检索结果-内蒙古大学图书馆

Joint UAV Deployment, Power Allocation, and Coalition Formation for Physical Layer Security in Heterogeneous Networks

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Yalin Gao, Xiaozheng Ye, Neng Niyato, Dusit Han, Zhu Yang, Kai Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In the coming sixth-generation era, unmanned aerial vehicles (UAVs) have been explored for convenient air access and strong environmental adaptability to provide communication services for ground Internet-of-Things (IoT) devices. However, the inherent broadcast nature of wireless communications makes UAV communications vulnerable to eavesdropping attacks. This paper explores a secure uplink communication in a heterogeneous network, where a UAV provides communication services to ground IoT nodes in the presence of an eavesdropper. We propose a transmission scheme to maximize the overall sum secrecy rate, in which the cooperative jamming technique and coalition formation game are employed, and the UAV endurance is considered. Specifically, a double-layer optimization problem is formulated, including a non-convex optimization problem in the lower layer, and a game theory problem in the upper layer. We use block coordinate descent and successive convex approximation methods to decompose the lower layer problem into UAV deployment and power allocation sub-problems, and convert them into a difference of two convex functions programming problem and a second-order cone programming problem, respectively. Moreover, we develop an algorithm to solve the upper layer problem, obtaining a stable coalition formation. Simulation results show that the proposed transmission scheme can effectively improve the overall sum secrecy rate, and has dynamic adaptability to eavesdropping attacks. © 1967-2012 IEEE.

关键词： Jamming

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

引用

2023 World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Dual-Scale Enhanced and Cross-Generative Consistency Learning for Semi-Supervised Medical Image Segmentation

SSRN

引用

SSRN 2024年

作者： Gu, Yunqi Zhou, Tao Zhang, Yizhe Zhou, Yi He, Kelei Gong, Chen Zhu, Huafu PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Information Science and Technology Nanjing210094 China School of Computer Science and Engineering Southeast University Nanjing211189 China Medical School Nanjing University National Institute of Healthcare Data Science Nanjing210023 China Institute of High Performance Computing A*STAR Singapore

Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. Moreover, accurately segmenting lesions poses challenges due to variations in shape, size, and location. To address these issues, we propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image Segmentation (DEC-Seg). First, we propose a Cross-level Feature Aggregation (CFA) module that integrates cross-level adjacent layers to enhance the feature representation ability across different resolutions. To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales. This constraint helps handle variations in lesion sizes and improves the robustness of the model. Furthermore, we propose a cross-generative consistency scheme, in which the original and perturbed images can be reconstructed using cross-segmentation maps. This consistency constraint allows us to mine effective feature representations and boost the segmentation performance. To produce more accurate segmentation maps, we propose a Dual-scale Complementary Fusion (DCF) module that integrates features from two scale-specific decoders operating at different scales. Extensive experimental results on polyp segmentation from colonoscopy images, skin lesion segmentation from dermoscopic images, and Brain glioma segmentation from MRI images demonstrate the effectiveness of our DEC-Seg against other state-of-the-art semi-supervised segmentation approaches. © 2024, The Authors. All rights reserved.

关键词： Computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Xuefei Li Mingzhe Chen Danpu Liu Zhilong Zhang Tony Q. S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

关键词： Base stations Correlation Network topology Simulation Collaboration Reinforcement learning Vectors Sensors Global communication Optimization

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

引用

ACM Transactions on Recommender Systems 2024年第4期2卷 1-27页

作者： Zhiyong Cheng Jianhua Dong Fan Liu Lei Zhu Xun Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shandong Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computing National University of Singapore Singapore Singapore School of Electronic and Information Engineering Tongji University Shanghai China School of Information Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China and Hefei Comprehensive National Science Center Hefei China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.

关键词： Multi-Behavior Recommendation disentangled representation learning graph convolutional network multi-behavior recommendation multi-task learning

来源：评论

学校读者我要写书评

暂无评论

SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency

arXiv

引用

arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

Using Simulated data to Predict Birthweight from Prenatal Ultrasound Images

Using Simulated Data to Predict Birthweight from Prenatal Ul...

引用

Western New York Image Processing Workshop (WNYIPW)

作者： Caitlin Dreisbach Arunaggiri Pandian Karunanidhi Yuka Shimazaki Neha Rana Loralei Thornburg Linwei Wang Susan Groth Stefanie Hollenbach School of Nursing and Goergen Institute for Data Science University of Rochester Rochester New York Goergen Institute for Data Science University of Rochester Rochester New York University of Rochester Medcical Center Rochester New York Department of Computing and Information Science Rochester Institute of Technology Rochester New York School of Nursing Unviersity of Rochester Rochester New York

Fetal weight estimation during pregnancy is critical for monitoring growth and planning delivery, yet current ultrasound-based estimations are prone to error. This study leveraged open access ultrasound images and simulated clinical data to develop a deep learning pipeline for fetal weight prediction. The dataset consisted of n=1,570 ultrasound images from 333 pregnant individuals at 18-40 weeks’ gestation. Relevant maternal and fetal attributes like body mass index, gestational age, amniotic fluid index, and newborn birth weight were simulated based on population averages. A convolutional neural network (CNN) using transfer learning was applied to extract imaging features. The pre-trained Xception model was adapted by adding a global average pooling layer and freezing layers during feature extraction. Extracted imaging features were combined with clinical metadata and input into linear regression and random forest models. Hyperparameter tuning via randomized search identified a random forest configuration minimizing error. On a 20% test set, the pipeline achieved a mean absolute error of 328.6 grams in predicting birth weight, suggesting feasibility of the approach. While clinical conclusions are limited without real-world data, this study demonstrates a pipeline leveraging open science resources to address an important maternal-fetal health problem. Tailored CNN architectures and further model optimization on population-representative data could enhance performance and clinical utility. This work highlights the potential of open datasets to enable rapid scientific progress and proposes a deep learning methodology for improved fetal weight assessment during pregnancy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter 31

Adversarial Color Enhancement: Generating Unrestricted Adver...

引用

31st British Machine Vision Conference, BMVC 2020

作者： Zhao, Zhengyu Liu, Zhuoran Larson, Martha Data Science Group Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands

We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification. Our approach, Adversarial Color Enhancement (ACE), generates unrestricted adversarial images by optimizing the color filter via gradient descent. The novelty of ACE is its incorporation of established practice for image enhancement in a transparent manner. Experimental results validate the white-box adversarial strength and black-box transferability of ACE. A range of examples demonstrates the perceptual quality of images that ACE produces. ACE makes an important contribution to recent work that moves beyond Lp imperceptibility and focuses on unrestricted adversarial modifications that yield large perceptible perturbations, but remain non-suspicious, to the human eye. The future potential of filter-based adversaries is also explored in two directions: guiding ACE with common enhancement practices (e.g., Instagram filters) towards specific attractive image styles and adapting ACE to image semantics. Code is available at https://***/ZhengyuZhao/ACE. © 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

A Fusion Neural Network Incorporating Attention for Sensor-Based Human Activity Recognition 3

A Fusion Neural Network Incorporating Attention for Sensor-B...

引用

3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Lu, Limeng Zhang, Chuanlin Cao, Kai Deng, Dao Institute of Chinese Ethnic Information Technology Northwest Minzu University Lanzhou China Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Lanzhou China Northwest Minzu University School of Mathematics and Computer Science Lanzhou China Northwest Minzu University Key Laboratory of Streaming Data Computing Technologies and Application Lanzhou China

ISBN: (纸本)9781665459112

Even though the RNN, LSTM, and other networks are used to extract dependencies in time series, sensor-based human behavior recognition (HAR) still faces some difficulties, and the ability of deep learning (DL) networks to extract features still needs to be improved. We propose a fusion neural network in which an optimized small optimized Convolutional Block Attention Module (MP-CBAM) is suitable for HAR tasks to extract samples. The MP-CBAM is added to two branches Convolutional Neural Network (CNN) with different convolution kernel sizes, and the fused features are labeled with GRU for temporal dependence. Then the softmax function is used for classification. We validate on benchmark datasets UCI-HAR and WISDM and accuracies of 97.15% and 9S.9S% are obtained, respectively, demonstrating the adaptation of our framework to both single-sensor and multi-sensor devices. © 2022 IEEE.

关键词： Convolution

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：