1TABLE OF CONTENT
Foreword
Vice-Chancellor of Universiti Sains Malaysia
Dean, School of Computer Sciences
Chair & Co-Chair of Big Data Summit 2 (2018)
Co-Chairs of PRAGMA Steering Committee
Background of Big Data Summit 2
Programme Objectives
Program Theme
Background of PRAGMA
Keynote & Invited Speakers
Programme Schedule of BDS2
BDS2 Poster Listing
PRAGMA 35 Poster Listing
BDS2 Project Abstracts
BDS2 Poster Abstracts
PRAGMA 35 Poster Abstracts
PRAGMA 35 Demo Abstracts
Program Committees
Partners & Sponsors
Personal Notes

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FOREWORD
Professor Datuk Dr. Asma Ismail, FASc.
Vice-Chancellor
Universiti Sains Malaysia

Assalamualaikum and Good Day,
The term ‘Big Data’ has been around for some time now. The ‘Big Data’ concept surfaced
resulting from the increased amounts of data available ever since the digital era started. All thesedata and information that have been collected especially from the use of the Internet andtechnology requires an efficient data storage system. Seeing that the digital realm is evolving
together with the increasingly large amounts of data, we can no longer rely on previouslypractised ways of storing data in small quantities. We are not only moving towards
accommodating bigger storage repositories but also on how to manipulate and programmecomputers in order to provide advantages across many different areas.
Data that are being generated at this moment represent the past and present and at the sametime can foretell future outcomes of a situation. Some processes in Big Data include comparing
different data to create relationships, building models from the collected data, runningsimulation, data tweaking, etc. which are done in an automated manner to cater to a specified ordefined problem. This is where ‘analytics’ plays a role, such as using Artificial Intelligence or A.I.
and machine learning to make sense of all the data. Many sectors such as healthcare,transportation, business, etc. are already exploring the benefit of data analytics.
This paradigm shift has led to an increase in the demands for powerful data analysing tools. Withpowerful data tools, data can be analysed effectively, hence increasing the ability to describe,
predict and prescribe outcomes based on the available data. That is why we are now moving
towards an ecosystem that utilizes large resources and amounts of data along with effective
analytics.
The 2018 Big Data Summit 2 is co-located with the PRAGMA 35th Meeting and is a satellite
event to the Big Data Week Asia held in Kuala Lumpur. This Summit focuses on empowering
existing data analytics with the advantages of High Performance Computing and Artificial
Intelligence. This is part of the efforts to provide a common platform for the Asia Pacificcommunities in Big Data where recent works can be shared and international collaboration
opportunities can be explored. Through such efforts, it is hoped that future potentials in data
analytics can be further uncovered and which would benefit many different areas.
Therefore, I would like to thank the people who have worked hard to realise this event,particularly the School of Computer Sciences and the National Advanced IPv6 Centre at USM in
collaboration with the PRAGMA community as well as the industry players within Malaysia. Allof these parties have made a big effort in making the summit a platform where activities to
explore the Big Data field can take place. I sincerely hope that this Summit will turn out to bebeneficial and fruitful to the delegates and participants, and able to open up doors for futurecollaborations on research and innovations among them.

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FOREWORD
Professor Dr. Ahamad Tajudin Khader
Dean
School of Computer Sciences
Universiti Sains Malaysia

Assalamualaikum and Good Day,Big Data. Initially, the term refers to the size and complexity of the data itself that traditional
data processing softwares are insufficient equipped to manage. But over the years, the term
refers more to the use of predictive analytics, user behaviour analytics and others to extract
valuable data from such huge data repositories. Everyone is aware of the benefits of having big
data but there is no specific method that fits all user goals. It can be seen in today’s scientists,business managers, healthcare practitioners and even governments that there are difficulties in
effectively utilizing big data in their daily activities.
It is easy to invest in big data projects but utilizing big data is hard and complex. Hence, more
recent research on improving big data usage and analytics has begun emerging. The ability todescribe, predict and prescribe outcomes based on existing data is the very essence in building an
ecosystem that supports large data resources through effective analytics methods. This is where
today’s Big Data Summit 2 which is co-located with PRAGMA 35th Meeting and is a satellite
event to the Big Data Week Asia held in Kuala Lumpur, focuses on improving existing data
analytics through the use of High-Performance Computing and Artificial Intelligence. By
integrating the two areas, a more comprehensive and automated analytics approaches could be
integrated into existing big data applications. By providing a common platform such as thisSummit for the EU and SEA communities in Big Data to share their recent works and explore
international collaboration opportunities, it is hope that further enhancements to the area could
be produced for the benefit of many areas.
Hence, I commend the effort made by the people who worked hard to realise this event
particularly the team from the School of Computer Sciences and the National Advanced IPv6
Centre in collaboration with the PRAGMA community. It is also hope that these Summit would
be an advantageous platform for researchers to identify strategies and challenges that not only
focuses on the area of Big Data, but also in other research areas. This can be seen in some of the
projects ideas that would be introduced throughout the summit. I sincerely hoped that by theend of this Summit, we all would be able to come together will be able to initiate collaboration
on research and innovation among the participants presence here today.
Thank you.

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FOREWORD
Dr. Nurul Hashimah Ahamed
Hassain Malim & Dr. Gan Keng Hoon
Chair & Co-Chair
Big Data Summit 2 (2018)

Greetings!
We welcome you to the Big Data Summit 2: HPC & AI Empowering Data Analytics (BDS2)
held October 3–6, 2018 in Penang, Malaysia. As a premier event in the field, BDS2 provides acollaborative platform for reporting the latest developments in the research and application ofHigh Performance Computing and Artificial Intelligence. Big Data Summit was first held in
2016, with the collaboration with CONNECT2SEA.
This year, we welcome the PRAGMA delegates and proudly present the summit as a jointly
event with the 35th PRAGMA meeting. This setting allows all the participants to take advantage
on the networking opportunities that could be leveraged from the PRAGMA community ofpractice that spans through the Pacific Rim. Although it is only in its second installment, BDS2
has already witnessed significant growth in its participations. The summit has attracted numerous
abstract submissions from both academia and industries from our calls for poster presentation as
well as project speakers. Spanning from tracks of big data, AI, HPC to data analytics, the summit
will show case a total of 44 posters comprising of late-breaking results, technical descriptions,student projects etc., 11 project demonstrations and 8 exciting project talks with partnering
opportunities. Apart of that, we are also proud to announce that the summit has attracted a totalof 130 participations from PRAGMA members, academics and industries.
Internally, BDS2 is a collaborative effort between three divisions in USM i.e. the School ofComputer Sciences, National Advanced IPv6 Center (NAv6) and Nexus (Sciences). Hence, wewish to thank the many people who have contributed their time, energy, and creativity to support
this year’s summit. Our thanks go first to all our patrons, presenters, speakers and delegates.
Next, we would like to warmly thank the technical reviewers who devoted much of their time toreview submissions and provide the comprehensive and constructive reviews. We would also liketo thank the keynotes and invited speakers for their invaluable contribution and for sharing theirvision in their talks. As for our partners, MDEC and PRAGMA, as well as our sponsors, Vitrox,
Silverlake, Hilti, Fusionex, Novorient, Intel and Sophic, this summit would like to thank you foryour generous and kind supports. And lastly, to our team of organizing committee, this summit
would not be possible without the excellent work of yours.
Once again, we welcome you to this wonderful event and hope that our exciting programs willfurther stimulate the research and networking in the areas of big data. Enjoy the summit!

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FOREWORD
Shava Smallen &Shinji Shimojo
Co-Chairs
PRAGMA Steering Committee

Greetings!
We would like to welcome everyone to the PRAGMA 35 workshop, HPC and AI empowering
Data Analytics. The Pacific Rim Application and Grid Middleware Assembly, PRAGMA, is an
open, grass-roots, international organization that makes cyberinfrastructure accessible, easy touse, and useful for long-tail-of-science communities to advance their science and address
societally important problems. Through active participation and contributions of all members,
PRAGMA focuses on how to make these new and rapidly changing CI technologies usable bythese communities of scientists, within a trusted envelop of shared, easy-to-use computer anddata resources and fosters new generations of researchers.
PRAGMA workshops are held twice a year and are an opportunity to share progress with one
another and plan our future activities. We are very happy to partner with the Big Data Summit
2 and explore opportunities for future collaboration. We would like to acknowledge the workof the PRAGMA Program Committee, in particular its Chair Dr. Nurul Malim, and the Big Data
2 Summit Committee members for their careful planning of this event. Our thanks as well to
our gracious hosts at Universiti Sains Malaysia as well as MDEC and the sponsors, namely
Vitrox, Silverlake, Hilti, Fusionex and Novorient. Also, a thank you to all of our participants forthe excellent posters and demonstrations and our Expedition and Working Group chairs forleading discussions that help us define our future activities and directions. We encourage
everyone to participate in the breakout sessions and provide us feedback that may improve
future workshops and collaborations.
Finally, we thank all participants for attending and we encourage everyone to participate, identify
activities for collaborations, engage others in those ideas for future projects, and provide us
feedback that may improve future workshops and collaborations.

5BIG DATA SUMMIT 2 (BDS2)

Programme Objectives
The Big Data Summit 2 (BDS2): HPC & AI Empowering Data Analytics co-locating with
PRAGMA 35 Meeting is a continuity of Big Data Summit (BDS) 2016 which was held on 5–6
May 2016 at Hotel Bangi Putrajaya in collaboration with CONNECT2SEA.
BDS2 is co-located with PRAGMA 35 and is a satellite event to Big Data Week Asia that is heldin Kuala Lumpur on 2 – 9 October 2018 hosted by Malaysia Digital Economy Corporation(MDEC). As such, this summit is more focused and specific to the applications of HPC and AI
for Data Analytics with the following objectives:To enlighten Malaysian Researchers the impact of HPC & AI progression in empowering(transforming) analytics

To highlight recent works on HPC & AI (e.g. Deep Learning) throughout the globe

To provide a platform for International collaboration to Malaysian researchers via
PRAGMA network

Program Theme“HPC and AI: Empowering Data Analytics”
Data analytics and insights are fueling innovation across scientific research, product and service
design, customer experience management, and process optimization. The trend has emerged
from generating insights (descriptive analytics) to predicting future trends (predictive analytics).
Four years ago, we never would have thought data analytics could evolved to prescriptive
analytics in a near future. Prescriptive analytics involves decision making based on viable
solutions to a problem and the impact of considering a solution on future trend. Certainly, its amassive and complex solution with tremendous data points to begin with. High Performance
Computing (HPC) accelerates innovation in diverse areas - ranging from molecular chemistry to
genome sequencing, energy exploration, and financial trading. Its capabilities to support real-time
analytics with in-memory computing; big data analytics; and simulation and modelling
empowered users to execute compute- and data-intensive workloads quickly and accurately.
Artificial Intelligence (AI) is the foundation for cognitive computing, an approach that enables
machines to mimic the neural pathways of the human brain to analyze vast datasets, make6

decisions in real time, and even predict future outcomes. Predictive analytics with AI are corecapabilities required by data-driven organizations looking to gain competitive advantage with
their digital transformation initiatives. Over the last several years, the computing community
witnessed the convergence of HPC and AI in predictive analytics. Predictive analytics with AI
applications are expected to execute increasingly difficult tasks and forecast evolving trends tosolve some of the world’s biggest scientific, engineering, and technological problems. These“high-performance computation” requires support of an advanced technology solution. HPC
environments is a good foundation for AI. By aggregating computing power to handle dataintensive tasks, HPC provide the extreme levels of scalability, performance, and efficiency
required by these complex applications.
The intertwined future of HPC and AI is changing the analytics landscape. Its accelerating
analytics towards prescriptive analytics. As more powerful purposely-built HPC solutions (e.g
DGX by Nvidia) being developed and new AI applications that took advantage of theseinfrastructures e.g. Deep Learning, this accelerated analytics pushes us towards prescriptive
analytics. Although limited, but such applications are already on the way. Google’s self-driving
car is a perfect example of prescriptive analytics. It analyzes the environment and decides the
direction to take based on data. It decides whether to slow down or speed up, to change lane ornot, to take a long cut to avoid traffic or prefer shorter route etc. In this way, it functions justlike a human driver by using data analysis at scale. While AI applications, such as machinelearning and deep learning, are transforming industries across the globe, HPC technologies
works silently behind-the-scenes to empower AI applications. The HPC infrastructure enables
AI applications to handle high-performance workloads, such as advanced analytics. HPC are
now becoming the engine of AI. Hence, any progression in AI or HPC will definitely
empowered (transformed) analytics. Therefore, this summit is a platform for Malaysian andInternational researchers to showcase their efforts in both fields of supporting analytics and toseek collaboration opportunities, international and local.

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PACIFIC RIM APPLICATION AND GRIDMIDDLEWARE ASSEMBLY (PRAGMA)

The Pacific Rim Application and Grid Middleware Assembly (PRAGMA) is an international,
distributed community of practice for technology and approaches that supports the long tail ofscience, namely enabling small- to medium-sized international groups, to make rapid progress in
conducting research and education by providing and developing international, experimental
cyberinfrastructure. To realize this mission, PRAGMA's current activities include fourinterrelated activities:
 Fostering international "scientific expeditions" by forging teams of domain scientists andcyberinfrastructure researchers who develop and test information technologies that areneeded to solve specific scientific questions and create usable, international-scale, cyberenvironments;
 Developing and improving a grassroots, international cyberinfrastructure for testing,computer science insight and, advancing scientific applications by sharing resources,
expertise and software; Infusing new ideas by developing young researchers who gain experience in cross-borderscience and by extending engagements with strategic partners;
 Building and enhancing the essential people-to-people trust and organization developed
through regular, face-to-face meetings - a core foundation of PRAGMA's success.
PRAGMA’s community of practice comprising individuals and institutions from around thePacific Rim that actively collaborate and meet-up to discuss progresses, issues and concerns bygroups. PRAGMA meetings are held twice a year in its distributed communities countries.
Malaysia had been the host for the 15th meeting in 2008. This summit is intended to co-locatewith the 35th PRAGMA meeting to take advantage on the networking opportunities that could
be leveraged from the PRAGMA community of practice that spans through the Pacific Rim.

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PRAGMA Expeditions
PRAGMA forges collaborative, multidisciplinary teams to address scientific questions of highsocietal impact. These questions define and drive PRAGMA's technology development efforts.
There are three current expeditions: Biodiversity: Understanding adaption in extreme environments.
 Limnology: Predicting impact of eutrophication on lake ecosystem services.
 ENT: Creating and utilizing an Experimental software-defined Network Testbed.

PRAGMA Working Groups
The goal of PRAGMA’s Working Groups is to identify activities for international collaborations
and engage others in those ideas for future projects. The current PRAGMA Working Groups
are described below:
Resources and Data: Investigates current technology trends and evaluates theirpotential beneficial impact on applications from PRAGMA’s applications. Current
projects include the PRAGMA Cloud Testbed, the Experimental Networking Testbed,
Open Data Platform, Containers/Kubernetes, GPUs/Machine learning, andMonitoring.
Chairs: Nadya Williams (University of California, San Diego), Hsiu-Mei Chou (National
Center For High-Performance Computing)

Telescience: Making and improving access to or use of remote equipment (e.g., tileddisplay walls or sensors). Current application areas of the group include environmental
monitoring and traffic flow.
Chairs: Shinji Shimojo (Osaka University, Fang-Pang Lin (National Center For HighPerformance Computing)

Biosciences: Creating stable infrastructure to perform computational genomics analyses
with a focus on rice breeding and integrating technologies to create an infrastructure toadvance the screening of potential compounds to combat infectious diseases
Chairs: Jason Haga (National Institute Of Advanced Industrial Science And Technology)

At the beginning of the PRAGMA workshop, Working Group updates will be given by thechairs. It is followed by two breakout sessions where attendees will have an opportunity to dive
deeper into topics, discuss new project ideas, have an open discussion, and set goals for the next
workshop. At the end of the workshop, the Working Group chairs will summarize the results ofthe discussions to everyone.

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KEYNOTE & INVITED SPEAKERS
Keynote 1
Dr. Dzaharudin Mansor
National Technology Officer
Microsoft Malaysia

Title: Democratizing AI
Abstract: As we transition into another industrial revolution, AI, in conjunction with the 3rd
and 4th Platforms, has become a key technology megatrend that is poised to further accelerate
the 4th Industrial Revolution. AI is not a new technology, but one that which become significant
due the lowering cost of computing and storage by hyper-scale cloud, access to huge amounts ofdata and tools that has made applying AI and Data Science simpler and more productive; inshort Democratization of AI. It is the author’s opinion there is an urgency to make AI amainstream in education and a core skill for everyone that deals with science and technology forwork or and research.

Dr. Dzaharudin Mansor is the National Technology Officer (“NTO”) for Microsoft Malaysia.
As the NTO, Dzahar drives the engagement with key national technology stakeholders, which
include academics, policy makers & advisors, and interest groups with the intention to builds
trust while contribute to national development. Dr Dzahar joined Microsoft in 2005 and has
more than 33 years of professional experience in ICT and telecommunications. He started his
career as a lecturer at the department of Computer Science, La Trobe University, and moved on
to as a R&D engineer at Telecom Australia Research Laboratories in Melbourne. On returning
to Malaysia, he joined Celcom as a R&D manager, and left the company as the Vice President
for R&D, Intelligent Network and IT divisions. He subsequently worked at HP in Singapore andother technology companies in R&D, operations, business, as well as leadership positions.
He also presently holds, and has held, several associate positions including as an Adjunct
Professor at IIUM, a councillor at PIKOM and academic advisor at several public and private
universities. In 2010, he had the honour of leading the Business Services Economic
Transformation Program (ETP) Labs that has been one of the key initiatives by the Malaysian
Government to transform Malaysia into a developed nation by 2020. He is a senior member ofIEEE. Dr. Dzahar received a First Class Honors Degree in Computer Systems Engineering fromMonash University, Australia in 1985, and subsequently awarded Australian University Graduate
scholarships to completed his PhD in Computer Science in 1988. In 1985, he was awarded theDigital Award for Computer Engineering, the University Tasmania award for achieving top 10
position in HSC, and the MCE Top Student Award at MCKK in 1979.
Dr. Dzahar is passionate about technology, where he works closely with academia and researchon topics such as Software Engineering, Computer Architectures, Cyber Security,
Telecommunications, Data Science and AI. He aspires to contribute towards the nation’s Digital
Economy initiative.

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KEYNOTE & INVITED SPEAKERS
Keynote 2
Dr. Jason Haga
Cyber-Physical Cloud Research GroupThe National Institiute of Advanced Industrial
Science and Technology (AIST), Japan

Title: Immersive Visualization and Analytics for Understanding Large-scale Datasets
Abstract: Today, data is accumulating at an unprecedented rate and is expected to reach tens ofzettabytes by the year 2020. These troves of big data can provide significant value to all sectorsof society, especially research activities. However, the sheer amount of data is creating significant
challenges to data-intensive science. To address this challenge, the visualization and analysis ofdata requires an interdisciplinary effort and next generation technologies, specifically interactive
environments that can immerse the user in data and provide tools for data analytics. Several
types of immersive technologies are becoming a viable, innovative solutions for a wide variety of
applications. To highlight this concept, this keynote will showcase scalable high-resolution
display technologies, virtual reality, and augmented reality technologies for data-intensive
applications through different application examples. These applications explore how
combinations of 2D and 3D representations of data can support and enhance data-intensive
efforts using these new technology platforms. This presentation is designed to inspire any
research community looking for novel data visualization solutions.

Dr. Jason Haga is currently a member of the Cyber-Physical Cloud Research Group in the
Information Technology Research Institute of The National Institute of Advanced Industrial
Science and Technology (AIST). For over 20 years, Dr. Haga has focused on multidisciplinary
research. Past research includes the design and implementation of biomedical applications forgrid computing environments and tiled display walls. He also has collaborated with cultural
heritage institutions to deploy novel interactive exhibits to engage the public in learning. Current
projects of interest include immersive visualization of data and user experience/user interface fordata intensive applications. He is actively involved with the Pacific Rim Application and Grid
Middleware Assembly (PRAGMA) community, where he leads the Biosciences working groupand has mentored over 70 students from around the world. Dr. Haga is continuing thismentorship effort at AIST by leading an international internship program that strategically
positions AIST as an international hub for computer science research training. With over 14
years of global collaborative efforts with technologists and domain scientists in the PRAGMA
community, he continues to look for interdisciplinary research opportunities connecting
scientists to advance research on a global scale. Dr. Haga earned a PhD in biomedical
engineering from the University of Tennessee, Memphis and did postdoctoral work at UC San
Diego. He currently lives in Tsukuba, Japan.

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KEYNOTE & INVITED SPEAKERS
Keynote 3
Prof. Dr. Rosni Abdullah
Director
National Advance IPv6 Centre
Universiti Sains Malaysia

Title: Big Data and Artificial Intelligence Meet Biology
Abstract: Donald Knuth in an interview with Computer Literacy Bookshops (CLB) on 7th
December, 1993 said, “Biology easily has 500 years of exciting problems to work on”. After 25
years, we are witnessing an exciting era of data explosion in biology where biological data fromvarious data sources is growing at an exponential rate. There is now demand for new approaches
and techniques to manage, organize and analyze this massive amount of data. In this talk, we will
present an overview on big data and artificial intelligence, how both technologies are poised tosolve the challenges in Big Biology.

Rosni Abdullah is a Professor in Parallel Computing at the School of Computer Sciences,
Universiti Sains Malaysia (USM) and is one of the national pioneers in this field. She obtained
her PhD in April 1997 from Loughborough University, United Kingdom specializing in the areaof Parallel Numerical Algorithms. Both her Bachelors degree and Masters degree in Computer
Science were obtained from Western Michigan University, Kalamazoo, Michigan, U.S.A. in 1984
and 1986 respectively. She has served as Dean of the School of Computer Sciences at Universiti
Sains Malaysia (USM) from 2004 to 2012, after having served as its Deputy Dean (Postgraduate
and Research) since 1999. She is also the Head of the Parallel and Distributed Processing
Research Group at the School since its inception in 1994. Her research areas include Parallel andDistributed Computing, Parallel Numerical Algorithms and Parallel Algorithms for
Bioinformatics. 20 PhD students have graduated under her supervision. She has led more than
20 research grants including 2 European Union grants and 2 Intel grants, and has publishedmore than 100 papers in journals and conference proceedings. She is currently the Director of
the National Advanced IPv6 Center (Nav6), a center of research excellence in USM that focus
on Cybersecurity and Internet of Things (IoT).

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KEYNOTE & INVITED SPEAKERS
Keynote 4
Dr. Fang-Pang Lin
National Center for High Performance Computing,
NARLabs Taiwan

Title: HPC in Applications of Big Data and IoT
Abstract: High performance computing (HPC) has been developed in order to achieve extreme
scale modeling for high-resolution floating-point solutions on traditional challenge of big sciencesuch as high energy physics, astronomy, brain science, molecular structure and turbulence … etc.
The international competition on whose machine is the fastest is only getting harsher and more
intense nowadays. Yet, with the rising of optical networks, 4G/5G wireless networks and theadvance of smart and connected end devices ranging from sensors, mobile phones, datacollected has been in exponential growth. To understand the data is usually not what traditional
HPC concerns. The modern machine learning technology, e.g. deep learning, requires only lower
resolution floating point solutions. It is easy to find the analogy between data observed from thegalaxy and the data from the sensors that deployed around the world. Compute power, however,is still the key to solutions of Big Data (BD) analytics. In this talk, the previous efforts in NCHC
on applications of BD and IOT will be introduced and it will be used to explain why our new
peta-scale machine needs to converge both HPC and BD for both modeling physics and learningfrom data.

Dr Fang-Pang Lin is the Senior Research Fellow at National Center for High Performance
Computing and Joint Appointment Professor at National Central University of Taiwan. He is
one of the key developers for developing the national cyber-infrastructure of Taiwan, namely
Knowledge Innovation National Grid. He co-founded the Global Lake Ecological Observational
Network and Global Coral Reef Environmental Observational Network. His major research
focuses on cyberinfrastructure for long term environmental and ecological observation. Recent
development includes Taiwan Earth Science Observatory Knowledgebase, EU FP7
Fish4Knowledge collaboration, real-time wide area flood monitoring and government big data.
The efforts also lead to US-East Asia collaborations to enable transnational cyberinfrastructure
applications, which based on shared software defined systems applying to issues on disastermanagement, environmental monitoring and simulation, and smart cities. Dr. Fang-Pang Lin
obtained his PhD in University of Wales at Swansea, UK. He worked in Rolls-Royce University
Computing Center in Oxford University for his postdoctoral research. He joined NCHC in Oct.,
1997 and has been working in numerical simulation and software engineering regarding
application integration. He was the winner of 2006 Outstanding Achievement Award in Scienceand Technology, the Executive Yuan of Taiwan.

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KEYNOTE & INVITED SPEAKERS
Invited Talk 1
Dr. Ryousei Takano
Information Technology Research Institute
The National Institiute of Advanced Industrial
Science and Technology (AIST), Japan

Title: ABCI: An Open Innovation Platform for Advancing AI Research and Deployment
Abstract: ABCI is an open innovation platform with world-class computing resources of 0.55
AI-EFLOPS / 37 PFLOPS (DP) for AI research and development. Through industry andacademia collaboration, Algorithms, Big Data, and Computing Power are leveraged in a single
common public platform. ABCI rapidly accelerates the deployment of AI into real business andsociety. ABCI is ranked at number 5 in the June 2018 TOP500 supercomputer ranking and theoperation starts from August, 2018.

Ryousei Takano is a research group leader of the Information Technology Research Institute, theInstitute of Advanced Industrial Science and Technology (AIST), Japan. He received his Ph.D.
from the Tokyo University of Agriculture and Technology in 2008. He joined AXE, Inc. in 2003
and then, in 2008, moved to AIST. His research interests include operating systems anddistributed parallel computing. He is currently exploring a highly efficient data center for AI and
Big Data processing, and an operating system for heterogeneous accelerator clouds.

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KEYNOTE & INVITED SPEAKERS
Invited Talk 2
Prof. Dr. Renato J. Figueiredo
Department of Electrical and Computer Engineering
University of Florida, USA

Title: On Lakes and Clouds: A Retrospective on the PRAGMA/GLEON Lake Expedition
Abstract: The PRAGMA Lake Expedition is an interdisciplinary collaboration with GLEON(The Global Lake Ecological Observatory Network) that is advancing the current understandingof the effects of climate change and eutrophication (i.e., increased nutrient pollution of nitrogenand phosphorus, leading to increased plant growth) on harmful algal blooms in lakes. Since its
inception in 2014, the lake expedition has brought together computer scientists and lake
ecologists (faculty, graduate, and undergraduate students) to address science questions usingstate-of-the-art (yet easy to use) cyber-infrastructure. In particular, the team has successfully
developed and deployed GRAPLEr, a novel system that combines open-source technologies
developed by PRAGMA (IP-over-P2P, IPOP overlay virtual networks) and leveraged from otherprojects (HTCondor high-throughput computing) to provide a user-friendly platform to execute
hundreds of thousands of lake model runs from a user's familiar R/R-Studio desktop
environment in cloud computing infrastructures (including PRAGMA-Cloud). More recently,
the lake expedition is investigating approaches that allow small computing devices (sensor
gateways) deployed in the field, at the network's "edge", to connect to the cloud infrastructure
via software-defined overlays. The talk will provide a retrospective on the lake expedition
activities, with a summary of techniques, technologies and lessons learned along the way.

Renato J. Figueiredo is a Professor at the Department of Electrical and Computer Engineeringof the University of Florida. Dr. Figueiredo received the B.S. and M.S. degrees in ElectricalEngineering from the Universidade de Campinas in 1994 and 1995, respectively, and the Ph.D.
degree in Electrical and Computer Engineering from Purdue University in 2001. From 2001 until
2002 he was on the faculty of the School of Electrical and Computer Engineering ofNorthwestern University at Evanston, Illinois, and from 2012 to 2013 he was a visiting
researcher at Vrije Universiteit, the Netherlands. His research interests are in the areas ofvirtualization, distributed systems, overlay and software-defined networks, cloud and edge
computing, and their applications in support of computational science in domains including lake
ecology, bio-diversity, and smart and connected communities. Dr. Figueiredo’s research teamleads the IPOP (IP-over-P2P) open-source overlay virtual network project.

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KEYNOTE & INVITED SPEAKERS
Invited Talk 3
Nadya Williams
Research Scientist
San Diego Supercomputer Center
University of California, San Diego, USA

Title: Toward the Global Research Platform
Abstract: From biomedical data to particle physics, today nearly all research and data analysis
involves remote collaboration. In order to work effectively and efficiently on multi-institutional
projects, researchers depend heavily on high-speed access to large datasets and computing
resources. To meet the needs of researchers in California and beyond, the National Science
Foundation (NSF) in the United States has awarded a five-year, $5 million grant to fund thePacific Research Platform (PRP). The PRP’s data-sharing architecture, with end-to-end 10100Gbps connections, enables region-wide virtual co-location of data with computing resources.
The PRP establishes a science-driven high-capacity data-centric network, enabling researchers tomove data between labs and collaborators’ sites, supercomputer centers or data repositories
without performance degradation. Today, dozens of top universities and research centers are
doing work across a broad range of data-intensive research projects that will have wide-reaching
impacts on science and technology worldwide in the areas of cancer genomics, galaxy evolution
research, and climate modeling. In this keynote, Nadya Williams will provide an overview of the
PRP and it’s architecture and describe a few use cases that describe how scientists are leveraging
the PRP to help them achieve their scientific goals.

Nadya Williams is a Research Scientist at the San Diego Supercomputer Center at UC San Diego.
She served as a functional lead in the design, specification and evaluation of softwarearchitectures for the scientific computing environments focusing on high performance, hightthroughput and virtual environments for PRAGMA and NBCR projects at UCSD. She isactively involved with PRAGMA since 2007 where she now leads Resources and Data working
group. Nadya has years of global collaborative experience working with the scientists from
Europe, PRAGMA and other communities. She recently joined the technical team for the
Pacific Research Platform where she continues to work in the area ofvirtualization, distributed
applications and cloud computing to provide a computational science support to the domainscientists. Nadya earned an M.S. in Oceanography and an M.S. in Computer Science.

16

KEYNOTE & INVITED SPEAKERS
Invited Talk 4
Kamal Hisham Kamaruddin
Head of Operation
MYREN Network Sdn. Bhd.

Title: Enabling Global HPC Collaboration through MYREN and NSRC
Abstract: Collaboration is key in this age of research and education both local and atinternational level. I will give an introduction for MYREN as the National Research &Education Network (NREN) in Malaysia and Network Startup Research Center (NSRC), an
organization based at University of Oregon. This talk will highlight efforts to improve the
network capabilities and engineering setup at R&E organization around the world, particularly
our recent joint effort during PRAGMA35 and Big Data Summit 2 to MYREN technicalcommunity.

Graduated from Sheffield Hallam University, UK in Bachelor of Computing (Networks &Communications), Kamal Hisham Kamaruddin is currently the Head of Operation at Malaysia
MYREN. Kamal has been with MYREN since its inception in 2005 and instrumental indeveloping MYREN2, MYREN3 network and all its sub projects to get Polytechnics,
Community Colleges and Teaching Hospitals connected to MYREN. He is the Governor for
Malaysia in an Asia- Europe collaboration project – Trans Eurasia Information Network (TEIN)
connecting Research & Education Network (REN) in Asia Pacific to Europe. He is also a
Steering Committee member for Asi@Connect project, a successor project for TEIN4.
Kamal is actively promoting REN best practices in the area of network design, security andnetwork operation both locally in Malaysia and in the region particularly in the ASEAN region.
He continues to assist network engineers from MYREN user community in Malaysia andvoluntarily runs technical workshops for MYREN user groups.

17

PROGRAMME SCHEDULE OF BDS2
Big Data Summit 2 2018

(3-6 October 2018)
Day 1 – 3rd October 2018, Wednesday
TIME
0830-0900

PROGRAM
Registration

0900-0915

Welcoming & Introduction to Big Data Summit 2
 Dr. Nurul Hashimah Ahamed Hassain Malim
Chair, Big Data Summit 2
 Dr. Gan Keng Hoon
Co-Chair, Big Data Summit 2
Introduction to PRAGMA
Shava Smallen
Interim Co-Chair, PRAGMAKEYNOTE 1:
DEMOCRATIZING AI
Dr. Dzaharudin Mansor
Microsoft Malaysia
COFFEE BREAK (Sponsored by HILTI)

0915-0930
0930-1010

1010-1030
1030-1110

1110-1310

KEYNOTE 2: (PRAGMA)IMMERSIVE VISUALIZATION AND ANALYTICS FOR
UNDERSTANDING LARGE-SCALE DATASETS
Dr. Jason Haga
National Institute of Advanced Industrial Science and Technology(AIST), Japan
Project Speaker Slots
1110 – 1130

1130 – 1150
1150 – 1210
1210 – 1230
1230 – 1250
1250 – 1310

[BS01] Reliability Assessment Framework using
Test-Defect Coverage Analytics Model
Dr. Sharifah Mashita Syed Mohamad, Universiti Sains
Malaysia[BS02] Type-2 Fuzzy Systems and the Variationsin the Uncertain Environments
Dr. Nur Syibrah Mohd Naim, Universiti Sains Malaysia[BS03] Data Mining System to Detect Finger
Motion in Offline Handwriting Forgery
Dr. Loh Wei Ping, Universiti Sains Malaysia[BS04] Strategic Cyber Threat Intelligence Using
External Unstructured Data
Mr. Kew Yoke Ling, KewMann[BS05] Fiber Tractography using Grid Computing
Dr. Anusha Achuthan, Universiti Sains Malaysia[BS06] Can I Trust You? Towards Modelling Trust
at Zero AcquaintanceDr. Syaheerah Lebai Lutfi, Universiti Sains Malaysia
18

Venue
Lobby Auditorium
Murad
Muhammad Noor
Auditorium Murad
Muhammad Noor

Auditorium Murad
Muhammad Noor
Auditorium Murad
Muhammad Noor
Lobby Ground
Floor
Auditorium Murad
Muhammad Noor

Auditorium Murad
Muhammad Noor

1310 -1410

BUSINESS LUNCH (Sponsored by VITROX)

1410-1450

KEYNOTE 3:
BIG DATA AND ARTIFICIAL INTELLIGENCE MEETBIOLOGY
Prof Rosni Abdullah
National Advance IPv6 Centre, Universiti Sains Malaysia
KEYNOTE 4: (PRAGMA)
HPC IN APPLICATIONS OF BIG DATA AND IOT
Dr. Fang-Pang Lin
National Center for High-Performance Computing,
NARLabs, Taiwan
Project Speaker Slots
1530 – 1550 [BS07] Data Science Analytics for Manufacturing
and Supply Chain
Dr. Umi Kalsom Yusof, Universiti Sains Malaysia
1550 - 1610
[BS08] Interfacing Chatbot with Data Retrieval andAnalytics Queries for Decision Making
Dr. Gan Keng Hoon, Universiti Sains Malaysia

1450-1530

1530-1610

1610-1630

COFFEE BREAK (Sponsored by HILTI) +POSTER SESSION NETWORKING

1630 -1805

Poster Session(Please refer to BDS2 poster listing)1830 -2030

PRAGMA Welcoming Reception(PRAGMA members + BDS2 Committee only)

Day 2 – 4th October 2018, Thursday
TIME
0830-0900

PROGRAM
Registration
Arrival of the Vice Chancellor, USM

0905-1015

Opening Ceremony
National & USM Anthem
Prayer Recitation
Welcoming Speech by Prof. Ahamad Tajudin Khader,Main Patron BDS2 & Dean, School of Computer Sciences
Speech by Dr. Peter Arzberger,
Founder, The Pacific Rim Application and Grid Middleware
Assembly (PRAGMA)
Opening Speech by Prof. Datuk Dr. Asma Ismail
Vice Chancellor, Universiti Sains Malaysia
Video Presentation
Group Photo
19

Lobby Ground
Floor
Auditorium Murad
Muhammad Noor

Auditorium Murad
Muhammad Noor

Auditorium Murad
Muhammad Noor

Lobby Auditorium
Murad
Muhammad Noor
Lobby Auditorium
Murad
Muhammad Noor
Clubhouse

Venue
Lobby Auditorium
Murad
Muhammad Noor
Auditorium
Murad
Muhammad Noor

1015-1035

COFFEE BREAK (Sponsored by Fusionex)

1035-1100

PRAGMA Welcoming Statement
Shava Smallen

1100-1130

1200-1300

INVITED TALK 1: (PRAGMA) ABCI: AN OPEN
INNOVATION PLATFORM FOR ADVANCING AIRESEARCH AND DEPLOYMENTDr. Ryousei Takano
National Institute of Advanced Industrial Science and Technology(AIST) JapanINVITED TALK 2: (PRAGMA) ON LAKES AND CLOUDS: A
RETROSPECTIVE ON THE PRAGMA/GLEON LAKE
EXPEDITION
Prof. Dr. Renato J. Figueiredo
University of Florida
PRAGMA Working groups and Expedition updates

1300-1400

BUSINESS LUNCH (Sponsored by Silverlake)

1400-1500

PRAGMA Working Groups (WG) Breakouts 1:
Resources WG + Cyberlearning WG
Telescience WG
Data/Geo/Bioscience WG
PRAGMA Lightning Talks
Chair: Wassapon Watanakeesuntorn

1130-1200

1500-1530
1530-1600

COFFEE BREAK (Sponsored by Fusionex)
POSTER SESSION NETWORKING

1600-1630

PRAGMA Poster Session(Please refer to PRAGMA 35 poster listing)

1630-1730

PRAGMA Demo Session 1
Session Chair: Assoc Prof. Dr. Kohei Ichikawa
1630 – 1645

1645 – 1700

1700 – 1715
1715 – 1730

[PD01] Integrating PRAGMA-ENT and InterCloud Platform using Dynamic L2VLAN Service
Kohei Ichikawa, Atsuko Takefusa, Yoshiyuki Kido,
Yasuhiro Watashiba and Susumu Date
[PD02] Extending SDN Networks from Cloud-toEdge using Virtual Private Networks with Peer-toPeer Overlay Links
Kensworth Subratie and Renato Figueiredo[PD03] Analysis of Load Balancing Performanceon Cluster Computing with PROXMOX VEAndi R. Darsono[PD04] Performance Comparison of Load
Balancing using Honeybee and Threshold
Algorithm
Aditya Efrian, Sri Chusri Haryanti, Sri Puji Utami and
Ridho Yanevan Pratama
20

Lobby Ground
Floor
Auditorium
Murad
Muhammad Noor
Auditorium
Murad
Muhammad Noor

Auditorium
Murad
Muhammad Noor
Auditorium
Murad
Muhammad Noor
Lobby Ground
Floor
Auditorium B
Main Board Room
Seminar Room 3
Auditorium
Murad
Muhammad Noor
Lobby Auditorium
Murad
Muhammad Noor
Lobby Auditorium
Murad
Muhammad Noor
Auditorium
Murad
Muhammad Noor

1750-2245

CONFERENCE DINNER
Butterworth 4 & 5, Holiday Inn, Batu Feringghi

Holiday Inn, Batu
Feringghi

1750 Departure from SAINS@USM
1900 Bus Arrival at Holiday Inn, Batu Feringghi
Participant can walk around or prepare for prayers
1945 Participants take place in ballroom
2000 VIP Arrival
2010 National & USM Anthems
2015 Appreciation Speech by Chair of Big Data Summit 2
Dr. Nurul Hashimah Ahamed Hassain Malim
2025 Speech by Vice Chancellor Universiti Sains Malaysia
Prof. Datuk Dr. Asma Ismail
2035 Dinner & Performances
2135 PRAGMA Appreciation Session
Shava Smallen & Dr. Peter Arzberger
2200 Adjourn – Night market walk
2245 Buses departure to Equatorial Hotel & sains@usm

Day 3 – 5th October 2018, Friday
TIME
0900-0930

0930-1000

1000-1020

PROGRAMINVITED TALK 3: (PRAGMA) TOWARD THE GLOBALRESEARCH PLATFORM
Nadya Williams
San Diego Supercomputer Center
University of California San Diego
INVITED TALK 4: ENABLING GLOBAL HPC
COLLABORATION THROUGH MYREN AND NSRC
Kamal Hisham Kamaruddin
MYREN Network Sdn. Bhd.
PRAGMA Wide Mentoring Update
Chair: Dr. Jason Haga

1020-1040

COFFEE BREAK (Sponsored by Novorient) + NETWORKING

1040-1210

PRAGMA Demo Session 2
Session Chair: Dr. Yoshiyuki Kido
1040 – 1055
1055 – 1110
1110 – 1125

1125 – 1140

[PD05] EDISON Data Platform for
Computational Science Data
Jaesung Kim, Jeongcheol Lee, Sunil Ahn and Jongsuk Lee[PD06] Web-based Compute-Data Research
Environment for Aircraft Airfoil Aerodynamics
James Junghun Shin, Kumwon Cho and Jonsuk Ruth Lee[PD07] Mobile-based Augmented Reality for
Sundanese Alphabets Education
Muhammad Reza Aditya, Resskytha Sari, Adri
Nursimarsiyan and Nova Eka Diana[PD08] Tuberculosis (TB) Disease Interactive Map
in Jakarta Capital Special Region
Nuraisah and Ummi Azizah Rachmawati
21

Venue
Auditorium
Murad
Muhammad Noor
Auditorium
Murad
Muhammad Noor
Auditorium
Murad
Muhammad Noor
Lobby Ground
Floor
Auditorium
Murad
Muhammad Noor

1140 – 1155

1210-1445
1445-1630

1630-1645
1645-1730

1800-2030[PD09] Neuro Data Platform for Neuroscientist
Nurul Hashimah Ahamed Hassain Malim, Jafri Malin
Abdullah, Sharifah Aida Sheikh Ibrahim, Nurfaten
Hamzah, Ariffin Marzuki Mokhtar, Putra Sumari,Ahamad Tajudin Khader and Muhammad Jaziem
Mohamad Javeed
1155 – 1210
[PD10] Deep Learning Classification for Liver
DiseaseAndi Batari Ahmad and Nova Eka Diana
LUNCH and PRAGMA Steering Committee Meeting
PRAGMA Working Groups (WG) Breakouts 2:
Resources WG + Cyberlearning WG
Telescience WG
Data/Geo/Bioscience WG
COFFEE BREAK (Sponsored by Novorient) + NETWORKING
CLOSING CEREMONY PRAGMA Best Poster Award & Student Presentation
 PRAGMA Working groups wrap-up
 PRAGMA 35 Wrap up by Shava Video presentation
 Invitation to PRAGMA 36 in Korea
 Big Data Summit 2 Poster Award
 Big Data Summit 2 wrap up by Prof. Dr. Rosni Abdullah,
Director, National Advanced IPv6 Centre, USM
PRAGMA Dinner (PRAGMA members & BDS2 committees only)
1800: Bus Departure to Bangkok Tomyam
1845: Dinner2030: Bus Departure to Equatorial Hotel

Auditorium B
Lobby Ground
Floor
Auditorium B
Main Board Room
Seminar Room 3
Lobby Ground
Floor
Auditorium
Murad
Muhammad Noor
Auditorium
Murad
Muhammad Noor

Bangkok Tomyam
Sunway Tunas

Day 4 – 6th October 2018, Saturday
TIME
0830-1700

PROGRAM
Excursion – (PRAGMA member only)
Pick-up point: Equatorial Hotel
0830 Participants gather at Equatorial Hotel
0845 Bus Departure from Equatorial Hotel to Penang Hill
0915 Arrival at Penang Hill
1200 Departure from Penang Hill to Esplanade
1245 Arrival at Esplanade (Lunch)
1400 Departure to Penang Heritage Trail
1600 Departure from Penang Heritage Trail to Equatorial Hotel & airport

22

BDS2 POSTER LISTING
Poster Presentation
PosterID
BP01

Title and Author
Towards Large-scale Text Annotation for Sentiment Analysis using Semisupervised Deep Learning
Vivian Lay Shan Lee, Gan Keng Hoon

BP02

Big-spatial Data Pre-processing Framework towards Flood Assessment
Ahmed Ndanusa, Zulhairi Dahalin, Azman Ta'a

BP03

Data Analytics of Malaysia’s Most Influential Entities in Social Media forCommercial Purpose
Yasmin M Yacob, Tong Hau Lee

BP04

Low Resolution to High Resolution Video Surveillance Image Enhancement
Using Deep Learning
Muhamad Faris Che Aminudin, Shahrel Azmin Suandi

BP05

Classification of Manufacturing High Dimensional Data Using Deep Learningbased Approach
Mohd Nor Akmal Khalid, Umi Kalsom Yusof

BP06

Copy-Move Forgery Detection Using Convolutional Neural Networks
Arfa Zainal Abidin, Azurah A. Samah, Hairudin Abdul Majid, Saleha Safie, Muhamad
Faiz Misman

BP07

Cognitive-based Approach for Business Intelligence
Herison Surbakti, Azman Taa

BP08

iFR: A New Framework for Real Time Face Recognition with Machine Learning
Syazwan Syafiqah Sukri, Nur Intan Raihana Ruhaiyem

BP09

Automatic Liver Tumor Detection using Deep Learning: Triplanar Convolutional
Neural Network Approach
Chung Sheng Hung, Gan Keng Hoon, Anusha Achuthan, Mandava Rajeswari

BP10

Selection Drought Index Calculation Methods Using ELECTRE (Elimination
and Choice Translation Reality)
Addy Suyatno Hadisuwito, Fadratul Hafinaz Hassan

BP11

Deep Bayesian for Opinion-Target Identification
Omar Al-Janabi, Cheah Yu-N, Nurul Hashimah Ahamed Hassain Malim

BP12

The Effect of Vocal Cues on Trust at Zero Acquaintance
Deborah Ooi, Syaheerah Lebai Lutfi
23

BP13

EEG Channels Selection Using Hybridizing Flower Pollination and β-Hill
Climbing Algorithm for Person Identification
Zaid Alyasseri, Ahamad Tajudin Khader

BP14

Exploring Whole-Brain Functional Networks of Music-Linguistic from Rhythmic
Quranic Recitations and Language Proficiency
Mas Syazwanee Shab, Muzaimi Mustapha, Aini Ismafairus Abd Hamid, Amiri Ab
Ghani, Nidal S. Kamel

BP15

Parallel Text Acquisition and English-Malay Machine Translation
Tan Tien Ping, Yin Lai Yeong, Gan Keng Hoon, Siti Khaotijah Mohammad

BP16

Characterizing Compute-Intensive Tasks as a Factor of Network Congestion
Norfazlin Rashid, Umi Kalsom Yusof

BP17

A Hyper-heuristic based Artificial Bee Colony Algorithm for the Traveling
Salesman Problem
Choong Shin Siang, Wong Li Pei

BP18

The Application of Machine Learning in Classifying Potential Vector Borne
Disease Awareness
Abrar Noor Akramin Kamarudin, Zurinahni Zainol, Nur Faeza Abu Kassim

BP19Supersymmetry Feature Decomposition for Classification Purpose
Nu’man Badrud’din

PRAGMA POSTER LISTING
Poster Presentation
PosterID
PP01

Title and Author
Mobile-based Augmented Reality for Sundanese Alphabets Education
Muhammad Reza Aditya, Resskytha Permata Sari, Adri Nursimarsiyan and Nova Eka
Diana

PP02

Deep Learning Classification for Liver Disease
Andi Batari Ahmad and Nova Eka Diana

PP03

Application of Deep Learning and Fingerprint Modeling Methods to Predict
Cannabinoid and Cathinone Derivatives
Widya Dwi Aryati, Gerry May Susanto, Muhammad Siddiq Winarko, Heru Suhartantoand Arry Yanuar

24

PP04

Building Smart City Datasets with Crowdsourcing for Safe Direction in Bangkok,
ThailandManassanan Boonnavasin, Suchanat Mangkhangjaroen and Prapaporn Rattanatamrong

PP05

A Network Performance Measurement in a Low-Cost Containerized Cluster
SystemThitiwut Chamornmarn, Vasaka Visoottiviseth and Ryousei Takano

PP06

A Prototype of Collaborative Augment Reality Environment for HoloLens
Dawit Chusetthagarn, Vasaka Visoottiviseth and Jason Haga

PP07

Analysis of Load Balancing Performance on Cluster Computing with Proxmox
VE
Andi Rasuna Dharsono and Sri Chusri Haryanti

PP08

Performance Comparison of Load Balancing using Honeybee and Threshold
Algorithm
Aditya Efrian, Sri Chusri Haryanti, Sri Puji Utami Atmoko and Ridho Yanevan Pratama

PP09

Data-centric Modeling of Gainesville Businesses
Michael Elliott, Erik Bredfeldt, Matthew Collins, Renato Figueiredo, Mark Girson,
Amardeep Siglani, Lila Stewart and Jose Fortes

PP10

Performance Analysis of GTX 980 GPU on Colon Histopathology Images
Training Based on Convolutional Neural Network
Toto Haryanto, Aniati Murni, Kusmardi Kusmardi, Li Xue and Suhartanto Heru

PP11

Dengue Hemorrhagic Fever Disease Data Clustering Based on Interactive Map inSpecial Region Jakarta Capital
Brian Hogantara and Ummi Azizah Rachmawati

PP12Curating Target-Activity Information for Nadi Compounds Based on CHEMBL
using Similarity Searching
Muhammad Jaziem Mohamed Javeed, Aini Atirah Rozali, Siti Zuraidah Mohamad Zobir,
Habibah Abdul Wahab and Nurul Hashimah Ahamed Hassain Malim

PP13

Design AR application using the tiled display walls
Jidapa Kongsakoonwong, Jason Haga and Boonsit Yimwadsana

PP14

Decision Support System based on Interactive Map of Measles and Rubella Datain Jakarta
Pravin Kumar, Elan Suherlan and Ummi Azizah Rachmawati

PP15RNA-seq transcriptome profiling of Desmos chinensis: revealing the molecularbasis of petal evolution in the custard apple family Annonaceae
Amy Wing-Sze Leung, Sangtae Kim and Richard Mark Kingsley Saunders

25

PP16

Computational Fluid Dynamics Study of Wind Environment in Urban Areas
Chun-Ho Liu, Wai-Chi Cheng, Wenye Li, Ziwei Mo, Zhangquan Wu, Lillian Y.L Chan,
W.K. Kwan and Hing Tuen Yau

PP17

Enhancing MedThaiSAGE: Decision Support System using Rich Visualization on
SAGE 2
Jarernsri Mitrpanont, Wudichart Sawangphol, Supakorn Silapadapong, Suthivich
Suthinuntasook, Wichayapat Thongrattana and Jason Haga

PP18

Tuberculosis (TB) Disease Interactive Map in Jakarta Capital Special Region
Nuraisah and Ummi Azizah Rachmawati

PP19

Digital Poster Management application on a SAGE2-based Multiple Display
systemPrakritchai Phanphila, Vasaka Visoottiviseth, Jason Haga and Ryousei Takano

PP20

Machine learning for processing image data for disaster management
Parintorn Pooyoi, Jason Haga and Worapan Kusakunniran

PP21

Room Auto Controlling Based on Occupant Body Condition Using Arduino andRaspberry Pi
Ahmad Sabiq, Nova Eka Diana, Debita Febriana and Sri Chusri Haryanti

PP22

Criminality Linguistics Detection on Social Networks Through Personality Traits
Saravanan Sagadevan, Nurul Hashimah Ahamed Hassain Malim, Nurul Izzati Ridzuwan
and Muhammad Baqir Hakim Mohammad Bashir

PP23

Performance Comparison of Dynamic Load Balancing Algorithm for Indonesian
e-Health Cloud
Ridho Yanevan Pratama, Aditya Efrian, Sri Chusri Haryanti and Sri Puji Utami

PP24

Using UAV images for smart agriculture to monitor rice paddy with artificial
intelligenceMing-Der Yang, Hui Ping Tsai, Yu-Chun Hsu and Cloud Tseng

PP25

rEDM Code Acceleration with ABCI Supercomputer
Wassapon Watanakeesuntorn, Kohei Ichikawa, Jason Haga, Gerald Pao, Erik Saberski

26

BDS2 PROJECT ABSTRACTS27

[BS01] Reliability Assessment Framework using Test-Defect Coverage Analytics
Model
S. M. Syed-Mohamad1,*, M. H. Husin1, W. M. N. W. Zainon1
1: School of Computer Sciences, Universiti Sains Malaysia, Malaysia* Correspondent author: mashita@usm.my
Keywords: Software Analytics, Software Testing, Visual Analytics, Software Reliability
Software testing is a process by which quality of the software
under test can be identified. Information collected during testingsuch as defect models is used to decide whether a piece of softwareis ready to be released. An early process was to freeze the software
code to prevent further additions to the software functionality andthen test it. Any defects revealed by the testing process were thenfixed and retested. The rate of detection and fixing of outstanding
defects and the overall decline in the number of outstanding defects
with respect to time or testing effort indicates the level of reliability
and this has led to various software reliability growth models [1].
However, these depended on being able to hold the code steady fora fixed period in order to observe the growing reliability. The
emergence of incremental development in its various forms such as
Agile and DevOps requires a different approach in determining thereadiness of the software for release [2]. This approach needs topredict how reliable the software is likely to be based on continuousplanning tests, not defect growth and decline. Continuous testing andautomation are a key aspect of rapidly evolving software which
results in continuous stabilization throughout the development
process. Yet, there has been little formalization of quality assurance
practices that support decision making for software developed using
the contemporary software practices. The ultimate goal of thisresearch project is therefore to establish an integrative reliability
assessment framework, driven by metrics and analytics-based
insight.
There is a positive correlation between coverage and failures[3]. The more coverage, the greater the chances for a defect to be
detected. Yet, test coverage should not be the main indicator to test
effectiveness [4]. These and, possibly other factors can be used to
predict differing levels of test coverage for different parts of software
under test. Therefore, Test-Defect Coverage Analytics model(TDCAM) is proposed to assist decision-making about quality ofsoftware, in particular related to reliability assessment. TDCAM
integrates test coverage and defect coverage related metrics into avisual form, enabling users to observe the information. Test coveragemeasures the amount of testing performed by a set of tests (a test
suite). It includes gathering information about which parts of aprogram are actually executed when running the test suite. It is used
to gauge the effectiveness and completeness of testing and indicatesreliability [1]. Currently, TDCAM focuses on code coverage, inwhich it measures the degree of testing of the source code. An
example of the measure of control flow is branch coverage which isthe percentage of branches that have been exercised by a test suite.
The second data element of TDCAM is defect coverage. Defectcoverage is the fraction of defects detected by a test suite during
testing. In our study, the defect coverage provides defect related
metrics such as number, types and severity of defects.
A case study on Apache POI, an open source project, has been
conducted to validate the proposed model, visualized in a bubble
chart, as depicted in Figure 1. The x- and y-axes represent test
coverage (in branch unit) and defect coverage (defect numbers),
respectively. The bubble size represents severity of defects. Basedon this empirical study, the “xssf” component contains the most highseverity or critical defects compared to other components. Testingteam may strategize their testing effort to focus on any critical
functions and resolving the defects.
Our future work is to extend TDCAM to become a suite of test
analytics tool with support from artificial intelligence andvisualization techniques. A more focused test effort can be achievedby using algorithms to automatically optimize test cases, forinstance, tests that cover a recurring issue making it ideal for
optimization. The new proposed framework will give a significant
impact on how software stakeholders can make an informed decision
about quality and deliver reliable software releases at higher
velocities which the modern society is increasingly relying upon [5].

Fig. 1. Test-defect coverage metrics mapped against severity of defects
References[1]. Lyu, M. R., “Software Reliability Engineering: A Roadmap”. In 2007
Future of Software Engineering IEEE Computer Society. pp. 153-170,
2007.
[2]. Syed-Mohamad, S. M., Haron, N. H. & McBride, T., (2017), “Test
Adequacy Assessment Using Test-Defect Coverage Analytic Model”.
Journal of Telecommunication, Electronic and Computer Engineering(JTEC), Vol. 9, Issue 3-5, pp.191-196.
[3]. Herzig, K. & Nagappan, A. N. (2014), “The impact of test ownershipand team structure on the reliability and effectiveness of quality testruns”, Proceedings of the 8th ACM/IEEE International Symposium on
Empirical Software Engineering and Measurement.
[4]. Inozemtseva, L. & Holmes, R. (2014), “Coverage Is Not Strongly
Correlated with Test Suite Effectiveness”, ICSE ’14, ACM, pp. 435445.
[5]. Elberzhager, F., Arif, T., Naab, M., Süß, I., Koban, S. (2017), “Fromagile development to DevOps: going towards faster releases at highquality – experiences from an industrial context. Springer. vol. 269,
pp. 33–44.

Authors Biographies
SHARIFAH MASHITA SYED-MOHAMAD is a seniorlecturer at the School of Computer Sciences, USM. Her
research interests include software analytics, software
reliability and testing, empirical software engineering,agile development and software metrics and measurement.
MOHD HEIKAL HUSIN is a senior lecturer at the Schoolof Computer Sciences, USM. His research interests
include e-government technology, Web 2.0, social media
adoptions, project development management and software
testing approaches.
WAN MOHD NAZMEE WAN ZAINON is a seniorlecturer at the School of Computer Sciences, USM. His
research interests are at the intersection of VisualComputing and software engineering with focus on
software reuse, requirement engineering practices, visual
data mining and multimedia information retrieval.

28

[BS02] Type-2 Fuzzy Systems and the Variations in the Uncertain Environments
Syibrah Naim
School of Computer Sciences, Universiti Sains Malaysia, Malaysia*syibrah@usm.my
Keywords: type-2 fuzzy sets, interval type-2 fuzzy sets, general type-2 fuzzy sets, intuitionistic fuzzy sets
Fuzzy system utilises set theory in a mathematical form torepresent vagueness of parameters by using fuzzy set representation.
In fuzzy system, the basic idea is that statements are not merely ‘true’or ‘false’ since partial truth is also accepted by introducing a degreeof membership from 0 to 1 in a fuzzy set (Uzokaa et al., 2011). It ismuch closer to human thinking and natural language by providing
an effective means of capturing the approximate, inexact nature of
the real world.

(postgraduate surveys) have been done. Moreover, secondary
datasets and expert opinions from clinicians have been acquired todiagnose new baby born condition (collaboration with Prof Jonathan
Garibaldi, University of Nottingham) to study the level of
uncertainties in type-1 and type-2 fuzzy theories. The results showthat by increasing number of experts/decision makers (alsoincreasing the data volume), general type-2 fuzzy systemoutperformed interval type-2, intuitionistic interval type-2, and type1.
Different real-world applications using higher-ordered fuzzy
system such as science maritime, ambient intelligent in smart homelaboratory for elderly and higher voltage insulator have been
investigated. Collaborations have been established in different
application areas such as to predict index deprivation in household,predict performance and health index from the students’ database,and currently learning fuzzy rule-based from the sensor signal torecognise human activity.
The aim is to expand the potential of implementing type-2 fuzzy
sets in big data analytics in many important application areas. The
result is expected to improve the conventional system and decisionmaking techniques to provide a system that able to mimic groups ofhuman decision.
 Uzokaa F E, Obotb O, K Barkerc, Osujid J (2011) An
experimental comparison of fuzzy logic and analytic hierarchy
process for medical decision support systems. Computer
Methods and Programs in Biomedicine 103:10-27.
 Naim N, Hagras H (2014) A type 2-hesitation fuzzy logic based
multi-criteria group decision-making system for intelligent
shared environments. Journal of Soft Computing 18(7): 1305–
1319.
 Zadeh L A (1975) The concepts of a linguistic variable and its
application to approximate reasoning part I. Information
Sciences 8:199–249.
 Wagner C, Hagras H (2010) Toward general type-2 fuzzy logic
systems based on zSlices. IEEE Transactions on Fuzzy Systems
18(4):637-660.
 Mendel J M, John R I B (2002) Type-2 fuzzy sets made simple.
IEEE Transactions on Fuzzy System 10(2):117-127.
 Golseﬁd S M M, Zarandin M H F (2016) Multi-central general
type-2 fuzzy clustering approach for pattern recognitions.
Information Sciences 328:172-188.
 Rakshit P. Saha S, Konar A (2016) A type-2 fuzzy classifier for
gesture induced pathological disorder recognition. Fuzzy Sets
and Systems 305:95-130.
 Zhai D, Mendel J (2011) Uncertainty measures for general Type2 fuzzy sets. Information Sciences 181(3):503-518.
 Almaraashi M, John R, Hopgood A, Ahmadi S (2016) Learningof interval and general type-2 fuzzy logic systems using
simulated annealing: Theory and practice. Information Science
360:21-42.

Type-2 Fuzzy Sets
Fuzzy sets have tremendously developed over the years. Firstly, in
1998, Karnik and Mendel proposed Type-2 Fuzzy Logic System(T2FLS) based on Type-2 Fuzzy Sets (T2FS), which was proposedby Zadeh (1975). Nowadays, the Interval Type-2 Sets (IT2FS) are
the common application of type-2 fuzzy sets. It has shown to be very
powerful in handling the uncertainties when compared to Type-1
Fuzzy Sets (T1FS, conventional fuzzy sets since 1975). Recently,
several researchers have started to explore the application of General
Type-2 Fuzzy Sets (GT2FS) and systems. Wagner and Hagras (2010)
first introduced GT2FS, which is one of the advanced extensions of
the type-2 fuzzy sets to capture high level of uncertainties.
In simple terms, general type-2 comprehends an nth number ofinterval fuzzy set simultaneously rather than just an interval (interval
type-2) during fuzzification. The conventional type-1 fuzzy set
handles a single fuzzy value, whilst the interval type-2 fuzzy set
defines an interval type-2 fuzzy value to measure the uncertainties.
Apart from this, fuzzy sets have developed in many ways to interpret
uncertainties, such as intuitionistic fuzzy set, which takes both
membership and non-membership values of fuzziness. In 2014,
Naim and Hagras have proposed Intuitionistic Interval Type-2 Fuzzy
Sets.

Type-2 Fuzzy System
However, Mendel and John (2002) stated that the type-2 fuzzy sets
are more difficult to use and understand than the type-1 fuzzy sets;hence, their use is not yet widespread. Researchers had difficulties
to make type-2 fuzzy sets easy to use and understand with the aimthat they would be widely used. This statement generalises theinterval type-2 fuzzy sets (IT2FS) and it was stated 5 years ago. It is
known now that the application of the general type-2 system(GT2FS) are minimal since Wagner and Hagras proposed Zslices in2010. In the literature, we found that when utilising the general type2 fuzzy sets, the complexity of the system increases in order toevaluate the higher level of uncertainties. Several works on thegeneral type-2 applications have been done in clustering andclassifier problems (Golsefid and Zarandin (2016), Rakshit et al.
(2016)). The remaining works mainly published results on the FLS(tuning general type-2 parameters) and the mathematical operators(Zhai and Mendel (2011), Almaraashi et al. (2016)).

Syibrah Naim is a senior lecturer at School of Computer
Science, Universiti Sains Malaysia since 2016. Syibrah
completed her Ph.D. at University of Essex, her masterand undergraduate studies at Universiti Malaysia
Terengganu. Her research interests are type-2 fuzzy
sets, fuzzy logic, fuzzy decision-making, andoptimisation. She has higher interest to adapt higherordered fuzzy theories into any potential application infuzzy environments.

The Application of Type-2 Fuzzy System
Currently, my research interest is to adapt type-2 fuzzy theories
into any potential application in fuzzy environments. The focus of
my research plan is to learn the basic system, finding the problem
and simultaneously applying the system to generate the optimised
output. Several fuzzy type-2 applications such as in the intelligent
environments (intelligent lighting for reading) and social surveys

29

[BS03] Data Mining System to Detect Finger Motion in Offline Handwriting Forgery
W. P. Loh1*, C. S. Cheng2
1: School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia.
2: Faculty of Information Sciences and Engineering, Management and Science University, University Drive, Seksyen 13, 40100 Shah Alam,
Selangor, Malaysia.
* Correspondent author: meloh@usm.my
Keywords: Data Mining, Finger Motion, Offline Handwriting, Classification, Forgery

Abstract

significant finger motion attribute was identified. A system wasdeveloped from Neuroph OCR (Optical Character Recognition) torecognize handwritten letters and characters. Further efforts wererequired to compare between the genuine and forged online
handwritings and to integrate finger motion data into the system.
This study contributes to the advancement of data analytics via an
automated process for forensic handwriting examination in the
cyber-security industry. Future extended works may involve theDivision of Commercial Crime Investigation Department, Royal
Malaysia Police and potential software development company tosupport the system implementation and testing.

Introduction: Every individual has their own unique handwriting
characters which may differ by line quality, spacing (line or spaces
between character and word), height, width and size of letters, pen
lifts and separations, connection strokes, beginning and ending
strokes, unusual letter formation, shading (pen pressure), slant,
baseline habits, flourishment, and embellishments and diacritic
placement.
Problem Statement: It is difficult to judge the genuine or
fraudulent nature of handwritten documents on the visual inspection
basis. In the pasts, the offline handwriting classification wasdetermined solely based on the handwriting pattern recognition [1][4]. The offline handwriting forgery issues are still unsolvable as thecounterfeiters’ tricky attempts on offline handwriting forgery are notproperly understood. In particular, there was no work that predicts
handwriting patterns from the finger motions.
Objectives: Therefore, this study aims to integrate finger motion
captures into offline handwriting data pattern detection with specificgoals (i) to recognize finger motion features to classify similar
handwriting patterns, (ii) to develop a data mining system that
detects offline handwriting patterns characterized by finger motion(iii) to analyze and compare the differences between genuine andforged offline handwritings.
Method: A total of 30 subjects was tasked to write the phrase"Sphinx of black quartz, judge my vow" using their dominant handswith a provided Pilot G2 05 gel ink rollerball pen for two repetitions;
under 60 fps video capture using 16 MP sports camera. The
handwritings were executed on a desk at 0.74 m height (elbow
height) with a sheet of survey form on it.
Data analysis was conducted at two-phase data mining analyses:
data pre-processing (video-image-numeric transformation, numeric
data screening) and handwriting feature classification using the J48
algorithm on 10-fold cross-validation mode aided by the WEKA
tool. The initial phase analysis translated the raw data into 15
attributes: writing time, three-finger (thumb, middle, index)
coordinates, pen-grip angles, and phrase length in two replicates; andphrase inclination, demographic data (gender, handedness). The
second phase analysis grouped data by similar handwriting patterns(phrase length, inclination angle) with the rule-reasoning
relationship application. The Neuroph OCR (Optical Character
Recognition) system in Java language was developed to recognize
handwriting characteristics (phrase length and inclination angle) on
different class patterns. The capability of the developed system will
be tested on Aspects of Consistency Level (ACL).
Results: Findings show consistent classification accuracies on the
handwriting inclination angle using Tree classifier algorithms. The
classification accuracy that was trained on the J48 algorithm could
achieve 98% with the thumb angle being the most discriminantfeature. A system which includes the finger motion features (threefinger coordinates) data to identify offline handwriting patterns is tobe developed.
Conclusion: This paper reports an ongoing sustainable researchin developing a data mining system to detect finger motion for
distinguishing the offline handwriting patterns. The experiment datacollection with its analyses was completed at the data mining level.
The findings from the data classification level were obtained andReferences
[1]

[2]

[3]

[4]

Zamora-Martínez F, Frinken V, España-Boquera S, CastroBleda MJ, Fischer A, and Bunke H (2014) Neural network
language models for off-line handwriting recognition. Pattern
Recognition 47(4):1642–1652.
Jayech K, Mahjoub MA, and Ben Amara NE (2016)
Synchronous multi-stream Hidden Markov Model for offline
Arabic handwriting recognition without explicit segmentation.
Neurocomputing 214:958–971.
Kamble PM and Hegadi RS (2015) Handwritten Marathi
character recognition using R-HOG feature. Procedia Comput.
Sci. 45:266–274.
Sueiras J, Ruiz V, Sanchez A, and Velez JF (2018) Offline
continous handwriting recognition using sequence to sequenceneural networks. Neurocomputing 289:119–128.

Authors Biographies
Loh Wei Ping received her Ph.D in Applied Mathematics,
from the School of Mathematical Sciences of Universiti
Sains Malaysia in 2009. She currently works as a seniorlecturer in School of Mechanical Engineering, Universiti
Sains Malaysia. She serves as a member of the Society
for Industrial & Applied Mathematics (SIAM) andAmerican Mathematical Society (AMS). Her main
research andinterests nclude data mining, mathematical modelling
applied biomechanics. She won the “Best Paper Award” in the International
Conference on Computer Science and Information Technology (ICCSIT
2018) in a joint research paper. To date, she has published over 50 journals,
conference proceedings and textbooks in these areas.
Cheng Chun Seong received his B.Sc Hons. Degree in
Computer Forensic, from Faculty of Information
Sciences and Engineering of Management and Science
University, Malaysia in 2018. He received the Best
Project Award for his excellent achievement in the Final
Year Project (2018). In 2016, he received his Diploma
in Computer Forensic and an Academic Award during
the 19th Convocation Ceremony 2016. He currently
works as a Software Engineer (Java) in SecureMetric Technology Sdn. Bhd.
He will be pursuing his MSc. in Computer Science soon in 2018.

30

Big Data Summit 2: HPC & AI Empowering Data Analytics (co-locate with PRAGMA 35 Meeting), Penang, Malaysia, Oct 3 - 6, 2018

[BS04]Strategic Cyber Threat Intelligence Using External Unstructured Data
Mohamad Nizam Kassim1,*, Kew Yoke Ling2
1: Cyber Security Responsive Services Division, CyberSecurity Malaysia
2: KewMann Sdn Bhd* Correspondent author: nizam@cybersecurity.my
Keywords: Cyber Threat Intelligence, Cybersecurity, Robotic Process Automation, Natural Language Processing, Unstructured Data

PurposeTerrorism Events, and South China Sea Conflict News). Data fromeach category was then visualized based on time series or geolocation, allowing cyber security analysts to discover any trends orpatterns within the collected data. Full-text search and filtering
capabilities were implemented so that cyber security analysts could
easily locate data points of interest and perform data drill-down
where required. As a result, the analysts were able to visually analyze
the data, detect specific events, and monitor entities of interest(people, groups, companies, industries, methods and locations). For
future works, deep learning techniques will be employed,
particularly Recurrent Neural Networks (RNN), to extract
knowledge and hidden patterns once a sufficiently large volume of
security-related news has been collected,

Cyber threats are continuously evolving, and represent potential
threats to national security. With the increasing number and variety
of cyber threats, cyber security analysts face multiple challenges in
obtaining the required data and performing the necessary analysis todetect, monitor and respond to such threats in a timely manner. The
goal of this project was to automatically acquire, process and analyze
cybersecurity news from external online sources, enabling cybersecurity analysts to perform data exploration and discovery througha series of dashboards, resulting in actionable insights and enhancing
their ability to formulate the right strategies to respond to possible
cyber threats.
The project was a research and development collaboration
between CyberSecurity Malaysia (project owner and subject matter
expert), KewMann (consultant and solution developer) and UTM
Skudai (through Cyber Threat Intelligence Lab, Faculty ofComputing). The scope of the project consisted of Threat Actors
Landscape, Global Cyber Attack Landscape, Terrorism Events inSouth East Asia and South China Sea Conflict News Landscape.

This project demonstrated that automatic processes can be applied
to data acquisition and analysis, greatly reducing the time and effort
between obtaining the raw data and useful end-user interactions,allowing actionable insights to be derived in a timely manner.

MethodReferences

Multiple software robots were designed and configured to acquire
unstructured data from specified cybersecurity online news websites
and news aggregators through Robotic Process Automation (RPA).
The collected data then underwent text pre-processing in preparation
for further text analysis, which involved extensive use of Natural
Language Processing (NLP) techniques (Schatz, Bashroush, & Wall,2017). Text preprocessing was first conducted to clean and structure
the collected texts, which included tokenization, stemming, text
duplication removal and filtering of irrelevant content. Termfrequency–inverse document frequency (TFIDF) (Ramos, 2013)
was then applied to extract relevant keywords, while customized
Name Entity Recognition (NER) was used to obtain security-related
information such as dates, persons (i.e. threat actors), methods (i.e.
cyber-attacks), organizations (i.e. industry verticals) and locations.

 Journal article: Ramos, J. (2003, December). Using tf-idf todetermine word relevance in document queries. In Proceedingsof the first instructional conference on machine learning (Vol.
242, pp. 133-142).
 Journal article: Schatz, D., Bashroush, R., & Wall, J. (2017).
Towards a more representative definition of cyber security.
Journal of Digital Forensics, Security and Law, 12(2), 8.
 Book: Tufte, E. R., Goeler, N. H., & Benson, R.
(1990). Envisioning information (Vol. 126). Cheshire, CT:
Graphics press.

Text analytic techniques enabled cyber threats profiles to bevisualized based on best practices set out by Tufte, Goeler, & Benson(1990), leading to strategic insights on cyber-attacks and threat
actors against specific industry verticals. This allowed importantquestions to be addressed accurately and in a timely manner:
 What are the main cyber threats against each industry vertical?
 What are the impacts of specified cyber threats to target victims?
 What are the cyber threats trends for specified time periods?
 Do threat actors change their tactics?
 How do these cyber threats impact our national cyber security?

Mohamad Nizam Kassim works as Specialist at Cyber
Security Responsive Services Division, CyberSecurity
Malaysia. He has a degree in Power Electrical Engineeringfrom University of Wollongong (Australia) and Master ofComputer Science from Universiti Teknologi Malaysia(Malaysia). Currently, he is pursuing his postgraduate studies in machinelearning and natural language processing. His current research analytic
projects at CyberSecurity Malaysia are developing predictive algorithm todetect scams and frauds websites and developing analytics platform for cyberthreat intelligence profiling.

Conclusion

Authors Biographies

Kew Yoke Ling is the founder and executive director of
KewMann, a regional big data and behavioral science
company. KewMann helps organizations predict andinfluence human behaviors through cutting-edge big datatechnologies, and provides consulting services to government
agencies, financial services, telco and other large organizations. Yoke Ling
has a Degree (Hons) in Computing from Staffordshire University, UK and
Postgraduate Diploma of Chartered Institute of Marketing (CIM), UK.

Results
The cyber threat landscape Overview consists of a composite
visualization of the number of occurrences of cyber security news in
the last 30 days by category (Threat Actors, Cyber Attack Methods,31

[BS05] Fiber Tractography using Grid Computing
A. Achuthan1, *, M. Mustapha2, B. Belaton3
1: Advanced Medical & Dental Institute, Universiti Sains Malaysia, 13200 Kepala Batas, Penang.
2: Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150 Kubang Kerian, Kelantan.
3: School of Computer Sciences, Universiti Sains Malaysia, 11800 Penang.
*anusha@usm.my
Keywords: Brain, Diffusion Tensor Imaging, Cerebral Small Vessel Disease, Tractography

Introduction
for tractography of similarly meaningful fiber bundles.
Then, in the third phase, it is proposed to perform tractography ina grid computing environment. Each of the anatomical structure will
be used as the starting point. The tractography originating from acorresponding anatomical structure will be directed to one Central
Processing Unit (CPU). A group of starting points will require
multiple CPUs, which will form a grid computing environment.
A work by Lee and Kim performed tractography starting frommultiple seed voxels in parallel instead of sequentially using
multiple Graphical Processing Unit (1). However, in their work, theseed selection does not correspond to any anatomical structure that
corresponds to a target functionality. Hence, in this proposed work,
it is modeled to first localize the anatomical regions that corresponds
to a meaningful fiber bundles and then construct its tractography inthe grid computing manner.

Cerebral small vessel disease (SVD) plays a major role indementia, stroke and ageing (3,4). The SVD is a pathologicalprocess that affects the small vessels of the brain. It has been highly
associated with cognitive disorders, and disturbances in mood andgait. In clinical practices, Diffusion Tensor Imaging (DTI) is widelyused as neuroimaging marker of cerebral SVD. DTI enables thevisualization of the brain microstructural connectivity. This
microstructural connectivity is constructed through fiber tracking(tractography) of the white matter tracts. Figure 1 shows an example
of whole brain tractography. Generally, a whole brain tractography
consists of 100,000 to 1,000,000 fiber tracts.

Grid
Computing
Framework

Figure 1: Whole Brain Tractography.
Visualization and quantification of white matter tracts is a very
crucial step to assess the severity of SVD. The knowledge on thenumber of tracts between related brain structures will be a useful
insight on the disease. Thus, the past decade has seen tremendous
research efforts on the development of various tractography
solutions (2). However, the tractography process is known to usehigh computing resources and time. Hence, most of the tractography
solutions could not be made applicable in clinical practice.

Figure 2: Proposed Whole Brain Tractography.

Research Contributions
The main challenge in this work in on the localization of
anatomical structures that can produce meaningfully similar fiber
bundles. Research effort in this work will focus at the anatomical
localization phase. Besides that, research into parallelizing thetractography and finally merging the multiple fiber bundles into awhole tractography will also be given attention.

Proposed Framework
In this work, it is planned to take advantage of grid computing in
the tractography process to assist timely clinical assessment. The
overall proposed framework is illustrated in Figure 2. This
framework consists of three main phases, namely (i) DTI
Preprocessing, (ii) Anatomical Localization and (iii) Tractography.
The first phase involves data format conversion, motion
correction, brain extraction and data preparation. This phase isrequired to ensure the dataset is available in a suitable format for thetractography phase.
The fiber tracts may be grouped accordingly in reference to theanatomical brain structure or its functionality. In this work, fiber
tracts are clustered into group based on its anatomical location. Thisis because fiber tracts originating from a similar anatomical location
may carry a similar functionally. This similarly meaningful fiber
tracts may form a fiber bundle. Hence, the second phase
concentrates on localizing the major anatomical brain structures
such as subcortical structures, frontal lobe and corpus callosum.
Each anatomical structure will be used as starting point

References
(1)

(2)

(3)

32

Lee J, Kim DS (2013) Divide et impera: Acceleration of DTI
tractography using multi‐GPU parallel processing. Imaging
Systems and Technology 23(3): 256-264.
Norton I, Ibn Essayed W, Zhang F, Pujol S, Yarmarkovich A,
Golby AJ, Kindlmann G, Wassermann D, Estepar RSJ, Rathi
Y, Pieper S, Kikinis R, Johnson HJ, Westin CF, O'Donnell LJ(2017) SlicerDMRI: Open source diffusion MRI software forbrain cancer research. Cancer Research 77(21): e101-e103.
Shi Y, Wardlaw JM (2016) Update on cerebral small vessel
disease: A dynamic whole-brain disease. Stroke and Vascular
Neurology 1(3):83-92.
Telgte A, van Leijsen E, Wiegertjes K, Klijn CJM, Tuladhar
AM, Erik de Leeuw F (2018) Cerebral small vessel disease:
From a focal to a global perspective. Nature Reviews
Neurology 14: 387-398.

[BS06] Can I Trust You? Towards Modelling Trust at Zero Acquaintance
Deborah Y. H. Ooi1, Zaher Bamasud, Syaheerah L. Lutfi*
Dept. of Computer Science, Universiti Sains Malaysia, Malaysia
Corresponding author: syaheerah@usm.my
Keywords: Facial expression, Gesture, Trust modelling, Zero Acquaintance, VoiceIntroduction

Each participant in this study will be asked to rate thetrustworthiness of 5 different video clips. The participant will submit
their ratings via a simple online assessment indicating the amount oftrust they would place in the speaker. Two different sets ofparticipants will rate how much they trust the speaker -- the first set
is based on face and gesture of the speaker in the videos that are
muted, and the second only based on vocal cues in a set of voice
clips extracted from the above mentioned videos.

Much research has already been carried out in this field ofaffective computing. Research trying to identify emotions from face,gesture and voice are abounding in number, each with their own
specific outcomes. However, there is one area that has yet to belooked into. Trust. Can you trust me? Will you trust what I am going
to present? Will you trust that I will deliver? How did I make you
trust me? These are the questions we are seeking to answer throughour work thus far. Why is this important, you may ask?

The characteristics of each video clip will also be analysed, alongwith the ratings from the participants. This will be analysed by using
a correlation matrix to determine the features that have a significant
impact on perceived trustworthiness.

Commercial dealings would never happen as neither party would
trust the other enough to deliver what they have promised. Ecommerce would never grow. Relationships would not last long andmore often than not end up in arguments or conflict. Even in aclassroom setting, students would have a hard time believing thethings taught by the teacher if they lack trust in the teacher.
Similarly, teachers would have a hard time teaching the students ifthey don’t trust that the students would readily absorb all that has
been given. This situation is especially prevalent in online tutoring,where there is a lack of personal connection between teacher andstudent. If technology was able to accurately measure how“trustworthy” the teacher is, the teachers could then take steps toimprove themselves in that area. For example, the teachers could
speak with more authority, or lower their voices to a more gentle
tone. Maybe smile more, or smile less. Such feedback would proveinvaluable to improve the teaching and learning process.

Expected Results
The potential outcomes of this investigation are plentiful. In
addition to precisely measuring the sole impact of facial features andgestures, as well as pitch or speech rate on perceived trustworthiness,this investigation would also be able to identify the effects of thesemantic content by analysing the difference in trustworthinessrating from one original audio clip and another. The outcomes are
truly endless and at the very least, a relationship is hoped to be
identified between facial features, gestures and voice characteristics
and perceived trustworthiness.
References

In recent years, several researches have been conducted on thefactors that determine trustworthiness. Particularly, many studies
have shown the significance and importance of voice in perceived
trustworthiness. For instance, from a medical perspective, a research
has found that audio communication shows a more significant
emergence of trust as compared to text chat. Yet anotherinvestigation has studied facial features and identified that men withgreater facial width were more likely to exploit the trust of others
and that people were more likely to trust male counterparts withnarrow rather than wide faces. This study attempts to model trust
using machine learning methods towards achieving a larger effort inbuilding synthetic agents that that can gauge trust, especially whenin a learning domain.

Barros, P., Churamani, N., Lakomkin, E. et al (2018). The omgemotion behaviour dataset. doi: arXiv preprint arXiv:1803.05434
Smith, B. L., Brown, B. L., Strong, W. J. et al (1975). Effects ofspeech rate on personality perception. Language and Speech, 18 (2),145-152. doi: 10.1177/002383097501800203
Trouvain, J., Schmidt, S., Schröder, M., Schmitz, M., & Barry, W.
J. (2006). Modelling personality features by changing prosody insynthetic speech. doi:10.22028/D291-25920

As of this moment, this gargantuan task is still in its early stages.
We have identified the modality (cues) that would used to minefeatures of trust - face, voice and gesture. Cues through expressions
conveyed via these sources would be analysed and quantified into amachine readable form, using image and voice processing
technologies. A model would be constructed using a machinelearning methods.
MethodTo carry out this investigation, a total of 120 different video
samples have been obtained from the OMG-Emotion behaviour
dataset (Barros et al., 2018). Each of these video samples will be
cropped to form a 10 second long video clip. To investigate the effectof facial features and gestures on trustworthiness, each video clip
will be muted and subjected to analysis by using a video data
analysis software.

33

Big Data Summit 2: HPC & AI Empowering Data Analytics (co-locate with PRAGMA 35 Meeting), Penang, Malaysia, Oct 3 - 6, 2018

[BS07] Data Science Analytics for Manufacturing and Supply Chain
Umi Kalsom Yusof1,* and Mohd Nor Akmal Khalid1
1: School of Computer Sciences, University Science Malaysia, Pulau Pinang, Malaysia* Correspondent author: umiyusof@usm.my
Keywords: Data analytics, industry 4.0, manufacturing, supply chain, big data, internet of things, artificial intelligence, cyber physicalsystem.

Background and Motivationand product services. This enable the product to be uniquely
identified for its entire life cycle as an active entity where self-awareand self-maintenance can be performed; thus, encouragetransparency among manufacturers, supply chains, and customers(Lee et al., 2014). This fundamentally changes how the
manufacturing operates, from reactive to proactive operations andultimately predictive operating models.
Achieving these feats require certain underpinning technologies toallow problems to be solved and adaptive decisions to be made withminimum human involvement. Artificial intelligence (AI) allows
such features by learning and reasoning that ultimately realize aconnected, intelligent, and ubiquitous industrial practice (Zhong etal., 2017). In tandem with the industry 4.0 framework, AI-enabledtechnologies with advanced data analytical capabilities, would
realize a prognostic-monitoring system that can derive competitive
advantage in design, monitoring, control, planning and execution
level of both the manufacturing and supply chain.

The paradigm of research had moved from empirical, theoretical,
simulation and to currently exploration of data or data-driven (Heyet al., 2009). The cycle of a research is supported by its data, fromdata capture and data curation to data analysis and data visualization.
However, good tools for both data curation and data analysis are stilllacking.
Since the advent of “big data”, lots of research efforts havedevoted themselves to this new research topic, and most of themfocus on social or commercial mining (Lee et al., 2014). However,those researches mostly focus on ‘human-generated data’ instead of‘machine-generated data’, which include machine controllers,sensors, and manufacturing systems. The latter leads to the emergentof industrial big data that leads to predictive analytics inmanufacturing, in realizing the demand of Industry 4.0.
In addition, under the Industry 4.0 concept, astounding growth in
the adoption of information technology and social media networks
has increasingly influenced consumers’ perception on productinnovation, quality, variety and speed of delivery (Zhong et al.,2017) in supply chain. As such, data analytics provide an opportunity
to the supply chain management through service innovation thattransparent throughout the product lifecycle; enabling to adapt to themarket requirements that are highly customizable and very tractabledemands.

Opportunities and Outlooks
With big data environment and emergent of industry 4.0, theprognostic-monitoring system is the major prospect for therealization of an industry-wide revolution. It would involve
advanced analytical tools and AI-enabled technologies that“proactively” materialize data-driven modeling, big data analytics,
data-enabled prediction, smart design and prototyping, real-time
information sharing and monitoring, and collaborative decision
making (Zhong et al., 2017).
Data-driven modelling and data-enabled prediction would drive
the quality, efficiency and agility of the manufacturing throughknowledge and insights gained from the data deluge. Real-time
information sharing and collaborative decision making would
augment the cognitive ability of humans while complement
limitations of the machine. Smart design and prototyping would
enable the creation of more industry-wide value added processes andservices (Lee et al., 2014; Zhong et al., 2017).
Nevertheless, materializing the intelligent factory of the future,through the prognostic-monitoring system with respect to industrial
settings, remain a huge challenge which requires a collaborative
effort by scientist, researcher, and practitioner.

Data Science and Analytics in Manufacturing and
Supply Chain
A unified information grid based on the recent developments of
the Internet of things (IoT) framework and the emergence of sensing
technology have created a tightly connected systems and humans,
which further populates the big data environment of the industry. In
today’s competitive business environment, big data, cloudcomputing and advance analytics are the necessary framework tosystematically process data for intelligent decision-making (Lee etal., 2014).
Cyber Physical System (CPS) and service innovations are two
inevitable trends for manufacturing industries. On one hand, CPS areautomated systems that enable communication of the operations of
the physical reality using computing infrastructures, utilizing
networked devices (Jazdi 2014). On the other, service innovations
that laid the foundation for Product Service System (PSS), in theform of software and embedded intelligence, are integrated into theindustrial products and systems (Lee et al., 2014).
CPS goes with the trend of having information and services
everywhere at hand encompassing the communication activities andinfrastructures such as Internet of things (IoT), Internet of Services(IoS) and Internet of People (IoP) (Hermann et al., 2016).
Manufacturing systems are able to monitor physical processes,
create a “digital twin” (or “cyber twin”) of the physical world, andmake smart decisions through real-time communication andcooperation with humans, machines, sensors, and so forth (Zhong etal., 2017). This enables all physical processes and information flows
to be available across holistic manufacturing supply chains, small
and medium-sized enterprises, and large companies.
PSS involves electronics and tether-free intelligence thatintertwine predictive technologies with intelligent algorithms topredict, manage and optimize autonomously product performance

References
Hey, T., Tansley, S. and Tolle, K. M. The fourth paradigm: dataintensive scientific discovery. Vol. 1. Redmond, WA: Microsoft
research, 2009.
Hermann, M., Pentek, T. and Otto, B. “Design principles forindustrie 4.0 scenarios.” In System Sciences (HICSS), 2016 49th
Hawaii International Conference on, pp. 3928-3937. IEEE, 2016.
Lee, J., Kao, H. A. and Yang, S. “Service innovation and smart
analytics for industry 4.0 and big data environment.” Procedia Cirp
16 (2014): 3-8.
Tjahjono, B., Esplugues, C., Ares, E. and Pelaez, G. “What does
industry 4.0 mean to supply chain?” Procedia Manufacturing 13(2017): 1175-1182.
Zhong, R. Y., Xu, X., Klotz, E. and Newman, S. T. “Intelligent
manufacturing in the context of industry 4.0: a review.” Engineering
3, no. 5 (2017): 616-630.

34

Big Data Summit 2: HPC & AI Empowering Data Analytics (co-locate with PRAGMA 35 Meeting), Penang, Malaysia, Oct 3 - 6, 2018

[BS08] Interfacing Chatbot with Data Retrieval and Analytics Queries for Decision
MakingGan Keng Hoon 1,*, Loo Ji Yong1
1: School of Computer Sciences, Universiti Sains Malaysia, Malaysia* Correspondent author: khgan@usm.my
Keywords: Chatbot, Information Extraction, Unstructured Texts, Natural Language Parsing, Structured Retrieval

Motivation
Conventional data analytics process uses dashboard with tables,
charts, summaries, search tool in projecting its analysis outcome toits user with the goal of enabling discovery of useful information,suggesting conclusions etc. to support decision-making [1][3]. Suchdecision-making mechanisms can be improved further by using
natural language interface in the dashboard components, e.g. using
natural language keywords to search the sales performance of a
product. Motivated by the needs to enable a user friendlier
interaction with analytics outcome, this project proposes a natural
language chatbot who can assist in the role of decision making by
delivering information of dashboard components with human likeconversational pattern.

‘period’ = “1Q” or ‘period’ = “2Q” GROUP by ‘period’;
Functional interfacefunction difference_values(pre_value, post_value) returnray(status_increase_decrease, diff_percentage, diff_value)

From the above example, although the desired result is to havethe chatbot to inform the user that sales has increased; there could beother variant of outcomes that partially satisfy the information needs,
e.g. display both sales of Q1 and Q2 (see example response in Figure1), letting user to figure out the increment.
Business Logic Flow"what is the sales increment for second
quarter compare to first"

From User Intent to Data Retrieval and Analytics
The process of data analytics started from data cleansing,transforming, modeling data and finally delivering the processeddata to the user. The data to be accessed is communicated using
dashboard via visual and information lookup tools. The needs ofbetter mechanism to communicate analytics data with user [2] canbe seen from the rapid development of many commercializedbusiness intelligence or analytics dashboard (e.g. Sisense, Kautilya
BI, Wizdee Natural Language BI, Pentaho etc.) on the market.
From data retrieval to analytics, current interaction between
users and the system can be improvised with the usage of human likeconversational chat agent. To connect the two ends between user
input and business data, the intent posed by the user must be able to
be interpreted to a corresponding business logic. The business logic
decides how data will be accessed and manipulated before it is sent
back as response to user.

Chatbot Scenario
A scenario of chatbot usage in data retrieval and analytics isshown in Figure 1. A question, i.e. "what is the sales increment forsecond quarter compare to first" was posed by the user to check thesales of his company. Although the information needs are clearlystated using standard question-type language, its corresponding
business logic is complex. The intent of finding out about“increment” requires data retrieval process to obtain the sales
amount of two quarters, followed by an increment function to
perform calculation on the difference between the quarters.
An example translated intent, i.e. from natural language intent to its
corresponding actionable statements for data interfaces (e.g. SQL,SOLR query) and functional interfaces (e.g. APIs calls) are as
follows.
Data interface
SELECT ‘period’, SUM(‘sales_amount’) FROM SALES WHEREar-// generate a corresponding JSON object
with the following contents
intent: comparison (from keyword"compare")target entity: sales
constraint entity: period, amountcomparing_object entity: 1Q, 2Q// from the object, SQL processing engine
will generate a query as following
SELECT 'period', SUM('sales_amount')
FROM SALESWHERE 'period' = "1Q" or 'period' = "2Q"
GROUP by 'period'

Figure 1: Example of chatbot conversation in data retrieval andanalytics scene.

Project Prospect
In conclusion, the uniqueness of the work lies in connecting natural
language with data accessing interface for analytics and decision
making. This project covers several areas of research anddevelopment such as data storage and retrieval, domain analytics
specification, natural language conversational modelling and querytransformation. Prospective collaborators include dashboard
solution providers, HPC centers and relevant interested parties.

References[1]

H. Chen, R.H.L. Chiang, V.C. Storey, Business intelligence andanalytics: From big data to big impact, MIS Quarterly, 36 (4) (2012),
pp. 1165-1188.
[2] Ron Kohavi, Neal J. Rothleder, and Evangelos Simoudis. 2002. Emerging
trends in business analytics. Commun. ACM 45, 8 (August 2002), 45-48.
[3] Ee-Peng Lim, Hsinchun Chen, and Guoqing Chen. 2013. BusinessIntelligence and Analytics: Research Directions. ACM Trans. Manage.
Inf. Syst. 3, 4, Article 17 (January 2013).

35

BDS2 POSTER ABSTRACTS36

[BP01] Towards Large-scale Text Annotation for Sentiment Analysis using Semisupervised Deep Learning
Vivian Lay Shan Lee1,*, Keng Hoon Gan1
1: Sch. of Computer Sciences, Universiti Sains Malaysia, Malaysia* Correspondent author: vivianlee@student.usm.my
Keywords: Data annotation, Sentiment classification, Semi-supervised learning, Deep learning

Background

MethodSentiment classification is a task of classifying an opinion text as
expressing a positive or negative sentiment. In general, methods
used in sentiment classification can be divided into two groups,
lexicon-based methods and machine learning-based methods. In
both methods, fully annotated corpora are required in evaluation
stage and model training stage respectively. Along with the successof deep learning application in many other domains, researchers insentiment classification also following in their footsteps (Nogueira
et al. 2014; Kim 2014). However, it takes tremendous amount oftime, cost and effort to build high quality fully annotated corpora.
Semi-supervised learning is proposed by the researchers with theaim to minimize time and cost in creating a fully annotated corpus.
Semi-supervised learning is a method that make use of a
combination of labelled data and unlabelled data. In recent past year
studies, researchers made efforts in exploring semi-supervised deeplearning models in performing sentiment classification and they
offer good results (Dai and Le 2015; Guan et al. 2016).

To address the first problem, two popular semi-supervised
methods self-learning and co-learning will be explored andcompared. For deep learning models, we proposed to use
sophisticated types of recurrent neural network (RNN) based models
which free from exploding gradient problem in modelling
representations for sentiment classification. The idea to use RNN
models is because it can learn long-term knowledge, the knowledge
that does not change much overtime, and does not suffer high model
complexity as RNN shares same parameters across whole learning
process (Lai et al. 2015; Chen et al. 2016).
As for the second problem, we will find out the text features thatare significant to classify an opinionated text. Our hypothesis is thatby labelling the text that are sentiment relevance, we can improve
the accuracy of classification. Subjective statements refer to internal
state of a person which cannot be directly observed. In contrast,
objective statements expressed factual information. But a subjective
statement does not always contain opinion and an objective
statement might express an opinion.
Lastly, for the third problem, we will review on previous works inbalancing class proportion of unlabeled data to reduce bias
propagation of model. There are three groups of approaches intackling the issue: data level approaches, algorithmic levelapproaches and cost-sensitive approaches. Methods under thesethree categories will be explored and analyzed.

Motivation
In this internet era, massive amount of user generated data makes
unlabeled data inexpensive and easy to acquire. To avoid the time
consuming and expensive process of labelling massive amount ofdata without compromising model performances, semi-supervised
learning appears as the most promising method to use. While manystudies promote an optimistic view that unlabeled data is useful inimproving model performances in semi-supervised learning (Nigam
et al. 2000; Dai and Le 2015), yet there are also publications reported
that adding unlabeled data reducing accuracy of the model (Zhang etal. 2015; Iosifidis and Ntoutsi 2017; Levatić et al. 2017). Therefore,
researchers are motivated to add in only the informative unlabeledinstances that have positive impacts on the model performances.
Also, model trained in semi-supervised methods tends to produce
unbalanced output without any constraints on data proportion(Iosifidis and Ntoutsi 2017; Levatić et al. 2017). In extreme cases,
the classifier will classify all the unlabeled data into one of theclasses which is undesirable.
In this work, we will focus on the document level sentimentclassification. Compared to aspect level or sentence level sentimentclassification, document level classification is more difficult as it ishighly subjective and often contain variables of opinions. Hence,rule-based method has difficulties in capturing the variations of how
opinions are presented in reviews (Guan et al. 2016). With theaccessibility of large amount of data, deep learning serves as anencouraging solution with its ability in modelling the classifier.
Whereas in semi-supervised deep learning, it is essential to select the
unlabeled data subset with care and to apply appropriate constraints
on data class proportions with the intentions of producing a good
accuracy learner. To this end, we proposed to address following threeproblems: (1) demand of extensive labelling in supervised deeplearning sentiment classification (2) unravel characteristics of
unlabeled data that have positive impacts in classifier performances(3) tendency of bias propagation in semi-supervised methods.

References
Chen, Sun, Tu, et al (2016) Neural Sentiment Classification with
User and Product Attention. In: Proc 2016 Conf Empir
Methods Nat Lang Process.
Dai, Le (2015) Semi-supervised sequence learning. In: NIPS’15
Proc 28th Int Conf Neural Inf Process Syst.
Guan, Chen, Zhao, et al (2016) Weakly-Supervised deep learning forcustomer review sentiment classification. In: IJCAI’16 Proc
25th Int Jt Conf Artif Intell.
Iosifidis, Ntoutsi (2017) Large scale sentiment learning with limitedlabels. In: Proc 23rd ACM SIGKDD Int Conf Knowl Discov
Data Min - KDD ’17.
Kim (2014) Convolutional neural networks for sentenceclassification. In: Proc Annu Meet Assoc Comput Linguist(ACL, 2014).
Lai, Xu, Liu, Jun (2015) Recurrent convolutional neural networks
for text classificaton. In: AAAI’15 Proc 29th AAAI Conf ArtifIntell.
Levatić, Ceci, Kocev, Džeroski (2017) Semi-supervisedclassification trees. J Intell Inf Syst 49:461–486.
Nigam, Mccallum, Thrun, Mitchell (2000) Text Classification fromLabeled and Unlabeled Documents using EM. Mach Learn
39:103–134.
Nogueira, Santos, Gatti (2014) Deep convolutional neural networks
for sentiment analysis of short texts. In: Proc COLING 2014,
25th Int Conf Comput Linguist.
Zhang, Tang, Yoshida (2015) TESC: An approach to TExt
classification using semi-supervised clustering. KnowledgeBased Syst 75:152–160.

37

[BP02] Big-spatial Data Pre-processing Framework towards Flood Assessment
Z. Dahalin1,*, A. Ta’a2, A. Ndanusa3
1,2,3: Faculty of Information Technology Universiti Utara Malaysia,* Correspondent author: elahmedn@gmail.com
Keywords: Flood causative factors, Flood vulnerability, GIS
Introduction
Floods are the most common natural hazards in the world, leading
to severe havocs on both lives and properties. Assessing flood
vulnerability and its related risks has long been identified as animportant approach for the formulation of policies as well as
strategies aimed at mitigating the impacts resulting from any
potential flooding events. Therefore, in this research, a frameworkthat integrates multiple, heterogenous and voluminous spatial data
sets was developed to obtain the required insights or knowledge thatcan efficiently aid in flood vulnerability assessment for proper
mitigating measures.
SignificanceCurrently, some studies propose the use of structural means, suchas dams and dikes as employed in Malaysia, Netherlands, Ngeria as
well as the Sponge City in China (Sang & Yang, 2017),(Abdul Mohit& Mohamed Sellu, 2017),(Baghel, 2014),(Olukanni, Adejumo, &Salami, 2016), for flood mitigation, however, these measures haveoverly not been efficient due to the current climate change. Whilesome have adopted topographical spatial data set for flood
vulnerability assessment, as in the case of Nigeria (Ikusemoran,
Kolawole, & Martins, 2014). Nonetheless, mitigating these risks areneither uniquely the product of structural systems, nor are theyefficiently assessed by concentrating homogenous non-structural
factors alone without considering the multiple flood causative
factors (FCFs) to derive a holistic insight on geomorphologicalfeatures of the Earth. This is because, the use of homogenous andscanty features is constrained in revealing other FCFs, such as
topography, hydrology and vegetation of the study area. Therefore,
to address these limitations, a framework based on heterogenous
FCFs is developed to have a holistic insight on the geomorphologicalcontents of the study area.
MethodIn addressing the aforementioned limitations, Figure 1 below
gives an illustrative approach.

Figure 1: Big-SPAPFA Flowchart
As illustrated by this procedural flowchart in Figure 1, thederivation of the framework is from the formulation of big spatial
data, which were pre-processed from various sources and of varied
forms. Precisely, the map representing the study area was clippedfrom LandSat-8, SRTM and TRMM. After this initial stage, theoutput was corrected geometrically and radiometrically to havecorresponding outputs depicting the hydrological, topographical andvegetal contents of the study area. The accuracy of the final output
was assessed using flood inventory and other GIS means.
Results
From the approaches employed in the preceding section, flood
vulnerability maps and their corresponding regional severity wasclassified based on the pre-processed multiple FCFs, which
eventually produced the Big-SPAPFA framework as illustrated in
Figure 2.

Figure 2: Big-SPAPFA Framework
From the above Figure, various FCFs were used to map out regional
flood vulnerability and the corresponding levels of severity.

Conclusion
Flood risk assessment and mapping approach which classifies
floodplains, are required in flood mitigation and proper decisionmaking. Thus, Big-SPAPFA framework was developed using
multiple flood causative factors. Ultimately, the accuracy and thereliability of the framework was ensured using a decadal floodinventory.
References
Abdul Mohit, M., & Mohamed Sellu, G. (2017). Development of
Non-structural Flood Mitigation Policies and Measures for
Pekan town, Malaysia. Asian Journal of Behavioural Studies,2(6), 9.
Baghel, R. (2014). River Control in India: Spatial, Governmental
and Subjective Dimensions. Springer International
Publishing.
Ikusemoran, M., Kolawole, M. S., & Martins, A. K. (2014). Terrain
Analysis for Flood Disaster Vulnerability Assessment : A
Case Study of Niger State , Nigeria. American Journal of
Georaphic Information System, 3(3), 122–134.
Olukanni, D. O., Adejumo, A., & Salami, W. (2016). Assessment ofjebba hydropower dam operation for improved energy
production and flood management. ARPN Journal ofEngineering and Applied Sciences, 11(13), 8450–8467.
Sang, Y. F., & Yang, M. (2017). Urban waterlogs control in China:
more effective strategies and actions are needed. Natural
Hazards, 85(2), 1291–1294.

38

[BP03] Data Analytics of Malaysia’s Most Influential Entities in Social Media forCommercial Purpose
T.H. Lee1, Y.M. Yacob1
1: School of Computer and Communication Engineering, Universiti Malaysia Perlis, Malaysia* Correspondent author: yasmin.yacob@unimap.edu.my
Keywords: data analytics, prediction, social media, key-influencer

Abstract
The social media agents such as Facebook and Twitter are
powerful tools to capture pools of interest, sentiment and preferenceof the social media users. Thus, the agents are extremely useful forcommercial purpose. Analyses of influential people were conductedin Sina Weibo [1], the most popular social media platform in China
and, Facebook [2]. Meanwhile, [3] performed predictions of Sina
Weibo users. [4] determined influential actors in Twitter. However,to date, no work was conducted to determine key-influencer for
Malaysian scope. This work is to determine Malaysia’s mostinfluential entities in social media for commercial-related purpose.
The entities in this study include people and business organizations.
This study is significant because it can benefit marketer as theinfluential entities become the market-movers that may affect thefans’ buying decision making. The key-influencers can bring much
more impact on the products that they promote due to their higher
popularity. Thus, a lot of profit will be earned by company as themore the followers get to see the products, the more the business
opportunities.
This work analyzed Malaysians 10 most influential entities asbusiness and marketing key-influencer. On top of that, analysis andpredictions of number of followers gained for a specified period oftime for the influential entities were determined. Two sets of 10,000
data were collected within a duration of one month; April 2018 and,covered social media users in Malaysia. The data sets were acquired
from Followerwonk and Twitter API which were obtained via a
subscription of US$29 per month.
In order to determine Malaysia’s 10 most influential entities, theTwitter data were imported to R Studio and edited from commacharacters. Then, bar chart was plotted and listed 10 entities withthe highest number of followers in ascending order. In order todetermine number of followers gained in a month, 2 data sets were
imported and cleaned from comma characters. Ten entities with thehighest number of followers were selected from each data set andthen merged. Bar chart was plotted based on the number ofdifferences of number of followers of the two merged data sets.
In order to predict number of followers gained, 100 tweets for eachmost influential entity was imported via Twitter API. Average
number of retweets of each entity was calculated and the results werecombined in a data frame. Then, the data frame was merged with thedata set. Later, suitable attributes were determined via correlation
plot and employed to be executed in linear regression function. If theresult is satisfactory, then the output is considered as a goodprediction.
As shown in Figure 1(a), Dato’ Seri Najib Tun Razak has thehighest number of followers followed by Khairy Jamaluddin. Air
Asia as business organization was ranked second of the entities with
3,191,072 followers in April 2018. The former prime minister also
topped in the number of followers gained in April 2018. Air Asia andKhairy Jamaluddin came in second and third, respectively in thementioned category in Figure 1(b).
Astro AWANI is predicted to have the highest number of followers
gain with 1,468,834 new followers. The prediction results are basedon the targeted average number of retweets. In summary, data
analytics is a powerful analysis tool to aid the market brandcompared to local brand traditional marketing way.

(a)

(b)
Figure 1 Bar chart of (a) Malaysians top 10 influential entities, (b)number of follower gained in each influencer in April 2018

Table 1 Prediction of number of followers gained in April 2018
[1] Liao Q, Wang W, Han Y, and Zhang Q (2013) Analyzing the
Influential People in Sina Weibo Dataset. GLOBECOM - IEEE
Global Telecommunication Conference, pp 3066–3071.
[2] Kao LJ, Huang YP and Sandnes FE (2016) Mining TimeDependent Influential Users in Facebook Fans Group. IEEE Int.
Conf. Syst. Man, Cybernetics, pp. 718–723.
[3] Zhou J, Wu G, Tu M, Wang B, Zhang V, and Yan Y (2017)
Predicting User Influence Under the Environment of Big Data.
IEEE 2nd Intl. Conf. Cloud Comp. on Big Data Analysis, pp
133–138.
[4] Qasem Z, Jansen M, Hecking T, and Hoppe HU (2015) On the
Detection of Influential Actors in Social Media. 11th Int. Conf.
Signal-Image Technol. Internet-Based Syst. (SITIS 2015), pp
421–427.

39

Big Data Summit 2: HPC & AI Empowering Data Analytics (co-locate with PRAGMA 35 Meeting), Penang, Malaysia, Oct 3 - 6, 2018

[BP04] Low Resolution to High Resolution Video Surveillance
Image Enhancement Using Deep Learning
Muhamad Faris1,*, Shahrel Azmin Suandi1
1: Intelligent Biometric Group,
School of Electrical and Electronics Engineering, Universiti Sains Malaysia,
Engineering Campus, Nibong Tebal 14300, Malaysia* Correspondent author: muhdfaris@student.usm.my
Keywords: Deep Learning, Convolutional Neural Network, Image Enhancement

Abstract

are method to assess image quality. PSNR quantifies the noise in theimage based on the pixel difference between two images (Hore andZiou, 2010). SSIM evaluates the image quality based on three visual
impacts; luminance, contrast, and structure (Wang et al., 2004). The
results of LR-to-HR Network are shown in Table 1, 2, and 3.

Surveillance cameras are widely used in this age to help
improving the security and surveillance. The abundant ofsurveillance cameras and its placement cause variation in image
quality captured by the camera because not all surveillance cameras
have the same quality. These factors would affect the overall image
quality capture by the surveillance camera. The low-quality
surveillance camera usually has low-resolution image and this would
cause problem for face recognition. The low-resolution (LR) image
usually is pixelated and the person face is hard to be identified. Face
recognition requires a good-quality image to extract all the features
but with low-resolution image, there is fewer information that canbe extracted. Therefore, image enhancement that able to improve thequality of LR image could improve the overall surveillance cameras
system.
The propose method to enhance the LR image is by employing
deep convolutional neural network (CNN). Based on research by
Kim et al. (2015), the LR image can be enhanced by utilizing theend-to-end relationship of CNN and reconstruct the image using
Super Resolution approach. In this research, SCface database is usedas training sample because it contains a good-quality image(mugshot) and low-resolution image (camera variation) (Grgic et al.,2011). Low-resolution to High-resolution (LR-to-HR) Network is
trained using mugshot image as a good-quality image reference forthe network. The depth of network influences the performance. The
mugshot image is trained using 25 convolutional layer between eachlayer contain rectified linear unit as activation function as shown in
Figure 1. The 25 layers show the most optimum choice considering
training time and its performance.

Table 1 : Comparison between original images with mugshot images.
PSNR(dB)/SSIM
Distance 1
Distance 2
Distance 3
Cam 1
10.140/0.246
10.432/0.3036
10.661/0.3137
Cam 2
10.173/0.2517
10.204/0.3025
10.217/0.3223
Cam 3
10.584/0.2599
10.517/0.3038
10.297/0.3288
Cam 4
9.552/0.2356
9.433/0.2915
9.288/0.3097
Cam 5
8.630/0.2346
8.617/0.2740
8.555/0.2550
Cam 6
9.185/0.2947
9.979/0.3122
9.866/0.3412
Table 2: Comparison between enhanced images with mugshot images.
PSNR(dB)/SSIM
Distance 1
Distance 2
Distance 3
Cam 1
10.810/0.2635
10.686/0.3251
10.813/0.3418
Cam 2
10.403/0.2678
10.401/0.3214
10.366/0.3504
Cam 3
10.782/0.2738
10.679/0.3238
10.428/0.3577
Cam 4
9.796/0.2631
9.601/0.3204
9.412/0.3467
Cam 5
9.048/0.2511
8.975/0.2971
8.860/0.2953
Cam 6
9.856/0.3220
10.856/0.3560
10.264/0.3600
Table 3: Difference between enhanced image and original images.
PSNR(dB)/SSIM
Distance 1
Distance 2
Distance 3
Cam 1
0.67/0.018
0.254/0.022
0.152/0.0281
Cam 2
0.23/0.016
0.197/0.019
0.149/0.0281
Cam 3
0.198/0.014
0.162/0.020
0.131/0.029
Cam 4
0.244/0.028
0.168/0.0289
0.124/0.037
Cam 5
0.418/0.017
0.358/0.023
0.305/0.04
Cam 6
0.671/0.027
0.877/0.044
0.398/0.019

The results show there is improvement after the image is enhanced
using LR-to-HR Network based on PSNR and SSIM values. The
higher PSNR value and the closer SSIM value to 1 meaning that thebetter the image quality.
The proposed LR-to-HR Network shows that it capable to enhance
the low resolution image by utilizing deep learning to learn required
parameters to reconstruct the image based on the good-quality
image. The pixelated image is smoothen out using this approach, andthis would help in analyzing the image further.

Figure 1: The architecture of LR-to-HR Network.

The trained LR-to-HR Network reconstructed the surveillance
image based on the learned parameter in the last residual layer. The
reconstruct image can be defined as:(1)
𝐼𝐻𝑅 = F(I𝐿𝑅 )where I𝐿𝑅 is the LR image and 𝐼𝐻𝑅 is the reconstructed highresolution image based on the last residual layer F. F is the last layer
in the architecture in this case, F is the 25th convolution layer. The
aim of LR-to-HR Network is to reconstruct I𝐿𝑅 as similar as 𝐼𝐻𝑅
based on the residual layer that are trained with mugshot image. The
network is trained using base learning rate of 0.1 and gradually
decreases by factor of 10 overtime. To avoid exploding gradient𝜃 𝜃dynamic gradient clipping is introduced using − , , where α is the𝛼 𝛼current learning rate and θ is the gradient. As the learning rate
decrease, the gradient clipping also changes. This make gradientclipping more dynamic and prevent the exploding gradient.
LR-to-HR Network is tested using surveillance image and theenhanced image is analyzed using Peak-Signal to Noise Ratio(PSNR) and Structural Similarity Index (SSIM). PSNR and SSIM

Reference Grgic, M., Delac, K., and Grgic, S. (2011) SCface – Surveillancecamera face database. Multimedia Tools and Applications,51(3):863-879.
 Hore, A. and Ziou, D. (2010). Image quality metrics: PSNR vs.
SSIM. In 2010 20th International Conference on Pattern
Recognition, pages 2366-2369.
 Kim, J., Lee, J. K., and Lee, K. M. (2015). Accurate Image Super
Resolution Using Very Deep Convolutional Network. IEEE
Transactions on Pattern Analysis and Machine Intelligence,(32(2):295-307.
 Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P
(2004). Image quality assessment: from error visibility to
structural similarity. IEEE Transactions on Image Processing,13(4):600-612.

40

Big Data Summit 2: HPC & AI Empowering Data Analytics (co-locate with PRAGMA 35 Meeting), Penang, Malaysia, Oct 3 - 6, 2018

[BP05] Classification of Manufacturing High Dimensional Data Using Deep
Learning-based Approach
Umi Kalsom Yusof1,* and Mohd Nor Akmal Khalid1
1: School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia* Correspondent author: umiyusof@usm.my
Keywords: Industry 4.0, big data analytics, high dimensional data, classification, machine learning and deep learning-based.

Background and Motivation

larger training sets with labeled examples; (ii) powerful GPU
implementations, making the training of very large models practicaland (iii) better model regularization strategies (Xie et al., 2017).
These factors enabled the training of truly deep learning models. In
addition, deep learning models can learn to recognize high-levelfeatures by themselves.

In recent years, manufacturing equipment are equipped withsensors to facilitate real-time monitoring of the production process(Tello et al., 2018). This provides an opportunity to perform quality
control by predicting which manufactured products are at risk ofbeing defective. Traditionally, defect was recognized by humanexperts inspecting each wafer. However, the size of electronic chips
has decreased over time and is currently ≈65 nm, while wafers are
becoming larger. A typical wafer may carry 1500–3000 chips,
making expert evaluation impractical (Kim et al., 2016).
Recent adoption of information technologies in the manufacturingsector have prompted the emergence of the “smart factory” that
employs an advanced information technologies such as the Internetof Things (IoT), the cloud, big data analytics and cyber-physicalsystems. The concept of a smart factory was introduced in the
Hannover Fair 2011 with the vision of Industry 4.0. The concept
affects most manufacturing activities such as demand forecasting,production planning and control, scheduling, inventory and logistics
management (Kim et al., 2016).
A predictive analytics system that identifies defects in products
before they are finalized, or even shipped, can result in significant
savings for the manufacturer (Maurya, 2016). However, predicting
defect in manufactured products is a challenging machine learning
task due to the rarity of such failures in modern manufacturing
processes. As such, the product defects can be predicted byclassifying such rarity as data points with binary classes.
Classification is an important task of knowledge discovery indatabases and data mining. Classification modelling is to learn afunction from training data while makes as few errors as possible
when being applied to data previously unseen (Sun et al., 2007).
With the emergence of “big data”, many classification algorithms in
manufacturing are facing challenges, even though they used to be
successful in different fields. High-dimensional data have beenubiquitous in various fields, such as biomedicine, cancer diagnosis
using DNA data, and image classification (Yin and Gai, 2015).

Deep Learning and Future Outlooks
The layers of deep learning networks have escalated from several
to hundreds in just few years when applied to large amount of datawith more abstract and expressive representations (Xie et al., 2017).
However, simply stacking more layers onto current architectures is
not a reasonable solution, which incurs vanishing gradients by thetime it reaches the end of the network.
Looking from another perspective, the classification problem is
actually a dynamic process where the context is important although
the input may be static (Kim et al., 2016). To utilize this information,
a top-down (or feedback) connections that propagate the input
downwards can be found in deep belief networks (DBNs). Deep
belief networks (DBNs) are multi-layer generative models that learn
one layer of features at a time from unlabeled data (Erfani et al.,2016). DBNs perform non-linear dimensionality reduction on verylarge data sets and learn high-dimensional manifolds from the data.
As such, a novel classification model based on a prominent deeplearning and DBNs model which addresses high-dimensional data
would potentially provide high predictive accuracy with practicalcomputational efficiency.

References
Erfani, S. M., Rajasegarar, S., Karunasekera, S., and Leckie, C.
(2016). High-dimensional and large-scale anomaly detection using alinear one-class svm with deep learning. Pattern Recognition 58,
121–134.
Kim, H., Choi, B. S., and Huh, M. Y. (2016). Booster in highdimensional data classification. IEEE Transactions on Knowledge
and Data Engineering 28(1), 29–40.
Maurya, A. (2016). Bayesian optimization for predicting rareinternal failures in manufacturing processes. In 2016 IEEE
International Conference on Big Data (Big Data), pp. 2036–2045.
IEEE.
Sun, Y., Kamel, M. S., Wong, A. K., and Wang, Y. (2007). Costsensitive boosting for classification of imbalanced data. Pattern
Recognition 40(12), 3358–3378.
Tello, G., Al-Jarrah, O. Y., Yoo, P. D., Al-Hammadi, Y., Muhaidat,
S. and Lee, U. (2018). Deep structured machine learning model forthe recognition of mixed-defect patterns in semiconductor
fabrication processes. IEEE Transactions on Semiconductor
Manufacturing 31(2), 315–322.
Yin, H. and K. Gai (2015). An empirical study on preprocessing
high-dimensional class imbalanced data for classification. In High
Performance Computing and Communications (HPCC), pp. 1314–
1319. IEEE.
Xie, D., Xiong, J. and Pu, S. (2017). All you need is beyond a good
init: exploring better solution for training extremely deep
convolutional neural networks with orthonormality and modulation.
In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition, pp. 6176—6185.

Classification of High Dimensional DataClassifying high dimensional data poses a challenge forclassification due to the following two factors (Erfani et al., 2016):(i) Exponential search space – The number of potential feature
subspaces grows exponentially with increasing input dimensionality.
(ii) Irrelevant features – A high proportion of irrelevant features
effectively creates noise in the input data, which mask the true classof the data. The challenge is to choose a subspace of the data thathighlights the relevant attributes or features known as “curse ofdimensionality” (Yin and Gai, 2015).
As was reported by Kim et al. (2016), most of the features of highdimensional data are irrelevant to the target feature and the
proportion of relevant features. Finding relevant features simplifies
learning process and increases classification accuracy. Classification
performance could severely degenerate if the learning methods
directly implemented on high-dimensional or small sample data sets.
Alternatively, a deep learning model is one of the methods that cancircumvent the challenges of feature extraction and has achieved acommanding lead on the state-of-the-art for solving highdimensional data. In recent years, interest in deep learning models
have triggered due to several factors: (i) the availability of much

41

Big Data Summit 2: HPC & AI Empowering Data Analytics (co-locate with PRAGMA 35 Meeting), Penang, Malaysia, Oct 3 - 6, 2018

[BP06] Copy-Move Forgery Detection Using Convolutional Neural Networks
A. Z Abidin, A. A Samah*, H. A Majid, S. Safie, M. F Misman
School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia* Correspondent author: azurah@utm.my
Keywords: copy-move, video forgery, deep-learning, video tampering, video forensic, convolutional neural networksVideo is widely used as supporting evidences and historical records
in various field as an entertainment industry, video surveillance,
medical imaging, military, legal evidence, political videos, video
tutorials, advertisements, etc. (Gavade & Chougule, 2015; Pandey,
Singh, & Shukla, 2016). Unfortunately, the growth of user friendly
and easy-to-use multimedia editing tools, the editing or tampering
video sequence has become easier and faster without leaving any
noticeable traces of the tampering operations. The result of thetampering has become a serious social security event (Rocha, 2011).
Therefore, effective detection of video tampering is of great
importance for digital video forensics which is the main focus of thisstudy. Copy-move video forgery is classified under spatial
tampering attack which is a typical video tampering that needs to bedone on all related frames to tamper some regions due to thecorrelation between frames (Chen et al., 2012). Although many
forgery detection techniques have been proposed till date, there is
still less study focusing on video copy-move forgery detection. The
difficulty on tampering detection is due to the original and tampered
region might be having the same artifacts or the forged area undergo
geometric transformations and retouching (Sitara & Mehtre, 2016).
Deep learning (DL) is a particular kind of machine learning that
accomplish significant ability and flexibility by learning using theconcept of nested hierarchy, with each concept defined in relation tosimpler concepts. In this study, we investigate and develop the deeplearning method used on features extraction which helps to reducethe computational time. DL provides a novel approach to theidentification of features for the forged regions, which inherently
represent characteristics of the forged regions appearing in thedataset. Thus, this method greatly saves time and energy to find newfeatures from a set of videos. Although some DL methods have been
implemented in video forgery detection, only a few have beenstudied for video copy-move forgery. The successful of DL in
producing the excellent prediction results depend on the rightarchitecture because of every architecture has their strengths andlimitations. DL architecture is including the development of pretraining layers and training layers, back propagation for parametersoptimization and regularization. The goal of DL architecture is tofind the optimal parameters in each layer. Therefore, by choosing
right architecture with the right type of data can boost the result
analysis. Hence in this on-going study, a method in Deep Learning,
ConvNet are proposed to detect video copy-move forgery. ConvNet
is one of the deep learning method which capable to extract featuresinstead of using the handcrafted feature. The network will extract themost suitable feature and output in the form of feature map. The
performances of the proposed method will be measured usingclassification accuracy, specificity and sensitivity. The measured
performance will be compared with previous method on video copymove forgery by using SULFA dataset. Recently, video forensics inparticular video forgery detection has emerged as an indispensable
research field. Our ongoing study has identified that video forensics
still presents many unexplored research issues, due to many factors
such as peculiarities of video signals with respect to images and thewider range of possible alterations that can be applied on this typeof digital content. Thus the research field offers a lot of additional
research work and potential future works that can be done incollaboration with other researchers. We welcome partnership
opportunities from other research institutes, industry andgovernment.









Chen, R., Dong, Q., Ren, H. and Fu, J. (2012). Video forgery
detection based on non subsampled contourlet transform andgradient information. Information Technology Journal.
11(10):1456-1462.
Gavade, J. D. and Chougule, S. R. (2015). Review of techniquesof digital video forgery detection. Advances in Computer Scienceand Information Technology (ACSIT). 2(3): 233-236.
Pandey, R. C., Singh, S. K. and Shukla, K. K. (2016). Passive
forensics in image and video using noise features: A review.
Digital Investigation. 19:1-28.
Rocha, A., Scheirer, W., Boult, T., et al.: Vision of the Unseen:
Current Trends and Challenges in Digital Image and Video
Forensics. ACM Computing Surveys 43(5), article number:26(2011).
K. Sitara, B.M. Mehtre, Digital video tampering detection: An
overview of passive techniques, Digital Investigation, Volume 18,
2016, Pages 8-22, ISSN 1742-2876.

Authors Biographies
A. Z Abidin is currently a MSc full time research student
at School of Computing, UTM Skudai. Her research area
is on Video Forgery Detection using Convolutional Neural
Network (CNN).

A.A Samah is a senior lecturer at School of Computing,
Her research interest includes Simulation Modelling,
Image Processing and Business Intelligence.

H.A Majid is a lecturer at School of Computing. His
research area is on Optimization, Image Processing, andBusiness Intelligence.

S. Safie is currently a PhD student at School ofComputing, UTM Skudai. Her research area is on Video
Forgery Detection using Optical Flow Method.

M.F Misman is currently a PhD student at School ofComputing, UTM. His works is on Video Forgery
Detection using Convolutional Neural Network (CNN)
and Long Short Term Memory (LSTM).

42

[BP07] COGNITIVE-BASED APPROACH FOR BUSINESS INTELLIGENCE
Herison Surbakti1,2, Azman Ta’a2
1: Fakultas Sains dan Teknologi, Program Studi Sistem Informasi, Universitas Respati Yogyakarta, Indonesia.
2: School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Malaysia.
E-mail: herisonsurbakti@respati.ac.id, azman@uum.edu.my
Keywords: Business Intelligence, Cognitive-based Approach, Data Analytics, Tacit Knowledge, Unstructured Data
Purpose of study
Business intelligence (BI) plays key role to empowering thedata and knowledge within the organization and uses extract,transform, and load (ETL) functions to capture, transform, andintegrate the structured and unstructured data into a structured data
warehouse (DW). Noticeably, the tacit knowledge in a higher
institution, especially in libraries department contains huge of
information and knowledge arises from the librarian inspiration andexperiences during exploring solutions to the various problems(Sihui and Xueguo, 2016). This situation has resulted multiple data
sources in the institutions often contradict each other, and cause pooroutcome in data analysis. Therefore, this study introduced anenhanced BI framework with cognitive-based approach tosystematically manage this problem.

Figure 1. BI Framework Using Cognitive Approach
The proposed model of BI framework has been developed byadapting KID model for data cycle and cognitive analytics to captureand extract tacit knowledge as shown in Figure 2.

Significance of study
Cognitive-based approach is a technique that ideally suited forthe capturing tacit knowledge from among the massive data available
these days. Moreover, the current BI system not be able to capture
the data from the human skills and experiences due to the limitationof its framework (Bakar and Ta’a, 2014). Therefore, the BI
framework produced from this study will helps developers to manage
tacit knowledge in data analytics efficiently.
Research method
This research has investigated the limitations of BI frameworkin capturing various data types to identify the problem in handlingthe tacit knowledge for data analysis. The proposed model for
managing tacit knowledge was adapted from Knowledge
Information Data (KID) and cognitive approach model that proposedby Sato and Huang (2015). The approaches are able to develop a newdata centric model that works with traditional structured data as wellas unstructured data including video, image, and digital signal
processing. The design will be used as the method to develop the
cognitive-approach for capturing tacit knowledge and combines withthe cognitive analytics to make the tacit knowledge as codified data
source in a BI system.

Figure 2. The KID model used in BI Framework
Conclusion
Cognitive system is the process of replicating the humanthought processes in a computerized system or a model. Thus, the
new approach for data analytics can be achieved by having the tacitknowledge to be analyzed in BI system. This research will assess thetechnique in capturing tacit knowledge, such as cognitive mapping
technique, convert the captured tacit knowledge into unstructured
data using cognitive analytics method, and absorb the incoming data
as well as update knowledge as it happens by human tasks using the
KID method. Thus, the captured tacit knowledge as mostly presented
in unstructured data can be used in BI system for data analysis
successfully.

The Business Intelligence Framework
Tacit knowledge can enrich the data analysis process. The formof tacit knowledge need to be converted as the codify data according
to the ability of BI framework in processing the available data. The
use of cognitive as an advanced approach to capture and extract tacitknowledge by elaborating the methods of predictive analytics,
stochastic analytics, and cognitive computing in BI system. Thus, the
new BI framework can be illustrated as shown in Figure 1.
Furthermore, to convert the tacit knowledge to be a codify data or
explicit knowledge, the cognitive approach will be needed such as
cognitive mapping and cognitive analytics process. The converted
tacit knowledge will be stored in the repository as the available data
to re-use by instantiation, interpretation, and assimilation cycle
process as shown in Figure 2.

References
Bakar, M. S. A. & Ta’a, A. (2014). Business Intelligence Modellingfor Graduate Enterpreneur Programme. Journal of Information andCommunication Technology (JICT), 13(1), 55–86.
Sato, A. & Huang, R. (2015). From Data to Knowledge: A Cognitive
ApproachtoRetailBusinessIntelligence.
210-217.
doi:10.1109/dsdis.2015.106.

43

Big Data Summit 2: HPC & AI Empowering Data Analytics (co-locate with PRAGMA 35 Meeting), Penang, Malaysia, Oct 3 - 6, 2018

[BP08] iFR: A New Framework for Real Time Face Recognition with Machine
Learning
Syazwan Syafiqah Sukri1,*, Nur Intan Raihana Ruhaiyem1
1: School of Computer Sciences, Universiti Sains Malaysia, Malaysia* syazwansyafiqah@student.usm.my
Keywords: Face Recognition, Machine Learning, Tensorflow, Deep learning, Eye blink detection

Abstract

benchmarks in recognizing face [3]. During the training process, thefaces are aligned using Multi-task Convolutional Neural Networks(MTCNN) [4] to identify detect and align the faces by making eyes
and bottom lip appear in the same location on each image.

Face authentication has a significant usage in various
applications. The trend of using face identification technology to
unlock screen in smartphones shows a good acceptances of faceauthentication as a viable security measure. Other than security
purposes, face authentication can be used for taking the attendance.
Although we have fingerprint option to take the attendance, there aresome flaws that we can highlight such as fingerprint can becontaminated and hygienic issue could arise as fingerprint is acontact-based authentication.
This research is carried out to study about deep learning
and Tensorflow framework which are implemented in developing areal time face recognition. As deep learning is using gradient-based
optimization, Tensorflow is especially suitable for it [1]. Apart from
that, we also want to eliminate spoofing by using eye blink detection.
We use eye blink detection to differentiate between real face andphoto attack. In average, a normal person will blink 15-20 times per
minute which is a person blinks in every four seconds. Besides, we
intent to develop a system without human intervention as machinelearning is capable to learn things and make decision by itself withthe given data.
This system starts with face detection, eye blink
detection and face recognition. In face detection phase, we are using
pre-built classifier which is Haar cascade classifier from OpenCV in
Python language to detect a face. We use two classifiers which arehaarcascade_eye.xml and haarcascade_frontalface_alt.xml. The
accuracy of face detected will be handled by scaleFactor parameter.
The lower the scaleFactor, the face detection will be less accurate
and vice versa. A bounding box will be drawn around the face(s)detected.
Next is eye blink detection. In this phase, we make useof OpenCV and dlib in Python language. Eye blink detection process
starts with facial landmark detection to localize the eyes in given
frame from video stream (webcam). Then, eye aspect ratio (EAR)
for each eye is computed. It gives us a singular value, relating the
distance between the vertical eye landmark points to the distances
between the horizontal eye landmark points. To determine a personis blinking or not, threshold is set where EAR will remain
approximately constant when the eyes are open and will rapidly
approach zero during a blink, then increase again when the eyes are
open. The system will terminate if the face is detected but eye blink
is not detected. For early stage, we set a time constraint for thesystem to detect eye blink of the face detected. If the time constraint
is exceeded, then the system will not proceed to recognition phase.
The process is followed by recognition phase. This iswhere deep leaning in Tensorflow framework is applied. We use a
pre-trained model, FaceNet [2] to build the face recognition networkin this system. In FaceNet model, the triplet loss is calculated fortraining. Triplet loss is about minimizing the distance between an
anchor and a positive which both have the same identity andmaximizing the distance between an anchor a negative which bothhave different identity. For our training data, we use CASIA Face
Image Database Version 5.0 which has 2500 color facial images of
500 subjects. This dataset has typical variations which include
illuminations, poses, expressions, eye-glasses and imaging distance.
Apart from that, we also use Labeled Faces in Wild (LFW) as our

Deep learning is the leading subset of artificial
intelligence currently been discussed among researches anddevelopers. Thus, this system is developed to take the advantage oflatest technology to improve the previous system [5]. In this researchstudy, we are mainly focusing on developing a system that is nothardware-dependent and that is why we emphasize on implementing
deep learning and eye blink detection in this system. As our future
work, we will focus on twin problem which is one of the challenging
problems in face recognition.

References
1.

2.

3.

4.

5.

44

Pattanayak, S. (2017). Introduction to Deep-Learning Concepts
and TensorFlow. Pro Deep Learning with TensorFlow, 89-152.
doi:10.1007/978-1-4842-3096-1_2
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A
unified embedding for face recognition and clustering. 2015
IEEE Conference on Computer Vision and Pattern Recognition(CVPR). doi:10.1109/cvpr.2015.7298682
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller.
Labeled faces in the wild: A database for studying facerecognition in unconstrained environments. Technical report,
Technical Report 07-49, University of Massachusetts, Amherst,
2007.
Liu, X., Fang, Z., Liu, X., Zhang, X., Gu, J., & Xu, Q. (2017).
Driver Fatigue Detection Using Multitask Cascaded
Convolutional Networks. IFIP Advances in Information andCommunication Technology Intelligence Science I, 143-152.
doi:10.1007/978-3-319-68121-4_15
Sukri, S. S., Ruhaiyem, N. I., & Mohamed, A. S. (2017). Face
Recognition with Real Time Eye Lid Movement Detection.
Advances in Visual Informatics Lecture Notes in ComputerBig Data Summit 2: HPC & AI Empowering Data Analytics (co-locate with PRAGMA 35 Meeting), Penang, Malaysia, Oct 3 - 6, 2018

[BP09] Automatic Liver Tumor Detection using Deep Learning: Triplanar
Convolutional Neural Network Approach
S. H. Chung1, 4*, K. H. Gan1, A. Anusha2, R. Mandava3,
1: School of Computer Sciences, Universiti Sains Malaysia, Malaysia
2: Advanced Medical and Dental Institute, Universiti Sains Malaysia, Malaysia
3: Faculty of Computing Engineering and Technology, Asia Pacific University, Malaysia
4: Centre for Research and Development in Learning, Nanyang Technological University, Singapore* Correspondent author: shenghung@hotmail.com
Keywords: Deep Learning, Triplanar Convolutional Neural Network, Liver Tumor Detection

Introduction
Convolutional Neural Networks (LeCun et al., 1990) haverecently demonstrated successes in computer vision tasks such asclassification, segmentation and object recognition (LeCun et al.,2015), (Greenspan et al., 2016). This study proposed Triplanar
Convolutional Neural Network (ConvNet) for automatic liver tumor
detection in Computed Tomography (CT) images. The Triplanar
ConvNet incorporates three different views of liver tumor, namely
axial, sagittal and coronal planes extracted based on center voxel ofinterests (VOI) to classify liver tumor and healthy liver in the LiverCT dataset obtained from MICCAI 2008 Liver Tumor Grand
Challenge (4 training, 6 test images) and MICCAI 2017 Liver Tumor
Segmentation Challenge (LiTS) (131 training, 70 test images) in
512x512 image resolution. Liver tumor is the second major causesof cancer death for 745,000 patients (World Health Organization,2014) and 788,000 patients (World Health Organization, 2015)
worldwide. In clinical routine, CT image is the most commonly used
modality for evaluation, surgery planning and progression
evaluation of liver tumor. However, challenges arise in segmenting
liver tumor from liver tissues such as low-contrast, variation of sizes
and irregular shapes. In this study, we overcome these limitations
using Triplanar ConvNet to automatically detect liver tumor basedon the discriminative features learnt from three orthogonal views ofliver tumor and healthy liver.

Fig.2 Triplanar Convolutional Neural Network framework.

Findings and Discussion
We trained Single-view and Triplanar ConvNet using GPU Tesla
K40 acceleration with 2880 cores. We used patches of 17x17 pixels
as the recommended optimum input dimension (Li et al., 2015)
extracted from the CT volumes for our ConvNet approaches. As
observed from the preliminary results (Table 1), the initial findingsof 5.5% accuracy improvement observed from 50 epochs to 300
epochs in the training dataset are promising to demonstrate the useof Triplanar views in gaining additional insights and comparativeevaluation for liver tumor segmentation in future work.
Table 1. Preliminary Evaluation Results

BackgroundA variety of algorithms including level-set, graph-cuts,
morphological operations, SVM and AdaBoost have been developed
for liver tumor classification and segmentation tasks. However, theapproach employed depend on hand-crafted features, the needs ofuser interactions and the algorithms performance highly depend on
the correct parameters during extraction and selection of features.
Inspired by the improved accuracy of Triplanar ConvNet shown inmedical imaging diagnosis work carried out in knee cartilage
segmentation, anatomical brain segmentation, lung noduleclassification and lymph node detection, we evaluated Single-view
ConvNet and Triplanar ConvNet for liver tumor segmentation.

Methods

Trainable
ParamsTriplanar

29474

Single-view

9826

Accuracy
Improvement~95% to ~97.5%

Training
epochs50 to 300

~90% to ~92%

50 to 300

Runtime(average)8s 17µs*
per epoch
2s 58µs*
per epoch

Acknowledgement
The authors would like to thank National Supercomputing
Computer, Singapore (https://www.nscc.sg). The computational
work for this article was done on resources of the National
Supercomputing Computer, Singapore.

Methodology

References

Fig. 1 and Fig. 2 illustrate Single-view and Triplanar ConvNet
respectively. Single-view ConvNet consists of 2 Conv. layers, 2
Max-pooling layers and fully-connected layer. Triplanar ConvNet isbuilt from three ConvNet architectures and accepts three streams of
inputs (1st input: Axial; 2nd input: Sagittal; 3rd input: Coronal). The
three views are subsequently merged using Merge layer (Fig.2) toextract learnable representation at the final layer.

Greenspan, H., Van Ginneken, B., & Summers, R. M. (2016). Guest editorial
deep learning in medical imaging: Overview and future promise of an
exciting new technique. IEEE transactions on medical imaging, 35(5), 11531159.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature,521(7553), 436-444.
LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E.,
Hubbard, W. E., & Jackel, L. D. (1990). Handwritten digit recognition witha back-propagation network. Advances in neural information processing
systems.
Li, W., Jia, F., & Hu, Q. (2015). Automatic segmentation of liver tumor in CT
images with deep convolutional neural networks. Journal of Computer andCommunications, 3(11), 146.
World Health Organization, W. (2014). World Cancer Report (Vol. 978-92832-0429-9).
World Health Organization, W. (2015). Cancer Factsheet.

Fig.1 Single-view Convolutional Neural Network framework*11 Microseconds (µs) = 1.0×10-6 Seconds

45[BP10] Selection Drought Index Calculation Methods Using ELECTRE(Elimination and Choice Translation Reality)
Addy Suyatno Hadisuwito1,* and Fadratul Hafinaz Hassan1
1: School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia*Correspondent author: addyshadisuwito@student.usm.my
Keywords: Drought Index, Electre, Calculation Methods, KBDI
Drought index is an important measurement to monitor a forestfrom potential fires. In most cases, forest fires occur because theforests have a high drought index. One of the causes of the highdrought index is a low rainfall condition. Drought is a recurring
natural hazard characterized as having below normal precipitation
over an extended period of time ranging from months to years (Dai,2011).

This research to find out the suitable methods to calculate
the drought index in a tropical forest. The Taman Hutan
Rakyat Suharto, District of Kutai Kartanegara, East
Kalimantan, Indonesia will be chosen as the case study as it
is one of the tropical forest. Also, the Taman Hutan Rakyat
Suharto are among the tropical forest with high risk in suffersfrom forest fire. The outcome of this study is that therecommended alternative becomes a reference in calculating
the drought index in the tropical forest.
Comparison between methods has also been done by Homdee,(2016) and Jain, (2015). (Homdee, Pongput and Kanae, 2016)
conducted a comparison the performance of three climatic drought
indices to characterize drought trends in the Chi River basin in
Northeast Thailand. Initially, the drought assessment was conductedusing the Standardized Precipitation Index (SPI), a precipitationbased index, and the Standardized Precipitation Evapotranspiration
Index (SPEI), an index taking into account the difference between
precipitation and potential evapotranspiration (PET). (Jain et al.,2015) did research to compare Standardized Precipitation Index(SPI), Effective Drought Index (EDI), statistical Z-Score, China ZIndex (CZI), Rainfall Departure (RD), Rainfall Decile basedDrought Index (RDDI) for their suitability in drought prone districtsof the Ken River Basin. Based on our knowledge both studies
compare the results, not the elements of the method.
In this paper we will compare seven types of drought index
calculation methods which are also known as ‘alternatives’. Thereare (1) the Palmer Drought Severity Index (PDSI; Palmer, 1965), (2)Keetch-Byram Drought Index (KBDI; Keetch et al., 1968), (3)Bhalme and Mooley Drought Index (BMDI), (4) the Standardized
Precipitation Index (SPI; McKee et al., 1993), (5) Effective Drought
Index (EDI), (6) The McArthur Forest Fire Danger Index (MFFDI)
and (7) the Standardized Precipitation Evapotranspiration Index(SPEI; Vicente-Serrano et al., 2010).
The method used to compare seven alternatives is the ELECTRE(Elimination and Choice Translation Reality) algorithm. The electre
algorithm uses a matrix calculation basis using five variables which
are also known as ‘criteria’ referenced in the decision. There are (1)calculation period, (2) the type of data needed for calculation, (3) thecomplexity of the formula used, (4) suitable for use in the tropicalforests, and (5) the type of calculation result scale. Rating suitabilityof each alternative on each criteria, rated 1 to 5 with the provisionsof 1 for ‘Very bad score’, 2 for ‘Bad score’, 3 for ‘Enough score’, 4for ‘Good score’, and 5 for ‘Very Good score’. For example, decision
makers give preference weights as W = [5, 3, 4, 4, 5].
ELECTRE consists of seven steps: (1) Normalization decision
matrix; (2) Weighted matrix normalized; (3) Determine the set ofconcordance and discordance index; (4) Calculate the matrix ofconcordance and discordance; (5) Determine the dominant matrix ofconcordance dan discordance; (6) Determine of agregat thedominant matrix; (7) Elimination of less favourable alternative.

46

The calculation results show that an alternative elimination matrix
that has a value of 1 is the element e65 and e26. The e65 elementmeans that the 6th alternative (MFFDI) is more recommended than
the 5th alternative (EDI). Whereas the e26 element means that the
2nd alternative (KBDI) is more recommended than the 6thalternative (MFFDI). So it can be concluded that the 2nd alternative(KBDI / Keetch-Byram Drought Index), the most recommended
alternative to calculate drought index at The Taman Hutan Rakyat
Suharto.

References Dai, A. (2011) Drought under global warming: a review. Wiley
Interdiscip. Rev. Clim. Change 2, 45e65.
 Homdee, T., Pongput, K. and Kanae, S. (2016) ‘A comparative
performance analysis of three standardized climatic drought
indices in the Chi River basin, Thailand’, Agriculture and Natural
Resources. Elsevier Ltd, 50(3), pp. 211–219. doi:
10.1016/j.anres.2016.02.002.
 Jain, V. K. et al. (2015) ‘Comparison of drought indices forappraisal of drought characteristics in the Ken River Basin’,
Weather and Climate Extremes. Elsevier, 8, pp. 1–11. doi:
10.1016/j.wace.2015.05.002.
 McKee, T.B., Doesken, N.J., Kleist, J. (1993) The relationship ofdrought frequency and duration to time scales. In: Proceedings of
the 8th Conference on Applied Climatology Boston, MA, USA,pp. 179e183.
 Palmer, W.C. (1965) Meteorological Drought. US Department ofCommerce, Weather Bureau Washington, DC, USA.
 Vicente-Serrano, S.M., Begueria, S., Lopaz-Moreno, J.I. (2010) A
multiscalar drought index sentitive to global warming: thestandardized precipitation evapotranspiration index. J. Clim. 23,
1696e1718.

Authors Biographies
Addy Suyatno Hadisuwito is postgraduated student(PhD) at the School of Computer Sciences,
Universiti Sains Malaysia. Completed bachelor's
degree in Informatics Engineering, University ofTechnology Yogyakarta, and completed master's
degree in Master of Computer Science, The
University of Gadjah Mada, Yogyakarta. The field of
research that is artificial intelligence, especially artificial neuralnetworks, expert systems, decission support system, fuzzy logic, andalgorithm analysis.
Fadratul Hafinaz Hassan is senior lecturer at Schoolof Computer Sciences, Universiti Sains Malaysia.
PhD graduate computer science in the Centre forIntelligent Data Analysis, Brunel University, West
London in 2013 and completed master’s degree in
Master of Science, Information Technology,
University of Science Malaysia. Specialising in
optimisation algorithms, also highly experience in statistical analysis
and pedestrian simulation, familiarity with pedestrian flow planning,
crowd behaviours and spatial planning.

[BP11] Deep Bayesian for Opinion-target identification
Omar Mustafa Al-janabi1, Nurul Hashimah Ahamed Hassain Malim2& Yu-N Cheah3
1,2,3: Universiti Sains Malaysia, School of Computer Sciences, Malaysia*Correspondent author: Omar37513@gmail.com
Keywords: Deep Learning, Bayesian Inference, Opinion Mining, opinion-target
performance of the Bayesian nonparametric model while it’s infer theposterior of hidden variables.

PurposeThe purpose of this work is to seek if the Deep Learning models canbe adapted to improve the performance of Bayesian nonparametricmodel. The nonparametric Stick Breaking process potently has
infinite number of components. It is proposed to identify the opiniontarget in opinion mining task. Nonparametric model is powerful todetermine the number of topics from the data. The given data are thedocuments and the components are the distributions of terms thatreflect the topics (or “clusters”). Implementing nonparametric modelon huge data requires multiple passes through all data and it’sinteractable for large scale application (Wang, Paisley, & Blei, 2011).
To improve the divergence with large scale of data (or “big data”), a
deep learning algorithm is being proposed to be integrated as
sampling algorithm into the nonparametric Stick Breaking process.

Generally, Deep learning uses many heuristics to train huge data andperform perfect prediction with highly sophisticated models (Silver
et al., 2016). Neural network is a parametrized function, where theparameters are the weights and biases of the neural network. From astatistical point of view, neural networks are efficient non-linear
function approximators and representation learners. While mostly
used for classifications, and they have been extended to unsupervised
learning with AutoEncoders to estimate multimodal distribution(Kingma & Welling, 2013).
However, as has been stated in the background, to infer the opiniontarget (or “word”) in hidden variable, a posterior inference
approximation is required. In this work we propose deep learning toinfer the posterior by denoting the weight of neural network as theposterior.

BackgroundNonparametric Bayesian models like Stick Breaking process is a
powerful model for unsupervised analysis of grouped data(Guo, Pan,
& Cai, 2017). Stick Breaking process is applied to document
collections, it’s provide a nonparametric topic model, where
document is viewed as group of observed opinion-target (or “words”),
and components are (presented as topics) distribution over words.
Unlike normal/traditional clustering, Stick Breaking process infer thenumber of topics from the data. However, Bayesian probabilistic
model allows very flexible interaction with the observed points of thedata. It is mainly concerned with insight and learning from the data.
The Stick Breaking process is inherently experience the Bayesian.
Where it is a joint distribution of hidden variables Z and observed
variables X as shown in the equation 1:

P Z, X

Conclusion
The performance of nonparametric probabilistic models has attracted
many researchers because it infers the topics (or “clusters”) based on
the given data. It is unlike the parametric/traditional clustering that isrequired to specify the number of cluster in advance. Yet, thedeficiency of nonparametric models experiences the Bayesianposterior inference, where these posteriors are intractable in when it’sconducting massive amount of data. This deficiency generated
because the sampling algorithms are required high number of
iteration to produce better inference. And that leads into inaccurate
clustering and time consuming. To improve the posterior inference a
deep learning is proposed to represent the weights in the neuralnetwork as posterior in the nonparametric Stick Breaking process.

1

References

Inference about unknown/hidden variable is through the posterior,the conditional distribution in equation 2 gives the observations.

P(Z, X)P ZX =
P(X)

Guo, X., Pan, B., & Cai, D. (2017). Robust Asymmetric Bayesian
Adaptive Matrix Factorization.

2

Kingma, D. P., & Welling, M. (2013). Auto-Encoding Variational
Bayes.

The Posterior inference or the denominator of the posterior is
intractable, and much research is dedicated to developing an inference
algorithm to tackle this deficiency(The, et al. 2006) (Zhuolin, et al,2014). Variational Inference has been recently developed (Wang etal., 2011) to draw the samples from the posteriors. E.g. fit a
distribution (e.g. normal distribution) to the posterior turning asampling problem into an optimization problem. Unfortunately, when
it is come the implementation of the variational inference on the
Machine Learning problems e.g. clustering algorithms, it faces
inadequacies. Such as, it’s often play second fiddle with posterior
inference in terms of accuracy and scalability. However, we haveproposed the deep learning mechanism to confront this issue as
elaborated in the next section.

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den
Driessche, G., … Hassabis, D. (2016). Mastering the game of
Go with deep neural networks and tree search.
Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006).
Hierarchical Dirichlet Processes. Journal of the American
Statistical Association,
Wang, C., Paisley, J., & Blei, D. M. (2011). Online Variational
Inference for the Hierarchical Dirichlet Process. Aistats 2011.
Zhuolin, Qiu Bin, Wu Bai, W., Chuan, S., & Le, Y. (2014). Collapsed
Gibbs Sampling for Latent Dirichlet Allocation on Spark.
JMLR: Workshop and Conference Proceedings, (2004), 17–
28.

MethodThe Deep Bayesian is the prior distributions on the weight. We tend
to put normal distribution over the weights to improve the47[BP12] The Effect of Vocal Cues on Trust at Zero Acquaintance
Deborah Y. H. Ooi1, Syaheerah L. Lutfi2
1: Dept. of Computer Science, Universiti Sains Malaysia, Malaysia
2: Dept. of Computer Science, Universiti Sains Malaysia, Malaysia
Corresponding author: syaheerah@usm.my
Keywords: Vocal Cues, Prosody, Trust, Zero Acquaintance, Voice
Trust is essential in many human relationships. In fact, trust isvital wherever risk, uncertainty, or interdependence exist. Without
trust, education in general would be much more difficult as thestudents would lack trust in their teachers and vice versa. Trust isnecessary to maximise learning in a teacher-student relationship.
Furthermore, trustworthiness can be gauged in a zero-acquaintancesituation. Zero acquaintance is a situation in which perceivers makejudgments about targets they are given no opportunity to interact
with. Intuitive judgments at zero acquaintance are proven to bestrong predictors of the performance of teachers, salespeople, andother professionals.
In recent years, several researches have been conducted on thefactors that determine trustworthiness. Particularly, many studies
have shown the significance of voice in perceived trustworthiness.
For instance, a research has found that verbal exchanges between aneonatal-care nurse and mother greatly influences a woman's
confidence. Another research supports that fact by indicating thataudio communication shows a more significant emergence of trust
than text chat.
Despite recent few research works that analysed the effect ofvarious vocal cues (e.g., phonetic segmentation, speech rates, andfundamental frequency(f0)) on trustworthiness, research addressing
the factors of trustworthiness at zero acquaintance is scarce andlimited. Especially, we are still far from consensus regarding thedirection of association between different vocal features ---in
particular, speech rate and fundamental frequency (f0)--- andtrustworthiness. Thus, there is a need to investigate deeper into thisarea and this paper plans to do just that. Furthermore, this paper also
aims to identify the characteristics that different ethnics in Malaysia
base their judgement of trustworthiness on.
MethodFigure 1: A general overview of the investigation flow

To carry out this investigation, 120 different voice samples havebeen obtained from the OMG-Emotion behaviour dataset (Barros etal., 2018). Each of these voice samples will be cropped, acousticallyanalysed and digitally manipulated in 4 different ways, namely
increasing and decreasing the rate and f0 respectively. The extent towhich it will be manipulated is based on previous research conductedby Trouvain et al. (2006) and Smith et al. (1975). This will result in
the formation of 600 audio clips, which are then divided into 120
sets respectively. Figure 1 shows a graphical representation of theoverall methodology used in this investigation. In this manner, thesemantic content of the audio clips within each set are completely
identical and controlled, hence enabling the researcher to moreclearly identify the impact of rate and f0 on trustworthiness ratings.
Each participant in this study will be asked to rate thetrustworthiness of 5 different sets on a scale of 1 to 10 (1 being least
trustworthy and 10 being most trustworthy). Since Peninsular
Malaysia consists of 3 main ethnic groups, namely Malay, Indianand Chinese, this research will also include participants from all48

these races. The ratings given by each ethnic group will be recorded
and analysed accordingly. Each set will be rated 3 times, one fromeach ethnic group. The participants will submit their ratings via a
simple online assessment indicating the amount of trust they would
place in the speaker.
Albeit a traditional approach, the assessment method will be able
to investigate the reasons behind perceived trustworthiness of anindividual. A more conventional approach such as the trust game is
not applicable to this study as the responses obtained might beaffected by the attitudes of the sender and receiver (whether they arerisk taking individuals, cautious individuals, etc).
Finally, the ratings of each set are analysed. Firstly, the effects ofrate and f0 on the trustworthiness ratings, as compared to the original
will be analysed. This scope of analysis will be termed ``intra-set''.
Within each set, the correlation between speech rate, f0 and thetrustworthiness ratings will be analysed by using a correlation
matrix. The difference in ratings between each ethnic group will also
be evaluated and examined.
Next, the effects of the semantic content of the audio clips will
also be analysed. This can be carried out by investigating andcomparing the ratings between sets. Characteristics such as numberof pauses, stutters and non-lexical conversation sounds (filler
sounds) such as “um”, “uh”, and “hmm” will be taken into
consideration. The correlation between these characteristics and thetrustworthiness ratings will be analysed. This scope of analysis will
be called “inter-set''. The results will be accurately documented,
analysed and discussed.
Expected Results
By manipulating only the f0 and speech rate of each audio clip
and grouping the audio clips into sets of 5, the semantic content(what is being said), as well as all the other paralinguistic effects
such as pauses will be controlled. Hence, the effects of thosecharacteristics will be nullified, enabling the research to be
conducted solely on speech rate and f0. Having 600 audio clips (120sets) to analyse will provide a wide and broad sample size to enableevaluation from a variety of different aspects. Hence, the potential
outcomes of this investigation are plentiful. In addition to precisely
measuring the sole impact of pitch or speech rate on perceived
trustworthiness, this investigation would also be able to identify theeffects of the semantic content by analysing the difference intrustworthiness ratings from one original audio clip and another.
Furthermore, given sufficient participants from each ethnic group,
the difference in characteristics of perceived trustworthiness basedon ethnic groups (if any) will be identified. The outcomes are truly
endless and at the very least, a relationship is hoped to be identified
between voice characteristics and perceived trustworthiness.
References
Barros, P., Churamani, N., Lakomkin, E. et al (2018). The omgemotion behaviour dataset. doi: arXiv preprint arXiv:1803.05434
Smith, B. L., Brown, B. L., Strong, W. J. et al (1975). Effects ofspeech rate on personality perception. Language and Speech, 18 (2),145-152. doi: 10.1177/002383097501800203
Trouvain, J., Schmidt, S., Schröder, M. et al (2006). Modellingpersonality features by changing prosody in synthetic speech.
doi:10.22028/D291-25920

[BP13] EEG Channels Selection Using Hybridizing Flower Pollination and β-Hill
Climbing Algorithm for Person Identification
Zaid Abdi Alkareem Alyasseri1,2, *, Ahamad Tajudin Khader1, Mohammed Azmi Al-Betar3
1: School of Computer Sciences, Universiti Sains Malaysia (USM), Malaysia
2: Dept. of ECE, Faculty of Engineering, University of Kufa, Iraq
3: Dept. of IT, Al-Huson University College, Al-Balqa Applied University, Al-Huson, Irbid, Jordan* Correspondent author: zaid.alyasseri@uokufa.edu.iq
Keywords: EEG, Biometric Authentication, Feature selection, Flower pollination algorithm, β-hill climbing

abstract

Results and Discussion

Recently, electroencephalogram (EEG) signal present a great
potential for a new biometric system. Several studies showed that
EEG presents uniqueness features, universality, and naturalrobustness to spoofing attacks. The EEG signals represent thegraphical recording of the brain electrical activity which can bemeasured by placing electrodes (sensors) in various positions of thescalp. This paper proposes a new method for EEG channels selectionthat maximizes the accuracy recognition rate, which is measured bymeans of the radial basis function kernel support vector machine(RBF-SVM) classifier. The proposed method using a hybridizing
meta-heuristic method based on binary flower pollination algorithm(FPA) and β-hill climbing (FPA-βhc) for reducing the required
electrodes (channels) while maintaining accuracy rate performance.
The EEG autoregressive with three different coefficients have beenused as features extraction. The (FPA- βhc) is tested using a standard
EEG signal dataset, namely, EEG motor movement/imagery dataset.
The experimental results show the proposed approach can make useof less than a half of the number of channels while maintaining
recognition rates up to 96%, which is crucial towards the effective
use of EEG in biometric applications. It is worth mentioning that the
proposed method can achieve results that are better than the state-ofthe-art ones, as well as we draw future directions towards theresearch area.

Figure 2 shows the comparison based the convergence rate between
the proposed method (FPA-βhc) and standard (FPA). It is clear toobserve that the hybridization method (FPA-βhc) achieve better thanstandard FPA algorithm where it is achieved recognition rates up to96%.

Proposed Method
This section provides a discussion of the proposed methodologyof the hybridizing flower pollination and β-hill climbing algorithm
for person identification. The proposed method (FPA-βhc)
framework run through four phases where the result of each phase isan input to the consecutive one. The four phases are presented in
Figure 1.

49

Figure 2 Convergence rate of the proposed method and FPA.

References
 Alyasseri, Z. A. A., Khader, A. T., Al-Betar, M. A., & Awadallah,
M. A. (2018). Hybridizing β-hill climbing with wavelet
transform
fordenoising
ECG
signals. Information
Sciences, 429, 229-246.
 Rodrigues, D., Silva, G. F., Papa, J. P., Marana, A. N., & Yang,
X. S. (2016). EEG-based person identification through binary
flowerpollination
algorithm. Expert
Systems
with
Applications, 62, 81-90.
 Alyasseri, Z. A. A., Khader, A. T., Al-Betar, M. A., Awadallah,
M. A., & Yang, X. S. (2018). Variants of the flower pollination
algorithm: a review. In Nature-Inspired Algorithms and Applied
Optimization (pp. 91-118). Springer, Cham.

Authors Biographies
Z.A.A. Alyasseri is a lecturer at the University of Kufa,
Iraq. received the BSc in computer science from Babylon
University in 2007 and MSc in computer science from
University Science Malaysia (USM) in 2013. Since
September 2016, He has joined the Computational
Intelligence research group at the School of Computer
Sciences, University Science Malaysia (USM) for pursuing
Doctor of philosophy in the field of Artificial Intelligence (Brain-Inspired
Computing).
A.T. Khader received the BSc. and M.Sc. in Mathematics
from the University of Ohio,USA, in 1982 and 1983,
respectively. He obtained his Ph.D. in Computer Sciencefrom University of Strathclyde, U.K., in 1993. He iscurrently a Professor in the School of Computer Sciences,
Universiti Sains Malaysia. His current position is the Deanof School of Computer Sciences, USM. His researchinterest Mainly focuses on Optimization and Scheduling.
M.A. Al-Betar is an associate professor in the Dept ofComputer Science at Al-Huson University College, AlBalqa Applied Univ., Jourdan. He received a B.Sc and
M.Sc from Computer Science Department at Yarmouk
University, Jordan in 2001 and 2003 respectively. He
obtained PhD from the School of Computer Sciences,
USM in 2010. His research interests are mainly directed to metaheuristic
optimization methods and hard combinatorial optimization problems.

[BP14] Exploring Whole-Brain Functional Networks of Music-Linguistic from
Rhythmic Quranic Recitations and Language Proficiency
M.S. Shab1,*, A.I. Abd Hamid1, A. Ab Ghani2, N.S. Kamel3, M. Muzaimi1
1: Dept. of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia (USM) Health Campus, Malaysia
2: Kolej Islam Antarabangsa Sultan Ismail Petra (KIAS), Malaysia
3: Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi Petronas (UTP), Malaysia*Correspondent author: massyazwanee@gmail.com
Keywords: Rhythmic Quranic recitations, Language proficiency, fMRI, Functional networks(i)
Native Arabic speakers and non-native Arabic speakers
Introduction(ii)
Quranic and non-Quranic stimuli
Prior research to extend our understanding of neural bases(iii)
Different styles of Quranic verse recitations
underlying music and language input typically used songs to feature
the two cognitive domains. Manifesting related properties with
Significancemusic, Quranic recitation can be considered in the same
By investigating functional connectivity during auditory task, byperspectives. By manipulating either the linguistic or musicalunbiased whole-brain and data-driven approach, it is hoped that
dimensions (or both) of Quranic recitation and studying theirdifferent networks bearing significant importance in learning can beinterconnection, it is possible to get further insight regarding neuraldelineated. Understanding the interconnection can aid in outlining
networks that are fundamental to music and language cognition.
theoretical foundations of music-language processing at brain levelfrom the manipulation of music properties (rhythm, melody, tempo)
Background and Objective
and language proficiency, as demonstrated by huffaz (those who
Quranic verses have been synonymously recited in melodious andhave completely memorized the Quran), and from application view,
rhythmic ways in Muslim cultures. Despite expressing bothin improving the outcomes of learning and listening-related tasks.
combination of music and language, neuroimaging studies using
Quranic stimulations focused exclusively on the musical elements
References[1-3] while very little addresses the language impact [4-5], meaning,[1] Abdurrochman, A., Wulandari, R. & Fatimah, N. (2007).
the musical and linguistic components of Quranic stimuli are studied
The Comparison of Classical Music, Relaxation Musicon separate basis. Employing functional magnetic resonanceand the Quranic Recital: an AEP Study. Abstrak.
imaging (fMRI) recordings as measured by the blood-oxygenation[2] Fauzan, N., Shahidan, S. N. & Amran, N. H. (2017).
level-dependent (BOLD) signal fluctuations, this study seeks the
Identification of Dominant Wave during the Recitation ofneural foundation for melodic, rhythmic recitation of Quranic verse
Al-Mulk Verse with (without) Understanding Using EEG
when integrated with subjects’ language proficiency level.
Signal. O-JIE: Online Journal of Islamic Education, 3, 17.
Methods[3] Hassan, M. S. S. A. O. & Othman, S. A. (2013). Effects ofThirty (N=30) healthy, Muslim subjects will be divided into two
Quran Listening and Music On Electroencephalogram
groups: native Arabic speakers, n=15, and non-native Arabic
Brain Waves. The Egyptian Journal of Experimental
speakers, n=15 with inclusion and exclusion criteria. The
Biology (Zoology), 9(1), 119-121.
determination of Arabic language proficiency among the subjects[4] Nasir, S. A. M. & Mahmud, W. M. H. W. (2015). Brain
will be done by administering the Basic Inventory of Natural
Signal Analysis Using Different Types of Music.
Languages to assign them as native or non-native speakers. Seven
International Journal of Integrated Engineering, 7(3).
modes of listening will be presented as auditory stimuli; pre-test[5] Saleem, A. (2015). Does memorization without
resting condition, three Quranic recitations (Ayatul Kursi verse incomprehension
resultinlanguagelearning?
Murattal Asim, Murattal Susi and Tarannum Asli styles, with each(Dissertation). Cardiff University
characterized by distinctive qiraat, tempo and accentuation), two
non-Quranic stimuli (Arabic news and Arabic poem) and post-test
resting condition. Each stimulus will be given in random order.
Imaging data of the subjects will be acquired using a 3-Tesla MRI
scanner at Hospital USM (HUSM). Following preprocessing, thefunctional images will be registered to Montreal Neurological
Institute (MNI) standard human brain template. Functional neuralnetworks will be extracted and distinguished by applying theIndependent Component Analysis (ICA). Resulting spatial maps
(indicative of brain region’s activation and functional connectivity
strength) from SPM12 and MATLAB software will be used to testfor groups’ differences. Applying Bonferroni test, the result will beconsidered significant at p<0.05.
Anticipated Result
Different functional neural networks will be manifested, among
them, plausibly: language network, default mode network, attention
network, working memory network, attention network, emotional
network and etc., with each corresponding to a distinct set of neuralsubstrates. Diverse spatial distributions and activation patterns may
be exhibited between:

50[BP15] Parallel Text Acquisition and English-Malay Machine Translation
T.-P. Tan*, Y.-L. Yeong K. H. Gan, S. K. Mohammad,
School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia
tienping@usm.my
Keywords: Parallel Text Acquisition, Statistical Machine Translation, Neural Machine Translation

Introductionreference translation.
For testing a MT, a different set of parallel text test was collected.
The tests evaluate MTs in both the news and computer science
domain. For news domain, parallel text was extracted from
MalaysiaKini news portal that produces news in English and Malay.
Most of the news generated by this portal contains passages andsentences that are similar. To match English and Malay news
sentences, the BleuAlign sentence alignment algorithm was applied.
For testing MT on a specialized domain, we extracted sentences in
the computer science domain from the exam questions of the Schoolof Computer Sciences, Universiti Sains Malaysia. These
examination papers are a good source for building a parallel text
corpus because the exam questions exist in bilingual since the year
2000.

Machine Translation (MT) is a process of translating text from asource language (for example English) to a target language (forexample Malay) using a software. MT provides the convenience oftranslation that is fast and at a low cost to consumers, and the qualityof the translation produced are improving. Two state-of-the-art MTarchitectures are Statistical Machine Translation (SMT) and Neural
Machine Translation (NMT). SMT modeling translations byextracting the statistics from text corpora. On the other hand, NMT
learns translation patterns using a neural network architecture. Bothapproaches create a translation model from translation examples fora pair of languages, which is known as parallel text. A parallel text
consists of aligned bilingual phrases or sentence pairs that aretranslation of each other. Usually, the bigger the parallel text corpus,
the better the quality of the translation produced.

Experiments and Results
Parallel Text Acquisition
We manage to collect more than 300 thousand English-Malay
parallel sentences for training. About 250 thousand pairs frombilingual dictionaries and 70 thousand pairs from thesis and articles.
On the other hand, 200 thousand and 70 thousand parallel sentences
for testing were extracted from news and computer science domain
respectively. We randomly selected 4 thousand sentences for testingon SMT and NMT. Moses toolkit (Koehn, et al., 2007) was used tobuild the English-Malay SMT. A Malay text corpus (Tan, et al.,2009) with about 870 MB was used to build a Malay 4-grams
language model using SRILM (Stolcke, 2002). The NTM has abidirectional encoder-decoder LSTM architecture with attention
mechanism (Bérard, et al., 2016). The BLEU score for SMT was
higher compared to NMT in the news domain. The baseline SMT
obtained a BLEU score of 48.35, while the BLEU score for thebaseline NMT is only 39.31. The usage of a language model in SMT
was the reason why SMT obtained a very high BLEU score in thenews domain. The BLEU score for computer science domain arecomparable at 21.13 and 21.21 for SMT and NMT respectively.

We collected an English-Malay PArallel Text (EMPAT) corpus.
This corpus consists of parallel text for training and testing. The
parallel text for training was extracted from bilingual dictionaries,
theses, and articles. The testing part on the other hand was extracted
from news articles and exam questions.
A bilingual dictionary contains many of translation examples thatcan be a good resource for building a parallel text corpus. An OCR
was first used to scan dictionaries. Normally, the translation
examples are in a particular order. Thus, regular expressions can beapplied to extract these sentences. In some cases, language
identification algorithm has to be used to separate the sentences. One
way to determine the language of a sentence is by using n-gram
language model. Spelling correction were also carried out. This weredone using minimum edit distance and n-gram model. Anothersource to acquire English-Malay parallel sentences is theses andarticles. In Malaysia, it is customary in many journals and theses tohave the abstract to be written in Malay and English. In addition, an
abstract from an article also contain many recent terms fromdifferent domains that are useful in translation. Thus, the abstract ofthese documents provides another source for us to extract EnglishMalay parallel text. The other source where English-Malay paralleltext can be found is in the abstract of a document. The theses
produced by Universiti Sains Malaysia and journal articles were
downloaded from an open access repository web site using web
crawlers. These documents were from various fields such as social
science, humanities, business, computer science, engineering and soforth. The title and abstract were identified using keywords. The text
was segmented based on sentence, and some pre-processing was
performed such as separating the punctuations from words andconverting uppercase letters to lowercase letters. The last step is toalign the sentences in the English abstract file to the corresponding
sentence in the Malay abstract file automatically using an alignment
algorithm. This was done because the sentences in both languages
are not necessarily in the same order. In addition, not every sentencein the abstract was translated. The BleuAlign tool (Sennrich & Volk,
2010) was used for aligning the sentences. The reference translation
was generated using an initial SMT. BleuAlign will then find thetarget language sentence that have the highest BLEU score for eachAcknowledgement
The work is supported by Fundamental Research Grant (FRGS)
from Ministry of Higher Education Malaysia.

References
Sennrich R, Volk M (2010) MT-based sentence alignment for OCRgenerated parallel texts. Proceedings of AMTA.
Koehn P, et al. (2007) Moses: open source toolkit for statisticalmachine translation. Proceedings of ACL.
Tan T.-P, et al. (2009) MASS: A Malay language LVCSR corpus
resource, Proceedings of Oriental Cocosda’09.
Stolcke A., SRILM – An extensible language modeling toolkit.
International Conference on Spoken Language Processing,
Denver, 2002.
Bérard A, Pietquin O, Besacier L, Servan C (2016). Listen andtranslate: A proof of concept for end-to-end speech-to-texttranslation, Proceedings of NIPS.

51

[BP16] Characterizing Compute-Intensive Tasks as a Factor of Network
Congestion
Norfazlin Rashid1,2,* and Umi Kalsom Yusof1
1: School of Computer Sciences, University Science Malaysia, Penang, Malaysia
2: Faculty of Engineering Technology, University Malaysia Perlis, Perlis, Malaysia* Correspondent author: norfazlin@unimap.edu.my
Keywords: computation congestion, cloud data centre, network congestion, characterization of computation congestion.

Purpose of Study

Google Cluster Trace, a set of production-run cloud computing
workload data consisting of 12 583 machines, with approximately
600 000 jobs and 20 million tasks which was collected for 29 days.
This data trace discloses some of the scheduling complexities that
affect Google’s workload, including the variety of job types,
complex scheduling constraints on some jobs, mixed hardware
types, and inaccurate user estimation of resource consumption(Reiss, Wilkes, & Hellerstein, 2011). It is believed that the onset ofcomputation congestion can be detected by characterizing computeintensive tasks in the data trace.

The purpose of this study is to characterize computation
congestion in a cloud network. The result will answer the question,“What are the network metrics that can reflect the occurrence ofcomputation congestion in a cloud data centre?”

Background and Motivation
Congestion in high capacity networks (e.g. cloud data centres) has
long been a matter of interest in research. To manage congestion, wemust know the factors that might contribute towards it. Generally,
researchers assumed that the factors of congestion are mostly caused
by link utilizations, such as insufficient bandwidth or inefficientrouting of packets through its respective paths. However, anotherpossible factor of congestion is expected in cloud data centres,
namely compute-intensive tasks; too many of these tasks may leadto computation congestion (Wang, Almeida, Blackburn, &Crowcroft, 2016).
To simply explain the scenario, we could describe the network
flow as akin to traffic on a highway; the number of lanes is analogous
to bandwidth while tolls are comparable to network nodes.
Congestion is usually thought to be caused by insufficient lanes, andthe solution has been by adding more lanes to occupy larger numberof vehicles (i.e. add more bandwidth for larger amount of datapassage) or by distributing the traffic to less occupied lanes (i.e.
network load balancing or congestion control mechanism). What if
the congestion happens because of the delay in the toll? Adding morelanes, instead of speeding up the process at the toll, would not help
reduce congestion. It is also possible that the lanes seem available(due to the large lane capacity), but processes are jam-packed at thetoll. Since current methods of identifying congestion does notdifferentiate the cause, it is imperative that we find a way to do so.

Proposed Methodology
Analysis will be done on Google Cluster Trace, taking into
account the task usage of CPU, memory, disk, and network.
According to (Chen & Katz, 2010), only CPU and memory are
constrained resources. Thus, it is highly likely that the metrics to bechosen are task duration in seconds, CPU usage in cores, andmemory usage in gigabytes. The characterization of the workload
will then be derived using statistical profiling, such as time series
and k-mean of several performance metric. The graph produced
during job launches during the workload is observed and comparedto the graph during job drops. A job launch is defined as the interval
during which we first see a task belonging to the job, while job drops
may be an indicator of congestion on the node.

Conclusion
From the analysis, it is anticipated that a specific pattern would
indicate the occurrence of computation congestion, most possibly
from CPU usage, memory usage and disk allocation. The pattern
would help to identify a threshold value for each field andsubsequently be used as the metric to characterize computation
congestion caused by compute-intensive tasks.

Computation Congestion in Cloud Data Centres
References
As mentioned earlier, in order to manage congestion, thecharacteristics of the congestion factor must be known. In the caseof managing computation congestion, we should be able to identify
when it happens or about to happen. Theoretically, computation
congestion might occur when there is a surge of compute-intensive
tasks at a certain point. Hence, it is reasonable to say that we need tohave a performance metric based on those tasks, where we ask “howmany tasks are too many?”
Currently, few research are done to address the issue ofcomputation congestion, particularly the mechanism of identifying
it. In proposing a proactive congestion control mechanism, Wang etal. (2016) estimated the probability of computation congestion bymonitoring CPU usage, memory usage, execution rate and request
arrival rate; the estimation was based on assumptions that the tasks
are Poisson processes, and the queue length of task requests are
similar to a simple M/M/1-Processor Sharing queuing system.
Besides using pure assumptions, some researchers have embarked
in analysing the behaviour of real workload to predict congestion(Beaumont, Eyraud-Dubois, & Lorenzo-Del-Castillo, 2016; Liu etal., 2017). Real data analysis are now possible with the release of

Beaumont, O., Eyraud-Dubois, L., & Lorenzo-Del-Castillo, J. A.
(2016). Analyzing real cluster data for formulating allocation
algorithms in cloud platforms. Parallel Computing, 54, 83–
96. https://doi.org/10.1016/j.parco.2015.07.001
Chen, Y., & Katz, R. H. (2010). Analysis and Lessons from a Publicly
Available Google Cluster Trace. Technical Report No.
UCB/EECS-2010-95.
Retrievedfrom
http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS2010-95.pdf
Liu, C., Liu, C., Shang, Y., Chen, S., Cheng, B., & Chen, J. (2017).
An adaptive prediction approach based on workload patterndiscrimination in the cloud. Journal of Network andComputer Applications, 80(June 2016), 35–44.
Reiss, C., Wilkes, J., & Hellerstein, J. J. L. (2011). Google clusterusage traces: format+ schema. Google Inc.,
Wang, L., Almeida, M., Blackburn, J., & Crowcroft, J. (2016).
C3PO: Computation Congestion Control (PrOactive). In
Proceedings of the 3rd ACM Conference on InformationCentric Networking (pp. 231–236). New York, NY, USA:52

[BP17] A Hyper-heuristic based Artificial Bee Colony Optimization for theTraveling Salesman Problem
S. S. Choong1,*, L. P. Wong1
1: School of Computer Sciences, Universiti Sains Malaysia, Malaysia* Correspondent author: css15_com047@student.usm.my
Keywords: swarm intelligence, bio-inspired metaheuristic, evolutionary algorithm, combinatorial optimization, Choice Function.
Research Background and Objective
Bees’ foraging behaviour has been computational realized asalgorithms to solve various optimization problems [1-3]. The
Artificial Bee Colony (ABC) algorithm [1] is a well-known beeinspired algorithm. In the ABC algorithm, a food source represents asolution to the optimization problem in the search space, and thenectar amount of the food source represents the fitness of thatsolution. ABC defines three types of bees: employed, onlooker, andscout bees. An employed bee looks for new food sources around theneighbourhood of the food source that it previously visited. An
onlooker bee observes dances and selects a relatively better foodsource to visit. A scout bee searches for new food sources randomly.
ABC is initially proposed to solve optimization of mathematical test
functions with a unique neighbourhood search mechanism [1].
Recently, ABC is modified to solve combinatorial optimization
problems, e.g. the Traveling Salesman Problem (TSP) [4]. However,its neighbourhood search mechanism cannot be directly applied forthis set of problems. Instead, the employed and onlooker bees areprescribed with one or more perturbative heuristics to produce new
solutions. These heuristics are problem-specific, for instance, theperturbative heuristics for TSP include insertion, swap, inversion, etc.
In view of the large availability of heuristics, the question concerning
the selection of a particular heuristic has been posed. This leads to thestudies on hyper-heuristics.

statistically outperforms Random-ABC. Moreover, comparison
studies among the state-of-the-art algorithms ascertain thecompetitiveness of MCF-ABC.
Conclusion
The Artificial Bee Colony (ABC) algorithm is a popular beeinspired optimization algorithm. When ABC is used to solve
combinatorial optimization problems, single or multiple perturbative
low-level heuristics (LLHs) are adopted as its neighbourhood searchmechanism. When there are multiple LLHs, the selection of these
LLHs has a significant impact on the performance of ABC. In thisstudy, we propose the use of a hyper-heuristic, i.e. Modified Choice
Function (MCF), to guide the selection of the LLHs in ABC. The
comparisons indicate that MCF-ABC is competitive among thestate-of-the-art algorithms.
Many problems in the Big Data domain, e.g. feature selection,clustering, and error minimization, can be framed as combinatorial
optimization problems. For future work, MCF-ABC can be adapted
to solve these problems.
References[1] D. Karaboga (2005) An idea based on honey bee swarm fornumerical optimization. Erciyes University, Engineering
Faculty, Computer Engineering Department, Technical report.
[2] L. P. Wong and S. S. Choong (2015) A bee colony optimization
algorithm with frequent-closed-pattern-based pruning strategy
for traveling salesman problem. In Proceeding of the Conference
on Technologies and Applications of Artificial Intelligence (TAAI2015), pp 308-314.
[3] S. S. Choong, L. P. Wong, and C. P. Lim (2018) A dynamic
fuzzy-based dance mechanism for the bee colony optimization
algorithm.
Computational
Intelligence.
https://doi.org/10.1111/coin.12159.
[4] M. S. Kıran, H. İşcan, and M. Gündüz (2013) The analysis ofdiscrete artificial bee colony algorithm with neighborhood
operator on traveling salesman problem. Neural computing andapplications 23:9-21.
[5] E. K. Burke, M. Gendreau, M. Hyde, G. Kendall, G. Ochoa, E.
Özcan, et al. (2013) Hyper-heuristics: A survey of the state of
the art. Journal of the Operational Research Society 64:16951724.
[6] S. S. Choong, L. P. Wong, and C. P. Lim (2018) Automatic
design of hyper-heuristic based on reinforcement learning.
Information Sciences 436-437:89-107.
[7] J. H. Drake, E. Özcan, and E. K. Burke (2012) An improved
choice function heuristic selection for cross domain heuristic
search. In Proceedings of the International Conference on
Parallel Problem Solving from Nature 2012, pp 307-316.
[8] S. S. Choong, L. P. Wong, and C. P. Lim (2017) An artificial bee
colony algorithm with a modified choice function for thetraveling salesman problem. In Proceedings of the 2017 IEEE
International Conference on System, Man, and Cybernetics(SMC) 2017, pp 357-362.
[9] S. S. Choong, L. P. Wong, and C. P. Lim (2018) An artificial bee
colony algorithm with a modified choice function for thetraveling salesman problem. Swarm and Evolutionary
Computation. https://doi.org/10.1016/j.swevo.2018.08.004.

A hyper-heuristic is a high-level automated methodology forselecting heuristics [5, 6]. The heuristics to be selected in a hyperheuristic are known as the low-level heuristics (LLHs). In thisstudy, a hyper-heuristic, i.e. Modified Choice Function (MCF)[7], is integrated in the ABC algorithm to select the perturbative
heuristics deployed by the employed and onlooker bees. The
proposed model is denoted as MCF-ABC.
The Proposed Model
MCF-ABC is an ABC variant with ten LLHs. It consists of fourphases: initialization, employed bee phase, onlooker bee phase, andscout bee phase. The initialization and scout bee phase of MCF-ABC
is similar with that of the ABC variants in [4]. In the employed andonlooker bee phases, a bee is aided by MCF to select an appropriate
LLH. MCF evaluates the performance score, F of each LLH using
three measurements, i.e. f1, f2, and f3. f1 represents the recent
performance of each LLH; f2 reflects the dependencies between
consecutive pairs of LLHs; f3 records the elapsed time since the last
execution of an LLH. For each neighbourhood search, an LLH withthe largest F score is selected. After each neighbourhood search, thegenerated solution is improved using a local search. Then, a greedy
acceptance method is applied to decide whether to accept the new
solution. After that, the F score of each LLH is updated. More detailsof MCF-ABC can be found in [8, 9].
Results and Findings
MCF-ABC is evaluated using 64 TSP instances. The aim ofsolving a TSP is to find a route that leads a person to visit eachcity once and only once and to return to the starting city with theminimum total cost. On average, MCF-ABC solves the 64 instances
to 0.055% from the known optimum within approximately 2.7
minutes. To examine the effectiveness of MCF-ABC, it is comparedwith a Random-ABC model which uses the same sets of LLHs anda random selection strategy. The results show that, MCF-ABC53

[BP18] The Application of Machine Learning in Classifying the Vector Borne
Disease Awareness
Abrar Noor Akramin Kamarudin1,*, Zurinahni Zainol1, Nur Faeza Abu Kassim2
1: School of Computer Science, Universiti Sains Malaysia, Malaysia
2: School of Biological Science, Universiti Sains Malaysia, Malaysia* Correspondent author: min268@gmail.com
Keywords: classification, vector borne disease, human behavior

Abstract

discovered variable, the knowledge of mosquito breeding sites andmosquitoes’ bite time which has never been used in any existing
predictive models.

This study compares the classification algorithms’ performance on
the awareness of mosquito-borne viral disease outbreak based on thehuman factors (awareness, knowledge, attitudes and practices). The
classification will be done by using several machine learningalgorithms (Naïve Bayes, Support Vector Machine, Decision Tree,
Artificial Neural Network and Logistic Regression) to predict theawareness of dengue risk among the community. The results will becompared to select the best machine learning algorithm in supporting
the initial design of a personalized educational program.
The Internet is one kind of instrument to hold the lifelong learning
among the communities. This includes the vector-borne disease(VBD) awareness campaign, especially the dengue fever. Althoughsuch campaigns are always seen on television, the rate of dengue
outbreak is constantly increasing each season (Boonchutima,
Kachentawa, Limpavithayakul, & Prachansri, 2017). This indicatesthat the awareness level among the communities are stillquestionable. They might not fully aware of their responsibility andneed to increase their knowledge regarding the vital issue. Current
VBD outbreak prediction system did not include human awarenessinto accounts (Kesorn et al., 2015). Constructed predictive modelthrough search queries, environment, and climate factors does not
solve the real issue comprehensively due to the exclusion of humanfactors. A new method which involves human behavior andeducation to the current prediction system can provide a heuristic
solution in VBD prevention.
An initial online survey was carried out with 171 individuals of
the Malaysian public aged 13 years old and above. The selfadministered structured questionnaire covers all aspects ofdemographic profile, self-assessment whether they have theawareness of dengue or not, and questionnaire regarding mosquitoes
breeding sites, mosquito’s prevention method and mosquitoes bitingtime. The machine learning algorithms are tested on 13 features ofhuman awareness, knowledge, attitudes and practices such as
cleanliness, waste management, clogged drains and stagnant water
management, aerosol spray/mosquito repellent usage, long-sleeved
shirt and long pant bright in color usage, mosquitoes breeding site,and mosquitoes bite time (Gyawali, Bradbury, & Taylor-Robinson,
2016; Aung et al., 2016). Fivefold cross-validation technique wasutilized in the present investigation for the training and testing. The
algorithms were performed by using WEKA, a data mining
application developed by Waikato Environment for Knowledge
Analysis.
Out of 171 respondents, 86% declare that they are aware of themosquitoes from breeding in their area. It is found that the Support
Vector Machine algorithm outperform other machine learningalgorithms for predicting human awareness with 88.3% correctclassification. It is followed by Decision Tree (86.55%), Logistic
Regression and Naïve Bayes algorithm (85.96%), while theArtificial Neural Network at 85.38%.
An effective prediction model of VBD awareness among citizens
is vital in a personalized e-learning program to highlight the risk ofcontracting dengue and increase their knowledge about dengue
prevention and treatment. Hence, this research proposes to use the
SVM algorithm to construct a predictive model for dengue
awareness using human behavior parameters and the newly

References








Aung, M. M. T., Hassan, A. B., Kadarman, N. B., Hussin, T. M.
A. B. R., Barman, A., Ismail, S. B., & Hashim, S. E. B. (2016).
Knowledge, attitude, practices related to dengue fever among
rural population in Terengganu, Malaysia. Malaysian Journal of
Public Health Medicine, 16(2), 15-23.
Boonchutima, S., Kachentawa, K., Limpavithayakul, M., &Prachansri, A. (2017). Longitudinal study of Thai people media
exposure, knowledge, and behavior on dengue fever prevention
and control. Journal of infection and public health, 10(6), 836841.
Gyawali, N., Bradbury, R. S., & Taylor-Robinson, A. W.
(2016). Knowledge, attitude and recommendations for practice
regarding dengue among the resident population of
Queensland, Australia. Asian Pacific Journal of Tropical
Biomedicine, 6(4), 360-366.
Kesorn, K., Ongruk, P., Chompoosri, J., Phumee, A., Thavara,U., Tawatsin, A., & Siriyasatien, P. (2015). Morbidity rate
prediction of dengue hemorrhagic fever (DHF) using the
support vector machine and the Aedes aegypti infection rate insimilar climates and geographical areas. PloS one, 10(5),e0125049.

Authors Biographies
Abrar Noor Akramin Kamarudin is a PhD student at Schoolof Computer Science, Universiti Sains Malaysia. His
research is focused on vector borne disease prediction,
personalization and e-learning.

Zurinahni Zainol is an Associate Professor at School ofComputer Science, Universiti Sains Malaysia. Her research
specialization are Theory of Database Design andManagement, XML Document Schema Design, Data
Model, and Software Development using Formal
Specification.

Nur Faeza Abu Kassim is a Senio r Lecturer at School of
Biological Science, Universiti Sains Malaysia. She is anexpert in medical and epidemiological entomology. Her
research specialization are mosquito -borne disease & therole of vectors in transmission disease. Development of theintegrated vector management, mosquito control,
monitoring/surveillance program, attractant and trapping
system.

54

[BP19] Supersymmetry Feature Decomposition for Classification Purpose
Nu’man Badrud’din
Department of Physics, International Islamic University Malaysia (IIUM) Kuantan
numan.badr@gmail.com
Keywords: Machine learning, feature decomposition, classification, supersymmetry, particle physics
Machine learning for classifying data is gaining popularity inmany fields but has yet to be seen as clear in improving particle
detection in particle physics. It provides the ability to classify highdimensional data such as the supersymmetry (SUSY) event based on
the dataset’s list of features. In an attempt to enhance the analysis, thefeatures were engineered in this research by using featuredecomposition alongside two methods of machine learningalgorithm: Random Forest and Artificial Neural Network. Featuredecomposition is also a type of data compression as it reduces thedimensionality of the dataset. Different combination of numbers offeatures and feature decomposition types are tested for both of thealgorithms. Therefore, the accuracy and precision of the classification
for both algorithms is compared with and without featuredecomposition to observe its effect on the data classification. For theartificial neural network algorithm, different activation function anddifferent training ratio are also being tested to observe the effect of
the feature decomposition on different parameter. The overall result
obtained refuted the initial hypothesis, showing that both accuracy
and precision with feature decomposition are lesser than without.
Conclusively, the compression of supersymmetry data by dimension
reduction is not feasible in classifying the dataset as it decreases its
accuracy and precision.
Purpose
1. To develop neural network classifier model for the supersymmetry
dataset.
2. To develop feature engineering code for the supersymmetry
database and compare the machine learning with and without featureengineering.
3. To compare the accuracy of the supersymmetry classification withand without feature engineering.
4. To check for consistency of the classification for artificial neuralnetwork algorithm.
Background
Particle physics simulation in finding new exotic particles
produces enormous amount of data and machine learning techniques
have yet to be seen as clear in improving the simulation. Therefore,feature decomposition is used to compress the size of the data withintention it could improve the classification of the dataset.
The research was intended to compare the accuracy andprecision of supersymmetry classification simulation with andwithout feature decomposition to give insights into the effect offeature decomposition to the classification of supersymmetry data.
MethodComputations were performed on a Linux based computer withan AMD Ryzen 7 octa-core processor, a 16GB NVIDIA GTX1070
graphics processor, and 32GB of RAM. All codes were written using
PyCharm IDE software and Python 3.6.1 programming language with
Anaconda library packages Keras neural network library.
Dataset used was mini supersymmetry dataset from Machine
Learning Repository of University of California, Irvine (UCI)consisting of 100,000 data and 18 features (dimensions). The dataset
has of about 46% of positive examples and the rest is negative
examples.
The classification of the dataset was being done andcompared by using two types machine learning algorithms –random forest (RF) classifier and artificial neural network (ANN).
Random forest classifier is one of the learning algorithms, and ANN
is Keras’ neural network for simple deep learning. Both classifier
codes were developed without feature decomposition first and theresult was the base reference for comparison to the result withfeature decomposition. 8 types of feature decomposition werepaired with respective count of 18 numbers of features ofsupersymmetry to find the ideal number of features needed to get
the highest accuracy for random forest algorithm. While for ANN,the 8 types of feature decomposition were paired with 6, 12 and 18
number of features respectively to observe the effect of featuredecomposition on the accuracy of the learning. The accuracy andprecision between both classifiers were compared with and without
feature decomposition.
Results
Both classifier algorithm of random forest (RF) and artificial
neural network (ANN) without feature decomposition (FD) output
higher accuracy than when all types of feature decomposition areused. The accuracies and precisions dropped when featuredecomposition or higher training data was used. Both classifiers are
consistent throughout repetition of the simulations.
Even without feature decomposition, both algorithms output
a fairly similar accuracy. This could be expected since the dataset
is sufficiently large to be fed for both of the machine learning.
Although ANN outputs higher accuracy, the minor difference of
0.1452% in accuracy between the two algorithms is noticeable but
less significant.
Although some of the result showed that featuredecomposition could increase the accuracy of the classification, it
also showed that it could decrease the accuracy and precision either
slightly or drastically. Nevertheless, the increases were
insignificant, and the precision dropped from when no featuredecomposition is used.
The precision overall is reduced after feature decomposition,
except for some combination of feature decomposition, activation
function and number of features yet their accuracy remains
decreased regardless.
From both of RF and ANN results, it can be observed that
there was no significant increase (by 3% or more) regardless offeature decomposition used and regardless of number of featureschosen. On the contrary, almost all feature decomposition reduces
the accuracy and precision of the machine learning for bothalgorithms. Although there are some that have higher accuracy, it
is considered to be insignificant in comparison with the reference
result of without feature decomposition. The difference inaccuracies in repeated simulations was less than 1% concluded
that the ANN classifier algorithm of this research is consistent.
Conclusion
The overall results from this research showed that featureengineering by feature decomposition decreases the classification
accuracy for both random forest and artificial neural networkclassifier. Early hypothesis that feature engineering will at least
increase the accuracy deemed to be wrong in this case; because of
the dimension reduction by the decomposition. Not only the reducein accuracy, but the precision also diminishes as features were being
decomposed. This shows that each feature in the supersymmetry
dataset are important as they are not redundant, or in other words
they are independent and non-derivative of each other. Therefore,
all and each feature should be considered when classifying thesupersymmetry dataset or other feature engineering techniques
have to be devised.

55

PRAGMA 35 POSTER ABSTRACTS56

[PP01] Mobile-based Augmented Reality for Sundanese Alphabets Education
Muhammad Reza Aditya, Resskytha Permata Sari, Adri Nursimarsiyan and Nova Eka Diana
Informatics Department, Faculty of Information Technology, Universitas YARSI, Indonesia
Rezaditya28@gmail.com, rereskytha@gmail, adrysiyan@gmail, nova.diana@yarsi.ac.id

Abstract
In 2017, the Indonesian Ministry of Higher Research and Education reported that thesmartphone user in Indonesia had achieved about 25% of the total citizen or about 65 million
people. This trend is beneficial to utilize smartphone for promoting traditional cultures inIndonesia, especially to the teenagers that are steadily moving forward to modern culture. This
research aims to build an Android-based application with an augmented reality feature toenhance the experience of learning traditional Indonesian heritage, especially traditional
alphabets. CARIOSAN is a mobile application with the purpose to preserve the Sundanesealphabets and prevent it from extinction. The main features in the application are AR-based
Sundanese alphabets recognition, Rarangken information, Sundanese alphabets writing canvas,
and quiz feature to assess user understanding of the Sundanese alphabets. We interviewed 35
respondents for measuring the usability, the easiness of use, the information coverage, theinterface, and the novelty of the application. The results showed that the developed application
was highly useful and interactive in providing the essential information of Sundanese alphabets
although with a novelty score only above 50 percent.

57

[PP02] Deep Learning Classification for Liver Disease
Andi Batari Ahmad and Nova Eka Diana
Informatics Department, Faculty of Information Technology, Universitas YARSI, Jakarta,
Indonesia
ndieta.tarii@gmail.com, nova.diana@yarsi.ac.id

Abstract
Liver disease is one of the top ten diseases with the highest mortality rate in Indonesia, with theincreasing rate of one percent per year. Type of liver disease most attacking Indonesian people is
Hepatitis. According to Basic Health Research (Riskesdas) 2013, Hepatitis had a prevalence
number of 1.2 percent that was double than the prevalence in 2007. There are some methods todiagnose the liver disease such as enzymes pattern analysis, excretion, metabolism,
electrophoresis and serologic test. This research focused on diagnosing the liver disease based on
enzymes pattern using Deep Learning approach. We used Indian Liver Patient Dataset (ILPD)
from UCI Machine Learning Repository with a total of 583 data (416 positives and 167
negatives) to build the classification model, with the training and testing rate of 0.7 and 0.3,
respectively. We conducted a preprocessing step for the training data using Synthetic Minority
Over-sampling Technique (SMOTE) with the percentage rate of 0.5 to balance the positive andnegative class. Experiment results reveal that the created model can classify liver disease with theaccuracy, sensitivity, and specificity rate of 0.89882, 0.84, and 0.9225 percent, respectively.

58

[PP03] Application of Deep Learning and Fingerprint Modeling
Methods to Predict Cannabinoid and Cathinone Derivatives
Widya Dwi Aryati, Gerry May Susanto, Muhammad Siddiq Winarko, Heru Suhartanto, Arry
Yanuar*Faculty of Pharmacy, Universitas Indonesia, Depok, West Java 16424, Indonesia
Faculty of Computer Sciences, Universitas Indonesia, Depok, West Java 16424, Indonesia

Abstract
In recent years, new psychoactive substances (NPS) have rapidly emerged in market purportedly
as “legal” alternatives to internationally controlled drugs, with the potential to pose serious
health risks. 21 of 56 NPS were found as cannabinoid derivatives in 2016 in Indonesia. From2013 to 2018 there was an increasing number of cathinone derivatives, 30 compounds in 2013
and 89 compounds in 2018. Artificial intelligence (AI) has become a tool for data processing andis applied for object recognition such as human pose and image classification. The purpose ofthis study is to apply and gain the best AI method to classify new cannabinoid and cathinone
derivatives by comparing deep learning method and fingerprint modeling method. The
pharmacophore modeling was used as the reference method.This study compared deep learningand fingerprint modeling methods. Both methods were compared with pharmacophoremodeling as the reference method. Physicochemical property descriptor will be used as learningparameters for the deep learning method. The two models produced by each method will be
used to classify new cannabinoid substances. As for the cathinone substances, the structure wastransformed into a fingerprint form. This method was also compared with pharmacophoremodeling as the reference method. Compared to the pharmacophore modeling method, the deeplearning method for cannabinoids classification showed the higher accuracy and Cohen Kappascores respectively (0,8958 and 0,396) and (0,8622 and 0,68) for pharmacophore modeling.
Pharmacophore modeling in the classification of cathinone derivatives showed accuracy(91.11%) and Cohen Kappa scores (0.708). However, fingerprint modeling gave accuracy(71.8%) and Cohen Kappa (0.637). These results conclude that the deep learning method withdescriptor is a better instrument to be used for cannabinoid classification compared topharmacophore modeling, but fingerprint modeling showed lower accuracy than pharmacophoremodeling in cathinone classification.

59

[PP04] Building Smart City Datasets with Crowdsourcing for Safe Direction in Bangkok,
ThailandManassanan Boonnavasin, Suchanat Mangkhangjaroen and Prapaporn Rattanatamrong
Thammasat University
oat.boonnavasin@gmail.com, mind_happy_ok@hotmail.com, rattanat@gmail.com

Abstract
There could be many problems when there is population living together in cities. For examples,
crime, robbery, attack and snatch. Having a crime map that can mark potential risk places orareas can help you avoid directions that not safe. Unfortunately, there is no such open data tocreate the crimp map for public in Thailand. This project presents our effort in building a smart
city dataset with crowdsourcing to collect information about unsafe or risky to crime
places/areas in Bangkok area. We first crawl crime scene information from online news. Using
only information from news has limits because we can only get the cases reported to the polices
and those that they. Officially announced. So, we need more data from active citizen by
crowdsourcing. The newly developed dataset will be used to draw points, lines, and appropriate
shapes on the map. After that directions from a source to a destination of user selection can beprovided with the avoidance of the risky area (if possible) or some warning information will be
provided along with the direction. Our main challenges are (1) how to build a successful
crowdsourcing that fits Thai people’s nature and yield quality data collection, (2) once data iscollected both from crowdsourcing and news crawling, how can we turn them into correct
latitude and longitude, and (3) how to generate route direction to avoid the unsafe areas on themap. We present our preliminary result based on the inputs achieved from a user survey and a
prototype mobile application with Google Map API, called Happy Map. We hope thisapplication can help people to have happy and safe travel in Bangkok, Thailand.

60

[PP05] A Network Performance Measurement in a Low-Cost Containerized Cluster
SystemThitiwut Chamornmarn, Vasaka Visoottiviseth and Ryousei Takano
Faculty of Information and Communication Technology, Mahidol University, Nakhon Prathom,ThailandInformation Technology Research Institute, National Institute of Advanced Industrial Scienceand Technology, Tsukuba, Japan

Abstract
This poster demonstrates the network performance in a low-cost containerized cluster system
which consists of five Raspberry Pi computers. We use Kubernetes for managing cluster
resources and executing containers on it. The NodePort service mechanism forwards externaltraffic to a corresponding container running on a worker node. We have conducted two
experiments micro benchmark with iperf and an application benchmark with the Apachebenchmark. The reasons of these experiments are measuring the performance of Kubernetes
networking topologies and also finding the limitation and durability of network functions in
Kubernetes. The first experiment measures TCP goodput with four possible communication
patterns: intra-node, inter-node, external with forwarding, and external without forwarding. The
second experiment measures HTTP request processing throughput using the Nginx web server
and the Apache benchmark with six different container deployment configurations.

61

[PP06] A Prototype of Collaborative Augment Reality Environment for HoloLens
Dawit Chusetthagarn, Vasaka Visoottiviseth and Jason Haga
Faculty of Information and Communication Technology, Mahidol University, Nakhon Prathom,ThailandInformation Technology Research Institute, National Institute of Advanced Industrial Scienceand Technology, Tsukuba, Japan
dawit.chu@student.mahidol.ac.th1, vasaka.vis@mahidol.edu1, jh.haga@aist.go.jp2

Abstract
The interest in augmented reality (AR) and virtual reality (VR) for a variety of applications has
surged in recent years. Although these technologies provide new ways to interact and understandinformation, often the experience is limited to a single person. To address this limitation, we
implemented the concept of spatial anchors from Microsoft using Holotoolkits to create acollaborative AR environment for a HoloLens application. The collaborative environment canwork through the server in Holotoolkits running with the Unity platform. Preliminary results ofour collaborative AR environment were demonstrated using a disaster management applicationthat can be shared in the HoloLens as a proof-of-concept application. The concept of ourapplication can be implemented in a variety of use-cases to provide greater collaborative
understanding including an indoor navigation system that shares user’s view point in HoloLens.
In the future, this environment will be used to visualize, explore, and understand large datasets indata-intensive science research from a variety of disciplines.

62

[PP07] Analysis of Load Balancing Performance on Cluster Computing with Proxmox
VE
Andi Rasuna Dharsono and Sri Chusri Haryanti
Faculty of Information Technology, Universitas YARSI, Indonesia
andi.rasuna@gmail.com, sri.chusri@yarsi.ac.id

Abstract
This research aims to examine the performance of load balancing for a cluster. Load balancing isa solution for a large access load and minimizes downtime in serving requests from users. Loadbalancing distributes loads of traffic evenly to the servers with particular algorithms. In thisresearch, the server for load balancing cluster is implemented using LVS topology via direct
routing and use round-robin algorithm on Proxmox VE for load balancing. Proxmox VE is incharge of dividing virtual resource servers into four virtual environments, two nodes as loadbalancing clusters and two nodes as web servers. The results of research conducted with loads of250, 500 & 1000 users show that load balancing system reduces the maximum response time up
to 4165 ms with 0% of packet loss, compared to utilization of single server with the maximum
response time up to 7269 ms and 22.48% packet loss. The load balancing system for clusters also
manages to failover with an average value of downtime 16.6 seconds.

63

[PP08] Performance Comparison of Load Balancing using Honeybee and Threshold
Algorithm
Aditya Efrian, Sri Chusri Haryanti, Sri Puji Utami Atmoko, Ridho Yanevan Pratama
Faculty of Information Technology, Universitas YARSI, Indonesia
adityaefrian@gmail.com, sri.chusri@yarsi.ac.id , puji.atmoko@yarsi.ac.id ,ridhoyanevan@gmail.com

Abstract
Cloud computing is currently developing rapidly. Load balancing technique is very crucial tobalance the load in the cloud. Load balancing is needed to distribute dynamic workload acrossresources in the cloud. CloudAnalyst can be used to simulate load balancing algorithm in cloud.
One advantage of CloudAnalyst is it applies a GUI. We implement Honeybee and Thresholdalgorithms into CloudAnalyst. We examine Honeybee and Threshold algorithms implementation
for Indonesian e-Health cloud model. There are 4 data center and 34 users based on the numberof regions and provinces in Indonesia. Two data center selection policies are used, i.e. closest
data center and optimized response time policy. The average response time is investigated for
different user data sizes. The simulation result shows that Threshold algorithm gives average
response time that is faster than the Honeybee. Nevertheless, Honeybee algorithm delivers better
performance for data size smaller than 80 bytes, and Threshold algorithm tends to give smaller
response time for data size 100 bytes.

64

[PP09] Data-centric Modeling of Gainesville Businesses
Michael Elliott, Erik Bredfeldt, Matthew Collins, Renato Figueiredo, Mark Girson, Amardeep
Siglani, Lila Stewart and Jose Fortes
ACIS Lab, University of Florida
mielliott@ufl.edu, fortes@acis.ufl.edu

Abstract
The purpose of this research is to investigate the extent to which data collected by the City ofGainesville can be used to model local business success and to improve our understanding of
how the city’s public services can influence that success. The scope of data utilized in this study
includes, but is not limited to, business location, age, crime rate, local and national economic
growth, utility consumption, and compliance with city regulations. To supplement publicly
available data, a survey was conducted of local business owners to assess their own perceived
success, which factors drove or inhibited that success or lack thereof, as well as how experienceswith the City of Gainesville may have affected their businesses. Extensive analysis is performed
with the considered data in order to identify potential correlations with local business success.
We explore data trends across previous years and geographically map a snapshot of recent data
to subsections of the city. However, meaningful conclusions are at times hindered by bothmissing information and the lack of any direct means of matching entities across separate
datasets. There is a wide range of actions the City of Gainesville can take to facilitate theadvancement of its own data infrastructure to make it robust enough to support practicalapplications and modeling. Measures can be taken to increase the integrity and usability ofexisting datasets, while future data collection could benefit from enhanced specificity, common
identifiers, and more rigorous data verification process. We are looking for collaboration
opportunities with researchers who are engaged in similar initiatives in other cities and data
scientists interested in modeling business lifecycles.

65[PP10] Performance Analysis of GTX 980 GPU on Colon Histopathology Images
Training Based on Convolutional Neural Network
Toto Haryanto, Aniati Murni, Kusmardi Kusmardi, Li Xue and Suhartanto Heru
Universitas Indonesia
toto.haryanto@ui.ac.id, aniati@cs.ui.ac.id, kusmardis@gmail.com,
xueli@itee.uq.edu.au, heru@cs.ui.ac.id

Abstract
Cancer diagnose based on the histopathology images is still become challenges recently. The
variation of images, high resolution of images, different pattern of cell on the images havepotential tend to miss-classification. Convolutional neural network has widely used in image
processing with its ability to extract and classify an object. Applying CNN on high resolution ofimages cause cost intensive in training process. For that, Graphics Processing Unit (GPU) has
important role to increase the speed-up. However, the problem in GPU is the size limitation ofmemory. This research focus on the way to utilize the GPU memory in the training of CNN
architecture. For training CNN architecture, NVIDIA GTX-980 are accelerated by customize
CUDA memory allocation from cnmem library. The parameter of cnmem are chosen from 0.6,
0.8, 1, 2 and 4 experimentally and the best value will be used to the next training. To enrich thedataset, augmentation such as rotation, zoom, shear and flip are conducted. Some optimization
technique are applied experimentally to determine the best model to classify two classes ofcancer, benign or malignant. We use image variation from 32x32, 64x64, 128x128, 180x180 and
200x200 in this research. While training, number of batch-size is selected experimentally from10, 20, 50, 100 and 150. According to the research, enabling cnmem with parameter 1 is selected
as the best value. The 200x200 images show the most significant efficiency of GPU performance
when training CNN. Speed-up are measure by comparing training time of GTX-980 with CPU
core i7 machine from 16, 8, 4, 2 cores and the single core. The highest speed-up GTX-980
obtained with enabling cnmem are 4.49, 5.00, 7.58, 11,97 and 16.19 compare to 16, 8, 4, 2 and 1
core processor respectively.

66

[PP11] Dengue Hemorrhagic Fever Disease Data Clustering Based on Interactive Map
in Special Region Jakarta Capital
Brian Hogantara and Ummi Azizah Rachmawati
Department of Informatics, Faculty of Information Technology, Universitas YARSI Jakarta
hogantara.brian@gmail.com; ummi.azizah@yarsi.ac.id

Abstract
Dengue fever is an infection caused by the dengue virus. The virus spread by Aedes aegypti
mosquito. Patients who are infected have symptoms such as fever, accompanied by a headache,pain in muscles and joints, until spontaneous bleeding. This research aims to develop aninteractive map that is presenting data with web media to obtain spatial information easily. Theinteractive map uses clustering to process the data from the Jakarta Health Service Office.
Clustering is a method of data analyzing, which aims to group data with similar characteristics tothe same 'region' and the data with different characteristics to the 'other area'. This method
separates the values of health indicators into a few groups which have significant value difference
among groups. The result of this research can be used by the policy maker in Jakarta to reduce
the spread of the disease and to decrease the mortality of the patient of dengue fever. Theinteractive map also can be used for a decision-making system for the government based on data
dengue fever in Jakarta. This research can help the government and society to take action related
to a characteristic of Jakarta areas that have a lot of cases of dengue hemorrhagic fever.

67[PP12] Curating Target-Activity Information for Nadi Compounds Based on CHEMBL
using Similarity Searching
Muhammad Jaziem Mohamed Javeed, Aini Atirah Rozali, Siti Zuraidah Mohamad Zobir,
Habibah Abdul Wahab and Nurul Hashimah Ahamed Hassain Malim
Universiti Sains Malaysia, Malaysian Institute of Pharmaceuticals and Nutraceuticals (Ipharm)muhammadjaziem21@gmail.com, ainiatirahrozali@yahoo.com, zuraidah@iphrm-nibm.my,habibahw@usm.my

Abstract

Natural products are generally considered as a rich source of biologically active substance. In the
period of 20 years (1981-2002), Food and Drug Administration (FDA) has claimed 5% of the1031 new chemical entities (NCE) approved as drugs are natural products and other 23% arenatural-product-derived molecules. In Malaysia, natural products are collected and stored in a
database known as Natural Product Discovery (NADI). However, the natural compounds in thisdatabase has not been classified in their respective activity classes yet. Curating the target-activityof NADI database is the focus of this study. It requires few methods and a database forreference. A publicly available database, CHEMBL, is used in this study as it covers a broad
range of curated and annotated data. Importantly, a curated linkage between indexed 2D
chemical structures and biological targets is provided. However, it is impractical to screen all thecompounds randomly because it will be time-consuming and computationally expensive.
Simplest approach used in virtual screening (VS) known as similarity searching (SS) techniquewhich is also widely used in drug discovery process is implemented to perform the screening taskof NADI and CHEMBL. The results from the similarity searching method is used to discover
the compounds with highest similarity value in ChEMBL with the query compound in NADI. It
will be then fed as input to pattern matching process in order to determine the target-activity for
NADI. If there is no target-activity information of certain compounds in ChEMBL, the targetactivity in NADI will be defined as undetermined. Compounds with target-activity information
will be a reference for the query NADI compounds. At the end of this study, our aim is to createa statistic based on NADI target activity information. Out of thousand compounds in NADI,
few should be able to be assigned to the target-activity reported in ChEMBL database that stores
millions of compounds. The finding from this study will be beneficial for those who are
conducting studies in drug discovery field.

68[PP13] Design AR application using the tiled display walls
Jidapa Kongsakoonwong, Boonsit Yimwadsana , Jason H. Haga
Information Technology Research Institute, National Institute of Advanced Industrial Science
andTechnology, Tsukuba, Japan
Faculty of Information and Communication Technology, Mahidol University, Bangkok,
Thailand,jidapa.kog@student.mahidol.ac.th, boonsit.yim@mahidol.ac.th, jh.haga@aist.go.jp

Abstract
Data visualisation plays an important role in understanding data through different graphicalrepresentations of the data. However, newer technologies have expanded data visualization
beyond a single computer screen and paper printouts. Often these traditional technologies lackcontext and can reduce the efficiency of understanding the data by the user. Hence, it isnecessary to develop visualization approaches that facilitate understanding and exploration ofdata in an easy manner by the user. One of technologies that can help us to add context to theinformation is augmented reality (AR). This technology has become very popular recently as ameans to add context to data objects and allow users to interact spatially with the information.
This paper uses the new ARKit tool to develop an AR application that creates virtual
information layers that are superimposed over data objects in the physical world. The user caninteract with the layers and manipulate the data to generate better understanding. Our proof-ofconcept AR application will be demonstrated with a tiled display walls. In the future, the conceptof our application can be applied to developing a dynamic AR application for data intensive
science.

69[PP14] Decision Support System based on Interactive Map of Measles and Rubella Datain Jakarta
Pravin Kumar, Elan Suherlan, Ummi Azizah Rachmawati
madnug93@gmail.com; elan.suherlan@yarsi.ac.id; ummi.azizah@yarsi.ac.id
Deaprtment of Informatics, Faculty of Information Technology, Universitas YARSI Jakarta

AbstractIndonesia is implementing a program towards the elimination of measles, with efforts to perform
case-based measles surveillance (CBMS) that confirmed by a laboratory. In line with the plan ofmeasles elimination, rubella control through the monitoring of rubella also implemented byintegrating with measles surveillance. This study aims to develop a decision support system basedon an interactive map that can be used to assist the Jakarta Health Department in monitoring
and to search for measles and rubella data in Jakarta Special Capital Region. This system is aform of effort that helped the Jakarta Health Department in monitoring patients with measles
and rubella and evaluate the spread of measles and rubella located in the Jakarta Special Capital
Region. This study uses the software development methodology, comprising the steps of aliterature study, data collection, data preparation, system design, system implementation, andtesting. The results show that more than 80% of users agree that the system is easy to understandand user-friendly, leading, and unconventional. The system performance is quick, and thefeatures in the system already support the decision correctly. The map is interactive, and 70%
stated that the information is complete and informative.

70

[PP15] RNA-seq transcriptome profiling of Desmos chinensis: revealing the molecularbasis of petal evolution in the custard apple family Annonaceae
Amy Wing-Sze Leung, Sangtae Kim and Richard Mark Kingsley Saunders
School of Biological Sciences, The University of Hong Kong, Hong Kong S.A.R
Department of Biology, Sungshin Women’s University, Republic of Korea
amywingsze@connect.hku.hk, saunders@hku.hk

Abstract
Although petals are not homologous within flowering plants different types of petals might
nevertheless share a deep homology at the molecular level. Sepal and petal differentiation has
evolved independently in the family Annonaceae and some core eudicots, with the developmentof “bract-like” and “stamen-like” petals, respectively. Little is known of the molecular control offloral development in the non-model family Annonaceae. In order to better understand floral
developmental genetics of the family, we profiled the transcriptome of a representative species,
Desmos chinensis, during its floral development using a high throughput RNA-seq technique.
Floral organ and leaf samples at developing and mature stages were obtained for transcriptome
sequencing. Over 200GB of Illumina data was generated using the HiSeq2000 platform. All
downstream bioinformatics work was performed on the High Performance Computing cluster
HPC2015 supported by the Information technology service of The University of Hong Kong. In
total, 22,112 assembled transcripts (c. 80% transcriptome completeness) were recovered using denovo assembly. The transcriptome was annotated and characterized based on publicly available
databases. Downstream expression level estimation and gene ontology enrichment analysis
suggested that a large proportion of D. chinensis transcripts are responsible for defense, withsome expressed strongly in sepals. Metabolism related transcripts, including those responsible forglycolysis, were found to be up-regulated in mature petals. 52 complete homeotic MADS-box
gene transcripts were obtained from the floral and leaf transcriptome with the aid of the draft
genome of a closely related species. The orthology of these putative MADS-box transcripts werevalidated using phylogenetic analyses, OrthoMCL and UniProt annotation.

71

[PP16] Computational Fluid Dynamics Study of Wind Environment in Urban Areas
Chun-Ho Liu, Wai-Chi Cheng, Wenye Li, Ziwei Mo, Zhangquan Wu, Lillian Y.L Chan, W.K.
Kwan and Hing Tuen Yau
Department of Mechanical Engineering, The University of Hong Kong
Information Technology Services, The University of Hong Kongliuchunho@graduate.hku.hk, 2wccheng2007@yahoo.com.hk, 3liwenye@connect.hku.hk,
4ziwei.mo@gmail.com, 5wzqmec@gmail.com, 6lilianyl@hku.hk, 7hcxckwk@hku.hk, 8billyau_hpc@hku.hk

Abstract
Economic activities and industrialization unavoidably lead to degrading wind environment andelevating pollutant concentrations in urban areas. Buildings, skyscrapers and infrastructures inmetropolises collectively form complicated urban morphology in which the dynamics is different
from that in the atmospheric boundary layer (ABL) aloft. Under this circumstance, theconventional (meso-scale) meteorology models would not be fully applicable to diagnose theproblems in details. Engineering computational fluid dynamics (CFD), such as OpenFOAM, iscommonly used to tackle the problems in refined micro-scale. The protocol of using building
information for OpenFOAM CFD studies in the city ventilation perspective is reported in thispaper. Detailed information of buildings and terrain is collected from the Lands Department,
The Hong Kong Special Administrative Region (HKSAR). The digital maps are
threedimensional (3D) spatial data of the HKSAR territory that include buildings (commercial
and residential), infrastructure (roads and bridges) and natural terrain (mountains and slopes) forland assessment, engineering visualization and air ventilation analysis, etc. The 3D geometricmodels are available in virtual reality modeling language (VRML) format. In this paper, we use
one of the HKSAR downtown areas as an example to demonstrate the solution protocol. The
digital models in the files are divided into tile basis so MeshLab is used to assemble and convert
the VRML files to STL format. The STL files of building information are then merged with the
OpenFOAM mesh generation utility blockMesh and are discretized by snappyHexMesh to 3D
unstructured meshes. The mesh generatorsnappyHexMesh uses the triangulated surface
geometries in the STL files to generate 3D meshes, approximating the solid surfaces. It also
refines the surfaces iteratively to morph the buildings by split-hex meshes to the facades andground in high spatial resolution. Additional layers of refined spatial resolution are fabricated aswell to improve the accuracy of near-wall-flow calculation. The STL model of downtown
HKSAR areas, which is reduced in scale approximately 1:300, is discretized into over 5 million
hexahedral cells for subsequent CFD calculation.

72

[PP17] Enhancing MedThaiSAGE: Decision Support System using Rich Visualization
on SAGE 2
Jarernsri Mitrpanont, Wudichart Sawangphol, Supakorn Silapadapong, Suthivich Suthinuntasook,
Wichayapat Thongrattana1, Jason Haga
Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom,Thailand, Information Technology Research Institute, National Institute of Advanced Industrial
Science and Technology, Tsukuba, Japan{jarernsri.mit, wudhichart.saw}@mahidol.ac.th,{supakorn.sil, suthivich.sut, wichayapat.tho}@student.mahidol.ac.th, jh.haga@aist.go.jp

Abstract
For several years, the Routine to Research (R2R) Organization has gathered medical research
project data in Thailand and transformed it into a valuable knowledge resource to improvehealthcare services and help healthcare policy makers to construct policies. However, the large
amount of historical data creates a challenge to understand this information.To address thisproblem, MedThaiVis has been proposed to serve as a tool to visualize this complex data. In
addition, MedThaiVis has been extended to operate on complex data in Scalable Amplified
Group Environment (SAGE2), called MedThaiSAGE. This platform can help users tounderstand the data, gain insights into the complex data, and provide a better comprehensive
view for exploration. SAGE2 is the platform that allows us to execute a visualization on scalable,
high-resolution, display walls. Furthermore, MedThaiSAGE has been developed as a Decision
Support System based on Association Rules, which is exploited to help healthcare policy makers
to see the relevant set of rules and help in developing policy. However, these two applications
perform independently. Therefore, we proposed and implemented an approach to integrate these
two applications in order to enhance their capability. In order to integrate Extended MedThaiVis
and MedThaiSAGE, there are three main issues that were overcome: (1) data integration, (2)communication and cooperative workflow between the two applications, and (3) fully support
on SAGE2. In conclusion, our approach can help users to explore overview and insights of R2R
data and increase the capability of healthcare policy makers.

73

[PP18] Tuberculosis (TB) Disease Interactive Map in Jakarta Capital Special Region
Nuraisah, Ummi Azizah Rachmawati
Faculty of Information Technology, Universitas YARSI Jakarta
nuraisaah07@gmail.com; ummi.azizah@yarsi.ac.id

Abstract
Tuberculosis (TB) is a disease that is easily contagious and can attack various organs of the body,especially the lungs. This disease can cause complications to cause death for the sufferer if not
appropriately treated. Currently, Indonesia is in the top six countries with the newest TB cases
and is ranked second with the most cases of TB patients in the world. Prevention can be done,
among others, by conducting regular visits to community homes to ensure that their place ofresidence has proper sanitation by established health standards. It is necessary to map problems
in each region to ensure what diseases are now spreading in the area. This study aims to monitor
Tuberculosis in the Special Capital Region of Jakarta, especially East Jakarta, by using interactive
maps to facilitate the DKI Jakarta Health Office in monitoring the control of Tuberculosis foraction and prevention. This research use SDLC methodology, which consisted of the stages ofsystem analysis, system design, system implementation, and system testing. The data used is fromthe Health Service Office of Jakarta Region. This interactive map-based tuberculosis information
system in the particular area of the capital Jakarta is a form of business that can help thecommunity and is expected to be useful for its users.

74

[PP19] Digital Poster Management Application on a SAGE2 based Multiple Display
SystemPrakritchai Phanphila, Vasaka Visoottiviseth Jason Haga, Ryousei Takano
Faculty of Information and Communication Technology Mahidol University, Bangkok, Thailand
Information Technology Research InstituteNational Institute of Advanced Industrial Science and Technology,Tsukuba, Japan
prakritchai.pha@student.mahidol.ac.th, vasaka.vis@mahidol.ac.thjh.haga@aist.go.jp, takano-ryousei@aist.go.jp

Abstract
Digital posters are an impressive presentation media that can replace paper postersused at
technical conferences. An organizer of such events requires effective managementof multiple
digital posters and multiple monitors. This paper proposes a digital postermanagement systemon a multiple display environment. To control multiple monitors we employ SAGE2 technology,
which is middleware for collaborative working in highresolution that allows multiple users tosimultaneously control the display of the samemonitor from browsers. Managing multiple
monitors is designed using the main SAGE2screen to manage and control other SAGE2 screens.
We have implemented a costeffective multiple display system using Raspberry Pi computers. For
digital posters, we have extended a PDF viewer to support automatic page advancing with anarbitraryinterval. Also, we have developed a SAGE2 multiple monitor application that can beusedto control the PDF viewer presentation on multiple SAGE2 displays. The
currentimplementation can manage and control what should be shown on each SAGE2
display.Future work includes implementing a PDF uploading function and evaluating theperformance and effectiveness in a real exhibition environment.

75

[PP20] Machine learning for processing image data for disaster management
Parintorn Pooyoi, Worapan Kusakunniran, Jason H. Haga
Mahidol University, National Institute of Advanced Industrial Science and Technology
parintorn.poo@gmail.com, worapan.kun@mahidol.edu, jh.haga@aist.go.jp

Abstract
Natural disasters are an important global problem affecting many different countries. In Japan, apublic website was made available to provide a variety of data from different sensors throughout
the country. This data includes information about river water levels, rainfall levels, and snowfall
level. Moreover, this information includes CCTV cameras positioned along the river, which
provide photos of the river conditions in real-time. This provides users with information on thecurrent status of the river, but does not provide any additional information and the user is left toprocess the information and make decisions. The goal of this project is to improve the usabilityof this CCTV image data through image processing with machine learning. Convolution Neural
Network is one of the most popular machine learning algorithms. As a first step to provide more
information from the camera images, we implemented this framework to detect snow in thecamera images. using a modified form of the transfer learning VGG19 model. This model wastrained with images that were positive for snow and negative for snow. The results confirmedthat it can detect snow areas on the ground in image. However, in some images had false
positives because it classified a clouds as snow. Because of this, we implemented postprocessing to correct the false positives and improved the accuracy of the classification. Future
work will extend the classification from a binary (i.e. yes or no snow) to a more quantitative
measure of the amount of snow in the images.

76

[PP21] Room Auto Controlling Based on Occupant Body Condition Using Arduino And
Raspberry Pi
Ahmad Sabiq, Nova Eka Diana, Debita Febriana, Sri Chusri Haryanti
Faculty of Information Technology, Universitas YARSI, Indonesia
ahmad.sabiq@yarsi.ac.id, nova.diana@yarsi.ac.id, debitafebriana@gmail.com, sri.chusri@yarsi.ac.id

Abstract
A cozy room should adjust its environment based on the condition of its occupants since it will
indirectly affect the moods and body conditions of people inside. This study aims to develop asystem for monitoring the human body condition using paired sensors on the Arduino Lilypad.
The system will send the sensors data to the Raspberry Pi 3 via Bluetooth to automaticallycontrol the electronic device inside the room based on the occupant body condition. The
developed system will automatically turn on or turn off the electronic device when the bodytemperature or the heart rate is higher than the specified threshold value.

77[PP22] Criminality Linguistics Detection on Social Networks Through Personality Traits
Saravanan Sagadevan, Nurul Hashimah Ahamed Hassain Malim, Nurul Izzati Ridzuwan and
Muhammad Baqir Hakim Mohammad Bashir
Universiti Sains Malaysia, Penang, Malaysia
nurulhashimah@usm.my

Abstract
As text messaging became a mainstream form of communication among online users, cybercriminal activities such as cyber bully and cyber harassing are more often executed through suchplatforms. However, linguistic markers in the text messages often act as fingerprints in revealing
the characters of the culprits who hide behind the anonymity provided by internet. Presently,
most of the text-based cybercriminal studies directly dives into linguistic analysis rather than
exploring through the psychology-personality aspects that acts as one of the root causes to thepresence of criminality. Therefore, this study attempts to investigate the criminality contents intweets messages through the point of view of personality trait that advocates criminal behaviour
and its representation towards languages measured through sentiment valences. Furthermore,most of the recent studies in personality detection domain used Big 5 Personality Model andAutomatic personality Recognition (APR) technique to examine the personality of social
networks users. As an alternative, this study intended to incorporate the Three Factor Personality
Model (PEN) traits especially Psychoticism that widely has been applied to evaluate thepersonality of criminals and employed an Automatic Personality Perception (APP) method to
guide the identification of sentiment seed terms that associated to PEN model traits. We havecollected English and Malay language messages tweets messages and annotated based on
sentiment valences identified through literature reviews. This study proposed a methodology tomake comparisons among Naïve Bayes (NB), Sequential Minimal Optimization (SMO), KNearest Neighbour (KNN), J48 and ZeroR classifier as a baseline based on the Chi Squarefeature selection and 10-fold cross validation. Our Machine Learning comparison showed that
NB classifier outperformed other classifiers while KNN performed worst among them.

78[PP23] Performance Comparison of Dynamic Load Balancing Algorithm for Indonesian
e-Health Cloud
Ridho Yanevan Pratama, Aditya Efrian, Sri Chusri Haryanti, Sri Puji Utami Atmoko
Faculty of Information Technology, Universitas YARSI, Indonesia
ridhoyanevan@gmail.com, adityaefrian@gmail.com , sri.chusri@yarsi.ac.id , puji.atmoko@yarsi.ac.id

Abstract
The popularity of cloud computing has been growing - including the use of cloud computing in
healthcare, as commonly called e-health cloud. The increasing number of cloud computing users
has driven the selection of an effective load balancing algorithm. This study has examined theperformance of load balancing algorithms for a cloud. The cloud is Indonesian e-health cloudusing Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms forload balancing. We used CloudAnalyst simulator, a cloud computing simulator that applies aGUI. Before running the scenario, PSO and ACO algorithms are added on CloudAnalyst. The
simulation is done for a cloud model with the number of users and data centers that adopt theneed of part of e-health cloud in Indonesia. 42 users were taken based on the number of cities,
while for 6 data centers were taken from 6 provinces on the Java Island. The data centerselection use optimized response time policy. The average response time and the average data
center processing time are investigated for different requests per user and data sizes. The results
show that the ACO algorithm provides a lower response time and data center processing timecompared to using the PSO algorithm for load balancing on the cloud.

79[PP24] Using UAV images for smart agriculture to monitor rice paddy with artificial
intelligenceMing-Der Yang, Hui Ping Tsai, Yu-Chun Hsu, and Cloud Tseng
Department of Civil Engineering, National Chung Hsing University, Taichung, Taiwan
Innovation and Development Center of Sustainable Agriculture, National Chung Hsing
University, Taichung, Taiwan
mdyang@nchu.edu.tw, huiping.tsai@nchu.edu.tw, daviddrmfsltd@gmail.com, khjhs60217@hotmail.com
Abstract
Rice is the staple food for over half of the world’s population. Especially in Asia, rice is central
to the food security (FAO 2014). Therefore, a smart rice paddy monitoring system is urgently
needed to ensure a sustainable rice production. With the intention to develop a smart rice paddy
monitoring system, the present project uses a huge amount of environmental data as the basis
for the development of an artificial intelligent (AI) agricultural cultivation support system
integrating unmanned aerial vehicle (UAV) surveillance. Meanwhile, a standard operatingprocedure (SOP) of the UAV field monitoring system will be established in order to combine
field monitoring networks and cloud-based image processing techniques. Three major highlights
are 1) establishing a UAV agricultural multi-source image database, 2) implementing a variety ofimage analyses combining AI techniques for growth monitoring, yield prediction, crop moisture
content evaluation, damage assessment, and disease monitoring, and 3) establishing a UAV
cloud-based platform to combine functions of UAV image analysis and expert advice support
system. Overall, the present study is expected to provide a valuable basis for smart agriculture
development and to benefit both farmers and management agencies in agriculture cultivation andmanagement in Taiwan and further southeast Asia countries.

80[PP25] rEDM Code Acceleration with ABCI Supercomputer
Wassapon Watanakeesuntorn, Kohei Ichikawa, Jason Haga, Gerald Pao, Erik Saberski
Nara Institute of Science and Technology
wassapon.watanakeesuntorn.wq0@is.naist.jp

Abstract
The rEDM code is the R package which written on C++ and R programming language. The
rEDM package is a collection of methods for Empirical Dynamic Modeling (EDM). In thiswork, we try to optimize and accelerate the code by using CUDA. We aim to run this programon the AI Bridging Cloud Infrastructure (ABCI) in AIST, Japan. We use the dataset of zebrafish
neural brain for running with the rEDM code from UCSD, USA. This is collaboration project of
NAIST, AIST and UCSD.

81

PRAGMA 35 DEMO ABSTRACTS82

[PD01] Integrating PRAGMA-ENT and Inter-Cloud Platform using Dynamic L2VLAN
Service
Kohei Ichikawa, Atsuko Takefusa, Yoshiyuki Kido, Yasuhiro Watashiba, Susumu Date
Nara Institute of Science and Technology, National Institute of Informatics,
Osaka University
ichikawa@is.naist.jp, takefusa@nii.ac.jp, {kido, watashiba-y, date}@cmc.osaka-u.ac.jp

Abstract
Effective sharable cyberinfrastructures that allow researchers to freely perform their researchexperiments on the environment are fundamental for collaborative research in widely distributed
environments. This demo will introduce an integration of an international Software-Defined
Networking testbed (PRAGMA-ENT) and an Inter-Cloud platform (Virtual Cloud Provider)using a dynamic VLAN service (NSI). Using these services and infrastructures, we candynamically design and deploy our own research testbed in the Inter-Cloud environment with an
on-demand manner. In the talk, we present the overview of these services and introduce some
applications on the testbed.

83

[PD02] Extending SDN Networks from Cloud-to-Edge using Virtual Private Networks
with Peer-to-Peer Overlay Links
Ken Subratie Renato Figueiredo
University of Florida
kcratie@ufl.edu renato@ece.ufl.edu

Abstract
While solutions to many challenges posed by IoT lie at the network’s edge, they cannot forego
services available in the cloud which has over a decade of research and engineering to beleveraged. To bridge this gap, hybrid approaches in networking that account for characteristics
of both edge and cloud systems are necessary. On cloud data centers, significant progress has
been made on applying Software Defined Networking (SDN) to address networking challenges
such as scalability, addressing, virtualization, and traffic engineering; administrators are now wellversed at managing data center SDN deployments in enterprise systems. However, theapplicability of SDN in edge networks has not yet been thoroughly investigated. I will
demonstrate a hybrid system that incorporates SDN software switches and overlay networks tobuild a dynamic layer 2 virtual network connecting hosts across the edge (and in the cloud) withlinks that are peer-to-peer Internet tunnels. These tunnels are terminated as subordinate devices
to SDN switches and seamlessly enable the traditional SDN functionalities such that cloud andedge resources can be aggregated.

84

[PD03] Analysis of Load Balancing Performance on Cluster Computing withPROXMOX VEAndi Rasuna Darsono, Sri Chusri Haryanti,
Faculty of Information Technology, Universitas YARSI, Indonesia
andi.rasuna@gmail.com, sri.chusri@yarsi.ac.id

Abstract
This research aims to examine the performance of load balancing for a cluster. Load balancing isa solution for a large access load and minimizes downtime in serving requests from users. Loadbalancing distributes loads of traffic evenly to the servers with particular algorithms. In thisresearch, the server for load balancing cluster is implemented using LVS topology via direct
routing and use round-robin algorithm on Proxmox VE for load balancing. Proxmox VE is incharge of dividing virtual resource servers into four virtual environments, two nodes as loadbalancing clusters and two nodes as web servers. The results of research conducted with loads of250, 500 & 1000 users show that load balancing system reduces the maximum response time up
to 4165 ms with 0% of packet loss, compared to utilization of single server with the maximum
response time up to 7269 ms and 22.48% packet loss. The load balancing system for clusters also
manages to failover with an average value of downtime 16.6 seconds.

85

[PD04] Performance Comparison of Load Balancing using Honeybee and Threshold
Algorithm
Aditya Efrian, Sri Chusri Haryanti, Sri Puji Utami Atmoko, Ridho Yanevan Pratama
Faculty of Information Technology, Universitas YARSI, Indonesia
adityaefrian@gmail.com , sri.chusri@yarsi.ac.id , puji.atmoko@yarsi.ac.id ,ridhoyanevan@gmail.com

Abstract
Cloud computing is currently developing rapidly. Load balancing technique is very crucial tobalance the load in the cloud. Load balancing is needed to distribute dynamic workload acrossresources in the cloud. CloudAnalyst can be used to simulate load balancing algorithm in cloud.
One advantage of CloudAnalyst is it applies a GUI. We implement Honeybee and Thresholdalgorithms into CloudAnalyst. We examine Honeybee and Threshold algorithms implementation
for Indonesian e-Health cloud model. There are 4 data center and 34 users based on the numberof regions and provinces in Indonesia. Two data center selection policies are used, i.e. closest
data center and optimized response time policy. The average response time is investigated for
different user data sizes. The simulation result shows that Threshold algorithm gives average
response time that is faster than the Honeybee. Nevertheless, Honeybee algorithm delivers better
performance for data size smaller than 80 bytes, and Threshold algorithm tends to give smaller
response time for data size 100 bytes.

86[PD05] EDISON Data Platform for Computational Science Data
Jaesung Kim, Jeongcheol Lee, Sunil Ahn, Jongsuk Ruth Lee
Korea Institute of Science Technology and Information (KISTI), Korea{jskim0116, jclee, siahn, jsruthlee}@kisti.re.kr

Abstract
This paper demonstrates the EDISON-DATA platform, which provides a way to easily publish,
preserve, share, and analyze computational science data. While the data is explosively generated
from computational science field, research on a computational science platform that provides away to utilize and analyze the data is still an early stage. One of the main issues in thecomputational science data platform is addressing the diversity and heterogeneity of data. Our
platform provides customized pre-processing services by data type to extract metadata
consistently and provides methods to analyze the metadata. Sharing and reusing computational
science data can avoid duplication of calculation time and cost. In addition, it is also possible toobtain new knowledge by analyzing the constructed data through artificial intelligence methods.
This platform was developed to support multi-disciplinary data. To test this platform's
functionality and applicability, we select the computational science data in the material field as apilot. We have constructed about 100,000 material simulation data on our platform. Based on
this data, we will describe the platform's main functionalities such as data management, data
analysis, and data property prediction service based on artificial intelligence.

87[PD06] Web-based Compute-Data Research Environment for Aircraft Airfoil
Aerodynamics
James Junghun Shin, Kumwon Cho, Jongsuk Ruth Lee
Korea institute of Science and Technology Information (KISTI)
shandy77@kisti.re.kr, ckw@kisti.re.kr, jsruthlee@kisti.re.kr

Abstract
Manufacturers in the aircraft industry need appropriate and efficient analysis frameworks todevelop reliable wings their design and manufacturing stages. Particularly two dimensional
airfoils are basic shape design elements in this technical area. In order to tackle the fieldtechnicians’ or engineers’ feeling difficulties on high performance computing simulation, then wedeveloped a cyber-infrastructural and web-based research environment. The first notable pointof this demo presentation is that a computer simulation environment was embodied in the web
based framework. It includes some essential computing elements such as automatic meshgeneration, airfoil shape parametrization, and high-fidelity flow solver. This framework enables
simulation-runners not to install any software with easy access, and then complications were
eliminated to consider computationally not professionally trained users who work in the
manufacturing fields. One more remarkable point is that this framework employed machinelearning driven tools for the aerodynamic performance inferences by machine learningtechniques with training and testing the dataset from the high-fidelity flow simulations. The
comparison between flow solver simulations and machine learning inferences showed that somemethods ranked higher accuracy and others were not. It was concluded that this virtual airfoil
research environment will help engineering designers and researchers conduct quicker decision
and analysis.

88[PD07] Mobile-based Augmented Reality for Sundanese Alphabets Education
Muhammad Reza Aditya, Resskytha Permata Sari, Adri Nursimarsiyan and Nova Eka Diana
Informatics Department, Faculty of Information Technology, Universitas YARSI, Indonesia
rezaditya28@gmail.com, rereskytha@gmail, adrysiyan@gmail, nova.diana@yarsi.ac.id

Abstract
In 2017, the Indonesian Ministry of Higher Research and Education reported that thesmartphone user in Indonesia had achieved about 25% of the total citizen or about 65 million
people. This trend is beneficial to utilize smartphone for promoting traditional cultures inIndonesia, especially to the teenagers that are steadily moving forward to modern culture. This
research aims to build an Android-based application with an augmented reality feature toenhance the experience of learning traditional Indonesian heritage, especially traditional
alphabets. CARIOSAN is a mobile application with the purpose to preserve the Sundanesealphabets and prevent it from extinction. The main features in the application are AR-based
Sundanese alphabets recognition, Rarangken information, Sundanese alphabets writing canvas,
and quiz feature to assess user understanding of the Sundanese alphabets. We interviewed 35
respondents for measuring the usability, the easiness of use, the information coverage, theinterface, and the novelty of the application. The results showed that the developed application
was highly useful and interactive in providing the essential information of Sundanese alphabets
although with a novelty score only above 50 percent.

89

[PD08] Tuberculosis (TB) Disease Interactive Map in Jakarta Capital Special Region
Nuraisah, Ummi Azizah Rachmawati
Faculty of Information Technology, Universitas YARSI Jakarta
nuraisaah07@gmail.com; ummi.azizah@yarsi.ac.id

Abstract
Tuberculosis (TB) is a disease that is easily contagious and can attack various organs of the body,especially the lungs. This disease can cause complications to cause death for the sufferer if not
appropriately treated. Currently, Indonesia is in the top six countries with the newest TB cases
and is ranked second with the most cases of TB patients in the world. Prevention can be done,
among others, by conducting regular visits to community homes to ensure that their place ofresidence has proper sanitation by established health standards. It is necessary to map problems
in each region to ensure what diseases are now spreading in the area. This study aims to monitor
Tuberculosis in the Special Capital Region of Jakarta, especially East Jakarta, by using interactive
maps to facilitate the DKI Jakarta Health Office in monitoring the control of Tuberculosis foraction and prevention. This research use SDLC methodology, which consisted of the stages ofsystem analysis,system design, system implementation, and system testing. The data used is fromthe Health Service Office of Jakarta Region. This interactive map-based tuberculosis information
system in the particular area of the capital Jakarta is a form of business that can help thecommunity and is expected to be useful for its users.

90

[PD09] Neuro Data Platform for Neuroscientist
Nurul Hashimah Ahamed Hassain Malim1, Jafri Malin Abdullah2, , Sharifah Aida Sheikh
Ibrahim2, Nurfaten Hamzah2, Muhammad Jaziem Mohamad Javeed1, Ariffin Marzuki3, Putra
Sumari1, Ahamad Tajudin Khader1
1School of Computer Sciences, Universiti Sains Malaysia
2
Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia
3
Hospital Universiti Sains Malaysia
1nurulhashimah@usm.my, 2brainsciences@gmail.com

Abstract
The University hospital (namely Hospital Universiti Sains Malaysia) holds a large collections of
Neuroimaging data from various neuroimaging machines. A collaborative effort on compilingand making use of these data work was initiated by a centre called P3Neuro (now is part of
Department of Neurosciences, School of Medical Sciences) where the School of Computer
Sciences was engaged to develop a platform that is not only aimed at hosting the data but also todevelop potential tailored diagnostic tools that could help the medical practitioners in theirdiagnosis decision makings. Since August 2017, the collaborative project has achieved its firsttwo milestones i.e. data pre-processing pipeline and initial data storage and retrieval mechanism.
Whilst, there is still a long way to go, this demo is intended to present the updates on on-goingdata pre-processing pipeline and also the initial data storage schema. This is followed by the next
phase planning on the analytics and visualization of these data.

91

[PD10] Deep Learning Classification for Liver Disease
Andi Batari Ahmad and Nova Eka Diana
Informatics Department, Faculty of Information Technology, Universitas YARSI, Jakarta,
Indonesia
ndieta.tarii@gmail.com, nova.diana@yarsi.ac.id

Abstract
Liver disease is one of the top ten diseases with the highest mortality rate in Indonesia, with theincreasing rate of one percent per year. Type of liver disease most attacking Indonesian people is
Hepatitis. According to Basic Health Research (Riskesdas) 2013, Hepatitis had a prevalence
number of 1.2 percent that was double than the prevalence in 2007. There are some methods todiagnose the liver disease such as enzymes pattern analysis, excretion, metabolism,
electrophoresis and serologic test. This research focused on diagnosing the liver disease based on
enzymes pattern using Deep Learning approach. We used Indian Liver Patient Dataset (ILPD)
from UCI Machine Learning Repository with a total of 583 data (416 positives and 167
negatives) to build the classification model, with the training and testing rate of 0.7 and 0.3,
respectively. We conducted a preprocessing step for the training data using Synthetic Minority
Over-sampling Technique (SMOTE) with the percentage rate of 0.5 to balance the positive andnegative class. Experiment results reveal that the created model can classify liver disease with theaccuracy, sensitivity, and specificity rate of 0.89882, 0.84, and 0.9225 percent, respectively.

92

PROGRAMME COMMITTEESPatrons

Prof. Dr. Ahamad Tajudin Khader
Dean, School of Computer Sciences, Universiti Sains Malaysia
Prof. Dr. Rosni Abdullah
Director, NAV6, Universiti Sains Malaysia
Prof. Dr. Mohd Nazalan Mohd Najimudin
Director, Nexus-Sciences, Universiti Sains Malaysia
Prof. Dr. Habibah Abdul Wahab
PRAGMA Steering Committee-Malaysia
Dr. Peter Azberger
Founding and Former Chair, PRAGMA Steering Committee

ChairsDr. Nurul Hashimah Ahamed Hassain Malim
School of Computer Science, Universiti Sains Malaysia & PRAGMA Member
Shava Smallen
PRAGMA Interim co-chair,
University of California, San Diego, USA

Co-chairs

Dr. Gan Keng Hoon
School of Computer Sciences, Universiti Sains Malaysia
Dr. Jongsuk (Ruth) Lee
National Supercomputing Division, Korea Institute of Science and Technology
Information (KISTI)

Secretary

Mdm. Eliza Yasmin Dahlan

Treasurer

Mr. Ahmad Anas Ismail
Dr. Zarul Fitri Zaaba

PRAGMA 35 Program
Committees

Prof. Renato Fuigeredo, University of Florida, USA
Prof. Shinji Shimojo, Osaka University, Japan
Prof. David Abramson, University of Queensland, Australia
Prof. Heru Suhartanto, Universitas Indonesia, Indonesia
Assoc. Prof. Kohei Ichikawa, National Institute of Advanced Industrial
Science and Technology (AIST), Japan
Assoc Prof. Putchong Uthayopas, Kasetsart University, Thailand
Nadya Williams, University of California, San Diego, USA
Dr. Jason Haga, National Institute of Advanced Industrial Science andTechnology (AIST), Japan
Dr. Ryousei Takano, National Institute of Advanced Industrial Science andTechnology (AIST), Japan
Dr. Yoshiyuki Kido, Osaka University, Japan
Dr. Fang Pang Lin, National Center for High Performance Computing, Taiwan
Dr. Prapaporn Rattanatamrong, Thammasat University, Thailand
Hsiu-Mei Chou, National Center for High Performance Computing, Taiwan
Aimee Stewart, KU Biodiversity Institute, USA
Weicheng Huang, National Applied Research Laboratories, Taiwan
Wassapon Watanakeesuntorn, Nara Institute of Science and Technology, Japan93

Big Data Summit 2
Committees

Technical
Reviewers

Assoc. Prof. Dr. Bahari Belaton
Assoc. Prof. Dr. Chan Huah Yong
Dr. Mohd Heikal Husin
Dr. Nur Syibrah Mohd Naim
Dr. Mohd Nadhir Ab Wahab
Dr. Anusha Achuthan
Dr. Mohamed F.R Anbar
Dr. Azleena Mohd Kassim
Dr. Mohd Halim Mohd Noor
Dr. Chew Xin Ying
Mr. Mohd Azam Osman
Mr. Iznan Husainy Hasbullah
Mdm. Zuhaida Ariffin
Mr. Mohamed Azahar Mustapha
Mr. Muhamad Hadzri Yaakop
Mdm. Nor Aida Lob Abu Bakar
Mdm. Sheela Muniandy
Mdm. Halizah Abdul Razak
Mdm. Nurul Nadiah Zambri
Mdm. Nur Sadrina Abd Rahim
Mdm. Rohana Omar
Mdm. Badriyah Che May
Mdm. Siti Zainura Abdul Kadir
Mr. Shik Abdulla Mohamed Ali
Mr. Ruslan Ahmad
Mr. Ramlee Yahaya
Mr. Mohamad Tarmizi Hat
Mr. Jasmi Chek Isa
Dr. Nurul Hashimah Ahamed Hassain Malim, USM
Dr. Shankar Karuppayah, USM
Dr. Wong Li Pei, USM
Dr. Tan Choo Jun, WOU
Dr. Sarina Sulaiman, UTM
Dr. Jasy Liew Suet Yan, USM
Mr. Mohd Azam Osman, USM
Dr. Fadratul Hafinaz Hassan, USM
Assoc. Prof. Dr. Zalinda Othman, UKM
Dr. Chew Xin Ying, USM
Dr. Liew Chee Sun, UM
Dr. Suraya Alias, UMS
Dr. Mohammed F. R. Anbar, USM
Prof. Dr. Khong Kok Wei, Nottingham University
Dr. Nor Asilah Wati Abdul Hamid, UPM
Dr. Umi Kalsom Yusof, USM
Dr Syaheerah Lebai Lutfi, USM
Dr. Gan Keng Hoon, USM
Dr. Tan Tien Ping, USM
Dr. Nur Syibrah Muhamad Naim, USM
Dr. Anusha Achuthan, USM
Assoc. Prof. Dr. Cheah Yu-N, USM
Dr. Nur Hana Samsudin, USM

94

PARTNERS & SPONSORS
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95

PERSONAL NOTES

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