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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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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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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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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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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

arXiv

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arXiv 2024年

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2024, CC BY-NC-SA.

关键词： Speech recognition

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PWPH:Proactive Deepfake Detection Method Based on Watermarking and Perceptual Hashing

PWPH:Proactive Deepfake Detection Method Based on Watermarki...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jian Li Shuanshuan Li Bin Ma Chunpeng Wang Linna Zhou Fei Wang Yule Wang Miaomiao Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The popularity of Deepfake technology has raised the challenge of recognizing real and fake faces. While detection methods already exist, most of them are passive forensics and face challenges of generalizability and migration. Currently, some research attempts to protect the original image by priorly inserting invisible information. However, there are still shortcomings in terms of image quality and information robustness due to information embedding, i.e., watermarking. Therefore, we employ the robustness of perceptual hash coding and combine it with information hiding techniques to propose a proactive Deepfake detection solution, referred to as PWPH in this paper. Our approach is simple and efficient: first, the image containing a face is divided into two parts: FA (face area), and NFA (non-face area). A perceptual hash code is generated from the non-face area (NFA). Then, the hash codes are embedded as watermarks into the FA. At the extraction stage, we use the same method as the encoder to retrieve the embedded watermark from FA. The watermark is then compared with the hash code generated from the NFA of the detected image. The extracted watermark is sensitive to distortion and may vanish during Deepfake processing. Experimental results validate that our method, requiring just one encoder and decoder, enables active detection and source tracking. Furthermore, its efficacy in typical Deepfake scenarios such as face swapping and expression reconstruction is confirmed through comparison with prior arts.

关键词： Learning systems Image quality Deepfakes Codes Face recognition Watermarking Robustness Decoding Faces Image reconstruction

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StarMFND: Star Topology Multi-Domain Fake News Detection

StarMFND: Star Topology Multi-Domain Fake News Detection

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Data Science and Information technology (DSIT), International Conference on

作者： Pengfei Gu Yibing Chen Wanlong Bing Yahui Liu Hongliang Ma Wei Wang School of Information Science and Technology Shihezi University Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China School of Computer Science and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection.

关键词： Economics Social networking (online) Computational modeling Stars Feature extraction Data models Topology Internet Fake news Information technology

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Generating Personalized Imputations for Patient Health Status Prediction in Electronic Health Records

Generating Personalized Imputations for Patient Health Statu...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Liu Weiyu Zhang Jiasheng Si Zhao Li Xueping Peng Wenpeng Lu 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Evay Info Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Electronic health records (EHRs) play a crucial role in the development of personalized treatment plans for patients. However, EHRs are often highly incomplete, posing significant challenges for predictive modeling. While existing deep learning models employ various imputation techniques to reconstruct missing values, they often fail to represent missing data in a personalized manner and do not learn from the missingness patterns in EHRs data. This limitation reduces their effectiveness in practical personalized healthcare applications. To address this issue, we propose SPIME, a self-supervised model that generates personalized imputations in EHRs data for patient health status prediction. We introduce a personalized missing mask (PMM) based on the frequency of feature measurements. Additionally, we incorporate a masked imputation task (MIT) loss that minimizes the loss of artificially introduced missing values, thereby enhancing the model’s capability to handle missing data. SPIME adopts self-supervised pretraining to learn representations from personalized missing patterns and reconstructs missing data in the latent space. To further enhance representation learning, two independent attention mechanisms are applied separately across the feature and temporal dimensions. Experimental results on two real-world EHRs datasets show that SPIME outperforms existing state-of-the-art methods in predicting in-hospital mortality and decompensation, demonstrating its effectiveness in reconstructing missing data and predicting patient health status. The code will be published at https://***/cling6666/SPIME.

关键词： Training Representation learning Biological system modeling Mortality Predictive models Biomedical measurement Imputation Data models Frequency measurement Electronic medical records

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MMSFD: Multi-grained and Multi-modal Fusion for Short Video Fake News Detection

MMSFD: Multi-grained and Multi-modal Fusion for Short Video ...

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Data Science and Information technology (DSIT), International Conference on

作者： Shuai Ren Yahui Liu Yaping Zhu Wanlong Bing Hongliang Ma Wei Wang School of Information Science and Technology Shihezi University Shihezi China Key Laboratory of Computing Intelligence and Network Information Security Xinjiang Production and Construction Corps Shihezi China School of Computer Science and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Short video platforms have become an essential part of people’s daily lives, while also facing the challenge of the rapid spread of fake news. Therefore, the detection of fake news in short videos has attracted increasing attention. Existing works mostly focus on the integration of textual and visual content, but fail to fully exploit multi-modal information at both fine-grained and coarse-grained levels. Moreover, multi-modal fake news detection is vulnerable to modality ambiguity due to potential conflicts or weak correlations between different modalities. In this paper, we propose a Multi-grained and Multi-modal fusion for Short video Fake news Detection (MMSFD). We encode textual and visual content using pre-trained models at both fine-grained and coarse-grained levels and fuse them through distinct branches. To tackle the ambiguity issue, we design a multi-modal fusion branch based on similarity weighting, as well as separate single-modal branches. Additionally, this paper presents a cross-modal attention mechanism for adaptively aggregating and reweighting features. The experimental results on a real-world short video dataset demonstrate that the proposed method outperforms state-of-the-art short video fake news detection methods.

关键词： Visualization Adaptation models Attention mechanisms Quantization (signal) Computational modeling Feature extraction Rendering (computer graphics) Encoding Real-time systems Fake news

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

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IEEE Transactions on Computers 2025年第07期74卷 2168-2182页

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. © 1968-2012 IEEE.

关键词： Scalability

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