2018 cbees-bbs chengdu, china conference ... 2018 program.pdfk0016: probabilistic time context...

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/

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]

http://dl.acm.org/

http://www.iccai.org/

mailto:[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.


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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.


- 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


- 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.


- 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).


- 21 -

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


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


10:50-11:25 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


Topic: Modern Information

Engineering and Technology

9 presentations

15:35-15:55 Coffee Break


15:55-16:10 Invited Speech III

Prof. Ming Chen, Zhejiang University, China


- 22 -

March 13,

2018

(Tuesday)

Afternoon

15:55-18:25 Session 4


Topic: Bioinformatics and Basic

Medicine

10 presentations

15:55-18:10 Session 5


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.


- 23 -

Detailed Schedule for Conference

March 12, 2018 (Monday)


10:00~18:00 Arrival and Registration (Hotel Lobby)

13:30-13:35

Opening Remarks



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


Topic: “On Poisson Models in the Analysis of RNA-seq Data”

14:55-15:35

Keynote Speech III



Topic: “3D Face Modeling: Images, Shapes, and DNA”


16:00-16:15

Invited Speech I

Prof. Zhiwei Qiao


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)


09:00-09:05

Opening Remarks

Prof. Yinglei Lai


09:05-09:45

Keynote Speech IV

Prof. Edwin Wang

University of Calgary, Canada

Topic: “From Health Genomics to Intelligent Precision Health”


- 24 -

09:45-10:25

Keynote Speech V

Prof. Kiyoshi Hoshino

University of Tsukuba, Japan

Topic: ―Technology for Acquiring Biosignals Generated during Eye

Movements‖


10:50-11:25

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


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


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


Topic: Modern Inf

ormation Engineering and Technology


15:55-16:10

Invited Speech III

Prof. Ming Chen


Topic: “Single Cell Big Data Analysis”

15:55-18:25


Topic: Bioinformatics and Basic

Medicine


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!


- 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


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


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.


- 26 -


Time: 16:00-18:00




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.


- 27 -


Time: 16:00-18:00




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 approach. 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

different 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 accuracy of 98% on Vision Research dataset.


- 28 -


Time: 16:00-18:00




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.


- 29 -


Time: 16:00-18:00




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


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.


- 30 -


Time: 16:00-18:00




M0022 Presentation 6 (17:15~17:30)

A Compression–Encryption Hybrid Algorithm Based on Compressive Sensing

Changzhi Yu, Hengjian Li and Jiwen Dong


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.


- 31 -


Time: 16:00-18:00




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.


- 32 -


Time: 16:00-18:00




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.


- 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


Session 2: Topic: “Genetic Engineering and Protein Structure Analysis”

Session Chair: Prof. Minghui Li


Estimating and Interpreting the Effects of Sequence Variants and Cancer Mutations on Protein

Function

Minghui Li


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.


- 34 -

Afternoon, March 13, 2018 (Tuesday)

Time: 13:20-15:35




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.


- 35 -


Time: 13:20-15:35




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


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.


- 36 -


Time: 13:20-15:35




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


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.


- 37 -


Time: 13:20-15:35




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


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.


- 38 -


Time: 13:20-15:35




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.


- 39 -


Time: 13:20-15:35




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.


- 40 -


Time: 13:20-15:35




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.


- 41 -


Time: 13:20-15:35




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.


- 42 -

Session 3


we strongly suggest that you attend the whole session.


Time: 13:20-15:35


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.


- 43 -


Time: 13:20-15:35




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


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.


- 44 -


Time: 13:20-15:35




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


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.


- 45 -


Time: 13:20-15:35




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.


- 46 -


Time: 13:20-15:35




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.


- 47 -


Time: 13:20-15:35




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.


- 48 -


Time: 13:20-15:35




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.


- 49 -


Time: 13:20-15:35




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.


- 50 -


Time: 13:20-15:35




K0042 Presentation 9 (15:20~15:35)

Human Segmentation with Deep Contour-Aware Network

Fiseha Berhanu, Hong Wu and William Zhu


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.



- 51 -

Session 4



Time: 15:55-18:25


Session 4: Topic: “Bioinformatics and Basic Medicine”

Session Chair: Prof. Ming Chen


Single Cell Big Data Analysis

Ming Chen


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).


- 52 -


Time: 15:55-18:25




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.


- 53 -


Time: 15:55-18:25




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.


- 54 -


Time: 15:55-18:25




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.


- 55 -


Time: 15:55-18:25




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.


- 56 -


Time: 15:55-18:25




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.


- 57 -


Time: 15:55-18:25




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.


- 58 -


Time: 15:55-18:25




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.


- 59 -


Time: 15:55-18:25




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


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.


- 60 -


Time: 15:55-18:25




M0021 Presentation 10 (18:10~18:25)

Generating Cancelable Palmprint Templates Based on Bloom Filters

Jian Qiu, Hengjian Li and Jiwen Dong


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.


- 61 -

Session 5



Time: 15:55-18:10


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


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.


- 62 -


Time: 15:55-18:10




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%.


- 63 -


Time: 15:55-18:10




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.


- 64 -


Time: 15:55-18:10




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.


- 65 -


Time: 15:55-18:10




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.


- 66 -


Time: 15:55-18:10




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.


- 67 -


Time: 15:55-18:10




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


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.


- 68 -


Time: 15:55-18:10




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


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.


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Time: 15:55-18:10




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.


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Poster Session March 13, 2018 (Tuesday)

Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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).


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


K0038 Poster 15

DeepXSS: Cross Site Scripting Detection Based on Deep Learning

Yong Fang, Yang Li, Cheng Huang and Liang Liu


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.


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Time: 09:00~18:20


K0040 Poster 16

Detecting Webshell Based On Random Forest with FastText

Yong Fang, Yaoyao Qiu, Cheng Huang and Liang Liu


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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


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.


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Time: 09:00~18:20


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


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.


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Time: 09:00~18:20


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.


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Time: 09:00~18:20


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


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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.

mailto:[email protected]


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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.


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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.


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Note


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Note


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Note


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