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arXiv 2020年

作者： Liu, Li Chen, Da Shu, Minglei Li, Baosheng Shu, Huazhong Paques, Michel Cohen, Laurent D. Qilu University of Technology Shandong Academy of Sciences Shandong Artificial Intelligence Institute Jinan250014 China Shandong's Key Laboratory of Radiation Oncology Shandong Cancer Hospital Shandong Academy of Medical Sciences Jinan China Department of Radiation Oncology Shandong Cancer Hospital & Institute Shandong Academy of Medical Sciences Jinan China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China The Centre Hospitalier National dOphtalmologie des Quinze-Vingts Paris France University Paris Dauphine PSL Research University CNRS UMR 7534 CEREMADE Paris75016 France

Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their strong ability in tracing tubular structures, by which a tubular structure can be naturally modeled as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulties such as the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek suitable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal paths-based tubular structure tracing algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Trees (mathematics)

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Industrial Air Pollution Prediction Using Deep Neural Network 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Pengfei, Yu Juanjuan, He Xiaoming, Liu Kai, Zhang School of Computer Science Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

In this paper, a deep neural network model is proposed to predict industrial air pollution, such as PM2.5 and PM10. The deep neural network model contains 9 hidden layers, each layer contains 45 neurons. The output of the hidden layer neurons is calculated using the ReLU activation function, which can effectively reduce the gradient elimination effect of the deep neural network. Twelve air pollutant indicators from industrial factories are collected as the input data, such as CO, NO2, O3, and SO2. About 180,000 real industrial air pollution data from Wuhan City are used to train and test the DNN model. Furthermore, the performance of our approach is compared with the SVM and Artificial neural network methods, and the comparison result shows that our algorithm is accurate and competitive with higher prediction accuracy and generalization ability. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Design of gear reducer based on FOA optimization algorithm 1st

Design of gear reducer based on FOA optimization algorithm

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1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Lin, Xiaojia Zhang, Fuquan Xu, Lin Department of Computer Science Fujian Business University Fuzhou350012 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang Univeristy Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China Innovative Information Industry Research Institute Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783319707297

In order to optimize the design of gear reducer, gear reducer optimal design to improve reliability and security, slow convergence and local optimum for FOA algorithm is proposed based on the improved type FOA gear reducer optimization design model. To avoid falling into local optimum Drosophila optimization algorithms, improved FOA algorithm by introducing a correction factor. Superior to the gear reducer seven variables optimization design model for the study, to ensure the safety and reliability of the premise, the FOA has the advantage of improved convergence speed and avoid local optimization problems, in order to verify the proposed method and reliability. © Springer International Publishing AG 2018.

关键词： Reliability

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Deep learning based joint resource scheduling algorithms for hybrid MEC networks

arXiv

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arXiv 2019年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and Business Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom

In this paper, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial vehicle (UAVs), all with mobile edge cloud installed to enable user equipments (UEs) or Internet of thing (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real-time, while considering the dynamic environment. To this end, we propose a hybrid deep learning based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LSFCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Secondly, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed integer nonlinear programming (MINLP) problems and generate the sample datasets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Thirdly, a DNN with the scheduling layer is introduced to provide user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from traditional DNN predictor, we only input one UEs information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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An Efficient Genetic Algorithm for Solving Constraint Shortest Path Problem Through Specified Vertices 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Kai, Zhang Yunfeng, Shao Zhaozong, Zhang Wei, Hu School of Computer Science Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430081 China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

Finding a constraint shortest path which passes through a set of specified vertices is very important for many research areas, such as intelligent transportation systems, emergency rescue, and military planning. In this paper, we propose an efficient genetic algorithm for solving the constraint shortest path problem. Firstly, the Dijkstra algorithm is used to calculate the shortest distance between any two specified vertices. The optimal solution change from the original problem into the Hamilton path problem with the specified vertices. Because the number of specified vertices is much less than the number of vertices for the whole road network, the search space would be reduced exponentially. Secondly, the genetic algorithm is adopted to search for the optimal solution of the Hamilton path problem. Thirdly our algorithm should detect and eliminate the cycle path. Finally, the performance of our algorithm is evaluated by some real-life city road networks and some randomly generated road networks. The computational results show that our algorithm can find the constraint shortest path efficiently and effectively. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Medical Image Registration Based on Moving Manifold Regularization

Medical Image Registration Based on Moving Manifold Regulari...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Wei Zhang Huabing Zhou Yicheng Yang Changcai Yang Zhenghong Yu Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China College of Computer and Information Sciences Fujian Agriculture and Forestry University FuZhou China College of Mechanical and Electrical Engineering Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

We propose a new registration framework, namely moving manifold regularization, for solving medical image registration problems. The proposed method first uses the Snake model to obtain the region of interest on the images, and then extracts the two landmark point sets from the segmented images. After that, a transformation model based on the two landmark point sets is used to register the medical images. This framework considers the error functional with moving manifold regularization as we formulate this transformation model as a vector-field interpolation problem. The regularized error function moves by pixels, and the manifold regularization is used as the prior of the transformation to capture the underlying geometry information of the input data. The transformation function for each pixel is specified in a special space namely Reproducing Kernel Hilbert Space. Extensive experiments on medical images showed that the proposed method, which overcomes the defects of manual selection error and improves the registration accuracy, is an effective and stable registration method.

关键词： Manifolds Medical diagnostic imaging Feature extraction Image registration Geometry Interpolation

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Fully distributed certificateless threshold signature without random oracles

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中国科学（信息科学） 2018年第9期61卷 253-255页

作者： Wenjie YANG Weiqi LUO Xizhao LUO Jian WENG Anjia YANG College of Cyber Security/College of Information Science and Technology Jinan University Guangzhou 510632 China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou 511443 China Guangzhou Key Laboratory of Data Security and Privacy Preserving Guangzhou 511443 China College of Cyber Security/College of Information Science and Technology Jinan University Guangzhou 510632 China School of Computer and Technology Soochow University Suzhou 215006 China

A certificateless (t,n) threshold signature (CLTS) scheme allows at least t members to cooperatively sign a message on behalf of an n-member group,which is a good way to share the responsibility and *** researchers have focused on CLTS schemes in the ***,these schemes numerously employ a single key generation center (KGC) and thus inevitably suffer from single point of failure and abuse of single key generator *** settle these problems,Xiong et al.[1] introduced the concept of fully distributed CLTS,in which multiple distributed KGCs take the responsibility of generating and allocating the entity partial private key to distributed ***,in their definition,each of the distributed KGCs directly generates and sends an entity partial private key share to a signer,which implicates two assumed *** is that the number of KGCs and signers is the same,which results in poor *** other one is that the collusion between KGCs and signers is forbidden,which weakens their security model.

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引用

25th International Conference on Neural information processing, ICONIP 2018

作者： Liu, Wenfeng Liu, Peiyu Yi, Jing Yang, Yuzhen Liu, Weitong Li, Nana School of Information Science and Engineering Shandong Normal University Jinan250014 China School of Computer Heze University Heze274015 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China

ISBN: (纸本)9783030041816

How to effectively measure the similarity between two sentences is a challenging task in natural language processing. In this paper, we propose a sentence similarity comparison method that combines word embeddings and syntactic structure. First of all, by generating the corresponding syntactic tree, we synthetically analyze the two sentences and block them according to the syntactic components. Secondly, we prune the syntactic tree, remove the stop words and perform morphological restoration. Then, some important operations will be performed, such as passive flipping, negative flipping, and so on. Finally, the similarity of two sentence pairs is calculated by weighting the block embeddings of the syntactic tree. Experiments show the effectiveness of this method. © Springer Nature Switzerland AG 2018.

关键词： Embeddings

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Crowd-Guided Entity Matching with Consolidated Textual Data

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Journal of computer Science & technology 2017年第5期32卷 858-876页

作者： Zhi-Xu Li Qiang Yang An Liu Guan-Feng Liu Jia Zhu Jia-Jie Xu Kai Zheng Min Zhang School of Computer Science and Technology Soochow University Suzhou 215006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China School of Computer Science and Technology Soochow University Suzhou 215006 China School of Computer South China Normal University Guangzhou 510631 China School of Computer Science and Technology Soochow University Suzhou 215006 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing 100872 China

Entity matching (EM) identifies records referring to the same entity within or across databases. Existing methods using structured attribute values (such as digital, date or short string values) may fail when the structured information is not enough to reflect the matching relationships between records. Nowadays more and more databases may have some unstructured textual attribute containing extra consolidated textual information (CText) of the record, but seldom work has been done on using the CText for EM. Conventional string similarity metrics such as edit distance or bag-of-words are unsuitable for measuring the similarities between CText since there are hundreds or thousands of words with each piece of CText, while existing topic models either cannot work well since there are no obvious gaps between topics in CText. In this paper, we propose a novel cooccurrence-based topic model to identify various sub-topics from each piece of CText, and then measure the similarity between CText on the multiple sub-topic dimensions. To avoid ignoring some hidden important sub-topics, we let the crowd help us decide weights of different sub-topics in doing EM. Our empirical study on two real-world datasets based on Amzon Mechanical Turk Crowdsourcing Platform shows that our method outperforms the state-of-the-art EM methods and Text Understanding models.

关键词： entity matching consolidated textual data crowdsourcing

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Opinion target extraction for the Chinese formal text based on dependency relations 1st

Opinion target extraction for the Chinese formal text based ...

引用

1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Yang, Xiao-Yan Xu, Ge Zhang, Fu-Quan Liao, Xiang-Wen Xu, Lin Department of Computer Science Minjiang University FuzhouFujian350121 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control FuzhouFujian350121 China School of Software Beijing Institute of Technology Beijing10081 China College of Mathematics and Computer Science Fuzhou University FuzhouFujian350108 China Innovative Information Industry Research Institute Fujian Normal University FuzhouFujian350300 China

ISBN: (纸本)9783319707297

Due to the increasing amount of opinion data on the internet, opinion mining has become a hot topic, in which extracting opinion targets is a key step. The state-of-the-art approaches only use direct dependency relation patterns to extract opinion targets and the indirect dependency relation patterns have not been used. In this paper, the dependency relations between opinion target and opinion word are defined, and direct and indirect dependency relation patterns are designed. Then, a bootstrapping approach is used to extract and evaluate both candidate patterns and opinion targets. The experimental results show that in formal text, the approach improves the performance compared with the state-of-the-art approaches for opinion target extraction. © Springer International Publishing AG 2018.

关键词： Sentiment analysis

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