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2022 International Conference on Electronic information Engineering, Big Data, and computer Technology, EIBDCT 2022

作者： Gong, Jibing Kou, Xiaomeng Zhang, Hanyun Peng, Jiquan Gong, Shishan Wang, Shuli School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China School of Information Science Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

This paper proposes an automatic data extraction algorithm for web pages based on noise reduction and visualization blocks' construction. In this algorithm, we first build an MD5 trigeminal tree of the web page's source files using a Message-Digest Algorithm. And then we arrange some noise reduction. Finally, we construct visualization blocks by an optimized cluster algorithm named BIRCH A-CF which could create an area clustering feature forest and complete the clustering by dynamically changing the circle's radius according to the correction factor. We perform experiments on eight different datasets to compare our method with eight baseline methods. The experimental results show that our approach outperforms current methods by providing more accuracy and robustness, it also accelerates noise reduction and reduces the number of nodes effectively. © 2022 SPIE.

关键词： Visualization

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Topology Uncertainty Modeling For Imbalanced Node Classification on Graphs

Topology Uncertainty Modeling For Imbalanced Node Classifica...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiayi Gao Jiaxing Li Ke Zhang Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Most existing graph neural networks work under a class-balanced assumption, while ignoring class-imbalanced scenarios that widely exist in real-world graphs. Although there are many methods in other fields that can alleviate this issue, they do not consider the special topology of the non-Euclidean graph. Hence, we propose Graph Topology Uncertainty (GraphTU), a novel probabilistic class-imbalanced solution specifically for graphs. Firstly, an invisible "uncertain gap" between under-represented minorities in training set and authentic minorities in unseen set is modeled by estimating statistical variances in topology. We extend the training distribution for minorities by sampling in this gap through a non-parametric way. Moreover, a gradient-guided mask is introduced to prevent biased statistics. Extensive experiments demonstrate the superior performance of GraphTU.

关键词： Training Uncertainty Network topology Signal processing Probabilistic logic Graph neural networks Acoustics

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RTS-DETR: Efficient Real-Time DETR for Small Object Detection

RTS-DETR: Efficient Real-Time DETR for Small Object Detectio...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wenqiang Li Aimin Li Zhiyao Li Xiaotong Kong Yuechen Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, object detection models DEtection TRansformer (DETR) series based on Transformer architecture have played a huge role in various fields. However, the DETR series models are not satisfactory in small object detection. Mainly due to the huge amount of calculation of DETR, a lot of feature information will be lost in the feature fusion stage and the low tolerance of small objects to Intersection over Union (IoU). In order to solve the above problems, we propose a near real-time detection model RTS-DETR. In this paper, we revisit Real-Time DEtection TRansformer (RT-DETR), which effectively handles multi-scale features by decoupling intra-scale interaction and cross-scale fusion, but this will lose a lot of positive local information. To this end, we have improved the efficient hybrid encoder. We propose a new positional encoding method that enables the hybrid encoder to more accurately convert the input feature sequence into a high-dimensional representation, and propose a new feature fusion module to enhance the model's ability to capture local features. Furthermore, in order to improve the tolerance of small objects to IoU, we combine Normalized Wasserstein Distance (NWD) with Shape-IoU for the optimization model. This method more accurately takes into account the shape and size of objects, thereby improving detection accuracy. Our model achieves an accuracy of 38.8% (in terms of mAP @0.5 ) on the widely used VisDrone dataset, which improves the accuracy by 2.5% compared to RT-DETR with ResNet-18 as the backbone network.

关键词： Accuracy Shape Fuses Optimization models Semantics Object detection Feature extraction Transformers Real-time systems Encoding

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Topgformer: Topological-Based Graph Transformer for Mapping Brain Structural Connectivity to Functional Connectivity

Topgformer: Topological-Based Graph Transformer for Mapping ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dalu Guo Ke Zhang Jiaxing Li Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Exploring the mapping between structural connectivity (SC) and functional connectivity (FC) is of essential importance to understanding the working mechanism of the human brain. Traditional methods are difficult to represent the complex relationship of high-order interaction between SC and FC. Recent learning-based methods can not well capture the important long-range interactions and edge information of brain connectivity. To address the issue, we propose a novel Topological-based Graph Transformer (TopGFormer) to generate functional connectivity from the structure connectivity with sufficient consideration of topological properties of brain connectivity. We propose a Topological Multi-Head Attention block that simultaneously uses centrality encoding and adjacency matrix to capture node and edge importance respectively. The centrality encoding reflects the importance of brain regions while the adjacency matrix captures the linking relationship of different regions. Experiments on HCP resting and emotion task datasets demonstrate the performance of the proposed method.

关键词： Learning systems Brain Correlation Signal processing Transformers Encoding Acoustics

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HSD-YOLO: A Lightweight and Accurate Method for PCB Defect Detection

HSD-YOLO: A Lightweight and Accurate Method for PCB Defect D...

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International Joint Conference on Neural networks (IJCNN)

作者： Zhiyao Li Aimin Li Wenqiang Li Xiaotong Kong Yuechen Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

PCB defect detection is a typical small objects detection task, as with other objects, there is a small object size, the detection process is susceptible to the problem of background interference. In practical industrial production, it is difficult for existing object detection models to realize the balance between accuracy and real-time performance. Therefore, we propose a new and lightweight object detection model. And we named it Hsd-YOLO. Firstly, HGNetv2, the backbone of the new paradigm RT-DETR for object detection, is chosen as the backbone of our model, which makes the model more lightweight and reduces the number of parameters and computation while guaranteeing the accuracy of the model’s detection. Secondly, we use the more lightweight convolutional GSConv, which is introduced into the neck to make the model balance between accuracy and speed. Finally, a unified dynamic head framework, DyHead (Dynamic Head), is introduced to make the model improve the representation of the object detection head without increasing the computational overhead. We perform comparison experiments as well as ablation experiments on a publicly available PCB defect datasets to fully illustrate the effectiveness of ours model. We conduct ablation experiments on the current state-of-the-art single-stage model YOLOv8, and our model improves AP, AR, mAP@0.5 and mAP@0.5-0.9 by 3.8%, 0.2%, 0.6% and 2.2%, respectively, and reduces the number of parameters in 385312 while ensuring accuracy. Comparing with major object detection models, our model performs the best in accuracy.

关键词： Accuracy Computational modeling Neural networks Object detection Quality control Production Real-time systems

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LDD-YOLO: An Improved Lightweight Detection Method for Steel Surface Defects Based on YOLOv8

LDD-YOLO: An Improved Lightweight Detection Method for Steel...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yuechen Zhang Aimin Li Zhiyao Li Xiaotong Kong Wenqiang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Steel is an indispensable raw material in the industrial field, steel surface defects seriously affect the quality of steel, in recent years a lot of research has been carried out on the detection of steel surface defects. Existing steel defect detection methods are unable to fully mine the underlying feature information of the target image and do not achieve a dynamic balance between accuracy and speed. To address the above problems, this paper proposes an optimised target detection algorithm based on YOLOv8. First, we proposed the DMCA module, which combines the ideas of deformable convolution and multi-channel self-attention mechanism. We developed a strengthen self-attention module to enhance the process of deformable convolutional generation of offsets, so that the model can better adapt to the complex shapes of different defective targets and extract features at a deeper level. Secondly, using the idea of LKA (Large Kernel Attention), we propose the LF-MSPP lightweight module with long-range dependence and adaptive capability to capture the tele-relationships with small computational cost and parameters, improved the problem of missing defective feature information. Finally, we replaced the head of the original YOLOv8 with a Dynamic Head and used the split attention mechanism to improve the head detection capabilities while ensuring lightweight. We conduct extensive experiments on the widely used Northeastern University steel defect dataset NEU-DET. Experimental results show that the improved model improves mAP@50, mAP@50-95, AP and AR indicators by 2.4%, 2.0%, 6.1%, and 3.4% respectively compared with the original YOLOv8 model, and the number of model parameters is reduced by 11.2%. The improved model is also better than mainstream defect target detection models such as SSD, Retinanet, FasterRCNN, YOLOv5, YOLOv6, YOLOv7, YOLOv8, etc, and can better meet the accuracy and speed requirements of actual industrial production for steel surface defect detection mode

关键词： YOLO Accuracy Head Three-dimensional displays Shape Convolution Feature extraction Steel Kernel Defect detection

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MF-YOLO: Multimodal Fusion for Remote Sensing Object Detection Based on YOLOv5s

MF-YOLO: Multimodal Fusion for Remote Sensing Object Detecti...

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International Conference on computer Supported Cooperative Work in Design

作者： Wenqiang Li Aimin Li Xiaotong Kong Yuechen Zhang Zhiyao Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Remote sensing object detection has flourished at a rapid rate of development. In remote sensing images (RSI), most algorithms perform well in detecting medium and large objects but perform poorly on small objects. This is because small objects occupy fewer pixels in the entire image, and the detector is more likely to be biased towards detecting large objects. In addition, the huge background in the image also introduces noise, causing many detection failures. Most approaches solve the problem of small object detection by increasing the depth of the neural network, but the results are not satisfactory. To solve the above problems, we propose a new method, MF-YOLO, that utilizes fused infrared (IR) and red-green-blue (RGB) images for remote sensing object detection. Through multimodal fusion (MF), we can obtain more positive information, thereby improving detection accuracy. In addition, we introduced the Bi-Level Routing Attention (BRA) module to improve the YOLOv5s model structure, and proposed a new loss function to enhance the model’s learning discrimination ability. Experimental results show that the accuracy of MF-YOLO on the VEDAI and NWPU VHR-10 datasets is 76.62% and 91.63% respectively (in terms of mAP @0.5 ). Compared with the existing state-of-the-art, the detection accuracy is significantly improved. In addition, our model has fewer weight parameters than YOLOv5s, allowing it to achieve relatively high near-real-time detection speed.

关键词： YOLO Accuracy Federated learning Noise Neural networks Prediction algorithms Routing

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Enhancing GNN-based CQA Spam Detection: Question-Answer-Pair Perspective with Supervised Neighbor Selection

Enhancing GNN-based CQA Spam Detection: Question-Answer-Pair...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Chenyang Wei Xi Luo Lu Zhang Gaofeng He Haiting Zhu School of Computer Science Nanjing Audit University Nanjing China Key Laboratory of Computer Network Information Integration (Southeast University) Ministry of Education School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Community question answering (CQA) portals have become very popular platforms attracting numerous participants to share and acquire knowledge and information on the Internet. However, many malicious users post suggestive questions and deceptive answers to promote a target (product or service), which can extremely distort the users' decision, and make the CQA environment less credible. Although various methods have been proposed to detect the fraud content from CQA, most of them detect deceptive questions and answers separately, thus suffer the problem of interactive information missing. In this paper, we take the Question-Answer-Pair (QAP) as the detecting target and propose a GNN-based method to detect the spam activities in the CQA. Specifically, we also design a label-aware neighbor selection module based on supervised learning to select more appropriate neighbors for attributes aggregating in G NN. Extensive experiments are conducted in a real-world dataset and results demonstrate the advantage and effectiveness of the proposed model for CQA spam detection.

关键词： Cloud computing Supervised learning Artificial neural networks Big Data Question answering (information retrieval) Graph neural networks Data models Fraud Portals

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Transmission Energy Allocation for Over-the-Air Computation with Energy Harvesting

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Journal of Communications and information networks 2024年第2期9卷 126-136页

作者： Siyao Zhang Zixiang Ren Xinmin Li Yin Long Jie Xu Shuguang Cui School of Computer Science and Technology Southwest University of Science and TechnologyMianyang 621010China Shenzhen Future Network of Intelligence Institute(FNii-Shenzhen) The Chinese University of Hong KongShenzhenGuangdong 518172China School of Information Science and Technology University of Science and Technology of ChinaHefei 230027China FNii-Shenzhen The Chinese University of Hong KongShenzhenGuangdong 518172China College of Computer Science Chengdu UniversityChengdu 610100China School of Science and Engineering FNii-Shenzhenand Guangdong Provincial Key Laboratory of Future Networks of IntelligenceThe Chinese University of Hong KongShenzhenGuangdong 518172China

Over-the-air computation(AirComp)has recently emerged as a promising multiple-access technique for fast wireless data aggregation(WDA)from distributed wireless devices(WDs). This paper investigates an energy harvesting (EH) AirComp system, in which multiple EH-powered single-antenna WDs simultaneously send wireless signals to a single-antenna access point (AP) with conventional energy supply for WDA via AirComp. Under this setup, we minimize the average computation mean square error(MSE)over a particular time period, by jointly optimizing the transmit energy allocation at the WDs and the AirComp denoising factors at the AP over time, subject to the energy causality constraints at individual WDs. First, we consider the offline scenario by assuming that the energy state information(ESI)and channel state information (CSI) are non-causally known at the beginning of the period, in which the formulated average MSE minimization corresponds to a non-convex optimization problem. We present a high-quality converged solution by using the techniques of alternating optimization and convex optimization. It is shown that for each WD,if the EH rate is sufficiently high,then the channel inversion power allocation is adopted;while if the EH rate is low, then all the harvested energy should be used up for transmission with proper energy allocation over time. Next, we consider the online scenario with causal ESI and CSI,in which the MSE minimization becomes a stochastic optimization *** this scenario, we present an offline-inspired online algorithm to obtain efficient online energy allocation designs by utilizing the obtained offline solutions. Finally,numerical results show that the proposed designs significantly outperform two benchmark schemes with power-halving and full-power transmission,respectively.

关键词： over-the-air computation(AirComp) power allocation energy harvesting(EH) optimization

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RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Prohibited Items

RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Proh...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaotong Kong Aimin Li Wenqiang Li Zhiyao Li Yuechen Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

X-ray image detection is essential for ensuring public safety, but traditional methods rely heavily on human analysis and are relatively inefficient. To address this issue, this paper proposes a new object detection model called RFSD-YOLO, which is based on the YOLOv8 model and is specifically designed for detecting prohibited items in X-ray images. The model adopts the RFCAConv structure instead of the conventional convolutional operation. This allows for independent parameterisation among the convolutional kernels, enhancing the model's ability to capture and express potential prohibited item features. The neck section of the model includes the GSConv and VoVgscspdesigns, which aim to balance complexity and parameter size while maintaining detection performance and reducing computational burden. Additionally, we have introduced a dynamic head structure, DyHead, which replaces the traditional detection head design and improves detection accuracy without adding computational cost. The experimental results demonstrate that our enhanced model surpasses the current state-of-the-art object detection models in detecting prohibited items. Additionally, we introduce a simplified version of the RFSD-YOLOnano model to cater to resource-constrained environments. This streamlined model improves AP, AR, and mAP by 4.4%, 14.6%, and 3.4%, respectively, compared to YOLOv8n. This series of innovations not only validates the superiority of our model, but also provides new solutions in the field of automated detection of prohibited items for X-ray security screening.

关键词： Technological innovation Head Accuracy Computational modeling Object detection Data models Public security Complexity theory Security X-ray imaging

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