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Strengthening Healthcare Cybersecurity by Optimizing Adversarial Attack Defences in Deep Learning Models using GPU and parallel processing Technologies 7

Strengthening Healthcare Cybersecurity by Optimizing Adversa...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Venugopal, Hridya Christo, Mary Subaja Kattankulathur Campus SRM Institute of Science and Technology Department of Networking and Communications Tamilnadu Chengalpattu603203 India

ISBN: (纸本)9798350372816

In healthcare, safeguarding patient data integrity is paramount, necessitating robust deep learning models. This research focuses on bolstering the cyber security defenses of these models, particularly against adversarial attacks, through the application of the Localized Adversarial Feature Attack (LAFEAT) technique. Leveraging Graphics processing Units (GPU) and the TensorFlow framework, the study explores algorithmic enhancements like the Fast Gradient Sign Method (FGSM) and Model-Agnostic Meta-Learning (MAML). These techniques perturb input data gradients or adapt model parameters, enhancing resilience against adversarial perturbations. Additionally, Particle Swarm Optimization (PSO), a metaheuristic optimization technique, is examined to fortify defense mechanisms further. parallel processing techniques, utilizing parallel GPU or distributed clusters, are implemented to expedite the optimization process, reducing computational burdens and enhancing scalability. Despite advancements, gaps remain in understanding how these models can be optimized for real-world healthcare applications, particularly in terms of balancing computational efficiency and robustness. The research demonstrates tangible benefits, with a significant 10% increase in success rates against adversarial attacks compared to baseline methods. Moreover, parallel processing implementation results in a 30% reduction in optimization time, improving the efficiency of cyber security defenses for deep learning models in healthcare. These numerical enhancements underscore the research's value in fortifying healthcare systems against adversarial threats, ensuring the security and integrity of patient data while addressing existing gaps in the optimization of adversarial defenses in healthcare applications. The strengths of this research lie in its robust adversarial attack optimization techniques, integration of parallel processing for efficiency, and application to critical healthcare tasks. Comprehensive

关键词： Graphics processing unit

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Initial Results on the Effectiveness of distributed Edge Detection for Large image Data Sets

Initial Results on the Effectiveness of Distributed Edge Det...

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SoutheastCon Conference

作者： Renato, Ivanescu Constantin Mihai, Mocanu Univ Craiova Dept Comp & Informat Technol Craiova Romania

ISBN: (纸本)9781665406529

image data is expanding rapidly, along with technology development, so efficient solutions must be considered to achieve high, real-time performance in the case of processing large image datasets. parallel processing is increasingly used as an attractive alternative to improve the performance, when using existing distributed architectures but also for sequential commodity computers. It can provide speedup, efficiency, reliability, incremental growth, and flexibility. We present such an alternative and stress the effectiveness of the methods to accelerate computations on a small cluster of PCs compared to a single CPU. Our paper is focused on applying edge detection on large image data sets, as a fundamental and challenging task in image processing and computer vision. Five different techniques, mainly Sobel, Prewitt, LoG, Canny, and Roberts, are compared in a simple experimental setup that includes the OpenCV library functions for image pixels manipulation. Gaussian blur is used to reduce high-frequency components to manage the noise that edge detection is impacted by. Overall, this work is part of a more extensive investigation of image segmentation methods on large image datasets, but the results presented are relevant and show the effectiveness of our approach.

关键词： parallel image processing edge detection OpenCV MPI

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Automated, high-throughput image calibration for parallel-laser photogrammetry

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MAMMALIAN BIOLOGY 2022年第3期102卷 615-627页

作者： Richardson, Jack L. Levy, Emily J. Ranjithkumar, Riddhi Yang, Huichun Monson, Eric Cronin, Arthur Galbany, Jordi Robbins, Martha M. Alberts, Susan C. Reeves, Mark E. McFarlin, Shannon C. George Washington Univ Ctr Adv Study Human Paleobiol Dept Anthropol Sci & Engn Hall800 22nd St NWSuite 6000 Washington DC 20052 USA Duke Univ Dept Biol 130 Sci Dr Durham NC 27708 USA Duke Univ Pratt Sch Engn 305 Teer Engn Bldg Durham NC 27708 USA George Washington Univ Dept Comp Sci Sci & Engn Hall800 22nd St NWRoom 4000 Washington DC 20052 USA Amazon Web Serv Ident Serv 410 Terry Ave N Seattle WA 98019 USA Duke Univ Duke Univ Lib 226 Bostock LibBox 104732 Durham NC 27708 USA George Washington Univ Dept Phys 725 21st St NW Washington DC 20052 USA Appl Mat Inc 3050 Bowers Ave Santa Clara CA 95054 USA Univ Barcelona Dept Clin Psychol & Psychobiol Campus MundetPg Vall dHebron 171 Barcelona 08035 Spain Max Planck Inst Evolutionary Anthropol Deutsch Pl 6 D-04013 Leipzig Germany Duke Univ Dept Evolutionary Anthropol 130 Sci Dr Durham NC 27708 USA

parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject's body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is also the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in imageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data.

关键词： Automation Baboon Gorilla image processing Machine learning parallel-laser photogrammetry

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Robustness and Accuracy in Pipelined Bi-conjugate Gradient Stabilized methods 24th

Robustness and Accuracy in Pipelined Bi-conjugate Gradient S...

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24th International Conference on Computational Science (ICCS)

作者： Havdiak, Mykhailo Aliaga, Jose, I Iakymchuk, Roman Ivan Franko Natl Univ Lviv Lvov Ukraine Univ Jaime I Castellon de La Plana Spain Umea Univ Umea Sweden Uppsala Univ Uppsala Sweden

ISBN: (纸本)9783031637582;9783031637599

In this article, we propose an accuracy-assuring technique for finding a solution for unsymmetric linear systems. Such problems are related to different areas such as image processing, computer vision, and computational fluid dynamics. parallel implementation of Krylov subspace methods speeds up finding approximate solutions for linear systems. In this context, the refined approach in pipelined BiCGStab enhances scalability on distributed memory machines, yielding to substantial speed improvements compared to the standard BiCGStab method. However, it's worth noting that the pipelined BiCGStab algorithm sacrifices some accuracy, which is stabilized with the residual replacement technique. This paper aims to address this issue by employing the ExBLAS-based reproducible approach. We validate the idea on a set of matrices from the SuiteSparse Matrix Collection.

关键词： Krylov subspace methods BiCGStab Residual replacement Numerical reliability ExBLAS HPC

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parallel vs distributed Edge Detection for Large Medical image Datasets 10

Parallel vs Distributed Edge Detection for Large Medical Ima...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Ivanescu, Renato Constantin Nagy, Rodica Ofiteru, Anca Istrate Comanescu, Cristina Manda, Alexandru Department of Computers and Information Technology University of Craiova Craiova Romania University of Medicine and Pharmacy of Craiova University of Craiova Craiova Romania

ISBN: (纸本)9781665485579

The medical routine of the future is strongly influenced by medical information technology. The quality and efficiency of medicine are at higher standards due to the image-based methods and the increase in computation power. parallel and distributed computing are attractive alternatives to improve processing performance in many areas of study and this paper investigates such solutions for increasing the throughput on large datasets in medical imaging. In this research, different edge detection techniques are used for image processing, as it is the most common and challenging task in the medical domain. The results are relevant and present the effectiveness of the two processing approaches compared to a single CPU for the acceleration of medical image processing. The CUDA processing platform using a hybrid streams-based programming model is the parallel approach. The distributed system is a Linux-based cluster, which uses message passing for communication and efficient load balancing between nodes for high performance. This paper highlights parallel computing as a very good solution for processing large image datasets in the medical domain. Good results were also obtained for the distributed system. © 2022 IEEE.

关键词： Computer operating systems

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An image generator based on neural networks in GPU

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第25期81卷 36353-36374页

作者： Silva, Thiago W. Reis, Halamo Melcher, Elmar U. K. Lima, Antonio M. N. Brito, Alisson, V Fed Univ Campina Grande UFCG Campina Grande Paraiba Brazil Fed Univ Ceara UFC Quixada Brazil Fed Univ Paraiba UFPB Joao Pessoa Paraiba Brazil

Existing image databases contain a few diversity of images. Likewise, there is no specific image base available in other situations, leading to the need to undertake additional efforts in capturing images and creating datasets. Many of these datasets contain only a single object in each image, but often the scenario in which projects must operate in production requires several objects per image. Thus, it is necessary to expand original datasets into more complex ones with specific combinations to achieve the goal of the application. This work proposes a technique for image generation to extend an initial dataset. It has been designed generically to work with various images and create a data set from some initial images. The generated set of images is used in a distributed environment. It is possible to perform image generation in this environment, producing datasets with specific images to work in certain applications. The generation of images consists of two methods: generation by deformation and generation by a neural network. With the proposed methods, this work sought to bring as main contributions the specification and implementation of an image generating component so that it is possible to easily integrate it with possible heterogeneous devices capable of parallel computing, such as General Purpose Graphics processing Unit (GPGPU). In comparison with the existing methods to the proposed one, this one proposes to use the image generator enlarging an initial image bank with the combination of two methods. Some experiments are presented doing generation with handwritten digits to validate the proposed approach. The generator was designed with CUDA and GPU-optimized libraries as TensorFlow-specific modules. The results obtained can optimize the integration process with the simulation of possible stimuli choices, avoiding problems in the generation of image phase tests.

关键词： image generation GPU Neural network Generation by deformation Functional verification

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Multimodal Interaction Fusion Network for Cloth-Changing Person Re-Identification 22

Multimodal Interaction Fusion Network for Cloth-Changing Per...

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22nd IEEE International Symposium on parallel and distributed processing with Applications, ISPA 2024

作者： Yuan, Caihong Xu, Yuanchen Guan, Zhijie Chen, Xiaopan Zhu, Xiaoke Liang, Wenjuan Henan University School of Computer and Information Engineering Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan Province Spatial Information Processing Engineering Technology Research Center Kaifeng China

ISBN: (纸本)9798331509712

Cloth-Changing Person Re-Identification (CC-ReID) refers to the technology that can identify the same individual in surveillance scenarios, even after changing their clothes. The core of CC-ReID is to exact cloth-irrelevant identity features. Existing methods usually extract them from body contours, skeletons, human faces, etc. However, information extracted from a single modality is often insufficient. Therefore, we propose a novel Multimodal Interaction Fusion (MIF) framework that could facilitate the deep integration of multimodal information, thereby enhancing the generalization ability of CC-ReID. Specifically, we design a triple-interaction collaborative module that strategically integrates features captured from body posture, original RGB images, and clothes-erased images, maximizing the advantages of each modality and overcoming their limitations. Additionally, we develop an Adapt-Blend Pool (ABP) module that effectively improves pooling methods to enhance the model's ability to process images with complex backgrounds and varied features. Following training, our framework effectively harnesses a variety of modal inputs to capture identity representations independent of clothing accurately. Extensive experimental results on PRCC, LTCC, and VC-Clothes datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： image enhancement

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Deep Neural Network Training With distributed K-FAC

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第12期33卷 3616-3627页

作者： Pauloski, J. Gregory Huang, Lei Xu, Weijia Chard, Kyle Foster, Ian T. Zhang, Zhao Univ Chicago Dept Comp Sci Chicago IL 60637 USA Texas Adv Comp Ctr Austin TX 78758 USA

Scaling deep neural network training to more processors and larger batch sizes is key to reducing end-to-end training time;yet, maintaining comparable convergence and hardware utilization at larger scales is challenging. Increases in training scales have enabled natural gradient optimization methods as a reasonable alternative to stochastic gradient descent and variants thereof. Kronecker-factored Approximate Curvature (K-FAC), a natural gradient method, preconditions gradients with an efficient approximation of the Fisher Information Matrix to improve per-iteration progress when optimizing an objective function. Here we propose a scalable K-FAC algorithm and investigate K-FAC's applicability in large-scale deep neural network training. Specifically, we explore layer-wise distribution strategies, inverse-free second-order gradient evaluation, and dynamic K-FAC update decoupling, with the goal of preserving convergence while minimizing training time. We evaluate the convergence and scaling properties of our K-FAC gradient preconditioner, for image classification, object detection, and language modeling applications. In all applications, our implementation converges to baseline performance targets in 9-25% less time than the standard first-order optimizers on GPU clusters across a variety of scales.

关键词： Training parallel processing Program processors Convergence Computational modeling Data models Deep learning Optimization methods neural networks scalability high-performance computing

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An FPGA-Based Energy-Efficient Real-Time Hand Pose Estimation System With an Integrated image Signal Processor for Indirect 3-D Time-of-Flight Sensors

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IEEE INTERNET OF THINGS JOURNAL 2025年第2期12卷 1817-1830页

作者： Kim, Yongsoo So, Jaehyeon Hwang, Chanwook Cheng, Wencan Choi, Jaehyuk Ko, Jong Hwan Sungkyunkwan Univ Dept Semicond & Display Engn Suwon 16419 South Korea Samsung Elect IP Dev Team Yongin 17113 South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea Natl Univ Singapore Dept Comp Sci Singapore Singapore

As artificial intelligence (AI) technology advances, Internet of Things (IoT) devices, such as mobile phones and augmented reality devices, are increasingly becoming crucial enablers of user-device interactions. Among the various methods of interaction, hand pose recognition and analysis is a crucial method to understand the intentions of users and perform precise functions. However, to perform such functions, a substantial amount of computation and resources are required, making it challenging to implement them on small form-factor devices with low-power consumption. For this reason, improving energy efficiency is a crucial objective in real-time hand pose estimation (HPE) applied to low-power platforms with limited resources. In this article, we introduce an FPGA-based energy-efficient real-time HPE system with an integrated image signal processor (ISP). The proposed system uses several low-power design techniques, including a systolic array with dynamic on/off control per processing element (PE), to minimize power consumption and save energy when not in use. In addition, we improve area efficiency by reducing the buffer size in the systolic array using a half-size shift buffer stack. Furthermore, the use of parallel and pipelined structures improved operational efficiency, resulting in a reduction in both operational time and power consumption. The evaluation results on a KU115 FPGA board show that the system achieves an error of 7.78 mm and can process 52 fps, demonstrating its capability for real-time HPE. Moreover, this system achieves high-energy efficiency, up to 61.74 GOPs/W, making it suitable for energy-efficient and accurate HPE in low-power environments.

关键词： Pose estimation Sensors Real-time systems Internet of Things Energy efficiency Three-dimensional displays Accuracy Convolutional neural network (CNN) FPGA hand pose estimation (HPE) image signal processor (ISP) Internet of Things (IoT) low-power architecture

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Speed Up Federated Learning in Heterogeneous Environments: A Dynamic Tiering Approach

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5026-5035页

作者： Mahmoud Sajjadi Mohammadabadi, Seyed Zawad, Syed Yan, Feng Yang, Lei Univ Nevada Reno Dept Comp Sci & Engn Reno NV 89557 USA IBM Res Almaden Res Dept San Jose CA 95120 USA Univ Houston Comp Sci Dept Houston TX 77004 USA Univ Houston Elect & Comp Engn Dept Houston TX 77004 USA

Federated learning (FL) enables collaborative training of a model while keeping the training data decentralized and private. However, in Internet of Things systems, inherent heterogeneity in processing power, communication bandwidth, and task size can significantly hinder the efficient training of large models. Such heterogeneity would render vast variations in the training time of clients, lengthening overall training and wasting resources of faster clients. To tackle these heterogeneity challenges, we propose dynamic tiering-based FL (DTFL), a novel system that leverages distributed optimization principles to improve the edge learning performance. Based on clients' resources, DTFL dynamically offloads part of the global model to the server, alleviating resource constraints on slower clients and speeding up training. By leveraging split learning, DTFL offloads different portions of the global model to clients in different tiers and enables each client to update the models in parallel via local-loss-based training. This helps reduce the computation and communication demand on resource-constrained devices, mitigating the straggler problem. DTFL introduces a dynamic tier scheduler that uses tier profiling to estimate the expected training time of each client based on their historical training time, communication speed, and dataset size. The dynamic tier scheduler assigns clients to suitable tiers to minimize the overall training time in each round. We theoretically prove the convergence properties of DTFL and validate its effectiveness by training large models (ResNet-56 and ResNet-110) across varying numbers of clients (from 10 to 200) using the popular image datasets (CIFAR-10, CIFAR-100, CINIC-10, and HAM10000) under both I.I.D and non-I.I.D systems. DTFL seamlessly integrates various privacy measures without sacrificing performance. Extensive experimental results show that compared with state-of-the-art FL methods, DTFL can significantly reduce the training time b

关键词： Training Computational modeling Internet of Things Servers Federated learning Accuracy Dynamic scheduling Performance evaluation Optimization Load modeling distributed optimization edge computing federated learning (FL) heterogeneous devices split learning (SL)

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