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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Wang, Huan Liu, Ao Zhang, Junjie Zhang, Qingming School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798331507398

Many existing studies employ graph neural networks (GNNs) to aggregate information from neighboring nodes and learn node embeddings in a low-dimensional space, aiming to produce layouts that satisfy established aesthetic criteria. However, current GNN approaches for graph layout tasks primarily focus on node features, often neglecting the complex relationships between edge features. To address this limitation and enhance the model's ability to capture complex graph structures, we propose a plug-and-play edge message passing enhancement module that can be easily integrated into existing layout frameworks. This module incorporates both edge distance features and angular relationships between edges, enabling more effective information exchange. We evaluate the usability and effectiveness of this module through quantitative and qualitative experiments. © 2024 IEEE.

关键词： Graph algorithms

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Optimizing Task Processing Efficiency in MEC Networks Through Cooperative Offloading and Resource Allocation 6

Optimizing Task Processing Efficiency in MEC Networks Throug...

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6th International Conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Liang, Yong Sun, Haifeng School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350353174

As Internet of Things (IoT) devices continue to proliferate and users' demands for low-latency and compute-intensive services grow, mobile edge computing (MEC) can effectively reduce task processing delay and energy consumption by tasks offloading to the MEC server. In the context of an MEC network comprising IoT mobile device (IMD), neighboring IMDs, and MEC servers, we propose a joint cooperative partial offloading and resource allocation scheme that selects a neighboring IMD and an MEC server as offloading assistants to assist the IMD in processing tasks in MEC systems. Further, the allocation of IMD computing resources should also be taken into consideration. By jointly optimizing the offloading decision and computational resource allocation, we minimize the weighted sum cost of task processing delay and energy consumption of all IMDs as an optimization problem and solve the non-convex problem by a deep Q-network (DQN) algorithm. Extensive experimental results show that the proposed scheme can effectively reduce the cost compared to other baseline schemes. © 2024 IEEE.

关键词： Computation offloading

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Knowledge Representation Learning Method Based on Semantic Enhancement of External Information

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Recent Advances in computer science and Communications 2024年第7期17卷 68-84页

作者： Li, Song Yang, Yuxin Zhang, Liping School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

Background: Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured information representation of triples or introducing only one additional kind of information, which has large limitations and reduces the representation efficiency. Objective: This study aims to combine entity description information and textual relationship description information with triadic structure information, and then use the linear mapping method to linearly transform the structure vector and text vector to obtain the joint representation vector. Methods: A knowledge representation learning (DRKRL) model that fuses external information for semantic enhancement is proposed, which combines entity descriptions and textual relations with a triadic structure. For entity descriptions, a vector representation is performed using a Bi-directional Long-and Short-term Memory Network (Bi-LSTM) model and an attention mechanism. For the textual relations, a convolutional neural network is used to vectorially encode the relations between entities, and then an attention mechanism is used to obtain valuable information as complementary information to the triad. Results: Link prediction and triadic group classification experiments were conducted on the FB15K, FB15K-237, WN18, WN18RR, and NELL-995 datasets. Theoretical analysis and experimental results show that the DRKRL model proposed in this paper has higher accuracy and efficiency compared with existing models. Conclusion: Combining entity description information and textual relationship description information with triadic structure information can make the model have better performance and effectively improve the knowledge representation learning ability. © 2024 Bentham science Publishers.

关键词： Classification (of information)

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An efficient algorithm for approximate Voronoi diagram construction on triangulated surfaces

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Computational Visual Media 2023年第3期9卷 443-459页

作者： Wenlong Meng Pengbo Bo Xiaodong Zhang Jixiang Hong Shiqing Xin Changhe Tu School of Computer Science and Technology Harbin Institute of TechnologyWeihai 264209China School of Computer Science and Technology Shandong UniversityQingdao 266237China

Voronoi diagrams on triangulated surfaces based on the geodesic metric play a key role in many applications of computer *** methods of constructing such Voronoi diagrams generally depended on having an exact geodesic ***,exact geodesic computation is time-consuming and has high memory usage,limiting wider application of geodesic Voronoi diagrams(GVDs).In order to overcome this issue,instead of using exact methods,we reformulate a graph method based on Steiner point insertion,as an effective way to obtain geodesic ***,since a bisector comprises hyperbolic and line segments,we utilize Apollonius diagrams to encode complicated structures,enabling Voronoi diagrams to encode a medial-axis surface for a dense set of boundary *** on these strategies,we present an approximation algorithm for efficient Voronoi diagram construction on triangulated *** also suggest a measure for evaluating similarity of our results to the exact *** our GVD results are constructed using approximate geodesic distances,we can get GVD results similar to exact results by inserting Steiner points on triangle *** results on many 3D models indicate the improved speed and memory requirements compared to previous leading methods.

关键词： geodesic Voronoi diagrams(GVDs) triangular surfaces mesh surfaces approximate geodesics Apollonius diagrams

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Hash-Based Food Image Retrieval with Enhanced Feature Correlation Vision Transformeraper 17

Hash-Based Food Image Retrieval with Enhanced Feature Correl...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Yuan, Xin Chen, YongHui Wang, FuPan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798331507398

Image retrieval is a crucial research task in the field of computer vision, and food image retrieval is an application of fine-grained image retrieval in food computation tasks. The feature extraction of food image visual spatial information is constrained by the subtle differences among various types of food. The effective combination of contextual and texture information also affects the final outcome of food image retrieval. Furthermore, differences in food processing methods introduce information errors, posing greater challenges for food image retrieval tasks. This paper is based on the pretrained Vision Transformer (ViT) model and introduces an enhanced image feature association ViT hashing network (IFCVH-Net). Targeting the associations between subcategories of food information, IFCVH-Net incorporates a weight feature integration module that uses a self-attention mechanism to assign different weights to encoded feature vectors, thereby enhancing the extraction of global features from food images. Additionally, to make full use of visual spatial information effectively, a dual-channel feature association module is designed. The IFCVH-Net model is evaluated using three popular hashing image retrieval frameworks: Greedy Hash (GreedyHash), Central Similarity Quantization (CSQ), and Deep Polarized Network (DPN). Experiments comparing the IFCVH-Net model with mainstream network models such as AlexNet, ResNet50, ViT-B_32, ViT-B_16, EVHNet-16, and EVHNet-32 on the Food-101 and Vireo Food-172 datasets show that the IFCVH-Net model outperforms the other models in terms of retrieval accuracy. © 2024 IEEE.

关键词： CBAM Hybrid Attention Food Image Retrieval Hash Retrieval Self-Attention Mechanism Vision Transformer Model

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GUI Element Detection Based on YOLOv5s 17

GUI Element Detection Based on YOLOv5s

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Wang, Yuqi Wu, Jue Yang, Lei School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798331507398

The identification of GUI elements in a user interface design diagram is fundamental to many software engineering tasks. In order to solve the problems of low detection accuracy of GUI element recognition, low recognition accuracy and systematic extraction of GUI element information, this paper proposes a GUI element extraction method based on user interface design diagram. Firstly, the improved object detection model YOLOv5s-Shuffle-CIOU was used to detect GUI elements in the image user interface. For GUI elements classified as text elements, the optical character recognition (OCR) model is used for character extraction. Finally, relative positions of text GUI elements and non-text GUI elements within the graphical user interface are performed, and the resulting integration is encapsulated as an integrated JSON object representing GUI elements. This method can effectively improve the detection and classification accuracy of different categories of GUI elements, and can systematically extract all GUI element attribute information in graphical user interface. This approach is platform-independent and helps with model building in code generation and GUI testing. © 2024 IEEE.

关键词： Optical character recognition

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Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

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computers, Materials & Continua 2024年第4期79卷 143-160页

作者： Jiao Wang Bin Wu Hongying Zhang School of Information Engineering Southwest University of Science and TechnologyMianyang621010China

Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.

关键词： Deep multi-view subspace clustering contrastive learning adaptive fusion self-expression learning

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Transformer-Based Person Re-Identification: A Comprehensive Review

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

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Sparse Sampling and Fusion of Multi-Fidelity Aerodynamic Heating Data 17

Sparse Sampling and Fusion of Multi-Fidelity Aerodynamic Hea...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Nie, Yutang Yang, Chunming Wei, Mingzhi Jiang, Xianwei Ma, Xin School of Computer Science and Technology Southwest University of Science and Technology China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9798331507398

To reduce the acquisition cost of aerodynamic heating data for high-speed aircraft by leveraging diverse data sources, this paper proposes a multi-source data fusion method based on sparse sampling and multi-fidelity neural network. First, a large volume of low-fidelity data is generated using low-cost engineering calculation methods along the flight trajectory, and sparse sampling is performed according to the data variation trends. Then, CFD numerical simulation is conducted on the sampled flight parameters to obtain a limited set of high-fidelity data. Finally, high-fidelity data across the flight trajectory is estimated by combining high-fidelity and low-fidelity data through multi-fidelity neural network. The effectiveness of the fusion is verified using heat flux data at the nose tip of a certain aircraft under the simulated flight trajectory. Results indicate that the proposed fusion approach can achieve a prediction error of less than 4% while using only 5% of the CFD data points from the original trajectory, significantly reducing the CFD simulation workload and demonstrating the method's potential for engineering applications. © 2024 IEEE.

关键词： Flight paths

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How graph convolutions amplify popularity bias for recommendation?

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Frontiers of computer science 2024年第5期18卷 121-132页

作者： Jiajia CHEN Jiancan WU Jiawei CHEN Xin XIN Yong LI Xiangnan HE School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China School of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computer Science and Technology Shandong UniversityQingdao 250100China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail items are less likely to be *** effect prevents the GCN-based RS from making precise and fair recommendations,decreasing the effectiveness of recommender systems in the long *** this paper,we investigate how graph convolutions amplify the popularity bias in *** theoretical analyses,we identify two fundamental factors:(1)with graph convolution(i.e.,neighborhood aggregation),popular items exert larger influence than tail items on neighbor users,making the users move towards popular items in the representation space;(2)after multiple times of graph convolution,popular items would affect more high-order neighbors and become more *** two points make popular items get closer to almost users and thus being recommended more *** rectify this,we propose to estimate the amplified effect of popular nodes on each node's representation,and intervene the effect after each graph ***,we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node,then remove the effect from the node embeddings at each graph convolution *** method is simple and generic-it can be used in the inference stage to correct existing models rather than training a new model from scratch,and can be applied to various GCN *** demonstrate our method on two representative GCN backbones LightGCN and UltraGCN,verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular *** are open-sourced^(1)).

关键词： recommendation graph convolution networks popularity bias

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