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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

engineering 2024年第2期33卷 178-189页

作者： Xiaoming Yuan Jiahui Chen Ning Zhang Qiang(John)Ye Changle Li Chunsheng Zhu Xuemin Sherman Shen Qinhuangdao Branch Campus Northeastern UniversityQinhuangdao 066004China State Key Laboratory of Integrated Services Networks&the Research Institute of Smart Transportation Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada SUSTech Institute of Future Networks Southern University of Science and TechnologyShenzhen 518055China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility *** order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client *** enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for *** problem is decoupled into two convex *** to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration *** on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their *** simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

关键词： Knowledge sharing Internet of Vehicles Federated learning Broad learning Reconfigurable intelligent surfaces Resource allocation

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A One-Dimensional Residual Network and Physical Fitness-Based Exercise Prescription Recommendation Method 10

A One-Dimensional Residual Network and Physical Fitness-Base...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Fan, Runqing Peng, Zhenlian Cao, Buqing Liu, Jianxun Che, Peng Chen, Tieping Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In the context of the national big data strategy, physical fitness test data has become one of the main influencing factors in guiding and promoting the participation of the population in sports and fitness. Recommending exercise prescriptions based on national physical fitness test data has become an important research topic. However, currently, there is little research on how to accurately use computer data processing technology to recommend exercise prescriptions based on physical fitness test data. In this study, we propose a ResNet-based Exercise Prescription (ResNet-EP) method that utilizes one-dimensional residual neural network technology to recommend exercise prescriptions based on physical fitness testing data. This method comprehensively analyzes physical fitness testing data and exercise prescription data and realizes the automatic recommendation of exercise prescriptions. Experimental results on a real dataset demonstrate that the ResNet-EP model outperforms other comparison models in terms of precision (79.98%), recall (83.73%), and F1 score (81.81%). This study provides novel insights into the combination of physical fitness testing and exercise. © 2023 IEEE.

关键词： Machine learning

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GCW-YOLOv8n: Lightweight Safety Helmet Wearing Detection Algorithm

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computer engineering and Applications 2025年第3期61卷 144-154页

作者： Xu, Zhuang Qian, Yurong Yan, Feng Key Laboratory of Software Engineering Xinjiang University Urumqi830091 China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China School of Computer Science and Technology Xinjiang University Urumqi830046 China

China is a major industrial country in the world. In various construction environments, the falling of construction materials and collisions on construction sites are the main causes of casualties. Accidents caused by head injuries often occur, and wearing safety helmets can ensure the safety of construction personnel to the greatest extent possible. In order to solve the problems of poor timeliness and low management efficiency of manual management, the existing models have strict requirements for computing power, large memory requirements, and handling of load and data transmission delay of industrial equipment, and to achieve edge computing and real-time control, a modified helmet wearing detection algorithm based on YOLOv8n is proposed. Firstly, a new GS-C2f module is proposed, which introduces GhostConv and SE (squeeze and excitation) attention mechanism, effectively reducing the computational complexity of the model and helping the network extract features effectively. Secondly, the CBAM attention mechanism is introduced in the Neck section to enhance the model focus on effective features. Finally, Wise-IoUv3 is introduced to further improve the accuracy of the model. Through experiments, compared with the original YOLOv8n model, this model achieves a 21.24% reduction in computational parameters and a 0.01 improvement in recognition accuracy, achieving satisfactory results between model accuracy and complexity. © 2025 Journal of computer engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： Office equipment

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Efficient and Lossless Integrity-preserving Training Scheme for High-dimensional Logistic Regression over Vertical Data

Efficient and Lossless Integrity-preserving Training Scheme ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Wei Xu Hui Zhu Ruikang Liu Yandong Zheng Fengwei Wang Dengguo Feng School of Cyber Engineering Xidian University Xi’an China State Key Laboratory of Computer Science Institute of Software Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Logistic regression is a widely used and efficient machine learning algorithm for data analysis. However, data is often distributed among different participants, and building high-quality models often involves data interaction, which might easily lead to sensitive information leakage. While some schemes have been proposed to address the data security issue through privacy-preserving technologies such as multi-party computation, homomorphic encryption, and trusted execution environment (TEE), they still suffer from some limitations in terms of security, efficiency, and model integrity and are not suitable for the secure outsourcing training. Therefore, in this paper, we propose an efficient and lossless integrity-preserving training for high-dimensional logistic regression over vertically partitioned data. First, we utilize a block strategy to encrypt the data in batches and outsource them to a cloud with TEE to achieve the non-interactive training. Considering TEE’s limited memory, we employ random sampling technology to achieve mini-batch training within TEE, which ensures the data security. We find that the cloud might launch the active attacks to drop the model accuracy, such as lazy attack and replacement attack. To prevent the attacks, we introduce an integrity protection mechanism to verify data integrity and reduce verification frequency using a mixed-probability checking method, including fixed and random probability checking. Detailed analysis has confirmed that our scheme is secure and integrity-preserving. Extensive experiments have demonstrated that our scheme can achieve lossless, non-interactive, and efficient model training.

关键词： Training Logistic regression Machine learning algorithms Computational modeling Distributed databases Information leakage Data models Multi-party computation Outsourcing Protection

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Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP

arXiv

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arXiv 2024年

作者： Li, Yayuan Guo, Jintao Qi, Lei Li, Wenbin Shi, Yinghuan National Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Southeast University China

Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existing CLIP-based methods in training-free FSL (i.e., without the requirement of additional training) mainly learn different modalities independently, leading to two essential issues: 1) severe anomalous match in image modality;2) varying quality of generated text prompts. To address these issues, we build a mutual guidance mechanism, that introduces an Image-Guided-Text (IGT) component to rectify varying quality of text prompts through image representations, and a Text-Guided-Image (TGI) component to mitigate the anomalous match of image modality through text representations. By integrating IGT and TGI, we adopt a perspective of Text-Image Mutual guidance Optimization, proposing TIMO. Extensive experiments show that TIMO significantly outperforms the state-of-the-art (SOTA) training-free method. Additionally, by exploring the extent of mutual guidance, we propose an enhanced variant, TIMO-S, which even surpasses the best training-required methods by 0.33% with approximately ×100 less time cost. Our code is available at https://***/lyymuwu/TIMO. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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There are More Fish in the Sea: Automated Vulnerability Repair via Binary Templates

arXiv

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arXiv 2024年

作者： Lin, Bo Wang, Shangwen Chen, Liqian Mao, Xiaoguang Key Laboratory of Software Engineering for Complex Systems College of Computer Science and Technology National University of Defense Technology Changsha China

As software vulnerabilities increase in both volume and complexity, vendors often struggle to repair them promptly. Automated vulnerability repair has emerged as a promising solution to reduce the burden of manual debugging and fixing activities. However, existing techniques exclusively focus on repairing the vulnerabilities at the source code level, which has various limitations. For example, they are not applicable to those (e.g., users or security analysts) who do not have access to the source code. Consequently, this restricts the practical application of these techniques, especially in cases where vendors are unable to provide timely patches. In this paper, we aim to address the above limitations by performing vulnerability repair at binary code level, and accordingly propose a template-based automated vulnerability repair approach for Java binaries. Built on top of the literature, we collect fix templates from both existing template-based automated program repair approaches and vulnerability-specific analyses, which are then implemented for the Java binaries. Our systematic application of these templates effectively mitigates vulnerabilities: experiments on the Vul4J dataset demonstrate that TemVUR successfully repairs 11 vulnerabilities, marking a notable 57.1% improvement over current repair techniques. Moreover, TemVUR securely fixes 66.7% more vulnerabilities compared to leading techniques (15 vs. 9), underscoring its effectiveness in mitigating the risks posed by these vulnerabilities. To assess the generalizability of TemVUR, we curate the ManyVuls4J dataset, which goes beyond Vul4J to encompass a wider diversity of vulnerabilities. With 30% more vulnerabilities than its predecessor (increasing from 79 to 103), ManyVuls4J serves as a robust benchmark for evaluating the performance of TemVUR. The evaluation on ManyVuls4J reaffirms TemVUR’s effectiveness and generalizability across a diverse set of real-world vulnerabilities. Copyright © 2024, The Authors.

关键词： Program debugging

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A Survey of Applications of Vision Transformer and its Variants

A Survey of Applications of Vision Transformer and its Varia...

引用

Intelligent Data and Security (IDS), IEEE International Conference on

作者： Chuang Wu Tingqin He Hunan Key Laboratory for Service computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

The Transformer architecture, renowned for its efficacy in natural language processing, encounters unique hur-dles when applied to computer vision. In response, the Vision Transformer (Vi $T$ ) emerges as a successful adaptation for image classification tasks. While ViT exhibits tremendous potential in revolutionizing computer vision, addressing its inherent chal-lenges and limitations stands as a critical endeavor. This compre-hensive survey meticulously scrutinizes the drawbacks associated with ViT, proposing bespoke adaptations tailored to specific applications while showcasing their remarkable performance across diverse visual tasks. Moreover, it delves into the evolution of ViT adaptations across various visual domains, elucidating four promising directions for future research and development in this dynamic field.

关键词： Surveys computer vision Visualization Computational modeling Transformers Real-time systems Data models

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Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition 26

Graph Convolutional Network with Long Time Memory for Skelet...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Qi, Yanpeng Pang, Chen Liu, Yiliang Liu, Hong Lyu, Lei Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China Shandong Provincial Academy of Education Recruitment and Examination Jinan China

ISBN: (纸本)9798350331684

Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120. © 2023 IEEE.

关键词： Recurrent neural networks

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A missing physical fitness test data classification method based on MLP 10

A missing physical fitness test data classification method b...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Che, Peng Peng, Zhenlian Cao, Buqing Liu, Jianxun Chen, Tieping Fan, Runqing Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In recent years, the physical health status of college students have been attached by the country and all universities. Much effort is spent every year to conduct physical health tests, generating a large amount of physical fitness data. Due to some irresistible reasons, such as entering incorrect data or students being unable to participate in some physical fitness programs, missing data exists in the physical fitness test, and the calculation of total points according to the existing rules of weighted summation may lead to inaccurate classification of some students grades. To this end, a classification method based on physical fitness test data with missing items is proposed in this paper. Firstly, the method collects the raw data set and constructs a structured data set. Then, the MLP method is used to construct a classifier model to classify the test data set with missing items. Finally, compared experiments are conducted with the training model constructed by GaussianNB, SVM and XGBoost and the results show that the classification effect of MLP is excellent, and the accuracy of classification can reach 93.29 %. © 2023 IEEE.

关键词： Classification (of information)

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OnMKD: An Online Mutual Knowledge Distillation Framework for Passage Retrieval 12th

OnMKD: An Online Mutual Knowledge Distillation Framework for...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Deng, Jiali Li, Dongyang Zhang, Taolin He, Xiaofeng School of Computer Science and Technology East China Normal University Shanghai China School of Software Engineering East China Normal University Shanghai China NPPA Key Laboratory of Publishing Integration Development ECNUP Shanghai China

ISBN: (纸本)9783031446955

Dense passage retriever recalls a set of relevant passages from a large corpus according to a natural language question. The dual-encoder architecture is prevalent in dense passage retrievers, which is based on large-scale pre-trained language models (PLMs). However, existing PLMs usually have thick structures and bulky parameters, resulting in large memory and time consumption. To overcome the limitation of PLMs, in this paper we apply online distillation to passage retrieval and propose an Online Mutual Knowledge Distillation framework (OnMKD). Specifically, we obtain a lightweight retriever by simultaneously updating two peer networks with the same dual-encoder structure and different initial parameters, named Online Mutual Knowledge Refinement. To further interact with the latent knowledge of intermediate layers, we utilize a novel cross-wise contrastive loss to alternate the representation of questions and passages. Experimental results indicate that our framework outperforms other small baselines with the same number of layers on multiple QA benchmarks. Compared to the heavy PLMs, OnMKD significantly accelerates the inference process and reduces storage requirements with only a slight sacrifice in performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Distillation

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