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Recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles 18th

Recommendation-Aware Collaborative Edge Caching Strategy in...

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18th international Conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ou, Pingjie Chen, Ningjiang Huang, Zizhan School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9783031714696

Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional caching methods based on learning and popularity tend to encounter data skew when handling vehicle requests, leading to untimely cache data updates. To address these issues, this paper proposes an efficient edge cooperative caching strategy with recommendation awareness. Firstly, a time series prediction model based on a multi-dense multilayer perceptron is designed. this model segments historical request information into sub-sequence level slivers to enhance positional correlations between information, predicting user requests through historical time series and covariates. Secondly, to optimize cache utilization, comprehensively considers user mobility and contribution to enable recommendation awareness. Additionally, an optimized lifecycle is introduced to design a cache replacement strategy. Simulation experiments demonstrate that, compared to caching schemes based on content popularity and deep reinforcement learning, the proposed method achieves lower caching costs and higher cache hit rates. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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18th symposium and Summer School on Service-Oriented computing, SummerSOC 2024

18th Symposium and Summer School on Service-Oriented Computi...

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18th symposium and Summer School on Service-Oriented computing, SummerSOC 2024

ISBN: (纸本)9783031725777

the proceedings contain 9 papers. the special focus in this conference is on Service-Oriented computing. the topics include: Integrating Artifact Translation Into Model Transformation Processes;evaluating Cloud-Native Deployment Options with a Focus on Reliability Aspects;towards a Taxonomy of Infrastructure as Code Misconfigurations: An Ansible Study;Minimial-Risk Training Samples for QNN Training from Measurements;exploring the Cost Landscape of Variational Quantum Algorithms;A Service-Based Pipeline for Complex Linguistic Tasks Adopting LLMs and Knowledge Graphs;design and Implementation of a high performance Domain Name Service on Commodity Hardware.

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Proceedings - 18th IEEE international symposium on high performance computer architecture, HPCA - 18 2012

Proceedings - 18th IEEE International Symposium on High Perf...

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18th IEEE international symposium on high performance computer architecture, HPCA - 18 2012

ISBN: (纸本)9781467308243

the proceedings contain 38 papers. the topics discussed include: MACAU: a Markov model for reliability evaluations of caches under single-bit and multi-bit upsets;booster: reactive core acceleration for mitigating the effects of process variation and application imbalance in low-voltage chips;MORSE: multi-objective reconfigurable self-optimizing memory scheduler;TAP: a TLP-aware cache management policy for a CPU-GPU heterogeneous architecture;SCD: a scalable coherence directory with flexible sharer set encoding;quasi-nonvolatile SSD: trading flash memory nonvolatility to improve storage system performance for enterprise applications;decoupled dynamic cache segmentation;AgileRegulator: a hybrid voltage regulator scheme redeeming dark silicon for power efficiency in a multicore architecture;dynamically heterogeneous cores through 3D resource pooling;and Pacman: tolerating asymmetric data races with unintrusive hardware.

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Personalized mmWave Signal Synthesis for Human Sensing 18th

Personalized mmWave Signal Synthesis for Human Sensing

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18th international Conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Zhang, Jiaqi Liu, Hankai Xie, Xin Tong, Xinyu Liu, Xiulong Li, Keqiu College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (纸本)9783031714665

the excellent sensing performance and privacy-preserving nature of millimeter-wave (mmWave) radar has led to its increasing prominence in the field of human sensing, enabling it to support a wide range of human sensing applications. However, the scarcity of mmWave datasets and the high cost of data collection limit the capability of deep learning. In this paper, we propose PmSyn, a system that synthesizes mmWave signals based on human body model provided by public human motion database and trains neural networks with signatures extracted from synthesized signals to achieve activity recognition and skeleton tracking. We analyze in depth the reflection of the human body model and the propagation principle of the signal. We propose to inject real user’s shape information into the public human body model to endow the synthesized signals with personalization. Experiments show that for activity recognition, PmSyn achieves 93.1%/98.6% accuracy under five activities when trained with synthesized data only/adding a small amount of real data. For skeleton tracking, PmSyn achieves an overall skeleton tracking error of 10.5 cm/6.2 cm when trained with synthesized data only/adding a small amount of real data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Musculoskeletal system

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Proc. 18th ACM international symposium on high performance Distributed computing, HPDC 09, Co-located with the 2009 international symposium on high performance Distributed computing Conf., HPDC'09

Proc. 18th ACM International Symposium on High Performance D...

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18th ACM international symposium on high performance Distributed computing, HPDC 09, Co-located with the 2009 international symposium on high performance Distributed computing Conference, HPDC'09

ISBN: (纸本)9781605585871

the proceedings contain 26 papers. the topics discussed include: harnessing parallelism in multicore clusters with the all-pairs and wavefront abstractions;pluggable parallelisation;DataStager: scalable data staging services for petascale applications;interconnect agnostic checkpoint/restart in Open MPI;maintaining reference graphs of globally accessible objects in fully decentralized distributed systems;adaptive run-time prediction in heterogeneous environments;performance prediction based on hierarchy parallel features captured in multi-processing;CLOUDLET: towards MapReduce implementation on virtual machines;investigating transactional memory performance on ccNUMA machines;an adaptive online system for efficient processing of hierarchical data;high performance wide-area overlay using deadlock-free routing;TakTuk, adaptive deployment of remote executions;live migration of virtual machine based on full system trace and replay;and trace-based evaluation of job runtime and queue wait time predictions in grids.

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SUMMNet: Using Transformer as a Summary of ConvNet for Image Classification 27th

SUMMNet: Using Transformer as a Summary of ConvNet for I...

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27th international Conference on Pattern Recognition, ICPR 2024

作者： Li, Yaqi Guo, Qingbei Li, Zhongtao Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9783031781858

Convolutional neural networks (CNNs) and Vision Transformers (ViTs) are essential in computer vision. While convolutional neural networks remain robust and are widely used in traditional computer vision tasks, ViTs are rapidly emerging as a new trend. Convolutional neural networks capture local features of images by local dependency and are computationally efficient, especially when dealing with large-scale image data. ViTs globally capture the relationships between input image sequences by self-attention. Still, the large amount of computational resources are required for the similarity computation between tokens and the computation complexity quadratically increases with the number of tokens. To address these issues, we propose a hybrid neural network architecture combining convolution and transformer, called SUMMNet, which consists of four stages, each containing two important blocks, i.e., Local Large Convolution Block (LLCB) and Global Self-Attention Block (GSAB). LLCB introduces the Large Kernel Convolution Attention (LKCA) to capture local detail features more efficiently. GSAB employs a new Lightweight Cross-Head Self-Attention (LCHSA) to enhance the interactions between heads for global abstract information, while decreasing the computation complexity with the dimensionality reduction of Key and Value in self-attention. the proposed SUMMNet has the advantages of both CNNs and ViTs in terms of efficiency and effectiveness. We evaluate our SUMMNet through extensive experiments and it shows promising performance in image classification tasks. Our SUMMNet achieves 84.1% top-1 accuracy with 11.4G FLOPs on the ImageNet-1K image classification task, surpassing Swin-Transformer by 0.6% with 36% less parameters and 30% fewer FLOPs. Our source code is available at https://***/YaqiLi01/***. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A high-Precision Generality Method for Chinese Nested Named Entity Recognition 18th

A High-Precision Generality Method for Chinese Nested Named...

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18th international Conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ji, Xiayan Chen, Lina Gao, Hong Shen, Fangyao Guo, Hongjie College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (纸本)9783031714696

Chinese Named Entity Recognition (CNNER) faces numerous challenges, including the diversity of the Chinese language, the complex representation of mixed Chinese and English characters and symbols in texts, the complexity of the Chinese language itself with long sentences containing multiple entities, and the uneven distribution of named entity classes in actual Chinese scenarios. To address these challenges, we propose a method called CPMFA (Character Pair-based method with Multi-feature representation and Attention mechanism). the CPMFA method predicts predetermined relations between character pairs, facilitating the identification of nested named entities based on these relations. Firstly, the method leverages the pre-trained language model LERT (Linguistically-motivated Bidirectional Encoder Representation from Transformer) and BiLSTM (Bidirectional Long Short-Term Memory) to generate comprehensive and accurate character embeddings. Secondly, it incorporates multi-feature representation to capture complex semantic information and introduces the Pyramid Squeeze Attention (PSA) module to emphasize key features. Finally, the PolyLoss function is integrated into the model training process to tackle the challenge of an imbalanced distribution of entity classes. We employed the DiaKG, Yidu-S4K and Weibo datasets to validate and evaluate the efficacy and adaptability of our method. the F1 obtained by the CPMFA on these three datasets is 83.79%, 72.03%, and 70.39%, in that order. the experimental results illustrate the outstanding performance of the proposed CPMFA method in both general knowledge and Chinese medical domains. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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A Satellite-Ground Link Handover Strategy in LEO Networks Using Advantage Actor-Critic Algorithm 20th

A Satellite-Ground Link Handover Strategy in LEO Networks U...

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20th IFIP WG 10.3 international Conference on Network and Parallel computing, NPC 2024

作者： Chen, Chen Tong, Chenqiang Cong, Li Wang, Zhiyi Jin, Fan Zhou, Xiaobo Pei, Qingqi School of Telecommunication Engineering Xidian University Xi’an710071 China Xidian Guangzhou Institute of Technology Xidian University Guangzhou China Xidian Hangzhou Institute of Technology Xidian University Hangzhou China The State Grid Jilin Province Electric Power Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China College of Intelligence and Computing Tianjin University Tianjin300350 China

ISBN: (纸本)9789819628636

Low Earth Orbit (LEO) satellite network is a crucial component of the Space-Air-Ground Integrated Network (SAGIN) architecture and Non-Terrestrial Networks (NTN). It can provide global, real-time, and seamless broadband data services to many users, playing a critical role in emergencies such as disaster relief. this paper proposes an LEO satellite networks satellite-ground link handover strategy based on Advantage Actor-Critic (A2C) to tackle challenges associated with high handover frequency among ground users, data transmission blocking, and access selection post-handover due to the high-speed cyclic motion of satellites. the strategy considers three handover factors: signal reception strength, satellite potential service time, and service quality. At first, the handover problem is modeled as a combinatorial optimization problem, subsequently transformed into a Markovian Action-Value Function (MAVF) based Multi-Agent Markov Decision Process (MAMDP), and solved in the framework of the A2C algorithm proposed in this paper. the results of simulations illustrate the superior performance of the proposed handover strategy compared to three alternative strategies, namely, Random Handover (RH), Maximum number of Free Channels (MFC), and Maximum Service Time (MST), in terms of both convergence and practicality. the average number of handover occurrences can be reduced by up to 58%, consequently lowering the probability of blocking data transmission. Moreover, signal quality exhibits more excellent stability and can be enhanced by up to 39%, thereby ensuring user communication quality. © IFIP international Federation for Information Processing 2025.

关键词： Markov processes

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Stock Market Growth and Decline Using ARIMA Model and Sentiment Analysis

Stock Market Growth and Decline Using ARIMA Model and Sentim...

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international symposium on Advanced computing and Communication (ISACC)

作者： Sandipan Biswas Bhaskar Roy Sujit Kumar Singh Nitin Nitin Saroj Kumar Sahoo Sanjaya Kumar Sarangi Dept. of CSE-AI Brainware University Kolkata India Dept. of CSE(AIML) Asansol Engineering College Asansol India Faculty of Information Technology and Computer Science Parul University Vadodara India Department of CSE Akhilesh Das Gupta Institute of Professional Studies Delhi India Department of CSE Centurion University of Technology and Management Bhubaneswar India Dept. of Computer Science Utkal University Bhubaneswar India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Researchers have been developing and entering the technological era since the 18th century. Before everyone else, social networks brought people from distant nations together. Social network applications such as Reddit, Twitter, Facebook, Instagram, LinkedIn, and others are examples of associate degree programs. Each application has a unique purpose. Investors who want to buy stocks take use of this social networking information. they stay up with a few companies thanks to this information. In order to assess people's sentiments regarding a certain stock, we will be using stock-related news data in this instance. Using TEXTBLOB sentiment analysis, this analysis will provide feedback on the corporate so that we may be prepared to see a rise or fall in relation to investors or firm performance.

关键词： Sentiment analysis Analytical models Social networking (online) Time series analysis Predictive models Data models Web sites Multimedia communication Stock markets Intelligent systems

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Proceedings of the 18th ACM international symposium on high performance Distributed computing, HPDC 09, Co-Located with the 2009 international: Foreword

Proc. 18th ACM International Symposium on High Performance D...

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Proc. 18th ACM international symposium on high performance Distributed computing, HPDC 09, Co-located with the 2009 international symposium on high performance Distributed computing Conf., HPDC'09 2009年 iii页

作者： Casanova, Henri Kranzlmüller, Dieter Gerndt, Michael Schiffers, Michael

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