检索结果-内蒙古大学图书馆

A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

computers, Materials & Continua 2025年第1期82卷 389-406页

作者： Ali Alqarni Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy *** key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and *** privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user *** address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving *** model analyzes data based on user demands and interactions with service providers or neighboring *** aims to minimize privacy risks while ensuring service continuity and *** SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy *** results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

关键词： Support vector machine big data IoV privacy-preserving

来源：评论

学校读者我要写书评

暂无评论

Attribute grouping-based naive Bayesian classifier

引用

science China(Information sciences) 2025年第3期68卷 125-149页

作者： Yulin HE Guiliang OU Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) College of Computer Science & Software Engineering Shenzhen University

The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large extent by the assumption of attribute independence. To address this issue, this paper proposes a novel attribute grouping-based NBC(AG-NBC), which is a variant of the classical NBC trained with different attribute groups. AG-NBC first applies a novel effective objective function to automatically identify optimal dependent attribute groups(DAGs). Condition attributes in the same DAG are strongly dependent on the class attribute, whereas attributes in different DAGs are independent of one another. Then,for each DAG, a random vector functional link network with a SoftMax layer is trained to output posterior probabilities in the form of joint probability density estimation. The NBC is trained using the grouping attributes that correspond to the original condition attributes. Extensive experiments were conducted to validate the rationality, feasibility, and effectiveness of AG-NBC. Our findings showed that the attribute groups chosen for NBC can accurately represent attribute dependencies and reduce overlaps between different posterior probability densities. In addition, the comparative results with NBC, flexible NBC(FNBC), tree augmented Bayes network(TAN), gain ratio-based attribute weighted naive Bayes(GRAWNB), averaged one-dependence estimators(AODE), weighted AODE(WAODE), independent component analysis-based NBC(ICA-NBC), hidden naive Bayesian(HNB) classifier, and correlation-based feature weighting filter for naive Bayes(CFW) show that AG-NBC obtains statistically better testing accuracies, higher area under the receiver operating characteristic curves(AUCs), and fewer probability mean square errors(PMSEs) than other Bayesian classifiers. The experimental results demonstrate that AG-NBC is a valid and efficient approach for alleviating the attribute i

关键词： naive Bayesian classifier attribute independence assumption attribute grouping dependent attribute group posterior probability class-conditional probability

来源：评论

学校读者我要写书评

暂无评论

Hierarchical style transfer for enhancing feature diversity in long-tailed visual recognition

引用

Cluster Computing 2025年第5期28卷 1-16页

作者： Hu, Lintao Chen, Zhao-Min Chen, Huiling Deng, Ruoxi Hu, Jie College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China

The long-tail distribution is prevalent in the real-world data, posing a challenge to the training of deep learning models. Previous methods predominantly alleviate the long-tail learning problem by reusing tail class samples. However, since reusing tail class samples does not improve the diversity of features in tail categories, it may result in overfitting on tail classes and suboptimal performance. To tackle this challenge, we propose a novel method that enhances the feature diversity of tail classes by transferring style information from head classes to tail classes. Our method comprises two key components: a style transfer module and a distribution alignment module. The style transfer module employs the fast Fourier transform to achieve the transfer of style information from head to tail classes and applies a hierarchical transfer strategy at different levels of the network to maximize the utilization of style features. The distribution alignment module leverages contrastive learning to ensure the consistency of the feature distribution of tail classes before and after style transfer. Our approach is highly flexible and versatile, serving as a plug-and-play module that can be integrated into long-tail learning models with ResNet and its derivative networks as the backbone, such as BBN and GLMC. Experimental results show that our approach significantly enhances model performance, especially in tail classes, on CIFAR100/10-LT, ImageNet-LT, and iNaturalist 2018 datasets. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fast Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

引用

computers, Materials & Continua 2025年第1期82卷 543-560页

作者： Hengyang Liu Yang Yuan Pengcheng Ren Chengyun Song Fen Luo School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China College of Artificial Intelligence Chongqing Technology and Business UniversityChongqing400067China

Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution ***,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than *** design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these *** be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled *** introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in *** the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local *** this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation *** two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation *** only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.

关键词： Semi-supervised medical image segmentation contrastive learning stochastic augmented

来源：评论

学校读者我要写书评

暂无评论

A survey on cross-user federated recommendation

引用

science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

来源：评论

学校读者我要写书评

暂无评论

Enhanced Network Security Hybrid Cloud Workflow Scheduling Using Levy-Optimized Slime Mould Algorithm

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第18期49卷 111-126页

作者： Guan, Jingang College of Artificial Intelligence Yantai Institute of Science and Technology Yantai265600 China

Mainstream network security models based on PSO and other algorithms have not been optimized for hybrid cloud environments, resulting in limitations in their application. To ensure network security in hybrid cloud environments, the study first introduces the Levy distribution, uses the Levy distribution to generate random numbers and redesign the position update formula. Then, the penalty parameters of SVM and the radial basis kernel function parameters of SVM are used as individuals to improve SMA. After iteration, the optimal position is the optimal solution of the two parameters. After determining the optimal parameters, an improved slime mould algorithm optimization algorithm is designed. Finally, the algorithm is applied to the encoded hybrid cloud workflow scheduling to propose a method based on the improved slime mould algorithm. The test results showed that when the number of tasks was 1000, the proposed algorithm took 13.7 hours to run, which was better than the traditional 18.2 hours and more than 50 hours of the particle swarm algorithm (P © 2025 Slovene Society Informatika. All rights reserved.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Enabled Edge Computing Techniques for Advanced Video Surveillance in Autonomous Vehicles

引用

computers, Materials & Continua 2025年第4期83卷 1239-1255页

作者： Mohammad Tabrez Quasim Khair Ul Nisa Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBishaP.O.Box 551Saudi Arabia

The blockchain-based audiovisual transmission systems were built to create a distributed and flexible smart transport system(STS).This system lets customers,video creators,and service providers directly connect with each ***-based STS devices need a lot of computer power to change different video feed quality and forms into different versions and structures that meet the needs of different *** the other hand,existing blockchains can’t support live streaming because they take too long to process and don’t have enough computer *** amounts of video data being sent and analyzed put too much stress on networks for vehicles.A video surveillance method is suggested in this paper to improve the performance of the blockchain system’s data and lower the latency across the multiple access edge computing(MEC)*** integration of MEC and blockchain for video surveillance in autonomous vehicles(IMEC-BVS)framework has been *** deal with this problem,the joint optimization problem is shown using the actor-critical asynchronous advantage(ACAA)method and deep reinforcement training as a Markov Choice Progression(MCP).Simulation results show that the suggested method quickly converges and improves the performance of MEC and blockchain when used together for video surveillance in self-driving cars compared to other methods.

关键词： Blockchain multiple access edge computing video surveillance autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

FreeDOM: Online Dynamic Object Removal Framework for Static Map Construction Based on Conservative Free Space Estimation

引用

IEEE Robotics and Automation Letters 2025年第6期10卷 5577-5584页

作者： Li, Chen Li, Wanlei Liu, Wenhao Shu, Yixiang Lou, Yunjiang Harbin Institute of Technology Shenzhen School of Intelligence Science and Engineering College of Artificial Intelligence Shenzhen518055 China

Online map construction is essential for autonomous robots to navigate in unknown environments. However, the presence of dynamic objects may introduce artifacts into the map, which can significantly degrade the performance of localization and path planning. To tackle this problem, a novel online dynamic object removal framework for static map construction based on conservative free space estimation (FreeDOM) is proposed, consisting of a scan-removal front-end and a map-refinement back-end. First, we propose a multi-resolution map structure for fast computation and effective map representation. In the scan-removal front-end, we employ raycast enhancement to improve free space estimation and segment the LiDAR scan based on the estimated free space. In the map-refinement back-end, we further eliminate residual dynamic objects in the map by leveraging incremental free space information. As experimentally verified on SemanticKITTI, HeLiMOS, and indoor datasets with various sensors, our proposed framework overcomes the limitations of visibility-based methods and outperforms state-of-the-art methods with an average F1-score improvement of 9.7%. © 2016 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

引用

science China(Information sciences) 2025年第1期68卷 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue QIAO Yifang QIN Xiao LUO Hui XIONG Ming ZHANG School of Computer Science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology Department of Computer Science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

关键词： graph representation learning graph neural networks graph contrastive learning graph augmentations graph pooling

来源：评论

学校读者我要写书评

暂无评论

An Interpolated Quantized Guard Band Algorithm for Physical Layer Key Generation

引用

Intelligent and Converged Networks 2025年第1期6卷 102-114页

作者： An, Yongli Zha, Kun Song, Wenfeng Ji, Zhanlin College of Artificial Intelligence North China University of Science and Technology Tangshan063210 China

With the continuous progress of communication technology, traditional encryption algorithms cannot meet the demands of modern wireless communication security. Secure communication based on physical layer encryption emerges as a solution. To meet the low Key Disagreement Rate (KDR) and high Key Generation Rate (KGR) requirements for physical layer key generation, this paper proposes two quantization algorithms, Improve-CQG and Interpolate-CQG, based on the Channel Quantization with Guard band (CQG) algorithm. The former divides the characteristic quantization into two phases: threshold filtering and guard band quantization, while the latter adds a step after these two phases: interpolation quantization. Compared to the CQG algorithm, the Improve-CQG algorithm enhances the granularity of filtered quantization values. The core concept of the Interpolate-CQG algorithm is to utilize threshold filtering and the rounded-off quantization values from the guard band quantization phase. The symbol information corresponding to these index values is replaced by a new interpolated symbol and inserted into the key by the agreed quantized coordinates. Simulation proves that the Interpolate-CQG is an effective quantization algorithm for the key generation with lower KDR and higher KGR than the Improve-CQA and Improve-CQG. © 2020 Tsinghua University Press.

关键词： Encryption algorithms

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：