检索结果-内蒙古大学图书馆

International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： R Subash Banu Priya Prathaban Ganeshkumar N Gopinathan S Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering GITAM School of Technology Gandhi Institute of Technology and Management GITAM (Deemed to be University) Bengaluru Karnataka India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Efficient route optimization is a crucial concern in modern transportation systems, influencing travel time, resource consumption, and user satisfaction. By empowering users to select their priority parameters, such as safety or efficiency, this project enables travellers to make eco-conscious choices and select routes that minimize their environmental footprint. Real-time data integration ensures users can bypass traffic jams and accidents, reducing emissions and fuel consumption. Furthermore, the focus on safety factors helps prevent accidents, thereby fostering safer road environments. Existing systems often fall short, providing limited customization and overlooking safety and sustainability priorities. To bridge these gaps, this project introduces an advanced route planning system that empowers users to select their priority parameters, integrates real-time data for up-to- the-minute updates, and prioritizes road safety. It introduces a pioneering approach that enhances the Dijkstra Algorithm by integrating real-time accident rate and travel time data into the route optimization process. By considering these additional factors, the algorithm produces routes that not only minimize distance but also prioritize safety and time efficiency in dynamic real-world conditions. The algorithm's multi-factor optimization considers distance, accident rates, and travel times, enabling the creation of routes that balance these criteria and offer improved decision-making for users through a user- friendly interface. The algorithm's dynamic adaptability, multi- factor optimization, and practical implementation showcase its potential to revolutionize how routes are planned, leading to safer, faster, and more informed travel experiences.

关键词： Analytical models Adaptation models Heuristic algorithms Real-time systems Road safety Safety Planning Optimization Public transportation Accidents

来源：评论

学校读者我要写书评

暂无评论

Stochastic Security Ephemeral Generation Protocol for 5G Enabled Internet of Things 7th

Stochastic Security Ephemeral Generation Protocol for 5G Ena...

引用

7th EAI International Conference on the Internet of Things as a Service, IoTaaS 2021

作者： Al Sibahee, Mustafa A. Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Computer Technology Engineering Department Iraq University College Basrah Iraq Faculty of Biological and Physical Sciences Tom Mboya University College Homabay Kenya Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen China

ISBN: (纸本)9783030959869

To ensure secure access to the data held in internet of things, many lightweight authentication schemes have been developed using approaches such as symmetric cryptography or hashing operations. Although these schemes achieve forward key secrecy and user anonymity, de-synchronization is a major problem in these protocols. As such, many other schemes have been presented to address this pertinent security challenge. However, some of these schemes are still susceptible to smart card loss attacks among others. In this paper, stochastic security ephemeral generation protocol for 5G enabled internet of things is presented. It is demonstrated to offer mutual authentication and session key agreement. It is also robust against packet replays, eavesdropping and man-in-the-middle attacks. In terms of performance, it has the lowest computation and communication overheads. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Examining the Effects of Applying AI to Enhance Big data Retrieval Efficiency

Examining the Effects of Applying AI to Enhance Big Data Ret...

引用

Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Nakul S. Sharma Dhananjay Yadav Manish Shrivastava Sanjeev Kumar Mandal G. Ravivarman D Little Femilin Jana Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This studies paper explores how synthetic Intelligence (AI) technologies may be used to improve the efficiency of information retrieval from huge databases, as applied in a diffusion of industries. The paper will discuss the concept of AI as a tool to reduce information complexity, at the same time as inspecting current studies projects inside the subject and unique programs for the enterprise and patron worlds. It'll then discover the demanding situations that get up from applying AI and massive records technology, and description a suggested framework for first-rate practices. The paper will recall how AI may be utilized to enhance information seek outcomes in real-time, and provide an explanation for the diverse ways wherein this might be carried out. Subsequently, the paper will discuss the capability implications of the usage of AI for large statistics Retrieval efficiency, as well as its destiny applications.

关键词： Industries databases Scalability Big data Information retrieval Real-time systems Security

来源：评论

学校读者我要写书评

暂无评论

Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

引用

arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy

ACGraph: Accelerating Streaming Graph Processing via Depende...

引用

Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Layers of technology in pluriversal design. Decolonising language technology with the LiveLanguage initiative

arXiv

引用

arXiv 2024年

作者： Koch, Gertraud Bella, Gábor Helm, Paula Giunchiglia, Fausto Department of Anthropological Studies in Culture and History University of Hamburg Hamburg Germany Department of Logic of Uses Social Sciences and Information IMT Atlantique Nantes France Department for Media Studies Data Science Center University of Amsterdam Amsterdam Netherlands Department of Information Engineering and Computer Science University of Trento Trento Italy

Language technology has the potential to facilitate intercultural communication through meaningful translations. However, the current state of language technology is deeply entangled with colonial knowledge due to path dependencies and neo-colonial tendencies in the global governance of artificial intelligence (AI). Language technology is a complex and emerging field that presents challenges for co-design interventions due to enfolding in assemblages of global scale and diverse sites and its knowledge intensity. This paper uses LiveLanguage, a lexical database, a set of services with particular emphasis on modelling language diversity and integrating small and minority languages, as an example to discuss and close the gap from pluriversal design theory to practice. By diversifying the concept of emerging technology, we can better approach language technology in global contexts. The paper presents a model comprising of five layers of technological activity. Each layer consists of specific practices and stakeholders, thus provides distinctive spaces for co-design interventions as mode of inquiry for de-linking, re-thinking and re-building language technology towards pluriversality. In that way, the paper contributes to reflecting the position of co-design in decolonising emergent technologies, and to integrating complex theoretical knowledge towards decoloniality into language technology design. © 2024, CC BY.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge Computing Networks

Energy-Efficient Client Sampling for Federated Learning in H...

引用

IEEE International Conference on Communications (ICC)

作者： Jian Tang Xiuhua Li Hui Li Min Xiong Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

To address network congestion and data privacy concerns, federated learning (FL) that combines multiple clients and a parameter server has been widely used in mobile edge computing (MEC) networks to process the abundant data generated by mobile clients. However, the existing client sampling methods do not adequately consider the data heterogeneity and system heterogeneity. Parameter server selects inappropriate clients to participate in the FL training process. This inevitably leads to slower convergence of the global model and higher energy consumption. In this paper, we design a client sampling model with the goal of selecting suitable clients to improve the energy efficiency of FL in heterogeneous MEC networks. Then we propose an energy-efficient client sampling strategy by quantifying the communication capability, computation capability and data quality of clients. Based on the quantization results, clients are assigned with a corresponding sampled probability. Simulation results show that our proposed strategy can effectively accelerate the convergence of the global model and reduce the energy consumption compared with the baseline schemes.

关键词： Training Energy consumption Multi-access edge computing Federated learning Computational modeling Simulation Sampling methods

来源：评论

学校读者我要写书评

暂无评论

Power-Measurement-Based Channel Autocorrelation Estimation for IRS-Assisted Wideband Communications

arXiv

引用

arXiv 2025年

作者： Sun, He Zhu, Lipeng Mei, Weidong Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China 611731 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user’s reference signal received power (RSRP) measurements with time-varying random IRS training reflections. As RSRP is readily accessible in existing communication systems, our proposed channel estimation method does not require additional pilot transmission in IRS-aided wideband communication systems. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS ref

关键词： Orthogonal frequency division multiplexing

来源：评论

学校读者我要写书评

暂无评论

EAViT: External Attention Vision Transformer for Audio Classification

arXiv

引用

arXiv 2024年

作者： Iqbal, Aquib Zim, Abid Hasan Tonmoy, Md Asaduzzaman Zhou, Limengnan Malik, Asad Kuribayashi, Minoru Department of Computer Science University of Massachusetts AmherstMA United States Department of Mechanical Engineering Aligarh Muslim University India Department of Electrical Engineering Aligarh Muslim University India School of Electronic and Information Engineering UESTC China School of Information Technology Monash University Malaysia Malaysia Center for Data-driven Science and Artificial Intelligence Tohoku University Japan

This paper presents the External Attention Vision Transformer (EAViT) model, a novel approach designed to enhance audio classification accuracy. As digital audio resources proliferate, the demand for precise and efficient audio classification systems has intensified, driven by the need for improved recommendation systems, and user personalization in various applications, including music streaming platforms and environmental sound recognition. Accurate audio classification is crucial for organizing vast audio libraries into coherent categories, enabling users to find and interact with their preferred audio content more effectively. In this study, we utilize the GTZAN dataset, which comprises 1,000 music excerpts spanning ten diverse genres. Each 30-second audio clip is segmented into 3-second excerpts to enhance dataset robustness and mitigate overfitting risks, allowing for more granular feature analysis. The EAViT model integrates multi-head external attention (MEA) mechanisms into the Vision Transformer (ViT) framework, effectively capturing long-range dependencies and potential correlations between samples. This external attention (EA) mechanism employs learnable memory units that enhance the network’s capacity to process complex audio features efficiently. The study demonstrates that EAViT achieves a remarkable overall accuracy of 93.99%, surpassing state-of-the-art models. © 2024, CC BY.

关键词： Audio streaming

来源：评论

学校读者我要写书评

暂无评论

Integrating Generative Artificial Intelligence Into Future Internet of Vehicles [From the Guest Editors]

引用

IEEE Vehicular Technology Magazine 2025年第2期20卷 40-42页

作者： Gang Sun Hongyang Du Abbas Jamalipour Gabriel-Miro Muntean Lin Cai Jiawen Kang Dusit Niyato University of Electronic Science and Technology of China Chengdu China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China University of Sydney Sydney NSW Australia Performance Engineering Laboratory Dublin City University Dublin Ireland Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada Guangdong University of Technology Guangzhou China College of Computing and Data Science Nanyang Technological University Singapore Singapore

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：