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Euromicro conference on parallel, distributed and Network-Based processing

作者： Pasquale De Luca Ardelio Galletti Livia Marcellino Mario Pianese Science and Technologies Department Parthenope University of Naples Naples Italy

The 3D surface reconstruction is critical for various applications, demanding efficient computational approaches. Traditional Radial Basis Functions (RBFs) methods are limited by increasing data points, leading to slower execution times. Ad-dressing this, our study introduces an experimental parallelization effort using Julia, as well-known for high-performance scientific computing. We developed an initial sequential RBF algorithm in Julia, then expanded it to a parallel model, exploiting Multi-Threading to enhance execution speed while maintaining accuracy. This initial exploration into Julia's parallel computing capabilities shows marked performance gains in 3D surface reconstruction, offering promising directions for future research. Our findings affirm Julia's potential in computationally intensive tasks, with test results confirming the expected time efficiency improvements.

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Human Pose Estimation Network Based On MultiHeatmap Fusion 5

Human Pose Estimation Network Based On MultiHeatmap Fusion

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5th Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2024

作者： Zhao, Yangguang Lu, Jun College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computingof Heilongjiang Province Haerbin China

ISBN: (纸本)9798350374407

Estimating human pose in complex multi-frame situations is a challenging task and has attracted intensive research by many researchers. Although 3D human pose estimation methods have achieved remarkable results in scenes based on single images, their performance often fails once these model transformations are applied to video sequences. Common problems with these models include inability to cope with motion blur, out-of-focus videos, and occlusion of human poses. In order to solve the above problems, this paper proposes a feature extraction and representation model MHMF, which is used in the feature extraction stage of the model. The initial features extracted by the backbone network HRNet-w32 are guided by the heat map to the attention layer, which improves the network's attention to important areas for predicting key points of human posture. At the same time, the integration of the aggregation heat map and the backbone network heat map improves the spatiotemporal consistency of the key points. In addition, to improve the accuracy of mesh pose estimation under occlusion, this paper proposes a transformer-based NewDSTformer model. By adjusting the structure of the Transformer encoder, increasing the encoder level and combining it with the dynamic progressive attention masking method. The model can adapt to different input situations, handle the positional relationship of local key points, and be able to perform accurate detection even under occlusion. It was evaluated on the 3DPW data set and improved the accuracy by $0.3 \%$, indicating that this paper effectively improved the performance of 3D human mesh reconstruction. © 2024 IEEE.

关键词： Mesh generation

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Eco-FL: Adaptive Federated Learning with Efficient Edge Collaborative Pipeline Training 51

Eco-FL: Adaptive Federated Learning with Efficient Edge Coll...

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51st International conference on parallel processing (ICPP)

作者： Ye, Shengyuan Zeng, Liekang Wu, Qiong Luo, Ke Fang, Qingze Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9781450397339

Federated Learning (FL) has been a promising paradigm in distributed machine learning that enables in-situ model training and global model aggregation. While it can well preserve private data for end users, to apply it efficiently on IoT devices yet suffer from their inherent variants: their available computing resources are typically constrained, heterogeneous, and changing dynamically. Existing works deploy FL on IoT devices by pruning a sparse model or adopting a tiny counterpart, which alleviates the workload but may have negative impacts on model accuracy. To address these issues, we propose Eco-FL, a novel Edge Collaborative pipeline based Federated Learning framework. On the client side, each IoT device collaborates with trusted available devices in proximity to perform pipeline training, enabling local training acceleration with efficient augmented resource orchestration. On the server side, Eco-FL adopts a novel grouping-based hierarchical architecture that combines synchronous intra-group aggregation and asynchronous inter-group aggregation, where a heterogeneity-aware dynamic grouping strategy that jointly considers response latency and data distribution is developed. To tackle the resource fluctuation during the runtime, Eco-FL further applies an adaptive scheduling policy to judiciously adjust workload allocation and client grouping at different levels. Extensive experimental results using both prototype and simulation show that, compared to state-of-the-art methods, Eco-FL can upgrade the training accuracy by up to 26.3%, reduce the local training time by up to 61.5%, and improve the local training throughput by up to 2.6x.

关键词： Federated learning edge intelligence pipeline parallelism parallel processing

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Research and Suggestions for Evaluation Metrics of On-board Video Desensitization

Research and Suggestions for Evaluation Metrics of On-board ...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Xiaolong Huang Te Hu Haojie Ji Yupeng Chen Wangdong Lu Peng He Guowei Sun Jingyan Wang State Key Laboratory of Intelligent Vehicle Safety Technology Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China China Automotive Engineering Research Institute Co. Ltd Chongqing China Zhongtong Bus Holding Co. Ltd Liaocheng China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Sensitive data leakage has become an urgent problem to be solved as more images based functionalities are being developed in vehicles. However, there is a scarcity of evaluation for on-board videos data desensitization. This research analyzes on-board video desensitization process including the image pre-processing stage, sensitive area localization stage and sensitive area desensitization stage. Considering that, this paper presents several evaluation methods and metrics of sensitive target detection performance, privacy-utility evaluation of video file metadata and image, so as to provide reference for the related research on the desensitization evaluation of on-board video.

关键词： Measurement Location awareness Information science Target recognition Current measurement distributed databases Object detection

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

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2024 IEEE International conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Pawlowicz, Paulina Sunderam, Vaidy Hung, Che-Lun Kwiecien, Andrzej Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Silesian University of Technology Department of Applied Informatics Gliwice Poland Emory University Department of Computer Science AtlantaGA United States National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei City Taiwan

ISBN: (纸本)9798350362480

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data. © 2024 IEEE.

关键词： Information granules

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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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High-speed and Low-area Implementations of a Generic and parallel Integral image Architecture 20

High-speed and Low-area Implementations of a Generic and Par...

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20th IEEE Interregional NEWCAS conference (IEEE NEWCAS)

作者： Chehaitly, Mouhamad Lalevee, Andre Napoleon, Thibault Jridi, Maher L bISEN ISEN Yncrea Quest Brest France

ISBN: (纸本)9781665401050

Nowadays many applications of computer vision and image processing are based on Integral image (IIM) algorithm. Since the first use of IIM by Viola-Jones in the face detection algorithm, its computation is still a real challenge and receive a great attention in the research area especially in the field of real time applications. The IIM is greedy in hardware resources where the total number of additions increases with the image size and requires frequent memory accesses which result in the bottleneck effect when realized with CPU. To improve the processing efficiency, we propose a generic parallel/pipeline architectures with and without memory based on dual direction (by columns then by rows) or two levels of data flow oriented in IIM computing architecture. An interesting characteristic of the proposed architectures is the genericity of the calculation, according to the degree of parallelism (the maximum column size and the maximum row size that can be calculated in a clock cycle), the quantization of image values, on the one hand and on the other hand these architectures can be used in two functional modes. Based on the synthesis results of the implementation using Altera Quartus prime lite edition targeting an Intel/Altera Cyclone V - (FPGA), the proposed architecture achieves a high-throughput and low-area, when compared to the state-of-theart methods. More particularly, for 480x 640 image size, the proposed IIM architecture involves 765 logic registers, 643 slice LUT, and just 13,7 kbits and it operates at a maximum frequency of 182.28 MHz. These results show that this approach is one of the best candidates for portable applications that require high speed processing and low hardware resources usage.

关键词： Integral image parallel/Pipeline architecture FPGA

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Design of Automatic Colouring of Sketch images Based on Deep Learning Algorithm

Design of Automatic Colouring of Sketch Images Based on Deep...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Lian Chen Chengjun Zhou School of Art and Design Hubei University of Technology China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

In this paper, we have designed an innovative deep learning model using U-net with PatchGAN discriminator aiming at automatic colouring of sketch images, which provides a novel solution for sketch colouring. Firstly, the image is passed through the dataset to the generator, which receives the input image and tries to convert it into the expected output image. Subsequently, in each iteration, the loss functions of the generator and the discriminator are calculated and the model parameters are updated by the optimiser. Finally, predictions are made on the test dataset and the results are plotted. By passing new unseen data to the trained generator, it was observed that the generation results were as expected and the deep learning coloured images were very close to the actual coloured images. This research brings important implications for the fields of digital art and computer vision. Firstly, it provides a completely new approach to processing sketch images, opening up more possibilities for artists and designers. Second, the model expands new ideas and examples for the application of deep learning techniques in the field of image processing. Finally, in practical applications, this auto-colouring technique is expected to greatly improve productivity and can be widely used in animation, comics and other fields.

关键词： Deep learning Productivity Information science Computer vision image processing Computational modeling Digital art

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Realistic Sketch Face Generation via Sketch-Guided Incomplete Restoration 29

Realistic Sketch Face Generation via Sketch-Guided Incomplet...

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

作者： Liu, Zijie Gu, Chunhua Xie, Ying Zhang, Huichen Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China Traffic Management Research Institute of the Ministry of Public Security Key Laboratory of Ministry of Public Security for Road Traffic Safety Wuxi China

ISBN: (纸本)9798350330717

Generating realistic sketches of human faces is an important research direction in the fields of computer vision and computer graphics. However, generating high-quality sketch faces remains challenging, especially when dealing with incomplete input sketch images. In this paper, we propose a framework based on the U-Net generative network, which progressively optimizes network performance through a three-stage training strategy. We introduce the Sketch Guidance and Completion Network (SGCN), which is capable of restoring missing features, completing blurry edges, and handling occluded regions in sketch images. By employing multiple discriminators and adversarial training strategies, we enhance the realism and preserve fine details in the generated results. Through experiments on publicly available sketch face datasets, we demonstrate significant improvements in reconstructing incomplete sketch faces using our approach. The generated images exhibit enhanced completeness, realism, and retain fine details and important facial features. Quantitative evaluations validate the effectiveness and superiority of our method. © 2023 IEEE.

关键词： Face synthesis GAN Sketch-based image synthesis

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An Adaptive Cooperative Coevolutionary Algorithm for parallel Feature Selection in High-Dimensional Datasets 30

An Adaptive Cooperative Coevolutionary Algorithm for Paralle...

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30th Euromicro International conference on parallel, distributed and Network-Based processing, PDP 2022

作者： Firouznia, Marjan Trunfio, Giuseppe A. Amirkabir University of Technology Electrical Engineering Department Tehran Iran University of Sassari Department of Biomedical Sciences Italy

ISBN: (纸本)9781665469586

Nowadays, it is common in many disciplines and application fields to collect large volumes of data characterized by a high number of features. Such datasets are at the basis of modern applications of supervised Machine Learning, where the goal is to create a classifier for newly presented data. However, it is well known that the presence of irrelevant features in the dataset can lead to a harder learning phase and, above all, can produce suboptimal classifiers. For this reason, the ability to select an appropriate subset of the available features is becoming increasingly important. Traditionally, optimization metaheuristics have been used with success in the task of feature selection. However, many of the approaches presented in the literature are not applicable to datasets with thousands of features since common optimization algorithms often suffer from poor scalability with respect to the size of the search space. In this paper, the problem of feature subset optimization is successfully addressed by a cooperative coevolutionary algorithm based on Differential Evolution. In the proposed algorithm, parallelized for multi-Threaded execution on shared-memory architectures, a suitable strategy for reducing the dimensionality of the search space and adapting the population size during the optimization results in a significant performance. A numerical investigation on some high-dimensional datasets show that, in most cases, the proposed approach can achieve smaller feature subsets and higher classification performance than other state-of-The-Art methods. © 2022 IEEE.

关键词： Optimization

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