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

共 6952 条记录，以下是4941-4950 订阅

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

引用

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

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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Hybrid Deep Learning Approach for Nonfunctional Software Requirements Classifications

Hybrid Deep Learning Approach for Nonfunctional Software Req...

引用

international conference on Communication, computing and Digital Systems (C-CODE)

作者： Kiramat Rahman Anwar Ghani Rashid Ahmad Syed Haseeb Sajjad Dept. of Software Engineering International Islamic University Islamabad Pakistan Big Data Research Center Jeju National University Jeju South Korea Dept. of Computer Science Inernational Islamic University Islamabad Pakistan

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts.

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Research on Sports Live Broadcast System under Computer Big data Technology

Research on Sports Live Broadcast System under Computer Big ...

引用

Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Weiqiang Zhang Student Office of Lanzhou Resource & Environment Voc-Tech University Lanzhou China

Based on the computer big data technology, this paper designs a system to realize the unattended live broadcast through the steps of video stitching, hot spot area determination, hot spot area tracking and so on. Then this paper designs the software and hardware environment of the system and determines the final implementation scheme and parameters through experiments. Secondly, this paper studies various related technologies involved in video stitching. In this paper, the concept of hot spot region is proposed and the detection algorithm of hot spot region is designed. The video splicing function is designed based on SURF feature point detection technology. After video fusion processing, the video Mosaic subsystem is finally realized. Finally, this paper designs the function and implementation of the video live relay subsystem. The simulation results show that this method can smoothly track the hot spots and extract them to realize real-time retransmission.

关键词： Target tracking TV Splicing Simulation Streaming media Big data Feature extraction

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Computer Network Security Detection Method Based on Virtual Operation

Computer Network Security Detection Method Based on Virtual ...

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Decision Science & Management (ICDSM), international conference on

作者： Yanbin Zhang Dan Liu Yang Su Guangzhou Customs Technology Center Guangzhou Guangzhou Branch China Electronic Port Data Center Guangzhou

This paper provides a network security service establishment method and system based on virtualization technology, including: obtaining computing node sets and host devices; Determine the link bandwidth according to the host equipment; The computing resource model is established according to the set of computing nodes and host devices; The computing resource model is used to deploy the service function chain to establish network security services. The method presented in this paper makes use of the characteristics of rapid deployment of container borne network functions, and proposes some dynamic function deployment methods. On the basis of the idea of integer programming optimization problem modeling, the overall problem is decomposed into independent subproblems, the scale of the optimization problem is reasonably limited, and the fast deployment of containers is utilized to ensure that the response time of service requests is low, while retaining the performance advantages of optimization algorithms over simple methods to some extent.

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A Method for Analyzing data in Measuring Devices Using a Specialized computing System for Synthesizing and Evaluating Statistical Hypotheses

A Method for Analyzing Data in Measuring Devices Using a Spe...

引用

international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ilya Tarasov Laboratory of Specialized Computer Systems RTU MIREA Moscow Russia

The article examines the application of a method for processing experimental data in measurement systems by generating hypotheses about the presence of a given set of parameters and estimating their probability. The difference between the method is the use of the error probability distribution function with an independent parameter "distribution scale" and the search for a combination of process model parameters at which the posterior probability function takes the maximum value. The method is characterized by high resistance to impulse noise, as a result of which it can be used in measuring systems. A special feature of the method is the large number of calculations required to generate hypotheses and test their probability. To solve this problem, a parallel computing system architecture has been proposed that generates hypotheses and evaluates them using independent processing elements.

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Anomaly Detection for Highly Imbalanced data–an Empirical Analysis

Anomaly Detection for Highly Imbalanced Data–an Empirical A...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Akshat Ajay Das Veena Mayya Manohara M M Pai Department of Electronics & Communications Manipal Academy of Higher Education Manipal Institute of Technology Manipal Karnataka India Department of Information & Communication Technology Manipal Academy of Higher Education Manipal Institute of Technology Manipal Karnataka India

An event or an observation that is statistically different from the others is termed an anomaly. Anomaly detection is the process of identifying such anomalies. Anomaly detection is an effective tool for risk mitigation, fraud detection, and improving the system's robustness. It is also an active research area, with numerous algorithms being proposed. In this paper, we compare the performance of various anomaly detection algorithms on mul-tivariate as well as univariate datasets. The assessment measures generated are important and can be beneficial for predicting anomalies in a timely and accurate manner. Experimental results demonstrate that on a univariate dataset, the auto-regressive moving average (ARMA), performs better than the local outlier factor (LOF), while on a multivariate dataset, the LOF model performs better. The prototype developed has been extensively tested on publicly available datasets and can be evaluated on larger, more comprehensive datasets for deployment in the real-time anomaly detection setup.

关键词： Deep learning Analytical models Computational modeling Prototypes Probability Prediction algorithms Real-time systems

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Federated data Augmentation Algorithm based on Heterogeneity Assessment

Federated Data Augmentation Algorithm based on Heterogeneity...

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Cloud computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Bang Hu Mingming Zhang Kai Liu Xianhui Li Yongli Wang School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Information and Communication Branch State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Jiangsu Ruizhong Data Co. Ltd State Grid Electric Power Research Institute Co. Ltd Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

In order to solve the problem of poor learning effect caused by data heterogeneity among different participants in the existing federated learning methods, this paper proposes a federated data augmentation algorithm based on heterogeneity assessment (FDA-HA) from the perspective of mitigating the effect of heterogeneity and protecting user data privacy, which mitigates the degree of data heterogeneity among participants by generating adversarial networks for data augmentation and at the same time safeguards fairness in the data augmentation process under the premise of protecting data privacy while guaranteeing fairness in the data enhancement process. Experimental results on MNIST, FashionMNIST, and Cifar10 datasets show that compared with mainstream federated learning algorithms, this algorithm improves the accuracy by 7.96% and 13.44% in data scenarios with different degrees of heterogeneity, and at the same time, it has a certain degree of fairness.

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data Engineering Challenges in AI automation

Data Engineering Challenges in AI automation

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international conference on computing, Electronics & Communications Engineering (iCCECE)

作者： Deexith Reddy Dallas TX United States of America

Artificial Intelligence (AI) systems have seen a meteoric rise in adoption, permeating across diverse sectors such as healthcare, finance, and e-commerce. The next step in the evolution of the practical use of AI is the automation of these systems. Since these systems hinge largely on the quality, volume, and careful management of data for training, optimization, and validation of data science models, the promise of AI automation hinges significantly on the ability to efficiently handle data ingestion, ensure real-time processing, and respond swiftly to changes in *** is a requirement of a multi-faceted approach in terms of data engineering to help practitioners implement automated AI. The need for robust data engineering practices that ensure scalability, efficiency, and reliability in AI applications and their automation is evident. Comprehensive investigations of the impact of these practices on the automation of AI are required to create future-proof, reliable AI *** paper will highlight some areas of data engineering that are important for automation of AI, the challenges they pose and the impact they have on performance of AI systems. There is a solution section for each problem that gives the scope of automation and human involvement required along with the tools that are currently being used.

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data Volume-Aware Computation Task Scheduling for Smart Grid data Analytic Applications

Data Volume-Aware Computation Task Scheduling for Smart Grid...

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IEEE international conference on Communications (ICC)

作者： Binquan Guo Hongyan Li Ye Yan Zhou Zhang Peng Wang State Key Laboratory of Integrated Service Networks Xidian University Xi'an P. R. China Tianjin Artificial Intelligence Innovation Center (TAIIC) Tianjin P. R. China

Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.

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Analysis of COVID-19 Using Machine Learning & data Science Technologies

Analysis of COVID-19 Using Machine Learning & Data Science T...

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Pervasive computing and Social Networking (ICPCSN), international conference on

作者： Bogasamudram Sravani Gumpena Hema Sri Venkata Sai Vytla Vyshnavi Talluri Sandeep M. M. Yamuna Devi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

The goal of this research study is to use data science and machine learning to anticipate and analyze the Covid-19 pandemic's spread. The main goal is to create a predictive algorithm that can be used to anticipate daily Covid-19 instances across various geographies. The proposed model will be based on historical data related to Covid-19 cases such as population density, demographics, weather conditions, and socio-economic factors. The model will use supervised learning algorithms to analyze the data and develop a predictive model. Real-world data will be used to validate the suggested model, and performance measures including accuracy, precision, recall, and F1 score will be used to assess the model's effectiveness. Public health experts will use the model's findings to learn more about the virus's transmission and to help them choose the best preventative measures. The proposed approach may prove to be an effective tool in the fight against the Covid-19 epidemic.

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