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Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

Science China(Information Sciences) 2017年第11期60卷 43-64页

作者： Jibing GONG Xiaoxia GAO Hong CHENG Jihui LIU Yanqing SONG Mantang ZHANG Yi ZHAO School of Information Science and Engineering Yanshan University The Key Lab for Computer Virtual Technology and System Integration Yanshan University State Key Lab of Mathematical Engineering and Advanced Computing Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

Friend recommendation is a fundamental service in both social networks and practical applications,and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend++, a hybrid multi-individual recommendation model that integrates a weighted average method(WAM) into the random walk(RW) framework by seamlessly employing social ties, behavior context, and personal information. In Friend++, the first plus signifies recommending a new friend through network features, while the second plus stands for using node features. To verify our method, we conduct experiments on three social datasets crawled from the Sina microblog system(Weibo). Experimental results show that the proposed method significantly outperforms six baseline methods in terms of recall, precision, F1-measure, and MAP. As a final step, we describe a case study that demonstrates the scalability and universality of our method. Through discussion, we reach a meaningful conclusion: although common interests are more important than user activities in making recommendations, user interactions may be the most important factor in finding the most appropriate potential friends.

关键词： multi-individual friend recommendation architecture behavior context analysis Intimacy degree random walk framework social networks

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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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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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iBole： A Hybrid Multi-Layer Architecture for Doctor Recommendation in Medical Social Networks

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Journal of computer Science & technology 2015年第5期30卷 1073-1081页

作者：宫继兵王立立孙胜涛彭思维 School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

In this paper, we try to systematically study how to perform doctor recommendation in medical social net- works （MSNs）. Specifically, employing a real-world medical dataset as the source in our work, we propose iBole, a novel hybrid multi-layer architecture, to solve this problem. First, we mine doctor-patient relationships/ties via a time-constraint probability factor graph model （TPFG）. Second, we extract network features for ranking nodes. Finally, we propose RWR- Model, a doctor recommendation model via the random walk with restart method. Our real-world experiments validate the effectiveness of the proposed methods. Experimental results show that we obtain good accuracy in mining doctor-patient relationships from the network, and the doctor recommendation performance is better than that of the baseline algorithms： traditional Ranking SVM （RSVM） and the individual doctor recommendation model （IDR-Model）. The results of our RWR-Model are more reasonable and satisfactory than those of the baseline approaches.

关键词： doctor recommendation architecture random walk with restart doctor-patient tie mining time-constraintprobability factor graph model medical social network

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Energy-efficient computation offloading via deep reinforcement learning in mobility-aware multi-access edge computing systems with diverse users

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Expert systems with Applications 2025年 286卷

作者： Haixing Wu Shunfu Jin School of Information Science and Engineering Yanshan University Qinhuangdao 066004 PR China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 PR China

Multi-access Edge Computing (MEC) has been a promising solution that enables Internet of Things (IoT) devices to support computation-intensive applications by offloading some tasks to the network edge. However, most existing offloading methods in MEC systems are based on the premise of scenarios with either single-type or stable users, which is not aligned with the practical situations of users’ diversity and mobility. In addition, these methods always ignore energy saving strategies in MEC systems, which inevitably degrades Utility Energy Efficiency (UEE). To tackle these issues, considering personalized requirements from diverse users and idle energy consumption in MEC servers, we propose a novel computation offloading architecture with adaptive sleeping mechanism and heterogeneous MEC servers. Based on the architecture, we investigate energy-efficient computation offloading in mobility-aware MEC systems with diverse users. Under the Deep Reinforcement Learning (DRL) framework, we propose a Twin Delayed Deep Deterministic Policy Gradient Algorithm with Bursty Traffic (TD3-BT), in which the inherent correlation of task arrivals is taken into account to capture the behavior of correlated traffic and provide rational metrics estimation within one time slot. A long-term system cost minimization problem is formulated to optimize computation offloading for the trade-off between system delay and UEE. Assisted by TD3-BT, the formulated problem is effectively solved and the decision making in each time slot is provided for diverse users. Experimental results demonstrate that our TD3-BT algorithm achieves superior performance under various offloading scenarios compared to some benchmarks.

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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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PromptHC: Multi-attention prompt guided haze-weather crowd counting

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Expert systems with Applications 2025年 286卷

作者： Jienan Shen Liangang Tong Shaohua Li Weihang Kong School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Information Technology Center Yanshan University Qinhuangdao 066004 China Hebei Key Laboratory of Computer Virtual Technology and System Integration Qinhuangdao 066004 China

Existing crowd counting methods encounter the challenge of degraded performance in hazy weather due to the blurring of pedestrian outlines. However, current hazy-weather crowd counting methods primarily focus on extracting crowd features, often neglecting the varying degrees of distortion in pedestrian outlines caused by inhomogeneous haze distribution. To this end, this paper develops a multi-attention prompt guided method for hazy-weather crowd counting, termed PromptHC. Specially, to explore the relationship between varying haze concentrations and the pedestrian outlines, a multi-attention dynamically adjustable prompt module is designed to provide crucial prompts about crowd features in hazy weather. Meanwhile, to further enhance the anti-interference capability of the model in hazy weather, a progressive guidance module is incorporated, which effectively reduces interference from different haze concentrations by guiding the learning of crowd attention. Furthermore, a global context-enhanced crowd feature extraction module is designed to capture precise global information. A series of ablation studies verify the actual effectiveness of each core component of the PromptHC. In addition, we conduct a performance comparison with the current mainstream methods on two hazy-weather datasets. Experimental results show the feasibility and superiority of the PromptHC for the hazy-weather crowd counting task.

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Software fault localization via mining program dynamic execution graph

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Journal of Computational Information systems 2014年第19期10卷 8563-8573页

作者： Zhang, Kai Yang, Ke Zhou, Lianbo Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

This paper is concerned with the problem of locating the code area related to software potential fault quickly and accurately in software testing period. A new method Sig BB based on graph model is proposed for mining the suspicious fault nodes from the passing and failing execution graphs. Representing each execution of a program as a graph, the graphs are divided into the passing and failing sets. By extracting the most representative passing and failing graphs based on these sets, the discriminative sub-graph is mined between the two representative graphs. First, Sig BB searches the max common graph, and then gets the opposite nodes set. The discriminative sub-graph is obtained by organizing and extending the set finally. Since the detected code scale is associated with the sorting of suspicious nodes, a suspicious metric strategy is also designed to sort the nodes in the discriminative sub-graph. Experimental results indicate that our method is both effective and efficient for software fault localization.

关键词： Software testing

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Improving neighbourhood quality for CHUCKY: An empirical study

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Journal of Computational Information systems 2015年第16期11卷 5967-5977页

作者： Zhao, Fengda Yang, Ke 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

Vulnerability discovery is at the centre of attention in computer security. Most vulnerability detection methods need considerable human auditing, thus making vulnerability detection inefficient and unreliable. Similar neighbourhood-based missing check detection method solves this problem to some degree. However, semantic noises that degrade the quality of the neighbourhood list remain. To reduce these noises, we firstly define a q-gram-based name similarity to measure the correlation of function names or file names. The context similarity of function is then defined to measure the commonness of two functional working environment. Finally, two filters are proposed: one is the naive neighbourhood filter which filters irrelative functions by name similarity;the other is the improved integrate neighbourhood filter, which awards strong correlated functions by recognising remarkable name and context similarity. The filter-based methods are experimentally evaluated in five widely used open-source software and are compared with the original method. Result shows that the improved neighbourhood rank lists are more accurate than the original list. © 2015 by Binary Information Press

关键词： Static analysis

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Road network information based edge bundling

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Journal of Information and Computational Science 2015年第13期12卷 4909-4918页

作者： Nie, Junlan Zhang, Jikai Xin, Shuyue Guo, Dongliang 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

To solve edge confusion problems in the geo-related graph visualization, here we present an edge bundling algorithm based on the road network information. First, the improved Dijkstra shortest path algorithm is employed to calculate the related path of original edges, and slicing quadratic Bezier curves are fitted out to replace original edges according to these path information. And then, the curve segments that satisfy conditions are merged together according to the control point consolidation strategy and thickened according to their weights, which simplifies the bundling result and also effectively shows the heat of each path;Finally, for the interested curves in the human-computer interaction, highlighted transition colors are introduced to character their directivity, and the effect is intuitive. The experimental results show that, our algorithm can effectively bundle original edges, which reduces the confusion degree of the graph. Meanwhile, the bundling result is more close to the original path, which improves the readability and understandability of the graph effectively. ©, 2015, Journal of Information and Computational Science. All right reserved.

关键词： Human computer interaction

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