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Classification of Arrhythmia Based on Extreme Learning Machine

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Classification of Arrhythmia Based on Extreme Learning Machi...

International Conference on Intelligent Human-Machine Systems and Cybernetics

作者： Xingjin Zhang Runchuan Li Yongpeng Liu Shan Gao Hongpo Zhang Shengya Shen Zongmin Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou Henan China School of Foreign Languages Zhengzhou University Zhengzhou Henan China

This paper proposes an arrhythmia classification method based on Extreme Learning Machine (ELM). Firstly, we extract a feature vector from the electrocardiogram (ECG) dataset as the input of the ELM model for each heartbeat and use the 10-fold cross validation method to test the model. Secondly, we complete the experiment on the MIT-BIH database according to the AAMI-reported arrhythmia classification criteria. Finally, compared with other methods the average accuracy of classification method proposed in this paper has a 1.7% increase, and the sensitivity of the N, S, V, and Q categories are 99.12%, 75.2%, 93.72%, and 98.64%, respectively. We found that the performance about class F is lower than the best performance in the literature, because the optimal outcome of neural network requires a relatively high number of training samples.

关键词： Arrhythmia Extreme learning machine Heartbeat classification

Understanding adversarial attacks on deep learning based medical image analysis systems

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arXiv 2019年

作者： Ma, Xingjun Niu, Yuhao Gu, Lin Wang, Yisen Zhao, Yitian Bailey, James Lu, Feng State Key Laboratory of VR Technology and Systems School of CSE Beihang University Beijing China School of Computing and Information Systems The University of Melbourne ParkvilleVIC3010 Australia Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Cixi Instuitue of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

A survey of industrial control system testbeds

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IOP Conference Series: Materials Science and engineering 2019年第4期569卷

作者： Yangyang Geng Yi Wang Wenwen Liu Qiang Wei Ke Liu Haolan Wu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001 China. Department of Research and Development Center Beijing sifang automation co.ltd Beijing 100085 China

Industrial Control System(ICS) testbed is the basis of ICS safety research. In order to build an ICS testbed, it is necessary to have a deep understanding of the current research status. This paper introduced the structure and composition of the typical ICS system. Then it simply analyzed the technical characteristics, system framework and advantages and disadvantages of four types of typical ICS testbeds. Finally, it summarized application scenarios of ICS testbed and pointed out some problems in testbed construction and the development direction of ICS testbed with the fusion of cyber and physical.

关键词：

Fair Efficiency Comparisons of Decoy-state Quantum key Distribution Protocols

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arXiv 2018年

作者： Hong-Xin, Li Ming, Gao Xue-Ping, Yan Bao, Yan Yu, Han Ling, Shan Zhi, Ma Department of Language Engineering LuoyangUniversity of Foreign Languages Luoyang Henan471003 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China College of Animal Science and Technology Henan University of Science and Technology Luoyang Henan471003 China

Secure key rate of decoy-state quantum key distribution protocols hasbeen improved with biased basis choice, however, the securitystandardsand parameters of current protocols are *** a result, we cannotgive an accurate key rate comparison between different kinds of protocols. Taking the schemes based on different formula of secure key rate as examples, we give a fair comparison between typical protocols under universalcomposable securitystandard. Through analyzing the relationship of security parameters in post-processing stage and final secure key, we achieve the unified quantification between protocols based on Gottesman-Lo-Lütkenhaus-Preskill formula and the ones under universal composable security. Based on the above research, the impact of different sending length and secure parameters on secure key rate is investigated, meanwhile, we give the dependent relationship between secure key rate and sending length under different secure parameters. Besides, we analyze the importance and conditions of fair comparison. For the first time we give a fair comparison betweenthe protocols based on GLLP formula and smooth entropy, and taking Raymond protocol and Toshiba protocol as examples, we analyze the way for improving secure key rate in the light intensity choice and the singlebit error rate estimation method. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum cryptography

Strong light illumination on gain-switched semiconductor lasers helps the eavesdropper in practical quantum key distribution systems

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arXiv 2018年

作者： Fei, Yang-yang Meng, Xiang-dong Gao, Ming Yang, Yi Wang, Hong Ma, Zhi State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengzhouHenan450001 China CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China HefeiAnhui230026 China

The temperature of the semiconductor diode increases under strong light illumination whether thermoelectric cooler is installed or not, which changes the output wavelength of the laser (Lee M. S. et al., 2017). However, other characteristics also vary as temperature increases. These variations may help the eavesdropper in practical quantum key distribution systems. We study the effects of temperature increase on gain-switched semiconductor lasers by simulating temperature dependent rate equations. The results show that temperature increase may cause large intensity fluctuation, decrease the output intensity and lead the signal state and decoy state distinguishable. We also propose a modified photon number splitting attack by exploiting the effects of temperature increase. Countermeasures are also proposed. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum cryptography

Attentive relational networks for mapping images to scene graphs

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arXiv 2018年

作者： Qi, Mengshi Li, Weijian Yang, Zhengyuan Wang, Yunhong Luo, Jiebo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Department of Computer Science University of Rochester United States

Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships. Despite the recent success in object detection using deep learning techniques, inferring complex contextual relationships and structured graph representations from visual data remains a challenging topic. In this study, we propose a novel Attentive Relational Network that consists of two key modules with an object detection backbone to approach this problem. The first module is a semantic transformation module utilized to capture semantic embedded relation features, by translating visual features and linguistic features into a common semantic space. The other module is a graph self-attention module introduced to embed a joint graph representation through assigning various importance weights to neighboring nodes. Finally, accurate scene graphs are produced by the relation inference module to recognize all entities and the corresponding relations. We evaluate our proposed method on the widely-adopted Visual Genome Dataset, and the results demonstrate the effectiveness and superiority of our model. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

Calibration of the air shower energy scale of the water and air Cherenkov techniques in the LHAASO experiment

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Physical Review D 2021年第6期104卷 062007-062007页

作者： F. Aharonian Q. An Axikegu L. X. Bai Y. X. Bai Y. W. Bao D. Bastieri X. J. Bi Y. J. Bi H. Cai J. T. Cai Zhen Cao Zhe Cao J. Chang J. F. Chang B. M. Chen E. S. Chen J. Chen Liang Chen Long Chen M. J. Chen M. L. Chen Q. H. Chen S. H. Chen S. Z. Chen T. L. Chen X. L. Chen Y. Chen N. Cheng Y. D. Cheng S. W. Cui X. H. Cui Y. D. Cui B. Z. Dai H. L. Dai Z. G. Dai Danzengluobu D. della Volpe B. D’Ettorre Piazzoli X. J. Dong K. K. Duan J. H. Fan Y. Z. Fan Z. X. Fan J. Fang K. Fang C. F. Feng L. Feng S. H. Feng Y. L. Feng B. Gao C. D. Gao L. Q. Gao Q. Gao W. Gao M. M. Ge L. S. Geng G. H. Gong Q. B. Gou M. H. Gu F. L. Guo J. G. Guo X. L. Guo Y. Q. Guo Y. Y. Guo Y. A. Han H. H. He H. N. He J. C. He S. L. He X. B. He Y. He M. Heller Y. K. Hor C. Hou H. B. Hu S. Hu S. C. Hu X. J. Hu D. H. Huang Q. L. Huang W. H. Huang X. T. Huang X. Y. Huang Z. C. Huang F. Ji X. L. Ji H. Y. Jia K. Jiang Z. J. Jiang C. Jin T. Ke D. Kuleshov K. Levochkin B. B. Li Cong Li Cheng Li F. Li H. B. Li H. C. Li H. Y. Li J. Li K. Li W. L. Li Xin Li X. R. Li Y. Li Y. Z. Li Zhe Li Zhuo Li E. W. Liang Y. F. Liang S. J. Lin B. Liu C. Liu D. Liu H. Liu H. D. Liu J. Liu J. L. Liu J. S. Liu J. Y. Liu M. Y. Liu R. Y. Liu S. M. Liu W. Liu Y. Liu Y. N. Liu Z. X. Liu W. J. Long R. Lu H. K. Lv B. Q. Ma L. L. Ma X. H. Ma J. R. Mao A. Masood Z. Min W. Mitthumsiri T. Montaruli Y. C. Nan B. Y. Pang P. Pattarakijwanich Z. Y. Pei M. Y. Qi Y. Q. Qi B. Q. Qiao J. J. Qin D. Ruffolo V. Rulev A. Sáiz L. Shao O. Shchegolev X. D. Sheng J. Y. Shi H. C. Song Yu. V. Stenkin V. Stepanov Y. Su Q. N. Sun X. N. Sun Z. B. Sun P. H. T. Tam Z. B. Tang W. W. Tian B. D. Wang C. Wang H. Wang H. G. Wang J. C. Wang J. S. Wang L. P. Wang L. Y. Wang R. N. Wang W. Wang X. G. Wang X. J. Wang X. Y. Wang Y. Wang Y. D. Wang Y. J. Wang Y. P. Wang Z. H. Wang Z. X. Wang Zhen Wang Zheng Wang D. M. Wei J. J. Wei Y. J. Wei T. Wen C. Y. Wu H. R. Wu S. Wu W. X. Wu X. F. Wu S. Q. Xi J. Xia J. J. Xia G. M. Xiang D. X. Xiao G. Xiao H. B. Xiao G. G. Xin Y. L. Xin Y. Xing D. L. Xu R. X. Xu L. Xue D. H. Yan J. Z. Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 69029 Heidelberg Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China 230026 Hefei Anhui China School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University 610031 Chengdu Sichuan China College of Physics Sichuan University 610065 Chengdu Sichuan China Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences 100049 Beijing China TIANFU Cosmic Ray Research Center Chengdu Sichuan China School of Astronomy and Space Science Nanjing University 210023 Nanjing Jiangsu China Center for Astrophysics Guangzhou University 510006 Guangzhou Guangdong China University of Chinese Academy of Sciences 100049 Beijing China School of Physics and Technology Wuhan University 430072 Wuhan Hubei China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences 210023 Nanjing Jiangsu China Hebei Normal University 050024 Shijiazhuang Hebei China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences 200030 Shanghai China Key Laboratory of Cosmic Rays (Tibet University) Ministry of Education 850000 Lhasa Tibet China National Astronomical Observatories Chinese Academy of Sciences 100101 Beijing China School of Physics and Astronomy & School of Physics (Guangzhou) Sun Yat-sen University 519082 Zhuhai Guangdong China School of Physics and Astronomy Yunnan University 650091 Kunming Yunnan China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet 1211 Geneva Switzerland Dipartimento di Fisica dell’Università di Napoli “Federico II” Complesso Universitario di Mo

The Wide Field-of-View Cherenkov Telescope Array (WFCTA) and the Water Cherenkov Detector Array (WCDA) of LHAASO are designed to work in combination for measuring the energy spectra of the cosmic ray species over a very wide energy range from a few TeV to 10 PeV. The energy calibration can be achieved with a proven technique of measuring the westward shift of the Moon shadow cast by galactic cosmic rays due to the geomagnetic field. This deflection angle Δ is inversely proportional to the cosmic ray rigidity. The precise measurement of the shifts by WCDA allows us to calibrate its energy scale for energies as high as 35 TeV. Through a set of commonly triggered events, the energy scales can be propagated to WFCTA. The energies of the events can be derived both by WCDA-1 and WFCTA with the median energies 23.4±0.1±1.3 TeV and (21.9±0.1 TeV), respectively, which are consistent within uncertainties. In addition, the propagation of the energy scale is also validated by the Moon shadow based on the same data selection criteria of the commonly triggered events. This paper reports, for the first time, an observational measurement of the absolute energy scale of the primary cosmic rays generating showers observed by air Cherenkov telescopes.

关键词： Cosmic ray composition & spectra

Discovery of a new γ-ray source LHAASO J0341+5258 with emission up to 200TeV

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arXiv 2021年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, B.D. Wang, C. Wang, H. Wang, H.G. Wang, J.C. Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Dipartimento di Fisica dell’Università di Napoli ‘Federico II" Complesso Universitario di Monte Sant’Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China D’epartement de Physique Nucl’eaire et Corpusculaire Facult’e de Sciences Universit’e de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute o

We report the discovery of a new unidentified extended γ-ray source in the Galactic plane named LHAASO J0341+5258 with a pre-trial significance of 8.2 standard deviations above 25 TeV. The best fit position is R.A.= 55.34◦ ± 0.11◦ and Dec= 52.97◦ ± 0.07◦. The angular size of LHAASO J0341+5258 is 0.29◦ ± 0.06◦stat ± 0.02◦sys. The flux above 25 TeV is about 20% of the flux of Crab Nebula. Although a power-law fit of the spectrum from 10 TeV to 200 TeV with the photon index α = 2.98±0.19stat±0.02sys is not excluded, the LHAASO data together with the flux upper limit at 10 GeV set by the Fermi LAT observation, indicate a noticeable steepening of an initially hard power-law spectrum spectrum with a cutoff at ≈ 50 TeV. We briefly discuss the origin of UHE gamma-rays. The lack of an energetic pulsar and a young SNR inside or in the vicinity of LHAASO J0341+5258 challenge, but do not exclude both the leptonic and hadronic scenarios of gamma-ray production. Copyright © 2021, The Authors. All rights reserved.

关键词： Gamma rays

A new aggregate signature scheme in cryptographic currency

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International Journal of Performability engineering 2017年第5期13卷 754-762页

作者： Yuan, Chao Xu, Mixue Si, Xueming State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China

With the rise of Bitcoin, cryptographic currencies have attracted more and more attention. Subsequently, other cryptographic currencies were gradually created, such as Zcash, Moreno, Dash and so on. In cryptographic currency, privacy preserving and expansion are two key technical points. In terms of privacy preserving, more effective solutions were proposed in Zcach, Moreno, Dash and other cryptographic currencies systems, in which ring signature, zero knowledge proof and other cryptographic techniques played important roles. But these schemes mainly considered protecting the addresses of both sides of the transaction. In terms of expansion, lightning network and other projects also give solutions. But most of these projects will bring other problems. In this paper, a signature scheme based on the aggregate signature and the elliptic curve algorithm is proposed to hide the transaction value of a single sender and receiver in the transactions which contain multiple inputs and outputs. This signature scheme achieves the purpose of privacy preserving from the transaction value. Further, the correctness proof and security analysis are given in this paper. In addition to that, another signature scheme that combines aggregation signature with bilinear ring signature is proposed. This aggregate ring signature scheme gives another attempt to solve the problem of expansion in the cryptographic currency system only using cryptographic technologies. At the same time, the sender's addresses can be hidden. Similarly, we also confirmed the correctness of this signature scheme.

关键词： Expansion