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Fire Science and Fire Protection engineering (ICFSFPE), Conference on

作者： Shenshi Huang Ruichao Wei Kai Shi Jinxing Hu Shouxiang Lu Liping Lian Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen China Institute of Advanced Materials Science and Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen China The Smart City Research Institute of China Electronics Technology Group Corporation Shenzhen China State Key Laboratory of Fire Science University of Science and Technology of China Hefei China School of Urban Planning and Design Peking University Shenzhen China

ISBN: (数字)9781728153223

ISBN: (纸本)9781728153230

This study is to evaluate the passenger evacuation of the high-speed train. Geometry was built up based on current Chinese train type CRH380B, and scenarios were designed according to the current emergency response plans. Simulation runs were performed by Pathfinder and uncertainty of evacuation time was obtained. It is observed that the passenger flow rates in train coaches are generally stable. Passenger transfer time and evacuation time are approximately proportional to passenger attendance rate. However, in some complicated scenarios, it is found that the process of passenger transfer affects the evacuation results a lot. Reasonable passenger transfer scale can maximize the effectiveness of emergency plans. This study provides an evaluation method on the topic of train car evacuation, and future work can be carried out to optimize the design of emergency response.

关键词： passenger evacuation high-speed train monteCarlo simulation high-speed train Evacuation passengers Evacuation Time evaluation method Educational attendance simulating Cars

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Mimic Defense Structured Information System Threat Identification and Centralized Control

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Journal of Physics: Conference Series 2019年第3期1187卷

作者： Bo Zhang Weichao Li Xin Sun Yufeng Zhao State Grid Key Laboratory of Information & Network Security Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China State Grid Zhejiang Electric Power Research Institute Hangzhou China

With the speeding up of the informatization process of our national economy and social development, the degree of informatization in our country is getting higher and higher. Information system has become the key infrastructure of our country. The security of these basic information networks and important information systems has been seriously related to national security and social stability, to the collective interests of enterprises, and to the vital interests of the broad masses of the people. Increasing application in improving work efficiency and economic benefits at the same time, the increasingly com-plex information system has also brought greater challenges to operation and maintenance managers. The use of known or unknown vulnerabilities, causing damage to information systems, stealing sensitive information, endangering the normal operation of business systems and other behaviors, has become one of the main causes of various security incidents. How to effectively deal with the vulnerability threat and various vulnerability problems of enterprise information systems, concentrate on the implementation of information security vulnerability warning related risks, and provide solutions to related vulnerability threats, have become an urgent problem to be solved. Therefore, we construct the mimic structured enterprise information system by establishing the threat identification system through unified management to form a comprehensive and secure cyber threat management system.

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Mobile APP user attribute prediction by heterogeneous information network modeling

arXiv

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arXiv 2019年

作者： Zhang, Hekai Gong, Jibing Teng, Zhiyong Wang, Dan Wang, Hongfei Du, Linfeng Bhuiyan, Zakirul Alam School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Lab for Computer Virtual Technology and System Integration Yanshan University Qinhuangdao066004 China School of Computer Science and Engineering Beihang University Beijing100191 China Shenyuan Honors College Beihang University Beijing100191 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214000 China Department of Computer and Information Sciences Fordham University

User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior analysis and other aspects. Although many scholars have made achievements in user attribute prediction and other related fields, there are still two main problems that impede further improvement on the accuracy of classification: (1) Traditional machine learning classification merely takes each object as a single individual, ignoring the relationship between them;(2) At present, the popular Heterogeneous Path-Mine Information Network only considers whether the user has a relationship with the attributes of other nodes, rather than the degree of correlation of the attributes. It employs a linear regression model to fit the weight of meta-path, which is highly sensitive to outliers. To solve the above two problems, this paper advances the HetPathMine model and puts forward TPathMine model. With applying the number of clicks of attributes under each node to express the user's emotional preference information, optimizations of the solution of meta-path weight are also presented. Based on meta-path in heterogeneous information networks, the new model integrates all relationships among objects into isomorphic relationships of classified objects. Matrix is used to realize the knowledge dissemination of category knowledge among isomorphic objects. The experimental results show that: (1) the prediction of user attributes based on heterogeneous information networks can achieve higher accuracy than traditional machine learning classification methods;(2) TPathMine model based on the number of clicks is more accurate in classifying users of different age groups, and the weight of each meta-path is consistent with human intuition or the real world situation. Copyright ©

关键词： Forecasting

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P2P Network Based on Neighbor-neighbor Lists

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Journal of Physics: Conference Series 2019年第3期1168卷

作者： Hao Zhao Chenyang Wang Yuefei Zhu Wei Lin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450001 China Information Engineering University Information Technology Section Zhengzhou Henan 450001 China

In order to solve the vulnerability of the network structure in the real world, this paper constructs a P2P network model based on neighbor-neighbor lists. We propose two algorithms, network repairing and pruning, to improve the effectiveness and robustness of the network. Simulation experiments show that the proposed model and algorithms can effectively enhance the self-healing of P2P network.

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Network Embedding based on External Word Vectors

Network Embedding based on External Word Vectors

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IEEE International Performance computing and Communications Conference

作者： XiaoKun Zhang Yan Liu Jing Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781538668092;9781538668085

Network embedding, which preserves network's sophisticated features, aims to effectively learn the low-dimensional embedding of vertices to lower the computing and storage costs. Content information network like Twitter is defined as network with rich text information. Most research about content information network are based on the information of the network itself. By introducing external word vectors into the modeling process, we can make use of the external syntactic and semantic features. In this paper, we propose NE-EWV (Network Embedding based on External Word Vectors), which preserves the feature fusion representation from both semantic feature space as well as structural feature space. Experiments are conducted in three real-world content information network datasets. In link predict task, the AUC increases 7% to 19% compared with the models which only considering the structural features, increases 1% to 12% compared with the model considering structural and text features in most cases.

关键词： Network embedding Content information network Auto-encoder External word vectors

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Sparse representation of gaussian molecular surface

arXiv

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arXiv 2019年

作者： Gui, Sheng Chen, Minxin Lu, Benzhuo STATE KEY LABORATORY of SCIENTIFIC and ENGINEERING COMPUTING NATIONAL CENTER for MATHEMATICS and INTERDISCIPLINARY SCIENCES ACADEMY of MATHEMATICS and SYSTEMS SCIENCE CHINESE ACADEMY of SCIENCES BEIJING100190 China SCHOOL of MATHEMATICAL SCIENCES UNIVERSITY of CHINESE ACADEMY of SCIENCES BEIJING100049 China DEPARTMENT of MATHEMATICS SOOCHOW UNIVERSITY SUZHOU215006 China

In this paper, we propose a model and algorithm for sparse representing Gaussian molecular surface. The original Gaussian molecular surface is approximated by a relatively small number of radial basis functions (RBFs) with rotational ellipsoid feature. The sparsity of the RBF representation is achieved by solving a nonlinear L1optimization problem. Experimental results demonstrate that the original Gaussian molecular surface is able to be represented with good accuracy by much fewer RBFs using our L1model and algorithm. The sparse representation of Gaussian molecular surface is useful in various applications, such as molecular structure alignment, calculating molecular areas and volumes, and the method in principle can be applied to sparse representation of general shapes and coarse-grained molecular modeling. Copyright © 2019, The Authors. All rights reserved.

关键词： Radial basis function networks

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Photocurrent imaging of multi-memristive charge density wave switching in two-dimensional 1T-TaS2

arXiv

引用

arXiv 2020年

作者： Patel, Tarun Okamoto, Junichi Dekker, Tina Yang, Bowen Gao, Jingjing Luo, Xuan Lu, Wenjian Sun, Yuping Tsen, Adam W. Institute for Quantum Computing Department of Physics and Astronomy Department of Electrical and Computer Engineering Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Institute of Physics University of Freiburg FreiburgD-79104 Germany Key Laboratory of Materials Physics Institute of Solid State Physics Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China High Magnetic Field Laboratory Chinese Academy of Sciences Hefei230031 China Collaborative Innovation Centre of Advanced Microstructures Nanjing University Nanjing210093 China

Transport studies of atomically thin 1T-TaS2 have demonstrated the presence of intermediate resistance states across the nearly commensurate (NC) to commensurate (C) charge density wave (CDW) transition, which can be further switched electrically. While this presents exciting opportunities for the material in memristor applications, the switching mechanism has remained elusive and could be potentially attributed to the formation of inhomogeneous C and NC domains across the 1T-TaS2 flake. Here, we present simultaneous electrical driving and scanning photocurrent imaging of CDWs in ultrathin 1T-TaS2 using a vertical heterostructure geometry. While micron-sized CDW domains form upon changing temperature, electrically driven transitions result in largely uniform changes, indicating that states of intermediate resistance for the latter likely correspond to true metastable CDW states in between the NC and C phases, which we then explain by a free energy analysis. Additionally, we are able to perform repeatable and bidirectional switching across the multiple CDW states without changing sample temperature, demonstrating that atomically thin 1T-TaS2 can be further used as a robust and reversible multi-memristor material. Copyright © 2020, The Authors. All rights reserved.

关键词： Memristors

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Improved Cryptanalysis of an ISO Standard Lightweight Block Cipher with Refined MILP Modelling 13th

Improved Cryptanalysis of an ISO Standard Lightweight Block ...

引用

13th International Conference on Information Securityand Cryptology, Inscrypt 2017

作者： Yin, Jun Ma, Chuyan Lyu, Lijun Song, Jian Zeng, Guang Ma, Chuangui Wei, Fushan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China National University of Defense Technology Changsha410073 China Army Aviation Institute Beijing101116 China

ISBN: (纸本)9783319751597

Differential and linear cryptanalysis are two of the most effective attacks on block ciphers. Searching for (near) optimal differential or linear trails is not only useful for the security evaluation of block ciphers against these attacks, but also indispensable to the cryptanalysts who want to attack a cipher with these techniques. In recent years, searching for trails automatically with Mixed-Integer Linear Programming (MILP) gets a lot of attention. At first, Mouha et al. translated the problem of counting the minimum number of differentially active S-boxes into an MILP problem for word-oriented block ciphers. Subsequently, in Asiacrypt 2014, Sun et al. extended Mouha et al.’s method, and presented a technique which can find actual differential or linear characteristics of a block cipher in both the single-key and related-key models. In this paper, we refine the constraints of the 2-XOR operation in order to reduce the overall number of variables and constraints. Experimental results show that MILP models with the refined constraints can be solved more efficiently. We apply our method to HIGHT (an ISO standard), and we find differential (covering 11 rounds) or linear trails (covering 10 rounds) with higher probability or correlation. Moreover, we find so far the longest differential and linear distinguishers of HIGHT. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： ISO Standards

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MemNetAR: Memory Network with Adversative Relation for Target-Level Sentiment Classification

MemNetAR: Memory Network with Adversative Relation for Targe...

引用

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yiwei Gao Jianwei Niu Xuefeng Liu Kaili Mao Shui Yu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Target-level sentiment classification aims to identify the sentiment of multiple targets in a sentence. Although existing approaches based on neural network have achieved good performance in this task, we find that many approaches tend to give the same predictions for instances that have multiple targets, and this tendency can make a low accuracy for those instances that have different classes for different targets. Based on this observation, we propose MemNetAR, a memory network which can explicitly leverage the adversative relation among multiple targets in a sentence. Specifically, we add an adversative loss to the cross-entropy loss when there are adversative words between targets. The experimental results on public laptop and restaurant datasets prove that our model can improve 0.84% and 0.8% on total test dataset, and improve 2.97% and 2.68% on the dataset consisting of those instances with multiple targets but different classes by leveraging this new adversative information.

关键词： Task analysis Feature extraction Sentiment analysis Portable computers Machine learning Training Predictive models

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Exploring Lorentz Invariance Violation from Ultrahigh-Energy γ Rays Observed by LHAASO

引用

Physical Review Letters 2022年第5期128卷 051102-051102页

作者： Zhen Cao 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 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. D’Ettorre Piazzoli B. Z. Dai H. L. Dai Z. G. Dai Danzengluobu D. della Volpe 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 X. 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 Cheng Li Cong Li F. Li H. B. Li H. C. Li H. Y. Li Jian Li Jie Li K. Li W. L. Li X. R. Li Xin 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. R. 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 Key Laboratory of Particle Astrophysics and Experimental Physics Division and 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 610000 Sichuan China 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 100049 Beijing China University of Science and Technology of China 230026 Hefei Anhui China School of Physical Science and Technology and School of Information Science and Technology Southwest Jiaotong University 610031 Chengdu Sichuan China College of Physics Sichuan University 610065 Chengdu Sichuan China School of Astronomy and Space Science Nanjing University 210023 Nanjing Jiangsu China Center for Astrophysics Guangzhou University 510006 Guangzhou Guangdong 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 and School of Physics (Guangzhou) Sun Yat-sen University 519000 Zhuhai Guangdong China Dipartimento di Fisica dell’Università di Napoli “Federico II ” Complesso Universitario di Monte Sant’Angelo via Cinthia 80126 Napoli Italy School of Physics and Astronomy Yunnan University 650091 Kunming Yunnan China Département de Physique Nucléaire et Corpusculaire Faculté de S

Recently, the LHAASO Collaboration published the detection of 12 ultrahigh-energy γ-ray sources above 100 TeV, with the highest energy photon reaching 1.4 PeV. The first detection of PeV γ rays from astrophysical sources may provide a very sensitive probe of the effect of the Lorentz invariance violation (LIV), which results in decay of high-energy γ rays in the superluminal scenario and hence a sharp cutoff of the energy spectrum. Two highest energy sources are studied in this work. No signature of the existence of the LIV is found in their energy spectra, and the lower limits on the LIV energy scale are derived. Our results show that the first-order LIV energy scale should be higher than about 105 times the Planck scale MPl and that the second-order LIV scale is >10−3MPl. Both limits improve by at least one order of magnitude the previous results.

关键词： Cosmic rays & astroparticles Gamma ray astronomy Lorentz symmetry

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