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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Ren, Jiadong Huang, Guoyan Wang, Qian He, Haitao Liu, Xinqian Zhao, Xiaolin Computer Virtual Technology and System Integration Laboratory of Hebei Province College of Information Science and Engineering Yanshan University Qinhuangdao Hebei China Beijing Key Laboratory of Software Security Engineering Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781450371582

The complexity of software directly leads to an increasing cost in software testing and maintenance. Finding the important nodes with significant vulnerability is helpful for fault discovery and further reduces the damage to the software system. In this paper, a new algorithm named MIN-REP (Mining the Important Nodes based on Ripple Effects of Probability) is proposed to find out the paths with greater possibility for fault propagation, and then the important nodes are mined. To build a model of directed unweighted software network, functions are taken as the nodes and the dependencies between the functions are regarded as the edges. Fault propagation tendency paths are discovered based on the function execution paths and minimum probability threshold. The frequency of each directed edge in the set of fault propagation tendency path is taken as the weight of the corresponding edge. Then some metrics related to ripple effects of probability are calculated. Finally, the nodes with the metric at top-k are taken as the important nodes. The experiment verifies the accuracy and efficiency of the algorithm MIN-REP. © 2019 Association for Computing Machinery.

关键词： Probability

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Multi-label feature selection with feature reconstruction and label correlations

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Expert systems with Applications 2025年 285卷

作者： Ning Zhang Ao Wang Pengwei Lu Tao Feng Ying Xu Guodong Du School of Information Science and Engineering Yanshan University Qinhuangdao 066004 PR China Hebei Key Laboratory of Computer Virtual Technology and System Integration Yanshan University Qinhuangdao 066004 PR China Department of Computer Science and Technology Tsinghua University Beijing 100084 PR China Shenzhen Research Institute of Yanshan University Shenzhen 518063 PR China

With the arrival of the big data era, multi-label data have frequently appeared in various fields. However, multi-label data often contain rich feature information, leading to a high-dimensional feature space. Thus, multi-label feature selection plays a crucial role in addressing this challenge as a preprocessing step. Multi-label data assign a set of labels to each instance simultaneously; therefore, capturing the correlations among labels is an effective means of enhancing the learning performance. However, current multi-label feature selection methods seldom consider label correlations. Moreover, redundant features can cause overfitting, increase the complexity, and reduce the interpretability. To address these issues, we propose a multi-label feature selection method that incorporates feature reconstruction and label correlations. Specifically, we construct a low-dimensional embedding from the original feature space using induction, which preserves the manifold structure of the feature space. Next, we explore the global and local label correlations using a manifold regularization term designed by the generated low-dimensional space. We then achieve feature reconstruction by minimising the reconstruction error and sparse regularization terms to learn the relationships between the features. Finally, we construct an optimal objective function with ℓ 2 , 1 -norm regularization to facilitate multi-label feature selection and ensure convergence. Comparative experiments on 11 datasets show that the proposed method exhibits notable performance improvements over existing methods(e.g., achieves 5.6 % higher Average precision than RFS on S c e n e and 3.6 % lower Coverage than CRASG on S c i e n c e ), validating the effectiveness and superiority of the proposed method.

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Outlier detection based on cluster outlier factor and mutual density

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International Journal of Intelligent Information and Database systems 2019年第1-2期12卷 91-108页

作者： Zhang, Zhongping Zhu, Mengfan Qiu, Jingyang Liu, Cong Zhang, Debin Qi, Jie School of Information Science and Engineering Yanshan University Qinghuangdao Hebei066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province 438 West Section of Hebei Street Haigang District Qinhuangdao City Hebei Province066004 China Hebei Education Examinations Authority 231 Hongqi Street Shijiazhuang City Hebei Province050000 China First Middle School of Qian An Country 347 Wutong Road Qianan Jilin131400 China

Outlier detection is an important task in data mining with numerous applications. Recent years, the study on outlier detection is very active, many algorithms were proposed including based on clustering. However, most outlier detection algorithms based on clustering often need parameters and it is very difficult to select a suitable parameter for different data set. In order to solve this problem, an outlier detection algorithm called outlier detection based on cluster outlier factor and mutual density is proposed in this paper. The mutual density and γ density is used to construct decision graph. The data points with γ density anomalously large in decision graph are treated as cluster centres. This algorithm detect the boundary of outlier cluster using cluster outlier factor called cluster outlier factor (COF), it can automatic find the parameter. This method can achieve good performance in clustering and outlier detection which be shown in the experiments. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Data mining

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Semantic recommendation through Reinforcement Learning and weighted meta-paths

Semantic recommendation through Reinforcement Learning and w...

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2022 International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2022

作者： Yu, Jing Lang, Yue Li, Xuewen Zhang, Jin Zheng, Shaojie Gong, Jibing School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

Explainable recommendation systems, which can produce high-accuracy recommendations and help users make quick decisions, have become a hotspot in research field. Most of existing research algorithms committed to improving the accuracy of recommendation, but ignored the interpretability of recommendation. We propose a semantic recommendation algorithm through Reinforcement Learning and weighted meta-paths which analyzes and selects the meta-path, and uses Reinforcement Learning network to train the weight of meta-path. This method can effectively solve data sparsity, improve the accuracy of recommendation, and increase interpretability. Compared with the baseline method, indicators of the recommended methods for integrating Reinforcement Learning with Heterogeneous Information Networks have been greatly improved, as verified and compared on a real MovieLens 1M dataset. Experimental results prove that the algorithm can effectively improve the accuracy and interpretability of the recommendation. © 2022 SPIE.

关键词： Reinforcement learning

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A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation

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Frontiers of Information technology & Electronic Engineering 2024年第5期25卷 664-684页

作者： Xiaojun BAI Yang ZHANG Haixing WU Yuting WANG Shunfu JIN School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan UniversityQinhuangdao 066004China

How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research *** this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance *** to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant *** meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,*** establish a four-dimensional continuous-time Markov chain as the system *** using the Gauss-Seidel method,we derive the stationary probability distribution of the system ***,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of *** experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading ***,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

关键词： Edge computing Ofloading scheme Cloud-edge device collaboration Markov chain Cost function

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Semantic analysis and retrieval of spatial data based on the uncertain ontology model in Digital Earth

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International Journal of Digital Earth 2015年第1期8卷 3-16页

作者： Shengtao Sun Lizhe Wang Rajiv Ranjan Aizhi Wu The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesBeijingChina Computational Informatics CSIROCanberraACTAustralia College of Vehicles and Energy Yanshan UniversityQinhuangdaoChina

Metadata are the information about and description of *** Digital Earth,metadata become variant and heterogeneous with many *** paper studies uncertain features in the generation and application of metadata,and two types of uncertainties(incomplete and imprecise)are described based on semantic quantitative measurement method semantic relationship quantitative measurement based on possibilistic logic and probability statistic(SRQ-PP).Moreover,in the case study,we apply two types of quantitative measurements based on SRQ-PP to describe incomplete(uncertain)knowledge and imprecise(vague)information separately in spatial data service retrieval,which in turn is helpful to identify additional potential data resources and provide a quantitative analysis of the results.

关键词： metadata semantic ontology uncertainty measurement spatial data digital earth

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K-means sequence clustering algorithm based on Two-stage center sequences selection

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Journal of Computational Information systems 2014年第6期10卷 2351-2358页

作者： Wu, Di Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Department of Information and Electronic Engineering Hebei University of Engineering Handan 056038 China Key Lab of Computer Virtual Manufacturing and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

K-means sequence clustering is a hot topic in data mining. However, the clustering quality of K-means is affected heavily by outliers and noises. In this paper, a new sequence similarity measurement, sequence density and Q neighborhoods of sequence are deffned and then a novel K-means sequence clustering algorithm TCSSK-means (Two-stage center sequences selection-based K-means) is proposed. In the first stage, the sequence with the minimum density neighborhood is viewed as initial clustering center, and the distances between each initial clustering center are as far as possible, the KICCSS (K initial clustering center sequences set) is obtained. In the second stage, by selecting the mean sequence of the larger similarity sequences set, which meets certain conditions with clustering centers in the T-1-th round, K clustering centers of sequence databases in the T-th round are updated. Repeat the renewal process of sequence clustering centers until the sequence clustering criterion function converges. K final sequence clusters are gained finally. The experimental results and analysis show that the performance of TCSSK-means is better in clustering quality and running time. Copyright © 2014 Binary Information Press.

关键词： Data mining

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Multi-resolution terrain rendering seamlessly by function curve fitting

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Journal of Computational Information systems 2012年第24期8卷 10399-10409页

作者： Nie, Junlan Guo, Dongliang Kong, Lingfu Wang, Yanfen The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

During the process of real-time multi-resolution terrain rendering, avoiding cracks between LOD (Level of Detail) has always been a tedious task that requires extra complicated handling. In this paper, we propose a novel algorithm called Function Curve Fitting (FCF) to repair T-junction cracks of regular data-based LODs. FCF, as a time-efficient algorithm, uses regular grid as rendering element, could be conveniently integrated with existing algorithms and achieve seamless meshes with excellent image quality and frame rates. Compared with other existing LOD crack-repairing algorithms based on terrain rendering, the experiments on surface quality and rendering performance demonstrate the effectiveness of our FCF approach. © 2011 by Binary Information Press.

关键词： Curve fitting

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A Robot Pick and Place Skill Learning Method Based on Maximum Entropy and DDQN Algorithm

A Robot Pick and Place Skill Learning Method Based on Maximu...

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2021 International Conference on Robotics Automation and Intelligent Control, ICRAIC 2021

作者： Wu, Peiliang Zhang, Yan Li, Yao Mao, Bingyi Chen, Wenbai Gao, Guowei School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei Qinhuangdao066004 China School of Automation Beijing Information Science and Technology University Beijing100192 China

Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the PAP skill based on current methods. In order to improve the learning efficiency of robot PAP skills, this paper proposes a Soft-DDQN-based PAP skill learning method. Firstly, the Soft-DDQN is proposed by introducing maximum entropy into robot DDQN framework, and the learning goal of Soft-DDQN is to maximize reward and information entropy. Secondly, PAP problem is modelled as a discrete form and Soft-DDQN is applied to solve the PAP problem. Finally, in order to verify the efficiency of Soft-DDQN-based PAP skill learning, comparisons have been given from two standard perspectives and shown that Soft-DDQN improves efficiency of PAP skill learning evidently. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Intelligent robots

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Dynamic community mining based on behavior prediction

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International Journal of Performability Engineering 2018年第7期14卷 1590-1599页

作者： Chen, Xiao Hu, Xinzhuan Pan, Xiao Guo, Jingfeng Network Technology Center Hebei Normal University of Science and Technology Qinhuangdao066004 China College of Economics and Management YanShan University Qinhuangdao066004 China College of Economic and Management Shijiazhuang Tiedao University Shijiazhuang050043 China College of Information Science and Engineering YanShan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China

Dynamic network research has been a new trend in recent years. Based on the influence of vertex behavior on community structure, this paper studies signed network dynamic community mining. Firstly, the set pair connection degree is introduced to describe the relation between vertices, and the edge prediction model of signed network is proposed by taking into account the variability of the relation between vertices. Secondly, based on the prediction model, a set pair signed networks dynamic model is proposed by adding time axis T to the signed network. Then, based on the dynamic model, the evolution of signed networks and community discovering are studied. Finally, network evolution law and community stability are analyzed by using the connection trend and connection entropy in set pair theory, and the accuracy and validity of the dynamic community mining algorithm are verified by experiments. © 2018 Totem Publishers Ltd. All rights reserved.

关键词： Dynamic models

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