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7th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Zhang, Yuru Zhao, Jiayuan Su, Xiaodong Xu, Hongyan Harbin University of Commerce College of Computer and Information Engineering Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China

ISBN: (纸本)9798350334197

The human pose estimation task requires the use of visual cues and anatomical relationships between joints to locate key points. Due to the structural dependencies between human joints, it is difficult to model the dependencies between joints with a convolutional neural network-based approach. In this paper, we propose a graph pose modeling network (GPMN) based on a graph model, which constructs a structural graph model between joints and then uses a convolutional neural network to explicitly model the dependencies between joints. Specifically, this method extracts features through a backbone network and uses a graph model to obtain proximity joint dependencies. At the same time, a cross-cross-attention mechanism is used to obtain the distant joint dependencies for reinforcing challenging key points. To solve the problem that the challenging key points of the network are weakened during the training process, this paper uses a focal loss function to make the network focus more on the challenging key points, such as knees and ankles. Under the same experimental conditions, HRNet, the highest accuracy feature extraction network, and ResNet, the classical feature extraction network, are used as the backbone network. The accuracy of the algorithm with HRNet network as the backbone is improved by 2.6% compared with the original network. © 2023 IEEE.

关键词： Extraction

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A Sharding Scheme Based on Graph Partitioning Algorithm for Public Blockchain

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computer Modeling in Engineering & Sciences 2024年第6期139卷 3311-3327页

作者： Shujiang Xu Ziye Wang Lianhai Wang Miodrag J.Mihaljevi′c Shuhui Zhang Wei Shao Qizheng Wang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan250014China Mathematical Institute The Serbian Academy of Sciences and ArtsBelgrade11000Serbia

Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business ***,transaction performance and scalability has become the main challenges hindering the widespread adoption of *** to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many *** improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling ***,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected *** paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction *** crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the ***,the scheme can achieve a high-frequency transaction as well as a better blockchain *** results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.

关键词： Blockchain sharding graph partitioning algorithm

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FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI Segmentation in Incomplete Multi-Modal Scenarios 24

FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI...

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7th International Conference on Image and Graphics processing, ICIGP 2024

作者： Yang, Rui Wang, Xiao Xu, Xin Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan China

ISBN: (纸本)9798400716720

Magnetic Resonance Imaging (MRI) is an invaluable tool for brain tumor segmentation. However, in clinical practice, certain modalities might be unavailable, leading to potential performance degradation in prediction tasks. According to current implementations, different modalities are treated as independent entities during the feature extraction phase, ignoring their inherent complementary nature. In this paper, inspired by knowledge distillation techniques, we introduce the Fusion-Inspired Modality Distillation (FIMD) framework. FIMD leverages the output from a fused multi-modal model as a teacher to guide the training of individual modalities. Each modality benefits from the collective knowledge of all modalities, enhancing its performance. Our FIMD method, devoid of specific architectural constraints, seamlessly integrates into existing multi-modal brain tumor segmentation frameworks. Notably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets indicate that FIMD can enhance the performance of current state-of-the-art (SOTA) algorithms on Dice by an average of 0.55-1.60%. Especially in the case of missing modalities, the effectiveness of the FIMD framework in enhancing the robustness and accuracy of brain tumor segmentation is demonstrated. © 2024 ACM.

关键词： Semantic Segmentation

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Metal Surface Defect Detection Algorithm Based on Improved YOLOv5 7

Metal Surface Defect Detection Algorithm Based on Improved Y...

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7th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Su, Xiaodong Zhang, Fengchun Zhang, Yurong Xu, Hongyan Chen, Xu Harbin-University of Commerce College of Computer And-Information:Engineering Harbin China Heilongiiang Provincial Key:Laboratory of Electronic Commerce and Information Processing Harbin China

ISBN: (纸本)9798350334197

Aiming at the problem of model misdetection and missed detection caused by the small defect and unclear features of industrial metal surfaces, this paper studies a large number of metal surface defects, and proposes a metal surface micro defect detection algorithm based on improved YOLOv5. First, this paper uses the FenceMask data enhancement method for regularization to solve the problem of few sample images. Then an enhanced multi-scale feature fusion pyramid network DS-FPN is proposed by introducing depthwise separable convolution, dilated convolution and spatial attention mechanism, so that the model can improve the ability to extract key information of images without adding additional parameters and calculations. An adaptive channel spatial attention mechanism SCBAM is also proposed, which adds non-local information to the original interaction with only local information, improving the feature expression ability of the model. Finally, through experimental verification, the detection accuracy of the model in this paper on the public data set GC10-DET has reached 74.9%, which is 4.7% higher than the original YOLOv5 model. © 2023 IEEE.

关键词： Convolution

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GM-HRNet: Human Pose Estimation Based on Global Modeling 7

GM-HRNet: Human Pose Estimation Based on Global Modeling

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7th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Su, Xiaodong Xu, Hongyan Zhao, Jiayuan Zhang, Fengchun Chen, Xu Harbin University of Commerce College of Computer and Information Engineering Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China

ISBN: (纸本)9798350334197

Convolutional neural networks have been widely used for human pose estimation tasks, but with some issues. It is limited to local receptive fields and it is difficult to capture global information. To address this problem, we propose the GM-HRNet network. The network aggregates the multi-stage feature information of HRNet, and makes full use of the criss-cross attention and channel attention to obtain the context information in the space and channel dimensions, and realizes the modeling of the global relationship, so as to effectively locate the keypoints of the human body. In this paper, HRNet is used as a benchmark to conduct experiments on MPII dataset. Experimental results show that the accuracy of GM-HRNet is 1.3 percent higher than that of HRNet under the same experimental conditions, which validates the effectiveness of the GM-HRNet model. © 2023 IEEE.

关键词： Convolutional neural networks

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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

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computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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A prediction model of integrated learning based on FCM and SAGA 4

A prediction model of integrated learning based on FCM and S...

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4th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Zhu, Yuhan Ning, Shiyong Harbin University of Commerce School of Computer and Information Engineering Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing China

ISBN: (纸本)9798350329490

Existing methods for big data analysis and prediction face difficulties due to the high dimensionality and disorderliness of big data, which cause noise problems. Moreover, the proportion of base learners in integrated learning often leads to redundancy. To address these issues, this paper proposes an integrated learning prediction model based on FCM-SAGA, which dynamically adjusts the learners by using the SAGA algorithm to achieve the optimal solution of the selector. For feature selection, this paper adopts FCM clustering to reduce the dimensionality and ensure that data clusters with similar variation curves can be aggregated together. Finally, the dataset selects real incremental one-year data to verify the advantages of the proposed algorithm over other mainstream algorithms in terms of accuracy. © 2023 IEEE.

关键词： Big data

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Graph External Attention Enhanced Transformer 41

Graph External Attention Enhanced Transformer

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41st International Conference on Machine Learning, ICML 2024

作者： Liang, Jianqing Chen, Min Liang, Jiye Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China

The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings. Despite making some progress, existing works tend to overlook external information of graphs, specifically the correlation between graphs. Intuitively, graphs with similar structures should have similar representations. Therefore, we propose Graph External Attention (GEA) - a novel attention mechanism that leverages multiple external node/edge key-value units to capture inter-graph correlations implicitly. On this basis, we design an effective architecture called Graph External Attention Enhanced Transformer (GEAET), which integrates local structure and global interaction information for more comprehensive graph representations. Extensive experiments on benchmark datasets demonstrate that GEAET achieves state-of-the-art empirical performance. The source code is available for reproducibility at: https://***/icm1018/GEAET. Copyright 2024 by the author(s)

关键词： Graph neural networks

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A dynamic and resource sharing virtual network mapping algorithm

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Digital Communications and Networks 2023年第5期9卷 1101-1112页

作者： Xiancui Xiao Xiangwei Zheng Ji Bian Cun Ji Xinchun Cui School of Information Science and Engineering Shandong Normal UniversityJinan250014China School of Information Engineering Shandong Management UniversityJinan250358China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014China School of Computer Science and Technology Qufu Normal UniversityRizhao276800China

Network virtualization can effectively establish dedicated virtual networks to implement various network ***,the existing research works have some shortcomings,for example,although computing resource properties of individual nodes are considered,node storage properties and the network topology properties are usually ignored in Virtual Network(VN)modelling,which leads to the inaccurate measurement of node availability and *** addition,most static virtual network mapping methods allocate fixed resources to users during the entire life cycle,and the users’actual resource requirements vary with the workload,which results in resource allocation *** on the above analysis,in this paper,we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE,first,we construct a new,more realistic network framework in which the properties of nodes include computing resources,storage resources and topology *** the node mapping process,three properties of the node are used to measure its mapping ***,we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties,so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link ***,we divide the resource requirements of Virtual Network Requests(VNRs)into basic subrequirements and variable sub-variable requirements to complete dynamic resource *** former represents monopolizing resource requirements by the VNRs,while the latter represents shared resources by many VNRs with the probability of occupying resources,where we keep a balance between resource sharing and collision among users by calculating the collision *** results show that the proposed NMAPRS-VNE can increase the average acceptance rate and network revenu

关键词： Network virtualization VNRs Network frameworks Dynamic resource allocation Resource sharing

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