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Machine Learning, Robotic 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 HICSS-53 mini-track is to present
both novel and industrial solutions to challenging
technical issues as well as compelling smart
application use cases. This mini-track will share
related practical experiences to benefit the reader
and will provide clear proof that machine learning
technologies are playing an ever-increasing
important and critical role in supporting robotic and
toy computing applications - a new cross-discipline
research topic in computer science, decision science,
management sciences, 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 machine learning technologies to support
their decisions and proper growing smart application
needs.
Machine learning employs software tools from
advanced analytics disciplines such as data mining,
predictive analytics, and text-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 machine learning applications present
methodological and technological challenges. Further
machine 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 Artificial Intelligence
(AI) 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 AI technologies have been applied to
robotic and toy computing. Robotic computing is one
branch of AI technologies, and their synergistic
interactions, that enable and are enabled by robots.
Robots now can easily capture a user's physical
activity state (e.g., walking, standing, running, etc.)
and store personalized information (e.g., face, voice,
location, activity pattern, etc.) through the camera,
microphone, and sensors by AI technologies. Toy
computing is a recently developing concept which
transcends the traditional toy into a new area of
computer research using AI technologies. A toy in
this context can be effectively considered a
computing device or peripheral called Smart Toys.
There are four research papers presented in this
mini-track. The first paper is “Assessing Mission
Performance for Technology Reliant Missions” by
DeMoes et al. The second paper is “IoT4Fun Rapid
Prototyping Toolkit for Smart Toys” by Priscilla de
Albuquerque et al. The third paper is “A Literature
Survey on Smart Toy-related Children's Privacy
Risks” by Fantinato et al. The fourth paper is
“Automatic Segmentation of Grammatical Facial
Expressions in Sign Language: Towards an Inclusive
Communication Experience” by Eduarda de Araújo
Cardoso et al.
Proceedings of the 53rd Hawaii International Conference on System Sciences | 2020
Page 1469
URI: https://hdl.handle.net/10125/63919
978-0-9981331-3-3
(CC BY-NC-ND 4.0)