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Knowledge Graph and Knowledge Reasoning:A Systematic Review

Journal of electronic science and technology 2022年第2期20卷 159-186页

作者： Ling Tian Xue Zhou Yan-Ping Wu Wang-Tao Zhou Jin-Hao Zhang Tian-Shu Zhang Kashi Institute of Electronics and Information Industry Kashi 844099 Shenzhen Institute of Information Technology Shenzhen 518172 the School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054 the School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054

The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.

关键词： methods. representation reasoning

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The application of a novel neural network in the detection of phishing websites

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 1865-1879页

作者： Feng, Fang Zhou, Qingguo Shen, Zebang Yang, Xuhui Han, Lihong Wang, JinQiang School of Information Science and Engineering Lanzhou University Gansu Lanzhou China School of Electronic and Information Engineering Lanzhou Institute of Technology Gansu Lanzhou China

In recent years, security incidents of website occur increasingly frequently, and this motivates us to study websites’ security. Although there are many phishing detection approaches to detect phishing websites, the detection accuracy has not been desirable. In this paper, we propose a novel phishing detection model based on a novel neural network classification method. This detection model can achieve high accu-racy and has good generalization ability by design risk minimization principle. Furthermore, the training process of the novel detection model is simple and stable by Monte Carlo algorithm. Based on testing of a set of phishing and benign websites, we have noted that this novel phishing detection model achieves the best Accuracy, True-positive rate (TPR), False-positive rate (FPR), Precision, Recall, F-measure and Matthews Correlation Coefficient(MCC) comparable to other models as Naive Bayes (NB), Logistic Regression(LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Linear Support Vector Machine (LSVM), Radial-Basis Support Vector Machine (RSVM) and Linear Discriminant Analysis (LDA). Furthermore, based upon experiments, we find that the proposed detection model can achieve a high Accuracy of 97.71% and a low FPR of 1.7%. It indicates that the proposed detection model is promising and can be effectively applied to phishing detection. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Websites

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Wireless Interference Classification with Low Complexity Multi-Branch Networks

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China Communications 2023年第4期20卷 382-394页

作者： Song Ma Yufan Cheng Ying Mou Pengyu Wang Qihang Peng Jun Wang National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential ***,deep learning(DL)based WIC methods have been ***,conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy,especially when the interference-tonoise ratio(INR)is *** this end,we propose three effective ***,we introduce multibranch convolutional neural networks(CNNs)for interference *** multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology,and it notably improves the recognition ability for *** design avoids the carefully crafted selection of each ***,multi-branch CNNs are computationally expensive and *** this end,we further propose Low complexity multibranch networks(LCMN),which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient ***,we present novel loss function,which encourages networks to have consistent prediction probabilities for samples with high visual similarities,resulting in increasing recognition accuracy of *** results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.

关键词： electromagnetic interference wireless interference identification deep learning multi-branch architectures

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Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

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Computer Modeling in engineering & sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang Yao Zhou Guangchun Luo Peng Chen Dezhong Peng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer Science Sichuan UniversityChengdu610065China National Innovation Center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

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BC-PC-Share:Blockchain-Based Patient-Centric Data Sharing Scheme for PHRs in Cloud Computing

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Computer Modeling in engineering & sciences 2023年第9期136卷 2985-3010页

作者： Caihui Lan Haifeng Li School of Electronic and Information Engineering Lanzhou City UniversityLanzhou730070China School of Software Dalian University of Foreign LanguagesDalian116044China School of Software Dalian University of TechnologyDalian116024China

Sharing of personal health records(PHR)in cloud computing is an essential functionality in the healthcare ***,how to securely,efficiently and flexibly share PHRs data of the patient in a multi-receiver setting has not been well *** instance,since the trust domain of the cloud server is not identical to the data owner or data user,the semi-trust cloud service provider may intentionally destroy or tamper shared PHRs data of user or only transform partial ciphertext of the shared PHRs or even return wrong computation results to save its storage and computation resource,to pursue maximum economic interest or other malicious ***,the PHRs data storing or sharing via the cloud server should be performed with consistency and integrity ***,the emergence of blockchain technology provides new ideas and prospects for ensuring the consistency and integrity of shared PHRs *** this end,in this work,we leverage the consortiumblockchain technology to enhance the trustworthiness of each participant and propose a blockchain-based patient-centric data sharing scheme for PHRs in cloud computing(BC-PC-Share).Different from the state-of-art schemes,our proposal can achieve the following desired properties:(1)Realizing patient-centric PHRs sharing with a public verification function,i.e.,which can ensure that the returned shared data is consistent with the requested shared data and the integrity of the shared data is not compromised.(2)Supporting scalable and fine-grained access control and sharing of PHRs data with multiple domain users,such as hospitals,medical research institutes,and medical insurance companies.(3)Achieving efficient user decryption by leveraging the transformation key technique and efficient user revocation by introducing time-controlled *** security analysis and simulation experiment demonstrate that the proposed BC-PC-Share scheme is a feasible and promising solution for PHRs data sharing via consortium b

关键词： Blockchain patient-centric personal health records data sharing attribute-based encryption

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Few-shot image semantic segmentation based on meta-learning: A review

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 351-367页

作者： Liu, Yu Zhu, Ye Chong, Haoze Yu, Ming School of Electronic and Information Engineering Hebei University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China College of Computer and Information Engineering Tianjin Agricultural University Tianjin China

Deep learning-based image semantic segmentation approaches heavily rely on large-scale training datasets with dense annotations and often suffer from scarce semantic labels for unseen categories. This limitation has spurred a research trend in Few-shot image Semantic Segmentation (FSS), which makes it possible to segment objects of new categories using only a few labeled samples. Although more and more FSS methods are emerging and gradually integrated into practical applications, a deep understanding of its achievements and issues is still missing. In this survey, we focus on the recent developments of FSS, specifically on FSS methods based on meta-learning. According to different network architectures, we summarize the related research into three classes, that are Convolutional Neural Network-based (CNN-based) models, Graph Neural Network-based (GNN-based) models, and Transformer-based models. Then, we explore the specific implementations of these models, including parameter-based methods, metric-based methods, attention-based methods, and optimization-based methods. Furthermore, we illustrate datasets and analyze the experimental results of various kinds of methods. Toward the end of the paper, we discuss the limitations of FSS and present its applications and challenges to provide further research directions. © 2024 - IOS Press. All rights reserved.

关键词： Semantic Segmentation

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Outlier item detection in bundle recommendation via the attention mechanism

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High-Confidence Computing 2024年第3期4卷 51-57页

作者： Yuan Liang School of Information Engineering Suqian UniversitySuqian 223800China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Bundle recommendation offers users more holistic insights by recommending multiple compatible items at ***,the intricate correlations between items,varied user preferences,and the pronounced data sparsity in combinations present significant challenges for bundle recommendation ***,current bundle recommendation methods fail to identify mismatched items within a given set,a process termed as‘‘outlier item detection’’.These outlier items are those with the weakest correlations within a *** them can aid users in refining their item *** the correlation among items can predict the detection of such outliers,the adaptability of combinations might not be adequately responsive to shifts in individual items during the learning *** limitation can hinder the algorithm’s *** tackle these challenges,we introduce an encoder–decoder architecture tailored for outlier item *** encoder learns potential item correlations through a self-attention ***,the decoder garners efficient inference frameworks by directly assessing item *** have validated the efficacy and efficiency of our proposed algorithm using real-world datasets.

关键词： Bundle recommendation Outlier item detection Attention mechanism Decoder encoder

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Feature selection for determining input parameters in antenna modeling

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Journal of Systems engineering and electronics 2025年第1期36卷 15-23页

作者： LIU Zhixian SHAO Wei CHENG Xi OU Haiyan DING Xiao School of Physics University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer and Information Engineering Xinjiang Agricultural UniversityUrumqi 830052China

In this paper,a feature selection method for determining input parameters in antenna modeling is *** antenna modeling,the input feature of artificial neural network(ANN)is geometric *** selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)*** information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear *** EM response range is utilized to evaluate the *** wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is *** the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly *** modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed *** number of input parameters decreases from8 to *** testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.

关键词： antenna modeling artificial neural network(ANN) feature selection maximal information coefficient(MIC)

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Fe doping 1T phase MoS_(2)with enhanced zinc-ion storage ability and durability for high-performance aqueous zinc-ion batteries

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Rare Metals 2025年第1期44卷 253-263页

作者： Jing-Yi Liu Rong-Jie Zhe Zhan-Hong Peng Yi-Hui Song Lin-Xuan Yang Chen Qing Jun-Ling Guo Jin-Ping Liu Yunnan Key Laboratory of Opto-Electronic Information Technology College of Physics and Electronic Information TechnologyYunnan Normal UniversityKunming 650000China Country State Center for International Cooperation on Designer Low-Carbon and Environmental Materials School of Materials Science and EngineeringZhengzhou UniversityZhengzhou 450001China School of Chemistry Chemical Engineering and Life ScienceState Key Laboratory of Advanced Technology for Materials Synthesis and ProcessingWuhan University of TechnologyWuhan 430070China

As a promising cathode material for aqueous zinc-ion batteries,1T-MoS_(2)has been extensively investigated because of its facile two-dimensional ion-diffusion channels and high electrical ***,the limited number of available Zn storage sites,i.e.,limited capacity,hinders its application because the inserted Zn^(2+),which form strong electrostatic interactions with 1T-MoS_(2),preventing subsequent Zn^(2+)***,the approach of enlarging the interlayer distance to reduce electrostatic interactions has been commonly used to enhance the capacity and reduce Zn^(2+)migration ***,an enlarged interlayer spacing can weaken the van der Waals force between 1T-MoS_(2)monolayers,easily disrupting the structural ***,to address this issue,an effective strategy based on Fe doping is proposed for 1T-MoS_(2)(Fe-1T-MoS_(2)).The theoretical calculations reveal that Fe doping can simultaneously moderate the rate of decrease in the adsorption energy after gradually increasing the number of stored atoms,and enhance the electron delocalization on metal-O ***,the experiment results show that Fe doping can simultaneously activate more Zn storage sites,thus enhancing the capacity,and stabilize the structural stability for improved cycling ***,Fe-1T-MoS_(2)exhibits a larger capacity(189 mAh·g^(-1)at 0.1 A·g^(-1))and superior cycling stability(78%capacity retention after 400 cycles at 2 A·g^(-1))than pure 1T-MoS_(2).This work may open up a new avenue for constructing high-performance MoS_(2)-based cathodes.

关键词： Aqueous zinc-ion battery 1T-MoS_(2) Fe doping More Zn storage sites Enhanced structural stability

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Stochastic Gradient Compression for Federated Learning over Wireless Network

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China Communications 2024年第4期21卷 230-247页

作者： Lin Xiaohan Liu Yuan Chen Fangjiong Huang Yang Ge Xiaohu School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing University of Aeronautics and AstronauticsNanjing 210016China School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan 430074China

As a mature distributed machine learning paradigm,federated learning enables wireless edge devices to collaboratively train a shared AI-model by stochastic gradient descent(SGD).However,devices need to upload high-dimensional stochastic gradients to edge server in training,which cause severe communication *** address this problem,we compress the communication by sparsifying and quantizing the stochastic gradients of edge *** first derive a closed form of the communication compression in terms of sparsification and quantization ***,the convergence rate of this communicationcompressed system is analyzed and several insights are ***,we formulate and deal with the quantization resource allocation problem for the goal of minimizing the convergence upper bound,under the constraint of multiple-access channel *** show that the proposed scheme outperforms the benchmarks.

关键词： federated learning gradient compression quantization resource allocation stochastic gradient descent(SGD)

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