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7 results on '"Jiang, Xiao-Jian"'

1. Deep Hashing for Signed Social Network Embedding

Author

Guo, Jia-Nan, Mao, Xian-Ling, Jiang, Xiao-Jian, Sun, Ying-Xiang, Wei, Wei, and Huang, He-Yan

Subjects
Computer Science - Social and Information Networks, Computer Science - Machine Learning
Abstract

Network embedding is a promising way of network representation, facilitating many signed social network processing and analysis tasks such as link prediction and node classification. Recently, feature hashing has been adopted in several existing embedding algorithms to improve the efficiency, which has obtained a great success. However, the existing feature hashing based embedding algorithms only consider the positive links in signed social networks. Intuitively, negative links can also help improve the performance. Thus, in this paper, we propose a novel deep hashing method for signed social network embedding by considering simultaneously positive and negative links. Extensive experiments show that the proposed method performs better than several state-of-the-art baselines through link prediction task over two real-world signed social networks., Comment: 9 pages, 9 figures

Published
2019

2. HSDS: An Abstractive Model for Automatic Survey Generation

Author

Jiang, Xiao-Jian, Mao, Xian-Ling, Feng, Bo-Si, Wei, Xiaochi, Bian, Bin-Bin, Huang, Heyan, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Li, Guoliang, editor, Yang, Jun, editor, Gama, Joao, editor, Natwichai, Juggapong, editor, and Tong, Yongxin, editor

Published
2019
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7. Comparison of spatial interpolation methods for daily meteorological elements.

Author

Jiang Xiao-jian, Liu Xiao-jun, Huang Fen, Jiang Hai-yan, Cao Wei-xing, and Zhu Yan

Abstract

A comparative study was made to evaluate the methods of inverse distance weighting (IDW), co-kriging (CK), and thin plate spline (TPS) in interpolating the average meteorological elements (including maximum air temperature, minimum air temperature, sunshine hours, and precipitation) of the 15th day per month from the 1951-2005 comprehensive observation data of 559 meteorological stations in China. The results showed that the RMSEs for the maximum and minimum air temperature in a year interpolated by TPS were the smallest (1.02 °C and 1.12 °C, respectively), and the R2 between the observed and predicted values were the highest (0.9916 and 0.9913, respectively), compared with those interpolated by IDW and CK. In four seasons, the smallest RMSEs for the maximum and minimum air temperature interpolated by TPS were observed in autumn (0.83 °C) and summer (0.86 °C), respectively, and the R2 between the observed and predicted values interpolated by TPS were higher in autumn than in other seasons. The RMSEs for the sunshine hours and precipitation in a year interpolated by TPS were the smallest (0.59 h and 1.01 mm, respectively), and the R2 between the observed and predicted values were the highest (0.9118 and 0.8135, respectively), compared with those interpolated by IDW and CK. In four seasons, the RMSE for the sunshine hours in winter interpolated by TPS was the smallest (0.49 h), and the R2 between the observed and predicted sunshine hours was the smallest (0.9293). The RMSE for the precipitation in winter interpolated by TPS was the smallest (0.33 mm), while the RMSE for the precipitation in summer interpolated by IDW was the smallest (2.01 mm). The R2 between the observed and predicted precipitation in winter interpolated by CK was the highest (0.8781). It was suggested that TPS could be the optimal spatial interpolation method in interpolating and rasterizing the daily meteorological elements in China. [ABSTRACT FROM AUTHOR]

Published
2010

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