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EvolveKG:a general framework to learn evolving knowledge graphs

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Frontiers of Computer Science 2024年第3期18卷 43-59页

作者： Jiaqi LIU zhiwen yu Bin GUO Cheng DENG Luoyi FU Xinbing WANG Chenghu ZHOU School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China Department of Computer Science Shanghai Jiao Tong UniversityShanghai 200240China Institute of Geographical Science and Natural Resources Research Chinese Academy of SciencesBejjing 100864China

A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical *** observation gives rise to evolving knowledge graphs whose structure temporally grows over ***,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain *** this end,we propose EvolveKG–a general framework that enables algorithms in the static knowledge graphs to learn the evolving *** quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge *** novelty of EvolveKG lies in Derivative Graph–a weighted snapshot of evolution at a certain ***,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with ***,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains *** four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.

关键词： knowledge graph evolution modal characterization algorithmic implementation

Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2204-2219页

作者： yuanxin Lin zhiwen yu Kaixiang Yang Ziwei Fan C.L.Philip Chen the School of Computer Science and Engineering in South China University of Technology IEEE the Pengcheng Laboratory

Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.

关键词： Learning systems Adaptation models Correlation Adaptive systems Annotations Boosting Classification algorithms Broad learning system label correlation mining label imbalance weighting multi-label imbalance CLASSIFICATION ENSEMBLE

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丝杠进给系统运动控制研究

机电工程技术 2024年第4期53卷 54-57页

作者：俞志文李钢南京工程学院南京211167

丝杠进给系统在运行过程中具有非线性、强耦合和参数时变等特点,由于传统PID控制器的参数固定,无法很好地适应系统变化,从而导致系统响应速度缓慢及跟踪性能较差。针对此类问题,将复杂的丝杠进给系统简化处理并建立二质量系统模型;对传... 详细信息

丝杠进给系统在运行过程中具有非线性、强耦合和参数时变等特点,由于传统PID控制器的参数固定,无法很好地适应系统变化,从而导致系统响应速度缓慢及跟踪性能较差。针对此类问题,将复杂的丝杠进给系统简化处理并建立二质量系统模型;对传统的模糊规则加以优化和改进,设计了一种模糊PI控制与加速度前馈补偿相结合的复合控制器。模糊PI控制器根据实际系统特性调整模糊规则,使得控制器参数自适应变化,提高控制效果;加速度前馈补偿控制器根据输入信号预先补偿系统,提升了系统带宽。在Simulink中搭建系统模型,在阶跃输入和干扰输入下对比观察2种控制器作用的系统响应曲线。经仿真验证,丝杠进给系统的动态特性、稳态特性和抗干扰能力都得到了明显地提升。

关键词：丝杠进给系统模糊PI控制前馈补偿动态特性

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Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

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Frontiers of Computer Science 2020年第6期14卷 55-69页

作者： Qianchen yu zhiwen yu Zhu WANG Xiaofeng WANG Yongzhi WANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China Shanxi Provincial Key Laboratory for Embedded System Xi'an 710072China College of Computer Science and Engineering North Minzu UniversityYinchuan 750021China College of Earth Exploration Science and Technology Jilin UniversityChangchun 130025China

Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been ***,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined *** propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in *** Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been ***,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the *** this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout *** conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.

关键词： graph convolutional neural network graph classification overlapping community detection nonparametric Bayesian generative model relational infinite latent feature model Indian buffet process uncollapsed Gibbs sampler posterior inference quality estimation

Human-machine interactive streaming anomaly detection by online self-adaptive forest

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Frontiers of Computer Science 2023年第2期17卷 145-156页

作者： Qingyang LI zhiwen yu Huang XU Bin GUO School of Computer Science Northwestern Polytechnical UniversityXi'an 710129China

At nomaly detectors are used to distinguish differences between normal and abnormal data,which are usually implemented by evaluating and ranking the anomaly scores of each instance.A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score *** real scenarios,anomaly detection often needs to be regulated by human feedback,which benefits adjusting anomaly *** this paper,we propose a human-machine interactive streaming anomaly detection method,named ISPForest,which can be adaptively updated online under the guidance of human *** particular,the feedback will be used to adjust the anomaly score calculation and structure of the detector,ideally attaining more accurate anomaly scores in the *** main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model *** approach is instantiated for the powerful class of tree-based streaming anomaly detectors,and we conduct experiments on a range of benchmark *** results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts.

关键词： anomaly detection human-machine interaction human feedback random space tree ensemble method

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Relative manifold based semi-supervised dimensionality reduction

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Frontiers of Computer Science 2014年第6期8卷 923-932页

作者： Xianfa CAI Guihua WEN Jia WEI zhiwen yu School of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou 510006 China School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China Shenzhen Key Laboratory of High Performance Data Mining Shenzhen 518055 China

A well-designed graph plays a fundamental role in graph-based semi-supervised learning; however, the topological structure of a constructed neighborhood is unstable in most current approaches, since they are very sensitive to the high dimensional, sparse and noisy data. This generally leads to dramatic performance degradation. To deal with this issue, we developed a relative manifold based semisupervised dimensionality reduction （RMSSDR） approach by utilizing the relative manifold to construct a better neighborhood graph with fewer short-circuit edges. Based on the relative cognitive law and manifold distance, a relative transformation is used to construct the relative space and the relative manifold. A relative transformation can improve the ability to distinguish between data points and reduce the impact of noise such that it may be more intuitive, and the relative manifold can more truly reflect the manifold structure since data sets commonly exist in a nonlinear structure. Specifically, RMSSDR makes full use of pairwise constraints that can define the edge weights of the neighborhood graph by minimizing the local reconstruction error and can preserve the global and local geometric structures of the data set. The experimental results on face data sets demonstrate that RMSSDR is better than the current state of the art comparing methods in both performance of classification and robustness.

关键词： cognitive law relative transformation relative manifold local reconstruction semi-supervised learning

Mobile crowd photographing: another way to watch our world

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Science China(Information Sciences) 2016年第8期59卷 250-274页

作者： Huihui CHEN Bin GUO zhiwen yu Xingshe ZHOU School of Computer Science Northwestern Polytechnical University School of Computer and Information Engineering Luoyang Institute of Science and Technology

People take and share pictures in the mobile network. Through collecting and computing pictures with built-in contexts, Mobile Crowd Photographing(MCP) can give us a new way to see this world. This paper focuses on participatory picture collection, which is one way of MCP. Three characteristic issues of MCP are proposed, and then our recent work to solve these issues will also be demonstrated.

关键词： mobile crowd photographing task-driven data collection task definition task assignment data selection

A framework based on sparse representation model for time series prediction in smart city

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Frontiers of Computer Science 2021年第1期15卷 99-111页

作者： Zhiyong yu Xiangping ZHENG Fangwan HUANG Wenzhong GUO Lin SUN zhiwen yu College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China Hangzhou Key Laboratory for IoT Technology&Application Zhejiang University City CollegeHangzhou 310015China School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China

Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the *** one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT *** this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this *** dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower *** choice of different dictionaries has a significant impact on the performance of *** paper focuses on the study of dictionary construction strategy and summarizes eight variants of *** results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.

关键词： sparse representation smart city time series prediction dictionary construction

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A survey on ensemble learning

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Frontiers of Computer Science 2020年第2期14卷 241-258页

作者： Xibin DONG zhiwen yu Wenming CAO Yifan SHI Qianli MA School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Department of Computer Science City University of Hong KongHong Kong SAR 999077China

Despite significant successes achieved in knowledge discovery,traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data,such as imbalanced,high-dimensional,noisy data,*** reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of *** this context,it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining *** learning,as one research hot spot,aims to integrate data fusion,data modeling,and data mining into a unified ***,ensemble learning firstly extracts a set of features with a variety of *** on these learned features,multiple learning algorithms are utilized to produce weak predictive ***,ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive *** this paper,we review the research progress of the mainstream approaches of ensemble learning and classify them based on different *** addition,we present challenges and possible research directions for each mainstream approach of ensemble learning,and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning,reinforcement learning,etc.

关键词： ensemble learning supervised ensemble classification semi-supervised ensemble classification clustering ensemble semi-supervised clustering ensemble