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2DCMA： An Effective Maintenance Algorithm of Materialized Views in Peer data Management Systems

Journal of Computer Science & Technology 2006年第4期21卷 503-512页

作者：覃飙王珊杜小勇 School of Information Renmin University of China Beijing 100872 P.R. China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 P.R. China

Update management is very important for data integration systems. So update management in peer data management systems （PDMSs） is a hot research area. This paper researches on view maintenance in PDMSs. First, the definition of view is extended and the peer view, local view and global view are proposed according to the requirements of applications. There are two main factors to influence materialized views in PDMSs. One is that schema mappings between peers are changed, and the other is that peers update their data. Based on the requirements, this paper proposes an algorithm called 2DCMA, which includes two sub-algorithms： data and definition consistency maintenance algorithm% to effectively maintain views. For data consistency maintenance, Mork＇s rules are extended for governing the use of updategrams and boosters. The new rule system can be used to optimize the execution plan. And are extended for the data consistency maintenance algorithm is based on the new rule system. Furthermore, an ECA rule is adopted for definition consistency maintenance. Finally, extensive simulation experiments are conducted in SPDMS. The simulation results show that the 2DCMA algorithm has better performance than that of Mork＇s when maintaining data consistency. And the 2DCMA algorithm has better performance than that of centralized view maintenance algorithm when maintaining definition consistency.

关键词： view maintenance peer data management system schema mapping ECA rule

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Dual-stream coupling network with wavelet transform for cross-resolution person re-identification

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Journal of Systems engineering and Electronics 2023年第3期34卷 682-695页

作者： SUN Rui YANG Zi ZHAO Zhenghui ZHANG Xudong Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education) Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China

Person re-identification is a prevalent technology deployed on intelligent *** have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open *** real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera *** low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR *** address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification ***,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR ***,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various *** qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.

关键词： cross-resolution feature invariant learning person re-identification residual knowledge transfer wavelet transform

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Attributes-based person re-identification via CNNs with coupled clusters loss

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Journal of Systems engineering and Electronics 2020年第1期31卷 45-55页

作者： SUN Rui HUANG Qiheng FANGWei ZHANG Xudong Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education) Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China

Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and *** attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and *** paper proposes an end-to-end framework of deep learning for attribute-based person *** the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN ***,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id *** coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of *** combined features are mapped into the Euclidean *** L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the *** experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.

关键词： person re-identification(re-id) convolutions neural network(CNN) attributes coupled clusters loss(CCL)

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ViGT: proposal-free video grounding with a learnable token in the transformer

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Science China(information Sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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A Review-Based Reputation Evaluation Approach for Web Services

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Journal of Computer Science & Technology 2009年第5期24卷 893-900页

作者：李海华杜小勇 Member 田萱 Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China School of Information Science & Technology Beijing Forestry University Beijing 100083 China

Web services are commonly perceived as an environment of both offering opportunities and threats. In this environment, one way to minimize threats is to use reputation evaluation, which can be computed, for example, through transaction feedback. However, the current feedback-based approach is inaccurate and ineffective because of its inner limitations （e.g., feedback quality problem）. As the main source of feedback, the qualities of existing on-line reviews are often varied greatly from low to high, the main reasons include：（1） they have no standard expression formats, （2） dishonest comments may exist among these reviews due to malicious attacking. Up to present, the quality problem of review has not been well solved, which greatly degrades their importance on service reputation evaluation. Therefore, we firstly present a novel evaluation approach for review quality in terms of multiple metrics. Then, we make a further improvement in service reputation evaluation based on those filtered reviews. Experimental results show the effectiveness and efficiency of our proposed approach compared with the naive feedback-based approaches.

关键词： Web services reputation evaluation review quality measurement

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CSMCCVA:Framework of cross-modal semantic mapping based on cognitive computing of visual and auditory sensations

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High Technology Letters 2016年第1期22卷 90-98页

作者：刘扬 Zheng Fengbin Zuo Xianyu Laboratory of Spatial Information Processing Henan University College of Computer Science and Information Engineering Henan University College of Environment and Planning Henan University Institute of Data and Knowledge Engineering Henan University

Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search *** study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory *** mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the ***,the algorithms of cross-modal semantic mapping were *** results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.

关键词： multimedia neural cognitive computing （MNCC） brain-like computing cross-modal semantic mapping （CSM） selective attention limbic system multisensory integration memory-enhancing mechanism

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A new dynamic hash index for flash-based storage

A new dynamic hash index for flash-based storage

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9th International Conference on Web-Age information Management, WAIM 2008

作者： Xiang, Li Zhou, Da Meng, Xiaofeng School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE

ISBN: (纸本)9780769531854

Compared with traditional magnetic disks, Flash memory has many advantages and has been used as external storage media for a wide spectrum of electronic devices (such as PDA, MP3, Digital Camera and Mobile Phone) in recent years. As the capacity increases and price drops, it looks like a perfect alternative for magnetic disks. However, due to hardware limitations of flash memory, techniques including storage subsystem and indexing originally designed for magnetic disks can not run smoothly in a flash memory without any modification. In this paper we explore problems of indexing flash-resided data and present a new dynamical hash index for flash memory in two schemas. The analysis and experimental results validate the efficiency of our design. © 2008 IEEE.

关键词： Flash memory

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Representation learning via an integrated autoencoder for unsupervised domain adaptation

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Frontiers of Computer Science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target *** key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain ***,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain ***,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain *** address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called *** capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature *** higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain *** addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local *** results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder

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Towards video management over relational database

Towards video management over relational database

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12th International Asia Pacific Web Conference, APWeb 2010

作者： Li, Hui Zhang, Xiao Wang, Shan Du, Xiaoyong School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering China

ISBN: (纸本)9780769540122

Video has become popular in our daily life for both professional and consumer applications. Both low level video processing and high level semantic video analysis are critically computational tasks in application domains. Most of current video computing tools are developed for specific analytic tasks, they are lack higher level interoperability with database and treat database merely as a relational data storage engine rather than an analytic platform, which causes inefficient data access and massive amount of data movement. In this paper, we study how to support video data management over relational database, and present our initial solutions of video data storage mechanism, video data access method and efficient video analytics. We also illustrate our ongoing prototype system HybVideo that developed in a novel architecture. It integrates above solutions to tackle the major challenges of providing a platform for both storage and analysis of video data. © 2010 IEEE.

关键词： information management

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Approximation algorithm for constructing data aggregation trees for wireless sensor networks

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中国高等学校学术文摘·计算机科学 2009年第4期3卷 524-534页

作者： Deying LI Jiannong CAO Qinghua ZHU Key Laboratory of Data Engineering and Knowledge Engineering School of InformationRenmin University of ChinaBeijing 100872China Internet and Mobile Computing Lab Department of ComputingHong Kong Polytechnic UniversityHong KongChina

This paper considers the problem of constructing data aggregation trees in wireless sensor networks (WSNs)for a group of sensor nodes to send collected information to a single sink *** data aggregation tree contains the sink node,all the source nodes,and some other non-source *** goal of constructing such a data aggregation tree is to minimize the number of non-source nodes to be included in the tree so as to save *** prove that the data aggregation tree problem is NP-hard and then propose an approximation algorithm with a performance ratio of four and a greedy *** also give a distributed version of the approximation *** simulations are performed to study the performance of the proposed *** results show that the proposed algorithms can find a tree of a good approximation to the optimal tree and has a high degree of scalability.

关键词： WSNs data aggregation tree approximation algorithm distributed algorithm

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