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Multi-Update Patterns and Validity Verification for Robust Visual Tracking

Journal of computers (Taiwan) 2020年第4期31卷 77-90页

作者： Wen, Jia Wang, Hong-Jun Li, Yan-Nan Zhao, Ji-Wei Yang, Yu-Ying School of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Key Laboratory of Software Engineering of Hebei Province China

The Object tracking is a challenging problem in computer vision field. Now, deep learning has made outstanding achievements in feature extraction. There are already some examples of deep learning applications in visual tracking. But, they almost paid attention to precision and sacrificed efficiency. So we propose a tracker named adaptive combination tracker(ACT), based on neural network model, which gives consideration to both precision and efficiency. Our ACT tracker gets rich features offline through Stacked Denoising Autoencoders (SDAE) and transfers them to online tracking. In tracking, a dynamic particle filter algorithm is proposed to speed up tracking. We update our tracker with different patterns according to the change in appearance. In order to ensure efficiency, our tracker regulates speed adaptively. Our tracker regulates speed precisely to avoid drift caused by fast motion as much as possible, and confirm and optimize the effectiveness of the results by comparison with previous target. We evaluate our tracker on OTB2013 datasets. Extensive experiments on OTB2013 datasets demonstrate that the proposed tracker performs favorably in relation to the state-of-the-art methods. © 2020 computer Society of the Republic of China. All rights reserved.

关键词： Efficiency

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New methods of constructing quaternary sequence Pairs

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ICIC Express Letters 2016年第9期10卷 2177-2183页

作者： Jia, Yanguo Shen, Xiumin Duan, Xiaobei Song, Xiaofei School of Information Sciences and Engineering Yanshan University China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave. Qinhuangdao066004 China

In this letter, new methods of constructing quaternary sequence pairs are presented based on binary sequence pairs with two-level autocorrelation, almost perfect binary sequence pairs and cyclic shift sequences. The quaternary sequence pairs con- structed by these methods are periodic autocorrelation signals. These methods are handy and exible, and can greatly enrich the theory of quaternary sequence pairs. © 2016 ICIC International.

关键词： Autocorrelation

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Time sequential influence maximization algorithm based on neighbor node influence

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High technology Letters 2022年第2期28卷 153-163页

作者： CHEN Jing QI Ziyi LIU Mingxin School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004P.R.China Hebei Key Laboratory of Virtual Technology and System Integration Qinhuangdao 066004P R.China Hebei Key Laboratory of Software Engineering Qinhuangdao 066004P R.China College of Electronic and Information Engineering Guangdong Ocean UniversityZhanjiang 524088P.R.China

In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this *** is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information ***,the propagation probability between nodes is calculated by the improved degree estimation ***,the weighted cascade model(WCM) based on static social network is not suitable for temporal social ***,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node *** combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it ***,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.

关键词： neighbor node influence time sequential social network influence maximization(IM) information propagation model

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3-SPS+RRS+PS mechanism analysis and hessian matrix solution

3-SPS+RRS+PS mechanism analysis and hessian matrix solution

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2014 International Conference on Logistics Engineering, Management and computer Science, LEMCS 2014

作者： Zhang-Yuan Wangzhen Guoansong Guoi-Juan Xi, Feng-Feng Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Hebei Normal University of Science and Technology Qinhuangdao Hebei066004 China Force Equipment Research Institute Airlines Beijing100076 China China National Heavy Machinery Research Institute Xian710032 China Department of Aerospace Engineering Ryerson University Toronto Canada

ISBN: (纸本)9789462520103

3-SPS+RRS+PS is a new type of mechanism. There is good application prospect in the field of aerospace. Especially some key kinetic characteristic calculation algorithms are implemented, which makes its calculation mechanized, thus it will contribute to make the dynamics performance of this mechanism optimized and ascended. In the paper, firstly, the degree of freedom is calculated and freedom nature is analyzed. Then the reverse solution is calculated, started with the nature of degree of freedom. And the mechanism velocity and acceleration inverse solutions are calculated in the direct derivation method. Although there are shortcomings that calculation process is complicated in direct derivation method, its calculation process is easy to realize mechanization. Then a kind of fast numerical calculation algorithm is given and MATLAB program is implemented. The algorithm has universal applicability to the mechanism of continuous differentiable inverse equation, and can be applied to general parallel mechanism. © 2014. The authors - Published by Atlantis Press.

关键词： Mechanisms

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Method of periodic dynamic pattern mining based on complex network

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Journal of Computational Information systems 2015年第21期11卷 7849-7856页

作者： Wang, Lei Jiang, Liya Dong, Jun Huang, Guoyan Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Hebei Normal University of Science and Technology Qinhuangdao China

Traditionally, analysis of dynamic network has been focused only on a single snapshot or integrated network obtained over a period of time. However, the temporal feature in dynamic network has been ignored. In this paper, periodic dynamic pattern (PDP) mining method is proposed to discover the dynamic evolution rule of social networks. We define a periodic dynamic pattern based on complex network, and construct matrix of graph (MG) to present the evolution of complex network over time. Moreover, a regular edge pattern searching (REPS) algorithm based on MG is designed. It is used to select frequent and regular edge existence sequence (EES) in MG with progressive scan. To improve efficiency of the algorithm, sequences according with frequency threshold and REP should be transformed to decimal integer. Then, depth-first search method is used to mine PDP, which owns the same REP in dynamic network. So, we can predict future network evolution and control its behavior. Experimental results demonstrate that our method is an effective periodic dynamic pattern mining. Copyright © 2015 Binary Information Press.

关键词： Complex networks

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Identifying important nodes in complex software network based on ripple effects

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ICIC Express Letters, Part B: Applications 2016年第2期7卷 257-264页

作者： Ren, Jiadong Wu, Hongfei Gao, Rui Huang, Guoyan Dong, Jun College of Information Science and Engineering Yanshan University China Computer Virtual Technology and System Integration Laboratory of Hebei Province No. 438 Hebei Avenue Qinhuangdao China

The structure of complex software systems can be extracted as a complex software network, and the quality of software systems largely depends on the topological structure of the software network. A small portion of important nodes plays a critical role in ensuring the stability and quality of the software system. Identifying these important nodes in software network has become an important and challenging task of software engineering. In this paper, firstly we construct a model of directed weighted software network. Then, by analyzing the bug propagation process on weighted software network, the values of some metrics related to ripple effects are calculated. Finally, important nodes whose ripple effects are larger than average ripple effect are identified by our proposed approach. Experimental results on real software data show the utility of the approach. © 2016 ICIC International.

关键词： computer software

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A novel approach for mining important nodes in directed-weighted complex software network

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Journal of Computational Information systems 2015年第8期11卷 3059-3071页

作者： Ren, Jiadong Wu, Hongfei Yin, Tengteng Bai, Lan Zhang, Bing College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Hebei Institute of Foreign Languages Qinhuangdao China

If the software system is abstracted as a software network, its quality largely depends on the topology structure. A tiny fraction key function nodes play a critical role in improving the stability, reliability and robustness of complex software network. How to identify the top-k key nodes accurately and effectively becomes more and more significant both in theoretical and practical. In this paper, a novel approach based on semi-global information of mining top-k key nodes in directed-weighted complex software network is proposed. Firstly, we map the function and relationship of function calling or dependency to a directed function dependency network (DFDN). Secondly, an algorithm DFS-WEIGHT based on Depth-First-Search strategy is used to transform a DFDN into a directed-weighted function dependency network (DWFDN). Thirdly, we treat function nodes of DWFDN as two different roles - the caller or callee. Callers with high ability of accumulating vulnerabilities and callees with high ability of propagating vulnerabilities are considered as key nodes. Then, two metrics SABAC and SABPR are defined to measure the importance of callers and callees respectively. Finally, a Software Network key Node miNing (SN-KNN) algorithm is proposed to mine most important callers and callees. Experimental results indicate that the algorithm SN-KNN is effective in mining key nodes. ©, 2015, Binary Information Press. All right reserved.

关键词： Software reliability

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A new method of identifying influential nodes in complex software network based on leaderrank

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ICIC Express Letters 2016年第12期10卷 2837-2844页

作者： Huang, Guoyan Liu, Jing Chen, Xiaojuan Ren, Jiadong College of Information Science and Engineering Yanshan University China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 West Hebei Ave. Qinhuangdao066004 China

Identifying influential nodes is crucial for understanding and improving the stability and robustness of complex software network. This paper presents a new method based on LeaderRank information to identify top-k influential nodes in directed-weighted complex software network. Firstly, the function and relationship of function calling or dependency are mapped to a Directed function dependency network (DN). Secondly, a so-called stem node connected with every other node by a bidirectional link is introduced into the original network. Thirdly, an algorithm Constructing DWN (C-DWN) is used to transform a DN into a Directed-Weighted function dependency network (DWN). Finally, the Identifying Influential Node in Software Network (IIN-SN) algorithm is proposed to identify influential nodes. Experimental results show that the proposed method is effective for identifying influential nodes with the final score ranking following power-laws. The top-k most influential nodes are the same and the final score (NS) of each node is a little larger during the software evolution. What is more, we present that some factors such as in-degree of nodes and influence of connected nodes are related to the influence of nodes. © 2016 ICIC International.

关键词： Complex networks

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An efficient approach to mine maximal continuous frequent patterns from software function call sequence

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Journal of Computational Information systems 2015年第8期11卷 3073-3085页

作者： He, Haitao Yin, Tengteng Wu, Hongfei Bai, Lan Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Hebei Institute of Foreign Languages Qinhuangdao China

Certain characteristics of software are often hidden in its structure and can only be discovered when the software is executed dynamically. So mining important patterns from dynamic call graph of software plays an important role in analyzing software security. However the general sequential pattern mining algorithms may lead to poor performance due to the lack of consideration on the continuous property of software function call sequence. In this paper, combining with the characteristics of software itself, function call sequence is modeled firstly. The sequence represents the call relationship among functions of software. Then a novel structure called Nitem-List is put forward. The structure stores both the next item and the support information which is heuristic information for pruning the search space about a pattern. On the basis of that, an efficient algorithm called Maximal Continuous Frequent Pattern Miner (MCFP-Miner) and an update algorithm UMCFP-Miner are proposed. After constructing the initial Nitem-Lists from a mined database, MCFP-Miner can mine continuous frequent patterns from these Nitem-Lists. In UMCFP-Miner, a tighter bound for pruning is proposed. Finally, extensive experiments on various databases show that algorithm MCFP-Miner and UMCFP-Miner have better efficiency. ©, 2015, Binary Information Press. All right reserved.

关键词： Miners

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An improved algorithm for community discovery in weighted social networks

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Journal of Information and Computational Science 2015年第18期12卷 6873-6881页

作者： Guo, Jingfeng Liu, Miaomiao Liu, Linlin College of Information Science and Engineering Yanshan University QinhuangdaoHebei China Northeast Petroleum University DaqingHeilongjiang China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei China

The problems of AGMA (Automatic Graph Mining Algorithm) are improved and a novel algorithm, namely CRMA (Clustering Re-clustering Merging Algorithm) is proposed which can realize more reasonable community division for weighted social networks. Firstly, when re-clustering nodes, AGMA neglected the clustering situation of its neighbors and didn't take into account the weight of the edge connecting the current node and its unclustered neighbor, which led to unreasonable clustering. Aiming to this, the concept of the connection compactness is introduced. Then according to the node weight, the edge weight and the friend coefficient, each node would be clustered into the community has the largest connection compactness with itself. Secondly, for community division in some unweighted networks and signed networks, if there are many nodes whose clustered neighbors is less than its unclustered neighbors, the division results of AGMA tend to produce many small communities, which would result in the lower modularity. In view of this problem, the concepts of positive edge weight density, the cluster density and the connection coefficient between clusters are proposed. Lastly, we merge communities based on the above three concepts and the modularity is effectively enhanced finally. The higher correctness and better generality of the improved algorithm are verified through experiments. © 2015 by Binary Information Press.

关键词： Clustering algorithms

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