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Retinotopic Foveated Rendering 31

Retinotopic Foveated Rendering

引用

ieee conference on Virtual Reality and 3D User Interfaces (VR)

作者： Zhang, Yan You, Keyao Hu, Xiaodan Zhou, Hangyu Kiyokawa, Kiyoshi Yang, Xubo Shanghai Jiao Tong Univ Shanghai Peoples R China Nara Inst Sci & Technol Nara Japan

ISBN: (纸本)9798350374025;9798350374032

Foveated rendering (FR) improves the rendering performance of virtual reality (VR) by allocating fewer computational loads in the peripheral field of view (FOV). Existing FR techniques are built based on the radially symmetric regression model of human visual acuity. However, horizontal-vertical asymmetry (HVA) and vertical meridian asymmetry (VMA) in the cortical magnification factor (CMF) of the human visual system have been evidenced by retinotopy research of neuroscience, suggesting the radially asymmetric regression of visual acuity. In this paper, we begin with functional magnetic resonance imaging (fMRI) data, construct an anisotropic CMF model of the human visual system, and then introduce the first radially asymmetric regression model of the rendering precision for FR applications. We conducted a pilot experiment to adapt the proposed model to VR head-mounted displays (HMDs). A user study demonstrates that retinotopic foveated rendering (RFR) provides participants with perceptually equal image quality compared to typical FR methods while reducing fragments shading by 27.2% averagely, leading to the acceleration of 1/6 for graphics rendering. We anticipate that our study will enhance the rendering performance of VR by bridging the gap between retinotopy research in neuroscience and computer graphics in VR.

关键词： computing methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality computing -methodologies-Computer graphics-Rendering

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Research on an Innovative Decision Algorithm Based on EWM

Research on an Innovative Decision Algorithm Based on EWM

引用

2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Deng, Yuchen Zhao, Huicui Advanced Research Institute Nanchang University Jiangxi Nanchang330036 China The International Process Academy Nanchang University Jiangxi Nanchang330036 China

ISBN: (纸本)9798350375275

With the iteration of decision algorithm, various decision problems emerge in an endless stream. This paper aims to propose a new decision algorithm to reduce the impact of decision problems on practice. Firstly, six factors are constructed in this paper, on which a decision algorithm based on entropy value is established. Entropy weight method (EWM) is used to optimize the decision problem. Forest decision problem is taken as the object of this study. The research results show that the new decision algorithm constructed in this paper can effectively solve the decision problem, and reduce the required decision time, and has the robustness and generalization performance of universities. This study provides a certain methodology for the development of decision algorithms. © 2023 ieee.

关键词： Iterative methods

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Towards a privacy-preserving distributed cloud service for preprocessing very large medical images

Towards a privacy-preserving distributed cloud service for p...

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ieee international conference on Digital Health (ieee ICDH) at the ieee World Congress on Services (SERVICES)

作者： Wang, Yuandou Kanwal, Neel Engan, Kjersti Rong, Chunming Zhao, Zhiming Univ Amsterdam Multiscale Networked Syst Amsterdam Netherlands Univ Stavanger Dept Elect Engn & Comp Sci Stavanger Norway LifeWatch ERIC Virtual Lab & Innovat Ctr VLIC Amsterdam Netherlands

ISBN: (纸本)9798350341034

Digitized histopathology glass slides, known as Whole Slide Images (WSIs), are often several gigapixels large and contain sensitive metadata information, which makes distributed processing unfeasible. Moreover, artifacts in WSIs may result in unreliable predictions when directly applied by Deep Learning (DL) algorithms. Therefore, preprocessing WSIs is beneficial, e.g., eliminating privacy-sensitive information, splitting a gigapixel medical image into tiles, and removing the diagnostically irrelevant areas. This work proposes a cloud service to parallelize the preprocessing pipeline for large medical images. The data and model parallelization will not only boost the end-to-end processing efficiency for histological tasks but also secure the reconstruction of WSI by randomly distributing tiles across processing nodes. Furthermore, the initial steps of the pipeline will be integrated into the Jupyter-based Virtual Research Environment (VRE) to enable image owners to configure and automate the execution process based on resource allocation.

关键词： Computational Pathology Cloud computing Privacy-preserving Image Preprocessing Virtual Research Environment Infrastructure Planning

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HyperNode: An Efficient Node Classification Framework Using HyperDimensional computing 42

HyperNode: An Efficient Node Classification Framework Using ...

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42nd ieee/ACM international conference on Computer-Aided Design (ICCAD)

作者： Li, Haomin Liu, Fangxin Chen, Yichi Jiang, Li Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China Tianjin Univ Tianjin Peoples R China

ISBN: (纸本)9798350322255

Graph Neural Networks (GNNs) are increasingly being recognized as an effective method for learning representations from graph-structured data. Despite their potential, the substantial computational and energy demands of current deep learning-based methods limit their practical applicability in real-world scenarios. HyperDimensional computing (HDC) offers a promising alternative, which is inspired by neuroscience. HDC leverages characteristics inherent in biological neural systems, such as high-dimensionality, randomness, and holographic representations, striking a balance between accuracy, efficiency, and robustness. In this paper, we introduce HyperNode, a cutting-edge node classification framework leveraging HDC for hardware-friendly computation. HyperNode encodes node features and edges using high-dimensional vectors in line with HDC principles. It establishes an HDC reference library by combining node classes. This library is subsequently employed during the node classification process, which aids in similarity checks of encoded query vectors. Remarkably, our framework drastically curtails the computational demands typical of conventional GNNs, supporting highly-parallelizable computation. Experimental results demonstrate that HyperNode achieves an average speed-up of 990.8x and a 7.53% accuracy improvement compared to GNN models with similar performance on widely-recognized graph learning benchmarks. The proposed framework offers a promising solution for efficient and effective graph-based machine learning tasks.

关键词： Hyperdimensional computing Graph neural networks Node classification

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VM CONSOLIDATION ALGORITHM FOR ENERGYEFFICIENCY AND QUALITY OF SERVICE

VM CONSOLIDATION ALGORITHM FOR ENERGYEFFICIENCY AND QUALITY ...

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2023 ieee international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Anusherly, S. Manikandan, S. Gunaseelan, M.S.V. Harshan, J. Information Technology Panimalar Engineering College India

ISBN: (纸本)9798350305708

Scheduling is undoubtedly one of the difficulties that the cloud, which is always developing, must overcome. Scheduling describes aspecific method of arranging the tasks that a laptopdevice should complete in the correct order. In this study, we used FCFS and round-robin schedulingto construct a standard priority seek set of criteria for assignment execution and assessment. The method has been tested on the cloud, and the outcomes demonstrate that it outperforms several traditional scheduling techniques. In distributed computing systems, a variety of scheduling techniques are used, task scheduling being one of them. For resource optimization, we specifically cover three methods: ANT colony, analytic, and genetic algorithms. We developed a new precedence set of rules with a limited number of activities;in the future, we will take on additional jobs and work to reduce the implemented executiontime. We may also expand this set of rules to the network environment and examine the time difference between the cloud and the network. The suggested approach is entirely based on queuing models. The burden, response time, and length of the common queue were all decreased by directingincoming requests to a low task. These results show that our version may increase the utilization of the global agenda while decreasing latency. The results of the experiments verified that the suggested version may lower power consumption, hence enhancing service quality inside the cloud architecture. Future iterations of the planned version would utilize cloud computing techniques built on parallel algorithms to speed up activation of user requests. We recommend the adoption of aheterogeneous resource allocation method called Skewness avoidance multi-resource allocation (SAMR) to distribute resources based on particularrequirements for particular types of assets. Our approach comprises of a set of VM provisioning guidelines to ensurethat heterogeneous constraints are properly distributed to preven

关键词： Cloud computing Green computing Resourceallocation Service optimization VM Migration

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Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications 54

Owl: Differential-based Side-Channel Leakage Detection for C...

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54th Annual ieee/IFIP international conference on Dependable systems and Networks (DSN)

作者： Zhao, Yu Xue, Wenjie Chen, Weijie Qiang, Weizhong Zou, Deqing Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & EngnHubei Key Lab Distributed Sy Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst LabCluster & Grid Comp Wuhan 430074 Peoples R China Jinyinhu Lab Wuhan 430074 Peoples R China

ISBN: (纸本)9798350341065;9798350341058

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： side-channel detection GPU CUDA applications

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Leveraging CNN-CPU Scheduling for Enhanced Resource Management in Storage Edge Network Frameworks 5

Leveraging CNN-CPU Scheduling for Enhanced Resource Manageme...

引用

5th international conference on Computer Engineering and Application (ICCEA)

作者： Alwahedi, Mazen Duanmu, Chunjiang Zhejiang Normal Univ Coll Comp Sci & Technol Jin Hua City Peoples R China Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jin Hua City Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Because the landscape of data processing created is likely to rise considerably in the next years due to the proliferation of devices that require data processing at edge computing, it is imperative for data processing issues at the network's edge storage frame. The goal of this study is to find out how deep learning can be used to speed up operation processing. Effective resource management is essential in the age of edge computing to maximize the performance of storage edge networked frameworks. The purpose of this work is to investigate how resource management in such systems could be improved by utilizing CNN-CPU scheduling approaches. Our goal is to optimize job allocation, prioritization, and scheduling in storage edge situations by fusing Convolutional Neural Networks (CNNs) with CPU-based scheduling methods. The suggested method aims to improve overall system performance, reduce latency, and maximize resource use. We illustrate the efficacy of CNN-CPU scheduling in improving resource management in storage edge networked frameworks with experimental validation and performance analysis. This research contributes to advancing the capabilities of edge computing systems by leveraging deep learning techniques for efficient resource allocation and management. The proposed approach seeks to overcome the limitations of traditional scheduling methods by leveraging the power of deep learning. CNNs are renowned for their ability to extract meaningful features from data, and by harnessing this capability, we can optimize resource utilization and minimize latency in storage edge networked frameworks. Through a combination of CNN-based feature extraction and CPU-based scheduling decisions, we strive to achieve efficient and intelligent resource management. This study investigates the potential of CNN-CPU scheduling techniques to address these challenges and enhance resource management in such frameworks. By integrating Convolutional Neural Networks (CNNs) with CPU-based sc

关键词： EDRs schedule enhanced send cpu-cnn enhanced computing RRAPS

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A Medical Pre-diagnosis Scheme Based on Neural Network and Inner Product Function Encryption 4

A Medical Pre-diagnosis Scheme Based on Neural Network and I...

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4th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Cui, Xu Liu, Hao Tang, Min Ma, Yihong School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin54004 China Guilin541002 China

ISBN: (纸本)9798350314496

Artificial neural networks are widely used in various fields, such as intelligent road networks, Internet of Things, and smart medical systems due to their ability to process large amounts of data in parallel, store information in a distributed manner, and self-organize and self-learn. Cloud computing technology has further expanded the development of neural network applications. However, user data often contains sensitive information, and once the data management right is transferred to the cloud, it faces serious security and privacy issues. In the medical field, privacy-preserving implementation of classification algorithms is crucial for ensuring the privacy of electronic medical diagnosis services. Current privacy-preserving medical pre-diagnosis schemes based on homomorphic encryption impose a significant computational and communication burden on users and servers. This paper proposes an efficient privacy-preserving medical pre-diagnosis scheme based on neural networks and inner product function encryption that protects user privacy during pre-diagnosis while having small computational and communication overheads. © 2023 ieee.

关键词： Cloud computing

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Smartwatch-Based Human Staff Activity Classification: A Use-Case Study in Internal Logistics systems Utilizing AGVs

Smartwatch-Based Human Staff Activity Classification: A Use-...

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2024 ieee international conference on Big Data, BigData 2024

作者： Pavliuk, Olena Mishchuk, Myroslav Lviv Polytechnic National University Department of Automated Control Systems Lviv Ukraine Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland

ISBN: (纸本)9798350362480

Recent advancements in the domain of human activity recognition (HAR) are increasingly aimed at developing methodologies, approaches, and models for real-time, multi-step activity recognition and analysis. This work presents a smartwatch-based approach for complex, real-time HAR that is applicable but not limited to internal logistics systems that use autonomous guided vehicles. A distributed smartwatch-based data collection system was developed, and a dataset was gathered and published, containing readings from human staff representatives executing activity sequences representing typical internal logistics tasks. A HAR-specific, pre-trained DenseNet121 was used for basic activity classification, achieving an F1-score of 91.01%. For multi-step activity classification, we compared models based on CNN, LSTM, BiLSTM, GRU, and BiGRU as meta-classifiers, employing different dataset-splitting strategies and models' configurations. The best-performing CNN-based model achieved an F1-score of 87.44% using the shared dataset utilization approach. Despite the challenges faced, the adaptability of the proposed approach suggests that it can be integrated into an intelligent enterprise management system to provide a robust and flexible HAR framework that enhances production efficiency. © 2024 ieee.

关键词： Enterprise resource management

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Autonomic Passive IT-OT Device Classification in ICS/SCADA Networks

Autonomic Passive IT-OT Device Classification in ICS/SCADA N...

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4th ieee Annual international conference on Cyber Security and Resilience (ieee CSR)

作者： Rubin, Benny Tekeoglu, Ali Rouff, Christopher Cornell Univ Comp Sci Dept Ithaca NY 14850 USA Leidos Innovat Ctr Reston VA USA Johns Hopkins Univ Whiting Sch Engn Baltimore MD USA Johns Hopkins Univ Appl Phys Lab Baltimore MD USA

ISBN: (纸本)9798350375367

Industrial Control systems (ICS) manage physical processes through Programmable Logic Controllers (PLCs) and are widely used in critical infrastructure (CI). Because of their wide use in CI, PLCs are a target for cyberattacks to disrupt CI. On CI and other networks, there is often a mix of Information Technology (IT) and Operational Technology (OT) systems. Detecting and classifying OT and IT assets on a network can help in providing targeted security measures for OT systems. Actively scanning for assets in ICS networks can lead to unexpected errors on PLCs. PLCs operate with limited resources and are notorious for producing errors or faults when presented with unexpected network packets. Special care must be taken when sending unsolicited network packets to these devices to avoid physical process interruption, catastrophic failures, even potential human injury. In this paper, we explore a collection of prevalent Machine Learning (ML) algorithms: k-Nearest-Neighbors (KNN), Support-Vector-Machines (SVM), Random Forest and AdaBoost in order to differentiate ICS network assets from IT machines solely from passive monitoring of network traffic flows. Furthermore, we integrate the proposed classification method into an autonomic architecture for continuous monitoring and identification of ICS assets in a SCADA network environment, both for offensive and defensive purposes.

关键词： ICS SCADA Network Monitoring Autonomic computing

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