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2020 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2020

作者： Sibahee, Mustafa A. Al Lu, Songfeng Abduljabbar, Zaid Ameen Liu, Erasmus Xin Ran, Yanli Al-Ashoor, Ahmed Abdulelah Jasim Hussain, Mohammed Abdulridha Hussien, Zaid Alaa Shenzhen Institute Huazhong University of Science and Technology Shenzhen China Iraq University College Basrah Iraq Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China University of Basrah Basrah Iraq Al-Kinoouze University College Basrah Iraq Neusoft Institute Guangdong Guangdong China Shenzhen University Shenzhen China Southern Technical University Basrah Iraq

ISBN: (数字)9781728172026

ISBN: (纸本)9781728172019

Document integrity and origin for E2E S2S in IoTcloud have recently received considerable attention because of their importance in the real-world fields. Maintaining integrity could protect decisions made based on these message/image documents. Authentication and integrity solutions have been conducted to recognise or protect any modification in the exchange of documents between E2E S2S (smart-to-smart). However, none of the proposed schemes appear to be sufficiently designed as a secure scheme to prevent known attacks or applicable to smart devices. We propose a robust scheme that aims to protect the integrity of documents for each users session by integrating HMAC-SHA-256, handwritten feature extraction using a local binary pattern, one-time random pixel sequence based on RC4 to randomly hide authentication codes using LSB. The proposed scheme can provide users with one-time bio-key, robust message anonymity and a disappearing authentication code that does not draw the attention of eavesdroppers. Thus, the scheme improves the data integrity for a users messages/image documents, phase key agreement, bio-key management and a one-time message/image document code for each users session. The concept of stego-anonymity is also introduced to provide additional security to cover a hashed value. Finally, security analysis and experimental results demonstrate and prove the invulnerability and efficiency of the proposed scheme. © 2020 IEEE.

关键词： Authentication

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Measurement of the neutron capture cross sections of rhenium up to stellar s- and r-process temperatures at the China Spallation Neutron Source Back-n facility

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Physical Review research 2024年第1期6卷 013225-013225页

作者： Gaole Yang Zhendong An Wei Jiang Xiankai Li Weiwei Qiu Zhengfa Liao Ziyue Zhuang Xiaoping Zhang Shengli Chen Chenchen Guo Erxi Xiao Xiao Fang Xinxiang Li Hongwei Wang Xinrong Hu Bing Jiang Wenqing Shen Jincheng Wang Jie Ren Xichao Ruan Dexin Wang Su-Yalatu Zhang Wen Luo Zhichao Zhu Haoyang Lan Zongwei Cao Xu Ma Yingdu Liu Pusen Wang Yi Yang Ping Su Xiangai Deng Wanbing He Yugang Ma Chunwang Ma Yuting Wang Zhitao Dai Ertao Li Pengqin He RenGuang Tang Tao Zhou Jing Wang Han Yi Yue Zhang Yonghao Chen Ruirui Fan Keqing Gao Qiang Li Kang Sun Zhixin Tan Minhao Gu Hantao Jing Jingyu Tang Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China School of Physics and Astronomy Sun Yat-sen University Zhuhai 519082 China State Key Laboratory of Lunar and Planetary Sciences Macau University of Science and Technology Macau 999078 China CNSA Macau Center for Space Exploration and Science Macau 999078 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China School of Nuclear Science and Technology University of South China Hengyang 421001 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai 519082 China University of Chinese Academy of Sciences Beijing 100049 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing 102413 China College of Mathematics and Physics Inner Mongolia Minzu University Tongliao 028000China Institute of Nuclear Physics Inner Mongolia Minzu University Tongliao 028000 China Shool of Materials Science and Engineering Xiangtan University Xiangtan 411100 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Department of Nuclear Science and Technology Fudan University Shanghai 200433 China Institute of Particle and Nuclear Physics Henan Normal University Xinxiang 453007 China School of Physics Henan Normal University Xinxiang 453007 China Shenzhen Center Cancer hospital Chinese Academy of Medical Sciences Shenzhen 518100 China Institute for Advanced Study in Nuclear Energy and Safety College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China School of Nuclear Science and Technology University of Science and Technology of China Hefei 230027 China

The neutron capture measurement of natural rhenium was performed with the time-of-flight technique at the Back-n facility of the China Spallation Neutron Source in the energy range from 1 to 500 eV of the resolved resonance region (RRR) and from 5 to 400 keV in the unresolved resonance region (URR). Prompt γ-rays originating from neutron-induced capture events were detected by four C6D6 detectors. The pulse height weighting technique and the double-bunch unfolding method based on the Bayesian theory were used in the data analysis. To obtain reliable measurement results, background subtraction, normalization, and corrections were carefully considered. The multilevel R-matrix Bayesian code SAMMY was used to extract the resonance parameters in the RRR. An absence of resonance near 392 eV is observed in our measurement, which has been observed in previous works. The average cross sections in the URR of natural rhenium relative to Au197 were obtained in logarithmical equidistant energy bins with 20 bins per energy decade. The talys code was used to describe the average cross sections in the URR; the Maxwellian-averaged cross sections (MACSs) of Re185 and Re187 are given from kT=5 to 100 keV. At a thermal energy of kT=30 keV, the MACS value for Re185 (1469±127 mb) is in good agreement with the Karlsruhe Astrophysical database of Nucleosynthesis in Stars (KADoNIS) recommended value (1535±62 mb) within the error bars. By contrast, the value of 1361±118 mb for Re187 shows a discrepancy with the KADoNiS recommended value (1160±57 mb).

关键词： Nuclear astrophysics Nuclear reaction rates Nuclear reactions Radiative capture Resonance reactions r process s process 150 ≤ A ≤ 189 Nuclear data analysis & compilation

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A new runoff calculation model based on multi-source radar remote sensing technology 6

A new runoff calculation model based on multi-source radar r...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Zhao, Jianhui Wang, Ning Wu, Lin Min, Lin College of Computer and Information Engineering Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

River runoff changing will directly affect the safety of the surrounding areas. In theory, river runoff can be calculated from water depth, river width and flow velocity. But in the actual monitoring, the observation of water depth, river width and flow velocity is complex. Radar remote sensing can observe target all-time and all-weather, and has the ability to penetrate clouds, rain and fog. The common Radar remote sensing runoff calculation model is depth-runoff relationship model, which is constructed by fitting water depth and in-situ runoff. The river depth is derived from Radar Altimetry (RA) data. But this model has only one input parameter, the accuracy of this model inverted runoff is not high. In view of the above problems, this paper proposed the Multi-source Radar Remote Sensing Runoff Calculation Model (MRRS-RCM). Firstly, river width and relative water depth were derived from Synthetic Aperture Radar (SAR) images and RA data. Secondly, based on the Manning's equation, the flow velocity were described from river width and relative water depth. Thirdly, river width relative water depth, and in-situ runoff were fitted to construct MRRS-RCM. Experiments show the accuracy of MRRS-RCM estimated runoff is better than depth-runoff relationship model, the RRMSE values of MRRS-RCM reach 13%. © 2019 IEEE.

关键词： Synthetic aperture radar

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Local Partial Zero-Forcing Combining for Cell-Free Massive MIMO Systems

arXiv

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arXiv 2022年

作者： Zhang, Jiayi Zhang, Jing Björnson, Emil Ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical Engineering Linköping University Linköping Sweden The Department of Computer Science KTH Royal Institute of Technology Kista Sweden The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is to realize signal processing in a scalable way. In this paper, we consider scalable full-pilot zero-forcing (FZF), partial FZF (PFZF), protective weak PFZF (PWPFZF), and local regularized ZF (LRZF) combining by exploiting channel statistics. We derive closed-form expressions of the uplink SE for FZF, PFZF, and PWPFZF combining with large-scale fading decoding over independent Rayleigh fading channels, taking channel estimation errors and pilot contamination into account. Moreover, we investigate the impact of the number of pilot sequences, antennas per AP, and APs on the performance. Numerical results show that LRZF provides the highest SE. However, PWPFZF is preferable when the number of pilot sequences is large and the number of antennas per AP is small. The reason is that PWPFZF has lower computational complexity and the SE expression can be computed in closed-form. Furthermore, we investigate the performance of PWPFZF combining with fractional power control and the numerical results show that it improves the performance of weak UEs and realizes uniformly good service for all UEs in a scalable fashion. Copyright © 2022, The Authors. All rights reserved.

关键词： Power control

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Corrigendum to “Ensemble framework for daily carbon dioxide emissions forecasting based on the signal decomposition–reconstruction model” [APEN 345 (2023)/121330]

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Applied Energy 2023年 349卷

作者： Chao Song Tao Wang Xiaohong Chen Quanxi Shao Xianqi Zhang School of Civil Engineering Sun Yat-sen University Guangzhou 510275 China Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China Sun Yat-sen University Guangzhou 510275 China Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute Sun Yat-sen University Guangzhou 510275 China CSIRO Data61 Australian Resources Research Centre 26 Dick Perry Avenue Kensington WA 6155 Australia North China University of Water Resources and Electric Power Collaborative Innovation Center of Water Resources Efficient Utilization Zhengzhou 450046 China Technology Research Center of Water Conservancy and Marine Traffic Engineering Henan Province Zhengzhou 450046 China

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Weighted Linear Multiple Kernel Learning for Saliency Detection 2nd

Weighted Linear Multiple Kernel Learning for Saliency Detect...

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Zhou, Quan Wu, Jinwen Fan, Yawen Zhang, Suofei Wu, Xiaofu Zheng, Baoyu Jin, Xin Lu, Huimin Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Information Engineering China University of Geosciences Wuhan China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

ISBN: (纸本)9783030177621

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： center-surround contrast Corner-surround contrast Global contrast Multiple kernel learning Saliency detection

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Attribute-Based Searchable Encryption Scheme Supporting Efficient Range Search in Cloud Computing

Attribute-Based Searchable Encryption Scheme Supporting Effi...

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IEEE Conference on Dependable and Secure Computing

作者： Yuan Li Haiyan Wang Shulan Wang Yong Ding College of Computer Science and Software Engineering Shenzhen University Shenzhen P. R. China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen P. R. China College of Big Data and Internet Shenzhen Technology University Shenzhen P. R. China Guangxi Key Laboratory of Cryptography Information Security Guilin P. R. China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

With the widespread application of cloud computing technology, data privacy security problem becomes more serious. The recent studies related to searchable encryption (SE) area have shown that the data owners can share their private data with efficient search function and high-strength security. However, the search method has yet to be perfected, compared with the plaintext search mechanism. In this paper, based LSSS matrix, we give a new searchable algorithm, which is suitable for many search method, such as exact search, Boolean search and range search. In order to improve the search efficiency, the 0, 1-coding theory is introduced in the process of ciphertext search. Meanwhile it is shown that multi-search mechanism can improve the efficiency of data sharing. Finally, the performance analysis is presented, which prove our scheme is secure, efficient, and human-friendly.

关键词： Cloud computing data privacy Search methods Conferences Encryption Performance analysis security

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Spatiotemporal estimation and local driver analysis of fractional vegetation coverage in the Three-North region, China

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Advances in Space research 2025年

作者： Yi Long Hua Sun Fugen Jiang Song Chen Jie Tang Research Center of Forestry Remote Sensing and Information Engineering Central South University of Forestry and Technology Changsha China Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province Changsha China Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern Area Changsha China

Understanding the spatio-temporal dynamics of fractional vegetation coverage (FVC) and its driving factors is imperative for advancing ecological management and fostering sustainable development. Towards the Three-North region of China, five distinct nonnegative matrix factorization models were employed to estimate FVC. The quadratic optimization derived from improved constrained nonnegative matrix factorization, IC-NMF 2 , was implemented to refine FVC estimation from 2000 to 2022. The spatial evolution pattern and change trend analysis were performed. The global and local drivers of FVC were analyzed using optimal parameter-based geographic detector (OPGD) and geographically weighted random forest (GWRF) model. The findings indicated that the IC-NMF 2 model outperformed other models in estimation accuracy and stability. FVC in the Three-North region had notable directional and regional characteristics, with a predominant tendency of either improvement or relative stability. However, localized degradation was observed, particularly in the Tianshan Mountains and the Beijing-Tianjin-Hebei region. Both OPGD and GWRF analyses consistently identified average relative humidity and accumulated precipitation as the primary global drivers of FVC, which showed a bi-enhance interactive effect. Average relative humidity was the first local influencing factor of FVC in most regions, showing strong spatial alignment with FVC distributions. By integrating local factor importance with trend analysis, more precise recommendations for ecological protection and management for sub-regions can be formulated. This study demonstrates great potential for application in other regions, especially in monitoring vegetation ecosystems and their dynamic responses to global climate changes in desert areas.

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VulDeeLocator: A deep learning-based fine-grained vulnerability detector

arXiv

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arXiv 2020年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Chen, Zhaoxuan Zhu, Yawei Jin, Hai The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Cyber Security and Computer Hebei University Baoding071002 China The Department of Computer Science University of Colorado Colorado Springs CO80918 United States University of Texas San Antonio United States

Automatically detecting software vulnerabilities is an important problem that has attracted much attention from the academic research community. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and the locating precision that would warrant their adoption for real-world use. In this paper, we present a vulnerability detector that can simultaneously achieve a high detection capability and a high locating precision, dubbed Vulnerability Deep learning-based Locator (VulDeeLocator). In the course of designing VulDeeLocator, we encounter difficulties including how to accommodate semantic relations between the definitions of types as well as macros and their uses across files, how to accommodate accurate control flows and variable define-use relations, and how to achieve high locating precision. We solve these difficulties by using two innovative ideas: (i) leveraging intermediate code to accommodate extra semantic information, and (ii) using the notion of granularity refinement to pin down locations of vulnerabilities. When applied to 200 files randomly selected from three real-world software products, VulDeeLocator detects 18 confirmed vulnerabilities (i.e., true-positives). Among them, 16 vulnerabilities correspond to known vulnerabilities;the other two are not reported in the National Vulnerability database (NVD) but have been "silently" patched by the vendor of Libav when releasing newer versions. Copyright © 2020, The Authors. All rights reserved.

关键词： Location

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Measurement of the Pr141(n,γ) cross section up to stellar s-process temperatures at the China Spallation Neutron Source Back-n facility

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Physical Review C 2023年第3期108卷 035802-035802页

作者： Xiankai Li Zhendong An Wei Jiang Jingyi Zhang Ruoran Bai Shaokun Liu Xinxiang Li Zhichao Zhu Gaole Yang Weiwei Qiu Zhengfa Liao Ziyue Zhuang Xiaoping Zhang Shengli Chen Chenchen Guo Erxi Xiao Xiao Fang Hongwei Wang Xinrong Hu Bing Jiang Wenqing Shen Jincheng Wang Jie Ren Xichao Ruan Wen Luo Haoyang Lan Zongwei Cao Xu Ma Yingdu Liu Pusen Wang Yi Yang Ping Su Xiangai Deng Wanbing He Yugang Ma Chunwang Ma Yuting Wang Zhitao Dai Ertao Li Pengqin He RenGuang Tang Tao Zhou Jing Wang Han Yi Yue Zhang Yonghao Chen Ruirui Fan Keqing Gao Qiang Li Kang Sun Zhixin Tan Minhao Gu Hantao Jing Jingyu Tang School of Nuclear Science and Technology University of South China Hengyang 421001 China School of Physics and Astronomy Sun Yat-sen University Zhuhai 519082 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China State Key Laboratory of Lunar and Planetary Sciences Macau University of Science and Technology Macau 999078 China CNSA Macau Center for Space Exploration and Science Macau 999078 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China University of Chinese Academy of Sciences Beijing 100049 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai 519082 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing 102413 China State Key Laboratory of Nuclear Physics and Technology and CAPT Peking University Beijing 100871 China Beijing Laser Acceleration Innovation Center Huairou Beijing 101400 China Shool of Materials Science and Engineering Xiangtan University Xiangtan 411100 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Department of Nuclear Science and Technology Fudan University Shanghai 200433 China Institute of Particle and Nuclear Physics Henan Normal University Xinxiang 453007 China School of Physics Henan Normal University Xinxiang 453007 China Shenzhen Center Cancer hospital Chinese Academy of Medical Sciences Shenzhen 518100 China Institute for Advanced Study in Nuclear Energy and Safety College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China

In nuclear astrophysics, Pr141 is mainly produced by an s process, which has a neutron magic number (N=82) leading to its low capture cross section and strongly affecting the s process. Its neutron capture cross section is an important input parameter for nuclear astrophysical network calculations. According to the EXFOR database, the measurement precision for the Pr141(n,γ) cross section should be improved in the resolved and unresolved resonance region. The (n,γ) cross section of Pr141 was measured between 1 eV and 500 keV at the Back-n facility of the Chinese Spallation Neutron Source using the time-of-flight (TOF) method. The prompt γ rays were detected by four C6D6 liquid scintillator detectors, and the data was analyzed using the pulse-height weighting technique (PHWT). The majority of results are in agreement with the evaluated data libraries ENDF/B-VIII.0, JENDL-5.0, and TENDL-2021, with some significant exceptions for small resonances. The multilevel R-matrix Bayesian code sammy was used to extract the resonance parameters of Pr141 in the resolved resonance region. And the Maxwell average cross section (MACS) from kT=5 to 100 keV is calculated in the temperature range of the s-process nucleosynthesis model. In particular, at kT=30 keV the MACS value of Pr141 is 145.0±13.7 mb, which shows an obvious discrepancy with the Karlsruhe Astrophysical database of Nucleosynthesis in Stars (KADoNiS) recommended value 111.4±1.4 mb.

关键词： Neutron physics Nuclear astrophysics Nuclear reactions Nucleosynthesis in explosive environments Radiative capture Resonance reactions 90 ≤ A ≤ 149 Nuclear data analysis & compilation Radiation detectors

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