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检索条件"任意字段=7th International Conference on Algorithms and Architectures for Parallel Processing"

共 2976 条记录，以下是301-310 订阅

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Formulating parallel Supervised Machine Learning Designs For Anomaly-Based Network Intrusion Detection in Resource Constrained Use Cases 19

Formulating Parallel Supervised Machine Learning Designs For...

引用

19th IEEE international conference on Mobile Ad Hoc and Smart Systems (MASS)

作者： Joshi, Varun Korah, John Georgia State Univ Data Sci Atlanta GA 30303 USA Calif State Polytech Univ Pomona Comp Sci Pomona CA 91768 USA

ISBN: (数字)9781665471800

ISBN: (纸本)9781665471800

One of the main problems with using supervised machine learning for anomaly-based Network Intrusion Detection (NID) in large cyber networks is the massive and dynamic data sets used in training and the computational overhead they induce in the training phase. In resource-constrained situations, there is a lack of powerful machines that would be traditionally used to deal with a high computational load. We focus on parallel processing designs that work in such resource-constrained use cases by lowering the computational overhead of supervised machine learning for anomaly-based NID, specifically for Mini-batch gradient descent. We avoid the black-box approach of traditional parallelization frameworks and allow the user to maximize the utilization of their scarce computational resources by granting greater control over parallelization while leveraging key aspects of the optimization algorithms being implemented. To demonstrate this, we implemented initial data and model parallel based designs, using the Compute Unified Device Architecture (CUDA) and Message Passing Interface (MPI) libraries, aimed at maximizing the use of a limited number of CPUs and GPUs. We conducted an initial comparative performance study using a large real-world network intrusion dataset called the KDD cup 1999;our results demonstrate up to 8.5 times more epochs per second using just 1 GPU (4000 threads) and up to 37 times faster convergence using just 1 compute node (7 cores) when compared to a serial approach.

关键词： parallelization gradient descent anomaly detection network intrusion detection resource constraints

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Multi-scale Image Decomposition Using a Local Statistical Edge Model 7

Multi-scale Image Decomposition Using a Local Statistical Ed...

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7th IEEE international conference on Virtual Reality (ICVR

作者： Wong, Kin-Ming City Univ Hong Kong Sch Creat Media Hong Kong Peoples R China

ISBN: (纸本)9781665423090

We present a progressive image decomposition method based on a novel non-linear filter named Sub-window Variance filter. Our method is specifically designed for image detail enhancement purpose;this application requires extraction of image details which are small in terms of both spatial and variation scales. We propose a local statistical edge model which develops its edge awareness using spatially defined image statistics. Our decomposition method is controlled by two intuitive parameters which allow the users to define what image details to suppress or enhance. By using the summed-area table acceleration method, our decomposition pipeline is highly parallel. the proposed filter is gradient preserving and this allows our enhancement results free from the gradient-reversal artefact. In our evaluations, we compare our method in various multi-scale image detail manipulation applications with other mainstream solutions.

关键词： multi-scale image decomposition image processing edge-preserving filter

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Deep Learning-based Text-in-Image Watermarking

Deep Learning-based Text-in-Image Watermarking

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IEEE conference on Multimedia Information processing and Retrieval (MIPR)

作者： Bishwa Karki Chun-Hua Tsai Pei-Chi Huang Xin Zhong Department of Computer Science University of Nebraska Omaha Omaha NE USA Information Systems and Quantitative Analysis University of Nebraska Omaha Omaha NE USA

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

In this work, we introduce a novel deep learning- based approach to text-in-image watermarking, a method that embeds and extracts textual information within images to en-hance data security and integrity. Leveraging the capabilities of deep learning, specifically through the use of Transformer- based architectures for text processing and Vision Transformers for image feature extraction, our method sets new benchmarks in the domain. the proposed method represents the first application of deep learning in text-in-image watermarking that improves adaptivity, allowing the model to intelligently adjust to specific image characteristics and emerging threats. through testing and evaluation, our method has demonstrated superior robustness compared to traditional watermarking techniques, achieving enhanced imperceptibility that ensures the watermark remains undetectable across various image contents.

关键词： Deep learning Computer vision Data security Watermarking Information processing Transformers Feature extraction Robustness Data mining Text processing

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Innovative Traffic Optimization Techniques: Integrating Braess' Paradox and Vision Transformer architectures (ViT) for Novel Roadway Modalities 23

Innovative Traffic Optimization Techniques: Integrating Brae...

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Proceedings of the 2023 7th international conference on Natural Language processing and Information Retrieval

作者： Siddhant Singh Archit thanikella Memorial High School United States of America Panther Creek High School United States of America

ISBN: (纸本)9798400709227

the Traffic Management System employs Braess' paradox to reduce congestion by altering driving patterns and utilizes the Frank-Wolfe method to create efficient routes. Integrated with a state-of-the-art pothole detection model, which utilizes Vision Transformer and Transformer architectures, the system dynamically adapts to road conditions. this innovative approach, trained on a Mumbai Hyperloop station dataset, includes a Two-Way Transformer network for precise road condition analysis, enhancing traffic flow and safety. It has achieved a 35% decrease in traffic congestion, 47% faster incident response, and 33% shorter trip durations. the system's integration of advanced technologies paves the way for future autonomous vehicle networks and offers significant benefits for urban transportation and planning, simultaneously addressing the challenges of potholes and congestion.

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A PID-Optimized Olen Campton Beamforming Method

A PID-Optimized Olen Campton Beamforming Method

引用

IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Wei Si Hua Yi Xin Yang Yu Xuan Chen Xiang Hao Hou Shaanxi Key Laboratory of Underwater Information Technology Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

An adaptive narrowband beamforming method based on proportional-integral-differential (PID) virtual interference control which is called PID-optimized Olen Campton beamforming method (PIDBF) is proposed, achieving high-precision and rapid low sidelobe beamforming. In the proposed algorithm, PID controlling gains are applied to iteratively adjust the virtual noise sources. thus, the Olen Campton beamforming method (OBF) is optimized by innovatively introducing PID control method in the iterative process of virtual interference sources, leading to faster and more stable sidelobe control. the superior performance of the proposed method is verified through a number of simulations.

关键词： PI control Array signal processing Optimization methods Process control Interference parallel processing Computational efficiency PD control Sensor arrays Convergence

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Multi-frame Video Super-resolution Based on Efficient and parallel Network 2

Multi-frame Video Super-resolution Based on Efficient and Pa...

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2nd IEEE international conference on Software Engineering and Artificial Intelligence (SEAI) / 7th international Workshop on Pattern Recognition (IWPR)

作者： Yang, Bin Jiang, Juhao Chen, Guannan Fujian Normal Univ Coll Photon & Elect Engn Fuzhou Peoples R China

ISBN: (数字)9781665482233

ISBN: (纸本)9781665482233

Multi-frame video super-resolution(VSR) aims to restore a high-resolution video from both its corresponding low-resolution frame and multiple neighboring frames, in order to make full use of the inter-frame information. However, vast computation complexity hinders the inference speed of video super-resolution. In order to increase the inference speed while ensuring the accuracy of the model, we proposed an efficient and parallel multi-frame VSR network, termed EPVSR. the proposed EPVSR is based on spatio-temporal adversarial learning to achieve temporal consistency and uses TecoGAN as the baseline model. By adding an improved non-deep network, which is composed of parallel subnetworks with multi-resolution streams, these streams are fused together at regular intervals to exchange information. we reduced the number of parameters and make the model lighter. Besides, we implement structural re-parameterization network acceleration technique to optimize the inference process of EPVSR network. Finally, our EPVSR achieves the real-time processing capacity of 4K@36.45FPS. compared with TecoGAN, we achieve 9.75 x performance speedups, but the effect is not reduced. the PSNR of EGVSR are increased by 3.36%. the experimental results show that the nondeep network can effectively speed up the model inference, and the proposed EPVSR has a good super-resolution effect.

关键词： multi-frame video super-resolution TecoGAN PaeNet real-time system non-deep network

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Profiler: An Exploration of Micro-Architecture-Oblivious GPU Program Characteristics Profiling and Analysis Framework

Profiler: An Exploration of Micro-Architecture-Oblivious GPU...

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Software Engineering and Computer Science (CSECS), international conference on

作者： Wang Peng Qi Kaiyuan Zhang Dong Liu Peng dept. Cloud Computing R&D Inspur Group Co. Ltd Jinan Inspur Data Technology Co. Ltd Jinan China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Given the swift advancements in GPU architectures, the process of assessing and improving GPU rendering performance has grown increasingly sophisticated and vital. To address these challenges and provide microarchitecture-agnostic insights, this paper introduces MICPAT, a tool for GPU characteristic profiling. MICPAT extracts key program characteristics such as instruction composition, basic block count, instruction frequency and memory allocation size across NVIDIA's Kepler, Maxwell, Pascal, and Volta GPU series. By analyzing these microarchitecture-agnostic characteristics, developers gain deep insights into the behavior and performance of their GPU programs. MICPAT supports precompiled applications utilizing CUDA, OpenACC, OpenCL, or CUDA Fortran. Serving as a versatile platform, MICPAT enables consistent analysis across this diverse set of GPU architectures and precompiled application environments. Utilizing Octane renderer, as well as Rodinia and Parboil benchmarks, extensive experimental evaluations across 100 GPU rendering applications have validated MICPAT's efficacy and its microarchitecture-agnostic nature. the open source repo is https://***/records/13623324.

关键词： Computer science Microarchitecture Graphics processing units Computer architecture Organizations Benchmark testing Rendering (computer graphics) Resource management Software engineering Radio spectrum management

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A Deep Learning Method for Ship-Radiated Noise Recognition Based on MFCC Feature

A Deep Learning Method for Ship-Radiated Noise Recognition B...

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international conference on Transportation Information and Safety (ICTIS)

作者： Fucai Hu Jinyang Fan Yiyang Kong Linke Zhang Xiaoxi Guan Yongsheng Yu School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan China Transportation College of Jilin University Jilin University Changchun China State Key Laboratory of Silicate Materials for Architectures Wuhan University of Technology Wuhan China

Underwater target recognition is one of the most challenging tasks in underwater signal processing. Previous deep learning methods have relied on fusing more acoustic features, ignoring the rich information contained in the time-frequency features of underwater acoustics. Furthermore, fusing features that are less relevant to the target may result in redundancy and affect the recognition performance of the model. In this paper, a novel method based on Multi-Scale feature extraction, Attention mechanism for feature fusion and Convolutional Recurrent Neural Network (MACRN) is proposed to fully exploit the deep features of Mel-Frequency Cepstral Coefficients (MFCC) for underwater target recognition. In comparative experiments conducted on the open-source dataset ShipsEar, the proposed method achieves an average recognition accuracy of 98.1%

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Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks

Counterfactual Gradients-based Quantification of Prediction ...

引用

IEEE conference on Multimedia Information processing and Retrieval (MIPR)

作者： Mohit Prabhushankar Ghassan AlRegib School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

the widespread adoption of deep neural networks in machine learning calls for an objective quantification of esoteric trust. In this paper we propose GradTrust, a classification trust measure for large-scale neural networks at inference. the proposed method utilizes variance of counterfactual gradients, i.e. the required changes in the network parameters if the label were different. We show that GradTrust is superior to existing techniques for detecting misprediction rates on 50000 images from ImageNet validation dataset. Depending on the network, GradTrust detects images where either the ground truth is incorrect or ambiguous, or the classes are co-occurring. We extend GradTrust to Video Action Recognition on Kinetics-400 dataset. We showcase results on 14 architectures pretrained on ImageNet and 5 architectures pretrained on Kinetics-400. We observe the following: (i) simple methodologies like negative log likelihood and margin classifiers outperform state-of-the-art uncertainty and out-of-distribution detection techniques for misprediction rates, and (ii) the proposed GradTrust is in the Top-2 performing methods on 37 of the considered 38 experimental modalities. the code is available at: https://***/olivesgatech/GradTrust

关键词： Uncertainty Codes Current measurement Measurement uncertainty Machine learning Information processing Artificial neural networks Reliability

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Adaptive load balancing Strategy for VR Microservices with Edge AI

Adaptive load balancing Strategy for VR Microservices with E...

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international conference on Electronics Technology (ICET)

作者： Liang Liu Yuxiang Chen Xun Shao Kailin Wang Yu Huang Zhongguancun Laboratory Beijing Beijing P.R.China University College London London United Kingdom Toyohashi University of Technology Toyohashi Japan Beijing University Of Posts and Telecommunications Beijing P.R.China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

the pervasiveness of information and communications technologies, such as 5 G and edge AI, has led to the booming growth of video applications, particularly in the realm of virtual reality (VR). However, due to the random distribution of the video traffic and the fluctuation of the computing power in Mobile Edge Computing (MEC) networks, traditional load balancing strategies lead to high video service response delay and low resource utilization. To this end, this paper designs and implements an adaptive dynamic load-balancing strategy for VR. Based on the performance monitoring function of the Kubernetes (k8s) system, we first utilize the reinforcement learning algorithm TD3 to find the optimal video service allocation at different edge nodes. then, considering the real-time GPU utilization of each edge computing node, the queue length, and the average transmission delay, we jointly optimize the average response delay of users and the work efficiency of nodes. Finally, we evaluate the proposed scheme on a real k8s system. Compared with traditional load balancing strategies, the proposed scheme reduces the latency by $16 \%$ to $52 \%$ and improves the work efficiency by $5 \%$ to $8 \%$.

关键词： Heuristic algorithms Microservice architectures Graphics processing units Edge AI Virtual reality Load management Real-time systems

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