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Smart World Congress (SWC), IEEE

作者： Qingqiong Cai Lu Yang Kunpeng Xiao Shenwei Huang College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

Traffic prediction plays a vital role in intelligent transportation systems, such as flow control and route planning. As a classical spatial-temporal prediction task, timely accurate traffic prediction is challenging due to complicated spatial-temporal correlation in traffic data. Traffic data contains short-term and long-term temporal sequential correlations, as well as local and global spatial correlations. In addition, traffic patterns at different time steps and the distribution of traffic data at various nodes may differ, so models need to have the ability to capture these temporal and spatial heterogeneities. To solve these issues, we propose a novel traffic prediction model, called Spatio-Temporal Attention network (STAN). It models short-term and long-term temporal correlations using Gate Recurrent Unit (GRU) and Heterogeneity-Aware Temporal Self-Attention (HA-TSA) mechanism, respectively. It further models local and global spatial correlations using Graph Attention network (GAT) and Heterogeneity-Aware Spatial Self-Attention (HA-SSA) mechanism, respectively. Based on traditional self-attention mechanism, HA-TSA and HA-SSA further learn different parameters for different nodes and different time steps to capture temporal and spatial heterogeneities. Besides, in order to extract the periodicity of traffic data, time stamps are concatenated to the original traffic features as auxiliary information. STAN also adds a centrality encoding based on node’s degree to measure the importance of a node in the traffic network, thus helping to discover similar traffic patterns between nodes. Experimental results on two benchmark traffic datasets show that compared with the state-of-the-art baseline method, Mean Absolute Errors (MAEs) of STAN decrease by 7.48% and 7.77%, and Root Mean Squared Errors (RMSEs) of STAN decrease by 1.89% and 5.19%, respectively.

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Social Internet of Tings Trust Management Based on Implicit Social Relationship 5th

Social Internet of Tings Trust Management Based on Implicit ...

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5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Zhang, Hongbin Fan, Fan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Jian School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

The "Social Internet of Things (SIoT)" is a combination of the Internet of Things (IoT) and social networks to form a new paradigm. The SIoT promotes the development of smart cities, smart transportation, and many other fields. In SIoT, the openness and mobility of objects are enhanced. However, this tends to lead to network data sparsity problems. By distinguishing explicit and implicit social relationships, we introduce an implicit social relationship-based trust management model (IRTM) for reliable service delivery in SIoT. IRTM establishes implicit social relationships among nodes by mining their latent characteristics and trust transitivity. It models SIoT by creating sub-networks for each social relationship as a way to fuse the impact of different types of social relationships on trust management. To address the problem of malicious attacks by malicious nodes in the network to protect their interests, it considers two metrics, node relationship strength, and recommendation reliability, to filter malicious recommendations. Experiments conducted in the presence of data sparsity and malicious objects show that IRTM can improve the accuracy and convergence of trust evaluation compared to other methods that ignore implicit social relationships when computing trust. In addition, our scheme can improve resistance to trust-related attacks. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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Dear-DIA^(XMBD): Deep Autoencoder Enables Deconvolution of data-Independent Acquisition Proteomics

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Research 2024年第1期6卷 707-720页

作者： Qingzu He Chuan-Qi Zhong Xiang Li Huan Guo Yiming Li Mingxuan Gao Rongshan Yu Xianming Liu Fangfei Zhang Donghui Guo Fangfu Ye Tiannan Guo Jianwei Shuai Jiahuai Han Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China School of Life Sciences Xiamen UniversityXiamen 361102China State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkXiamen 361102China Department of Computer Science Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine School of MedicineXiamen UniversityXiamen 361102China Bruker(Beijing)Scientific Technology Co.Ltd. BeijingChina Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024China Westlake Omics Ltd. Yunmeng Road 1HangzhouChina Department of Electronic Engineering Xiamen UniversityXiamen 361005China

data-independent acquisition(DIA)technology for protein identification from mass spectrometry and related algorithms is developing *** spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising *** this paper,we proposed an untargeted analysis method,Dear-DIA^(XMBD),for direct analysis of DIA ***-DIA^(XMBD) first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms,then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes,and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and *** show that Dear-DIA^(XMBD) performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms.

关键词： Deep Auto Independent

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Research on Domain Event Extraction Methods Based on Pre-Trained Model

Research on Domain Event Extraction Methods Based on Pre-Tra...

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2022 International Conference on Signal Processing, Computer networks, and Communications, SPCNC 2022

作者： Meng, Fanshen Zhang, Yonggang Liu, Xuhong Liu, Xiulei Yu, Junyang Institute of Data Science and Information Analysis Beijing Information Science & Technology University Beijing100029 China Center for Information Research Academy of Military Sciences Beijing100142 China Beijing Key Laboratory of Network Culture and Digital Communication Beijing Information Science & Technology University Beijing100029 China Beijing Advanced Innovation Center of Materials Genome Engineering Beijing Information Science & Technology University Beijing100029 China School of Software Henan University Henan 475001 China

ISBN: (纸本)9781510664616

One of the important research directions in information extraction is event extraction(EE). It aims at recognizing event types and event arguments from natural language texts, which is an important technical basis for artificial intelligence application that serves for the information work in business, science and technology, military and other fields. Currently, data annotation samples of the relevant field based on encyclopedia and news data are relatively rare and lack relevant datasets. Therefore, there are only a few public research on the event extraction in the relevant intelligence field. By integrating universal information extraction, the event extraction method which use the pre-trained model for the relevant intelligence field can handle the problem of rare data annotation samples for the event extraction in the relevant field. By expanding training samples automatically, the context information of encyclopedia and news data is learnt effectively to extract relevant events from encyclopedia and news data. Compared with other event extraction methods, using precision, recall and F1 value as assessment indicators, in event recognition tasks, the value of F1 enhances 1.26%, and in argument recognition tasks, the value of F1 enhances 1.58%. The method can significantly boost the extraction performance in small samples shown in the experimental results. © 2023 SPIE.

关键词： Information retrieval

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A Virtual network Mapping Method Based on Compound Particle Swarm Optimization 7

A Virtual Network Mapping Method Based on Compound Particle ...

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7th International Conference on Cyber Security and Information Engineering, ICCSIE 2022

作者： Huang, Jianhua Tang, Yunlong Wei, Yongjun Wang, Huan Zhang, Haifeng Zhang, Bing School of Computer Science and Technology School of Software Guangxi University of Science and Technology Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Guangxi Education System Network Security Monitoring Center Liuzhou China Liuzhou Railway Vocational Technical College Liuzhou China

ISBN: (纸本)9781665455749

Aiming at problems of low credibility of results and low accuracy of the scheme in the mapping process of virtual networks. It is suggested to use a composite particle swarm optimization approach for virtual network mapping. Firstly, to derive the maximum number of virtual cyberspaces, we construct the virtual network model and the physical network model;secondly, resource limitations are used to build the node mapping and link mapping models, and once more, the mapping scheme of the virtual network on the physical network is solved using the composite particle swarm algorithm to improve the acceptance rate of virtual network requests. In simulation experiments, we compare our established algorithm with two algorithms, D-ViNE-SP and VNE-R-PSO, in two dimensions, and the results show that our proposed VNE-MM-PSO mapping method improves the acceptance rate of network requests by 8% and also improves on the benefit-cost ratio. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Influencing factors and mechanism of high-temperature oxidation of high-entropy alloys: A review

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International Journal of Minerals,Metallurgy and Materials 2021年第6期28卷 915-930页

作者： Ya Wei Yu Fu Zhi-min Pan Yi-chong Ma Hong-xu Cheng Qian-cheng Zhao Hong Luo Xiao-gang Li National Materials Corrosion and Protection Data Center Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China Key Laboratory for Corrosion and Protection of The Ministry of Education(MOE) Beijing 100083China BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Graduate School of University of Science and Technology BeijingFoshan 528000China

High-temperature oxidation is a common failure in high-temperature environments,which widely occur in aircraft engines and aerospace thrusters;as a result,the development of anti-high-temperature oxidation materials has been ***-based alloys are a common high-temperature material;however,they are too ***-entropy alloys are alternatives for the anti-oxidation property at high temperatures because of their special structure and *** recent achievements of high-temperature oxidation are reviewed in this *** high-temperature oxidation environment,temperature,phase structure,alloy elements,and preparation methods of high-entropy alloys are *** reason why high-entropy alloys have anti-oxidation ability at high temperatures is *** research,material selection,and application prospects of high-temperature oxidation are introduced.

关键词： high-entropy alloy high-temperature oxidation influencing factors oxidation mechanism

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PENG ZU Study on Healthy Aging in China(PENG ZU Cohort):Design and Goals

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China CDC weekly 2024年第34期6卷 876-882页

作者： Ju Cui Jing Pang Liqun Zhang Juan Li Xiaolan Wu Xinyi Zhu Wei Ma Senlin Luo Huafang Gao Zhao Wang Jianping Cai Tiemei Zhang The Key Laboratory of Geriatrics Beijing Institute of GeriatricsInstitute of Geriatric MedicineChinese Academy of Medical ScienceBeijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina Beijing Institute of Geriatrics Beijing HospitalBeijingChina Institute of Geriatric Medicine Chinese Academy of Medical ScienceBeijing HospitalBeijingChina Center on Aging Psychology CAS Key Laboratory of Mental HealthInstitute of PsychologyChinese Academy of SciencesBeijingChina Institute of Healthy Ageing China Research Center on AgeingBeijingChina Institute of Statistics and Big Data Renmin University of ChinaBeijingChina Beijing Institute of Technology BeijingChina National Human Genetic Resources Center National Research Institute for Family PlanningBeijingChina Department of Pharmacology School of Pharmaceutical SciencesTsinghua UniversityBeijingChina

Life expectancy is increasing,leading to the continuous aging of the population in *** the health status of the older population is crucial to achieving healthy *** primary objective of the PENG ZU Study on Healthy Aging in China(PENG ZU Cohort)is to understand the natural progression of health status among the aging Chinese ***,the PENG ZU cohort aims to identify and validate multidimensional aging markers,uncover the underlying mechanisms of systemic aging and functional decline,and develop novel strategies and measures to delay functional decline and adverse health outcomes,while maintaining overall good *** PENG ZU cohort consists of 26,000 individuals aged 25 to 89 years from seven major geographical regions in *** data and biospecimens are collected according to standardized procedures at baseline and follow-up *** recruitment for the PENG ZU cohort was completed in October *** extensive analysis of multidimensional health-related data and bioresources collected from the cohort is anticipated to develop methods for evaluating functional status and elucidating multilevel,cross-scale interactions and regulatory mechanisms of healthy *** findings from this study will enhance the understanding of health changes due to aging,facilitate efficient and effective interventions to maintain functional ability,and reduce the incidence and severity of age-related diseases,thereby further promoting healthy aging.

关键词： aging thereby maintain

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NIR-II fluorescence molecular tomography based on transformer encoder architecture

NIR-II fluorescence molecular tomography based on transforme...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Bao, Jie Xiao, Anqi Han, keyi Pei, Ziyu Fu, Lidan Tian, Jie Hu, Zhenhua CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi’an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (纸本)9781510685987

Fluorescence molecular tomography (FMT) is a highly sensitive method for depicting the three-dimensional distribution of fluorescent targets within biological tissues. However, traditional FMT reconstruction can be affected by the severe photon scattering and the modeling errors of the photon propagation model, making it difficult to obtain accurate reconstruction results. Deep learning methods can directly learn the nonlinear mapping between the photon intensity on the surface and the internal light sources, providing a promising approach for addressing the inverse problem in FMT. Since the second near-infrared window (NIR-II, 1000-1700) fluorescence imaging has a higher tissue penetration depth and imaging contrast, this work constructs a deep learning model suitable for the fluorescence images in this spectrum. The model introduced in this study, akin to grasping the contextual nuances of word vectors in the domain of machine translation, endeavors to comprehend the process of photon transmission within biological tissues. Inspired by the transformer encoder, we proposed a network mainly consist of positional encoding, dual-head attention mechanism, residual connection, and fully connected layers. It has achieved significantly better results than comparative methods on the NIR-II dataset constructed by the Monte Carlo method. © 2025 SPIE.

关键词： Fluorescence imaging

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Community discovery method based on complex network of data fusion based on the super network perspective

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International Journal of Computers and Applications 2021年第4期43卷 383-390页

作者： Pei, Li School of Computer Science & Technology Xi‘an University of Posts and Telecommunications Xi‘an China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi‘an University of Posts and Telecommunications Xi‘an China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi‘an Jiaotong University Xi‘an China

To enhance the computational efficiency and precision of community discovery, a community discovery algorithm with the mixed label based on the minimum description length (MDI) of information compression is proposed in this paper. Firstly, the community detection is converted to the information compression problem of seeking for an effective network structure and the quality evaluation function is constructed based on the MDL criterion. Secondly, the community discovery algorithm with heuristic mixed label movement is constructed based on the label node movement algorithm and Louvain community addition algorithm so as to reduce the quality evaluation function. Finally, the simulation experiment in the standard test set and API capture Sina microblog dataset shows that the proposed algorithm is superior to the selected comparison algorithm in computational efficiency and precision. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Computational efficiency

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UAPE: Information Propagation Model Based on User Attitude and Public Opinion Environment

UAPE: Information Propagation Model Based on User Attitude a...

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IEEE International Conference on Communications (ICC)

作者： Xinyu Li Jinyang Huang Xiang Zhang Peng Zhao Meng Wang Guohang Zhuang Huan Yan Xiao Sun School of Computer and Information Hefei University of Technology Hefei China CAS Key Laboratory of Electromagnetic Space Information University of Science and Technology of China Hefei China School of Big Data and Computer Science Guizhou Normal University Guiyang China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Modeling the information propagation process in social networks is a challenging problem. Despite numerous attempts to address this issue, existing studies often assume that user attitudes have only one opportunity to alter during the information propagation process. Additionally, these studies tend to consider the transformation of user attitudes as solely influenced by a single user, overlooking the dynamic and evolving nature of user attitudes and the impact of the public opinion environment. In this paper, we propose a novel model, UAPE, which considers the influence of the aforementioned factors on the information propagation process. Specifically, UAPE regards the user's attitude towards the topic as dynamically changing, with the change jointly affected by multiple users simultaneously. Furthermore, the joint influence of multiple users can be considered as the impact of the public opinion environment. Extensive experimental results demonstrate that the model achieves an accuracy range of 91.62% to 94.01 %, surpassing the performance of existing research.

关键词： Accuracy Social networking (online) Market research

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