检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Liu, Zhuocheng Shen, Zhishu Zhou, Pan Zheng, Qiushi Jin, Jiong School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

With the proliferation of data-driven services, the volume of data that needs to be processed by satellite networks has significantly increased. Federated learning (FL) is well-suited for big data processing in distributed, resource-constrained satellite environments. However, ensuring its convergence performance while minimizing processing time and energy consumption remains a challenge. To this end, we propose a hierarchical clustered federated learning framework, FedHC. This framework employs a satellite-clustered parameter server (PS) selection algorithm at the cluster aggregation stage, grouping nearby satellites into distinct clusters and designating a cluster center as the PS to accelerate model aggregation. Several communicable cluster PS satellites are then selected through ground stations to aggregate global parameters, facilitating the FL process. Moreover, a meta-learning-driven satellite re-clustering algorithm is introduced to enhance adaptability to dynamic satellite cluster changes. The extensive experiments on satellite networks testbed demonstrate that FedHC can significantly reduce processing time (up to 3x) and energy consumption (up to 2x) compared to other comparative methods while maintaining model accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation

引用

IEEE Transactions on Medical Imaging 2023年第11期42卷 3323-3335页

作者： Li, Zeju Kamnitsas, Konstantinos Dou, Qi Qin, Chen Glocker, Ben Imperial College London BioMedIA Group Department of Computing LondonSW7 2AZ United Kingdom University of Oxford Department of Engineering Science OxfordOX3 7DQ United Kingdom University of Birmingham School of Computer Science BirminghamB15 2TT United Kingdom The Chinese University of Hong Kong Department of Computer Science and Engineering Shatin999077 Hong Kong Imperial College London I-X and the Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom

This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta-learning scheme to explicitly align the distribution of training and validation data which is used as a proxy for unseen test data. We improve the current data augmentation strategies with two core designs. First, we learn class-specific training-time data augmentation (TRA) effectively increasing the heterogeneity within the training subsets and tackling the class imbalance common in segmentation. Second, we jointly optimize TRA and test-time data augmentation (TEA), which are closely connected as both aim to align the training and test data distribution but were so far considered separately in previous works. We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net. Extensive experimentation shows that the proposed data augmentation framework can significantly and consistently improve the segmentation performance when compared to existing solutions. Code is publicly available at https://***/ZerojumpLine/JCSAugment. © 1982-2012 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Analysis of 2.4 GHz Circular Patch Antenna on FR4 Substrate for Wi-Fi and Bluetooth: Exploring Reflection Coefficient, Gain, VSWR, and Current Density Characteristics

Analysis of 2.4 GHz Circular Patch Antenna on FR4 Substrate ...

引用

2023 Intelligent computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Kinol, A. Mary Joy Bommi, R.M. Vijayaraj, N. Nalini, M. Institute of Ece Saveetha School of Engineering Simats Chennai India Chennai Institute of Technology Department of Ece Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Eie Chennai India

ISBN: (纸本)9798350394580

A metal patch is attached to one side of a dielectric substrate to create a small, planar antenna that is frequently employed in wireless communication applications. The advantages of circular patch antennas include their ease of production, low profile, and lightweight design. An antenna with a circular radiating metal patch is called a circular patch antenna and is most frequently employed in wireless communication systems. Design circular microstrip patch antennas for this study's FR4 substrate to analyse 2.4 GHz reflection coefficient, gain VSWR, and current distribution. The HFSS software was used to construct a circular patch antenna. © 2023 IEEE.

关键词： Antenna Design FR4 Gain Novel Circular Patch Antenna (CPA) VSWR Wireless Communication

来源：评论

学校读者我要写书评

暂无评论

Socio-technical Approaches to Solid Waste Management in Rural India: A Case Study of Smart Pipe Composting in Raichur District, Karnataka 8th

Socio-technical Approaches to Solid Waste Management in Rura...

引用

8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Chethan, R.K. Jambha, Aniketh V. Pathania, Chirag Siddhartha, Mutyala Sai Sri Sanjay Arifuzzaman Darshan, K. Cornet, Souresh Jayaprakash, Sajithkumar K. Department of Commerce and Management School of Arts Humanities and Commerce Amrita Vishwa Vidyapeetham Mysuru India Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Artificial Intelligence Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus Karnataka Mysuru India Department of Visual Communication School of Arts Humanities and Commerce Amrita Vishwa Vidyapeetham Mysuru India Amrita School for Sustainable Futures Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994885

Rural areas in developing countries are plagued with complex, intricate challenges. In India, rural communities are seriously lagging regarding Sanitation and Waste management facilities. The majority of communities lack the resources to address those issues. Government programs have partly been able to address those issues, but much still needs to be done to solve the challenges completely. This study aimed to understand better the challenges associated with Solid Waste Management in rural India. It specifically explores how technology can help to complete the last mile of sustainability through a user-centered and participatory problem-solving approach. Taking the case of a community in Raichur district, Karnataka, a smart pipe composting design is proposed to address the Solid Waste Management challenges in the community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Waste management

来源：评论

学校读者我要写书评

暂无评论

Graph Contrastive Learning Reimagined: Exploring Universality 24

Graph Contrastive Learning Reimagined: Exploring Universalit...

引用

33rd ACM Web Conference, WWW 2024

作者： Zhuo, Jiaming Cui, Can Fu, Kun Niu, Bingxin He, Dongxiao Wang, Chuan Guo, Yuanfang Wang, Zhen Cao, Xiaochun Yang, Liang School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Cybersecurity Northwestern Polytechnical University Xi'an China School of Cyber Science and Technology Shenzhen Campus Sun Yat-Sen University Shenzhen China

ISBN: (纸本)9798400701719

Real-world graphs exhibit diverse structures, including homophilic and heterophilic patterns, necessitating the development of a universal Graph Contrastive Learning (GCL) framework. Nonetheless, the existing GCLs, especially those with a local focus, lack universality due to the mismatch between the input graph structure and the homophily assumption for two primary components of GCLs. Firstly, the encoder, commonly Graph Convolution Network (GCN), operates as a low-pass filter, which assumes the input graph to be homophilic. This makes it challenging to aggregate features from neighbor nodes of the same class on heterophilic graphs. Secondly, the local positive sampling regards neighbor nodes as positive samples, which is inspired by the homophily assumption. This results in feature similarity amplification for the samples from the different classes (i.e., FALSE positive samples). Therefore, it is crucial to feed the encoder and positive sampling of GCLs with homophilic graph structures. This paper presents a novel GCL framework, named gRaph cOntraStive Exploring uNiversality (ROSEN), designed to achieve this objective. Specifically, ROSEN equips a local graph structure inference module, utilizing the Block Diagonal Property (BDP) of the affinity matrix extracted from node ego networks. This module can generate the homophilic graph structure by selectively removing disassortative edges. Extensive evaluations validate the effectiveness and universality of ROSEN across node classification and node clustering tasks. © 2024 ACM.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Quantum Key Distribution Protocols with Machine Learning Techniques

Enhancing Quantum Key Distribution Protocols with Machine Le...

引用

2023 Intelligent computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Bommi, R.M. Nalini, M. Vijayaraj, N. Kinol, A.Mary Joy Chennai Institute of Technology Department of Ece Chennai India Sri Sairam Engineering College Department of Eie Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Institute of Ece Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798350394580

Quantum Key Distribution (QKD) promises theoretically unbreakable encryption by using fundamental quantum physics principles. QKD has emerged as a contender in the next generation of encryption protocols as the demand for more secure communication grows in our linked society. However, practical QKD implementations confront obstacles such as noise, interception attempts, and flaws in quantum hardware. We investigate the possibilities of Machine Learning (ML) techniques to improve the efficiency, resilience, and security of QKD protocols in this study. We provide a novel system that uses deep learning models to optimize photon polarization states and hence increase the key generation rate. Our approach detects and mitigates eavesdropping attempts using unsupervised learning algorithms, substantially enhancing the security of quantum communications. To achieve security, the BB84 protocol makes use of fundamental quantum mechanics features. Furthermore, we use reinforcement learning to adaptively alter system settings in real time to account for the quirks and noise profiles of different quantum channels. In comparison to typical QKD setups, experimental results show that our ML-enhanced QKD system is more resilient to known quantum attacks and provides a more consistent and efficient key distribution rate. Our findings indicate that the interaction of quantum physics and machine learning can pave the way for more secure and practical quantum communication networks in the near future. © 2023 IEEE.

关键词： BB84 protocol Machine Learning Techniques Quantum bit error rate (QBER) Quantum Key Distribution style

来源：评论

学校读者我要写书评

暂无评论

REMOTELY CONTROLLABLE DNA MICROFLOW 26

REMOTELY CONTROLLABLE DNA MICROFLOW

引用

26th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2022

作者： Udono, Hirotake Nomura, Shin-Ichiro M. Takinoue, Masahiro Department of Computer Science School of Computing Tokyo Institute of Technology Japan Department of Robotics Graduate School of Engineering Tohoku University Japan

ISBN: (纸本)9781733419048

Despite an emerging interest in fluid manipulation within confined environment such as biological cells and micro-scale biomimics, remotely controllable micro-scale flow remains unexplored. However, intracellular microfluidics is not compatible with established top-down microfluidic methods because of limited accessibility. Here, we demonstrate photoresponsive DNA fluid, condensate of DNA nanostructures (DNA motifs), as a versatile mechanistic tool for the remote control of microflow. Insertion site of azobenzene, introduced for photocontrollability, determines an extent of dynamic shift in binding enthalpy between DNA motifs, providing the underpinning mechanism for drastic and facile flow-mode switching. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Remote control

来源：评论

学校读者我要写书评

暂无评论

Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation 22

Securing Consumer IoT Swarms Using Graph Transformers and SR...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Kohli, Varun Kohli, Bhavya Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln United States

ISBN: (纸本)9798331508050

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a ∼ 10-4 second inference latency on a laptop CPU. © 2025 IEEE.

关键词： computer viruses

来源：评论

学校读者我要写书评

暂无评论

Leveraging Machine Learning to Predict Disease Risk Levels from Multiple Eye Condition Indicators

Leveraging Machine Learning to Predict Disease Risk Levels f...

引用

Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Mudumala Varnika Narayani Nunnaguppala Rohit Naga Ruthvika Durupudi Tejashwini Vadeghar Jyotsna C. Aiswariya Milan K. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

This paper presents a review of machine learning methodologies applied to disease risk evaluation, leveraging ocular condition indices derived from the RFMiD dataset. To enhance the performance of classification, a formal and structured mechanism of statistical analysis and also the method of model prediction was applied. Data mining techniques which include Decision Trees, Random Forest, Naive Bayes, Gradient Boosting, and Stacking Classifiers were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. For the cases of coarse models applied to healthcare XAI approaches such as SHAP and LIME were used to gain better interpretability. This paper points to the potential of machine learning for disease risk modelling and provides recommendations for generating accurate and easily-interpreted models in order to increase the diagnostic accuracy and, therefore, the quality of care for patients.

关键词： Analytical models Accuracy Computational modeling Medical services Predictive models Retina Real-time systems Bayes methods Wearable devices Diseases

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：