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30th Euromicro International Conference on parallel, distributed and Network-Based processing (PDP)

作者： Umair, Areeba Masciari, Elio Univ Naples Federico II Dept Elect Engn & Informat Technol Naples Italy

ISBN: (纸本)9781665469586

Coronovirus has emerged as challenge for the whole mankind causing illness worldwide. To eradicate the disease, global efforts are put increasing to develop its vaccine. In order to achieve the immunity against the virus, wide provision of vaccine is necessary. To make sure the distribution of vaccines, the sentiments of people for vaccines must be analyzed. Now-a-days, people share their thoughts, feelings and feedback about anything they experience on social media platforms. In this study, high performance approaches have been used for the analysis of the sentiments of people about vaccines. In this study, we have used the freely available data and applied pre-processing over it. We found out the polarity values of the tweets using TextBlob() function of Python and drew the wordclouds for positive, negative and neutral tweets. We used BERT model for understanding the people's feelings and feedback about vaccines. The model evaluation was performed using precision, recall and F measure. The BERT model achieved achieved 55 % & 54 % precision, 69 % & 85 % recall and 58 % & 64 % F score for positive class and negative class respectively. Therefore, the use of artificial intelligence in social media analysis produce fruitful results while determining the people's attitude towards ant new trend, topic and any emergency situation. These methods helps to grow the vaccines campaigns timely by solving the people's concerns about vaccines.

关键词： Coronavirus COVID vaccine sentimental analysis vaccine hesitancy high performance approaches

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Low-Dimensional Feature Representation with Hybrid Attention for Few-Shot image Classification 29

Low-Dimensional Feature Representation with Hybrid Attention...

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29th IEEE International Conference on parallel and distributed Systems, ICPADS 2023

作者： Yao, Xin-Wei Yuan, Zhi-Heng Fang, Yu-Li He, Chuan Zhang, Yu-Chen Li, Qiang Zhejiang University of Technology College of Computer Science and Technology Hangzhou China Zhejiang University of Technology Taizhou Research Institute Taizhou China Zhejiang University of Technology Institute for Frontier and Interdisciplinary Sciences Hangzhou China

ISBN: (纸本)9798350330717

Learning effective image representation and constructing a suitable metric space are two main challenges in few-shot image classification. Existing methods normally consider the joint characteristic distribution of the image to improve the image representation ability, but it also brings high computational cost and produces high-dimensional embedding vectors, which limit their real-world applicability. In this paper, in order to reduce computational complexity and embedding dimension, we propose an effectively low-dimensional feature representation module (LDFR), which introduces a window mechanism to make the model focus on the correlation between local channels by using Brownian Distance Covariance. Furthermore, we combine LDFR with Hybrid Attention (LDFR-HA) to solve the problem of unbalanced sample features in few-shot image classification. Specifically, weighted average is adopted in the Hybrid Attention to construct the metric space and perform classification. Numerous experiments are conducted on two standard few-shot image classification benchmarks, i.e., general object recognition and fine-grained categorization. Extensive evaluations demonstrate that LDFR-HA significantly outperforms existing approaches. On popular datasets miniimageNet and CUB, LDFR-HA achieves 3.52 percentage point(pp)/2.29pp and 2.1pp/1.45pp gains over the state-of-the-art method on 5-way 1-shot/5-shot tasks, respectively. © 2023 IEEE.

关键词： Brownian Distance Covariance Few-shot image classification Hybrid Attention Low-Dimensional Feature

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Effects of spatial decomposition on the efficiency of kNN search in spatial interpolations

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INTERNATIONAL JOURNAL OF parallel EMERGENT AND distributed SYSTEMS 2022年第1期37卷 103-121页

作者： Fan Naijie Mei Gang Ding Zengyu Cuomo, Salvatore Xu Nengxiong China Univ Geosci Beijing Sch Engn & Technol Beijing 100083 Peoples R China Univ Naples Federico II Dept Math & Applicat Naples Italy

Spatial interpolations are commonly used in geometric modelling in life science applications such as medical image processing. In large-scale spatial interpolations, it is always needed to find a local set of data points for each interpolated point using the k Nearest Neighbor (kNN) search. To improve the efficiency of kNN, the uniform grid is commonly employed to fastly locate neighbours, and the size of grid cell could strongly affect the efficiency of kNN search. In this paper, we evaluate effects of the size of uniform grid cell on the efficiency of kNN search which is implemented on the CPU and GPU. We employ the Standard Deviation of points' coordinates to measure the spatial distribution of scattered points. For irregularly distributed scattered points, we perform several series of kNN search in two- and three-dimensions. Benchmark results indicate that: for both the sequential version implemented on the CPU and the parallel version implemented on the GPU, with the increase of the Standard Deviation of points' coordinates, the relatively optimal size of the grid cell decreases and eventually converges. Moreover, relationships between the Standard Deviation of scattered points' coordinates and the relatively optimal size of grid cell are fitted. [GRAPHICS] .

关键词： Spatial interpolation k nearest neighbours (kNN) search uniform grid spatial distribution standard deviation

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GPU-Accelerated FDTD Solver for Electromagnetic Differential Equations 24th

GPU-Accelerated FDTD Solver for Electromagnetic Differential...

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24th International Conference on Computational Science (ICCS)

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. Univ Sydney Ctr Distributed & High Performance Comp Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9783031637537;9783031637513

Computational electromagnetics plays a crucial role across diverse domains, notably in fields such as antenna design and radar signature prediction, owing to the omnipresence of electromagnetic phenomena. Numerical methods have replaced traditional experimental approaches, expediting design iterations and scenario characterization. The emergence of GPU accelerators offers an efficient implementation of numerical methods that can significantly enhance the computational capabilities of partial differential equations (PDE) solvers with specific boundary-value conditions. This paper explores parallelization strategies for implementing a Finite-Difference Time-Domain (FDTD) solver on GPUs, leveraging shared memory and optimizing memory access patterns to achieve performance gains. One notable innovation presented in this research involves utilizing strategies such as exploiting temporal locality and avoiding misaligned global memory accesses to enhance data processing efficiency. Additionally, we break down the computation process into multiple kernels, each focusing on computing different electromagnetic (EM) field components, to enhance shared memory utilization and GPU cache efficiency. We implement crucial design optimizations to exploit GPU's parallel processing capabilities fully. These include maintaining consistent block sizes, analyzing optimal configurations for field-updating kernels, and optimizing memory access patterns for CUDA threads within warps. Our experimental analysis verifies the effectiveness of these strategies, resulting in improvements in both reducing execution time and enhancing the GPU's effective memory bandwidth. Throughput evaluation demonstrates performance gains, with our CUDA implementation achieving up to 17 times higher throughput than CPU-based methods. Speedup gains and throughput comparisons illustrate the scalability and efficiency of our approach, showcasing its potential for developing large-scale electromagnetic simulations on

关键词： Numerical Computational Electromagnetics GPU Accelerators Finite-Difference Time-Domain Solver Partial Differential Equations Geometric Discretization

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distributed Deep Learning-based Model for Large image Data Classification 7

Distributed Deep Learning-based Model for Large Image Data C...

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7th International Conference on Future Networks and distributed Systems, ICFNDS 2023

作者： Nurnoby, M Faisal Shawarib, Khalid A. Abu Khan, Ayaz Ul Hassan King Fahd University Of Petroleum And Minerals Dhahran Saudi Arabia King Fahd University Of Petroleum And Minerals Khobar Saudi Arabia SDAIA-KFUPM Joint Research Center For Artificial Intelligence King Fahd University Of Petroleum And Minerals Dhahran Saudi Arabia

ISBN: (纸本)9798400709036

Artificial intelligence has shown great potential in a variety of applications, from natural language models to audio visual recognition, classification, and manipulation. AI Researchers have to work with massive amount of collected data for use in machine learning, raising some challenges in effectively managing and utilizing the collected data in the training phase to develop and iterate on more accurate, and more generalized models. In this paper we conducted a review on parallel and distributed machine learning methods and challenges. We also propose a distributed and scalable deep learning model architecture which can span across multiple processing nodes. We tested the model on the MIT Indoor dataset, to evaluate the performance and scalability of the model using multiple hardware nodes, and showed the scaling characteristics of the different model using different model sizes. We find that distributed training is 80% faster using 2 GPUs than 1 GPU. We also find that the model keeps the benefits of distributed training such as speed and accuracy regardless of its size or training batch size. © 2023 ACM.

关键词： image classification

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LOFS: A Lightweight Online File Storage Strategy for Effective Data Deduplication at Network Edge

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第10期33卷 2263-2276页

作者： Cheng, Geyao Guo, Deke Luo, Lailong Xia, Junxu Gu, Siyuan Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Edge computing responds to users' requests with low latency by storing the relevant files at the network edge. Various data deduplication technologies are currently employed at edge to eliminate redundant data chunks for space saving. However, the lookup for the global huge-volume fingerprint indexes imposed by detecting redundancies can significantly degrade the data processing performance. Besides, we envision a novel file storage strategy that realizes the following rationales simultaneously: 1) space efficiency, 2) access efficiency, and 3) load balance, while the existing methods fail to achieve them at one shot. To this end, we report LOFS, a Lightweight Online File Storage strategy, which aims at eliminating redundancies through maximizing the probability of successful data deduplication, while realizing the three design rationales simultaneously. LOFS leverages a lightweight three-layer hash mapping scheme to solve this problem with constant-time complexity. To be specific, LOFS employs the Bloom filter to generate a sketch for each file, and thereafter feeds the sketches to the Locality Sensitivity hash (LSH) such that similar files are likely to be projected nearby in LSH tablespace. At last, LOFS assigns the files to real-world edge servers with the joint consideration of the LSH load distribution and the edge server capacity. Trace-driven experiments show that LOFS closely tracks the global deduplication ratio and generates a relatively low load std compared with the comparison methods.

关键词： Servers Indexes Costs Redundancy Throughput image edge detection Resource management Data deduplication locality sensitivity hash edge computing storage systems

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No-reference stereo image quality evaluation method integrating monocular and binocular features 4

No-reference stereo image quality evaluation method integrat...

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4th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

作者： Bai, Jinxin Hu, Xiaojuan School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130000 China

ISBN: (纸本)9798400708831

In recent years, the widespread adoption of 3D technology has created a need for accurate and efficient stereoscopic image quality assessment methods. Such methods aim to automatically supervise and optimize stereoscopic image and video processing systems. Based on the characteristics of human vision, we propose a method that integrates monocular and binocular features. Initially, monocular quality-sensitive features are extracted separately for the left and right views through two parallel CNN sub-networks. Subsequently, a third CNN sub-network extracts binocular quality-sensitive features for stereo image pairs. Quality scores are then computed based on both monocular and binocular features, and the final predicted quality score is obtained through quality fusion. The proposed method is validated on the Waterloo dataset, achieving a PLCC value of 0.979 and SROCC value of 0.975 on Waterloo P-I, and a PLCC value of 0.973 and SROCC value of 0.970 on Waterloo P-ii. The results demonstrate that the predicted scores obtained by the proposed method exhibit excellent consistency with subjective evaluation scores. © 2023 ACM.

关键词： Binoculars

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Area-NeRF: Area-based Neural Radiance Fields 2

Area-NeRF: Area-based Neural Radiance Fields

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2nd International Conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Ye, Zonxin Li, Wenyu Qiao, Peng Dou, Yong National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing School of Computer Changsha China

ISBN: (纸本)9798350331417

Neural Radiance Field (NeRF) has received widespread attention for its photo-realistic novel view synthesis quality. Current methods mainly represent the scene based on point sampling of ray casting, ignoring the influence of the observed area changing with distance. In addition, The current sampling strategies are all focused on the distribution of sampling points on the ray, without paying attention to the sampling of the ray. We found that the current ray sampling strategy for scenes with the camera moving forward severely reduces the convergence speed. In this work, we extend the point representation to area representation by using relative positional encoding, and propose a ray sampling strategy that is suitable for camera trajectory moving forward. We validated the effectiveness of our method on multiple public datasets. © 2023 IEEE.

关键词： NeRF neural radiance field neural rendering novel view synthesis

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Frequency Decoupled Domain-Irrelevant Feature Learning for Pan-Sharpening

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第2期35卷 1237-1250页

作者： Zhang, Jie Cao, Ke Yan, Keyu Lin, Yunlong He, Xuanhua Wang, Yingying Li, Rui Xie, Chengjun Zhang, Jun Zhou, Man Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Grad Sch Sci Isl Branch Hefei 230026 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Xiamen Univ Key Lab Multimedia Trusted Percept & Efficient Com Minist Educ China Xiamen 361005 Peoples R China Chinese Acad Sci High Magnet Field Lab Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

Pan-sharpening aims to generate high-detail multi-spectral images (HRMS) through the fusion of panchromatic (PAN) and multi-spectral (MS) images. However, existing pan-sharpening methods often suffer from significant performance degradation when dealing with out-of-distribution data, as they assume the training and test datasets are independent and identically distributed. To overcome this challenge, we propose a novel frequency domain-irrelevant feature learning framework that exhibits exceptional generalization capabilities. Our approach involves parallel extraction and processing of domain-irrelevant information from the amplitude and phase components of the input images. Specifically, we design a frequency information separation module to extract the amplitude and phase components of the paired images. The learnable high-pass filter is then employed to eliminate domain-specific information from the amplitude spectrums. After that, we devised two specialized sub-networks (AFL-Net and PFL-Net) to perform targeted learning of the frequency domain-irrelevant information. This allows our method to effectively capture the complementary domain-irrelevant information contained in the amplitude and phase spectra of the images. Finally, the information fusion and restoration module dynamically adjusts the feature channel weights, enabling the network to output high-quality HRMS images. Through this frequency domain-irrelevant feature learning framework, our method balances generalization capability and network performance on the distribution of training dataset. Extensive experiments conducted on various satellite datasets demonstrate the effectiveness of our method for generalized pan-sharpening. Our proposed network outperforms state-of-the-art methods in terms of both quantitative metrics and visual quality, showcasing its superior ability to handle diverse, out-of-distribution data.

关键词： Representation learning Frequency-domain analysis Feature extraction Data mining Training Spatial resolution Circuits and systems image restoration Fourier transforms Distortion Frequency domain-irrelevant pan-sharpening learnable filter amplitude spectrums

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distributed Cloud Computing Architecture in Hyperspectral Remote Sensing image Classification under Big Data 5

Distributed Cloud Computing Architecture in Hyperspectral Re...

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5th IEEE International Conference on Information Systems and Computer Aided Education, ICISCAE 2022

作者： Feng, Kai Wu, Yi Shaanxi Academy of Aerospace Technology Application Co. Ltd. Xi'an China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

By using the cloud computing platform that has achieved excellent commercial results to perform parallel classification processing of massive remote sensing data, it can meet the requirements of improving the parallel processing efficiency of massive remote sensing data. The purpose of this paper is to study the application of distributed cloud computing architecture in hyperspectral remote sensing image classification based on big data. The related technologies of cloud computing are expounded, and the model of SCSRC, a sparse representation classification method based on spatial correlation constraints, is proposed. By analyzing the principle and solution method of SCSRC model, a distributed classification algorithm DP-SCSRC based on spatial correlation constraint sparse representation is proposed on the Spark platform, and the following optimization methods are designed for the core steps. Finally, through experiments with different computing nodes and different amounts of computing data, it is proved that the DP-SCSRC algorithm has a good distributed speedup ratio and data scalability under the premise of ensuring the classification accuracy. © 2022 IEEE.

关键词： distributed cloud

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