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arXiv 2021年

作者： Li, Xiaoxu Yang, Xiaochen Ma, Zhanyu Xue, Jing-Hao School of Computer and Communication Lanzhou University of Technology China Pattern Recognition and Intelligent System Laboratory School of Information and Communication Engineering Beijing University of Posts and Telecommunications China School of Mathematics and Statistics University of Glasgow United Kingdom Department of Statistical Science University College London United Kingdom

Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images. One main solution to few-shot image classification is deep metric learning. These methods, by classifying unseen samples according to their distances to few seen samples in an embedding space learned by powerful deep neural networks, can avoid overfitting to few training images in few-shot image classification and have achieved the state-of-the-art performance. In this paper, we provide an up-to-date review of deep metric learning methods for few-shot image classification from 2018 to 2022 and categorize them into three groups according to three stages of metric learning, namely learning feature embeddings, learning class representations, and learning distance measures. With this taxonomy, we identify the novelties of different methods and problems they face. We conclude this review with a discussion on current challenges and future trends in few-shot image classification. © 2021, CC BY.

关键词： Image classification

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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Robustify a job-shop schedule according to the payload of the machine

Robustify a job-shop schedule according to the payload of th...

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International Conference on Services systems and Services Management, ICSSSM

作者： Zhiyang You Zhigang Sun Xibin Zhao Daxin Zang Department of Computer Science and Technology Tsinghua University Beijing China School of Software Tsinghua University Beijing China Key Laboratory of Information System Security Ministry of Education School of Software Tsinghua University Beijing China Software Engineering Institute Beijing Aeronautics and Astronautics University Beijing China

Robust scheduling is an effective way to solve job-shop scheduling problem (JSP) under a dynamic and stochastic environment with uncertainties, which may lead to numerous schedule disruptions. Maintaining the optimization obtained, the robust schedule is more stable in execution. The vast majority of the researches on robust scheduling concentrate on the development of the robustness and stabilization with an optimization target that minimizes makespan. However, other optimization targets are needed in reality, and in this paper, we introduce another optimization target that maximizes the payload of the machine, which can reflect the capacity of machine. This work applies a two-step way to achieve a robust schedule. A baseline schedule in the deterministic environment is generated first, which pursues standard optimization criterions, and then the temporal buffers are inserted into the baseline schedule which make the schedule absorb some level of unexpected disruption without rescheduling. According to the payload of each machine which should be close to the maximum at any time in execution, we can get each size of the temporal buffers inserted into the different operations. An extensive experiment is used to show the relative performance of the proposed robust schedule. It is shown that the schedule is robust and stable under the stochastic disturbance coupling with minimal makespan tardiness and symmetry payload.

关键词： Robustness Payloads Job shop scheduling Uncertainty Processor scheduling Dynamic scheduling Stochastic processes Switches computer science Space technology

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans

Implementation of a Hip Fracture Classification Network Base...

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International Conference on computer and Communications (ICCC)

作者： Kui Zhao Meng Yang Ti Zhang Die Hu Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Science Shenyang China Department of Orthopedics Peking University Third Hospital Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Hip fractures is the most common hip clinical trauma. The majority of elderly patients experience a decrease in bone density due to osteoporosis, making them highly prone to various types of hip fractures. In this context, accurate diagnosis and classification of hip fractures are crucial for guiding clinical decision-making and ensuring the best possible patient outcomes. The BV C- N et proposed in this study is a two-stage model for the identification of hip fracture types, which is based on the integration of the V-Net and ConvNeXt architectures. First, deep learning segmentation model is trained to generate candidate fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to ascertain the type of fracture. The results show that the two-stage deep learning model proposed in this study enhances the accuracy of hip fracture type recognition and reduces the time required for manual classification, which can better assist doctors in fracture region recognition.

关键词： Deep learning Image segmentation Adaptation models Osteoporosis Accuracy Computed tomography Transfer learning Predictive models Older adults Hip

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Bidirectional Visual-Tactile Cross-Modal Generation Using Latent Feature Space Flow Model

SSRN

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SSRN 2023年

作者： Fang, Yu Zhang, Xuehe Xu, Wenqiang Liu, Gangfeng Zhao, Jie State Key Laboratory of Robotics and System Harbin Institute of Technology No.2Yikuang StreetNangang District Heilongjiang Harbin150001 China Department of Computer Science and Engineering Shanghai Jiao Tong University No.800 Dongchuan Road Minhang District Shanghai Shanghai200240 China

Inspired by visual-tactile cross-modal bidirectional mapping of the human brain, this paper introduces a novel approach to bidirectional mapping between visual and tactile data, an area not fully explored in the predominantly unidirectional existing studies. First, we adopt separate Variational AutoEncoder (VAE) models for visual and tactile data. Furthermore, we introduce a conditional flow model built on the VAE latent feature space, enabling cross-modal bidirectional mapping between visual and tactile data using one model. The experimental results show that our method achieves excellent performance in terms of the similarity between the generated data and the ground truth (SSIM of visual data: 0.57, SSIM of tactile data: 0.80), the classification accuracy on generated data (visual data: 93.63\%, tactile data: 88.52\%), and the zero-shot classification accuracy between generated data and language (visual data: 45.14\%, tactile data: 45.57\%). To the best of our knowledge, the method proposed in this paper is the first one to utilize a single model to achieve bidirectional mapping between visual and tactile data. Our model and code will be made public after the acceptance of the paper. © 2023, The Authors. All rights reserved.

关键词： Mapping

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Improved vicinal SV algorithm

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Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology 2005年第11期25卷 967-970页

作者： Bi, Le-Peng Zheng, Zhi-Yun Song, Han-Tao Lu, Yu-Chang Department of Computer Science and Engineering School of Information Science and Technology Beijing Institute of Technology Beijing 100081 China State Key Laboratory of Intelligent Technology and System Tsinghua University Beijing 100084 China

Two improvements are introduced into vicinal-risk-minimization based support vector (SV) algorithm. Since the misclassified samples must be support vectors, a scheme for pruning hard-to-learn samples from the training set based on support vectors is presented. The parameter's determination of Gaussian vicinal function is proposed to be modified, based on the maximum likelihood criterion. Preliminary experimental results show that the pruning scheme and improvement of the parameter's determination of vicinal function much improved vicinal SV algorithm's generality, and can outperform support vector machine (SVM) by about 0.5% in test accuracy.

关键词： Learning algorithms

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Open Call [IEEE Radio Communications: Components, systems, and Networks]

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IEEE Communications Magazine 2007年第9期45卷 38-38页

作者： Joseph Evans Zoran Zvonar Department of Electrical Engineering & Computer Science University of Kansas RF & Wireless System Business Unit

The three articles in this special section are an open call for papers that address the following topics: unified spectrum management; soft-decision in UMTS systems; and polar transmitters for wireless communications.

关键词： Radio communication computer networks Laboratories Radio frequency Radio transmitters Wireless communication OFDM modulation GSM Pervasive computing 3G mobile communication

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A survey of public auditing for secure data storage in cloud computing

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International Journal of Network Security 2016年第1期18卷 133-142页

作者： Hsien, Wei-Fu Yang, Chou-Chen Hwang, Min-Shiang Department of Management Information System National Chung Hsing University No. 500 Lioufeng Rd. Wufeng Taichung41354 Taiwan Department of Computer Science and Information Engineering Asia University No. 500 Lioufeng Rd. Wufeng Taichung41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University No. 91 Hsueh-Shih Road Taichung40402 Taiwan

Cloud computing has been popular as the IT architecture. Cloud service providers offer many services based on cloud computing. Cloud storage service is the cloud services which can provide a huge storage space to solve the bottleneck of the storage space of local end users. However, cloud storage service may have data security because the users' data is not stored in their own storage. In this paper, we will focus on data integrity in the cloud storage service. Public auditability is a model of outsourcing data integrity verification, which can achieve efficiency and security. Therefore, we survey the previous researches of data integrity based on public auditability which includes collecting the basic requirements and evaluation metrics, providing the representative with approaches to analyze security and efficiency. Finally, we propose some future developments.

关键词： Hash functions

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TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China National University of Singapore Singapore

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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