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Towards self-interpretable review spammer group detection

Information Sciences 2025年 716卷

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

Due to data imbalance, existing spammer group detection methods often yield suboptimal performance. Moreover, many of these approaches operate as black boxes, offering little to no interpretability for their detection results. To address these issues, we propose a self-interpretable approach for detecting review spammer groups. Our method begins by constructing a user-product bipartite graph with edge attributes. We integrate user review information with a novel fitness function to develop an adaptive genetic algorithm that effectively identifies high-quality candidate groups. Next, we introduce a hybrid graph neural network enhanced with active learning to generate vector representations of these candidate groups. We then design and construct a prototype layer and a group classification layer to detect spammer groups accurately. To provide interpretability, we incorporate prototype learning to create an interpretation mechanism that explains detection outcomes. Experimental results demonstrate that our method achieves substantial improvements in Precision@k and Recall@k at the top-1000 ranking, outperforming state-of-the-art solutions on the Amazon, YelpChi, YelpNYC, and YelpZip datasets by [11.53 %, 97.36 %, 51.37 %, 32.06 %] and [12.65 %, 54.65 %, 64.19 %, 47.37 %], respectively. Additionally, the Fidelity of our interpretability results under varying Sparsity levels is approximately 4 %, 9 %, 8 %, and 8 % higher than those of existing methods on the same datasets. © 2025 Elsevier Inc.

关键词： Active learning

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Fast Smallest Lowest Common Ancestor Computation Based on Stable Match

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Journal of computer Science & technology 2013年第2期28卷 366-381页

作者：周军锋蓝国翔陈子阳汤显 School of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University School of Economics and Management Yanshan University

In this paper, we focus on efficient processing of XML keyword queries based on smallest lowest common ancestor （SLCA） semantics. For a given query Q with m keywords, we propose to use stable matches as the basis for SLCA computation, where each stable match M consists of m nodes that belong to the m distinct keyword inverted lists of Q. M satisfies that no other lowest common ancestor （LCA） node of Q can be found to be located after the first node of M and be a descendant of the LCA of M, based on which the operation of locating a stable match can skip more useless nodes. We propose two stable match based algorithms for SLCA computation, i.e., BSLCA and HSLCA. BSLCA processes two keyword inverted lists each time from the shortest to the longest, while HSLCA processes all keyword inverted lists in a holistic way to avoid the problem of redundant computation invoked by BSLCA. Our extensive experimental results verify the performance advantages of our methods according to various evaluation metrics.

关键词： XML keyword search smallest lowest common ancestor

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The constructions of ternary sequence pairs with two-level autocorrelation

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Journal of Information and Computational Science 2014年第13期11卷 4605-4612页

作者： Jia, Yanguo Duan, Xiaobei Shi, Yan 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 this letter, a periodic autocorrelation signal is presented, which is the ternary sequence pair with two-level autocorrelation. The methods of constructing ternary sequence pairs based on binary sequence pairs and cyclotomy are presented by us. Then we have a discussion about perfect binary sequence pairs, pseudorandom binary sequence pairs, difference set pairs and ternary sequence pairs. These signals enrich the theory of sequence pair discrete correlation signals. Copyright © 2014 Binary Information Press.

关键词： Autocorrelation

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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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Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study

arXiv

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arXiv 2025年

作者： Liu, Tianhao Liu, Jiqiang Zhang, Tao Wang, Jian Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100045 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication. © 2025, CC BY.

关键词： Generative adversarial networks

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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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