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38th International Cosmic Ray Conference, ICRC 2023

作者： Zhang, Hengying He, Huihai Feng, Cunfeng Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin 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.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Key Laboratory of Particle Astrophysics 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 Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 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 School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 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 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Facu

The kilometer-square array (KM2A) of the Large High Altitude Air Shower Observatory (LHAASO, located at 4410 m above sea level with an atmospheric depth of 600 g/cm2) can simultaneously measure air shower sizes of both electromagnetic particles and muon contents with high precision for cosmic rays with energies in the knee region. The energy is reconstructed by combining parameters of muons and electromagnetic particles, which is weakly dependent on the mass composition of cosmic rays. We study the measurement method of the all particle spectrum and mean logarithmic mass through the simulation data, and discuss the corresponding systematic errors. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Systematic errors

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Accurate ECG Data Generation with a Simple Generative Adversarial Network

Accurate ECG Data Generation with a Simple Generative Advers...

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作者： Haiyan Wang Zhaoyang Ge Zongmin Wang Collaborative Innovation Centre for Internet Healthcare Zhengzhou University State Key Laboratory of Mathematical Engineering and Advanced Computing Simulation Experiment Centre Zhengzhou University of Aeronautics School of Information Engineering Zhengzhou University

Since ECG data is highly sensitive medical data, the acquisition of ECG data is highly restricted. However, with the increasing demand for ECG big data research, the improvement of computer computing capabilities, and the development of deep learning, the direction of ECG intelligent analysis is facing a serious lack of standard clinical data. In order to generate more precise ECG data, this paper proposes a GAN architecture for generating ECG heartbeat data. The network structure is simple and does not require any domain knowledge. In this paper, the MIT-BIH Arrhythmia database is selected, from which all left bundle branch block heartbeats are selected to form a training dataset. The training process shows that the proposed GAN structure is effective and accurate, and the generated results show that not only the generated simulated ECG heartbeat data is diverse, but also highly similar to the real data.

关键词： ECG data generation heartbeat left bundle branch block deep learning model GAN

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Observation of Large-Scale Anisotropy of Proton above Tens of TeV with LHAASO-KM2A 38

Observation of Large-Scale Anisotropy of Proton above Tens o...

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38th International Cosmic Ray Conference, ICRC 2023

作者： He, Jiayin Zhao, Shiping Guo, Yingying Zhang, Yi Yuan, Qiang Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin 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.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences 9 Jiangsu Nanjing210023 China University of Science and Technology of China Anhui Hefei230026 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Astrophysics & 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 Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 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 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Cor

We present a measurement for large-scale anisotropy of pure protons with the Large High Altitude Air Shower Observatory (LHAASO). We analyze the data collected from the full array of KM2A in one year’s operation. To select the proton data set, we employed a technique similar to the gamma/background discrimination method in KM2A, using a cut on the ratio of muonic and electron magnetic components. The purity of the proton sample is up to 90%, and the reconstructed energy is from 10 TeV to 270 TeV. We first perform the energy evolution analysis of the dipole anisotropy of protons at this energy range. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Anisotropy

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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第32届中国控制与决策会议

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang University School of Engineering RMIT University

This brief explores the approximation properties of a unique basis expansion based on Pascal’s triangle,which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time *** roles of certain parameters,such as sampling time interval or model order,and signal characteristics,i.e.,its curvature,on the approximation are *** errors in one and multiple-step predictions are ***,time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Signal approximation Sampling Discretization

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Incorporating Multiple Features to Predict Bug Fixing Time with Neural Networks

Incorporating Multiple Features to Predict Bug Fixing Time w...

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International Conference on Software Maintenance (ICSM)

作者： Wei Yuan Yuan Xiong Hailong Sun Xudong Liu SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China SKLSDE Lab School of Software Beihang University Beijing China

Debugging is a well-known time-consuming task, and knowing how long it would take to resolve bugs is of great importance for allocating the limited resources in a software development team. However, it is challenging to predict bug fixing time since fixing bugs is subject to a plethora of uncertain factors such as types of bugs, program complexity and developers' abilities. Existing work mainly focuses on developers' activities in a bug lifecycle and ignores other important factors. In light of the limitations of existing work, we propose a novel approach to predicting the bug fixing time by incorporating a comprehensive set of relevant features. Specifically, we consider four types of features including developers' activities, developers' sentiments, semantics of bugs, and efforts caused by understanding and analyzing source code, and design particular neural networks to take advantage of these features and make them work efficiently. Experimental results on four real application datasets demonstrate that on the one hand, our approach outperforms the state-of-the-art by over 5% in accuracy and 7.3% in F1-score on average; on the other hand, each type of the features considered in our approach plays an important part in the prediction.

关键词：

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Homogeneous and Heterogeneous Graph Learning for Hybrid Beamforming in mmWave Systems

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Yuhang Lu, Yang Zhang, Guangyang Ai, Bo Niyato, Dusit Cui, Shuguang State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Shenzhen518066 China

Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeterwave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adaptation to dynamic environments. To address the issue of limited feature extraction capability of the existing single-type GNNs, such as node-GNN or edge-GNN, we model the mmWave communication systems as both homogeneous and heterogeneous graphs, and separate the HBF design into node- and edge-level subtasks. Then, the two graphs are presented by two novel models based on homogeneous graph attention network (GAT) and heterogeneous GAT (HGAT), respectively, and mapped to the desired power allocation, radio frequency precoder and baseband precoder. Both the proposed GAT and HGAT are generalizable in the user variation scenarios, while the HGAT is also generalizable in antenna variation scenarios through the incorporation of a complex embedding layer. Furthermore, we introduce a constraint adaptive layer in the GAT and HGAT to ensure feasible outputs. Extensive numerical results based on the public dataset DeepMIMO are provided to evaluate the GAT and HGAT. The proposed approaches generally outperform existing baselines in terms of adaptability to system settings and generalizability to (unseen) problem parameters/sizes, while the HGAT can even achieve faster and better inference than traditional optimization approaches. © 2002-2012 IEEE.

关键词： Network theory (graphs)

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Detection of two TeV gamma-ray outbursts from NGC 1275 by LHAASO

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Monthly Notices of the Royal Astronomical Society 2025年第2期540卷 1860–1869页

作者： Cao, Zhen Aharonian, F Axikegu Bai, Y X Bao, Y W Bastieri, D Bi, X J Bi, Y J Bian, W Bukevich, A V Cao, Q Cao, W Y Cao, Z Chang, J Chang, J F Chen, A M Chen, E S Chen, H X Chen, L Chen, M J Chen, M L Chen, Q H Chen, S Chen, S H Chen, S Z Chen, T L Chen, Y Cheng, N Cheng, Y D Chu, M C Cui, M Y Cui, S W Cui, X H Cui, Y D Dai, B Z Dai, H L Dai, Z G Danzengluobu Dong, X Q Duan, K K Fan, J H Fan, Y Z Fang, J Fang, J H Fang, K Feng, C F Feng, H Feng, L Feng, S H Feng, X T Feng, Y Feng, Y L Gabici, S Gao, B Gao, C D Gao, Q Gao, W Gao, W K Ge, M M Ge, T T Geng, L S Giacinti, G Gong, G H Gou, Q B Gu, M H Guo, F L Guo, J Guo, X L Guo, Y Q Guo, Y Y Han, Y A Hannuksela, O A Hasan, M He, H H He, H N He, J Y He, Y Hor, Y K Hou, B W Hou, C Hu, H B Hu, Q Hu, S C Huang, C Huang, D H Huang, T Q Huang, W J Huang, X T Huang, X Y Huang, Y Huang, Y Y Ji, X L Jia, H Y Jia, K Jiang, H B Jiang, K Jiang, X W Jiang, Z J Jin, M Kang, M M Karpikov, I Khangulyan, D Kuleshov, D Kurinov, K Li, B B Li, C M Li, C Li, D Li, F Li, H B Li, H C Li, J Li, K Li, S D Li, W L Li, X R Li, X Li, Y Z Li, Z Liang, E W Liang, Y F Lin, S J Liu, B Liu, C Liu, D Liu, D B Liu, H Liu, H D Liu, J Liu, J L Liu, M Y Liu, R Y Liu, S M Liu, W Liu, Y Liu, Y N Luo, Q Luo, Y Lv, H K Ma, B Q Ma, L L Ma, X H Mao, J R Min, Z Mitthumsiri, W Mu, H J Nan, Y C Neronov, A Ng, K C Y Ou, L J Pattarakijwanich, P Pei, Z Y Qi, J C Qi, M Y Qiao, B Q Qin, J J Raza, A Ruffolo, D Sáiz, A Saeed, M Semikoz, D Shao, L Shchegolev, O Sheng, X D Shu, F W Song, H C Stenkin, Yu V Stepanov, V Su, Y Sun, D X Sun, Q N Sun, X N Sun, Z B Takata, J Tam, P H T Tang, Q W Tang, R Tang, Z B Tian, W W Wan, L H Wang, C Wang, C B Wang, G W Wang, H G Wang, H H Wang, J C Wang, K Wang, L P Wang, L Y Wang, P H Wang, R Wang, W Wang, X G Wang, X Y Wang, Y Wang, Y D Wang, Y J Wang, Z H Wang, Z X Wang, Z Wei, D M Wei, J J Wei, Y J Wen, T Wu, C Y Wu, H R Wu, Q W Wu, S Wu, X F Wu, Y S Xi, S Q Xia, J Xiang, G M Xiao, D X Xiao, G Xin, Y L Xing, Y Xiong, D R Xiong, Z Xu, D L Xu, R F Xu, R X Xu, W L Xue, L Yan, D H Yan, J Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences 100049 Beijing China University of Chinese Academy of Sciences 100049 Beijing China TIANFU Cosmic Ray Research Center Chengdu Sichuan China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 D-69029 Heidelberg Germany School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 Sichuan China School of Astronomy and Space Science Nanjing University Nanjing 210023 Jiangsu China Center for Astrophysics Guangzhou University Guangzhou 510006 Guangdong China Tsung-Dao Lee Institute & School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Institute for Nuclear Research of Russian Academy of Sciences Moscow 117312 Russia Hebei Normal University Shijiazhuang 050024 Hebei China University of Science and Technology of China Hefei 230026 Anhui China Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing 210023 Jiangsu China State Key Laboratory of Particle Detection and Electronics China Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou 311121 Zhejiang China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai 200030 China School of Physics and Astronomy Yunnan University Kunming 650091 Yunnan China Key Laboratory of Cosmic Rays (Tibet University) Ministry of Education Lhasa 850000 Tibet China Department of Physics The Chinese University of Hong Kong Shatin New Territories Hong Kong China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing 100101 China School of Physics and A

The Water Cherenkov Detector Array (WCDA) is one of the components of Large High Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 h per day with >98 per cent duty cycle. In this work, we report the detection of two outbursts of the Fanaroff–Riley I radio galaxy NGC 1275 that were detected by LHAASO-WCDA between 2022 November and 2023 January with statistical significance of 5.2σand 8.3σ⁠. The observed spectral energy distribution in the range from 500 GeV to 3 TeV is fitted by a power law with the best-fitting spectral index ofα=−3.37±0.52and−3.35±0.29⁠, respectively. The outburst flux above 0.5 TeV was (⁠4.55±4.21)×10−11cm−2s−1and (⁠3.45±1.78)×10−11cm−2s−1⁠, corresponding to 60 per cent and 45 per cent of Crab Nebula flux, respectively. Variation analysis reveals the variability time-scale of days at the TeV energy band. A simple test by one-zone synchrotron self-Compton model reproduces the data in the gamma-ray band well.

关键词： methods: data analysis methods: statistical galaxies: active galaxies: jets gamma-rays: general

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Design and Implementation of Variable Precision Algorithm for Transcendental Functions

Design and Implementation of Variable Precision Algorithm fo...

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作者： Hao Jiangwei Xu Jinchen Guo Shaozhong Xia Yuanyuan Liu Dan State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University

Mathematical function is an essential part of numerical program, and it is also the key factor that affects the precision and performance of a program. In the design of mathematical functions, in order to satisfy most application scenarios, it is necessary to correctly round and cover the calculated interval as much as possible. But in certain applications, correct rounding and full coverage of definition domain may not be ***, mathematical functions can be customized according to application requirements to avoid precision waste as well as improve performance. However, manually implementing mathematical functions is a time-consuming and error-prone task. Tools like Metalibm is designed to automatically generate mathematical function code, which is difficult to take advantage of all the mathematical properties of a function and results in the generated code performing slowly than the corresponding mathematical library function. A method of generating variable precision code based on mathematical properties is proposed for the transcendental function in this work. Experiments showed the performance of the proposed method is comparable to Glibc mathematical function.

关键词： Taylor Design and Implementation of Variable Precision Algorithm for Transcendental Functions

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Defense Strategy Selection Method for Stackelberg Security Game Based on Incomplete Information

Defense Strategy Selection Method for Stackelberg Security G...

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作者： Xiaoyue Ge Tianyang Zhou Yichao Zang State Key Laboratory of Mathematical Engineering and Advanced Computing Information & Engineering University

Network attack and defense confrontation is essentially a game process between unequal subjects on both sides of attack and *** are some defects in the prior hypothesis that the subject status of both sides of the attack and defense game is equal and the active defense ability is insufficient in the research of the existing network defense *** this paper,game theory unequal thought and defender active release incentive technology into network defense strategy generation model construction process,and a Stackelberg Security Game defense strategy generation method based on incomplete information is *** establishing the network model,the strong equilibrium strategy algorithm of Stackelberg Security Game with incomplete information is used to generate the optimal defense strategy of the *** defender can actively release the incentive strategy without reducing the accuracy of defense strategy *** experimental results provide a method for the selection of optimal defense strategy,and can strengthen the security of the system.

关键词： network attack and defense Stackelberg Security Game incentive strategy policy selection

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NIG-AP: a new method for automated penetration testing

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Frontiers of Information Technology & Electronic engineering 2019年第9期20卷 1277-1288页

作者： Tian-yang ZHOU Yi-chao ZANG Jun-hu ZHU Qing-xian WANG State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China China National Digital Switching System Engineering and Technological R&D Center Zhengzhou 450001China

Penetration testing offers strong advantages in the discovery of hidden vulnerabilities in a network and assessing network ***,it can be carried out by only security analysts,which costs considerable time and *** natural way to deal with the above problem is automated penetration testing,the essential part of which is automated attack *** previous studies have explored various ways to discover attack paths,all of them require perfect network information beforehand,which is contradictory to realistic penetration testing *** vividly mimic intruders to find all possible attack paths hidden in a network from the perspective of hackers,we propose a network information gain based automated attack planning(NIG-AP)algorithm to achieve autonomous attack path *** algorithm formalizes penetration testing as a Markov decision process and uses network information to obtain the reward,which guides an agent to choose the best response actions to discover hidden attack paths from the intruder’s *** results reveal that the proposed algorithm demonstrates substantial improvement in training time and effectiveness when mining attack paths.

关键词： Penetration testing Reinforcement learning Classical planning Partially observable Markov decision process

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