Qingyang Zhang, Quan Zhang, Weisong Shi, Hong Zhong
AMBER alert systems are inefficient since object searching heavily relies on reports of witnesses, who might miss alerts and cannot search enough areas of city. Using automatic license plate recognition (ALPR) technique, city-wide video surveillance is of great improvement for vehicle searching. However, analyzing huge amount of video data in the cloud leads to vast cost of data transmission and high response latency. Edge computing as an emerging computing paradigm can significantly reduce the cost of data transmission and response latency for latency-sensitive applications due to the data processing at the proximity of data sources. In this poster, we propose an enhanced AMBER alert system using collaborative edges, called AMBER Alert Assistant (A3 in short), which can search the suspect vehicle by analyzing static and mobile cameras' data in real time fashion. We propose location-direction-related diffusion that effectively optimizes the searching area for vehicle searching. The evaluation results show that real-time video analytics can be achieved by collaboratively leveraging multiple edge nodes.
{"title":"Enhancing AMBER alert using collaborative edges: poster","authors":"Qingyang Zhang, Quan Zhang, Weisong Shi, Hong Zhong","doi":"10.1145/3132211.3132459","DOIUrl":"https://doi.org/10.1145/3132211.3132459","url":null,"abstract":"AMBER alert systems are inefficient since object searching heavily relies on reports of witnesses, who might miss alerts and cannot search enough areas of city. Using automatic license plate recognition (ALPR) technique, city-wide video surveillance is of great improvement for vehicle searching. However, analyzing huge amount of video data in the cloud leads to vast cost of data transmission and high response latency. Edge computing as an emerging computing paradigm can significantly reduce the cost of data transmission and response latency for latency-sensitive applications due to the data processing at the proximity of data sources. In this poster, we propose an enhanced AMBER alert system using collaborative edges, called AMBER Alert Assistant (A3 in short), which can search the suspect vehicle by analyzing static and mobile cameras' data in real time fashion. We propose location-direction-related diffusion that effectively optimizes the searching area for vehicle searching. The evaluation results show that real-time video analytics can be achieved by collaboratively leveraging multiple edge nodes.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125904394","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}
Image recognition applications are on the rise. Increasingly, applications on edge devices such as mobile smartphones, drones and cars, are relying on recognition techniques to provide interactive and intelligent functionality. Given the complexity of these techniques, and resource constrained nature of edge devices, applications rely on offloading compute intensive recognition tasks to the cloud. This has also lead to the rise of cloud-based recognition services. This involves sending captured images to remote servers across the Internet, which leads to slower responses. With the rising numbers of edge devices, both, the network and such centralized cloud-based solutions, are likely to be under stress, and lead to further slower responses. To reduce the recognition latency, and provide better scalability to the cloud-based solutions, we propose Precog. Precog employs selective computation on the devices to reduce the need to offload images to the cloud. In coordination with edge servers, it uses prediction to prefetch parts of the trained classifiers used for recognition onto the devices, and uses these smaller models to accelerate recognition on devices. Our evaluation shows that Precog can reduce latency by up to 5×, better utilize edge and cloud resources and also increase accuracy. We believe that Precog is the first system to use devices and edge servers collaboratively to enable prefetching and caching on the devices, and drive down recognition latency for mobile applications.
{"title":"Precog: prefetching for image recognition applications at the edge","authors":"Utsav Drolia, Katherine Guo, P. Narasimhan","doi":"10.1145/3132211.3134456","DOIUrl":"https://doi.org/10.1145/3132211.3134456","url":null,"abstract":"Image recognition applications are on the rise. Increasingly, applications on edge devices such as mobile smartphones, drones and cars, are relying on recognition techniques to provide interactive and intelligent functionality. Given the complexity of these techniques, and resource constrained nature of edge devices, applications rely on offloading compute intensive recognition tasks to the cloud. This has also lead to the rise of cloud-based recognition services. This involves sending captured images to remote servers across the Internet, which leads to slower responses. With the rising numbers of edge devices, both, the network and such centralized cloud-based solutions, are likely to be under stress, and lead to further slower responses. To reduce the recognition latency, and provide better scalability to the cloud-based solutions, we propose Precog. Precog employs selective computation on the devices to reduce the need to offload images to the cloud. In coordination with edge servers, it uses prediction to prefetch parts of the trained classifiers used for recognition onto the devices, and uses these smaller models to accelerate recognition on devices. Our evaluation shows that Precog can reduce latency by up to 5×, better utilize edge and cloud resources and also increase accuracy. We believe that Precog is the first system to use devices and edge servers collaboratively to enable prefetching and caching on the devices, and drive down recognition latency for mobile applications.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131785410","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}
J. Dick, Caleb Phillips, S. H. Mortazavi, E. D. Lara
The use of unmanned aerial vehicles (UAV), or drones, has in recent years seen explosive growth due to lower costs and technology advances in mobile computing, batteries, sensors, and control systems. Drones are now used in a multitude of applications, from natural resource exploration, the film and entertainment industry, to urban surveillance, and defense. The image processing demands of these applications requires higher powered computing capabilities than those available locally to the drone, prompting the offloading of these tasks to the cloud. However, the latency requirements of the cloud are beyond those acceptable for many applications. This paper proposed the use of a server on the network edge to optimize both processing capability as well as latency for applications requiring real-time communication between a drone and a cloud server. We propose to test the limits of this model by implementing a system for real-time tracking of golf drives on a golf course.
{"title":"High speed object tracking using edge computing: poster abstract","authors":"J. Dick, Caleb Phillips, S. H. Mortazavi, E. D. Lara","doi":"10.1145/3132211.3132457","DOIUrl":"https://doi.org/10.1145/3132211.3132457","url":null,"abstract":"The use of unmanned aerial vehicles (UAV), or drones, has in recent years seen explosive growth due to lower costs and technology advances in mobile computing, batteries, sensors, and control systems. Drones are now used in a multitude of applications, from natural resource exploration, the film and entertainment industry, to urban surveillance, and defense. The image processing demands of these applications requires higher powered computing capabilities than those available locally to the drone, prompting the offloading of these tasks to the cloud. However, the latency requirements of the cloud are beyond those acceptable for many applications. This paper proposed the use of a server on the network edge to optimize both processing capability as well as latency for applications requiring real-time communication between a drone and a cloud server. We propose to test the limits of this model by implementing a system for real-time tracking of golf drives on a golf course.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114463009","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}
Augmented reality (AR) augments a real-world environment by computer-generated sensory information such as text, sound, and graphics. With advanced AR technologies, the information about a person's surrounding physical environment can be brought out of the digital world and overlaid with the person's perceived real world. Pokemon Go is an example of location-based AR applications [1]. According to Digi-Capital, mobile AR could become the primary driver of a $108 billion VR/AR market by 2021 [2].
{"title":"Fast and accurate object analysis at the edge for mobile augmented reality: demo","authors":"Qiang Liu, Siqi Huang, T. Han","doi":"10.1145/3132211.3132458","DOIUrl":"https://doi.org/10.1145/3132211.3132458","url":null,"abstract":"Augmented reality (AR) augments a real-world environment by computer-generated sensory information such as text, sound, and graphics. With advanced AR technologies, the information about a person's surrounding physical environment can be brought out of the digital world and overlaid with the person's perceived real world. Pokemon Go is an example of location-based AR applications [1]. According to Digi-Capital, mobile AR could become the primary driver of a $108 billion VR/AR market by 2021 [2].","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117307394","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}
Mobile Edge Computing (MEC) has received much attention from the research community in recent years. A significant part of the published work has studied the telecom-centric MEC architecture, which assumes that the computing resource is located at the edge of the mobile access network (e.g., the Evolved Packet Core), typically at the first aggregation level. Many authors make a silent assumption in their analyses that the latency at this stage of the network is negligible. In this paper we show not only that this assumption false, but that in some common cases the latency of the first-aggregation stage dominates the end-to-end latency. We challenge the latency argument in the context of present-day access networks and discuss what must be done to pave the way for practical deployments of MEC.
{"title":"Edge computing in the ePC: a reality check","authors":"I. Hadžić, Yoshihisa Abe, Hans C. Woithe","doi":"10.1145/3132211.3134449","DOIUrl":"https://doi.org/10.1145/3132211.3134449","url":null,"abstract":"Mobile Edge Computing (MEC) has received much attention from the research community in recent years. A significant part of the published work has studied the telecom-centric MEC architecture, which assumes that the computing resource is located at the edge of the mobile access network (e.g., the Evolved Packet Core), typically at the first aggregation level. Many authors make a silent assumption in their analyses that the latency at this stage of the network is negligible. In this paper we show not only that this assumption false, but that in some common cases the latency of the first-aggregation stage dominates the end-to-end latency. We challenge the latency argument in the context of present-day access networks and discuss what must be done to pave the way for practical deployments of MEC.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124970529","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}
Karim Habak, E. Zegura, M. Ammar, Khaled A. Harras
Edge computing offers an alternative to centralized, in-the-cloud compute services. Among the potential advantages of edge computing are lower latency that improves responsiveness, reduced wide-area network congestion, and possibly greater privacy by keeping data more local. In our previous work on Femtoclouds, we proposed taking advantage of clusters of devices that tend to be co-located in places such as public transit, classrooms or coffee shops. These clusters can perform computations for jobs generated from within or outside of the cluster. In this paper, we address the full requirements of workload management in Femtoclouds. These functions enable a Femtocloud to provide a service to job initiators that is similar to that provided by a centralized cloud service. We develop a system architecture that relies on the cloud to efficiently control and manage a Femtocloud. Within this architecture, we develop adaptive workload management mechanisms and algorithms to manage resources and effectively mask churn. We implement a prototype of our Femtocloud system on Android devices and utilize it to evaluate the overall system performance. We use simulation to isolate and study the impact of our workload management mechanisms and test the system at scale. Our prototype and simulation results demonstrate the efficiency of the Femtocloud workload management mechanisms especially in situations with potentially high churn. For instance, our mechanisms can reduce the average job completion time by up to 26% compared to similar mechanisms used in traditional cloud computing systems when used in situations that suggest high churn.
{"title":"Workload management for dynamic mobile device clusters in edge femtoclouds","authors":"Karim Habak, E. Zegura, M. Ammar, Khaled A. Harras","doi":"10.1145/3132211.3134455","DOIUrl":"https://doi.org/10.1145/3132211.3134455","url":null,"abstract":"Edge computing offers an alternative to centralized, in-the-cloud compute services. Among the potential advantages of edge computing are lower latency that improves responsiveness, reduced wide-area network congestion, and possibly greater privacy by keeping data more local. In our previous work on Femtoclouds, we proposed taking advantage of clusters of devices that tend to be co-located in places such as public transit, classrooms or coffee shops. These clusters can perform computations for jobs generated from within or outside of the cluster. In this paper, we address the full requirements of workload management in Femtoclouds. These functions enable a Femtocloud to provide a service to job initiators that is similar to that provided by a centralized cloud service. We develop a system architecture that relies on the cloud to efficiently control and manage a Femtocloud. Within this architecture, we develop adaptive workload management mechanisms and algorithms to manage resources and effectively mask churn. We implement a prototype of our Femtocloud system on Android devices and utilize it to evaluate the overall system performance. We use simulation to isolate and study the impact of our workload management mechanisms and test the system at scale. Our prototype and simulation results demonstrate the efficiency of the Femtocloud workload management mechanisms especially in situations with potentially high churn. For instance, our mechanisms can reduce the average job completion time by up to 26% compared to similar mechanisms used in traditional cloud computing systems when used in situations that suggest high churn.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131384194","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 this demo we fabricate 4 development boards using the APlix CSR1010 modules and then establish a Bluetooth Low Energy (BLE) mesh network, which is suitable for power-limited and low-complexity IoT applications with low-priority and infrequent data traffic. In addition to the basic operations such as sensing and actuating devices, this demo also shows a BLE star-mesh integration topology to extend the connectivity range to cover 4 laboratories.
{"title":"Establishing a BLE mesh network with fabricated CSRmesh devices: demo abstract","authors":"Xiaokun Yang, Xianlong He","doi":"10.1145/3132211.3132460","DOIUrl":"https://doi.org/10.1145/3132211.3132460","url":null,"abstract":"In this demo we fabricate 4 development boards using the APlix CSR1010 modules and then establish a Bluetooth Low Energy (BLE) mesh network, which is suitable for power-limited and low-complexity IoT applications with low-priority and infrequent data traffic. In addition to the basic operations such as sensing and actuating devices, this demo also shows a BLE star-mesh integration topology to extend the connectivity range to cover 4 laboratories.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"6 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132532507","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 poster presents a smart building system integrated with the emerging edge computing technology and IoT Mesh networks. More specifically, first we have established an IoT Mesh network with one IoT host/server and three devices. And the next step is to develop the data analysis algorithm at the network edge and to connect to the GoKit cloud service with a GizWits V3.0 board. The system will show an effective solution to smart home/building with proposing a cloud-edge-IoT system, and fundamentally extending the connection area to cover an entire farm or factory.
{"title":"A smart building system integrated with an edge computing algorithm and IoT mesh networks: demo abstract","authors":"Archit Gajjar, Yunxiang Zhang, Xiaokun Yang","doi":"10.1145/3132211.3132462","DOIUrl":"https://doi.org/10.1145/3132211.3132462","url":null,"abstract":"This poster presents a smart building system integrated with the emerging edge computing technology and IoT Mesh networks. More specifically, first we have established an IoT Mesh network with one IoT host/server and three devices. And the next step is to develop the data analysis algorithm at the network edge and to connect to the GoKit cloud service with a GizWits V3.0 board. The system will show an effective solution to smart home/building with proposing a cloud-edge-IoT system, and fundamentally extending the connection area to cover an entire farm or factory.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134311122","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}
Autonomous machine vision is a powerful tool to address challenges in multiple domains including national security (for example, video surveillance), health care (for example, patient monitoring), and transportation (for example, autonomous vehicles). Distributed vision, where multiple cameras observe a specific geographic area 24/7, enables smart understanding of events in a physical environment with minimal human intervention. We observe that the cloud paradigm alone does not offer a pathway to real-time distributed vision processing. With potentially thousands of cameras, hundreds of gigabytes data per second needs to be transferred to the cloud, saturating the bandwidth of the network. More importantly, vision applications are inherently latency-critical with a high demand for real-time scene analysis (for example, feature extraction and object tracking). To meet latency requirements, computation - including both processing of raw video streams to identify objects, and analytics on this data, needs to be brought to the edge of the network. While object recognition may be done locally at the end node (next to the camera), vision analytics requires access to data generated across different nodes. For example, a subject of interest may need to be tracked across multiple cameras to identify the nature of activities. This creates a need for a low latency distributed data store communicating over a dynamic communication network (most often wireless), to be implemented at the edge. Moreover, the data store must be able to address the limited storage at the end nodes (typically gigabytes). Additionally, privacy and security are prime concerns in the design of such a distributed edge storage.
{"title":"Edge datastore for distributed vision analytics: poster","authors":"Yang Deng, A. Ravindran, T. Han","doi":"10.1145/3132211.3132463","DOIUrl":"https://doi.org/10.1145/3132211.3132463","url":null,"abstract":"Autonomous machine vision is a powerful tool to address challenges in multiple domains including national security (for example, video surveillance), health care (for example, patient monitoring), and transportation (for example, autonomous vehicles). Distributed vision, where multiple cameras observe a specific geographic area 24/7, enables smart understanding of events in a physical environment with minimal human intervention. We observe that the cloud paradigm alone does not offer a pathway to real-time distributed vision processing. With potentially thousands of cameras, hundreds of gigabytes data per second needs to be transferred to the cloud, saturating the bandwidth of the network. More importantly, vision applications are inherently latency-critical with a high demand for real-time scene analysis (for example, feature extraction and object tracking). To meet latency requirements, computation - including both processing of raw video streams to identify objects, and analytics on this data, needs to be brought to the edge of the network. While object recognition may be done locally at the end node (next to the camera), vision analytics requires access to data generated across different nodes. For example, a subject of interest may need to be tracked across multiple cameras to identify the nature of activities. This creates a need for a low latency distributed data store communicating over a dynamic communication network (most often wireless), to be implemented at the edge. Moreover, the data store must be able to address the limited storage at the end nodes (typically gigabytes). Additionally, privacy and security are prime concerns in the design of such a distributed edge storage.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122887893","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}
We present compression algorithms for analog responses of Passive Infra-Red (PIR) sensors and a corresponding benchmarking framework based on ARM Cortex-M4 micro-controller. Compression ratio, reconstruction accuracy, memory footprint, and running times for a compression algorithm based on Discrete Cosine Transform (DCT) are presented. Analog responses can be compressed by up to 90% and recovered with less than 10% error. Our framework presents a first step in overcoming the computational limitations of the edge nodes in connected lighting systems to collect fine-grained occupancy patterns and enable beyond-lighting applications, such as Space Optimization and Heating Ventilation and Air Conditioning (HVAC) controls.
{"title":"Embedded sensor-data compression frameworks for connected lighting systems: poster","authors":"Arvind Ramesh, Olaitan Olaleye, A. Murthy","doi":"10.1145/3132211.3132212","DOIUrl":"https://doi.org/10.1145/3132211.3132212","url":null,"abstract":"We present compression algorithms for analog responses of Passive Infra-Red (PIR) sensors and a corresponding benchmarking framework based on ARM Cortex-M4 micro-controller. Compression ratio, reconstruction accuracy, memory footprint, and running times for a compression algorithm based on Discrete Cosine Transform (DCT) are presented. Analog responses can be compressed by up to 90% and recovered with less than 10% error. Our framework presents a first step in overcoming the computational limitations of the edge nodes in connected lighting systems to collect fine-grained occupancy patterns and enable beyond-lighting applications, such as Space Optimization and Heating Ventilation and Air Conditioning (HVAC) controls.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128688393","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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