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25th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2024

ISBN: (纸本)9789819642069

The proceedings contain 55 papers. The special focus in this conference is on parallel and distributed Computing: Applications and Technologies. The topics include: A Meta-reinforcement Learning Framework for Adaptive Quadrotor UAV Attitude Control;Securing Energy Transactions for Electric Vehicles: The Blockchain Approach and Encrypted NFTs;optimizing Task Allocation in Heterogeneous Agent Manufacturing Systems;MPG: Multi-modal Personal Health Graph for Alzheimer’s Disease Diagnosis;SMAC: A Secure Multi-authority Access Control Scheme with Attribute Unification for Fog Enabled IoT in E-Health;Convolutional Neural Networks Parameter Training for SCM Algorithm Based on Hausdorff Difference;handling Non-stationarity with Distribution Shifts and Data Dependency in Time Series Forecasting;the Two-Stage Stochastic Facility Location Game;regularized Non-monotone γ-weakly Submodular Maximization;fed-MoE: Efficient Federated Learning for Mixture-of-Experts Models via Empirical Pruning;WaitIO-Hybrid: Communication for Coupling MPI Programs Among Heterogeneous Systems;the Material Delivery Route Prediction Method Based on Deep Reinforcement Learning;privacy-Preserving in Medical image Analysis: A Review of methods and Applications;research on Task Migration Problem Based on Link Uncertainty in Adversarial Scenarios;optimizing Production Component Scheduling in Multivariate Industrial Networks with Dynamic Changes in Production Costs;multi-agent Collaboration for Time-Sensitive Tasks in Multiple Networked Adversarial Scenarios;containerized Data-Flow processing for Scalable Real-Time Analytics on Edge Devices;fast Approximation for Scheduling Malleable Jobs on parallel Batch Machines with Rejection;real-Time and In-Situ Temperature Profiling for Determining Detonation of White Dwarf Mergers;accparser: A Standalone OpenACC Parser and Its Usage on Mapping OpenACC to OpenMP Directives;Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams;long-Term and Periodicity-Aware Spatio

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A Differential Privacy image Publishing Method Based on Wavelet Transform 22nd

A Differential Privacy Image Publishing Method Based on Wave...

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22nd International Conference on parallel and distributed Computing, Applications and Technologies (PDCAT 2021)

作者： Zhang, Guifen Wei, Hangui Ge, Lina Qin, Xia Guangxi Univ Nationalities Sch Artificial Intelligence Nanning Peoples R China Guangxi Univ Nationalities Key Lab Network Commun Engn Nanning Peoples R China

ISBN: (纸本)9783030967727;9783030967710

image is an important information-bearing medium with many important attributes. If the image data is released directly, personal privacy will be compromised. This paper aims at how to use the method of differential privacy to protect the privacy of image data and make the image data have high usability. In this paper, a WIP method based on wavelet change is proposed. Firstly, wavelet transform is used to compress the image. Then, noise is added to the main features after transformation to obtain the published image satisfying the differential privacy. It solves the problem of low usability of large images and the problem that Fourier transform cannot deal with abrupt signal. Experimental results show that compared with similar methods in the frequency domain, the denoised image obtained by the proposed WIP method is more distinguishable and the information entropy is closer to the original image. The accuracy is 10% higher than other methods. Compared with other frequency-domain methods for image differential privacy protection, the proposed WIP method has higher usability and robustness.

关键词： image processing Differential privacy Privacy protection The wavelet transform

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QGIP: A Framework Bridging Quantum Grayscale image processing and Applications

QGIP: A Framework Bridging Quantum Grayscale Image Processin...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Xilong Che Jiale Zhang Shuo Chen Shun Peng Juncheng Hu College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Quantum computing offers parallel processing capabilities and resource-saving advantages, particularly useful for managing expansive datasets and complex image processing tasks. Grayscale images, being the simplest single-channel image mode, are frequently employed in artificial intelligence training. Before actual image applications, various image processing operations are typically required. However, the restoration of a grayscale image of dimensions 2 n × 2 n after a series of linear transformations poses a challenge. Existing methods typically involve finding the inverse of the most recent linear transformation or re-encoding the image followed by repeated operations until the final transformation, resulting in excessive computational overhead and disconnection from subsequent quantum grayscale image applications. To address this issue, we propose a universal quantum linear restoration algorithm for grayscale image, denoted as QLR, which effectively bridges the stages of linear transformation and subsequent image applications. QLR reduces the time complexity from O(2 n ) to O(n) compared to classical counterpart. Building upon the QLR algorithm, we further propose two quantum resource-optimized compression methods for optional lossless image storage. Combining with other quantum algorithms and techniques, we design a framework (QGIP) aimed at bridging the processes of quantum grayscale image processing and applications. Experiments simulated on the IBM Quantum platform validate the correctness and efficiency of our proposal.

关键词： Training distributed processing Quantum algorithm image coding Gray-scale parallel processing image restoration Proposals image storage Time complexity

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A fast and fully distributed method for region-based image segmentation Fast distributed region-based image segmentation

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JOURNAL OF REAL-TIME image processing 2021年第3期18卷 793-806页

作者： Mazouzi, Smaine Guessoum, Zahia Skikda Univ 20 Aout 1955 Comp Sci Dept Skikda Algeria Univ Reims Sorbonne Univ CReSTIC Reims Lip6 Paris France

distributed and parallel computing techniques allow fast image processing, namely when these techniques are applied at the low and the medium level of a vision system. In this paper, a collective and distributed method for image segmentation is introduced and evaluated. The method is modeled as a multi-agent system, where the agents aim to collectively produce a region-based segmentation. Each agent starts searching for an acceptable region seed by randomly jumping within the image. Next, it performs a region growing around its position. Thus, several agents find themselves within the same homogeneous region and are organized in a graph where two agents are connected if they are within the same region. So, a unifying of the labels in a same region is collaboratively performed by the agents themselves. The proposed method was experimented on real range images from the ABW dataset and the Object Segmentation Database (OSD) one, and the obtained results were compared to those of some well-referenced methods from the literature. The evaluation results show that the proposed method provides fast and accurate image segmentation, allowing it to be deployed for real-time vision systems.

关键词： image segmentation Region growing Pixel labeling distributed computing Real-time computer vision systems

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image Compression and Reconstruction Based on Quantum Network

Image Compression and Reconstruction Based on Quantum Networ...

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IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： J. Xun Qin Liu Shan Huang Andi Chen W. Shengjun National Laboratory of Solid State Microstructure School of Physics and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China Institute for Brain Sciences and Kuang Yaming Honors School Nanjing University Nanjing China Hefei National Laboratory University of Science and Technology of China Hefei China

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks make image reconstruction more efficient and accurate. They can also process more complex image information using fewer bits and faster parallel computing capabilities. Therefore, this paper will discuss image reconstruction methods based on our quantum network and explore their potential applications in image processing. We will introduce the basic structure of the quantum network, the process of image compression and reconstruction, and the specific parameter training method. Through this study, we can achieve a classical image reconstruction accuracy of 97.57%. Our quantum network design will introduce novel ideas and methods for image reconstruction in the future.

关键词： Training distributed processing image coding Accuracy Conferences Quantum mechanics Reconstruction algorithms

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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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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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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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