that are distributed across various geographical locations. These data centers consume a significant amount of energy to power and cool their servers. This paper investigates the joint effectiveness of using two new cooling technologies - open air cooling (OAC) and thermal energy storage (TES) - in CDNs to reduce their dependence on traditional chiller-based cooling and minimize its energy costs. Our Lyapunov-based online algorithm optimally distributes workload to data centers leveraging price and weather variations. We conduct a trace based simulation using weather data from NOAA and workload data from a global CDN. Our results show that CDNs can achieve at least 64% and 98% cooling energy savings during summer and winter respectively. Further, CDNs can significantly reduce their cooling energy footprint by switching to renewable open air cooling. We also empirically evaluate our approach and show that it performs optimally.
{"title":"Cost Minimization Using Renewable Cooling and Thermal Energy Storage in CDNs","authors":"Stephen Lee, Rahul Urgaonkar, R. Sitaraman, P. Shenoy","doi":"10.1109/ICAC.2015.39","DOIUrl":"https://doi.org/10.1109/ICAC.2015.39","url":null,"abstract":"Content delivery networks (CDNs) employ hundreds of data centers that are distributed across various geographical locations. These data centers consume a significant amount of energy to power and cool their servers. This paper investigates the joint effectiveness of using two new cooling technologies - open air cooling (OAC) and thermal energy storage (TES) - in CDNs to reduce their dependence on traditional chiller-based cooling and minimize its energy costs. Our Lyapunov-based online algorithm optimally distributes workload to data centers leveraging price and weather variations. We conduct a trace based simulation using weather data from NOAA and workload data from a global CDN. Our results show that CDNs can achieve at least 64% and 98% cooling energy savings during summer and winter respectively. Further, CDNs can significantly reduce their cooling energy footprint by switching to renewable open air cooling. We also empirically evaluate our approach and show that it performs optimally.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"682 1","pages":"121-126"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76872450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a canonical correlation analysis (CCA) based workload-performance-resource (WPR) model which can capture and compare the complex many-to-many workload, performance and resource consumption relationship of an application running in physical and in virtual machines. The model can also establish complex relationships of the usage variables of four potentially interrelating resources (CPU, memory, disk I/O and network I/O) used by the application. The model is intended to be used in planning application resource requirements prior to cloud migration. Experimental results show that the WPR model can model and capture the complex resource consumption behavior of an application and the system modules that perform operations on its behalf, as well as the intricate correlation between the four types of resources, and gives good prediction performance.
{"title":"A Workload, Performance and Resource Usage","authors":"Yeali S. Sun, Cheng-En Du, Meng Chang Chen","doi":"10.1109/ICAC.2015.36","DOIUrl":"https://doi.org/10.1109/ICAC.2015.36","url":null,"abstract":"This paper proposes a canonical correlation analysis (CCA) based workload-performance-resource (WPR) model which can capture and compare the complex many-to-many workload, performance and resource consumption relationship of an application running in physical and in virtual machines. The model can also establish complex relationships of the usage variables of four potentially interrelating resources (CPU, memory, disk I/O and network I/O) used by the application. The model is intended to be used in planning application resource requirements prior to cloud migration. Experimental results show that the WPR model can model and capture the complex resource consumption behavior of an application and the system modules that perform operations on its behalf, as well as the intricate correlation between the four types of resources, and gives good prediction performance.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"4 1","pages":"215-218"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83529014","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 computing, featured by shared servers and location independent services, has been widely adopted by various businesses to increase computing efficiency, and reduce operational costs. Despite significant benefits and interests, enterprises have a hard time to decide whether or not to migrate thousands of servers into the cloud because of various reasons such as lack of holistic migration (planning) tools, concerns on data security and cloud vendor lock-in. In particular, cloud security has become the major concern for decision makers, due to the nature weakness of virtualization -- the fact that the cloud allows multiple users to share resources through Internet-facing interfaces can be easily taken advantage of by hackers. Therefore, setting up a secure environment for resource migration becomes the top priority for both enterprises and cloud providers. To achieve the goal of security, security policies such as firewalls and access control have been widely adopted, leading to significant cost as additional resources need to employed. In this paper, we address the challenge of the security-aware virtual server migration, and propose a migration strategy that minimizes the migration cost while promising the security needs of enterprises. We prove that the proposed security-aware cost minimization problem is NP hard and our solution can achieve an approximate factor of 2. We perform an extensive simulation study to evaluate the performance of the proposed solution under various settings. Our simulation results demonstrate that our approach can save 53%moving cost for a single enterprise case, and 66% for multiple enterprises case comparing to a random migration strategy.
{"title":"Towards Security-Aware Virtual Server Migration Optimization to the Cloud","authors":"Bowu Zhang, Jinho Hwang, Liran Ma, Timothy Wood","doi":"10.1109/ICAC.2015.45","DOIUrl":"https://doi.org/10.1109/ICAC.2015.45","url":null,"abstract":"Cloud computing, featured by shared servers and location independent services, has been widely adopted by various businesses to increase computing efficiency, and reduce operational costs. Despite significant benefits and interests, enterprises have a hard time to decide whether or not to migrate thousands of servers into the cloud because of various reasons such as lack of holistic migration (planning) tools, concerns on data security and cloud vendor lock-in. In particular, cloud security has become the major concern for decision makers, due to the nature weakness of virtualization -- the fact that the cloud allows multiple users to share resources through Internet-facing interfaces can be easily taken advantage of by hackers. Therefore, setting up a secure environment for resource migration becomes the top priority for both enterprises and cloud providers. To achieve the goal of security, security policies such as firewalls and access control have been widely adopted, leading to significant cost as additional resources need to employed. In this paper, we address the challenge of the security-aware virtual server migration, and propose a migration strategy that minimizes the migration cost while promising the security needs of enterprises. We prove that the proposed security-aware cost minimization problem is NP hard and our solution can achieve an approximate factor of 2. We perform an extensive simulation study to evaluate the performance of the proposed solution under various settings. Our simulation results demonstrate that our approach can save 53%moving cost for a single enterprise case, and 66% for multiple enterprises case comparing to a random migration strategy.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"11 1","pages":"71-80"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90551080","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}
Phyllis R. Nelson, C. Landauer, K. Bellman, Shotaro Goto, Jesse Taylor
In previous work we showed that reflection and Wrappings are useful tools for system integration. But System of Systems integration also needs to accommodate specific hardware challenges. We discuss simple examples from the operation of a single cyber-physical agent that accepts top-down commands, but uses the Wrappings architecture to self-organize its context specific implementation of them. Our experimental context is CARS (Computational Architectures for Reflective Systems) a test bed for exploring the behavior of distributed autonomous cyber-physical agents in a complex environment.
{"title":"System of Systems Integration Also Includes Hardware Integration: A Small Demonstration of Providing Some Reflection Processes for HW","authors":"Phyllis R. Nelson, C. Landauer, K. Bellman, Shotaro Goto, Jesse Taylor","doi":"10.1109/ICAC.2015.74","DOIUrl":"https://doi.org/10.1109/ICAC.2015.74","url":null,"abstract":"In previous work we showed that reflection and Wrappings are useful tools for system integration. But System of Systems integration also needs to accommodate specific hardware challenges. We discuss simple examples from the operation of a single cyber-physical agent that accepts top-down commands, but uses the Wrappings architecture to self-organize its context specific implementation of them. Our experimental context is CARS (Computational Architectures for Reflective Systems) a test bed for exploring the behavior of distributed autonomous cyber-physical agents in a complex environment.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"280 1","pages":"285-288"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90789166","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}
There are several research projects ongoing to apply cloud computing to industrial systems. The main focus of them is real-time performance of virtual machines (VMs) since it is important to guarantee a time-critical feature of industrial systems. However, there is another important issue that how much computing resource (CPU, memory, etc.) should be allocated to each VM which runs processes of an industrial system. In this paper, we propose a resource management method which manages VM resources with millisecond precision. In the proposed method, resource usage is measured and predicted, considering several microseconds allocation delay of Xen. Our experimental results show that the proposed method can guarantee 99% operation timing with higher CPU utilization in comparison with conventional resource management methods.
{"title":"A Virtual Machine Resource Management Method with Millisecond Precision","authors":"Y. Kaneko, Toshio Ito, Tomonori Maegawa","doi":"10.1109/ICAC.2015.24","DOIUrl":"https://doi.org/10.1109/ICAC.2015.24","url":null,"abstract":"There are several research projects ongoing to apply cloud computing to industrial systems. The main focus of them is real-time performance of virtual machines (VMs) since it is important to guarantee a time-critical feature of industrial systems. However, there is another important issue that how much computing resource (CPU, memory, etc.) should be allocated to each VM which runs processes of an industrial system. In this paper, we propose a resource management method which manages VM resources with millisecond precision. In the proposed method, resource usage is measured and predicted, considering several microseconds allocation delay of Xen. Our experimental results show that the proposed method can guarantee 99% operation timing with higher CPU utilization in comparison with conventional resource management methods.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"71 1","pages":"223-226"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89764605","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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