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检索条件"任意字段=4th International Multi-Conference on Computing in the Global Information Technology"

共 11800 条记录，以下是101-110 订阅

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Coded Distributed computing with Wireless Shared Heterogeneous Workers 4

Coded Distributed Computing with Wireless Shared Heterogeneo...

引用

4th IEEE international conference on Electronic technology, Communication and information, ICETCI 2024

作者： Lei, Yu Li, Mingming Zhao, Guangquan Peng, Xiyuan Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Norinco Group Air Ammunition Research Institute Co. LTD Test Technology Department Harbin China

ISBN: (纸本)9798350361643

Distributed computing can parallelize large-scale computing tasks. However, due to the straggler effect, the performance of distributed systems may be diminished. Coded distributed computing mitigates the straggler effect by introducing redundant loads. In this research, we investigate a wireless network comprising multiple masters and heterogeneous workers, with each master allocated a distinct task. We divide computing tasks into two types: long-term computing tasks and short-term computing tasks. For long-term computing tasks, we propose the Static Worker Assignment algorithm (SWA), which balances the assignment of workers and minimizes the single iteration time of all tasks. Comparing SWA with two reference benchmarks reveals significant speed improvements of up to 54.03% and 51.14% over the respective benchmarks. For short-term tasks, we propose the Dynamic Worker Assignment algorithm (DWA), which reduces task reloading time without significantly increasing single iteration time. Compared to reloading by SWA, the DWA method may result in a 0.89% increase in single iteration time, while the task reloading delay decreases by 52.70%. © 2024 IEEE.

关键词： Distributed computer systems

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Edgraph: Dynamic GNN Inference with multi-edge Clouds Collaboration 4

Edgraph: Dynamic GNN Inference with Multi-edge Clouds Collab...

引用

4th international conference on Computer Science, Electronic information Engineering and Intelligent Control technology, CEI 2024

作者： Shi, Chenglong Xiao, Wenjing Chen, Miaojiang Wang, Rui Hao, Yixue Hu, Long Guangxi University School of Computer Electronics and Information Nanning China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350378542

In edge computing (EC) scenarios such as traffic flow prediction and social relationship recommendations, the emerging Graph Neural Networks (GNNs) is the promising deep learning model to tackle these applications. However, since GNNs can cause a large amount of cross-server communication during their inference process, there is still a big challenge to provide cost-efficient intelligent services in EC for GNN processing. To address this issue, this paper proposes Edgraph, an efficient EC architecture for GNN computing, with the goal of minimizing the system's energy and time cost for processing user tasks. Specifically, at each time step, the architecture first perceives the user topology and represents their data association as a graph layout. then the graph layout is optimized by calling our proposed hierarchical traversal graph cutting algorithm (HTGC). Finally, based on the optimized graph layout, our proposed deep reinforcement learning (DRL) based graph offloading algorithm (DRLGO) is executed to obtain the optimal offloading strategy for the tasks of users. Based on this strategy, the tasks of users are offloaded to edge servers and processed by the deployed GNN model. Experimental results show the effectiveness of our proposed architecture because it can fully learn dynamic scenario information and minimize the energy and time cost of the GNN-based EC system. © 2024 IEEE.

关键词： Graph neural networks

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Research on 5G Communication Control Strategies for Autonomous Driving 4

Research on 5G Communication Control Strategies for Autonomo...

引用

4th international conference on Electronic information Engineering and Computer Communication, EIECC 2024

作者： Liu, Huanyi Guangxi Polytechnic of Construction School of Information Engineering Guangxi China Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle Guangxi Vocational Normal University Nanning China

ISBN: (纸本)9798331534622

this paper expounds the construction of an autonomous driving wireless communication system, underscoring the pivotal function of 5G technology in providing high bandwidth and low-latency communication, as well as the advantages of edge computing in data processing and response time reduction. It introduces a multi-path data forwarding strategy that intelligently selects data transmission paths through URL parsing. this strategy, combined with an enhanced SF-RED algorithm and end-to-end dual-connection dual-link technology, effectively reduces packet loss. Furthermore, an optimized task allocation strategy for 5G edge access nodes is proposed, aimed at efficient task delegation and maximizing system utility through reduced execution duration at edge access nodes. Empirical results show that the proposed strategies significantly enhance communication quality, response speed, and control accuracy within autonomous driving systems, thereby offering strong support for the development of autonomous driving technology. © 2024 IEEE.

关键词： 5G mobile communication systems

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Enhancing Security and Privacy Implications in 5G Network Slicing 4

Enhancing Security and Privacy Implications in 5G Network Sl...

引用

4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sukumar, Aadith Singh, Aniket Gupta, Apoorv Singh, Mangal Symbiosis Institute of Technology Pune412115 India

ISBN: (纸本)9798350343670

5G network slicing has emerged as a transformative technology, enabling the creation of customized virtual networks tailored to diverse service requirements. this paper delves into the theoretical underpinnings of 5G network slicing, exploring cutting-edge solutions, recent innovations, and future trajectories. the paper highlights the significance of efficient resource management and orchestration. Addressing the challenges posed by the multi-tenant environment, the study proposes advanced encryption techniques, access controls, and segmentation methods while delves into the critical security and privacy dimensions of 5G network slicing. through a meticulous analysis of vulnerabilities and the development of comprehensive strategies, this research strengthens the security and privacy framework within 5G network slices. the paper concludes by emphasizing the importance of a solid theoretical foundation for designing and deploying network slices that meet specific service requirements and performance objectives. © 2024 IEEE.

关键词： 5G Network Computer Network information Security Mobile Communications Network Slicing Security and Privacy

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Research on distributed cooperative silicon based photonic computing chip 4

Research on distributed cooperative silicon based photonic c...

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4th international conference on Electronic information Engineering and Computer Communication, EIECC 2024

作者： Zhu, Kejian Li, Tuo Wang, Changhong Wang, Menglong Zou, Xiaofeng Sun, Pengfei Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd. Shandong Jinan China Peking University School of Electronics Beijing China

ISBN: (纸本)9798331534622

In this paper, an architecture of a photonic neural network chip with distributed multi-scenarios is presented. the distributed computing chip has five computing modules, which are characterized by three-dimensional imaging processing, convolutional operation, matrix operation, low-energy operation, and high-speed operation, respectively. through the cooperative work of multiple modules, self-adaptive high-speed and energy-efficient computation of large-scale computing tasks can be realized. © 2024 IEEE.

关键词： Photonic integrated circuits

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BiLSTM model with Attention mechanism for multi-label news text classification 4

BiLSTM model with Attention mechanism for multi-label news t...

引用

4th international conference on Neural Networks, information and Communication Engineering, NNICE 2024

作者： Duan, Aihua Raga, Rodolfo C. National University College of Computing and Information Technologies Manila0900 Philippines Anhui University of Finance and Economics School of Management Science and Engineering Bengbu233030 China José Rizal University Department of Computer Studies and Engineering Mandaluyong City Philippines

ISBN: (纸本)9798350394375

multi-label text classification is very important in text research. A news will have multiple classification labels, and the number of labels is uncertain, so how to accurately learn labels from text is difficult. In this paper, GloVe is used for vectorization, and BiLSTM is used to learn the context, Use the Attention mechanism to focus on important words in the sentence, and finally focal loss function is used as the loss function. the experimental results show that the accuracy of this method reaches 84,43%% and AUR rate is 95.98%, which are higher than the evaluation scores of other five models. © 2024 IEEE.

关键词： Classification (of information)

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A multi-level and multi-agent collaborative control platform for city-scale virtual power plants based on a cloud-pipe-edge-end fusion architecture 4

A multi-level and multi-agent collaborative control platform...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Baorong, Zhou Jiangnan, Li Wenmeng, Zhao Tao, Wang Biao, Zhao Wenyuan, Zheng Guanglei, Huang Mingyu, Ou Electric Power Research Institute China Southern Grid Guangzhou510530 China Shenzhen Power Supply Bureau Co. Ltd. Shenzhen518000 China Tsinghua University Shenzhen International Graduate School Shenzhen518000 China

ISBN: (纸本)9798331528676

With the continuous expansion and complexity of urban power grids, traditional power control methods are difficult to meet the needs of multi-agent, multi-energy and multi-level in modern power grids. To address this problem, this paper proposes a multi-level and multi-agent collaborative control platform for urban-scale virtual power plants based on a cloud-pipe-edge-end fusion architecture. By introducing technologies such as cloud computing, edge computing and the Internet of things, the platform designs a multi-level control architecture that includes functional modules such as physical information modeling, resource aggregation optimization, automatic power control and instruction decomposition. the innovation of the platform lies in its ability to enable the full process of adjustable resources on the load side of the virtual power plant to participate in the automatic generation control (AGC) and automatic voltage control (AVC) of the power grid, and integrates intelligent monitoring and traceability technology of electric carbon, providing real-time and efficient power control and carbon emission management. through actual case analysis and verification, the platform is significantly superior to traditional systems in terms of supply and demand balance, response speed, energy efficiency optimization and carbon emission management, which improves the overall operation efficiency and stability of the power grid and provides strong technical support for the intelligent management of urban power grids. ©2024 IEEE.

关键词： Cloud platforms

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Weather recognition using DenseNet with multi-head attention mechanism 4

Weather recognition using DenseNet with multi-head attention...

引用

4th international conference on Computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Zhang, Xian Yuan, Shaoxin Chao, Qiluo Shen, Haichao School of Information Engineering Chang’an University Xi’an China The 20th Research Institute of China Electronics Technology Group Corporation Xi’an China

ISBN: (纸本)9781510683242

In the field of weather image recognition, the recognition of weather categories should not only consider local features, but also needs to consider the global multi-feature synthesis to avoid misjudge. To address this issue, a weather recognition method based on image organic fusion of deep neural network DenseNet and multi-head attention mechanism is proposed. Among them, DenseNet is responsible for capturing local features in weather images, while the attention mechanism is responsible for focusing on the global feature information of the image, so that the organic fusion of local and global features can improve the accuracy of weather recognition. the proposed method can recognize six types of weather—cloudy, haze, rainy, snow, sunny and *** experimental results show that the model using DenseNet alone achieves 85.82% accuracy on the test set, while the accuracy of the model with the fusion of the attention mechanism increases to 87.45%. this suggests that fusing deep neural networks with attention mechanisms is an effective way to recognize weather categories. © 2024 SPIE.

关键词： Deep neural networks

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Towards computing Power Networks: A Case of Enhancing Eavesdropping-Mitigation Mechanisms with Programmable Data Planes 4

Towards Computing Power Networks: A Case of Enhancing Eavesd...

引用

4th international conference on Electronic information Engineering and Computer technology, EIECT 2024

作者： Zu, Xuefeng Gu, Kepeng Zhou, Lei Unit 31401 of People's Liberation Army of China Changchun China

ISBN: (纸本)9798331528850

the Programmable Data Plane (PDP) is a paradigm of providing in-network computing for computing Power Networks (CPN). With PDP, multiple computing services (e.g., DNS) can be offloaded on edge-network-devices. these in-network services have advantages of decreasing their response delay and increasing the utilization of computing power for edge-network-devices. However, it is a vacant area for CPN to enhance eavesdropping-mitigation mechanisms using the PDP. In this paper, we propose a novel CPN-powered eavesdropping-mitigation mechanism with the PDP. In particular, the mechanism provides an in-network privacy-level classification for traffic packets. the classification first extracts payloads from packets, then analyzes the payloads using an AI algorithm on the PDP, finally forwards the packets according to the analysis results. Experimental results show that the proposed mechanism can classify and forward packets effectively. © 2024 IEEE.

关键词： computing power

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Crop Intelligence: Optimizing Agriculture through Machine Learning-guided Recommendations 4

Crop Intelligence: Optimizing Agriculture through Machine Le...

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4th international conference on multimedia Processing, Communication and information technology, MPCIT 2024

作者： Ankitha, K. Patil, Shruti Dept. of Artificial Intelligence and Data Science Nitte Karkala India SJEC Dept. of Intelligent Computing and Business System Vamanjur Mangalore India

ISBN: (纸本)9798350375466

Crop Intelligence, powered by Machine Learning (ML) and data analytics, is a transformative paradigm poised to revolutionize modern agriculture. this approach involves harnessing the capabilities of Machine Learning (ML) to provide tailored recommendations and insights to farmers, enabling data-driven decision-making for optimizing crop yield, resource utilization, and sustainability. this paper explores the potential and advancements in crop intelligence, focusing on how ML-guided recommendations can significantly enhance agricultural practices. Key challenges such as data quality, local adaptability, and user acceptance are identified and discussed, providing a holistic view of the landscape. Additionally, the paper highlights future directions, including personalized recommendations, integration of IoT and remote sensing, and collaborative platforms, as crucial steps toward realizing the full potential of crop intelligence. Ultimately, crop intelligence stands as a promising avenue to bolster global food security, sustainable farming, and the livelihoods of farmers through the fusion of agricultural knowledge with cutting-edge technology. © 2024 IEEE.

关键词： Artificial Intelligence Crop prediction KNN Machine Learning

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