With the rapid development of artificial intelligence,face recognition systems are widely used in daily lives.Face recognition applications often need to process large amounts of image data.Maintaining the accuracy an...With the rapid development of artificial intelligence,face recognition systems are widely used in daily lives.Face recognition applications often need to process large amounts of image data.Maintaining the accuracy and low latency is critical to face recognition systems.After analyzing the two-tier architecture“client-cloud”face recognition systems,it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded,and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network.This paper proposes a flexible and efficient edge computing accelerated architecture.By offloading part of the computing tasks to the edge server closer to the data source,edge computing resources are used for image preprocessing to reduce the number of images to be transmitted,thus reducing the network transmission overhead.Moreover,the application code does not need to be rewritten and can be easily migrated to the edge server.We evaluate our schemes based on the open source Azure IoT Edge,and the experimental results show that the three-tier architecture“Client-Edge-Cloud”face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.展开更多
This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of co...This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].展开更多
Reconstructing a three-dimensional(3D)environment is an indispensable technique to make augmented reality and augmented virtuality feasible.A Kinect device is an efficient tool for reconstructing 3D environments,and u...Reconstructing a three-dimensional(3D)environment is an indispensable technique to make augmented reality and augmented virtuality feasible.A Kinect device is an efficient tool for reconstructing 3D environments,and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces.To employ multiple devices simultaneously,Kinect devices need to be calibrated with respect to each other.There are several schemes available that calibrate 3D images generated frommultiple Kinect devices,including themarker detection method.In this study,we introduce a markerless calibration technique for Azure Kinect devices that avoids the drawbacks of marker detection,which directly affects calibration accuracy;it offers superior userfriendliness,efficiency,and accuracy.Further,we applied a joint tracking algorithm to approximate the calibration.Traditional methods require the information of multiple joints for calibration;however,Azure Kinect,the latest version of Kinect,requires the information of only one joint.The obtained result was further refined using the iterative closest point algorithm.We conducted several experimental tests that confirmed the enhanced efficiency and accuracy of the proposed method for multiple Kinect devices when compared to the conventional markerbased calibration.展开更多
This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedes...This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedestrian detection algorithms during training. Furthermore, it is dedicated to addressing occlusion issues in pedestrian detection by using Azure Kinect for body tracking and integrating a robust occlusion management algorithm, significantly enhancing detection efficiency. In experiments, an average latency of 204 milliseconds was measured, and the detection accuracy reached an outstanding level of 97%. Additionally, this approach has been successfully applied in creating a simple yet captivating augmented reality game, demonstrating the practical application of the algorithm.展开更多
Windows Azure作为微软公司赢在未来的云计算平台,具有许多新的特性,正逐渐受到业界的青睐,因此在现阶段研究基于该平台来设计和开发大规模Web应用程序十分必要。首先分析Windows Azure云服务中与编程相关的关键组件,如计算服务、存储...Windows Azure作为微软公司赢在未来的云计算平台,具有许多新的特性,正逐渐受到业界的青睐,因此在现阶段研究基于该平台来设计和开发大规模Web应用程序十分必要。首先分析Windows Azure云服务中与编程相关的关键组件,如计算服务、存储服务和数据库服务的特点,特别是它们与本地开发的差异;在此基础上根据平台提出两个设计大规模Web应用程序的要点:计算资源的"无状态"处理和数据库的"横向"扩展,并给出了相应的实例进行说明。展开更多
基金This work is supported by the National Key Research and Development Program of China under Grant(No.2016YFB1000302)the National Natural Science Foundation of China under Grant(No.61832020).
文摘With the rapid development of artificial intelligence,face recognition systems are widely used in daily lives.Face recognition applications often need to process large amounts of image data.Maintaining the accuracy and low latency is critical to face recognition systems.After analyzing the two-tier architecture“client-cloud”face recognition systems,it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded,and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network.This paper proposes a flexible and efficient edge computing accelerated architecture.By offloading part of the computing tasks to the edge server closer to the data source,edge computing resources are used for image preprocessing to reduce the number of images to be transmitted,thus reducing the network transmission overhead.Moreover,the application code does not need to be rewritten and can be easily migrated to the edge server.We evaluate our schemes based on the open source Azure IoT Edge,and the experimental results show that the three-tier architecture“Client-Edge-Cloud”face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.
文摘This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Korea Government(MSIT)(Grant No.NRF-2022R1A2C1004588).
文摘Reconstructing a three-dimensional(3D)environment is an indispensable technique to make augmented reality and augmented virtuality feasible.A Kinect device is an efficient tool for reconstructing 3D environments,and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces.To employ multiple devices simultaneously,Kinect devices need to be calibrated with respect to each other.There are several schemes available that calibrate 3D images generated frommultiple Kinect devices,including themarker detection method.In this study,we introduce a markerless calibration technique for Azure Kinect devices that avoids the drawbacks of marker detection,which directly affects calibration accuracy;it offers superior userfriendliness,efficiency,and accuracy.Further,we applied a joint tracking algorithm to approximate the calibration.Traditional methods require the information of multiple joints for calibration;however,Azure Kinect,the latest version of Kinect,requires the information of only one joint.The obtained result was further refined using the iterative closest point algorithm.We conducted several experimental tests that confirmed the enhanced efficiency and accuracy of the proposed method for multiple Kinect devices when compared to the conventional markerbased calibration.
文摘This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedestrian detection algorithms during training. Furthermore, it is dedicated to addressing occlusion issues in pedestrian detection by using Azure Kinect for body tracking and integrating a robust occlusion management algorithm, significantly enhancing detection efficiency. In experiments, an average latency of 204 milliseconds was measured, and the detection accuracy reached an outstanding level of 97%. Additionally, this approach has been successfully applied in creating a simple yet captivating augmented reality game, demonstrating the practical application of the algorithm.
