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ieee international conference on High Performance computing and Communications (HPCC)

作者： Zhiquan Lai Yujie Liu Wei Wang Yanqi Hao Dongsheng Li National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

As deep learning grows rapidly, model training heavily relies on parallel methods and there exist numerous cluster configurations. However, current preferences for parallel training focus on data centers, overlooking the financial constraints faced by most researchers. To attain the best performance within the cost limitation, we introduce a throughput-cost metric to accurately characterize clusters' cost-effectiveness. Based on this metric, we design a cost-effective cluster featuring the 3090 with NVLink. The experiment results demonstrate that our cluster achieves remarkable cost-effectiveness in various distributed model training schemes.

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Safe Dynamic Reconfiguration of Concurrent Component-based Applications 19

Safe Dynamic Reconfiguration of Concurrent Component-based A...

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19th ieee international conference on Software Architecture (ICSA)

作者： Farhat, Salman Bliudze, Simon Duchien, Laurence Univ Lille INRIA CNRS Cent LilleUMR CRIStAL 9189 F-59000 Lille France

ISBN: (纸本)9781665494939

Cloud computing and cyber-physical systems involve software capable of adapting at run time to remain compliant with user demands and environmental constraints. This calls for extending the life cycle of software systems with a reconfiguration step to go beyond analysis, design, development and deployment. Existing approaches compute a new valid configuration at design time, at run time, or both, inducing computational or validation overheads for each reconfiguration step. We propose an approach that relies on variability models to acquire a representation of the set of valid configurations of a system. We use feature models to automatically generate a JavaBIP run-time variability model. The generated model monitors and controls the application behaviour by intercepting reconfiguration requests and executing them in such a manner as to ensure that all reachable configurations are valid without the need of pre-computing the possible configurations neither at design time nor at run-time while only inducing a minimal run-time computational overhead.

关键词： distributed systems Concurrent Component-based systems Variability Models Self-Configuration Dynamic Reconfiguration

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RAW Optimization of ieee 802.11ah Networks 4

RAW Optimization of IEEE 802.11ah Networks

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4th international conference on computing and Communication systems, I3CS 2023

作者： Mondal, Md. Arifuzzaman Khongjoh, Saradaphisha Hussain, Md. Iftekhar North-Eastern Hill University Department of Information Technology Shillong India

ISBN: (纸本)9798350323771

ieee 802.11ah (also known as WiFi Halow) is a new WiFi standard for sub-l GHz communications intended to address key challenges of the Internet of Things. ieee 802 working group introduces ieee 802.11ah to overcome the disadvantages of various previously introduced protocols (more data rate and very less transmission range, and more transmission range and very less data rate) by providing much data rate and transmission range of up to 1 km and has the capacity of 8192 devices connecting simultaneously with only one access point. The ieee 802.11ah standard was released to support Machine-to-Machine (M2M) Communication between a large number of sensor devices that are rapidly increasing with the Internet of Things (IoT). ieee 802.11ah protocol introduces the RAW Mechanisms in the MAC layer to avoid channel contention, especially when there is a large number of stations trying to access the channel simultaneously, thereby increasing scalability in dense deployments. In this paper, we analyze the network performance in terms of throughput through the proper allocation of nodes. This article investigates the impact of the number of stations and the number of groups on network performance, specifically throughput, in wireless networks. The article examines how varying the number of stations and groups affects the network's performance and identifies the optimal values for these parameters that can maximize the network's throughput. © 2023 ieee.

关键词： Network performance

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Quantitative Assessment of Limb Stability from Handwriting Images 23

Quantitative Assessment of Limb Stability from Handwriting I...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： He, ZhiFei Zeng, Luchuan Xie, Ruijiang Li, Keyi Su, Hai Lei, Jie Lan, Li Xia, Linglin Lin, Deyu NanChang University School of Software Jiang'Xi China Yuan Ze University Taoyuan City Taiwan Rutgers University New Jersey United States Zhejiang University of Water Resources and Electric Power Zhejiang China

ISBN: (纸本)9781665494571

Limb stability refers to the ability of limbs to maintain posture during physical movements. Observing limb movements and quantizing limb stability is crucial for tremor detection. In this paper, we propose a quantitative metric to quantify tremor of limbs. We applied the Otsu threshold segmentation method to quantify features of handwriting images that were digitized and identified. Then we employed the eight-connected components to quantify the deviation degree, obtained two parameters that can quantitatively evaluate the patient's left and right hand's stability. Further we studied the correlation between the patient's living habits and limb stability to predict latent tremor. The tremor pre-diagnosis based on the C4.5 algorithm reached a classification accuracy of 97.10%. Experimental results demonstrated that the parameters obtained by our method reach higher accuracy and reliability when quantitatively evaluating the stability of limbs. This method is beneficial for tremor detection of the patient's handwriting. Further research includes efforts on creating metrics that can be assessed in absolute scales, using a broader set of samples, and integrate the metrics into clinical practice for tremor diagnosis. © 2021 ieee.

关键词： Measurement Image segmentation Handwriting recognition Correlation Smart cities Prediction algorithms sensor systems

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Optimal sensor Network Solution Based Smart Drip Irrigation System

Optimal Sensor Network Solution Based Smart Drip Irrigation ...

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2023 international conference on Smart systems for Applications in Electrical Sciences, ICSSES 2023

作者： Mallikarjun, B.C. Reddy, B.A.Pramod Shivakumar, Parinitha Jain Tv, Varun Kumar Rakshith, M.D. Manoj, K.R. Siddaganga Institute of Technology Dept. of Electronics and Telecommunication Tumkur India

ISBN: (纸本)9798350347296

Water is a precious and limited resource in agriculture, and its efficient management is becoming increasingly important. With factors such as population growth, urbanization, and climate change affecting water availability, it is crucial to manage water resources effectively in agriculture. A monitoring and control system with an optimal networking solution for irrigation must be put in place in order to maximize water usage. A smart drip irrigation system with an optimal sensor network solution, which uses sensors to keep track of soil moisture, temperature, humidity, and other environmental variables, is proposed and implemented in order to maximize water use and crop growth. The system realized combines the Internet of Things (IoT), cloud computing, and optimization tools that makes the sensor network system scalable and adaptable to different agricultural settings, promoting sustainable agriculture. The results justifies the effectiveness of the solution © 2023 ieee.

关键词： sensor networks

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Deploying Hierarchical Mesh Networks for Supporting distributed computing in Industrial Internet of Things

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ieee systems JOURNAL 2022年第3期16卷 4433-4444页

作者： Ma, Chaofan Zheng, Meng Liang, Wei Kasparick, Martin Lin, Yufeng Zhongyuan Univ Technol Software Coll Zhengzhou 450007 Peoples R China Chinese Acad Sci State Key Lab Robot Shenyang 110016 Peoples R China Tech Univ Berlin D-10587 Berlin Germany Fraunhofer Heinrich Hertz Inst D-10587 Berlin Germany Cent Queensland Univ Ctr Intelligent Syst Townsville Qld 4810 Australia

The centralized computing model in industrial Internet of Things (IIoT) leads to large delay and unbalanced traffic, which strictly restricts the adoption of IIoT in industrial applications demanding high network performance. To cope with the problem, researchers resort to the in-network computation model in which the computation capability is distributed among nodes in IIoT. Existing works on the in-network computation assume that the network connectivity built in advance meets the performance requirement of the in-network computation model. Nevertheless, no node placement methods have been proposed to build network connectivity supporting in-network computation. For this reason, we propose an in-network-computation-oriented node placement (INP) algorithm. The INP algorithm first decomposes the whole problem into several delay constrained relay node placement problems, and then, solves them sequentially. Moreover, we prove that the INP algorithm ensures explicit time complexity and approximation ratio. Finally, we verify the efficiency of this work through extensive simulations and preliminary experiments.

关键词： Computational modeling Industrial Internet of Things Delays Task analysis Data models Edge computing Computer architecture In-network computation industrial Internet of Things (IIOT) relay node placement wireless sensor networks

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Optimal Error Exponent of Type-Based distributed Hypothesis Testing over AWGN Channels 59

Optimal Error Exponent of Type-Based Distributed Hypothesis ...

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59th Annual Allerton conference on Communication, Control, and computing, Allerton 2023

作者： Tong, Xinyi Huang, Shao-Lun Tsinghua University Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China

ISBN: (纸本)9798350328141

This paper investigates the distributed hypothesis testing (DHT) problem over AWGN channels, where the distributed nodes are restricted to transmit functions of the empirical distributions of the observed data sequences due to practical computational constraints. The goal of this problem is to design the transmitted signals of the distributed nodes, so that the decision center can make a decision based on the received signals with the maximal decision error exponent. In order to solve this problem, we first propose a mixture coding strategy for each node, which combines the decode-and-forward and amplify-and-forward strategies, and then the achievable error exponent is computed. Moreover, we present a genie-aided approach to prove the converse result, thereby demonstrating the optimality of the achievable error exponent. In summary, our results provide coding guidance for distributed systems, and could be applied to more complicated problems. © 2023 ieee.

关键词： White noise

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Perceiving the Narrative Style for Fake News Detection Using Deep Learning 23

Perceiving the Narrative Style for Fake News Detection Using...

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作者： Sun, Mengtao Hameed, Ibrahim A. Wang, Hao Pasquine, Mark Norwegian University of Science and Technology Dept. Ict and Natural Sciences Ålesund Norway Norwegian University of Science and Technology Dept. Computer Science Gjovik Norway Norwegian University of Science and Technology Dept. International Business Ålesund Norway

ISBN: (纸本)9781665494571

Existing deep-learning-based features have shown strong enough results (more than 90% accuracy) if a large amount of annotated data is available. However, in reality, data annotation is labor-intensive and expensive. This work proposes a novel approach for fake news detection by perceiving narrative style with deep learning to alleviate the problem. Deep-learning-based features are represented as embedding vectors. Traditional embedding vectors ingest word context information, but the training requires considerable annotated datasets. Many linguistics studies have shown that written styles, such as the usage of punctuation, repetition of words, and grammatical order, are significant for distinguishing fake news. Our model takes advantage of the word-to-word dependency relationship, describing the styles of the news utterances. We denote the proposed model as Syntax Graphical Thread (SGT) network. We utilize a trainable randomly initialized embedding and Gated Recurrent Unit (GRU) layer to capture the context vector, while a Graph Attention (GAT) layer is used to capture the narrative features. The experimental result manifests that our method can significantly mitigate the reliance on data scale and present better classification results when the dataset is limited. © 2021 ieee.

关键词： Context Deep learning Training Smart cities Text categorization Syntactics Logic gates

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Enabling Intelligence Inclusiveness in Edge to Cloud Continuum: Challenges and Opportunities 9

Enabling Intelligence Inclusiveness in Edge to Cloud Continu...

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9th ieee international conference on Network Softwarization (ieee NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Palomares, Javier Coronado, Estefania Cervello-Pastor, Cristina Siddiqui, Shuaib I2CAT Fdn Barcelona Spain Univ Castilla La Mancha High Performance Networks & Architectures Albacete Spain Univ Politecn Cataluna Dept Network Engn Barcelona Spain

ISBN: (纸本)9798350399806

Edge to Cloud Continuum is a concept that integrates cloud computing and cellular networks that has been gaining popularity due to its potential to provide a seamless user experience and address the challenges of managing complex multi-domain networks involving massive IoT devices. Enabling intelligence in the Edge to Cloud Continuum can further enhance its capabilities, offering benefits such as reduced latency, improved scalability, enhanced resource utilization, and increased context awareness. This paper provides insights into the opportunities and challenges of enabling intelligence in Edge to Cloud Continuum, highlighting the potential of this technology. This study presents a comprehensive review of the existing literature on enabling intelligence in Edge to Cloud Continuum, to reach the research questions that will construct the PhD. Various tools and technologies that can be used to integrate intelligence into the Edge to Cloud Continuum system were explored and analyzed. In addition, this study provides a detailed work plan for the upcoming months of the project.

关键词： Edge to Cloud Continuum Multi-domain Enabling Intelligence distributed systems 6G

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sensorDAO: A New Framework of sensor Governance for Internet of Vehicles 3

SensorDAO: A New Framework of Sensor Governance for Internet...

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3rd ieee international conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Liu, Yuhang Jiang, Tai Li, Juanjuan Ding, Wenwen Wang, Fei-Yue Chinese Academy of Science Institute of Automation Beijing China Macau University of Science and Technology Faculty of Innovation Engineering China Macau University of Science and Technology Macao Institute of Systems Engineering China

ISBN: (纸本)9798350318470

High-performance sensors play a crucial role in IoV (Internet of Vehicles) to achieve advanced autonomous driving. Typically, IoV involves extensive data sharing and centralizes data storage, posing significant security challenges for sensors. Although blockchain offers a decentralized platform for data exchange, it can't guarantee the reliability and trustworthiness of each sensor in IoV. Besides, blockchain primarily focuses on technical aspects and falls short of establishing a comprehensive organizational and operational framework for sensors. To address these challenges, we introduce the concept of DAO (Decentralized Autonomous Organization and Operation) into sensors and propose a new framework for reliable sensor operations in IoV, namely sensorDAO. It utilizes the collaboration of on-chain and off-chain layers to support various scenarios at the application layer. We also provide key technical elements to construct sensorDAO, including distributed storage, intelligent algorithms, governance, and incentive mechanisms. © 2023 ieee.

关键词： Blockchain

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