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COVER PAGE

2020 The 3rd International Conference on

Software Engineering and Information Management

（ICSIM 2020）

Workshop

The 3rd International Conference on

Big Data and Smart Computing

(ICBDSC 2020)

Sydney, Australia | January 12-15, 2020

Co-Organized By

Published By

CONTENTS

Welcome ............................................................................................................................................................................ 1

Timetable.......................................................................................................................................................................... 2

Venue with Map ............................................................................................................................................................. 3

Detailed Agenda ............................................................................................................................................................ 5

Introduction of Keynote Speakers ......................................................................................................11

Introduction of Invited Speaker ..........................................................................................................13

Session 1- Big data science and data analysis .................................................................................15

Session 2- Machine learning and intelligent computing .............................................................21

Session 3- Software Engineering and Image Processing .............................................................27

Session 4- Computer Science and Information Technology .......................................................34

Poster ............................................................................................................................................................40

WELCOME

1

Dear distinguished delegates,

It is our great honor and pleasure to welcome you to 2020 The 3rd International Conference on Software

Engineering and Information Management (ICSIM 2020) and its workshop-The 3rd International

Conference on Big Data and Smart Computing (ICBDSC 2020). The conferences are held in Sydney,

Australia on January 12-15, 2020.

ICSIM and ICBDSC 2020 keep promoting the information exchange on Software Engineering and Big Data

and aims to promote international cooperation and provide an opportunity for researchers around the

world to exchange ideas and the latest research results. The evaluation of all the papers was performed

based on the reports from anonymous reviewers, who are qualified in the field of Software Engineering and

Information Management as well as Big Data and Smart Computing. As a result of their hard work, we are

pleased to have accepted 55 presentations coming from initially from around 100 submissions. The

presentations are divided into 1 poster session and 4 parallel sessions with the topic on Big Data Science

and Data Analysis; Machine Learning and Intelligent Computing; Software Engineering and Image

Processing as well as Computer Science and Information Technology.

A word of special welcome is given to our keynote speakers and invited speaker who are pleased to make

contributions to our conference and share their new research ideas with us. They are Prof. Yonghui Li, from

University of Sydney, Australia; Prof. Xiangjian (Sean) He, from University of Technology Sydney (UTS),

Australia; and Prof. Eko K. Budiardjo, from University of Indonesia, Indonesia.

We’d like to express our heartfelt appreciation to our conference chairs, keynote speakers, invited speaker,

session chairs, authors, and audiences. Thanks to your support and help, we can hold this conference

successfully and always keep making progress. We wish and hope that you will enjoy this conference in a

comprehensive experience embracing Software Engineering and Big Data as well as culture, friendship, and

this famous city. Wish you all enjoy your staying here. Thank you for your attention!

We look forward to meeting you again next time!

Yours sincerely,

Conference Chairs Prof Suhuai Luo, University of Newcastle Australia, Australia

AGENDA OVERVIEW

2

January 12, 2020 (Sunday) | Conference Preparations

10:00-17:00 Registration & Materials Collection State Room (Ground floor)

January 13, 2020 (Monday) Morning | Keynote Speeches & Invited Speech

09:30-9:40 Opening Remarks-Prof. Suhuai Luo State Room (Ground floor)

09:40-10:20 Keynote SpeechⅠ- Prof. Yonghui Li State Room (Ground floor)

10:20-10:30 Group Photo

10:30-10:50 Coffee Break & Poster Presentations

10:50-11:30 Keynote SpeechⅡ- Prof. Xiangjian (Sean) He State Room (Ground floor)

11:30-12:00 Invited Speech - Prof. Eko K. Budiardjo State Room (Ground floor)

12:00-13:30 Lunch Restaurant (Ground floor)

January 13, 2020 (Monday) Afternoon | Author Presentations

13:30-16:30 Session1-- Big Data Science and Data Analysis State Room (Ground floor)

Session2-- Machine Learning and Intelligent Computing

Capitol Room (3rd floor)

16:30-16:45 Coffee Break

16:45-19:30

Session3-- Software Engineering and Image Processing State Room (Ground floor)

Session4-- Computer Science and Information Technology Capitol Room (3rd floor)

19:30-21:00 Dinner Restaurant (Ground floor)

January 14, 2020 (Tuesday) | Social Program

07:00-17:00 Social Program Hotel Pickup

January 15, 2020 (Wednesday) | Social Program

Local Custom Self-Experience

VENUE

3

Conference Venue

Rendezvous Hotel Sydney Central

Add: Cnr of George and Quay Streets, Sydney, New South Wales, Australia, 2000

Rendezvous Hotel Sydney Central is located adjacent to Railway Square, 6 minutes' walk from

Sydney Paddy's Market and 4 minutes' walk from the University of Technology Sydney. The

world-famous Sydney Opera House and Sydney Harbor Bridge are just a 15-minute drive

away. It is also only 6.4km away from Sydney Airport.

Sales Manager: Maria Colobig Tel: + 61 2 9212 2544 Email: [email protected] Web: https://www.rendezvoushotels.com/hotel/sydney-central

VENUE

4

======================================================================== How to get to the Rendezvous Hotel Sydney Central from Sydney Kingsford Smith International Airport? ========================================================================

Taxi

Taxi----Around 19 minutes (10.4km)

The affordable way:

Bus+ Walking---around 20 minutes

Get on the City Circle via Airport (T8) at Sydney Domestic Airport Station

$ Get off at Central

$ 450 meters

$ Rendezvous Hotel Sydney Central

Tips:

Currency: Australian Dollar

Emergency Call:000

DETAIL AGENDA

5

January 12, 2020 (Sunday) | 10:00-17:00 Registration & Materials Collection

State Room (Ground floor)

Give your Paper ID to the staff.

Sign your name in the attendance list and check the paper information.

Check your conference kit, which includes conference bag, name tag, lunch coupon, conference program, the receipt of the payment, the USB of conference proceeding.

! Attention

In order to keep everyone's property safe, kindly notice that only the participants wearing the attendance card can be allowed to enter the meeting room. If you have any accompanying person, please do inform our staff in advance when you do the registration. Thanks for your understanding and cooperation.

Please always keep your belongings with you. The organizer of the conference does not assume any responsibility for the loss of personal stuff of the participants.

Don’t stay too late in the city, don’t be alone in the remote area. Be aware of the strangers who offer you service, signature of charity, etc., at many scenic spots.

DETAIL AGENDA

6

Poster Guideline

Please read it carefully:

Please bring your own poster.

Prepare the Poster

*Your poster should cover the KEY POINTS of your work.

*The title of your poster should appear at the top about 25mm (1”) high.

*The author(s) name(s) , affiliation(s) and mailbox are put below the title.

*Posters are required to be condensed and attractive. The characters should be large enough so that they are visible from 1 meter apart. Suggested Poster with size of A1 (594mm×840mm width*height), with conference short name and paper ID on right up corner.

During poster session, the author should stand by your poster, explaining and answering doubts or questions.

Carefully prepare your poster well before the conference. All illustrations, charts, etc., to be posted should be prepared in advance as materials for these purposes will not be available at the meeting site.

Oral Presentation Guideline

Get your presentation PPT files prepared. Please copy your PPT to the computer 15 minutes before your session on January 13. The size of PPT is 16:9.

Regular oral presentation: 15 minutes (including Q&A).

Laptop, projector & screen, laser sticks will be provided by the conference organizer.

Certificate of Presentation will be awarded after your presentation by the session chair.

One Best Presentation will be selected from each parallel session and the author of best presentation will be announced and awarded after the session by the session chair.

DETAIL AGENDA

7

[January 13, 2020 (Monday)] Morning

Opening & Keynote Speeches & Invited Speech State Room (Ground Floor)

09:30-09:40 Opening Remarks Prof. Suhuai Luo University of Newcastle Australia, Australia

9:40-10:20 Keynote Speech I Prof. Yonghui Li, IEEE Fellow

University of Sydney, Australia Speech Title: 5G IoT networks

10:20-10:30 Group Photo

10:30-10:50 Coffee Break & Poster Presentations

A1-015, A1-041-A, A2-004, A2-030, A1-052, A1-053, A1-054, A1-055, A1-061-A, A1-047

10:50-11:30 Keynote Speech Ⅱ

Prof. Xiangjian (Sean) He University of Technology Sydney (UTS), Australia

Speech Title: Performance-Enhancing CCNN for Crowd Counting

11:30-12:00 Invited Speech

Prof. Eko K. Budiardjo

University of Indonesia, Indonesia Speech Title: Assessment of Software Engineering

Process Based on CMMI-QFD Framework

Lunch @ Restaurant (Ground floor) <12:00-13:30>

DETAIL AGENDA

8

[January 13, 2020 (Monday)] Afternoon Authors’ Parallel Presentations

State Room (Ground floor)

13:30-16:15

Session 1 -- Big Data Science and Data Analysis Chaired by -- TBA

11 Presentations A1-024, A1-044, A1-045, A2-007, A2-019,

A2-029, A2-021, A2-017-A, A2-033, A2-034, A2-013-A

16:15-16:30 Coffee Break | Outside Meeting Room

16:30-19:15

Session 3 -- Software Engineering and Image Processing Chaired by -- TBA

11 Presentations A1-017, A1-023, A1-030, A1-032, A1-051,

A1-058, A1-008, A1-012, A1-021, A2-015-A, A1-031

Capitol Room (3rd floor)

13:30-16:30

Session 2 -- Machine Learning and Intelligent Computing Chaired by -- Prof. Chung-Ming Huang National Cheng Kung University, Taiwan

12Presentations A1-007, A1-011-A, A1-018, A2-011, A2-018, A2-001, A2-023, A1-009, A1-1001-A, A1-060, A2-022, A1-056

16:30-16:45 Coffee Break | Outside Meeting Room

16:45-19:30

Session 4-- Computer Science and Information Technology Chaired by -- Assoc. Prof. Razali Yaakob

Universiti Putra Malaysia, Malaysia

11 Presentations A2-025-A, A1-004, A1-010, A2-032, A1-022,

A1-014, A1-028-A, A1-033, A1-039, A1-040, A1-057

Dinner @ Restaurant (Ground floor) <19:30-21:00>

DETAIL AGENDA

9

[January 14, 2020 (Tuesday)] Duration Time: 07:00-17:00 Social Program

Assembly Time: The tour guide will inform you in advance after booking Assembly Point: Hotel Pick-up Minimum: Four People (if less than 4, the tour will be cancelled)

Overview Blue Mountains National Park

Three Sisters (Echo Point)- Waradah Australian Centre- Leura Village- Featherdale Wildlife Park

Three Sisters: The Three Sisters is the Blue Mountains’ most spectacular landmark. The Three Sisters is essentially an unusual rock formation representing three sisters who according to Aboriginal legend were turned to stone. The character of the Three Sisters changes throughout the day and throughout the seasons as the sunlight brings out the magnificent colours. The Three Sisters is also floodlit until around 11pm each evening looking simply spectacular set against the black background of the night sky.

Waradah Australian Centre: One of Australia’s best Aboriginal cultural centers, Waradah is the place to learn more about Australia’s unique heritage and first peoples, as well as witness traditional Aboriginal dance and didgeridoo performances. Various shows featuring Aboriginal dancers or musicians in traditional costume are scheduled throughout the day and include an introduction to the story and an explanation of the significance of each performance.

Leura Village: Hailed as the 'jewel in the crown' of the Blue Mountains, classy, vintage style Leura Village is now as popular as neighbouring iconic tourist town Katoomba. Less hippy and more North Shore than Katoomba, Leura is renowned for stunning bush-walks, small town village appeal, tree-lined streets, nineteenth century cottages and gorgeous cold climate gardens, with the annual Leura Garden Festival a key Blue Mountains calendar event.

Featherdale Wildlife Park: Featherdale provides a home to over 1,700 Australian native animals from more than 250 different species and serves as Australia’s largest native collection. Focusing solely on native animals, at Featherdale you will find the largest collection of koalas in New South Wales, 3 open space enclosures filled with kangaroos and wallabies, as well as loads of other iconic Australian animals, such as dingos, echidnas, wombats, bilbies, Tasmanian devils, reptiles and a stunning collection of birds from Australia and around the world.

DETAIL AGENDA

10

Note This social program is optional and chargeable, and the specific tour time are subject to the

arrangement that day. The driver will pick you up at your hotel and take you back to the hotel after the trip. If you are interested, please give your feedback before December 28. If you miss this date, we can’t

accept your request anymore. Please keep all your belongings at any time. The organizer of the conference does not assume any

responsibility for the loss of personal stuff of the participants.

Included Hotel pickup, National Park fees, Featherdale Wildlife

Park fees, Driver/Guide, Air-conditioned vehicle

Not Included Lunch, 3 rides at scenic world, Personal expenses,

Personal Insurance

KEYNOTE SPEAKER

11

Prof.Yonghui Li, IEEE Fellow

University of Sydney, Australia

Speech Title: 5G IoT networks

Abstract: Connected smart objects, platforms and environments have been identified as the next big technology development, enabling significant society changes and economic growth. The entire physical world will be connected to the Internet, referred to as Internet of Things (IoT). The intelligent IoT network for automatic interaction and processing between objects and environments will become an inherent part of areas such as electricity, transportation, industrial control, utilities management, healthcare, water resources management and mining. Wireless networks are one of the key enabling technologies of the IoT. They are likely to be universally used for last mile connectivity due to their flexibility, scalability and cost effectiveness. The attributes and traffic models of IoT networks are essentially different from those of conventional communication systems, which are designed to transmit voice, data and multimedia. IoT access networks face many unique challenges that cannot be addressed by existing network protocols; these include support for a truly massive number of devices, the transmission of huge volumes of data burst in large-scale networks over limited bandwidth, and the ability to accommodate diverse traffic patterns and quality of service (QoS) requirements. Some IoT applications have much stringent latency and reliability requirements which cannot be accommodated by existing wireless networks. Addressing these challenges requires the development of new wireless access technologies, underlying network protocols, signal processing techniques and security protocols. In this talk, I will present the IoT network development, architecture, key challenges, requirements, potential solutions and recent research progress in this area, particularly in 5G. Bio: Yonghui Li is a Professor and Director of Wireless Engineering Laboratory, in School of Electrical and Information Engineering, the University of Sydney. He is the recipient of the prestigious Australian Research Council (ARC) Queen Elizabeth II Fellowship in 2008 and ARC Future Fellowship in 2012. His current research interests are in the area of wireless communications, Internet of Things, Wireless networks, 5G and wireless AI. He participated in $500million Australian national Smart Grid Smart City project, the world first large-scale demonstration project. He has published more than 200 papers in IEEE journals and conferences. Several of his journal papers have been included in ESI highly cited papers. According to google scholar, his research works have been cited more than 7000 times. His now an editor for IEEE Transactions on Communications, and IEEE Transactions on Vehicular Technology. He also served as a guest editor for several special issues of IEEE journals, such as IEEE JSAC special issue on Millimeter Wave Communications, IEEE Communications Magazine on Wireless AI, IEEE Access. He received the best paper awards from IEEE International Conference on Communications (ICC) 2014, IEEE PIMRC 2017 and IEEE Wireless Days Conferences (WD) 2014.

KEYNOTE SPEAKER

12

Prof. Xiangjian (Sean) He

University of Technology Sydney (UTS), Australia

Speech Title: Performance-Enhancing CCNN for Crowd Counting

Abstract: Crowd counting, for estimating the number of people in a crowd using vision based computer techniques, has attracted much interest in research community. Although many attempts have been reported, real world problems, such as huge variation in subjects’ sizes in images and serious occlusion among people, make it still a challenging problem. In this study, we propose an Adaptive Counting Convolutional Neural Network (A-CCNN) and consider the scale variation of objects in a frame adaptively so as to improve the accuracy of counting. The existing approaches also typically end up with a complicated network model resulting in a challenge for real-time processing. In our approach, a new pruning strategy is proposed by considering the contributions of various filters to the final classification. The filters in the original CCNN model are grouped into positive, negative and irrelevant types. We prune the irrelevant filters of which feature maps contain little information, and the negative filters determined by a mask learned on a training dataset. We demonstrate the advantages of our proposed approach on a crowd counting problem. Our experimental results on benchmark datasets show that the proposed network model improves the counting accuracy. Bio: Prof. Xiangjian He is the Director of Computer Vision and Pattern Recognition Laboratory at the Global Big Data Technologies Centre (GBDTC) at the University of Technology Sydney (UTS). He is an IEEE Senior Member and has been an IEEE Signal Processing Society Student Committee member. He received a UTS Chancellor's Award for Research Excellence in 2018. He has also been awarded 'Internationally Registered Technology Specialist' by International Technology Institute (ITI). He has been carrying out research mainly in the areas of image processing, network security, pattern recognition, computer vision and machine learning in the previous years. He is a leading researcher in several research areas, and has recently been leading his reesarch teams for deep-learning-based and/or machine-learning-based research in the areas of human behavious recognition on a single image, human counting in a crowd, tiny object detection, 3D medical image restoration, image processing based on hexagonal structure, authorship identification of a document and a document’s components (e.g., sentences, sections etc.), network and cyber security, car license plate recognition of high speed moving vehicles with changeable and complex background, and video tracking with motion blur. He has played various chair roles in many international conferences such as ACM MM, MMM, ICDAR, IEEE BigDataSE, IEEE TrustCom, IEEE CIT, IEEE AVSS, IEEE TrustCom, IEEE ICPR and IEEE ICARCV. He has received many competitive national or regional grants including FIVE grants awarded by Australian Research Council (ARC), three grants awarded by National Natural Science Foundation of China (NSFC), and one grant awarded by Hong Kong Research Grants Council (RGC). Very recently, he has received an ARC-LP grant of $540,000 for over three years from 2019-2022 and close to two-million dollars of industry grants awarded by Cisco, SAS, Sydney Trains, Data 61, RMCRC etc. In recent years, he has many high quality publications in prestigious journals such as Journal of the

KEYNOTE SPEAKER

13

Association for Information Science and Technology and ACM Computing Surveys, IEEE Transactions journals such as IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Mobile Computing, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Cloud Computing, IEEE Transactions on Reliability and IEEE Transactions on Consumer Electronics, and in Elsevier’s journals such as Pattern Recognition, Signal Processing, Automation in Construction, Information Sciences, Neurocomputing, Future Generation Computer Systems, Computer Networks, Computer and System Sciences, and Network and Computer Applications. He has also had papers published in premier international conferences and workshops such as ACL, IJCAI, CVPR, ECCV, ACM MM, TrustCom and WACV.

INVITED SPEAKER

14

Prof. Eko K. Budiardjo

University of Indonesia, Indonesia

Speech Title: Assessment of Software Engineering Process Based on CMMI-QFD Framework

Abstract: Competition in the software development businesses make some companies race in producing qualified product in which expected by its stakeholder. The quality of software development product, in which known as work product or artifact, depend on the goodness of its development process. Nowadays, software businesses competition become tighter in term of achieving expected quality. Lean and agile process must be the fundamental mindset in defining the process. Software organization capability is the major factor for goodness and continuous process improvement. Capability Maturity Model Integration for Development (CMMI-Dev) gives a framework for defining continuous implementation roadmap, practices to be implemented and its prioritization, for process capability improvement either in stage or continuous representation. With respect to software quality mindset in establishing the priority improvements to the software engineering process, Quality Function Deployment (QFD) framework for CMMI-Dev continuous representation consisting of four phases Requirement Elicitation / Integration, CMMI PAs Prioritization, Practices Prioritization and Prioritization Action Plan. Furthermore, capability of the evaluation results is then arranged on prioritizing improvements along with software engineering process. Based on some conducted case-based research, it shows that the crucial one is how we can determine appropriate practices priority in order to meet the expected quality. QFD methodology for CMMI-Dev will be presented based on the case-based research in applying QFD for CMMI-Dev. Bio: Dr. Eko K. Budiardjo has been the faculty member of the Faculty of Computer Science - University of Indonesia since 1985. Teaching, research, and practical services are aligned; give result in a full spectrum of academic achievement. Majoring in Software Engineering as professional track record, he has made some scientific contribution such as Software Requirement Specification (SRS) patterns representation method, R3 Method, ZEF Framework, FrontCRM Framework, and Social CRM Framework for Higher Education Institution. Graduated from Bandung Institute of Technology (ITB) in 1985, holds Master of Science in Computer Science from the University of New Brunswick – Canada in 1991, and awarded Philosophical Doctor in Computer Science from the University of Indonesia in 2007. He is a member of the International Association of Engineers (IAENG); a senior member of the International Association of Computer Science and Information Technology (IACSIT). Currently he is the Head of Reliable Software Engineering (RSE) Lab., and Chairman of The Indonesian ICT Profession Society (IPKIN).

SESSION 1

15

January 13, 2020 Session 1

Big Data Science and Data Analysis

13:30-16:15 State Room (Ground floor)

Chaired by TBA

11 Presentations— A1-024, A1-044, A1-045, A2-007, A2-019,

A2-029, A2-021, A2-017-A, A2-033, A2-034, A2-013-A

*Note:

Please arrive 30 minutes ahead of the session to prepare and test your PowerPoint.

Certificate of Presentation will be awarded to each presenter by the session chair when the session is

over.

One Best Presentation will be selected from each parallel session and the author of best presentation

will be announced and awarded when the session is over.

Please keep all your belongings at any time!

SESSION 1

16

A1-024

Application of Cloud Computing for Big Data in the X-Ray Crystallography Community Tosson, M. Shokr and U. Pietsch University of Siegen, Germany Abstract- The X-ray crystallography community has recently been affected by a significant increase in data volume caused by the use of advanced detector technologies and the new generation of high brilliance light sources. The fact that forced the decision makers to implement Big Data analytics, aiming to achieve a suitable environment for scientists at experimental and post-experimental phases. This paper demonstrates an extension of our approach towards a compact platform which provides the scientists with the digital ecosystem for the systematic harvest of data. It introduces an innovative solution to use warehousing and cloud computing to manage datasets collected by 2D energy-dispersive detectors, for an example. Moreover, it suggests that, deploying a Software as a Service (SaaS) cloud model, a public cloud data center, and cloud-based in-memory warehousing architecture, it is possible to dramatically reduce both hardware and processing costs.

A1-044

Big Data and Its Effect on the Music Industry Omar Hujran, Ahmad Alikaj, Usman Khan Durrani and Nidal Al-Dmour United Arab Emirates University, United Arab Emirates Abstract- This research discusses the effect of big data and Internet technologies on the music industry. Specifically, this paper addresses two research questions; (1) how do modern businesses in the music industry implement the use of Internet technologies and big data to ensure their success in the market, and (2) what are the advantages and drawbacks of implementing digital business models in the music industry? To answer the research questions, two real-life cases (i.e. Shazam and Spotify) were analyzed to show how modern businesses in the music industry implement big data and Internet technologies to ensure their success in the market. Furthermore, previous literature and secondary resources were used to explain the development of traditional business models into digital business models in the music industry. In addition to discussing the benefits and drawbacks of implementing the modern digital business model.

A1-045

A Data Mining Approach for Student Referral Service of the Guidance Center: An Input in Designing Mediation Scheme for Higher Education Institutions of the Philippines Joey Cabrera, Markdy Orong, Nelpa Capio, Arnel Pilarca, Eden Neri and Ariel Clarin Misamis University, Philippines Abstract- The academic guidance office of an educational institution holds pertinent data of all the students in the institution such as psychological examination results, students’ referral records and the like. Further, the office offered orientation services, testing services, counseling and follow-up services, individual inventory services, career guidance services, research & evaluation services and placement services. In this paper, a data mining approach was used to produce a trend analysis through time series and forecasted data using the Autoregressive Integrated Moving Average (ARIMA) of the student referral details from one of the Higher Education Institutions in the Philippines. Student referral historical data from the second semester of school year 2016- 2017, first semester of school year 2017-2018, second

SESSION 1

17

semester of school year 2017-2018 and the first semester of school year 2018- 2019 was used in the study. Results showed that absenteeism, poor attendance and poor academic performance were the highest number of recorded students' referrals over the others in which poor attendance yields a decreasing pattern among the three. On the other hand, based on the forecasted data, only poor academic performance and poor attendance showed a slight increasing patterns among others. These further signify that a proper program should be in place by the school counselors in mitigating the occurrence of referrals especially on the reasons showing an increase of prediction data.

A2-007

A Novel Approach for Blog Feeds Recommendation Based on Meta-data Jaekwang Kim Sungkyunkwan University, South Korea Abstract- As the blogosphere continues to grow, finding good quality blog feeds has been very time consuming and requires much effort. So, recommending blog feeds, which handle topics close to user interests, can be useful. Recently, the number of bloggers who use the subscription services has been increasing. Subscription is a service using protocols like RSS and ATOM, which notify users when new entries are posted on the blogs that the users register for subscription. In this paper, we present an effective and efficient approach to recommending log feeds based on the subscription lists and meta-data of blogs. In order to find blogs that handle topics close to the blogs in subscription lists, we first model the topic of blogs by collecting and expanding tags in the blogs, and we then compare the topic models of blogs for recommendation. Also, the usefulness of blogs is an important factor. For choosing useful blogs, we adopt the update frequency and number of subscribers because useful blogs are the ones in which new entries will be frequently posted and to which many users will subscribe. In order to validate the proposed blog recommendation algorithm, experiments on real blog data have been conducted, and the results of experiments show that our blog feed recommendation can satisfy users who want to subscribe to blog feeds.

A2-019

Database Concept for Transcription of Registry Records into Digital Form Radek Koci, Jaroslav Rozman and Frantisek Zboril Brno University of Technology, Czech Republic Abstract- This paper describes our design of database for crowdsourcing project designed to transcribe data from old parish book registers – baptisms, weddings and burials. The information about names, occupations, addresses or personal relationships will be stored in this database. It is based on information extracted from real records from various parishes and times, which was done by professional historians. The nature of data and the requirement to transcribe it as faithfully as possible entails several problems. These problems and limitations will be discussed and the resulting database structure will be presented.

A2-029

Massively Scalable Image Processing on the HPCC Systems Big Data Platform Tanmay Sanjay Hukkeri, Shubham Milind Phal, Yatish H R, Shobha G, Jyothi Shetty and Naweed Mohammed RV College of Engineering, India

SESSION 1

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Abstract- Today’s fast-moving world sees an abundance of image data in everyday life. From messages to insurance claims to even judicial systems, image data plays a pivotal role in facilitating several critical Big Data applications. Some of these applications such as automatic license plate recognition (ALPR) use CCTV cameras to capture snapshots of traffic from real-time video, inadvertently resulting in the generation vast amounts of image data on a daily basis. This brings with it the herculean task of processing these images to extract the essential information as efficiently as possible. The conventional method of processing images in a sequential manner can be very time consuming on account of the vast multitude of images and the intensive computation involved in order to process these. Distributed image processing seeks to provide a solution to this problem by splitting the computations involved across multiple nodes. This paper presents a novel framework to implement distributed image processing via OpenCV on HPCC Systems distributed node architecture*, a set of high-performance computing clusters. The proposed approach when tested on the Indian License Plates Dataset was found to be 85 percent accurate. Additionally, a 30 percent decrease in computation time was observed when executed on a multi-node setup without any impact to accuracy.

A2-021

Anti-disturbance Control Based on Uncertain Data Peng Cheng, Feng Pan, Yanyan Yin, Lingshuang Kong and Song Wang Jiangnan University, China Abstract- In this paper, we describe a novel method for constructing probabilistic robust disturbance rejection control for systems contain uncertain data in which a scenario optimization method is used to deal with the nonlinear and unbounded uncertainties. For anti-disturbance, a reduced order disturbance observer is considered and a state-feedback controller is designed. Sufficient conditions are presented to ensure that the resulting closed-loop system is stable and a prescribed H∞ performance index is satisfied. A numerical example is presented to illustrate the effectiveness of the techniques proposed and analyzed.

A2-017-A

Development of Framework for R&D Economic Feasibility Analysis through hybrid approach of Big Data and Technology Usage Scenario: Focused on low latency of 5G Byungun Yoon, Hyejin Jang and Sunhye Kim Dongguk University, South Korea Abstract- R&D feasibility study consists largely of three sectors: technical feasibility, political feasibility, and economic feasibility. Among these, economic feasibility analysis is critical to determine the efficiency of government R&D investments. In that reason, the establishment of systematic evaluation methodologies has emerged as a major issue. Furthermore, various data including market analysis or expert-based technology assessment reports, patent data has been cumulated as Big Data source for R&D economic feasibility analysis. However existing studies have limitations from three perspectives as below. There are ongoing questions about the adequacy of the detailed evaluation processes and the reliability and objectivity of the evaluation methods for economic analysis of existing national R&D projects. In addition, in analyzing R&D economics, they hardly reflect the characteristics of R&D projects having various indirect benefits by using only direct economic benefits. Lastly, it is difficult to apply standardized economic analysis methods because R&D projects need to predict and apply

SESSION 1

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market changes and uncertain future values. Therefore, in the case of the infra-technologies of the fourth industrial revolution, such as artificial intelligence, big data, and 5G, which are recently emerging, there is a limit in performing the impact of technology development. The purpose of the study is to develop a framework for technology R&D economic feasibility analysis through hybrid approach of Big Data and usage scenario. First, we analyze the usage scenario of the technology to be evaluated and select the appropriate economic feasibility evaluation methodology for each scenario. Based on the selected methodology for each scenario, the economic value for the life of the technology is derived by analyzing Big Data of market analysis report and patent application data. The parameters of the estimation model are defined in consideration of the competitive technology relationship of each scenario and the R&D contribution of the technology. The final value is presented by adding up the economic value of each scenario. To verify the technology development impact analysis framework proposed in this study, 5G low latency technology was selected. The usage scenario of 5G technology was defined as autonomous driving, smart fact, etc., and appropriate economic impact assessment was selected for each scenario. For example, for a cooperative driving scenario where there is no markets and no comparable alternatives, CVM (Contingent Valuation Method) was chosen as the most appropriate methodology. The contributions of this study are as follows. R&D feasibility can be assessed by considering the ripple effect based on an abundant amount of information and detailed usage scenario of technology, proposed in this study, not the simple outcome view. It can be used to evaluate the feasibility of R&D projects and can be used as a reference for materiality assessments such as R&D priority evaluation.

A2-033

Big Data Analyses of ZeroNet Sites for Exploring the New Generation DarkWeb Jianwei Ding, Xiaoyu Guo and Zhouguo Chen CETC 30, China Abstract- ZeroNet is a new generation typical dark web, which uses the Bitcoin encryption algorithm and BitTorrent technology to build a distributed and censored-resistant communication network. Based on our cumulative studies on the onion router, we present a big data analyses framework for automated multi-categorization of ZeroNet websites to facilitate analyst situational awareness of new content that emerges from this dynamic landscape. Over the last two years, our team has developed a distributed crawling infrastructure called ZeroCrawler that automatically crawls and updates ZeroNet websites in realtime. It stores data into a research repository designed to help better understand ZeroNet’s hidden service ecosystem. The analysis component of our framework is called Automated Multi-Categorization Labeling (AMCL), which introduces a three-stage thematic labeling strategy: (1) it learns descriptive and discriminative keywords for different categories, and (2) get a probability distribution of the keywords for different categories, and then (3) uses these terms to map ZeroNet website content to several labels. We also present empirical results of AMCL and our ongoing experimentation with it, as we have gained experience applying it to the entirety of our ZeroNet repository, now over 3000 indexed websites. The experimental results show that AMCL can discover categories on previously unlabeled websites, and we discuss applications of AMCL in supporting various analyses and investigations of the ZeroNet websites.

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A2-034

Feature Selection for the Classification of Alzheimer’s Disease Data Hany Alashwal, Mohamed El Halaby, Areeg Abdalla and Ahmed A. Moustafa College of IT, UAEU, United Arab Emirates Abstract- In this paper, we describe the features of our large dataset (6400+ rows and 400+ features) that includes Alzheimer’s disease (AD) patients, individuals with mild cognitive impairment (MCI, prodromal stage of Alzheimer’s disease), and healthy individuals (without AD or MCI). We also, present a feature selection method applied on the dataset. Unlike prior data mining models that were applied to AD, our dataset is big in nature and includes genetic, neural, nutritional, and cognitive measures of all the individuals. All of these measures in the data have been shown by empirical studies to be related to the development of AD. We used a random forest classifier to discover which features best classify and differentiate between AD patients and healthy individuals. Identifying these features will likely provide evidence for protective factors against the development of AD.

A2-013-A

A Systematic Approach for Keyword-Based Edge Weight to Identify Main Path through Big Technology Data Myeongji Oh and Byungun Yoon Dongguk University, Republic of Korea Abstract- Extracting technological intelligent information from big data plays an important role for forecasting technological trend. For this, it is necessary to be concerned with the technology development process from the first appearance to the present point. The amount of data such as patent data base has been massively accumulated, systematic approach of main path analysis, which can handle crucial information selected from huge dataset, has surfaced as a serious issue. Main path analysis is one of the methodologies for analyzing big data with a time series information. This can be a practical tool for visualizing complex and large data and identifying key evolution routes of research through the sequential relationships of patents or articles. However, existing main path analysis has constructed network based on citation information, which has a limitation that they cannot include contents information such as title or abstract although they can find impactful patent on the network. Keyword similarity-based patent network was suggested to supplement the limitation of citation-based network but it is difficult to figure out the importance of edge between two patents on the flow from the source technology to the current technology. Therefore, this study proposes a content based main path analysis method that can offer technical or functional information that patent contains. The research process is as follows. First, a citation-based network is constructed to consider the influence of the order information of patent data. Second, the weight of the edges is calculated by edge weights based on patent text information considering both the importance of nodes and the importance of connections between two patents. Third, a main path on the network is investigated by calculating the suggested edge weight and existing algorithms of global main path and key-route main path. The proposed methodology is illustrated by the case of the self-driving car. It is possible to consider not only citation information but also content information in terms of methodology. Also, it will enable to visually cast the overall development structure and the main trend of technology.

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January 13, 2020

Session 2

Machine Learning and Intelligent Computing

13:30-16:30 Capitol Room (3rd floor)

Chaired by Prof. Chung-Ming Huang

National Cheng Kung University, Taiwan

12 Presentations— A1-007, A1-011-A, A1-018, A2-011, A2-018, A2-001,

A2-023, A1-009, A1-1001-A, A1-060, A2-022, A1-056

*Note:



over.




SESSION 2

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A1-007

FriendNet Backdoor: Indentifying Backdoor Attack that is Safe for Friendly Deep Neural Network Hyun Kwon, Hyunsoo Yoon and Ki-Woong Park KAIST, Republic of Korea Abstract- Deep neural networks (DNNs) provide good performance in image recognition, speech recognition and pattern analysis. However, DNNs are vulnerable to backdoor attacks. Backdoor attacks allow attackers to proactively access training data of DNNs to train additional malicious data, including the specific trigger. In normal times, DNNs correctly classify the normal data, but the malicious data with the specific trigger trained by attackers can cause misclassification of DNNs. For example, if an attacker sets up a road sign that includes a specific trigger, an autonomous vehicle equipped with a DNN may misidentify the road sign and cause an accident. Thus, an attacker can use a backdoor attack to threaten the DNN at any time. However, this backdoor attack can be useful in certain situations, such as in military situations. Since there is a mixture of enemy and friendly force in the military situations, it is necessary to cause misclassification of the enemy equipment and classification of the friendly equipment. Therefore, it is necessary to make backdoor attacks that are correctly recognized by friendly equipment and misrecognized by the enemy equipment. In this paper, we propose a friendnet backdoor that is correctly recognized by friendly classifier and misclassified by the enemy classifier. This method additionally trains the friendly and enemy classifier with the proposed data, including the specific trigger that is correctly recognized by friendly classifier and misclassified by enemy classifier. We used MNIST and Fashion-MNIST as experimental datasets and Tensorflow as a machine learning library. Experimental results show that the proposed method in MNIST and Fashion-MNIST has 100% attack success rate of the enemy classifier and the 99.21% and 92.3% accuracy of the friendly classifier, respectively.

A1-011-A

Deep Learning-Based Defect Analysis of Multicrystalline Silicon Solar Cells in Electroluminescence Images Keh-Moh Lin, You-Teh Lin, Swapnil Shinde, Horng-Horng Lin and Harshad Kumar Dandage Southern Taiwan University of Science and Technology, Taiwan Abstract- In this study, a deep learning image recognition method is used to classify the defect types of mc-Si solar cells. The Convolutional Neural Network (CNN) method based on Tensorflow framework is applied to identify the different types of defect patterns on Electroluminescence (EL) images. In the first part, we will use the EL images provided by a cell manufacturer as the training data and train the Resnet101 model to identify microcracks, electrode lines, dense dislocation areas, low efficiency areas, and high efficiency areas. There will be at least 2000 EL images in the training database. Next, we take a sample EL image and use the trained Resnet101 model to mark various defect patterns in the EL image. Finally, we will be able to use this method to establish an automatic defect inspection system. By using the Resnet101 model, the accuracy of the defect patterns classification was more than 80%.

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A1-018

Profiling the Natural Environment Using Acoustics: Long-Term Environment Monitoring through Cluster Structures Adikarige Madanayake, Mangalam Sankupellay and Ickjai Lee James Cook University, Australia Abstract- Eco-acoustic recordings of the natural environment are becoming an increasingly important technique for ecologists to monitor and interpret long-term terrestrial ecosystems. Visualisation has been a popular approach to analyse short-term eco-acoustic recordings, but it is practically not feasible for long-term monitoring. Unsupervised machine learning could be a solid candidate to find clustering structures within this long-term eco-acoustic data, and this paper investigates if unsupervised machine learning is able to find any clustering structural difference around an important environmental event, in particular with k-means clustering. Experimental results reveal that there are clear clustering structural changes in general geophony and biophony sounds before and after a bushfire in our study region which indicates that clustering approaches could be used to identify important environmental events.

A2-011

Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models Pratik Patil, Ching-Seh Wu, Katerina Potika and Marjan Orang San Jose State University, United States Abstract- Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random guess, they consider nothing but a proper selection of stocks and time interval in the experiments. In this paper, a novel approach is proposed using graph theory. This approach leverages Spatio-temporal relationship information between different stocks by modeling the stock market as a complex network. This graph-based approach is used along with two techniques to create two hybrid models. Two different types of graphs are constructed, one from the correlation of the historical stock prices and the other is a causation-based graph constructed from the financial news mention of that stock over a period. The first hybrid model leverages deep learning convolutional neural networks and the second model leverages a traditional machine learning approach. These models are compared along with other statistical models and the advantages and disadvantages of graph-based models are discussed. Our experiments conclude that both graph-based approaches perform better than the traditional approaches since they leverage structural information while building the prediction model.

A2-018

Automated Detection of Social Roles in Online Communities using Deep Learning Piyumini Rasangika Wijenayake, Damminda Alahakoon, Daswin de Silva and Saliya Kirigeeganage La Trobe University, Australia

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Abstract- Online communities are an increasingly important aspect in the digital age, for business organizations, diverse industry sectors and overall, in modern society. The social role of each end-user, influencers to followers, and content providers to receivers is a primary consideration when evaluating the purpose and contribution of any online community. Most existing research on the detection of social roles in online communities is based on manual observations and analysis. This paper introduces a technique for automating the detection and extraction of social roles from online communities. Given the large volume of text and value of content, it is no longer viable to manually encode and detect social roles and contributions. The machine learning approach is based on a deep recurrent neural network and a word embedding model. A dataset consisting of over 1.2 million textual posts extracted from an online community on higher education in Australia was used to demonstrate the technique. This technique can be applied to any online community to automatically identify social roles, their influence and interactions.

A2-001

A Study on Determining Household Poverty Status: SVM Based Classification Model Maricel Paulino Naviamos and Jasmin Niguidula Technological Institute of the Philippines Manila, Philippine Abstract-Poverty is the normal challenge faced by the worldwide community. The human society has never ceased to fight against poverty. This research study focuses on determining significant attributes that can be utilized to distinguish poor and non-poor household units. At least one selected community in the Philippines is utilized to validate and test the model for Classification using Support Vector Machine (SVM) algorithm. To check the accuracy and evaluate the model 80% of the total poor and non-poor households are used as a training set and the remaining 20% as a testing set to minimize the impact of disparities and determine whether the model’s classifications are correct. Accuracy, Precision, Recall and F1-Score are likewise done to interpret and gauge the performance of the SVM algorithm for the binary classification model in which the outcome indicates 88.64% precise.

A2-023

Survey of Swarm Intelligence Algorithms Suganya Selvaraj and Eunmi Choi Kookmin University, South Korea Abstract- Swarm Intelligence (SI) is an AI technique that has the collective behavior of a decentralized, self-organized system. SI has more advantages such as scalability, adaptability, collective robustness and individual simplicity and also has the ability to solve complex problems. Besides, SI algorithms also have few issues in time-critical applications, parameter tuning, and stagnation. SI algorithms need to be studied more to overcome these kinds of issues. In this paper, we studied a few popular algorithms in detail to identify important control parameters and randomized distribution. We also studied and summarized the performance comparison of SI algorithms in different applications.

A1-009

An Integrated of Fuzzy Rule Base System and TOPSIS Technique for Multi-Attribute Decision Making Saeed Bahrami, Razali Yaakob, Azreen Azman and Rodziah Atan University Putra Malaysia, Malaysia

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Abstract- Multi-attribute decision making (MADM) is one of important issues in make-decision have been conducted many research in recent years. Indeed, the main objective of this research is developing an extended Fuzzy rule based method based on TOPSIS conventional technique. This paper aids to solve a MADM problem by means of fuzzy environment under a group decision making. To do so, fuzzy rule based system (FRBS) is used to obtain final score of alternatives toward each individual expert’s opinions. And also, TOPSIS technique is used in order to final aggregation of expert's results and makes a unique decision. This study makes an investigation on data about a supplier selection problem (SSP) as a case-based problem has been taken from a valid research. Robustness and validity of the proposed method is indicated with a numerical example, and compare the output of results with another validated approach.

A1-1001-A

The Proximity Service (ProSe) of Sharing the Downloaded Data in Mobile Social Network in Proximity (MSN-P) using the Moving Mobile Edge Computing (MEC) Server Approach Chung-Ming Huang and Pin-Jui Chen National Cheng Kung University, Taiwan Abstract- A Mobile Social Networks in Proximity (MSN-P)-centric group Point Of Interests (POIs) downloading and proximate sharing system based on the proposed k-Connection-Limited and n-Hop (kCL-nH) tree topology’s construction method was proposed in this paper. Let there be m mobile users’ handheld devices that are grouped and organized in an n-level tree topology to share the root handheld device’s downloaded data through 4G/5G cellular network. The proposed method selects one handheld device acting as the root node of the tree and plays the role of the Mobile Edge Computing (MEC) server. The root handheld device, i.e., the MEC server, is responsible for downloading POIs’ contents through its 4G/5G cellular network interface and forwarding the downloaded POIs’ contents to group members’ handheld devices in the tree using the Device-To-Device (D2D) communication way. Since the number of connected handheld devices for a given handheld device is limited, e.g., k at most, using the D2D communication way, e.g., Wi-Fi Direct used in this work, the MEC server, i.e., the root handheld device, needs to initialize and maintain the kCL-nH tree using the periodically reported context from each group member’s handheld device. Since the root handheld device consumes much more battery power than others, this work devised a control scheme that has each group member’s handheld device to play the root handheld device alternatively based on the remaining battery power of handheld devices. The performance analysis has shown the tradeoff between (1) the reduction of the downloaded data’s volume and the associated network traffic, which also denotes the expense of using the 4G/5G cellular network, and (2) the consumed battery power using the n-level tree’s D2D-based proximate sharing way.

A1-060

Identification of writing on Bulletin Board via Tor Noriaki Yoshiura and Kaichiro Iida Saitama University, Japan Abstract- In recent years, awareness of protection of personal information has been increased, and anonymous communication that enables communication with hiding personal information attracts attention. Tor (The Onion Router) realizes anonymous communication most widely,

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but this anonymization is used for crime. This paper aims to identify users who maliciously write to the Internet bulletin board using Tor. This paper proposes a method of detecting Tor packet of writing on bulletin board. This method captures Tor packets and decides whether the packets is used for writing on bulletin board by using fingerprint. This paper implements the method and experiments to evaluates the method.

A2-022

TKM Ontology Integration and Visualization Suganya Selvaraj and Eunmi Choi Kookmin University, South Korea Abstract- Ontology is the most efficient way of representing knowledge and influence relationship about diseases, symptoms, medications, and diagnosis in the traditional medical field. Since integrating traditional medicine ontologies with modern medical ontologies can benefit to the treatment process for effectively using traditional medicines, there is a need for integration and visualization of traditional medicine ontology. Furthermore, an effective ontology visualization is useful to design, manage, and browse the traditional medicine ontology successfully. In this paper, we construct the traditional Korean medicine (TKM) ontology using TKM domain knowledge with a few imported classes from modern medicine ontology and traditional Chinese medicine ontology (TCM) and also visualize the TKM using Web-based Visualization of Ontologies (WebVowl).

A1-056

IP Traceback method by OpenFlow Noriaki Yoshiura and Hayata Yano Saitama University, Japan Abstract- IP traceroute is used to find the routes of egress packets, while IP traceback is used to find the routes of ingress packets. DDoS attacks spoof source IP address and IP traceback is useful to detect true source IP address of DDoS attacks. This paper proposes the method of IP traceback and implements the method at OpenFlow controllers. This paper also evaluates the load of IP traceback on OpenFlow controllers by experiments.

SESSION 3

27

January 13, 2020

Session 3

Software Engineering and Image Processing

16:30-19:15 State Room (Ground floor)

Chaired by TBA

11 Presentations— A1-017, A1-023, A1-030, A1-032, A1-051,

A1-058, A1-008, A1-012, A1-021, A2-015-A, A1-031

*Note:



over.




SESSION 3

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A1-017

Scaling Agile Software Development Approach in Government Organization in New Zealand Dipendra Ghimire, Stuart Charters and Shirley Gibbs Aspire2 International New Zealand, New Zealand Abstract- Agile methods are based on an iterative and incremental cycles of development where requirements and solutions are developed through collaboration and coordination between cross-functional teams and their customer. Agile software development focuses on flexibility, allowing changes in requirements to occur during the software development process. The use of Agile software development is increasing in both the private and public sectors. However, there is little knowledge about the use of Agile in the public sector. This paper presents important factors such as communication, transparency, feedback, product owner engagement, confidence and organization culture that contribute to the outcome of Agile Software development projects in public sector organisations. This study compares the main challenges in the public and the private sector. Transparency and Product Owner engagement were found to be the main difference in the challenges in the public and private sectors

A1-023

Agile Project Management and Mapping Solutions: A Systematic Literature Review Teguh Raharjo and Betty Purwandari Universitas Indonesia, Indonesia Abstract- The survey stated that the project management with agile approach has become more popular. It brings a significant impact on business growth and project performance. However, the implementation is challenging. Therefore, a Systematic Literature Review (SLR) was employed to reveal the challenges of agile project execution. The Knowledge Area from Project Management Body of Knowledge (PMBOK) was adopted to classify the challenges. A total of 23 papers from 400 papers was identified as the result of the SLR extraction. The challenges from related studies were categorized into the Knowledge Area of PMBOK. A mapping from the challenges to the solution was performed using the PMBOK Guide, Prince2 Agile, Agile Practice Guide, and other related references. This study provides a list of agile challenges and their mapped solutions. The biggest challenge comes from stakeholder management, which contains the challenges for agile adaption, agile transition, and agile transformation. Other challenges cover project resource management, project integration management, project scope management, and project schedule management. For academicians, this study provides a new understanding of agile challenges and their mapped solutions from the perspective of project management. For practitioners, the findings provide potential lesson-learned and recommendations to deal with the challenges.

A1-030

Continuous Conflict Prediction during Collaborative Software Development: A step-before Continuous Integration Ritu Arora, Anand Wani, Ankur Vineet, Bhavik Dhandhalya, Yashvardhan Sharma and Sanjay Goel Birla Institute of Technology and Science, India Abstract- Concurrent activities of collaborative developers over shared project repositories might lead to direct and indirect conflicts. Software Configuration Management systems are designed to capture direct or merge conflicts which arise due to concurrent editing of same

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shared artifact. However, inconsistencies caused owing to indirect conflicts which arise because of concurrent editing of related artifacts might enter the codebase, since SCM systems have limited capabilities to capture these. Although, Continuous Integration process which is deployed to build the entire codebase with every commit, is quite effective in capturing several type of inconsistencies. However, still few categories of behavioral semantic inconsistencies might evade the build process and penetrate into codebase. In this paper, we propose the Continuous Conflict Prediction Framework which describes a cyclic, real-time, continuous process for conflict prediction which is executed during the process of code creation by collaborative developers. This framework entails a critical conflict-prediction and awareness-generation process which helps in capturing conflicts during development process itself and hence minimizes the number of conflicts entering the project codebase. The proposed framework is realized through implementation of the tool named Collaboration Over GitHub.

A1-032

Software Engineering Wastes – A Perspective of Modern Code Review Nargis Fatima, Sumaira Nazir and Suriayati Chuprat Universiti Teknologi Malaysia, Malaysia. Abstract- Identification and eradication of waste are the principal emphases of lean thinking. Waste is defined as any activity that consumes resources but does not deliver any value to the stakeholder and it can also be demarcated as an impediment to process flow. Lean thinking has been applied in the software engineering domain concerning overall software development, however, still, there is a need to take action regarding waste identification and elimination concerning specific software engineering activities. This paper describes the wastes generated during Modern Code Review (MCR). MCR is a software engineering activity and acknowledged as a lightweight process for defect identification, code improvement and software quality enhancement. Although it provides various benefits, however as it is a socio-technical process that involves coordination and communication among multiple team members having different personalities, preferences, and technical skills, it can generate multiple types of wastes. Therefore, the study has two objectives that are to recognize and report various wastes generated during MCR and to map the identified MCR wastes on the existing software engineering wastes. Systematic Literature Review has been conducted to identify the MCR wastes. The research evaluated papers concerning MCR from 2013 to 2019. Grounded theory has been utilized to recognize and produce a unique list of the waste generated during MCR. The identified unique list of MCR wastes and their mapping on existing software engineering wastes are validated through software engineering experts. The study findings report 28 unique wastes based on the waste definition found in lean software engineering. Out of 28 identified MCR wastes 25 wastes map to the existing software engineering wastes. However, 3 wastes such as negative emotions, inequality/biasness and insignificant feedback are not reported in the existing software engineering literature. The study will be useful for researchers to identify the wastes in same context for other software engineering activities and to provide the strategies to minimize the generation of identified wastes.

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A1-051

A Novel Graph-Based Program Representation for Java Code Plagiarism Detection Hayden Cheers and Yuqing Lin The University of Newcastle, Australia Abstract- Source code plagiarism is a long-standing issue in undergraduate computer science education. Identifying instances of source code plagiarism is a difficult and time-consuming task. To aid in its identification, many automated tools have been proposed to find indications of plagiarism. However, prior works have shown that common source code plagiarism detection tools are susceptible to plagiarism-hiding transformations. In this paper a novel graph-based representation of Java programs is presented which is resilient to plagiarism-hiding transformations. This graph is titled the Program Interaction Dependency Graph (PIDG) and represents the interaction and transformation of data within a program, and how this data interacts with the system. To show the effectiveness of this graph, it is evaluated on a data set of simulated source code plagiarism. The results of this evaluation indicate the PIDG is a promising means of representing programs in a form that is resilient to plagiarism.

A1-058

Hard and Soft Skills for Scrum Global Software Development Teams Anita Hidayati, Eko K.Budiardjo and Betty Purwandari Universitas Indonesia, Indonesia Abstract- Scrum is considered as one of the solutions to overcome the problems encountered in Global Software Development (GSD). The success of the scrum GSD project is largely determined by the skills of the scrum team. This study aims to identify and rank essential skills for scrum GSD teams. Firstly, the identification of hard skills and soft skills was obtained by conducting a literature study and depth interviews with a software engineering expert. It produced five soft skills and five hard skills. Secondly, the skills were arranged into a questionnaire, which was distributed to thirty undergraduate students taking software engineering modules. They are considered as scrum beginners in development teams. The questionnaire data were analyzed by ranking the importance of skills. Thirdly, the ranks were validated in a Focus Group Discussion (FGD) by four practitioners, two academics, one policymaker, and one representative of an association. All of them have at least five-year experience in scrum GSD. Fourthly, a frequency analysis was employed to achieve a consensus among the experts. Fifthly, a round table discussion was conducted to confirm the consensus. The results show that programming skill is the most important hard skill. Meanwhile, analytical thinking is the most important soft skill. The least important hard skill is database expertise, whilst the least significant soft skill is leadership. The rank of importance of these skills is by following under the nature of scrum and GSD. These can be used as a foundation to construct competencies in scrum GSD teams.

A1-008

Improving Core Topics Discovery in Semantic Markup Literature: A Combined Approach Carlos Montenegro and Rosa Navarrete Escuela Politécnica Nacional, Ecuador Abstract- This research configures a corpus of articles related to the aspects being investigated in Semantic Markup, knowledge domain that has evolved and expanded over the last decade and conduct a manual process to identify the Topics being addressed. Then, it is used LDA, an

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unsupervised probabilistic topic model, and other tools, for automatically recognize the topics of interest within this corpus; this aims to interpret, validate and complement the results manually obtained. The results let us argue that using combined techniques contribute to improving the human expert analysis, and it is helpfully for the discovery of core topics in Semantic Markup Literature.

A1-012

Dynamic Image Quality Analysis and Comparison between 6MHz and 8MHz Bandwidths for DVB-C Digital TV System Chin-Ta Chen and Chih-Chung Yang Zhaoqing University, China Abstract- The object of this research is to analyze and compare the dynamic image quality between the DVB-C digital TV systems with bandwidth of 8 MHz and 6 MHz, in Europe and in Taiwan, respectively. Under the RF output level from -50dBm to-79dBm, with carrier modulation of either 64QAM or 256QAM, we simulate the dynamic image transmission over the digital TV system with standard measuring equipment on the following criteria, namely, the picture quality rating (PQR), the differential mean opinion score (DMOS), and the peak signal to noise ratio (PSNR). The standard measuring equipment, which is the property of the Telecommunication Technical Center (TTC), is composed of the instruments from the Tektronix and the Rode & Swazi (R & S). Based on our experiment setup, under the 64QAM carrier modulation at -78dBm level, the scores for the above three criteria, i.e., PQR, DMOS, and PSNR, are 4, 11, and 33, respectively for the European DTV system. Under the 64QAM carrier modulation at -72dBm level, the scores for the above three criteria, are 5, 14, and 38, respectively for the Taiwan DTV system. Under the 256QAM carrier modulation at -73dBm level, there is observable mosaic on the image for the European system. However, under the 256QAM carrier modulation at -68dBm level, there is serious mosaic on the image for the Taiwan system.

A1-021

Internet Addiction and Mental Health Prediction Using Ensemble Learning Based on Web Browsing History Betty Purwandari, Wayan Surya Wibawa, Nilam Fitriah, Mellia Christia and Dini Rahma Bintari Universitas Indonesia, Indonesia Abstract- Huge penetration of Web browsing may lead to the Internet Addiction Disorder (IAD), which brings bad impact on Web users’ general health status. Young people who are very active online are prone to suffer from the IAD. It negatively affects their academic performance and social lives. The earlier the detection, the better the treatment. Therefore, this pilot study aims to predict the IAD among the youth to encourage early treatment. The samples were 30 undergraduate students at Universitas Indonesia (UI). Their Web browsing history recorded from their laptops for five weeks and analyzed using the Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel as a machine learning method for prediction. The results were subsequently compared with implementation of ensemble learning such as Random Forest and Gradient Boosting. It was then matched with respondents’ responses to the Internet Addiction Test (IAT) questionnaire, which measures the IAD level. Respondents’ general health data were collected with the 12-item General Health

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Questionnaire (GHQ-12). Features from Web browsing histories were extracted to classify activities in five types, i.e. information retrieval (IR), instant messaging (IM), social networking services (SNS), leisure, and online shopping. The extracted features became inputs to classify participants’ IAD. The results were compared with their IAD results from the IAT questionnaire. The machine learning was also employed to classify the input into respondents’ general health (GH) status, which were matched with their responses to the GHQ-12 questionnaire. The findings show that the prediction accuracies are 66.67% for the IAD status and 65.17% for the GH status by employing SVM. Moreover, the precision for predicting IAD and GH are 63.33% and 44.33% by applying Random Forest, 63.33% and 67.17% by using Gradient Boosting. It indicates that Random Forest decreasing the prediction accuracies, but Gradient Boosting has a slight difference compared to SVM. For each classifier, IAD status was predicted more accurately than GH status. An alternative to improve the outcomes is by gaining data from the Internet firewall instead of Web browsing history from users’ laptops. It can provide richer and more realistic records of Web access, which are collected from any devices connected to the university computer networks. However, it requires consent from the participants and authority, who manages the infrastructure. Moreover, when each class has a balanced example, it is planned to add more features and employ the other types of ensemble learning to achieve higher accuracy. Furthermore, performing a multi-class prediction can show specific IAD severity level and the class of mental health status, i.e. anxiety and depression.

A2-015-A

Identifying Technological Topics by Clustering Scientific Papers through Word-Embedding Model Inchae Park, Songhee Kim, Taeyeoun Roh, Jaehyeong An and Byungun Yoon Dongguk University, Republic of Korea Abstract- Patent and scientific paper that stands for applied research and fundamental basic research respectively are widely used as an important technology information source. Because it is difficult to derive meaningful implications in terms of future technological strategy with scattered data per se of both patent and scientific articles, there have been many attempts to identify the emerging technological topics by clustering them. Patent includes refined bibliographical information such as IPC, CPC code etc., whereas scientific paper does not have appropriate bibliographical information for clustering. Although there have been several studies on clustering documents using textual information from full-text or abstract in both scientific paper and patent, the results of clustering are still required to be improved since the used textual information includes too much noise. In order to overcome the limitation of low performance in text-based clustering and the lack of appropriate bibliographical information, this research identifies technology topics by clustering the scientific paper based on the textual information from a series of titles of references in scientific paper. The clustering is conducted by using word-embedding model which is recently used to analyze textual information and has better performance compared to the pervious keyword based text mining model. Automobile industry is selected as a case study to identify the emerging technological topics. The present research contributes to propose a new approach in that the titles of references in scientific article are utilized as a new source that implies not only citation information but also

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its contents. The performance of the proposed approach will be suggested by comparing to the results of previous clustering techniques.

A1-031

Energy Balanced Threshold Using Game Theory Algorithm for Wireless Sensor Networks Optimization Nina Hendrarini, Muhamad Asvial and Riri Fitri Sari Universitas Indonesia, Indonesia Abstract- The wireless sensor network (WSN) as a supporting monitoring system requires stable conditions. The clustering mechanism in wireless sensor networks has been implemented to reduce energy waste. Therefore, maintaining energy in a balanced cluster head is very important. Logically, the distance between the cluster member nodes and the cluster head, and the distance of the head to sink node can affect the stability of the network while it is related to energy resources. To maintain a balanced environment, head cluster energy configuration management is a priority. One effective way to extend network life is to maintain energy balance. The main objective of this paper is to optimize the sensor network by modifying the Distributed Energy Efficient Clustering (DEEC) protocol using the Game Theory algorithm. Here, game theory has been introduced into the solution of problems by finding threshold values. Nash Equilibrium, a concept of game theory is used to have a correlation between energy variables, and non-cooperative behaviour as a character of game theory. In this work, DEEC protocol - a heterogeneous cluster protocol - has been modified in terms of threshold factors to improve the performance of wireless sensor networks. The threshold will filter out nodes that are not suitable to be cluster heads, so the wrong cluster head selection will not occur.

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January 13, 2020

Session 4

Computer Science and Information Technology

16:30-19:30 Capitol Room (3rd floor)

Chaired by Assoc. Prof. Razali Yaakob

Universiti Putra Malaysia, Malaysia

11 Presentations— A2-025-A, A1-004, A1-010, A2-032, A1-022,

A1-014, A1-028-A, A1-033, A1-039, A1-040, A1-057

*Note:



over.




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A2-025-A

Trend of Sea Level Change in the Gulf of Thailand Phimphaka Taninpong, Watha Minsan, Salinee Thumronglaohapun and Kanyarat Luangdang Chiang Mai University, Thailand Abstract- This study aims to examine the trend of sea level change in the Gulf of Thailand over the past 40 years. The sea level data from the 18 tide gauge stations from January 1977 to November 2017 from Marine Department, the Ministry of Transport in Thailand are used in this study. The monthly sea level data from each tide gauge stations are cleaned and seasonally adjusted. The missing values are imputed by using the overall monthly average sea level. The autocorrelation is removed from the time series, and trend is assessed by linear trend using the least square method. The results show that the rate of change in sea level varies from station to station. Sea level change has linearly increased from 0.72 to 16.91 mm/year. However, sea level change of Khlong Yai and Pak Panang station has linearly decreased of -2.30 and -5.72 mm/year, respectively. The average rate of sea level change of the northern of the gulf of Thailand is higher than another coast since sea level change of both stations, Samut Sakorn and Samut Songkram, has linearly increased of 8.15 and 13.20 mm/year, respectively. The highest average rate of sea level change appears in the eastern coast of the gulf of Thailand since sea level change at Ao Udom, Chonburi province has linearly increased of 16.91 mm/year while sea level change in the southern coast of the gulf of Thailand is lower than another coast.

A1-004

Marker less Hand Gesture Tracking using Smart Phones Ronie C. Bituin and Elijah Christian Bautista Systems Plus College Foundation, Philippines Abstract- Most of today's small developer forms of mixed reality involves the use of markers to anchor virtual objects in the real world. However, the exponential increase of today's modern hardware in computing power, which can be further increased thanks to the advent of cloud computing, has led to new possibilities not realized before. The researchers’ interest then for this study is to harness this increased computing power in knowing how the digital medium could be used to immerse one's self into a completely new environment such as simulated realities as another research into the field of synthesis of congruent technological paradigms. In this paper, the researchers demonstrate how modern tools can fuel a mobile phone such that it can perform a marker-less tracking (QR and AR Code free) and mimic hand gestures similar to what Microsoft Kinect© and Leap Motion© can do while only relying on the mobile phone's camera and presented as under the domain of Mixed Reality, which is a combination of Augmented and Virtual Reality. Also discussed are the improvements and benefits of this new method compared to other developments in this area.

A1-010

Efficient Semantic Segmentation through Dense Upscaling Convolution Kurt Schoenhoff, Jason Holdsworth and Ickjai Lee James Cook University, Australia Abstract- Semantic segmentation is the classification of each pixel in an image to an object, the resultant pixel map has significant usage in many fields. Some fields where this

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technology is being actively researched is in medicine, agriculture and robotics. For uses where the resources or power requirements are restricted such as robotics or where large amounts of images are required to process, efficiency can be key to the feasibility of a technique. Other applications that require real-time processing have a need for fast and efficient methods, especially where collision avoidance or safety may be involved. We take a combination of existing semantic segmentation methods and improve upon the efficiency by the replacement of the decoder network in ERFNet with a method based upon Dense Upscaling Convolutions, we then add a novel layer that allows the fine tuning of the decoder channel depth and therefore the efficiency of the network. Our proposed modification achieves 20-30% improvement in efficiency on moderate hardware (Nvidia GTX 960) over the original ERFNET and an additional 10% efficiency over the original Dense Upscaling Convolution. We perform a series of experiments to determine viable hyperparameters for the modification and measure the efficiency and accuracy over a range of image sizes, proving the viability of our approach.

A2-032

Tamper Resistance Evaluation of TWINE Implemented on 8-bit Microcontroller Yusuke Nozaki and Masaya Yoshikawa Meijo University, Japan Abstract- Lightweight ciphers, which can be used in limited resources of internet of things devices, have been attracted attention in recent years. In particular, TWINE has good performances in software implementation of a small embedded device. Even though encryption algorithm is computationally secured, the threat of power analysis which can easily estimate a secret key stored into a cryptographic circuit is pointed out. This study proposes a power analysis method for a lightweight cipher TWINE of software implementation to evaluate the tamper resistance (security evaluation). The proposed method introduces two attack points which are obtained by an analysis of assembly code of TWINE round function. Evaluation experiments use an AVR 8-bit microcontroller Atmega328P mounted on Arduino-UNO. These experiments revealed the vulnerability of TWINE software implementation against the proposed power analysis method.

A1-022

Buying the Unreal: Drivers of Virtual Item Purchase in Video Games Andy Syahrizal, Betty Purwandari, Muhammad Mishbah and Muhammad Fadhil Dzulfikar Universitas Indonesia, Indonesia Abstract- In the past couple of years, video games have become a big influence on the consumption of virtual goods. In addition to the advancement of video game markets, virtual items in game industries are also growing. There is a substantial amount of money going into the trading of virtual items. However, most of it is spent on cosmetic goods. They give better aesthetics to the game characters, but they do not grant any direct advantages to the character owners in gameplay. Why do people buy these items? Do they want to attract more attention from other players? This investigation was conducted to address the questions. A systematic literature review was employed to identify factors affecting individuals to purchase virtual goods in video games. The analysis of selected references was mapped into two models. The first one is Stimulus-Organism- Response (SOR) model to explain the purchase behavior. The second one is Attention, Interest, Desire, and Action (AIDA) model to reflect the monetization

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behind the purchase. The findings improve the understanding of driving factors of the virtual item purchase. It gives a potential contribution to game industries as well as to the regulators.

A1-014

A Cooperative Trust Evaluation Scheme for Tactical Wireless Sensor Networks Jihun Lim, Dooho Keum and Young-Bae Ko Ajou University, Republic of Korea Abstract- In this paper, we address an important issue of trust evaluation for tactical wireless sensor networks, where energy-hungry sensor nodes are deployed in harsh and hostile areas for the purpose of surveillance and reconnaissance. In particular, due to the limited resources of the node, it is of great importance to detect and isolate malicious nodes in an energy-efficient manner. Most of previous work adopt some proactive scanning method, requiring periodical exchange of control messages even in ordinary times and thus significant computational overhead for updating trust values per all nodes over the network. In order to provide more energy efficient evaluation method (while yet preventing any intelligent attacks such as selective forwarding and false positive problem attacks), we propose a novel cooperative trust evaluation scheme in which the initial trust level of nodes measured by on- demand trust rating approach is re-evaluated later on by a root or gateway node based on a pattern analysis. Through a preliminary simulation study using OPNET simulator, we compare our scheme to the existing work and prove that it can achieve the higher detection rate with much less energy consumption.

A1-028-A

Immune-based Artificial Intelligence Approach for the Periodic Recycle Routing Problem Yi-Chih Hsieh, Peng-Sheng You and Ta-Cheng Chen National Formosa University, Taiwan Abstract- This study investigates the periodic recycle routing problem (PRRP). This problem is related to the periodic vehicle routing problem, and it’s also an extension of the vehicle routing problem. In the PRRP, multiple vehicles have to collect recycle wastes from several buildings in which each building may have different periodic demand and frequency for the recycle collection. For example, some buildings need the recycle collection every day, and some buildings need the recycle collection once per two days or once per three days. The goal of the PRRP is to schedule the buildings and the routes for vehicles every day, so as to minimize the total distance of all vehicles. In this study, we apply an Immune-based Algorithm (IBA) to solve the problem. Besides, we also propose a new encoding method to convert any permutation of integer sequence into a feasible solution of the problem, including the combination of buildings and their routes, for each vehicle every day. In this study, we solved a practical example in Taichung, Taiwan. The numerical results showed that the IBA can effectively and efficiently solve the considered PRRP.

A1-033

Modern Code Review Benefits–Primary findings of a systematic literature review Sumaira Nazir, Nargis Fatima and Suriayati Chuprat UniversitI Teknologi Malaysia, Malaysia. Abstract- Modern Code Review (MCR) an effective quality assurance technique that can ensure software quality and customer satisfaction through the identification of defects, code improvement and accelerating the development process. It is an asynchronous and lightweight

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review process supported with review tools, for instance, Gerrit. It is a light version of Fagan’s inspection process which was introduced solely for defect detection from source code. MCR has developed as a practice for open-source and industrial software development. Researches have been conducted in the context of MCR utilizing various data collection methodologies such as interviews, surveys and comment analysis from review tools. Besides defect detection, other benefits have been reported concerning MCR process adoption, for instance, knowledge sharing, team awareness, collaboration, etc. However, the team members involved in MCR activities are not aware of the benefits of MCR activities as the literature is dispersed. No, systematize study available reporting benefits concerning MCR. As a consequence, there is a lack of actual awareness of the adoption of the MCR process. Therefore, the objective of the study is to systematically analyze and report the benefits of the MCR process. Systematic Literature Review has been utilized to identify MCR benefits. Thematic analysis has been performed to group the identified benefits into the relevant themes. The themes and reported benefits are validated by the experts for their relevancy. The study findings report 54 unique benefits, grouped into 9 themes. This research has implications for the software industry, engineer and researchers. The industry can incorporate the MCR process widely, whereas software engineers being aware of the real benefits of MCR can provide their participation effectively in achieving those benefits in reality. In future, the researchers can extend this study by identifying more benefits in different research settings and by quantifying the reported benefits of MCR.

A1-039

E-Government Inter-Organizational Integration: Types and Success Factors Mieke Eka Putri, Dana Indra Sensuse, Muhammad Mishbah and Pudy Prima Universitas Indonesia, Indonesia Abstract- The rapid development of ICT in Indonesia encourages governments to implement e-government for supporting their services so they can improve their service delivery, strengthen accountability, and increase transparency. Unfortunately, there is a major drawback regarding the implementation of e-government. One of the drawbacks is that most organizations develop silos online services. This condition reflects a lack of e-government inter-organizational integrations. E-government integration among organizations or agencies is necessary to promote a more efficient process, accurate information, and seamless services, which are the objectives of the e-government itself. E-government integration is a complicated process as stakeholders need to consider and plan several factors to make it successful. This study identified the types of e-government inter-organizational integrations through a systematic literature review using the Kitchenham method. The study reveals the success factors which need to be concerned to implement e-government successfully. This study can be used as a reference by stakeholders to initiate an implementation of e-government inter-organizational integration.

A1-040

A Predictive Analytics Approach in Determining the Predictors of Student Attrition in the Higher Education Institutions in the Philippines Markdy Y. Orong, Roseclaremath A. Caroro, Geraldine D. Durias, Joey A. Cabrera, Herwina Lonzon and Gretel Ricalde Misamis University, Philippines

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Abstract- The paper identified the predictors of student attrition in the Higher Education Institution (HEI) through predictive analytics approach. The prediction model used in the study includes variable optimization through Genetic Algorithm (GA) and decision tree generation phase through C4.5 algorithm. The college student leavers’ data from one of the Higher Education in the Philippines from the school year 2008-2009 until the school year 2018-2019 was used as datasets of the study. Out of forty identified reasons for leaving as variables, there were nine (9) identified predictors of student attrition. Through the identified predictors, administrators of educational institutions may design intervention plans related to the student attrition.

A1-057

Status of Bachelor Science in Information Technology Program in MIMAROPA SUCs: Basis for Regional Qualification Framework towards ASEAN Connectivity Ailen Garcia and Mario Marigmen Occidental Mindoro State College, Philippines Abstract- This paper aimed to determine the status of BSIT program among State Universities and Colleges (SUCs) in Mindoro, Marinduque, Romblon and Palawan (MIMAROPA) Region 4B in terms of administration qualification, teacher competency, infrastructure, and student competency basis for regional qualification framework towards ASEAN connectivity. Thirty-seven (37) BSIT program heads/directors and teachers and 244 4th year BSIT students were participants of the study. Findings revealed that administration qualification among teachers was low which indicates that the teacher qualifications needs to be improved and strengthened. Findings also revealed that teacher competency is on the high extent which shows that teachers are competent to teach the assigned subjects. The infrastructures were proved as on the high extent which indicates that SUCs in MIMAROPA provides good services to the needs of the BSIT students. Lastly, the student competency revealed as high which shows that student acquire the basic knowledge and skills. However, there are some areas or skills that need to be enhanced to be eligible to ASEAN member countries. But as for local and national level, the BSIT program among SUCs in MIMAROPA are complaint with the basic standards. Based on the result, the proposed regional qualification framework to improve the teachers and students competencies is recommended to be eligible and skilled in the ASEAN Economic Community.

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January 13, 2020

Poster Presentations

10:30-10:50 State Room (Ground Floor)

Chaired by TBA

10 Presentations— A1-015, A1-041-A, A2-004, A2-030, A1-052,

A1-053, A1-054, A1-055, A1-061-A, A1-047

A1-015

The Moderating Effects of Mentoring in ERP Systems Su-Tzu Hsieh Zhaoqing University, China Abstract- A failure implementation of enterprise resource planning, ERP System can be a disaster for organizations, as the fee of a ERP system is sky-high. Furthermore, a familiar user of an ERP system is required an exhaustive business knowledge to analysis business operation flows to diagnose and solve business problems. These complex business analysis, diagnose and issues solving may require mentoring rather than training of a particular skill. There is, however, only very limited research addresses to the effects of mentoring on ERP system implementation. This paper argues that mentoring can play an important role of moderating in affecting user’s perceived usefulness, satisfaction and intention to continue use.

A1-052

Weak Sparsity Adaptive Matching Pursuit Algorithm based on Environmental Monitoring Sensor Network Data Peipei Zhao, Xuewen Liu, Mingliang Li and Jiajing Ding Hebei GEO University, China

POSTER

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Abstract- Due to the undetermined signal sparsity in environmental monitoring applications, the compressed sensing reconstruction algorithm with sparsity adaptive characteristics has better application value. In order to improve the reconstruction accuracy of the reconstruction algorithm, this paper proposes a weak sparsity adaptive matching pursuit algorithm. Firstly, the algorithm constructs the candidate set by weak selection, and then introduces the backtracking idea to filter the candidate set atoms and form a support set. In addition, the algorithm applies the idea of variable step size, and selects different step sizes for different iterations to achieve more accurate and complete reconstruction. Simulation experiments show that the improved algorithm proposed in this paper has higher reconstruction accuracy than similar algorithms.

A1-041-A

Continuous Use Behavior of Travel Booking App from the Perspective of User Perception: Second-order Construct Based on the Information System Quality Xiaoke Yang, Qiuhua Chen and Qian Chen Fujian Agriculture and Forestry University, China Abstract- The development of mobile internet has driven the rise of e-commerce. In several years, the cross-border integration of tourism and e-commerce has made the travel App have a large number of users. How to better analyze the use behavior of users and influence their continuous use behavior in a more “smart” form has become a major problem for App operators. In this study, practical problems of continuous use of travel booking App were analyzed. Based on the user perception control and ECM-ISC model, the second-order construct of information system quality was formed by the characteristics of travel booking App, and the continuous use behavior model of travel booking App was established. It is found that: (1) Information quality, system quality, and service quality constitute the second-order information system quality in the use process, which has an influence on user satisfaction, and promotes users to form continuous use willingness ; (2) The user's expectation confirmation has a positive influence on the continuous use willingness through satisfaction and perceived usefulness; (3) The user's continuous use willingness and contributing factors together has a positive influence on the continuous use behavior; (4) The self-efficacy perceived by the user has no significant influence on the continuous use willingness, indicating that the user has no perception control difficulty in terms of self-efficacy. The application of ECM-ISC model in practice is extended from the perspective of mobile marketing; The applicability of user perception control and information system quality in management practice model is enhanced; The micro-mechanism of continuous use behavior of travel booking App is revealed; And the marketing management suggestions are proposed for App operators from three dimensions of information system quality.

A1-053

WSN Signal Reconstruction Based on Unknown Sparse Compressed Sensing Yanli Wang, Xuewen Liu, Mingliang Li and Xueqing Li Hebei GEO University, China Abstract- For the signal reconstruction problem of unknown signal sparsity in compressed sensing, this paper proposes a Sparsity Adaptive Stagewise Orthogonal Matching Pursuit algorithm (SAOMP), which realizes the reconstructed signal under the condition of unknown signal sparsity. The algorithm combines the idea of adaptive thinking, variable step size

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iteration and piecewise orthogonal thinking. Under the condition of unknown signal sparsity, the number of supporting set atoms is adaptively selected, and finally the signal reconstruction is realized. The experimental results show that the proposed algorithm is better than the Orthogonal Matching Pursuit algorithm, the Regularized Orthogonal Matching Pursuit algorithm and the Stagewise Orthogonal Matching Pursuit algorithm for the 128-bit observation set and the 256-bit length.

A2-004

A Transfer Learning Approach for Handwritten Numeral Digit Recognition Le Zhang Hubei University, China Abstract- Handwritten numeral digit recognition is a classical problem in the field of computer vision, which has a wide range of applications in various fields including financial and post services. The accuracy of handwritten numeral digit recognition has been greatly improved by using deep learning in the past few years. However, deep learning relies on a large amount of training data and time-consuming calculation. In this paper, we adopt a transfer learning approach for handwritten numeral digit recognition and use both the multi-layer perceptron and convolutional neural network models to share the feature extraction process among five handwritten numerical datasets, namely, Tibetan, Arabic, Bangla, Devanagari, and Telugu. We compare the transfer learning scheme with the model based on a single dataset. We find that using the transfer learning method can significantly reduce the training time of the deep learning models, and slightly reduces the recognition accuracy.

A2-030

Safety Production Process Hazard Situation Analysis System Based on Large-scale Data Real-time Processing Wenchi Du, Niansong Zhang and Aimin Wang Nanjing University of Science and Technology, China Abstract- In order to give full play to the effects of big data in production safety monitoring and to meet the purpose of upgrading the safety management of enterprises, reducing personnel and increasing efficiency, this paper designs and implements a dangerous situation analysis system for production safety based on real-time processing of large-scale data. The platform adopts a layered architecture design method, and is constructed based on security parameter monitoring and monitoring technology, large-scale real-time data processing technology, dangerous situation assessment system, knowledge base, and SPC process analysis and early warning technology. The platform mainly covers three types of data source systems: people, things, and environment. It has basic information management, dynamic monitoring, big data analysis, and intelligent early warning. The results show that the platform can collect and share a large amount of data on safe production, summarize the rules of accidents through a big data analysis model, and finally achieve fine management of the safety of dangerous goods production.

A1-054

Research on Security Location of Malicious Node Filtering for Environmental Monitoring Sensor Networks Chaoyang Wang, Mingliang Li, Xuewen Liu and Xuejiao Wu Hebei GEO University, China

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Abstract- To solve the wireless sensor network node is difficult to find false data to filter out attack which led to the problem of positioning accuracy decline. Aiming at the reason that the traditional DV-HOP positioning algorithm is vulnerable, an improved DV-HOP algorithm is proposed. The algorithm can combine multiple communication radii to reduce the hop error of the anchor node in communication with other nodes, and make the positioning more accurate by the hop-weighted hyperbolic algorithm. Further, consistency checking mechanism between packet information aggregator implemented filtering malicious nodes. The simulation results show that the improved DV-HOP algorithm has significantly higher positioning accuracy than the traditional DV-HOP algorithm under the condition that the detection mechanism filters malicious nodes.

A1-061-A

An Efficient Feature Extraction Approaches for Fatty liver Grade Classification using Ultrasound Images Moo Jung Seo and Jae Chern Yoo Sungkyunkwan University, Republic of Korea Abstract- Fatty liver is one of those diseases which can be detected by the relationship between the triglyceride of hepatocytes and the abnormal presence of other fats. Fatty liver can lead to liver cirrhosis in extreme conditions, resulting in permanent liver damage. However, this condition can be recovered if it is found at an early stage. Therefore, there is a high need of early diagnosis. Early diagnosis requires a highly efficient computer-aided design that allows you to diagnose fatty liver with minimal time. This study presents enhanced Gray Level Co-occurrence Matrix, an efficient method of functional extraction for identifying fatty liver tissue using B-scan ultrasound images. The technique proposed in this study is high effective when compared with the latest techniques proposed in previous studies. Proposed technique have improved the accuracy 6.05% compared to the conventional method.

A1-055

Research on Differential Particle Swarm Optimization Algorithm for Wireless Network Location in Seismic Exploration Xianhua Kong, Junjian Kang, Mingliang Li and Jiting Li Hebei GEO University, China Abstract- A novel hybrid optimization algorithm (DEPSO) is proposed for TDOA location and data security optimization problem in wireless network of 4G seismic exploration instrument based on the combination of the difference evolution algorithm (DE) and particle swarm optimization (PSO). Based on the DE, the algorithm is based on the neighborhood structure of cell topology, and avoids the injection of false information based on distributed compressed sensing technology. The novel algorithm which establishes the information sharing mechanism between the improved DE and the PSO can avoid to fall into local optimum and slow convergence problems. The simulation result shows that the improved algorithm improves the accuracy, robustness and data security, than the classical algorithm’s so that it makes 4G seismic exploration instrument far more feasible and superior in practice than ever.

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A1-047

Human capital and Their affect in service quality dimensions Mariam Ibrahim, Azhara Aziz and Marwah Abdulkareem University of kufa, Iraq Abstract- The purpose of this study is to investigate the effect of human capital components on quality of educational service through its dimensions. questionnaire were used to extract the components of human capital (HC) that affect the service quality dimensions. These results were then tested using program SPSS, EXCEL. Each components of HC was then tested on the service quality (SQ) using correlation, regression coefficient. The analysis yielded five HC components, which were termed as Knowledge, Experience, skill, Innovation and Talent. It was found that all Knowledge, Experience, skill, Innovation and job-related talent significantly positively predict the SQ. The study offered experiential data to support the contention that university should develop and engage capabilities of lecturers to enhance the quality of educational service.

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