Big Data, Cloud Computing, and Data Science Engineering

222 Pages·2020·11.07 MB·English

by  Roger Lee

Studies in Computational Intelligence 844
Roger Lee Editor
Big Data, Cloud
Computing, and
Data Science
Engineering
Studies in Computational Intelligence
Volume 844
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
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Roger Lee
Editor
Big Data, Cloud Computing,
and Data Science
Engineering
123
Editor
RogerLee
Software Engineering andInformation
Technology Institute
Central Michigan University
Mount Pleasant,MI, USA
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-030-24404-0 ISBN978-3-030-24405-7 (eBook)
https://doi.org/10.1007/978-3-030-24405-7
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Foreword
The purpose of the 4th IEEE/ACIS International Conference on Big Data, Cloud
Computing, Data Science and Engineering (BCD) held on May 29–31, 2019 in
Honolulu, Hawaii was for researchers, scientists, engineers, industry practitioners,
and students to discuss, encourage and exchange new ideas, research results, and
experiencesonallaspectsofAppliedComputersandInformationTechnology,and
to discuss the practical challenges encountered along the way and the solutions
adoptedtosolve them. Theconferenceorganizers haveselectedthebest 13papers
from those papers accepted for presentation at the conference in order to publish
theminthisvolume.Thepaperswerechosenbasedonreviewscoressubmittedby
members of the program committee and underwent further rigorous rounds of
review.
In chapter “Robust Optimization Model for Designing Emerging Cloud-Fog
Networks”, Masayuki Tsujino proposes a robust design model for economically
constructing IoT infrastructures. They experimentally evaluated the effectiveness
of the proposed model and the possibility of applying the method to this model to
practical scaled networks.
In chapter “Multi-task Deep Reinforcement Learning with Evolutionary
Algorithm and Policy Gradients Method in 3D Control Tasks”, Shota Imai,
Yuichi Sei, Yasuyuki Tahara, Ryohei Orihara, and Akihiko Ohsuga propose a
pretrainingmethodtotrainamodelthatcanworkwellonvarietyoftargettasksand
solve the problems with deep reinforcement learning with an evolutionary algo-
rithm and policy gradients method. In this method, agents explore multiple envi-
ronments with a diverse set of neural networks to train a general model with
evolutionary algorithm and policy gradients method.
In chapter “Learning Neural Circuit by AC Operation and Frequency Signal
Output”, Masashi Kawaguchi, Naohiro Ishii, and Masayoshi Umeno used analog
electronic circuits using alternating current to realize the neural network learning
model. These circuits are composed of a rectifier circuit, voltage-frequency con-
verter, amplifier, subtract circuit, additional circuit, and inverter. They suggest the
realization of the deep learning model regarding the proposed analog hardware
neural circuit.
v
vi Foreword
In chapter “IoTDoc: A Docker-Container Based Architecture of IoT-Enabled
Cloud System”, Shahid Noor, Bridget Koehler, Abby Steenson, Jesus Caballero,
DavidEllenberger,andLucasHeilmanintroduceIoTDoc,anarchitectureofmobile
cloud composed of lightweight containers running on distributed IoT devices. To
explorethebenefitsofrunningcontainersonlow-costIoT-basedcloudsystem,they
use Docker to create and orchestrate containers and run on a cloud formed by
cluster of IoT devices. Their experimental result shows that IoTDoc is a viable
option for cloud computing and is a more affordable, cost-effective alternative to
large platform cloud computing services.
Inchapter“ASurvivalAnalysis-BasedPrioritizationofCodeCheckerWarning:
A Case Study Using PMD”, Hirohisa Aman, Sousuke Amasaki, Tomoyuki
Yokogawa, and Minoru Kawahara propose an application of the survival analysis
method to prioritize code checker warnings. The proposed method estimates a
warning’slifetime withusingtherealtrendofwarningsthroughcodechanges;the
brevity of warning means its importance because severe warnings are related to
problematic parts which programmers would fix sooner.
In chapter “Elevator Monitoring System to Guide User’s Behavior by
Visualizing the State of Crowdedness”, Haruhisa Hasegawa and Shiori Aida pro-
posethatevenoldequipmentcanbemadeefficientusingIoT.TheyproposeanIoT
system that improves thefairness and efficiency byvisualizing thecrowdedness of
anelevator,whichhasonlyonecage.Whenacertainfloorgetscrowded,unfairness
arisesintheusersontheotherfloorsastheyarenotabletotaketheelevator.Their
proposed system improves the fairness and efficiency by guiding the user’s
behavior.
In chapter “Choice Behavior Analysis of Internet Access Services Using
Supervised Learning Models”, Ken Nishimatsu, Akiya Inoue, Miiru Saito, and
Motoi Iwashita conduct a study to try to understand the Internet-access service
choice behavior considering the current market in Japan. They propose supervised
learning models to create differential descriptions of these user segments from the
viewpoints of decision-making factors. The characteristics of these user segments
are shown by using the estimated models.
In chapter “Norm-referenced Criteria for Strength of the Upper Limbs for the
Korean High School Baseball Players Using Computer Assisted Isokinetic
Equipment”, Su-Hyun Kim and Jin-Wook Lee conducted a study to set the
norm-referencedcriteriaforisokineticmuscularstrengthoftheupperlimbs(elbow
and shoulder joint) for the Korean 83 high school baseball players. The provided
criteria of peak torque and peak torque per body weight, set through the computer
isokinetic equipment, are very useful information for high school baseball player,
baseball coach, athletic trainer, and sports injury rehabilitation specialists in injury
recovery and return to rehabilitation, to utilize as an objective clinical assessment
data.
In chapter “A Feature Point Extraction and Comparison Method Through
Representative Frame Extraction and Distortion Correction for 360° Realistic
Contents”, Byeongchan Park, Youngmo Kim, Seok-Yoon Kim propose a feature
point extraction and similarity comparison method for 360° realistic images by
Foreword vii
extracting representative frames and correcting distortions. The proposed method is
shown, through the experiments, to be superior in speed for the image comparison
than other methods, and it is also advantageous when the data to be stored in the
server increase in the future.
Inchapter“DimensionReductionbyWordClusteringwithSemanticDistance”,
Toshinori Deguchi and Naohiro Ishii propose a method of clustering words using
the semantic distances of words, the dimension of document vectors is reduced to
the number of word clusters. Word distance is able to be calculated by using
WordNet. This method is free from the amount of words and documents. For
especiallysmalldocuments,theyuseword’sdefinitioninadictionaryandcalculate
the similarities between documents.
Inchapter“Word-EmotionLexicon forMyanmarLanguage”,ThiriMarlar Swe
and Phyu Hninn Myint describe the creation of Myanmar word-emotion lexicon,
M-Lexicon, which contains six basic emotions: happiness, sadness, fear, anger,
surprise, and disgust. Matrices, Term-Frequency Inversed Document Frequency
(TF-IDF), and unity-based normalization are used in lexicon creation. Experiment
shows that the M-Lexicon creation contains over 70% of correctly associated with
six basic emotions.
In chapter “Release from the Curse of High Dimensional Data Analysis”,
Shuichi Shinmura proposes a solution to the curse of high dimensional data anal-
ysis.Inthisresearch,theyintroducethereasonwhynoresearcherscouldsucceedin
the cancer gene diagnosis by microarrays from 1970.
In chapter “Evaluation of Inertial Sensor Configurations for Wearable Gait
Analysis”, Hongyu Zhao, Zhelong Wang, Sen Qiu, Jie Li, Fengshan Gao, and
JianjunWangaddresstheproblemofdetectinggaiteventsbasedoninertialsensors
and body sensor networks (BSNs). Experimental results show that angular rate
holds themost reliable informationfor gait recognitionduringforward walking on
level ground.
It isour sincere hope that this volume provides stimulation and inspiration, and
that it will be used as a foundation for works to come.
May 2019 Atsushi Shimoda
Chiba Institute of Technology
Narashino, Japan
Prajak Chertchom
Thai-Nichi Institute of Technology
Bangkok, Thailand
BCD 2019 Program Co-chairs
Contents
Robust Optimization Model for Designing Emerging Cloud-Fog
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Masayuki Tsujino
Multi-task Deep Reinforcement Learning with Evolutionary
Algorithm and Policy Gradients Method in 3D Control Tasks. . . . . . . . 19
Shota Imai, Yuichi Sei, Yasuyuki Tahara, Ryohei Orihara
and Akihiko Ohsuga
Learning Neural Circuit by AC Operation and Frequency
Signal Output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Masashi Kawaguchi, Naohiro Ishii and Masayoshi Umeno
IoTDoc: A Docker-Container Based Architecture of IoT-Enabled
Cloud System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Shahid Noor, Bridget Koehler, Abby Steenson, Jesus Caballero,
David Ellenberger and Lucas Heilman
A Survival Analysis-Based Prioritization of Code Checker Warning:
A Case Study Using PMD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Hirohisa Aman, Sousuke Amasaki, Tomoyuki Yokogawa
and Minoru Kawahara
Elevator Monitoring System to Guide User’s Behavior
by Visualizing the State of Crowdedness . . . . . . . . . . . . . . . . . . . . . . . . 85
Haruhisa Hasegawa and Shiori Aida
Choice Behavior Analysis of Internet Access Services Using
Supervised Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Ken Nishimatsu, Akiya Inoue, Miiru Saito and Motoi Iwashita
ix
x Contents
Norm-referenced Criteria for Strength of the Upper Limbs
for the Korean High School Baseball Players Using Computer
Assisted Isokinetic Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Su-Hyun Kim and Jin-Wook Lee
A Feature Point Extraction and Comparison Method Through
Representative Frame Extraction and Distortion Correction
for 360° Realistic Contents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Byeongchan Park, Youngmo Kim and Seok-Yoon Kim
Dimension Reduction by Word Clustering with Semantic Distance . . . . 141
Toshinori Deguchi and Naohiro Ishii
Word-Emotion Lexicon for Myanmar Language . . . . . . . . . . . . . . . . . . 157
Thiri Marlar Swe and Phyu Hninn Myint
Release from the Curse of High Dimensional Data Analysis . . . . . . . . . 173
Shuichi Shinmura
Evaluation of Inertial Sensor Configurations for Wearable Gait
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
Hongyu Zhao, Zhelong Wang, Sen Qiu, Jie Li, Fengshan Gao
and Jianjun Wang
Author Index.. .... .... .... ..... .... .... .... .... .... ..... .... 213