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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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Factors promoting and hindering informatization of nursing institutions for the aged: A toe theoretic perspective 18

Factors promoting and hindering informatization of nursing i...

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18th Pacific Asia Conference on Information Systems, PACIS 2014

作者： He, Yingzhao Zuo, Meiyun Chen, Jie Key Laboratory of Data Engineering and Knowledge Engineering School of Business Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China

This paper uses the theoretic framework of TOE(technology-organization-environment), identifies the factors promoting and hindering informatization of nursing institutions for the aged. This case study of six nursing institutions for the aged finds that different ownership institutions have some different factors promoting and hindering the process of informatization. In general, compared with public nursing institutions for the aged, private nursing institutions have more both promoting and hindering factors in technological and organizational aspect, but less hindering factors in the environmental aspect;and, risk management is an important promoting factor in the application of information systems for these institutions, it is fewer reported in other industries' study. These findings extend the application field of the TOE theory;extend the informatization study in nursing institutions for the aged. In practical,we suggest that different ownership's nursing institutions for the aged should take good advantage of promoting factors considering their own resources, overcome the hindering factors to ensure the successful implementation of the informatization.

关键词： Informatization

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knowledge sharing of senior employees in the context of task-based cooperation: A game theoretic analysis 18

Knowledge sharing of senior employees in the context of task...

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18th Pacific Asia Conference on Information Systems, PACIS 2014

作者： Wang, Changyu Zuo, Meiyun Quan, Xuefei Key Laboratory of Data Engineering and Knowledge Engineering School of Business Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China

The aging of population has become a global trend and the critical knowledge of enterprises may be lost with the retirement of senior employees. Task-based cooperation is an important method to prevent the loss of knowledge and to promote the knowledge sharing behavior between senior employees and young ones. This paper constructs the knowledge contribution payoff functions of senior employees and young ones in the context of task-based cooperation and analyzes the knowledge contribution behavior using static game theory with complete information, and then we obtain Nash equilibrium solutions under different conditions. The results suggest that enterprises can promote knowledge contribution of senior employees and young ones in the process of task-based cooperation by improving social rate of return(e.g., creating a good cultural atmosphere), reducing cost rate of knowledge contribution (e.g., training on knowledge expression), raising economic rate of return and providing monetary compensation according to the contribution of knowledge, whether the marginal payoff of employees' knowledge contribution is increasing, invariant or diminishing. Since the formation of social return is difficult in the short term, giving economic incentives can be an effective way at the beginning. With the progress of task-based cooperation between senior employees and young ones, the amount of employees' knowledge (especially of young ones'), the productivity of knowledge contribution, the collaborative efficiency and social rate of return will increase and therefore economic incentives contributing to cooperation between both players may decrease appropriately. The findings of this paper are beneficial to understand knowledge sharing behavior in the context of task-based cooperation, and provide several incentive mechanisms for enterprises to promote knowledge sharing between senior employees and young ones.

关键词： Game theory

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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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Efficient Adaptive Label Refinement for Label Noise Learning

arXiv

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arXiv 2025年

作者： Zhang, Wenzhen Cheng, Debo Lu, Guangquan Zhou, Bo Li, Jiaye Zhang, Shichao School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guangxi Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Guilin541004 China UniSA STEM University of South Australia AdelaideSA5095 Australia Guangxi Collaborative Innovation Center of Modern Sericulture and Silk Hechi University Guangxi Hechi546300 China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou310027 China

Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined criteria, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy labels while thoroughly learning clean samples. However, this often results in overly complex and difficult-to-train models. To address this issue, we decouple the tasks of avoiding fitting incorrect labels and thoroughly learning clean samples and propose a simple yet highly applicable method called Adaptive Label Refinement (ALR). First, inspired by label refurbishment techniques, we update the original hard labels to soft labels using the model’s predictions to reduce the risk of fitting incorrect labels. Then, by introducing the entropy loss, we gradually ‘harden’ the high-confidence soft labels, guiding the model to better learn from clean samples. This approach is simple and efficient, requiring no prior knowledge of noise or auxiliary datasets, making it more accessible compared to Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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HGFormer: Topology-Aware Vision Transformer with HyperGraph Learning

arXiv

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arXiv 2025年

作者： Wang, Hao Zhang, Shuo Leng, Biao School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing100044 China

The computer vision community has witnessed an extensive exploration of vision transformers in the past two years. Drawing inspiration from traditional schemes, numerous works focus on introducing vision-specific inductive biases. However, the implicit modeling of permutation invariance and fully-connected interaction with individual tokens disrupts the regional context and spatial topology, further hindering higher-order modeling. This deviates from the principle of perceptual organization that emphasizes the local groups and overall topology of visual elements. Thus, we introduce the concept of hypergraph for perceptual exploration. Specifically, we propose a topology-aware vision transformer called HyperGraph Transformer (HGFormer). Firstly, we present a Center Sampling K-Nearest Neighbors (CS-KNN) algorithm for semantic guidance during hypergraph construction. Secondly, we present a topology-aware HyperGraph Attention (HGA) mechanism that integrates hypergraph topology as perceptual indications to guide the aggregation of global and unbiased information during hypergraph messaging. Using HGFormer as visual backbone, we develop an effective and unitive representation, achieving distinct and detailed scene depictions. Empirical experiments show that the proposed HGFormer achieves competitive performance compared to the recent SoTA counterparts on various visual benchmarks. Extensive ablation and visualization studies provide comprehensive explanations of our ideas and contributions. Copyright © 2025, The Authors. All rights reserved.

关键词： k-nearest neighbors

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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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Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022

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International Journal of Digital Earth 2025年第1期18卷

作者： Wang, An Su, Hua Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China Fuzhou University Fuzhou China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou China Department of Geography Ghent University Ghent Belgium

Long time series and accurate subsurface temperature data in the global ocean are essential for ocean warming and climate change studies. The sparse in situ observations in the pre-Argo era hinder the reconstruction of long-time series observational data for the global ocean. This study proposes a novel Adaptive Spatio-TEmporal Neighbors with two-point differences (ASTEN) method for subsurface temperature reconstruction, which adaptively learns and adjusts spatio-temporal neighbors depending on the distribution of in situ observations to ensure robust gaps-filling performance across four dimensions. By integrating geoscience domain knowledge and utilizing spatiotemporal autocorrelation, ASTEN simultaneously learns the spatial pattern and temporal variation of subsurface temperature, and significantly enhances the interpretability and accuracy of ocean temperature reconstructions over a long time series compared to the DINCAE and DINEOF. The ASTEN reconstructed temperature data for the upper 1000 m from 1960 to 2022 can effectively track the ocean warming process for more than six decades. This study demonstrates the ASTEN method is well suited for subsurface temperature reconstruction, and holds great potential in the gaps-filling of sparse ocean observations with high missing rates over a large scale. The new reconstruction of subsurface temperature can effectively reduce the uncertainty of subsurface ocean warming analysis. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Global warming

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Author Name Disambiguation Based on Rule and Graph Model 9th

Author Name Disambiguation Based on Rule and Graph Model

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9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020

作者： Zhang, Lizhi Ban, Zhijie Inner Mongilia A.R. Key Laboratory of Data Mining and Knowledge Engineering College of Computer Science Inner Mongolia University Huhhot China

ISBN: (纸本)9783030604493

Author name disambiguation has long been viewed as a challenging problem in scientific literature management, and with the substantial growth of the scientific literature, the solution to this problem has become increasingly difficult and urgency. In this paper, we conduct research on the author name disambiguation problem in large-scale academic papers. In our method, we combine the paper feature information and the relation information between the papers for disambiguation. Based on the Aminers disambiguation framework, we present a novel method to constructing the paper relation graph based on atomic cluster and propose an efficient post processing algorithm, aiming to improve the disambiguation performance by rule-based clustering, this algorithm utilizes similarity features based on metadata information and implement two types of disambiguation rules. We carefully evaluate the proposed disambiguation method on real-world large data and experimental result shows that our method achieves clearly better performance than the state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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