ho chi minh city, vietnam conference abstracts jan. 13 … schedule.pdf · the weather situation of...

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Ho Chi Minh City, Vietnam

CONFERENCE ABSTRACTS

Jan. 13-16, 2017

International Conference on Machine Learning and Soft

Computing (ICMLSC 2017)

The 9th International Conference on Computer Research

and Development (ICCRD 2017)

ALAGON CENTRAL HOTEL & SPA IN HO CHI MINH CITY

52B - 62 - 64 Pham Hong Thai Street, Ben Thanh Ward,

District 1, Ho Chi Minh City, VietNam

Tel: (848) 3824 5888 – Fax: (848) 3827 2158

Email: [email protected]

http://www.alagonhotels.com/alagon-central-hotel-spa.html

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Welcome Address

We are pleased to welcome you to the 2017 Ho Chi Minh City conference, which will take place at ALAGON CENTRAL HOTEL & SPA IN HO CHI MINH CITY, Ho Chi Minh City, Vietnam, from Jan. 13-16,

2017.

After several rounds review procedure, the program committee accepted those papers to be

published in conference proceedings. We wish to express our sincere appreciation to all the

individulas who have contributed to ICMLSC 2017 and ICCRD 2017 conferences in various ways.

Special thanks are extended to our colleagues in program committee for their thorough review of all

the submissions, which is vital to the success of the conference, and also to the members in the

organizing committee and the volunteers who had delicated their time and efforts in planning,

promoting, organizing and helping the conference. Last but not least, our special thanks go to

conference chair Prof. Pham The Bao for all the kind and patient support and assistance offered to

our whole conference procedure. Without him, our conference could not be prepared so smoothly,

thanks again.

This conference program is highlighted by three Keynote Speakers: Prof. Pham The Bao, from

University of Science, Vietnam; Prof. Hieu Trung Huynh, Dean of IT faculty of Industrial University of

Ho Chi Minh City, Vietnam; and Prof. Genci Capi, from Hosei University, Japan.

One best presentation will be selected from each session, evaluated from: Originality; Applicability;

Technical Merit; PPT; English. The best one will be announced at the end of each Session, and

awarded the certificate over the Dinner.

Ho Chi Minh City is the heart and soul of Vietnam. Not only the largest city of the country, it is the

most bustling, dynamic and industrious centre of commerce, economy, science, technology and

tourism, as well as a cultural trendsetter.

We wish you a successful conference and enjoyable visit in Ho Chi Minh City!

Conference Organizing Committee

Ho Chi Minh City, Vietnam

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Organizing Committee

Advisory Chairs

Prof. Phuoc Tran Vinh, University of Information Technology, Vietnam

Prof. Nguyen-Vu Truong, Vietnam Academy of Science and Technology, Vietnam

Prof. Jia-Ching Wang, Department of Computer Science and Information Engineering, National

Central University, Taiwan

Conference Chairs

Prof. Pham The Bao, University of Science, Vietnam

Prof.Hieu Trung Huynh, Dean of IT faculty of Industrial University of Ho Chi Minh City, Vietnam

Prof. Genci Capi, Hosei University, Japan

Program Chairs

Prof. Le Hoai Bac, University of Science, Vietnam, Ho Chi Minh City, Vietnam

Prof. Cheol-Young Ock, University of Ulsan, Korea

Technical Committee

Taejun Cho, Daejin University, Korea

Rositca Nikolova, University of Sofia "St. Kliment Ohridski", Bulgaria

Seonghwan Yoon, Dept. of Architecture, Pusan National University, Korea

Victoria Tuzlukova, Sultan Qaboos University, Oman

Ngo Quoc Viet, Ho Chi Minh City University of Pedagogy, Vietnam

Yao-Ming Hong, MingDao University, Taiwan

Ro-Yu Wu, Lunghwa University of Science and Technology, Taiwan

Chandrasekaran Subramaniam, Anna University, India

Sharad K. Pradhan, National Institute Of Technical Teachers’ Training & Research,India

Benilda Eleonor V. Comendador, Polytechnic University of the Philippines, Philippines

Jamaiah Yahaya, Universiti Kebangsaan Malaysia, Malaysia

Hung Manh La, University of Nevada, Reno, USA

Pham Cong-Kha, Department of Engineering Science University Of Elctro Communications Chofu,

Tokyo, Janpan

Chang Gyoon Lim，Chonnam National University, Korea

Jarot S. Suroso, Indonesia Bina Nusantara University, Indonesia

JinFeng Wang, South China Agricultural University, China

Hung Manh La, University of Nevada, Reno, USA

Amir H. Alavi, Michigan State University, USA

Dong Si, Computing and Software Systems (CSS), University of Washington Bothell,Bothell, WA, USA

Doo-Hwan Bae, School of Computing, KAIST, Korea

JungHyun Han, Korea University, Korea

Yao-Ming Hong, MingDao University, Taiwan

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Ro-Yu Wu, Lunghwa University of Science and Technology, Taiwan

Ngo Quoc Viet, Ho Chi Minh City University of Pedagogy, Vietnam

Jamaiah Yahaya, Universiti Kebangsaan Malaysia, Malaysia

Zainiharyati Mohd Zain, Faculty of Applied Sciences, Universiti Teknologi MARA, Malaysia

Jyothi Singaraju, Sri Padmavati Mahila Visvavidyalayam (Women’s University), India

Baij Nath Kaushik, ABES Engineering College, Ghaziaba,India

S.Felix Wu, University of California, Davis，U.S.

Hieu Minh Nguyen, Academy of Cryptography Techniques,Vietnam

Dr. Izzuddin Zaman, Universiti Tun Hussein Onn, Malaysia

Samarjeet Borah, Sikkim Manipal University, India

Kuo-Yuan Kao, National Penghu University, Taiwan

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Conference will be held at ALAGON CENTRAL HOTEL & SPA IN HO CHI MINH CITY.

Address: 52B - 62 - 64 Pham Hong Thai Street, Ben Thanh Ward, District 1, Ho Chi Minh City,

VietNam.

UTC/GMT+7

The Weather Situation of Ho Chi Minh City in January

Average daily minimum temperature

21℃

Average daily highest temperature

32℃

It gets pretty crowded from November to March and in June and August. Prices tend to peak over the

Christmas and New Year period, but if you don’t fancy sharing the sites with the masses, try to avoid

these busy times.

Some travellers like to time a visit with Tet(Vietnamese New Year), which is the biggest festival in the

calendar in late January or early February.

Currency

The currency of Vietnam is "Dong" (abbreviated "d" or VND). Bank notes are: 100d , 200d and 500d

(too small value - rarely used); 1,000d; 2,000d; 5,000d; 10,000d, 20,000d, 50,000d and 100,000d

(each has two versions - cotton and polymer), 200,000d and 500,000d.

US dollar is widely accepted while most major currencies can be exchanged at leading banks in

Vietnam (Vietcombank, ANZ, ACB, VIB Bank…) or some hotels and jewelry shops. With the relatively

low value of Dong, you are recommended to carry US dollar in small notes; it will help you to change

easily.

Health Care

Health issues and the quality of medical facilities vary enormously depending on where and how you

travel in Vietnam. Some international hospitals/clinics in Hanoi and Ho Chi Minh City (American,

French and German doctors on staff)

In Ho Chi Minh City: (tel code: 84-8)

International SOS

65, Nguyen Du Str.

Tel: 829-8520, emergency: 829-8424

Saigon International Clinic

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8 Alexandre de Rhodes Str., District 1

Tel: 823-8888

Gia Dinh International Hospital

1 Trang Long Str., Bin Thanh District

Tel: 803-0678

Franco Vietnamese Hospital

6 Nguyen Luong Bang Str., District 7

Tel: 411-3333

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Instructions for Oral & Poster

Presentations

Oral Presentations

Timing: a maximum of 15 minutes total, including speaking time and discussion. Please make

you’re your presentation is well timed. Please keep in mind that the program is full and that the

speaker after you would like their allocated time available to them.

You can use CD or USB flash drive (memory stick), make sure you scanned viruses in your own

computer. Each speaker is required to meet her/his session chair in the corresponding session

rooms 10 minutes before the session starts and copy the slide file (PPT or PDF) to the computer.

It is suggested that you email a copy of your presentation to your personal inbox as a backup. If

for some reason the files can’t be accessed you’re your flash drive, you will be able to download

them to the computer from your email.

Please note that each session room will be equipped with a LCD projector, screen, point device,

microphone, and a laptop with general presentation software such as Microsoft PowerPoint and

Adobe Reader. Please make sure you’re your files are compatible and readable with our

operation system by using commonly used front sand symbols. If you plan to use your own

computer, please try the connection and make sure it works before your presentation.

Movies: If your PowerPoint files contain movies please make sure that they are well formatted

and connected to the main files.

Poster Presentations

It is suggested to prepare poster with size of 60cm*80cm.

Posters are required to be condensed and attractive. The characters should be large enough so

that they are visible from 1 meter apart.

Please note that during your poster session, the author should stay by your poster paper to

explain and discuss your paper with visiting delegates.

Dress code

Please wearing formal clothes or national characteristics of clothing

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Keynote Speeches

Prof. Pham The Bao

University of Science, Vietnam

Pham The Bao received the B.S. degree from Ho Chi Minh City University, in 1995 and the

Ph.D. degree from University of Science, in 2009. He is Professor of Mathematics &

Computer Science faculty, University of Science. His interesting research is image

processing, pattern recognition and computing: intelligent computing, Biocomputing,

high-performance computing

“SEGMENTATION LIVER FROM 3D ABDOMINAL CT IMAGES BY ENHANCED OTSU METHOD”

Abstract: Segmentation of the whole liver region from computed tomography (CT) image is the ?rst step

in the computer-aided diagnosis for liver disease. In this paper, we propose a novel method for

segmenting liver region from 3D CT images of abdomen using enhanced Otsu method. Our algorithm use

Otsu method with some improvements to construct intensity model and shape model for liver. First, the

3D CT image of abdomen is cropped, preserving only the part that contains liver. Second, the liver region

in each slice of preserved part is segmented using intensity model. Finally, the segmented liver regions

from all slices are post-processed to increase performance and merged into a single liver region.

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Prof. Hieu Trung Huynh

Dean of IT faculty of Industrial University of Ho Chi Minh City,

Vietnam

Dr. Hieu Trung Huynh is the dean of Faculty of Information Technology, Industrial

University of Ho Chi Minh city. His research interests include machine learning, intelligent

computation, medical data analysis, and computer-aided diagnosis (CAD). He received his

B.S. degree in Computer Science at University of Technology in 1998 and then became a

lecturer there. He passed M.S courses in the University of Technology, VietNam, realized his

thesis research work at Osaka Sangyo University, Japan, and received M.E. degree of

Computer engineering at University of Technology in 2003. He did his PhD at Intelligent

Computing Laboratory at Chonnam National University, Korea, and obtained his PhD degree

in 2009. He then worked in this Laboratory until 2010. From 2011 to 2012, he has joined the

Department of Radiology, University of Chicago, US. He has returned Viet Nam and

appointed as dean of Faculty of Information Technology. He has published more than 40

papers primarily in the fields of machine learning, intelligent computation and medical data

analysis. He is a member of IEICE, IEEE and committee member of several international

conferences

“Liver tumor volumetry on MR images using the feedforward neural network trained by

a non-iterative algorithm”

The hepatic cell carcinoma (HCC) is one of the most common cancers and rapidly growing worldwide. The

early detection of patients with the liver cancer plays an important role to improve the treatment

performance. The basic criterion to evaluate the tumor response to the treatment is the tumor size.

Several computerized schemes have been developed for the liver tumor segmentation on CT images.

These methods include probability, multiple thresholding, watershed like, deformable models, paintbrush

algorithm, adaptive, level-set and active contour techniques, etc. In comparison with CT images, the

number of approaches developed for liver tumor segmentation on MR images is limited, while the

increasing use of liver MRI as a single exam for liver disease leads to imperative demands for investigating

researches in the computerized MRI liver tumor volumetry. In this talk, we focus on a very challenging

task of the liver tumor volumetry in abdominal MR images. The proposed scheme consists of five main

stages. Firstly, the liver in the T1-weighted MR image series was segmented automatically. The region of

interest (ROI) image which contains the liver tumor region was extracted. Teacher regions were

generated by using the 3D fast marching algorithm. A neural network trained by a non-iterative algorithm

was employed to classify the voxels, which were not processed by the fast-marching algorithm. Finally,

the post-processing stage was applied to refine the liver tumor boundaries. The liver tumor volumes

computed by using our developed scheme excellently agreed with the “gold-standard” manual volumes

by the radiologist.

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Prof. Genci Capi

Hosei University, Japan

Genci Capi received the B.E. degree from Polytechnic University of Tirana, in 1993 and the

Ph.D. degree from Yamagata University, in 2002. He was a Researcher at the Department of

Computational Neurobiology, ATR Institute from 2002 to 2004. In 2004, he joined the

Department of System Management, Fukuoka Institute of Technology, as an Assistant

Professor, and in 2006, he was promoted to Associate Professor. He is currently a Professor

in the Department of Electrical and Electronic Systems Engineering, University of Toyama.

His research interests include intelligent robots, BMI, multi robot systems, humanoid robots,

learning and evolution. He has been committee member of several international

conferences, and editor of special issues of some journals.

"Challenges in assistive intelligent robotics"

Abstract: Soon robots are expected to operate in our homes, hospitals and offices. Therefore, they have

to process multiple sensors data and adapt the policy as the environment changes. In this talk, I will

overview the existing efforts including our attempts at creating intelligent robots operating in everyday life

environments. In particular, I will focus on remotely operating surveillance robot, robot navigation in

urban environments, and assistive humanoid robot. I will show experimental results that demonstrate the

effectiveness of proposed algorithms.

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Daily Schedule of Events D

1/

Jan. 13 Registration(Lobby): Celine Xi, Amy Hu

Note:

*Collecting conference materials;

*Certificate will be get at the registration desk;

*Accommodation not provided, and it’s suggested to make an early reservation.

10:00-17:00

D2/

Jan. 14

Cocochine

Opening Remarks:

Prof. Pham The Bao, University of Science, Vietnam 9:00-9:05

Keynote Speech I:

Prof. Hieu Trung Huynh, IT faculty of Industrial University of Ho Chi Minh City, Vietnam

“Liver tumor volumetry on MR images using the feedforward neural network trained by a

non-iterative algorithm”

9:05-9:55

Keynote Speech II:

Prof. Genci Capi, Hosei University, Japan

“Challenges in assistive intelligent robotics”

9:55-10:45

Coffee Break & Group Photo 10:45-11:10

Keynote Speech III:

Prof. Pham The Bao, University of Science, Vietnam


11:10-12:00

Lunch at Restaurant 12:00-13:00

Room A Room B

Session A-1

Computer theory and Application Technology

Session B-1

Mechanical and systems engineering 13:00-15:30

Coffee Break 15:30-15:45

Session A-2

Machine learning and image processing

Session B-2

Communication network and Information

Engineering

15:45-19:00

Poster 13:00-18:00

Dinner at Restaurant 19:00-20:00

D3/

Jan.1

5

Campus Visit in University of Science, Vietnam 14:00-16:00

D4/

Jan.1

6

One Day Tour in Ho Chi Minh City 8:00-17:00

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Technical Program

Morning, Day 2/Jan. 14th

Chair: Prof. Pham The Bao, University of Science, Vietnam

Venue: Cocochine Room

9:00-12:00

9:00-9:05

Opening Remarks

Prof. Pham The Bao


9:05-9:55

Keynote Speech I

Prof. Hieu Trung Huynh

IT faculty of Industrial University of Ho Chi Minh City, Vietnam

“Liver tumor volumetry on MR images using the feedforward neural network trained by a

non-iterative algorithm”

9:55-10:45

Keynote Speech II

Prof. Genci Capi


“Challenges in assistive intelligent robotics”

10:45-11:10 Coffee Break & Group Photo

11:10-12:00

Keynote Speech III

Prof. Pham The Bao



*The Group Photo will be updated online.

**One best presentation will be selected from each session, the best one will be announced at the end of each

session and awarded certificate during the dinner, winners’ photos will be updated online.

***Best Presentation will be evaluated from: Originality; Applicability; Technical Merit; PPT; English.

****Please arrive at the conference room10 minutes earlier before the session starts, copy your PPT to the

laptop.

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Afternoon, Day 2/Jan. 14th

Session A-1< Computer theory and Application Technology >

Venue: Room A

Chair:

Time:13:00-15:30

Note:

* The certification of Oral/Poster presentations, listeners, will be awarded at the end of each session.

* For the Best Presentation of each session, it is encouraged to award it to student author prior.

*To show the respect to other authors, especially to encourage the student authors, we strongly

suggest you attend the whole session, the scheduled time for presentations might be changed due to

unexpected situations, please come as early as you could.

*session photo will be taken at the end of the session and update online

Time:13:00-13:15

Probabilistic Quantitative Temporal Constraints: Representation, Reasoning, and Query

Answering

Prof. Paolo Terenziani, Antonella Andolina

DISIT, Univ. del Piemonte Orientale, Alessandria, Italy

Temporal reasoning, in the form of propagation of temporal constraints, is an important topic

in Artificial Intelligence, to which many papers, workshops and conferences have been

devoted. Many application areas, including, e.g., scheduling and planning, demand for the

treatment of different forms of uncertainty and\or preferences. To cope with such a need, the

current literature in the area is moving from the treatment of “crisp” temporal constraints to

fuzzy or probabilistic constraints. However, despite the huge amount of work in the area, the

spectrum of possible solutions has not been fully explored. In particular, despite their

practical relevance, no approach has been developed to represent and perform temporal

reasoning on probabilistic quantitative temporal constraints, and to support query answering

on a knowledge base of such constraints. We propose an original approach overcoming such

limitations of the current literature.

Time:13:15-13:30

An Application Intended to Provide Customer Loyalty in Karaman Retail Sector by Using

Sequential Pattern Mining Methods

Mr. Mehmet Ali Canbolat and Hakan Candan

Karamanoğlu Mehmetbey University, Turkey

Sequential pattern mining is one of data mining methods used for analyzing customer

behaviors. The aim of this study is the creation of the campaign designing model which

provides continuity and keeps loyalty of customers that have regular shopping trend. 100

customers that have customer cards of a local retail chain where operates in Karaman

province and regularly buy “1 kg black tea every month”, have been included this study. Last

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six months shopping trend of these customers have been measured and in next month before

the purchasing process, the related product’s campaign information has been introduced via

SMS. When 48% customer/product loyalty had been provided before the SMS information,

after the SMS information 71% customer/product loyalty have been provided. When the

discount/campaign message was sent for related product in the near term shopping times of

customers who have regular shopping trend for the same product, it has been observed that

soon they have already purchased the product which they would need, from the same

shopping point.

Time:13:30-13:45

Comparing Computer and Internet Usage with Macroeconomic Indicators under the Title of

Development: The Case of Turkey

Bülent Darıcı and Mr. Mehmet Ali Canbolat

Karamanoğlu Mehmetbey University, Turkey

The concept of development is closely related to many subjects. Considering the broadness

of the development issues, identifying a country’s growth rates and comparing these rates

with the other countries is substantially important for determining the country’s position. In

addition to the environment, health, education, social, cultural and infrastructure indicators,

which are the primary development subjects; there’s also the necessity of an evaluation of

creating values, introducing these values to the countries, and obtaining and utilizing

technological information.

The statistics of the utilization of computer and internet, which can be regarded as an

indicator of the utilization of technology that some people regard as the fifth factor of

production and represents a dimension of the development phenomenon, expresses an

important development data also for Turkey, as well as the other developing countries.

In addition to the statistics that reflect the utilization of computer and internet from 2000’s till

today, determining the direction of the mutual relations in the development process by

comparing them with the development of various macro dimensions of those years, and ideas

on the sources of the problems encountered in creating values will be able to reveal the

dimensions of the study. In this context, it will be possible to identify a roadmap for the other

countries that are on the path of the development, based on the example of Turkey.

Time:13:45-14:00

A Computer Based Professional Development Model that Brings Innovation to Teaching

Practice

Dr. Victoria Tuzlukova

Sultan Qaboos University, Oman

Rooted in the belief that professional development has a visible influence on the educational

process and eventually leads to students’ achievements, this paper discusses in-service

professional development in the context of a language institution in the Sultanate of Oman

with a particular focus on the benefits of computer based virtual environments on designing

and implementing professional development and training programs that will enable teachers

to bring innovation and creativity to their teaching practice. It starts with an overview of the

crucial principles and components of the professional development aimed at addressing

teachers’ perceived needs and desires, as well as fostering critical reflection of teaching

practices and meaningful collaboration. Then it considers some possible goals of using

computer based virtual environments through Moodle platform, namely providing faculty

with an open dynamic solution for peer-to-peer e-professional development; enabling

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teachers to benefit from flexible professional development opportunities and social computer

mediated interactions; encouraging reading and open sharing of ideas and content, practice

and experience, and enhancing teachers’ beliefs in their abilities to competently use

computers and manage computer-mediated communications and interactions. At end the

paper presents some approaches for designing a successful computer based virtual

professional development context, and steps to set up this format of professional

development.

Time:14:00-14:15

Cloud Service Selection using TOPSIS and Fuzzy TOPSIS with AHP and ANP

Mr. Akshay Jaiswal, Ravi Bhushan Mishra

IIT (BHU) Varanasi, India

The growing demand and availability of cloud services have triggered the need for

comparison of their features available to customers at different prices and performance. It is

necessary to be said that relevant and fair comparison is still challenging due to diverse

deployment options and unique features of different services. The aim of this paper is to rank

cloud services based on quantified QoS (Quality of Service) attributes using Technique for

Order of Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy TOPSIS, and

comparing them to find out which method suits more in different scenarios.

A comparative study of Analytic Hierarchy Process (AHP) and Analytic Network process

(ANP) is also done while extracting the weights of criteria for TOPSIS and fuzzy TOPSIS.

Time:14:15-14:30

An Experimental Analysis of Clustering Sentiments for Opinion Mining

Prof. Anjan Babu G, Kamakshaiah K, Surya Kumari S

Sri Venkateswara University, Tirupati, India

Social Media Analytics playing a major role in e-commerce for extracting the useful

information of a product/service. Opinion Mining has become the key process of Social

Media Analytics. In this paper, the process of opinion mining in social media while dealing

with different kind of opinionated documents and the challenges associated to opinion

mining from social media has been discussed. The twitter is a big online social activity where

some millions of people share their opinions. Applying sentiment analysis on social media

data to get product reviews based on the product features is one major concern. K-means

clustering technique applied on a sample twitter dataset to cluster different sentiments in

context with different features of products and been evaluated and explained with the help of

a machine learning tool.

Time:14:30-14:45

Content-based filtering for movie recommendation using ego-focused network analysis

Ms. Jieun Son, Seoung Bum Kim

Korea university, South Korea

Content-based filtering (CBF), one of the most successful recommendation techniques, is

based on correlation between contents. CBF uses item information represented as attributes

to calculate similarity. This paper suggests a novel CBF method using a multiattribute

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networkwhich effectively reflects several attributes while calculating correlation.In network

analysis, weighted links represent the degree of correlation between directly linked items,

and centralities measure the similarity between indirectly linked items. This mechanism

results ina variety of items being recommended to the user and improves performance. We

comparedthe proposed approach with existing approaches using MovieLens data and found

that our approach outperformed the existing ones in terms of accuracy and robustness.

Time:14:45-15:00

Computational simulations of catalytic systems using quantum chemical methods

Prof. Georgi Nikolov Vayssilov, Rositca Nikolova and Hristiyan Aleksandrov

University of Sofia, Bulgaria

Quantum chemical simulations using parallel calculations become particularly useful in

various fields of modern Chemistry. The present contribution is focused on application of

these methods for understanding the behavior of heterogeneous catalytic systems of interest

to chemical, petrochemical and automotive industry.

In order to clarify different aspects of the structure and properties of ceria-containing systems

we investigated computationally the redox properties of such systems and their interaction

with noble metal species or gaseous adsorbates using periodic density functional calculations

with DFT+U approach. As a general measure of the redox properties we determined the

energy for formation of oxygen vacancies in ceria and have shown that the nanoparticles are

reduced easier than the stable single crystal surfaces [1].

Combining computational modeling with different experimental methods we investigated

interaction of platinum clusters and mononuclear platinum species with ceria support [2,3].

For platinum, supported on ceria we were able to explain the strong dependence of the

catalytic activity on the morphology of the support by oxygen spillover onto the platinum

particle [2]. For mononuclear species we considered the influence of the oxidation state of

the species on their relative stability and reactivity as measured by adsorption energy of CO.

As a general trend, the binding energy of CO depends on the coordination of the platinum

species and their oxidation state. The CO2 formation on such species was also found to be

rather favorable process.

The modeling of various carbonate, hydrogen carbonate, formate structures [4] allowed us to

suggest a general classification of these surface species and to identify the regions of

vibrational frequencies characteristic for different types. In continuation of those studies, we

modelled the interaction of NO and reduced ceria. Our results suggest formation of new

types of species: azides (N3-, 2044-2042 cm-1) [3] and nitric oxide dianions (NO2-,

1010-980 cm-1). These species are established for the first time after NO adsorption on solid

surfaces and their relative concentrations strongly depend on the sample morphology [5]. At

further stages of interaction, N2O, N2, nitrites and nitrates are formed [4]. The present

findings impose revision of some current views of catalytic NO conversion.

Acknowledgments. The financial support by the European Commission (H2020 projects

Materials Networking) and the computational resources from the Bulgarian Supercomputer

Center are gratefully acknowledged.

Time:15:00-15:15

Improving Trend Analysis Utilizing Misra-Gries Algorithm of User Responses acquired from

an Audience Response System

Prof. Sarah Doniza, and Dennis Martillano

Malayan Colleges Laguna and STI- Southwoods, Philippines

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Innovation in response systems has evolved over time, moving away from hardware that

requires extensive wiring to achieve network connectivity. There has been an increasing

demand for clickers, a hand-held remote control device, used to convey responses to

questions. However, clicker-based audience response systems, have been difficult to use and

deploy. In today’s mobile-centric world, an individual with a mobile device has access to

infinite opportunities. This study aims to utilize mobile technology to enable members of the

audience to respond to questions through their mobile devices instead of additional hardware,

which is inconvenient and expensive. This mobile application provides presenters with an

important analytics tool that would help process identified aspects, based on participants’

responses, with illustrated graphs and identify the most frequent items with minimal user

time and effort using Misra-Gries Algorithm.

Time:15:15-15:30

Towards A Soft Computing Approach to Document Clustering

Mr. Muhammad Rafi

FAST-NU Karachi Campus, Pakistan

Soft computing refers to partnership of methods to produce an approximate and low cost

solution for hard problems. We believe that document clustering is one such problem and

trust that the soft computing approach will be a good candidate for this problem. In this

paper, we propose a generalized document clustering approach using soft computing

techniques. We de_ne two methods for document clustering using k-Mean partition

algorithm: (i) A Genetic Algorithm (GA) based k-Mean algorithm that optimized to _nd a

local optimal solution and (ii) A Harmony Search (HS) based k-Mean algorithm that

optimized to _nd a global optimal solution. We also proposed a novel soft computing

partnership method (Hybrid) that uses solution produced from either (GA k-Mean) or (HS

k-Mean) method to seed the other for improvement. We extensively performed experiments

with our proposed method on standard text mining datasets like: (i) NEWS20, (ii) Reuters

and (iii) WebKB and evaluated the results on Purity and Silhouette. In comparison the

proposed outperform the basic k-Mean and the hybrid approach performs exceptionally good.

A Fast-training Approach Using ELM for Satisfaction Analysis of Call Centers

Ms. Jing Liu, Yingnan Zhang, Jin Hu, Xiang Xie, Shilei Huang

Beijing Institute of Technology, China

Analysis of the customers’ satisfaction guarantees the improvement of service quality in call

centers. In this paper, an intelligent satisfaction recognition system is introduced to analyze

the customers’ satisfaction through the customers’emotion recognition. The nature dialogues

are collected from the Chinese call center. Support Vector Machine (SVM) and Extreme

Learning Machine (ELM) are used for the mapping model respectively. According to the

experiment, the best F score of SVM is 0.71. Compared to SVM, the best F of ELM is up to

0.723.The training time of SVM ranges from 1268s to 5002s while ELM’s only ranges from

7.28s to 15.82s, with a decrease of 99%. ELM shortens the training time largely without

damaging the performance. Because of the faster training speed, ELM is more beneficial to

the model updating in real time. Therefore, ELM has a great edge on online learning.

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Robust Recognition of Mandarin Vowels by Articulatory Manners

Mr. Jin Hu, Jing Liu, Yingnan Zhang, Zhuanling Zha, Xiang Xie, Shilei Huang


This paper proposes a robust classifier for Mandarin vowels considering articulatory manners

(AMs) which include the height of the body of the tongue, the front-back position of the

tongue, and the degree of lip rounding. Firstly, the articulatory manners of each vowel are

encoded to a 3-dimension vector pattern. Then, acoustic features are extracted and mapped to

the articulatory manner vector by ELM. Finally, the nearest vowel to the articulatory manner

vector is chosen as the recognized result. Comparison between our method and the direct

method without considering the articulatory manners shows that the proposed method has an

improvement of 7.1 percentage points. Tests with three kinds of noisy data in the Aurora-4

show it also outperforms the normal method with an about a gain of about 4 percentage

points.

Improving Availability Applying Intelligent Replication in Federated Cloud Storage Based on

Log Analysis

Ms.Cindy Pamela López Chulca, Rene Heinsen, Eui-Nam Huh

Kyung Hee University / South Korea

This study is focusing on improving the availability of federated storage services in order to

provide better quality-of-service (QoS) to the customer with the minimum use of resources.

One of the most efficient solutions to get the best experience in the cloud is to combine the

services offered. In order for this to happen, there exist different approaches for selecting the

best subset of services to reach the optimal performance. However, those works focus on one

time selection processes, despite of customer's requirements are continuously changing and

demanding adaptable storage service. In this research, I propose a method to improve storage

availability through log sentiment analysis and intelligent replication. This methodology is

based on the merging of two types of log analysis and the measurement of availability and

performance metrics in order to select the best subset of services in cloud storage service

federation.

- 20 -

Session B-1< Mechanical and Systems Engineering>

Venue: Room B

Chair: Prof. Genci Capi

Time: 13:00-15:30

Note:






* session photo will be taken at the end of the session and update online

Time:13:00-13:15

A Novel Biological Based Method for Robot Navigation and Localization

Endri Rama, Prof. Genci Capi, Yusuke Fujimura and Mitsuru Jindai


The mobile robot ability to navigate autonomously in its environment is very

important. Even though the advances in technology, robot self-localization and goal

directed navigation in complex environments are still challenging tasks. In this article,

we propose a novel method for robot navigation based on rat`s brain signals (Local

Field Potentials). It has been well known that rats accurately and rapidly navigate in a

complex space by localizing themselves in reference to the surrounding environmental

cues. As the first step to incorporate the rat’s navigation strategy into the robot control,

we analyzed the rats’ strategies while it navigates in a multiple Y-maze, and recorded

Local Field Potentials (LFPs) simultaneously from three brain regions. Next, we

processed the LFPs, and the extracted features were used as an input in the artificial

neural network to predict the rat`s next location, especially in the decision-making

moment, in Y-junctions. We developed an algorithm by which the robot learned to

imitate the rat`s decision-making by mapping the rat`s brain signals into its own

actions. Finally, the robot learned to integrate the internal states as well as external

sensors in order to localize and navigate in the complex environment.

Time:13:15-13:30

Optimizing Deep Learning Parameters using Genetic Algorithm for Object

Recognition and Robot Grasping

Delowar Hossain, Genci Capi and Mitsuru Jindai

University of Toyama, Japan

The performance of deep learning networks has been increasing by elaborate network

structures. However, there have many parameters, which have a lot of influence on the

networks performance. We propose a Genetic Algorithm based Deep Belief Neural

Network (DBNN) method for robot object recognition and grasping purpose. This

method optimizes the parameters of DBNN method, such as, the number of hidden

units, the number of epochs and the learning rates, which reduce the object recognition

- 21 -

error and the network training time. After object recognition, the robot performs the

pick-and-place operation. We build a database of six objects for experimental purpose.

Our experimental evolution demonstrates that our method outperforms on the

optimized robot object recognition and grasping tasks.

Time:13:30-13:45

Fracture Stopper for stress corrosion cracking inprestressed foundation structures

Prof. Taejun Cho

Daejin University, Republic of Korea

This research project aims to resolve the integrity assessment of the foundation

structures under an ambient corrosive environment, from both a deterministic

approach and a reliability-based approach. The deterministic approach focuses on the

understanding the mechanics and mechanisms of the fracture failure for

stress-corrosion cracking (SCC) on a material level and that on a structural system

level, followed by a design of fracture stopper on the expected spots of SCC. The

probabilistic approach will develop a risk assessment program of SCC in a structural

system considering fracture resistance in prestressed foundation system based on real

time measured data. ACKNOWLEDGMENT: This research was supported by Human

resources Exchange program in Scientific technology through the National Research

Foundation of Korea(NRF) funded by the Ministry of Science, ICT and future

Planning(2016H1D2A2915738)

Time:13:45-14:00

Design Analysis of Mechanical Jack For Assembling Canopy

Dr. Izzuddin Zaman, Shiau Wei Chan, Mohd Shahrir Mohd Sani

Universiti Tun Hussein Onn Malaysia

The canopy has been used widely all over the world due to the ability of the canopy to

provide protection to human from the scorching sun or rain. However, the erection of

the canopy is a tedious work. It needs four or manpower in order to lift a canopy frame

at the same time, thus requires more spending in order to hire a worker. The canopy

jack was designed to make the erection of canopy easier at the same time to reduce the

usage of workers. There are several mechanical jack that available in the market, but

there is no specification of design analysis. This research is conducted to design

canopy jack and determine the static analysis by using SolidWorks software. The

design process was done by following the design engineering process which consisted

of four main phases; defined task, conceptual design, embodiment design and lastly is

the detail drawing. The static analysis was carried out by using SolidWorks to

determine the maximum stress and strain, displacement and the safety factor of the

canopy jack. The main bolt was the critical part of the canopy jack. Based on the result

of static analysis, it shows that the maximum stress analysis reading is 362 MPa and

the minimum factor of safety reading of the main bolt is 1.71, thus shows the main

bolt was safe to be used. Overall, it can be concluded that the canopy jack was safe

and strong enough to be use in order to lift the maximum load of 200kg.

- 22 -

Time:14:00-14:15

Transmission error estimation and reduction by gear profile modification

Mr. Moonyoung Yoon, Jeongseok Lee, Kwangsuk Boo and Heungseob Kim

Department of Mechanical Engineering, Inje University, South Korea

In this paper, we introduce method to reduce transmission error through gear profile

modification affecting transmission error reduction. Gear noise in power transmission

system originates from misalignment, profile error, and tooth deflection. However, the

main reason of transmission is tooth deflection in mesh, and it has been studied over

the decades. In order to minimize tooth deflection generating transmission error, load

sharing on tooth flank is widely need to improve contact ratio of gear pair in mesh

through tooth profile modification. Therefore, involute curve needs to be optimized by

improving method to design tooth profile.

In order to obtain the results of transmission error reduction, a gear pair was modelled

using finite elements by FEA software, Abaqus. The gear mesh was simulated with

several cases of tooth modification under static conditions. The results obtained was

used to estimate how profile modification with optimized design affect transmission

error reduction.

In addition, test bench was set with power brake making load to driven gear, two

torque meters, two encoders with high resolution(18,000 lines/rev) on the shafts, and

motor to measure transmission error with actual gear pair designed by tooth

modification, and these two results was compared.

Time:14:15-14:30

Intelligent Information System for Philippine Public Transport – A Big Data

Framework for Urban

Transportation Development

Mr. Brian J. Rafor and Rosicar E. Escober

Polytechnic University of the Philippines, Philippines

The main objective of the study was to unravel the answer on the following

exploratory questions: (1) the benefit of having an Intelligent Information System for

Philippine Public Transport (IISPPT) as perceived by the respondents; (2) the essential

features of IISPPT as perceived by the respondents; (3) the essential inferential

information of IISPPT that should be rendered by the system; and (4) the effective

framework to establish it. In undertaking this study, the exploratory method of

research was employed. An extensive review of literature and studies related to the

subject matter of this research combined with the inputs of the 400 respondents were

the principal sources of inference used in developing the appropriate framework for

IISPPT. The respondents of this research were primarily qualified by the

characteristics that they are persons who have used or are currently using the public

transport facilities in the Philippines regardless of type and frequency. Based on the

findings, the following conclusions were derived: (1)The IISPPT shows a valid need to

be implemented; (2) It can hold a lot of possible features and that the implementation

of this solution should prioritize the launch of the ability to send alerts on public

transport service interruption and advisories; (3) It can give a lot of possible inferential

information and that the travel duration is currently what tops the need of the travelers;

(4) The framework developed by the researcher is the most suited way considering all

- 23 -

the factors relevant to the overall architecture.

Time:14:30-14:45

Estimation Method for Advanced Driver Assistance System and Real-Time

Context-Aware

Dr. ByungHun Oh, HyoHaeng Lee

Sungkyunkwan University, Korea

This paper suggests an efficient estimation method for the ADAS (advanced driver

assistance system) and Real-Time Context-aware. It also examines a prototype system

that employs the aforementioned method. The suggested system uses the vehicle’s

location and driver’s information to deal with accident vulnerable points. It informs

drivers of appropriate distance of approach and alerts them so that drivers can

efficiently recognize communication information. For the performance evaluation of

the suggested system, the NASA-TLX (Task Load Index) and SUS (System Usability

Scale) evaluation were performed. The results of the analysis demonstrate that this

system is more efficient than existing systems.

Time:14:45-15:00

The Decision Support System by Optimization and Dynamic analysis for

Transportation Routing

Asst. Prof. PRAPAI SRIDAMA

Bansomdejchaopraya Rajabhat University, Thailand

The objective of this research is developing the decision support system by

optimization and dynamic analysis (DSS_ODA) algorithm. Many techniques are used

in this algorithm such as K-means algorithm, Hierarchical Clustering, Partition

clustering, Lagrange multiplier method and the second condition. The checking rout

paths procedure, the checking capacity procedure and optimization and dynamic

analysis procedure are main elements. The DSS_ODA algorithm is tested by 50

customers from a shipping company at Bangkok in Thailand. The results showed that,

the DSS_ODA algorithm offers shipping cars for customers, reduces time and errors in

finding the transportation routing from staff. Furthermore, the DS_ODA algorithm

decreases the time of transaction documents and reduces the time to decide from

customer. This research surveys satisfaction of 50 customers. This survey finds the

average of satisfaction is score 4.52 from score 5. In addition, the number of

customers increases by 22.63 percent.

Time:15:00-15:15

Towards Automated Counter-Melody Generation For Monophonic Melodies

Mr. Luke Prudente and Andrei Coronel

Ateneo de Manila University, Philippines

Algorithmic composition has focused on creating music from algorithms, stemming

from the capacity to convert notes into numbers and vice versa, thus allowing simple

to complex algorithmic manipulations. The focus of these studies has either been the

- 24 -

creation of melodies, chords, accompaniments or entire songs. This study focuses on a

relatively underexplored topic on the algorithmic generation of a counter-melody from

a given melody. Using a method based on existing knowledge of generating chords

and music theory on compatible notes and chord progressions, combined with

concepts of machine learning and tree traversal techniques for generating chords, this

study was able to generate 200 counter-melodies from 100 inputs, involving two

generation techniques per input. The results show that counter-melodies were

successfully generated based on chord progression generation and note selection

approaches, and after subjecting the counter-melodies to proper subject evaluation, the

average scores of 2.89 and 3.02 on a 5-point evaluation criteria reveal that the

counter-melodies are musically fit for the original melodies they were based from.

Time:15:15-15:30

Data Analytics based Driver Assistance System in Vehicular Ad-Hoc Networks

Jetendra Joshi, Mr. Dandu Geet Kamal Tej, Pranith Kumar, Rohith Samineni ,S R

Rahul, Siddhanth Polepally and Vishal Rajapriya

Department of Electronics and Communications NIIT University, Rajasthan, India

Vehicular Networks has a great potential to enhance the lives of people. When

vehicular networks are integrated with Mobile Application, Web Application, cloud

technology and Internet of Things, it helps to make the driving experience pleasant

and safe. This paper aims to enhance the driver assistance by assisting in directions,

transit time, and to cope up with the avoidable situations and emergency situations by

also monitoring the vehicle’s position, speed, lane tracking. The defined model aims to

deliver reliable help in emergency situations by using efficient protocols to

communicate to the cloud and efficient database retrieval methods to make the query

respond faster and to provide a unique and safe experience to the driver.

Genetic algorithm based on attribute correlation for multi-label classification

Ms. Hou Manli, Wang Zhihai

Beijing Jiaotong University School of Computer and Information Technology, China

The classifier chains (CC) model has been used widely for multi-label classification,

its remarkable characteristic is in consideration of the association between the labels,

and the CC method adds the classifiers before it to predict the current instance. Then,

the association between the labels is added to each of the current classification of the

instance. However, because the CC model requires all the labels to join the chain, the

disadvantage of the CC model is that the labels with wrong or redundant information

will affect the performance of the classifier. Considering the issue, this paper proposes

a genetic algorithm (GA) based on attribute correlation for multi-label classification.

The results of the experiments prove that the performance of classification can be

improved.

A Distributed Incremental Learning Method for Sparse Kernel Machine over Wireless

Sensor Networks

Ms. Xinrong Ji, Cuiqin Hou, Yibin Hou

Beijing University of Technology, China

- 25 -

In wireless sensor networks, centralized learning methods have very high

communication costs and energy consumption. These are caused by the need to

transmit scattered training samples from various sensor nodes to the central fusion

center where a classifier or a regression machine is trained by batch learningwith all

training samples. To decrease the communication costs and energy consumption, a

distributed learning problem of kernel machine by incorporating 1 norm

regularization is investigated,and a novel distributed incremental learning algorithm

for the 1 -regularized kernel minimum mean squared error (KMSE) machine

isproposed.The proposed algorithm relies on the incremental learning method and the

collaborationbetween single-hop neighboring nodes based on Markov Chains

Theory.This paper evaluates the proposed algorithm with respect to the prediction

accuracy, the sparse rate of modeland the communication costs on synthetic and real

datasets.The simulation results show that the proposed algorithm can obtain

approximately the same prediction accuracy as that obtained by the batch learning

method and the current state-of-the-art distributed algorithms. Moreover, it is

significantly superior in terms of the sparse rate of model and communication costs.

Research on the de-noising of train wheel-rail sound signal

Ms. WANG Qian,ZHOU Li juan and CHEN Qing hua

School of Electrical and Information Engineering, Guangxi University of Science and

Technology, Liuzhou 545006, China

Based on a new algorithm that combines spectral subtraction of multitaper estimation

with signal self-correlation analysis, wheel-rail sound signal has been theoretically

processed in this article. The simulation results show that the new algorithm results in

better signal audio-visual and de-noising effects than the individual spectral

subtraction of mulitaper without self-correlation one.

- 26 -

Session A-2< Machine learning and image processing>

Venue: Room A

Chair:

Time:15:45-19:00

Note:







Time:15:45-16:00

A study integrated lane detection and vehicle tracking method with camera system

Duksun Yun, Bupjin Lee, Mr. Youngbong Kim, Quyet Nguyen Van

Department of Mechanical Engineering, inje University, South Korea

In this paper, we introduce an approach method to detect information about lane and vehicle

for driver assistance, lane keeping system, or lane change system. Most previous research

work only can detect the lanes or vehicle separately. However, the combination between lane

information and vehicle information are able to support well for lane keeping system, or lane

change system. And this combination are able to increase the reliable results.

In order to keep vehicle on the lane, Lane keeping system (LKS) have to know lane

information such as width of lane, the road curve. For lane change system (LCS), the system

have to detect both lanes (left side lane, right side lane), and the system must discover other

vehicles around the host vehicle. Therefore in this study, we used three cameras, one is to

detect the front of the host vehicle. The remaining cameras are equipped under the wing

mirror both left and right side to detect information from both sides of the behind of the host

vehicle.

In lane detection, line detection, edge detection are used. For the vehicle detection, based on

the brightness and darkness between vehicle and road, vehicle is detected in real-time using

the simple algorithm. The system was tested on the high way in Korea.

Time:16:00-16:15

Korean Morphological Analysis for Korean-Vietnamese Statistical Machine Translation

Mr. Nguyen Quang Phuoc, Joon-Choul Shin, and Cheol-Young Ock

University of Ulsan, Korea

This paper describes experiments with Korean to Vietnamese statistical machine translation

(SMT). The fact that Korean is a morphologically complex language that does not have clear

optimal word boundaries causes a major problem of translating into or from Korean. To solve

this problem, we present a method to conduct a Korean morphological analysis by using a

pre-analyzed partial word-phrase dictionary. Besides, building a Korean-Vietnamese parallel

- 27 -

corpus for training SMT models by collecting texts from multilingual magazines. Then, we

apply such a morphology analysis to Korean sentences that are included in collected parallel

corpus as a preprocessing step. The experiment results demonstrate remarkable improvement

of Korean to Vietnamese translation quality in term of BLEU.

Time:16:15-16:30

Application of Manifold Learning in Human Activity Recognition Environment

Asst. Prof. Jian-hua Yeh, Bo-xun Wang

Aletheia University, Taiwan, R.O.C.

The human activity recognition has gradually become an irreplaceable application in today's

society, there are ways to identify the human activity through image processing with a fixed

perspective, and however, this is not always suitable personally. Using wearable devices to

collect data from the sensors, which are mounted to the wearable device, to identify the

human activity. This belongs to personal activity recognition, which is also a commonly seen

method that still has many problems regarding accuracy. The main method in this study uses

time windows and manifold learning as the basis for feature extraction. Public data sets are

used for all the processes in the experiment. First, the data sets will be segmented through

time windows. Secondly, the system feature exaction process will be processed in each time

windows to obtain feature vector. The feature vector contains general statistical feature and

manifold feature from the displacement path acceleration sensors. Finally, the classifier

models such as the decision tree, nearest neighbor method, Bayesian classifier, Bayesian

network, support vector machines, bagging decision trees, bagging Bayesian classifier

classification model, etc. are used to recognize the common life action including walking,

running, running up or down the stairs, circling to the right or left, turn left, turn right,

jumping, lying, standing, sitting, pushing a wheelchair and other activities. According to the

experimental results, we demonstrate that the proposed new features aids in the body motion

recognition.

Time:16:30-16:45

Machine Learning Based Cloud Integrated Farming

Jetendra Joshi, Mr. Siddhanth Polepally, Pranith Kumar, Rohith Samineni ,S R Rahul,

Kaushal Sumedh, Dandu Geet Kamal Tej, and Vishal Rajapriya

Department of Electronics and Communications NIIT University, Rajasthan, India

With the increasing population, the demand for more agricultural production is only surged.

This increasing demand can only be attained with progressing technology. Internet of Things

(IoT) is dramatically advancing the way we live our life by full-scale control over data with

minimal human involvement. Using IoT to meet this high demand for production is achieved.

In this paper we propose a bot that can be used for small scale farming in areas like gardens

or backyard. A simple web application is provided for the user to decide the plants to be

farmed and the bot does rest of the work. This bot can plant the seeds, water each plant at

required intervals and even plot the weed to bury it. A database is provided with the

information about several parameters to be taken care of for each kind of plant. Different

sensors are used to sense the properties of soil and environment which can be used to

anticipate the near changes and take necessary steps. Image processing is being used for

detection and prevention of weed growth. We adopted Bayesian methods of machine learning

to efficiently estimate the performance parameters by probability distribution. .

- 28 -

Time:16:45-17:00

Solving the Tower of Hanoi using neural network approach

Mr. Sang Hun Bang

France University Paris 8, France

Our hypothesis is that we can infer the rules of the game not through the description of rules

of the game, but through the movement of the hand the disc. Therefore, we study this

problem solving through the action of others and the change of states. To solve the problem

of tower of Hanoi, we make use of machine learning methodology. We develop an

intelligence that learns and infers the rules of the game by supervised learning for neural

network. Based on the understanding of rules, the intelligence finds also an optimal solution

by means of reinforcement learning method. Finally, we show that the artificial intelligence

learns the problem of Tower of Hani from the movement of body, behaviors, and the change

of states.

Time:17:00-17:15

Fast Singular Value Projection For Low-rank Matrix Completion

Dr. Quan Liu, Yanbo Wang, Shuchang Zhang, Bo Yuan

Shanghai Jiao Tong University, China

Matrix completion problem aims to recover a low-rank matrix from a sampling of its entries.

Singular value projection (SVP) is a projected gradient descent method, which iteratively

makes an orthogonal projection onto a set of low-rank matrices. In this paper, we propose a

more efficient SVP by applying the expectation-maximization (EM) algorithm for principal

component analysis (PCA). EM for PCA is an alternating minimization approach for finding

the principal subspace. With warm starts, the projection process can be computed efficiently.

Under the sparse plus low-rank structure, we show that EM for PCA can be implemented

with the complexity in the order linear to the number of observed entries. Another important

problem is how to determine the rank of the matrix. We thus propose an empirically approach

based on the edge distribution (ED) algorithm to estimate the rank. An imputation method is

used in ED to enhance its applicability for matrix completion problem. Numerical

experiments show that our method is promising for a wide range of problems.

Time:17:15-17:30

Investigating the Accuracy of Test Code Size Prediction using Use Case Metrics and

Machine Learning Algorithms: An Empirical Study

Mourad Badri,Linda Badri ,William Flageol, Prof. Fadel Toure

University of Quebec, Trois-Rivières, Québec, Canada

Software testing playsa crucial role in software quality assurance. It is, however, a time and

resource consuming process. It is, therefore, important to predict as soon as possible the

effort required to test software, so that activities can be planned and resources can be

optimally allocated. Test code size, in terms of Test Lines Of Code (TLOC), is an important

testing effort indicator used in many empirical studies. In this paper, we investigate

empirically the early prediction of TLOC for object-oriented software using use case metrics.

We used different machine learning algorithms (linear regression, k-NN, Naïve Bayes, C4.5,

Random Forest, and Multilayer Perceptron)to build the prediction models. We performed an

- 29 -

empirical studyusingdata collected from five Java projects. The use case metrics have been

compared to the well-known Use Case Points (UCP) method. Results show that the use case

metrics-based approach gives a moreaccurate prediction of TLOC than the UCP method.

Time:17:30-17:45

Gyroscope-based game revealing progress of children with autism

Agnieszka Landowska, Dr. Agata Kołakowska, Katarzyna Karpienko

Gdańsk University of Technology, Poland

The paper concerns the automation of measuring progress of children with autism spectrum

disorder. The proposed approach combines diverse approaches: e-technologies and mobile

applications for autism, behavioral metrics derived from gyroscope and game state with

machine learning methods to find interconnections between the metrics and the progress of a

child. The paper presents a gyroscope-based game, specifically designed as an investigation

tool for therapy progress monitoring. The game enables registration of behavioral patterns of

use of the applications and tablet. The paper presents how the game was used in a study of

behavioral metrics. 31 children with autism took part in the study. Each of them played the

game several times during a 6-months period. The data gathered during the gameplay are

used to calculate a set of metrics, that might be used in evaluation of a child's progress.

Results in terms of classification accuracy reach 80%, however they depend on the particular

skill category. The best accuracies are obtained for evaluation of stereotypic behaviors and

gross motor skills of a child. The approach presented in the study is novel and was not

applied before, therefore it might be interesting for other researchers working on supporting

technologies for autism. The results might be also interesting for practitioners applying

e-technologies in autistics therapy.

Time:17:45-18:00

Image Feature Extraction with Homomorphic Encryption on Integer Vector

Yunfan Huang, Assoc. Prof. Haomiao Yang, Mengxi Nie, Honggang Wu

University of Electronic Science and Technology of China, China

With the amount of user-contributed image data increasing, it is a potential threat for users

that everyone may have the access to gain privacy information. To reduce the possibility of

the loss of real information, this paper combines homomorphic encryption scheme and image

feature extraction to provide a guarantee for users’ privacy. In this paper, the whole system

model mainly consists of three parts, including social network service providers (SP), the

Interested party (IP) and the applications. Except for the image preprocessing phase, the main

operations of feature extraction are conducted in ciphertext domain, which means only SP

has the access to the privacy of the users. The extraction algorithm is used to obtain a

multi-dimensional histogram descriptor as image feature for each image. As a result, the

histogram descriptor can be extracted correctly in encrypted domain in an acceptable time.

Besides, the extracted feature can represent the image effectively because of relatively high

accuracy. Additionally, many different applications can be conducted by using the encrypted

features because of the support of our encryption scheme.

Time:18:00-18:15

Hybrid Convolutional Autoencoders for Feature Extraction

Mr. Young Joon Park, Seoung Bum Kim

- 30 -

Korea University in South Korea

Dimensionality reduction based on supervised and unsupervised feature extraction has

received considerable attention in various areas for which datasets with huge amounts of

variables are available. However, applying unsupervised or supervised feature extraction

methods separately has limitations because they are incapable of preserving label information

and reconstruction information on extracted features. To address this problem, hybrid training

that utilizes the information from both features and output labels together has been

introduced. In this study, we propose a hybrid convolutional autoencoder that minimizes both

reconstruction and classification errors. The proposed method extracts the features from the

neural network structure that consists of convolutional encoderdecoder pairs and an output

layer containing label information. We examined the quality of the extracted features in terms

of classification and clustering errors. The experimental results with image datasets

demonstrated that the proposed method outperformed other competitors such as traditional

autoencoders and supervised deep neural networks.

Time:18:15-18:30

MSMS: A Multi-section Multi-signature Model with Distinguished Signing Responsibilities

Mr. Dang Minh Tuan

Vietkey Group – Vietnam

This paper proposes a concept of multi-section multi-signature model with distinguished

signing responsibilities. The model has overcome the limitation of some previous

multi-signatures models by allowing every signer to sign and be responsible for one or

multiple sections of the signed message. A theoretical analysis against two common types of

digital signature attacks has proved the model security assurance level.

Time:18:30-18:45

Sentiment Analysis of e-Learning Binus University using Naive Bayes Algorithm

Prof. Sfenrianto

Bina Nusantara University, Indonesia

There are so many e-learning used by educational institutions in Indonesia to support

students’ learning process. Binus University has using the e-learning served as a learning

platform for students. However student satisfactions have been very important issues for the

success of e-learning. In this study, we report the finding of sentiment analysis that influence

satisfaction online learning at e-learning Binus University. The samples in this study are

taken from 80 students who used the e-learning. Naïve Bayes Algorithm is applied to classify

the data positive or negative on the review of e-Learning Binus University. Results show that

Naïve Bayes is a good accuracy (91.00%).

Time:18:45-19:00

Corrective Learning of Assistance Force for Opening/Closing of Automobile Door Based on

Support Vector Machine.

Mr. Hyun-Chan Shin, Hac-Jin Yang and Seong-Kun Kim

Hoseo University, Korea

We developed a corrective learning model of assistance force for the opening/closing of an

- 31 -

automobile door depending on the various condition of the parking ground. Candidates of

learning models for the assistance operating force were compared to determine the proper

estimation of the assistance according to the slope and user's force, etc. Experimental model

was manufactured in scale to obtain learning data for the estimation. Learning algorithm was

composed to predict the assistance force to incorporate real assistance force data. Among

these machine learning algorithms, an Artificial Neural Network (ANN) and Support Vector

Machine(SVM) were applied and the adaptability was compared between these models. The

SVM provided more adaptability for the learning process of the door assistance force. This

paper proposes a system for determining the assistance force to control a door motor to

compensate for the deviation of required door force in the slope condition, as needed in the

plane condition.

Development of Engagement Scale in E-learning Environment

Ms. Jeongju Lee, Hae-Deok Song

Chung-Ang University, Korea

Recently, the number of cases of using e-learning has significantly increased(Allen &

Seaman, 2008; Chen, Lambert, & Guidry, 2010; Hawkins & Rudy, 2008). E-learning make

learners learning beyond time and space(Malik, 2013). So it can give an opportunity to

students as well as adult learners for learning(Richardson & Swan, 2003). But there exist a

problem that consistently appears: dropout rates. Generally, learners in e-learning

environment appear higher dropout rates than learners in traditional learning

environment(Levy, 2007) and the key reason for this is a lack of learning engagement(Lee &

Choi, 2011; Sun & Rueda, 2012). There occur relatively low level of interaction between

learners and instructors in e-learning environment so that the learners can’t engage in

learning well(Cho & Cho, 2014; Jun, 2005). Learning engagement is a precedence indicator

of learning dropout so dropout rates can decrease if level of engagement increased(Appleton,

Christenson, & Furlong, 2008).

In addition, E-learning is more suitable for self-directed or independent learners than passive

and dependent learners(Diaz & Cartnal, 1999). Therefore less self-directed or less

independent learners can’t engage in learning enough so that they just use e-learning as a tool

for complete learning course(Malik, 2013). So in e-learning environment, the learning

engagement should be considered for important factor to get a positive learning effect. If we

can measure an engagement in e-learning, we can predict learner’s dropout of learning and

can try to raise insufficient factors of engagement. For those reason, the purpose of this paper

was to develop an instrument that measure engagement, the engagement scale, for learners in

E-learning environment.

Identifying university-level flipped classroom learner competencies

Ms. Rang Kim, Hae-Deok Song

Chung-Ang University, Korea

The goal of this study is to identify university-level flipped classroom learner competencies.

To achieve the goal, three-phased competency modeling method was employed. In Phase I,

as a primary process of gathering empirical information about behaviors, strategies, and

attitude leading to successful engagement and performance in the flipped classroom,

Behavioral Event Interviews with eight learners was conducted. In Phase II, as a process of

- 32 -

identifying competencies, interview data was analyzed and coding was conducted, followed

by testing reliability of the coding. Based on the meaning codes, 26 behavioral indexes were

developed and structured into 11 competencies in four clusters. In Phase III, validation of the

competency model was employed by four experts. The finding will be used as a behavioral

guidance for the learners.

Metadata Management and Materialization of Composite Documents in a Digital Library

Dr. Lý Anh Tuấn, Nicolas Spyratos

ThuyLoi University, Viet Nam

In this paper we present a method for the materialization of a (virtual) composite document

that is the creation of a paper version of the composite document including a table of contents

and an index. First, we introduce a simple model for the creation of composite documents

from other, simpler documents and for the metadata management during the creation process;

then we present algorithms for generating the table of contents and the index of a composite

document to be used for the document’s materialization.

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Session B-2<Communication network and Information Engineering>

Venue: Room B

Chair:

Time:15:45-19:00

Note:







Time:15:45-16:00

Speed up Querying Encrypted Data on Outsourced Database

Mr. Kim Giau Ho, Ly Vu, Nam Hai Nguyen, Hieu Minh Nguyen

Le Quy Don Technical University, Vietnam

Therapid development of cloud computing has appeared outsourced database and that is

essential solution to reduce the cost for data owners (DOs). The user's data which is stored on

the cloud will face many risks from attackers including service providers. To ensure the

security of data, theDOsencrypt data before storing on the cloud. However, the encrypting

before storing will increase the processing time to encode/decode records when querying to the

database. Therefore, speeding up querying on the encrypted database is essential in an

environment where data need to be encrypted before pushing to the cloud. In this paper, we

propose a new method to improve the speed of querying on the encrypted database using

parallel computing. The experimental results prove the effectiveness of our proposed method.

Time:16:00-16:15

Development of It Risk Management Framework Using Cobit 4.1, Implementation In It

Governance For Support Business Strategy

Bayu Rahadi, Assoc. Prof. Jarot S. Suroso


Extensive use of information technology in companies put IT into a position which is of

considerableconcern, especially in large companies that put IT becomes a strategic part of the

company. The importanceof IT division, make the companies willing to pay big to get the

benefits offered by IT itself, but on the otherhand appears disappointment incurred from

investments are not comparable with the results obtained. Untilthe threat appear and disrupt the

business of the company. By doing risk management using the IT risk management framework

by Cobit 4.1, the combining between business strategy Goals and IT Goals can assist

companies in identifying risks that might occur and Companies can design how to mitigate if

risks occur. IT governance should be able to support the company's business strategy by

managing and memange risks in order to avoid large financial losses to the company due to the

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lack of identifying and analyzing risks in the company.

Time:16:15-16:30

Analysis on Factors Influencing Enterprises’ Introduction of Mobile Payment Service with

ANP Method

Prof. Hsin-Pin Fu and Chang-Chan Lee

National Kaohsiung first university of science and technology, Taiwan (R.O.C.)

At present, mobile payment is a hot topic in the field of mobile business. However, most

previous research on mobile payments was conducted from the perspectives of consumers and

users, while mobile payment from the perspective of enterprise has been discussed in few

researches, and is a key factor influencing consumers’ adoption of mobile payment by

enterprises. To popularize mobile payment, it is noteworthy to understand the key factors

influencing an enterprise to introduce such service. Therefore, the author, with a

technologyorganization- environment (TOE) as the basic theoretical frame, collected relevant

influential factors considered by enterprises when introducing the service of mobile payment,

established a hierarchical table of three-order factors with relevant references, and ranked and

analyzed the weighted factors according to the analytic network process (ANP).

According to relevant research, enterprises considered the security of technology, enterprise

scale of organization, and governmental support of environment as the most important

influential factors. Finally, the practical implication proposed in this paper is only for the

reference of future enterprises in the process of introducing mobile payment. At the same time,

it can be referred by mobile payment tool suppliers when developing relevant solutions and

researching marketing strategies.

Time:16:30-16:45

Bit Rate Adaptation Based UEs Power Reduction Evolution in LTE Wireless Networks

Ms. Ruchi Sachan, Chang Wook Ahn

Sungkyunkwan University, South Korea

At present, the major drawback for mobile phones is the issue of power consumption.

Alternatives to decrease the power consumption of standard and usually power-hungry

location-based services requires knowledge of how individual phone features consume power.

A typical phone feature for e.g. related to multimedia streaming utilizes more power while

receiving, processing and displaying the multimedia content, thus contributing to the increased

power consumption. Streaming applications using the internet are quickly growing popular

among mobile users due to the development in data speed, high bandwidth and low latency

offered with increasing LTE technology. There is a growing concern that current battery

modules have limited capability in fulfilling the long-term power need for the progress on the

mobile phone because of increasing in power consumption during multimedia streaming

processes. Considering this, many solutions found to reduce battery power consumption for

mobile devices have presented of which suggested mostly based on streaming multimedia

contents over Wi-Fi, 3G, and 4G technologies. In this paper, we provide an offline meaning

Sleep-Mode method to compute the minimum power consumption comparing with Power-ON

solution to save power by implementing energy rate adaptation mechanism based on mobile

residue energy level as a mean to save battery power use. Our simulation results show that our

method preserves efficient power while achieving a better throughput compared to without rate

adaptation mechanism.

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Time:16:45-17:00

Social Coordinates: A Scalable Embedding Framework for Online Social Networks

Mr. Phuong Duy Pham, Fredrik Erlandsson and S.Felix Wu

UC Davis, CA

We present a scalable framework to embed nodes of a large social network into an Euclidean

space such that the proximity between embedded points reflects the similarity between the

corresponding graph nodes. Axes of the embedded space

are chosen to maximize data variance so that the dimension of the embedded space is a

parameter to regulate noise in data. Using recommender system as a benchmark, empirical

results show that similarity derived from the embedded coordinates outperforms similarity

obtained from the original graph-based measures.

Time:17:00-17:15

An Improved Location Estimation with Modular Neural Fuzzy Approach using Received

Signal Strength in Wireless Sensor Networks

Prof. Chang Gyoon Lim, Yong Min Kim

Chonnam National University, South Korea

Recently, the range-free methods have more and more popularity, because they provide simpler

and economic estimates than range-based ones, though their results are not as precise as those

of range-based methods are. In this paper, we introduced a range-free localization methods

based on RSS information. Namely, some nodes, which are already known their locations, are

called anchors, and other nodes compute their locations based on these anchors.

Modular Neural Fuzzy (MNF) Approach system is a fuzzy logic system integrated with neural

networks, which is optimized by a genetic algorithm. The location accuracy can be highly

achieved by using the proposed approach with the optimal number of neurons in the hidden

layer, which also leads types of membership function in fuzzy rules. We have analyzed the

main ideas and performance of existing location estimation methods, which are RSS-based

(Received Signal Strength) in WSN and compared the new localization method with the

previous existing ones. An average error of the localization method by GRNN is 0.5843, the

largest error is 2.0906, and the smallest error is 0.0086. Although performance of GRNN

approach has outperformed most other methods, the proposed approach get an advantage over

it. The proposed approach is averaged at 0.5565% of error rate having used the same

experimental conditions and using the same training and validation sets intended for training

the modules.

Time:17:15-17:30

UiTiOt: A Container-based Wireless Network Emulation Testbed

Mr. Chuong Dang Le Bao, Nhan Ly Trong, Quan LE-TRUNG

University of Information Technology, Vietnam National University HCM City, Vietnam

In this paper, we introduce an emulation testbed, namely UiTiOt, a container-based testbed

aims to provide a usable and scalable way to establish the wired-network infrastructure to

perform wireless network emulation. The testbed utilizes the cloud infrastructure at University

of Information and Technology (UiT) campus to deploy experiment nodes that integrated the

wireless network emulation tool QOMET in order to mimic, in real time, the wireless

- 36 -

communication behavior in the wired-network. In addition, a web application is developed to

enhance the user effort in designing topology and deploying the virtual nodes and network

based on user-defined experiment scenario. The use-cases and effectiveness of the testbed are

also discussed.

Time:17:30-17:45

Logistics Indicators Could Improve Logistics Performance of Hospitals

Mr. Andre M. R. Wajong


Hospitals are institutions in whichnumerous transactions take place. A significant amount of

those transactions happen in the realm of logistics. Logistifics is one of the concern of the

hospital’s managing entities. Hospitals must provide a variety of logistics within a limited

amount of time. The implementation of hospital logistics indicators were expected to assist the

management in improving performance in the logistics units of hospitals in the Province of

Jakarta. It was expected that by attending to a number of variables, namely information

technology support, logistics management, logistics business intelligence, and logistics

performance, such improvement could be achieved. The result of this study provided a

description of major indicators that could improve thelogistics performance in hospital industry

located within the Province of Jakarta.

Time:17:45-18:00

Comparative Research of the Source Localization and Connectivity Based on LORETA and

WMNE Algorithm

Mr. Zhengkai Weng,Tao Xie, Wei Chen, Fei Chen

School of Information Engineering, Wuhan University of Technology. No.205, Luoshi Road,

Hongshan District, Wuhan City, Hubei Province, China.

EEG (electroencephalograph) are the brain activity signals measured on the scalp. By

conducting LORETA (Low Resolution Brain Electromagnetic Tomography) and WMNE

(Weighted Minimum Norm Estimate) algorithms for source localization, the EEG can be

inversely traced to the source areas that generates the EEG on the cortex, and the corresponding

source current density can also be obtained. Next, by applying the PS (Phase Synchronization)

method, the PLV (Phase Locking Value) matrices of source localization results are obtained,

and these matrices denote the connectivity between each pair of ROI (Region of Interest). The

results of comparative analysis show that there are no significant differences between their

connectivity. However, the overall values and the degree of dispersion of the source current

density data from group LORETA are both far greater than group WMNE. Overall, the results

indicate that these two algorithms perform similarly when they are used to calculate the

connectivity of each pair of ROI, but LORETA performs better than WMNE when they are

used to calculate the results of source localization.

Time:18:00-18:15

Proportional Betting Strategy for Monte Carlo Tree Search

Assoc. Prof. Kuo-Yuan Kao

National Penghu University, Taiwan

Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in sequential

- 37 -

decision problems, typically move planning in combinatorial games. Each round of Monte

Carlo tree search consists of four major steps: (1)selection, (2)expansion, (3)simulation and

(4)backpropagation. This paper introduces a proportional betting strategy for the selection step

of Monte Carlo tree search. The strategy first computes for each child an index based on its

estimated winning probability and actual proportion of plays and then selects the child with the

highest index. The main idea is making the accumulated plays of the children

proportional to their corresponding estimated winning probabilities. The numerical experiment

carried out in this paper shows that proportional betting strategy outperforms both UCB and

Thompson sampling at Monte Carlo tree search.

Time:18:15-18:30

Brain Hemorrhage Diagnosis by Deep Learning

Mr. Tong Duc Phong

Pham Ngoc Thach University of medicine, Viet Nam

We propose an approach to diagnosing brain hemorrhage by using deep learning. In particular,

three types of convolutional neural networks that are LeNet, GoogLeNet, and Inception-ResNet

are employed. In the training phase, we only train the last fully-connected layers

of GoogLeNet and Inception-ResNet, but do train all layers of LeNet. We build a dataset

consisting of 100 cases collected from the 115 Hospital, Ho Chi Minh City, Vietnam. The

experimental results show that LeNet, GoogLeNet, and Inception-ResNet achieve accuracy of

0.997, 0.982, and 0.992 respectively on the dataset. Through experimental results, we found

that convolutional neural networks are pre-trained with non-medical images like GoogLeNet or

Inception-ResNet can be used in medical image diagnosis, particularly in brain hemorrhage

diagnosis. And, we confirm that among the three deep models, LeNet is the most

time-consuming model.

Time:18:30-18:45

Background Check System for Turkish IT Companies

Assoc. Prof. Arzu Baloğlu, Uğur Kaplancalı

Marmara University, Turkey

This paper focuses on Background Check Systems and Pre-Employment Screening. In our

study we attempted to make an online background-checking site that will help employers when

hiring employees. Our site has two types of users which are free and powered user. Free users

are the employees and powered users are the employers, which will hire employers. The

database of the site will contain all the information about the employees and employers who are

registered in the system so the employers can make a search based on their searching criteria to

find the suitable employee for the job. The web site also has a comments and points system.

The current employer can make comments to his/her employees and can also give them points.

The comments will be shown on employee’s profile, so when an employer searches for an

employee he/she can check the points and comments of the employee to see whether he or she

is capable of the job or not. This website was designed and implemented with using ASP.NET,

C# and JavaScript. The outputs have a user-friendly interface. The interface is also aimed at

providing the useful information for Turkish technology companies.

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Time:18:45-19:00

Aspect Sentiment Analysis and Latent Topic Modeling for Cyber-Attack Analytics

Otto K.M. Cheng and Dr. Raymond Lau

City University of Hong Kong, Hong Kong

Recent Internet attack reports reveal that there is a growing number of cybercrimes around the

world. Moreover, there are increasing evidences showing that hackers tend to exchange

cybercrime knowledge, or even transact to acquire cyber-attack tools via “black markets”

established in online social media. Accordingly, it presents unprecedented opportunities to

security intelligence experts or researchers to tap into online social media to mine knowledge

about cybercrime activities so as to better combat cybercrime in general and cyber-attack in

particular. The main contributions of this paper are the design, development, and evaluation of

a Latent Dirichlet Allocation (LDA) and aspect-oriented sentiment analysis based cybercrime

analytics methodology. Our preliminary evaluation of the proposed cybercrime analytics

methodology based on a real-world data set crawled from Twitter and Blog sites shows that it is

effective to mine prominent knowledge of real-world cyber-attack incidents for cybercrime

forensics.

Intersection safety using navigation image combine with camera vision sensor

Mr. Quang Nguyen Van, ChangJun Seo, KwangSuck Boo

Inje University, Korea

Safety is the most important requirement for any system including autonomous vehicle. When

vehicles change lane or keep lane, the system on the vehicle have to detect lane, other vehicle,

and objects on the road. Especially, vehicles go through the complex traffic scenarios like road

intersection. In road intersection, other vehicles come from many directions, people cross the

road, or many road signs. It is difficult to recognize all of that.

Therefore, in this paper, our idea is use information from navigation and camera vision to

detect and estimate road intersection situation. A host vehicle is moving on the road, firstly,

navigation is used to detect road intersection. In case that, road intersection is detected,

algorithm computes distance between the host vehicle and the road intersection, when the

distance is less than 50 meter, the host vehicle speeds down, then the camera vision is used to

detect lane, vehicles, and pedestrian. This study combines some image processing algorithms to

detect lane, vehicle, and pedestrian such as line detection, edge detection, aggregate channel

feature…

Modeling and Simulation of Voltage Source Inverter with Voltage Drop and Its Application for

Direct Torque Control of Induction Motors

Mr. H. L. Bui, Shoudao Huang, and D. C. Pham

Hunan University, China

Power electronics and electrical machines nowadays offer an extremely wide range of

industrial applications. Their modeling and simulation are also a great interest to engineers. In

some simulation applications, nonlinear of the power electronic devices is neglected due to its

- 39 -

simplicity. Therefore, the performance of the control system obtained is not the same as

experimental results. In this paper, a model of two-level three-phase voltage source inverter

having its voltage drops is proposed. Then application to direct torque control of three-phase

induction motors with the proposed model has studied. Finally, the simulation results are

provided to verify the effectiveness of the proposed work.

- 40 -

Poster Session

Venue: Room Cocochine

Time:13:00-18:00

Note:


*To show the respect to other authors, especially to encourage the student authors, we strongly suggest you

attend the whole session, the scheduled time for presentations might be changed due to unexpected situations,

please come as early as you could.

Algebraic and dynamic analysis on the modified swarm optimizers

Prof. JiaoWeidong, Jiang Yonghua, Shi Jizhong, Wang Xiaoyan, Yang Shixi

School of Engineering, Zhejiang Normal University 688 Yingbin Avenue 321004 Jinhua,

China

The modified particle swarm optimizers were proposed that use special velocity-updating

modes and velocity-changing tracks to control velocity of evolved particles, and to tune the

search process for the globally-optimal solution. Based on a reduced one-dimensional PSO

system with only one particle, contrastive researches were made to interpret essential

reasonability of the modified swarm optimizers, from both algebraic and dynamic

viewpoint. Optimization example showed that the modified swarm optimizers are superior

to the BPSO, on not only convergence precision but also computation expense.

A life-cycle workflow architecture for Linked Data

Prof. Yongju Lee

Kyungpook National University, Korea

Linked Data proliferation gives rise to phenomenal growth of Open Data applications on the

Web. While many standards, methods and technologies are applicable for Linked Data,

there are still a number of open problems in the area of Linked Data. Particularly, there have

been tremendous amounts of efforts of Semantic Web community to develop the Linked

Data platform but there is still lack of a systematic approach on methodologies,

technologies and mechanisms. Therefore we propose a novel Linked Data life-cycle

workflow architecture and discuss a systematic overview by describing individual

components in the architecture. We also outline how we are currently addressing these

aspects.

Social Personality Evaluation Based on Prosodic and Acoustic Features

Mr. Yingnan Zhang, Jing Liu, Jin Hu, Xiang Xie, Shilei Huang


In recent decades, personality as a long term paralinguistic information has attracted more

and more researchers. The main idea of the personality refers to the characteristics which

acts as interactions between persons and the social occasions This paper proposes an

approach for the automatic prediction of the Big-Five personality traits and 30 sub

- 41 -

dimensions the listeners attribute to a speaker they don’t know. The experiments are

performed over a corpus of 1031 speech clips (337 identities in total) annotated not only

Big-Five personality traits, but also all 30 sub-dimensions by using The Revised NEO

Personality Inventory (NEO PI-R). The results show that it is possible to predict some

particular sub-dimension with high accuracy (more than 75%) whether a person is perceived

to be in the higher or lower part of the scales corresponding to each of the 30subdimensions,

these sub dimensions give personality more accurate descriptions to lay the foundation for a

more diversified personality classification

A comparison of SimpSVM and RVM for sign language recognition

Mr. Pham Quoc Thang, Nguyen Duc Dung and Nguyen Thanh Thuy

Tay Bac University, Vietnam

Sign language recognition is a rather new field and many challenges, especially when

motion capture devices become more popular. In this paper, we study the feasibility and

effectiveness of two classification methods, namely Simplification of Support Vector

Machine (SimpSVM) and Relevance Vector Machine (RVM), and also give some

comparative results of them for the sign language recognition problem. The experimental

results on the Auslan data set and ASLID data set show that SimpSVM and RVM could

achieve good predictive performances and SimpSVM is better as compared to RVM on sign

recognition. They also pointed out that prediction behaviors of them are similar in terms of

the prediction accuracy when the amount of data or the number of feature changed and sign

discrimination. However, SimpSVM requires fewer training time than RVM in training

phase.

Efficient Executable File Similarity Detection Scheme by Exploiting Executable Fingerprint

Sujin Oh, Jin Kim, Prof. YoungWoong Ko

Hallym university, South Korea

These days, Windows Operating System is widely used and it is dominant than other

commercial operating systems. Besides, most of the commercial software vendors provide a

number of software mainly for Windows OS. Therefore, software similarity analysis

systems for Windows OS are highly useful for software plagiarism detection and copyright

protection. However, mostly the software similarity analysis systems and techniques work

on source codes, not executables, even though the majority of commercial software barely

makes public its source codes. In this paper, we propose a novel scheme for software

plagiarism detection for Windows executables. Our technique exploits both extended basic

blocks’ opcode sequences and imported API functions information in executable files. We

implemented our scheme and measured its performance through several kinds of

experiments using code samples and different versions of commercial software.

Automated therapy monitoring for children with autism spectrum disorder

Agata Kołakowska, Agnieszka Landowska, Ms. Katarzyna Karpienko

Gdańsk University of Technology, Poland

- 42 -

The presentation will report the research conducted under the AUTMON project, that is a

research project carried on at Gdansk University of Technology. Its aim is to identify

children behavioral patterns observed while using a tablet, which are correlated with autism

and then to develop a system able to track these patterns in order to monitor therapy

progress. Five specially designed games have been implemented to reach the stated goal.

Each game constitutes a data source of different type. Apart from standard information on a

game flow, all sensors present in a tablet send valuable data. Gestures read by touch screen,

moves and tilts read by accelerometer and gyroscope are all taken into account. The raw

data are processed in order to calculate a number of characteristics.Data from touch screen

for example let extract information on the precision of drawing paths. Data from

accelerometer and gyroscope may inform about the intensity of some movements etc. All

calculated parameters are then evaluated to find out a set of metrics with satisfying

discriminative ability in the therapy progress recognition task.

More than 30 autistic children took part in the data collection stage of the project. Once a

month a child was supposed to play the games and a therapist filled a questionnaire on the

child’s progress in the following areas of development: communication skills, fine and gross

motor skills, following instructions, self-reliance, social and emotional skills, stereotypical

behaviors, reaction to stimulation, attention control, difficult behaviors. To reflect progress

between the sessions differences between the values of the gathered parameters are taken

into account. Data extracted from the games accompanied by the therapists’ evaluations

are part of training data sets used in the next stage. A number of machine learning

approaches have been applied to build classification models for recognizing progress. There

are both single classifiers, such as for exampledecision trees, Bayesian nets, neural

networks, and combinations of them, e.g. random forests, rotation forests, AdaBoost. Their

performance has been evaluated and compared and will be reported during the presentation.

The ability to recognize progress has been assessed not only separately for each game but

also for their combination by applying voting. The first results, already obtained, confirm

that it is possible to reveal the subtle changes in some areas of development which correlate

with therapy progress.There is still much work to do to improve the performance of the

models, but the authors believe that eventually these efforts will result in creating a tool,

which could be helpful in preparing personalized therapy plans. Appropriate autism therapy

can prevent social exclusion of the children and provide a chance to make them independent

in adult life.

Automatic classification of thangka headdresses based on convolutional depth neural

networks

Assoc. Prof. Liu Huaming, Bi Xuehui, Wang Xiuyou and Wang Weilan

Fuyang Normal College, China

As a representative of Tibetan culture, people’s headdresses in Thangka can be divided into

hairpin, monk hat and crown. In order to meet users’ demand for accurate retrieval of

Thangka, the category information can be used to mark headdresses of Thangka, thereby

increasing the accuracy. Existing headdress classifiers suffer from a common problem:

image segmentation is required before classification. When segmentation is not satisfactory,

the human interaction is also required. This paper presents a classification method for

Thangka headdress based on convolutional deep neural networks, without segmentation and

human interaction, ease of application. First, top features of headdresses are unsupervised

learned by self-encoding; then enter labeled training samples to train a softmax classifier

after the convolution and pooling operation process; and finally using the test sampled to

test classifier’s performance. Compared with other methods, experimental results show that

this method can be a good automatic classifier of headdresses, and can be more readily

applied to headdress labeling.

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Research on the application of Web service matching strategy in the Pre-IPO

Ms. ZangFang

Hunan University College of information science and engineering, Hunan Electronic

Science And Technology Institute Mechanical and electronic information engineering

branch, China

Based on the function of OWL-S, this paper obtain an extended semantic Web service

description model OWL-S+, with some additional information such as reliability, time,

price and so on. According to this model, a comprehensive consideration of the matching

strategy of service function and quality performance is proposed. Finally, in contrast to a

query recall and precision this paper proves the efficiency of OWL-S+ model and matching

strategy. This research will improve the efficiency of the daily work of the listing

corporation effectively, As well as promoting the Web services.

Group-based extension and computation of argumentation framework

Dr. Fangfang Xie

Department of Information Science School of Mathematical Sciences Peking University,

Beijing, China

Abstract argumentation framework may lead to counter intuitive results according to the

attack relation between single arguments. In this paper, we first present the group-based

argumentation framework to deal with the attack relation among sets of arguments. We

define annotate arguments that contain the labels of groups to extend the argumentation

framework. We prove that the new framework is a general case of abstract argumentation

framework without self-attacks. We then present an approach to compute the preferred

extension and winner of the group-based argumentation.

- 44 -

ListenersNote:

*To show the respect to authors, especially to encourage the student authors, we strongly suggest you attend the

whole session, the scheduled time for presentations might be changed due to unexpected situations, please

come as early as you could.

Prof. Joon Shik Yoon

Korea University Medical Center, Korea

Prof. Seoung Bum Kim

Korea university, South korea

Asst. Prof. Junyeob Yeo

Department of Physics, Kyungpook National University, South Korea

- 45 -

Day 3/Jan. 15 (14:00-16:00)

Campus Visit At downtown campus of University of Science

(Vietnam National University Ho Chi Minh City)

----Special thanks to Prof. Pham The Bao

Address: 227 Nguyen Van Cu Str., Dist. 5, HCMC

Gathering Time&Venue: 13:20 at conference hotel lobby

Transportation: On foot

Details about Visit:

- history of the University

- the model of Hochiminh National University

- historical place

- University library

- faculties

Down town campus of University of Science is less than 2 KMs aways from Conference hotel, and it

will be an interesting experience to walk there.

- 46 -

The University of Science is the public university which plays a particularly important role in

education and scientific research in Vietnam National University-Ho Chi Minh City.

A: Conference Hotel--Alagon Central Hotel & Spa in Ho Chi Minh City

B: Downtown campus of University of Science(quite near the Hotel Nikko Saigon)

Downtown campus of University of Science

- 47 -

Day 4/Jan. 16 (8:00-17:00)

One Day Tour In Ho Chi Minh City

At 08:00 of Jan. 16, you will be picked up at conference hotel lobby.

Attention!

This trip will charge 100USD for each one. (Pay to join before Jan. 3, 2017), or you could

choose to enjoy free time on Jan.16 to explore the city by yourself.

Please be there on time, or you will miss the tour.

Route：

you will visit:

The Notre Dame Cathedral and the General Post Office. Travel along Dong Khoi Street, the

Opera House and the Peoples’ Committee Building: Built in 1897 by French architect Ferret

Eugene, the 800 seat Opera House was used as the home of the Lower House Assembly of South

Vietnam after 1956. Continue to visit The Reunification Palace: Here on April 30th 1975 the

‘American War’ officially ended when tank number 843 of the North Vietnamese Army crashed

through the gates of what was, at the time the residence of the President of the Republic of Vietnam

(This palace can be closed for meeting).You will enjoy lunch at a local Vietnamese restaurant.

In the afternoon, you will visit: The War Remnants Museum: Formerly known as the Museum of

American War Crimes, this is a poignant display of the futility of war. Thien Hau Pagoda: Located in

Cho Lon, the city’s Chinatown, this pagoda is dedicated to the Goddess of the Sea. The most

impressive features of this structure are the intricately friezes and the carved tableaus towards the

front of the pagoda. The impressive incense coils in the interior also make for some great photos.

Binh Tay Market: The name ‘Cho Lon’ can be roughly translated as ‘Big market’. Although the

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whole district lives up to its name a visit to this market really drives the point home. Selling

everything from hats to dried squid, you are sure to enjoy a walk through the maze of stalls in this

bustling market. Visit a Lacquer ware workshop.

17:00h: End of the tour.

Service excludes:

- Admission fee

- English speaking guide

- Transportation

- Lunch.

Service excludes:

Personal expenses (not mentioned above).

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MEMO

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