检索结果-内蒙古大学图书馆

IEEE Joint International information Technology and Artificial Intelligence Conference (ITAIC)

作者： Wenkang Ding Ruirui Sun Qinglong Meng Zhong Wan Guanghe Cheng Huijuan Hao Jinqiang Bai 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

With the continuous increase of IoT devices, the management and optimization of devices become particularly important. The update cycle of device functions is short, and in order to facilitate management and optimize device performance, a fast and convenient upgrade method is needed to solve this problem. Remote upgrade technology is mainly divided into full package upgrade and differential upgrade. Compared to full package upgrade, differential upgrade has obvious advantages in remote upgrade of large files. The main problem with current differential upgrade is the problem of slow generation speed while ensuring a certain size range of differential packages. This article proposes an improved Bsdiff algorithm to solve the problem of slow generation speed of differential packages. Experimental verification shows that the efficiency of remote upgrade has been improved, Improved the speed of differential packet generation and upgrade efficiency while ensuring the size of differential packets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NextSeg: Automatic COVID-19 Lung Infection Segmentation from CT Images Based on Next-ViT

NextSeg: Automatic COVID-19 Lung Infection Segmentation from...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Aimei Dong Ruixin Wang Xuening Zhang Jian Liu 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 Faculty of Computer Science and Technology 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

The spread of COVID-19 has brought great disaster to the world, and automatic segmentation of the infected region can help doctors make a quick diagnosis and reduce workload. However, automatic segmentation of infected regions in computed tomography (CT) images still faces many challenges. The most central problem at the moment is that when capturing low-frequency signals and high-frequency signals, the use of Transformer blocks for capturing low-frequency signals may deteriorate the high-frequency signals (local texture information) to some extent. For this reason, in this paper, we propose a new network for automatic segmentation of COVID-19 lung infections from CT images, called NextSeg. NextSeg follows an encoder-decoder architecture, where the encoder part uses NCB and NTB convolutional attention modules to extract better features while reducing the deterioration of texture profile information. The decoder part uses an attention fusion module to learn the weight relationship between features and retain more important feature information. In addition to the difficulty of extracting the boundary details of the COVID-19 infected region as well as the loss of details the deeper the network is, we designed a new hybrid strategy. Traditional and state-of-the-art approaches to the COVID-19 segmentation domain are compared. Extensive experiments show that our proposed framework outperforms existing competitors in both qualitative and quantitative results.

关键词： COVID-19 Image segmentation Convolution Computed tomography Neural networks Lung Medical services

来源：评论

学校读者我要写书评

暂无评论

Non-aligned Supervision for Real Image Dehazing

arXiv

引用

arXiv 2023年

作者： Fan, Junkai Guo, Fei Qian, Jianjun Li, Xiang Li, Jun Yang, Jian Key Lab of Intelligent Perception and Systems for HighDimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computer Science Nankai University Tianjin China

Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in the misalignment of hazy and clear image pairs. In this paper, we propose an innovative dehazing framework that operates under non-aligned supervision. This framework is grounded in the atmospheric scattering model, and consists of three interconnected networks: dehazing, airlight, and transmission networks. In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network. To implement this, we present a multi-scale reference loss that compares the feature representations between the referred image and the dehazed output. Our scenario makes it easier to collect hazy/clear image pairs in real-world environments, even under conditions of misalignment and shift views. To showcase the effectiveness of our scenario, we have collected a new hazy dataset including 415 image pairs captured by mobile Phone in both rural and urban areas, called"Phone-Hazy". Furthermore, we introduce a self-attention network based on mean and variance for modeling real infinite airlight, using the dark channel prior as positional guidance. Additionally, a channel attention network is employed to estimate the three-channel transmission. Experimental results demonstrate the superior performance of our framework over existing state-of-the-art techniques in the real-world image dehazing task. Phone-Hazy and code will be available at https://***/projectpage/NSDNet/***. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

Research on Classroom Behavior Target Detection Algorithm based on YOLOv7

Research on Classroom Behavior Target Detection Algorithm ba...

引用

IEEE Advanced information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Wenke Qiu Xiaoming Wu Xiangzhi Liu Computer Science and Qilu University of Technology (Shandong Academy of Sciences) Jinan ShanDong China 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

The integration of video information for the purpose of detecting students’ behavior in the classroom, with the aim of enhancing teaching efficiency and maintaining a stable classroom environment, offers significant assistance. However, existing models usually struggle to achieve good accuracy due to the lack of datasets, dense targets, interclass occlusion, and other issues. To address these difficulties, the CD-YOLO classroom behavior detection algorithm is proposed in this paper. And the classroom behavior dataset (DSL-Dataset) is created. We propose a loss function named VAIoU, which can be combined with vector ratio to filter the prediction box. To further improve the detection accuracy of occluded targets, we propose an attention module CM module with command feature fusion. Moreover, in this paper, we improve the convolutional kernel to design a lightweight convolutional kernel FFconv. Experimental results on DSL-Dataset show that CD-YOLO outperforms existing one-stage detection models in terms of detection accuracy.

关键词： Accuracy Education Object detection Filtering algorithms Feature extraction Vectors information management

来源：评论

学校读者我要写书评

暂无评论

Uncertainty-Guided Diffusion Model for Camouflaged Object Detection

引用

IEEE Transactions on Multimedia 2025年

作者： Yang, Jinsheng Zhong, Bineng Liang, Qihua Mo, Zhiyi Zhang, Shengping Song, Shuxiang Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China

Recently, diffusion models have significantly improved the performance of Camouflaged Object Detection (COD) by adding noise to a mask and iteratively denoising it to match the target distributions. Due to the direct extraction of features from noisy masks and the lack of conditional constraints on a prediction area, the diffusion model may deviate from a correct prediction range and produces mispredictions in regions with high uncertainty. To address this issue, we propose an uncertainty-guided diffusion model (UGDNet) for COD, which explicitly quantifies uncertainty and integrates it as an anchor condition into the diffusion models to provide an initialization of the diffusion regions. The core idea is first to utilize a probability representation and transformer to explicitly model uncertainty, aiming to identify areas where a model may generate overconfident mispredictions. Then, we use the uncertainty as an anchor condition to provide a reference prediction range for the diffusion model, guiding each step of the diffusion process. Furthermore, we use uncertainty to guide feature aggregation, prompting the model to pay extra attention to the semantic features of regions with high uncertainty to refine the segmentation results further. The experimental results indicate that our proposed UGDNet achieves higher accuracy than existing state-of-the-art models on five COD benchmarks, including COD10K, NC4K, CAMO, CHAMELEON, and CDS2K. © 1999-2012 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

CA-STCNN: An Attention-based Hybrid Deep Learning Model for Encrypted Traffic Classification

CA-STCNN: An Attention-based Hybrid Deep Learning Model for ...

引用

Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Hongyang Sun Huiling Shi Lei Zhang Lei Huang Wei Zhang 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

With the increasing trend of network traffic encryption, both normal and abnormal traffic have begun to utilize encryption techniques to conceal their behavior, making accurate identification of encrypted traffic an essential prerequisite for network management and security. Current traffic classification methods generally suffer from inadequate feature extraction and lower multi-classification accuracy. This paper proposes a multiclassification hybrid deep learning model for encrypted traffic, called channel attention-based spatiotemporal convolutional neural network (CA-STCNN). The model extracts spatial features by convolutional neural network (CNN) and then introduces temporal convolutional neural network (TCN) to extract the temporal features. Using the convolution structure with more stable gradients can ensure the robustness of spatiotemporal feature extraction. Furthermore, the model also introduces the channel attention mechanism to assign weights to features in the channel dimension according to their contributions to classification. In the multi-classification experiments conducted using the ISCXVPN dataset, CA-STCNN outperforms existing methods in terms of accuracy, precision, recall, and F1 score.

关键词：

来源：评论

学校读者我要写书评

暂无评论

RLGNN: Robust Graph Neural networks with Noisy label Learning

RLGNN: Robust Graph Neural Networks with Noisy Label Learnin...

引用

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Haodong Qian De Li Qiyu Li Jinyan Wang Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Graph Neural networks (GNNs) have become a powerful tool for processing graph-structured data. However, real-world data often faces the problem of label noise and sparsity, which will greatly affect the application of graph neural networks in the real world. Hence, we introduce Robust Graph Neural networks with Noisy label Learning (RLGNN) to tackle the challenges posed by label noise and sparsity in graph-structured data. Its key components include unsupervised contrastive learning for deriving robust node representations, a two-stage clean sample selection mechanism, and a semi-supervised loss function to harness information from unlabeled nodes. RLGNN ingeniously merges contrastive learning with the prediction space, refining the label selection process and boosting the model’s robustness and generalizability. Comprehensive experiments indicate that RLGNN significantly surpasses existing models in managing label noise in semi-supervised node classification scenarios.

关键词： Filters Noise Refining Contrastive learning Semisupervised learning Predictive models Graph neural networks Robustness Noise measurement Faces

来源：评论

学校读者我要写书评

暂无评论

FedISMH: Federated Learning Via Inference Similarity for Model Heterogeneous

FedISMH: Federated Learning Via Inference Similarity for Mod...

引用

IEEE International Conference on Big Data

作者： Yongdong Li Li-e Wang Zhigang Sun Xianxian Li Hengtong Chang Jinke Xu Caiyi Lin School of Computer Science and Engineering Guangxi Normal University Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Federated Learning (FL) is a privacy-preserving machine learning paradigm, enabling decentralized devices to collaboratively train models without sharing local data. Traditional FL approaches, however, rely on averaging parameters across clients with homogeneous models, which limits their applicability in scenarios where clients require heterogeneous models. In this paper, we propose FedISMH, a novel approach to address model heterogeneity in FL. Instead of directly applying knowledge distillation, FedISMH clusters clients based on the structural similarities of client models, where clients’ structural features can be extracted through either labeled or unlabeled dataset This allows the proposed model to identify clients with similar model architectures while preserving privacy. Additionally, FedISMH introduces a dynamic mechanism to manage noise clients by aligning them with the most structurally similar clusters, ensuring that their inclusion promote the performance of the cluster. Experimental results on MNIST and SVHN demonstrate that FedISMH consistently outperforms state-of-the-art methods in both IID and Non-IID settings, offering improved accuracy, robustness, and flexibility in heterogeneous FL environments.

关键词： Privacy Data privacy Accuracy Federated learning Computational modeling Noise Feature extraction Data models Servers Protection

来源：评论

学校读者我要写书评

暂无评论

BS-YOLOv5s: Insulator Defect Detection with Attention Mechanism and Multi-Scale Fusion

BS-YOLOv5s: Insulator Defect Detection with Attention Mechan...

引用

IEEE International Conference on Image Processing

作者： Zengbin Zhang Guohua Lv Guixin Zhao Yi Zhai Jinyong Cheng 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

With the rapid development of deep learning, the use of object detection algorithms for aerial insulator image defect detection has become the main way. To address the problems of low detection accuracy for small targets, weak representation ability of feature maps, insufficient extracted key information, and the shortage of aerial insulator defect datasets, this paper proposes an improved insulator defect detection method named BS-YOLOv5s based on 3-D attention mechanism and Bi-Slim-neck using YOLOv5s as the base network. Additionally, to solve the problem of the shortage of aerial insulator datasets, this paper proposes a new aerial insulator dataset Weather-Insulator (WI) containing a variety of defect scenarios. The experimental results demonstrate that the proposed method not only greatly improves the detection accuracy, but also maintains a high detection speed, satisfying the engineering requirements for insulator defect detection. The dataset and code for this paper are publicly available at https://***/jspron/insulator-defect.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Multiple-Image Authentication Method Based on Phase-Only Hologram and Logistic Map

SSRN

引用

SSRN 2024年

作者： Liansheng, Sui Zishan, Jiang Haiwei, Lu Zhaolin, Xiao Tianyu, Zeng School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control Shijiazhuang050024 China

An interesting security method for multiple-image authentication scheme is proposed based on computer-generated holograms and logistic map. First, each original image is encoded as the complex-valued hologram under the point light source model. The resulting hologram is then converted to a phase-only hologram using the Floyd-Steinberg dithering algorithm. Second, each phase-only hologram is randomly sampled by the aid of a binary mask. Through the catenation of all selected pixels, a phase-only pixel sequence is formed. Finally, a non-periodic and non-converging sequence generated with logistic map is used to scramble this sequence. After only preserving the phase data of the scrambled sequence, the real-valued ciphertext carrying the information of all original images is obtained. In the process of authentication, although no valid information can be discerned from noisy reconstructed images at a small sampling rate, the verification of original images can be efficiently accomplished using the nonlinear correlation maps. Besides binary masks, the parameters of logistic map are served as secret keys. Due to their high sensitivity, the security of the proposed method is greatly enhanced. The proposed authentication mechanism has been demonstrated to be effective and robust through experiments. To our best knowledge, it is first time to implement multiple-image authentication using phase-only holograms, which can provide a new perspective for optical information security. © 2024, The Authors. All rights reserved.

关键词： Holograms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：