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A novel collaborative recommendation algorithm integrating probabilistic matrix factorization and neighbor model

Journal of Information and Computational Science 2015年第5期12卷 2011-2019页

作者： Yu, Hongtao Dou, Lisha Zhang, Fuzhi The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China

The existing collaborative recommendation algorithms suffer from lower recommendation precision due to the problem of data sparsity. To solve this problem, we propose a novel collaborative recommendation algorithm which integrates the probabilistic matrix factorization and neighbor models. We first propose a method to calculate the similarity between users or items based on the probabilistic matrix factorization model and construct a natural exponential function to compute the weighted similarity. Then we devise a collaborative recommendation algorithm to make recommendations for the target user, which dynamically adjusts the recommendation results for user-and item-based models by the balance adjustment factor. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing algorithms in terms of prediction accuracy. Copyright © 2015 Binary Information Press.

关键词： Exponential functions

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The synthesis of dynamic junction over-constrained parallel-parallel mechanism based on the weighted directed graph theory

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ICIC Express Letters 2013年第11期7卷 3057-3065页

作者： Guo, Xijuan Zhang, Dayu Geng, Qingjia Zhang, Wenyuan The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China

Several dynamic junction over-constrained parallel-parallel mechanisms are proposed in this paper with a new method based on graph theory synthesis. This kind of mechanism is the pivotal part of the equivalent mechanism in helicopter automatic tilting device. The purpose of synthesis is that the synthesis result is helpful to optimize the structure and optimal design for helicopter automatic tilting device, and the reason why a graph method is chosen is that it can be programmed easily. The characteristic of "Dynamic Junction" is proposed relative to fixed junction. Because the joint of connecting two parallel mechanisms is dynamic, these two mechanisms can motion relatively. A mount of permutation and combination calculating processes are needed because of dynamic characteristic. In this view, choosing a synthesis method which can program easily in computer is appropriate. The graph method is the best choice. However, classic graph method is difficult to discriminate the graph isomorphism and limited to apply in spatial mechanism. Therefore, this paper proposed a new graph way. First, mechanism is modeled in weighted directed graph, then based on equal freedom of degree, many other graph models are got which have the same motion characteristic. Finally many target mechanisms can be derived by those models. The new method avoids the problem of classic graph method and is easy to program and run in computer, it is proved effectively because several dynamic junction over-constrained parallel-parallel mechanisms are successfully synthesized by it. © 2013 ISSN 1881-803X.

关键词： Dynamics

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Robust recommendation algorithm based on the identification of suspicious users and matrix factorization

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Journal of Information and Computational Science 2014年第13期11卷 4769-4777页

作者： Yi, Huawei Zhang, Fuzhi Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China

The existing recommendation algorithms have lower robustness against shilling attacks. With this in mind, in this paper we propose a robust recommendation algorithm based on the identification of suspicious users and matrix factorization. We first give the computational methods of Deviation Degree about the Number of Ratings (DDNR) and Average Similarity of Neighbors (ASN) according to the distribution of users' ratings. Then we identify suspicious users based on the differences of computational results of users' DDNR and ASN. Finally, we introduce the matrix factorization technology which incorporates indicator function and the identification results of suspicious users to make recommendations for users. Experimental results show that the proposed algorithm not only improves the recommendation accuracy, but also has better robustness. Copyright © 2014 Binary Information Press.

关键词： Matrix algebra

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Automatic web page data extraction through MD5 trigeminal tree and improved BIRCH

Automatic web page data extraction through MD5 trigeminal tr...

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

作者： Gong, Jibing Kou, Xiaomeng Zhang, Hanyun Peng, Jiquan Gong, Shishan Wang, Shuli 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 School of Information Science Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

This paper proposes an automatic data extraction algorithm for web pages based on noise reduction and visualization blocks' construction. In this algorithm, we first build an MD5 trigeminal tree of the web page's source files using a Message-Digest Algorithm. And then we arrange some noise reduction. Finally, we construct visualization blocks by an optimized cluster algorithm named BIRCH A-CF which could create an area clustering feature forest and complete the clustering by dynamically changing the circle's radius according to the correction factor. We perform experiments on eight different datasets to compare our method with eight baseline methods. The experimental results show that our approach outperforms current methods by providing more accuracy and robustness, it also accelerates noise reduction and reduces the number of nodes effectively. © 2022 SPIE.

关键词： Visualization

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New construction of quaternary sequence pairs with even period

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Journal of Information and Computational Science 2014年第13期11卷 4613-4621页

作者： Jia, Yanguo Shi, Yan Duan, Xiaobei Shen, Xiumin Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China

In the paper, we present a new method for constructing a class of quaternary sequence pairs with even period 2N from the known binary sequence pairs with odd period N by using the reverse Gray mapping and interleaving technique. The distribution of correlation function of the proposed quaternary sequence pairs is also derived, which is decided by the properties and the periods of the known binary sequence pairs. Copyright © 2014 Binary Information Press.

关键词： Mapping

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A virtualized data center energy-saving mechanism based on switching operating mode of physical servers and reserving virtual machines

A virtualized data center energy-saving mechanism based on s...

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作者： Yin, Chunxia Liu, Jian Jin, Shunfu College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Ocean College of Hebei Agricultural University Qinhuangdao China Institute of Computing Technology Chinese Academy of Sciences Beijing China

The energy consumption of virtualized data centers has grown very fast in last several years. Because a large number of hosts are running in an idle state, virtualized data centers waste a large amount of electric energy. To save more energy for virtualized data centers, an energy-saving mechanism is proposed based on switching operating mode of physical servers and reserving virtual machines (VMs). The main idea is that when the amount of idle VMs reaches twice of a specified threshold, half of these idle VMs are reserved to process the new task that is about to arrive, and the physical server which hosts the other half of the idle VMs is switched to the sleep mode. From the perspective of task arrival rate and sleep parameters, we use two-dimensional Markov processes to analyze the proposed energy-saving mechanisms. By using matrix geometry solutions, we theoretically estimate energy consumption and response performance. According to the numerical experiments, the proposed energy-saving mechanism obviously cut down electric energy consumption and ensures response performance. Finally, the specified threshold for the amount of VMs is optimized by building a cost function. © 2020 John Wiley & Sons, Ltd.

关键词： Cost functions

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Outlier detection based on cluster outlier factor and mutual density 10th

Outlier detection based on cluster outlier factor and mutual...

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10th International Symposium on Intelligence Computation and Applications, ISICA 2018

作者： Zhang, Zhongping Zhu, Mengfan Qiu, Jingyang Liu, Cong Zhang, Debin Qi, Jie School of Information Science and Engineering Yanshan University QinhuangdaoHebei066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei066004 China Hebei Education Examinations Authority ShijiazhuangHebei050000 China The First Middle School of Qian An Country Qian’anJilin131400 China

ISBN: (纸本)9789811364723

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 that 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 which combining the natural neighbor search algorithm of the Natural Outlier Factor (NOF) algorithm and based on the Density and Distance Cluster (DDC) algorithm. 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 centers. 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. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Data mining

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A metadata extraction approach from papers based on meta-learning

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Journal of Information and Computational Science 2013年第4期10卷 1121-1129页

作者： Zhang, Fuzhi Zhao, Zihao School of Information Science and Engineering The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

To improve the accuracy of paper metadata extraction, a paper metadata extraction approach based on meta-learning is presented. Firstly, we propose a construction method of base-classifiers, which combines the Support Vector Machine (SVM) with the created diverse base-level training sets to construct some base-classifiers with larger diversity. The training sets are created according to the Open Access (OA) journal categories. Secondly, we present a paper metadata extraction algorithm based on meta-learning, which uses the meta-classifier to integrate the classification results of the base-classifiers and generates the final extraction results. The proposed approach guarantees extraction accuracy and independence of the base-classifiers, increases the diversities of them, and improves the classification performance of the meta-classifier, as well as reduces the degree of correlation of misclassifications. The experimental results show that our approach is superior to other single machine learning algorithm and the accuracy of paper metadata extraction is improved. © 2013 by Binary Information Press.

关键词： Support vector machines

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Opnet: Deep Occlusion Perception Network with Boundary Awareness for Amodal Instance Segmentation

Opnet: Deep Occlusion Perception Network with Boundary Aware...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shihui Zhang Ziteng Xue Yuhong Jiang Houlin Wang School of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

The Amodal Instance Segmentation (AIS) task aims to infer the visible and occluded regions of an object instance. Existing AIS methods typically focus on directly predicting visible and occluded regions or leveraging prior knowledge to guide predictions. However, these methods often ignore the perception of occluded views, leading to inaccurate results. To address this issue and achieve high-quality AIS, we propose a boundary-aware Occlusion Perception Network (OPNet). OPNet consists of three main components: the Dynamic Feature Augmentation Pyramid (DFAP), the Dual-path Boundary Aware Module (DBAM), and the Shape-guide Refinement Module (SRM). Specifically, DBAM employs an occlusion-perception strategy to learn discriminative features with boundary information, enabling it to distinguish occlusion from multiple views. Additionally, DFAP and SRM optimize the results by enhancing feature aggregation and imposing geometric constraints. Experiments on the D2SA, KINS, and CWALT datasets show that OPNet significantly outperforms state-of-the-art AIS methods that without prior knowledge. Code is available at https://***/ZitengXue/OPNet.

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Robust Recommendation Method Based on Shilling Attack Detection and Matrix Factorization Model

Robust Recommendation Method Based on Shilling Attack Detect...

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2017 2nd International Conference on Communications, Information Management and Network Security (CIMNS2017)

作者： Yu-qi HU Kai LIU Fu-zhi ZHANG School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

The existing robust collaborative recommendation algorithms have low robustness against PIA and Ao P attacks. Aiming at the problem, we propose a robust recommendation method based on shilling attack detection and matrix factorization model. Firstly, the type of shilling attack is identified based on statistical characteristics of attack profiles. Secondly, we devise corresponding unsupervised detection algorithms for standard attack, Ao P and PIA, and the suspicious users and items are flagged. Finally, we devise a robust recommendation algorithm by combining the proposed shilling attack detection algorithm with matrix factorization model, and conduct experiments on the Movie Lens dataset to demonstrate its effectiveness. Experimental results show that the proposed method exhibits good recommendation precision and excellent robustness for shilling attacks of multiple types.

关键词： Collaborative recommendation Shilling attack Attack type identification Attack detection Matrix factorization model

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