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http://www.adma2017.net/
Program
The 13th International Conference on Advanced Data
Mining and Applications (ADMA)
Collocated with
The 26th ACM International Conference on Information
and Knowledge Management (CIKM)
Singapore, 5-6 November 2017
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Conference Venue
NTU Alumni House at Marina Square
6 Raffles Boulevard, #02-27/28, Marina Square Shopping Mall, Singapore 039594
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TABLE OF CONTENT
ORGANIZATION .................................................................................................................. 3
KEYNOTES ......................................................................................................................... 7
INDUSTRY TALKS ............................................................................................................ 10
CONFERENCE PROGRAM .............................................................................................. 14
CONFERENCE DAY 1 ...................................................................................................... 15
CONFERENCE DAY 2 ...................................................................................................... 19
MAP ................................................................................................................................... 23
SPONSORSHIP ................................................................................................................. 24
ADMA 2017
The 13th International Conference on Advanced Data Mining and Applications aims at
bringing together the experts on data mining from all over the world, and provides an
international forum for the dissemination of original research results in data mining,
spanning applications, algorithms, software and systems, and different applied disciplines
with potentials in data mining, such as social media and social network mining, bio-
medical science, green computing, and smart nation applications. ADMA will promote the
same close interaction and collaboration among practitioners and researchers.
CIKM 2017 CIKM is a premier forum for research on topics at the confluence of information retrieval,
data management, and knowledge management. This year, CIKM highlights
technologies and insights that materialize the “Smart Cities, Smart Nations” vision
shared by many urban areas and their countries, while it brings together international and
Singapore technology providers and seekers to explore technology and business
collaboration opportunities through open innovation.
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ADMA 2017 General Chairs Aixin Sun, Nanyang Technological University
Hady Lauw, Singapore Management University
Program Chairs Gao Cong, Nanyang Technological University
Wen-Chih Peng, National Chiao Tung University
Proceedings Chairs Chenliang Li, Wuhan University Wei Emma Zhang, Macquarie University
Publicity Chairs Ju Fan, Renmin University of China Hongzhi Yin, University of Queensland
Special Issue Chairs Michael Sheng, Macquarie University Xiuzhen Zhang, RMIT University
Local Arrangement Chairs David Lo, Singapore Management University Zhen Hai, Institute for Infocomm Research, A*STAR
Registration Chair Victor Chu, Nanyang Technological University
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Demo Chairs Zhifeng Bao, RMIT University
Xin Cao, The University of New South Wales
Awards Committee Chair Xiaoli Li, Institute for Infocomm Research, A*STAR
Web Chair Yihong Zhang, Nanyang Technological University
Steering Committee Xue Li, University of Queensland, Australia (Chair) Jie Cao, Nanjing University of Finance and Economics, China Michael Sheng, Macquarie University, Australia Jie Tang, Tsinghua University, China Shuliang Wang, Beijing Institute of Technology, China Kyu-Young Whang, Korea Advanced Institute of Science and Technology, Korea Min Yao, Zhejiang University, China Osmar Zaiane, University of Alberta, Canada Chengqi Zhang, University of Technology Sydney, Australia Shichao Zhang, Guangxi Normal University, China
Program Committee
Swati Agarwal, IIIT-Delhi
Djamal Benslimane, Lyon 1 University
Tossapon Boongoen, Mae Fah Luang University
Yi-Shin Chen, National Tsing Hua University
Lisi Chen, Hong Kong Baptist University
Yuan Fang, Institute for Infocomm Research
Kaiyu Feng, Nanyang Technological University
Philippe Fournier-Viger, Harbin Institute of Technology Shenzhen Graduate School
Tao Guo, Nanyang Technological University
Jialong Han, Nanyang Technological University
Bryan Hooi, Carnegie Mellon University
Wei Hu, Nanjing University
Guangyan Huang, Deakin University
Chih-Chieh Hung, Tamkang University
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Shafiq Joty, University of British Columbia
Jianxin Li, University of Western Australia
Xutao Li, Harbin Institute of Technology
Haiquan Li, University of Arizona
Cheng-Te Li, National Cheng Kung University
Jinyan Li, University of Technology Sydney
Gang Li, Deakin University
Guosheng Lin, Nanyang Technological University
Bin Liu, IBM Thomas J. Watson Research Center
Cheng Long, Queen's University Belfast
Xudong Luo, Guangxi Normal University
Marco Maggini, University of Siena
Toshiro Minami, Kyushu Institute of Information Sciences and Kyushu University
Yasuhiko Morimoto, Hiroshima University
Gunarto Sindoro Njoo, National Chiao Tung University
Tuan Anh Pham, Nanyang Technological University
Hai Phan, University of Oregon
Tieyun Qian, Wuhan University
Yongrui Qin, University of Huddersfield
Wenjie Ruan, University of Oxford
Dharmendra Sharma, University of Canberra
Michael Sheng, Macquarie University
Hyun Ah Song, Carnegie Mellon University
Guojie Song, Peking University
Eiji Uchino, Yamaguchi University
Xianzhi Wang, Singapore Management University
Hongzhi Wang, Harbin Institute of Technology
Qinsi Wang, Carnegie Mellon University
Chenguang Wang, IBM Research
Wei Wei, Huazhong University of Science and Technology
Yu-Ting Wen, National Chiao Tung University
Feng Xia, Dalian University of Technology
Zhipeng Xie, Fudan University
Guandong Xu, University of Technology Sydney
Hui-Gwo Yang, National Chiao Tung University
Zijiang Yang, York University
Dezhong Yao, Nanyang Technological University
Lina Yao, The University of New South Wales
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Qi Yu, Rochester Institute of Technology
Quan Yuan, University of Illinois at Urbana-Champaign
Guang Lan Zhang, Boston University
Wei Emma Zhang, Macquarie University
Shichao Zhang, Guangxi Normal University
Kaiqi Zhao, Nanyang Technological University
Yong Zheng, Illinois Institute of Technology
Xiaofeng Zhu, Guangxi Normal University
Demo Program Committee
Yingke Chen, Sichuan University
Xin Huang, Hong Kong Baptist University
Yuchen Li, National University of Singapore
Xutao Li, Harbin Institute of Technology, Shen Zhen
Hua Mao, Sichuan University
Ryan McConville, Bristol University, UK
Ruiming Tang, Huawei Noah Ark lab
Qingyao Wu, South China University of Technology
Jianqiu Xu, Nanjing University of Aeronautics and Astronautics
Yi Yu, National Institute of Informatics, Japan
Chao Zhang, University of Illinois at Urbana-Champaign
Zhiwei Zhang, Hong Kong Baptist University
Yuxin Zheng, Tencent
Jingbo Zhou, Baidu Research
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Sunday 5 Nov 2017, 8:30am-9:30am
Keynote Speech I: On Application-Aware Information Extraction for Big Data in Social Networks
Due to the paradigm shift to the Cloud computing, data has been accumulated at fast pace in various
applications. Among others, the number of social network activities is increasing drastically. It has
become very desirable to conduct various analyses for applications on social networks. However, as
the scale of a social network has become prohibitively large, it is infeasible to scrutinize the data and
extract the key essence from the entire social network. This issue becomes further complicated due
to the heterogeneous nature of the data. As a result, a significant amount of research effort has been
elaborated upon extracting the essential application-dependent information from a social network. In
this talk, we shall examine some recent studies on data processing and information extraction for
social networks. Explicitly, we shall explore the methods for three levels of information extraction in a
social network, namely, parameter extraction, information extraction, and structure extraction, and
interpret them from their respective objectives. We then comment on how to conduct application-
aware information extraction for big data in social networks.
Ming-Syan Chen received the Ph.D. degrees in Computer,
Information and Control Engineering from The University of Michigan,
Ann Arbor, MI, USA. He is now an NTU Chair Professor and also the
Dean of the College of Electrical Engineering and Computer Science
at National Taiwan University. He was a research staff member at
IBM Thomas J. Watson Research Center, NY, USA, the
President/CEO of Institute for Information Industry (III), and the
Director of Research Center of Information Technology Innovation
(CITI) in the Academia Sinica. His research interests include
databases, data mining, social networks, and IoT applications. He is a recipient of the National Chair
Professorship and also the Academic Award of the Ministry of Education, the NSC (National Science
Council) Distinguished Research Award, Y.Z. Hsu Science Chair Professor Award, Pan Wen Yuan
Distinguished Research Award, Teco Award, Honorary Medal of Information, and K.-T. Li Research
Breakthrough Award for his research work, and also the Outstanding Innovation Award from IBM
Corporate for his contribution to a major database product. Dr. Chen is a Fellow of ACM and a Fellow
of IEEE.
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Sunday 5 Nov 2017, 9:30am-10:30am
Keynote Speech II: I’m Telling You - You Ain’t the Only One Who Needs MapReduce Algorithms!
There is a growing trend of applications including Internet-of-Things (IoT) that should handle big data.
For such applications, the MapReduce framework has recently attracted a lot of attention. Google’s
MapReduce or its open-source equivalent Hadoop is a powerful tool for building applications to deal
with big data. MapReduce is a programming model that allows easy development of scalable parallel
applications to process big data on large clusters of commodity machines. I will introduce Hadoop
MapReduce available to everyone and present how to design efficient MapReduce algorithms for big
data analysis based on my experiences. Since Spark is recently developed to overcome the
shortcomings of MapReduce which it is not optimized for iterative algorithms, I will finally discuss
Spark.
Kyuseok Shim is currently a professor at electrical and computer
engineering department in Seoul National University, Korea. Before
that, he was an assistant professor at computer science department in
KAIST and a member of technical staff for the Serendip Data Mining
Project at Bell Laboratories. He was also a member of the Quest Data
Mining Project at the IBM Almaden Research Center. Kyuseok has
been working in the area of databases focusing on data mining,
search engines, recommendation systems, MapReduce algorithms,
privacy preservation, query processing and query optimization. His
writings have appeared in a number of professional conferences and journals including ACM, VLDB
and IEEE publications. He served as a Program Committee Co-Chair for PAKDD 2003, WWW 2014,
ICDE 2015 and APWeb 2016. Kyuseok was previously on the editorial board of VLDB as well as
IEEE TKDE Journals. He was named an ACM Fellow for his contributions to scalable data mining and
query processing research in 2013. He is currently a member of the VLDB Endowment Board of
Trustees and a steering committee member of PAKDD as well as DASFAA conferences.
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Monday 6 Nov 2017, 8:30am-9:30am
Keynote Speech III: LAMP and GENIE: Just-in-Time Model Construction for Predictive Traffic Analytics
Traffic situations are often dynamic and predictive models constructed for traffic analytic can often be
outdated by the time sufficient data are collected to construct them. In this talk, we will look at LAMP
and GENIE, a system that we built to support just-in-time model construction over dynamically
changing data. LAMP (semi-LAzy Mining Paradigm) is a new data mining paradigm for predictive
analytics. Instead of pre-computing big, complex models, LAMP build small, local prediction model
during prediction time. This is done by dynamically searching for data that are representative of the
existing situation and then building a model from the selected data. To facilitate the efficient search of
data from a huge database, we build GENIE (GEneric InvErted index), a unified framework to support
storage and retrieve of Big Data with various types of structure. GENIE is developed to leverage on
the ever growing parallel processing power of Graphics Processing Units (GPUs) to support efficient
search. By utilizing the efficient search engine of GENIE, we are able to ensure that LAMP is efficient
enough for dynamic car trajectories prediction. In the talk, we will also look at some of our ongoing
work on traffic analytics including traffic visualization, obstacle detection and intention analysis.
Information on LAMP and GENIE is available at http://www.comp.nus.edu.sg/~atung/gl/ .
Dr Anthony K. H. Tung is currently an Associate Professor in the
Department of Computer Science, National University of Singapore
(NUS). He received both his B.Sc.(2nd Class Honour) and M.Sc. in
computer sciences from the National University of Singapore in 1997 and
1998 respectively. In 2001, he received the Ph.D. in computer sciences
from Simon Fraser University (SFU). Dr Anthony Tung main research
areas are on searching, mining and visualizing complex data. More
recently, he also looks into the creation of innovative big data
applications over the data processing techniques that he had developed
over the past 18 years. Anthony is also the deputy director of NUS SeSaMe research center
(http://sesame.comp.nus.edu.sg/).
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Monday 6 Nov 2017, 9:30am-10:00am
Industry Talk I: Data Mining-based Cost Optimisation for Electricity Retailers
Singapore’s electricity market will be fully liberalised in 2018. With more than 25 electricity retailers,
there will be adoption of various new business models and technologies, which will benefit end-
consumers. In this talk, we will highlight AI / machine learning / data mining techniques for cost
optimisation, which can help these electricity retailers gain competitive advantage. We will give real-
world examples of their business problems, data mining objectives, and types of energy and
maintenance use cases.
Dr Phua Chun Wei Clifton is at NCS Group, working on artificial intelligence, machine learning, and
advanced analytics under NCS Digital. He leads a team of more than 30 highly capable data
scientists, and mentor them in their activities. In his free time, Clifton volunteers professional services
to events, conferences, and journals (related to data mining/analytics, security and health informatics).
Was also part of teams which won some analytics competitions. His specialization was Big Data
analytics in public security (attack and disaster preparation/recovery/response; cyber security; internal
security; and predictive policing) and fraud (government, banking, and insurance).
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Monday 6 Nov 2017, 10:00am-10:30am
Industry Talk II: Advancing Ensembles for Population Data Mining
Ensembles remain the model builder of choice for data miners in real world applications. Since the
introduction of the concept in the 1980's they have proven to be a reliable modeller of first choice, with
variations to the algorithms advancing over the years, from random forests through to extreme
gradient boosted trees. Indeed, even neural networks can be viewed within an ensemble framework.
However, a focus within data mining has remained on building point (ensemble) models in business
applications. The advent of general access to massive compute over massive data, as now available
through cloud services, has benefited the application of neural network algorithms which are data and
compute hungry. We have an opportunity now to also consider the case for data and compute hungry
massive ensemble modelling. In this presentation I will review the ensemble landscape and share the
ensemble driven concept of data genetics using massive ensembles over customer populations as a
new approach for data mining, utilising the massive compute and data now available.
Dr Graham Williams recently joined Microsoft as Director of Data Science Asia/Pacific after over 30
years as researcher, developer and educator in Artificial Intelligence, Machine Learning, Data Mining,
Analytics and Data Science. He was previously lead Data Scientist for the Australian Government’s
Center of Excellence in Data Analytics, Director of Data Science at the Australian Taxation Office,
and Principal Research Scientist with CSIRO Australia. Over 30 years he has also been an active
contributor to the Open Source ecosystem with open source software projects across Data Mining,
Data Science, and R. Graham has authored many books, papers, Internet resources and software
packages for data mining. His latest book released in July 2017 introduces the Essentials of Data
Science. His research contributions have included developing the concept of ensembles of models
through Ensemble Decision Tree Induction (1989), HotSpots for identifying target areas in very large
data collections (1992), WebDM data mining as a service (1995), and Rattle as a simple to use
Graphical User Interface for data mining using R (2005).
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Monday 6 Nov 2017, 11:00am-11:30am
Industry Talk III: Personalised News and Video Recommendation System at LinkSure
In recent years, the Internet industry has shifted more and more towards digital content distribution
through online services. In this talk we overview the overall system design and architecture of
LinkSure News and Video Recommendations, the challenges encountered in practice, and the
lessons learned from the production deployment of these systems at LinkSure. Specifically, we will
highlight how news selection and personalisation of recommendations are formulated and addressed
at LinkSure. By presenting our experiences of applying techniques at the intersection of
recommender systems, information retrieval, machine learning, and statistical modelling in a large-
scale industrial setting and highlighting the open problems, we hope to stimulate further research and
collaborations.
Dr Rubing Duan is the director of big data department at LinkSure (the leading company of internet
access services) and a manager of the Asia Big Data Association. He received B.Sc. in computer
science from National University of Defence Technology, China, M.Sc. in data science from Central
South University, China, Ph.D. in data mining and distributed computing from the University of
Innsbruck, Austria. Previously, he has worked as a postdoctoral research fellow at the University of
Souther California, USA. His main research interests include recommender system, quantitative
analysis and strategies, deep learning, large-scale computing system, etc. In last few years, his team
has won more than 10 prodigious awards and achievements (funded by Rakuten, SingTel, IEEE,
ACM etc.) in the area of big data models and algorithms.
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Monday 6 Nov 2017, 11:30am-12:00pm Industry Talk IV: Innovation Opportunities in the Logistics Industry
Learn about DHL’s trend research approach as well as how it applies cutting edge technologies such
as robotics, augmented reality, IOT and analytics to meet the evolving needs of the supply chain
industry.
Timothy Kooi is currently an Innovation Leader at the DHL’s Innovation Center, where he is
responsible for trend research and converting new trends into proof-of-concepts. He is also the Head
of Data Analytics for the Customer Solutions and Innovation BU for the region, responsible for
deepening the use of data analytics to generate new value-add and business growth opportunities.
Prior to DHL, he was the Asia-Pacific Head of development and profitability for the Burger King Brand.
He was also with EDBI, the strategic investment arm of the Singapore Economic Development Board
(EDB) for a number of years, focused on Singapore-based investments. He started out his career
with the EDB with their Logistics industry development group.
Monday 6 Nov 2017, 12:00pm-12:30pm Industry Talk V: AI in Healthcare - Opportunities and Challenges from a Health System’s Perspective
Artificial intelligence and its application in healthcare industry are gaining momentum in the past two
years. We are going to talk about the challenges we face and opportunities we see in developing and
deploying AI-driven solutions to improve clinical outcome, operation efficiency and reduce cost of care.
Sijia Wang is team lead from Health Insights Department, Integrated Health Information
Systems(IHiS). Sijia currently lead hospital data science projects and data science competency
center. IHiS is the technology agency of Ministry of Health Holdings Singapore with mission to
digitize, connect and analyze Singapore’s health ecosystem.
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7:00pm - 9:00pm Saturday 4 Nov 2017 Reception at Conference Venue
Time Sunday 5 Nov 2017 Monday 6 Nov 2017
07:30am -
08:10am
Registration
Conference Venue
08:10am -
08:30am
Opening Session
Basil 1&2&3
Registration
Conference Venue
08:30am -
09:30am
Keynote I
On Application-Aware Information Extraction for Big Data in Social Networks
Prof. Ming-Syan Chen
Basil 1&2&3
Keynote III
LAMP and GENIE: Just-in-Time Model Construction for Predictive Traffic Analytics
Prof. Anthony K. H. Tung
Basil 1&2&3
09:30am -
10:30am
Keynote II
I’m Telling You - You Ain’t the Only One Who Needs MapReduce Algorithms!
Prof. Kyuseok Shim
Basil 1&2&3
Industry Talk I by Dr Phua Chun Wei Clifton
Industry Talk II by Dr Graham Williams
Basil 1&2&3
10:30am -
11:00am
Morning Tea
Conference Venue
Morning Tea
Conference Venue
11:00am -
12:30pm
Session 1-1 Machine Learning
Basil 3
Session 1-2 Social Network and Social Media Mining
Basil 2
Industry Talk III by Dr Rubing Duan
Industry Talk IV by Timothy Kooi
Industry Talk V by Sijia Wang
Basil 1&2&3
12:30pm -
01:30pm
Lunch
Conference Venue
Demo Session 1
Basil 1
Lunch
Conference Venue
01:30pm -
03:00pm
Session 2-1 User Behavior
and Profile Analysis
Basil 3
Session 2-2 Medical Informatics
Basil 2
Session 4-1 Text Mining and Natural Language Processing
Basil 3
Session 4-2 Distributed and High
Performance Computing
Basil 2
03:00pm -
03:30pm
Afternoon Tea
Conference Venue
Demo Session 2
Basil 1
Afternoon Tea
Conference Venue
03:30pm -
05:10pm
Session 3-1 Data Mining Algorithms
Basil 3
Session 3-2 Recommendation Systems
Basil 2
Session 5-1 Data Mining Applications
Basil 3
Session 5-2 Smart Nation Applications
Basil 2
07:00pm -
09:00pm
Conference Diner
Mortar Restaurant & Bar
Conference Banquet and Award Function
Marina Mandarin Singapore
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Sunday 5 Nov 2017 – ADMA NTU Alumni House at Marina Square 6 Raffles Boulevard, #02-27/28, Marina Square Shopping Mall, Singapore 039594
TIME Function Location
7:30am Registration Conference Venue
8:10am Conference Opening Basil 1&2&3
08:30am
9:30am
Keynote Speech I
On Application-Aware Information Extraction for Big Data in Social Networks
Prof. Ming-Syan Chen College of Electrical Engineering and Computer Science National Taiwan University
Basil 1&2&3
09:30am
10:30am
Keynote Speech II
I’m Telling You - You Ain’t the Only One Who Needs MapReduce Algorithms! Prof. Kyuseok Shim Electrical and Computer Engineering Department Seoul National University, Korea
Basil 1&2&3
10:30am
11:00am
Morning Tea
Conference venue
11:00am – 12:30pm Parallel Paper Presentations
Session 1-1: Machine Learning 11:00am – 12:30pm, Basil 3 Session Chair: Dr Zhen Hai, Institute for Infocomm Research, A*STAR, Singapore Mixed Membership Sparse Gaussian Conditional Random Fields (Spotlight)
Jie Yang, Henry C.M. Leung, S.M. Yiu and Francis Y.L. Chin Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning (Spotlight) Shichao Zhang, Yue Fang, Cong Lei, Jilian Zhang and Xiaoyi Hu StruClus: Scalable Structural Graph Set Clustering with Representative Sampling (Spotlight)
Till Schäfer and Petra Mutzel Effects of Dynamic Subspacing in Random Forest
Md Nasim Adnan and Zahid Islam
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Employing Hierarchical Clustering and Reinforcement Learning for Attribute-based Zero-Shot Classification
Liu Bin, Li Yao, Junfeng Wu and Xiaosheng Feng
Session 1-2: Social Network and Social Media Mining 11:00am – 12:30pm, Basil 2 Session Chair: Dr Guojie Song, Peking University, China From Mutual Friends to Overlapping Community Detection: A Non-negative Matrix Factorization Approach (Spotlight) Xingyu Niu, Hongyi Zhang, Micheal R. Lyu and Irwin King A Feature-based Approach for the Redefined Link Prediction Problem in Signed Networks (Spotlight)
Xiaoming Li, Hui Fang and Jie Zhang A Solution to Tweet-Based User Identification across Online Social Networks
Yongjun Li, Zhen Zhang and You Peng (Video is available at https://pan.baidu.com/s/1c2cuyvm) Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities
Areej Alsini, Amitava Datta, Jianxin Li and Du Huynh Language-independent Twitter Classification using Character-based Convolutional Networks Shiwei Zhang, Xiuzhen Zhang, Jeffrey Chan and Stephen Wa FRISK: A Multilingual Approach to Find twitteR InterestS via wiKipedia Coriane Nana Jipmo, Gianluca Quercini and Nacéra Bennacer
12:30pm
01:30pm
Lunch
Demo Session
Conference venue
Basil 1
1:30pm – 3:00pm Parallel Paper Presentations
Session 2-1: User Behavior and Profile Analysis 1:30pm – 3:00pm, Basil 3 Session Chair: Dr Chu Wing Yan Victor, Nanyang Technological University, Singapore
Modeling Check-in Behavior with Geographical Neighborhood Influence of Venues (Spotlight)
Thanh-Nam Doan and Ee-Peng Lim Your Moves, Your Device: Establishing Behavior Profiles using Tensors (Spotlight) Eric Falk, Jérémy Charlier and Radu State An Approach for Identifying Author Profiles of Blogs
Chunxia Zhang, Yu Guo, Jiayu Wu, Shuliang Wang and Zhendong Niu Generating Topics of Interests for Research Communities
Nagendra Kumar, Rahul Utkoor, Bharath Kumar Reddy Appareddy and Manish Singh
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Session 2-2: Medical Informatics 1:30pm – 3:00pm, Basil 2 Session Chair: Dr Xiuzhen Zhang, RMIT University, Australia Predicting Clinical Outcomes of Alzheimer's Disease from Complex Brain Networks (Spotlight) Xingjuan Li, Yu Li and Xue Li
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties (Spotlight)
Simon Razniewski, Vevake Balaraman and Werner Nutt Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers
Zibo Yi, Shasha Li, Jie Yu, Yusong Tan and Qingbo Wu
Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure Mohammad Khubeb Siddiqui, Md. Zahidul Islam and Muhammad Ashad Kabir Multiclass Lung Cancer Diagnosis by Gene Expression Programming and microarray datasets
Hasseeb Azzawi, Jingyu Hou, Russul Alanni and Yong Xiang
03:00pm
03:30pm
Afternoon Tea
Demo Session
Conference venue
Basil 1
3:30pm – 5:10pm Parallel Paper Presentations
Session 3-1: Data Mining Algorithms 3:30pm – 5:10pm, Basil 3 Session Chair: Dr Chih-chieh Hung, Tamkang University, Taiwan
Querying and Mining Strings Made Easy (Spotlight)
Majed Sahli, Essam Mansour and Panos Kalnis A Higher-Fidelity Frugal Quantile Estimator
Anis Yazidi, Hugo Lewi Hammer and John Oommen Discovering Group Skylines with Constraints by Early Candidate Pruning
Ming-Yen Lin, Yueh-Lin Lin and Sue-Chen Hsueh Mobile Robot Scheduling with Multiple Trips and Time Windows
Shudong Liu, Shili Xiang, Xiaoli Li and Huayu Wu Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining
Mun Hou Wong, Vincent S. Tseng, Sun-Wei Liu and Cheng-Hung Tsai Environmental Sound Recognition using Masked Conditional Neural Networks
Fady Medhat, David Chesmore and John Robinson
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Session 3-2: Recommendation Systems 3:30pm – 5:10pm, Basil 2 Session Chair: Dr Kaiqi Zhao, Nanyang Technological University, Singapore
Fair Recommendations Through Diversity Promotion (Spotlight) Pierre-René Lhérisson, Fabrice Muhlenbach and Pierre Maret A Hierarchical Bayesian Factorization Model for Implicit and Explicit Feedback Data (Spotlight)
Binh Nguyen and Atsuhiro Takasu Group Recommender Model Based on Preference Interaction
Wei Zheng, Bohan Li, Wang Yanan, Hongzhi Yin, Xue Li, Donghai Guan and Xiaolin Qin Identification of Grey Sheep Users By Histogram Intersection In Recommender Systems
Yong Zheng, Mayur Agnani and Mili Singh An Evolutionary Approach for Learning Conditional Preference Network from Inconsistent Examples
Mohammad Haqqani and Xiaodong Li Color-sketch simulator: a guide for color-based visual known-item search
Jakub Lokoc, Anh Nguyen Phuong, Marta Vomlelová and Chong-Wah Ngo
Demo Sessions Time: 12:30pm – 1:30pm; 3:00pm – 3:30pm Venue: Basil 1 Demo Chairs: Dr Zhifeng Bao, RMIT University, Australia Dr Xin Cao, The University of New South Wales, Australia
An Interactive Web-based Toolset for Knowledge Discovery from Short Text Log Data
Michael Stewart, Wei Liu, Rachel Cardell-Oliver and Mark Griffin Tools and Infrastructure for Supporting Enterprise Knowledge Graphs
Sumit Bhatia, Nidhi Rajshree and Anshu Jain Carbon: Forecasting Civil Unrest Events by Monitoring News and Social Media Wei Kang, Jie Chen, Jiuyong Li, Jixue Liu, Lin Liu, Grant Osborne, Nick Lothian, Brenton Cooper, Terry Moschou and Grant Neale SWYSWYK: a new Sharing Paradigm for the Personal Cloud
Paul Tran-Van, Nicolas Anciaux and Philippe Pucheral Detect Tracking Behavior among Trajectory Data
Jianqiu Xu A System for Querying and Analyzing Urban Regions
Wee Boon Koh 7:00pm
9:00pm
Conference Dinner Mortar Restaurant & Bar
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Monday 6 Nov 2017 – ADMA NTU Alumni House at Marina Square 6 Raffles Boulevard, #02-27/28, Marina Square Shopping Mall, Singapore 039594
TIME Function Location
7:30am Registration Conference Venue
8:30am
9:30am
Keynote Speech III
LAMP and GENIE: Just-in-Time Model Construction for Predictive Traffic Analytics Prof. Anthony K. H. Tung Department of Computer Science National University of Singapore
Basil 1&2&3
9:30am
10:00am
Industry Talk I
Data Mining-based Cost Optimisation for Electricity Retailers Dr Phua Chun Wei Clifton NCS Group
Basil 1&2&3
10:00am
10:30am
Industry Talk II
Advancing Ensembles for Population Data Mining Dr Graham Williams Cloud AI Research, Microsoft
Basil 1&2&3
10:30am
11:00am
Morning Tea Conference Venue
11:00am
11:30am
Industry Talk III
Personalised News and Video Recommendation System at LinkSure Dr Rubing Duan Big Data Department, LinkSure
Basil 1&2&3
11:30am
12:00pm
Industry Talk IV
Innovation opportunities in the Logistics industry Dr Timothy Kooi Asia Pacific Innovation Center, DHL Customer Solutions and Innovation
Basil 1&2&3
12:00pm
12:30pm
Industry Talk V
AI in Healthcare - Opportunities and Challenges from a Health System’s Perspective Sijia Wang Health Insights Department, Integrated Health Information Systems
Basil 1&2&3
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12:30pm
1:30pm
Lunch Conference Venue
1:30pm – 3:00pm Parallel Paper Presentations
Session 4-1: Text Mining and Natural Language Processing 1:30pm – 3:00pm, Basil 3 Session Chair: Dr June-jae Kim, Institute for Infocomm Research, A*STAR, Singapore
Calling for Response: Automatically Distinguishing Situation-aware Tweets During Crises (Spotlight)
Xiaodong Ning, Lina Yao, Xianzhi Wang and Boualem Benatallah Feature Analysis for Duplicate Detection in Programming QA Communities (Spotlight) Wei Emma Zhang, Michael Sheng and Yanjun Shu Structured Sentiment Analysis
Abdulqader Almars, Xue Li, Xin Zhao, Ibrahim A.Ibrahim, Weiwei Yuan and Bohan Li Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning
Yuanhao Zheng, Bifan Wei, Jun Liu, Meng Wang, Weitong Chen, Bei Wu and Yihe Chen
Improving Chinese Sentiment Analysis via Segmentation-based Representation Using Parallel CNN Yazhou Hao, Qinghua Zheng, Yangyang Lan, Yufei Li, Meng Wang, Sen Wang and Chen Li Entity Recognition by Distant Supervision with Soft List Constraint
Hongkui Tu, Zongyang Ma, Aixin Sun, Zhiqiang Xu and Xiaodong Wang
Session 4-2: Distributed and High Performance Computing 1:30pm – 3:00pm, Basil 2 Session Chair: Dr Zhifeng Bao, RMIT University, Australia
Distributed Training Large-Scale Deep Architectures (Spotlight)
Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu, Chun-Nan Chou, Chia-Chin Tsao, Kuan-Chieh Tung, Ting-Wei Lin, Cheng-Lung Sung and Edward Chang
Fault Detection and Localization in Distributed Systems using Recurrent Convolutional Neural Networks (Spotlight)
Guangyang Qi, Lina Yao and Anton Uzunov Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data
Premysl Cech, Jakub Marousek, Jakub Lokoc, Yasin Silva and Jeremy Starks Hybrid Subspace Mixture Models For Prediction and Anomaly Detection in High Dimensions Jenn-Bing Ong and Wee-Keong Ng Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach
Bassma Al-Jubouri and Bogdan Gabrys
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03:00pm
03:30pm
Afternoon Tea Conference Venue
3:30pm – 5:10pm Parallel Paper Presentations
Session 5-1: Data Mining Applications 3:30pm – 5:10pm, Basil 3 Session Chair: Dr Xin Cao, University of New South Wales, Australia
Improving Real-Time Bidding Using a Constrained Markov Decision Process (Spotlight)
Manxing Du, Redouane Sassioui, Georgios Varisteas, Radu State, Mats Brorsson and Omar Cherkaoui Efficient Revenue Maximization for Viral Marketing in Social Networks (Spotlight) Yuan Su, Xi Zhang, Sihong Xie, Philip S. Yu and Binxing Fang A Joint Human/Machine Process for Coding Events and Conflict Drivers (Spotlight)
Bradford Heap, Alfred Krzywicki, Susanne Schmeidl, Wayne Wobcke and Michael Bain An Intelligent Weighted Fuzzy Time Series Model Based on A Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting
Ruixin Yang, Mingyang Xu, Junyi He, Stephen Ranshous and Nagiza Samatova Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments
Anis Yazidi, John Oommen and Morten Goodwin Making Use of External Company Data to Improve the Classification of Bank Transactions
Erlend Vollset, Eirik Folkestad, Jon Atle Gulla and Marius Rise Gallala
A Framework for Clustering and Dynamic Maintenance of XML Documents
Ahmed Al-Shammari, Chengfei Liu, Mehdi Naseriparsa, Bao Quoc Vo and Tarique Anwar
Session 5-2: Smart Nation Applications 3:30pm – 5:10pm, Basil 2 Session Chair: Dr Jianqiu Xu, Nanjing University of Aeronautics and Astronautics, China
An empirical study on collective online behaviors of extremist supporters (Spotlight)
Jung-Jae Kim, Yong Liu, Wee Yong Lim and Vrizlynn L. L. Thing
People-Centric Mobile Crowdsensing Platform for Urban Design
Shili Xiang, Lu Li, Si Min Lo and Xiaoli Li PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network
Yao Cheng, Chang Xu, Daisuke Mashima, Vrizlynn L. L. Thing and Yongdong Wu Generating Life Course Trajectory Sequences with Recurrent Neural Networks and Application to Early Detection on Social Disadvantage
Lin Wu, Michele Haynes, Andrew Smith, Tong Chen and Xue Li
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STA: a Spatio-temporal Thematic Analytics Framework for Urban Ground Sensing
Guizi Chen, Wee Siong Ng and Usha Nanthani Kunasegaran Mining Load Profile Patterns for Australian Electricity Consumers
Vanh Khuyen Nguyen, Wei Emma Zhang, Quan Z. Sheng and Jason Merefield Location-aware Human Activity Recognition
Tam Nguyen, Daniel Fernandez, Quy T.K Nguyen and Ebrahim Bagheri Privacy and Utility Preservation for Location Data Using Stay Region Analysis
Manoranjan Dash and Sin Teo 7:00pm
9:00pm
Conference Banquet and Award Function Marina Mandarin Singapore
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NTU Alumni House at Marina Square
6 Raffles Boulevard, #02-27/28, Marina Square Shopping Mall, Singapore 039594
Tel: (+65) 6252 7277
Email: [email protected]