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2012 International Conference on Engineering Materials, ICEM 2012

作者： Li, Wenjie Liu, Si Hu, Yu Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology 300384 Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology 300384 Tianjin China School of Computer Science and Technology Tianjin University 300072 Tianjin China

ISBN: (纸本)9783037856130

In homogeneous or heterogeneous databases, the main direction of research on XML-based information interaction between databases is taking XML as middleware to realize the information exchange between data in the environment of small and medium-sized databases. Considering the insufficiency of the traditional text file-based information interaction method, the paper puts forward an XML-based method of information exchanging between databases, and the corresponding database exchanging system is developed. The utmost feature and advantage of the system is taking hibernate XML mapping table file for converting middleware to realize information interaction between databases. The experiment result shows that the system provides a certain reference value for data mining and data warehouse. © (2013) Trans Tech Publications, Switzerland.

关键词： Middleware

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Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

Multi-View Representation Learning for Multi-Instance Learni...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhao, Lu Yuan, Liming Li, Zhenliang Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using 1 times1 convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance;ii) Compared to building multi-view representations directly from scratch, extracting them automatically using 1 times1 convolutions is more economical, and may be more effective since 1 times1 convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability. © 2022 IEEE.

关键词： Convolution

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A hybrid efficient short-term traffic flow forecast technology

A hybrid efficient short-term traffic flow forecast technolo...

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6th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2009

作者： Lin, Xin Wang, Xiaoye Xiao, Yingyuan Zhang, Degan Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300191 China

ISBN: (纸本)9780769537351

This paper presents a hybrid short-term traffic flow forecast technology. For the uncertainty, the short-term traffic flow forecast is complicated, and the accuracy is not high. This strategy combines the RBF neural network and ant colony clustering algorithm to forecast the traffic flow. It used ant colony clustering algorithm to get the centers of hidden layer neurons. To find the best clustering result, local search is used in ant colony algorithm. The model has strong local generalization abilities and high accuracy. The simulation experiment results illuminate that the application is fairly effective. © 2009 IEEE.

关键词： Forecasting

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Deep Multi-Instance Learning with Adaptive Recurrent Pooling for Medical Image Classification

Deep Multi-Instance Learning with Adaptive Recurrent Pooling...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ding, Yi Zhao, Lu Yuan, Liming Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key issue for these multi-instance neural networks is how to aggregate all patch (instance) features into an entire image (bag) representation, referred to as multi-instance pooling, e.g., max, mean, and attention based pooling. Nevertheless, these multi-instance pooling operations do not take the structural information within an image into account. This is obviously inappropriate for medical image classification since there often exist certain dependencies among regional patches/lesions. We propose an adaptive recurrent pooling based deep multi-instance neural network in this paper. In this network, we first extract multi-view global structural features from every bag using the self-attention mechanism, and then aggregate these multi-view features into a whole bag representation based on the adaptive recurrent pooling operation in order to further capture the contextual information within the bag. Moreover, we introduce the cross-normalization operation used in the Unit Force Operated vision Transformer into the self-attention module to reduce its computational complexity. We have experimentally evaluated the performance of the proposed network on three medical image datasets, namely UCSB breast, Messidor, and Colon cancer. The results demonstrate the advantage of our network over current state-of-the-art deep multi-instance networks in terms of classification accuracy and interpretability. © 2022 IEEE.

关键词： Aggregates

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Knowledge discovery approach based on closeness relationship of FC

Knowledge discovery approach based on closeness relationship...

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6th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2009

作者： Zhang, Degan Li, Miao Zhang, Xiaoli Wang, Jinhui Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300191 China

ISBN: (纸本)9780769537351

A fuzzy clustering is one of important and valid methods to knowledge discovery. One of problems in fuzzy clustering is to determine a certain fuzzy sample classification in given limited sample space. Another is its validity, that is to say, if the sample is resemble in sample space, its fuzzy type will be resemble too. In our research, firstly, using triangle arithmetic operator and triangle transference, we extend fuzzy equivalence relationship to fuzzy closeness relationship, and cluster. Secondly, importing fuzzy coverage based on fuzzy closeness relationship, we judge resemble type of resemble sample. Thirdly, we introduce the clustering method based on fuzzy closeness relationship and fuzzy coverage. The method can overcome information more loss in fuzzy equivalence. Finally, evaluating its validity, we test its feasibility. The method above is applied to knowledge discovery for electronic commerce on Internet, we have got good result. © 2009 IEEE.

关键词： Fuzzy clustering

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Light field imaging for computer vision:a survey

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Frontiers of Information Technology & Electronic Engineering 2022年第7期23卷 1077-1097页

作者： Chen JIA Fan SHI Meng ZHAO Shengyong CHEN Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China

Light field(LF)imaging has attracted attention because of its ability to solve computer vision *** this paper we briefly review the research progress in computer vision in recent *** most factors that affect computer vision development,the richness and accuracy of visual information acquisition are *** imaging technology has made great contributions to computer vision because it uses cameras or microlens arrays to record the position and direction information of light rays,acquiring complete three-dimensional(3D)scene *** imaging technology improves the accuracy of depth estimation,image segmentation,blending,fusion,and 3D *** has also been innovatively applied to iris and face recognition,identification of materials and fake pedestrians,acquisition of epipolar plane images,shape recovery,and LF ***,we further summarize the existing problems and the development trends of LF imaging in computer vision,including the establishment and evaluation of the LF dataset,applications under high dynamic range(HDR)conditions,LF image enhancement,virtual reality,3D display,and 3D movies,military optical camouflage technology,image recognition at micro-scale,image processing method based on HDR,and the optimal relationship between spatial resolution and four-dimensional(4D)LF information *** imaging has achieved great success in various *** the past 25 years,more than 180 publications have reported the capability of LF imaging in solving computer vision *** summarize these reports to make it easier for researchers to search the detailed methods for specific solutions.

关键词： Light field imaging Camera array Microlens array Epipolar plane image computer vision

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Research on multi-dimensional association rules mining in distributed environments based on advanced SQL query

Research on multi-dimensional association rules mining in di...

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6th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2009

作者： Zhang, Lei Qiao, Mei Zhang, Degan Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300191 China

ISBN: (纸本)9780769537351

This paper proposes the MDMA (Multi-dimensional Distributed Mining Association rules) algorithm based on advanced SQL query. The algorithm works on star-style structure networks. It uses CUBE operator in new standard SQL and combines with SQL powerful query function. So there is no need for the proposed algorithm to conduct a great deal of iterations to generate frequent itemsets in the process of mining association rules. Therefore, no matter what the number of the site in the distributed environments and the scale of the local database in the local site is, the algorithm always needs only two times of scanning the database and only three times of network communications to generate all the global frequent itemsets. Consequently, this algorithm has the merits of light network traffic, low time cost, better scalability and more simplicity. © 2009 IEEE.

关键词： Association rules

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A local trust model based on time influence function in P2P networks

A local trust model based on time influence function in P2P ...

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

作者： Yan-xia Cui Hong-yun Ning Yun-yun Du Key Laboratory of Computer Vision and System Tianjin University of Technology Tianjin China

ISBN: (纸本)9781479983544

In peer-to-peer network environment, there is no centralized management authority to be relied on, so the service provider and service requester have to establish some assessment model, and evaluate the dynamic behavior of others, then choose more credible trusted entities to interact. Only in this way can they ensure security, safety and reliable decisions. On the basis of EigenTrust model, a local trust value computation model considering time effect function is put forward, and it reduces the network overhead, increases the binding force of malicious nodes, and makes the trust value computation model more reliable and accurate. Simulation experiments show that the improved model proposed in this paper greatly reduces the network overhead indeed, and transaction success rate of nodes is higher than EigenTrust model in the presence of malicious nodes in the P2P system.

关键词： Peer-to-peer computing Computational modeling Analytical models Time-frequency analysis Security History Laboratories

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A novel two-step-deformation active contour with application to facial features extraction

A novel two-step-deformation active contour with application...

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2009 2nd International Congress on Image and Signal Processing, CISP'09

作者： Hua, Zhang Yuanquan, Wang Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300191 China

ISBN: (纸本)9781424441310

In allusion to the boundary leaking problem of traditional active contours when used to extract facial features, two kinds of local information are introduced to enhance the weak boundary, then to build new external force for active contours. The prior knowledge of the local shape of the facial features is incorporated into the evolution of active contour. The snake contour in each evolution step just serves as prediction, and then is ruled by the prior local shape;this way, the snake contour deforms via prediction-correction two steps. This two-step-deformation (TSD) snake can utilize the a priori shape of objects effectively. The proposed strategies are evaluated on several images and the experimental results validate the high performance of this TSD snake for facial features segmentation. ©2009 IEEE.

关键词： Deformation

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An Adaptive Contourlet-domain Watermarking Algorithm Based on Chaotic Encryption

An Adaptive Contourlet-domain Watermarking Algorithm Based o...

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2012 International Conference on Convergence computer Technology(IC3T 2012)

作者： Liu Yuanchao Wang Huaibin Wang Chundong Li Jinfeng Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology

In this paper an adaptive Contourlet-domain watermarking algorithm is proposed based on chaotic encryption. In order to enhance the security of watermarking, the watermark is processed using chaotic encryption. The wavelet-based Contourlet transform is applied on the host image. When being embedded, the encrypted watermark is adaptively embedded into the Contourlet coefficients with larger magnitude that correspond to the edges. And then, the algorithm automatically adjusts the intensity of embedding. Furthermore a blind detection method is presented using Neyman-Pearson criterion. The experimental results prove the validity of this proposed algorithm and good robustness against several common watermarking attacks.

关键词： Digital Watermarking, Contourlet Transform, Chaotic Encryption

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