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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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Convergence Perceptual Model for computing Time Series data on Fog Environment 1

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4th international conference on Recent Trends in Advanced computing - Computer Vision and Machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

作者： Kesavan, Rupa Poorani, S. Iyswarya, R. Muthunagai, S.U. Anitha, R. Vijayaraja, L. Sri Venkateswara College of Engineering Pennalur Sriperumbudur India Sri Sairam Institute of Technology West Tambaram Chennai India

ISBN: (数字)9789811971693

ISBN: (纸本)9789811971686

The evolution of fog/edge paradigm is at the rising edge that complements data analysis and data computing algorithms in a big data platform, The role of fog nodes in a fog computing set up shall best suit time sensitive applications especially on virtual clusters supporting edge devices in sensing, processing, controlling and action planning, there by replacing the non-virtualized complicated computing mechanisms resolving the existing complexity in cloud computing storage and retrieval algorithms. There is an emerging need for addressing collaborative processes in various sectors not limiting to computing digital data on big data platform but also the futuristic industrial revolution to replace the existing techniques and technologies in industrial automation. The proposed work reveals a novel framework that supports the cutting edge of fog node fitting as an intermediate layer between the edge devices and cloud storage. Computational by means of fog-node attracts the need by replacing existing cloudlets time complexity in resource management. Hence fog as things operates as a controller in cyber physical systems like controlling transmission lines of high volt system connections, monitoring smart metering that has a wide range of applications through the integration of the industrial automation process. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog

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An Object-Based Extremely Weak Supervised Learning Paradigm: Focusing on the Introduction of Algorithm

An Object-Based Extremely Weak Supervised Learning Paradigm:...

引用

2023 international conference on Machine Vision, Image processing and Imaging Technology, MVIPIT 2023

作者： Zhou, Min Luo, Xiaoyan Zhao, Bo Tang, Panpan Jiaxing Statistical Investigation Center Jiaxing Bureau of Statistics Jiaxing China Nanhu Laboratory Research Center of Big Data Technology Jiaxing China

ISBN: (纸本)9798350306545

Although deep learning-based algorithms have achieved satisfactory classification performance, they require a large amount of annotated training data. To overcome this issue, we proposed an extremely-weak supervised learning algorithm for remote sensing (RS) classification tasks, which only requires several carefully chosen ground-feature sample labels as input. To be specific, our method consists of three subnetworks working in tandem and under the object-based framework. The first one produces pseudo labels by using two unsupervised autoencoders with shared weights, the second one attempts to optimize the pseudo labels through noisy learning and post-processing, and the third one involves running inference with a powerful residual network for labeling the unseen data. To the extent of our information, we are the first to introduce in the RS classification task the autoencoder and noisy learning modules, which minimized the data acquisition and human labeling efforts. Due to the space limitations, this article focuses only on the algorithms and theory of computation. Readers are referred to the companion piece for detailed case studies. © 2023 IEEE.

关键词： Remote sensing

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Prefetching-based Adaptive Video Streaming Strategy on Edge computing 29

Prefetching-based Adaptive Video Streaming Strategy on Edge ...

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Xuanyu Zhou, Jipeng Jinan University Department of Computer Science Guangzhou China Jinan University School of Data Science of Guangzhou Huashang College Department of Computer Science Guangzhou China

ISBN: (纸本)9798350330717

Dynamic Adaptive Streaming over HTTP (DASH) is a widely adopted video streaming protocol. Adaptive Bitrate Streaming (ABR) algorithm is utilized to dynamically switch between different bitrates. However, traditional ABR algorithms have gradually failed to meet users' demands for high-quality video transmission, especially in complex network environments. Thus, the current research focus has shifted towards enhancing the accuracy of algorithm. The advent of edge computing has brought new possibilities for DASH transmission. Edge computing-based algorithms can make decisions from a more macroscopic perspective, which can enhance algorithm efficiency. In this paper, we propose an edge computing-based strategy that enables the edge server to obtain the actual file size of the next segment at every bitrate. As a result, edge servers can obtain more informations. We further propose an edge-based system model that assists the ABR algorithm in achieving better operational efficiency. With additional information, the algorithm located at the edge node possesses the capability to make more precise decisions, which is advantageous for enhancing the quality of experience (QoE) for users. Our experiments demonstrate that the proposed strategy can significantly enhance QoE and network resource utilization. © 2023 IEEE.

关键词： DASH Edge computing Video Streaming

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27th international conference on Principles of Distributed Systems, OPODIS 2023

27th International Conference on Principles of Distributed S...

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27th international conference on Principles of Distributed Systems, OPODIS 2023

ISBN: (纸本)9783959773089

The proceedings contain 32 papers. The topics discussed include: from consensus research to Redbelly Network Pty Ltd;quantum distributed computing: potential and limitations;distributed algorithms as a gateway to deductive learning;the synchronization power of auditable registers;multi-valued connected consensus: a new perspective on crusader agreement and adopt-commit;energy-constrained programmable matter under unfair adversaries;a fair and resilient decentralized clock network for transaction ordering;discrete incremental voting;on the convergence time in graphical games: a locality-sensitive approach;eating sandwiches: modular and lightweight elimination of transaction reordering attacks;improved distributed algorithms for random colorings;fever: optimal responsive view synchronization;and improved deterministic distributed maximum weight independent set approximation in sparse graphs.

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Disentangling Client Contributions: Improving Federated Learning Accuracy in the Presence of Heterogeneous data 21

Disentangling Client Contributions: Improving Federated Lear...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liu, Chunming Alghazzawi, Daniyal M Cheng, Li Liu, Gaoyang Wang, Chen Zeng, Cheng Yang, Yang Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350329223

Federated Learning (FL) is a promising paradigm that leverages distributed data sources to train machine learning models, thereby offering significant privacy advantages. However, the inherent statistical heterogeneity among clients, characterized by distinct data distributions and varying model performance, poses a substantial challenge. Such heterogeneity adversely affects both the convergence and overall performance of the global model within the FL framework. To address this issue, we introduce a novel FL algorithm, FedVa, which considers both the local data volume and model accuracy to determine client contributions, assigning respective weights. This consideration facilitates greater engagement from key clients during the aggregation process, thereby enhancing their influence on the global model refinement. Experimental results indicate that FedVa surpasses prevailing methods in managing statistical heterogeneity and enhancing global model accuracy, without incurring additional communication costs. Specifically, FedVa achieves a convergence speed 3.9 times faster than FedAvg on the MNIST dataset, and on the CIFAR-10 dataset, it results in a 2.3% increase in final model accuracy compared to FedAvg. Our code is publicly available at https://***/ChunmingLiu23/FedVa. © 2023 IEEE.

关键词： Learning systems

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Identification and Analysis of Email Spam using Filtering Techniques

Identification and Analysis of Email Spam using Filtering Te...

引用

2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Ojha, Ananta Chakravarty, Ajay Teerthanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Jain University College of Computing Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The majority of transactions and commerce in this e-world use emails. Due to the time and money savings, email has developed into a strong tool for communication today. However, the majority of emails now include spam, which is undesired information, thanks to social networks and advertisements. Although several algorithms have been created for the classification of email spam, none of them can categorise spam emails with 100% accuracy. In order to investigate the most effective classifier for email spam classification, the spam dataset is examined in this research utilising the TANAGRA data mining tool. Fisher filtering, FCBF, MOD tree, and MIFS are some of the classifiers used for identification and analysis. Finally, based on calculation time in seconds, the top classifier for email spam is determined. © 2023 IEEE.

关键词： data mining E-mail spam and Phishing Filtering

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Research on the Collaborative Interaction between Electricity and computing Power for Green Development-Taking data Centers as an Example 3

Research on the Collaborative Interaction between Electricit...

引用

3rd international Signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Tang, Wei Zhang, Chenglong Duan, Jinhui Hu, Zhenda State Grid Energy Research Institute Co. Ltd. Beijing China Institute of State Grid Fujian Electric Power Company Center of Energy and Research Power Economic Research Fuzhou China

ISBN: (纸本)9798350307023

The allocation of electricity and computing resources is an important factor affecting economic development and social stability. Through research on the coordinated operation of computing power and electricity, resource allocation can be optimized, resource utilization efficiency can be improved, investment in power grid construction can be reduced, fossil energy consumption and carbon emissions can be reduced, and low-carbon and green development of electricity can be promoted. This article takes data centers as an example to study and analyze the planning and operation related issues of computing power collaboration. The focus is on the perspective of power supply and demand balance, and proposes a theoretical method for data centers to participate in demand response and achieve collaborative interaction with the power grid. Firstly, the composition of the adjustable load potential of the data center and the mechanism of participating in grid interaction were analyzed. Then, a data center energy supply and load energy consumption model is established. Finally, considering delay sensitive loads and delay tolerant loads, based on the time scheduling mechanism, spatial scheduling mechanism, and server optimization management mechanism of the data center, a potential calculation model for the participation of the data center in power demand response is proposed. This study provides theoretical support for the inclusion of the data center in power grid scheduling. © 2023 IEEE.

关键词： computing power

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Enhancing Accuracy in Handwritten Text Recognition with Convolutional Recurrent Neural Network and data Augmentation Techniques 3

Enhancing Accuracy in Handwritten Text Recognition with Conv...

引用

3rd international conference on Secure Cyber computing and Communications, ICSCCC 2023

作者： Minz, Shivanshu Kanojia, Ritika Yadav, Tushar Jayanthi, N. Delhi Technological University Department of Electronics and Communication Engineering Delhi110042 India

ISBN: (纸本)9798350300710

Handwriting recognition is a challenging research area in pattern recognition and image processing that has various applications, such as reading assistance for the blind and digitizing handwritten documents. This study proposes using a convolutional recurrent neural network (CRNN) architecture for handwritten text recognition without the need for segmentation, allowing for the image to be processed in one step. To improve the quality of results and the model, the study employs the CTC decoder to handle the unpredictability that arises when processing multiple alphabets and diverse handwriting. data augmentation techniques are also utilized to increase the efficiency and accuracy of the model. The study provides detailed information on how to overcome these challenges and improve the accuracy of the proposed model. © 2023 IEEE.

关键词： Recurrent neural networks

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Keynote Talk: Leveraging the Edge-Cloud Continuum to Manage the Impact of Wildfires on Air Quality

Keynote Talk: Leveraging the Edge-Cloud Continuum to Manage ...

引用

2023 IEEE international conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Balouek-Thomert, Daniel Perez, Ismael Faulstich, Sam D. Holmes, Heather A Altintas, Ilkay Parashar, Manish Imt Atlantique Nantes Université École Centrale Nantes Cnrs Inria LS2N Umr 6004 NantesF-44000 France University of Utah Salt Lake CityUT United States University of California La Jolla San DiegoCA United States

ISBN: (纸本)9781665453813

The emergence of large-scale cyberinfrastructure composed of heterogeneous computing capabilities and diverse sensors and other data sources are enabling new classes of dynamic data-driven 'urgent' applications. However, as the variety of data sources, and the volume and velocity of data grow, processing this data while considering the uncertainty of infrastructure and timeliness constraints of urgent application workflows can be nontrivial and presents a new set of challenges. In this paper, we use an application workflow that monitors and manage the air quality impacts of remote wildfires to illustrate how the R-Pulsar programming system, leveraging the SAGE and WIFIRE platforms, can enable urgent analytics across the computing continuum. R-Pulsar supports urgent data-processing pipelines that tradeoff the content of data, cost of computation, and urgency of the results to support such workflows. We also discuss research challenges associated with programming urgent application workflows and managing resources in an autonomic manner. © 2023 IEEE.

关键词： data handling

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Tabular data Extraction From Documents 11th

Tabular Data Extraction From Documents

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11th international conference on Recent Trends in computing, ICRTC 2022

作者： Madake, Jyoti Pandey, Sameeran Vishwakarma Institute of Technology Maharashtra Pune India

ISBN: (纸本)9789811988240

data is everywhere, and data is the new oil, meaning it is omnipresent and valuable. Unfortunately, much of this data is not digitized and that calls for a technology capable of translating a hardcopy into usable softcopy. Text extraction or "textraction" is the key. Using image processing and advanced machine learning, a technique to perform text extraction comes into the picture which is optical character recognition. But, the OCR’s scope is limited to just extraction of data. Making sense of that data by reorganizing it is a different story altogether. This project proposes a couple of methods to extract data from tables and then re-tabulate it in the database using the two approaches. The first method straightaway utilizes the processing power of Google’s vision API and later processes the data obtained from its OCR while the second method focuses on image processing algorithms to extract the data from the columns of the tables and use it to repopulate the digital database. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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