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IEEE International Conference on Software Engineering and service Sciences (ICSESS)

作者： Nan Zhang Haixia Xu Liming Yuan Xianbin Wen Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (纸本)9781538665664;9781538665657

Due to the high resolution and multipolarization of Synthetic Aperture Radar (SAR), dimensionality reduction is often needed as a pre-processing step for SAR target recognition. For this purpose, Laplacian Eigenmaps (LE)can be naturally regarded as a candidate, but it neglects the difference between the intra-class and inter-class discriminant information. In this paper, we attempt to integrate LE with Linear Discriminant Analysis (LDA)in order to reduce features for SAR objects. Owing to the inherent imaging mechanism and speckle noise of SAR images, we also propose applying hypergraph to formalize the complex relationships between SAR objects. We have performed extensive experiments on the Moving and Stationary Target Acquisition and Recognition database. Experimental results demonstrate the effectiveness of the presented combination of LE, LDA, and hypergraph on SAR target recognition.

关键词： Synthetic aperture radar Target recognition Laplace equations Feature extraction Manifolds Matrix converters Dimensionality reduction

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A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

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7th International Conference on Computational Data and Social Networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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Hybrid Filtrations Recommendation system based on Privacy Preserving in Edge computing

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Procedia Computer Science 2018年 129卷 407-409页

作者： Lina Ni Hongdi Lin Mengmeng Zhang Jinquan Zhang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

It is challenging to design a secure recommendation system on the Internet which can help users to select their favorite products as less privacy leaked as possible. In this paper, we present a hybrid filtrations recommendation system based on privacy preserving in edge computing (HFRS-PP), which can prevent the users’ privacy information from being leaked via the merits of edge computing in the process of computing and ensure the real-time, accuracy and stability of the query results. Particularly, we propose a privacy-preserving recommendation algorithm to obtain the desired results for the end users through hybrid filtrations. The filtration-rough set theory algorithm is given to distinguish the valid reviews from spam reviews for the next filtration.

关键词： Privacy protection edge computing recommendation system rough set theory

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A survey on edge computing systems and tools

arXiv

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arXiv 2019年

作者： Liu, Fang Tang, Guoming Li, Youhuizi Cai, Zhiping Zhang, Xingzhou Zhou, Tongqing School of Data and Computer Science Sun Yat-sen University GuangzhouGuangdong China Key Laboratory of Science and Technology on Information System Engineering National University of Defense Technology ChangshaHunan China School of Compute Science and Technology Hangzhou Dianzi University China College of Computer National University of Defense Technology ChangshaHunan China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

—Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy efficiency

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A Location Privacy Preserving Scheme Based on Repartitioning Anonymous Region in Mobile Social Network

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Procedia Computer Science 2018年 129卷 368-371页

作者： Lina Ni Yanfeng Yuan Xiao Wang Mengmeng Zhang Jinquan Zhang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Applying the proliferated location-based services (LBS) to social networks has spawned mobile social network (MSN) services that it allows users to discover potential friends around them. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a location privacy preserving (RPAR) scheme via to repartition anonymous region where the central anonymous location minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location.

关键词： Privacy preserving mobile social network location-based service location privacy anonymous region repartition

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Load Balancing Across Microservices

Load Balancing Across Microservices

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yipei Niu Fangming Liu Zongpeng Li Key Laboratory of Services Computing Technology and System Huazhong University of Science and Technology University of Calgary Canada

ISBN: (纸本)9781538641293

With the advent of cloud container technology, enterprises develop applications through microservices, breaking monolithic software into a suite of small services whose instances run independently in containers. User requests are served by a series of microservices forming a chain, and the chains often share microservices. Existing load balancing strategies either incur significant networking overhead or ignore the competition for shared microservices across chains. Furthermore, typical load balancing solutions leverage a hybrid technique by combining HTTP with message queue to support microservice communications, bringing additional operational complexity. To address these challenges, we propose a chain-oriented load balancing algorithm (COLBA) based solely on message queues, which balances load based on microservice requirements of chains to minimize response time. We model the load balancing problem as a non-cooperative game, and leverage Nash bargaining to coordinate microservice allocation across chains. Employing convex optimization with rounding, we efficiently solve the problem that is proven NP-hard. Extensive trace-driven simulations demonstrate that COLBA reduces the overall average response time at least by 13% compared with existing load balancing strategies.

关键词： Load management Time factors Containers Computer architecture Load modeling Conferences Logic gates

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Author Correction: Maximizing carbon sequestration potential in Chinese forests through optimal management

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Nature communications 2024年第1期15卷 4020页

作者： Zhen Yu Shirong Liu Haikui Li Jingjing Liang Weiguo Liu Shilong Piao Hanqin Tian Guoyi Zhou Chaoqun Lu Weibin You Pengsen Sun Yanli Dong Stephen Sitch Evgenios Agathokleous Key Laboratory of Ecosystem Carbon Source and Sink China Meteorological Administration (ECSS-CMA) School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China. zyu@***. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 100091 Beijing China. zyu@***. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 100091 Beijing China. liusr@***. Key Laboratory of Forest Management and Growth Modelling China's National Forestry and Grassland Administration Research Institute of Forest Resource Information Techniques Chinese Academy of Forestry 100091 Beijing China. Forest Advanced Computing and Artificial Intelligence Laboratory (FACAI) Department of Forestry and Natural Resources Purdue University West Lafayette IN 47907 USA. College of Forestry Northwest agriculture and Forestry University Yangling 712100 China. Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University 100871 Beijing China. Schiller Institute for Integrated Science and Society Department of Earth and Environmental Sciences Boston College Chestnut Hill Massachusetts MA 02467 USA. Key Laboratory of Ecosystem Carbon Source and Sink China Meteorological Administration (ECSS-CMA) School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China. Department of Ecology Evolution and Organismal Biology Iowa State University Ames IA 50011 USA. College of Forestry Fujian Agriculture and Forestry University Fuzhou 350002 China. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 10

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Data analytics for fog computing by distributed online learning with asynchronous update

arXiv

引用

arXiv 2019年

作者： Li, Guangxia Zhao, Peilin Lu, Xiao Liu, Jia Shen, Yulong Shaanxi Key Laboratory of Network and System Security Xidian University China State Key Laboratory of Integrated Service Networks Xidian University China School of Computer Science and Technology Xidian University China Tencent AI Lab Tencent Inc. China Department of Electrical and Computer Engineering University of Alberta Canada

—Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive applications. As the popularity of fog applications increases, there is a demand for the development of smart data analytic tools, which can process massive data streams in an efficient manner. To satisfy such requirements, we propose a system in which data streams generated from distributed sources are digested almost locally, whereas a relatively small amount of distilled information is converged to a center. The center extracts knowledge from the collected information, and shares it across all subordinates to boost their performances. Upon the proposed system, we devise a distributed machine learning algorithm using the online learning approach, which is well known for its high efficiency and innate ability to cope with streaming data. An asynchronous update strategy with rigorous theoretical support is applied to enhance the system robustness. Experimental results demonstrate that the proposed method is comparable with a model trained over a centralized platform in terms of the classification accuracy, whereas the efficiency and scalability of the overall system are improved. Copyright © 2019, The Authors. All rights reserved.

关键词： Fog computing

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Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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Correction to: A method of VR‑EEG scene cognitive rehabilitation training

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Health information science and systems 2021年第1期9卷 7页

作者： Wenjun Tan Yang Xu Pan Liu Chunyan Liu Yujin Li Yanrui Du Chao Chen Yuping Wang Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang 110189 China. Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou 510006 China. Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China. Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin 300384 China. Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne VIC 8001 Australia.

[This corrects the article DOI: 10.1007/s13755-020-00132-6.].

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