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The FORA European Training network on Fog Computing for Robotics and Industrial Automation

The FORA European Training Network on Fog Computing for Robo...

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Barzegaran, Mohammadreza Pop, Paul Univ Calif Irvine Ctr Pervas Commun & Comp Irvine CA USA Tech Univ Denmark DTU Dept Appl Math & Comp Sci Lynbgy Denmark

ISBN: (纸本)9798350396249

Fog Computing for Robotics and Industrial Automation, FORA, was a European Training network which focused on future industrial automation architectures and applications based on an emerging technology, called Fog Computing. The research project focused on research related to Fog Computing with applicability to industrial automation and manufacturing. The main outcome of the FORA project was the development of a deterministic Fog Computing Platform (FCP) to be used for implementing industrial automation and robotics solutions for Industry 4.0. This paper reports on the scientific outcomes of the FORA project. FORA has proposed a reference system architecture for Fog Computing, which was published as an open Architecture Analysis Design Language (AADL) model. The technologies developed in FORA include fog nodes and hypervisors, resource management mechanisms and middleware for deploying scalable Fog Computing applications, while guaranteeing the non-functional properties of the virtualized industrial control applications, and methods and processes for assuring the safety and security of the FCP. Several industrial use cases were used to evaluate the suitability of the FORA FCP for the Industrial IoT area, and to demonstrate how the platform can be used to develop industrial control applications and data analytics applications.

关键词： Fog and Edge Computing Industry 4.0 Deterministic Virtualization Time-Sensitive networking

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A Comparison Between Convolutional and Transformer architectures for Speech Emotion Recognition

A Comparison Between Convolutional and Transformer Architect...

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Iyer, Shreyah Glackin, Cornelius Cannings, Nigel Veneziano, Vito Sun, Yi Univ Hertfordshire Sch Phys Engn & Comp Sci Hatfield Herts England Intelligent Voice Ltd London England

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Creating speech emotion recognition models comparable to the capability of how humans recognise emotions is a long-standing challenge in the field of speech technology with many potential commercial applications. As transformer-based architectures have recently become the state-of-the-art for many natural language processing related applications, this paper investigates their suitability for acoustic emotion recognition and compares them to the well-known AlexNet convolutional approach. This comparison is made using several publicly available speech emotion corpora. Experimental results demonstrate the efficacy of the different architectural approaches for particular emotions. The results show that the transformer-based models outperform their convolutional counterparts yielding F1-scores in the range [7033 %, 75.76 %1. This paper further provides insights via dimensionality reduction analysis of output layer activations in both architectures and reveals significantly improved clustering in transformer-based models whilst highlighting the nuances with regard to the separability of different emotion classes.

关键词： speech emotion recognition transformers wav2vec2 convolutional neural networks alexnet transfer learning mel spectrograms

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Human Action Recognition Using Hybrid Deep Evolving Neural networks

Human Action Recognition Using Hybrid Deep Evolving Neural N...

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作者： Dasari, Pavan Zhang, Li Yu, Yonghong Huang, Haoqian Gao, Rong Royal Holloway Univ London Dept Comp Sci Surrey TW20 0EX England Nanjing Univ Posts & Telecommun Nanjing Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing Peoples R China Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Human action recognition can be applied in a multitude of fully diversified domains such as active large-scale surveillance, threat detection, personal safety in hazardous environments, human assistance, health monitoring, and intelligent robotics. Owing to its high demands in real-world applications, it has drawn significant attention. In this research, we propose hybrid deep neural networks, i.e. Convolutional Long Short-Term Memory (ConvLSTM) networks, Long-term Recurrent Convolutional networks (LRCN), for tackling video action classification. In particular, for the LRCN model, different CNN encoder architectures such as VGG16, ResNet50, DenseNet121 and MobileNet, as well as several Long Short-Term Memory (LSTM) variant decoder architectures, such as LSTM, bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), are used for spatial-temporal feature extraction to test model performance. We adopt diverse experimental settings including using different numbers of frames per video and learning configurations to optimize performance. The empirical results indicate the superiority of MobileNet in combination with a BiLSTM network over other hybrid network settings for the action classification using the UCF50 dataset. Owing to the lightweight MobileNet encoder, this LRCN model also achieves a better trade-off between performance and training and inference computational costs, while outperforming existing state-of-the-art methods.

关键词： Human Action Recognition Video Analytics Convolutional Neural network Long Short-Term Memory (LSTM) Gated Recurrent Unit Long-term Recurrent Convolutional network ConvLSTM.

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Layer-aware Collaborative Microservice Deployment toward Maximal Edge Throughput 41

Layer-aware Collaborative Microservice Deployment toward Max...

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41st ieee conference on Computer Communications (ieee INFOCOM)

作者： Gu, Lin Chen, Zirui Xu, Honghao Zeng, Deze Li, Bo Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & Technol Serv Comp Technol & Syst LabCluster & Grid Comp Wuhan Peoples R China China Univ Geosci Sch Comp Sci Wuhan Hubei Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781665458221

Lightweight container-based microservice has been widely advocated to promote the elasticity of edge cloud. The inherent layered structure of containers offers a compelling way to cope with the resource scarcity of edge servers through layer sharing, which can significantly increase storage utilization and improve the edge throughput. Recent studies show that it is possible to share layers not only within the same server but also between servers, which microservice deployment can take full advantage of. In this paper, we investigate the problem of how to collaboratively deploy microservices by incorporating both intra-server and inter-server layer sharing to maximize the edge throughput. We formulate this problem into an integer linear programming form and prove it as NP-hard. We propose a randomized rounding based heuristic algorithm, and conduct formal analysis on the guaranteed approximation ratio. Through extensive experiments, we verify the efficiency of our proposed algorithm, and the results demonstrate that it can deploy 6x and 12x more microservice instances, and improve the edge throughput by 27.74% and 38.46% in comparison with state-of-the-art strategies.

关键词： Heuristic algorithms conferences Microservice architectures Collaboration Integer linear programming Elasticity Containers

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PerfHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing

PerfHD: Efficient ViT Architecture Performance Ranking using...

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2023 ieee/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Ma, Dongning Zhao, Pengfei Jiao, Xun Villanova University VillanovaPA19085 United States Beijing Xiaochuan Technology Co. Ltd. Beijing100191 China

ISBN: (纸本)9798350302493

Neural Architecture Search (NAS) aims at identifying the optimal network architecture for a specific need in an automated manner, which serves as an alternative to the manual process of model development, selection, evaluation and performance estimation. However, evaluating performance of candidate architectures in the search space during NAS, which often requires training and ranking a mass amount of architectures, is often prohibitively computation-demanding. To reduce this cost, recent works propose to estimate and rank the architecture performance with-out actual training or inference. In this paper, we present PerfHD, an efficient-while-accurate architecture performance ranking approach using hyperdimensional computing for the emerging vision transformer (ViT), which has demonstrated state-of-the-art (SOTA) performance in vision tasks. Given a set of ViT models, PerfHD can accurately and quickly rank their performance solely based on their hyper-parameters without training. We develop two encoding schemes for PerfHD, Gram-based and Record-based, to encode the features from candidate ViT architecture parameters. Using the VIMER-UFO benchmark dataset of eight tasks from a diverse range of domains, we compare PerfHD with four SOTA methods. Experimental results show that PerfHD can rank nearly 100K ViT models in about just 1 minute, which is up to 10X faster than SOTA methods, while achieving comparable or even superior ranking accuracy. We open-source PerfHD in PyTorch implementation at https://***/VU-DETAIL/PerfHD. © 2023 ieee.

关键词： network architecture

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Safety-Preserving Filters Against Stealthy Sensor and Actuator Attacks 62

Safety-Preserving Filters Against Stealthy Sensor and Actuat...

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62nd ieee conference on Decision and Control (CDC)

作者： Escudero, Cedric Murguia, Carlos Massioni, Paolo Zamai, Eric Univ Lyon Univ Claude Bernard Lyon 1 UMR5005CNRSINSA Lyon Ecole Cent LyonAmpere F-69621 Villeurbanne France Eindhoven Univ Technol Dept Mech Engn Dynam & Control Grp Eindhoven Netherlands

ISBN: (纸本)9798350301243

This article proposes a novel strategy based on control input filtering for mitigating the effects of deception attacks on control and sensor measurement signals. We assume an adversary who can tamper data transmitted from a communication network in order to degrade the plant performance. The proposed strategy consists on adding multiple-input multiple-output (MIMO) filters to the control loop, between the received control actions and the plant actuators. The filter's goal is to dynamically steer the reachable set induced by the attack signals to a safe region of the state space. The article provides a filter synthesis method under the form of a semidefinite programming problem, yielding such filters in a way that attack-free control signals are distorted as little as possible, and plant trajectories are contained in the safe set. At the end of the paper, a set of simulations demonstrate the effectiveness of the approach.

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Deep Single Shot Musical Instrument Identification using Scalograms 5

Deep Single Shot Musical Instrument Identification using Sca...

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5th International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Chatterjee, Debdutta Dutta, Arindam Sil, Dibakar Chandra, Aniruddha Natl Inst Technol Dept Elect & Commun Engn Durgapur 713209 India Indian Inst Sci Dept Computat & Data Sci Bangalore India Steradian Semicond Pvt Ltd Bangalore India

ISBN: (纸本)9781665456456

Musical instrument identification has for long had a reputation of being one of the most ill-posed problems in the field of musical information retrieval. Despite several robust attempts made at solving the problem, a timeline spanning over the last five odd decades, the problem remains an open conundrum. In this work, we take on a further complex version of the traditional problem, we attempt to solve the problem with minimal data available - one audio excerpt per class. We propose to use a convolutional Siamese network and a residual variant of the same to identify musical instruments based on the corresponding scalograms of their audio excerpts. Results obtained for two publicly available datasets validate our algorithm, achieving over 80% accuracy with only 5 sets of training data. Moreover, our proposed architectures work for both spectrograms as well as scalograms, and exhibit improvements, albeit marginal (approximate to 3%), for the later input class.

关键词： Audio excerpts scalogram one-shot learning convolutional Siamese network

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DNF: Decouple and Feedback network for Seeing in the Dark

DNF: Decouple and Feedback Network for Seeing in the Dark

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jin, Xin Han, Ling-Hao Li, Zhen Guo, Chun-Le Chai, Zhi Li, Chongyi Nankai Univ VCIP CS Tianjin Peoples R China Hisilicon Technol Co Ltd Shenzhen Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

The exclusive properties of RAW data have shown great potential for low-light image enhancement. Nevertheless, the performance is bottlenecked by the inherent limitations of existing architectures in both single-stage and multi-stage methods. Mixed mapping across two different domains, noise-to-clean and RAW-to-sRGB, misleads the single-stage methods due to the domain ambiguity. The multi-stage methods propagate the information merely through the resulting image of each stage, neglecting the abundant features in the lossy image-level dataflow. In this paper, we probe a generalized solution to these bottlenecks and propose a Decouple aNd Feedback framework, abbreviated as DNF. To mitigate the domain ambiguity, domain-specific subtasks are decoupled, along with fully utilizing the unique properties in RAW and sRGB domains. The feature propagation across stages with a feedback mechanism avoids the information loss caused by image-level dataflow. The two key insights of our method resolve the inherent limitations of RAW data-based low-light image enhancement satisfactorily, empowering our method to outperform the previous state-of-the-art method by a large margin with only 19% parameters, achieving 0.97dB and 1.30dB PSNR improvements on the Sony and Fuji subsets of SID.

关键词： Low-level vision

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Online Knowledge Distillation with History-Aware Teachers

Online Knowledge Distillation with History-Aware Teachers

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作者： Li, Chengcheng Wang, Zi Qi, Hairong Univ Tennessee Knoxville TN USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

In this work, we propose a novel online knowledge distillation (OKD) approach, built upon the classical deep mutual learning framework in which peer networks (students) treat each other as teachers by learning from their predictions. The proposed method traces and leverages two levels of information encoded in each peer's learning trajectory to dynamically construct superior teachers to supervise other students. We first build a recurrent neural network associated with each peer, which takes both the network's current and previous logits as input and outputs integrated logits with the same dimension as the transferred knowledge. By doing so, the teachers provide an enhanced representation of knowledge. Beyond that, we also build a weight-averaged surrogate for each network, which maintains the exponential moving average of its learned parameters during the online training procedure. The proposed approach exploits the hidden information behind the online learning process instead of myopically learning from peers' outputs at a single time/iteration step. It potentially reduces uncertainties from peers as suffered in previous OKD studies with more stabilized transferred knowledge. We evaluate the proposed approach with benchmark image classification datasets and network architectures. Experimental results demonstrate its effectiveness with clear performance improvement over state-of-the-arts.

关键词： online knowledge distillation history-aware knowledge network-based OKD learning trajectory

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An Architecture-Independent CGRA Compiler enabling openMP Applications 36

An Architecture-Independent CGRA Compiler enabling OpenMP Ap...

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36th ieee International Parallel and Distributed Processing Symposium (ieee IPDPS)

作者： Kojima, Takuya Adhi, Boma Cortes, Carlos Tan, Yiyu Sano, Kentaro Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan RIKEN Ctr Computat Sci R CCS Kobe Hyogo Japan

ISBN: (纸本)9781665497473

Coarse-Grained reconfigurable architecture (CGRA) is a promising platform for HPC systems in the post-Moore's era. A single-source programming model is essential for practical heterogeneous computing. However, we do not have a canonical programming model and a frontend compiler for it. Existing versatile CGRAs, in respect to their execution model, computational capability, and system structure, magnify the difficulty of orchestrating the compiler techniques. It consequently forces designers of the CGRAs to develop the compiler from scratch, working only for their architectures. Such an approach is outdated, given other successful accelerators like GPU and FPGAs. This paper presents a new CGRA compiler framework in order to reduce development efforts of CGRA applications. openMP annotated codes are fed into the proposed compiler, as recent openMP support device offloading to the accelerators. This property improves the reusability of the existing source code for HPC workloads. The design of the compiler is inspired by LLVM, which is the most famous compiler framework so that the frontend is built to be architecture-independent. In this work, we demonstrate that the proposed compiler can handle different types of CGRAs without changing the source codes. In addition, we discuss the effect of architecture-independent optimization algorithms. We also provide an open-source implementation of the compiler framework at https://***/hal-lab-u-tokyo/CGRAOmp.

关键词： Codes Runtime Computational modeling Graphics processing units programming Heterogeneous networks Reconfigurable architectures

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