检索结果-内蒙古大学图书馆

Cost-Efficient Deep Neural Network Placement in Edge Intelligence-Enabled Internet of Things

ACM Transactions on Sensor Networks 1000年

作者： Hao Tian Xiaolong Xu Hongyue Wu Qingzhan Zhao Jianguo Dai Maqbool Khan State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Nanjing China School of Software Nanjing University of Information Science and Technology Nanjing Jiangsu China Nanjing China College of Intelligence and Computing Tianjin University Tianjin China Tianjin China Geospatial Information Engineering Research Center College of Information Science and Technology Shihezi University Shihezi Xinjiang China Shihezi China Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Pakistan Haripur Pakistan

Edge intelligence (EI) integrates edge computing and artificial intelligence empowering service providers to deploy deep neural networks (DNNs) on edge servers in proximity to users to provision intelligent applications (e.g., autonomous driving) for ubiquitous Internet of Things (IoT) in smart cities, which facilitates the quality of experience (QoE) of users and improves the processing and energy efficiency. However, considering DNN is typically computational-intensive and resource-hungry, conventional placement approaches ignore the influence of multi-dimensional resource requirements (processor, memory, etc.), which may degrade the real-time performance. Moreover, with the increasing scale of geo-distributed edge servers, centralized decision-making is still challenging to find the optimal strategies effectively. To overcome these shortcomings, in this paper we propose a game theoretic DNN placement approach in EI-enabled IoT. First, a DNN placement optimization problem is formulated to maximize system benefits, which is proven to be \(\mathcal {N}\mathcal {P}\)-hard and model the original problem as an exact potential game (EPG). Moreover, an EPG-based DNN model placement algorithm, named EPOL, is designed for edge servers to make sub-optimal strategies independently and theoretical analysis is possessed to guarantee the performance of EPOL. Finally, real-world dataset based experimental results corroborate the superiority and effectiveness of EPOL.

关键词： Edge intelligence DNN placement Game theory Internet of Things

来源：评论

学校读者我要写书评

暂无评论

SRF: SpectrumRecombineFormer for Hyperspectral Image Classification

引用

ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Weipeng Jing Peilun Kang Donglin Di Juntao Gu Linhui Li Mahmoud Emam Linda Mohaisen Xun Yang Chao Li College of Computer and Control Engineering Northeast Forestry University China Li Auto China Heilongjiang Cyberspace Research Center China Faculty of Artificial Intelligence Menoufia University Egypt Information Technology Faculty of Computing and Information Technology King Abdulaziz University Saudi Arabia University of Science and Technology of China School of Information Science and Technology China

Hyperspectral (HS) imaging is a valuable technique for accurately classifying materials because of the abundance of spectral information and high resolution it provides. However, the characteristics of Hyperspectral images (HSI), such as high-dimensional features and information redundancy, pose significant challenges to data processing. Traditional dimensionality reduction methods often have information loss, high computational complexity, and easy to ignore the strong correlation between HSI spectral bands when dealing with HSI data. Although other methods can achieve satisfactory classification performance, they do not consider the dimensionality reduction of HSI, and they focus on the model performance, which limits further improvement in classification performance. This paper proposes a transformer-based framework called “SpectrumRecombineFormer” (SRF), which is composed of two key modules, namely “Spatial Spectral ReCombination” (SSRC) and “Cross-layer Fusion” (CF). The SSRC is capable of utilizing both adjacent and non-adjacent spectrums to generate the spatial-sequential perceptive representations, which alleviates the effect of the strong correlation between HSI spectral bands. The CF can avoid the loss of information during the feed-forward procedure among layers. Extensive experiments on five existing datasets (widely-adopted Indian Pines, Houston2013, Pavia University, Salinas and KSC) demonstrate the capability of our proposed method to address the above mentioned challenges. Both quantitative and qualitative experimental ablation studies, including visualization results, reveal that the proposed SRF method can successfully and efficiently classify hyperspectral images and surpass the other state-of-the-art methods. For access to the source code, please visit https://***/kangpeilun/SRF-HSI-Classification-master.

关键词： hyperspectral image classification dimensionality reduction skip fusion swin-transformer remote sensing

来源：评论

学校读者我要写书评

暂无评论

Bioinformatics Research and Applications 1

引用

丛书名： Lecture Notes in computer science

1000年

作者： Mark Borodovsky Johann Peter Gogarten Teresa M. Przytycka Sanguthevar Rajasekaran

来源：评论

学校读者我要写书评

暂无评论

GSFL: A Privacy-preserving Grouping-Split Federated Learning Approach in Resource-constrained Edge computing Scenarios

引用

ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Qi Liu Zhilu Wang Xiaokang Zhou Yonghong Zhang Xiaodong Liu Haiyang Lin Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China and Nanjing University of Information Science and Technology School of Software China Nanjing University of Information Science and Technology School of Computer Science China Kansai University Faculty of Business Data Science Japan and RIKEN Center for Advanced Intelligence Project Japan Nanjing University of Information Science and Technology School of Automation China Edinburgh Napier University School of Computing UK Nanjing University of Information Science and Technology School of Software China

The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount of distributed data, leading to new prospects for deep learning. However, this data is confined within data silos and contains sensitive information, making it difficult to be processed in a centralized manner, particularly under stringent data privacy regulations. Federated learning (FL) offers a solution by enabling collaborative learning while ensuring privacy. Nonetheless, data and device heterogeneity complicate FL implementation. This research presents a specialized FL algorithm for heterogeneous edge computing. It integrates a lightweight grouping strategy for homogeneous devices, a scheduling algorithm within groups, and a Split Learning (SL) approach. These contributions enhance model accuracy and training speed, alleviate the burden on resource-constrained devices, and strengthen privacy. Experimental results demonstrate that the GSFL outperforms FedAvg and SplitFed by 6.53× and 1.18×. Under experimental conditions with \(\alpha=0.05\), representing a highly heterogeneous data distribution typical of extreme Non-IID scenarios, GSFL showed better accuracy compared to FedAvg by 10.64%, HACCS by 4.53%, and Cluster-HSFL by 1.16%. GSFL effectively balances privacy protection and computational efficiency for real-world applications in mobile multimedia communications.

关键词： Federated learning Split learning Non-IID Clustering

来源：评论

学校读者我要写书评

暂无评论

Social Informatics 1

引用

丛书名： Lecture Notes in computer science

1000年

作者： Karl Aberer Andreas Flache Wander Jager Ling Liu Jie Tang Christophe Guéret

ISBN: (数字)9783642353864

ISBN: (纸本)9783642353857

This book constitutes the proceedings of the 4th International Conference on Social Informatics, SocInfo 2012, held in Lausanne, Switzerland, in December 2012. The 21 full papers, 18 short papers included in this volume were carefully reviewed and selected from 61 submissions. The papers are organized in topical sections named: social choice mechanisms in the e-society,computational models of social phenomena, social simulation, web mining and its social interpretations, algorithms and protocols inspired by human societies, socio-economic systems and applications, trust, privacy, risk and security in social contexts.

关键词： Information Systems Applications (incl. Internet) computer Communication Networks Artificial Intelligence computer Appl. in Social and Behavioral sciences User Interfaces and Human computer Interaction Data Mining and Knowledge Discovery

来源：评论

学校读者我要写书评

暂无评论

EEG data Classification: Review and Taxonomy

ACM Transactions on Computing for Healthcare

引用

ACM Transactions on computing for Healthcare 1000年

作者： Asma Belhadi Anis Yazidi Pedro G. Lind Youcef Djenouri Department of Computer Science OsloMet – Oslo Metropolitan University N-0130 Oslo Norway OsloMet Artificial Intelligence Lab Pilestredet 52 N-0166 Oslo Norway Department of Informatics University of Oslo 0316 Oslo Norway Department of Computer Science OsloMet – Oslo Metropolitan University N-0130 Oslo Norway School of Economics Innovation and Technology Kristiania University of Applied Sciences 0153 Oslo Norway Simula Research Laboratory Numerical Analysis and Scientific Computing 0164 Oslo Norway University of South-Eastern Norway Norwegian Research Center Oslo Norway

EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from raw data, eliminating the need for manual feature extraction. However, in the context of medical data, and in particular for EEG analysis, the use of deep learning approaching while having been very successful is not being included in medical diagnosis routines, yet. The aim of this survey is twofold. On one side, it provides a comprehensive overview of the current state-of-the-art in EEG data classification, with a specific focus on the use of deep learning techniques. On the other side, it also addresses the clinician community, explaining the power and trustfulness of such new approaches. The survey begins with an introduction highlighting the limitations of traditional model-based approaches and the potential of deep learning in EEG data classification. The fundamental principles and architectures of deep learning models are presented, including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Graph Convolution Neural Network (GCNNs) that have been successfully applied to EEG data classification tasks. A detailed review and analysis of existing literature on deep learning-based EEG data classification are provided, categorizing the studies based on the type of the input data, e.g., sequences, images, graphs or multi-modalities. We also discuss about the existing tools and technologies for EEG data classification and highlights the challenges and limitations associated with deep learning in EEG data classification, including limited data availability, interpretability of deep models and bias mitigation. Potential solutions and ongoing research efforts to overcome these challenges are explored, providing insights into the future directions of this field. This survey serves as a valuable resource for researchers, practitioners

关键词： EEG Data Classification Deep Learning Healthcare Applications Clinical Diseases

来源：评论

学校读者我要写书评

暂无评论

Information Retrieval technology 1

引用

丛书名： Lecture Notes in computer science

1000年

作者： Gary Geunbae Lee Dawei Song Chin-Yew Lin Akiko Aizawa Kazuko Kuriyama Masaharu Yoshioka Tetsuya Sakai

ISBN: (数字)9783642047695

ISBN: (纸本)9783642047688

This book constitutes the refereed proceedings of the 5th Asia Information Retrieval Symposium, AIRS 2009, held in Sapporo, Japan, in October 2009. The 18 revised full papers and 20 revised poster papers presented were carefully reviewed and selected from 82 submissions. All current aspects of information retrieval - in theory and practice - are addressed; working with text, audio, image, video and multimedia data.

关键词： Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Information Storage and Retrieval Algorithm Analysis and Problem Complexity Data Structures Data Storage Representation

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions

引用

ACM computing Surveys 1000年

作者： Md Palash Uddin Yong Xiang Mahmudul Hasan Jun Bai Yao Zhao Longxiang Gao School of Information Technology Deakin University Burwood Australia Deakin University Burwood Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, scienceDirect, Springer, Web of science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.

关键词： Federated Learning Robustness Attack Countermeasure Defence.

来源：评论

学校读者我要写书评

暂无评论

Complex Networks and Their Applications VII 1

引用

丛书名： Studies in Computational Intelligence

1000年

作者： Luca Maria Aiello Chantal Cherifi Hocine Cherifi Renaud Lambiotte Pietro Lió Luis M. Rocha

ISBN: (数字)9783030054144

ISBN: (纸本)9783030054137

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

关键词： Computational Intelligence Artificial Intelligence

来源：评论

学校读者我要写书评

暂无评论

PRIMA 2018: Principles and Practice of Multi-Agent Systems 1

引用

丛书名： Lecture Notes in computer science

1000年

作者： Tim Miller Nir Oren Yuko Sakurai Itsuki Noda Bastin Tony Roy Savarimuthu Tran Cao Son

ISBN: (数字)9783030030988

ISBN: (纸本)9783030030971

This book constitutes the refereed proceedings of the 21st International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2018, held in Tokyo, Japan, in October/November 2018. The 27 full papers presented and 31 short papers were carefully reviewed and selected from 103 submissions.;PRIMA presents subjects in many application domains, particularly in e-commerce, and also in planning, logistics, manufacturing, robotics, decision support, transportation, entertainment, emergency relief and disaster management, and data mining and analytics.

关键词： Artificial Intelligence computer Communication Networks Special Purpose and Application-Based Systems Algorithm Analysis and Problem Complexity Software Engineering/Programming and Operating Systems

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：