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Smart World Congress (SWC), IEEE

作者： Shanshan Liu Xiangzhi Liu Jiazheng Man Bingyang Cao Yunfeng Dong Xiaoming Wu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The large variation in target scales within steel surface defect images often results in low detection accuracy. To address this issue, we have developed a multi-scale defect detection method named SGNet. Initially, we introduced a Self-Guided Bi-Level Routing Transformer (SG-BiFormer) as the backbone network of our model. SG-BiFormer utilizes a self-guided strategy to model global information and dynamically reallocates tokens based on the importance of image regions, increasing token quantity in significant areas to preserve fine-grained details and reducing it in less critical areas. This strategy significantly enhances the accuracy and efficiency of the model in handling multi-scale targets. Subsequently, we incorporated Grouped Separable Convolutions (GSConv) to manage local information of multi-scale features. GSConv automatically adjusts the size and shape of the convolution kernels according to different input features. This design not only improves global information processing capabilities but also ensures sensitive and precise detail capture. Lastly, we employed MPDIoU as the loss function for our model, which optimizes corner positioning to tackle the challenges of regression tasks, substantially improving the accuracy of the model in handling complex defects. Experimental evidence confirms the effectiveness of this method. Our SGNet achieved a 77.1% mAP on the NEU-DET dataset, demonstrating outstanding performance.

关键词： Visualization Accuracy Shape Detectors Transformers Routing Feature extraction Steel Kernel Defect detection

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Crop Classification Methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing Data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An Accountable Anonymous Voting Scheme Based on One-time Ring Signature

An Accountable Anonymous Voting Scheme Based on One-time Rin...

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Frontiers of Electronics, information and Computation Technologies (ICFEICT), International Conference on

作者： Yunqi Gu Zhuowei Shen School of Cyber Science and Engineering Southeast University Key Laboratory of Computer Networks and Information Integration (Southeast University) Ministry of Education Nanjing China

Electronic voting plays an increasingly important role in both economic life and social activities. The electronic voting scheme based on one-time ring signature and blockchain solves the problems of voter anonymity, double voting, and public verification, while achieving the openness and transparency of voting. However, it is not applicable to the multi-round voting scenario where the voting group is relatively fixed, nor does it consider the need for anonymous accountability and differential privacy protection. In this paper, based on one-time ring signature, stealth address, blacklist and whitelist, an improved accountability anonymous voting scheme is proposed to enhance the practicability of the scheme. The security analysis shows that the scheme confirms to the basic security standards of an electronic voting system.

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Spatial-Spectral Transformer for Local and Global Hyperspectral Image Classification

Spatial-Spectral Transformer for Local and Global Hyperspect...

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Signal, information and Data Processing (ICSIDP), IEEE International Conference on

作者： MengXin Cao Guixin Zhao Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science 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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Deep learning techniques have become increasingly prevalent in hyperspectral image (HSI) classification. However, convolutional neural network (CNN)-based approaches often struggle with inefficient utilization of spectral sequence data and inadequate processing of fine-grained spectral details. In contrast, transformer models are inherently designed to capture sequence dependencies across spectral bands. Yet, most existing approaches tend to focus on either local or global feature extraction separately, which limits their ability to fully exploit the spectral information. To overcome these limitations, we propose the Local and Global Fusion Spectral Transformer (LGFST), an innovative architecture that combines Swin Transformer blocks, standard Transformer blocks, and a novel Local-Global Fusion (LGF) module. This architecture is designed to simultaneously capture both local and global features, thereby improving the extraction of detailed spectral information. The Swin Transformer block, integrated with patch merging and hierarchical attention mechanisms, expands the receptive field and enhances local feature representation through both intra- and cross-window interactions. Meanwhile, the Transformer block performs self-attention across all tokens to extract global features. The fusion of local and global features from these two blocks effectively improves the texture and discriminative power of spectral data, leading to enhanced classification performance, especially in scenarios with dense class distributions. Finally, the LGF module facilitates interaction between local and global feature sets, ensuring a seamless integration of information from both blocks. Extensive experiments on three benchmark HSI datasets demonstrate that LGFST outperforms conventional transformer models and sets new benchmarks in performance compared to state-of-the-art methods.

关键词： Merging Benchmark testing Transformers Feature extraction Distortion Sensors Data mining Standards Hyperspectral imaging Image classification

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Infringement Detection and Traceability Scheme Based on Ciphertext Feature Extraction

Infringement Detection and Traceability Scheme Based on Ciph...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Lianhai Wang Xinlei Wang Tianrui Liu Yingxiaochun Wang Qi Li 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid growth of healthcare data transactions has fostered win-win developments in healthcare technology innovation and economic benefits. However, the issue of secondary transactions makes data ownership vulnerable to infringement due to the easily replicable nature of the data. As the importance of ownership and privacy increases, more and more users protect the privacy of data through encryption methods, making it impossible for existing methods to make infringement judgments on ciphertext data. To solve this problem, we propose an infringement detection and traceability scheme based on feature extraction of ciphertext data. Firstly, the characteristics of fully homomorphic encryption are utilized to obtain data features in the ciphertext state, and the plaintext state of these features is used as the basis for infringement judgment. Additionally, during the data transaction process, the ownership registration of the sales data is verified through smart contracts to ensure the effective association between the transaction data and the seller’s address. The experimental results show that the scheme can accurately determine infringement while protecting data security and privacy.

关键词： Economics Data privacy Technological innovation Data security Smart contracts Cyberspace Medical services Data science Feature extraction Homomorphic encryption

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The Adaptive Fault-tolerant Routing Based on an Improved Local Security information Model of the Exchanged Hypercube 21

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Yazhi Luo, Chuanwen Wang, Guijuan Zhang, Li Lv, Mengjie Yu, Jiguo Qilu University of Technology School of Computer Science and Technology Jinan China Beijing Forestry University School of Information Science and Technology Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Nanjing University of Posts and Telecommunications College of Computer Nanjing China Qilu University of Technology Shandong Academy of Sciences Big Data Research Institute Jinan China

ISBN: (纸本)9798350329223

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length. © 2023 IEEE.

关键词： Hypercube networks

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LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising

arXiv

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arXiv 2023年

作者： Wang, Dayang Xu, Yongshun Han, Shuo Wu, Zhan Zhou, Li Morovati, Bahareh Yu, Hengyong The Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States The Laboratory of Image Science and Technology Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In the fields of computer vision and natural language processing, masked autoencoders (MAE) have been recognized as an effective label-free self-pretraining method for transformers, due to their exceptional feature representation ability. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE’s robustness and generability across a variety of noise levels. Experiments results show that the proposed LoMAE can enhance the transformer’s denoising performance and greatly relieve the dependence on the ground truth clean data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels. © 2023, CC BY.

关键词： Image quality

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Modeling and application of service relationships 3

Modeling and application of service relationships

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3rd IEEE International Conference on Civil Aviation Safety and information Technology, ICCASIT 2021

作者： Shaoxi, Liu Jun, Shen Jie, Ren Ying, Huang Southeast University Key Laboratory of Computer Network & Information Integration of Ministry of Education Nanjing China AVIC Chengdu Aircraft Design and Research Institute Chengdu China Nanjing Origin Information Technology Co. Ltd Nanjing China

ISBN: (纸本)9781665425186

In some new network architectures, in order to improve the scalability of services, scholars in recent years have been trying their best to explore the strategy of combining several small-grained services into large-grained services that can respond to user needs. In response to this problem, this paper studies and analyzes the relationship between services, and proposes a service relationship model based on fragmented logic. The model defines the service relationship structure and its related relationship operations. Then the model is applied in the SDEN network to complete the system requirements of service dynamic combination, thereby verifying that the model can improve the flexibility and scalability of the actual application system. © 2021 IEEE

关键词： Scalability

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Text Guided Road Marking Point Cloud Completion

Text Guided Road Marking Point Cloud Completion

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Next Generation Data-driven networks (NGDN), International Conference on

作者： Xu He Qihong Xu Huan Luo College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

With the continuous development of intelligent transportation technologies such as autonomous driving and navigation, accurate perception of road markings becomes crucial. However, due to limitations in sensor perspectives and obstacles blocking the view, the scanned point cloud of road markings is often incomplete, potentially leading to erroneous decisions in intelligent transportation systems. Therefore, it becomes imperative to recover complete road markings from these incomplete point clouds. This paper proposes a text-guided road marking completion method, which integrates text information with point cloud data using attention mechanisms. By leveraging text information to guide the network in completing road marking point clouds, the proposed method aims to enhance the perception accuracy and completeness of road markings. Experimental validation on road marking datasets demonstrates the effectiveness and feasibility of the proposed approach.

关键词： Point cloud compression Accuracy Attention mechanisms Navigation Fuses Roads Transportation Diffusion models Robustness Next generation networking

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Multi Multi-Cell Uplink Interference Management Cell Uplink Interference Management: A Distributed Power Control Method

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ZTE Communications 2022年第S01期20卷 56-63页

作者： HU Huimin LIU Yuan GE Yiyang WEI Ning XIONG Ke School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing 100044China ZTE Corporation Shenzhen 518057China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China

This paper investigates a multi-cell uplink network,where the orthogonal frequency division multiplexing(OFDM)protocol is considered to mitigate the intra-cell *** optimization problem is formulated to maximize the user sup-porting ratio for the uplink multi-cell system by optimizing the transmit *** paper adopts the user supporting ratio as the main performance *** goal is to improve the user supporting ratio of each *** the formulated optimization problem is non-convex,it cannot be solved by using traditional convex-based optimi-zation ***,a distributed method with low complexity and a small amount of multi-cell interaction is *** results show that a notable perfor-mance gain achieved by our proposed scheme compared with the traditional one is without inter-cell interaction.

关键词： uplink interference multi-cell uplink network non-cooperative game in-teractive scheme

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