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arXiv 2021年

作者： Tian, Lei Wang, Peng Liang, Guoqiang Shen, Chunhua School of Computer Science Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China School of Computer Science The University of Adelaide Australia

Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods. In this paper, we design a novel generative adversarial network (GAN) to improve the localization accuracy of visible joints when some joints are invisible. The network consists of two simple but efficient modules, i.e., Cascade Feature Network (CFN) and Graph Structure Network (GSN). First, the CFN utilizes the prediction maps from the previous stages to guide the prediction maps in the next stage to produce accurate human pose. Second, the GSN is designed to contribute to the localization of invisible joints by passing message among different joints. According to GAN, if the prediction pose produced by the generator G cannot be distinguished by the discriminator D, the generator network G has successfully obtained the underlying dependence of human joints. We conduct experiments on three widely used human pose estimation benchmark datasets, i.e., LSP, MPII and COCO, whose results show the effectiveness of our proposed framework. © 2021, CC BY.

关键词： Generative adversarial networks

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Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Hu, Shishuai Liao, Zehui Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'An710072 China Ningbo Institute Northwestern Polytechnical University Ningbo315048 China Research and Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adaptation and multi-source domain generalization methods have been proposed, which, however, are less favorable for clinical practice due to the cost of acquiring target-domain data and the privacy concerns associated with redistributing the data from multiple source domains. In this paper, we propose a Channel-level Contrastive Single Domain Generalization (C2SDG) model for medical image segmentation. In C2SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations. The segmentation is performed based solely on the structure representations. Our method is novel in the contrastive perspective that enables channel-wise feature disentanglement using a single source domain. We evaluated C2SDG against six SDG methods on a multi-domain joint optic cup and optic disc segmentation benchmark. Our results suggest the effectiveness of each module in C2SDG and also indicate that C2SDG outperforms the baseline and all competing methods with a large margin. The code will be available at https://***/ShishuaiHu/CCSDG. © 2023, CC BY-NC-ND.

关键词： Deep learning

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EIAS:An Efficient Identity-Based Aggregate Signature Scheme for WSNs Against Coalition Attack

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computers, Materials & Continua 2019年第6期59卷 903-924页

作者： Yong Xie Fang Xu Xiang Li Songsong Zhang Xiaodan Zhang Muhammad Israr Department of Computer Technology and Application Qinghai UniversityXiningChina School of Computer and Information Science Hubei Engineering UniversityXiaoganChina Department of Computer Sciences COMSATS UniversityAbbottabadPakistan

Wireless sensor networks(WSNs)are the major contributors to big data *** authenticity and integrity of the data are two most important basic requirements for various services based on big *** aggregation is a promising method to decrease operation cost for resource-constrained ***,the process of data acquisitions in WSNs are in open environments,data aggregation is vulnerable to more special security attacks with hiding feature and subjective fraudulence,such as coalition *** to provide data authenticity and integrity protection for WSNs,an efficient and secure identity-based aggregate signature scheme(EIAS)is proposed in this *** security proof shows that our proposed scheme can be secure against all kinds of *** performance comparisons shows EIAS has clear advantages in term of computation cost and communication cost when compared with similar data aggregation scheme for WSNs.

关键词： Wireless sensor networks(WSNs) big data signature aggregation efficiency coalition attack

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A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization

SSRN

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SSRN 2023年

作者： Cao, Jian Qian, Chen Huang, Yihui Chen, Dicheng Gao, Yuncheng Dong, Jiyang Guo, Di Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Implicit regularization induced by gradient optimization is an important way to understand the generalization in neural networks. Recent theory explains implicit regularization over the model of deep matrix factorization (DMF) and analyzes the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics can mathematically characterize the practical learning rate of adaptive gradient optimization such as RMSProp. Discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization of RMSProp, i.e. landscape analysis. It mainly focuses on gradient regions like saddle points and local minima. We investigate that increasing learning rates benefit saddle point escaping (SPE) stages. In elucidating implicit regularization through the convergence of RMSProp, we prove that for a rank-R matrix reconstruction, DMF will converge to a second-order critical point after R stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials

SSRN

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SSRN 2024年

作者： Qi, Yaping Hu, Dan Zheng, Ming Jiang, Yucheng Chen, Yong P. Tohoku University Sendai980-8577 Japan Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long 999078 China Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University Aarhus-C8000 Denmark School of Materials Science and Physics China University of Mining and Technology Xuzhou221116 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Jiangsu Suzhou215009 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. The proposed CNN model achieves an impressive accuracy of 98.8%. Experiments illustrate the effectiveness of DDPM in addressing data limitations and significantly improved the performance of the classification model. Notably, the DDPM-CNN method shows high reliability, with 100% classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, presenting a promising avenue for rapid and automated materials analysis via spectroscopy. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Method of Realizing External Network Access to Intranet for Embedded Web Server

A Method of Realizing External Network Access to Intranet fo...

引用

Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Xian Zhang Taiguo Qu Pengju Tang Yiwen Liu School of Computer Science and Engineering Huaihua University Huaihua China Key Laboratory of Intelligent Control Technology for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Key Laboratory of Wuling Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China

ISBN: (纸本)9781665464024

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects.

关键词： Performance evaluation Uniform resource locators Smart agriculture Web servers Software Real-time systems Organ transplantation

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Efficient and privacy-preserving deep inference towards cloud–edge collaborative

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Applied Soft Computing 2025年 180卷

作者： Yulong Wang Guoxin Zhong Yubing Duan Yunchang Cheng Mingyong Yin Run Yang Institute of computer application China Academy of Engineering Physics Mianyang China School of Cyberspace Science Harbin Institute of Technology Harbin China

The cloud–edge collaborative inference approach splits deep neural networks (DNNs) into two parts to run collaboratively on resource-constrained edge devices(AIoT devices) and cloud servers, aiming at minimizing inference latency and protecting data privacy for AIoT computing system. However, despite not exposing the raw input data from edge devices directly to the cloud, state-of-the-art attacks can still target collaborative inference to reconstruct the raw private data from exposed local models’ intermediate outputs, introducing serious privacy risks. In this paper, we propose a secure privacy inference framework for cloud–edge collaboration system towards AIoT network, called CIS ( C ollaborative I nference S hield), which supports adaptively partitioning the network according to dynamically changing network bandwidth and fully releases the computational power of edge devices. To mitigate the influence introduced by private perturbation, CIS provides a way to achieve differential privacy protection by adding refined noise to the intermediate layer feature maps offloaded to the cloud. Meanwhile, given a total privacy budget, the budget is reasonably allocated by the size of the feature graph rank generated by different convolution filters, making cloud inference robust to the perturbed data, thus effectively trading-off between privacy and availability. Finally, we construct a real cloud–edge collaborative inference computing scenario to verify the effectiveness of inference latency and model partitioning on resource-constrained edge devices. Furthermore, the state-of-the-art cloud–edge collaborative reconstruction attack is utilized to evaluate the practical availability of the end-to-end privacy protection mechanism provided by CIS.

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Prediction of chilling damage risk in maize growth period based on probabilistic neural network approach

引用

International Journal of Agricultural and Biological engineering 2021年第2期14卷 120-125页

作者： Chunqiao Mi Changhua Zhao Qingyou Deng Xiaowu Deng School of Computer Science and Engineering Huaihua UniversityHuaihua 418000HunanChina Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture Huaihua 418000HunanChina Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua 418000HunanChina Human Resource Department Huaihua UniversityHuaihua 418000HunanChina

Low temperature chilling damage is one of the most serious disasters in maize production,which is a typical non-linear complex issue with numerous influencing factors and strong *** to predict it is not only a hot theoretical research topic,but also an urgent practical problem to be ***,most of the current researches are focusing on post-disaster static descriptive assessment rather than pre-disaster dynamic predictive analysis,resulting in the problems such as no indicative result and low *** this study,the satisfaction rate of environmental accumulated temperature for maize production was used to measure the chilling damage risk,and a model for maize chilling damage risk prediction based on probabilistic neural network was *** model was composed of input layer,pattern layer,summation layer and output *** obtained results showed that the prediction accuracy for the most serious risk level was as high as 0.91,and the rates of the Type I Error and Type II Error made by the model were 0.1 and 0.09,*** indicated that the model employed was promising with good *** results of this research are of both theoretical significance for providing a new reference method of pre-disaster prediction to study maize chilling disaster risk and practical significance for reducing maize production risk and ensuring yield safety.

关键词： maize chilling damage risk prediction probabilistic neural network

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HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging

arXiv

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arXiv 2022年

作者： Jia, Haozhe Bai, Chao Cai, Weidong Huang, Heng Xia, Yong Research & Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'An710072 China School of Computer Science University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States JD Finance America Corporation California CA94043 United States

In our previous work, i.e., HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging. In this paper, we extend our HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks to further exploit global semantic discrimination for the obtained high-resolution features. We trained and evaluated our HNF-Netv2 on the multi-modal Brain Tumor Segmentation Challenge (BraTS) 2021 dataset. The result on the test set shows that our HNF-Netv2 achieved the average Dice scores of 0.878514, 0.872985, and 0.924919, as well as the Hausdorff distances (95%) of 8.9184, 16.2530, and 4.4895 for the enhancing tumor, tumor core, and whole tumor, respectively. Our method won the RSNA 2021 Brain Tumor AI Challenge Prize (Segmentation Task), which ranks 8th out of all 1250 submitted results. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature 23

Decentralized and Lightweight Cross-Chain Transaction Scheme...

引用

23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Zou, Huiying Duan, Jia Liu, Xi Ren, Wei Li, Tao Zheng, Xianghan Choo, Kim-Kwang Raymond Hunan Engineering Research Center of Geographic Information Security and Application The Third Surveying and Mapping Institute of Hunan Province Hunan China China University of Geosciences School of Computer Science Wuhan China Sichuan University Ministry of Education Key Laboratory of Data Protection and Intelligent Management China Casm State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Mnr Beijing China Fuzhou University College of Computer and Big Data China Sanya College School of Information and Intelligent Engineering China University of Texas at San Antonio Department of Information Systems and Cyber Security San Antonio United States

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach's security. © 2024 IEEE.

关键词： blockchain transaction cross-chain technology notary schemes proxy re-signature

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