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2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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2018 CBEES-BBS CHENGDU, CHINA
CONFERENCE ABSTRACT
2018 International Conference on Computing and
Artificial Intelligence (ICCAI 2018)
March 12-14, 2018
Skytel Hotel Chengdu, Chengdu, China
Sponsored by
Published and Indexed by
http://www.iccai.net/
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Table of Contents 2018 CBEES-BBS Chengdu, China Conference Introduction 7
Presentation Instruction 8
Keynote Speaker Introduction 9
Brief Schedule for Conference 21
Detailed Schedule for Conference 23
Session 1: Medical Image Analysis and Processing Technology
Invited Speech: Study on EPR Oxygen Imaging For Oxygen-Image-Guided Precise
Radiation
Zhiwei Qiao
25
M1001: Super-Resolution Based on Noise Resistance Deep Convolutional Network
Hengjian Li, Yunxing Gao, Jiwen Dong and Guang Feng
26
K0017: Malicious Code Detection based on Image Processing Using Deep Learning
Rajesh Kumar, Zhang Xiaosong, Riaz Ullah Khan, Ijaz Ahad and Jay Kumar
27
M0029: Class Balanced PixelNet for Neurological Image Segmentation
Mobarakol Islam and Hongliang Ren
28
K0019: Evaluating the Performance of ResNet Model Based on Image Recognition
Riaz Ullah Khan, Xiaosong Zhang, Rajesh Kumar and Emelia Opoku Aboagye
29
M0022: A Compression–Encryption Hybrid Algorithm Based on Compressive Sensing
Changzhi Yu, Hengjian Li and Jiwen Dong
30
K0013: Attribute-based Face Recognition and Application of Intelligent Factory Safety
Detection
Xiangfeng Chen, Wenbai Chen, Peichao Xu, and Mengyao Lv
31
M0001: A Method of Constructing Vertebral 3D Statistical Model Based on Gaussian
Curvature
Du Jing, Yu Bin, Hui Yu, Wu Jun-Sheng and Zhang Chen
32
Session 2: Genetic Engineering and Protein Structure Analysis
Invited Speech: Estimating and Interpreting the Effects of Sequence Variants and
Cancer Mutations on Protein Function
Minghui Li
33
M0036: Biased Distribution of Amino Acid in Intrinsically Disordered Proteins and
Regions
Zhengyu Ding, Tian Feng, Fangbo Nan, Yu Wang and Bo He
34
M0011: Predicting Intrinsically Disordered Proteins Based on Different Feature Teams
Bo He, Wenliang Zhang, Haikuan Gao, Chengkui Zhao and Weixing Feng
35
M0003: Charactering and Predicting E3-Substrate Interactions by Systematically
Integrating Omics, Networks and Pathways
36
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Di Chen and Hai-Long Piao
M0049: Predicting Intrinsically Disordered Regions Based on The Structural Bias of
Amino Acid Dimers
Tian Feng, Zhengyu Ding, Fangbo Nan, Yu Wang and Bo He
37
M0010: One-Dimensional and Two-Dimensional Linear Mixed Models to Accurately
Dissect Causal Genetic Effects in Associative Omics Studies
Patrick Xuechun Zhao, Wenchao Zhang, Bongsong Kim, Xinbin Dai and Shizhong
Xu
38
M0002: A Dynamic Pooling Approach to Extract Complete Allele Signal Information in
Somatic Copy Number Alternations Detection
Long Cheng, Pengfei Yao, Jianwei Lu, Ke Hao and Zhongyang Zhang
39
M0008: A Computational Framework to Simultaneously Quantify DNA Methylation,
Somatic Copy Number Alternation and DNA Heterogeneity from Low Coverage Plasma
Circulating DNA Sequencing
Pengfei Yao, Long Cheng, Jianwei Lu, Ke Hao and Zhongyang Zhang
40
M0015: RNA-Seq Based Sensitive and Comprehensive Mutation Detection and
Interpretation System for Precision Medicine
Zhifu Sun
41
Session 3: Modern Information Engineering and Technology
B0068: Both Chargaff Second Parity Rule and the Strand Symmetry Rule are Inaccurate
Zhiyu Chen
42
K0030: Statistical Analysis of Extracted Video Data by Using Web Crawler
Md Khalid Hossen, Yong Wang, Hussain Ahmed Tariq, Gabriel Nyame and Raphael
Elimeli Nuhoho
43
K0026: Effective and Explainable Detection of Android Malware based on Machine
Learning Algorthims
Rajesh Kumar, Zhang Xiaosong, Riaz Ullah Khan, Jay Kumar and Ijaz Ahad
44
K0007: Sentiment Analysis on the online reviews based on Hidden Markov Model
Xiaoyi Zhao and Yukio Ohsawa
45
K0018: Improvement on Speech Emotion Recognition Based on Deep Convolutional
Neural Networks
Yafeng Niu, Dongsheng Zou, Yadong Niu, Zhongshi He and Hua Tan
46
K0021: Fuzzy-Based Indoor Positioning by Using the Neighbor Points
Chih-Yung Chen, Shen-Whan Chen, Yu-Ju Chen and Rey-Chue Hwang
47
M0019: The Application of Multi–Source Information Fusion Technology in Vehicle
Integrated Navigation System
Binhui Tang, Weijun Zeng and Zhen-xing Zhou
48
K0002: Combining Explicit and Implicit Semantic Similarity Information for Word
Embeddings
Shi Yin, Yaxi Li and Xiaoping Chen
49
K0042: Human Segmentation with Deep Contour-Aware Network
Fiseha Berhanu, Hong Wu and William Zhu
50
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Session 4: Bioinformatics and Basic Medicine
Invited Speech: Single Cell Big Data Analysis
Ming Chen
51
M0044: Leaf Shape Variation and Its Correlation to Phenotypic Traits of Soybean in
Northeast China
Fei Huang, Yangjing Gan, Dongdong Zhang, Fei Deng and Jing Peng
52
M0032: YeastSCI: A Web Tool Integrating Zinc Cluster Protein Information of
Saccharomyces and Candida
Pitchya Tangsombatvichit, Utharn Buranasaksee and Suwut Tumthong
53
M0028: Prediction of Continuous B-cell Epitopes Using Long Short Term Memory
Networks
Cheng Bin, Liu Lingyun, Qi Zhaohui and Yang Hongguang
54
M0048: The Repertoire of Mutational Signatures in Human Cancer
Steven G. Rozen, Ludmil Alexandrov, Jaegil Kim, Nicholas Haradhvala, Mi Ni
Huang, Alvin Wei Tian Ng, Gad Getz, Michael R Stratton and Pan
55
M0039: Inhibition Assessment of Anticancer Drugs for ALK Gene Variation Target
Chang-Sheng Chiang and Pei-Chun Chang
56
M0017: A Framework of an Unconstrained Sleep Monitoring System
Annan Dai, Xiangdong Yang, Wei Li and Ken Chen
57
M0023: Improving Medical Ontology based on Word Embedding
Gao Mingxia, Furong Chen and Rifeng Wang
58
M0027: Leveraging Word Embeddings and Semantic Enrichment for Automatic
Clinical Evidence Grading
Haolin Wang, Yuming Qiu, Jun Jiang, Ju Zhang and Jiahu Yuan
59
M0021: Generating Cancelable Palmprint Templates Based on Bloom Filters
Jian Qiu, Hengjian Li and Jiwen Dong
60
Session 5: Intelligent Computing and Computer Applications
M0030: Automated Encoding of Clinical Guidelines into Computer-interpretable
Format
Yuming Qiu, Peng Tang, Haolin Wang, Jun Jiang, Ju Zhang and Nanzhi Wang
61
K0031: Principal Component Analysis for Financial Time Series Prediction
Li Tang, Heping Pan and Yiyong Yao
62
M0037: Classification and Feature Extraction for Text-based Drug Incident Report
Takanori Yamashita, Naoki Nakashima and Sachio Hirokawa
63
K0043: Thermo-Economic Multi-objective Optimization of Adiabatic Compressed Air
Energy Storage (A-CAES) System
Wenjing Hong and Longxiang Chen
64
K0005: The Optimal Crane Scheduling for Chemical Polishing Process Based on Expert
System
Chi-Yen Shen, Shuming T. Wang, Kaiqi Zhou, Hanlin Shen and Rey-Chue Hwang
65
K0003: Extended Movement Unit for Pepper 66
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Naoki Igo, Daichi Fujita, Ryusei Yamamoto, Toshifumi Satake, Satoshi Mitsui,
Tetsuto Kanno and Kiyoshi Hoshino
K0016: Probabilistic Time Context framework for Big Data Collaborative
Recommendation
Emelia Opoku Aboagye, Gee C. James, Gao Jianbin, Rajesh Kumar and Riaz Ullah
Khan
67
K0029: Optimizing a Deep Learning Model in order to have a Robust Neural Network
Topology
Riaz Ullah Khan, Rajesh Kumar, Nawsher Khan, Xiaosong Zhang and Ijaz Ahad
68
K0052: Automatic Clustering of Natural Scene Using Color Spatial Envelope Feature
Haifeng Wang, Xiaoyan Wang and Yuchou Chang
69
Poster Session
M0013: The Efficacy of Peg-IFNα Anti-Viral Treatment were Evaluated by Variation of
Peripheral Th17 Cells in Chronic Hepatitis C Patients
Yizhang Xu
70
M0020: Synonymous Permutation Reveals Selection for Less Out-of-Frame Stop
Codons
Jingrui Zhong and Nanyan Zhu
71
M0025: Predicting Drug-target Interaction via Wide and Deep Learning
Yingyi Du, Jihong Wang, Xiaodan Wang, Jiyun Chen and Huiyou Chang
72
M0031: Research of Heart Rate Variability Analysis System Based on Cloud Model
Zhangyong Li, Yaoming An and Shangzhi Xiang
73
M0042: FlexSLiM: a Novel Approach for Short linear Motif Discovery in Protein
Sequences
Xiaoman Li, Ping Ge and Haiyan Hu
74
M0043: Neural Correlates of Emotional Regulation Processing: Evidence from ERP and
Source Current Density Analysis
Zhen-Hao Wang, Yi Wang, Dong-Ni Pan and Xuebing Li
75
M3001: Shorten Bipolarity Checklist for the Differentiation of Subtypes of Bipolar
Disorder using Machine Learning
Chaonan Feng, Huimin Gao, Xuefeng B Ling, Jun Ji and Yantao Ma
76
K0009: Optimization of Contract Distribution Based on Multi-objective Estimation of
Distribution Algorithm
Laihong Hu, Xiaogang Yang and Hongdong Fan
77
K0011: A Denoising Autoencoder Approach for Credit Risk Analysis
Qi Fan and Jiasheng Yang
78
K0020: Supervised Prediction of China's Seven-Day Interbank Pledged Repo Rate
Yiwu Lin and Liping Shen
79
K0023: Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles
Firas Gerges, Germain Zouein and Danielle Azar
80
K0025: Remote Intelligent Position-Tracking and Control System with 81
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MCU/GSM/GPS/IoT
Jianpei Shi, Liqiang Zhang and Daohan Ge
K2001: Fuzz Testing Based On Virtualization Technology
Longbin Zhou and Zhoujun Li
82
K0034: Image Authenticity Decision Based on Random Sample Consensus and Circular
Feature Selection
Xueyan Li
83
K0038: DeepXSS: Cross Site Scripting Detection Based on Deep Learning
Yong Fang, Yang Li, Cheng Huang and Liang Liu
84
K0040: Detecting Webshell Based On Random Forest With FastText
Yong Fang, Yaoyao Qiu, Cheng Huang and Liang Liu
85
K0047: A Multi-Layer Neural Network Model Integrating BiLSTM and CNN for
Chinese Sentiment Recognition
Shanliang Yang, Qi Sun, Huyong Zhou and Zhengjie Gong
86
K0048: A Topic Detection Method Based on KeyGraph and Community Partition
Shanliang Yang, Qi Sun, Huyong Zhou, Zhengjie Gong, Yangzhi Zhou and Junhong
Huang
87
K0050: A Topic Detection Method Based on KeyGraph and Community Partition
Shanliang Yang, Qi Sun, Huyong Zhou, Zhengjie Gong, Yangzhi Zhou and Junhong
Huang
88
K4001: Analysis and Design of Item Bank System Based on Improved Genetic
Algorithm
Jie Zhang
89
K4002: Cloud Based Face Recognition for Google Glass
Zeeshan Shaukat, Juan Fang, Muhammad Azeem, Faheem Akhtar and Saqib Ali
90
Conference Venue 91
One Day Tour 92
Note 94
Feedback Information 97
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2018 CBEES-BBS Chengdu, China
Conference Introduction
Welcome to 2018 International Conference on Computing and Artificial Intelligence (ICCAI 2018) which is sponsored by Hong Kong Chemical, Biological & Environmental Engineering Society (CBEES) and Biology and Bioinformatics (BBS). The objective of 2018 International Conference on Computing and Artificial Intelligence (ICCAI 2018) is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in Computing and Artificial Intelligence.
Papers will be published in one of the following conference proceedings or journals:
ACM Conference Proceedings (ISBN: 978-1-4503-6419-5). Archived in
the ACM Digital Library, and indexed by Ei Compendex and submitted to be
reviewed by Scopus and Thomson Reuters Conference Proceedings Citation
Index (ISI Web of Science).
Genomics, Proteomics and Bioinformatics (GPB) (ISSN: 1672-0229). Indexed
by Science Citation Index Expanded (SciSearch), Journal Citation
Reports/Science Edition, PubMed/Medline, SCOPUS, EMBASE and so on,
CiteScore: 2.99, SCImago Journal Rank (SJR): 1.329.
Journal-Interdisciplinary Sciences: Computational Life Sciences (ISSN:
1913-2751 (print version); ISSN: 1867-1462 (electronic version)). Indexed
by Science Citation Index Expanded (SciSearch), Journal Citation
Reports/Science Edition, PubMed/Medline, SCOPUS, EMBASE and so on, Impact
Factor: 0.753.
Journal of Computers (JCP, ISSN: 1796-203X). Indexed by DBLP, EBSCO,
DOAJ, ProQuest, EI INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,
etc.
Journal of Advances in Information Technology (JAIT, ISSN:1798-2340).
Indexed by EI INSPEC; EBSCO; ULRICH's Periodicals Directory; WorldCat;
CrossRef; Genamics JournalSeek; Google Scholar; Ovid LinkSolver; etc.
Conference website and email: http://www.iccai.org/; [email protected]
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Presentation Instruction
Instruction for Oral Presentation
Devices Provided by the Conference Organizer:
Laptop Computer (MS Windows Operating System with MS PowerPoint and Adobe Acrobat
Reader)
Digital Projectors and Screen
Laser Stick
Materials Provided by the Presenters:
PowerPoint or PDF Files (Files should be copied to the Conference laptop at the beginning of
each Session.)
Duration of each Presentation (Tentatively):
Regular Oral Presentation: about 12 Minutes of Presentation and 3 Minutes of Question and
Answer
Keynote Speech: about 35 Minutes of Presentation and 5 Minutes of Question and Answer
Plenary Speech: about 30 Minutes of Presentation and 5 Minutes of Question and Answer
Invited Speech: about 12 Minutes of Presentation and 3 Minutes of Question and Answer
Instruction for Poster Presentation
Materials Provided by the Conference Organizer:
The place to put poster
Materials Provided by the Presenters:
Home-made Posters
Maximum poster size is A1
Load Capacity: Holds up to 0.5 kg
Best Presentation Award One Best Oral Presentation will be selected from each presentation session, and the
Certificate for Best Oral Presentation will be awarded at the end of each session on March 12
and 13, 2018.
Dress code Please wear formal clothes or national representative of clothing.
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Keynote Speaker Introduction
Keynote Speaker I
Prof. Bijoy K. Ghosh
Texas Tech University, USA
Bijoy received the B. Tech and M. Tech degrees in Electrical and Electronics Engg. from
BITS Pilani and the Indian Institute of Technology, Kanpur, India, and the Ph.D. degree in
Engineering Sciences from the Decision and Control Group of the Division of Applied
Sciences, Harvard University, Cambridge, MA, in 1977, 1979 and 1983, respectively. From
1983 to 2007 Bijoy was with the Department of Electrical and Systems Engineering,
Washington University, St. Louis, MO, USA, where he was a Professor and Director of the
Center for BioCybernetics and Intelligent Systems. Currently he is the Dick and Martha
Brooks Regents Professor of Mathematics and Statistics at Texas Tech University, Lubbock,
TX, USA. He received the Donald P. Eckmann award in 1988 from the American Automatic
Control Council, the Japan Society for the Promotion of Sciences Invitation Fellowship in
1997. He became a Fellow of the IEEE in 2000, and a Fellow of the International Federation
on Automatic Control in 2014. Currently he is the IEEE Control Systems Society
Representative to the IEEE-USA's Medical Technology Policy Committee. Bijoy had held
visiting positions at Tokyo Institute of Technology, Osaka University and Tokyo Denki
University, Japan, University of Padova in Italy, Royal Institute of Technology and Institut
Mittag-Leffler, Stockholm, Sweden, Yale University, USA, Technical University of Munich,
Germany, Chinese Academy of Sciences, China and Indian Institute of Technology,
Kharagpur, India. Bijoy's current research interest is in BioMechanics and Control Problems
in Rehabilitation.
Topic: “Iterative Learning Control Problems in Medical Rehabilitation‖
Abstract—In this talk, I shall review learning control problems from the point of view of
medical rehabilitation of stroke patients. After initially surveying the field, a new Cooperative
Learning Control problem is introduced where a dynamical system is controlled by the sum of
two controllers. Each of the two controllers, we design, has the structure of an iterative
learning controller, which learns to track a desired, a priori chosen, output sequence. Once
learned, the strength of one of the controller is reduced while this loss of control is iteratively
transferred to the other controller. There is no direct communication between the two
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controllers and each controller updates iteratively, using the error signal between the system
and the desired output. We show that ‗controller participation‘ can be iteratively transferred
until one controller has completely acquired full control of the closed loop system. An
important application of the proposed cooperative control system is in rehabilitation of stroke
patients, wherein a loss of control in the arm movement is initially aided by additive
‗functional electrical stimulus‘ signals generated through a computer. Subsequently, with
therapeutic recovery, dependence on the computer control is reduced while the patient learns
to be self-reliant on his/her own motor control capability.
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Keynote Speaker II
Prof. Yinglei Lai
The George Washington University, USA
Dr. Yinglei Lai is Professor of Statistics in the Department of Statistics at the George
Washington University. His research interest is to develop statistical and computational
methods in bioinformatics, computational biology and biostatistics. He received his B.S. in
Information & Computation Sciences and Business Administration from the University of
Science and Technology of China in 1999. Dr. Lai received his Ph.D. in Applied Mathematics
(Computational Biology) from the University of Southern California in 2003. After his
postdoctoral training at Yale University School of Medicine, he joined as a faculty member in
the Department of Statistics at the George Washington University in 2004.
Topic: “On Poisson Models in the Analysis of RNA-seq Data‖
Abstract—High-throughput genome-wide RNA sequencing (RNA-seq) data have been
increasingly collected for biomedical studies. Differential expression analysis and correlation
analysis of RNA-seq data are important to understand the biological functions of genes and
how genes interact with each other. RNA-seq data are generally count-type observations.
Furthermore, many genes have multiple isoforms. Therefore, it can be challenging to conduct
differential expression and correlation analysis of RNA-seq data. Poisson and related models
have been widely used in the analysis of RNA-seq data. We extend the Poisson model
approach so that the wide range of RNA-seq observations can be accommodated. We also
propose a multivariate approach for the correlation analysis of RNA-seq data. Our simulation
study demonstrates the advantage of our method. We use the RNA-seq data collected by The
Cancer Genome Atlas (TCGA) project to illustrate our method.
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Keynote Speaker III
Assoc. Prof. Qijun Zhao
Sichuan University, China
Qijun Zhao is currently an associate professor in the College of Computer Science at Sichuan
University. He obtained his B.Sc. and M.Sc. degrees in computer science both from Shanghai
Jiao Tong University, and his Ph.D. degree in computer science from the Hong Kong
Polytechnic University. He worked as a post-doc research fellow in the Pattern Recognition
and Image Processing lab at Michigan State University from 2010 to 2012. His research
interests lie in biometrics, particularly, face perception and affective computing, with
applications to intelligent video surveillance, public security, healthcare, and human-computer
interactions. Dr. Zhao has published more than 50 papers in academic journals and
conferences, and participated in many research projects either as principal investigators or as
primary researchers. He is a reviewer for many renowned field journals and conferences. He
served as a program committee co-chair in organizing the 11th Chinese Conference on
Biometric Recognition (CCBR2016) and the 2018 IEEE International Conference on Identity,
Security and Behavior Analysis (ISBA).
Topic: ―3D Face Modeling: Images, Shapes, and DNA”
Abstract—The face reveals a lot of information of humans, for example, identity, race, gender,
age, emotion, intention, and health. 3D face models are thus widely used in many applications,
from security to healthcare, from education to entertainment, and from human-computer
interaction to computer vision. Yet, acquisition of 3D faces is still much more expensive than
acquisition of 2D face images. This talk will introduce our recent work on reconstructing 3D
face shapes from 2D images, including 3D face reconstruction via cascaded regression in
shape space, joint face alignment and 3D face reconstruction, disentangling features in 3D
face shapes for joint face reconstruction and recognition, and mug-shot-based 3D face
reconstruction for arbitrary view face recognition. To better understand the diversity of human
3D face shapes, this talk will analyze the impact of ethnicity on 3D face modeling, review
related research on 3D face modeling from the biological perspective, and discuss future
research directions. We believe that 3D faces will play increasingly important roles in many
applications with the rapid development of both 3D face acquisition techniques and 3D face
modeling methods.
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Keynote Speaker IV
Prof. Edwin Wang
University of Calgary, Canada
Dr. Edwin Wang is Professor and AISH Chair in Cancer Genomics at the University of
Calgary. He was a Senior Investigator at the National Research Council Canada and Professor
at McGill University. He has a undergraduate training in Computer Science and a PhD
training in Experimental Molecular Genetics (UBC, 2012). He is the member of the
AACR-Cancer Systems Biology Think Tank, which consists of ~30 world leaders in the field
for discussing key problems and cutting-edge directions. He is an Editor of PLoS
Computational Biology, the top journal in the field of bioinformatics. He has edited the book
of Cancer Systems Biology (2010), the first book of the field. His pioneering work of
microRNA of singling networks opens the new research area: network biology of non-coding
RNAs. His pioneering work of cancer network motifs has been featured in the college
textbook, GENETICS (2014/2017) written by a Nobel Laureate, Dr. Hartwell and the father
of systems biology, Dr. Hood.
Topic: ―From Health Genomics to Intelligent Precision Health”
Abstract—Cancer is the leading cause of death and the third largest burden in the healthcare
system in the world. Each year, more than 15 million new cancer patients are diagnosed and
7-8 million people die from cancer in the world. Current precision oncology is focusing on
cancer treatment, however, with some notable exceptions, improvements in overall survival
and morbidity over the past few decades have been modest. Historical data suggest that early
detection of cancer is crucial for its ultimate control and prevention. To meet the challenges of
the surge in cancer cases in the future, it is envisioned that, besides the promotion of lifestyle
changes, improving early diagnosis is the best strategy for reducing the impact of
carcinogenesis.
Both genetic and environmental factors (e.g., pollution, lifestyle and so on) interact to induce
cancer initiation, progression and metastasis. Therefore, we are aiming to combine the
genome sequencing, imaging and electronic medical records of individuals to identify
high-risk cancer individuals, ‗healthy lifestyle patterns‘ for cancer prevention, and monitor
high-risk cancer individuals for cancer early detection. To do so, we have complied a cohort
which contains 5 million people whose medical records have been collected. Among them,
0.5 million people‘ genomic information has been determined. We are developing new
algorithms by applying machine learning and deep learning approaches to the cohort to meet
the goals mentioned above.
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Keynote Speaker V
Prof. Kiyoshi Hoshino
University of Tsukuba, Japan
Prof. Kiyoshi Hoshino received two doctor's degrees; one in Medical Science in 1993, and
the other in Engineering in 1996, from the University of Tokyo respectively. From 1993 to
1995, he was an assistant professor at Tokyo Medical and Dental University School of
Medicine. From 1995 to 2002, he was an associate professor at University of the Ryukyus.
From 2002, he was an associate professor at the Biological Cybernetics Lab of University of
Tsukuba. He is now a professor. From 1998 to 2001, he was jointly appointed as a senior
researcher of the PRESTO "Information and Human Activity" project of the Japan Science
and Technology Agency (JST). From 2002 to 2005, he was a project leader of a SORST
project of JST. His research interests include biomedical measurement and modelling,
medical engineering, motion capture, computer vision, and humanoid robot design.
Topic: ―Technology for Acquiring Biosignals Generated during Eye Movements”
Abstract—The objective of our study is to provide a method for measuring user‘s eye
movements day and night with a high degree of accuracy without imposing a psychological
burden on a device-wearer, regardless of brightness of image contents. Specifically, our
method, in particular, makes possible; (1) tracing the points where the user is looking at (i.e.
line of sight); (2) detection of any of bad physical conditions, such as dizziness and
sick-feeling, or the signs of them (i.e. nystagmus or cycloduction); and (3) estimation of the
degree of distraction of attention (i.e. the degree of heterophoria between the eyes).
To this end, a faint blue light with less brightness is illuminated in the vicinity of the eyeballs
as an auxiliary light to improve the grayscale contrast of the blood vessels in the tunica
conjunctiva or sclera of an eyeball. Moreover, the above method is used together with a
combination of techniques for equalizing the individual image partitions of the gray-level and
for determining a banalization threshold based on the difference in grayscale value between
the target and its adjacent pixels, so as to remove eyelashes and faint-colored thin blood
vessels, achieving an improvement in grayscale contrast of the characteristic blood vessels.
Furthermore, using a method for tracing the images of the characteristic template blood
vessels is used to measure the user‘s eye movements.
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Plenary Speaker I
Prof. Ralf Hofestädt
Bielefeld University, Germany
Prof. Ralf Hofestädt studied Computer Science and Bioinformatics at the University of Bonn.
He finished his PhD 1990 (University Bonn) and his Habilitation (Applied Computer Science
and Bioinformatics) 1995 at the University of Koblenz. From 1996 to 2001, he was Professor
for Applied Computer Science at the University of Magdeburg. Since 2001, he is Professor
for Bioinformatics and Medical Informatics at the University Bielefeld. The research topics of
the department concentrate on biomedical data management, modeling and simulation of
metabolic processes, parallel computing and multimedia implementation of virtual scenarios.
Topic: “Medical Omics for the Detection of Comorbidity between Asthma and Hypertension‖
Abstract—In general, comorbidity between two diseases will point to a causal relationship,
which may be explained by the presence of common pathways or biochemical processes.
Furthermore, comorbidity may be the result of non-obvious cofounder effects, e.g. life-style
or environmental factors, predisposing to multiple health problems. Hypertension is observed
by around 30% of the adult population and its prevalence is growing together with the age.
Hypertension causes many other cardiovascular diseases, including heart failure. Asthma is a
chronic respiratory disease and seems to be the result of complex interactions between genetic
and environmental risk factors. Many studies report association between asthma and
hypertension in different patient cohorts showing that asthmatic patients are more predisposed
to hypertension. Presence of hypertension, in turn, is associated with increased frequency and
severity or asthma. Considering that both asthma and hypertension have a strong genetic
component, several attempts to find shared genes in order to explain comorbidity between
asthma and hypertension have been made. This talk will present the genetic analysis of both
diseases and the side effects of the drug treatment in practise.
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Plenary Speaker II
Assoc. Prof. Hu Han
Chinese Academy of Sciences, China
Hu Han is an Associate Professor of the Institute of Computing Technology (ICT), Chinese
Academy of Sciences (CAS). He received the B.S. degree from Shandong University, and the
Ph.D. degree from ICT, CAS, in 2005 and 2011, respectively, both in computer science.
Before joining faculty of ICT, CAS in 2015, he was a Research Associate in the Department
of Computer Science and Engineering at Michigan State University, and a visiting researcher
at Google in Mountain View from 2011 to 2015. His research interests include computer
vision, pattern recognition, and image processing, with applications to biometrics. He has
authored or co-authored more than 30 scientific papers, including IEEE Trans. PAMI, IEEE
Trans. IFS, Pattern Recognition, ECCV, etc., with over than 930 citations according to Google
Scholar (Aug. 2017). He has served as the program committee member of a number of
international conferences on computer vision and biometrics, such as ICB, IJCB, ACCV, and
CCBR. He was a recipient of the ICCV2015 apparent age estimation competition runner-up
award, the CCBR2016 Best Student Paper award, and ACCV2012 Best Reviewer Award. He
is a member of the IEEE.
Topic: “Attribute Estimation from Face: Approaches and Applications‖
Abstract—Face attribute estimation has many potential applications in video surveillance,
face retrieval, and social media. Despite tremendous progress in attribute learning in recent
years, joint estimate of a wide variety of face attributes accurately and efficiently from a
single face image remains a challenging problem due to the data imbalance, label noise, etc.
In this talk, I will briefly review the representative approaches for face attribute learning and
highlight some of our latest research work on facial attribute from the exterior to the interior.
In particular, I will cover our face attribute learning approaches in terms of the feature
representation methods and the classification models. In addition, we also extend face
attribute estimation into a more general scope, i.e., from the exterior to the interior such as
heart rate estimation from the face.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 17 -
Invited Speaker I
Prof. Zhiwei Qiao
ShanXi University, China
Zhiwei Qiao received his PhD degree in transportation information engineering and control
from Beijing Jiaotong University in 2011. He was a Postdoctoral Scholar and Visiting
Professor with Department of Radiation and Cellular Oncology, The University of Chicago,
Chicago, IL, USA, from August 2012 to August 2014 and from January 2017 to August 2017,
respectively. He is currently a professor with School of Computer and Information
Technology, Shanxi University, Taiyuan, Shanxi, China. His research interests include
electron paramagnetic resonance imaging (EPRI), computed tomography (CT) and magnetic
resonance imaging (MRI) etc. He mainly focuses on image reconstruction algorithm, signal
processing and high performance computing. He has published a series of papers on CT and
EPRI image reconstruction, especially two papers on Journal of Magnetic Resonance. Now,
he is constructing the China-USA united lab for medical imaging, supported by Shanxi
University and The University of Chicago.
Topic: “Study on EPR Oxygen Imaging for Oxygen-Image-Guided Precise Radiation‖
Abstract—Electron paramagnetic resonance imaging (EPRI) can yield the 3-dimensional (3D)
spatial distribution of the unpaired-electron spin-density (UESD) from which the spatial
distribution of oxygen concentration within tumor tissue, referred to as the oxygen image, can
be derived. In pulsed 3D EPRI, the 3D Radon transform is used for modeling the imaging
process, and existing algorithms such as the standard 3D filtered-backprojection (FBP) can be
used for reconstructing images through inverting the 3D Radon transform. However, the
existing algorithms often require data collected at a large number of densely sampled
projection views, which can lead to a prolonged data-acquisition time especially in in vivo
animal EPR imaging. Therefore, there always exists a strong interest in shortening
data-acquisition time through reducing the number of data samples collected in EPRI, and one
approach is to acquire data at a reduced number of sparsely distributed projection views from
which existing algorithms such as FBP may reconstruct images with sampling artifacts. In the
work, we investigate and develop an optimization-based image reconstruction from data
collected at sparse views in EPRI. Specifically, we design a convex optimization program to
which the EPR image of interest is formulated as a solution and then tailor the primal-dual,
Chambolle-Pock (CP) algorithm to reconstruct the image by solving the convex optimization
program. We have performed studies using simulated and physical-phantom data on the
verification and characterization of the optimization-based image reconstruction. Results of
the studies suggest that the optimization-based image reconstruction may yield accurate
reconstructions from sparse-view projections, thus enabling fast EPRI with reduced
acquisition time.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 18 -
Invited Speaker III
Prof. Minghui Li
Soochow University, China
Dr. Minghui Li is currently a professor in School of Biology & Basic Medical Sciences at
Soochow University. Dr. Li received her Ph.D. in Computational Biophysics from the State
Key Laboratory of Theoretcial and Computational Chemistry at Jilin Univeristy, China in
2010. Upon completion of her Ph.D., Dr. Li spent two years as a Postdoctoral Fellow in
Computational Biophysics at The State University of New York at Buffalo in the USA. Then
she moved to National Center for Biotechnology Information (NCBI), National Institutes of
Health (NIH) and worked as a Postdoctoral Fellow and Research Fellow from 2012 to 2016.
Dr. Li‘s primary research interests are in understanding the mechanisms of molecular
recognition in biological systems, identifying disease-causing/cancer driver nonsynonymous
mutations and building the relationship between genotype and phenotype at the molecular and
atomic level using computational biophysics-based and bioinformatics methods. She has
balanced method development with the application of these powerful tools to relevant cancer
related targets. She will continue her research towards developing and applying powerful
computational methods and tools for understanding, identifying and predicting
disease-causing nonsynonymous mutations as well as their molecular mechanism analysis of
pathogenesis in collaboration with biologists.
Topic: “Estimating and Interpreting the Effects of Sequence Variants and Cancer Mutations
on Protein Function‖
Abstract—There has been a rapid development of genome-wide techniques in the last decade
along with significant lowering of the cost of gene sequencing, which generated rich and
widely available genomic data. However, the interpretation of such genomic data as well as
predicting the association of genetic variations with diseases and phenotypes still needs
significant improvement. Missense mutations can render proteins nonfunctional and may be
responsible for many diseases. The effects caused by missense mutations can be pinpointed by
in silico modeling that makes it more feasible to find a treatment and reverse the effect.
Specific human phenotype is largely determined by stability, activity, and interactions
between proteins and with other biomolecules which work together to provide specific
cellular functions. Therefore, the analysis of the effect of missense mutations on proteins and
their complexes would give us important clues for identifying functional important missense
mutations and understanding the molecular mechanisms of diseases and facilitated their
treatment and prevention. Cancer genome sequencing projects reveal vast amounts of somatic
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 19 -
missense mutations in proteins. However, not all cancer mutations provide a selective growth
advantage to cancer cells. Many mutations whose impact on protein function is either minor
or the affected proteins are not important for tumor progression. The important question is to
determine which mutations are likely to be drivers. One can considerably decrease the number
of potential driver candidates by determining the functional impact of each mutation on
protein.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 20 -
Invited Speaker III
Prof. Ming Chen
Zhejiang University, China
Ming Chen received his PhD in Bioinformatics from Bielefeld University, Germany, in 2004.
Currently he is working as a full Professor in Bioinformatics at College of Life Sciences,
Zhejiang University. His group research work mainly focuses on the systems biology,
computational and functional analysis of non-coding RNAs, and bioinformatics research and
application for life sciences. Prof. Chen is serving as an academic leader in Bioinformatics at
Zhejiang University. He chairs the Bioinformatics society of Zhejiang Province, China. He is
a committee member of Chinese societies for "Modeling and Simulation of Biological
Systems", "Computational Systems Biology", "Functional Genomics & Systems Biology" and
"Biomedical Information Technology".
Topic: “Single Cell Big Data Analysis‖
Abstract—With the development of CyTOF and single cell sequencing technology, high
dimension and large scale data have being accumulated, and the analysis of these data become
indispensable. This talk will briefly introduce several bioinformatics approaches for analyzing
such data. We developed a semi-automatic cell clustering platform to identify cell populations
in flow cytometry data. We dissected global ccRCC metastasis associated lncRNAs based on
single-cell RNA-seq data analysis. Using Microwell-seq, we analyzed more than 400,000
single cells covering all of the major mouse organs and constructed a basic scheme for a
Mouse Cell Atlas (MCA).
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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Brief Schedule for Conference
March 12,
2018
(Monday)
10:00~18:00 Arrival Registration
Venue: Hotel Lobby
Venue: Activated Room 1&2 (1st Floor)
13:30-13:35 Opening Remarks
Assoc. Prof. Qijun Zhao, Sichuan University, China
13:35-14:15 Keynote Speech I
Prof. Bijoy K. Ghosh, Texas Tech University, USA
14:15-14:55 Keynote Speech II
Prof. Yinglei Lai, The George Washington University, USA
14:55-15:35 Keynote Speech III
Assoc. Prof. Qijun Zhao, Sichuan University, China
15:35-16:00 Coffee Break & Group Photo
16:00-16:15 Invited Speech I
Prof. Zhiwei Qiao, ShanXi University, China
16:00-18:00 Session 1
Topic: Medical Image Analysis and Processing Technology
8 presentations
March 13,
2018
(Tuesday)
Morning
Venue: Activated Room 1&2 (1st Floor)
09:00-09:05 Opening Remarks
Prof. Yinglei Lai, The George Washington University, USA
09:05-09:45 Keynote Speech IV
Prof. Edwin Wang, University of Calgary, Canada
09:45-10:25 Keynote Speech V
Prof. Kiyoshi Hoshino, University of Tsukuba, Japan
10:25-10:50 Coffee Break & Group Photo
10:50-11:25 Plenary Speech I
Prof. Ralf Hofestädt, Bielefeld University, Germany
11:25-12:00 Plenary Speech II
Assoc. Prof. Hu Han, Chinese Academy of Sciences, China
12:00-13:20 Lunch (Yue Club)
March 13,
2018
(Tuesday)
Afternoon
Venue: Activated Room 1 (1st Floor)
13:20-13:35 Invited Speech II
Prof. Minghui Li, Soochow University, China
13:20-15:35 Session 2
Venue: Activated Room 1
Topic: Genetic Engineering and
Protein Structure Analysis
9 presentations
13:20-15:35 Session 3
Venue: Activated Room 2
Topic: Modern Information
Engineering and Technology
9 presentations
15:35-15:55 Coffee Break
Venue: Activated Room 2 (1st Floor)
15:55-16:10 Invited Speech III
Prof. Ming Chen, Zhejiang University, China
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 22 -
March 13,
2018
(Tuesday)
Afternoon
15:55-18:25 Session 4
Venue: Activated Room 1
Topic: Bioinformatics and Basic
Medicine
10 presentations
15:55-18:10 Session 5
Venue: Activated Room 2
Topic: Intelligent Computing and
Computer Applications
9 presentations
18:30~20:00 Dinner (Yue Club)
March 14,
2018
(Wednesday)
08:30~17:30 One Day Tour
Tips: Please arrive at the Conference Room 10 minutes before the session begins to upload PPT into
the laptop.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 23 -
Detailed Schedule for Conference
March 12, 2018 (Monday)
Venue: Activated Room 1&2 (1st Floor)
10:00~18:00 Arrival and Registration (Hotel Lobby)
13:30-13:35
Opening Remarks
Assoc. Prof. Qijun Zhao
Sichuan University, China
13:35-14:15
Keynote Speech I
Prof. Bijoy K. Ghosh
Texas Tech University, USA
Topic: “Iterative Learning Control Problems in Medical Rehabilitation”
14:15-14:55
Keynote Speech II
Prof. Yinglei Lai
The George Washington University, USA
Topic: “On Poisson Models in the Analysis of RNA-seq Data”
14:55-15:35
Keynote Speech III
Assoc. Prof. Qijun Zhao
Sichuan University, China
Topic: “3D Face Modeling: Images, Shapes, and DNA”
15:35-16:00 Coffee Break & Group Photo
16:00-16:15
Invited Speech I
Prof. Zhiwei Qiao
ShanXi University, China
Topic: “Study on EPR Oxygen Imaging for Oxygen-Image-Guided Precise
Radiation”
16:00-18:00 Session 1
Topic: Medical Image Analysis and Processing Technology
March 13, 2018 (Tuesday)
Venue: Activated Room 1&2 (1st Floor)
09:00-09:05
Opening Remarks
Prof. Yinglei Lai
The George Washington University, USA
09:05-09:45
Keynote Speech IV
Prof. Edwin Wang
University of Calgary, Canada
Topic: “From Health Genomics to Intelligent Precision Health”
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 24 -
09:45-10:25
Keynote Speech V
Prof. Kiyoshi Hoshino
University of Tsukuba, Japan
Topic: ―Technology for Acquiring Biosignals Generated during Eye
Movements‖
10:25-10:50 Coffee Break & Group Photo
10:50-11:25
Plenary Speech I
Prof. Ralf Hofestädt
Bielefeld University, Germany
Topic: ―Medical Omics for the Detection of Comorbidity between Asthma
and Hypertension‖
11:25-12:00
Plenary Speech II
Assoc. Prof. Hu Han
Chinese Academy of Sciences, China
Topic: ―Attribute Estimation from Face: Approaches and Applications‖
12:00-13:20 Lunch (Yue Club)
13:20-13:35
Invited Speech II
Prof. Minghui Li
Soochow University, China
Topic: “Estimating and Interpreting the Effects of Sequence Variants and
Cancer Mutations on Protein Function”
13:20-15:35
Session 2 (Activated Room 1)
Topic: Genetic Engineering and Protein
Structure Analysis
Session 3 (Activated Room 2)
Topic: Modern Inf
ormation Engineering and Technology
15:35-15:55 Coffee Break
15:55-16:10
Invited Speech III
Prof. Ming Chen
Zhejiang University, China
Topic: “Single Cell Big Data Analysis”
15:55-18:25
Session 4 (Activated Room 1)
Topic: Bioinformatics and Basic
Medicine
Session 5 (Activated Room 2)
Topic: Intelligent Computing and
Computer Applications
18:30-20:00 Dinner (Yue Club)
Note: (1) The registration can also be done at any time during the conference.
(2) The organizer doesn’t provide accommodation, and we suggest you make an early reservation.
(3) One Best Oral Presentation will be selected from each oral presentation session, and the
Certificate for Presentation will be awarded at the end of each session on March 12 and 13, 2018.
Let’s move to the session!
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 25 -
Session 1
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session.
Afternoon, March 12, 2018 (Monday)
Time: 16:00-18:00
Venue: Activated Room 1&2 (1st Floor)
Session 1: Topic: “Medical Image Analysis and Processing Technology”
Session Chair: Prof. Zhiwei Qiao
Invited Speech Presentation 1 (16:00~16:15)
Study on EPR Oxygen Imaging for Oxygen-Image-Guided Precise Radiation
Zhiwei Qiao
M3002: Study on EPR Oxygen Imaging For Oxygen-Image-Guided Precise Radiation
ShanXi University, China
Abstract—Electron paramagnetic resonance imaging (EPRI) can yield the 3-dimensional
(3D) spatial distribution of the unpaired-electron spin-density (UESD) from which the spatial
distribution of oxygen concentration within tumor tissue, referred to as the oxygen image,
can be derived. In pulsed 3D EPRI, the 3D Radon transform is used for modeling the
imaging process, and existing algorithms such as the standard 3D filtered-backprojection
(FBP) can be used for reconstructing images through inverting the 3D Radon transform.
However, the existing algorithms often require data collected at a large number of densely
sampled projection views, which can lead to a prolonged data-acquisition time especially
in in vivo animal EPR imaging. Therefore, there always exists a strong interest in shortening
data-acquisition time through reducing the number of data samples collected in EPRI, and
one approach is to acquire data at a reduced number of sparsely distributed projection views
from which existing algorithms such as FBP may reconstruct images with sampling artifacts.
In the work, we investigate and develop an optimization-based image reconstruction from
data collected at sparse views in EPRI. Specifically, we design a convex optimization
program to which the EPR image of interest is formulated as a solution and then tailor the
primal-dual, Chambolle-Pock (CP) algorithm to reconstruct the image by solving the convex
optimization program. We have performed studies using simulated and physical-phantom
data on the verification and characterization of the optimization-based image reconstruction.
Results of the studies suggest that the optimization-based image reconstruction may yield
accurate reconstructions from sparse-view projections, thus enabling fast EPRI with reduced
acquisition time.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 26 -
Afternoon, March 12, 2018 (Monday)
Time: 16:00-18:00
Venue: Activated Room 1&2 (1st Floor)
Session 1: Topic: “Medical Image Analysis and Processing Technology”
Session Chair: Prof. Zhiwei Qiao
M1001 Presentation 2 (16:15~16:30)
Super-Resolution Based on Noise Resistance Deep Convolutional Network
Hengjian Li, Yunxing Gao, Jiwen Dong and Guang Feng
University of Jinan, China
Abstract—In this paper, we present a novel deep network model which is designed to deal
with medical image super-resolution and has some resistance to noise contamination. We are
mainly aimed at the medical image susceptible to noise contamination in the collection and
transmission process, and the noise in medical images will be amplified after super-resolution
reconstruction. We improve the Super-Resolution Convolution Neural Network (SRCNN)
model mainly in two aspects. First, in order to make our model with noise resistance, we use
discrete Harr wavelet transform as preprocessing algorithm. Second, we use adaptive partition
algorithm based on image content to block the original image which can reduce the time
complexity. The experimental results show that our model still achieves a good objective
evaluation index (PSNR) and subjective visual effect on medical images that add Gaussian
white noise. Our model is fast and effective and also has important guiding significance for
the difficulty and risk assessment of surgical feasibility.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 27 -
Afternoon, March 12, 2018 (Monday)
Time: 16:00-18:00
Venue: Activated Room 1&2 (1st Floor)
Session 1: Topic: “Medical Image Analysis and Processing Technology”
Session Chair: Prof. Zhiwei Qiao
K0017 Presentation 3 (16:30~16:45)
Malicious Code Detection based on Image Processing Using Deep Learning
Rajesh Kumar, Zhang Xiaosong, Riaz Ullah Khan, Ijaz Ahad and Jay Kumar
University of Electronic Science and Technology of China, China
Abstract—In this study, we have used the Image Similarity technique to detect the unknown
or new type of malware using CNN ap- proach. CNN was investigated and tested with three
types of datasets i.e. one from Vision Research Lab, which contains 9458 gray-scale images
that have been extracted from the same number of malware samples that come from 25
differ- ent malware families, and second was benign dataset which contained 3000 different
kinds of benign software. Benign dataset and dataset vision research lab were initially exe-
cutable files which were converted in to binary code and then converted in to image files. We
obtained a testing ac- curacy of 98% on Vision Research dataset.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 28 -
Afternoon, March 12, 2018 (Monday)
Time: 16:00-18:00
Venue: Activated Room 1&2 (1st Floor)
Session 1: Topic: “Medical Image Analysis and Processing Technology”
Session Chair: Prof. Zhiwei Qiao
M0029 Presentation 4 (16:45~17:00)
Class Balanced PixelNet for Neurological Image Segmentation
Mobarakol Islam and Hongliang Ren
National University of Singapore, Singapore
Abstract—In this paper, we propose an automatic brain tumor segmentation approach (e.g.,
PixelNet) using pixel level convolutional neural network (CNN). The model extracts feature
from multiple convolutional layers and concatenates them to form a hyper-column where
samples a modest number of pixels for optimization. Hyper-column ensures both local and
global contextual information for pixel wise predictor. The model confirms the statistical
efficiency by sampling few number of pixels in training phase where spatial redundancy
limits the information learning among the neighboring pixels in conventional pixel-level
semantic segmentation approaches. Besides, label skewness in training data leads the
convolutional model often converge to the certain classes which is a common problem in the
medical dataset. We deal this problem by selecting an equal number of pixels for all the
classes in sampling time. The proposed model has achieved promising results in brain tumor
and ischemic stroke lesion segmentation datasets.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 29 -
Afternoon, March 12, 2018 (Monday)
Time: 16:00-18:00
Venue: Activated Room 1&2 (1st Floor)
Session 1: Topic: “Medical Image Analysis and Processing Technology”
Session Chair: Prof. Zhiwei Qiao
K0019 Presentation 5 (17:00~17:15)
Evaluating the Performance of ResNet Model Based on Image Recognition
Riaz Ullah Khan, Xiaosong Zhang, Rajesh Kumar and Emelia Opoku Aboagye
University of Electronic Science and Technology of China, China
Abstract—In this study, we have used two different Datasets to evaluate the performance of
ResNet model. First dataset consists of images about healthcare data while second dataset
consists of malware and benign _les. We performed experiments to predict cancer on the first
dataset and detect malware on the second dataset. ResNet models i.e. Resnet18, ResNet50,
ResNet101 and ResNet152 are investigated and tested which belong to Microsoft. The neural
networks system has been turned out to be _t for approximating any ceaseless capacity, and
all the more as of late profound neural systems (DNNs) have been observed to be viable in a
few spaces, going from PC vision, speech recognition, to text processing. The purpose of this
paper is to make recommendations prediction of the cancer disease adopting Neural networks
and detecting the malware _les through the same ResNet model. We evaluated the
performance of ResNet model on two different datasets.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 30 -
Afternoon, March 12, 2018 (Monday)
Time: 16:00-18:00
Venue: Activated Room 1&2 (1st Floor)
Session 1: Topic: “Medical Image Analysis and Processing Technology”
Session Chair: Prof. Zhiwei Qiao
M0022 Presentation 6 (17:15~17:30)
A Compression–Encryption Hybrid Algorithm Based on Compressive Sensing
Changzhi Yu, Hengjian Li and Jiwen Dong
University of Jinan, China
Abstract—This paper introduces a simple and effective image encryption algorithm based on
Compressive Sensing. Firstly, in order to obtain the sparse matrix of the plain image, we
select the dual tree complex wavelet to transform the plain image into frequency domain and
then we take noise shaping (NS) to make the sparse matrix coefficient more concentrated.
Secondly, we use logistic and sine chaotic map system (LSS) to generate an encrypted
measurement matrix. Finally, the sparse matrix and the encrypted measurement matrix are
used to make a compression sensing operation. In order to improve the security of the
proposed algorithm, we divide the resulting ciphertext into four parts and then use Arnold
and LSS system (ALS) to encrypt them twice. Experiments and security analysis demonstrate
the algorithm‘s excellent performance in image encryption and various attacks.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 31 -
Afternoon, March 12, 2018 (Monday)
Time: 16:00-18:00
Venue: Activated Room 1&2 (1st Floor)
Session 1: Topic: “Medical Image Analysis and Processing Technology”
Session Chair: Prof. Zhiwei Qiao
K0013 Presentation 7 (17:30~17:45)
Attribute-based Face Recognition and Application of Intelligent Factory Safety Detection
Xiangfeng Chen, Wenbai Chen, Peichao Xu, and Mengyao Lv
Beijing Information Science & Technology University, China
Abstract—In order to meet intelligent factory safety requirements, a safety monitoring
system based on multi-attribute face recognition is introduced in this paper. The
multi-attribute face recognition model is obtained by fine-tuning Resnet-50, which is applied
in the mobile robot platform. When the target appears in the field of monitoring area, the
multiple attributes of the target can be detected by the model. Then, the system makes the
appropriate decision according to the predicted result. The experiments show that the
multiple attributes of the target face can be recognized by the model. In particular, whether
the target wears a helmet or not can be detected by the monitoring system. Further, the safety
of intelligent factory would be improved, reducing the reliance on labor force.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 32 -
Afternoon, March 12, 2018 (Monday)
Time: 16:00-18:00
Venue: Activated Room 1&2 (1st Floor)
Session 1: Topic: “Medical Image Analysis and Processing Technology”
Session Chair: Prof. Zhiwei Qiao
M0001 Presentation 8 (17:45~18:00)
A Method of Constructing Vertebral 3D Statistical Model Based on Gaussian Curvature
Du Jing, Yu Bin, Hui Yu, Wu Jun-Sheng and Zhang Chen
Northwestern Polytechnical University, China
Abstract—Aiming at the difficult problem of inaccurate model of medical spine 3D statistical
model library, this paper studies a method of constructing medical spine 3D statistical model
based on the feature points of Gaussian curvature flow localization model. In this method, the
human lumbar vertebrae model is physically positioned based on the feature points of the
Gaussian curvature flow to generate the sample matrix of feature points of each spine sample
model. Then the sample matrix of the feature point is aligned and registered by the ICP
iterative algorithm. Finally, PCA analysis is used to train and study the spine model samples
after registration, and finally a more accurate vertebral 3D statistical model library is obtained.
By comparing the performance parameters of the experimental results, the model constructed
by this method is more accurate than before.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 33 -
Session 2 Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session. Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 1 (1st Floor)
Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”
Session Chair: Prof. Minghui Li
Invited Speech Presentation 1 (13:20~13:35)
Estimating and Interpreting the Effects of Sequence Variants and Cancer Mutations on Protein
Function
Minghui Li
Soochow University, China
Abstract—There has been a rapid development of genome-wide techniques in the last decade
along with significant lowering of the cost of gene sequencing, which generated rich and
widely available genomic data. However, the interpretation of such genomic data as well as
predicting the association of genetic variations with diseases and phenotypes still needs
significant improvement. Missense mutations can render proteins nonfunctional and may be
responsible for many diseases. The effects caused by missense mutations can be pinpointed
by in silico modeling that makes it more feasible to find a treatment and reverse the effect.
Specific human phenotype is largely determined by stability, activity, and interactions
between proteins and with other biomolecules which work together to provide specific
cellular functions. Therefore, the analysis of the effect of missense mutations on proteins and
their complexes would give us important clues for identifying functional important missense
mutations and understanding the molecular mechanisms of diseases and facilitated their
treatment and prevention. Cancer genome sequencing projects reveal vast amounts of
somatic missense mutations in proteins. However, not all cancer mutations provide a
selective growth advantage to cancer cells. Many mutations whose impact on protein
function is either minor or the affected proteins are not important for tumor progression. The
important question is to determine which mutations are likely to be drivers. One can
considerably decrease the number of potential driver candidates by determining the
functional impact of each mutation on protein.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 34 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 1 (1st Floor)
Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”
Session Chair: Prof. Minghui Li
M0036 Presentation 2 (13:35~13:50)
Biased Distribution of Amino Acid in Intrinsically Disordered Proteins and Regions
Zhengyu Ding, Tian Feng, Fangbo Nan, Yu Wang and Bo He
Harbin Engineering University, China
Abstract—The analysis on structural characteristic of proteins is helpful to understand
molecular mechanisms of disordered structure formation and principles of protein folding,
and can provide a foundation for predicting model of intrinsically disordered proteins. In this
thesis, the significance test of structural bias of amino acid monomer, dimer and trimer
between disordered and ordered regions is carried out by using Fisher Exact Test. The
purpose is to probe the difference of amino acid compositions between disordered and
ordered regions and analyze the effect of interaction between amino acids on structural
formation. The results show that there are significant different amino acid compositions
between disordered and ordered regions and between different length disordered regions. It is
also found that the effect of every kind of amino acid on structural formation is different by
the analysis of amino acid dimer and trimer. Therefore, it is a good way to achieve the folding
principles of proteins by analyzing amino acids coding.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 35 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 1 (1st Floor)
Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”
Session Chair: Prof. Minghui Li
M0011 Presentation 3 (13:50~14:05)
Predicting Intrinsically Disordered Proteins Based on Different Feature Teams
Bo He, Wenliang Zhang, Haikuan Gao, Chengkui Zhao and Weixing Feng
Harbin Engineering University, China
Abstract—The characteristics of intrinsically disordered proteins depend on their length. An
obvious fact is that the composition of amino acid sequences is different for different length
disordered regions. In order to improve the performance of the predicting model, a new
method was proposed to predict disordered regions of diverse length disordered regions in
proteins by using different feature teams. Taking into account the relevance between their
characteristics and length of intrinsically disordered proteins, different feature teams were
constructed for different length disordered regions. In every feature team, the selection of
window sizes and features could meet the demand of the corresponding length disordered
region. Comparing with the traditional method, this method could consider not only the
influence of the window sizes but also the effect of the feature information. According to
every feature team, a basic predictor was required to built by SVM. By integrating these basic
predictors, the final decision could be made by the majority voting method. Subsequent
simulation suggests that the proposed method can consider the information from the long and
short disordered regions simultaneously and get a good predicting accuracy for IDPs,
especially for short disordered regions.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 36 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 1 (1st Floor)
Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”
Session Chair: Prof. Minghui Li
M0003 Presentation 4 (14:05~14:20)
Charactering and Predicting E3-Substrate Interactions by Systematically Integrating Omics,
Networks and Pathways
Di Chen and Hai-Long Piao
Chinese Academy of Sciences, China
Abstract—E3 ubiquitin ligases (E3s) play a critical role in disease progression. However, a
large number of E3-substrate interactions (ESIs) remain unrevealed. Here, we took advantage
of the increasing multi-omics data and biological knowledge to characterize and identify ESIs.
Multidimensional features were designed to describe the association profiles between E3 and
substrates in terms of expression level, network connection and pathway dependency.
Compared to three negative categories, ESI-specific association patterns emerged. Based on
such features, we constructed an ensemble prediction model for ESIs and confirmed its
reliability by both crossover and independent validations. Interestingly, substrates didn't
exhibit directly negative correlations with E3s in omics, although they mainly underwent
degradation. Nonetheless, integrating omics with networks or pathways provided meaningful
insights into ESI interpretation. Notably, our evaluations on FBXL family produced consistent
results with a proteomic-based study and the recall was improved. Moreover, a cancer
hallmark ESI landscape was predicted. Taken together, our study catches the first glimpse at
the ESI association patterns in a data-driven way and provides a valuable resource for deeply
characterizing and predicting ESIs.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 37 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 1 (1st Floor)
Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”
Session Chair: Prof. Minghui Li
M0049 Presentation 5 (14:20~14:35)
Predicting Intrinsically Disordered Regions Based on the Structural Bias of Amino Acid
Dimers
Tian Feng, Zhengyu Ding, Fangbo Nan, Yu Wang and Bo He
Harbin Engineering University, China
Abstract—Due to many important functions of intrinsically disordered proteins, it has already
become hotter and hotter research topic to distinguish intrinsically disordered regions from
amino acid sequences. To accurately predict intrinsically disordered regions from amino acid
sequences, a novel method was proposed to construct feature vectors based on structural bias
of amino acid dimers. Compared with the frequency of amino acid monomers and dimers, the
new features based on the structural bias of dimers cannot only provide the information of the
components of amino acids sequence but also involve the arrangement of sequences. With the
new features, BP neural network and SVM were introduced to predict intrinsically disordered
regions respectively. Subsequent simulation shows improvement of predicting accuracy. It
also proves the effectiveness of new features based on structural bias of amino acid dimers.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 38 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 1 (1st Floor)
Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”
Session Chair: Prof. Minghui Li
M0010 Presentation 6 (14:35~14:50)
One-Dimensional and Two-Dimensional Linear Mixed Models to Accurately Dissect Causal
Genetic Effects in Associative Omics Studies
Patrick Xuechun Zhao, Wenchao Zhang, Bongsong Kim, Xinbin Dai and Shizhong Xu
Noble Research Institute, USA
Abstract—Associative omics studies have rapidly become a major tool for identifying and
deciphering the interrelationship between an living organism‘s characteristics (phenotypic
variations) and its genetic variants at different ‗omics‘ levels (genotypic variants). Phenotypes
are often governed by individual genes (G), the gene-gene interactions (GxG) and
gene-environment interactions (GxE). Therefore, genotype-phenotype association discovery
and genetic variances analysis demands accurately dissecting these genetic causal effects,
facilitating our understanding of how living organisms develop, interact with and adapt their
physical and biological environment. We present our novel one-dimensional (1D) and
two-dimensional (2D) linear Mixed Models (LMMs), and a trio of genotype-phenotype
association analysis tools, namely 1) GWASPRO (bioinfo.noble.org/GWASPRO/), which
adopts a simple LMM for the analysis of additive genetic effects and is specially optimized
for the analysis of ―big data‖ generated from large-scale genome-wide association studies
(GWASs); 2) PEPIS (bioinfo.noble.org/PolyGenic_QTL/), which adopts a full polygenic
LMM to analyze the additive, dominance effects and epistatic effects such as additive x
additive, additive x dominance, dominance x additive, dominance x dominance in GWASs
and quantitative trait loci (QTL) mapping; and 3) PATOWAS (bioinfo.noble.org/PATOWAS/),
which further extends the 2D GWAS LMM for broader associative omics studies, such as
transcriptomics-wide association studies and metabolomics-wide association studies.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 39 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 1 (1st Floor)
Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”
Session Chair: Prof. Minghui Li
M0002 Presentation 7 (14:50~15:05)
A Dynamic Pooling Approach to Extract Complete Allele Signal Information in Somatic
Copy Number Alternations Detection
Long Cheng, Pengfei Yao, Jianwei Lu, Ke Hao and Zhongyang Zhang
Tongji University, China
Abstract—Accurately characterizing somatic copy number alterations (SCNAs) in cancers are
of great importance in both deciphering tumorigenesis and progression and improving clinical
diagnosis/treatment. Many computational methods in detecting SCNAs were proposed in
recent years, and saas-CNV is among the best performers evaluated with empirical datasets.
However, saas-CNV method inefficiently uses the allele dosage information in
next-generation sequencing or microarray data. To this regard, we proposed and implemented
a novel approach to extract the complete allele signal information for SCNA detection.
Evaluated in an empirical dataset of hepatocellular carcinoma, we demonstrated the novel
approach enhanced data signal-to-noise ratio, and resulted in improved detection of copy
number alternations especially focal genome changes.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 40 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 1 (1st Floor)
Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”
Session Chair: Prof. Minghui Li
M0008 Presentation 8 (15:05~15:20)
A Computational Framework to Simultaneously Quantify DNA Methylation, Somatic Copy
Number Alternation and DNA Heterogeneity from Low Coverage Plasma Circulating DNA
Sequencing
Pengfei Yao, Long Cheng, Jianwei Lu, Ke Hao and Zhongyang Zhang
Tongji University, China
Abstract—Genome of Hepatocellular Carcinoma (HCC) undergoes profound changes,
including DNA hypomethylation and somatic copy number alternations (SCNA). These two
characteristics provide orthogonal information for HCC early diagnosis, and can be assessed
by whole-genome bisulfite sequencing (WGBS) of the plasma circulating DNA. We
proposed a computational framework to simultaneously quantify DNA methylation and
SCNA from plasma circulating DNA WGBS, and further estimate the heterogeneity of the
circulating DNA. Our approach reliably detected global DNA hypomethylation and SCNA
from low coverage WGBS of tumor and plasma circulating DNA from HCC subjects
compared to healthy control individuals. The chromosomal pattern of SCNA detected from
tumor DNA and plasma DNA are highly consistent. The computational framework we
proposed make efficient use of WGBS and able to simultaneously characterize DNA
hypomethylation SCNA, which provide orthogonal evidence in HCC early diagnosis.
Importantly, our approach estimated the tumor DNA fraction in plasma circulating DNA,
ranging from 38.55% to 1.79%, and is correlated with tumor size (Spearman‘s correlation
coefficient = 0.68, p-value=0.0049). We estimate that the tumor DNA content in plasma
could be below 2% for HCC tumor of 2cm or smaller in diameter, which requires relatively
high coverage WGBS for reliable assessment.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 41 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 1 (1st Floor)
Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”
Session Chair: Prof. Minghui Li
M0015 Presentation 9 (15:20~15:35)
RNA-Seq Based Sensitive and Comprehensive Mutation Detection and Interpretation System
for Precision Medicine
Zhifu Sun
Mayo Clinic, USA
Abstract—RNA-seq is the most commonly used sequencing application. Not only does it
measure gene expression but it is also an excellent media to detect important structural
variants such as single nucleotide variants (SNVs), insertion/deletion (Indels) or fusion
transcripts. However, detection of these variants is challenging and complex from RNA-seq.
We have developed a sensitive and accurate analytical system which detects various
mutations at once for translational precision medicine. The pipeline incorporates most
sensitive aligners for micro-indels in RNA-seq, the best practice for data pre-processing and
variant calling, and STAR-fusion is for chimeric transcripts. Variants/mutations are annotated
and key genes are extracted for further investigation and clinical actions. For the well-defined
variants from NA12878 by GIAB project, about 95% and 80% of sensitivities were obtained
for SNVs and indels, respectively. For the lung cancer dataset with 41 known and oncogenic
mutations, 39 were detected by the pipeline with STAR aligner and all by the GSNAP aligner.
An actionable EML4-ALK fusion was also detected in one of the tumors. For 9 spiked-in
fusions with different concentrations, the pipeline was able to detect all. In conclusion, the
new RNA-seq workflow provides an accurate and comprehensive mutation profiling from
RNA-seq for personalized medicine.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 42 -
Session 3
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session.
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 2 (1st Floor)
Session 3: Topic: “Modern Information Engineering and Technology”
Session Chair: to be added
B0068 Presentation 1 (13:20~13:35)
Both Chargaff Second Parity Rule and the Strand Symmetry Rule are Inaccurate
Zhiyu Chen
Peiyou Education School, China
Abstract—In order to check Chargaff Second Parity Rule, we find the strands are asymmetric
in human DNA, this breaks the strand symmetry rule. We calculate the ratio between
oligonucleotide ATGC and oligonucleotide CGTA, and we compare the sample sequence
average ratio ATGC/CGTA and the complementary sequence average ratio ATGC/CGTA. We
find evolution degree bigger, and then the strand symmetry deviation will be bigger. Sequence
and its complementary strand sequence obviously have two different characters, include
physical property, chemical property and biological property. It is very important, based on
this asymmetry, we can find some new and special theories in biology to explain how
chromosome communicates and works in the future. We also find both leukemia and breast
cancer are weakening the DNA‘s asymmetry degree. Here need more research and check,
maybe we can find an easy diagnosing method to leukemia and breast cancer, if my result
here is right at last, it will benefit to the world, thanks to other researchers.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 43 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 2 (1st Floor)
Session 3: Topic: “Modern Information Engineering and Technology”
Session Chair: to be added
K0030 Presentation 2 (13:35~13:50)
Statistical Analysis of Extracted Video Data by Using Web Crawler
Md Khalid Hossen, Yong Wang, Hussain Ahmed Tariq, Gabriel Nyame and Raphael Elimeli
Nuhoho
University of Electronic Science and Technology of China, China
Abstract—Crawling is the process of exploring web applications automatically. The web
crawler aims at discovering the web pages of a web application by navigating through the
application. Before the analyses, the information and the characteristics of the structure have
to be obtained. The main complexities are to collect the video data. In this paper we will
discuss the design and implementation of a crawler for online video data and propose
countermeasures for technical challenges. Then we will do the statistical analysis of the
crawled data and visualize in graph. We can easily identify which sites are popular
comparing different video category. We also identify the similarities the video keyword
among the site. The correlation between the number of viewers, like and dislike from the
crawling the data.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 44 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 2 (1st Floor)
Session 3: Topic: “Modern Information Engineering and Technology”
Session Chair: to be added
K0026 Presentation 3 (13:50~14:05)
Effective and Explainable Detection of Android Malware based on Machine Learning
Algorthims
Rajesh Kumar, Zhang Xiaosong, Riaz Ullah Khan, Jay Kumar and Ijaz Ahad
University of Electronic Science and Technology of China, China
Abstract—The across the board reception of android devices and their ability to get to critical
private and secret data have brought about these devices being focused by malware engineers.
Existing android malware analysis techniques categorized into static and dynamic analysis. In
this paper, we introduce two machine learning supported methodologies for static analysis of
android malware. The First approach based on statically analysis, content is found through
probability statistics which reduces the uncertainty of information. Feature extraction was
proposed based on the analysis of existing dataset. Our both approaches were used to
high-dimension data into low-dimensional data so as to reduce the dimension and the
uncertainty of the extracted features. In training phase the complexity was reduced 16.7% of
the original time and detect the unknown malware families were improved.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 45 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 2 (1st Floor)
Session 3: Topic: “Modern Information Engineering and Technology”
Session Chair: to be added
K0007 Presentation 4 (14:05~14:20)
Sentiment Analysis on the online reviews based on Hidden Markov Model
Xiaoyi Zhao and Yukio Ohsawa
The University of Tokyo, Japan
Abstract—In this study, a new sentiment analysis model of online-shopping reviews based on
hidden Markov model has been proposed. Both the influence of the latest two comments and
the most popular comment from the Amazon Japan review page are taken into consideration.
The supervised training method is used to train this model, and then the model is optimized
by using a variation of genetic algorithm. The performance is evaluated through an
experiment of sentiment classification of online-shopping reviews of Amazon Japan‘s tea
category comparing to other methods from previous ones such as Support Vector Machine,
Logistic Regression with built-in cross-validation and so on. The result shows that the adapted
hidden Markov model has the highest f1 score among the other baseline methods.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 46 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 2 (1st Floor)
Session 3: Topic: “Modern Information Engineering and Technology”
Session Chair: to be added
K0018 Presentation 5 (14:20~14:35)
Improvement on Speech Emotion Recognition Based on Deep Convolutional Neural
Networks
Yafeng Niu, Dongsheng Zou, Yadong Niu, Zhongshi He and Hua Tan
Chongqing University, China
Abstract—Speech emotion recognition (SER) is to study the formation and change of
speaker‘s emotional state from the speech signal perspective, so as to make the interaction
between human and computer more intelligent. SER is a challenging task that has
encountered the problem of less training data and low prediction accuracy. Here we propose
a data processing algorithm based on the imaging principle of the retina and convex lens
(DPARIP), to acquire the different sizes of spectrogram and get different training data by
changing the distance between the spectrogram and the convex lens. Meanwhile, with the
help of deep learning to get the high-level features, we apply the AlexNet on the IEMOCAP
database and achieve the average accuracy over 48.8% on six emotions. The experimental
results indicate that our proposed data preprocessing algorithm is effective and more accurate
compared to existing emotion recognition algorithms.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 47 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 2 (1st Floor)
Session 3: Topic: “Modern Information Engineering and Technology”
Session Chair: to be added
K0021 Presentation 6 (14:35~14:50)
Fuzzy-Based Indoor Positioning by Using the Neighbor Points
Chih-Yung Chen, Shen-Whan Chen, Yu-Ju Chen and Rey-Chue Hwang
Shu-Te University, Taiwan
Abstract—This paper presents a fuzzy-based indoor positioning system (IPS) by using the
information of neighbor points to estimate the location of object. An 8x8 square meters
indoor area was used as the experimental area. In the experimental field, the received signal
strength (RSS) of 288 points, 392 points, 440 points and 704 points were sensed and
collected by a hexagonal positioning station which is composed of six printed-circuit board
SPARKLAN AX-106M antennas and Zigbee module. The sensed RSS values are then used
to be the information of fuzzy system for the object‘s position estimation. From the
experimental results shown, the proposed IPS and fuzzy estimation method do have the
accurate positioning performance and indeed has the potential in the real application.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 48 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 2 (1st Floor)
Session 3: Topic: “Modern Information Engineering and Technology”
Session Chair: to be added
M0019 Presentation 7 (14:50~15:05)
The Application of Multi–Source Information Fusion Technology in Vehicle Integrated
Navigation System
Binhui Tang, Weijun Zeng and Zhen-xing Zhou
College of Sichuan University, China
Abstract—By analyzing the advantages and disadvantages of GPS, Compass satellite
positioning and navigation system and inertial navigation system, a design method of
Compass / GPS / INS integrated navigation system is proposed. This method uses the
improved Kalman filter algorithm, and combines the integrated navigation system with
multi-sensor such as lidar for sufficient information fusion. The integrated navigation system
model uses the dispersive filter structure of federated filtering to establish partial filters
respectively and deduces the state equation and observation equation of combined navigation.
The filter cross-covariance matrix is used to solve the cross-correlation between local filters
and affect the positioning accuracy problem. The simulation results show that the integrated
navigation system has higher positioning accuracy than single navigation system, and can
effectively enhance the real-time and fault-tolerance of the system and facilitate the
troubleshooting and isolation of the navigation subsystems.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 49 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 2 (1st Floor)
Session 3: Topic: “Modern Information Engineering and Technology”
Session Chair: to be added
K0002 Presentation 8 (15:05~15:20)
Combining Explicit and Implicit Semantic Similarity Information for Word Embeddings
Shi Yin, Yaxi Li and Xiaoping Chen
University of Science and Technology of China, China
Abstract—In this paper, we propose a new framework that combines both explicit and
implicit semantic similarity information for training word embeddings. While the former
determines the similarity degree between two words explicitly, the latter reflects word
similarities implicitly through contextual and relational similarity. We also propose a novel
concept called relative similarity in vocabulary, which deliberately utilizes explicit semantic
similarity information (word's definition in particular) for word embeddings. We conduct
experimental studies on various word similarity and word categorization datasets. The results
show that our framework compares favorably to a number of state-of-the-art approaches for
word embeddings.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 50 -
Afternoon, March 13, 2018 (Tuesday)
Time: 13:20-15:35
Venue: Activated Room 2 (1st Floor)
Session 3: Topic: “Modern Information Engineering and Technology”
Session Chair: to be added
K0042 Presentation 9 (15:20~15:35)
Human Segmentation with Deep Contour-Aware Network
Fiseha Berhanu, Hong Wu and William Zhu
University of Electronic Science and Technology of China, China
Abstract—Human detection and segmenting are important computer vision problems with
applications in indexing, surveillance, 3D reconstruction and action recognition. The
figure-ground segmentation of humans in images captured in real-world environment is a
challenge problem due to a variety of viewpoints, articulated skeletal structure, complex
backgrounds, varying body proportions and clothing, etc. In this paper, we proposed a new
approach to human segmentation in still images based on Deep Contour-Aware Network
(DCAN), which is a unified multi-task deep learning framework combining the
complementary object and contour information simultaneously for better segmentation
performance. Experimental results on a large-scale human dataset indicate our human
segmentation method can achieve a marginally better segmentation accuracy than the state of
the art works.
15:35-15:55 Coffee Break
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 51 -
Session 4
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session. Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
Invited Speech Presentation 1 (15:55~16:10)
Single Cell Big Data Analysis
Ming Chen
Zhejiang University, China
Abstract—With the development of CyTOF and single cell sequencing technology, high
dimension and large scale data have being accumulated, and the analysis of these data become
indispensable. This talk will briefly introduce several bioinformatics approaches for analyzing
such data. We developed a semi-automatic cell clustering platform to identify cell populations
in flow cytometry data. We dissected global ccRCC metastasis associated lncRNAs based on
single-cell RNA-seq data analysis. Using Microwell-seq, we analyzed more than 400,000
single cells covering all of the major mouse organs and constructed a basic scheme for a
Mouse Cell Atlas (MCA).
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 52 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
M0044 Presentation 2 (16:10~16:25)
Leaf Shape Variation and Its Correlation to Phenotypic Traits of Soybean in Northeast China
Fei Huang, Yangjing Gan, Dongdong Zhang, Fei Deng and Jing Peng
Wuhan University of Technology, China
Abstract—Leaves are the main plant organs which play an important role in plant‘s life.
Soybean, as a major legume crop, has diverse leaf shapes among its genotypes. The
motivation of this study is to analyze the leaf shape variety of 206 soybean genotypes from
northeast China and its correlation to other phenotypic traits. Morphological operations have
been adopted to extract the features of leaves. The results show significant differences of
phenotypic traits among leaf shape groups, which indicate that the lanceolate leaf group has
the highest mean plant height (89.62 cm) and the largest number of nodes per plant (15.72
nodes/plant); the round leaf group has the lowest mean 100-seed weight (18.14 g) and seed
weight per plant (17.92 g); the lowest mean number of pods (41.86 pods/plant) is in the
elliptical group. In terms of the maturity period, most of the lanceolate leaves (67.24%)
belong to the late maturity group, in which there are only few of the elliptical leaves (11.54%).
Our results suggest that the variation in leaf shape is an important indicator of other
phenotypic characteristics, which could provide more information for soybean classification,
as well as for cultivating new varieties in northeast China.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 53 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
M0032 Presentation 3 (16:25~16:40)
YeastSCI: A Web Tool Integrating Zinc Cluster Protein Information of Saccharomyces and
Candida
Pitchya Tangsombatvichit, Utharn Buranasaksee and Suwut Tumthong
Rajamangala University of Technology Suvarnabhumi, Thailand
Abstract—The zinc cluster proteins act as transcriptional regulators only found in fungi. The
human pathogen Candida, zinc cluster transcription factors that involve in controlling the
expression of virulence genes and play roles in multidrug resistance. The yeast
Saccharomyces cerevisiae has a close relationship to Candida as it has genes encoded with
zinc cluster proteins. Previously, the researchers need to search from the publications
manually or from the Candida Genome Database and Saccharomyces Genome Database
separately. This is a time-consuming process. In this paper, we have developed the web tool
accessible online called Yeast Saccharomyces and Candida Integrated (YeastSCI). The tool
provides the integration information of Saccharomyces and Candida from popular databases.
Furthermore, a hybrid cacheable technique is proposed to make the tool self-updatable and
provide a real-time information efficiently.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 54 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
M0028 Presentation 4 (16:40~16:55)
Prediction of Continuous B-cell Epitopes Using Long Short Term Memory Networks
Cheng Bin, Liu Lingyun, Qi Zhaohui and Yang Hongguang
Hebei Academy of Sciences, China
Abstract—B-cell epitopes play a vital role in the epitope-based vaccine design. The
accumulation of epitope sample data makes it possible to predict epitopes using machine
learning methods. Compared with the experimental tests, the computational methods are
faster and more economic. Several machine learning computational methods have been
applied to improve the accuracy of epitope predictions. These methods have been improved
several times in the epitope prediction has made some achievements, but there are also
deficiencies. The commonly used propensity scale methods for the prediction are
physicochemical properties of amino acid sequences. It is difficult to get a good classification
result in the network training using only the physicochemical properties of the sample
sequence. In this study, we have developed a novel method for predicting continuous B-cell
epitope. We adopted the Long Short Term Memory network (LSTM) and relevance of amino
acids pair (RAAP) feature scale. LSTM can make up for the lack of RNN algorithm, which is
very suitable for epitope prediction. We have been adopted the performance of LSTM and
RAAP in three aspects, and achieved a certain result.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 55 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
M0048 Presentation 5 (16:55~17:10)
The Repertoire of Mutational Signatures in Human Cancer
Steven G. Rozen, Ludmil Alexandrov, Jaegil Kim, Nicholas Haradhvala, Mi Ni Huang, Alvin
Wei Tian Ng, Gad Getz, Michael R Stratton and Pan
Duke-NUS Medical School, Singapore
Abstract—Somatic mutations in cancer genomes are caused by multiple mutational processes,
each of which generates a characteristic mutational signature. Using ~84,000,00 somatic
mutations from ~4,500 whole cancer genome and ~18,500 exome sequences encompassing
most cancer types, we characterised 44 mutational signatures for single base substitutions, 11
mutational signatures for doublet base substitutions, and 17 signatures for small insertions and
deletions. The substantial size of the data set compared to previous analyses enabled
discovery of new signatures, enabled separation of overlapping signatures, and enabled
decomposition of signatures into components that may represent associated, but distinct, DNA
damage, repair and/or replication mechanisms. Estimation of the contribution of each
signature to the mutational spectra of individual cancer genomes revealed associations with
exogenous and endogenous exposures and with defective DNA maintenance processes. For
example, two new signatures are probably due to prior platinum therapy, another new
signature, in squamous skin carcinomas, is probably due to prior azathioprine therapy, and yet
another new signature, in colorectal and pancreatic endocrine cancers, probably stems from
inactivating germline or somatic mutations in the MUTYH gene, which encodes a component
of the base excision repair machinery. However, many signatures have unknown causes. In
summary, our analysis provides a comprehensive perspective on the repertoire of mutational
processes contributing to the development of human cancers. This will provide a foundation
for future research into (i) geographical and temporal differences in cancer incidence to
elucidate underlying differences in aetiology, (ii) the mutational processes and signatures
present in normal tissues and caused by non-neoplastic disease states, (iii) clinical and public
health applications of signatures as indicators of sensitivity to therapeutics and past exposure
to mutagens, and (iv) mechanistic understanding of the mutational processes underlying
carcinogenesis.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 56 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
M0039 Presentation 6 (17:10~17:25)
Inhibition Assessment of Anticancer Drugs for ALK Gene Variation Target
Chang-Sheng Chiang and Pei-Chun Chang
Asia University, Taiwan
Abstract—Cancer is a serious and potentially vital illness. There are multiple types of cancer,
many of which can be effectively treated today as to reduce or slow the evolution of the
disease. Therefore, to select the best anticancer drug for the patient genotype is a very
important thing. This study combines pharmacogenomics data and protein mutations data to
establish a regression model for assessing the efficacy of anticancer drugs regarding
variations of ALK target. This assessment model could help patients to select the best
anticancer drug to target ALK protein. Our results show that the R-squared values of the
regression model are 0.77 and 0.88 for anticancer drug PF2341066 and TAE684 respectively.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 57 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
M0017 Presentation 7 (17:25~17:40)
A Framework of an Unconstrained Sleep Monitoring System
Annan Dai, Xiangdong Yang, Wei Li and Ken Chen
Tsinghua University, China
Abstract—In this paper a framework is proposed to regularize the procedure of developing an
unconstrained monitoring system. An unconstrained sleep monitoring system is a healthcare
system aimed to monitor one‘s physiological parameters during sleep without interfering with
his or her sleep. The framework consists of 3 parts: hardware, software and evaluation method.
The hardware is dedicated to collect physiological signal generated by sleepers, and the
software is aimed to process the collected signal data to extract the physiological parameters.
The evaluation method assesses the extracted result to evaluate the performance of the
hardware and the software, and in turn helps modify the hardware or the software. All the 3
components need to meet certain requirements to guarantee the adaptability and feasibility of
the system. An example system is presented to instantiate the framework, in which
experiments of several nights were conducted to validate the monitoring system. This paper
generalizes a new framework on the development of an unconstrained sleep monitoring
system and provides researchers and developers with a comprehensive view over healthcare
systems.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
M0023 Presentation 8 (17:40~17:55)
Improving Medical Ontology based on Word Embedding
Gao Mingxia, Furong Chen and Rifeng Wang
Beijing University of Technology, China
Abstract—Medical ontology learning or improving is automatically learning the knowledge in
ontology format from medical data, mainly text data. With the rise of the word vector space,
improving ontology using word embedding has become a hot spot. Most of previous studies
have focused on how to acquire different ontological elements using all kinds of learning
technologies. Few studies focus on the prior knowledge in a given ontology. In essence,
ontology learning or improving is still a learning process based on existing samples. So, the
type and number of knowledge acquired is limited by existing samples in a given ontology.
This paper firstly formalizes several kinds of prior knowledge for classes in a given ontology.
Then we propose a method, named improving medical ontology based on word embeddings
(IMO-WE), to enrich different types of knowledge from medical text according to
characteristics of different types of prior knowledge. At last, the paper collects the PubMed
Central (PMC) data and the PHARE ontology, and finishes a series of experiments to evaluate
the IMO-WE. The experimental results yield the following conclusions. The first one is that
the data-rich model can achieve higher accuracy for the IMO-WE under same setting in
training progress. So, collecting and training big medical data is a viable way to learn more
useful knowledge. The second one is that the IMO-WE can be used to improving ontology
knowledge when medical data is sufficiently abundant and the ontology has appropriate prior
knowledge. Moreover, in the task of improving synonymous labels through similarity distance,
the accuracy of IMO-WE is significantly better than that of the Random indexing method.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 59 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
M0027 Presentation 9 (17:55~18:10)
Leveraging Word Embeddings and Semantic Enrichment for Automatic Clinical Evidence
Grading
Haolin Wang, Yuming Qiu, Jun Jiang, Ju Zhang and Jiahu Yuan
Chinese Academy of Sciences, China
Abstract—Clinical practice guidelines are supported by the best available evidence from
biomedical publications to assist clinical decision making. The recent technological advances
in natural language processing and text mining have the potential in reducing the labor cost
and time consumption of creating and updating the guidelines, and improving the quality of
clinical recommendations. In order to identify high-quality biomedical publications
automatically, we proposed an approach to classify unstructured biomedical text documents
into predefined clinical evidence levels based on the linguistic features and semantic
enrichment. We investigated the feasibility of leveraging word embeddings for clinical
evidence grading that is formulated as a text classification problem, and proposed some
strategies for semantic enrichment by incorporating the domain knowledge extracted from the
knowledge bases and semantic networks. Moreover, we evaluated the proposed method by
applying it to the clinical guidelines of breast cancer. The preliminary results demonstrated
that the proposed method performed better than the widely-used baseline methods, and
appropriate semantic enrichment could further improve the performance for this challenging
task.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 60 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:25
Venue: Activated Room 1 (1st Floor)
Session 4: Topic: “Bioinformatics and Basic Medicine”
Session Chair: Prof. Ming Chen
M0021 Presentation 10 (18:10~18:25)
Generating Cancelable Palmprint Templates Based on Bloom Filters
Jian Qiu, Hengjian Li and Jiwen Dong
University of Jinan, China
Abstract—In order to provide privacy protection and security authentication for palmprint, a
novel scheme for generating cancelable palmprint templates based on Bloom filters is
proposed in this paper. Firstly, Gabor filters are used to extract CompCode from palmprint.
Then, we propose a coding rule to encode CompCode blocks into transformation blocks.
Nextly, these transformed blocks are mapped to the Bloom filters to form a cancelable
palmprint features. Lastly, SVM classifiers are used for classification. The transformed blocks
realize a layer of security protection. On the basis of the recognition rate, Boom filters
provides effective protection of palmprint features. Experimental results on the Hong Kong
PolyU Palmprint Database verify that the proposed cancelable scheme can achieve high
recognition rate and protect palmprint templates at high security levels.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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Session 5
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session. Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:10
Venue: Activated Room 2 (1st Floor)
Session 5: Topic: “Intelligent Computing and Computer Applications”
Session Chair: Prof. Rey-Chue Hwang
M0030 Presentation 1 (15:55~16:10)
Automated Encoding of Clinical Guidelines into Computer-interpretable Format
Yuming Qiu, Peng Tang, Haolin Wang, Jun Jiang, Ju Zhang and Nanzhi Wang
Chinese Academy of Sciences, China
Abstract—Computer-interpretable guidelines (CIGs) are critical knowledge source for clinical
decision support systems (CDSS). However, most of current CIGs are encoded by medical
experts and knowledge engineers based on the clinical practice guidelines (CPGs). It is
complex, time-consuming and error-prone. This paper proposes a model and a system
framework that automates large part of the encoding process. The model employs a directed
graph representing the knowledge of a guideline, and the framework consists of a pipeline of
three steps: semi-structural guideline generation, graph reduction and validation, and CIG
construction. Furthermore, we chose two CPGs issued by National Comprehensive Cancer
Network (NCCN) to illustrate the use of this proposed framework. Automated encoding them
into semi-products saves a tremendous amount of time, reducing 25 workdays for manual
encoding work to 15 minutes of automated encoding plus 5 hours manual validation and
correction. This indicates that automated encoding tools based on rigorous models is of
practical value in a proper work framework.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 62 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:10
Venue: Activated Room 2 (1st Floor)
Session 5: Topic: “Intelligent Computing and Computer Applications”
Session Chair: Prof. Rey-Chue Hwang
K0031 Presentation 2 (16:10~16:25)
Principal Component Analysis for Financial Time Series Prediction
Li Tang, Heping Pan and Yiyong Yao
University of Electronic Science Technology of China, China
Abstract—This paper constructs an integrated model called PCA-KNN model for financial
time series prediction. Based on a K-Nearest Neighbor (KNN) regression, a Principal
Component Analysis (PCA) is applied to reduce redundancy information and data
dimensionality. In a PCA-KNN model, the historical data set as input is generated by a sliding
window, transformed by PCA to principal components with rich-information, and then input
to KNN for prediction. In this paper, we integrate PCA with KNN that can not only reduce the
data dimensionality to speed up the calculation of KNN, but also reduce redundancy
information while remaining effective information improves the performance of KNN
prediction. Two specific PCA-KNN models are tested on historical data sets of EUR/USD
exchange rate and Chinese stock index during a 10-year period, achieving the best hit rate of
77.58%.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 63 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:10
Venue: Activated Room 2 (1st Floor)
Session 5: Topic: “Intelligent Computing and Computer Applications”
Session Chair: Prof. Rey-Chue Hwang
M0037 Presentation 3 (16:25~16:40)
Classification and Feature Extraction for Text-based Drug Incident Report
Takanori Yamashita, Naoki Nakashima and Sachio Hirokawa
Kyushu University, Japan
Abstract—Medical institutions have been constructed incident report system, then
accumulating incident data. Incident data compose text-based data and some structured
attributes. We considered based on the analysis result with clustering for drug incident report.
Firstly, we generated a network of documents and words from the text-based data. Secondly,
Louvain method was applied to the network and 11 clusters were generated. We confirmed
the contents of each cluster from feature words extracted by TF-IDF. Then, we compare
clusters of text-based data with structured attributes and grasp the trend of the incident. This
proposed method showed the possibility of clinical support toward reduction incident from
text-based data.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 64 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:10
Venue: Activated Room 2 (1st Floor)
Session 5: Topic: “Intelligent Computing and Computer Applications”
Session Chair: Prof. Rey-Chue Hwang
K0043 Presentation 4 (16:40~16:55)
Thermo-Economic Multi-objective Optimization of Adiabatic Compressed Air Energy
Storage (A-CAES) System
Wenjing Hong and Longxiang Chen
University of Science and Technology of China, China
Abstract—Adiabatic compressed air energy storage (A-CAES) has been accepted as a
promising and emerging storage technology due to its excellent power and storage capacities.
Traditional A-CAES systems often store the compressed air in nature storage vessels, such as
underground hard-rock and salt caverns, thus depending heavily on geographical conditions.
This problem can be mitigated by introducing artificial vessels. However, the artificial vessels
could be very costly since their construction requires a large number of steels, accounting for
a large proportion of the capital investment of A-CAES. For a given output, the capital
investment and the performance of A-CAES system are depend on the operation pressure of
each component (compression train, expansion train, thermal energy storage tanks and
artificial vessels). In this work, both thermodynamic and economic performance of A-CAES
have been investigated through a multi-objective evolutionary optimization, and the four
operation pressures from different components are considered. Experimental results show that
the Round trip efficiency (RTE) is improved by 4.41%, the system total investment cost (TIC)
is decreased by 4.55% and the Profit is increased by 8.91% compared with conventional
A-CAES. Hence, the designed A-CAES is more efficient and more economic than the
conventional one.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 65 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:10
Venue: Activated Room 2 (1st Floor)
Session 5: Topic: “Intelligent Computing and Computer Applications”
Session Chair: Prof. Rey-Chue Hwang
K0005 Presentation 5 (16:55~17:10)
The Optimal Crane Scheduling for Chemical Polishing Process Based on Expert System
Chi-Yen Shen, Shuming T. Wang, Kaiqi Zhou, Hanlin Shen and Rey-Chue Hwang
I-Shou University, Taiwan
Abstract—It is well known that the manufacturing process of many industrial products
requires the crane lifting and delivering. The use of crane can not only reduce the cost of
manual handling, but also increase the production‘s capacity. Thus, how to design an accurate,
efficient and optimal crane scheduling becomes a very important issue in the industrial
manufacturing process, especially to the electronic industry. This paper presents an optimal
crane scheduling and control for the multiple manufacturing processes of electronic surface
treatment based on the entire plant design. The expert system with ―time-axis‖ method is used
to find the minimum number of cranes needed for the entire plant design. The surface
treatment of electronic industry is used as the example for the whole design process. The
result shows that the optimal crane scheduling developed can not only have the optimal
cranes‘ control, but also fit the requirement of minimum cycling time of each manufacturing
process.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 66 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:10
Venue: Activated Room 2 (1st Floor)
Session 5: Topic: “Intelligent Computing and Computer Applications”
Session Chair: Prof. Rey-Chue Hwang
K0003 Presentation 6 (17:10~17:25)
Extended Movement Unit for Pepper
Naoki Igo, Daichi Fujita, Ryusei Yamamoto, Toshifumi Satake, Satoshi Mitsui, Tetsuto
Kanno and Kiyoshi Hoshino
Asahikawa College, Japan
Abstract—This research realizes an extended movement unit for expanding the movement
range of Pepper. Pepper moves by the omni wheel. However, omni wheel is difficult to freely
move the floor with structurally large steps and irregularities. To extend Pepper's movement,
other devices are needed. We produced extended movement unit. The extended movement
unit aims at a unit that can be used without remodeling Pepper. The extended movement unit
consists of a mobile robot that extends mobility and a docking unit that joins the Pepper and
mobile robot. The mobile robot is based on the robot we developed. The docking unit realizes
a mechanism that can be joined to the mobile robot without modifying the Pepper. Pepper can
move various floor surfaces by docking with extended movement unit. The docking unit can
be stored and the mobile robot can be used for other tasks. By changing the docking unit
shape, the extended movement can also be used for other robots.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 67 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:10
Venue: Activated Room 2 (1st Floor)
Session 5: Topic: “Intelligent Computing and Computer Applications”
Session Chair: Prof. Rey-Chue Hwang
K0016 Presentation 7 (17:25~17:40)
Probabilistic Time Context framework for Big Data Collaborative Recommendation
Emelia Opoku Aboagye, Gee C. James, Gao Jianbin, Rajesh Kumar and Riaz Ullah Khan
University of Electronic Science and Technology of China, China
Abstract—A parallel scheme based on Probabilistic Tensor Factorization which addresses the
scalability problem of Collaborative Filtering (CF) is proposed for big data processing.
Parallel algorithms for large scale recommendation problems have witnessed advancements in
the big data era in recent times. Matrix Factorization models have been enormously used to
tackle such constraints, which we see as not scalable and does not converge easily unless
numerous iterations making it computationally expensive. This study proposes a novel
coordinate descent based probabilistic Tensor factorization method; Scalable Probabilistic
Time Context Tensor Factorization (SPTTF) for collaborative recommendation. Our
experiments with natural datasets show its efficiency.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 68 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:10
Venue: Activated Room 2 (1st Floor)
Session 5: Topic: “Intelligent Computing and Computer Applications”
Session Chair: Prof. Rey-Chue Hwang
K0029 Presentation 8 (17:40~17:55)
Optimizing a Deep Learning Model in order to have a Robust Neural Network Topology
Riaz Ullah Khan, Rajesh Kumar, Nawsher Khan, Xiaosong Zhang and Ijaz Ahad
University of Electronic Science and Technology of China, China
Abstract—In this study, a method based on different feature engineering / feature extraction /
feature derivation is proposed for improving air passenger forecasting by machine learning
existing libraries. In this kind of formulation, we kept focus on creating different kinds of
datasets that differ one from another by methodology so we extracted new features and
compared new feature space with original feature space in terms of variable importance. We
conducted experiments to improve the variance by aggregating all the features in final feature
space. Finally, we optimized a deep learning model to have a Robust Neural Network
Topology.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 69 -
Afternoon, March 13, 2018 (Tuesday)
Time: 15:55-18:10
Venue: Activated Room 2 (1st Floor)
Session 5: Topic: “Intelligent Computing and Computer Applications”
Session Chair: Prof. Rey-Chue Hwang
K0052 Presentation 9 (17:55~18:10)
Automatic Clustering of Natural Scene Using Color Spatial Envelope Feature
Haifeng Wang, Xiaoyan Wang and Yuchou Chang
Yuxi Normal University, China
Abstract—A video scene can be defined as a fixed subdivision of a video, or a group of video
frames having the same semantic contents. This paper presents a method to perform scene
classification under unsupervised clustering environment. A holistic representation of the
Spatial Envelope has been proposed to model the scene. One drawback of Spatial Envelope
features is that it uses R, G, and B channels separately to extract features for processing.
However, individual R, G, and B channels cannot describe color visual information of the
image accurately. In this paper, a novel different color channel generated with Fibonacci
lattice color quantization indexes is applied to generate Spatial Envelope features to address
this drawback. An unsupervised clustering method named as Hyperclique Pattern-KMEANS
(HP-KMEANS) is proposed to automatically select constraints for image clustering.
Evaluation of the proposed feature extraction algorithm shows promising results for natural
scene classification.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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Poster Session March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
M0013 Poster 1
The Efficacy of Peg-IFNα Anti-Viral Treatment were Evaluated by Variation of Peripheral
Th17 Cells in Chronic Hepatitis C Patients
Yizhang Xu
Georgetown Preparatory School, USA
Abstract—IL-17-producing T helper (Th17) cells have been shown to play an important role
in many liver diseases. The aim of this study is to investigate changes in the frequency of
Th17 cells in peripheral blood of chronic hepatitis C (CHC) patients. The Th17 frequencies of
36 chronic hepatitis C patients were compared with those of 20 normal controls. All samples
were quantitatively analyzed by flow cytometer. Serum IL-17 levels were evaluated using the
ELISA assay. There was a higher frequency of circulating Th17 cells and IL-17 levels in CHC
patients than controls (3.46±1.53% and 2.05±0.88% for Th17 cells, 86.21±29.28 pg/ml and
58.05±14.17 pg/ml for IL-17 levels) (P < 0.01). There were no significant differences in Th17
frequency and IL-17 levels between the groups of CHC patients with HCV RNA genotype 1b
and 2a. The percentage of circulating Th17 cells increased significantly, correlating positively
with ALT and negatively with HCVRNA. After 4 weeks of peg- IFNα-2a treatment, the
patients who acquired rapid virological response (RVR) had a higher pretreatment Th17
frequency compared with that of patients without RVR. During the 24 weeks of treatment
with peg-IFNα-2a, Th17 cell frequency increased during the initial 4 weeks, then
subsequently declined. In conclusion, Th17 cells and IL-17 were significantly increased in
CHC patients and they were a positive correlation with ALT but a negative correlation with
HCV RNA. Results suggest that an increase in Th17 cells is associated with inflammatory
liver damage and persistent infection of HCV. The characteristics of Th17 variation during
peg-IFNα-2a treatment imply that Th17 cells may serve as potent immunological markers for
evaluating the efficacy of peg-IFNα anti-viral treatment.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
M0020 Poster 2
Synonymous Permutation Reveals Selection for Less Out-of-Frame Stop Codons
Jingrui Zhong and Nanyan Zhu
Tsinghua University, China
Abstract—One important source of premature stop codons is processivity error. Although
Nonsense Mediated Decay (NMD) could degrade transcripts that contain a premature stop
codon, it has a fitness cost. Thus, it is commonly assumed that it is advantageous for genes to
stop early after a frameshift. However, we didn‘t identify any pattern for excessive
Out-of-frame Stop Codons (OSC) in S. cerevisiae. We shuffled the synonymous codons in
genes without changing codon preference and amino acid sequence and found fewer stop
codons were selected for in +1 reading frame, while no significant selection force was
detected in -1 reading frame. Moreover, we checked when the first OSC appears; it is shown
that there is also a selection force to avoid its early appearance. Hereby, with the support of
some experimental result from another study, we raise a new hypothesis for the cost of OSC:
it is more advantages to move on translating instead of truncating the peptide because the
former might still to give rise to some functional products, while the latter could not. In
general, the relative fitness cost of losing the possible functional products is higher than the
possible costs of degrading the non-functional products.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
M0025 Poster 3
Predicting Drug-target Interaction via Wide and Deep Learning
Yingyi Du, Jihong Wang, Xiaodan Wang, Jiyun Chen and Huiyou Chang
Sun Yat-Sen University, China
Abstract—Identifying the interactions of approval drugs and targets is essential in medicine
field, which can facilitate the discovery and reposition of drugs. Due to the tendency towards
machine learning, a growing number of computational methods have been applied to the
prediction of the drug-target interactions (DTIs). In this paper, we propose a wide and deep
learning framework combining a generalized linear model and a deep feed-forward neural
network to address the challenge of predicting the DTIs precisely. The proposed method is a
joint training of the wide and deep models, which is implemented by feeding the weighted
sum of the results obtained from the wide and deep models into a logistic loss function using
mini-batch stochastic gradient descent. The results of this experiment indicate that the
proposed method increases the accuracy of prediction for DTIs, which is superior to other
methods.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
M0031 Poster 4
Research of Heart Rate Variability Analysis System Based on Cloud Model
Zhangyong Li, Yaoming An and Shangzhi Xiang
Chongqing University of Post and Telecom, China
Abstract—The heart rate variability (HRV) has been used to analysis many diseases due to the
non-invasive characteristic. In recent years, researches show that there exists relationship
between mental stress and HRV. With the development of science and technology, health
monitoring is becoming more and more intelligent. In this paper, a heart rate variability based
on cloud model has been proposed. The HRV analysis system has been deployed on the cloud
platform, which can realize the basic analysis of HRV. Meanwhile, this paper presents a
quantitative model of mental stress based on fuzzy analytic hierarchy process (FAHP). The
results demonstrate that the system can well realize the above analyses. It is significant to
human health.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 74 -
March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
M0042 Poster 5
FlexSLiM: a Novel Approach for Short linear Motif Discovery in Protein Sequences
Xiaoman Li, Ping Ge and Haiyan Hu
University of Central Florida, USA
Abstract—Short linear motifs are 3 to 11 amino acid long peptide patterns that play important
regulatory roles in modulating protein activities. Although they are abundant in proteins, it is
often difficult to discover them by experiments, because of the low affinity binding and
transient interaction of short linear motifs with their partners. Moreover, available
computational methods cannot effectively predict short linear motifs, due to their short and
degenerate nature. Here we developed a novel approach, FlexSLiM, for reliable discovery of
short linear motifs in protein sequences. By testing on simulated data and benchmark
experimental data, we demonstrated that FlexSLiM more effectively identifies short linear
motifs than existing methods. We provide a general tool that will advance the understanding
of short linear motifs, which will facilitate the research on protein targeting signals, protein
post-translational modifications, and many others.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 75 -
March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
M0043 Poster 6
Neural Correlates of Emotional Regulation Processing: Evidence from ERP and Source
Current Density Analysis
Zhen-Hao Wang, Yi Wang, Dong-Ni Pan and Xuebing Li
Institute of Psychology, Chinese Academy of Sciences; University of Chinese Academy of
Sciences, China
Abstract—The history of human industrial development has undergone three industrial
revolutions. Based on the occurrence time sequences of the three industrial revolutions, the
industrial revolution time prediction function based on quadratic function and the time
prediction model based on gray GM (1,1) model are established. The prediction results show
that the fourth industrial revolution will take place around 2055. Based on the new
technologies, such as the Internet of things, big data, cloud computing and intelligent
manufacturing, the characteristics of the fourth industrial revolution is predicted.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 76 -
March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
M3001 Poster 7
Shorten Bipolarity Checklist for the Differentiation of Subtypes of Bipolar Disorder using
Machine Learning
Chaonan Feng, Huimin Gao, Xuefeng B Ling, Jun Ji and Yantao Ma
Qingdao University, China
Abstract—The differentiation of type I and type II of bipolar disorder is difficult. In clinical
practices, corresponding diagnostic operability is poor since their criterions are similar and do
not include past or lifetime characteristics. The aim of this study was to generate the clinical
feasible scale by using machine learning algorithms based on the analysis of a Chinese
multi-center cohort data. To evaluate the importance of each item of Affective Disorder
Evaluation(ADE), a case-control study of Chinese samples including 281 type I of bipolar
disorder and 79 type II of bipolar disorder patients conducted from 9 Chinese health facilities
participating in СΑFÉ-BD. The novel scale was formed by selected items from ADE
according to its importance calculated by mutual information criteria of
minimal-redundancy-maximal-relevance(mRMR).
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0009 Poster 8
Optimization of Contract Distribution Based on Multi-objective Estimation of Distribution
Algorithm
Laihong Hu, Xiaogang Yang and Hongdong Fan
Xi'an Research Inst. of Hi-tech, China
Abstract—Contract distribution is widely exists in modern commercial society, which mainly
depends on qualitative analysis, and there still lack studies of quantitative analysis. Based on
multi-objective estimation of distribution algorithm (MOEDA), quantitative research idea on
contract distribution is explored in this article. First of all, Multi-objective optimization model
is built for contract distribution. Then, the algorithm flow base on MOEDA is designed. At
last, simulations are carried out and compare with multi-objective genetic algorithm (MOGA).
The simulation results show that the MOEDA performs better than MOGA, and verify the
effectiveness and robustness of the proposed method in optimization of contract distribution.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 78 -
March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0011 Poster 9
A Denoising Autoencoder Approach for Credit Risk Analysis
Qi Fan and Jiasheng Yang
Nankai University, China
Abstract—Credit risk evaluation is a key consideration in financial activities. Financial
institutions such as banks rely on credit risk analysis for determining the potential risk
involved in financial activities and then decide the degree of involvement in such activities as
well as the appropriate interest rate and the amount of capital that should be reserved. The
recent development of machine learning has provided powerful tools for computer-aided
credit risk analysis, and neural networks are one of the most promising approaches. However,
conventional artificial neural networks involve multiple layers of neurons which then become
a universal function that can approximate any function. Therefore, it will learn from not only
the information in the training data set but also from the noise in it. It is critical to remove the
noise in order to improve the accuracy and efficiency of such algorithms. In this paper, a
denoising autoencoder approach is proposed for the training process for neural networks. The
denoising-autoencoder-based neural network model is then applied to credit risk analysis, and
the performance is evaluated.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0020 Poster 10
Supervised Prediction of China's Seven-Day Interbank Pledged Repo Rate
Yiwu Lin and Liping Shen
Shanghai Jiao Tong University, China
Abstract—In this paper, we try to predict the China's seven-day interbank pledged repo rates
of T + 1, T + 7 and T + 30. Repo rates are crucial for bankers to determine the level of money
availability in the market. We use time series prediction to model this problem and try three
categories of supervised learning algorithms on a real-world data set. Up to 312 kinds of
features are used and models as linear regression, support vector regression and LSTMs are
tried. We find that the T + 1 case is quite predictable, whose best result of 89.1% accuracy and
0.226 RMSE is obtained using lasso regression, which belongs to the category of linear
regression. However, the T + 7 and T + 30 case cannot be predicted that accurately with
whatever methods.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 80 -
March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0023 Poster 11
Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles
Firas Gerges, Germain Zouein and Danielle Azar
Lebanese American University, Lebanon
Abstract—Sudoku is a popular combinatorial number puzzle game and is widely spread on
online blogs and in newspapers worldwide. However, the game is very complex in nature
and solving it gives rise to an NP-Complete problem. In this paper, we introduce a heuristic to
tackle the problem. The heuristic is a genetic algorithm with modified crossover and mutation
operators. In addition, we present a new approach to prevent the genetic algorithm from
getting stuck in local optima. We refer to this approach as the ―purge approach‖. We test our
algorithm on different puzzles of different difficulty levels. Results show that our algorithm
outperforms several existing methods.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
- 81 -
March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0025 Poster 12
Remote Intelligent Position-Tracking and Control System with MCU/GSM/GPS/IoT
Jianpei Shi, Liqiang Zhang and Daohan Ge
Jiangsu University, China
Abstract—In this paper, we applied IoT (Internet of things) technology and SMS (short
message service) technology to vehicle security system, and designed vehicle remote control
system to ensure the vehicle security. Besides, we discussed the method that converted the
displacement increment to latitude and longitude increment in order to solve the problem that
how to accurately obtain the current location information when GPS (Global Positioning
System) failed. The hardware system can realize such function that owners by sending an
SMS, or by sending the password through web side of IoT platform, you can remotely control
the car alarm system opening or closing, and query vehicle position and other functions.
Through this method, it is easy to achieve security for vehicle positioning and tracking.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K2001 Poster 13
Fuzz Testing Based On Virtualization Technology
Longbin Zhou and Zhoujun Li
Beihang University, China
Abstract—As people pay more and more attention to software security, the technology of
vulnerability mining has gradually become the research hotspot in the industry. Fuzz testing is
the mainstream of the vulnerability mining technology. In order to solve the shortcomings of
the traditional document fuzz testing, such as efficiency is not high and the function is
missing, so a new method of document fuzz testing will be introduced. In this paper, there
will be a new way to streamline the test sample. It depends on the code coverage. So the
smallest sample set of maximum code coverage will be gotten by using this method. It relies
on virtual machine technology, it is more reliable and more accurate than Binary
instrumentation technology. This method can effectively reduce a large number of invalid test.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0034 Poster 14
Image Authenticity Decision Based on Random Sample Consensus and Circular Feature
Selection
Xueyan Li
Wuhan University of Science and Technology, China
Abstract—In order to reduce the complexity of forgery detection algorithm and improve the
accuracy, this paper proposes an image forgery detection algorithm based on DCT coupled
random sample consensus optimization. First of all, the initial image is divided into
sub-blocks of uniform size and DCT coefficients for each block is obtained through DCT to
represent each blocks; then, circular feature screening mechanism is established to extract
four features of the block, thereby reducing the feature dimension of each block. Finally, each
eigenvector is ordered in a lexicographical manner and prior threshold is used to match the
feature, reduce the image block false matching rate optimized by random sample consensus,
thus completing the image authenticity for decision making. Experimental results show that,
compared with the current image forgery detection algorithm, this algorithm has better
robustness, efficiency and accuracy, and good detection effects on the fuzzy and noise forgery.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0038 Poster 15
DeepXSS: Cross Site Scripting Detection Based on Deep Learning
Yong Fang, Yang Li, Cheng Huang and Liang Liu
Sichuan University, China
Abstract—Nowadays, Cross Site Scripting (XSS) is one of the major threats to Web
applications. Since it‘s known to the public, XSS vulnerability has been in the TOP 10 Web
application vulnerabilities based on surveys published by the Open Web Applications Security
Project (OWASP). How to effectively detect and defend XSS attacks are still one of the most
important security issues. In this paper, we present a novel approach to detect XSS attacks
based on deep learning (called DeepXSS). First of all, we used word2vec to extract the
feature of XSS payloads which captures word order information and map each payload to a
feature vector. And then, we trained and tested the detection model using Long Short Term
Memory (LSTM) recurrent neural networks. Experimental results show that the proposed
XSS detection model based on deep learning achieves a precision rate of 99.5% and a recall
rate of 97.9% in real dataset, which means that the novel approach can effectively identify
XSS attacks.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0040 Poster 16
Detecting Webshell Based On Random Forest with FastText
Yong Fang, Yaoyao Qiu, Cheng Huang and Liang Liu
Sichuan University, China
Abstract—Web-based remote access Trojan (or webshell) is a kind of tool for network
intrusion, which can be uploaded to a website to access web service management authority.
Once attacker injected successfully, it can cause great damage so that it is crucial to detect
webshell effectively. Webshells are flexible and changeable by using of obfuscation
techniques, which compounds the difficulties of detecting. A PHP webshell detection model is
proposed in this paper, which based on a combination of fastText and random forest algorithm
and called FRF-WD. The PHP opcode sequences as an important feature applied for webshell
detection. The experimental results show that the model can provide high detection rate and
low false alarm rate, which proved the feasibility and validity of the model.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0047 Poster 17
A Multi-Layer Neural Network Model Integrating BiLSTM and CNN for Chinese Sentiment
Recognition
Shanliang Yang, Qi Sun, Huyong Zhou and Zhengjie Gong
Communication University of China, China
Abstract—Technology of artificial intelligent has become research focus. Natural language
understanding (NLU) is regarded as core technology of AI. Sentiment recognition is a
difficult task in NLU; however it is advantageous to business market and public opinion
analysis. We proposed a multi-layer neural network model through integrating LSTM and
CNN to improve the performance of sentiment recognition. The structure of LSTM is
appropriate to storage text sequence information, and CNN has ability to extract salient
features for sentiment recognition task. We implemented models of LSTM-CNN and
BiLSTM-CNN, and conduct experiments on different dataset. In the end, we contrast our
proposed method with certain baseline methods. The result shows that the proposed method
outperforms single layer model and other statistic learnint method.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0048 Poster 18
A Topic Detection Method Based on KeyGraph and Community Partition
Shanliang Yang, Qi Sun, Huyong Zhou, Zhengjie Gong, Yangzhi Zhou and Junhong Huang
Communication University of China, China
Abstract—More and more media stream data is created on the Internet every day. It‘s more
difficult for persons to obtain valuable information due to information overload. Topic
detection is the method that extracts valuable hot topics from media stream data. It is the tool
to help to solve the problem of overload information. The topic positive accuracy of cluster
method is very low. In this paper, we proposed one topic detection method based on
KeyGraph to improve the positive accuracy, and took experiments compared with baseline
method on corpus marked by graduate students. In the result, the positive accuracy of
KeyGraph method reaches 88.48% with great improvement. The result verified the
effectiveness of our proposed method.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K0050 Poster 19
A Topic Detection Method Based on KeyGraph and Community Partition
Shanliang Yang, Qi Sun, Huyong Zhou, Zhengjie Gong, Yangzhi Zhou and Junhong Huang
Communication University of China, China
Abstract—More and more media stream data is created on the Internet every day. It‘s more
difficult for persons to obtain valuable information due to information overload. Topic
detection is the method that extracts valuable hot topics from media stream data. It is the tool
to help to solve the problem of overload information. The topic positive accuracy of cluster
method is very low. In this paper, we proposed one topic detection method based on
KeyGraph to improve the positive accuracy, and took experiments compared with baseline
method on corpus marked by graduate students. In the result, the positive accuracy of
KeyGraph method reaches 88.48% with great improvement. The result verified the
effectiveness of our proposed method.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K4001 Poster 20
Analysis and Design of Item Bank System Based on Improved Genetic Algorithm
Jie Zhang
Beijing Institute of Technology, China
Abstract—The application of the item bank system can effectively ensure the quality of the
examination questions and the stability of the level of the problem, and can achieve the
purpose of testing better. According to the changes of modern teaching thought and teaching
means, in the era of rapid development of distance education, the traditional item bank system
needs further analysis and optimization. In order to make the item bank system more
intelligent, with the help of the connotation of EAI and the depth study of the theory test
paper algorithm, an improved genetic algorithm is introduced into the system to solve the test
paper problem. As a result, the efficiency of the test paper system is improved, the reliability
and validity of the test papers are also effectively improved. Finally the network item bank
system is optimized.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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March 13, 2018 (Tuesday)
Time: 09:00~18:20
Venue: Activated Room 1&2 (1st Floor)
K4002 Poster 21
Cloud Based Face Recognition for Google Glass
Zeeshan Shaukat, Juan Fang, Muhammad Azeem, Faheem Akhtar and Saqib Ali
Beijing University of Technology, China
Abstract—Face recognition applications can benefit from the cloud computing as they
become widely available and easy to acquire today. There are numerous applications of face
recognition in terms of security, assistance, guidance and so on. By performing the face
recognition on cloud, we can greatly reduce the processing time and clients will not have to
store the big data for the image verification on their local machine (cell phones, pc's etc).
Cloud computing increases the processing power and storage with very less cost comparing to
the cost of acquiring an equally strong server machine. In this research the plan is to enhance
the user experience of augmented display wearing google glass, and for doing that, this
system is being proposed in which a person wearing google glass will send an image of a
person to cloud server powered by Hadoop (open-source software for reliable, scalable,
distributed computing) cloud server will recognize the face from the database already present
on server and then response to client device (google glass). Then google glass will display the
face details in a form of augmented display to the person wearing them. By moving the face
recognition process on cloud, the device will require less processing power, and by having the
database on cloud server, multiple clients will no longer require to maintain their local
database.
Dinner
18:30-20:00 Yue Club
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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Conference Venue
Skytel Hotel Chengdu, Chengdu, China
Add: No. 15, South Railway Station West Road, Wuhou District, Chengdu, Sichuan Province,
China
中国四川省成都市武侯区火车南站西路 15号
Contact Person: Dan Yuan
Tel.: +86-18030613968
Contact email: [email protected]
Skytel Hotel Chengdu is constructed by Sichuan Xingzhong Investment Co., Ltd. and under the
management of Grand Skylight Hotel Management Co., Ltd. Skytel is located at No. 15, South
Railway Station Road West, with advantageous location and convenient transportation, covering an
area of about 16,000m2. It is ten-minute drive from Shuangliu International Airport in the west,
Tianfu Square in the North, Hi-tech Industrial Development Zone and New International Convention
& Exhibition Center in the south.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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ONE DAY VISIT 08:30-17:30 March 14, 2018
Chengdu, China
Chengdu is a starting point for the national historical and cultural city, the best tourist city in
China and the southern Silk Road. It is one of the 'Top 10 Ancient Capital Cities', and it was
built around the 5th century BC. In the Western Han Dynasty, it became one of the six major
cities in China. During the Northern Song Dynasty, Chengdu people jointly issued the earliest
banknotes in the world, and the government set up the world's earliest managed savings bank
in Chengdu. More than 2,600 years of history of the city gave birth to Dujiangyan, Wuhou
Temple, Du Fu Thatched Cottage, Jinsha sites and many other places of interest.
Travel Schedule
Morning: Chengdu Research Base of Giant Panda (熊猫基地) Jinli (锦里)
Afternoon: Temple of Marquis (武侯祠) Kuan & Zhai Ally (宽窄巷子)
Chengdu Research Base of Giant Panda Breeding(熊猫基地) Chengdu Research Base of Giant Panda Breeding, or simply Chengdu Panda Base, is a non-profit research
and breeding facility for giant pandas and other rare animals. It is located in Chengdu, Sichuan, China.
Chengdu Panda Base was founded in 1987. It started with 6 giant pandas that were rescued from the wild.
By 2008, it had 124 panda births, and the captive panda population has grown to 83. Its stated goal is to
"be a world-class research facility, conservation education center, and international educational tourism
destination."
Jinli (锦里) Jinli is a street about 550 meters long. There are many bars, inns, snack stores and souvenir shops. The
street was renovated in 2004. In 2005, Jinli was named as “National Top Ten City Commercial Pedestrian
Street”. In 2006, Jinli was named as “National Demonstration Base of the Cultural Industry” by the
Ministry of Culture.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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Temple of Marquis (武侯祠) Wuhou Temple (Memorial Temple of Marquis Wu) is dedicated to Zhuge Liang, the Marquis Wu (Wuhou)
of Kingdom of Shu in the Three Kingdoms Period (220 - 280). Zhuge Liang was the personification of noble
character and intelligence. Memorial architectures erected in many places after his death include a
famous one in Chendu. Located in the south suburb of Chengdu, the temple covers 37,000 square meters.
It was combined with the Temple of Liu Bei at the beginning of the Ming Dynasty; consequently, the
entrance plaque reads 'Zhaolie Temple of Han Dynasty' (Zhaolie is the posthumous title of Liu Bei). The
current temple was rebuilt in 1672. Surrounded by old cypresses and classical red walls, it evokes
nostalgia.
Kuan & Zhai Ally (宽窄巷子)
Kuan Alley is a relatively large-scale ancient Qing Dynasty street left in Chengdu. Together with Daci
Temple and Wenshu Monastery, it is also known as the Three Preservation Historical and Cultural Cities
Block in Chengdu. Kuan & Zhai Alley is a long history card of Chengdu, where you can touch the traces of
history, but also appreciate the original taste of Chengdu leisure lifestyle, into the width of the alley,
walked into the most Chengdu, the world, the oldest and most fashionable old Chengdu business card.
Kuan & Zhai alley is a microcosm of the ancient and young city in Chengdu, a symbol of memory. Chengdu
people's generalization is more refined: Wide Alley: Chengdu's 'free life'; Narrow Alley: Old Chengdu's
'slow life'; Alley: Chengdu's 'new life.'
Note:
Lunch is not included.
Pick up at Skytel Hotel Chengdu at 8:30 a.m.
Guests are responsible for their belongings.
The above places are for references, and the final schedule should be adjusted to the actual
notice.
2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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2018 CBEES-BBS CHENGDU, CHINA CONFERENCE
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