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International Conference on software Maintenance (ICSM)

作者： Kun Yin Meng Yan Ling Xu Zhou Xu Zhao Li Dan Yang Xiaohong Zhang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data & Software Engineering Chongqing University China

ISBN: (数字)9781728156194

ISBN: (纸本)9781728156200

Logs are universally available in software systems for troubleshooting. They record system run-time states and messages of system activities. Log analysis is an effective way to diagnosis system exceptions, but it will take a long time for engineers to locate anomalies accurately through logs. Many automatic approaches have been proposed for log-based anomaly detection. However, most of the prior approaches did not consider the corresponding system component of a log message. Such component records the log location, which can help detect the location-sequence-related anomalies. In this paper, we propose LogC, a new Log -based anomaly detection approach with Component-aware analysis. LogC contains two phases: (i) turning log messages into log template sequences and component sequences, (ii) feeding such two sequences to train a combined LSTM model for detecting anomalous logs. LogC only needs normal log sequences to train the combined model. We evaluate LogC on two open-source log datasets: HDFS and ThunderBird. Experimental results show that LogC overall outperforms three baselines (i.e., PCA, IM, and DeepLog) in terms of three metrics (precision, recall, and F-measure).

关键词： Measurement software maintenance Turning software systems Anomaly detection Open source software Principal component analysis

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Convolutional Neural Network Pruning: A Survey

Convolutional Neural Network Pruning: A Survey

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第三十九届中国控制会议

作者： Sheng Xu Anran Huang Lei Chen Baochang Zhang School of Automatic Science and Eletrical Engineering Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing

Deep convolutional neural networks have enabled remarkable progress over the last years on a variety of visual tasks,such as image recognition, speech recognition, and machine translation. These tasks contribute many to machine ***, developments of deep convolutional neural networks to a machine terminal remains challenging due to massive number of parameters and float operations that a typical model contains. Therefore, there is growing interest in convolutional neural network pruning. Existing work in this field of research can be categorized according to three dimensions: pruning method,training strategy, estimation criterion.

关键词： convolutional neural networks machine intelligence pruning method training strategy estimation criterion

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Research on The Framework of The GIS-document Workflow System

Research on The Framework of The GIS-document Workflow Syste...

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IEEE International Conference on software engineering and Service Sciences (ICSESS)

作者： Ying Du Pu Cheng School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China School of Software Henan University Kaifeng China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Geographic information system (GIS)-document workflows are formed by combining workflow and GIS, and have the capabilities of geographic data processing, analysis, and visualization. They are widely used in areas with “integration of map and document.” An increasing number of complex business processes have great demands for business logic control and geographic data processing. Therefore, we established the framework of GIS - document workflow system in order to face the complex business processes and large geographic data processing processes.

关键词： Process control data visualization data processing Faces Geographic information systems Business software engineering

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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第32届中国控制与决策会议

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang University School of Engineering RMIT University

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 *** roles of certain parameters,such as sampling time interval or model order,and signal characteristics,i.e.,its curvature,on the approximation are *** errors in one and multiple-step predictions are ***,time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Signal approximation Sampling Discretization

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Research on water quality prediction based on SARIMA-LSTM: A case study of beilun estuary 21

Research on water quality prediction based on SARIMA-LSTM: A...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xu, Rui Xiong, Qingyu Yi, Hualing Wu, Chao Ye, Jianxin Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education School of Big Data AND Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781728120584

As water environment is an important part of mangrove ecosystem, an efficient prediction of water quality is the foundation for judging the health of wetland ecosystem. And it also contributes a lot to the smooth development of environmental protection work. Based on the data of water quality and weather in Beilun Estuary, this paper chooses permanganate index and the content of ammonia nitrogen, which can reflect the water quality, as forecasting targets. We propose a multi-feature prediction method called SARIMA-LSTM on the basis of seasonal autoregressive integrated moving average model and long short-term memory. Through the combination of linear and non-linear model, this method can possess a better prediction effect considering the influence of weather on water quality. And the experimental results of four models show that this method has higher accuracy, stability and reliability. © 2019 IEEE.

关键词： Long short-term memory

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BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China The Department of Computer Science City University of Hong Kong Hong Kong The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Information Science and Engineering Ningbo University Ningbo315211 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. © 2022, CC BY-NC-SA.

关键词： COVID-19

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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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Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation

arXiv

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arXiv 2021年

作者： Peng, Yang Liu, Ping Luo, Yawei Zhou, Pan Xu, Zichuan Liu, Jingen Center for Frontier AI Research Agency for Science Technology and Research Singapore Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Liaoning Dalian China

Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning paradigm, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, which try to mimic the human learning process either by making the student directly copy behavior from the teacher or selecting reliable learning materials, we propose to conduct further exploration to imitate the human learning process from different aspects, i.e., adaptively updating learning materials, selectively imitating teacher behaviors, and analyzing learning materials structures. The explored three components, collaborate together to constitute a new method for unsupervised domain adaptive person re-identification, which is called Human Learning Imitation framework. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Students

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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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Multi-Grained Selection and Fusion for Fine-Grained Image Representation

Multi-Grained Selection and Fusion for Fine-Grained Image Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jianrong Jiang Hongxing Wang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

How to learn a good fine-grained image representation is a key problem for fine-grained tasks. Most previous supervised methods suffer from insufficient training data, which require laborious annotations of fine-grained objects. In this paper, we propose an annotation-free method for fine-grained image representation, dubbed Multi-Grained Selection and Fusion (MGSF). The proposed MGSF extracts two types of visual features, i.e., fine-grained discriminative features that highlight informative convolutional parts by spatial selection and channel selection, and coarse-grained scene-level features that provide context information for fine-grained objects. Extensive experiments in fine-grained image retrieval demonstrate the superiority of our proposed representation compared with the state-of-the-art approaches on several fine-grained datasets.

关键词： Feature extraction Image retrieval Image representation Semantics Visualization Image classification Task analysis

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