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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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A parallel compression framework for fractal images using DCT block classification

A parallel compression framework for fractal images using DC...

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IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Jiahao Pang Jie He Hui Guo Minglang Chen Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

Fractal image compression has higher space domain compression performance compared to the mainstream image compression algorithms using discrete cosine transform, but the search time for coding fractal image compression is too long and less practical. In this paper, we propose a fractal image block classification scheme based on discrete cosine transform, and use NVIDIA CUDA architecture to achieve parallel coding, which shows that this scheme can achieve 107 times faster speedup than the basic fractal scheme, and the coding time is significantly reduced, and the PSNR decreases only about 0.4, so this scheme has greater practicality.

关键词： Image coding Graphics processing units computer architecture Ubiquitous computing Fractals Classification algorithms Discrete cosine transforms

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A standardized Edge computing Infrastructure of LoRaWAN Using IEEE 2668

A Standardized Edge Computing Infrastructure of LoRaWAN Usin...

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IEEE international Conference on Industrial Informatics (INDIN)

作者： Zhifu Zhang Yucheng Liu Gerhard P. Hancke Kim Fung Tsang Department of Computer Science City University of Hong Kong Hong Kong SAR China Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China

LoRaWAN is an overwhelmingly popular wide area networking protocol that is capable of deploying city-wide Internet of Things (IoT) networks. Increase on the deployment of LoRaWAN in the last few years has led to exponential increase on the number of LoRa sensory and actuating end devices and results in more congestion in LoRaWAN network traffic. Moreover, the evolving AI-IoT (AIoT) applications (e.g., AI-powered environment monitoring system) create needs for more computing power with low latency requirements. To mitigate the high latency issue and the shortage of computing power due to continuously demanding quality of service (QoS) requirements of IoT applications, the Edge-Cloud server structure has been explored in a thorough manner in research area in recent years. An IEEE 2668 standardized IoT infrastructure system is presented in this work focusing on analyzing the performance of applying Edge-Cloud network server structure to LoRaWAN networks and provide standardized edge computing LoRaWAN infrastructure. This work uses queuing network to quantitively analyze the performance of an edge computing infrastructure applied to LoRaWAN and uses IEEE 2668 to quantitively present the performance and applicability of the Edge-Cloud LoRaWAN infrastructure.

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Holistic Design of Economical and Secure Data Centers for Developing Countries: A Free Integrative Approach

Holistic Design of Economical and Secure Data Centers for De...

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ACS/IEEE international Conference on computer Systems and Applications

作者： Mohamed Ali Bouri Habib M. Kammoun Mohamed Ben Aouicha Dept. Computer Sciences and Communications Data Engineering and Semantics Sfax Tunisia Dept. Computer Sciences and Communications Research Groups in Intelligent Machines University of Sfax Sfax Tunisia Dept. Computer Sciences and Communications Data Engineering and Semantics University of Sfax Sfax Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

This proposal aims to suggest improvements for the article titled “Low-Cost Data Centers in Developing Countries” with the aim of enhancing its content, relevance, and practicality. Our expertise in data infrastructure, we believe these improve-ments will provide valuable insights and guidance to readers interested in establishing efficient data centers in developing countries.

关键词： Data centers Fault tolerance Power demand high performance computing Fault tolerant systems Organizations Developing countries Resource management Proposals Artificial intelligence

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Optimizing Split Algorithm performance: A Heuristic Method for Enhanced Tensor Product Matrix Computations

Optimizing Split Algorithm Performance: A Heuristic Method f...

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IEEE India Conference (INDICON)

作者： Biswajit Bhowmik Sunil Kumar Sanjay Ramapogu Raju Anupam Prakash Omkar Mense Department of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Lab National Institute of Technology Surathkal Mangalore Karnataka India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Optimizing tensor product matrix computations is critical for enhancing computational efficiency in high-performance applications. Traditional algorithms, like the Split algorithm, often struggle due to the unique properties of each matrix involved. This paper presents a novel heuristic method that optimizes the selection of cutting points and matrix ar-rangement, significantly reducing redundant calculations and minimizing memory usage. The proposed approach adapts to the varying characteristics of tensor products, improving performance across different computational scenarios. Enhancing floating-point operation efficiency and CPU utilization delivers substantial speed and efficiency gains, particularly in large-scale tensor product matrix operations, offering a robust solution for complex computational tasks.

关键词： Wireless sensor networks Tensors high dimensional data Scalability Heuristic algorithms high performance computing Robustness Computational efficiency Distributed computing Optimization

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Blind Asynchronous Goal-Oriented Detection for Massive Connectivity 21

Blind Asynchronous Goal-Oriented Detection for Massive Conne...

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21st international symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Daei, Sajad Razavikia, Saeed Kountouris, Marios Skoglund, Mikael Fodor, Gabor Fischione, Carlo School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Communication Systems Department EURECOM Sophia Antipolis06410 France Ericsson Research Sweden

ISBN: (纸本)9783903176553

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels’ angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks. © 2023 IFIP.

关键词： Clustering algorithms

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Embodiment in Interactive Installations: Results from a Systematic Literature Review 22

Embodiment in Interactive Installations: Results from a Syst...

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21st Brazilian symposium on Human Factors in computing Systems, IHC 2022

作者： Duarte, Emanuel Felipe Mendoza, Yusseli Lizeth Méndez De Queiroz, Maria Jêsca Nobre Baranauskas, M. Cecília C. Institute of Computing University of Campinas Campinas Brazil Institute of Computing & PPGInf University of Campinas Federal University of Paraná Curitiba Campinas Brazil

ISBN: (纸本)9781450395069

Ubiquitous and pervasive technologies, alongside the expanding role of the computer in our lives, present new dimensions to the concept of embodiment within the Human-computer Interaction (HCI) field. Interactive installations, in particular, afford the technological and conceptual exploration of embodiment due to a high degree of creative freedom and more open objectives, making it a promising context to be investigated. In this paper, we present a systematic literature review on approaches to embodiment in the context of interactive installations. We searched four major digital libraries and screened a total of 3245 studies following a protocol described in the paper. The final selection consists of 67 entries. Our analysis shows a multitude of theoretical and practical approaches to embodiment in interactive installations, but also significant compatibility and combined use. We present detailed summaries of and discussion on how embodiment is conceptualized, put into practice, and what technologies are involved, revealing new perspectives and possibilities for investigating embodiment in HCI. © 2022 ACM.

关键词： Human computer interaction

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Minimizing Age of Information for Mobile Edge computing Systems: A Nested Index Approach

Minimizing Age of Information for Mobile Edge Computing Syst...

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international symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Shuo Chen Ning Yang Meng Zhang Jun Wang Institute of Automation Chinese Academy of Sciences Beijing China ZJU-UIUC Institute Zhejiang University Zhejiang China Department of Computer Science University College London UK

Exploiting the computational heterogeneity of mobile devices and edge nodes, mobile edge computation (MEC) provides an efficient approach to achieving real-time applications that are sensitive to information freshness, by offloading tasks from mobile devices to edge nodes. We use the metric Age-of-Information (AoI) to evaluate information freshness. An efficient solution to minimize the AoI for the MEC system with multiple users is non-trivial to obtain due to the random computing time. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in a MEC system. We first reformulate the problem as a Restless Multi-Arm-Bandit (RMAB) problem and establish a hierarchical Markov Decision Process (MDP) to characterize the updating of AoI for the MEC system. Based on the hierarchical MDP, we propose a nested index framework and design a nested index policy with provably asymptotic optimality. Finally, the closed form of the nested index is obtained, which enables the performance tradeoffs between computation complexity and accuracy. Our algorithm leads to an optimality gap reduction of up to 40%, compared to benchmarks. Our algorithm asymptotically approximates the lower bound as the system scalar gets large enough.

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Reinforcement Learning-based Virtual Sensors Provision in Internet of Vehicles (IoV)

Reinforcement Learning-based Virtual Sensors Provision in In...

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IEEE international symposium on Network computing and Applications

作者： Slim Abbes Slim Rekhis Communication Networks and Security Research Lab University of Carthage Higher School of Communication of Tunis (SUP’COM) LR11TIC04 Tunisia

ISBN: (纸本)9798350397314

The Internet of Vehicles (IoV) has been recognized as a powerful application of the Internet of Things (IoT) in the Intelligent Transportation System (ITS), providing intelligence for interconnection between devices, interaction with the environment, and thus, greater efficiency in sensor data exploitation. Therefore, leveraging the huge capability of sensors embedded in vehicles to offer a Sensing As A Service (Se-aaS) represents a great solution to exploit under-used sensor resources and continue providing sensors despite their positions and mobility patterns. Nevertheless, the high network mobility and the fast topology changes in IoV impact the vehicle availability and complicate the service provision. To this aim, we propose a vehicle sensor virtualization in a Cloud IoV architecture that encompasses functional blocks of mobile sensor suppliers, Sensor Cloud Service Provider (SCSP), and service consumers. Moreover, we propose a reinforcement learning-based solution for vehicle sensor selection to predict and dynamically select the physical sensors composing the vehicle virtual sensor. The conducted simulations show the effectiveness of the proposed solution.

关键词： Space vehicles Cloud computing Soft sensors Simulation computer architecture Reinforcement learning Topology

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SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud 21

SAED: Edge-Based Intelligence for Privacy-Preserving Enterpr...

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21st IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Zobaed, Sakib M. Salehi, Mohsen Amini Buyya, Rajkumar Univ Louisiana Lafayette Sch Comp & Informat High Performance Cloud Comp HPCC Lab Lafayette LA 70504 USA Univ Melbourne Cloud Comp & Distributed Syst CLOUDS Lab Sch Comp & Informat Syst Parkville Vic 3010 Australia

ISBN: (纸本)9781728195865

Cloud-based enterprise search services (e.g., AWS Kendra) have been entrancing big data owners by offering convenient and real-time search solutions to them. However, the problem is that individuals and organizations possessing confidential big data are hesitant to embrace such services due to valid data privacy concerns. In addition, to offer an intelligent search, these services access the user's search history that further jeopardizes his/her privacy. To overcome the privacy problem, the main idea of this research is to separate the intelligence aspect of the search from its pattern matching aspect. According to this idea, the search intelligence is provided by an on-premises edge tier and the shared cloud tier only serves as an exhaustive pattern matching search utility. We propose Smartness at Edge (SAED mechanism that offers intelligence in the form of semantic and personalized search at the edge tier while maintaining privacy of the search on the cloud tier. At the edge tier, SAED uses a knowledge-based lexical database to expand the query and cover its semantics. SAED personalizes the search via an RNN model that can learn the user's interest. A word embedding model is used to retrieve documents based on their semantic relevance to the search query. SAED is generic and can be plugged into existing enterprise search systems and enable them to offer intelligent and privacy-preserving search without enforcing any change on them. Evaluation results on two enterprise search systems under real settings and verified by human users demonstrate that SAED can improve the relevancy of the retrieved results by on average approximate to 24% for plain-text and approximate to 75% for encrypted generic datasets.

关键词： Enterprise-search Semantic Edge Context-aware

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Enabling Efficient Large Recommendation Model Training with Near CXL Memory Processing

Enabling Efficient Large Recommendation Model Training with ...

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Annual international symposium on computer architecture, ISCA

作者： Haifeng Liu Long Zheng Yu Huang Jingyi Zhou Chaoqiang Liu Runze Wang Xiaofei Liaot Hai Jinf Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab Huazhong University of Science and Technology China School of Computer Science and Engineering University of New South Wales Australia Zhejiang Lab Hangzhou China

ISBN: (数字)9798350326581

ISBN: (纸本)9798350326598

Personalized recommendation systems have become one of the most important Internet services nowadays. A critical challenge of training and deploying the recommendation models is their high memory capacity and bandwidth demands, with the embedding layers occupying hundreds of GBs to TBs of storage. The advent of memory disaggregation technology and Compute Express Link (CXL) provides a promising solution for memory capacity scaling. However, relocating memory-intensive embedding layers to CXL memory incurs noticeable performance degradation due to its limited transmission bandwidth, which is significantly lower than the host memory bandwidth. To address this, we introduce ReCXL, a CXL memory disaggregation system that utilizes near-memory processing for scalable, efficient recommendation model training. ReCXL features a unified, hardwareefficient NMP architecture that processes the entire embedding training within CXL memory, minimizing data transfers over the bandwidth-limited CXL and enhancing internal bandwidth. To further improve the performance, ReCXL incorporates softwarehardware co-optimizations, including sophisticated dependencyfree prefetching and fine-grained update scheduling, to maximize hardware utilization. Evaluation results show that ReCXL outperforms the CPU-GPU baseline and the naïve CXL memory by $7.1 \times \sim 10.6 \times(9.4 \times$ on average) and $12.7 \times \sim 31.3 \times(22.6 \times$ on average), respectively.

关键词： Training Degradation Scalability Prefetching Memory management Web and internet services Bandwidth

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