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The Analysis of Phase Synchronisation in the Uniform Scale-Free Hypernetwork 9th

The Analysis of Phase Synchronisation in the Uniform Scale-F...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Du, Juan Ma, Xiujuan Ma, Fuxiang Zhou, Bin Yu, Wenqian College of Computer Qinghai Normal University Qinghai Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9789819959709

Many phenomena in realistic complex systems can be explained by the synchronisation behavior of complex systems, such as cricket chirping in unison. The synchronisation behavior occurring on a hypernetwork can be used to explain the swarming behavior occurring on a multivariate interacting system, such as the synchronised forwarding of group messages. There is a lack of results related to phase synchronization of hypernetwork in the existing studies on the synchronization behavior of hypernetworks. To address this problem, this paper investigates the node-based and hyperedge-based phase synchronisation of a scale-free hypernetwork using the Kuramoto model with the order parameter r as the synchronisation degree indicator. The comparative analysis reveals that the phase synchronisation of the scale-free hypernetwork is related to the uniformity k of the hypernetwork but not to the number of nodes and hyperedges, and the phase synchronisation based on hyperedges is more likely to occur than that based on nodes as the coupling strength increases. In addition, the degree of phase synchronisation of scale-free hypernetworks is related to the number of new_nodes of newly added nodes when the hyperedge grows during the construction of the hypernetwork, which shows that the smaller the new_nodes is, the better the degree of synchronisation of the hypernetwork is. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Synchronization

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Building Edge Detection Technology from Remote Sensing Image Based on NSCT and Tensor Voting 8

Building Edge Detection Technology from Remote Sensing Image...

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2024 8th International Conference on Machine Vision and information Technology, CMVIT 2024

作者： Wu, Yingbin Zhao, Peng Zhou, Mingquan Geng, Shengling Zhang, Dan School of Computer Science Qinghai Normal University Qinghai Xining China School of Mathematics and Information Technology Yuncheng University Shanxi Yuncheng China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining China

An edge detection technology based on the combination of non-downsampling contour wave transform (NSCT) and tensor voting is proposed, which aims to obtain more accurate and detailed edge information of buildings in remote sensing images. Firstly, NSCT is used for image decomposition to obtain the subband frequency information of different scales and angles. Then, position encoding is performed on these subband coefficients to obtain second-order symmetric tensors at the corresponding positions. Tensors of different scales and angles at the same position are weighted and summed to complete feature fusion. Finally, the edge features of the image are obtained based on tensor voting theory. The experimental results show that compared to common edge detection technologies, such as Canny, Fast Edge (Fast edge detection using structured forests) and HED (Holistically-Nested Edge Detection), our method can more accurately and intensively reflect the boundaries of buildings and the edge information of roofs, providing better support for the analysis of building types and architectural styles. Compared to the HED method, which is based on deep learning, our method improves PSNR and SSIM metrics by 0.98 and 0.03, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Edge detection

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Multi-scale Heterogeneous Graph Contrastive Learning∗

Multi-scale Heterogeneous Graph Contrastive Learning∗

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Li, Mingyuan Meng, Lei Ye, Zhonglin Zhao, Haixing Xiao, Yuzhi Cao, Shujuan Qinghai Norm. Univ. the State Key Lab. of Tibetan Intelligent Information Processing and Application College of Computer Xining China

ISBN: (纸本)9798350324457

In recent years, heterogeneous graph neural networks have become the mainstream approach for handling heterogeneous graph data. However, due to the sparsity of labels, most existing methods for heterogeneous graph neural networks typically employ a semi-supervised learning approach, which has certain limitations in practical applications. To address this issue, we propose a self-supervised heterogeneous graph representation learning method, namely Multi-scale Heterogeneous Graph Contrastive Learning (MHGCL). This approach decodes encoded information from two perspectives: meta-paths and network patterns, in a multi-scale fashion. It uses a loss function that maximizes the similarity between positive pairs at different scales and minimizes the similarity between negative pairs. This encourages related nodes and edges to be close to each other in the embedding space, while unrelated nodes and edges are pushed farther apart. Experimental results demonstrate that MHGCL comprehensively captures semantic information at different scales between nodes. It exhibits excellent performance in node classification tasks, validating its effectiveness in heterogeneous graph node embedding learning. © 2023 IEEE.

关键词： contrastive learning heterogeneous graph meta-path multi-scale

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A review of computational offloading research based on game theory in a vehicular edge computing environment 7

A review of computational offloading research based on game ...

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7th International Conference on Mechatronics and intelligent Robotics, ICMIR 2023

作者： Qu, Zijian Tong, Yinghua Li, Maocuo School of Computer Qinghai Normal University Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Xining810008 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

A new computing paradigm that extends the capabilities of the cloud to the edge of the network, vehicular edge computing (VEC), provides vehicles with a wealth of resources that can accommodate the rising demand for in-vehicle application computing and storage resources. This is accomplished by seamlessly fusing mobile edge computing with invehicle networks (MEC). It provides low-latency, high-bandwidth, and extremely dependable computing services to vehicles and other users with limited computing resources. This paper reviews existing game theory-based task offloading mechanisms in the vehicle edge environment. The first section introduces the current research status of vehicle edge computing in terms of its features, architecture, and applications. It covers the background and significance of task offloading in vehicle edge computing, the VEC architecture, the advantages of VEC, and several alluring application scenarios. A thorough analysis of the body of prior research is then presented, followed by a categorical discussion of game theory-based computational offloading mechanisms in the context of vehicle edges. Finally, predictions for the future of technology are made. © 2023 SPIE. All rights reserved.

关键词： Computer games

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Method for identifying vital nodes in the scientific-citation double-layer hypernetwork based on the h-index

Method for identifying vital nodes in the scientific-citatio...

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2024 International Conference on Computer application and information Security, ICCAIS 2024

作者： Guo, Lei Liu, Wei Zhang, Xin Hu, Feng School of Computer Qinghai Normal University Qinghai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9781510689312

Existing studies on scientific collaboration networks and citation networks are often limited to the analysis of a single relational dimension, making it challenging to comprehensively capture the interactions between collaboration and citation relationships within multilayered networks and their characteristics in knowledge dissemination. At the same time, traditional vital node identification methods are mostly based on evaluation metrics from single-layer networks, making it difficult to comprehensively and accurately identify vital nodes in multivariate, multilayer and multidimensional relationship hypernetworks. Therefore, based on the characteristics of scientific collaboration and citation networks, and in combination with hypernetwork theory based on hypergraphs, this paper proposes the construction of a scientific-citation double-layer hypernetwork model. Subsequently, based on the concept of aggregated networks, the double-layer hypernetwork is aggregated into a weighted hypernetwork. A method for identifying vital nodes in the hypernetwork based on the h-index is proposed, where the h-index is applied to the weighted aggregated hypernetwork and combined with the K-shell algorithm and hyperdegree metric to identify vital nodes. Finally, this method is applied to the empirical analysis of the scientific-citation double-layer hypernetwork constructed from the dataset of Acta Physica Sinica from 2014 to 2024. The results, based on the analysis of network propagation dynamics, network statistical characteristics, and AUC accuracy evaluation metrics, demonstrate that the proposed identification method is accurate and effective. This study provides valuable insights into how to construct multilayer hypernetwork models based on the characteristics of real-world networks, as well as the application of multilayer hypernetworks in real networks and the identification of vital nodes. © The Authors.

关键词： Multivariant analysis

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Building Segmentation from Remote Sensing Image via DWT Attention Networks 23

Building Segmentation from Remote Sensing Image via DWT Atte...

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5th International Conference on Pattern Recognition and intelligent Systems, PRIS 2023

作者： Wu, Yingbin Zhou, Mingquan Geng, Shengling Zhang, Dan College of Computer Qinghai Normal University Xining China College of Math and Information Yuncheng University Yuncheng China State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9781450399968

The attention mechanism has been widely used and achieved good results in many visual tasks. But the calculations of attention mechanism in vision tasks consume huge spaces and times, which is the obvious disadvantage of this method. In order to alleviate this problem, we use the DWT(Discrete Wavelet Transform) method to reduce the complexity of attention calculation. DWT can transform an N-dimensional vector into two vectors, one is the low-frequency component of N/2 dimension and the other is high-frequency component of N/2 dimension too. We only use the low-frequency to calculate the attention matrixes, which can reduce the complexity of matrix multiplication, then the time and space consumption of the network is reduced significantly. We also find that the building segmentation in the remote sensing image is different from the other scene segmentation, that the sizes and numbers of different classes of the targets in the general scene images are obvious. Despite all this, our method is still applicable for the targets with large numbers and sizes in general scene images, but not for the targets with small sizes and numbers, and this view is also verified by the subsequent experiments on different datasets. We apply our method on three typical networks (Danet, Swin and Segmenter), and carry out comprehensive experiments on the Cityscape dataset and three building segmentation datasets (Inria Aerial Dataset, Massachusetts Buildings Dataset and Chinese Style Architecture Dataset). The experiments show that, our method is more suitable for building segmentation and can reduce the complexity of the model calculation in building segmentation, and the Mean IoU of segmentation results is not reduced clearly, some even improved. © 2023 ACM.

关键词： Remote sensing

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Minimum Strong Radius of the Strong Product of Paths 2

Minimum Strong Radius of the Strong Product of Paths

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2nd International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2022

作者： Liu, Shuyang Li, Feng College of Computer Science Qinghai Normal University Xi'ning810003 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xi'ning810003 China

ISBN: (纸本)9781510663183

Let D is a strong oriented graph, the strong distance sd(u, v) is the number of arcs of D containing u and v. For a vertex v of D, the strong eccentricity se(v) is the strong distance between v and a vertex farthest from v. The strong radius srad(D) is the minimum strong eccentricity among the vertices of D. The minimum strong radius srad(G) of a graph D is the minimum strong radius over all strong orientations of G. And G1 ⊗ G2 denote the strong product of graphs G1 and G2. In this paper, according to the order of paths we give the result of the minimum strong radius of the strong product of paths, and propose a new directed network design method. © 2023 SPIE.

关键词： Graph theory

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Research on tibetan-Chinese Neural Machine Translation Integrating Statistical Method 6

Research on Tibetan-Chinese Neural Machine Translation Integ...

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6th International Conference on Machine Learning and Natural language processing, MLNLP 2023

作者： Zhou, Maoxian The Computer College of Qinghai Normal University Qinghai Xining810016 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Qinghai Xining810008 China Qinghai Engineering and Technology Research Center for Tibetan Information Processing Qinghai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9798400709241

In recent years, with the emergence of deep learning methods, Neural Machine Translation has become a new research direction of machine translation. Due to the scarcity of digital resources in tibetan, there is only a small-scale tibetan-Chinese bilingual parallel corpus to train translation models. In the case of insufficient resources, the performance of Neural Machine Translation decreases significantly. In this regard, considering that the word alignment results in tibetan-Chinese Statistical Machine Translation are better, and the alignment information in tibetan-Chinese Neural Machine Translation model is significantly different from that in tibetan-Chinese Statistical Machine Translation, this paper proposes a tibetan-Chinese Neural Machine Translation method that integrates statistical methods. We using Statistical Machine Translation method to generate word alignment information of tibetan-Chinese parallel corpus to supervise the training process of tibetan-Chinese Neural Machine Translation model to make the model achieve more accurate translation and alignment effect. Experimental results show that this method can effectively improve translation quality in low resource environment. © 2023 ACM.

关键词： Neural machine translation

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Cascading failure analysis of uniform double-layer hyper-networks based on the Couple Map Lattice model 24

Cascading failure analysis of uniform double-layer hyper-net...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and application, HPCC/DSS/SmartCity/DependSys 2022

作者： Gao, Shujie Ma, Xiujuan Ma, Fuxiang Zhou, Bin School of Computer Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9798350319934

Cascading failures are a class of dynamical behaviors that are widely present in various types of complex systems. The study of cascading failures has also been a hot topic for researchers. The results have shown that the process of cascading failures varies greatly depending on the structure of the network. Based on the double-layer hyper-network structure, considering the types of hyper-networks and the way of inter-layer connection, we construct four types of 3 uniform double-layer hyper-networks. In addition, the Couple Map Lattice (CML) cascading failure model is proposed which is suitable for describing the cascading failure behavior of a double-layer hyper-network. By this model, we analyzed the cascading failure processes happen in hyper-edges of a uniform double-layer hyper-network. Furthermore, through theoretical analysis, an approximate solution for the external perturbation threshold is obtained, which makes the double-layer hyper-network reaches the global collapse. The results of this paper provide an important reference for optimizing the robustness of realistic double-layer hyper-networks. In practical applications, the robustness of the double-layer hyper-networks can be improved by reducing the inter-layer connectivity probability p12 and the coupling strength ϵ between hyper-edges. © 2022 IEEE.

关键词： Network layers

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Microblog Authenticity Detection Based on Human-machine Collaboration 22

Microblog Authenticity Detection Based on Human-machine Coll...

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2nd International Conference on Human Machine Interaction, ICHMI 2022

作者： Yang, Fan Yang, Manyuan Yu, Zhiwen Northwestern Polytechnical University China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University China

ISBN: (纸本)9781450396615

For the sake of determining the reliability of an event, this paper proposes a method of identifying the authenticity of a microblog event based on human-machine collaboration. Using the Weibo that was spoken in the incident and the public information of the users participating in the event to identify false incidents. Then separately calculate the user's credibility and the emotional stance of Weibo and comments. The calculation of user credibility takes the complete information of the user as a static description of the user. When performing emotional standpoint detection, we divide it into two steps. For a piece of Weibo, its comments are tested for further computation, for different microblog, the emotions between microblog are tested. Building a graph model for an event. While processing the model, some special microblogs are extracted. Human will deal with these microblogs and give feedback to the graph model. Then the model will be changed accordingly. Finally, GCN is leveraged to process the model to obtain the true and false of the event. Through extensive experiments to verify the proposed model and show a comparative performance, the accuracy is 89.34 and the F-score reaches0.9375. © 2022 ACM.

关键词： Social networking (online)

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