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PI-Parking: A Physics Informed neural network Approach for Parking Availability Prediction 29

PI-Parking: A Physics Informed Neural Network Approach for P...

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

作者： Dong, Yijun Kuang, Haoxuan Li, Jun Sun Yat-Sen University School of Intelligent Systems Engineering Shenzhen China

ISBN: (纸本)9798350330717

Parking availability prediction is an important part of urban intelligent transportation systems (ITS), which allows drivers to be informed of the parking availability in advance, thereby reducing cruising for parking time and reducing pollution emissions. However, existing studies of parking availability prediction rarely incorporate the effect of price factors and cannot be trained to obtain the price elasticity of parking demand. In this paper, we propose a neural network-based approach called physics-informed parking (PI-parking). The model employs physics-informed neural networks (PINNs) to add price information into model training, which not only achieves the parking availability prediction, but also calculates the price elasticity of demand of different parking lots during the training process. On the SF Park project, experiment results show that our model outperforms 17.711% on average than compared models. For price elasticity of parking demand, we find that the values are distributed between 0.02 and 0.31, and most of the values are between 0.25 and 0.27. In addition, there is a similarity in the elasticity values for adjacent neighborhoods. © 2023 IEEE.

关键词： intelligent transportation systems parking availability prediction physics-informed neural networks

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A Bit Level Acceleration of Mixed Precision neural network 29

A Bit Level Acceleration of Mixed Precision Neural Network

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

作者： Qiu, Dehui Wang, Jing Hou, Yi Zhang, Weigong Gao, Lan Capital Normal University Information Engineering College Beijing China Renmin University of China School of Information Beijing China

ISBN: (纸本)9798350330717

With the growth of the convolutional neural network (CNN) parameters, the hardware resources become limited when deploying CNN models. Single bit-width quantization may lead to degradation of accuracy, while mixed-precision quantization models maintain higher accuracy. However, current mixed-precision quantization accelerators only consider the algorithm level quantization and fixed-bit-width processing elements (PEs) without fully utilizing the resources. Therefore, we propose a neural network acceleration architecture based on bit-level computational units to improve resource utilization and throughput of mixed-precision quantization accelerators. The 2-bit and 3-bit low-bit computational units are designed to implement the high-bit quantization. We also propose spatio-temporal fusion to satisfy the unique bit widths in each layer with mixed precision quantization. In particular, we use the 2-bit and 3-bit computational units to achieve dynamic layer-level quantization. We also discuss various combinations of different bit widths, which can be dynamically implemented according to the requirements of accuracy, execution time, etc. The proposed acceleration architecture is implemented in Verilog, verified using three network models: ResNet18, VGG7 and LeNet-5, and tested against three accelerators: Eyeriss, Stripes and Bit Fusion. The experimental results show that our accelerators provide more accuracy and the grouping operation reduces the area overhead. It provides a 3.08 to 3.54 times acceleration ratio and 2.4 to 2.57 times reduction in energy consumption on the three networks. © 2023 IEEE.

关键词： bit level computing unit convolutional neural network FPGA hardware accelerator

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Graph neural networks for Sound Source Localization on distributed Microphone networks 48

Graph Neural Networks for Sound Source Localization on Distr...

引用

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

作者： Grinstein, Eric Brookes, Mike Naylor, Patrick A. Imperial College London Department of Electrical and Electronic Engineering United Kingdom

ISBN: (纸本)9781728163277

distributed Microphone Arrays (DMAs) present many challenges with respect to centralized microphone arrays. An important requirement of applications on these arrays is handling a variable number of input channels. We consider the use of Graph neural networks (GNNs) as a solution to this challenge. We present a localization method using the Relation network GNN, which we show shares many similarities to classical signal processing algorithms for Sound Source Localization (SSL). We apply our method for the task of SSL and validate it experimentally using an unseen number of microphones. We test different feature extractors and show that our approach significantly outperforms classical baselines. © 2023 IEEE.

关键词： Microphone array

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Innovative Approaches for Enhanced Security in Cloud network Traffic: An Adaptive Deep Learning Framework 1

Innovative Approaches for Enhanced Security in Cloud Network...

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1st International conference on Software, Systems and Information Technology, SSITCON 2024

作者： Mala, K. Annapurna, H.S. Cit Gubbi Department of Ise Tumkur India Ssit Saahe University Department of Ise Tumkur India

ISBN: (纸本)9798350352931

The rapid growth of cloud computing has introduced significant security challenges, particularly in protecting network traffic from advanced cyber threats. This research presents an adaptive deep learning framework to enhance cloud network security by detecting and mitigating threats in real-time. By leveraging multi-modal data sources, such as network traffic logs, system performance metrics, and user behavior analytics, the framework identifies threats like distributed Denial of Service (DDoS) attacks, malware, and unauthorized access. A comprehensive dataset, including network traffic data, system logs, and security alerts from various cloud environments, was collected. Pre-processing involved normalization, augmentation, and feature extraction tailored to multi-modal inputs. Convolutional neural networks (CNNs) analyzed network traffic features, while Random Forest and Gradient Boosting algorithms examined system and user behavior. The research also explores the impact of network conditions like bandwidth fluctuations and latency on model performance and proposes Internet of Things (IoT)-based monitoring for real-time data collection. © 2024 IEEE.

关键词： Cloud security

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Brand Logo Image Classification Based on Deep Convolutional Generative Adversarial network 3

Brand Logo Image Classification Based on Deep Convolutional ...

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3rd IEEE International conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Wang, Yang Guangzhou Institute Of Science Technology Guangzhou China

ISBN: (纸本)9798350318609

Brand logo image examines how a critical dimension of logo design, namely the naturalness of the logo color, influences brand design induced by the logo. However, high complexity is observed, requiring higher quality data and susceptibility to overfitting when there is insufficient training data. The implementation of the proposed system involved the utilization of a Deep Convolutional Generative Adversarial network (DCGAN) reduce overfitting and efficiently handle noise and distortion in the logo image, which is essential in brand logo image processing. The brand logo image dataset forwarded to the preprocessing stage included pixel values, which may lead to the loss of contrast, shadow, sharpness, noise removal, and structure in some relevant images. The classification involved DCGAN, aiming to achieve high-quality logo image prediction, noise reduction, and handling of overfitting by incorporating synthetic logo images. The proposed DCGAN method achieves promising performance, including a mean Average Precision (mAP) of 52.25, Accuracy of 97.09%, Precision of 97.01 %, Recall of 98.10%, and F1-score of 97.11 %. This performance is compared to existing methods such as YOLOv3 and Convolutional neural network (CNN) - Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Generative adversarial networks

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Combination of Hybrid Feature Selection and LSTM-AE neural network for Enhancing DDOS Detection in SDN 20

Combination of Hybrid Feature Selection and LSTM-AE Neural N...

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20th International Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Setitra, Mohamed Ali Agbley, Bless Lord Y. El Abidine Bensalem, Zine Fan, Mingyu University of Electronic Science and Technology of China Chengdu611731 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

distributed Denial of Service (DDoS) attacks pose significant threats in Software-Defined network (SDN) environments. To enhance DDoS detection in SDN, this study presents a novel approach that combines Hybrid Feature Selection and Long Short-Term Memory (LSTM)-Autoencoder (AE) neural network. The feature selection process initially utilizes Information Gain (IG) to select 50% of the most important ones. Subsequently, the SHapley Additive exPlanations (SHAP) method is employed to identify relevant and interdependent features. The LSTM-AE neural network then captures the temporal characteristics and nonlinear patterns in the system's response, creating a low-dimensional data representation. Experimental evaluation using an SDN dataset demonstrates the effectiveness of the proposed approach, achieving an overall accuracy of 99.23%. The hybrid feature selection and LSTM-AE model offer improved DDoS detection capabilities in SDN environments. © 2023 IEEE.

关键词： Long short-term memory

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PS-Based Heterogeneous Computing Framework 6

PS-Based Heterogeneous Computing Framework

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6th International conference on Next Generation Data-Driven networks, NGDN 2024

作者： Guo, Xi He, Ming Qin, Shouhao Wang, Dexuan Li, Chenjun China United Network Communications Corporation Research Institute Beijing China GuoChuang Software Co. Ltd. Hefei China

ISBN: (纸本)9798350388374

In the current heterogeneous computing environment, different types of accelerated computing resources such as GPU(Graphics processing Unit) and NPU(neural processing Unit) coexist in the cluster. However, because these resources use different communication protocols and architectures, heterogeneous computing resources cannot effectively communicate and collaborate. To solve this problem, we propose a heterogeneous distributed computing framework based on the Parameter Server(PS) architecture and achieve heterogeneous parallel computing capabilities across hardware devices. This allows users to uniformly use all available heterogeneous computing resources in the cluster to perform large-scale data parallel deep learning training tasks. We have conducted experiments and evaluations on this framework in multiple scenarios, and the results show that the NPU and GPU computing power in a heterogeneous computing cluster can be used simultaneously to complete a same computing task without affecting accuracy, while ensuring high training efficiency. Overall, this framework provides an effective solution for high-performance distributed computing in heterogeneous computing environments. © 2024 IEEE.

关键词： Computing power

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Decentralised Biomedical Signal Classification using Early Exits 21

Decentralised Biomedical Signal Classification using Early E...

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21st IEEE Interregional NEWCAS conference (NEWCAS)

作者： Li Xiaolin Vandierendonck, Hans Nikolopoulos, Dimitrios S. Ji, Bo Cardiff, Barry John, Deepu Univ Coll Dublin Dublin Ireland Queens Univ Belfast Belfast Antrim North Ireland Virginia Tech Blacksburg VA USA

ISBN: (纸本)9798350300246

This paper presents a decentralised signal classification approach for data acquired using Internet of Things (IoT) wearable sensors. Traditionally, data from IoT sensors are processed in a centralised fashion, and in a single node. This approach has several limitations, such as high energy consumption on the edge sensor, longer response times, etc. We present a distributed processing approach for convolutional neural network (CNN) based classifiers where a single CNN model can be split into multiple sub-networks using early exits. To reduce the transfer of large feature maps between sub-networks, we introduced an encoder-decoder pair at the exit points. processing of inputs that can be classified with high confidence at an exit point will be terminated early, without needing to traverse the entire network. The initial sub-networks can be deployed on the edge to reduce sensor energy consumption and overall complexity. We also experimented with multiple exit point locations and show that the point of exit can be adjusted for trade-offs between complexity and performance. The proposed system can achieve a sensitivity of 98.45% and an accuracy of 97.55% for electrocardiogram (ECG) classification and save 60% of the data transmitted wirelessly while reducing 38.45% of the complexity.

关键词： ECG classification Arrhythmia Decentralised Inferencing distributed network Deep Learning

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distributed Online Convex Optimization with Compressed Communication 36

Distributed Online Convex Optimization with Compressed Commu...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Tu, Zhipeng Wang, Xi Hong, Yiguang Wang, Lei Yuan, Deming Shi, Guodong Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Univ Sydney Australian Ctr Field Robot Sch AMME Sydney Australia Tongji Univ Coll Control Sci & Engn Shanghai Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China

ISBN: (纸本)9781713871088

We consider a distributed online convex optimization problem when streaming data are distributed among computing agents over a connected communication network. Since the data are high-dimensional or the network is large-scale, communication load can be a bottleneck for the efficiency of distributed algorithms. To tackle this bottleneck, we apply the state-of-art data compression scheme to the fundamental GD-based distributed online algorithms. Three algorithms with difference-compressed communication are proposed for full information feedback (DC-DOGD), one-point bandit feedback (DC-DOBD), and two-point bandit feedback (DC-DO2BD), respectively. We obtain regret bounds explicitly in terms of time horizon, compression ratio, decision dimension, agent number, and network parameters. Our algorithms are proved to be no-regret and match the same regret bounds, w.r.t. time horizon, with their uncompressed versions for both convex and strongly convex losses. Numerical experiments are given to validate the theoretical findings and illustrate that the proposed algorithms can effectively reduce the total transmitted bits for distributed online training compared with the uncompressed baseline.

关键词： distributed computer systems

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BBSN: Bilateral-Branch Siamese network for Imbalanced Multi-label Text Classification 29th

BBSN: Bilateral-Branch Siamese Network for Imbalanced Multi-...

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29th International conference on neural Information processing

作者： Zhao, Jiangjiang Li, Jiyz Fukumoto, Fumiyo Hangzhou Dianzi Univ Hangzhou Peoples R China Univ Yamanashi Kofu Yamanashi Japan

ISBN: (纸本)9783031301100;9783031301117

In multi-label text classification, the numbers of instances in different categories are usually extremely imbalanced. How to learn good models from imbalanced data is a challenging task. Some existing works tackle it through class re-balancing strategies or imbalanced loss objectives, but their performance remains limited in the cases of imbalanced distributed data. In this work, we propose a model, which combined Siamese network and Bilateral-Branch network to deal with both representation learning and classifier learning simultaneously. In the siamese network component, we propose a category-specific similarity strategy to improve the representation learning and adapt a novelty dynamic learning mechanism to make the model end-to-end trainable, and in the bilateral-branch network, we adopt the cumulative learning strategy to shift the learning focus from universal pattern to tail learning. In general, we adopt a multi-task architecture to ensure that both the head categories and the tail categories are adequately trained. The experiments on two benchmark datasets show that our method can improve the performance on the entire and tail categories, and achieves competitive performance compared with existing approaches.

关键词： Multi-label Text Classification Imbalanced Data

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