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Fog computing for vehicular Ad-hoc networks: paradigms, scenarios, and issues

The Journal of China Universities of Posts and Telecommunications 2016年第2期23卷 56-65,96页

作者： Kang Kai Wang Cong Luo Tao School of Software Engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts Telecommunications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts Telecommunications

Vehicular Ad-hoc networks（VANETs） are kinds of mobile Ad-hoc networks（MANETs）, which consist of mobile vehicles with on-board units（OBUs） and roadside units（RSUs）. With the rapid development of computation and communication technologies, peripheral or incremental changes in VANETs evolve into a revolution in process. Cloud computing as a solution has been deployed to satisfy vehicles in VANETs which are expected to require resources（such as computing, storage and networking）. Recently, with special requirements of mobility, location awareness, and low latency, there has been growing interest in research into the role of fog computing in VANETs. The merging of fog computing with VANETs opens an area of possibilities for applications and services on the edge of the cloud computing. Fog computing deploys highly virtualized computing and communication facilities at the proximity of mobile vehicles in VANET. Mobile vehicles in VANET can also demand services of low-latency and short-distance local connections via fog computing. This paper presents the current state of the research and future perspectives of fog computing in VANETs. Moreover, we discuss the characteristics of fog computing and services based on fog computing platform provided for VANETs. In this paper, some opportunities for challenges and issues are mentioned, related techniques that need to be considered have been discussed in the context of fog computing in VANETs. Finally, we discuss about research directions of potential future work for fog computing in VANETs. Within this article, readers can have a more thorough understanding of fog computing for VANETs and the trends in this domain.

关键词： VANET fog computing cloud computing application vehicular cloud computing

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MM-PBSA: Challenges and opportunities 10

MM-PBSA: Challenges and opportunities

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10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017

作者： Do Vale Hipolito Cavalheiro, Joao Pedro Pires, Nuno Miguel Matos Dong, Tao Institute of Applied Micro-Nano Science and Technology-IAMNST Chongqing Key Laboratory of Colleges Universities On Micro-Nano Systems Technology and Smart Transducing Chongqing Engineering Laboratory for Detection Control and Integrated System National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China Department of Microsystems-IMS Faculty of Technology Natural Sciences and Maritime Sciences University College of Southeast Norway Kongsberg Norway P.O. Box 5003 ÅsN-1432 Norway

ISBN: (纸本)9781538619377

Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) is arguably the most popular method for free binding energy prediction since it is considerably more accurate than docking or scoring methods, but considerably less demanding than Free Energy Perturbation (FEP). In this review, the most important challenges and opportunities associated to this method were addressed and evaluated. The following conclusions were obtained: Accuracy of MM-PBSA calculations still strongly depends on the settings used for the prediction, the conditions in which Molecular Dynamics simulations (MD) are made, the conditions from which the experimental data are obtained, it depends on the presence of the complex structure and this method has also shown to be unable to provide accurate results for charged molecules. On the other hand, it presents many opportunities since many improvements were based on MM-PBSA calculations such as Single Amino Acid Mutation based change in Binding free Energy (SAAMBE), bootstrap, dampened Molecular Mechanics Poisson-Boltzmann Surface Area (dMM-PBSA), a high throughput method called g-mmpbsa, a method based on both MM-PBSA calculations and a scoring function, force fields for inhomogeneous protein specific environments and many other new ways of improving the accuracy of this promising method. This study intends to clarify what are the major trends in this field and thus contribute to increase MM-PBSA's applicability and efficiency. © 2017 IEEE.

关键词： Molecular dynamics

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Local shrunk discriminant analysis (LSDA)

arXiv

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arXiv 2017年

作者： Gao, Zan Zhang, Guotai Nie, Feiping Zhang, Hua Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology 300384 Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University Xian Shanxi710072

Dimensionality reduction is a crucial step for pattern recognition and data mining tasks to overcome the curse of dimensionality. Principal component analysis (PCA) is a traditional technique for unsupervised dimensionality reduction, which is often employed to seek a projection to best represent the data in a least-squares sense, but if the original data is nonlinear structure, the performance of PCA will quickly drop. An supervised dimensionality reduction algorithm called Linear discriminant analysis (LDA) seeks for an embedding transformation, which can work well with Gaussian distribution data or single-modal data, but for non-Gaussian distribution data or multimodal data, it gives undesired results. What is worse, the dimension of LDA cannot be more than the number of classes. In order to solve these issues, Local shrunk discriminant analysis (LSDA) is proposed in this work to process the non-Gaussian distribution data or multimodal data, which not only incorporate both the linear and nonlinear structures of original data, but also learn the pattern shrinking to make the data more flexible to fit the manifold structure. Further, LSDA has more strong generalization performance, whose objective function will become local LDA and traditional LDA when different extreme parameters are utilized respectively. What is more, a new efficient optimization algorithm is introduced to solve the non-convex objective function with low computational cost. Compared with other related approaches, such as PCA, LDA and local LDA, the proposed method can derive a subspace which is more suitable for non-Gaussian distribution and real data. Promising experimental results on different kinds of data sets demonstrate the effectiveness of the proposed approach1. Copyright © 2017, The Authors. All rights reserved.

关键词： Principal component analysis

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Finite-time and Fixed-time Stability and Synchronization 35

Finite-time and Fixed-time Stability and Synchronization

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第35届中国控制会议

作者： LIU Xiwei LU Wenlian CHEN Tianping Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education School of Mathematics Fudan University Centre for Computational System Biology Fudan University School of Computer Sciences Fudan University

ISBN: (纸本)9781509009107

Recently,finite-time and fixed-time stability has been a hot topic in the study of dynamical *** this paper,we will provide an insightful view on this issue and explore the intrinsic property by mathematical *** theorems and corollaries show how to find the settling ***,we apply the corresponding theory on the finite-time and fixed-time synchronization problem of nonlinearly coupled systems and a numerical simulation is also given to demonstrate its efficiency.

关键词： Finite-Time Fixed-Time Nonlinear Coupling Stability Synchronization

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Examining scientific writing styles from the perspective of linguistic complexity

arXiv

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arXiv 2018年

作者： Lu, Chao Bu, Yi Wang, Jie Ding, Ying Torvik, Vetle Schnaars, Matthew Zhang, Chengzhi School of Economics and Management Nanjing University of Science and Technology Nanjing Jiangsu China School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Information Management Nanjing University Nanjing Jiangsu China School of Information Management Wuhan University Wuhan Hubei China School of Information Sciences University of Illinois UrbanaIL United States Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing Jiangsu China

Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. In order to uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (1) syntactic complexity, including measurements of sentence length and sentence complexity;and (2) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity. Copyright © 2018, The Authors. All rights reserved.

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Improving stock market prediction via heterogeneous information fusion

arXiv

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arXiv 2018年

作者： Zhang, Xi Zhang, Yunjia Wang, Senzhang Yao, Yuntao Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Computer Science University of Illinois at ChicagoIL60607 United States Institute for Data Science Tsinghua University Beijing100084 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan Guangdong523808 China

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people’s sentiments towards the market and stocks, have been proved to play important roles in the stocks’ volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users’ sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsicrelations among the events and the investors’ sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Author Correction: Contribution of Ryugu-like material to Earth’s volatile inventory by Cu and Zn isotopic analysis

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Nature Astronomy 2023年 633页

作者： Marine Paquet Frederic Moynier Wei Dai Yan Hu Martin Bizzarro Marc Chaussidon Tetsuya Yokoyama Ikshu Gautam Makiko K. Haba Akira Ishikawa Yuki Masuda Yoshinari Abe Jérôme Aléon Conel M. O’D. Alexander Richard W. Carlson Larry Nittler Sachiko Amari Yuri Amelin Ken-ichi Bajo Noriyuki Kawasaki Sohei Wada Hisayoshi Yurimoto Audrey Bouvier Byeon-Gak Choi Nicolas Dauphas Andrew M. Davis Tommaso Di Rocco Andreas Pack Wataru Fujiya Ryota Fukai Tomohiro Usui Kanako Sakamoto Masanao Abe Akiko Miyazaki Aiko Nakato Masahiro Nishimura Tatsuaki Okada Toru Yada Kasumi Yogata Satoru Nakazawa Takanao Saiki Satoshi Tanaka Yuichi Tsuda Makoto Yoshikawa Yuki Hibiya Hiroshi Hidaka Sei-ichiro Watanabe Hisashi Homma Peter Hoppe Gary R. Huss Alexander N. Krot Kazuhide Nagashima Kiyohiro Ichida Shintaro Komatani Mayu Morita Morihiko Onose Tsuyoshi Iizuka Trevor R. Ireland Motoo Ito Takaaki Noguchi Shoichi Itoh Noriko T. Kita Kouki Kitajima Thorsten Kleine Ming-Chang Liu Kevin D. McKeegan Lauren Tafla Haolan Tang Edward D. Young Kazuko Motomura Izumi Nakai David Nesvorný Ann N. Nguyen Changkun Park Laurette Piani Liping Qin Sara S. Russell Naoya Sakamoto Maria Schönbächler Kentaro Terada Yasuko Terada Meenakshi Wadhwa Richard J. Walker Katsuyuki Yamashita Qing-Zhu Yin Shigekazu Yoneda Hiroharu Yui Ai-Cheng Zhang Tomoki Nakamura Hiroshi Naraoka Ryuji Okazaki Hikaru Yabuta Fuyuto Terui Shogo Tachibana Université Paris Cité Institut de Physique du Globe de Paris CNRS Paris France Centre for Star and Planet Formation GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Earth and Planetary Sciences Tokyo Institute of Technology Tokyo Japan Graduate School of Engineering Materials Science and Engineering Tokyo Denki University Tokyo Japan Institut de Minéralogie de Physique des Matériaux et de Cosmochimie Sorbonne Université Muséum National d’Histoire Naturelle CNRS UMR 7590 IRD Paris France Earth and Planets Laboratory Carnegie Institution for Science Washington DC USA McDonnell Center for the Space Sciences and Physics Department Washington University St. Louis MO USA Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou China Natural History Sciences IIL Hokkaido University Sapporo Japan Bayerisches Geoinstitut Universität Bayreuth Bayreuth Germany Department of Earth Science Education Seoul National University Seoul Republic of Korea Department of the Geophysical Sciences and Enrico Fermi Institute The University of Chicago Chicago IL USA Faculty of Geosciences and Geography University of Göttingen Göttingen Germany Faculty of Science Ibaraki University Mito Japan ISAS/JSEC JAXA Sagamihara Japan General Systems Studies The University of Tokyo Tokyo Japan Earth and Planetary Sciences Nagoya University Nagoya Japan Osaka Application Laboratory SBUWDX Rigaku Corporation Osaka Japan Max Planck Institute for Chemistry Mainz Germany Hawai’i Institute of Geophysics and Planetology University of Hawai’i at Mānoa Honolulu HI USA Analytical Technology Horiba Techno Service Co. Ltd Kyoto Japan Earth and Planetary Science The University of Tokyo Tokyo Japan School of Earth and Environmental Sciences The University of Queensland St Lucia Queensland Australia Kochi Institute for Core Sample Research JAMSTEC Kochi Japan Earth and Planetary Sciences Kyoto University Kyoto Japan Geoscience Universit

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Deep hybrid residual learning with statistic priors for single image super-resolution

Deep hybrid residual learning with statistic priors for sing...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Risheng Liu Xiangyu Wang Xin Fan Haojie Li Zhongxuan Luo Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Department of Computing The Hong Kong Polytechnic University DUT-RU International School of Information & Software Engineering Dalian University of Technology

ISBN: (纸本)9781509060689

This paper considers single image super-resolution (SISR), which is an important low-level vision task and has various applications in multimedia society. Recently, deep neural networks have archived good performance on this field. But most of existing deep models are based on the fully data-dependent network architecture, thus missing majority of domain-knowledge of the super-resolution task. To address this limitation, we develop a new hybrid residual learning approach to leverage priors of SISR within the maximum a posteriori framework for network architecture design. We demonstrate that it can incorporate both image priors and data fidelity into the network, leading to a novel cascaded residual learning system for SISR process. Extensive experimental results on real-world images show that the proposed algorithm performs favorably against state-of-the-art methods.

关键词： Image resolution Network architecture Radio frequency Training Degradation Dictionaries Convolution

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Leveraging geometric correlation for input-adaptive facial landmark regression

Leveraging geometric correlation for input-adaptive facial l...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuyao Feng Risheng Liu Xin Fan Kang Huyan Zhongxuan Luo Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT-RU International School of Information & Software Engineering Dalian University of Technology Department of Computing The Hong Kong Polytechnic University

ISBN: (纸本)9781509060689

Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions on controlled facial images can successfully capture the appearance variations, the performance of these appearance-based models are tightly related to the quantity and quality of the training data. In this paper, we develop a novel framework, named geometric correlated landmark regression (GCLR), to inherit the advantages but overcome limitations of these two categories of methods. Specifically, we first establish a landmark-to-landmark regression to estimate the geometry of facial images. By further incorporating a sparse coding term into the regression framework, we can successfully leverage the geometric correlations between the test image and the shape dictionary, thus significantly enhance the geometry regression performance. Experimental results on various challenging facial data sets verify the effectiveness and efficiency of GCLR.

关键词： Training Correlation Shape Face Geometry Databases Encoding

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Reverse testing image set model based multi-view human action recognition 22nd

Reverse testing image set model based multi-view human actio...

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Gao, Z. Zhang, Y. Zhang, H. Xu, G.P. Xue, Y.B. Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9783319276700

Recognizing human activities from videos becomes a hot research topic in computer vision, but many studies show that action recognition based on single view cannot obtain satisfying performance, thus, many researchers put their attentions on multi-view action recognition, but how to mine the relationships among different views still is a challenge problem. Since video face recognition algorithm based on image set has proved that image set algorithm can effectively mine the complementary properties of different views image, and achieves satisfying performance. Thus, Inspired by these, image set is utilized to mine the relationships among multi-view action recognition. However, the studies show that the sample number of gallery and query set in video face recognition based on image set will affect the algorithm performance, and several ten to several hundred samples is supplied, but, in multi-view action recognition, we only have 3–5 views (samples) in each query set, which will limit the effect of image set. In order to solve the issues, reverse testing image set model (called RTISM) based multi-view human action recognition is proposed. We firstly extract dense trajectory feature for each camera, and then construct the shared codebook by k-means for all cameras, after that, Bag-of-Word (BoW) weight scheme is employed to code these features for each camera;Secondly, for each query set, we will compute the compound distance with each image subset in gallery set, after that, the scheme of the nearest image subset (called RTIS) is chosen to add into the query set;Finally, RTISM is optimized where the query set and RTIS are whole reconstructed by the gallery set, thus, the relationship of different actions among gallery set and the complementary property of different samples among query set are meanwhile excavated. Large scale experimental results on two public multi-view action3D datasets - Northwestern UCLA and CVS-MV-RGBD-Single, show that the reconstruction of query

关键词： Cameras

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