Shanghai, China | December 20-23, 2019 中国上海 | 2019 年 12 月 20-23 日

FRONT PAGE

2019 The 7th International Conference on Information Technology: IoT and Smart City (ICIT 2019) 第七届信息技术国际会议 :物联网与智慧城市

2019 The 3rd International Conference on

Video and Image Processing (ICVIP 2019) 第三届视频和图像处理国际会议

Shanghai, China | December 20-23, 2019 中国上海 | 2019 年 12 月 20-23 日

Co-sponsored by

Published by PCO

CONTENTS

Welcome Message ...... 1

Agenda Overview ...... 2

Venue ...... 4

Guideline ...... 5

Warm Tips ...... 6

Detailed Agenda ...... 7

Introduction of Speakers ...... 16

Session 1- Bioinformatics and Computational Biology ...... 21

Session 2- Target Detection ...... 27

Session 3- Electronic Information Engineering and Technology ...... 33

Session 4- Text analysis and High-performance Computing ...... 39

Session 5- Pattern Recognition and Image Security ...... 44

Session 6- Image Analysis and Calculation ...... 50

Session 7- Image Processing Technology and Method ...... 56

Session 8- Computer Photography and Video Processing Technology ...... 61

Session 9- Digital Communication and Wireless Technology ...... 67

Session 10- Internet of Things and Information Network ...... 73

Session 11- Computer Information Technology and Software Engineering ...... 78

Session 12- Computer and Business Intelligence ...... 84

Posters ...... 90

WELCOME

Dear distinguished delegates,

On behalf of the conference committees, we are pleased to welcome you to the 7th International Conference on Information Technology: IoT and Smart City (ICIT 2019) and the 3rd International Conference on Video and Image Processing (ICVIP 2019), which are held in Shanghai, China on Dec. 20-23, 2019, sponsored by Singapore Institute of Electronics and School of Electronic Information and Electrical Engineering of Shanghai Jiao Tong University.

The conference features peer-reviewed presentations showcasing the latest technologies, applications and services. Prestigious experts and professors have been invited to deliver the latest information in their respective expertise areas. The evaluation of all the papers was performed based on the reports from anonymous reviewers, who are qualified in their field. As a result of their hard work, we are pleased to have accepted 166 presentations coming from initially from 293 papers which were submitted to ICIT & ICVIP from universities, research institutes, and industries. The presentations are divided into 2 poster sessions and 12 parallel sessions with topics including Bioinformatics and Computational Biology, Target Detection, Electronic Information Engineering and Technology, Text Analysis and High-performance Computing, Pattern Recognition and Image Security, Image Analysis and Calculation, Image Processing Technology and Method, Computer Photography and Video Processing Technology, Digital Communication and Wireless Technology, Internet of Things and Information Network, Computer Information Technology and Software Engineering, Computer and Business Intelligence.

We'd like to express our sincere gratitude to everyone who has contributed to this conference as its success could have only been achieved through a team effort. A word of special welcome is given to our keynote and invited speakers who are pleased to make contributions to our conference and share their new research ideas with us. They are Prof. Maode Ma from Nanyang Technological University, Singapore, Prof. David Zhang from Chinese University of Hong Kong, Shenzhen, China, Prof. Saman Halgamuge from the University of Melbourne, Australia, Prof. Hesheng Wang from Shanghai Jiao Tong University, China and Assoc. Prof. Manhua Liu, Shanghai Jiao Tong University, China. Additionally, our special thanks go to all committee members for their excellent work in reviewing the papers and their other academic support efforts.

Hope you will enjoy the charming environment of Shanghai, " Paris of the East"! There are historical and cultural architectural wonders lining the Bund and Xintiandi, as well as fashionable attractions in Pudong. You can go to the Former French Concession’s Tianzifang and Chenghuang (City God) Temple in the Yu Garden complex to experience what the area was once like centuries ago. Shanghai is a multifaceted place no one will regret visiting.

We believe that by this excellent conference, you can get more opportunity for further communication with researchers and practitioners with the common interest in this field. We are dedicated to higher and better international conference experiences. We will sincerely listen to any suggestion and comment. Wish you will enjoy this conference, contribute effectively toward it and take back with your knowledge, experiences, contacts and happy memories of these days.

We look forward to meeting you again next time! Yours sincerely, Conference Chairs Prof. Maode Ma, Nanyang Technological University, Singapore Prof. Hesheng Wang, Shanghai Jiao Tong University, China Program Chair Prof. Xuefeng Liang, Xidian University, China

AGENDA OVERVIEW

December 20, 2019 (Friday)

Lobby, Grand Metro Park Jiayou Hotel 10:00-17:00 Registration & Materials Collection 上海佳友唯景大酒店大堂 December 21, 2019 (Saturday) Morning

Chaired by Prof. Xuefeng Liang, Xidian University, China

Opening Remarks – Prof. Tao Han, Vice Dean Jiayou Room (Level 1, Building B) 09:00-09:10 School of Electronic Information and Electrical B 栋一楼佳友厅 Engineering, Shanghai Jiao Tong University, China Keynote Speech I - Prof. Maode Ma Jiayou Room (Level 1, Building B) 09:10-09:50 Nanyang Technological University, Singapore B 栋一楼佳友厅

09:50-10:00 Group Photo

10:00-10:30 Coffee Break Invited Speech - Assoc. Prof. Manhua Liu Jiayou Room (Level 1, Building B) 10:30-11:00 Shanghai Jiao Tong University, China B 栋一楼佳友厅 Jiayou Room (Level 1, Building B) 11:00-12:00 Poster Presentations I B 栋一楼佳友厅 Barossa Room (Level 1, Building A) 12:00-13:30 Lunch A 栋一楼巴罗莎 December 21, 2019 (Saturday) Afternoon

Session 1 - Bioinformatics and Computational Meeting Room 1 (Level 3, Building A) 13:30-16:00 Biology A 栋三楼会议室 1 生物信息学与计算生物 Session 2 - Target Detection Meeting Room 2 (Level 3, Building A) 13:30-16:00 目标检测 A 栋三楼会议室 2 Session 3 - Electronic Information Engineering and Meeting Room 9 (Level 3, Building A) 13:30-16:00 Technology A 栋三楼会议室 9 电子信息工程与技术

16:00-16:15 Coffee Break & Group Photo Session 4 - Text analysis and High-performance Meeting Room 1 (Level 3, Building A) 16:15-18:45 Computing A 栋三楼会议室 1 文本分析与高性能计算 Session 5 - Pattern Recognition and Image Security Meeting Room 2 (Level 3, Building A) 16:15-19:00 模式识别与图像安全 A 栋三楼会议室 2 Session 6 - Image Analysis and Calculation Meeting Room 9 (Level 3, Building A) 16:15-19:00 图像分析与计算 A 栋三楼会议室 9 Barossa Room (Level 1, Building A) 19:00-20:30 Dinner A 栋一楼巴罗莎 2

AGENDA OVERVIEW December 22, 2019 (Sunday) Morning

Chaired by Prof. Maode Ma, Nanyang Technological University, Singapore Keynote Speech I - Prof. David Zhang Jiayou Room (Level 1, Building B) 09:00-09:40 Chinese University of Hong Kong, Shenzhen, China B 栋一楼佳友厅 Keynote Speech II - Prof. Saman Halgamuge Jiayou Room (Level 1, Building B) 09:40-10:20 The University of Melbourne, Australia B 栋一楼佳友厅

10:20-10:40 Coffee Break & Group Photo Keynote Speech III - Prof. Hesheng Wang Jiayou Room (Level 1, Building B) 10:40-11:20 Shanghai Jiao Tong University, China B 栋一楼佳友厅 Jiayou Room (Level 1, Building B) 11:20-12:00 Poster Presentations II B 栋一楼佳友厅 Barossa Room (Level 1, Building A) 12:00-13:30 Lunch A 栋一楼巴罗莎 December 22, 2019 (Sunday) Afternoon Session 7 - Image Processing Technology and Meeting Room 1 (Level 3, Building A) 13:30-16:00 Method A 栋三楼会议室 1 图像处理技术与方法 Session 8 - Computer Photography and Video Meeting Room 2 (Level 3, Building A) 13:30-16:00 Processing Technology A 栋三楼会议室 2 计算机摄影学与视频处理技术 Session 9 - Digital Communication and Wireless Meeting Room 9 (Level 3, Building A) 13:30-15:45 Technology A 栋三楼会议室 9 数字通信与无线技术

15:45-16:15 Coffee Break & Group Photo Session 10 - Internet of Things and Information Meeting Room 1 (Level 3, Building A) 16:15-18:45 Network A 栋三楼会议室 1 物联网与信息网络 Session 11 - Computer Information Technology and Meeting Room 2 (Level 3, Building A) 16:15-18:45 Software Engineering A 栋三楼会议室 2 计算机信息技术与软件工程 Session 12 - Computer and Business Intelligence Meeting Room 9 (Level 3, Building A) 16:00-19:00 计算机与商务智能 A 栋三楼会议室 9 Restaurant (Level 2, Building A) 19:00-20:30 Dinner A 栋二楼中餐厅

December 23, 2019 (Monday) | Social Program

09:00-17:00 Social Program

VENUE

Grand Metro Park Jiayou Hotel

上海佳友唯景大酒店(或上海佳友美仑国际酒店) Add:No.159, Xin Jin Qiao Rd., Pudong, Shanghai, China (In Google Map, 6HWW+WQ) 地址：上海浦东新区新金桥路 159 号酒店官网：http://www.grandmetrohotel.com/

Getting Here Shanghai Pudong International Airport, (30-minute drive, about 100.00 CNY by taxi) Shanghai Hongqiao International Airport, (50-minute drive, about 130.00 CNY by taxi) Hongqiao Railway Station(West) (45-minute drive, about 130.00 CNY by taxi)

Metro+ Walking---- around 1 hr 30 min (Ticket: 7.00 CNY)

Shanghai Pudong International Airport Subway Station Take the Metro Line 2 (Toward East Xujing) (Ride 14 stops,53min) $ Transfer at Century Avenue $ Take the Metro Line 09 (Toward Caolu) (Ride 4 stops,14min) $ Taierzhuang Road (Follow signs for Exit 3) $

Walk 16 min

GUIDELINE

Poster Guideline

Please read it carefully:

 Please bring your own poster.

 Prepare the Poster

*Your poster should cover the KEY POINTS of your work.

*The title of your poster should appear at the top about 25mm (1”) high.

*The author(s) name(s) and affiliation(s) are put below the title

*Posters are required to be condensed and attractive. The characters should be large enough so that they are visible from 1 meter apart. Suggested Poster with size of A1 (594mm×840mm width*height), with conference short name and paper ID on right up corner.

 During the poster session, authors should stay next to their posters, explain and discuss the papers, under the guidance of the session chair.

 Carefully prepare your poster well in advance of the conference. All illustrations, charts, etc., to be posted should be prepared in advance as materials for these purposes will not be available at the meeting site.

 Certificate of Poster Presentation will be awarded after your presentation by the session chair.

Oral Presentation Guideline

 Get your presentation PPT files prepared. Please copy your slide files to the conference laptop before the session start. The size of PPT is 16:9.

 Regular oral presentation: 15 minutes (including Q&A).

 Laptop, projector & screen, laser sticks will be provided by the conference organizer.

 Certificate of Oral Presentation will be awarded after your presentation by the session chair.

WARM TIPS

Tips for Participants  Your punctual arrival and active involvement in each session will be highly appreciated.  The listeners are welcome to register at any working time during the conference. Listener’s Certificate can be collected along with your conference kits at the registration desk.  A “Best Presentation” award will be selected from each session, and will be announced and awarded by the session chair at the end of each session.  Please wear your name tag for all the conference activities. Lending your participant card to others is not allowed. If you have any companying person, please do inform our staff in advance when you do the registration.  Please keep all your belongings at any time. The organizer of the conference does not assume any responsibility for the loss of personal stuff of the participants.

Currency & Banking Foreign banknotes can be exchanged at People's Bank of China and other authorized money changers. Banking hours 09:00 - 17:00, Monday through Friday

Credit Card Visa and MasterCard are accepted at almost all chain stores, but Diners Club and American Express may only be accepted at major hotels, shops and restaurants. Check with your credit card company for details on merchant acceptance and other available services. Electricity Outlets for 220 Volts/50Hz are mostly used in China. In order to convert the power into 110 volts please contact the hotel housekeeping and current transformer will be available.

Emergency Dial Number Police: 110 Fire: 119 Ambulance: 120 ※ These services are available 24 hours

DETAILED AGENDA

December 20, 2019 (Friday) 10:00-17:00

Registration & Materials Collection

会议签到+资料领取

Lobby, Grand Metro Park Jiayou Hotel

上海佳友唯景大酒店大堂

Give your Paper ID to the staff.

Sign your name in the attendance list and check the paper information.

Check your conference kit, which includes conference bag, name tag, lunch & dinner coupon, conference program, the receipt of the payment, the USB of paper collection. ------

� ICIT 2019 � ICVIP 2019 TEL: +86-1348-2222-225 TEL: +86-1329-8699-999 Email: [email protected] Email: [email protected] Wechat group：conference_ac Wechat group：conference_org

如需微信咨询，请搜索以上微信号或扫描二维如需微信咨询，请搜索以上微信号或扫描二维码。验证时请输入会议简称+文章 ID, 如码。验证时请输入会议简称+文章 ID, 如 ICIT2019+V1-XXX. ICVIP2019+V2-XXX.

DETAILED AGENDA

[December 21, 2019 (Saturday)] Morning Opening & Keynote/Invited Speeches Chaired by Prof. Xuefeng Liang Jiayou Room (Level 1, Building B) B 栋一楼佳友厅

Prof. Tao Han, Vice Dean 09:00-09:10 Opening Remarks School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China

Prof. Maode Ma Nanyang Technological University, Singapore 09:10-09:50 Keynote Speech I Speech Title: A Secure and Efficient Fast Initial Link Setup Scheme against Key Reinstallation Attacks

09:50-10:00 Group Photo

10:00-10:30 Coffee Break

Assoc. Prof. Manhua Liu Shanghai Jiao Tong University, China 10:30-11:00 Invited Speech Speech Title: Brain image computing and analysis based on deep learning Poster Presentations I V1-3007, V1-0116, V1-0120, V1-0153, V2-0053, V2-0065, V1-0096, V1-0008 11:00-12:00 V2-0021, V2-0060, V2-0049, V1-0145, V2-0050, V1-0035, V1-0020, V1-0053 V1-0094, V1-0117, V1-3006, V2-0005, V2-0063, V2-0037, V2-0068

Lunch @ Barossa Room (Level 1, Building A)

午餐 | A栋一楼巴罗莎 <12:00-13:30>

DETAILED AGENDA [December 21, 2019 (Saturday)] Afternoon Authors’ Parallel Presentations

Meeting Room 1 (Level 3, Building A) | A 栋三楼会议室 1

Session 1 - Bioinformatics and Computational Biology 生物信息学与计算生物 Chaired by Prof. Hong-In Cheng 13:30-16:00 Kyungsung University, South Korea 10 Presentations V1-0144, V1-0062, V1-0064, V1-0067, V1-0127 V1-0132, V1-0049, V1-0081, V1-3005, V2-0066

16:00-16:15 Coffee Break & Group Photo

Session 4 - Text Analysis and High-performance Computing 文本分析与高性能计算 Chaired by Assoc. Prof. Stephne Karungaru 16:15-18:45 Tokushima University, Japan 10 Presentations V1-0047, V1-0058, V1-0048, V1-0123, V1-0018 V1-0104, V1-0119, V1-0113, V1-0150, V2-0054

Meeting Room 2 (Level 3, Building A) | A 栋三楼会议室 2

Session 2 - Target Detection 目标检测 Co-chaired by 13:30-16:00 Assoc. Prof. Archana Vasant Mire, Terna Engineering College, India Mr. Muhammad Bilal, Zhejiang University, China

10 Presentations V2-0034, V1-0060, V1-0061, V2-0001, V2-0008 V2-0035, V2-0044, V2-0061, V1-0140, V1-0156

16:00-16:15 Coffee Break & Group Photo

DETAILED AGENDA Session 5 - Pattern Recognition and Image Security 模式识别与图像安全 Chaired by 16:15-19:00 Dr. Kai Yang, China Mobile Research Institute, China 11 Presentations V2-0013, V1-0073, V1-0122, V1-0109, V2-0020 V2-0052, V1-1001, V2-0014, V1-1002, V2-0031, V1-3008-A

Meeting Room 9 (Level 3, Building A) | A栋三楼会议室9

Session 3 - Electronic Information Engineering and Technology 电子信息工程与技术 Chaired by Assist. Prof. Jie Gu 13:30-16:00 Northwestern University, USA

10 Presentations V1-0141, V1-0034, V1-0030, V1-0068, V1-0133 V1-0148-A, V1-0114, V1-0044, V1-0026, V1-0090

16:00-16:15 Coffee Break & Group Photo

Session 6 - Image Analysis and Calculation 图像分析与计算 Chaired by Assist. Prof. Jiawei Zhu 16:15-19:00 Chang'an University, China

11 Presentations V1-0039, V1-0147, V2-0018, V2-0032, V2-0039 V1-0031, V2-0046, V2-0062, V2-0064, V1-0126, V2-0056

Dinner @ Barossa Room (Level 1, Building A) 晚餐 | A栋一楼巴罗莎 <19:00-20:30>

DETAILED AGENDA

[December 22, 2019 (Sunday)] Morning Keynote Speeches Chaired by Prof. Maode Ma Jiayou Room (Level 1, Building B) B栋一楼佳友厅

Prof. David Zhang 09:00-09:40 Keynote Speech I Chinese University of Hong Kong, Shenzhen, China

Prof. Saman Halgamuge 09:40-10:20 Keynote Speech II The University of Melbourne, Australia

10:20-10:40 Coffee Break & Group Photo

Prof. Hesheng Wang 10:40-11:20 Keynote Speech III Shanghai Jiao Tong University, China Speech Title: Visual Servoing of Robots Poster Presentations II V1-0097, V1-0121, V1-0125, V1-0139, V1-0142, V1-0045 11:20-12:00 V1-0041, V1-0019, V2-0036, V1-0083, V1-0102, V1-0103, V1-0118 V2-0003, V2-0043, V1-0092, V1-0106, V1-0124, V2-0038, V2-0025

Lunch @ Barossa Room (Level 1, Building A) 午餐 | A栋一楼巴罗莎 <12:00-13:30>

DETAILED AGENDA [December 22, 2019 (Sunday)] Afternoon Authors’ Parallel Presentations

Meeting Room 1 (Level 3, Building A) | A 栋三楼会议室 1

Session 7 - Image Processing Technology and Method 图像处理技术与方法

13:30-16:00 Chaired by Prof. Yihong Zhang DongHua University, China 10 Presentations V1-0075, V2-0004, V2-0011, V2-0017, V2-0047 V2-0019, V2-0048, V2-0058, V2-0059, V1-0010

16:00-16:15 Coffee Break & Group Photo Session 10 - Internet of Things and Information Network 物联网与信息网络 16:15-18:45 Chaired by Assoc. Prof. Dharmendra Singh Rajput VIT Vellore, India 10 Presentations V1-0009, V1-0065, V1-0078, V1-0108, V1-0131 V1-0146, V1-0072, V1-0076, V1-0098, V1-0088

Meeting Room 2 (Level 3, Building A) | A 栋三楼会议室 2

Session 8 - Computer Photography and Video Processing Technology 计算机摄影学与视频处理技术 Co-chaired by 13:30-16:00 Dr. Cut Maisyarah Karyati, Gunadarma University, Indonesia Mr. Sergey Volkov, National Research Moscow State University of Civil Engineering, Russia 10 Presentations V1-0054, V2-0012, V1-0069, V2-0016, V2-0045 V1-0017, V2-0067, V1-0037, V2-0006, V1-0038

16:00-16:15 Coffee Break & Group Photo

DETAILED AGENDA Session 11 - Computer Information Technology and Software Engineering 计算机信息技术与软件工程 Co-chaired by 16:15-18:45 Assist. Prof. Ting Ma, Southwest Petroleum University, China Dr. Chunling Tu, Tshwane University of Technology, South Africa

10 Presentations V1-0006, V1-0087, V1-0007, V1-0046, V1-0086 V1-0111, V1-0130, V1-0134, V2-0070, V1-0110

Meeting Room 9 (Level 3, Building A) | A 栋三楼会议室 9

Session 9 - Digital Communication and Wireless Technology 数字通信与无线技术 Co-chaired by 13:30- 15:45 Dr. Warusia Mohamed Yassin, Universiti Teknikal Malaysia Melaka, Malaysia Senior Lecturer. Qin Li, HuaZhong Agricultural University, China

9 Presentations V1-0021, V1-1003, V1-0040, V1-0055 V1-0059, V1-0091, V1-0152, V1-0014, V2-0015

15:45-16:00 Coffee Break & Group Photo Session 12 - Computer and Business Intelligence 计算机与商务智能 16:00-19:00 Chaired by Assoc. Prof. Dejun Xie Xi’an Jiaotong Liverpool University, China

12 Presentations V1-0013, V1-0057, V1-0028, V1-0052, V1-0115, V1-0136 V1-0015, V1-0003, V1-0107, V1-0155, V1-0135, V1-3002

Dinner @ Restaurant (Level 2, Building A) 晚餐 | A栋二楼中餐厅 <19:00-20:30>

DETAILED AGENDA

[December 23, 2019 (Monday)]

Social Program

Duration Time: 09:00-17:00

❉ Assembly Time: 08:50 am ❉Assembly Point: Grand Park Jiayou Hotel 上海佳友唯景大酒店(或上海佳友美仑国际酒店) ❉Return Location: Grand Park Jiayou Hotel 上海佳友唯景大酒店(或上海佳友美仑国际酒店)

Address: No.159, Xin Jin Qiao Rd., Pudong, Shanghai, P.R.China 上海浦东新区新金桥路 159 号

Overview

Expo Park - Tianzifang - City God Temple - The Oriental Pearl Tower - The Bund - Huangpu River Cruise

世博园+田子坊+城隍庙+东方明珠+外滩+浦江游船 The Shanghai Expo Park is a riverside park in Shanghai’s Pudong District. The park and surrounding area were the site for the Expo 2010 World Expo. The park retains the iconic “One Axis and Four Pavilions” motif: the Expo Axis, the Chinese Art Palace (formerly the China National Pavilion), the Expo Theme Pavilion, the Expo Center, and the Expo Cultural Center (now the Mercedes-Benz Cultural Center). Following a re-purposing, the area contains numerous stores, restaurants, and recreation areas. It is a pleasant place for a stroll along the river and offers a great view on some of Shanghai's more iconic

architecture. Tianzifang is an arts and crafts enclave developed from a renovated residential area in the former French Concession part of Shanghai. It has become a major tourist attraction featuring more than 200 diverse small businesses such as cafes, bars, restaurants, art galleries, craft stores, design houses, and studios. Part of the appeal comes from Tianzifang having retained its residential feel. The narrow alleyways and traditional Shikumen architecture are things you will not want to miss. The City God Temple is located in the Chenghuangmiao Tourist Area in Shanghai. It is an important Taoist Temple in Shanghai and has a history of nearly 600 years. The "City God Temple" signboard is hung on the entrance to the main hall, while famous Han Dynasty general Huo Guang, Celebrity of Yuan Dynasty Qin Yubo and Opium War Hero Chen Huacheng are worshipped inside. The Oriental Pearl TV Tower combines ancient concepts such as the spherical pearls, with 21st Century technology, commerce, recreation, educational and conference facilities. It really is a TV and radio tower that services the city with more than nine television channels and upwards of ten FM radio channels. The tower is brightly lit in different LED sequences at night.

DETAILED AGENDA The Bund is a waterfront promenade and famous business district in central Shanghai. The walking areas extend roughly 1.6 km along the west bank of the Huangpu River. Owing to its historic status and zoning restrictions, the Bund contains fantastic examples of classic Shanghai architecture. For this reason, it is a popular spot for photography, offering the best views across the river to Shanghai’s modern skyline in Lujiazui. Huangpu River Cruise Taking a tour of the Huangpu River aboard the “Pujiang River” cruise is one of the best ways to enjoy the city’s bustling scenery. The west bank of the Huangpu River is home to the famous cosmopolitan buildings of the Bund, while the east bank is the modern Lujiazui financial district. When night falls, lights on both sides of the river shine bright and the scenery is stunning.

❉ Included 费用包含 ❉ Not Included 费用不含

- Round trip private car - Lunch - English tourist guide - Personal expenses such as souvenirs, the entrance fee for 此次行程包含来回接送车费,导游费。 individual attractions in scenic spots, Boat ticket on Huangpu

River 此次行程不包含午餐,个人消费,如纪念品、景点门票、黄浦江船票等

❉ Note  This social program is optional and chargeable.  This route is a small group of authors. If fewer than three applicants, the route will be cancelled.  The guide will leave on time. Please arrive the assembly point 5 minutes earlier.  If you are interested, please give your feedback before December 16. If you miss this date, we can’t accept your request anymore.  Please keep your belongings with you. The conference organizer and travel agency will not responsible for the loss of your personal property.

KEYNOTE SPEAKER Prof. Maode Ma, Fellow of IET Nanyang Technological University, Singapore

Speech Title--- A Secure and Efficient Fast Initial Link Setup Scheme against Key Reinstallation Attacks

Speech Abstract--- With the increasing demands for secure wireless link connections to the access points (APs) supporting large quantities of devices in wireless local networks (WLANs), the Fast Initial Link Setup (FILS) is a recently standardized approach specified in IEEE 802.11ai. It is a new amendment to IEEE 802.11 standard family to support massively deployed wireless nodes. However, security concerns on the link connection have not been fully eliminated, especially for the authentication process. For example, a type of recently revealed malicious attack, Key Reinstallation Attack (KRA) might be a threat to the FILS authentication. To prevent the success of the KRAs, in this talk, I will introduce the FILS scheme and present a novel protocol named as Secure and Efficient FILS (SEF) protocol as the optional substitute. The SEF is designed to eradicate potential threats from the KRAs without degrading the network performance.

BIO--- Prof. Maode Ma, a Fellow of IET, received his Bachelor degree from Department of Computer Science and Technology in Tsinghua University in 1982, his Master degree from Department of Computer Science and Technology in Tianjin University in 1991, and his Ph.D. degree in Department of Computer Science from Hong Kong University of Science and Technology in 1999. Now, Dr. Ma is a tenured Associate Professor in the School of Electrical and Electronic Engineering at Nanyang Technological University in Singapore. He has extensive research interests including network security and wireless networking. He has led 25 research projects funded by government, industry, military and universities in various countries. He has supervised over 20 research students to get their Ph. D degree. He has been a conference chair, technical symposium chair, tutorial chair, publication chair, publicity chair and session chair for over 100 international conferences. He has been a member of the technical program committees for more than 200 international conferences. Dr. Ma has more than 400 international academic publications including about 200 journal papers and more than 200 conference papers. He has edited 4 technical books and produced over 25 book chapters. His publication has received more than 4500 citation in Google Scholar. He has delivered about 70 keynote speeches and 10 tutorials at various international conferences. He currently serves as the Editor-in-Chief of International Journal of Computer and Communication Engineering, Journal of Communications and International Journal of Electronic Transport. He also serves as a Senior Editor for IEEE Communications Surveys and Tutorials, and an Associate Editor for International Journal of Security and Communication Networks, International Journal of Wireless Communications and Mobile Computing and International Journal of Communication Systems. He had been an Associate Editor for IEEE Communications Letters from 2003 to 2011. Dr. Ma is a senior member of IEEE Communication Society and IEEE Education Society, and a member of ACM. He is now the Secretary of the IEEE Singapore Section and the Chair of the ACM, Singapore Chapter. He has served as an IEEE Communication Society Distinguished Lecturer from 2013 to 2016.

KEYNOTE SPEAKER Prof. David Zhang, Fellow of IEEE and IAPR Chinese University of Hong Kong, Shenzhen, China

Speech Title--- Medical Biometrics

Speech Abstract--- Traditional Chinese Medicine (TCM) diagnosis methods are mainly relied on Doctor's experience and not quantified. In this presentation, we will try to develop a novel approach by using Medical Biometrics technology to solve these problems. By some TCM-orient diagnosis acquisition devices, we could collect many kinds of date like tongue/pulse/odor with a priori knowledge from Hospitals. Then, we use a statistical pattern recognition method to extract all possible features from these images/waveforms, including color, texture, shape, and so on. After matching between our training data and testing data, some decision rules will be made. Finally, we apply our results to the practical diseases diagnosis to illustrate the effectiveness of our approach.

BIO--- David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in both Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. He has been a Chair Professor at the Hong Kong Polytechnic University where he is the Founding Director of Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government since 2005. Currently he is Presidential Chair Professor in the Chinese University of Hong Kong (Shenzhen). Over past 30 years, he has been working on pattern recognition, image processing and biometrics, where many research results have been awarded and some created directions, including palmprint recognition, computerized TCM and facial beauty analysis, are famous in the world. So far, he has published 21 monographs, over 400 international journal papers and 38 patents from USA/Japan/HK/China. He has been continuously listed as a Highly Cited Researcher in Engineering by Clarivate Analytics during 2014-2019. He is also ranked about 82 with H-Index 110 at Top 1,000 Scientists for international Computer Science and Electronics. Professor Zhang is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and an IEEE Life Fellow and IAPR Fellow.

KEYNOTE SPEAKER Prof. Saman Halgamuge, Fellow of IEEE The University of Melbourne, Australia

Speech Title--- Supervised and Unsupervised Deep Learning

Speech Abstract--- World saw the first image of a blackhole in April 2019 which will not be the last extraordinary image we see due to the vast advancement in data visualization happening today. The global technology landscape is undergoing a dramatic shift towards an exciting space of overwhelmingly complex and abundant data. Being prepared for this reality is paramount; however, it is quickly becoming apparent that new innovative methods are required to leverage the kind of “wicked” datasets we are increasingly confronted with. We are already witnessing this paradigm shift in wide-ranging domains such as neural engineering, pharmaceutical drug development, and microbial ecology, which are empowered by rapidly-advancing technologies that can quickly generate terabytes of data for analysis of advanced processes, compounds and organisms. These technologies have been spurred by recent advances in Deep Learning coupled with improvements in processor technology (e.g. GPU), that have allowed practitioners and researchers to overcome the computational limitations of many Neural Networks that depend on fully human curated (i.e. labeled) data (i.e. Supervised Learning). The following fundamental question then naturally arises: What happens when curated information or labels capture only a subset of critical classes, or the curation process itself is not fault- or error-free, i.e., a presence of uncertainty, as is often the case in the aforementioned domains? Undoubtedly, the algorithm’s perceived reality will distort any subsequent analysis of these data, which may have detrimental downstream effects when new discoveries and critical decisions are made on a basis of these analyses. In such scenarios, learning algorithms that can find models –underlying structures or distinct patterns within data – without relying on labels (i.e. using Unsupervised Learning), have made great progress toward answering these sorts of questions; however, these algorithms only address part of the problem. Unsupervised Learning algorithms do not take into account any available and potentially reliable information or domain knowledge, which could prove useful in developing a robust model of the data. It can be advantageous to consider such information as well as any other available domain knowledge, not as ground truth but as a starting point to build a more complete picture of the problem under investigation. The frequently used learning strategies also include generative techniques: Variational Autoencoders and Generative adversarial nets (GANs) that are widely used to learn the data sampling process. The performance of GANs and their future applications heavily depend on the improvements to learning algorithm.

BIO--- Saman Halgamuge, FIEEE is a Fellow of Institute of Electrical and Electronics Engineering (IEEE), USA, and a Distinguished Lecturer/Speaker appointed by IEEE in the area of Computational Intelligence. He is currently a Professor in the Department of Mechanical Engineering, School of Electrical, Mechanical and Infrastructure Engineering at the University of Melbourne, an honorary Professor of School of Electrical, Energy and Materials Engineering at Australian National University (ANU). He was previously the Director of the Research School of Engineering at the Australian National University (2016-18) and held Professor, Associate Dean International, Associate Professor and Reader and Senior Lecturer positions at University of Melbourne (1997-2016). He graduated with Dipl.-Ing and PhD degrees in Data Engineering from Technical University of Darmstadt, Germany and B.Sc Engineering degree in Electronics and Telecommunication from University of Moratuwa, Sri Lanka. His research interests are in Machine Learning including Deep Learning, Big Data Analytics and Optimization and their applications in Energy, Mechatronics, Bioinformatics and Neuro-Engineering. His fundamental research contributions are in Unsupervised and Near Unsupervised type learning as well as in transparent Deep Learning and Bioinspired Optimization. His h-index is 42 (9100 citations) in Google Scholar and he graduated 50 PhD students as the primary supervisor. He has also been a keynote speaker for 40 research conferences. In China, he presented research seminars at many research institutions including Chinese Academy of Sciences, Tsinghua University, Peking University, Shanghai Jiao Tong University, Harbin Institute of Technology and Tong Ji University. He is also invited to serve as a Distinguished Professor at multiple Universities in China. 18

KEYNOTE SPEAKER Prof. Hesheng Wang Shanghai Jiao Tong University, China

Speech Title--- Visual Servoing of Robots

Speech Abstract--- Visual servoing is an important technique that uses visual information for the feedback control of robots. By directly incorporating visual feedback in the dynamic control loop, it is possible to enhance the system stability and the control performance. Dynamic visual servoing is to design the joint inputs of robot manipulators directly using visual feedback. In the design, the nonlinear dynamics of the robot manipulator is taken into account. In this talk, various visual servoing approaches will be presented to work in uncalibrated environments. These methods are also implemented in many robot systems such as manipulator, mobile robot, soft robot, quadrotor and so on.

BIO--- Hesheng Wang received the Ph.D. degree in Automation & Computer-Aided Engineering from Chinese University of Hong Kong. Currently, he is a Professor of Department of Automation, Shanghai Jiao Tong University, China. He worked as a visiting professor at University of Zurich in Switzerland. His research interests include visual servoing, service robot, robot control and artificial intelligent. He has published more than 100 papers in refereed journals and conferences. He has received a number of best paper awards from major international conferences in robotics and automation. He is an associate editor of Assembly Automation, International Journal of Humanoid Robotics and IEEE Transactions on Robotics. He was the general chair of IEEE RCAR2016 and program chair of IEEE AIM2019 and IEEE ROBIO2014. He was a recipient of Shanghai Rising Star Award in 2014, The National Science Fund for Outstanding Young Scholars in 2017 and Shanghai Shuguang Scholar in 2019. He is a Senior Member of IEEE.

INVITED SPEAKER Assoc. Prof. Manhua Liu Shanghai Jiao Tong University, China

Speech Title--- Brain Image Computing and Analysis based on Deep Learning

Speech Abstract--- Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to brain diseases. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD). Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish disease subjects from other groups. This talk will present our research works on application of deep learning in brain image computing and analysis including the hippocampus segmentation and classification using structural MRIs and multimodal image classification for brain disease diagnosis.

BIO--- Dr. Manhua Liu is currently an Associate Professor with the Artificial Intelligence institute, Shanghai Jiao Tong University. She received the Ph.D. degree from Nanyang Technological University, Singapore, in 2008. Her research interests include multi-modality brain image computing and analysis, biometrics, and machine learning and their applications to normal early brain development and disorders. She has published more than 60 SCI/EI papers in journals and proceedings of international conferences. As the PI, Dr. Liu has also successfully collaborated on 3 NSFC projects and National Key National Key Research and Development Program sponsored projects.

SESSION I

December 21, 2019 Session 1

Bioinformatics and Computational Biology 生物信息学与计算生物

13:30-16:00 Meeting Room 1 (Level 3, Building A) | A 栋三楼会议室 1

Chaired by Prof. Hong-In Cheng Kyungsung University, South Korea

10 Presentations— V1-0144, V1-0062, V1-0064, V1-0067, V1-0127 V1-0132, V1-0049, V1-0081, V1-3005, V2-0066

*Note:

* Please arrive at the conference rooms 30 minutes before the session start. * Certificate of Presentation will be awarded to each presenter by the session chair at the end of each session. * One Best Presentation will be selected from each parallel session and the author of best presentation will be announced and awarded when the session is over. * Please keep all your belongings at any time!

SESSION I The Effectiveness of Music Therapy System for the Elderly with Mild Cognitive Disability Hong-In Cheng, Rizky Alifa and Haegoo Lee Kyungsung University, South Korea

Abstract- Every human being must experience aging eventually. The global population aged 60 years or over was 962 million in 2017. It is expected the number of seniors will be approximately 2.1 billion in 2050 (United Nation, 2017). With the increasing number of elderly, the number of senior adults with MCI (mild cognitive impairment) and early-stage dementia is rising as well. Aging is a worldwide problem and many countries are preparing hard to cope with it. Music therapy has V1-0144 proven to be an effective method to decrease the development of dementia as well 13:30-13:45 as enhancing the behavior and cognitive abilities of seniors. A music therapy system was developed in this study for the elderly especially with MCI. The system is a kind of meaningful game for music therapy. Product designers and ergonomists have designed and redesigned the system. This music therapy system was chosen to be developed after conducting a survey. A specific instrument was selected and an usability test was conducted with a functional prototype. Before deciding the product design, an ergonomic pilot study was performed with a paper mock-up. Ten functional prototypes of the music therapy system were finally produced for a 10 week clinical test. The clinical test was performed in a dementia center and the results showed music therapy was effective for the elderly with MCI. The performance of the senior participants was also analyzed to examine their behavioral reactive abilities. Key Success of Technology Acceptance to Develop Mobile Application Supparang Ruangvanich and Pallop Piriyasurawong Chandrakasem Rajabhat University, Thailand

Abstract- The development of mobile applications remains to get a great deal of awareness between researchers due to their proliferation and pervasiveness, especially in the context of developing mobile applications of educational institutes. Nevertheless, their low acceptance and usage are experimental. Hereafter, in-depth investigations are required in order to understand the factors behind the low V1-0062 acceptance and development of mobile applications. Therefore, this research aimed 13:45-14:00 to empirically explore the acceptance of developing mobile applications with a proposed model that is developed from the Unified Theory of Acceptance and Use of Technology (UTAUT). The study objects to deliver crucial critical success on the acceptance of developing mobile applications. The researchers established the hypothesized internal hierarchy among performance expectancy, effort expectancy, social cloud, interface design, and functionality, of the development of the mobile application for students in the 21st century. The informed hypothesis was attained from a scientific method known as the systematic literature review. After that, the students in the 21st century for Thailand had given attention to the burgeoning of

SESSION I development on the mobile application due to the additional request in line with technological progress for tertiary education in the nation. The findings support as a guide for effective decisions in the design and development of mobile applications. Furthermore, the outcomes can be utilized in the resource allocation process for ensuring the success of developing the mobile application's vision and mission. As a consequence, it was seen that pupils' attitude plays a vital role in contributing to the development of the mobile application for students in the 21st century. The consequence of this study is anticipated to improve and upgrade the development of the mobile application to be beneficial according to the needs of the students in the 21st century. Implementation of Biometrics Recognition System based on Zynq SoC Platform and Cloud Server Jin Li, Wei Huang, Zhenyu Dai and Xiaowen Bian Inner Mongolia University, China

Abstract- Biometrics plays a key role in the recognition system. In order to improve efficiency and operability of recognition, this study presents an approach V1-0064 for a Biometrics recognition system. The system consists of Zynq SoC platform and 14:00-14:15 cloud server, which can perform data acquisition and data calculations respectively. In the proposed system, images are captured by OV5640 sensor. Then the images are pre-processed in Zynq SoC platform and uploaded to cloud server. Lastly, the subjects are recognized when the algorithms in cloud server extract biological features from images. The system is demonstrated by implementing gait recognition. This achieved implementation result shows that the system has not only implemented Biometrics recognition, but it also is more efficient and convenient for user. Time Series Analysis of Dengue Fever Cases in Thailand Utilizing ARIMA Model Patsaraporn Somboonsak Chandrakasem Rajabhat University, Thailand

Abstract- Forecasting epidemics of dengue in the population as time series models is a challenging task for providing useful information in planning public health surveillance. While Thailand's dengue usually outbreaks in the rainy season every year, and the number of patients is increasing. Although the SARIMA model is a V1-0067 tool for developing an appropriate model for dengue fever outbreak prediction, the 14:15-14:30 user needs to find out the optimal value for each parameter. This article aims to find an appropriate model with optimal parameter values, to predict the occurrence of dengue fever in Thailand provinces in the eastern and central regions of Thailand. Moreover, the authorities are possible to use these models for posing strategies for the prevention and control of dengue fever. Time series analysis with the SARIMA model developed on the data of dengue patients from January 2014 to December 2018, collected from the laboratory of the Bureau of Epidemiology (BoE), Ministry of Public Health Thailand. The

SESSION I Box-Jenkins method is applied to suit the seasonal autoregressive integrated moving average model to forecast dengue patients. In model performance evaluation, we plot the mean absolute percentage error (MAPE). The SARIMA model (1,2,2) (1,1,1)12 is the best model for the prediction. The number of dengue patients in each month can be estimated from the trend of dengue cases that occurred one, two, and twelve months in the past. The predicted value for 2019 is quite close to the observed value. The model was examined by the dengue fever, significant correlations at lag 12, 10, and 2. This article has results as an indication that the SARIMA model is a useful tool for monitoring dengue incidence. We also found that the SARIMA model can accurately show the number of dengue patients in the next year. The model can be used to plan the allocation of resources for public health services. Furthermore, these results offer innovations to prediction, surveillance, and alert the people in the eastern and regions central of Thailand via smartphone. Multi-task Learning for Animal Species and Group Category Classification Donghyeon Kim, Younglo Lee and Hanseok Ko Korea University, South Korea

Abstract- Accurate animal sound classification is an important task in automated animal monitoring system. Such monitoring system is essential for preventing epidemics caused by animal disease. Based on such needs, there has been a variety of efforts to develop an accurate system performing animal sound classification in V1-0127 deep learning framework. Although many research issues and methods to address 14:30-14:45 the issues were introduced, no one has yet to address overcoming the machine learning barriers induced by a single objective function. As learnable parameters only consider a single penalty at the output prediction for training, they cannot capture other characteristics contained in the dataset to extract more generalized prediction. This paper proposes a deep learning based multi-task learning framework for animal sound classification. Both animal species and group classification are performed in an end-to-end learning process. Experimental results show that the proposed multi-task method outperforms single-task method in our recorded animal sound dataset. Feature Analysis to Estimate Sleep Time based on Simple Measurement of Biological Information after Awakening Mahiro Imabeppu, Ren Katsurada and Tatsuhito Hasegawa University of Fukui, Japan

V1-0132 Abstract- Currently, many people wear a wristband type device while sleeping to 14:45-15:00 automatically record how many hours they sleep. Even a system without a wearing device, such as a smartphone application, needs to be set in advance. Therefore, automatic recording of sleep time cannot be realized without advanced measurement preparation. In this study, we propose a method to estimate sleep time without advanced preparation based on a simple measurement of biological

SESSION I information after awakening. We extracted 97 types of features from sensor data that were measured using wearable devices. We analyzed whether significant differences between each feature appear according to the previous sleep time. Furthermore, we evaluated the accuracy when the sleep time is estimated by machine learning using features with a significant difference. We adopted Support Vector Machine (SVM) as a machine learning algorithm and Leave-One-Session-Out Cross Validation (LOSO-CV) as an evaluation method. Consequently, there were seven features with significant differences when the biological information was measured one hour after awakening. By using machine learning, the accuracy of the previous sleep time (three sleep time categories: short, medium, or long) was estimated to be 62.5%. A Linear Time Algorithm for Finding Tandem Repeat in DNA Sequences Tida Butrak and Supaporn Chairungsee Walailak University, Thailand

Abstract- Tandem repeats cause several genetic diseases in humans and play an V1-0049 essential role in DNA fingerprinting for paternity analysis, forensic investigations, 15:00-15:15 tracing the evolution of populations, and disease diagnosis. However, the process of tandem repeat detection in DNA sequences takes time and requires significant computational memory. To improve the efficiency of this process, we present an algorithm that runs in linear time toward finding tandem repeats in DNA sequences with the Longest Previous non-overlapping Factor (LPnF) table and use the suffix tray data structure. Motion Monitoring for Limb Exercise Ziqi Li, Xiao Ma and Meizhen Liu Shaanxi Normal University, China

Abstract- In this paper, we proposed an motion monitoring strategy for limb exercise. The proposed strategy combines three important elements of limb motion: V1-0081 the motion pattern, the number of repetitions, and the period of each repetition. Two 15:15-15:30 methods are adopted to recognize the motion pattern: support vector machine and MoveNet, which is a deep neural network we proposed base on CNN and LSTM. A method combining zero-crossing detection and wavelet transform is used to count the number of repetitions and analyze the period of each repetition. The experimental results illustrate that the precision of workout-action identification is up to 97.71%, and the average error between the period calculated and the actual value is 4.03%. Nonlinear Discriminant Analysis for MR Brain Images Classification via Kernel Function V1-3005 Farzaneh Elahifasaee, Manhua Liu and Ming Yan 15:30-15:45 Shanghai Jiao Tong University, China

Abstract- This paper recommends a new algorithm for magnetic resolution (MR)

SESSION I brain imaging called kernel discriminant analysis (KDA). Actually, our paper contributions can be mentioned as following: firstly, it is for the first time of using KDA and combines it with statistically feature selection method (t-test) for a new scheme to classify of the brain image and mentioning that our proposed technique would be suitable in dealing with sparse data using for the brain image classification. It is evaluated proposed technique on 427 observations which are including of 198 AD moreover 229 normal control (NC) cases. Results of experimental indicating through employing our approved method accuracy of 88.07% could be obtained for AD/NC for classification of Alzheimer disease (AD) vs. NC. We are mentioning that, proposed method promising performance. 3D Saliva Ferning Images to Determine The Women’s Fertility Rates Cut Maisyarah Karyati, Aries Muslim and Daryl Diningrat Gunadarma University, Indonesia

Abstract- In this paper we will discuss the process of processing salivary images which will be represented by salivary fern patterns into 3D shapes and determine V2-0066 the level of fertility visually with existing theories. The author raises this theme 15:45-16:00 because it can make it easier to recognize the pattern of ferns in saliva in determining the level of female fertility in the health field. The work stages in this paper start from the study of literature, data collection, designing 3D applications, making 3D applications and testing. The stages of processing the saliva image used by the author’s computer in the process of image improvement, edge detection, cropping and masking, segmentation and 3-dimensional image representation.

Coffee Break & Group Photo <16:00-16:15>

SESSION II

December 21, 2019 Session 2

Target Detection 目标检测

13:30-16:00 Meeting Room 2 (Level 3, Building A) | A 栋三楼会议室 2

Co-chaired by Assoc. Prof. Archana Vasant Mire, Terna Engineering College, India Mr. Muhammad Bilal, Zhejiang University, China

10 Presentations— V2-0034, V1-0060, V1-0061, V2-0001, V2-0008 V2-0035, V2-0044, V2-0061, V1-0140, V1-0156

*Note:

SESSION II Analysis of Forensic Fingerprints in Facebook Images, The universal Anti forensic Attack Archana Vasant Mire and Shaveta Malik Terna Engineering College, India

Abstract- Now days Facebook is used as one of the most effective communication media. Cheaper digital devices have made it easy, to capture, edit and share images on Facebook. Consequently, image integrity is often an issue for these images. Most of the internet images do not use any embedded active security mechanism, such as V2-0034 watermarking and steganography. Hence, passive digital image forensic techniques 13:30-13:45 are often used to investigate these images. These techniques analyze various fingerprints introduced during tampering attacks, e.g. Noise, JPEG fingerprints, resampling artefacts, etc. Anitforensic attacks searches techniques to hide these forensic fingerprints. Most of the antiforensic techniques target a single forensic fingerprint. In this paper, we have investigated some of the very common forensic techniques on Facebook images. We found that JPEG fingerprints fail on Facebook images. In addition, the common resampling detector and noise inconsistency also fail on these images. Hence, Facebook can be considered as the universal antiforensic attack without degrading visual quality of an image. A New Approach for IoT-based Fall Detection System using Commodity mmWave Sensors Kai Wang, Guanyi Zhan and Wai Chen China Mobile Research Institute, China

Abstract- Falling poses a significant risk to the elderly people living alone, and timely detection when a fall occurs to an elderly has attracted much research attention. Among the variety of technologies proposed, mmWave sensor based technology has shown promising performance in this task due to its strength in V1-0060 body-pose recognition. However, previous studies have focused on using 13:45-14:00 sophisticated mmWave radars, whereas cheaper commodity mmWave sensors have not received much attention due to their low spatial resolutions. In this paper, we propose a fall detection system based on commodity mmWave sensors along with body-feature estimation algorithm to overcome the low-resolution deficiency. We first compare several body-feature estimation algorithms and select the optimized one. Then we illustrate the potential of body-feature estimation algorithms via a case study of a threshold-based fall detection system. We construct a database that includes 348 datasets; and our experimental evaluation of the datasets demonstrates that our algorithm is effective for detecting falls. Number Recognition of Parts Book Using LSTM and CTC Algorithm Heeran Shin, Jangsik Park and Jong-Kwan Song V1-0061 Kyungsung University, South Korea 14:00-14:15

Abstract- Optical character recognition (OCR) is a technology which convert the

SESSION II encoded text within an image. OCR is widely used by industries with a range of OCR needs for make pixel images to searchable data. Making image pixel to searchable data, it can save a huge amount of time and effort. Especially in large volume of patent drawings, storing them images aren’t efficient. It is just collection of pixels. OCR can make them to searchable data then it can create database. This is can be searched by users who may be faced with tens of thousands of images. In this paper, we propose the OCR on the machine parts book which contains large volume of drawing images for machine parts dealer. Proposed OCR consists of three parts: Pre-processing, Classification part and Post-processing. Pre-processing part which using the Mathematical morphology filters for remove the noise which cause false recognition. After finding contours in the image and put it into the deep learning network for classification. Proposed OCR adopt Shi et al’s CRNN architecture which consists of Convolutional layers, Long short-term memory layers with Connectionist temporal classification algorithm. Finally, drop the low probabilities for better OCR accuracy. Testing proposed OCR on the provided machine drawing from industry and check the recall and the precision, it shows 97.95% of recall and 91.12% of precision on about 2,223 drawings. Pulmonary Tuberculosis Detection Using Deep Learning Convolutional Neural Networks Michael Norval, Zenghui Wang and Yanxia Sun University of South Africa, South Africa

Abstract- Tuberculosis (TB) is classified as one of the top ten reasons for death from an infectious agent. This paper is to investigate the accuracy of two methods to V2-0001 detect Pulmonary Tuberculosis based on the patient chest X-ray images using 14:15-14:30 Convolutional Neural Networks (CNN). Various image preprocessing methods are tested to find the combination that yields the highest accuracy. Moreover, a hybrid approach using the original statistical computer-aided detection method combined with Neural Networks was also investigated. Simulations have been carried out based on 406 normal images & 394 abnormal images. The simulations show that a cropped region of interest coupled with contrast enhancement yields excellent results. When further enhancing the images with the hybrid method even better results are achieved.

A Non-template based Automatic Landmarking on 3D Face Data SukTing Pui and Jacey-Lynn Minoi University Of Malaysia Sarawak, Malaysia V2-0008 14:30-14:45 Abstract- The standard initial stage for the extraction of information from human face image data is the detection of key anatomical landmarks, which is a vital stage for several face recognition, facial analysis and synthesis applications. Locating facial landmarks in images is an important task in image processing, and detecting

SESSION II these landmarks automatically still remains a challenge. The appearance of facial landmarks may vary tremendously due to facial expressions and pose variations. Detecting and localising landmarks from raw face data are often performed manually by trained and experienced scientists or clinicians, and this process is laborious and tedious. In order to overcome these challenges, a novel non-template based automatic landmarking method on 3D face data is presented. The geometric approach is employed through utilising the surface curvatures, primitive surfaces information to detect and extract potential features. Subsequently, K-means clustering is applied to categorise and obtain the centroid of extracted features, which is later use to estimate and localise facial landmarks. The overall performance and accuracy of the proposed approach demonstrates the effectiveness and robustness of its method. Results of 95.6% of accurately locating the feature proved the effectiveness of the algorithm. Fast Infrared Target Detection under Backlighting Maritime Environment: via RVAM and Adaptive Threshold Segmentation Dongdong Ma, Lili Dong and Wenhai Xu Dalian Maritime University, China

Abstract- When detecting the infrared ocean target on a sunny day, it is easy for the target to appear backlighting phenomenon between the infrared thermal imager and the sunshine , which makes the gray level of the target on the infrared image is V2-0035 obviously lower than that of the wave and the background, which will greatly 14:45-15:00 reduce the detection accuracy. To solve this problem, we propose an revised visual attention model(RVAM) and adaptive threshold segmentation(ATS) method by analyzing the characteristics of infrared image. Its main contribution is to apply "center-surround" differences(CSD) operations under different sea waves, an average contrast(AC) method for judging the strength of ocean waves and a fast image segmentation method is proposed. This research can significantly improve the success rate and efficiency of searching maritime targets in different sea wave backlighting environment(BE) using infrared imager. Multi-level Up-sampling Network for Infrared Ship Saliency Object Detection Tianpeng Jiang, Zhaoying Liu and Yujian Li Beijing University of Technology, China

Abstract- Deep convolutional neural networks have been widely used for saliency detection. However, most of the previous works focus on the visible light image. In V2-0044 this paper, there are mainly two contributions. First, we propose a new architecture 15:00-15:15 named Multi-level Up-sampling Network (MLUNet) for infrared (IR) ship object saliency detection. Specifically, the architecture of MLUNet is an Encoder-Decoder like network embedded with subtraction feature filtering module (SFFM). The encoder uses the DenseNet like architecture, and the decoder part use two upsampling methods, which are deconvolution and sub-pixel convolution. SFFM is a feature subtraction module which is in charge of feature filtering. In our proposed

SESSION II MLUNet, SFFM is embedded after each convolution and deconvolution block. Secondly, we construct an IR ship object image dataset for saliency detection. This dataset includes 3845 IR images and ground-truth images with different backgrounds and different objects. Experimental results show that our method outperforms the state-of-the-art methods in terms of regional evaluation measures. An Efficient Method of Histological Cell Image Detection based on Spatial Constrained Convolution Neural Network Qiang Qi, Hong Wang and LiKang Peng Wuhan University of Technology, China

Abstract- As an important research direction in the field of medical images, histopathological cell image detection has been widely used in computer-aided diagnosis, biological research fields. With the rise of deep learning, neural network is applied to medical image analysis, which can realize the automatic detection and classification of histological cell images. In order to solve the problem that the V2-0061 output of the existing neural network is affected by spatial information factors in its 15:15-15:30 topological domain, on the basis of the traditional convolution neural network. Combined with the spatial position information, an improved convolution neural network model for histological cell image detection is proposed. Taking the traditional convolution neural network as the carrier, the convolution neural network model based on spatial information is constructed, which makes the model has the ability to fuse spatial information and eigenvector. Histopathological cell images were preprocessed by color deconvolution. Finally, a model verification experiment based on colorectal cancer image dataset is designed. The model proposed in this paper shows better performance than the state-of-the-art methods in four different categories (more than 20000 experimental images): the experimental accuracy is 75.8%, and the recall rate is 82.3%. F1 reached 80.1%. Gas Leak Segmentation Comparison Using Different Activation Function on Fully Convolutional Network Marshall Wiranegara, Jang-Sik Park and Jong-Kwan Song Kyungsung University, South Korea

Abstract- This paper explores the performance of a fully convolutional network, specifically the performance of differing activation functions, on a network on its V1-0140 ability to segment gas leakage images for gas detection. Quality management and 15:30-15:45 safety control is an integral part in preserving workplace safety. This is especially true if the workplace deals with hazardous and dangerous materials, such natural-gas processing plant and chemical plant. In such working environment, one of the safety measures that can be taken is by having early detection of any possible leak. This paper tries to use the images of gas leak from thermal camera to train a semantic segmentation network to classify regions with gas leakage. Since the dataset requires videos recorded using thermal camera of gas leakage, collecting real life data has its own barriers (safety reason, availability, etc.). To help

SESSION II supplement the lack of available data, transfer learning on smoke video which share similar characteristics with the gas leakage video. By changing the last activation function on the fully convolutional network, we observed a difference in their performance. The Taxonomy of Smart City Core Factors Hemalata Vasudavan and Saraswathy Shamini Gunasekaran Asia Pacific University of Technology & Innovation, Malaysia

Abstract- Cities build a pivotal role in modelling their environment and socioeconomic elements at the globalization level. The rapid urbanization in cities creates numerous demand and challenges for resources, mobility, space and quality of life for citizens. In order to address the challenges of rapid urbanization, the V1-0156 concept of ‘smart cities’ is introduced as an emerging trend to tackle the problems 15:45-16:00 that is facing by cities as well as to find measurement to enhance city’s management. This study explores the current literatures by analyzing and discussing the important findings from the available research on common characteristics and dimensions of smart city. The study focusses on correlating the identified five characteristics with the six dimensions to determine core factors for initiating smart city. The comprehensive correlation leads towards smart city core factors taxonomy. This study attempt to introduce a broad smart city core factors taxonomy to support smart city experts, city council and governments in planning their urban spaces innovatively as well as to serve as a reference model

Coffee Break & Group Photo <16:00-16:15>

SESSION III

December 21, 2019 Session 3

Electronic Information Engineering and echnology 电子信息工程与技术

13:30-16:00 Meeting Room 9 (Level 3, Building A) | A 栋三楼会议室 9

Chaired by Assist. Prof. Jie Gu Northwestern University, USA

10 Presentations— V1-0141, V1-0034, V1-0030, V1-0068, V1-0133 V1-0148-A, V1-0114, V1-0044, V1-0026, V1-0090

*Note:

SESSION III Design and Implementation of Distributed Time-Triggered System based on RT-Thread Chunmeng Zhong, Haifeng Zhang, Yidong Yuan, Sukun Zhang and Hai Wan Tsinghua University, China

Abstract- Internet of Things (IoT) technologies are driving changes in Industry 4.0. Applications developed based on the IoT system may have high requirements on the real-time property of data transmission. To provide the real-time data transmission service for time-sensitive applications, Time-Sensitive Networking (TSN) standards V1-0141 are proposed. Though TSN standards have relatively perfect support for real-time 13:30-13:45 transmission at the switch level, it pays little attention to the real-time guarantee of end devices. In order to ensure the real-time behavior of end systems, a time-triggered thread-level scheduling mechanism is devised. Based on this scheduling mechanism, time sensitive tasks, implemented as threads, are executed periodically at predefined times according to a global notion of time, which as a result can ensure the end to end delay of a message. Design and implementation of this mechanism are completed based on RT-Thread, a widely used lightweight embedded real-time operating system. Comprehensive test and experiments are conducted to show the effectiveness of the method proposed. Design and Implementation of Intelligent Home Power Control Systems by Using Raspberry Pi and AWS Cloud Service Yichen Pan and Jaesoo Kim Kyungpook National University, South Korea

Abstract- In the early years many fire disasters caused by electrical appliance which is dangerous and seriously influence people’s daily life. With the development of technology in the field of Internet of things, more and more V1-0034 intelligent electric appliances are appearing. In this situation, how to effectively and 13:45-14:00 securely control different devices has become an important research direction. In this paper, we design and implement an intelligent home power control system to manage electric appliances and share collected data by using Raspberry pi and amazon cloud service. It greatly improves the security of electric appliances and provides data support for the establishment of a smart city. Different to other studies, this paper redefines intelligent home power control systems from three aspects: low-energy, security and data sharing. By analyzing and processing the collected sensor information, it can provide an accurate prediction for the safety of household and regional electricity consumption. Smart City Concepts and Dimensions Weihua Duan, Rouhollah Nasiri and Sasan Karamizadeh V1-0030 ICT Research Institute, Iran 14:00-14:15 Abstract- In the fresh age of urbanization, and granted that the world's urban population encroaches 50 percent, the size and speed of urbanization are

SESSION III accelerating into what is referred to as the second wave of urbanization. In the meanwhile, the growth of urbanization has taken on a more rapid pace than in the past, since the start of the third millennium, which is the epoch of the rule of information technology in different fields of urban life, which may be named the third wave of urbanization. In this article we will talk about the components that make a city smart. A set of common multidimensional components is identified under the concept of smart city and the key factors of smart city's successful initiative are identified by studying the definitions of smart city's current work and the sort of concepts similar to smart city. This report introduces the strategic principles which aligned with the three main dimensions of the smart city namely (technology, people, and institutions): The integration of technology-mediated infrastructures and services, social learning to strengthen human infrastructure and organization to improve institutional and citizen interaction An Experimental Study on the Modeling System of the Public Bus Traffic Delay Index Giljong Song, Uiyong Jung, Namhyun Yoo and Seocho Kim NZERO Corporation, South Korea

Abstract- C-ITS is a system that provides automobiles and autonomous vehicles on the road with real-time traffic information, so it requires to collect data from traffic infrastructures and vehicles on the road, in real time. Among various traffic information sources, public transportation service, such as taxies and buses, V1-0068 provides and receives the large volume of data in the simple manner. In this study, 14:15-14:30 the traffic delay index, which can model the traffic information, provided by public buses, is suggested in order to have the C-ITS provide systematic traffic information in the systematic information. The traffic delay index is a model that could identify the section and cause of traffic congestion by using an individual bus’s real-time location information, in seconds; the traffic delay index of each bus was modelled through the use of the bus route information, actual time of arrival and departure, intersection crossing, and location and time of a periodical event at 30 second intervals, collected from the bus information system operated by the City S in Korea, and it was found that this traffic delay index was almost identical with actual traffic conditions. Estimation of Sidewalk Surface Type with a Smartphone Satoshi Kobayashi, Ren Katsurada and Tatsuhito Hasegawa University of Fukui, Japan

V1-0133 Abstract- In this study, we developed a method for estimating the type of sidewalk 14:30-14:45 surface with acceleration data measured by a smartphone’s accelerometer while the user is walking. If the type of sidewalk surface can be detected automatically, a large amount of sidewalk information, such as which sidewalks are easy to walk on and which are difficult, can be collected just by many people walking while carrying their smartphones. We can contribute to the creation of a safe society by

SESSION III sharing the accumulated sidewalk information with a map application. We propose a method that estimates type of sidewalk by machine learning using feature values extracted from acceleration data. The proposed method detects six types of sidewalk: asphalt, gravel, lawn, grass, sand, and mat, imitating snowy sidewalk. Through experiments, we considered how far the shape of the sidewalk surface can be classified from smartphone sensor data. In this study, we used Random Forest as the machine learning algorithm. We conducted experiments on seven subjects and evaluated the method’s accuracy using Leave-One-Subject-Out Cross Validation (Subject-CV) and Leave-One-Session-Out Cross Validation (Session-CV). The results showed that the proposed method can detect the type of sidewalk surface with 44.9% accuracy when evaluated with Subject-CV and 83.5% accuracy when evaluated with Session-C V. Experimental Study of Big Data Analytics Dashboard to Provide Food Safety through Integrating Two Different Domain Data Sang-Yong Rhee and Nam-Hyun Yoo KyungNam University, South Korea

Abstract- As the cases of smart cities spread, services that provide various information to citizens in real time are increasing. In particular, as interestin food safety increases, various services increase, but the most classic system related to food safety is the traceability system. The biggestproblem with the traceability V1-0148-A system is that it only manages the production and distribution of produce or 14:45-15:00 seafood. To complement thisproblem, some tourist towns adjacent to the sea have implemented a sushi restaurant food safety service to ensure the safety of the raw fishbasedfoods served by seafood restaurants. This service installs various sensors in the fish tank of the restaurant to check the status of thefish in the fish tank in real time and provides this information to the citizens and tourists. In this paper, a big data analysis dashboardsystem was designed and implemented that can integrate and analyze the data of the seafood traceability system and the sushi restaurantfood safety service. Analyze and verify the results of various algorithms developed in the process of combining data from two domainswith similar and different characteristics and deriving correlations between the data. Wind Power Prediction based on BP Algorithm with NWP Guo Peng and Wang Cun Inner Mongolia University, China

Abstract- In this paper, the back propagation (BP) neural network wind power V1-0114 prediction model and the BP neural network wind power model with numerical 15:00-15:15 weather prediction are established. The research shows that the BP neural network model with numerical weather prediction can improve the prediction accuracy more effectively.

SESSION III Available Discharge Capacity Estimation According to C-rate Variation of Second-Used Battery Chungu Lee, Taesic Kim, Jonghoon Kim and Joung-Hu Park Soongsil University, Korea

Abstract- As the number of used batteries increases, recycling the used batteries becomes an issue. One of the most notable ways to recycle used batteries is to create a new energy storage system with a used battery. In case of used battery, however, the life expectancy is low. Therefore, if a used battery is required to construct a low-cost system to perform a grid-scale energy storage, it should be considered how to use batteries efficiently. The fresh-battery oriented state-of-charge (SOC) or state-of-health (SOH) algorithms such as Kalman filter or extended Kalman filter V1-0044 which require the internal parameter of battery is not proper for used battery system. 15:15-15:30 In general, experimental steps to measure the internal parameters of a battery take a long time. Whereas, the ampere counting method is almost real time process, so more appropriate for the used battery system. Basically, it can be used only with measuring the available discharge capacity of a battery. However, in the case of used lithium-ion battery, the variation of the available discharge capacity is greater as the C-rate changes, than that of the fresh battery is. In this paper, algorithm for estimation about available discharge capacity according to C-rate of second used battery is proposed. Since the proposed algorithm in this paper estimates the available discharge capacity according to the C-rate and SOH variations, it can minimize an error of the ampere counting. Since the base of the algorithm is ampere counting, it does not require a high-performance computing processor. Therefore, this second used battery system can be configured in low cost. Security Threat Assessment of Aircraft System using FSS Dharmendra Singh Rajput, Rajesh Kaluri and Harshita Patel VIT Vellore, India

Abstract- The security and schedule management of an aircraft system has become V1-0026 much more important and very complicated issue because of information age's 15:30-15:45 advances to solve this complication. We are using fuzzy soft set with the decision-making problem in aircraft system. This research uses the Fuzzy Soft Set Decision-Making Method (FSS_DMM) to determine the weight of assessment criteria and to synthesize the score of aircraft. This concept is used to acquire the complete performance value for each alternative, to make the final choice. A High-Resolution Model for Simulation of Flexible Residential Electricity Demand Jiawei Zhu, Qiang Liao, Yishuai Lin, Weidong Lei and Jingjie Gao V1-0090 Chang'an University, China 15:45-16:00

Abstract- Residential electricity consumption accounts for almost one third of the total, and the electricity use pattern is highly dependent upon the activities of

SESSION III occupants. This paper presents a high-resolution model for domestic electricity demand simulation. Behaviors of household occupants are modeled by a Markov Chain Monte Carlo approach. The data of American Time Use Survey 2015 is employed to define the activity transition probability matrices of the Markov chains. By corresponding typical behaviors to common electric appliances and incorporating models of shiftable devices in the simulation, flexible residential energy demand can be estimated. The experimental results show high accuracy of the model, and effectiveness of flexible appliances that can shift and reduce peak loads to decrease individual electricity cost and improve global energy efficiency.

Coffee Break & Group Photo <16:00-16:15>

SESSION IV

December 21, 2019 Session 4

Text Analysis and High-performance Computing 文本分析与高性能计算

16:15-18:45 Meeting Room 1 (Level 3, Building A) | A 栋三楼会议室 1

Chaired by Assoc. Prof. Stephne Karungaru Tokushima University, Japan

10 Presentations— V1-0047, V1-0058, V1-0048, V1-0123, V1-0018 V1-0104, V1-0119, V1-0113, V1-0150, V2-0054

*Note:

SESSION IV Knowledge-based Ontology Development for Folk Medicine Salintip Ratsamano and Supaporn Chairungsee Walailak University, Thailand

Abstract- This research sought to develop a semantic ontology of knowledge about the introduction of folk medicine. In this research, the content of folk medicine is V1-0047 presented comprehensively and the research goals or procedures are defined as 16:15-16:30 follows: (1) determine the purpose and scope of the ontology; (2) develop the ontology by using the Hozo Ontology Editor program (Osaka University, Osaka, Japan); and (3) have an expert evaluate the ontology that is developed. The purpose of this research is to clarify the system-recommended treatment involving folk medicine that can guide patients well. Ideally, the treatment selection process will be accurate 100% of the time and can be used in related research. Aggregating Filter Feature Selection Methods to Enhance Multiclass Text Classification Rhodessa Cascaro, Bobby Gerardo and Ruji Medina Technological Institute of the Philippines, Philippines

Abstract- Text data usage has increased rapidly and simultaneously resulted in setbacks, such as high dimensionality of text data becoming a prominent problem. Hence, this study aimed to assess the application of filter feature selection techniques on text data. In this study, features were ranked from highest to lowest by the selected filter feature selection methods in each generated feature subset. Thereafter, a new feature subset was obtained using the proposed method. This study yielded that the accuracy of Information Gain is 1.12 percentage points higher in comparison to the accuracy of Chi-square. Moreover, classification accuracy obtained from aggregation exhibits a rise of 0.93 percentage points compared to the V1-0058 accuracy of Information Gain and 2.05 percentage points against Chi-square. 16:30-16:45 Classification accuracy improved when the features are aggregated. On Precision, in comparison to that of the aggregation, results show the differences in percentage points of 1.41 and significant 11.64 for Information Gain and Chi-square respectively. About Recall, there is a 5.54 percentage points improvement on Information Gain and 3.03 percentage points improvement on Chi-square. Then, in F1, the score for aggregation is quite low. It may mean that the classifier has problems with false positives or false negatives. Thus, the classifier needs to be checked using a confusion matrix or check on the dataset, which was not done in the experiment. Dataset imbalance was also not addressed in this study. For future work, the imbalanced class-dataset issue should be addressed. Also, the performance of other filter methods could be compared as well as utilize other classifiers that support multiclass tasks to determine which is suitable for multiclass text classification.

SESSION IV Algorithm for Palindrome Detection by Suffix Heap Surangkanang Charoenrak and Supaporn Chairungsee Walailak University, Thailand

Abstract- In this article, we present an efficient algorithm for linear-time suffix V1-0048 heap construction to use in detecting a pattern of palindrome, which can be 16:45-17:00 applied in a field of bioinformatics for detecting a pattern that may denote a cancer cell. We used the concept of the Longest Previous reverse Factor (LPrF) table and suffix heap construction to develop the algorithm. From such, we obtained a new algorithm that demonstrates efficient time and space attributes for detecting all reverse substrings in a string. The palindrome detection algorithm runs with high performance in data processing. Extracting Structured Data from Unstructured Text Using Conditional Random Field and Jaccard Similarity Sarun Wiriyapistan and Sukree Sinthupinyo Chulalongkorn University, Thailand

Abstract- Nowadays, purchasing information is usually found in form of formal messages such as an email. However, most messages are written in natural language V1-0123 form which is difficult to extract data. Our approach paper uses Conditional 17:00-17:15 Random Fields and Words Similarity to extract data from customers’ purchase order emails. We started from dividing the words in the email into a sequence of words. Then, we added features of each word. After that, we establish an appropriate template, characteristics and training sample sequences. Next, purchasing information was used for text extraction. From our experiment, it was found that adapting Words Similarity with Conditional Random fields can enhance the accuracy of extracting data significantly. How Big Data Analytics Impacts Agility: The moderation Effect of Orientation of interactive team cognition Qinxian Liu, Youyung Hyun, Ryuichi Hosoya and Taro Kamioka Hitotsubashi University, Japan

Abstract- Nowadays, organizations have been faced with the rapidly changing environment; thus, in order to survive in such volatile business environment, agility V1-0018 which is the ability to sense opportunities and defend threats has become a critical 17:15-17:30 issue. Big data analytics (BDA) has been known to positively influence the agility; however, to our knowledge, the impact of BDA use on agility has not been studied abundantly in relation to the perspective of team cognition. To fill this research gap, we developed a new construct called orientation of interactive team cognition (OITC) based on the interactive team cognition (ITC) theory. After analyzing the survey data from 173 respondents, our paper found that OITC plays a positive role in moderating the relationship between the use of BDA and agility.

SESSION IV Research of Cross-domain Cloud Trust Model and its Defense Abilities Analysis Minghui Yang and Linlin Zhang The Third Research Institute of the Ministry of Public Security, China

Abstract- Under the open, distributed and dynamic cloud environment, entities interactions make it more complex to build up the trust relationship. Recently, trust has been recognized as an important factor to improve cloud security and becomes a V1-0104 research hotspot. In this paper, we proposes a new multi-level trust model to 17:30-17:45 manage cloud entities’ relationship from the view of ‘virtual organization-sub domain-entity’, and measure trust relationships in a numerical way. By keeping updating the trust information, the model can identify different entity types and defense bad entities’ behaviors, optimizes resource allocation. The results show that this model is efficient to identify malicious entities and improves the ratio of service request response. So this model has good defense ability and is able to provide a better secure cloud environment. An Effective Verifiable Symmetric Searchable Encryption Scheme in Cloud Computing Kangle Wang, Xiaolei Dong, Jiachen Shen and Zhenfu Chao East China Normal University, China

Abstract- With the booming Internet industry, users' demand for resources is also increasing. In order to meet the needs of users, cloud computing came into being. In V1-0119 today's era, cloud storage is already the most popular application in cloud 17:45-18:00 computing, and users are increasingly inclined to store important and private information in the cloud. One of the most common methods is to encrypt the information before uploading it to the cloud, but searching the information it needs in the cloud server becomes an obstacle, and symmetric searchable encryption can help users achieve this. In this paper, we design a verifiable method based on an efficient symmetric searchable encryption scheme to verify the correctness of search results returned by the cloud server. Through safety analysis and performance evaluation tests, we demonstrate that our solutions are safe and effective. Distributed Deep Neural Network Training with Important Gradient Filtering, Delayed Update and Static Filtering Kairu Li, Yongyu Wu, Jia Tian, Wentao Tian and Zuochang Ye Tsinghua University, China & Cortex Lab

V1-0113 Abstract- With the increasing number of computing nodes in current computer 18:00-18:15 clusters, the performance of large-scale deep neural network training is essentially limited by the communicational cost, especially for transferring gradients among nodes during iteration. In this paper, three methods are proposed to reduce the communicational cost: important gradient filtering, delayed update and static filtering. Important gradient filtering algorithm selects the most important gradients to reduce the size of gradients to be transferred and help convergence. While

SESSION IV delayed update algorithm significantly reduces the gradient broadcasting time. Static filtering filters the gradient with very small variance. Results show that a combination of the proposed methods achieves 2.91× to 5.58× communication cost reduction in a cluster with inexpensive commodity Gigabit Ethernet interfaces.

SoCo-ITS: Service Oriented Context Ontology for Intelligent Transport System Santosh Pattar, Sandhya C R, Darshil Vala, Rajkumar Buyya, Venugopal K R, S S Iyengar and L M Patnaik University Visvesvaraya College of Engineering, India

Abstract- Intelligent Transport System (ITS) is a culmination of technologicaland application systems that are contrived to improvethe performance of road transportation and upgradethe commuter’s experience. The integration of Internet V1-0150 ofThings (IoT) with the transport system has contributed tothe development of ITS. 18:15-18:30 In this paper, we concentrate on thecommercial servitization standpoint of the application. Westructure and formulate an ontology called Service-OrientedContext Ontology for Intelligent Transport System: SoCo-ITS.This ontological framework abets in identifying appropriateservices required by the commuters in transit based on theirsituation, predilection and ITS environmental information.We discuss the detailed implementation description and alsoaccentuate its role in ITS through a use case scenario andan exemplar application portraying the importance of theproposed ontological model. Kitchen Utensils Recognition using Fine Tuning and Transfer Learning Stephen Karungaru Tokushima University, Japan

Abstract- To support blind persons at home especially in the kitchen, this work V2-0054 proposes the recognition of kitchen utensils using video sunglasses. The recognition 18:30-18:45 system is based on transfer learning/fine tuning an existing deep learning algorithms, VGG16. Initially, our system can recognize 6 kitchen items using 1354 images in 6 classes. The training/validation and evaluation sets are set at 80% and 20% respectively. Most of the training data was downloaded from the Internet. In this challenging and noisy data, we achieved and accuracy of 95% using the fine tuning learning.

Dinner @ Barossa Room (Level 1, Building A) 晚餐 | A栋一楼巴罗莎 <19:00-20:30>

SESSION V

December 21, 2019 Session 5

Pattern Recognition and Image Security 模式识别与图像安全

16:15-19:00 Meeting Room 2 (Level 3, Building A) | A 栋三楼会议室 2

Chaired by Dr. Kai Yang China Mobile Research Institute, China

11 Presentations— V2-0013, V1-0073, V1-0122, V1-0109, V2-0020 V2-0052, V1-1001, V2-0014, V1-1002, V2-0031, V1-3008-A

*Note:

SESSION V Attention based Generative Graph Convolutional Network for Skeleton-Based Human Action Recognition Kai Yang, Xiaolu Ding and Wai Chen China Mobile Research Institute, China

Abstract- Skeleton-based action recognition is a challenging field in computer vision. Graph representations of skeleton are used to learn the connection patterns of human joints. However, the fixed handcraft graph of human skeleton topology can not well represent all the connection patterns. In this work, we propose an end-to-end V2-0013 generative graph convolution network to learn the joints graph connection patterns 16:15-16:30 directly from data.We use self attention to construct the spatial adjacency matrix of each skeleton frame. Graph is generated progressively by learning the weighted adjacency matrix sequence from skeleton frames. In order to extract temporal dynamics effectively, velocity semantic information is also used to generate graphs. An online graph generative mechanism is proposed to enhance the model adaption. Extensive experiments on two large-scale action recognition datasets are preformed to verify the effectiveness of our approach. The comparison results show that our method achieves remarkable improvement compared with the state-of-the-art approaches. Design of Beverage Packaging Identification and Recycling System based on Zedboard Shaoting Li, Kai Sun, Meijing Qi, Jiahui Liu and Wei Huang Inner Mongolia University, China

Abstract- This article describes a Zedboard-based beverage packaging identification recycling system. In today's rapid development of image recognition technology, image recognition is applied to beverage packaging recycling, which enables beverage packaging to be recycled through image recognition in the case of unrecognizable barcodes, enabling more flexible identification and recycling. This V1-0073 article uses the open source Zedboard development board of the ZYNQ7000 16:30-16:45 platform for system design. The image processing IP core which designed in Vivado HLS is used to realize the acceleration of image processing in hardware; the high-speed image processing system is designed in Vivado. The control of the IP core through the program is implemented in the software part. In addition, the design of the barcode recognition function, the establishment of the beverage packaging database, and the preparation of the image comparison algorithm are completed. The type of beverage package was finally determined and sorted and recycled. By testing the system, the overall recognition function of this design has a high accuracy rate, and it can make up for the shortcomings that cannot be recovered when the barcode is not recognized, and achieves the expected goal. WiNum: A WIFI Finger Gesture Recognition System based on CSI V1-0122 Yong Zhang, Kangle Xu and Yujie Wang 16:45-17:00 Hefei University of Technology, China

SESSION V Abstract- Gesture recognition is an important part of human-computer interaction, which enables people to interact naturally with the machine. In this paper, we design a gesture recognition system called WiNum based on Gradient Boosting Decision Tree (GBDT) algorithm, which uses the commercial router to extract channel state information (CSI) in WiFi environment. Our system uses discrete wavelet transform (DWT) to preprocess the data in order to eliminate the noise in the original data. We design an adaptive gesture segmentation algorithm (AGS) based on the difference in information entropy between action and non-action parts to segment gestures. Different from KNN, SVM and other machine learning methods, we use GBDT ensemble learning algorithm to realize finger gesture recognition. The experimental results show that our system has an average recognition accuracy of 91 % for finger gestures. Implementation of Dairy Cows Individual Identification based on ZYNQ SoC Zhenyu Dai, Kai Sun, Jin Li and Wei Huang Inner Mongolia University, China

Abstract- Individual identification technology of dairy cows plays an important role in modern automated farming, which facilitates the management of various behaviors of dairy cows and improves the cow's health protection. In this paper, the ZYNQ-7000 hardware platform is used to preprocess the real-time cow frame image obtained by the camera, and then compared with the cow images in the database, so V1-0109 as to determine the identity information of the cow in the current frame picture and 17:00-17:15 complete the task of individual identification of dairy cows. After the camera acquires the cow picture, the image preprocessing is carried out in the programmable logic part. Image pre-processing IP cores are designed in Vivado HLS, including median filtering IP core and adaptive binary IP core. Additionally, the image comparison algorithm is completed in Vivado SDK, and the similarity comparison is performed between the SD card template image and the current frame picture in the DDR. Finally, the recognition results are shown in two ways: one is to output the corresponding cow number in the database through the serial port on the experiment host, and the other is to simulate the output result by turning on and off the LED on the development board. View Resistant Gait Recognition Anna Sokolova, Anton Konushin National Research University Higher School of Economics, Russia

Abstract- Human gait is one of the biometric characteristics that a person can be V2-0020 identified by. However, the wide applicability of gait recognition in real life is 17:15-17:30 prevented by a great variety of conditions that affect the gait representation, such as different viewpoints. In this work, we present a novel view resistant approach to overcome the multi-view recognition challenge. The new loss function is proposed to increase the stability of the model to view changes. Besides this, the cross-view embedding of the gait features is made to enhance their discriminant ability which

SESSION V improves the recognition accuracy as well. The proposed approaches show a significant gain in quality and allow to achieve the state-of-the-art accuracy on the most common benchmark and outperform the most successful model on the majority of the views and on average. Application of Improved LeNet-5 Network in Traffic Sign Recognition Wenlong Li, Xingguang Li, Yueya Qin, Wenjun Song and Wei Cui Changchun University of Science and Technology, China

Abstract- Considering that most convolutional neural network (CNN) models designed for traffic sign recognition (TSR) have sacrificed more resources and complicated network model development while pursuing higher performance, LeNet-5 shallow CNN with low complexity has been selected for improvement. V2-0052 Increasing the number of convolution kernel in the first convolution layer (C1 layer) 17:30-17:45 and the third convolution layer (C3 layer) while reducing the size of the convolution kernel in C3 layer. Introducing Rectified Linear Unit (ReLU) function with better performance. The maximum pooling is introduced instead of mean pooling. Besides, the output layer employs support vector machine (SVM) to shorten the operation time. The research results demonstrate that the improved LeNet-5 network has an identification accuracy rate of 98.12% and the identification time is 0.154s for traffic signs in German Traffic Sign Recognition Benchmark (GTSRB), which could guarantee the real-time performance of the system and effectively reduce the complexity of the system on the basis of a high recognition rate. Watermark Embedding and Extraction Based on LSB and Four-Step Phase Shift Method Xin Kuang, Wang An Ling, Li Shi Ke, Guo Lei, Pang Jian Ping, Liu Zhi Yue and Liu Fu Ping Beijing Institute of Graphic Communication, China

Abstract- A method of color watermark embedding and extraction based on LSB and four-step phase shift method is proposed for information hiding. The method firstly decomposes the color watermark into three primary colors, and decomposes them into grayscale images of R, G, and B channel image. The four-step phase shift V1-1001 method 17:45-18:00 respectively generates four computer-generated hologram (CGH) for each grayscale images, and one of the CGH will be embedded into the corresponding channel image of the color carrier image by the LSB algorithm; the watermark carrier image is decomposed into the R, G, and B channel images, the CGH is extracted from the channel image and The channel images are reconstructed from the CGH by the four-step phase shift method, the original watermark image is synthesized by the channel images, and the watermark recovery is completed. This watermark embedding extraction method based on LSB and the four-step phase shift improves the security during the watermark embedding process.

SESSION V LEFV: A Lightweight and Efficient System for Face Verification with Deep Convolution Neural Networks Ming Liu, Qingbao Li, Jinjin Liu and Zhifeng Chen State Key Laboratory of Mathematical Engineering and Advanced Computing, China

Abstract- The emergence of deep learning has made great progress in face V2-0014 recognition. With the popularization of embedded devices, deploying the deep model 18:00-18:15 on embedded devices has become a trend. Most high-precision models require lots of computation costs. Therefore, developing a lightweight deep face recognition system running on embedded devices is a hot topic in current research. To achieve high-accuracy real-time performance of an embedded device, we now present a simple and effective face recognition system LCFR, including face detection, face normalization and face recognition. The quantitative experiments on two large-scale challenging datasets, WIDER FACE dataset and IJB-A dataset, show competitive performances on both runtime and accuracy. Computational Holographic Image Watermarking Algorithm based on Discrete Cosine Transform Zhiyue Liu, Anling Wang,Xiaofeng Zhu and Fuping Liu Beijing Institute of Graphic Communication, China

Abstract- In order to improve the robustness and invisibility of watermark, a grayscale computing holographic image watermarking algorithm based on discrete cosine transform has been proposed. First, the watermark image is calculated to generate a hologram to improve the security of the watermark. The carrier image is divided into 4×4 blocks, and each block is separately subjected to discrete cosine V1-1002 transform, and the low-frequency coefficients of each block are extracted to form a 18:15-18:30 new matrix.Then, the watermark image is subjected to discrete cosine transform, and embed it into the carrier image according to prescript embedding rules,thereby completing the embedding process of the watermark.The extraction process is the inverse of the embedding process. The obtained image is decomposed into 4×4 blocks, and each block is subjected to discrete cosine transform, and extracted according to the inverse law of the embedding.Through test, the experimental results of the method show that the proposed algorithm has strong robustness in image clipping, rotation, translation and Gaussian noise attack. It ensures that the watermark is hidden while resisting a certain degree of attack to meet the needs of copyright identification. Artifacts Reduction for Compression Image with Pyramid Residual Convolutional Neural Network V2-0031 Chunmei Nian, Ruidong Fang, Jucai Lin,Zhengteng Zhang, Jun Yin and Xingming 18:30-18:45 Zhang Zhejiang Dahua Technology Co.,Ltd, China

SESSION V Abstract- A pyramid residual convolutional neural network (PRCNN) is proposed to restore image with compression artifacts in this paper, and the method is enable to process multiple resolutions by fixing patch size extracted from whole image. Convolutional neural network has reached great performance in image processing (e.g. denoise, deblur, super-resolution), however deeper network may cause vanishing or exploding gradient problems, and it is hard to apply in realistic scene for high complexity. Thus, the residual blocks (RB) are proposed to balance between performance and application, besides, this paper exploits pyramid convolutional neural network to learn coarse-fine feature. In order to handle various resolutions, the fixed patch based method is used to adapt realistic scene. The experiment shows that the proposed algorithm can reduce compression artifacts through objective and subjective assessment, and the training/testing data are collected with H.264 coding. The proposed method can improve PSNR and SSIM from 0.54dB to 1.41dB, 0.01 to 0.04 while compression artifacts are reduced in visual quality, respectively. Integrated platform for Financial Fraud Detection using Analytics, Machine Learning, MDR and SIEM Barani Shaju Avinashilingam University for Women, India

Abstract- Financial sectors focus on providing quality investment services guarded with security measures. Any event or trigger with harmful intent, needs to be identified and tracked to cease. Data Analytics provide a great platform to stimulate thinking and observations based on large data volumes. With an integrated approach of Analytics and Machine Learning, financial sectors can derive at a streamlined, strategic approach towards Fraud Management. Currently, industrial approach also use data analytics and machine learning for business predictions and forecasts. In this platform, there is always a need for shielded security measure to support V1-3008-A investigation towards fraud prevention and control over false positive alarms. This 18:45-19:00 makes the context of financial fault detection more challenging and need for vigilant monitoring measures. Use of exploratory data analytics and application of machine learning methods with time series data can help us to detect frauds in an enhanced and easy manner. Employing a cutting-edge solution system with analytical ability and machine learning has become an essential march for financial companies to strive and stay ahead. Proposed approach will be an enhancement with re-engineering to achieve higher accuracy levels in detection. It aims to provide a system with an ability to tune itself as a "Self-Adaptive, Self-Healing and a Self-Configurable" with use of Machine Detection Response (MDR) Solution. Topping it with Security Information and Event Management (SIEM) provides an integrated platform to achieve holistic and highly technology-driven Solution. SIEM aims to reduce mean time to detect and mean time to respond.

Dinner @ Barossa Room (Level 1, Building A) 晚餐 | A栋一楼巴罗莎 <19:00-20:30>

SESSION VI

December 21, 2019 Session 6

Image Analysis and Calculation 图像分析与计算

16:15-19:00 Meeting Room 9 (Level 3, Building A) | A 栋三楼会议室 9

Chaired by Assist. Prof. Jiawei Zhu Chang'an University, China

11 Presentations— V1-0039, V1-0147, V2-0018, V2-0032, V2-0039 V1-0031, V2-0046, V2-0062, V2-0064, V1-0126, V2-0056

*Note:

SESSION VI A Fast Real-time Map-Matching for Unstable Sampling-rate GPS Trajectories Jieli Peng, Yang Cao, Zhiming Ding and Jin Yan Beijing University of Technology, China

Abstract- Map matching is the process of matching GPS points to roads. Local methods and incremental methods usually have faster running speed, however, the performance of these methods may be insufficient for complex road networks and V1-0039 data with significant error. Global methods can get better results in complex 16:15-16:30 situations, while most of them handle high frequency GPS data slowly. In order to solve these problems, a weight based algorithm(FWMM) is developed in this paper. We introduce a new method of weight fusion to avoid the influence of dimension and adopt Particle Swarm Optimization( PSO) for parameter estimation. In order to accelerate the matching process of data with high sampling rate, we designed a mechanism which can adaptively accelerate high frequency data piece. Thus this method can be more accurate than existing methods mentioned above An Unsupervised Approach for 3D Medical Image Registration Yingjun Ma, Jinshuo Zhang, Dongmei Niu, Muhammad Umair Hassan and Xiuyang Zhao University of Jinan, China

Abstract- Deformable image registration is of great importance in many clinical applications. In this work, we propose an unsupervised end-to-end registration V1-0147 method for 3D medical images. The proposed method takes a pair of moving and 16:30-16:45 fixed images as input and directly estimates the spatial transformation parameters, which enable spatial transformation layer to generate the registered image. In particular, the registration network is designed in the underlying M-Net architecture that consists of encoding path, decoding path, left leg, and right leg. Moreover, we introduce a novel loss function to guide the training. The proposed method is evaluated on the public brain image dataset ADNI. Experimental results demonstrate that our method achieves promising performance. SRM-Net: An Effective End-to-end Neural Network for Single Image Dehazing Yu Li, Wenbin Yu, Lu Ding, Lingya Liu and Yiyin Wang Shanghai Jiao Tong University, China

Abstract- Recently, the great development of deep learning has prompted many V2-0018 neural networks for single image dehazing to occur. How-ever, due to the ill-posed 16:45-17:00 nature of haze, an excellent charac-teristics representation capacity is still challenging. In this paper, we propose a lightweight yet effective senet-residual (SE-Res) multiscale end-to-end neural network named SRM-Net. Inspired by the remarkable performance of residual networks, we intro-duce a SE-Res structure which is an improved residual framework with an embedded SE unit to obtain feature maps. These maps pass through a multiscale mapping layer which can

SESSION VI aggregate characteristics in different receptive fields. Notably, the utilization of all point-wise convolutions in the SRM-Net leads to fewer parameters for training, and the reuse of feature maps makes it more lightweight. Through extensive numerical experiments on three datasets including real hazy images, synthetic indoor and outdoor hazy images, the proposed SRM-Net achieves superior performances on subjective visual results and objective evaluation metrics compared to the state-of-the-art methods. Wavelet Denoising of Remote Sensing Image based on Adaptive Threshold Function Yuqing Ma, Juan Zhu and Jipeng Huang Northeast Normal University, China

Abstract- Aiming at the problem of edge feature loss caused by conventional threshold function in wavelet transform, a new adaptive threshold function denoising algorithm is proposed based on improved threshold. The algorithm takes V2-0032 advantages of the improved threshold functions, and takes the scale of the current 17:00-17:15 wavelet decomposition as a function adjustment factor, so that the function can be adjusted by adaptive scale transformation, which is more in line with the actual distribution of noise in each scale. A few noisy remote sensing images are tested and the simulation results of MATLAB confirm the merits of the proposed denoising technique compared with other wavelet-based techniques by measuring evaluation metrics such as signal-to-noise ratio and mean square error. Furthermore, the improved threshold function can obtain better visual effects which ensures the detail features in remote sensing images are better preserved. Collaborative Semantic Segmentation with Image Labels Zhikang Li, Ya Zhang and Yanfeng Wang Shanghai Jiao Tong University, China

Abstract- Weakly-supervised semantic segmentation has recently received much attention since it needs less fine-grained annotations than fully-supervised learning. Most existing studies use attention maps from the classification network as supervision, which suffers from only locating small discriminative parts of objects and lacking precise boundaries. In this study, we propose a collaborative V2-0039 segmentation network consisting of a localization sub-network and a segmentation 17:15-17:30 sub-network. The localization sub-network takes only image-level labels as supervision. The pseudo masks generated by the segmentation sub-network and the localization sub-network, providing rough localization information of objects and the shape information respectively, are mixed-up adaptively and used as supervision for the segmentation sub-network. The two sub-networks share the same backbone and are shown to mutually enhance each other during training. We evaluate the proposed method on PASCAL VOC 2012 Semantic Segmentation benchmark and achieve new state-of-the-art.

SESSION VI Automatic Classifications and Recognition for Recycled Garbage by Utilizing Deep Learning Technology Huiyu Liu, Ganiyat Owolabi and Sung-Ho Kim Kyungpook National University, South Korea

Abstract- The increasingly serious environmental problems have posed new challenges to the survival of human beings, an average of 1269 tons of garbage is V1-0031 produced every day in Kwangju, South Korea. Most of them can be recycled, 17:30-17:45 however, it costs a lot of manpower and resources and satisfactory classification efficiency was not achieved. Therefore, how to classify recycled garbage efficiently and accurately has become an important research direction. This paper proposes a novel classification method to automatically identify the type of garbage by utilizing deep learning technology. Deep learning technology is wildly utilized for image classification. Therefore, it also applies to the classification of recycled garbage. It not only saves a lot of manpower and resources but also improves the utilization of recycled resources. Unsupervised Single Image Dehazing via Disentangled Representation Qian Liu Shanghai Jiao Tong University, China

Abstract- Image dehazing aims to recover the latent clear content from the corresponding degraded hazy image. In this paper, we propose an unsupervised method for single image dehazing based on disentangled representation. Our proposed method does not rely on the physical scattering model and does not need V2-0046 paired of training data. We propose a content encoder and a haze encoder to 17:45-18:00 disentangle the content and hazy information from a hazy image respectively. We propose a latent regression loss to encourage the generated image to preserve haze information and to force the haze encoder to extract haze information from the haze image. The cycle-consistency loss are introduced to ensure that the dehazed images have the same content structures with the original images. We also use an adversarial loss on the dehazed images to guarantee haze free and visually realistic. Extensive experiment results on the public dehazing dataset RESIDE demonstrate that the proposed method outperforms state-of-the-art unsupervised methods, and can achieve comparable performance with the state-of-the- art supervised methods. Lossless Compression Algorithm based on Context Tree Jiong Wang, Jianhua Chen and Zhiyuan He Yunnan University, China

V2-0062 Abstract- In order to deal with the context dilution problem introduced in the 18:00-18:15 lossless compression of M-ary sources, a lossless compression algorithm based on a context tree model is proposed. By making use of the principle that conditioning reduces entropy, the algorithm constructs a context tree model to make use of the correlation among adjacent image pixels. Meanwhile, the M-ary tree is transformed

SESSION VI into a binary tree to analyze the statistical information of the source in more details. In addition, the escape symbol is introduced to deal with the zero-frequency symbol problem when the model is used by an arithmetic encoder. The increment of the description length is introduced for the merging of tree nodes. The experimental results show that the proposed algorithm can achieve better compression results. Generative Transferable Adversarial Attack Yifeng Li, Ya Zhang, Rui Zhang and Yanfeng Wang Shanghai Jiao Tong University, China

Abstract- Despite their superior performance in computer vision tasks, deep neural networks are found to be vulnerable to adversarial examples, slightly perturbed examples that can mislead trained models. Moreover, adversarial examples are often transferable, i.e., adversaries crafted for one model can attack another model. Most existing adversarial attack methods are iterative or optimization-based, consuming relatively long time in crafting adversarial examples. Besides, the crafted examples V2-0064 usually underfit or overfit the source model, which reduces their transferability to 18:15-18:30 other target models. In this paper, we introduce the Generative Transferable Adversarial Attack (GTAA), which generate highly transferable adversarial examples efficiently. GTAA leverages a generator network to produce adversarial examples in a single forward pass. To further enhance the transferability, we train the generator with an objective of making the intermediate features of the generated examples diverge from those of their original version. Extensive experiments on challenging ILSVRC2012 dataset show that our method achieves impressive performance in both white-box and black-box attacks. In addition, we verify that our method is even faster than one-step gradient-based method, and the generator converges extremely rapidly in training phase. Awareness and Settlement of IT Field Key Challenges for Next Generation Development in World Muhammad Bilal, Zhi Yu, Abid Bashir and Muhammad Ali Hussain Zhejiang University, China

Abstract- Information Technology (IT) is playing a vital role in all aspects of our lives nowadays. IT is used for fast communication links and transferring useful information from one place to another. But around the world, unfortunately, IT is V1-0126 not used properly. Without IT and Information and Communication Technology 18:30-18:45 (ICT) world will become a dark place. Most of the research in the IT field is inadequate. A subjective IT field development framework is designed with the assistance of better Internet features utilized properly in all developing countries around the world. The proposed IT development framework can be used innovatively by adopting a wireless-based network in developing country growth around the world. We analyzed wired and wireless systems on the basis of six common key Internet feature parameters for IT field improvement in developing countries around the world. In this empirical research, a total of 72 IT professional

SESSION VI specialists included on the basis of the statistical technique judgment sampling. The questionnaire survey results signify that speed, security, and reliability Internet features in the wired network are better than the wireless network. Moreover, mobility, coverage area, and cost relevant Internet features in the wireless network are much better than a wired network. According to expert decision-makers' strategy, the wireless network transmission medium is better to develop improvement in the world. Therefore, it is concluded that developing countries can also play a significant role in the IT field as well as other fields around the world if Internet features are provided in the form of a wireless-based network appropriately. In the future, security and speed relevant problems will be resolved in the wireless-based system through improving routing algorithms. Rare Traffic Sign Recognition using Synthetic Training Data Vlad Shakhuro, Boris Faizov and Anton Konushin Lomonosov Moscow State University, Russia

Abstract- Modern computer vision methods usually require lots of labelled data for training. Besides price of labelling, problems with rare object classes and adaptation to new domain or task arise. One of the promising methods to solve these problems V2-0056 is to generate synthetic training data. In this work we focus on task of traffic sign 18:45-19:00 detection. We consider several methods for generating synthetic data for training traffic sign detectors: random placement of signs of different quality (simple synthetic, CGI based and CGI improved using generative adversarial network). We also propose a method to replace real signs with synthetic signs. Experimental evaluation shows that proposed method improves quality of detection of rare traffic signs and that usage of synthetic data is very helpful for improving training of traffic sign classifier.

Dinner @ Barossa Room (Level 1, Building A) 晚餐 | A栋一楼巴罗莎 <19:00-20:30>

SESSION VII

December 22 2019 Session 7

Image Processing Technology and Method 图像处理技术与方法

13:30-16:00 Meeting Room 1 (Level 3, Building A) | A 栋三楼会议室 1

Chaired by Prof. Yihong Zhang DongHua University, China

10 Presentations— V2-0047, V1-0075, V2-0004, V2-0011, V2-0017 V2-0019, V2-0048, V2-0058, V2-0059, V1-0010

*Note:

SESSION VII A Real-Time Detection Drone Algorithm based on Instance Semantic Segmentation Zihao Liu, Haiqin Xu, Yihong Zhang, Zhouyi Xu,Sen Wu and Di Zhu DongHua University, China

Abstract- With the rapid development of drones, drones are widely used in various fields and bring convenience to people's production and life. However, they also bring security problems to society and the country. Especially in airports or military areas, the flight of drones can cause some problems. In order to effectively supervise the drone, this paper proposes a real-time detection drone algorithm HR-YOLACT which is based on instance semantic segmentation, and designed a V2-0047 new drone data set. The proposed algorithm combines the real-time instance 13:30-13:45 semantic segmentation algorithm YOLACT with the deep high-resolution representation classification network HRNet. Firstly, feature maps are extracted by HRNet's backbone network. Secondly, the feature pyramid network is used to further extract image features, so that the network has better classification ability. Finally, the improved prediction head is utilized to detect the boxes of drones. In addition, this paper uses cross entropy instead of focal loss as the loss function to obtain better network training speed and quality. The experimental results show that HR-YOLACT has faster detection speed and higher detection precision than existing popular real-time object detection and real-time instance semantic segmentation algorithms. Research on the Influence of Kmeans Cluster Preprocessing on Adversarial Images Guilin Chen, Guanwu Wang, Jian Hu and Xianglin Wei NUDT, China

Abstract- Deep learning is the core of the current artificial intelligence. Neural networks, represented by the depth of learning technology, has been widely applied to the field of computer vision, such as automatic driving, and face recognition. But recent research has shown that if the original picture is added visually imperceptible V1-0075 perturbations, it can fool the neural network to misclassify it. These adversarial 13:45-14:00 images can be generated very easily, and it poses a great threat to computer vision security. Therefore, more and more researches involve the defenses against adversarial images. With the use of the characteristics of the neural network's own fitting and generalization, we perform Kmeans clustering on the images that need to be identified, and then evaluate the impact of different clustering values on the classification of adversarial images. The experimental results show that for small amplitude perturbations images, the use of smaller clustering values can largely reverse the decline of neural network accuracy. However, as the magnitude of the perturbations increases, the defensive effect of simple clustering becomes weaker. Sketch based Image Retrieval with Adversarial Network V2-0004 Zhe Bai, Hong Hou,Ni Kong and Xiaoqun Guo 14:00-14:15 Northwest University, China

SESSION VII Abstract- Sketch retrieval is a specific cross-domain retrieval task. The core of sketch retrieval is to learn a common feature subspace, where the features of sketches and natural images can be both discriminative and domain-invariant. However, similarity constraints can impair the performance of the feature extractor, resulting in unsatisfactory retrieval accuracy. For this problem, we propose a novel sketch based image retrieval method based on adversarial network. Our method is demonstrated as follows: Firstly, we train the sketch image network and natural image network to improve the ability of classification; secondly, we train the adversarial network to promote the feature fusion of sketches ,of which the network is constituted by feature extractor and domain classifier; thirdly, we use the deep convolutional neural network to extract the deep feature to achieve retrieval. Experiments on retrieval show positive results. Attentional Bi-directional LSTM for Semantic Attribute Prediction Mengling Shen, Xianlin Zhang and Xueming Li Beijing University of Posts and Telecommunications, China

Abstract- Attribute prediction is a basic task in CV field, and it belongs to a multi-label prediction problem in practical terms. Most studies using deep features to handle the problem while ignoring the potential dependencies that exist within V2-0011 attributes and images. Differ from previous work, we propose a novel deep 14:15-14:30 architecture named ABLSTM, which not only taking advantage of CNN and Bi-LSTM, but also utilizing a multi-task-multi-loss design for attribute detection. Based on ABLSTM, we further construct a simple but extremely effective regression module to improve the accuracy of high-level abstract semantic attributes. Extensive experiments on the largest attribute prediction dataset of DeepFashion show the consistency superiority and efficiency of the proposed model. Multiple Ship Targets Association Method of Remote Sensing Images Based on SIFT and Bags of Visual Words Model Lichun Yang, Dan Yang and Jianghao Wu Beihang University, China

Abstract- Ships with unique moving and group activity characteristics can be considered as a type of very important military targets in marine situation V2-0017 awareness. We propose a novel method for multiple ship targets association with 14:30-14:45 utilizing multi-temporal optical satellite images. First, a large number of local invariant features are extracted from the region of the optical satellite imagery containing the ship targets. Second, we present a weighted Bag of Visual Words model to perform transforming the 128-dim features to high-order semantic features. Finally, the optimization model based on the Associated Cost Matrix is constructed to solve the target optimal correlation matching. The experimental results clearly demonstrate that the proposed method is robust to multi-target association ambiguity and produces good matching accuracy with low

SESSION VII computational effort. MDAnet: Multiple Fusion Network with Double Attention for Visual Question Answering Junyi Feng, Ping Gong and Guanghui Qiu Beijing University of Posts and Telecommunications, China

Abstract- The majority of existing methods for Visual Question Answering are V2-0019 primarily based on Recurrent Neural Networks with attention to extract question 14:45-15:00 features and outputs the last hidden state for modal fusion. However, only the last hidden state cannot preserve precise location information, which may lead to semantic confusion. Our work adopts Multiple Encoder Block to replace Recurrent Neural Networks, and propose a Double Attention Module which includes Objective Attention and Spatial Attention to acquire more important regions or objects. The proposed method is evaluated on VQA 2.0 and CLEVR, and obtain competitive result. High-Frequency Feature Learning in Image Super- Resolution with Sub-Pixel Convolutional Neural Network Jiang Xiao-yuan and Chen Xi-hai NARI Group Corporation, China

Abstract- Sub-pixel convolutional neural network is efficient for image super-resolution. However, the images generated are relatively smooth. Improving the learning ability of high-frequency features is of great significance for sub-pixel convolutional neural network to get better performance. In the paper, we propose an improved algorithm of sub-pixel convolutional neural network based on V2-0048 high-frequency feature learning for image super-resolution, it optimizes the 15:00-15:15 traditional sub-pixel convolutional structure. Firstly we introduce a residual convolutional layer in the generation net. it assigns the residual factor to each sub-pixel feature map and forces each pixel feature map to adaptively use the input information. Furthermore, a method for high frequency feature mapping is proposed. During image super-resolution training stage, the multi-task learning function, combining the pixel-level loss function with high-frequency contrast loss function, make the generation images getting closer to the target super-resolution images in high-frequency domain. The experiments on CelebA dataset show that our proposed method can effectively improve the quality of super-resolution images by contrast to the traditional sub-pixel convolutional neural network. Self-supervised Image Classification based on the Distances of Deep Feature Space Zhuoxun He, Ya Zhang and Yanfeng Wang Shanghai Jiao Tong University, China V2-0058

15:15-15:30 Abstract- Deep neural networks have demonstrated their effectiveness in computer vision, especially for image classification and detection. Mixup [1] is recently proposed as a data augmentation scheme, which applies linear combination of two

SESSION VII random training examples and corresponding targets. However, the linear assumption is inappropriate for training a non-linear model. In this paper, we propose a self-supervised method which requires the consistency of original and mixed images on feature space. Our work is motived by the semantic information is related to the relative position of features. To implement this idea effectively and efﬁciently, we perform two-stage of training procedure, i.e., running estimation of class centers of original data in feature space, and training deep neural networks with modified loss term of Mixup. Besides, the proposed approach is also compatible with other variants of Mixup. We validate our approach on two popular image classiﬁcation datasets, CIFAR10 and CIFAR100 by a variety of advanced deep neural networks, and demonstrate consistent generalization improvements, sometimes significantly. We also conduct analytical experiments to evaluate the robustness of our method to hyperparameters. Electromagnetic Environment Effect Analysis of Transmission Projects on Animal Population in Tibet Plateau Using Fine-grained Recognition Xiao Buqiong, Tashi Quda, Yang He, Guangzhou Zhang, Peng Fan and Gong Hao State Grid Electric Power Research Institute, China

Abstract- In this paper, fine-grained classification is applied to get animal V2-0059 population for evaluate electromagnetic environment effect of transmission line on 15:30-15:45 ecosystem in Tibet Plateau. Spectrum features of bird songs and photography of bird image have be for the specific characteristics with significance in classification by Fine-grained classification (FGC), which is used to get animal population, and then bird population growth or not is direct index for the electromagnetic environment effect evaluation. Using the method, the number of cranes is increasing year by year near the converter station, which indicates effect is less likely. A Multi-Objective Optimization Model for Bike-Sharing Yu Shan, Dejun Xie and Rui Zhang Xi’an Jiaotong Liverpool University, China

Abstract- The study proposes a multi-objective optimization model for bike-sharing industry by monitoring, with high accuracy, the user demand and providing the suitable number of bikes at selected stations. One of the key factors for designing an V1-0010 optimized bike sharing system is to balance the demand of pick-ups (drop-offs) 15:45-16:00 around a given station and the number of available bikes (vacant lockers) in the station throughout the day. The model optimizes the location of bicycle stations and the number of parking slots that each station should have by taking account of the main contributing factors including the total budget of the bike sharing system, the popularity of riding in the city, and the expected proximity of the stations. A case study using the bike-sharing in New York was conducted to test the effectiveness of the model.

Coffee Break & Group Photo <16:00-16:15> 60

SESSION VIII

December 22 2019 Session 8 Computer Photography and Video Processing Technology 计算机摄影学与视频处理技术

13:30-16:00 Meeting Room 2 (Level 3, Building A) | A 栋三楼会议室 2

Co-chaired by Dr. Cut Maisyarah Karyati, Gunadarma University,Indonesia Mr. Sergey Volkov, National Research Moscow State University of Civil Engineering, Russia 10 Presentations— V1-0054, V2-0012, V1-0069, V2-0016, V2-0045 V1-0017, V2-0067, V1-0037, V2-0006, V1-0038

*Note:

SESSION VIII Germplasm Selection based on Machine Vision Wu Yin, Chen Zhao Shenzhen Wissea Electronic Technology Co., Ltd, China

Abstract- With the agricultural Internet of Things technology, real-time images of tomato plants can be obtained and processed through the remote video surveillance V1-0054 system. The image processing technology and the conventional neural network 13:30-13:45 (CNN) based on visual algorithms will be used to process the collected images so as to accomplish the acquisition, processing and analysis of physiological indexes of tomato plants, which can identify the growth status of plants by breeading good traits and obtaining high-quality germplasm resources, improve agricultural production effciency and mitigate the loss caused by pest, insufficient of nutrition of soil and so forth. A Video Analysis Method on Wanfang Dataset via Deep Neural Network Jinlong Kang, Jiaxiang Zheng and Pengfei Xu Northwest University, China

Abstract- The topic of object detection has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, such as small object, compact and dense or highly overlapping object. Existing methods can detect multiple objects wonderfully, but because of the slight changes between frames, the detection effect of the model will become unstable, the detection results may result in dropping or increasing the object. In the pedestrian flow detection task, such phenomenon can not accurately calculate the flow. To solve this problem, in this paper, we describe the new function for real-time multi-object detection in sports competition and pedestrians flow detection in public based on deep learning. Our work is to extract a V2-0012 video clip and solve this frame of clips efficiently. More speciﬁcally, our algorithm 13:45-14:00 includes two stages: judge method and optimization method. The judge can set a maximum threshold for better results under the model , the threshold value corresponds to the upper limit of the algorithm with better detection results. The optimization method to solve detection jitter problem. Because of the occurrence of frame hopping in the video, and it will result in the generation of video fragments discontinuity. We use optimization algorithm to get the key value, and then the detection result value of index is replaced by key value to stabilize the change of detection result sequence. Based on the proposed algorithm, we adopt wanfang sports competition dataset as the main test dataset and our own test dataset for YOLOv3-Abnormal Number Version(YOLOv3-ANV), which is 5.4% average improvement compared with existing methods. Also, video above the threshold value can be obtained for further analysis. Spontaneously, our work also can used for pedestrians flow detection and pedestrian alarm tasks. Further more, all the code are publicly available for further research: https://github.com/kangjinlong/yolov3_Abnormal-video. 62

SESSION VIII Video Dynamic Target Processing Method based on FPGA Meijing Qi, Wei Huang, Shaoting Li and Pei Zhou Inner Mongolia University, China

Abstract- As the "eyes" of the city, video surveillance technology is getting more attention. For many complex scenes in video surveillance, such as criminal detectives, illegal vehicle identification, mall anti-theft, etc., detecting moving targets has become a demand of people. In response to such a situation, this design uses an FPGA development board with ZYNQ-7000 SoC to design a detection and processing method for moving targets. The design uses the camera to capture video V1-0069 images and store the data in multiple VDMAs, and then sends them into the image 14:00-14:15 detection module, extracts the contour of the moving target by the frame difference method, and finally stores the result for data output display and analog alarm. The IP core of the detection algorithm is realized by Vivado HLS design in computer. The two-frame difference method and the three-frame difference method are improved by adding optimization instructions and image processing methods. In the same video, the detection results of these two methods are more intuitive than the background subtraction method. At the same time, they are not affected by the surrounding environment and are not sensitive to illumination. The system finally realizes the detection speed and clear contour extraction effect in real time, and can be applied to the field of video surveillance. Video Super-Resolution Using Wave-Shape Network Yanan Wu and Sei-ichiro Kamata Waseda University, Japan

Abstract- Video super-resolution (VSR) aims to restore a high-resolution (HR) image from multiple low-resolution (LR) frames. Previous works deal with inputs LR frames by stacking or warping and only use single scale features for V2-0016 reconstruction. Most of them didn’t consider fusing multi-scale spatial and 14:15-14:30 inter-frame temporal information, which may result in loss of details. In this paper, a novel architecture named Wave-shape network is proposed, which is designed to treat each frame as a separate source of information and fuse different temporal frames through a multi-scale structure. This fusion strategy enables us to capture more complete structure and context information for HR image quality improvement. We evaluate this model on Vid4 dataset and the results show Wave-shape network not only achieves significant improvement in vision but also obtains much higher PSNR and SSIM than most previous VSR methods. Research on Occlusion Relationship Recognition based on Distance Measurement of Moving Objects in Video V2-0045 Yandong Li, Zhaogong Zhang and Zongchao Huang 14:30-14:45 Heilongjiang University, China

Abstract- Aiming at the problem of moving objects occluded in the field of

SESSION VIII automatic driving, in this paper, we propose a framework for occlusion relationship recognition based on distance measurement of the moving objects in video. The framework performs moving objects detection by YOLOv3 and occlusion recognition of moving objects by the information entropy. When the occlusion occurs, the framework will obtain the distance between the moving objects and the camera by binocular stereo vision distance measurement. Finally, the framework completes occlusion relationship recognition using a principle derived from this paper. The experimental results show that distance measurement of moving objects in video is effective and feasible for occlusion relationship recognition, which can provide some help for visual technology in the field of automatic driving. The Influence of Light Environment on Eye-control Human-computer Interaction Deqiang Fu, Ning Li and Yingwei Zhou China Institute of Marine Technology & Economy, China

Abstract- The eye-controlled operation has attracted more and more attention in recent years, especially in the field of advanced human-computer interaction. It is V1-0017 often used to analyze the friendliness of human-computer interface design and guide 14:45-15:00 human-computer interface design. In addition, the eye movement tracking method based on eye tracker can directly or indirectly capture the user's intention, habit, interest area, historical eye movement track, and other information. However, few people study the influence of light environment on eye control operation. This paper aims to study the influence of light environment on eye-control operation behavior during human-computer interaction task, and design human-computer interaction experiments under different illumination and color temperature conditions. Spatiotemporal Antialiasing for Rendering 3D Scene with Specular Effect based on Virtual Hit Points Weidong Liang, Chunyi Chen, Xiaojuan Hu, Qiwei Xing and Huamin Yang Changchun University of Science and Technology, China

Abstract- In the past few decades, anti-aliasing method has been widely investigated in the field of computer graphics. Compared with the traditional V2-0067 anti-aliasing method, the anti-aliasing method with temporal coherence can avoid 15:00-15:15 the computational cost of rendering additional samples. However, most of the existing temporal coherence anti-aliasing methods have limitations in performing anti-aliasing of specular materials. In this paper, we propose an optimized anti-aliasing method based on virtual hit point, which can avoid sampling error of specular surface. We designed an anti-aliasing pipeline based on virtual hit points to ensure the correct reprojection of specular samples. We use ray-tracing pipeline to render the image and display the results. Experimental results show that our method can achieve anti-aliasing effect with real-time performance. Aerial Visual Information Acquisition System Based on EEG Control V1-0037 Jialiang Zhang, Jianying Wang, Wenting Fu and Ling Guo 15:15-15:30 Xinjiang University, China

SESSION VIII Abstract- Designing an aerial visual information acquisition system based on EEG control to verify the feasibility and application of brain-computer interface technology in controlling aircraft visual information acquisition via brain-computer interface, aircraft control and wireless image transmission technology. The system uses the EEG signal acquired by the external dry electrode of the TGAM EEG signal acquisition chip as the control signal of the aircraft to complete the specified flight action, using Android APP to control and realize image real-time transmission, information display, storage and management. The result of the experiment demonstrated that it is feasible and promising to apply brain-computer interface technology to the control of aircraft visual information collection area. A Framework for Automatic Building Detection From Low-contrast VHR Satellite Imagery Junjun Li and Jiannong Cao Chang'an University, China

Abstract- Automatic separation of buildings from built-up area has attracted considerable interest in computer vision and digital photogrammetry field. While many efforts have been made for building extraction, none of them address the problem completely. This even a greater challenge in low-contrast very-high V2-0006 resolution (VHR) panchromatic satellite images. To alleviate this issue, a 15:30-15:45 framework for automatic building detection approach using dominant structural feature (DSF) is proposed in this study. Firstly, in order to suppress noise while enhancing structural feature, contourlet transform based image contrast enhancement is employed followed by directional morphological filtering operation. Considering the structural characteristics of buildings which are significantly different from the other non-manmade objects. We then exploit DSF by means of windowed structure tensor analysis. Candidate building edges are generated using multi-seed classification technique in DSF space, subsequently. Finally, a series rule- and knowledge-based criterions are elaborate designed for false alarm reduction procedures. The Research and Practice of Teaching Model of Electronics and Information Practice Courses based on Moodle Platform Jianying Wang, Fei Shi and Juan Chen Xinjiang University, China

Abstract- In recent years, with the rapid development of mobile Internet technology V1-0038 and the rapid popularization of mobile intelligent terminal, it has a great impact on 15:45-16:00 the practical teaching in Colleges and universities. In this paper, we has explored a set of teaching model for the practice courses of electronic information specialty in our school. We carefully comb the teaching present situation of electronic information practice courses in school of information science and engineering, Xinjiang University. Using the Moodle platform and introducing the diversified evaluation methods application of mobile Internet technology, it has effectively

SESSION VIII solved the problems of preview, course evaluation and feedback in class under the traditional experimental model. The experimental results show that the model can arouse the students' initiative and enthusiasm in experimental learning, to achieve better teaching effect.

Coffee Break & Group Photo <16:00-16:15>

SESSION IX

December 22 2019 Session 9

Digital Communication and Wireless Technology 数字通信与无线技术

13:30-15:45 Meeting Room 9 (Level 3, Building A) | A 栋三楼会议室 9

Co-chaired by

Dr. Warusia Mohamed Yassin, Universiti Teknikal Malaysia Melaka, Malaysia

Senior Lecturer. Qin Li, HuaZhong Agricultural University, China

9 Presentations— V1-0021, V1-1003, V1-0040, V1-0055 V1-0059, V1-0091, V1-0152, V1-0014, V2-0015

*Note:

SESSION IX Securing 5G HetNets using Mutual Physical Layer Authentication Ting Ma, Feng Hu and Maode Ma Southwest Petroleum University, China

Abstract- The upcoming 5G ultra wideband mobile network is expected to provide a fundamental framework for a huge number of devices, which indicates the presence of dynamical users join and leave events. Due to highly heterogeneous V1-0021 architecture, it is crucial to address security in 5G HetNets. In this paper, we focus 13:30-13:45 on presenting a mutual physical layer handover authentication approach for software-defined networking (SDN) assisted 5G HetNets. The legitimacy of user equipment (UE) and access points (AP) are both verified based on extracted physical layer characteristics of wireless channel links with the help of base station (BS). Furthermore, a verification is employed to enhance authentication performance. The parameters of are discussed in our simulations and Figure of Merit (FoM) is applied in the simulation to evaluate the performance of proposed authentication scheme. Design of Intelligent Container Terminal Communication System based on 5G Technology Jiemin Yang, Xiangqian Ding,Liangang Zhang and Jiancheng Lin Ocean University of China, China

Abstract- In the intelligent container terminal, the main loading-and-unloading equipment relies entirely on the automatic control of computer system, which puts V1-1003 forward more stringent requirements for low delay, high bandwidth and high 13:45-14:00 reliability of communication system. In order to satisfy these requirements, a communication system framework is designed based on 5G technology. Taking the automation wharf of Qingdao Port as an example, the system test is carried out. The results shows that the proposed system framework can achieve reliable synchronous transmission of control signal, video, image, voice and other information between automated operation equipment, and provide a more economical, flexible and reliable communication mode for intelligent container terminal. An Animal Respiration Monitoring System based on Channel State Information of Wi-Fi Network Yusheng Hao, Jincheng Li, Weilan Wang and Qiang Lin Northwest Minzu University, China

V1-0040 Abstract- Monitoring respiratory rate of animals is critical in Precesion Animal 14:00-14:15 Hunsbandry. In this paper, a novel non-contact respiration monitoring system using off-the-shelf Wi-Fi devices is proposed based on Channel State Information(CSI) of Wi-Fi network. Empirical test shows that Wi-Fi signals transmitted from transmitter to recevier are affacted by abdonimal fluctuations of pig’s and the CSI of some subcarriers share the same rhythm with the pig’s breathing. After the preprocessing steps of abnormal subcarriers filtering and curve smoothing, the periodicity level of

SESSION IX the CSI sequence of each subcarrier was estimated using the proposed index of . Then, the period and frequency of the CSI sequence was estimated based on its autocorrelation function. Finally, the respiratory rate of the pig was determined by Weighted Average algorithm. The proposed system can calculate the respiratory rate of pigs with a CSI sequence of 30 seconds in length with an accuracy of 99.87%. Pseudonymous Mobile Node Reauthentication Scheme for Mobile Wireless Sensor Networks Bosung Kim and Jooseok Song Yonsei University, South Korea

Abstract- Mobile wireless sensor networks (MWSNs) are a new type of wireless sensor network (WSN) where sensor nodes are mobile. Compared to static WSNs, MWSNs provide many advantages, but the mobility introduces a problem of V1-0055 frequent reauthentication. To efficiently resolve this problem, several mobile node 14:15-14:30 reauthentication schemes based on symmetric key cryptography have been proposed. However, most of the existing schemes do not preserve identity privacy of the mobile node and allow an adversary to track the movement of the mobile node. In this paper, we propose a pseudonymous mobile node reauthentication scheme for MWSNs that provides a mobile node reauthentication mechanism preserving the identity privacy of the mobile node. Furthermore, our scheme considers the node rejoin situation which was not considered in previous studies. By providing a simple reauthentication process for the node rejoin, our scheme reduces the energy consumption and latency in the reauthentication. Heuristic Routing with Infrastructure Nodes for Data Dissemination in Vehicular Networks Longdy Torn, Ammar Hawbani, Xingfu Wang, Omar Busaileh and Muhammad Umar Farooq University of Science and Technology of China, China

Abstract- This paper presents a protocol called Heuristic Routing with Infrastructure Nodes (HRIN) for data dissemination in ve-hicular networks. The main goals of this work aims to increase the packet delivery ratio and reduce the V1-0059 end-to-end delay. To achieve these goals, we propose two heuristic func-tions for 14:30-14:45 Road Segments Selection (RSS) and Intermediate Nodes Selection (INS). Further, we propose the deployment of the Roadside Unit (RSU) on each road junction. RSUs can further assist the vehicles in data dissemination. The RSS is the combination of two probability functions, shortest distance, and higher connectivity. On the other hand, the INS is based on four quantities, vehicle's speed difference, vehicle's moving direction, number of packets on the vehicle, and signal fading. The simulation results of our proposed protocol achieved reasonable results for packet delivery ratio and end-to-end delay compared to the existing protocols.

SESSION IX Joint Optimization QoS and Security of Wireless Communication Networks Xiaochun Su, Yanheng Liu, Jian Wang and Yuming Ge Jilin University, China

Abstract- Currently, security and quality of service (QoS) are critical for any network. The main purpose of security mechanisms is to provide identity authentication, protect information, and ensure that the received information is not tampered with; the aim of QoS is to provide better network communication services. Security and QoS are not independent of each other. The security mechanism affects QoS and vice versa. Both QoS and security pose resource management problems and are conflicting when resources are limited. QoS requires security mechanisms to ensure proper allocation and service cost, and an inappropriate security mechanism will reduce the performance of the communication quality. Currently, numerous real-time, big data, and diverse applications and services have emerged. V1-0091 Moreover, the number of potential threats is expected to increase dramatically. 14:45-15:00 Therefore, combining QoS and security is important. However, as they are difficult to model owing to their internal conflict, QoS and security are normally separated or optimized at a particular point using game theory. From the perspective of resource allocation, this study uses optimization method and considers security and QoS resource optimization by dividing resources into two categories: space (bandwidth) and time (delay). In order to harmonize the QoS and security, in this paper, the two conflicting terms "communication" and "security" are considered as two individual aspect. Furthermore, we construct a model wherein the joint utility is maximized by controlling the packet length. We formulate the utility functions of these two aspects with respect to the effective throughput. The results show that the presented optimization method enables a mobile node to adaptively select specific packet length based on the optimum curve. With the obtained packet length values, the joint utility reach to the maximum so that satisfactory communication quality can be realized without compromising the security, thereby improving resource utilization. Optimized Handover Algorithm for Vehicular Ad hoc Networks Gbenga Oladosu, Chunling Tu and Pius Adewale Owolawi Tshwane University of Technology, South Africa

Abstract- The Internet of Things (IoT) has played a tremendous change in the Information Technology (IT) environments, and thus its importance has also been V1-0152 realized on networks such as Vehicular Ad hoc Networks (VANETs). VANETs are 15:00-15:15 subsets of MANETs wherein vehicles are employed as mobile nodes and outfitted with On-Board Units (OBS). The main goal of VANETs is to convey solace and safety applications to ensure safety for road users. However, it’s been realized that more and advanced vehicles continue to be introduced and employed on the roads of VANETs. This seems to lead to network bottleneck such as handover delay, end-to-end delay and thus degrades network throughput and could cause issues during the handover process. In this paper, an algorithm called Optimized Handover

SESSION IX Algorithm (OHA) is proposed to solve the bottleneck incurred during the handover process in VANETs. The proposed algorithm integrates two well-known optimization algorithms known as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). PSO defines priority limits for vehicle nodes to provide road safety on the network. Meanwhile, ACO is used by vehicles to establish the most cost-effective path between its origin and final destination. Furthermore, we designed and implemented the Congestion Problems Reduction (CPR) algorithm to limit the number of vehicle requests to be processed in priority order. It is exhibited by Network Simulator-2 (NS-2) simulations that the proposed algorithm yields to a decrease handover delay, increased average throughput and offers a reasonable decreased latency at different vehicle speeds and packet sizes. The proposed algorithm also provides improvements to the Quality of Service (QoS). Multi-layer 3D MIMO Precoding Algorithm based on Partitioned Regions Wanping Liu and Yunchao Song Nanjing university of posts and telecommunications, China

Abstract- 3D MIMO is an effective communication technology to improve system capacity. Through of vertical dimension and horizontal, a 3D MIMO precoding algorithm with higher efficiency spectrum and lower complexity can be designed. Traditional MIMO precoding depends on instantaneous channel characteristics and V1-0014 has high dimensional matrix computational complexity, which reduces the system 15:15-15:30 spectrum efficiency. This paper proposes a multi-layer 3D MIMO precoding algorithm based on region partition horizontal and vertical, the first layer designs to eliminate the interference between different regions based on the statistics of channel information. On the horizontal dimension, the first layer of precoding will be designed based on and rate maximization guidelines. The second layer of precoding for each group of users between the interference to zero processing. Simulation results show that, compared with traditional multi-layer precoding and traditional ZF precoding, this algorithm has low complexity while maintaining good spectral efficiency. Intelligent Mattress Aliasing Signal Decomposition based on SVD Lei Zhang, Qingfeng Zhou and Min Peng Hefei University of Technology, China

Abstract- The physiological diseases caused by sleep are becoming more and more V2-0015 serious. Real-time monitoring of physiological characteristics during sleep helps 15:30-15:45 detect and prevent disease. In this paper, a non-contact physiological monitoring system for intelligent mattress based on piezoelectric ceramics is designed. Then, for the original aliased signals of multiple channels and multiple physiological features, a Hankel matrix is constructed for the signal, and then noise ﬁltering and signal separation are implemented by a singular value decomposition (SVD) method. Finally, compare the experimental results with a ﬁnger pulse oximeter. The

SESSION IX results show that the method has high accuracy and can obtain the physiological characteristics of human sleep state.

Coffee Break & Group Photo <15:45-16:00>

SESSION X

December 22 2019 Session 10

Internet of Things and Information Network 物联网与信息网络

16:15-18: 45 Meeting Room 1 (Level 3, Building A) | A 栋三楼会议室 1

Chaired by Assoc. Prof. Dharmendra Singh Rajput VIT Vellore, India

10 Presentations— V1-0009, V1-0065, V1-0078, V1-0108, V1-0131 V1-0146, V1-0072, V1-0076, V1-0098, V1-0088

*Note:

SESSION X A Novel Dynamic Energy Model for the Energy-Harvesting IoT Node Haipeng Zhang, Ningning Lu, Hucheng Sun, Jie Li and Ruiliang Song The 54th Research Institute of CETC, China

Abstract- In this paper, we propose a novel dynamic energy model composed of a differential equation to describe the energy evolution of an IoT node, especially for V1-0009 the energy-harvesting-basedIoT node. By analyzing the state which is defined as the 16:15-16:30 energy buffered in the node, a novel dynamic energy model is developed to characterize the process of energy harvest and consumption. By analyzing the specific energy harvesting mode and consuming activity, the concrete forms of the model can be obtained. Then some characteristics of the energy harvesting IoT node support, can be obtained. Finally, some experiments are designed to verify the model. NSIIC - A Novel Framework of Nodes’ Smart Connection Oriented to Collaborative IoT Wentong Zhang, Cheng Chi and Sheng Zhang Tsinghua Shenzhen International Graduate School, China

Abstract- The continuous progress of mobile communication and artificial intelligence technology has promoted the development of Internet of Things (IoT) industry. However, due to inconsistency in devices' software and hardware interfaces from different manufacturers, it is difficult for devices to interact with V1-0065 each other in traditional IoT solutions. Getting inspiration from collaborative 16:30-16:45 communication, we move the management of communication processes from the application layer to the bottom of the protocol layer. By defining a uniform data format scheme and designing an efficient inter-node communication cooperation strategy, the competition of different communication services can be effectively avoided. Based on this, here we propose A new Framework of Nodes’Smart Connection Oriented to Collaborative IoT - NSIIC, which means three stages of Non-homologous nodes' communication: Sense, Identification and Inter-Collaboration. A smart-home demo system shows its superiority over traditional IoT solution based on centralized architecture. An IoT Botnet Prediction Model Using Frequency based Dependency Graph: Proof-of-concept Warusia Mohamed Yassin, Mohd Faizal Abdollah, Mohd Zaki Mas'Ud and Farah Adeliena Bakhari Universiti Teknikal Malaysia Melaka, Malaysia V1-0078

16:45-17:00 Abstract- Malware attacks are widespread in an era of growing technology by targeting most computing resources. Plenty of the technology nowadays is based on digital data exchange and it leads to the Internet of Things (IoT) development. A massive growth of IoT technology attracts attackers’ interest in exploiting a number of IoT devices using a variety of attacks. Consequently, this has caused difficulty to

SESSION X the researcher in distinguishing a characteristic of such variant specifically for IoT botnet-based attack. Current approaches are weak in recognizing such behavior by analyzing registry information more accurately due to the fact that the attack pattern usually hard to construct. Hence, in this paper, selected features of suspicious registry information that’s been affected by IoT botnet action i.e. Mirai is further analyzed using the graph-theoretical approach. Using a dependency graph, the similar and dissimilar pattern of distinct botnet composed to facilitate the process of malware variant characteristic identification. As a result of doing this, a precise attack pattern can be constructed and could be considered for future botnet prediction. A series of experiments conducted as a proof-of-concept in order to assess and validate the formed attack pattern. The findings have shown that the proposed prediction model could overcome the issues of undetectable IoT botnet behavior. From this forward, this model could be used to obtain accurate detection results for any variant of malware. A Study on Function for Grasping Location of Device in An IoT Device List Visualization System Yuto Egawa, Yoshiaki Taniguchi and Nobukazu Iguchi Kindai University, Japan

Abstract- A variety of IoT devices have been widely used. However, IoT devices are sometimes unmanaged and may be left without sufficient security measures. To easily grasp the list of IoT devices in a wireless LAN, we have developed an IoT device list visualization system. The system operates on a laptop PC and obtains the V1-0108 list of IoT device addresses by monitoring and analyzing wireless frames. In this 17:00-17:15 paper, to support association between device addresses and actual IoT devices, we introduce two support functions for the system. For association of an IoT device that the user knows the location, we propose the device association support function. For association of that the user does not know the location, we propose the navigation function. In order to confirm the feasibility of these support functions, we placed multiple IoT devices in a room and conducted experiments. As a result of the experiment, it was confirmed that these functions are feasible. By implementing this function, IoT devices can be managed even in homes and small companies where it is difficult to introduce expensive systems. City services Management Methodology based on Socio-Cyber-Physical Approach Sergey Volkov National Research Moscow State University of Civil Engineering, China

V1-0131 Abstract- This article presents the management methodology of city services based 17:15-17:30 on socio-cyber-physical systems. The methodology development is caused by the need to form the stable basis for the constantly developing city services. Based on modern socio-cyber-physical systems, urban consumers can receive more and more services, while the number of potential opportunities provided by these services multiplies every day. Such an active development of urban services poses a

SESSION X challenge for city administrations to choose which services to develop, how to transform existing and what services need to be created for urban consumers. The developed algorithm allows to evaluate the current state of urban services, to determine options for development taking into consideration the city's development vector and compare the costs of creating services with the ability of residents to pay for them. An Adaptive Negative Network Concurrent Service Scheme in Single-machine Scenario Xuwang Teng and Jingyang Lv Beijing University of Posts and Telecommunications, China

Abstract- The author introduces the basic flow of building a network concurrent programming model, analyzes the common network service load types, and the V1-0146 limitations of common network concurrency models such as Apache and Nginx 17:30-17:45 models in high concurrency scenarios. Finally presents an adaptive network concurrent service scheme in a single-machine scenario, analyzed its performance when faced with different types of network service load. The new model based on the IO multiplexing model, which can effectively utilize the multi-core CPU resources, and can be adaptively processing IO-intensive and CPU-intensive service requests, also can be customized with the custom load balancing strategy, so as to ensure the low average delay of the service request. Applying LSTM to Enable Cache Prefetching to Optimize Flow Table Update Efficiency in SDN Switches Liangding Li, Jiapeng Chi and Jun Wang University of Central Florida, USA

Abstract- Due to the fast growth of the Internet and social network, a massive amount of data has been generated and move to the cloud. With the ability to separate the control and data plane, SDN (Software Defined Network) provide an emerging solution for data transportation management tasks in the data center. In recent years, more literature focused on using SDN to manage data center network. V1-0072 It has been shown that SDN switch can support fine-grained rule matching with 17:45-18:00 more than 12-tuple flow. However, Ternary Content Addressable Memory (TCAM), which used to store the flow table in SDN switch, has limited capacity and power-hungry. The performance of the data center throughput would reduce dramatically due to flow table overflow. Previous literature proposed two kinds of solutions, rule replacement and rule caching. In this paper, we propose a new rule caching method based on Long short-term memory (LSTM) to improve the cache hit ratio in SDN switches. From the experiment result, we surprisingly find that the deep learning based prefetching model can predict future flow rules with high accuracy. And then improve the cache hit ratio on TCAM compare with the famous FIFO and LRU cache.

SESSION X Formalizing Railway Network Using Hierarchical Timed Coloured Petri Nets Lalita Thampibal and Wiwat Vatanawood Chulalongkorn University, Thailand

Abstract- It is necessary for having the formal model of railway network in order to ensure the desirable behaviors of the rail transportation system. An alternative of V1-0076 formalizing the high-level railway network is proposed using hierarchical timed 18:00-18:15 coloured Petri nets. The train stations and their interlink railways are concerned as the high-level building blocks to construct a specific railway network. The timed coloured Petri nets would be formally defined for a particular building block of the network hierarchically. The lower level modules construction and rules for making the hierarchical railway network model are provided. The resulting model is verified by using CPN tools to ensure the correctness, safety, and liveness. Identity Authentication Chain based on Encrypted Database in Heterogeneous Alliance Network Linlin Zhang, Xiang Zou and Minghui Yang The Third Research Institute of the Ministry of Public Security, China

Abstract- In this paper, a chain structure of identity authentication based on encrypted database in heterogeneous alliance network was constructed to implement V1-0098 the cross domain access. For preventing the member with lower level from reaching 18:15-18:30 the member with higher level, two judgement nodes were designed in a whole chain. One is the node between III sub chain and II sub chain. The other one is between II sub chain and I sub chain. Moreover, the information of chains was saved in the encrypted database. These strategies could resist general network attacks. In addition, we design the protocols of identity association and cross domain access. The analysis of security can illustrate that the chain was feasible and protected. A Survey on Computation Oﬄoading for Vehicular Edge Computing Shuang Yuan, Yanfang Fan and Ying Cai Beijing Information Science & Technology University, China

Abstract- Vehicular Edge Computing (VEC) is a promising new paradigm that has received a lot of attention recently. Computation offloading (CO) can migrate computing tasks to the vehicular network edge of the VEC, which is critical for V1-0088 mobile applications that are sensitive to computation power. However, the dynamic 18:30-18:45 and randomness of Internet of Vehicles (IoV) lead to new features and challenges in vehicular computation offloading. Therefore, we focus on the CO in VEC. This paper depicts a broad methodical literature analysis of CO scheme and CO methods in VEC domains and divides the current works of CO into various categories. The methodical analysis of this research will help researchers to find the important characteristics of CO and select the most appropriate algorithm for computing tasks. Challenges and research directions have also been suggested in this paper.

Dinner @ Restaurant (Level 2, Building A) 晚餐 | A栋二楼中餐厅 <19:00-20:30>

SESSION XI

December 22, 2019 Session 11

Computer Information Technology and Software Engineering 计算机信息技术与软件工程

16:15-18:45 Meeting Room 2 (Level 3, Building A) | A 栋三楼会议室 2

Co-Chaired by Assist. Prof. Ting Ma, Southwest Petroleum University, China Dr. Chunling Tu, Tshwane University of Technology, South Africa

10 Presentations— V1-0006, V1-0087, V1-0007, V1-0046, V1-0086, V1-0111, V1-0130, V1-0134, V2-0070, V1-0110

*Note:

SESSION XI Research on Improving the User Experience and Usability Evaluation of Tomato Work Method App- Using the Forest App as An Example Chung-Shun Feng, Tsu-Wu Hu, Yi-Ru Chen and Chu-Yin Tsai Chaoyang University of Technology, Taiwan

Abstract- With the popularity of smart phones and the lives of people, it is normal to use smart phones at any time. With the evolution of technology, smart phones have become an indispensable part of our lives. In the field of education and digital learning, it is also due to the maturity of wireless network technology, the development of mobile networks, and the popularity of smart phones. A new type of mobile service and digital learning. The long-term use of mobile services and means of the complex information and m has led to the user's easy loss of focus, inability to concentrate and work delays, and V1-0006 the rapid formation of web messages and fragmented knowledge and unstructured 16:15-16:30 reading and learning. This study provides a focus on mobile device services to help users' productivity. This study uses the tomato work method-based productivity type App for digital learning and research analysis. The purpose of this study is: (1) to increase concentration. (2) Verify the usability assessment of the Forest App. (3) Optimize the interface design and user experience of the Forest App. (1) to increase attention. (2) Verify the usability assessment of the Forest App. (3) Improve the interface design and user experience of the Forest App. The results of the second experiment were that the average of "operational function is flexible", "feedback is encouraging", and "app content meets user's concentration needs" is a significant improvement. This result represents the user's operation of the interface design, so this study found that the user first operated this type of app, Attention must be paid to the flexibility of functional operation and intuitiveness of the interface operation and mobile learning to achieve the development of attention. Dynamic Pricing of Edge Cooperative Offloading Using a Simple Heuristic Bo Huang, Yaling Sun and Dapeng Li Nanjing University of Posts and Telecomm., China

Abstract- In this paper, we study a dynamic pricing of edge cooperative offloading scenario in a multi-periods system that maximizes the total expected discounted profit of requesting MEC server over a finite horizon. Keeping track of the pricing V1-0087 decisions in each of the resource scheduling waiting time gives rise to the curse of 16:30-16:45 dimensionality. In order to deal with the curse-of-dimensionality problem, we apply a simple heuristic algorithm. The idea of the heuristic stems from analytical findings which suggest that the shape of the myopic price is close to that of the optimal pricing policy. We firstly introduce the myopic price as the heuristic resource pricing policy, which is a function of the expected resource request quantity. For further simplified calculation, we then construct a linear approximation to the myopic expected resource request, which is a function of the initial computing

SESSION XI resource of requesting MEC server. Finally, we illustrate the heuristic resource pricing and data offloading policy in detail. The proposed algorithm can transform a recurrence summation optimization problem into a single-period optimization, and efficiently make a decision of resource pricing and data offloading policy in collaborative computation offloading scenario. Simulation results show that the proposed algorithm is near-optimal. Research on Interface Improvement of English Vocabulary Learning App Tsu-Wu Hu, Chung-Shun Feng, Chia-Cheng Hsu and Cheng-Yu Liu Chaoyang University of Technology, Taiwan

Abstract- This study is mainly to explore the improvement of the auxiliary learning English vocabulary APP, to stimulate the motivation of autonomous learning and V1-0007 the interest in language learning, and to promote the lifelong learning of mobile 16:45-17:00 devices. The research method first analyzes the usability of the existing English vocabulary learning APP, and then improves the new version of the vocabulary learning APP for the image and the use process, and then compares the items of the usability problem with the questionnaire. From the comparison of interface layout, image and pleasure, we can clearly understand the needs of people's visual experience, and then show the connection between learning experience and learning effect, and achieve better learning results. An Efficient Surface Remeshing Algorithm Based On Centroidal Power Diagram Minfeng Xu, Shiqing Xin and Changhe Tu Shandong University, China

Abstract- We propose an efficient algorithm for remeshing models under the framework of Centroidal Voronoi tessellation (CVT). Instead of using the Voronoi V1-0046 diagram, we use a power diagram to optimize the movement of sites on the surface 17:00-17:15 to avoid the intersection computation between the Voronoi cell and the input surface. Given a set of sites on the surface and their normal, paired sites are computed by offsetting the surface sites along their normal, a power diagram is constructed from the two sets. According to the paired sites, tangent planes, extracted from the power diagram, are used to constrain the movement of sites on the surface. We do a series of experiments on different models and show that the proposed algorithm is fast while the mesh quality is comparable. Analyzing Online Transaction Data using Association Rule Mining: Misumi Philippines Market Basket Analysis Jeanie Delos Arcos and Alexander Hernandez Technological Institute of the Philippines, Philippines V1-0086

17:15-17:30 Abstract- Association Rule Mining is a data mining designed to discover the real connections of data items in transaction data build on associativity. The technique utilizes the Apriori Algorithm to discover association rules. Furthermore, the Apriori Algorithm widely treated to discover frequent itemsets in transaction data. This

SESSION XI study aims to enhance the efficiency of the Apriori Algorithm in the mining of association rule as a reference to identifying mixed item deals as a regular promo to offer to customers build on item frequency of buying. The study shows that Association rule mining implementation through enhanced Apriori Algorithm generates results at a higher performance rate or lesser runtime rate compared with the original Apriori Algorithm, and it helps the organization in selecting customer product deals. Anticipated in business flow, the study produced a list of package items for consumers based on strong rules generated by association rule mining at a lesser runtime rate. The Constructing and Application Case of Online Virtual Exhibits Arrangement System for Museum Learning Yuhui Yang and Hu Yue Zhejiang University, China

Abstract- As the second class of the school, museum learning is popular with students of all ages, and even some virtual museums have more visitors than physical ones. However, the current construction of virtual museum has defects such as low development efficiency, high development difficulty and cost, which affect the popularization and development of online museum learning activities. The V1-0111 maturity of Web3D technology provides an opportunity to solve this dilemma. First, 17:30-17:45 the development trend of museum learning, virtual museum and related technologies are introduced. Secondly, aiming at the current weak links in the presentation and development of virtual museum, a framework of online virtual exhibits arrangement system for museum learning is proposed. Finally, this online virtual exhibits arrangement system has been applied to the construction of rural virtual museum in Mt. Mogan. The application results show that compared with traditional virtual museums, online virtual exhibits arrangement system have the advantages of high development efficiency, low development difficulty, low development cost, and high user satisfaction in aspects of immersion, operability, perceived usefulness and experience of museum learning, which will provide reference for building online museum learning environment. Food safety Knowledge Graph and Question Answering System Qin Li, Hao Zhigang and Zhao Liang HuaZhong Agricultural University, China

Abstract- The issue of food safety in recent years has always been the focus of V1-0130 public opinion. Every time there are unqualified foods, it will cause widespread 17:45-18:00 panic and rumor spread, which has a great impact on social stability. Therefore, this paper crawled the data of unqualified foods officially released in recent years from the network, and designed the extraction algorithm of food general entities, food domain entities and relationships between entities for these data. The extracted entity pairs were stored in the gStore database. In order to solve the problem of association of knowledge in knowledge graph, this paper also designed the food

SESSION XI safety ontology which organized the concepts, classifications and relationships about food production and food inspection. Finally, this paper also built an intelligent question answering system by means of gStore's http service to help person grasp the unqualified food information through natural language. Interaction to Support the Learning of Typing for Beginners on Physical Keyboard by Projection Mapping Chikako Sono and Tatsuhito Hasegawa University of Fukui, Japan

In this study, we developed an interactive method to support the learning of typing for beginners by projecting the directions onto the physical keyboard. Memorizing key placement on a keyboard is the first problem that beginners struggle with when V1-0134 learning typing. This system adds the procedure of lighting the key that should be 18:00-18:15 pushed next, announcing the key’s location, and displaying a visual effect when to the correct key is pushed in a typing practice application. We expect the procedure to support the memorization of key placement and make typing more enjoyable, even for beginners. The purpose of this study is to build an environment in which beginners can learn typing without being discouraged by the need to memorize key placement. We verified the usefulness of our proposed method through an experiment that had people use our system and reply questionnaire, and it was useful. Visualized Estimation of Word Frequency for Second Vocabulary Acquisition Yo Ehara Shizuoka Institute of Science and Technology, Japan

Abstract- This paper proposes a novel supervised visualization method of word usages. Previous visualization studies for second vocabulary acquisition visualize V2-0070 words by plot each word as a point in a two-dimensional space. As each word is 18:15-18:30 merely a point, from these visualizations, learners cannot obtain detailed information essential for second language learners as to how to use words. Our method plots the usages of one input word so that semantically similar usages be plotted nearby and enables learners to understand how to use words intuitively. Our visualization can also identify the usages that a learner knows. Experiments by using real vocabulary test data of second language learners, we show that our method can improve the predictive accuracy of the learners' responses. Semi-supervised Community Detection: A Survey Mingwei Leng Northwest Minzu University, China V1-0110 18:30-18:45 Abstract- Community structure detection is of great importance because itcan help in discovering the relationship between the function andthe topology structure of a network. Many community detectionalgorithms have been proposed to identify the communitystructure. Unfortunately, due to the lack of prior knowledge, mostof

SESSION XI unsupervised algorithms fail to discover the communitystructure. Prior knowledge is utilized by semi-supervisedcommunity detection algorithms to improve their performances.In recent years, a certain number of semi-supervised communitydetection algorithms have been proposed in the literature. Asurvey of the existing semi-supervised algorithms of detectingcommunities in social networks is presented, and this paper alsodiscusses some applications of semi-supervised communitydetection.

Dinner @ Restaurant (Level 2, Building A) 晚餐 | A栋二楼中餐厅 <19:00-20:30>

SESSION XII

December 22, 2019 Session 12

Computer and Business Intelligence 计算机与商务智能

16:00-19:00 Meeting Room 9 (Level 3, Building A) | A 栋三楼会议室 9

Chaired by Assoc. Prof. Dejun Xie Xi’an Jiaotong Liverpool University, China

12 Presentations— V1-0013, V1-0057, V1-0028, V1-0052, V1-0115, V1-0136 V1-0015, V1-0003, V1-0107, V1-0155, V1-0135, V1-3002

*Note:

SESSION XII An Analysis of IPOs Short and Long-term Effect of E-commerce Companies among China A-share, Hong Kong, and U.S. Market Fang-Cih Lin, Tzh-Han Weng, Hai-Yen Chang, Fu-Ming Lai, Wei-Ling Kung and Yi-Hsien Wang Chinese Culture University, Taiwan

Abstract- Chinese e-commerce companies have been pushinginitial public listings(IPOs)on different stock exchanges in order to raise money and benefit from excess profits in the booming China market. This paper examines whether IPOs on different exchanges exhibit significant abnormal returns(AR) and explores what the influencing factors are by sampling Chinese e-commerce companies that went public in China A-share, Hong Kong, and U.S exchanges between 1999 and 2018. V1-0013 We deploy the event study method to delve into the short-term and long-term 16:00-16:15 abnormal returns of these Chinese e-commerce IPOs on different stock exchanges, conducting cross-section regression and quantile regression analyses to examine what impacts cumulative abnormal returns (CAR). The empirical evidence suggests that IPOs by Chinese e-commerce companies present significant abnormal returns in the A-share, Hong Kong, and U.S. stock markets. Quantile regression analysis finds that share price fluctuation limits, trading volumes, and the 2008 global financial crisis influence CAR.The research findings can serve as a reference to the management of Chinese e-commerce companies planning their IPOs in one or more of these three markets. This study focuses on expanding the research scope of industrial informatics in the relevant e-commerce industry through the market value presented by IPO activities in different capital markets Factors Influencing University Students’ Intention to Redeem Digital Takeaway Coupons - Analysis Based on A Survey in China Guihang Guo, Ying Li and Siqi Zheng Guangdong University of Foreign Studies, China

Abstract- Digital coupon has become an uprising power in internet marketing. As an integration of online and offline resources, it is widely applied in takeaway market. The key to realize the marketing value of digital takeaway coupon is to spur V1-0057 consumers’ intention to use it. Literature at home and abroad has investigated the 16:15-16:30 factors influencing consumers’ intention to use mobile coupons, but there is little research on digital takeaway coupons. This study draws on previous study results, integrates Technology Acceptance Model with consumer characteristics to investigate university students’ attitude toward the coupons and the determinants influencing their intention to use them. The results of a questionnaire-based research show that perceived economic benefit, attitude, perceived enjoyment and coupon proneness have significant influence on intention to use digital takeaway coupons among university students, with perceived economic benefit having the greatest impact while coupon proneness the least.

SESSION XII Overview of Cashier-free Stores and A Virtual Simulator Jixuan Leng, Yunfei Feng, Jiaxuan Wu and Junteng Li Northwood High School, United States

Abstract- Grocery shopping has always played an importantessential role in our daily lives. It dramatically drives the development of online and offline trading. Several physical cashier-free stores have gradually emerged nowadays, including Amazon Go in Seattle USA, Sam's Club Now in Dallas USA, Jingdong store in Beijing China, and Alibaba TaoCafe in Hangzhou China. To empower a cashier-free V1-0028 store, various technologies are feasible;, for example, indoor localization, 16:30-16:45 self-checkout systems, computer vision, and others. Alongside, these methods need large computations and data storage which require high- performance infrastructures. High- performance infrastructures are major obstacles hindering cashier-free stores. Moreover, the installation of these high-performance infrastructures isare costly and challenging. In this paper, we first discuss various advanced technologies applied to cashier-free stores in detail. Then, we propose a supermarket simulator that profiles virtual scenes. This simulator serves as a baseline platform that assists researchers in this area to evaluate their add-on algorithms and technologies before applying them in an actual store. Retailer’s Ordering Decision with Overconfident Consumers Ying Li and Guihang Guo Guangdong University of Foreign Studies, China

Abstract- In this paper, the decision maker’s overconfidence is taken into V1-0052 consideration for the different members in the supply chain. Through the analysis of 16:45-17:00 overconfident consumer’s decision making process, this paper construct the profit functions for the fully rational retailer and overconfident retailer, conduct the optimal analysis to get the optimal ordering decisions, and conduct comparative study to analyze the effect of retailer’s overconfidence level and consumer’s overconfidence level on the optimal decisions’ deviation. Analysis of Research Hotspots in the Field of Sharing Economy in China--based on Co-word Analysis and Social Network Analysis Tao Huang, Ran Tian, Lifang Niu and Shujian Xiang Zhejiang Gongshang University, China

Abstract- Sharing Economy, as an emerging economic situation in the internet era, V1-0115 has produced compelling economic benefits in many fields, and has also triggered a 17:00-17:15 research boom in academic community on sharing economy. This paper discusses the hot spots of sharing economy research, which is helpful to clarify and deepen the relevant research direction. Based on CNKI literature database, we analyzes the research status of domestic sharing economy and summarizes the hot spots of sharing economy by using literature measurement method, Co-word analysis method, Social network analysis and other methods. The results indicate: the

SESSION XII evolution process of the hot spots of sharing economy research is from Weitzman Martin 's western sharing theory, Li's sharing economy theory with Chinese characteristics, to the sharing economy relying on modern technology. The hot spots of modern sharing economy mainly focus on the definition of concept boundary, discussion of business operation mode, description of social value and legal and governmental regulatory challenges. Deep Metric Learning for Sensor-based Human Activity Recognition Makoto Mizuno and Tatsuhito Hasegawa University of Fukui, Japan

Abstract- Although human activity recognition using wearable sensors has become a useful technology, activity recognition using acceleration sensor data is still under V1-0136 development. We obtained ideas from the image field and verified the method of 17:15-17:30 introducing deep metric learning into sensor-based activity recognition. As verification of this method, we confirmed the effect on the estimation accuracy and the effect of the visualization of the feature representation. In addition, we proposed three methods for verification and searched for suitable methods by comparing them. There was a difference in the estimation accuracy and the visualization result with the proposed method. We also confirmed that significant features can be obtained for activity recognition when a suitable method is used. A Study on the Establishment of A User's Product Attribute Requirement Data Verification Model Yu Xiaohong, Huang Yu-Che and Chang Yu-Ling Chaoyang University of Technology, Taiwan

Abstract- Every consumer has different product preferences, so finding a product portfolio that is accepted by most consumers and creating products that most consumers are willing to buy is an important prerequisite for product development. Sporting goods have always been products that require knowledge and technology. They must not only provide users with a good function, but also must provide correct sports knowledge and better health. Therefore, sports products must stand V1-0015 out, except for the products themselves. In addition to functions and special attribute 17:30-17:45 appeals, whether existing product attributes are the same as consumers' perceptions, and whether existing products can really help users to improve their sports needs, etc., are all issues to be explored. This study will take the concept of product attributes as the core, clarify the product's own attribute function, and provide a reference data database for future product design and development, and also introduce the user's physiological reaction experiment design. Construct a model to provide more information to help sporting goods designers find out the user's perception of the product and the functional differences that the product itself gives through experimental verification, providing the driving force for continuous innovation in the future product development process.

SESSION XII BI&A capability: A Discussion on the Mechanism of Value-Creating Xueliang Han and Huifang Wu Henan Univercity of Economics and Law, China

Abstract- Based on integrating dynamic capability theory, resource-based theory and industrial organization theory, this paper combs the existing literature of BI&A, V1-0003 supplements and extends the composition of BI&A capability, and puts forward a 17:45-18:00 model to promote enterprise value creation. Model: this paper describes the components of BI&A capability and their interconnections, reveals the degree of technological resources and industry competition of enterprises and their interaction, the effect and influence mechanism of dynamic management capability on BI&A capability, and the business benefits brought by BI&A capability. The research content not only enriches the existing research on BI&A theory but also brings enlightenment to enterprises in the practice of BI&A. Application of Crime Related Index Model in Violence Related Cases Qilu Wu and Yonglu Zhang Lanzhou University, China

Abstract- In the era of big data, how to effectively mine and use relevant data to describe the behavior characteristics of criminals and the hierarchical structure of criminal gangs has become the top priority. However, there are few effective and V1-0107 efficient analyzing models for individual criminal cases. In this paper, KL 18:00-18:15 divergence, social networks and other theories are used to build a model based on crime related index. By analyzing the characteristics of call behavior and the relationship between friends, the model designs index functions to measure highly related people. The actual application results of our model show that it is promising in criminal cases investigation and can effectively screen and analyze the data, quickly compare the similarity between the target characters and other characters, and find the key suspects to provide effective support for the study and analysis of the cases. Design of Integrated Platform for Clothing and Accouterment Support based on Big Data Zhai Chenggong, Fei Xiande and Yang Zhiwei Department of Quartermaster Procurement Army Logistics University of PLA , China

V1-0155 Abstract- Department of Quartermaster Procurement Army Logistics University of 18:15-18:30 PLA , China Based on the in-depth analysis of the necessity and feasibility of the optimization of big data-based quilt support, this paper puts forward the overall framework and implementation concept of the optimization of big data-based quilt support, and describes how to build the basic matching of the optimization of big data-based quilt support and the concept of big data information system system. Design of integrated

SESSION XII platform for clothing and accouterment support based on big data that collects, stores, processes, analyzes and mines the supply data of conscription bedding by using big data, cloud computing and other information technologies, which plays an important role in deepening the reform of military bedding and strengthening the scientific management of bedding. User Needs-driven Enrichment of Ontology: A case study in Sri Lankan Agriculture R.S.I. Wilson, Athula Ginige and J.S. Goonetillake University of Ruhuna, Sri Lanka

Abstract- This study describes the mobile-based user needs-driven knowledge management system that supports the decision making process by considering user needs and preferences. Agriculture is one of the domains, in which, users seek specific information and knowledge relevant to their needs rather than searching and accessing general information from the Web, books, magazines or other information V1-0135 sources. Thus, the conceptualized solution was created by applying participatory 18:30-18:45 sensing, natural language processing and ontology theories and techniques in a novel way in order to satisfy the user needs. The user-centered agriculture ontology that has been developed in our previous work is extended to make an up-to-date knowledge base by capturing user needs and preferences through participatory sensing. The methods of ontology evolution from unstructured data were analyzed to build a technique to enrich the user-centered ontology. The Modified Delphi method is used for verifying the correctness and relevancy of the ontology and the application-based evaluation is applied for checking the functional correctness of the system. Super-peer Selection Algorithm Based on AHP in Mobile Peerto-Peer Network Li Qing, Fu Xuan-li, Hou Yu-ke and He Wan-jie China Aerodynamics Research and Development Center, China

Abstract- In mobile p2p network, peers have strong dynamic character and mobility. It makes the topology of network change frequently and the network V1-3002 performance worsen, and also cause high failure rate of peers and instability of 18:45-19:00 system. This paper proposed a super-peer selection algorithm based on AHP for mobile p2p network. It takes the performance of bandwidth, CPU, storage, on-life time and mobility into account, and adopts AHP to select the most suitable peer in the partitioned region of network as super-peer. It effectively improves network performance and system stability in mobile p2p network. The experimental results show that the proposed algorithm can reduce the index information failure rate, the average search path length and super-peer failure rate, shorten the query delay.

Dinner @ Restaurant (Level 2, Building A) 晚餐 | A栋二楼中餐厅 <19:00-20:30>

POSTER Poster Presentations I

December 21, 2019 (Saturday)

11:00-12:00

Jiayou Room (Level 1, Building B) B 栋一楼佳友厅

Design and Development of Smart Home Sensing Supported by Blockchain Technology Maode Ma, Zhaozheng He, Quanqing Xu, Xue Jun Li

This paper presents the design and implementation of a smart home system in the context of Internet of Things (IoT) with Ethereum private Blockchain, Raspberry V1-3007 Pi, Blynk platform, DHT11 temperature and humidity sensors. By Raspberry Pi, it collects real-time room humidity and temperature information by DHT11. The data is then uploaded to the Blynk App, which is stored on the smart contract deployed with the Ethereum private Blockchain. When the real-time humidity or temperature value exceeds a predefined threshold value, warnings are given by turning on LEDs. The system functions as a proof-of-concept prototype, showing the feasibility of applying blockchain in smart homes with IoT functionalities. Open-Domain Dialogue Generation: Presence, Limitation and Future Directions Haodong Yang, Wenge Rong and Zhang Xiong Beihang University, China

Abstract- Dialogue systems have received more and more attention from researchers, especially open-domain dialogue generation system. The application scenario of it, also chat-bot, is very extensive, from website customer service to dialogue robot such as Siri and Microsoft Xiaoice. More and more studies have V1-0116 been done on open-domain dialogue generation. In recent years, deep learning is gradually emerging which can leverage a massive amount of data to learn meaningful feature representations. In this paper, we first give the development of deep learning in Natural Language Processing (NLP) especially in dialogue generation. Next we analyze the current problems and introduce several improvements which are the current research hotspot. The purpose of this paper is to sort out the research results of recent years and make contributions to the research and development of the dialogue system in the future. An Damage Identification System Based on Deep Learning V1-0120 Wu Yao, Ye Zuochang and Wang Yan Tsinghua University, China 90

POSTER Abstract- Deep Learning has achieved good results in many tasks, especially computer vision tasks such as image classification, object detection and semantic segmentation. And these tasks are usually part of a practical application such as automatic driving and face payment. In this paper, we propose an architecture and implementation of vehicle damage identification system based on deep learning. The system consists of damage identification module, mponent segmentation module and image alignment module. With this system, we only need to provide the the close and long view photos of the car, and the system will automatically identify damage and calculate the claim amount. Edge Assisted Object Detection for Mobile Application Wenbo Cheng, Qibo Sun and Yasheng Zhang Beijing University of Posts and Telecommunications, China

Abstract- Object detection for mobile devices is meaningful especially in the field of IoT. Limited by computing power and network transmission, it’s challenging to V1-0153 get high accuracy in mobile object detection. To solve this question, this article designs a system that enables high accuracy object detection running at 30fps for 720p videos. The system employs the object tracking technique, uses the caching technique, decouples the rendering pipeline from the offloading pipeline, and uses dynamic RoI encoding technique to get high detection accuracy. The result of the experiment shows that it can get 88% detection success rate. And it can also increase the detection accuracy by 17.7% and decrease the bandwidth by 52.6%. Saliency and Tracking based Semi-supervised Learning for Orbiting Satellite Segmentation Peizhuo Li, Yunda Sun and Xue Wan Chinese Academy of Sciences, China

Abstract- The trajectory and boundary of an orbiting satellite are fundamental information for on-orbit repairing and manipulation by space robots. This task, however, is challenging owing to the freely and rapidly motion of on-orbiting satellites, the quickly varying background and the sudden change in illumination conditions. Traditional segmentation usually relies on a large annotated dataset and V2-0053 needs to be pre-trained for each target, which exhausts much time in both training and testing due to the large number and resolution of the images. In this paper, we proposed a STSS (Saliency and Tracking based Semi-supervised Learning for Segmentation) algorithm that provides the segmentation binary mask of target satellites at 12 frames per second without requirement of annotated data. Our method, STSS, improves the segmentation performance by generating a saliency map based semi-supervised on-line learning approach within the initial bounding box estimated by tracking. Experiment is evaluated on our generated dataset, which contains various challenges including variation in target, background and illumination condition.

POSTER Automated Detection of Steel Defects via Machine Learning based on Real-Time Semantic Segmentation Kun Qian Huazhong University of Science and Technology, China

Abstract- To improve automation, increase efficiency, and maintain high quality in the production of steel, applying modern machine learning techniques to help detect steel defects has been the research focus in the steel industry, since an unprecedented revolution in image semantic segmentation has been witnessed in the past few years. In the traditional production process of steel materials, localizing and classifying surface defects manually on a steel sheet is inefficient and error-prone. Therefore, it’s a key challenge to achieve automated detection of V2-0065 steel surface defects in image pixel level, leaving an urgent and critical issue to be addressed. In this paper, to accomplish this crucial task, we apply a series of machine learning algorithms of real-time semantic segmentation, utilizing neural networks with encoder-decoder architectures based on Unet and feature pyramid network (FPN). The image dataset of steel defects is provided by Severstal, the largest steel company in Russia, through a featured code competition in the Kaggle community. The results show that the ensemble algorithm of several neural networks with encoder-decoder architectures has a decent performance regarding both time cost and segmentation accuracy. Our machine learning algorithms achieve dice coefficients over 0.915 and 0.905 at a speed of over 1.5 images per second on the public test set and private test set on the Kaggle platform, respectively, which locates at the top 2% among all teams in the competition. Research on Customer Deposit Order based on Data Mining Technology Aiting Xu and Yingying Zhou Zhejiang Gongshang University, China

Abstract- This paper takes the time deposit order data of banking institutions as the research object. Firstly, the data of banking institutions are analyzed and cleaned to convert the categorical data into numeric data. Then, to “split” preprocessing the data after processing, divided into training set and test set. In the training set, LR, Naive Bayes, K-NN, Decision Tree, Random Forests, Extra - trees, AdaBoost, V1-0096 GBDT, XGBoost, LightGBM and CatBoost algorithm (via the grid search method GridSearchCV) are established to study the customer's deposit ordering tendency respectively. Finally, the Stacking fusion algorithm in Ensemble Learning(The meta-learner adopts LR algorithm) is used to fuse each single model to build the best bank customer time deposit ordering classification model. The research results show that using python, based on the different dimensions of bank customer information, the Stacking fusion algorithm based on Ensemble algorithm has better prediction effect than the single model, and the Stacking fusion algorithm has better robustness. Therefore, this paper takes the Stacking fusion model as the final prediction model to effectively identify bank customers and help banking

POSTER institutions understand customer subscription tendency. A Scoring Approach for the Assessment of Study Skills and Learning Styles Aytac Gogus and Gurdal Ertek UAE University, UAE

Abstract- This paper presents the application of a scoring method and algorithm, adapted from the domain of financial risk management, for the computer-based assessment of study skills and learning styles of university students. The goal is to provide a single score that summarizes the overall intensity of a student’s study skills and, in effect, develop a deeper understanding of the relation between learning styles and study skills. The dimensionality reduction obtained through the V1-0008 scoring algorithm also enables comparing the single-dimensional study skill scores of students for various learning styles. The algorithm computes a weight for each study skill to measure its linear contribution to the overall study skill score, also providing a natural ranking of various study skills with respect to impact on total score. Statistical tests have been conducted to measure the differences in scores for various styles in Kolb’s four-region and nine-region models. The results suggest that students with different learning styles can have statistically significant differences in their overall study skill scores. The primary contribution of the study is illustrating how a scoring approach, based on unsupervised machine learning, can enable a deep understanding of learning styles and development of educational strategies. Pose Estimation of Complex Human Motion Fei Lei, Zhanghong An and Xueli Wang Beijing University of Technology,China

Abstract- In this paper, a human body pose estimation method based on skeleton V2-0021 matching is proposed. In the process of human body pose skeleton matching, we obtain the key point feature information of the human body by performing CNN operation on the input image: the joint point heat map, and the joint point according to the specific connection rules are connected to form a human skeleton. This method is not only suitable for image data, but the accuracy of human skeleton matching after inputting video data is also verified. Improving the Performance of Inter-node Data Exchange in Cloud-based Distributed Ray Tracing by Compression Yunbiao Liu, Chunyi Chen, Xiaojuan Hu and Huamin Yang Changchun University of Science and Technology, Changchun

V2-0060 Abstract- To improve the performance of cloud-based rendering systems, it not only requires a good load balancing algorithm, but also needs less cost for data transfer. This paper proposes an internal data compression method in the cloud, which reduces the cost of data transfer between worker nodes on the basis of Hadoop distributed framework. In this method, the coordinate system 93

POSTER transformation and simple vector addition are used to reduce the storage space of each pixel's primary ray-object intersection point and unit normal vector. We implemented the experiment of data transfer in a Hadoop ray tracing cluster with gigabit switch. First, we compared the cost under different types of data transfer. Secondly, we measured the execution time of the compression method. Finally, we analyze the reason for the error between the decompressed data and the origin data. The results show that the cost of data transfer in our method is approximately 66% of that of the three-dimensional coordinate representation method, and the calculation error is less than 10-6. Thus, our proposed method can effectively improve the transfer efficiency of cloud-based rendering system. Remote Sensing and Time Series Data Fused Multimodal Prediction Model based on Interaction Analysis Zhang Zhiwei and Wang Dong Shanghai Jiao Tong University, China

Abstract- With the rapid development of the times, human’s life is becoming more and more modern, helping people experience surrounding environment better. People can see attractive scenery, hear marvelous voice, smell fragrant flavor, touch soft objects and taste delicious food. All these feelings can be generalized by V2-0049 ‘Modality’. As there are heterogeneous modalities, the way to learning from multiple such modalities become an emerging research topic. Multimodal machine learning has a wide range of applications while it still has many challenges. Challenges can be included in five categories: Representation, Translation, Alignment, Fusion, Co-learning. In this paper, we focus on the representation and fusion problem of multimodal and solve a practical problem of urban functional area classification. In this paper, we propose a scalable interaction model based on Squeeze-and-Excitation block to fuse image modality from remote sensing images and temporal modality from user visit sequence. Crucially, our model produces improvements over typical multimodal methods. Anomaly Detection of Wind Turbine Generator based on Temporal Information Na Song, Xiangzhi Hu and Ning Li Shanghai Jiao Tong University, China

Abstract- Anomaly detection of wind turbines (WTs) and their components is of great significance due to the downtime reduction of WT and greater economic efficiency. Generator is one of the essential components of the WT and its most V1-0145 frequent fault is rear bearing temperature fault. In order to detect the anomaly of WT generator, a temporal information based normal behavior model is proposed in this paper, employing data from Supervisory Control and Data Acquisition (SCADA) system. First, a sliding window is used to reorganize data samples. Then, a prediction model based on Gated Recurrent Unit (GRU) network is constructed to extract temporal information and correlation among state parameters. Additionally, utilizing Mahalanobis distance (MD), the prediction residual of GRU model is

POSTER calculated to establish a health indicator (HI). The warning threshold is decided by distribution characteristic of HI in normal data. The exponentially weighted moving Average (EWMA) is introduced to smooth HI and reduce the effects of noise and outliers. Finally, effectiveness of proposed method is validated on some reported fault cases in a real WT. Results show that the proposed method can accurately and effectively detect anomaly in WT generator. Dangerous Driving Behavior Detection with Attention Mechanism Kun Wang, Xianqiao Chen and Rui Gao Wuhan University of Technology, China

Abstract- In order to reduce the incidence of traffic accidents caused by dangerous driving, a dangerous driving behavior recognition model based on convolutional neural network (CNN) and long short-term memory network (LSTM) is proposed. V2-0050 Aiming at the problem of low accuracy of the network model identification, the algorithm is optimized by introducing the unsupervised attention mechanism. The model focuses on a specific visual area and improves the recognition accuracy of the algorithm to some extent by integrating the attention weighted module and the convolution LSTM. The experimental results show that the detection accuracy and detection rate of the algorithm are improved compared with the Two-Stream method and C3D behavior recognition algorithm in the dangerous driving behavior recognition task. Exclusion-Net: Accurate Detection Algorithm for Crowd Objects Jin Wang, Zhiming Liu, Ming Shao, Kaide Li, Yaonong Wang and Miao Cheng Zhejiang Dahua Technology Co., Ltd., China

Abstract- Nowadays, the object detection algorithms have been widely used in many scenarios, but the accurate detection of crowd objectives in real-life scenarios is still a challenging problem, such as the detection problem of single human in a crowded scene, and vehicle positioning with multiple similar license plates V1-0035 (domestic Guangdong, Hong Kong and Macao license plates). Aiming at this kind of scenario, we designs a network structure based on multi-scale features. In addition, we also propose a new bounding box regression method with crowd multi-objectives, which has a significant improvement on the detection effect of crowd objectives. The network structure (Exclusion-Net) proposed in this paper, with very few parameters (1000K), achieves SOTA results on the VOC2007 public dataset and the Guangdong, Hong Kong and Macao license plate dataset of the actual traffic scene. On the NVIDIA 1080 graphics card, the running speed of our model can reach 40 FPS, which is easy to deploy on embedded devices. Component Deformation Measurement based on Point Cloud Guangkun Zhai, Yue Zhou V1-0020 Shanghai Jiao Tong University, China

Abstract- For the problem of fuel rod bending and distortion, we independently

POSTER developed a point cloud processing system based on monocular laser. The monocular laser device is used to obtain an outer edge contour image of the fuel rod, and the cross-sectional outer edge point cloud is processed based on the outer edge point. The cloud is adapted to the cross section and the fuel rod deformation is calculated based on the cross section. This article focuses on the use of cross sections to calculate fuel rod deformation and bending deformation. The method described in this paper is to obtain the outer cross-point of the fuel rod after using the upper and lower sets of monocular laser systems, and then use the total least squares method to fit the upper cross section and the lower cross section. The lower part of the point cloud data fitting part is used as the reference plane, and then the centroid displacement and the normal vector offset of the other cross sections relative to the reference plane are calculated. Finally, the two parameters are used to indicate the bending and twisting of the fuel rod. Domain Neural Chinese Word Segmentation with Mutual Information and Entropy Jun Wang, Bin Ge and Chunhui He National University of Defense Technology, China

Abstract- Chinese word segmentation (CWS) is an important basic task for NLP. However, the word segmentation model trained by the generic domain corpus has a significant decline in performance in the word segmentation task oriented to the specific domain. Aiming at the features of domain segmentation, this paper using V1-0053 domain corpus as the training samples, and proposed combined with the terminology dictionary, new word detection and Bi-LSTM-CRF segmentation method to improve the problem of out-of-vocabulary (OOV). The word segmentation experiment was carried out on the corpus of the automotive domain. The results show that the precision and recall of the word segmentation have reached 0.95, and the value of F1 also achieved 0.95, and they are better than state-of-the-art method. This method can also be combined with N-gram and chi-square statistic to further improve the recognition accuracy of OOV. Research on User Comments of Douban Animation Made in China based on Text Mining Technology Siyu Sun, Yingjie Gai, Yingying Zhou and Aiting Xu Zhejiang Gongshang University, China

Abstract- With the advent of the "Internet plus" era, the online film criticism has springing up rapidly. The success of “NeZha” has brought the domestic animation V1-0094 film to an unprecedented high tide and brought a lot of information about the film reviews. Based on Chinese natural language processing technology, this paper takes the Douban movie review of domestic animation series as the research object, uses Python to crawl the movie review data, on the basis of the preprocessing of data cleaning, data normalization, Chinese word segmentation and removal of stop words, etc., carries out machine learning based emotional tendency analysis,and the visual analysis of word cloud and the analysis of sentiment orientation based on

POSTER machine learning ,At last, uses LDA theme model to mine movie reviews in depth. The research shows that: the audience pays more attention to the plot setting, character shaping, picture presentation, production ability, line performance and other aspects of domestic animation series films; Douban users tend to be positive attitude of domestic animation, but there is still a large proportion of negative emotion;the praise and popularity of domestic animated films continue to rise, but the uneven quality of domestic animation films still needs to be improved. High Accuracy Time Difference Measurement of the Periodic Pulse Signal with the Stochastic Resonance Filtering Zhi Xia, Qi Li, Lu Ma and Haodong Han Harbin Engingeering University, China

Abstract- A method of high accuracy time difference measurement for the periodic pulse signal is proposed in this paper. Normally, the time difference between the periodic pulse signals received by two channels is estimated by the phase shift of V1-0117 the harmonic on the cross-spectrum between the received signals. However, the cross-spectrum method would be inaccurate in the low SNR environment. Dealing with this problem, an improved method based on the stochastic resonance (SR) is proposed in this paper. A SR filtering system is established to highlight the periodic pulse in the received signal. Then, an accurate time difference is estimated by the cross-correlation between the SR filtering outputs. The simulation results show that, comparing with the cross-spectrum method, the proposed method decreases the normalized mean square error (NMSE) of the time difference estimation from 0.6 to 0.1 with an input SNR of 5dB. Research on Automatic Wharf Unmanned Gate System based on Artificial Intelligence Jiemin Yang, Yongcui Li, Jiancheng Lin Ocean University of China, China

Abstract- For the needs of automated container terminal business processes,logic verification, information filing and port unit supervision, Qingdao Port Automation V1-3006 Container Terminal conducts research and design from the aspects of layout, business process and information collection of intelligent gates, And research on the method of port container attribute identification based on deep convolutional neural network. The method makes full use of the frequency selection characteristics of the convolutional neural network itself and the ability to generate translation, rotation and scaling invariant features. Finally through the experiment, Good results have been achieved in the detection of relevant targets such as container doors and seals, the effectiveness of the method was verified. A Dual-camera Surveillance Video Summarization Generating Strategy for Multi-target Capturing V2-0005 Qingyun Shen, Cihui Yang and Shipin Wen NanChang Hangkong University, China

POSTER Abstract- Traditional surveillance video contains a large amount of information which is too jumbled. Real-time video summarization can solve this problem but also face much challenges. Different from file summarization, real-time video summarization requires higher efficiency. Meanwhile, the validity and quality of a summarization should be ensured. To tackle these problems, we propose a real-time video summarization strategy based on dual-camera. In our strategy, a static camera and a PTZ camera are necessary. The static camera is used to monitor the scene to detect and track moving targets, and the PTZ camera is used to capture the close-up information of moving targets as video summarization, and the collaboration of these two cameras is crucial. Specifically, in order to obtain multi-target summarization efficiently and effectively, the priority of target capturing is determined by its spatial information and historical representation in the scene. Extensive experiments are performed on real-time outdoor scene with our method. Experimental results show that our proposed method is robust enough to capture multiple targets in the same scene at the same time. Human Pose Estimation of Diver based on Improved Stacked Hourglass Model Fei Lei, Junyou Yan and Xueli Wang Beijing University of Technology, China

Abstract- In this paper, a network structure is proposed for the task of single person pose estimation in a complex environment. This method improves the stacked hourglass model, achieves the feature extraction on most scales, and raises V2-0063 the detection accuracy of human key points. In the hourglass module, we use convolution operation to complete the upsampling to get more semantic information. When the responses of the two residual elements are added, we replace the identity mapping in the residual element with the 1×1 convolution element module to improve the phenomenon of variance explosion. We conducted model evaluation experiments on MPII and LSP data sets, and the results showed that the average detection accuracy of key points was improved by 0.2% and 0.8% respectively through our improvement on the stacked hourglass model. Building Extraction in Mountainous Environment based on Improved Watershed Algorithm Xiyan Sun, Huien Shi and Wentao Fu Guilin University of Electronic Technology, China

Abstract- Making full use of satellite remote sensing technology to analyze and V2-0037 study high-resolution remote sensing images is helpful to the efficient supervision of urban land use. However, the segmentation and extraction of building boundary in mountainous area has always been a difficult problem to be solved. Therefore, it is particularly important to carry out the research on the segmentation and extraction of building boundary in mountainous area. In this paper, based on the traditional watershed algorithm, the gray image gradient operator and morpho-logical opening and closing operation are combined to improve the

POSTER accuracy of image segmentation in mountainous area, and the experimental results are quantified by structural similarity to verify the improved algorithm Watershed algorithm is superior to traditional water-shed algorithm in building segmentation accuracy of high-resolution remote sensing image in multi mountainous area. Residual-based Fast Single Image Fog Removal Yeming Lin, Yunjian Zhang, Tong Li and Jingguo Ge University of Chinese Academy of Science, China

Abstract- Single image haze removal is a challenging problem to address, and various constraints/priors have been previously considered to obtain acceptable dehazing solutions. In this paper, we propose a trainable end-to-end system for single image dehazing called ReDehazeNet based on the residual and dilation V2-0068 convolutional neural networks. The first part of the networks incorporated into the system is used for recovering a coarse clear image, which is predicted by adopting a context aggregation sub-network that can capture the global structure information. The second part of the network adopts a novel hierarchical convolutional neural network to further refine the details of the clean image by integrating the local context information. Experiments on benchmark images show that ReDehazeNet outperforms several existing state-of-the-art methods while being highly efficient and easy to use. Poster Presentations II

December 22 2019 (Saturday)

11:20-12:00 Jiayou Room (Level 1, Building B) B 栋一楼佳友厅

Movie Genre Classification using TF-IDF and SVM Ning Fei and Yangyang Zhang Nanjing University of Posts and Telecommunications, China

Abstract- This paper studies the classification principle and process of SVM algorithm, and classifies the text containing movie information, so as to achieve the V1-0097 research purpose of movie classification. It focuses on the various steps that need to be completed in the process of classification, such as text word segmentation, feature engineering, text representation, etc., and designs and implements a movie classification system based on SVM algorithm. In addition, the following work has been done: i) In the process of sample selection and processing, all the sample data used in the experiment come from the film profile information of Douban Film 99

POSTER Network. In order to facilitate data acquisition and formatting, the function of automatically crawling the film profile web page is also implemented in the experiment. ii) By combining document frequency with word frequency, text feature selection can effectively avoid the disadvantage of "low word frequency first" caused by using document frequency alone. The experimental results show that the size of word frequency also has a certain influence on the classification results when feature selection is carried out. iii) In the parameter optimization stage of SVM, the principle of using kernel function and the influence of the classification model trained by observing different parameter values on the classification results are analyzed. The cross-checking method based on grid search is used to find the relatively optimal parameters. The results proves that SVM is capable for movie classification with relatively high F-Score. The performance evaluation indicates that non-linear kernel such as RBF cannot outperform linear kernel when dealing with large amount of features. An Efficient Non-local Attention Network for Video-based Person Re-identification Zhen Wang, Shixian Luo, He Sun, Huadong Pan and Jun Yin Advanced Research Institute of Zhejiang Dahua Technology Co. Ltd, China

Abstract- A spatial and temporal attention strategy based on Non-local Networks is proposed for video-based person re-identification. The most existing methods design attention mechanisms on high-level features, which ignore the low-level features with more details. The proposed method adopts non-local networks which can aggregate features according to feature correlation at any level. There are V1-0121 two contributions of this work can be summarized as follows: (i) The spatial and temporal redundancy in video-based person Re-ID is analyzed in this work; (ii) An Efficient Non-local Attention Network is designed to reduce the computation complexity by exploring spatial and temporal redundancy for video-based person Re-ID. We conduct extensive experiments on two large-scale benchmarks, i.e. MARS and DukeMTMC-VideoReID. The experiments show that our model achieve 85.2% mAP , 88.3% rank-1 accuracy on MARS dataset and 95.4% mAP, 95.6% rank-1 on DukeMTMC-VideoReID without re-ranking, which significantly outperforms the state-of-arts. Trafﬁc Flow Prediction based on Self-attention Mechanism and Deep Packet Residual Network Jia Xuebin, Li Tong, Zhu Rui, Wang Zhan, Zhang Zehui and Wang Jiawei Yunnan University, China

V1-0125 Abstract- Traffic flow forecasting is an important function of a traffic information system. In recent years, it has become a research hotspot of experts and scholars in the field of transportation. The traffic flow is affected by spatiotemporal factors, so the traffic flow data is highly nonlinear and complex, which makes the flow data difficult to predict accurately. Therefore, the previous traffic prediction model does not have accuracy and reliability. Under the circumstances, this paper proposes a

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POSTER self-attention mechanism-based packet residual network model (SA-Res2Net) to predict urban traffic flow. Firstly, the internal pixel connection and global correlation of the New York bike data is enhanced through the Self-Attention mechanism. Then, the Attention Map is input into the Res2Net model and divided into 8 channels of in and out traffic data to extract the spatiotemporal correlation of traffic flow with finer-grained multi-scale features. The experimental results show that compared with other traditional models, the model in this paper is of satisfactory predictive value. Java Session Language：A Session-Based Language for Cloud Service Composition Dongming Jiang and Yuan Jiang Jiangxi Science & Technology Normal University, China

Abstract- Since the limited capability of single one, cloud services need to be assembled to form business process, which crate for users demands. However, to realize service composition is a time-consuming task which requires users know both the business logic and techniques of service composition. Its main reason is V1-0139 that current solution lacks a suitable language which can implement cloud service and easy to use. Regrading this issue, we put forward JSL, a new dialect language for integrating services. Because interaction is the basis of service composition, JSL apply session to depict the operation of interaction, then express complex composition further. JSL also use functions operators to shield the prone-to-error concurrent programming. Moreover, we designed model-driven framework to support JSL Simulation experiences demonstrates the efficiency of JSL since JSL and its framework can speed up the development and decrease the concurrent mistakes. Session-based Recommendation with Context-Aware Attention Network Jinsheng Wu, Zhonghong Ou and Meina Song Beijing University of Posts and Telecommunications, China

Abstract- Session-based recommendation aims to generate recommendation results based on user’s anonymous session. Previous studies model the session as a sequence and use Recursive Neural Network (RNN) to represent user behavior for recommendations. Although achieved promising result, previous studies ignore the relationship between session’s items and external context of session, which fails in V1-0142 revealing the intrinsic relation between them. To tackle the problem mentioned above, we propose a novel method, i.e., Session-based Recommendation with Context-Aware Attention Network, SR-CAAN, which enhances the ability of modeling the user preference by combining sequence prediction with session external context aware method. In the proposed method, we incorporate external knowledge with Knowledge Graph (KG) to obtain the external context of session by using attention mechanism. Each session is presented as a composition of the external context of session and user’s long-short term interest is obtained by Recurrent Neural Networks (RNNs). Experiments conducted on real word datasets

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POSTER demonstrate that SR-CAAN outperform the state-of-the-art significantly. Multi-level Data Filter for Speaker Recognition on Imbalanced Datasets Chen Sun, Yunjie Zhang and Yiwei Liu Dalian Maritime University, China

Abstract- End to end speaker embedding systems have shown promising performance on speaker recognition tasks. However, speaker recognition system often degrade obviously when there are different scale and discrepant distribution of training datasets. In this paper, we proposed a novel model comprised of V1-0045 MultiReader technique and ResNet-GhostVLAD network, which makes the performance on speaker recognition task more stable and excellent. The MultiReader technique and dictionary-based GhostVLAD layer enable our model to filter data at data-level and feature-level respectively, and make it robust for imbalanced datasets which contain noisy and irrelevant information. The proposed model has been trained on the VoxCeleb and AISHELL combined dataset, then tested on AISHELL dataset. Evaluations for different weights of the multiple datasets show that our model outperforms the method of directly mixing training set by obvious margins, which are 10.7% and 44.3% relative improvement. Improvement of Congestion Control Algorithms for Streaming Media TFRC Guo Hui and JI Song-bo

Abstract- Inner Mongolia University of Technology School of Information Engineering China TFRC(TCP-Friendly Rate Control) congestion control algorithm not only solves the transmission quality problem of streaming media to a certain extent, but also V1-0041 has good friendliness to TCP algorithm. Based on the analysis of traditional TFRC congestion control algorithm, this paper proposes a method to modify the throughput calculation formula by introducing adjustment factor, which takes into account the change of adjacent round-trip time and the delay jitter of adjacent time, and dynamically changes the sending rate of the sender. The simulation results show that the improved TFRC algorithm has better smoothness and throughput than the original one. The Information Security Risk Assessment Model based on Improved Electre Method Tongjuan Wang Shandong Polytechnic College, China

V1-0019 Abstract- Information security is a global problem and has attracted worldwide attention. Information security risk assessment is of great significance to understand the information security situation clearly and put forward pertinent measures. Information security risk assessment is system engineering, involving standards, technology, management and other factors. Owing to the complexity and uncertainty of information system, information system security assessment 102

POSTER inevitably involves a certain degree of fuzziness. This paper brings forward the information system security assessment model based on improved Electre method with linguistic variables. First, a two-level index system for information system security is constructed based on the feasibility and maneuverability condition. Second, linguistic variables are adopted to represent evaluation values of second-level indicators and WAA operator is adopted to calculate the evaluation values for first level indicators. Then the improved Electre method is adopted to evaluate and rank the alternatives. The Results show that the improved Electre method is more effective and more practical for security risk assessment of information system. Improving Variational Auto-Encoder with Self-Attention and Mutual Information for Image Generation Lizhi Lin, Xinyue Liu and Wenxin Liang Dalian University of Technology, China

Abstract- When Variational Auto-Encoder (VAE) is applied to the image V2-0036 generation task, it tends to generate images with blurred borders. To solve this problem, we use mutual information to maximize the similarity between the data feature generated by the encoder and the shallow feature, and use self-attention to weight the features of different dimensions to increase the correlation of similar features. Our experiments show that our Self-Attention AE can generate higher quality samples than VAE and Wasserstein Auto-Encoder (WAE), as measured by the Freshet Inception Distance (FID) score. A Master-Slave Chain Architecture Model for Cross-Domain Trusted and Authentication of Power Services Zhengwen Zhang, Cheng Zhong and Shaoyong Guo Zhang Fan Beijing University of Posts and Telecommunications, China

Abstract- With the gradual complexity of China's electricity consumption information, the current power business is diversified, and multi-service integration has increasingly become the direction of power business development. However, the problem of converged business trust and mutual trust has not been effectively V1-0083 solved, and it will bring huge economic losses to the power grid. Therefore, while effectively isolating multiple services, how to ensure multi-service integration and credibility is an urgent security issue. As a decentralized distributed storage peer-to-peer trusted network, blockchain is highly transparent, decentralized, trusted, unchangeable, and anonymous. This paper introduces a master-slave chain architecture based on blockchain for cross-domain trusted authentication of power services. It uses the slave chain to isolate multiple services. The backbone ensures the trust of the business and minimizes the security risk of untrustworthiness.

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POSTER Malware Detection Method based on Control Flow Analysis Peng Shan, Qingbao Li, Ping Zhang, Yanyang Gu and Xinbing Zhu State Key Laboratory of Mathematical Engineering and Advanced Computing, China

Abstract- With the rapid development of computer technology and the iteration of malware, numerous computer malware detection methods have been proposed, among which malware detection based on machine learning has become an important research direction. Opcode sequences are often used as an important feature of machine learning models for training and testing. However, opcode sequences extracted orderly from raw disassembly text may not adequately reflect the behavior of executables. In order to solve this problem, this paper proposes a new malware detection method based on the control flow of executables. This V1-0102 method analyzes the control flow of executables and extracts execution traces from by the unit of function. This paper uses the execution traces to represent the behavior of the executables and present the execution traces by opcode sequences. In order to improve the training efficiency and reduce the feature dimensionality, this paper propose an x86 intermediate code. Then we convert the opcode sequences into the corresponding intermediate code sequences and vectorize them using Vector Space Model (VSM) for training and testing. This paper implements Naïve Bayes Classifier, Support Vector Machines and Random Forest for classification. The experiment result shows that the Random Forest algorithm has the best performance, with an accuracy of 90.8% and the model establishment time is 22s. Compared with the traditional methods based on disassembly text, the accuracy is improved by 1.4%, compared with the methods using x86 opcode, the efficiency is increased by 62%. Load Forecasting model of Mobile Cloud Computing based on Glowworm Swarm Optimization LSTM Network Yi Zhuang, Zhenhua Zhang, Wei Zhu and Wei Zhong Jiangsu Automation Research Institute, China

Abstract- Aiming at the problem of host load forecasting in mobile cloud computing, the Long Short Term Memory networks (LSTM) is introduced, which is suitable for the complex V1-0103 and long-time series data of the cloud environment and a load forecasting algorithm based on Glowworm Swarm Optimization LSTM neural network is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are superior to similar prediction algorithms in prediction accuracy.

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POSTER Double Mix-Zone for Location Privacy in VANET Yuye Zhou and Dongmei Zhang Beijing University of Posts and Telecommunications, China

Abstract- The mix-zone is considered to be a spatial privacy-based location privacy protection method. The mix-zone confuses the tracker's sight by replacing the pseudonym when exiting the mix-zone, destroying the continuity of the location information, thereby ensuring vehicle safety. However, the mix-zone is generally distributed at traffic junctions such as intersections. There are many vehicles and V1-0118 there are restrictions such as traffic lights. Although the traffic volume is large, the speed of the vehicle is limited. Therefore, the attacker can lock the information by the relative time, position and speed of the pseudonyms to increase the pseudonym's linkability..Thereby greatly reducing the protection of the mix-zone. In this paper, for inference attacks, we group vehicles in the mix-zone according to the direction of the exit and add noise into the vehicle location data to reduce the tracking probability. At the same time, in the subsequent time, the vehicle location is slowly adjusted and restored to the actual value, reducing the impact of the noise on LBS(Location Based Service). Infrared Stripe Noise Correction Algorithm based on Multi-scale Analysis and One-dimensional Vector Convolution Ping Jiang, Ende Wang and Daqi Feng Chinese Academy of Sciences, China

Abstract- Aiming at the problem of stripe noise in uncooled infrared imaging system, a multi-scale analysis and one-dimensional vector horizontal convolution stripe noise non-uniformity correction algorithm is proposed. The algorithm uses the wavelet transform to extract the vertical component of the original infrared V2-0003 image, and uses the Gaussian kernel function to generate the one-dimensional vector to smooth the vertical component, and the wavelet reconstruction outputs the corrected image. The algorithm can accurately remove infrared noise without causing more troublesome "ghosting" problems. Multiple sets of different infrared image data were selected for simulation experiments, and compared with the current advanced infrared stripe non-uniform correction algorithm and qualitative and quantitative analysis, which proves that the algorithm proposed in this paper has a good visual effect, and the image quality evaluation parameters can also get good results. Fast Image Dehazing based on Guided Filter and Look-up-table Yiwen Jian, Xiao Wei, Xinyue Zhang and Sen Xiang Wuhan University of Science and Technology, China V2-0043 Abstract- Video surveillance has been applied to all aspects of daily life such as security surveillance and driving records. However, it is inevitable that bad weather such as fog and haze will greatly reduce the visibility of the image, and make it

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POSTER impossible to record valuable data. In order to solve this problem, we propose in this paper a fast image dehazing algorithm based on dark channel prior and guiding filter. In addition, we further improve the processing speed by using look-up-table. Based on the prior theory of dark channel, this algorithm first extracts coarser transmission in blocks, then joint bilateral filtering is applied to refine the details. Finally, clear images are recovered with the refined dark channel map and the transmission map. In addition, in order to reduce the computation load in joint bilateral filtering and accelerate the algorithm, we use a two-dimensional look-up-table to avoid the cumbersome computation in weight calculation. Experimental results show that the proposed algorithm can improve the image quality, especially it removes halos on the edges at object boundaries. In addition, the method has low computation and can be applied in practice. Textual Information Extraction Model of Financial Reports Ding Pan and Zhuoqian Liang Jinan University, China

Abstract- This paper proposes a model to extract textual information from financial reports automatically. It takes event extraction as the core, maps narrative information of financial reports into the concepts of financial accounting field, and V1-0092 forms the integration of heterogeneous data of distributed financial information. This study shows that the model can identify text events in large-scale financial reporting corpus, automatically extract events and theirs attribute information, and convert them into structured data. Moreover, this paper presents and evaluates the effect of the model in extracting information from annual reports of listed companies. The experimental results turn out that the model provides semantic mapping between text events and domain knowledge concepts, which is reasonable and reliable to be applied in the field of financial statement analysis. Construction of Driving Conditions based on Multi-segment Clustering Algorithm Yanxiong Sun, Yeli Li, Qingtao Zeng, Yuning Bian, Xinyu Sun and Linxuan Yu Beijing Institute of Graphic Communication, China

Abstract- In this paper, a new method of building k-means and DBSCAN multi-segment clustering algorithm slots on the driving conditions of driverless cars. First, data such as GPS speed obtained by driverless vehicles is used to pre-process the data using ff vehicle data pre-processing models. Then, the model is V1-0106 extracted using Kin-se kin-se kine kinesiology fragments and all kinematic fragments are extracted. Finally, using the Driv-D-means clustering model, the kinematics fragments are used to construct a driving curve for cars that can reflect the data collected by driverless cars. The experimental results show that the algorithm can more effectively express the characteristics contained in the original data than the k-means algorithm.

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POSTER Text Analysis of Enterprise Financial Report Based on Semantic Perception Ding Pan, Yuan Deng and Zhuoqian Liang Jinan university, China

Abstract- Nowadays, purchasing information is usually found in form of formal messages such as an email. However, most messages are written in natural language form which is difficult to extract data. Our approach paper uses V1-0124 Conditional Random Fields and Words Similarity to extract data from customers’ purchase order emails. We started from dividing the words in the email into a sequence of words. Then, we added features of each word. After that, we establish an appropriate template, characteristics and training sample sequences. Next, purchasing information was used for text extraction. From our experiment, it was found that adapting Words Similarity with Conditional Random fields can enhance the accuracy of extracting data significantly. EM Image Segmentation of Nerve Cells based on Conditional Random Field Model Fuyun He, Yan Liang and Xiaoming Huang Guangxi Normal University, China

Abstract- Image segmentation of nerve cells is of great value in neuroscience research. To solve the problem that the EM image cannot be accurately segmented due to the complexity of the submicroscopic structure of nerve cells, and the loss and blurring of the boundary, an EM image segmentation method of nerve cells based on Conditional Random Field model is proposed. Firstly, the initial seed V2-0038 points of organelle and background to be segmented are automatically selected by superpixel clustering, and then the probability density distribution of target and background which selected as seed points is estimated by Gaussian Mixture Model. Then the energy function of superpixel features is established by using Conditional Random Field model. Finally, the automatic segmentation of nerve cell image is realized by minimizing the energy function. The effect of the automatically segmented nerve cell target and background is compared with that of the manually segmented images. The experimental results show that the nerve cell segmentation effect is improved, the method is robust, and the submicroscopic structure of EM image of nerve cells can be well characterized. Captioning Images on Mobile Devices Using Semi-Statistical Extraction Ari Ernesto Ortiz Castellanos and Jorge Enrique Roman Avalos National Taipei University of Technology, Taiwan

Abstract- In this work, we propose a semi-statistical Image Extracting Image V2-0025 Semantic Framework using TensorFlow for Mobile Devices. We used datasets for training and generate extraction from other datasets of sentences using the N-Best algorithm selecting the closest sentence associated to images. In addition, we test our experiment with two models of Object Recognition and make comparisons between the different datasets of images and sentences.

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