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Deep Learning, Ubiquitous and Toy Computing Minitrack
Patrick C. K. Hung
Faculty of Business and Information Technology, University of Ontario Institute of Technology, Canada
patrick.hung@uoit.ca
Shih-Chia Huang
Department of Electronic Engineering, National Taipei University of Technology, Taiwan
schuang@ntut.edu.tw
Sarajane Marques Peres
School of Arts, Sciences and Humanities, University of Sao Paulo, Brazil
sarajane@usp.br
Abstract
The goal of this minitrack is to present both novel
and industrial solutions to challenging technical
issues as well as compelling smart application use
cases. This minitrack will share related practical
experiences to benefit the reader and will 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.
1. Introduction
The pervasive nature of digital technologies as
witnessed in industry, services and everyday life has
given rise to an emergent, data-focused economy
stemming from many aspects of human individual
and ubiquitous applications. The richness and
vastness of these data are creating unprecedented
research opportunities in many fields including urban
studies, geography, economics, finance,
entertainment, and social science, as well as physics,
biology and genetics, public health and many other
smart devices. In addition to data, text and machine
mining research, businesses and policymakers have
seized on deep learning technologies to support their
decisions and proper growing smart application
needs.
As businesses build out emerging hardware and
software infrastructure, it becomes increasingly
important to anticipate technical and practical
challenges and to identify best practices learned
through experience in this research area. 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
designed to give contextual, semantic data to entities
in a ubiquitous environment that previously could not
contribute intelligence to key decisions and smart
devices. Recently deep learning technologies have
been applied to 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.
There are two research papers presented in this
minitrack. The first paper “Twitter Connections
Shaping New York City” by Sobolevsky et al.
presents a novel way of constructing a spatial social
network based on such data to analyze its structure
and evaluate its utility for delineating urban
neighborhoods. The second paper “Perceived
Innovativeness and Privacy Risk of Smart Toys in
Brazil and Argentina” by Fantinato et al. studies
Brazilian and Argentinian consumers’ perceived
innovativeness, risks and benefits of smart toys and
their purchase intention toward such toys.
Proceedings of the 51st Hawaii International Conference on System Sciences |2018
URI: http://hdl.handle.net/10125/50013
ISBN: 978-0-9981331-1-9
(CC BY-NC-ND 4.0)
Page 1007