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Corrigendum to “Fine-Grained Control-Flow Integrity Based on Points-to Analysis for CPS”

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Security and Communication Networks 2018年第1期2018卷

作者： Weizhong Qiang Shizhen Wang Hai Jin Jiangying Zhong Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 ***

REFF: REliable and Fast Forwarding in Vehicular Ad-hoc Network 1st

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REFF: REliable and Fast Forwarding in Vehicular Ad-hoc Netwo...

1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Dai, Ziyi Zheng, Kunxian Zhou, Jianshan Duan, Xuting Guo, Peng Rong, Hui Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Vehicular Ad-Hoc Network (VANET) has emerged as an increasingly dominant technology for future connected vehicle and vehicular networks, where the focus of the development of VANET lies in the standardization of message transmission and dissemination via multi-hop broadcasting. However, the current communication protocols concerning VANET face many challenges, including data flooding and collision, transmission delay and other problems. Most of the challenges are closely related to next-hop selection. Therefore, this paper proposes a new routing protocol named REliable and Fast Forwarding (REFF) to optimize the selection of nodes in VANET. In this protocol, node filtering and node evaluation are two main steps. Distance between previous node and candidate node, relative velocity between previous node and candidate nodes, included angle between direction of target node’s velocity and candidate node’s velocity and transmission power of candidate node are adopted as indexes to help select a specific node as the next hop using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). By using this technique, the number of candidates as next-hop is largely reduced, avoiding the data flooding and resulting transmission relay. In addition, simulations based on experiments are done to verify the feasibility. The results show that message achieves a faster and more reliable transmission using REFF. © 2018, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicular ad hoc networks

A Vehicular Positioning Enhancement with Connected Vehicle Assistance Using Extended Kalman Filtering 1st

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A Vehicular Positioning Enhancement with Connected Vehicle A...

1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Liu, Wenhao Duan, Xuting Rong, Hui Guo, Peng Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

In this paper, we consider the problem of vehicular positioning enhancement with emerging connected vehicles (CV) technologies. In order to actually describe the scenario, the Interacting Multiple Model (IMM) filter is used for depicting varies of observation models. A CV-enhanced IMM filtering approach is proposed to locate a vehicle by data fusion from both coarse GPS data and the Doppler frequency shifts (DFS) measured from dedicated short-range communications (DSRC) radio signals. Simulation results state the effectiveness of the proposed approach called CV-IMM-EKF. © 2018, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Global positioning system

WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

Modeling and Simulation on Cooperative Movement of Vehicle Group Based on the Behavior of Fish 1st

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Modeling and Simulation on Cooperative Movement of Vehicle G...

1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Kang, Lu Zheng, Kunxian Duan, Xuting Rong, Hui Guo, Peng Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No.37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No.68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No.30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No.37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Fatigue driving might affect the traffic safety when the vehicles are on the cruising state in highway. Trying to solve this problem, this paper uses the movement pattern of the fish to the vehicles fleet, and develops a model of vehicle group with realistic restrictions based on the existed fish algorithms, the mobile behavioral model and cluster behavior model, and then demonstrates the feasibility of applying the fish behavior to the cooperative movement of vehicle groups through analyzing the trajectory, velocity and spacing of vehicles. © 2018, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicles

Gaia data Release 3: Analysis of RVS spectra using the General Stellar Parametriser from spectroscopy

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arXiv 2022年

作者： Recio-Blanco, Alejandra de Laverny, Patrick Palicio, P.A. Kordopatis, G. Álvarez, M.A. Schultheis, M. Contursi, G. Zhao, H. Torralba Elipe, G. Ordenovic, C. Manteiga, M. Dafonte, C. Oreshina-Slezak, I. Bijaoui, A. Frémat, Y. Seabroke, G. Pailler, F. Spitoni, E. Poggio, E. Creevey, O.L. Abreu Aramburu, A. Accart, S. Andrae, R. Bailer-Jones, C.A.L. Bellas-Velidis, I. Brouillet, N. Brugaletta, E. Burlacu, A. Carballo, R. Casamiquela, L. Chiavassa, A. Cooper, W.J. Dapergolas, A. Delchambre, L. Dharmawardena, T.E. Drimmel, R. Edvardsson, B. Fouesneau, M. Garabato, D. García-Lario, P. García-Torres, M. Gavel, A. Gomez, A. González-Santamaría, I. Hatzidimitriou, D. Heiter, U. Jean-Antoine Piccolo, A. Kontizas, M. Korn, A.J. Lanzafame, A.C. Lebreton, Y. Le Fustec, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Lobel, A. Lorca, A. Magdaleno Romeo, A. Marocco, F. Marshall, D.J. Mary, N. Nicolas, C. Pallas-Quintela, L. Panem, C. Pichon, B. Riclet, F. Robin, C. Rybizki, J. Santoveña, R. Silvelo, A. Smart, R.L. Sarro, L.M. Sordo, R. Soubiran, C. Süveges, M. Ulla, A. Vallenari, A. Zorec, J. Utrilla, E. Bakker, J. Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Nice France CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain CIGUS CITIC Department of Nautical Sciences and Marine Engineering University of A Coruña Paseo de Ronda 51 A Coruña15071 Spain Royal Observatory of Belgium 3 avenue circulaire Brussels1180 Belgium Mullard Space Science Laboratory University College London Holmbury St Mary Dorking SurreyRH5 6NT United Kingdom CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Dpto. de Matemática Aplicada y Ciencias de la Computación Univ. de Cantabria ETS Ingenieros de Caminos Canales y Puertos Avda. de los Castros s/n Santander39005 Spain GEPI Observatoire de Paris Université PSL CNRS 5 Place Jules Janssen Meudon92190 France Centre for Astrophysics Research University of Hertfordshire College Lane HatfieldAL10 9AB United Kingdom 10025 Italy Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium Theoretical Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Data Science and

Context. The chemo-physical parametrisation of stellar spectra is essential for understanding the nature and evolution of stars and of Galactic stellar populations. A worldwide observational effort from the ground has provided, in one century, an extremely heterogeneous collection of chemical abundances for about two million stars in total, with fragmentary sky coverage. Aims. This situation is revolutionised by the Gaia third data release (DR3), which contains the parametrisation of Radial Velocity Spectrometer (RVS) data performed by the General Stellar Parametriser-spectroscopy, GSP-Spec, module. Here we describe the parametrisation of the first 34 months of Gaia RVS observations. Methods. GSP-Spec estimates the chemo-physical parameters from combined RVS spectra of single stars, without additional inputs from astrometric, photometric, or spectro-photometric BP/RP data. The main analysis workflow described here, MatisseGauguin, is based on projection and optimisation methods and provides the stellar atmospheric parameters;the individual chemical abundances of N, Mg, Si, S, Ca, Ti, Cr, Fe i, Fe ii, Ni, Zr, Ce and Nd;the differential equivalent width of a cyanogen line;and the parameters of a diffuse interstellar band (DIB) feature. Another workflow, based on an artificial neural network (ANN) and referred to with the same acronym, provides a second set of atmospheric parameters that are useful for classification control. For both workflows, we implement a detailed quality flag chain considering different error sources. Results. With about 5.6 million stars, the Gaia DR3 GSP-Spec all-sky catalogue is the largest compilation of stellar chemo-physical parameters ever published and the first one from space data. Internal and external biases have been studied taking into account the implemented flags. In some cases, simple calibrations with low degree polynomials are suggested. The homogeneity and quality of the estimated parameters enables chemo-dynamical studies of G

关键词： Stars

ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

DYNAMICAL BEHAVIOR OF A STOCHASTIC HBV INFECTION MODEL WITH LOGISTIC HEPATOCYTE GROWTH

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Acta Mathematica Scientia 2017年第4期37卷 927-940页

作者：刘群蒋达清史宁中 Tasawar HAYAT Ahmed ALSAEDI School of Mathematics and Statistics Key Laboratory of Applied Statistics of MOENortheast Normal UniversityChangchun 130024China School of Mathematics and Statistics Guangxi Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulinGuangxi 537000China Nonlinear Analysis and Applied Mathematics(NAAM)Research Group Department of MathematicsFaculty of ScienceKing Abdulaziz UniversityJeddah 121589Saudi Arabia College of Science China University of Petroleum(East China)Qingdao 266580China Department of Mathematics Quaid-i-Azam University 45320Islamabad 44000Pakistan

This paper is concerned with a stochastic HBV infection model with logistic growth. First, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic stationary distribution of the solution to the HBV infection model. Then we obtain sufficient conditions for extinction of the disease. The stationary distribution shows that the disease can become persistent in vivo.

关键词： stochastic HBV infection model extinction stationary distribution Lyapunov function

Quantifying china's regional economic complexity

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arXiv 2017年

作者： Gao, Jian Zhou, Tao CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China

China has experienced an outstanding economic expansion during the past decades, however, literature on nonmonetary metrics that reveal the status of China's regional economic development are still lacking. In this paper, we fill this gap by quantifying the economic complexity of China's provinces through analyzing 25 years' firm data. First, we estimate the regional economic complexity index (ECI), and show that the overall time evolution of provinces' ECI is relatively stable and slow. Then, after linking ECI to the economic development and the income inequality, we find that the explanatory power of ECI is positive for the former but negative for the latter. Next, we compare different measures of economic diversity and explore their relationships with monetary macroeconomic indicators. Results show that the ECI index and the non-linear iteration based Fitness index are comparative, and they both have stronger explanatory power than other benchmark measures. Further multivariate regressions suggest the robustness of our results after controlling other socioeconomic factors. Our work moves forward a step towards better understanding China's regional economic development and non-monetary macroeconomic indicators. Copyright © 2017, The Authors. All rights reserved.

关键词： Regional planning