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- mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink. More information about this series at http://www.springer.com/series/7092 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 ©SpringerNatureSwitzerlandAG2020 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. 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ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland 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