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Robust distributed Bayesian Learning with Stragglers via Consensus Monte Carlo

Robust Distributed Bayesian Learning with Stragglers via Con...

引用

IEEE Global Communications conference (GLOBECOM)

作者： Chittoor, Hari Hara Suthan Simeone, Osvaldo Kings Coll London Dept Engn KCLIP Lab London England

ISBN: (纸本)9781665435406

This paper studies distributed Bayesian learning in a setting encompassing a central server and multiple workers by focusing on the problem of mitigating the impact of stragglers. The standard one-shot, or embarrassingly parallel, Bayesian learning protocol known as consensus Monte Carlo (CMC) is generalized by proposing two straggler-resilient solutions based on grouping and coding. Two main challenges in designing straggler-resilient algorithms for CMC are the need to estimate the statistics of the workers' outputs across multiple shots, and the joint non-linear post-processing of the outputs of the workers carried out at the server. This is in stark contrast to other distributed settings like gradient coding, which only require the per-shot sum of the workers' outputs. The proposed methods, referred to as Group-based CMC (G-CMC) and Coded CMC (C-CMC), leverage redundant computing at the workers in order to enable the estimation of global posterior samples at the server based on partial outputs from the workers. Simulation results show that C-CMC may outperform G-CMC for a small number of workers, while G-CMC is generally preferable for a larger number of workers.

关键词： distributed Bayesian learning stragglers Consensus Monte Carlo grouping coded computing

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Hourglass Dehazing Network Based on Multi-scale parallel Fusion

Hourglass Dehazing Network Based on Multi-scale Parallel Fus...

引用

International conference on image processing, Computer Vision and Machine Learning (ICICML)

作者： Mao, Yishu Song, Xingcehn Zhang, Xinman Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China

ISBN: (纸本)9781665464680

As a common weather phenomenon, haze currently has some negative effects on advanced computer vision tasks. image dehazing, as a significant technique, plays a crucial role in deploying downstream vision tasks. In recent years, CNN-based end- to-end models have been proved to have great advantages over traditional image enhancement-based methods in removing image haze. However, some previous works failed to effectively utilize multi-scale features and perform reasonable feature fusion, resulting in the model losing the ability to restore details and colors. In this work, we adopt hourglass network which is a mature framework to better utilize the bottom-up and top-down symmetric topology structure for feature fusion and representation, so as to better recover the details and color information of haze-free images. At the same time, for the phenomenon of relatively indirect feature fusion in hourglass network, we propose a multi-scale parallel feature fusion module to perform more direct and effective feature fusion, thereby promoting the flow of features within different scales. What's more, for the sake of strengthening the recovery power of the network for high-frequency information, we raise a spatial-frequency domain channel attention mechanism, so as to more effectively utilize spatial and frequency information for feature representation and enhancement. Comprehensive experiments on the public RESIDE dataset confirm the effectiveness of our proposed network that can satisfactorily reconstruct more elaborate and realistic colors.

关键词： image enhancement image dehazing multi-scale fusion channel attention

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N-MlpE: Optimizing Multilayer Perceptron Network-based Knowledge Graph Embedding Model with Neighborhood Information 29

N-MlpE: Optimizing Multilayer Perceptron Network-based Knowl...

引用

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

作者： Xu, Qing Ren, Xiaoli Ren, Kaijun Lin, Jiarun Li, Xiaoyong National University of Defense Technology College of Meteorology and Oceanography Changsha China

ISBN: (纸本)9798350330717

As an effective knowledge organizing and modeling technique, knowledge graph has become a key topic in graph research, but the practical application of KG is limited by its incompleteness. In recent years, many knowledge graph embedding(KGE) methods for knowledge graph completion(KGC) based on graph neural networks(GNN) have been proposed. However, most GNN-based KGC models are still suffer from the encoder-decoder structure of low efficiency in aggregating neighborhood information and the difficulty of model training. This paper present an optimized model that incorporates Neighborhood information into knowledge inference, to improve the performance of KGC models based on multilayer perceptron network(MLP), which is named N-MlpE. We generate an input sequence that includes the query triplet and its neighbor entities and relationships, and then feed it to an adaptive filter module to remove useless neighbors for the inference to improve the accuracy of the inference, and reduce the computational complexity of training the model. The filtered sequence is then fed into a weight calculation module and a feature extraction module simultaneously, the former is designed based on selfattention to model the relevant rule inference, which enhances the interpretability of KGE models, and the latter is based on MLP and used to capture the long-distance interactions between triplets, which can significantly improve the accuracy of inference. Extensive experiments are conducted on two standard KG datasets WN18RR and FB15k237 to verify the effectiveness of N-MlpE, the results show that the accuracy of N-MlpE model outperforms most GNN-based models. © 2023 IEEE.

关键词： distributed representation inference knowledge graph completion knowledge graph embedding link prediction

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Design and Improvement of Firefly Algorithm Based on Ternary Optical Computer 2

Design and Improvement of Firefly Algorithm Based on Ternary...

引用

2nd IEEE International conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Tong, Yihui Sun, Shaojiang Qi, Baofeng Zhang, Jie Wang, Xuanchuan Wang, Zhehe Wang, Xianchao Fuyang Normal University School of Computer and Information Engineering Anhui Fuyang China Fuyang Normal University School of Mathematics and Statistics Anhui Fuyang China Hainan Tropical Ocean University School of Computer Science and Technology Hainan Sanya China

ISBN: (纸本)9798350347456

The firefly algorithm is used in the fields of image processing and task scheduling. The traditional design of this algorithm suffers from defects such as too slow convergence speeds and an easy tendency to fall into local optimal solutions. To this end, this paper designs a modified firefly algorithm based on the ternary optical computer, which has the characteristics of megabits, parallelism, and assignability by bit, and improves the classical firefly algorithm, mainly by using chaotic mapping for population initialization, introducing an adaptive dynamic step size, and making out-of-bounds adjustments to equilibrium. The experimental results show that the modified Firefly algorithm based on the ternary optical computer has enhanced merit-seeking ability and increased convergence speed. © 2023 IEEE.

关键词： parallel processing systems

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Domain Adaptive Semantic Segmentation via image Translation and Representation Alignment 19

Domain Adaptive Semantic Segmentation via Image Translation ...

引用

19th IEEE International Symposium on parallel and distributed processing with Applications (IEEE ISPA)

作者： Kang, Jingxuan Zang, Bin Cao, Weipeng Univ Liverpool Sch Comp Sci Liverpool Merseyside England Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781665435741

Domain Adaptation for semantic segmentation is of vital significance since it enables effective knowledge transfer from a labeled source domain (i.e., synthetic data) to an unlabeled target domain (i.e., real images), where no effort is devoted to annotating target samples. Prior domain adaptation methods are mainly based on image-to-image translation model to minimize differences in image conditions between source and target domain. However, there is no guarantee that feature representations from different classes in the target domain can be well separated, resulting in poor discriminative representation. In this paper, we propose a unified learning pipeline, called image Translation and Representation Alignment (ITRA), for domain adaptation of segmentation. Specifically, it firstly aligns an image in the source domain with a reference image in the target domain using image style transfer technique (e.g., CycleGAN) and then a novel pixelcentroid triplet loss is designed to explicitly minimize the intraclass feature variance as well as maximize the inter-class feature margin. When the style transfer is finished by the former step, the latter one is easy to learn and further decreases the domain shift. Extensive experiments demonstrate that the proposed pipeline facilitates both image translation and representation alignment and significantly outperforms previous methods in both GTA5 -> Cityscapes and SYNTHIA -> Cityscapes scenarios.

关键词： Domain Adaptation Semantic Segmentation Style Transfer Centroid Alignment

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Intelligent System of Sports Injury Evaluation Based on MRI image Analysis 2

Intelligent System of Sports Injury Evaluation Based on MRI ...

引用

2nd International conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Xue, Haitao Xianyang Normal University Sports College Shaanxi Xiangyang712000 China

ISBN: (纸本)9781665400527

Intelligent system of sports injury evaluation based on MRI image analysis is studied in the paper. People have developed a variety of medical image segmentation methods. The comprehensive application of various methods and giving full play to their respective advantages for image processing will become the development trend in this field. The foundation and language of scientific morphology is set theory. Its application can simplify image data, and then maintain their basic shape characteristics, and remove irrelevant structures, so that it has a natural parallel implementation structure. With this general consideration, the MRI image analysis model is designed and then applied to the scenario of the sports injury evaluation. For the comprehensive analysis and evaluation, the comparison result of the experiment is conducted. © 2022 IEEE.

关键词： Intelligent systems

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Extracting the Brain-Like Representation by an Improved Self-Organizing Map for image Classification 48

Extracting the Brain-Like Representation by an Improved Self...

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhang, Jiahong Cao, Lihong Zhang, Moning Fu, Wenlong Communication University of China State Key Laboratory of Media Convergence and Communication China Communication University of China Neuroscience and Intelligent Media Institute Beijing China

ISBN: (纸本)9781728163277

Backpropagation-based supervised learning has achieved great success in computer vision tasks. However, its biological plausibility is always controversial. Recently, the bioinspired Hebbian learning rule (HLR) has received extensive attention. Self-Organizing Map (SOM) uses the competitive HLR to establish connections between neurons, obtaining visual features in an unsupervised way. Although the representation of SOM neurons shows some brain-like characteristics, it is still quite different from the neuron representation in the human visual cortex. This paper proposes an improved SOM with multi-winner, multi-code, and local receptive field, named mlSOM. We observe that the neuron representation of mlSOM is similar to the human visual cortex. Furthermore, mlSOM shows a sparse distributed representation of objects, which has also been found in the human inferior temporal area. In addition, experiments show that mlSOM achieves better classification accuracy than the original SOM and other state-of-the-art HLR-based methods. The code is accessible at https://***/JiaHongZ/mlSOM. © 2023 IEEE.

关键词： image classification Self Organizing Maps Unsupervised Learning

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VLAB – ENHANCED VIDEO AND DATA PRE-processing 58

VLAB – ENHANCED VIDEO AND DATA PRE-PROCESSING

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58th Annual International Telemetering conference, ITC 2023

作者： Hardt, Simon Faber, Marc Safran Data Systems GmbH Friedrich-Ebert-Str 75 Bergisch Gladbach51429 Germany

In today's Flight Test Instrumentation (FTI) video telemetry applications, parallel video channels of the same video signal are acquired with the on-board data recorder. One is typically a high-quality video channel that is recorded directly in the recorder and the other is typically a reduced bit rate channel for real-time telemetry downlink to a ground receiving station. Thus, a certain amount of bandwidth is still required for the telemetry, even though not all image content may be urgently needed. In our approach, we offer an embedded video toolbox to the instrumentation engineer. A way to perform pre-processing, custom composition and extraction of video streams in the onboard recorder. The results are generated using Artificial Intelligence (AI) and create/hide video streams that, among other things, contain only information or regions of interest of video streams in an extremely bandwidth efficient manner. In addition, the AI can be used for other data interpretation methods. © 2023 International Foundation for Telemetering. All rights reserved.

关键词： Telemetering

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Liu, Mingyu Wang, Yijie Zhou, Xiaohui Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors. © 2025 IEEE.

关键词： Anomaly detection Graph structure learning Multivariate time series Window size selection

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Optimization of Micro-object Identification Based on the Mellin Transform and the Use of parallel Computing

Optimization of Micro-object Identification Based on the Mel...

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2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Jumanov, Isroil I. Kholmonov, Sunatillo M. Samarkand State University Intelligent Systems and Computer Technologies Samarkand Uzbekistan

ISBN: (纸本)9781665464291

Scientific and methodological foundations for the optimal identification of non-stationary objects based on the use of neural networks have been developed. Models and algorithms for detection, extraction of hidden relationships, useful properties and patterns in data, formation of a database and knowledge bases are proposed. Mechanisms have been developed for using the statistical, dynamic and specific characteristics of images, unique features of three, five-layer neural networks and combined models for setting variables with typical recognition and classification tools. Have been developed computational schemes for determining and adjusting the weights of neurons, choosing a suitable activation function, coefficients of synaptic and interneuronal connections, rational neural network architecture, the number of layers and neurons in the layers of the network, a set of functions of nonlinear dependencies "inputs-outputs". Data pre-processing algorithms are implemented that perform the functions of informative features selection, segmentation, object image contour extraction, search based on methods with annealing, prohibition, and stochastic search. Tested neural networks of Hopfield, Hamming, Hebb, Kohonen, bidirectional associative memory were tested. Schemes for two and three-dimensional image reconstruction based on the synthesis of tools for calculating Mellin transform functions, initial values of centroids, and the formation of a suboptimal set of variables are proposed. The identification software package in C++ was developed and implemented in the CUDA parallel computing environment. © 2023 IEEE.

关键词： Extraction

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