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Editor’s Note - Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

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Part of the following topical collections:
  1. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications
  2. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

World Wide Web gratefully acknowledges the editorial work of the scholars listed below on the special issue entitled “Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications.”

Jingkuan Song

University of Electronic Science and Technology of China

China

jingkuan.song@gmail.com

Shuqiang Jiang

Chinese Academy of Sciences

China

sqjiang@ict.ac.cn

Elisa Ricci

University of Perugia

Italy

elisa.ricci@unipg.it

Zi Huang

The University of Queensland

Australia

huang@itee.uq.edu.au

This special issue contains the following twenty-six papers:
  • On fusing the latent deep CNN feature for image classification by Xueliang Liu, Rongjie Zhang, Zhijun Meng, Richang Hong, and Guangcan Liu

     https://doi.org/10.1007/s11280-018-0600-3

  • A resource-aware approach for authenticating privacy preserving GNN queries by Yan Dai, Jie Shao, Gang Hu, and Long Guo

     https://doi.org/10.1007/s11280-017-0507-4

  • Effective shortest travel-time path caching and estimating for location-based servicesby...

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

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