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Deep Learning, Ubiquitous and Toy Computing
Patrick C. K. Hung1,2, Shih-Chia Huang2,1, Sarajane Marques Peres3
1Faculty of Business and IT, University of Ontario Institute of Technology, Canada
2Department of Electronic Engineering, National Taipei University of Technology, Taiwan
3School of Arts, Sciences and Humanities, University of São Paulo, Brazil
patrick.hung@uoit.ca; schuang@ntut.edu.tw; sarajane@usp.br
Welcome to the 1st Year of Deep Learning,
Ubiquitous and Toy Computing minitrack under
Decision Analytics, Mobile Services, and Service
Science track in HICSS-50!
Deep learning employs software tools from
advanced analytics disciplines such as data mining,
predictive analytics, text and machine learning based
on a set of algorithms that attempt to model high-level
abstractions in data by using multiple processing layers
with complex structures or non-linear transformations.
At the same time, the processing and analysis of deep
learning applications present methodological and
technological challenges. Further deep learning
applications are advantaged by a rise in sensing
technologies as witnessed in both the number of
sensors and the rich diversity of sensors ranging from
cell phones, personal computers, and health tracking
appliances to Internet of Things (IoT) technologies.
Recently deep learning technologies have been applied
into toy computing. Toy computing is a recently
developing concept which transcends the traditional
toy into a new area of computer research using
ubiquitous technologies. A toy in this context can be
effectively considered a computing device or
peripheral called Smart Toys.
This new minitrack includes three papers which
present both novel solutions to provide clear proof that
deep learning technologies are playing an ever-
increasing important and critical role in supporting
ubiquitous and toy computing applications - a new
cross-discipline research topic in computer science,
decision science, and information systems.
We want to take this opportunity to express our
sincere thanks to the HICSS-50 conference and all
other sponsors for their strong support. Many people
have worked very hard to make this minitrack possible.
We would like to thank all who have helped in making
this new minitrack a success. The Program Committee
Members and Referees each deserve credit for the
excellent final program that resulted from the diligent
review of the submissions. Special thanks go to the
HICSS-50 Program Chairs, Track Chairs of Decision
Analytics, Mobile Services, and Service Science, and
all the other organizing committee members. It has
been a great team work. Enjoy your stay in Big Island,
Hawaii, USA!
Acknowledgements
This minitrack was supported by the Natural
Sciences and Engineering Research Council of Canada
(NSERC), under Discovery Grants Program: RGPIN-
2016-05023; the Ministry of Science and Technology
(MOST), Taiwan, under MOST Grants: 105-2923-E-
002 -014 -MY3, 105-2923-E-027 -001 -MY3, 105-
2221-E-027 -113, & 105-2811-E-027 -001; and the
São Paulo Research Foundation (Fapesp) under Grants
2015/16615-0 and 2016/00014-0.
1206
Proceedings of the 50th Hawaii International Conference on System Sciences | 2017
URI: http://hdl.handle.net/10125/41296
ISBN: 978-0-9981331-0-2
CC-BY-NC-ND