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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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Comparative Analysis of Machine Learning Models for the Detection of Diabetic Retinopathy: A Performance Evaluation

Comparative Analysis of Machine Learning Models for the Dete...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Deepali Yuvraj Singh Kulreen Kaur Himanshu Verma Rajni Mohana ASET Amity University Punjab Mohali Punjab India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Diabetic retinopathy (DR) is a common complication of diabetes and a leading cause of blindness worldwide. Early detection and timely intervention are critical for preventing vision loss in affected individuals. In this research paper, we explore applying four machine-learning algorithms to detect diabetic retinopathy. Specifically, we investigate the effectiveness of convolutional neural networks (CNNs), transfer learning, and MobileNetV2 in analyzing retinal images to identify signs of diabetic retinopathy. Through comprehensive experimentation and evaluation on the same dataset, we compare the performance of these algorithms in terms of accuracy, sensitivity, specificity, and computational efficiency. Our findings provide insights into the potential of machine learning techniques for assisting clinicians in the early diagnosis and management of diabetic retinopathy, thereby contributing to improved healthcare outcomes for diabetic patients.

关键词： Performance evaluation Diabetic retinopathy Machine learning algorithms Accuracy Sensitivity Computational modeling Transfer learning Retina Convolutional neural networks Residual neural networks

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18th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2021

18th IFIP WG 10.3 International Conference on Network and Pa...

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18th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2021

ISBN: (纸本)9783030935702

The proceedings contain 20 papers. The special focus in this conference is on Network and parallel computing. The topics include: A Novel Task-Allocation Framework Based on Decision-Tree Classification Algorithm in MEC;qoS-Aware Scheduling for Cellular networks Using Deep Reinforcement Learning;preface;High Resolution of City-Level Climate Simulation by GPU with Multi-physical Phenomena;Adaptive Buffering Scheme for PCM/DRAM-Based Hybrid Memory Architecture;efficiency-First Fault-Tolerant Replica Scheduling Strategy for Reliability Constrained Cloud Application;Towards an Optimized Containerization of HPC Job Schedulers Based on Namespaces;architecture of an On-Time Data Transfer Framework in Cooperation with Scheduler System;data Delta Based Hybrid Writes for Erasure-Coded Storage Systems;BDCuckoo: an Efficient Cuckoo Hash for Block Device;A Two Tier Hybrid Metadata Management Mechanism for NVM Storage System;A Novel CFLRU-Based Cache Management Approach for NAND-Based SSDs;taming Congestion and Latency in Low-Diameter High-Performance Datacenters;evaluation of Topology-Aware All-Reduce Algorithm for Dragonfly networks;dgQuEST: Accelerating Large Scale Quantum Circuit Simulation through Hybrid CPU-GPU Memory Hierarchies;MPICC: Multi-Path INT-Based Congestion Control in Datacenter networks;vSketchDLC: A Sketch on distributed Deep Learning Communication via Fine-grained Tracing Visualization;Scalable Algorithms Using Sparse Storage for parallel Spectral Clustering on GPU.

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On Estimating the Average Response Time of High-Performance computing Environments 25th

On Estimating the Average Response Time of High-Performanc...

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25th international conference on distributed and Computer and Communication networks, DCCN 2022

作者： Gorbunova, A.V. Vishnevsky, V.M. V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences 65 Profsoyuznaya Street Moscow117997 Russia

ISBN: (纸本)9783031232060

parallel and distributed computing have become widespread as one of the main ways to improve the performance of computing environments when processing big data. The issue of assessing the main performance indicators of such systems remains relevant to the present time due to the complexity of such analysis. A fork-join queuing system is well suited for modeling parallel data processing. The article explores the possibility of estimating the average response time of the system using an analytical expression for the upper bound of the maximum order statistics. Two options are described, depending on the amount of knowledge available about the operation of a high-performance environment. The results of the numerical experiment confirm the efficiency of the approach for a fairly wide range of parameters, one of the main advantages of which is the ease of use. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data handling

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Enhancing Performance of Multi-Layered Deep Neural networks in Image Classification through Efficient Optimization Techniques

Enhancing Performance of Multi-Layered Deep Neural Networks ...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Durgesh Srivastava Sandhiya B Aparna Shankar Patil Vivek Kumar. M Sonali K Pawar Keshav Kaushik Chitkara University Institute of Engineering & Technology Chitkara University Rajpura Punjab India Department of Computer Science Christ University Bengaluru Karnataka India Department of Electronics and Communication Engineering Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India Department of Electronics & Communication Engineering Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune Maharashtra India Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

They demonstrated remarkable performance, and the same happened with deep neural networks, which consisted of multiple layers, allowing them to learn complex patterns and features directly from raw data. But, in many cases, training these deep nets is usually a time and resource-consuming process. Researchers have suggested several optimization techniques to improve the performance of multi-layered deep neural networks to overcome this challenge. These techniques aim to increase the learning rate of networks, reduce overfitting, and reach an optimal solution faster. Applying these efficient optimization approaches will further improve the performance of deep neural networks with large numbers of layers for image classification, resulting in a more general and accurate model suitable for real-world scenarios.

关键词： Training Accuracy Stochastic processes Artificial neural networks Grid computing Hardware Data models Optimization Image classification Overfitting

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distributed Exact Structural Clustering on Large Graph

Distributed Exact Structural Clustering on Large Graph

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international conference on parallel and distributed Systems (ICPADS)

作者： Jinyu Zhou Chuitian Rong Ding Liu Zhengyi Chai Tiangong University Tianjin China

Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. The existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. The graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. The distributed processing algorithm is called for processing large graphs. This work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.

关键词： distributed processing Costs Social networking (online) Scalability Clustering algorithms Focusing Cyberspace

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Verifying Sequential Programs in Untrusted Environments

Verifying Sequential Programs in Untrusted Environments

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international conference on parallel distributed and Grid computing (PDGC)

作者： Renju Rajeev Independent Researcher Kerala India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The lack of centralized control and regulatory oversight in an open and anonymous computing market presents significant challenges when verifying computation results. Within this environment, computing providers are highly incentivized to exploit the system by intentionally delivering incomplete or incorrect results for personal gain. Consequently, there is an urgent need to develop a robust system capable of accurately validating computation results obtained from untrusted sources. This research focuses on addressing the complex verification challenge in large-scale sequential tasks, particularly within non-deterministic processes like neural networks. To tackle this challenge, I propose a meticulous algorithm that verifies program execution integrity, ensuring precise alignment with the intended code. The proposed approach is based on the assumption of rational behavior from providers, where actions leading to negative expected utility are avoided, and task regularity, where the difficulty of each step remains independent of previous values. By addressing this fundamental problem, the proposed algorithm plays a crucial role in establishing trust in algorithmic execution within an open market framework. It offers a potential solution for accurate computation verification, especially in the context of complex sequential tasks and non-deterministic algorithms. Ultimately, this research enables the reliable utilization of computing resources in decentralized and unregulated environments, effectively meeting the critical need for trustworthy computation in such settings.

关键词： Codes Accuracy Computational modeling Neural networks Grid computing Reliability Cryptography Resilience Centralized control

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Memristor Based Federated Learning for Network Security on the Edge using Processing in Memory (PIM) computing

Memristor Based Federated Learning for Network Security on t...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / international Joint conference on Neural networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Alam, Shahanur Yakopcic, Chris Taha, Tarek M. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Artificial Intelligence (AI) is moving towards the edge. Training an AI model for edge computing on a centralized server increases latency, and the privacy of edge users is jeopardized due to private data transfer through a less secure communication channels. Additionally, existing high-power computing systems are battling with memory and data transfer bottlenecks between the processor and memory. Federated Learning (FL) is a collaborative AI learning paradigm for distributed local devices that operates without transferring local data. Local participant devices share the updated network parameters with the central server instead of sending the original data. The central server updates the global AI model and deploys the model to the local clients. As the local data resides only on the edge, these devices need to be protected from cyberattacks. The Federated Intrusion Detection System (FIDS) could be a viable system to protect edge devices as opposed to a centralized protection system. However, on-device training of the model in resource constrained devices may suffer from excessive power drain, in addition to memory and area overhead. In this work we present a memristor based system for AI training on edge devices. Memristor devices are ideal candidates for processing in memory, as their dynamic resistance properties allow them to perform multiply-add operations in parallel in the analog domain with extreme efficiency. Alternatively, existing CMOS-based PIM systems are typically developed for edge inference based on pretrained weights, and are not equipped for on-chip training. We show the effectiveness of the system, where successful learning and recognition is achieved completely within edge devices. The classification accuracy of the memristor system shows negligible loss when compared a software implementation. To the best of our knowledge, this first demonstration of a memristor based federated learning system. We demonstrate the effectiveness of this system as

关键词： PIM memristor Federated Learning low power Intrusion Detection

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Kubernetes WANWide: a Deployment Scenario to Expose and Use Edge computing Resources? 29

Kubernetes WANWide: a Deployment Scenario to Expose and Use ...

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29th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Manaouil, Karim Lebre, Adrien INRIA LS2N IMT Atlantique STACK Res Grp Nantes France

ISBN: (纸本)9781665414555

Cloud computing have highlighted the importance of container orchestration to manage distributed implication's life-cycle. With the advent of Edge computing, DevOs expect to find die features of containers in the cloud, also at the edge. However, orchestration systems have MA been designed to deal with geo-distribution aspects such as latency, intermittent networks, etc. In other words, it is unclear whether they could he directly used on top of massively distributed edge infrastructures without revision. In this paper, we provide an evaluation of Kubernetes in a WANWide context. Precisely, we present and discuss results we obtained during an experimental campaign to analyze the impact of WAN links on its behaviour. While there exist initiatives investigating Kubernetes revisions to deal with distribution aspects, there is, to the best of our knowledge. no rigorous performance evaluations to disqualify the vanilla code.

关键词： Kubernetes Wide Area Network Performance evaluations

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Part-of-Speech Tagging of parallel Corpus based on Convolutional Neural Network

Part-of-Speech Tagging of Parallel Corpus based on Convoluti...

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distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Nancao Ma Liaoning Institute of Science and Engineering Jinzhou China

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

parallel corpora part-of-speech tagging is an indispensable part in the application field of natural language information processing. The core work of this paper is to build a mathematical model of part-of-speech tagging based on CNNbased parallel corpus, which includes three aspects. Firstly, the basic CNN model is studied. Based on the artificial neuron model, the convolutional layer, the pooled layer and the fully connected layer are studied. Then, we study the maximum entropy model, integrate various information into one model, and carry out parts-of-speech tagging and syntactic analysis to improve the accuracy of natural language processing. Finally, the learning process of CNN is studied, the output is obtained by using forward propagation, and the error is fed back to the trainable parameters of the network through back propagation to realize parameter update. On this basis, NIST corpus is selected to compare the model proposed in this paper with the model commonly used in CNN, which proves that the model proposed in this paper has advantages in the accuracy of part-of-speech tagging.

关键词： Accuracy Computational modeling Semantics Information processing Tagging Feature extraction Mathematical models Entropy Convolutional neural networks Machine translation

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