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arXiv 2022年

作者： Song, Yisheng Wang, Ting Mondal, Subrota K. Sahoo, Jyoti Prakash The Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education The Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China The Faculty of Information Technology Macau University of Science and Technology Macao China The Department of Computer Science & Information Technology Institute of Technical Education and Research Siksha 'o' Anusandhan University India

Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge. In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of strengths and weaknesses of the existing works. For the sake of avoiding conceptual confusion, we first elaborate and compare a set of similar concepts including few-shot learning, transfer learning, and meta-learning. Furthermore, we propose a novel taxonomy to classify the existing work according to the level of abstraction of knowledge in accordance with the challenges of FSL. To enrich this survey, in each subsection we provide in-depth analysis and insightful discussion about recent advances on these topics. Moreover, taking computer vision as an example, we highlight the important application of FSL, covering various research hotspots. Finally, we conclude the survey with unique insights into the technology evolution trends together with potential future research opportunities in the hope of providing guidance to follow-up research. © 2022, CC BY.

关键词： Surveys

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Prophet: SSD Failure Analysis and Prediction Guided by Flash Reliability Characteristics in Data centers

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IEEE Transactions on computers 2025年

作者： Song, Yunpeng Liang, Yujiong Liu, Jialin Shi, Liang East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China East China Normal University School of Computer Science and Technology Shanghai China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan China

Solid-state drives (SSDs) are massively deployed in various fields, especially in data centers, for their excellent cost-effectiveness. However, SSDs may fail due to their imperfect manufacturing processes, resulting in system-level failures and even downtime in data centers. This makes SSD failure prediction critical. Current studies focus on dealing with data missing, numerical normalization, and other statistical issues in using machine learning methods, but the consideration of the reliability characteristics of the underlying flash media of SSDs and the timeliness (time duration between predicted failure and real failure) of SSD failure prediction result is missing. In this work, we study the failure characteristics of over 200,000 drives from industry data centers over a 4-year period, as well as daily data. The relationship between SSD attribute values and failures is first investigated. Then, we analyzed the SSD failure characteristics from several aspects (causes, differences between failures, and timeliness of prediction results) relying on flash reliability characteristics. Based on these, a novel SSD failure prediction method (Prophet) is proposed. Specifically, Prophet contains the following two components. First, to cope with the differences between failures, a diff-state method is proposed for differential machine learning modeling of SSDs in different "States". We define the "State" of an SSD, which represents the range of values in which the SSD currently lies in terms of some key attributes. Through flash reliability characteristics, we distinguish between different failures before training the model to obtain accurate predictions of different failure behaviors. Second, a recovery period method is proposed to enhance the timeliness of SSD failure prediction result by designing the sample selection method. The enhanced timeliness can be utilized by operations personnel to handle failed SSDs, such as replacement and repair. The evaluation results of

关键词： Failure rate

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Vibration Fault Detection Cyber-physical System with Service Support 2

Vibration Fault Detection Cyber-physical System with Service...

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2nd International conference on Safety Produce Informatization, IICSPI 2019

作者： Xiao, Bo Zhu, Minghua East China Normal University MOE Research Center for Software/Hardware Co-Design Engineering and Application Shanghai China

ISBN: (纸本)9781728145662

Vibration fault detection becomes increasingly used for economical and safety reasons. However, current research reveals some problems such as low accuracy of fault detection, few effective in-depth calculation of vibration data and few flexible architectures in that area. In this paper, a vibration fault detection Cyber-Physical System (CPS)with service support (S-VFDCPS) is presented. From the Service-Oriented Architecture (SOA) point of view, S-VFDCPS consists of enterprise level, gateway level and endpoint level, with relevant vibration detection services on each level. From the Cyber-Physical system (CPS) point of view, it contains three layers: the monitoring and control layer, the network layer and the device layer. Data processing algorithms and services are proposed quantifying the criteria of fault evaluation. It is shown by testing results that our proposed approach and the improvement in design make S-VFDCPS more flexible and accurate in real-time vibration fault detection. © 2019 IEEE.

关键词： Cyber Physical System

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Secure and Reliable Parking Protocol Based on Blockchain for VANETs

Secure and Reliable Parking Protocol Based on Blockchain for...

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IEEE conference on Wireless communications and Networking

作者： Yan Zhang Lei Zhang Burong Kang Yue Ma Tong Chen Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing School of Software Engineering East China Normal University Shanghai China

ISBN: (纸本)9781728195056;9781728195063

With the development of vehicular ad hoc network (VANET) technology, more and more smart parking systems in VANETs are presented. However, existing solutions suffer from the challenges of dishonest parking lots that may claim there are more empty spots than they actually have, malicious occupation of parking spots and multi-reservation attack, etc. To address these challenges, we propose a secure and reliable smart parking protocol based on blockchain and linkable group signatures. For the security of our scheme, it not only realizes traditional authentication, privacy preservation and message confidentiality requirements but also prevents multi-reservation attack and the dishonest parking lots that claim more empty spots than they actually have.

关键词： Privacy Protocols conferences Vehicular ad hoc networks Authentication Blockchain Reliability

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RTStitch: Real-time Stitching of High-resolution PCB Images *

RTStitch: Real-time Stitching of High-resolution PCB Images ...

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IEEE International conference on Systems, Man and Cybernetics

作者： Wenbin Yang Xianzhong Liu Shanghai Key Laboratory of Trustworthy Computing School of Software Engineering East China Normal University Shanghai China Software/Hardware Co-design Engineering Research Center MoE School of Software Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665442084

In the Automatic Optical Inspection (AOI) system, it is necessary to stitch high-resolution Printed Circuit Board (PCB) partial images in real time to obtain a panoramic image. However, the proportion of the overlapping area of these images is usually relatively small, and there are few feature points in the overlap area of some images. Traditional image stitching algorithms are not effective in processing such images, and it is difficult to meet the requirements of real-time stitching. Therefore, we propose RTStitch, a method specially used for real-time stitching of high-resolution PCB images in AOI systems. We divide the images into two categories. An image with a large change in the pixel gray value of the first column in the overlapping area is the first type of image, otherwise, it is the second type of image. We use different methods to obtain a relatively accurate maximum overlap position for these two types of images. Besides, we propose a new method of selecting template images, then perform template matching to obtain horizontal and vertical errors. We focus on the areas where the gray level changes drastically in the overlapping area to improve the accuracy of the registration. Our experimental results show that for the first type of image, RTStitch improves the registration accuracy by more than 2%, while for the second type of image, the registration accuracy is improved by more than 10%. And more than doubled the stitching speed. Moreover, RTStitch can meet the high-precision and real-time requirements of the AOI system.

关键词： Splicing conferences Printed circuits Cameras Real-time systems Automatic optical inspection Image stitching

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Fast and robust template matching based on ring projection and pseudo-Zernike moments for automatic optical inspection*

Fast and robust template matching based on ring projection a...

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IEEE International conference on Systems, Man and Cybernetics

作者： Jinming Liu Xianzhong Liu Shanghai Key Laboratory of Trustworthy Computing School of Software Engineering East China Normal University Shanghai China Software/Hardware Co-design Engineering Research Center MoE School of Software Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665442084

The traditional template matching methods are very time-consuming and cannot deal with the problems of rotation, scaling, and multi objects matching at the same time. In this paper, we propose a two-stage fast and robust template matching method based on ring projection transformation and pseudo-Zernike moments, which can be used in automatic optical inspection. In the first stage, we use dynamic time warping to estimate the scaling factors and obtain the preliminary candidates according to the extracted ring projection vectors. And then the candidates of the multi objects are obtained by non-maximum suppression. In the second stage, the fine matching process based on pseudo-Zernike moments is performed. Besides, we apply the image pyramid search strategy to improve the computational efficiency. The rotation angle is estimated by the improved ring-shifted method. The experimental results show that the mean errors of rotation, scaling, and translation for the image with or without noise or illumination changes are less than 0.2412°, 0.0037, and 1.0826 pixels, respectively. compared with the conventional search method, the image pyramid search strategy increases the computational efficiency by 50 times. The experiments of robustness demonstrate that the template matching method we proposed can still obtain high accuracy even in the case of noise or illumination changes. In addition, it can be used for multi objects detection.

关键词： conferences Lighting Estimation Object detection Search problems Robustness computational efficiency

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Providing Service composition to Cyber-Physical System for Industrial Fault Detection 3rd

Providing Service Composition to Cyber-Physical System for I...

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3rd International conference on Mechatronics and Intelligent Robotics, ICMIR 2019

作者： Xiao, Bo Zhao, Kang Tang, Haitao MOE Research Center for Software/Hardware Co-Design Engineering and Application East China Normal University 3663 North Zhongshan Rd. Shanghai200062 China Siemens Factory Automation Engineering Ltd Shanghai No.1 Branch 1089 South Second Zhongshan Rd. Shanghai200030 China

ISBN: (纸本)9789811502378

This paper presents an approach to providing service composition to cyber-physical system (CPS) for industrial fault detection and vibration detection in particular. While vibration fault detection has been increasingly adopted in industrial sectors and critical infrastructure for economical and safety concerns, architectural and performance restrictions of most systems in this area impose restrictions on its wide and flexible usage. Recent research shows that it is promising to applying service-oriented architecture to developing industrial systems. We regard that enhancing CPS with services and service composition schemes in industrial vibration fault detection be feasible and promising. The architecture of our vibration fault detection CPS system, consisting of the enterprise level, gateway level and endpoint level, is integrated with various vibration detection services at relevant levels. Service composition with mapping from physical services to virtual services and to composition services is designed. It is shown by testing results that the proposed approach makes the system for real-time vibration fault detection more flexible and effective. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Cyber Physical System

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Arrhythmia Classification with Attention-Based Res-BiLSTM-Net 1st

Arrhythmia Classification with Attention-Based Res-BiLSTM-Ne...

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1st International Workshop on Machine Learning and Medical engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on computing and Visualization for Intravascular Imaging and computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2019

作者： Huang, Chengbin Zhao, Renjie Chen, Weiting Li, Huazheng MOE Engineering Research Center for Hardware/Software Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9783030333263

In the modern clinical diagnosis, the 12-lead electrocardiogram (ECG) signal has proved effective in cardiac arrhythmias classification. However, the manual diagnosis for cardiac arrhythmias is tedious and error-prone through ECG signals. In this work, we propose an end-to-end deep neural network called attention-based Res-BiLSTM-Net for automatic diagnosis of cardiac arrhythmias. Our model is capable of classifying ECG signals with different lengths. The proposed network consists of two parts: the attention-based Resnet and the attention-based BiLSTM. At first, ECG signals are divided into several signal segments with the same length. Then multi-scale features are extracted by our attention-based Resnet through signal segments. Next, these multi-scale features from a same ECG signal are integrated in chronological order. In the end, our attention-based BiLSTM classifies cardiac arrhythmias according to combined features. Our method achieved a good result with an average F1score of 0.8757 on a multi-label arrhythmias classification problem in the First China ECG Intelligent competition. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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Efficient Federated Learning for AIoT applications Using Knowledge Distillation

arXiv

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arXiv 2021年

作者： Liu, Tian Ling, Zhiwei Xia, Jun Fu, Xin Yu, Shui Chen, Mingsong MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University Shanghai200062 China Department of Information Science and Engineering Zaozhuang University Zaozhuang277160 China Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States School of Computer Science University of Technology Sydney Australia

As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which makes it widely used by Artificial Intelligence Internet of Things (AIoT) applications. However, the traditional FL suffers from model inaccuracy, since it trains local models only using hard labels of data while useful information of incorrect predictions with small probabilities is ignored. Although various solutions try to tackle the bottleneck of the traditional FL, most of them introduce significant communication overhead, making the deployment of large-scale AIoT devices a great challenge. To address the above problem, this paper presents a novel Distillation-based Federated Learning (DFL) method that enables efficient and accurate FL for AIoT applications. By using Knowledge Distillation (KD), in each round of FL training, our approach uploads both the soft targets and local model gradients to the cloud server for aggregation, where the aggregation results are then dispatched to AIoT devices for the next round of local training. During the DFL local training, in addition to hard labels, the model predictions approximate soft targets, which can improve model accuracy by leveraging the knowledge of soft targets. To further improve our DFL model performance, we design a dynamic adjustment strategy of loss function weights for tuning the ratio of KD and FL, which can maximize the synergy between soft targets and hard labels. comprehensive experimental results on well-known benchmarks show that our approach can significantly improve the model accuracy of FL without introducing significant communication overhead. copyright © 2021, The Authors. All rights reserved.

关键词： Distillation

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Satellite Federated Fine-Tuning for Foundation Models in Space computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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