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Multi-layer network embedding on scc-based network with motif

Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Faster AMEDA-A Hybrid Mesoscale Eddy Detection Algorithm

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Computer Modeling in engineering & Sciences 2024年第11期141卷 1827-1846页

作者： Xinchang Zhang Xiaokang Pan Rongjie Zhu Runda Guan Zhongfeng Qiu Biao Song Organization Department Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Teacher Education Nanjing University of Information Science and TechnologyNanjing210044China School of Electronics&Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is *** recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean *** it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean *** this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center *** demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learning-based eddy detection method *** results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.

关键词： Mesoscale eddy detection object detection contour detection remote sensing

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Advanced DAG-Based Ranking(ADR)Protocol for Blockchain Scalability

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Computers, Materials & Continua 2023年第5期75卷 2593-2613页

作者： Tayyaba Noreen Qiufen Xia Muhammad Zeeshan Haider International School of Information Science and Engineering Dalian University of TechnologyDalian116620China School of Software Dalian University of TechnologyDalian116620China

In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive ***,current blockchain systems face the problems of limited throughput,poor scalability,and high *** to the failure of consensus algorithms in managing nodes’identities,blockchain technology is considered inappropriate for many applications,e.g.,in IoT environments,because of poor *** paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking(ADR)protocol to improve blockchain scalability and *** ADR protocol uses the directed acyclic graph ledger,where nodes are placed according to their ranking positions in the *** allows honest nodes to use theDirect Acyclic Graph(DAG)topology to write blocks and verify transactions instead of a chain of *** using a three-step strategy,this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and *** first step involves the safe entry of nodes into the system by verifying their private and public *** next step involves developing an advanced DAG ledger so nodes can start block production and verify *** the third step,a ranking algorithm is developed to separate the nodes created by *** eliminating attacker nodes,the nodes are ranked according to their performance in the system,and true nodes are arranged in blocks in topological *** a result,the ADR protocol is suitable for applications in the Internet of Things(IoT).We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious *** on the simulation results,this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications(IOTA)and ByteBall.

关键词： Blockchain scalability directed acyclic graph advanced DAG-based ranking protocol

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COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Generative adversarial networks based motion learning towards robotic calligraphy synthesis

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CAAI Transactions on Intelligence technology 2024年第2期9卷 452-466页

作者： Xiaoming Wang Yilong Yang Weiru Wang Yuanhua Zhou Yongfeng Yin Zhiguo Gong Department of Computer and Information Science University of MacaoMacaoChina School of Software Beihang UniversityBeijingChina Department of Computer Science and Technology Faculty of Information TechnologyBeijing University of TechnologyBeijingChina School of Foreign Languages Guangzhou Huashang CollegeGuangzhouChina

Robot calligraphy visually reflects the motion capability of robotic *** traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article presents a generative adversarial network(GAN)-based motion learning method for robotic calligraphy synthesis(Gan2CS)that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy *** key technologies in the proposed approach include:(1)adopting the GAN to learn the motion parameters from the robot writing operation;(2)converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration;(3)reproducing high-precision calligraphy works by synthesising the writing motion data *** this study,the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot *** robot performs the writing with motion planning,and the writing motion parameters of calligraphy strokes are learnt with *** the motion data of basic strokes is synthesised based on the hierarchical process of‘stroke-radicalpart-character’.And the robot re-writes the synthesised characters whose similarity with the original calligraphy characters is *** calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

关键词： calligraphy synthesis generative adversarial networks Motion learning robot writing

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DIEONet:Domain-Invariant information Extraction and Optimization Network for Visual Place Recognition

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Computers, Materials & Continua 2025年第3期82卷 5019-5033页

作者： Shaoqi Hou Zebang Qin Chenyu Wu Guangqiang Yin Xinzhong Wang Zhiguo Wang School of Computer Science and Technology Xinjiang UniversityUrumqi830046China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Institute of Public Security Kash Institute of Electronics and Information IndustryKashi844000China

Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and *** is well known that previous VPR algorithms emphasize the extraction and integration of general image features,while ignoring the mining of salient features that play a key role in the discrimination of VPR *** this end,this paper proposes a Domain-invariant information Extraction and Optimization Network(DIEONet)for *** core of the algorithm is a newly designed Domain-invariant information Mining Module(DIMM)and a Multi-sample Joint Triplet Loss(MJT Loss).Specifically,DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group,which enhances the model’s attention to the domain-invariant static object *** Loss introduces the“joint processing of multiple samples”mechanism into the original triplet loss,and adds a new distance constraint term for“positive and negative”samples,so that the model can avoid falling into local optimum during *** demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative *** particular,the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.

关键词： Visual place recognition domain-invariant information mining module multi-sample joint triplet loss

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A topical VAEGAN-IHMM approach for automatic story segmentation

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Mathematical Biosciences and engineering 2024年第7期21卷 6608-6630页

作者： Yu, Jia Peng, Huiling Wang, Guoqiang Shi, Nianfeng School of Computer and Information Engineering Luoyang Institute of Science and Technology China Software Research Institute Technological University of Shannon Ireland

Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance. © 2024 the Author(s).

关键词： Hidden Markov models

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Research on Recommendation Algorithm Based on Improved Collaborative Filtering 2

Research on Recommendation Algorithm Based on Improved Colla...

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2nd IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Li, Xin School of Software Beijing University of Technology Faculty of Information Science Beijing China

ISBN: (纸本)9798350360240

With the large-scale application of personalized recommendation system, the importance of user privacy protection has become increasingly prominent. Among them, collaborative filtering recommendation algorithm, as one of the widely used recommendation technologies, not only realizes personalized recommendation, but also draws attention to its potential privacy disclosure risk. Therefore, how to adopt effective privacy protection policies within the framework of collaborative filtering to ensure the security of user data has become one of the core areas of current research. Existing research generally adopts the method of adding noise to user-item rating data to protect user privacy, but this usually leads to a significant decline in recommendation accuracy. In order to overcome the problem that excessive noise may affect the quality of recommendation and even lead to the failure of recommendation, an innovative adaptive noise adding method is proposed in this paper. According to the similarity difference between users, this method specifically integrates different degrees of Laplacian noise into the user similarity matrix, aiming to accurately regulate the amount of noise injection, so as to minimize the interference to the original data while maintaining high recommendation accuracy. In order to further improve the efficiency of the algorithm, the algorithm also combines the optimized k-means algorithm to construct a more refined neighbor set by intelligent clustering of users. The experimental results show that the proposed algorithm has good performance and is compared with other privacy-protecting recommendation algorithms. The experimental results confirm the effectiveness and feasibility of the algorithm, indicating that it can maintain high quality personalized recommendation effect while protecting user privacy. © 2024 IEEE.

关键词： Differential privacy

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Comparisons of{100}texture improvement and formability in hot-rolled non-oriented electrical steel by austenite-ferrite phase transformation and shear deformation

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Journal of Iron and Steel Research International 2025年第1期32卷 171-185页

作者： Chi-hao Yu Hong-jiang Pan Yan-ping Zhao De-ting Tang Hai-jun Wang Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093YunnanChina School of Electronics and Information Engineering Anshun UniversityAnshun561000GuizhouChina School of Metallurgical Engineering Anhui University of TechnologyMa’anshan243032AnhuiChina

Over the years,the high magnetic induction of industrial Mn-added electrical steel is assumed to be the enhancement of{100}texture derived from its austenite-ferrite phase transformation during hot rolling(phase transformation(PT)method).However,it is still undetermined without straightforward experimental *** reason for{100}texture improvement of Mn-added electrical steel is experimentally confirmed due to the recrystallization induced by the austenite-ferrite phase transformation during hot ***,a more promising methodology to further improve{100}texture and formability of hot-rolled electrical steel is promoted by the control of hot rolling deformation condition(shear deformation(SD)method).The results show that the nucleation mechanisms of{100}oriented recrystallized grains are different in the samples by SD and PT methods,which are in-depth shear deformation and austenite-ferrite phase transformation,*** this case,coarse{100}oriented recrystallized grains and low residual stress are obtained in the sample by SD method,which is responsible for its superior{100}texture and *** contrast,the sample by PT method forms fine recrystallized grains with random orientations and accumulates severe residual stress.

关键词： Non-oriented electrical steel {100}texture Formability Austenite-ferrite phase transformation Shear deformation

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