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A novel deep neural design and efficient Pipeline architecture for Person Re-Identification in high resolution Video

A novel deep neural design and efficient Pipeline architectu...

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International conference on COMmunication Systems and networkS (COMSNETS)

作者： Mattela, Govardhan Tripathi, Manmohan Pal, Chandrajit Indian Inst Technol IIT Hyderabad India Ceremorph India Pvt Ltd Hyderabad Telangana India

ISBN: (纸本)9781728191270

The primary objective of person re-identification (Re-ID) is to retrieve a person of interest across different non-intersecting cameras for managing in distributed surveillance systems. This has added to its increasing popularity on account of its widespread use, applications and research significance. In this study, we have proposed a novel pipelined deep learning architecture which acts as a robust feature extractor and also helps in reducing down the search space by generating feature embeddings followed by executing a distance metric measurement for finding the similar neighbourhood embeddings and subsequently sorting the cluster centroids of the matching embeddings for finding a set of the nearest match before passing down to a siamese network for similarity checking in the reidentification process. Our experiments reported to achieve a performance accuracy of 85 similar to 90% with a model size of 288 MB executing at 30 fps in real-time.

关键词： ReID (reidentification) COTS (component of the shelf) GPU (Graphics processing unit) Residual network (ResNet)

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Adaptive Splitting Algorithm for neural network Modelling in AMP

Adaptive Splitting Algorithm for Neural Network Modelling in...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Kaifei Zhang Yuxiang Ma Yulei Wu Tao Chen Huijie Ma School of Computer and Information Engineering Henan University Kaifeng China School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol UK

Artificial Intelligence algorithms usually consume a lot of energy and computing resources during training, so data is usually sent to high-performance devices to complete model training. However, such a computing mode cannot make good use of computing resources in the network and is not conducive to the privacy protection of user information. In recent years, some studies have used end devices to participate in model training, among which federated learning is a typical representative. Due to the heterogeneous computing resources of the end devices, how to split the neural network model to achieve an effective balance between training time and energy consumption is an important issue. In this paper, we propose a novel model splitting method that includes the steps of collecting data, correcting the data, and determining the splitting layers to complete the splitting learning. We consider four factors (training time, energy consumption, CPU utilisation, and memory utilisation) to jointly decide the splitting layer and propose a scheme for adaptively revising the model. We use the VGG16 model for evaluation. The results show that the efficiency of our method is improved by 64.5% compared to the recently proposed ARES scheme. At the same time, our proposed method does not affect the convergence and accuracy of the model.

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Edge Computing-Enabled Lightweight Deep neural network for Real-Time Video Surveillance in Maritime Cyber-Physical Systems

Edge Computing-Enabled Lightweight Deep Neural Network for R...

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Caofei Luo Fumin Wu Renhao Xiong Guanhua Xu Wen Liu CETC (Ningbo) Maritime Electronics Research Institute Co. Ltd. Ningbo China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Navigation Wuhan University of Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the rapid developments of low-end IoT devices and artificial intelligence (AI), the heterogeneous and dynamic data has been increasing drastically, especially in the era of AI-enabled maritime cyber-physical systems (CPS). It is intractable to provide the efficient and flexible computer vision applications through traditional cloud-centric data processing strategies, which commonly suffer from the limitations, e.g., high cost, high- latency communication, and high occupied storage, etc. To meet these challenges, edge computing has meaningfully evolved into a flexible and powerful distributed architecture. It contributes to improvements in bandwidth savings and response time in practical applications. To guarantee efficient video surveillance in AI-powered maritime CPS, the edge computing-based lightweight deep network (termed ShipNet-YOLOv3), incorporated with Kalman filtering and Hungarian algorithm, is proposed to implement real-time detection and tracking of moving ships. In particular, ShipNet-YOLOv3 adopts Darknet-53 as the backbone network (i.e., feature extractor) with three prediction boxes. The channel pruning and fine-tuning strategy are further introduced to balance the trade-off between computational cost, network size, and detection (or tracking) accuracy. Experimental results on both synthetic and realistic scenarios have illustrated that the proposed method could provide superior ship detection and tracking results under different adverse imaging conditions. It thus has the capacity of promoting real-time video surveillance in AI -enabled maritime CPS.

关键词： Performance evaluation Filtering Image edge detection Imaging Video surveillance Feature extraction Real-time systems Kalman filters Marine vehicles Artificial intelligence

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28th International conference on neural Information processing, ICONIP 2021

28th International Conference on Neural Information Processi...

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28th International conference on neural Information processing, ICONIP 2021

ISBN: (纸本)9783030923099

The proceedings contain 403 papers. The special focus in this conference is on neural Information processing. The topics include: Learning Discriminative Representation with Attention and Diversity for Large-Scale Face Recognition;multi-task Perceptual Occlusion Face Detection with Semantic Attention network;RAIDU-Net: Image Inpainting via Residual Attention Fusion and Gated Information Distillation;sentence Rewriting with Few-Shot Learning for Document-Level Event Coreference Resolution;a Novel Metric Learning Framework for Semi-supervised Domain Adaptation;generating Adversarial Examples by distributed Upsampling;CPSAM: Channel and Position Squeeze Attention Module;A Multi-Channel Graph Attention network for Chinese NER;GSNESR: A Global Social network Embedding Approach for Social Recommendation;classification Models for Medical Data with Interpretative Rules;stochastic Recurrent neural network for Multistep Time Series Forecasting;contrastive Goal Grouping for Policy Generalization in Goal-Conditioned Reinforcement Learning;global Fusion Capsule network with Pairwise-Relation Attention Graph Routing;MA-GAN: A Method Based on Generative Adversarial network for Calligraphy Morphing;one-Stage Open Set Object Detection with Prototype Learning;aesthetic-Aware Recommender System for Online Fashion Products;DAFD: Domain Adaptation Framework for Fake News Detection;document Image Classification Method Based on Graph Convolutional network;continual Learning of 3D Point Cloud Generators;attention-Based 3D ResNet for Detection of Alzheimer’s Disease Process;generation of a Large-Scale Line Image Dataset with Ground Truth Texts from Page-Level Autograph Documents;speaker Verification with Disentangled Self-attention;DAP-BERT: Differentiable Architecture Pruning of BERT;trash Detection on Water Channels;tri-Transformer Hawkes Process: Three Heads are Better Than One;phenoDeep: A Deep Learning-Based Approach for Detecting Reproductive Organs from Digitized Herbarium Specimen I

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distributed Deep Learning In Open Collaborations 35

Distributed Deep Learning In Open Collaborations

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

作者： Diskin, Michael Bukhtiyarov, Alexey Ryabinin, Max Saulnier, Lucile Lhoest, Quentin Sinitsin, Anton Popov, Dmitry Pyrkin, Dmitry Kashirin, Maxim Borzunov, Alexander del Moral, Albert Villanova Mazur, Denis Kobelev, Ilia Jernite, Yacine Wolfz, Thomas Pekhimenko, Gennady Yandex Moscow Russia Hugging Face New York NY USA HSE Univ Moscow Russia Moscow Inst Phys & Technol Moscow Russia Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781713845393

Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High-Performance Computing clusters, whose construction and maintenance are both environmentally costly and well beyond the budget of most organizations. As a result, some research directions become the exclusive domain of a few large industrial and even fewer academic actors. To alleviate this disparity, smaller groups may pool their computational resources and run collaborative experiments that benefit all participants. This paradigm, known as grid- or volunteer computing, has seen successful applications in numerous scientific areas. However, using this approach for machine learning is difficult due to high latency, asymmetric bandwidth, and several challenges unique to volunteer computing. In this work, we carefully analyze these constraints and propose a novel algorithmic framework designed specifically for collaborative training. We demonstrate the effectiveness of our approach for SwAV and ALBERT pretraining in realistic conditions and achieve performance comparable to traditional setups at a fraction of the cost. Finally, we provide a detailed report of successful collaborative language model pretraining with 40 participants.

关键词： Deep learning

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Implementation of Data Stream Classification neural network Models Over Big Data Platforms 16th

Implementation of Data Stream Classification Neural Network ...

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18th International conference on Quantitative Evaluation Systems (QEST)

作者： Puentes-Marchal, Fernando Dolores Perez-Godoy, Maria Gonzalez, Pedro Jose Del Jesus, Maria Univ Jaen Jaen Spain

ISBN: (纸本)9783030850999;9783030850982

Streaming is being increasingly demanded because it helps in analyzing data in real-time and in decision making. Over time, the number of existing devices increases continuously, generating a huge amount of data. processing this data with traditional algorithms is impractical, so it is necessary to apply distributed algorithms in a Big Data context. In this paper, Apache Spark is used to implement some distributed versions of algorithms based on Extreme Learning Machine (ELM). In addition, these algorithms are evaluated with different real and synthetic datasets by performing two experiments. The first one tries to demonstrate that the performance of the distributed algorithms is the same as that of the sequential versions. The second experiment is a study about the behaviour of the algorithms in the presence of concept drift, an important research area within streaming.

关键词： Datastream Classification Extreme learning machine Big data Spark streaming

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28th International conference on neural Information processing, ICONIP 2021

28th International Conference on Neural Information Processi...

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28th International conference on neural Information processing, ICONIP 2021

ISBN: (纸本)9783030923068

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Training Large-Vocabulary neural Language Models by Private Federated Learning for Resource-Constrained Devices

Training Large-Vocabulary Neural Language Models by Private ...

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

作者： Mingbin Xu Congzheng Song Ye Tian Neha Agrawal Filip Granqvist Rogier van Dalen Xiao Zhang Arturo Argueta Shiyi Han Yaqiao Deng Leo Liu Anmol Walia Alex Jin Apple

Federated Learning (FL) is a technique to train models on distributed edge devices with local data samples. Differential Privacy (DP) can be applied with FL to provide a formal privacy guarantee for sensitive data on device. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, the noise required to guarantee differential privacy increases as the model size grows, which often prevents convergence. We propose Partial Embedding Updates (PEU), a novel technique to reduce the impact of DP-noise by decreasing payload size. Furthermore, we adopt Low Rank Adaptation (LoRA) and Noise Contrastive Estimation (NCE) to reduce the memory demands of large models on compute-constrained devices. We demonstrate in simulation and with real devices that this combination of techniques makes it possible to train large-vocabulary language models while preserving accuracy and privacy.

关键词： Training Adaptation models Privacy Differential privacy Vocabulary Federated learning Computational modeling

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MoNet: A Mixture of Experts Solution for Multilingual and Low-Resource ASR Challenges

MoNet: A Mixture of Experts Solution for Multilingual and Lo...

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International Joint conference on neural networks (IJCNN)

作者： Yongchao Li Lixu Sun Yineng Cai Nurmemet Yolwas School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contemporary end-to-end automatic speech recognition models have demonstrated remarkable performance in monolingual contexts with abundant corpora. However, their efficacy is considerably compromised in multilingual and resource-scarce environments due to the models’ inability to effectively learn the diverse linguistic features from unevenly distributed corpora during training. Hence, enabling these models to assimilate a broader spectrum of effective features presents a significant challenge. In this paper, we introduce a novel multilingual automatic speech recognition network, MoNet, predicated on the Mixture of Experts (MoE) approach, which is capable of automatically and efficiently processing multilingual speech data without the need for any language-specific cues. Specifically, within MoNet, the MoE layer employs a router to select a fixed number of optimal experts from an expert pool to guide the network’s learning and provide efficacious reasoning, followed by the use of joint CTC/Attention training and the implementation of a secondary scoring mechanism to further enhance the model’s performance. We conducted hybrid training and evaluation of the proposed model using a compendium of corpora from six linguistically diverse languages with considerable variance in total training duration—Turkish, Uighur, Bashkir, Uzbek, Kyrgyz, and Russian—sourced from the publicly accessible CommonVoice dataset. Our findings indicate that compared to the baseline model, MoNet achieves significant improvements, with a maximum reduction in Word Error Rate (WER) of up to 4.1% for individual languages and an average decrease of 1.78% in WER across multiple languages, while still maintaining an inference cost comparable to that of the baseline model. The experimental outcomes underscore the significant advantages of our proposed model for low-resource, multilingual speech recognition tasks.

关键词： Training Costs Error analysis neural networks Linguistics Cognition Acoustics

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FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective 35

FL-WBC: Enhancing Robustness against Model Poisoning Attacks...

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

作者： Sun, Jingwei Li, Ang DiValentin, Louis Hassanzadeh, Amin Chen, Yiran Li, Hai Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA Accenture Accenture Labs Secur R&D Dublin Ireland

ISBN: (纸本)9781713845393

Federated learning (FL) is a popular distributed learning framework that trains a global model through iterative communications between a central server and edge devices. Recent works have demonstrated that FL is vulnerable to model poisoning attacks. Several server-based defense approaches (e.g. robust aggregation) have been proposed to mitigate such attacks. However, we empirically show that under extremely strong attacks, these defensive methods fail to guarantee the robustness of FL. More importantly, we observe that as long as the global model is polluted, the impact of attacks on the global model will remain in subsequent rounds even if there are no subsequent attacks. In this work, we propose a client-based defense, named White Blood Cell for Federated Learning (FL-WBC), which can mitigate model poisoning attacks that have already polluted the global model. The key idea of FL-WBC is to identify the parameter space where long-lasting attack effect on parameters resides and perturb that space during local training. Furthermore, we derive a certified robustness guarantee against model poisoning attacks and a convergence guarantee to FedAvg after applying our FL-WBC. We conduct experiments on FasionMNIST and CIFAR10 to evaluate the defense against state-of-the-art model poisoning attacks. The results demonstrate that our method can effectively mitigate model poisoning attack impact on the global model within 5 communication rounds with nearly no accuracy drop under both IID and non-IID settings. Our defense is also complementary to existing server-based robust aggregation approaches and can further improve the robustness of FL under extremely strong attacks. Our code can be found at https://***/jeremy313/FL- WBC.

关键词： network security

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