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2019 2nd International Conference on computer Information Science and Artificial Intelligence, CISAI 2019

作者： Zhang, Bing Sun, Shengting Hao, Xiaobing School of Information Science and Engineering Yanshan University Hebei Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City066004 China

Given that analysis on the vulnerability of functions is helpful to the detection and improvement of software security, this paper aims to propose an efficient methods to identify the vulnerable nodes (ITVN) in different software by the interdependence of functions. First, the dynamic software execution process was constructed as Software Execution Dependency Network (SEDN) based on Complex network theory. Second, by analyzing the dependency relationship among functions, the algorithm calVulAndScoOfNodes (CVSN) was designed to compute the vulnerability and the affected scope of each node for further analysis. Third, in order to measure the functions vulnerability in the whole software network, the algorithm calVulDegreeOfNodes (CVDN) was put forward to calculate the vulnerable degree of each node. Finally, the Vulnerable Nodes in different software were obtained by ITVN. Experimental results show that the vulnerable nodes selected as important nodes are well-reasoned in software network by testing different software, and the measures are effective for evaluating nodes vulnerability. © Published under licence by IOP Publishing Ltd.

关键词： Software testing

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Detecting shilling profiles in collaborative recommender systems via multidimensional profile temporal features

Detecting shilling profiles in collaborative recommender sys...

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作者： Hao, Yaojun Zhang, Fuzhi School of Information Science and Engineering Yanshan University Qinhuangdao Hebei Province China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao Hebei Province China Department of Computer Science and Technology Xinzhou Teachers University Xinzhou Shanxi Province China

To defend recommender systems, various methods have been proposed to detect shilling profiles, which can be categorised as user- and item-based detection methods. Most of the user-based methods identify shilling profiles via statistical signatures of rating values and suffer from low precision when detecting different types of attacks. Most of the item-based methods use temporal information to detect the anomaly items, but they assume that the fake ratings were injected in short periods. So they are invalid for the long duration and decentralised injection attacks. To address these limitations, the authors extract the multidimensional profile temporal features and present a shilling detection method. First, from the user profile view, user rating behaviours are characterised by corrected conditional entropy and the dissimilarity with the rest-rating model. Second, from the item profile view, the user features are extracted according to item temporal popularity. Third, the features based on weighted deviation from dynamic mean are extracted according to the fact that the items mean changes with time. Finally, support vector machine is exploited to detect shilling profiles based on the proposed features. Experimental results on the Netflix dataset indicate that the performance of the proposed method is better than that of the benchmark methods. © The Institution of Engineering and technology 2018.

关键词： User profile

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Analysis of a new type islanding detection method for micro-grid based on the calculation of variance 8

Analysis of a new type islanding detection method for micro-...

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8th IEEE International Power Electronics and Motion Control Conference, IPEMC-ECCE Asia 2016

作者： Ying, Zhang Wang, Xiaohuan Zhang, Chunjiang Feng, Jianzhou Yanshan University Key Lab of Power Electronics for Energy Conservation and Motor Drive of HeBei Province Qinhuangdao China Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9781509012107

UL1741 rules that to realize the islanding detection, the inverter must stop running within 2s after the main grid failure occurs. However, with the increasing of the capacity of distributed generation system, in order to guarantee the stability and reliability of power supply simultaneously, IEEE Std.1547 proposed the concept that DG runs off-grid after islanding occurs. The concepts of the traditional islanding protection were attacked, the original islanding detection methods are no longer suitable for the flexible micro power grid operation system. This paper presents a new active islanding detection method which could suitable for micro-grid operation and control. The method, which under the premise of without destroying islanding operation conditions, fine-tunes the PWM modulation wave, real-time acquisition the voltage of point of common coupling, using the mathematical statistics method to extract the quadratic differential of the output voltage, according to the changes of the mathematical characteristic value in the grid connected mode and the islanding mode to judge if there was islanding occurred, and the theory basis of quantitative analysis of the disturbance quantity selection. Simulation and experiment results verify the correctness and feasibility of the proposed method. © 2016 IEEE.

关键词： Distributed power generation

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Capacity planning of the registration server in cloud storage

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International Journal of High Performance systems Architecture 2017年第2期7卷 105-112页

作者： Gu, Rui Jin, Shunfu Wu, Haixing School of Information Science and Engineering Yanshan University No. 438 Hebei Avenue Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Avenue Qinhuangdao066004 China Science and Technology on Communication Networks Laboratory No. 589 West Zhongshan Road Shijiazhuang050081 China

Cloud storage is one of the key services in cloud computing. In order to appeal more users to the cloud storage, free cloud experience is provided to potential users. A registration server is set specially for intentional users who are satisfied with the free experience and want to register as very important persons (VIPs). In order to mathematically evaluate the system performance and optimally plan the registration server, we establish a queueing network-based system model. We analyse the steady-state distribution of the system model and derive the performance measures of the system in terms of the average response time of potential users and the utilisation of registration server. For balancing different performance measures, we establish a cost function and develop an optimisation algorithm to plan the capacity of the registration server with a reasonable service rate. © Copyright 2017 Inderscience Enterprises Ltd.

关键词： Queueing networks

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Model for Software Behaviour Detection Based on Process Algebra and system Call

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China Communications 2013年第11期10卷 24-36页

作者：申利民王涛马川 College of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei Normal University of Science and Technology

Behaviour detection models based on automata have been studied widely. By add- ing edge ε, the local automata are combined into global automata to describe and detect soft- ware behaviour. However, these methods in- troduce nondeterminacy, leading to models that are imprecise or inefficient. We present a model of software Behaviour Detection based on Process Algebra and system call （BDPA）. In this model, a system call is mapped into an action, and a function is mapped into a process We construct a process expression for each function to describe its behaviour. Without con- strutting automata or introducing nondeter- minacy, we use algebraic properties and algo- rithms to obtain a global process expression by combining the process expressions derived from each function. Behaviour detection rules and methods based on BDPA are determined by equivalence theory. Experiments demon- strate that the BDPA model has better preci- sion and efficiency than traditional methods.

关键词： intrusion detection software be-haviour model static analysis process algebra system call

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A sketch recognition method based on bi-modal model using cooperative learning paradigm

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Neural Computing and Applications 2024年第23期36卷 14275-14290页

作者： Zhang, Shihui Wang, Lei Cui, Zhiguo Wang, Shi School of Information Science and Engineering Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China School of Information Technology Hebei University of Business and Economics Xuefu Road Hebei Province Shijiazhuang050061 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Hebei Street West Section Hebei Province Qinhuangdao066000 China

Static image is an important form of displaying a sketch, representing the appearance information of the sketch. And a stroke sequence composed of several points can also express the shape and contour information of the sketch. Therefore, it is very reasonable to treat a sketch as point-modal data and image-modal data simultaneously. In this paper, a method based on bi-modal model using cooperative learning paradigm is proposed for the sketch recognition task. Specifically, in the point-modal branch, a structural point convolution block is developed by properly dividing local regions to preserve the structural information. In the image-modal branch, the hierarchical residual structure is used to fully extract image-modal features. To reduce the negative impact of noisy samples on the recognition performance, a cooperative learning paradigm is designed based on different perceptual abilities of two modal branches on noisy samples, that is, when training the two branches, the noisy samples can be filtered out through information exchanges and mutual learning. Extensive experiments on the sketch datasets TU-Berlin and QuickDraw show that the proposed method outperforms most baseline methods and has many advantages such as no dependence on additional data and stroke information. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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Research on Software Community Division Method Based on Inter-node Dependency 16th

Research on Software Community Division Method Based on Inte...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Dong, Jun Hao, Chengqian Ren, Jiadong Lu, Haitao College of Information Science and Engineering Yanshan University Qinhuangdao066000 China The Key Computer Virtual Technology and System Integration Laboratory of Hebei Province Qinhuangdao066000 China

ISBN: (纸本)9783030607982

The mining of software community structure is of great significance in identifying software design pattern, software maintenance, software security and optimizing software structure. To improve the accuracy of description of software execution status model, A new method to construct merge software execute graph is proposed that based on the software execution network model. And then to get the software execution path through the software execution sequence analysis, The ND (Node Dependence) was proposed to measure the tightness of inter-node dependencies based on the design principle of "high cohesion, low coupling" of software. At last, depends on the software execution path, the SLCA (Software Label Communities Algorithm) community division algorithm is proposed to improve stability of community partition algorithm and validity of software module partition. © 2020, Springer Nature Switzerland AG.

关键词： Complex networks

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Research on the Influence of Improved K-shell Algorithm on Commodity Profit 2

Research on the Influence of Improved K-shell Algorithm on C...

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2nd International Conference on Artificial Intelligence and computer Science, AICS 2020

作者： Chen, Jing Huang, Jincheng College of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China Software Engineering Laboratory in Hebei Province Qinhuangdao066004 China

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN (Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities. © Published under licence by IOP Publishing Ltd.

关键词： Profitability

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Research on a Novel Influence Maximization Algorithm Based on Community Structure 2

Research on a Novel Influence Maximization Algorithm Based o...

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2nd International Conference on Artificial Intelligence and computer Science, AICS 2020

作者： Chen, Jing Liu, Jiangchuan College of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China Software Engineering Laboratory in Hebei Province Qinhuangdao066004 China

With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model. © Published under licence by IOP Publishing Ltd.

关键词： community division community structure independent cascade model influence maximization overlapping influence Seed node

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Learning to Cluster Person in the Video via Graph Convolution Network 13

Learning to Cluster Person in the Video via Graph Convolutio...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Li, Wei Liang, Sixin Bian, Ang Du, Guodong College of Computer Science Sichuan University Chengdu China School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350319156

Video person re-identification is receiving academic interest. However, the practical application of the algorithm is hardly supported because of prohibitive annotated data. Hence, the study for unlabeled data will lead to an attractive alternative. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the video through graph convolutional networks. In this paper, we find that the possibility of inter-frame linkage can be inferred from context. Therefore, a pose-guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose-guided representation module;(ii) a pose-guided embedding module;(iii) a link prediction module. Firstly, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross-frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior. © 2023 IEEE.

关键词： Convolution

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