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arXiv 2021年

作者： Li, Jianxin Fu, Xingcheng Peng, Hao Wang, Senzhang Zhu, Shijie Sun, Qingyun Yu, Philip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China State Key Laboratory of Software Development Environment Beihang University Beijing100083 China School of Computer Science and Engineering Central South University Changsha410083 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States

Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high quality graph embedding vectors various types of graphs. However, most existing methods usually randomly select the negative samples from the original graph to enhance the training data without considering the noise. In addition, most of these methods only focus on the explicit graph structures and cannot fully capture complex semantics of edges such as various relationships or asymmetry. In order to address these issues, we propose a robust and generalized framework for adversarial graph embedding based on generative adversarial networks. Inspired by generative adversarial network, we propose a robust and generalized framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution, and enables the discriminator and generator to jointly learn each node's robust and generalized representation. Based on this framework, we propose three models to handle three types of graph data and derive the corresponding optimization algorithms, i.e., UG-AGE and DG-AGE for undirected and directed homogeneous graphs, respectively, and HIN-AGE for heterogeneous information networks. Extensive experiments show that our methods consistently and significantly outperform existing state-of-the-art methods across multiple graph mining tasks, including link prediction, node classification, and graph reconstruction. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

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G-Strong Chain Recurrent Point And G-Chain Equivalent Point of Topological G-Conjugacy On Metric G-Space

G-Strong Chain Recurrent Point And G-Chain Equivalent Point ...

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2019虚拟现实与智能系统国际会议

作者： Ji Zhan-jiang Tu Jing-xian School of Data Science and Software Engineering Wuzhou University Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University

ISBN: (纸本)9781728150505

In this paper,we study the dynamical properties of Gstrong chain recurrent point,G-chain point set and G-chain equivalent point of topological G-conjugacy on metric *** inference,we give the following conclusions that if let f:X→X and f:Y→Y be two continous map of metric G-space X and *** the map h:X→Y is a topogical G-conjugacy from f to f,then（1） h(SCR（f）)=SCR（f）;（2） h(S（x,f）)=S(h（x）,f);（3）h(CE（x,f）)=CE(h（x）,f).These results will enrich the theory of G-strong chain recurrent point,G-chain point and G-chain equivalent point of topological G-conjugacy on metric G-space

关键词： metric G-space topological G-conjugacy G-strong chain recurrent point G-chain point G-chain equivalent point

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Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning

Weakly Supervised Fine-Grained Image Classification via Guas...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhihui Wang Shijie Wang Shuhui Yang Haojie Li Jianjun Li Zezhou Li International School of Information Science & Engineering Dalian University of Technology China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China School of Computer Science and Technology Hangzhou Dianzi University China Shanghai Pinlan Data Technology

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly. We discover that due to the operation of stacking local receptive filed, Convolutional Neural Network causes the discriminative region diffusion in high-level feature maps, which leads to inaccurate discriminative region localization. In this paper, we propose an end-to-end Discriminative Feature-oriented Gaussian Mixture Model (DF-GMM), to address the problem of discriminative region diffusion and find better fine-grained details. Specifically, DF-GMM consists of 1) a low-rank representation mechanism (LRM), which learns a set of low-rank discriminative bases by Gaussian Mixture Model (GMM) to accurately select discriminative details and filter more irrelevant information in high-level semantic feature maps, 2) a low-rank representation reorganization mechanism (LR2M) which resumes the space information of low-rank discriminative bases to reconstruct the low-rank feature maps. By recovering the low-rank discriminative bases into the same embedding space of highlevel feature maps, LR2M alleviates the discriminative region diffusion problem in high-level feature map and discriminative regions can be located more precisely on the new low-rank feature maps. Extensive experiments verify that DF-GMM yields the best performance under the same settings with the most competitive approaches, in CUBBird, Stanford-Cars datasets, and FGVC Aircraft.

关键词： Gaussian mixture model Correlation Feature extraction Kernel Image recognition Semantics

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Implementation of the parallel mean shift-based imagesegmentation algorithm on a GPU cluster

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International Journal of Digital Earth 2019年第3期12卷 328-353页

作者： Fang Huang Yinjie Chen Li Li Ji Zhou Jian Tao Xicheng Tan Guangsong Fana School of Resources&Environment University of Electronic Science and Technology of ChinaChengduPeople’s Republic of China Institute of Remote Sensing Big Data Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China School of Construction&Management Engineering Xihua UniversityChengduPeople’s Republic of China Texas A&M Engineering Experiment Station and High Performance Research Computing Texas A&M UniversityCollege StationTXUSA International School of Software Wuhan UniversityWuhanPeople’s Republic of China

The mean shift image segmentation algorithm is very computationintensive. To address the need to deal with a large number of remotesensing (RS) image segmentations in real-world applications, this studyhas investigated the parallelization of the mean shift algorithm on asingle graphics processing unit (GPU) and a task-scheduling methodwith message passing interface (MPI)+OpenCL programming model on aGPU cluster platform. This paper presents the test results of the parallelmean shift image segmentation algorithm on Shelob, a GPU clusterplatform at Louisiana State University, with different datasets andparameters. The experimental results show that the proposed parallelalgorithm can achieve good speedups with different configurations andRS data and can provide an effective solution for RS image processingon a GPU cluster.

关键词： Mean shift algorithm GPU cluster task scheduling MPI OpenCL

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Study on the Influence of Seasonal Diversity Deduction of Landscape Plants on Landscape Vision

Study on the Influence of Seasonal Diversity Deduction of La...

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作者： Shengju Li Xue Wang Yaosheng Zhang Yuehua Zhang Li Lan Jiamusi University School of Data Science and Software Engineering Wuzhou University

Urban garden landscape is a topic of great concern in the process of modern urban *** belongs to the landscape with vitality in the construction *** plant landscape is also the product of the combination of economy,politics,culture and *** cold land plants belong to the only life-changing and cyclical elements in the garden design *** use the seasonal changes of their distinctive features to realize the planning and design,which can effectively enrich the overall landscape *** seasonal changes of garden plants in cold regions mainly include space and *** seasonal changes of color include leaves,flowers,fruits and branches,and the spatial characteristics are different periods of *** the process of realizing the garden configuration,it is important to comprehensively study the seasonal changes of plants,which can effectively improve the role of *** study analyzes the influence of seasonal changes of garden plants on the landscape construction of garden space from three aspects:function,season and ***,the characteristics of seasonal changes of garden plants in cold regions are analyzed,so as to improve the rationality of landscape plant configuration in different seasons,and improve the level of garden landscape construction.

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Study on the Changes of Plant Ecological Communities and Environmental Factors in Cold Cities

Study on the Changes of Plant Ecological Communities and Env...

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作者： Shengju Li Xue Wang Yaosheng Zhang Yuehua Zhang Lan Li Jiamusi University School of Data Science and Software Engineering Wuzhou University

There are certain geographical differences in the design process of green areas along the streets of the cold *** cold climate brings special geographical features to the *** snow and ice add new vitality and characteristics to the city,and at the same time public demand new challenges for the excavation and creation of cold garden *** definition of standards for cold cities and the contribution of cold cities are movements to cold *** space next to the city streets are systematically defined,and the role and influence of public life generated by the concept of greenbelt are *** paper analyzes the design principles and theoretical basis of the green space design along the street in the cold *** example is given to analyze the problems and optimization suggestions that should be paid attention to during the design process of the green space next to the *** the solution of the problematic elements in the greening landscape design along the cold city street,the methods and strategies of the greening landscape design along the cold city street are analyzed from the aspects of plant.

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Deep Forest with Hashing Screening and Window Screening

arXiv

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arXiv 2022年

作者： Ma, Pengfei Wu, Youxi Li, Yan Guo, Lei Jiang, He Zhu, Xingquan Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjing300401 China Hebei Key Laboratory of Big Data Computing Tianjing300401 China School of Economics and Management Hebei University of Technology Tianjing300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjing300401 China School of Software Dalian University of Technology Dalian116023 China Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Hefei University of Technology Hefei230009 China Mininglamp Academy of Sciences Mininglamp Technology Beijing100084 China

As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vectors, and this increases the time cost of the model. To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening. HW-Forest employs perceptual hashing algorithm to calculate the similarity between feature vectors in hashing screening strategy, which is used to remove the redundant feature vectors produced by multi-grained scanning and can significantly decrease the time cost and memory consumption. Furthermore, we adopt a self-adaptive instance screening strategy to improve the performance of our approach, called window screening, which can achieve higher accuracy without hyperparameter tuning on different datasets. Our experimental results show that HW-Forest has higher accuracy than other models, and the time cost is also reduced. Copyright © 2022, The Authors. All rights reserved.

关键词： Scanning

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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Chinese Control and Decision Conference, CCDC

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Engineering RMIT University Melbourne VIC Australia

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This brief explores the approximation properties of a unique basis expansion based on Pascal's triangle, which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time representation. The roles of certain parameters, such as sampling time interval or model order, and signal characteristics, i.e., its curvature, on the approximation are investigated. Approximate errors in one and multiple-step predictions are analyzed. Furthermore, time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Shape Electronic mail Analytical models Mathematical analysis data models Upper bound

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A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

arXiv

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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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System architecture for high-performance permissioned blockchains

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Frontiers of Computer science 2019年第6期13卷 1151-1165页

作者： Libo FENG Hui ZHANG Wei-Tek TSAI Simeng SUN State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China Digital Society&Blockchain Laboratory School of Computer ScienceBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

Blockchain(BC),as an emerging distributed database technology with advanced security and reliability,has attracted much attention from experts who devoted to efinance,intellectual property protection,the internet of things(IoT)and so ***,the inefficient transaction processing speed,which hinders the BC’s widespread,has not been well tackled *** this paper,we propose a novel architecture,called Dual-Channel Parallel Broadcast model(DCPB),which could address such a problem to a greater extent by using three methods which are dual communication channels,parallel pipeline processing and block broadcast *** the dual-channel model,one channel processes transactions,and the other engages in the execution of *** parallel pipeline processing allows the system to operate *** block generation strategy improves the efficiency and speed of *** experiments have been applied to BeihangChain,a simplified prototype for BC system,illustrates that its transaction processing speed could be improved to 16K transaction per second which could well support many real-world scenarios such as BC-based energy trading system and Micro-film copyright trading system in CCTV.

关键词： blockchain concurrency performance dualchannel model parallel pipeline consensus algorithm

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