Joint Conference
2016 International Conference on
Artificial Intelligence and Computer Engineering
(AICE 2016)
2016 International Conference on
Economics and Management Engineering
(ICEME 2016)
2016 年人工智能与计算机工程国际会议
2016 年经济与管理工程国际会议
June 18-19, 2016
Wuhan, Hubei, China
AICE2016 & ICEME2016
Program Book
Organizer:
Publisher:
SCHEDUAL OF THE CONFERENCE
Saturday, June 18, 2016
10:00-16:30
Conference Registration, Future City Hotel Wuhan, Wuhan, Hubei, China
Sunday, June 19, 2016
9:00-9:10
Opening Ceremony, 4F, Lotus Room, Future City Hotel Wuhan (四楼荷花厅)
9:10-10:10
Keynote Speech
10:10-10:20
Tea Break
10:20-10:50
Keynote Speech
10:50-12:00
Oral Presentation
12:00-14:00
Lunch, 3F, Future City Hotel Wuhan (三楼餐厅)
14:00-14:40
Oral Presentation 4F, Lotus Room, Future City Hotel Wuhan (四楼荷花厅)
14:40-15:00
Tea Break
15:00-17:00
Oral Presentation
KEYNOTE SPEECH
Keynote Speaker
Prof. CHO Siu Yeung, David
Department of University of Nottingham Ningbo China (UNNC), China
Title: Self-Organizing Cortical Processing with Visual Feature Selection for Pattern Recognition
Abstract: Pattern recognition has been studied extensively, and many algorithms have been
established. It generally makes use of discriminant functions to learn the pattern in data. These
discriminate functions are developed to be simplistic so as to warrant fast computations. In addition,
simple evaluation functions are easier to learn because there are lesser parameters to estimate.
However, this simplicity may not work well when new ‘pattern’ is in data surfaces. Humans
recognize an object or pattern from surrounding world in split second; however this involves many
processing in the human visual system. Human gathers most of the sensory information through
sight. Visual-perceptual processing covers approximately one-fourth of the cortex. Visual
information processing is also the most complex, most studied sensory system of the brain.
It is envisaged that if the visual cortex can process information in such a lightning speed, there
should exist some combinations of feature selection and pattern classification which are close
enough to provide such capability. The motivation behind the research of this talk is to establish a
computational framework that attempts to emulate the visual cortical processing in the human brain.
The aim is to recognize a pattern in short computation time even when sparse data is presented.
Majority of the existing classifiers are only trained by datasets which are with balanced distribution
(i.e. equal number of positive and negative samples). These classifiers will pose a problem when the
data is imbalanced.
Prof. CHO Siu Yeung, David is the Head of Department and Associate Professor in Electrical and
Electronic Engineering at The University of Nottingham Ningbo China (UNNC) since Sept. 2011.
During the period of Oct. 2011 to Dec. 2013, he has served as the Founding Associate Dean for
Teaching and Learning in the Faculty of Science and Engineering at the same university. In his
tenure of the associate deanship, he has been proactively involved in overviewing the whole faculty
in Teaching and Learning policy, chairing the faculty’s T&L committee, sitting in the UK faculty
T&L board and Campus Teaching Committee to give advices for Teaching and Learning policy.
Concurrently, he is an Adjunct Professor at Ningbo University since Jan 2014. Prior to join in
UNNC, Prof Cho was working as a faculty in the School of Computer Engineering at Nanyang
Technological University (NTU). He was the Director of Forensics and Security Laboratory (ForSe
Lab) in the same school. He has proactively involved creating and organizing the lab dedicated to
research in the application of computational techniques to biometrics and forensics analysis. Prof.
Cho earned his PhD from the City University of Hong Kong in 1999. His research topic was to
develop an effective neural network learning algorithms in applying to the 3D shape reconstruction.
He is the co-inventor of the neural color reflectance model to tackle the multi-coloured shape from
shading problem in which he has published several papers in the premium international journals. He
has published one monograph book, five book chapters, and over 50 journal papers in which more
than 20 papers are in the top-notch international journals (IEEE Trans. Neural Network, IEEE Trans.
Knowledge & Data Engineering, Neural Computation, Pattern Recognition,… etc). Prof. Cho
received numerous research grants as a principal investigator, funded by the government and local
industries. He has jointly received up to 10 million RMB funding.
KEYNOTE SPEECH
Keynote Speaker
Dr. Lawrence, Wing-chi CHAN
Department of Health Technology and Informatics, Hong Kong Polytechnic University
Title: Biomedical Big Data Analytics – Current Trend and New Perspective
Abstract: Big data becomes the hottest topic in many fields, especially biomedical disciplines. The
analysis of big data is challenging because the data is not only large in size but also complex in
structural organization. One of the representative big data examples is Electronic Health Record
(EHR) database, which provides a digital and structural form of patient records and supports the
clinical decision, patient care and patient advice. EHR database is still an under-explored big data
resource that has hosted a large number of cases with complete recovery, good prognosis, reliable
diagnostic tests and effective treatments. This keynote speech reports and discusses some big data
studies led by the speaker. The themes of these studies cover novel EHR search engine,
disease-specific imaging probe profiling and effective neuronal connectivity. These EHR-based
biomedical applications thoroughly reveal the current trend and new perspective of big data
computing and intelligence.
Dr. Lawrence W.C. Chan received his PhD in Artificial Intelligence in 2001 from the University
of Hong Kong. Dr. Chan is currently Associate Professor in the Department of Health Technology
and Informatics, the Hong Kong Polytechnic University. He has been appointed as Editorial Board
Member, Engineering Applications of Artificial Intelligence (EAAI); Associate Editor, Frontiers in
Non-Coding RNA; and Affiliate member, Hong Kong Society of Medical Informatics (HKSMI). Dr.
Chan’s research interest includes bioinformatics, imaging informatics and clinical decision support.
Dr. Chan received a number of competitive research grants, including General Research Fund (GRF:
PolyU 5118/11E) for his project about EHR Clinical Decision Support System, and Health and
Medical Research Fund (HMRF 02131026) for his project about targeted therapy resistance in lung
cancer.
KEYNOTE SPEECH
Keynote Speaker
Prof. Yizhou Yu
Department of Computer Science, The University of Hong Kong
Title: Visual Intelligence Based on Deep Learning
Abstract: Deep learning is a powerful machine learning paradigm that involves deep architectures,
and is capable of extracting high-level representations from high-dimensional sensory data. Such
high-level representations are essential for many intelligence related tasks, including visual
recognition, speech perception, and language understanding. In this talk, I present two deep learning
projects for visual intelligence carried out in my research group. The first project focuses on
computational visual saliency, which attempts to determine the amount of attention steered towards
various regions in an image by the human visual and cognitive systems. We discover that a
high-quality visual saliency model can be learned from multiscale features extracted using deep
convolutional neural networks (CNNs), which have had many successes in visual recognition tasks.
For learning such saliency models, we introduce a neural network architecture, which has fully
connected layers on top of CNNs responsible for extracting features at multiple scales. We then
propose a refinement method to enhance the spatial coherence of our saliency results. Finally, we
point out that aggregating multiple saliency maps computed for different levels of image region
decomposition can further boost the performance, yielding saliency maps better than those
generated from a single region decomposition. To promote further research and evaluation of visual
saliency models, we also construct a large database of 4447 challenging images and their pixelwise
saliency annotation. Experimental results demonstrate that our proposed method significantly
outperforms all existing saliency estimation techniques.
The second project focuses on visual object recognition. Recognizing the category of a visual object
is a challenging computer vision problem. We develop a novel deep learning method that facilitates
example-based visual object category recognition. Our deep learning architecture consists of
multiple stacked layers and computes an intermediate representation that can be fed to a
nearest-neighbor classifier. This intermediate representation is discriminative and
structure-preserving. It is also capable of extracting essential characteristics shared by objects in the
same category while filtering out nonessential differences among them. Each layer in our model is a
nonlinear mapping, whose parameters are learned through two sequential steps that are designed to
achieve the aforementioned properties. The first step computes a discrete mapping called supervised
Laplacian eigenmap. The second step computes a continuous mapping from the discrete version
through nonlinear regression. We have extensively tested our method and it achieves high
recognition rates on a number of benchmark datasets.
ORAL PRESENTATION OVERVIEW
ID
Paper ID
Title
1
ICEME2136
2
AICE1103
3
ICEME2139
On the Evaluation of the Enterprises’ Leadership Effectiveness
4
AICE1104
Parallel Sentiment Analysis with Storm
5
ICEME2145
6
AICE1129
7
ICEME2159
Asymmetric Adjustments of U.S. Interest Rates
8
AICE1163
Constructing User Interaction Behaviors Net from System Log
9
AICE1180
Computing Eye Tracking Metric for a Radar Display Using a
Remote Eye Tracker
Implementation of an SDN-based Security Defense Mechanism
against DDoS Attacks
Relevance Vector Machine Classification of Hyperspectral Data
Based on Principal Component Analysis and Linear Discriminant
Analysis
The Development of common duct in Taiwan and Its
Enlightenment to China
Declarative Dependency Specification for Inter-connected
Large-scale Cyber-Physical Systems
Sunday, June 19, 2016
Plenary Session
Conference Room-4F, Lotus Room (四楼荷花厅)
Lunch-3F (三楼餐厅)
9:00-9:10 Opening Ceremony
9:10-10:10 Keynote Speech
10:10-10:20 Tea Break
10:20-10:50 Keynote Speech
10:50-12:00 Oral Presentation
12:00-14:00 Lunch
14:00-14:40 Oral Presentation
14:40-15:00 Tea Break
15:00-17:00 Oral Presentation
Implementation of an SDN-based Security Defense Mechanism against DDoS Attacks ICEME2136
Hsiao-Chung, Lin, Ping Wang
Relevance Vector Machine Classification of Hyperspectral Data Based on Principal Component
Analysis and Linear Discriminant Analysis AICE1103
Wen-Xing BAO, Bin LI, Rong REN
On the Evaluation of the Enterprises’ Leadership Effectiveness ICEME2139
Qing-Wu Sun
Parallel Sentiment Analysis with Storm AICE1104
Otto K.M. CHENG, Raymond Y.K. LAU
The Development of common duct in Taiwan and Its Enlightenment to China ICEME2145
Qi-Ming CUI, Hong ZHANG, Xiang WEI
Declarative Dependency Specification for Inter-connected Large-scale Cyber-Physical Systems
AICE1129
Jing-Quan XIE, Thomas DOLL
Asymmetric Adjustments of U.S. Interest Rates ICEME2159
Sainan HUANG, Songlin ZENG
Constructing User Interaction Behaviors Net from System Log AICE1163
Hua-Qiang SUN, Shu-Leng DONG, Bing-Xian MA
Computing Eye Tracking Metric for a Radar Display Using a Remote Eye Tracker AICE1180
Hong-Jie WEE, Sun-Woh LYE, Jean-Philippe PINHEIRO
CONFERENCE MAP GUIDE
All presentations and activities will be held at Future City Hotel Wuhan
Address: No.147 South Street,Port Luoshi WuChang HongShan District Wuhan,
430000, P.R. China
Mobile for Reservation: 86- 13986142255
E-mail for Reservation: [email protected]
本次联合会议将于 6 月 18-19 日在武汉未来城大酒店举行
酒店地址：中国湖北省武汉市武昌洪山区街道口珞狮南路 147 号
酒店订房电话：+86 13986142255
酒店订房邮箱：[email protected]
Conference Secretariat
Any question about the conference, please contact the conference secretary at +(86)
15391507782 (AICE2016) or +(86) 15377572654 (ICEME2016).