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distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space 36

Distributed Learning of Conditional Quantiles in the Reprodu...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Lian, Heng City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China City Univ Hong Kong Dept Math Hong Kong Peoples R China

ISBN: (纸本)9781713871088

We study distributed learning of nonparametric conditional quantiles with Tikhonov regularization in a reproducing kernel Hilbert space (RKHS). Although distributed parametric quantile regression has been investigated in several existing works, the current nonparametric quantile setting poses different challenges and is still unexplored. The difficulty lies in the illusive explicit bias-variance decomposition in the quantile RKHS setting as in the regularized least squares regression. For the simple divide-and-conquer approach that partitions the data set into multiple parts and then takes an arithmetic average of the individual outputs, we establish the risk bounds using a novel second-order empirical process for quantile risk.

关键词： Hilbert spaces

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18th International conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

18th International Conference on Intelligent Information Hid...

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18th International conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

ISBN: (纸本)9789819906048

The proceedings contain 38 papers. The special focus in this conference is on Intelligent Information Hiding and Multimedia Signal processing. The topics include: An Optimal Power System Multi-objective Environmental-Economic Planning Based on Moth-Flame Optimization;a Power-System Economic Dispatching Based on Enhanced Group Search Optimizer;research Review on Security Protection Scheme of distributed Photovoltaic Grid-Connected network;a Method of Constructing Fine-Grained Pose Evaluation Model;Research on Meteorological Public Opinion Combining Domain Keywords and LinearSVC;study on Dynamic Evaluation of Limiting Factors of High-Speed Railway Station Operation Efficiency;a Novel Algorithm for Video Frame Prediction Based on Convolutional neural network;GVFNet: Graph-Voxel Fusion network for 3D Object Detection;relation Extraction Model Based on Global Pointer and Potential Relation Embedding;distributed Face Biometric with Blockchain Federated Learning;ECG Heartbeat Segmentation with Bidirectional LSTM Deep Learning neural network;Representing Scenes as Compositional Generative neural Feature Fields Based on GIRAFFE for 3D Reconstruction of Classroom Scenes;pipe Attitude Recognition and Control System Based on Machine Vision;short-Term Forecast Based on Generalized Maximum Correntropy Criterion and Kernel Extreme Learning Machine;a Document Management System with Multi-biometric Watermarking for Educational Purpose;radar Waveform Gaming Configurable Framework Based on Deep Reinforcement Learning;Super Resolution of Unpaired MR Images Based on Domain Migration;optical Stealth Algorithm Under Photoelectric Countermeasures and Its Effectiveness Evaluation;brainwave Analysis of the Correlation Between Listening to the Rain/Water Sounds and Reducing Human-Perceived Temperature;Detection of Unknown DDoS Attack Using Modified Dynamic High-Resolution network;region Guide Point for Depth Estimation;dam Control System’s Cybersecurity Testbed.

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Research on Abnormal Power Consumption Behavior Detection and Load Forecasting System of Power Big Data Based on FCM and Wavelet neural network 14th

Research on Abnormal Power Consumption Behavior Detection an...

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14th International conference on Frontier Computing, FC 2024

作者： Pan, Jingni Chai, Yufeng Lv, Jiayu Wang, Xiaotian State Grid Shizuishan Power Supply Co. Ningxia China State Grid Ningxia Power Supply Co. Ningxia China State Grid Wuzhong Power Supply Co. Ningxia China

ISBN: (纸本)9789819627974

With the progress of smart grid, power systems are faced with more and more complex data challenges, including large-scale data, multi-sample types and low resource integration. This research takes power big data as the object, carries out abnormal power consumption behavior detection and load prediction based on Hadoop platform, and successfully realizes the visualization processing of power data. We thoroughly analyzed the needs of power big data visualization and designed the overall Hadoop-based architecture. In terms of key technologies, we adopt the visualization method based on geographic information, and use MapReduce to carry out parallel computation, and effectively identify and display different electrical behaviors. In addition, we proposed a wavelet neural network power load forecasting method based on FCM, and personalized classification of power users through FCM clustering. Combined with the prediction ability of wavelet neural network, accurate power load prediction was successfully realized, and parallel calculation was carried out on the distributed platform to further improve the prediction accuracy. Finally, we built a Hadoop big data experiment platform to verify the experimental results and performance of the proposed method. The experimental results show that our research has the ability to effectively cope with the big data challenges of power system, and provides important support and guidance for the safe and stable operation of power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Abnormal Power Consumption Behavior Detection FCM Hadoop Platform Power Big Data Wavelet neural network

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High-performance network Traffic Classification Based on Graph neural network 6

High-performance Network Traffic Classification Based on Gra...

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6th IEEE Information Technology, networking, Electronic and Automation Control conference, ITNEC 2023

作者： Pang, Bo Fu, Yongquan Ren, Siyuan Jia, Yan Harbin Institute of Technology College of Computer Science and Technology Shenzhen China National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing College of Computer Changsha China Peng Cheng Laboratory Shen Zhen China

ISBN: (纸本)9781665460033

network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets' flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets' flows together with the packets' features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification. © 2023 IEEE.

关键词： Graph neural networks

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18th International conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

18th International Conference on Intelligent Information Hid...

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18th International conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

ISBN: (纸本)9789819901043

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Collaborative Inference Acceleration with Non-Penetrative Tensor Partitioning

Collaborative Inference Acceleration with Non-Penetrative Te...

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

作者： Liu, Zhibang Xu, Chaonong Lv, Zhenjie Liu, Zhizhuo Zhao, Suyu Beijing Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

ISBN: (纸本)9798350368741

The inference of large-sized images on Internet of Things (IoT) devices is commonly hindered by limited resources, while there are often stringent latency requirements for Deep neural network (DNN) inference. Currently, this problem is generally addressed by collaborative inference, where the large-sized image is partitioned into multiple tiles, and each tile is assigned to an IoT device for processing. However, since significant latency will be incurred due to the communication overhead caused by tile sharing, the existing collaborative inference strategy is inefficient for convolutional computation, which is indispensable for any DNN. To reduce it, we propose Non-Penetrative Tensor Partitioning (NPTP), a fine-grained tensor partitioning method that reduces the communication latency by minimizing the communication load of tiles shared, thereby reducing inference latency. We evaluate NPTP with four widely-adopted DNN models. Experimental results demonstrate that NPTP achieves a 1.44-1.68× inference speedup relative to CoEdge, a state-of-the-art (SOTA) collaborative inference algorithm. © 2025 IEEE.

关键词： collaborative inference distributed computing edge intelligence

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Avoiding training in the platform-aware optimization process for faster DNN latency reduction

Avoiding training in the platform-aware optimization process...

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1st International conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Marichal, Raul Dufrechou, Ernesto Ezzaui, Pablo Fac Ingn Inst Comp Montevideo Uruguay

ISBN: (纸本)9798350364613;9798350364606

Many real-world deep learning applications are sensitive to the size and latency of the models, especially when inference is performed on low-power devices. In many scenarios, it is possible to optimize DNNs to meet latency and energy budgets without sacrificing too much accuracy. NetAdapt, a newly developed tool, tackles this issue by optimizing the network through the removal of layers based on practical latency measurements specific to the target device. In this work, we improve two stages of NetAdapt's algorithm. First, we enhance the candidate models' search procedure by improving the mechanism that interpolates latency measurements for configurations not available in the look-up table. Secondly and more importantly in terms of novelty, we study the potential benefits of integrating No-training NAS techniques into the hardware-aware optimization algorithm to avoid the fine-tuning and evaluation stages with minimum accuracy loss. The experimental evaluation shows that the optimizations applied can accelerate NetAdapt's algorithm up to 37x for a large network and 4x for a small one.

关键词： Deep learning Edge computing Efficient computing neural network optimizations Hardware-aware optimizations Heterogeneous computing NetAdapt

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Automatic Mixed Precision and distributed Data-Parallel Training for Satellite Image Classification using CNN 15

Automatic Mixed Precision and Distributed Data-Parallel Trai...

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15th International conference on Machine Vision (ICMV)

作者： Nuwara, Yohanes Kitt, Wong W. Juwono, Filbert H. Ollivierre, Gregory Asia Pulp & Paper Sinarmas Land Plaza MH Thamrin Jakarta Indonesia Curtin Univ Malaysia CDT 250 Bellevue WA 98009 USA Univ Southampton Malaysia Iskandar Puteri 79100 Johor Malaysia OmegaCrop 71-75 Shelton St London England

ISBN: (纸本)9781510666184;9781510666191

Deep learning models for computer vision in remote sensing such as Convolutional neural network (CNN) has benefitted acceleration from the usage of multiple CPUs and GPUs. There are several ways to make the training stage more effective in terms of utilizing multiple cores at the same time by processing different image mini-batches with a duplicated model called distributed Data Parallelization (DDP) and computing the parameters in a lower precision floating-point number called Automatic Mixed Precision (AMP). We would like to investigate the impact of DDP and AMP training modes on the overall utilization and memory consumption of CPU and GPU, as well as the accuracy of a CNN model. The study is performed on the EuroSAT dataset, a Sentinel-2-based benchmark satellite image dataset for image classification of land covers. We compare training using 1 CPU, using DDP, and using both DDP and AMP over 100 epochs using ResNet-18 architecture. The hardware that we used are Intel Xeon Silver 4116 CPU with 24 cores and an NVIDIA v100 GPU. We find that although parallelization of CPUs or DDP takes less time to train on the images, it can take 50 MB more memory than using only a single CPU. The combination of DDP and AMP can release memory up to 160 MB and reduce computation time by 20 seconds. The test accuracy is slightly higher for both DDP and DDP-AMP at 90.61% and 90.77% respectively than without DDP and AMP at 89.84%. Hence, training using distributed Data Parallelization (DDP) and Automatic Mixed Precision (AMP) has more benefits in terms of lower GPU memory consumption, faster training execution time, faster convergence towards solutions, and finally, higher accuracy.

关键词： Automatic Mixed Precision Convolutional neural network distributed Data-Parallel Graphics processing Unit Remote Sensing

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Internet of Things Enabled Smart Energy Management of distributed Energy Resources using Self-Adaptive Physics-Informed neural network Optimized with Green Anaconda Optimization 4

Internet of Things Enabled Smart Energy Management of Distri...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ghongade, Rahul D. Arunaa, J. Sri Latha Mageshwari, P.S. Vinoth, K. Shanmugathai, M. Mercy Rani, J. Femila P. R. Pote Patil College of Engineering and Management Department of Electronics and Telecommunication Engineering Maharashtra Amravati444602 India Dr. M. G. R Educational and Research Institute Department of Electronics and Communication Engineering Tamil Nadu Chennai600095 India Tamil Nadu Kavaraipettai601206 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Sri Sairam Engineering College Department of English Tamil Nadu Chennai600044 India Department of Mathematics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798331523923

Internet of Things (IoT)-enabled Smart Energy Management (SEM) in distributed Energy Resources (DERs), while crucial for optimizing energy distribution and resource management faces challenges such as data inconsistencies and the variability of renewable energy generation. These issues result in inaccurate demand forecasts, leading to suboptimal allocation of resources and inefficient Energy Management (EM). Additionally, as energy demand and generation patterns are influenced by factors like weather conditions, time of day, and energy consumption behaviors, prediction models may struggle to capture these dynamic changes, affecting the reliability of forecasts. To address these limitations, this manuscript proposes a novel approach for energy demand prediction. Data is collected from IoT-enabled sensors monitoring DERs. The data undergoes pre-processing, where the Fast Resampled Iterative Filtering (FRIF) method is used to eliminate missing values and normalize the inputs. The Self-Adaptive Physics-Informed neural network (SAPINN) model then utilizes the processed data to forecast energy demand, renewable energy generation, and storage levels. Green Anaconda Optimization (GAO) is applied to optimize the weight parameters of the SAPINN model. The proposed SAPINN-GAO method is implemented using the MATLAB platform and compared with existing models, such Stacked Convoluted Bi-Directional Gated Attention network-Hybrid Darts Seagull Optimizer (SConBGAN-HDSO), Recurrent neural network (RNN), and Support Vector Machine-Particle Swarm Optimization (SVM-PSO). The SAPINN-GAO method achieves an accuracy of 99.2%, precision of 99.2%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in energy demand prediction. The SAPINN-GAO method's higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for energy demand, renewable energy generation, and storage levels forecasting in IoT-enabled SEM syst

关键词： Recurrent neural networks

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A Moving Target Defense Approach for the distributed Dynamic network 21

A Moving Target Defense Approach for the Distributed Dynamic...

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21st IEEE International Symposium on Parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Lin Guo, Yunchuan Leng, Siyuan Li, Zifu Li, Fenghua Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

The distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed network Address Shuffling (DNAS), is proposed to thwart attackers' network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected neural network (FCNN) to recognize the attacker's scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning. © 2023 IEEE.

关键词： network security

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