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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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Dynamic Offloading Control for Waste Sorting Based on Deep Q-Network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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24th International Conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. Thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. The method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： distributed Resource Allocation Intelligent Waste Management Waste Sorting Wireless Communication Network

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The Art of Deception: Black-box Attack Against Text-to-image Diffusion Model 29

The Art of Deception: Black-box Attack Against Text-to-Image...

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

作者： Lu, Yuetong Xu, Jingyao Li, Yandong Lu, Siyang Wei, Xiang Lu, Wei Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Computer and Information Technology Beijing China University of Central Florida Department of Computer Science OrlandoFL United States

ISBN: (纸本)9798350330717

With the rise of Foundation models, Text-to-image models, as one of its important branches, have been increasingly applied. While focusing on the impressive generation capabilities of these models, it is also crucial to pay attention to the robustness of the models against attacks. In this paper, we shift our focus towards studying the vulnerability of Text-to-image (T2I) models. To this end, we propose a black-box attack method and demonstrate that T2I models are susceptible to adversarial text attacks. Specifically, this method can disrupt T2I models by making subtle modifications to the model's input (i.e., prompt) without accessing the model parameters, resulting in the generation of incorrect images. It is worth mentioning that we discovered the ability to switch different types of tokenizers within this black-box framework to handle text, furthermore, this attack framework can be applied to target different versions of T2I models. The experiments indicate that the images generated through adversarial text exhibit noticeable errors. We also employ CLIP score, a metric used to evaluate the similarity between images and image descriptions, to assess the results. The findings demonstrate a significant decrease in the visual-textual similarity after the model is subjected to attacks. Additionally, we have identified a specific type of error that T2I models tend to make when facing attacks - when confronted with unrecognizable text, the model often interprets it as human-related content. This paper not only highlights the vulnerability of T2I models to adversarial text attacks but also further discusses potential methods that could enhance the robustness of these attack techniques. This provides a valuable reference for future research directions in this field. © 2023 IEEE.

关键词： Diffusion ModelAdversarial Attack Foundation Model Text-to-image

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DEF-Net: A Face Aging Model by Using Different Emotional Learnings

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第5期32卷 3012-3022页

作者： Duan, Mingxing Li, Kenli Liao, Qing Tian, Qi Hunan Univ Sch Informat Sci & Engn Changsha 410082 Peoples R China Natl Univ Def Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410082 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Huawei Technol Cloud BU Shenzhen 518000 Guangdong Peoples R China

Face aging has attracted widespread attention in recent years, but most studies are based on the same emotional situation. Is the same person's aging in different emotional situations the same? To solve the above confusion, this paper proposes a novel face aging model DEF-Net, which consists of two parts: different emotional learnings (Emotion-Net) and face aging (Age-Net). Given a target emotion category, DEF-Net first assists the image from the original dataset to learn the emotion features through Emotion-Net and the generated dataset is used as the inputs of Age-Net. At the same time, multiple loss functions are used to ensure that the crucial information of the original image is not lost. Secondly, Age-Net, which has been pre-trained on the original dataset, began to adopt the generated dataset to learn the aging distribution under different emotions. Designed loss functions are utilized to ensure that the realistic target images generated by Age-Net do not lose the learned emotional characteristics. Finally, extensive experiments are used to verify the performance of DEF-Net. Compared with other state-of-the-art methods: (1) DEF-Net can learn different facial emotions across different datasets and generate corresponding realistic aging images;(2) the results achieved by our DEF-Net are demonstrated to be better than those by the model that performs face aging first and then learns different emotional characteristics.

关键词： Faces Aging Generative adversarial networks Training image reconstruction Task analysis Shape Age-net emotion-net face aging loss functions

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Edge-enhanced GAN with Vanishing Points for image Inpainting 23

Edge-enhanced GAN with Vanishing Points for Image Inpainting

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23rd ACIS International Summer Virtual Conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing, SNPD-Summer 2022

作者： Masaoka, Kei Sari, Irawati Nurmala Du, Weiwei Kyoto Institute of Technology Department of Information Science Kyoto Japan Kyoto Institute of Technology Information and Human Science Kyoto Japan

ISBN: (纸本)9798350396379

Reconstructing the damaged images with perspective views has an extensive range in the field of image inpainting. However, most existing methods generated inadequately realistic restored images. Accomplishing this problem, we propose an edge-enhanced image generation model considering viewpoints. Our method applies edge map information to guide image generation based on the perspective views of an image using vanishing points detection. Texture synthesis will be presented as post-processing to complete the remaining missing regions. Experiment shows that our approach can generate perspective images with convincing details, such as indoor and outdoor facades. © 2022 IEEE.

关键词： image enhancement

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Cuckoo Search Optimized Histogram Equalization for Low Contrast image Enhancement 7

Cuckoo Search Optimized Histogram Equalization for Low Contr...

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7th International Conference on parallel, distributed and Grid Computing, PDGC 2022

作者： Thakur, Niveditta Khan, Nafis Uddin Sharma, Sunil Datt Jaypee University of Information Technology Solan India

ISBN: (纸本)9781665454018

image enhancement is a process to improve the visual standard of image so as to extract spatial features of image. Histogram Equalization is method by which image can be improved for better perception and interpretation by different automated image processing system and human beings. Although, for low-contrast images (with lower dynamic range) traditional histogram equalization deteriorate quality of the output image by introducing washed-out form caused by too much brightness change. This paper presents histogram equalization based image enhancement method which is optimized using cuckoo search algorithm. Foremost purpose of the proposed method is to control over and under enhancement by calculating clip limits of the image histogram before performing histogram equalization. This method uses cuckoo search algorithm to automatically control the clip limit according to the image nature using improved fitness function. Effectiveness of the proposed method in comparison to other methods is analyzed using well known image quality metrics. © 2022 IEEE.

关键词： image enhancement

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image Inpainting using Automatic Structure Propagation with Auxiliary Line Construction 23

Image Inpainting using Automatic Structure Propagation with ...

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23rd ACIS International Summer Virtual Conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing, SNPD-Summer 2022

作者： Urano, Yuto Sari, Irawati Nurmala Du, Weiwei Kyoto Institute of Technology Department of Information Science Kyoto Japan Kyoto Institute of Technology Information and Human Science Kyoto Japan

ISBN: (纸本)9798350396379

Existing image inpainting methods used traditional and deep learning methods to restore a large missing region in the damaged image. This often leads to color discrepancy and blurriness. Pre-processing of prior line detection by user assistance is usually employed to reduce the blurry of center region by segmenting the large region into more minor. However, it operates manually, which is time-consuming. This paper introduces a technique to generate two-line types: penetrator and interactor in constructing auxiliary lines as guidance. These lines assist structure propagation established automatically, while the remaining small regions are filled by texture propagation. Experiments on large regular masks demonstrate that our proposed approach generates higher-quality results than other methods. © 2022 IEEE.

关键词： Textures

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FCTNet: A CNN-Transformer Hybrid for Single Remote Sensing image Super-Resolution

FCTNet: A CNN-Transformer Hybrid for Single Remote Sensing I...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Ning Shi Hui Zhou Chunyang Ye Biyuan Yao Yilin Wu Bo Li Hainan University Haikou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Convolutional Neural Network (CNN) and Transformer architectures have been extensively applied in the domain of remote sensing image super-resolution. However, to achieve optimal performance, many existing methods are designed with a large number of parameters, thereby increasing the complexity of the model and hindering practical deployment. To address this issue, we propose an innovative model that synergizes CNN and Transformer architectures while employing a minimal number of their respective modules. This approach significantly reduces the parameter count while maintaining superior performance. Firstly, by constructing additional shallow feature representations as input, we enhance the feature extraction capabilities for individual images. Secondly, we utilize residual connections between various modules to integrate multi-scale, high-dimensional feature information, thus ensuring efficient transmission. Finally, the image reconstruction module is employed to restore the high-resolution image. Experimental results show that FCTNet significantly outperforms existing methods while maintaining a substantially lower parameter count, as demonstrated through evaluations on two public datasets.

关键词： Visualization distributed processing Superresolution Feature extraction Transformers Sensors image restoration Convolutional neural networks Remote sensing image reconstruction

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Cervical cell classification based on attention mechanism and multi-resolution feature fusion 3

Cervical cell classification based on attention mechanism an...

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3rd International Conference on Information Science, parallel and distributed Systems, ISPDS 2022

作者： Yu, Jingya Wang, Guoyou Cheng, Shenghua School of Automation Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing Wuhan China Britton Chance Center School of Engineering Sciences Huazhong University of Science and Technology Moe Key Laboratory for Biomedical Photonics Wuhan China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

Liquid-based thin-layer cell smears are very important for the early screening and prevention of cervical cancer, and computer-aided diagnosis can reduce the workload of pathologists. The cell classification method based on deep learning can process data efficiently. However, most classification methods are based on a single resolution for recognition. When the resolution is low, the processing speed of the whole slide image is faster, but lack of picture details, which makes the identification inaccurate. When the resolution is high, it takes more time to process the whole slide image, but with more image detail. To this end, we propose a model based on Attention Mechanism and Multi-resolution Feature Fusion Module (AMFM), which combine the advantages of various resolutions to classify cervical cells. Experiments show that the accuracy is increased by 3.93% and the AUC is improved by 0.022 on the four-classification task of the cervical cell compared to the model based on a single resolution. © 2022 IEEE.

关键词： Computer aided diagnosis

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Communication-Efficient Federated Learning With Compensated Overlap-FedAvg

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第1期33卷 192-205页

作者： Zhou, Yuhao Ye, Qing Lv, Jiancheng Sichuan Univ Coll Comp Sci Chengdu 610017 Peoples R China

While petabytes of data are generated each day by a number of independent computing devices, only a few of them can be finally collected and used for deep learning (DL) due to the apprehension of data security and privacy leakage, thus seriously retarding the extension of DL. In such a circumstance, federated learning (FL) was proposed to perform model training by multiple clients' combined data without the dataset sharing within the cluster. Nevertheless, federated learning with periodic model averaging (FedAvg) introduced massive communication overhead as the synchronized data in each iteration is about the same size as the model, and thereby leading to a low communication efficiency. Consequently, variant proposals focusing on the communication rounds reduction and data compression were proposed to decrease the communication overhead of FL. In this article, we propose Overlap-FedAvg, an innovative framework that loosed the chain-like constraint of federated learning and paralleled the model training phase with the model communication phase (i.e., uploading local models and downloading the global model), so that the latter phase could be totally covered by the former phase. Compared to vanilla FedAvg, Overlap-FedAvg was further developed with a hierarchical computing strategy, a data compensation mechanism, and a nesterov accelerated gradients (NAG) algorithm. In Particular, Overlap-FedAvg is orthogonal to many other compression methods so that they could be applied together to maximize the utilization of the cluster. Besides, the theoretical analysis is provided to prove the convergence of the proposed framework. Extensive experiments conducting on both image classification and natural language processing tasks with multiple models and datasets also demonstrate that the proposed framework substantially reduced the communication overhead and boosted the federated learning process.

关键词： Collaborative work Data models Training Servers Computational modeling Convergence Deep learning distributed computing federated learning overlap efficient communication

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