检索结果-内蒙古大学图书馆

TechRxiv 2024年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Wang, Wen Jiang, Xuefeng Gao, Bo Lu, Jinda Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Computer and Information Technology The Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China School of Information Science and Technology University of Science and Technology of China Hefei China

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across multiple devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which suffers from unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache. Empirical experiments on four datasets demonstrate that FedCache achiev

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

LI-FPN: Depression and Anxiety Detection from Learning and Imitation

LI-FPN: Depression and Anxiety Detection from Learning and I...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang 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 Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM Dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM Dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

关键词：

来源：评论

学校读者我要写书评

暂无评论

Service-Oriented Multipath Scheduling for Integrated Satellite-Terrestrial networks

Service-Oriented Multipath Scheduling for Integrated Satelli...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Man Ouyang Ran Zhang Bingqing Wang Jiang Liu Weihua Zhuang State Key Laboratory of Networking and Switching Technology BUPT Beijing P.R.China Future Network Research Center Purple Mountain Laboratories Nanjing P.R.China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Low earth orbit (LEO) satellite networks can seamlessly supplement terrestrial networks by providing a high capacity, wide coverage, and cost-effective solution. Positioned to play a significant role in the upcoming 5G/6G era thanks to reduced launch expenses, LEO satellite networks offer benefits such as multi-path transmission, aggregated link bandwidth, redundant paths, and enhanced mobility support. These advantages necessitate further exploration in integrated satellite-terrestrial networks. In this work, we leverage network conditions, underlying link status, and real-time service characteristics to achieve effective synergy, aiming to fulfill application requirements. We formulate the service-oriented multi-path scheduling (SOMPS) problem as a bounded multi-knapsack problem and employ dynamic programming methods for its solution. Simulation results demonstrate that our proposed scheme provides high transmission rate, low latency, and customized information delivery for services, in comparison with baseline schemes.

关键词： Satellites Simulation Low earth orbit satellites Quality of service Space-air-ground integrated networks Dynamic scheduling Real-time systems Dynamic programming Low latency communication Interoperability

来源：评论

学校读者我要写书评

暂无评论

An avoiding overlap method between acoustic shadow and organ for automated ultrasound diagnosis and treatment 11

An avoiding overlap method between acoustic shadow and organ...

引用

11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Matsuyama, Momoko Koizumi, Norihiro Nishiyama, Yu Tsumura, Ryosuke Tsukihara, Hiroyuki Numata, Kazushi The University of Electro-Communications Department of Mechanical and Intelligent Systems Engineering Chofu Japan The University of Electro-Communications Department of Computer and Network Engineering Chofu Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Line 4 Tsukuba Japan The University of Tokyo Department of Cardiothoracic Surgery Bunnkyo-Ku Japan Yokohama City University Medical Center Gastroenterological Center Yokohama Japan

ISBN: (纸本)9781665492324

Ultrasound diagnosis and treatment have attracted attention because they are noninvasive and allow real-time observation of lesions. However, it is difficult to accurately estimate the location of the treatment area because of information loss due to acoustic shadows caused by the reflection of sound waves from hard tissues such as bone. In this study, we propose a probe manipulation method that uses deep learning to avoid overlap between organs and shadows for automated diagnosis. © 2022 IEEE.

关键词： Ultrasonics

来源：评论

学校读者我要写书评

暂无评论

Federated Learning with Only Positive Labels by Exploring Label Correlations

arXiv

引用

arXiv 2024年

作者： An, Xuming Wang, Dui Shen, Li Luo, Yong Hu, Han Du, Bo Wen, Yonggang Tao, Dacheng School of Information and Electronics Beijing Institute of Technology Beijing100081 China National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China JD Explore Academy Beijing100000 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Federated learning aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this paper, we study the multi-label classification problem under the federated learning setting, where trivial solution and extremely poor performance may be obtained, especially when only positive data w.r.t. a single class label are provided for each client. This issue can be addressed by adding a specially designed regularizer on the server-side. Although effective sometimes, the label correlations are simply ignored and thus sub-optimal performance may be obtained. Besides, it is expensive and unsafe to exchange user’s private embeddings between server and clients frequently, especially when training model in the contrastive way. To remedy these drawbacks, we propose a novel and generic method termed Federated Averaging by exploring Label Correlations (FedALC). Specifically, FedALC estimates the label correlations in the class embedding learning for different label pairs and utilizes it to improve the model training. To further improve the safety and also reduce the communication overhead, we propose a variant to learn fixed class embedding for each client, so that the server and clients only need to exchange class embeddings once. Extensive experiments on multiple popular datasets demonstrate that our FedALC can significantly outperform existing counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

A Measurement Study of DNS Query Protocols in Mobile networks: Efficiency, Reliability and Choice

A Measurement Study of DNS Query Protocols in Mobile Network...

引用

International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Liangyi Gong Sai Wang Lanqi Yang Chun Long Xiaochen Fan Daibo Liu Changhua Pei Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronic Engineering Tsinghua University Beijing China Institute for Electronics and Information Technology in Tianjin Tsinghua University Tianjin China College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

The Domain Name System (DNS) runs as a fundamental infrastructure of the mobile Internet. Various DNS protocols employed in the current network ecology can be predominantly classified as unencrypted DNS and encrypted DNS. However, existing research mainly focuses on assessing DNS performance within conventional internet structures, neglecting their evaluation in mobile contexts. In our pioneering study examining DNS within mobile networks, we developed an Android-based application to evaluate the efficiency and reliability of DNS protocols. The App issues nine domain name lookups to four cloud DNS providers supporting unencrypted and encrypted DNS protocols. Collaborating with volunteers from four countries, we collected about 52,000 test records. Our findings reveal substantial variability in the efficiency and reliability of all DNS protocols across different mobile scenarios. Overall, encrypted DNS protocols exhibit superior efficiency compared to plaintext DNS when oriented towards cloud DNS resolvers. In high-speed mobile scenarios, all DNS protocols demonstrate reduced efficiency, with encrypted DNS protocols showing relatively higher reliability. Our broad-scale measurement results indicate that the performance of DNS protocols varies across mobile contexts, but users are typically uninformed about these differences and do not realize how to break free. Intending to assist users in selecting an optimal DNS protocol, we propose a protocol choice model based on auto-encoding LSTM networks which leverages features of networking and protocols to predict the most suitable DNS protocol with reduced query time and enhanced reliability in the current scenario. Notably, we have achieved the prediction of the optimal DNS protocol for the future by foreseeing the network status ahead. Empirical results demonstrate an impressive 98.73% accuracy in prediction of DNS protocol selection.

关键词： Cloud computing Protocols Accuracy Current measurement Predictive models User experience Domain Name System Reliability Cryptography Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Look from Coarse to Fine: A Hierarchical Lane Detection network

Look from Coarse to Fine: A Hierarchical Lane Detection Netw...

引用

Artificial Intelligence Innovation (ICAII), International Conference on

作者： Xichen Yu Yanduo Zhang Tao Lu Xun Li Jun Chang Hubei Provincial Key Laboratory of Intelligent Robot School of Computer Science and Engineering & School of Artificial Intelligence Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and School of Computer Science Wuhan University Wuhan China

Lane detection involves identifying a high-level semantic identifier with a slender structure, which requires both high-level feature detection for existence and low-level features for shape and position determination. The objective of this study is to introduce a framework called hierarchical lane detection (HLDNet), which from coarse to fine established stronger relationships between the global and local lanes to achieve stable and accurate lane detection. Specifically, we reframe the existing lane identification method by expressing lanes as a combination of lane instances (for existence detection) and lane point sets (for shape detection). Our proposed Lane IoU loss improves the model performance by regressing the lane instance as a whole. To enhance stability, we capture the complete lane with high-level features using multiple receptive fields. Additionally, our model adaptively fuses high- and low-level features, propagates key features between channels, and further enhances lane local feature representation. Our experimental results show significant improvements in the stability and accuracy of lane detection across multiple benchmark datasets and can run in realtime at a speed of 91 FPS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

引用

International Conference on computer Supported Cooperative Work in Design

作者： Wei Ding Luyao Wang Wei Zhang Myung Jin Lee Ling Wang Kwang Woo Nam 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Industry-University Cooperstion Foundation Chungbuk National University Cheongju Korea School of Computer Science Northeast Electric Power University Jilin China School of Computer Science and Engineering Kunsan National University Gunsan Korea

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this paper, we propose a new UAV video similarity measurement algorithm to cluster and analyze UAV videos by measuring the similarity between UAV videos and train them in sub-groups, which in turn improves the efficiency of UAV video data mining. Our preliminary work introduces the maximum common view subsequence (LCVS) to compute the similarity between UAV videos, but LCVS needs to frequently compute the common visible region of video frames generating extremely high time cost. In order to solve the problem of high computational cost of LCVS, we propose a similarity algorithm based on minimum tilted outer rectangle clustering (MTORC) for UAV videos. The MTORC algorithm adopts minimum tilted outer rectangle to cluster the field of view of the marked points in each frame, which reduces the time complexity of calculating the common visible region. By comparing experiments with LCSS and LCVS, the experimental results prove that MTORC is better than the present previous research in terms of algorithm accuracy and time cost.

关键词： Training Visualization Costs Accuracy Clustering algorithms Autonomous aerial vehicles Time measurement

来源：评论

学校读者我要写书评

暂无评论

VagueGAN: A GAN-Based Data Poisoning Attack Against Federated Learning Systems

VagueGAN: A GAN-Based Data Poisoning Attack Against Federate...

引用

IEEE Communications Society Conference on Sensor and Ad Hoc Communications and networks (SECON)

作者： Wei Sun Bo Gao Ke Xiong Yang Lu Yuwei Wang Engineering Research Center of Network Management Technology for High-Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China

Federated learning (FL) is a privacy-preserving distributed learning paradigm relying on but without directly accessing privately owned datasets. However, the ‘‘available but not visible’’ nature of training data in FL leads to security risks. In particular, ‘‘not visible’’ local data can easily become the best targets of poisoning attacks. Although existing data poisoning methods may successfully attack FL systems, they mostly lead to significant data statistical changes and thus can be not hard to detect. In this paper, we propose VagueGAN, a new data poisoning attack model that unconventionally leverages the power of generative adversarial network (GAN) to generate seemingly legitimate vague data with appropriate amounts of poisonous noise. The quality of such vague data can be controlled on demand to achieve a balanced trade-off between attack effectiveness and stealthiness. Extensive experiments show that data poisoning attacks enhanced by our VagueGAN not only better degrade FL outcomes with low efforts but also are generally much less detectable.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adaptive ELM-Based Time-Constrained Control of Uncertain Robotic Manipulators With Minimum Learning Computation

引用

IEEE Transactions on Emerging Topics in Computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

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：