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Binary semantic Similarity Comparison based on software gene

Journal of Physics: Conference Series 2019年第1期1325卷

作者： Wenjie Sun Yihang Chen Zheng Shan Fudong liu Qiuna Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Materials branch of State Grid Henan Electric Power Company Zhengzhou China

The similarity of binary code is widely used in code copyright protection, vulnerability mining, malicious code analysis and etc. In this paper, we proposed a method for measuring/evaluating the similarity of two binary files based on software genes. Some of Natural language processing methods were introduced into program semantics analysis, including word2vec and doc2vec models to generate assembly instruction embeddings and gene semantic embeddings. Then the longest common subsequence method was applied to evaluate the software similarity. Experiments show that our method can effectively evaluate the similarity of binary files.

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Constraints on Heavy Decaying Dark Matter from 570 Days of LHAASO Observations

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Physical Review Letters 2022年第26期129卷 261103-261103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 J. T. Cai Zhe Cao J. Chang J. F. Chang E. S. Chen Liang Chen Long Chen M. J. Chen M. L. Chen Q. H. Chen S. H. Chen S. Z. Chen T. L. Chen Y. Chen H. L. Cheng 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 K. K. Duan J. H. Fan Y. Z. Fan Z. X. Fan J. Fang K. Fang C. F. Feng L. Feng S. H. Feng X. T. Feng Y. L. Feng B. Gao C. D. Gao L. Q. Gao Q. Gao W. Gao W. K. 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 S. L. He X. B. He Y. He M. Heller Y. K. Hor C. Hou X. Hou H. B. Hu Q. Hu S. Hu S. C. Hu X. J. Hu D. H. Huang W. H. Huang X. T. Huang X. Y. Huang Y. Huang Z. C. Huang X. L. Ji H. Y. Jia K. Jia K. Jiang Z. J. Jiang M. Jin M. M. Kang T. Ke D. Kuleshov K. Levochkin B. B. Li Cheng Li Cong Li F. Li H. B. Li H. C. Li H. Y. Li J. Li Jian Li Jie Li K. Li W. L. Li X. R. Li Xin 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 W. J. Long R. Lu Q. Luo H. K. Lv B. Q. Ma L. L. Ma X. H. Ma J. R. Mao A. Masood Z. Min W. Mitthumsiri Y. C. Nan Z. W. Ou B. Y. Pang P. Pattarakijwanich Z. Y. Pei M. Y. Qi Y. Q. Qi B. Q. Qiao J. J. Qin D. Ruffolo A. Sáiz C. Y. Shao 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. Wang R. N. Wang W. Wang X. G. 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 X. F. Wu Y. S. Wu S. Q. Xi J. Xia J. J. Xia G. M. Xiang D. X. Xiao G. Xiao G. G. Xin Y. L. Xin Y. Xing Z. Xiong D. L. Xu R. X. Xu L. Xue D. H. Yan J. Z. Yan Key Laboratory of Particle Astrophyics 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 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 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 Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences 210023 Nanjing Jiangsu 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 Hebei Normal University 050024 Shijiazhuang Hebei China School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai) Sun Yat-sen University 519000 Zhuhai & 510275 Guangzhou 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 Sci

The kilometer square array (KM2A) of the large high altitude air shower observatory (LHAASO) aims at surveying the northern γ-ray sky at energies above 10 TeV with unprecedented sensitivity. γ-ray observations have long been one of the most powerful tools for dark matter searches, as, e.g., high-energy γ rays could be produced by the decays of heavy dark matter particles. In this Letter, we present the first dark matter analysis with LHAASO-KM2A, using the first 340 days of data from 1/2-KM2A and 230 days of data from 3/4-KM2A. Several regions of interest are used to search for a signal and account for the residual cosmic-ray background after γ/hadron separation. We find no excess of dark matter signals, and thus place some of the strongest γ-ray constraints on the lifetime of heavy dark matter particles with mass between 105 and 109 GeV. Our results with LHAASO are robust, and have important implications for dark matter interpretations of the diffuse astrophysical high-energy neutrino emission.

关键词： Dark matter Gamma ray astronomy

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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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Constraining the Cosmic-ray Energy Based on Observations of Nearby Galaxy Clusters by LHAASO

arXiv

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arXiv 2025年

作者： Cao, Zhen Aharonian, F. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cai, C.M. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.B. Chen, X.J. 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 Diao, Y.X. 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, X.Y. He, Y. Hernández-Cadena, S. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, S.C. Huang, C. Huang, D.H. Huang, J.J. 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. Kaci, S. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, L. Li, R.L. Li, S.D. Li, T.Y. 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, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.R. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, X. Liu, Y. Liu, Y. Liu, Y.N. Lou, Y.Q. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mou, G.B. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ni, M.Y. Nie, L. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Key Laboratory of Particle 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 University of Science and Technology of China Anhui Hefei230026 China Yerevan State University 1 Alek Manukyan Street Yerevan0025 Armenia Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275

Galaxy clusters act as reservoirs of high-energy cosmic rays (CRs). As CRs propagate through the intracluster medium, they generate diffuse γ-rays detectable by arrays such as LHAASO. These γ-rays result from proton-proton (pp) collisions of very high-energy cosmic rays (VHECRs) or inverse Compton (IC) scattering of positron-electron pairs created by pγ interactions of ultra-high-energy cosmic rays (UHECRs). We analyzed diffuse γ-ray emission from the Coma, Perseus, and Virgo clusters using LHAASO data. Diffuse emission was modeled as a disk of radius R500 for each cluster while accounting for point sources. No significant diffuse emission was detected, yielding 95% confidence level (C.L.) upper limits on the γ-ray flux: for WCDA (1–25 TeV) and KM2A (>25 TeV), less than (49.4, 13.7, 54.0) and (1.34, 1.14, 0.40) ×10−14 ph cm−2 s−1 for Coma, Perseus, and Virgo, respectively. The γ-ray upper limits can be used to derive model-independent constraints on the integral energy of CRp above 10 TeV (corresponding to the LHAASO observational range > 1 TeV under the pp scenario) to be less than (1.96, 0.59, 0.08) × 1061 erg. The absence of detectable annuli/ring-like structures, indicative of cluster accretion or merging shocks, imposes further constraints on models in which the UHECRs are accelerated in the merging shocks of galaxy clusters. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic rays

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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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Accurate ECG Data Generation with a Simple Generative Adversarial Network

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Journal of Physics: Conference Series 2020年第1期1631卷

作者： Haiyan Wang Zhaoyang Ge Zongmin Wang Collaborative Innovation Centre for Internet Healthcare Zhengzhou University 75 Daxue Road Zhengzhou 450052 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450003 China Simulation Experiment Centre Zhengzhou University of Aeronautics Zhengzhou 450046 China School of Information Engineering Zhengzhou University Zhengzhou 450001 China

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.

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Brief Investigation on Square Root of a Node of <i>T</i><sub>3</sub>Tree

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Advances in Pure Mathematics 2018年第7期8卷 666-671页

作者： Guihong Chen Jianhui Li Department of Computer Science and Technology Neusoft Institute Guangdong Foshan China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

The article investigates some properties of square root of T3 tree’s nodes. It first proves several inequalities that are helpful to estimate the square root of a node, and then proves several theorems to describe th... 详细信息

关键词： Square Root Node Valuated Binary Tree Inequality

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Bidirectional spin-wave-driven domain wall motion in ferrimagnets

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Physical Review B 2019年第17期100卷 174403-174403页

作者： Se-Hyeok Oh Se Kwon Kim Jiang Xiao Kyung-Jin Lee Department of Nano-Semiconductor and Engineering Korea University Seoul 02841 Korea Department of Physics and Astronomy University of Missouri Columbia Missouri 65211 USA Department of Physics and State Key Laboratory of Surface Physics Fudan University Shanghai 200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China Institute for Nanoelectronics Devices and Quantum Computing Fudan University Shanghai 200433 China Department of Materials Science and Engineering Korea University Seoul 02841 Korea KU-KIST Graduate School of Converging Science and Technology Korea University Seoul 02841 Korea

We investigate ferrimagnetic domain-wall dynamics induced by circularly polarized spin waves theoretically and numerically. We find that the direction of domain-wall motion depends on both the circular polarization of spin waves and the sign of net spin density of the ferrimagnet. Below the angular momentum compensation point, left- circularly (right-circularly) polarized spin waves push a domain wall towards (away from) the spin-wave source. Above the angular momentum compensation point, on the other hand, the direction of domain-wall motion is reversed. This bidirectional motion originates from the fact that the sign of spin-wave-induced magnonic torque depends on the circular polarization and the subsequent response of the domain wall to the magnonic torque is governed by the net spin density. Our finding provides a way to utilize a spin wave as a versatile driving force for bidirectional domain-wall motion.

关键词： Domain walls Spin waves Ferrimagnets

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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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BENCHIP： Benchmarking Intelligence Processors

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Journal of Computer Science & Technology 2018年第1期33卷 1-23页

作者： Jin-Hua Tao Zi-Dong Du Qi Guo Hui-Ying Lan Lei Zhang Sheng-Yuan Zhou Ling-Jie Xu Cong Liu Hai-Feng Liu Shah Tang Allen Rush Willian Chen Shao-Li Liu Yun-Ji Chen Tian-Shi Chen State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China Intelligent Processor Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Cambricon Ltd. Beijing 100190 China A libaba Infrastructure Service A libaba Group Hangzhou 311121 China Iflytek Co. Ltd. Hefei 230088 China Beijing Jingdong Century Trading Co. Ltd. Beijing 100176 China RDA Microdectronics Inc. Shanghai 201203 China Advanced Micro Devices Inc. Sunnyvale CA 94085 U.S.A.

The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization （in both software and hardware）. However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in BENCHIP consists of two sets of benchmarks： microbenchmarks and macrobenchmarks. The microbenchmarks consist of single-layer networks, They are mainly designed for bottleneck analysis and system optimization. The macrobenchmarks contain state-of-the-art industrial networks, so as to offer a realistic comparison of different platforms. We also propose a standard benchmarking methodology built upon an industrial software stack and evaluation metrics that comprehensively reflect various characteristics of the evaluated intelligence processors, BENCHIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. BENCHIP will be open-sourced soon.

关键词： deep learning intelligence processor benchmark

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