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Cloud detection from visual band of satellite image based on variance of fractal dimension

Journal of Systems engineering and Electronics 2019年第3期30卷 485-491页

作者： TIAN Pingfang GUANG Qiang LIU Xing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-Time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

Cover ratio of cloud is a very important factor which affects the quality of a satellite image, therefore cloud detection from satellite images is a necessary step in assessing the image quality. The study on cloud detection from the visual band of a satellite image is developed. Firstly, we consider the differences between the cloud and ground including high grey level, good continuity of grey level, area of cloud region, and the variance of local fractal dimension (VLFD) of the cloud region. A single cloud region detection method is proposed. Secondly, by introducing a reference satellite image and by comparing the variance in the dimensions corresponding to the reference and the tested images, a method that detects multiple cloud regions and determines whether or not the cloud exists in an image is described. By using several Ikonos images, the performance of the proposed method is demonstrated.

关键词： cloud detection visual image satellite image variance of local fractal dimension (VLFD)

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MTSC-GE: A Novel Graph based Method for Multivariate Time Series Clustering 12

MTSC-GE: A Novel Graph based Method for Multivariate Time Se...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Yang, Ze Tai, Changyang Wu, Gongqing Zhang, Zan Bao, Xianyu Key Laboratory of Knowledge Engineering With Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438582

Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance. © 2021 IEEE.

关键词： data mining

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Gaussian Model-Based Fully Convolutional Networks for Multivariate Time Series Classification 12

Gaussian Model-Based Fully Convolutional Networks for Multiv...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Tai, Changyang Yang, Ze Zhang, Huicheng Wu, Gongqing Lv, Junwei Bao, Xianyu Key Laboratory of Knowledge Engineering With Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438582

Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dimensional MTS modeling has a large time and space consumption. This paper proposes a novel method, Gaussian Model-based Fully Convolutional Networks (GM-FCN), to improve the performance of high-dimensional MTS classification. Each original MTS is converted into multivariate Gaussian model parameters as the input of FCN. These parameters effectively capture the correlation be-tween MTS variables and significantly reduce the data scale by aligning an MTS size to its dimension. FCN is designed to learn more in-depth features of MTS based on these parameters for modeling the correlation between samples. Thus, GM-FCN can not only model the correlation between variables, but also the correlation between samples. We compare GM-FCN with nine state-of-the-art MTS classification methods, INN-ED, INN-DTW-i, INN-DTW-D, KLD-GMC, MLP, ResNet, Encoder, MCNN, and MCDCNN, on four high-dimensional public datasets, experimen-tal results show that the accuracy of G M - FCN is significantly superior to the others. Besides, the training time of GM-FCN is dozens of times faster than FCN using the original equal-length MTS data as input. © 2021 IEEE.

关键词： data mining

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A Deletable and Modifiable Blockchain Scheme Based on Record Verification Trees and the Multisignature Mechanism

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Computer Modeling in engineering & Sciences 2021年第7期128卷 223-245页

作者： Daojun Han Jinyu Chen Lei Zhang Yatian Shen Yihua Gao Xueheng Wang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China Department of Network and Finance Agricultural Bank of China Limited Xuchang BranchXuchang461000China School of Software Pingdingshan UniversityPingdingshan467000China Department of Network and Finance Agricultural Bank of China Limited Nanyang BranchNanyang473000China

As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various ***,data on a blockchain cannot be *** a result,it is impossible to clean invalid and sensitive data and correct erroneous ***,to a certain extent,hinders the application of blockchains in supply chains and Internet of *** address this problem,this study presents a deletable and modifiable blockchain scheme(DMBlockChain)based on record verification trees(RVTrees)and the multisignature scheme.(1)In this scheme,an RVTree structure is designed and added to the block *** RVTree can not only ensure that a record is true and valid but,owing to its unique binary structure,also verify whether modification and deletion requests are valid.(2)In DMBlockChain,the multisignature mechanism is also *** mechanism requires the stakeholders’signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily.A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature *** analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.

关键词： Blockchain record verification trees multisignature DMBlockChain

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An MRC Framework for Semantic Role Labeling 29

An MRC Framework for Semantic Role Labeling

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29th International Conference on Computational Linguistics, COLING 2022

作者： Wang, Nan Li, Jiwei Meng, Yuxian Sun, Xiaofei Qiu, Han Wang, Ziyao Wang, Guoyin He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China China Shannon.AI Zhejiang University China Tsinghua University China University of Leeds United Kingdom Amazon Alexa AI

Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two sub-tasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we propose to use the machine reading comprehension (MRC) framework to bridge this gap. We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense. The chosen predicate sense is then used to determine the semantic roles for that predicate, and these semantic roles are used to construct the query for another MRC model for argument labeling. In this way, we are able to leverage both the predicate semantics and the semantic role semantics for argument labeling. We also propose to select a subset of all the possible semantic roles for computational efficiency. Experiments show that the proposed framework achieves state-of-the-art or comparable results to previous work. Code is available at https://***/ShannonAI/MRC-SRL. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Performance study on logging to another main memory database

Performance study on logging to another main memory database

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作者： Qin, Xiongpai Xiaoyun, Zhou Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China Department of Computer Science and Technology Xuzhou Normal University Xuzhou Jiangsu 221008 China

ISBN: (纸本)9788988678183

In update intensive main memory database applications, huge volume of log records is generated, to maintain the ACID properties of the database system, the log records should be persistent efficiently. Delegating logging of one main memory database to another main memory database is proposed. The scheme is elaborated in detail in terms of architecture, logging & safeness levels, checkpointing, and recovery. Strict durability and relax durability are provided. When some form of non-volatile memory is used to temporarily holding log records, not only logging efficiency is improved, but also the scheme could guarantee full ACID of the system. We also propose using parallel logging to speedup log persistence by writing logs to multiple disks in parallel. Since interconnection network techniques progress by leaps and bounds, the scheme eliminates the concern about whether the system's overall performance may be slowed down by bandwidth and latency limitations. Experiment results demonstrate the feasibility of the proposal.

关键词： Interconnection networks (circuit switching)

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Power conservation for strongly connected topology control in wireless sensor network

Power conservation for strongly connected topology control i...

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1st ACM International Workshop on Foundations of Wireless Ad Hoc and Sensor Networking and Computing 2008, FOWANC'08

作者： Li, Deying Du, Hongwei Chen, Wenping School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing 100872 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781605581491

Topology control is one of the most elementary topics in wireless sensor networks. Typically, most of the research only considered the bidirectional communication and symmetric weighted communication model, while the real wireless world contains not only bidirectional but also unidirectional communication links and asymmetric weighted communication model. In this paper, we present two heuristics for the minimum power topology control problem on general model, i.e., given a set of sensors in the Euclidean plane and a transmission power threshold for each directed pair of sensors, to find a power assignment for each sensor to achieve a strong connectivity with minimum total transmission power. Extensive results in simulation evaluate the efficiency of the proposed algorithms. Copyright 2008 ACM.

关键词： Wireless sensor networks

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An Efficient Customized Blockchain System for Inter-Organizational Processes

An Efficient Customized Blockchain System for Inter-Organiza...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Puwei Sun, Zhouxing Li, Rui Chen, Jinchuan Gong, Ping Du, Xiaoyong Renmin University of China School of Information China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China Renmin University of China School of Mathematics China Fujian Normal University College of Computer and Cyber Security China

ISBN: (纸本)9798350304855

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently. © 2023 IEEE.

关键词： Blockchain

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An Integrated Semantic-Syntactic SBLSTM Model for Aspect Specific Opinion Extraction 15th

An Integrated Semantic-Syntactic SBLSTM Model for Aspect Spe...

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15th Web Information Systems and Applications Conference, WISA 2018

作者： Han, Zhongming Jiang, Xin Li, Mengqi Zhang, Mengmei Duan, Dagao School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China

ISBN: (纸本)9783030029333

Opinion Mining (OM) of Internet reviews is one of the key issues in Natural Language Processing (NLP) field. This paper proposes a stacked Bi-LSTM aspect opinion extraction model in which semantic and syntactic features are both integrated. The model takes embedded vector which is composed by word embedding, POS tags and dependency relations as its input while taking label sequence as its output. The experimental results show the effectiveness of this structural features embedded stacked Bi-LSTM model on cross-domain and cross-language datasets, and indicate that this model outperforms the state-of-the-art methods. © 2018, Springer Nature Switzerland AG.

关键词： Syntactics

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MDIot: IoT device identification method based on traffic characteristics 1

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19th Chinese Intelligent Systems Conference, CISC 2023

作者： Zheng, Hanxi Liu, Ruijun Yin, Huanpu Li, Haisheng School of Computer and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China

ISBN: (数字)9789819968824

ISBN: (纸本)9789819968817

In recent years, the rapid growth of the Internet of Things (IoT) devices has brought with it security risks, highlighting the need for management for IoT devices. Identification and classification are key components of devices management. However, existing work on IoT device identification is based on prior knowledge to manually extract features, resulting in too many redundant features and reducing devices identification accuracy. In this paper, we propose amodel called MDIoT. Our model use the multi-votingmethod for feature selection, and is combined with Multiobjective Genetic Algorithm to reduce redundancy features and noise. Experimental results show that our feature selection method is more efficient in feature selection and the classification accuracy is over 95%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Internet of things

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