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2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication conference, IEMCON 2022

2022 IEEE 13th Annual Information Technology, Electronics an...

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13th IEEE Annual Information Technology, Electronics and Mobile Communication conference, IEMCON 2022

ISBN: (纸本)9781665463164

The proceedings contain 93 papers. The topics discussed include: edge-cloud synergy: unleashing the potential of parallel processing for big data analytics;access delegation framework for private decentralized patient health records sharing system based on blockchain;fair and robust bio-inspired resource sharing in distributed dynamic spectrum access networks;trust development for blockchain interoperability using self-sovereign identity integration;abnormality detection in network traffic by classification and graph data analysis;an automated system to calculate marks from answer scripts;the problem with regular multiple byte block boundaries in encryption;on the identification of isomorphic graphs for graph neural network using multi-graph approach;a hybrid method based on machine learning to predict the stock prices in Bangladesh;cataract detection and grading using ensemble neural networks and transfer learning;and a tool for the dynamic allocation of multiple marine activities.

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WIDE AND DEEP GRAPH neural networkS WITH distributed ONLINE LEARNING

WIDE AND DEEP GRAPH NEURAL NETWORKS WITH DISTRIBUTED ONLINE ...

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IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Gao, Zhan Ribeiro, Alejandro Gama, Fernando Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)9781728176055

Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures, rendering them well-suited candidates for decentralized learning. Oftentimes, this decentralized graph support changes with time due to link failures or topology variations. These changes create a mismatch between the graphs on which GNNs were trained and the ones on which they are tested. Online learning can be used to retrain GNNs at testing time, overcoming this issue. However, most online algorithms are centralized and work on convex problems (which GNNs rarely lead to). This paper proposes the Wide and Deep GNN (WD-GNN), a novel architecture that can be easily updated with distributed online learning mechanisms. The WD-GNN comprises two components: the wide part is a bank of linear graph filters and the deep part is a GNN. At training time, the joint architecture learns a nonlinear representation from data. At testing time, the deep part (nonlinear) is left unchanged, while the wide part is retrained online, leading to a convex problem. We derive convergence guarantees for this online retraining procedure and further propose a decentralized alternative. Experiments on the robot swarm control for flocking corroborate theory and show potential of the proposed architecture for distributed online learning.

关键词： Graph neural networks graph filters distributed learning online learning convergence analysis

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Autoencoder-Based Gradient Compression for distributed Training 29

Autoencoder-Based Gradient Compression for Distributed Train...

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29th European Signal processing conference (EUSIPCO)

作者： Abrahamyan, Lusine Chen, Yiming Bekoulis, Giannis Deligiannis, Nikos Vrije Univ Brussel Dept Elect & Informat Brussels Belgium IMEC Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (纸本)9789082797060

Large-scale distributed training has recently been proposed as a solution to speed-up the training of deep neural networks on huge datasets. distributed training, however, entails high communication rates for gradient exchange among computing nodes and requires expensive high-bandwidth network infrastructure. Various gradient compression methods have been proposed to overcome this limitation, including sparsification, quantization, and entropy encoding of the gradients. However, most existing methods leverage only the intra-node information redundancy, that is, they compress gradients at each node independently. In contrast, we advocate that the gradients across the nodes are correlated and propose a method to leverage this inter-node redundancy to obtain higher compression rates. In this work, we propose the Learned Gradient Compression (LGC) framework to reduce communication rates within a distributed training with the parameter server communication protocol. Our framework leverages an autoencoder to capture the common information in the gradients of the distributed nodes and eliminate the transmission of redundant information. Our experiments show that the proposed approach achieves significantly higher gradient compression ratios than state-of-the-art approaches like DGC and ScaleCom.

关键词： Deep learning data-parallel distributed training gradient compression autoencoders

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distributed SCHEDULING USING GRAPH neural networkS

DISTRIBUTED SCHEDULING USING GRAPH NEURAL NETWORKS

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IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhao, Zhongyuan Verma, Gunjan Rao, Chirag Swami, Ananthram Segarra, Santiago Rice Univ Houston TX 77005 USA US Armys CCDC Army Res Lab Aberdeen Proving Ground MD USA

ISBN: (纸本)9781728176055

A fundamental problem in the design of wireless networks is to efficiently schedule transmission in a distributed manner. The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted independent set (MWIS) problem, which is NP-hard. For practical link scheduling schemes, distributed greedy approaches are commonly used to approximate the solution of the MWIS problem. However, these greedy schemes mostly ignore important topological information of the wireless networks. To overcome this limitation, we propose a distributed MWIS solver based on graph convolutional networks (GCNs). In a nutshell, a trainable GCN module learns topology-aware node embeddings that are combined with the network weights before calling a greedy solver. In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.

关键词： Maximum weighted independent set graph convolutional network wireless network scheduling

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4th International conference on Machine Learning for Cyber Security, ML4CS 2022

4th International Conference on Machine Learning for Cyber S...

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4th International conference on Machine Learning for Cyber Security, ML4CS 2022

ISBN: (纸本)9783031200984

The proceedings contain 152 papers. The special focus in this conference is on Machine Learning for Cyber Security. The topics include: Machine Learning Based Security Situation Awareness Method for network Data Transmission Process;multi-objective Hydrologic Cycle Optimization for Integrated Container Terminal Scheduling Problem;High Voltage Power Communication network Security Early Warning and Monitoring System Based on HMAC Algorithm;Large Scale network Intrusion Detection Model Based on FS Feature Selection;Privacy Preserving CSI Fingerprint Device-Free Localization;Research on Intelligent Detection Method of Automotive network Data Security Based on FlexRay/CAN Gateway;adversarial Attack and Defense on Natural Language processing in Deep Learning: A Survey and Perspective;a Novel Security Scheme for Mobile Healthcare in Digital Twin;Construction of Security Risk Prediction Model for Wireless Transmission of Multi Axis NC Machining Data;spiking neural networks Subject to Adversarial Attacks in Spiking Domain;Diverse Web APIs Recommendation with Privacy-preservation for Mashup Development;network Security Evaluation Method of College Freshmen Career Counseling Service Based on Machine Learning;FedTD: Efficiently Share Telemedicine Data with Federated Distillation Learning;increase Channel Attention Based on Unet++ Architecture for Medical Images;distributed Power Load Missing Value Forecasting with Privacy Protection;A Novel Blockchain-MEC-Based Near-Domain Medical Resource Sharing Model;differentially Private Generative Model with Ratio-Based Gradient Clipping;differential Privacy Protection Algorithm for Data Clustering Center;improved Kmeans Algorithm Based on Privacy Protection;symmetry Structured Analysis Sparse Coding for Key Frame Extraction;data Reconstruction from Gradient Updates in Federated Learning;natural Backdoor Attacks on Speech Recognition Models;boarding Pass Positioning with Jointly Multi-channel Segmentation and Perspective Transformation Co

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Sports Event Detection Using a 3D Convolutional neural network with Equiangular Basis Vector in Video processing

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： M. Ashok Kumar Laith H. Jasim Sudhakar K R. Archana Reddy N. Naga Saranya Department of Computer Science Software Engineering and Cyber Security Skyline University Nigeria Kano State Nigeria Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept. of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal India Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional neural network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional neural network with a Bidirectional Recurrent neural network (HCNN-BiRNN) model, Deformable Convolution networks (DCN), 2D-Convolutional neural network (2D-CNN), Convolutional neural network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%).

关键词： Geometry Deformable models Visualization Three-dimensional displays Sensitivity Event detection Feature extraction

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Preserved central model for faster bidirectional compression in distributed settings 35

Preserved central model for faster bidirectional compression...

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35th Annual conference on neural Information processing Systems (NeurIPS)

作者： Philippenko, Constantin Dieuleveut, Aymeric Ecole Polytech CMAP Inst Polytech Paris Paris France

ISBN: (纸本)9781713845393

We develop a new approach to tackle communication constraints in a distributed learning problem with a central server. We propose and analyze a new algorithm that performs bidirectional compression and achieves the same convergence rate as algorithms using only uplink (from the local workers to the central server) compression. To obtain this improvement, we design MCM, an algorithm such that the downlink compression only impacts local models, while the global model is preserved. As a result, and contrary to previous works, the gradients on local servers are computed on perturbed models. Consequently, convergence proofs are more challenging and require a precise control of this perturbation. To ensure it, MCM additionally combines model compression with a memory mechanism. This analysis opens new doors, e.g. incorporating worker dependent randomized-models and partial participation.

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4th International conference on Machine Learning for Cyber Security, ML4CS 2022

4th International Conference on Machine Learning for Cyber S...

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4th International conference on Machine Learning for Cyber Security, ML4CS 2022

ISBN: (纸本)9783031201011

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An approach towards AGV battery cell voltage prediction using DL and data pre-processing 18

An approach towards AGV battery cell voltage prediction usin...

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18th IEEE International conference on Computer Science and Information Technologies, CSIT 2023

作者： Medykovskyy, Mykola Cupek, Rafal Pavliuk, Olena Mishchuk, Myroslav Lviv Polytechnic National University ACS Department Lviv Ukraine Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland

ISBN: (纸本)9798350360462

Modern methods for solving the AGV battery cell voltage prediction problem include a symbiosis of probabilistic and machine learning methods. In this study, we propose to use two RNN-based approaches with preliminary data analysis and processing. In the first approach, we make predictions based on smoothed data. Three filters were chosen for smoothing: exponential smoothing;moving average;weighted moving average. The moving average method showed the best results, but according to the Dickey-Fuller test, the predicted signal was still not stationary. The second approach was based on a centered stationary signal obtained by subtracting the value of the moving average from the original *** forecast accuracy was evaluated using the following errors: MAPE, RMSE, MSPE, RMSPE, MFE, SEBIAS, ME. Adequacy of the proposed neural network model based on the coefficient of determination R2. As can be seen from Table 2, a significant improvement in prediction accuracy is observed for the centered signal. The maximum error was reduced by 5.6 times R2 increasing by 0.0596. At the same time, both models are adequate. © 2023 IEEE.

关键词： Forecasting

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4th International conference on Machine Learning for Cyber Security, ML4CS 2022

4th International Conference on Machine Learning for Cyber S...

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4th International conference on Machine Learning for Cyber Security, ML4CS 2022

ISBN: (纸本)9783031200953

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