iMLSE 2021

3rd International Workshop on Machine Learning Systems Engineering​

December 6, 2021, Virtual - in Conjunction with APSEC 2021
 

イベント概要

1/3

開催日

5/17 

開催​時間

14:00〜17:45

機械学習工学工学研究会
キックオフシンポジウム

我が国で唯一の機械学習とソフトウェア工学の接点である機械学習工学研究会。その最初の公式活動として、機械学習・ソフトウェア工学を代表する研究者・エンジニアが一同に会するシンポジウムを開催します。

Overview

The International Workshop on Machine Learning Systems Engineering aims to bring together leading software engineers, machine learning experts and practitioners to reflect on and discuss the challenges and implications of building software for complex Artificial Intelligence (AI) systems by using Machine Learning (ML) techniques.
 
The core idea behind this workshop is a growing concern that we have as software engineers in a world where data science, deep learning, and AI are becoming increasingly pervasive. Although AI research has allowed the development of novel algorithms capable of learning new tasks, adapting to the environment, and evolving, their implementation in software systems remains challenging. From an engineering perspective, once an algorithm is implemented, it requires a solid architecture, model/data validation, proper monitoring for changes, dedicated release engineering strategies, judicious adoption of design patterns and security checks, and thorough user experience evaluation and adjustment. All these activities require a combined knowledge in software engineering, data science, and machine learning. A failure to properly address these challenges in such complex software systems can lead to catastrophic consequences. An example of such failure is the recent human toll incidence caused by the $47-million Michigan Integrated Data Automated System (MiDAS)(see https://www.bridgemi.com/public-sector/broken-human-toll-michigans-unemployment-fraud-saga) or the recent finding that simple tweaks can fool neural networks in identifying street signs (see  https://iotsecurity.eecs.umich.edu/#roadsigns ).
 
The source of emerging difficulties is the shift in the development paradigm. Classically, we have constructed software systems in a deductive way, or by writing down the rules that govern the system behaviors as program code. With machine learning techniques, we generate such rules in an inductive way from training data. This shift does not only simply require new tools that intensively deal with data but also introduces unique characteristics. The resulting system behaviors are uncertain: black-box and less explicable. They are intrinsically imperfect and it is practically impossible to reason their correctness in a deductive way.
 
Given the critical and increasing role of AI-based systems in our society, it is now imperative to engage software engineers and machine learning experts in in-depth conversations about the necessary perspectives, approaches and road-maps to address these challenges and concerns.

Workshop Date:

6th December 2021 

Venue:

Virtual

Virtual Team Meeting

Important Dates

 Paper Deadline: 24 October 2021  (extended from 15 October 2021) 

Notification: 10 November 2021

Camera-ready: 15 November 2021

 

Program

Invited Speaker

washizaki-face.jpg
Hironori Washizaki (Professor at Waseda University, Japan)
Title: Software Engineering Patterns for Machine Learning Applications: Research and Practice
Biography: Prof. Dr. Hironori Washizaki is a Professor and the Associate Dean of the Research Promotion Division at Waseda University in Tokyo and a Visiting Professor at the National Institute of Informatics. He also works in the industry as Outside Directors of SYSTEM INFORMATION and eXmotion. He received his Ph.D. in information and computer science from Waseda University in 2003. He is leading a framework and patterns team at JST MIRAI eAI (engineerable AI) project. He has been the lead on a large-scale grant at MEXT called enPiT-Pro Smart SE, which encompasses professional training and education in IoT, AI, software engineering, and business. He currently serves as IEEE Computer Society Vice President for Professional and Educational Activities. He also serves as Associate Editor of IEEE Transactions on Emerging Topics in Computing, Editorial Board Member of MDPI Education Sciences, Steering Committee Member of the IEEE Conference on Software Engineering Education and Training, Advisory Committee Member of the IEEE CS flagship conference COMPSAC, and Convener of ISO/IEC/JTC1 SC7/WG20.

Detailed Program

10:00-14:40 GMT+8 (Taipei Time)  Monday 6th December 2021
11:00-15:40(JST) Monday 6th December 2021
2:00-6:40AM(GMT) Monday 6th December 2021
21:00(EST) Sunday - 1:40AM(EST)  Monday 6th December 2021
18:00-22:40(PST) Sunday 5th December 2021

The following times are in GMT+8 (Taipei Time).

10:00-10:05 Opening
10:05-10:45 [Invited Talk] Chair:
Hiroshi Maruyama
Title: Software Engineering Patterns for Machine Learning Applications: Research and Practice
Speaker: Hironori Washizaki (Professor at Waseda University)

10:45-10:50 <short break>


[Session 1 (10:50-12:00)] Chair: David Lo
10:50-11:10  FAILS: a tool for assessing risk in ML systems
- Gonzalo Aguirre Dominguez, Keigo Kawaai and Hiroshi Maruyama

11:10-11:30 Landscape of Requirements Engineering for Machine Learning-based AI Systems
- Nobukazu Yoshioka, Jati H. Husen, Hnin Thandar Tun, Zhenxiang Chen, Hironori Washizaki and Yoshiaki Fukazawa

11:30-11:50 How Data Plays in the Requirements of Face Recognition System: A Concern Driven Systematic Literature Review
- Zhijun Shao, Ji Wu, Wenxiao Zhao, Liping Wang, Hanjiao Wu and Qing Sun

11:50-12:00 Discussion

12:00-13:30 <break>

[Session 2 (13:30-14:40)] Chair: 
Lei Ma
13:30-13:50 Goal-Centralized Metamodel Based Requirements Integration for Machine Learning Systems
- Hnin Thandar Tun, Jati H. Husen, Nobukazu Yoshioka, Hironori Washizaki and Yoshiaki Fukazawa

13:50-14:10 Reference Model for Agile Development of Machine Learning-based Service Systems
- Hironori Takeuchi, Haruhiko Kaiya, Hiroyuki Nakagawa and Shinpei Ogata

14:10-14:30 Qunomon: A FAIR testbed of quality evaluation for machine learning models
- Kenichirou Narita, Michitaka Akita, Kyoung-Sook Kim, Yuta Iwase, Yuuichi Watanaka, Takao Nakagawa and Qiang Zhong

14:30-14:40 Discussion & Closing
  • Each author of a paper has 15 minutes to present the paper and 5 minutes for Q&A.
  • Please note that the given times of the program are subject to change.
 
 

SCOPE

We call for contributions that address challenges or provide practical insights in the engineering of components or systems constructed by using machine learning techniques. Topics include requirements engineering, design, construction, testing, quality assurance, operation, maintenance, and evolution, but not limited to these examples.

SUBMISSION TYPES

We call for four types of contribution, research papers, experiment reports, tool demonstrations, and position talks. Each submission will be reviewed by at least three reviewers. In either case, at least one author must attend and present their work if the submission is accepted and is welcome to the poster sessions.

RESEARCH PAPERS

Research papers focus on advanced and novel theories, methodologies, or mechanisms. Submitted papers must have been neither previously accepted for publication nor concurrently submitted for review in another journal, book, conference, or workshop.

All submissions must be in English, must not exceed 10 pages, and must come in A4 paper size PDF format and conform, at time of submission, to the IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt font, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf option).

EXPERIENCE PAPERS

Experience papers focus on the critical challenges that the industry faces in machine learning applications, innovative solutions, and experience getting insights. Submitted papers must have been neither previously accepted for publication nor concurrently submitted for review in another journal, book, conference, or workshop.

All submissions must be in English, must not exceed 10 pages, and must come in A4 paper size PDF format and conform, at time of submission, to the IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt font, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf option).

SHORT RESEARCH/EXPERIENCE PAPERS

Short papers illustrate an emerging idea of innovative solutions, challenges that the industry faces in machine learning applications, or initial experience getting insights. Submitted papers must have been neither previously accepted for publication nor concurrently submitted for review in another journal, book, conference, or workshop.

All submissions must be in English, must not exceed 4 pages, and must come in A4 paper size PDF format and conform, at time of submission, to the IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt font, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf option).

TOOL DEMONSTRATIONS

Tool demonstrations papers provide a highly interactive venue for researchers and practitioners to demonstrate their tools and discuss them with attendees. Demonstrations should be tool-based and describe novel aspects of early prototypes or mature tools. Submitted papers must have been neither previously accepted for publication nor concurrently submitted for review in another journal, book, conference, or workshop.

All submissions must be in English, must not exceed 2 pages, and must come in A4 paper size PDF format and conform, at time of submission, to the IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt font, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf option).

POSITION TALKS

Position talks aim at directly speaking to the community about new and emerging ideas or practical insights and experiences. Submissions for position talks consist of the title and abstract in 500 words. The abstracts are NOT included in the workshop proceedings.

 

PROCEEDINGS

Accepted full research and experence papers will be included in the APSEC 2021 workshop proceedings in IEL.

SUBMISSION PROCEDURE

Easychar: https://easychair.org/conferences/?conf=imlse2021

Call for Paper

機械学習工学キックオフシンポジウム

Organizers

Program Committee

  • Paolo Arcaini, National Institute of Informatics, Japan

  • Davide Dell'Anna, Delft University of Technology, Netherlands

  • Simos Gerasimou, University of York, United Kingdom

  • Junji Hashimoto, GREE, Inc., japan

  • Falk Howar, TU Claustha, Germany

  • Atsushi Igarashi, Kyoto University, Japan

  • Takeo Imai, Idein inc., Japan

  • Fuyuki Ishikawa, National Institute of Informatics, Japan

  • Tsutomu Kobayashi, National Institute of Informatics, Japan

  • Hironobu Kuruma, Hitachi, Ltd., Japan

  • Hiroshi Kuwajima, DENSO CORPORATION, Japan

  • Yi Li, Nanyang Technological University, China

  • Yepang Liu, Southern University of Science and Technology, China

  • Yuta Maezawa, National Institute of Informatics, Japan

  • Yutaka Matsuno, Nihon University, Japan

  • Karl Meinke, KTH Royal Institute of Technology, Sweden

  • Ettore Merlo, Ecole Polytechnique de Montreal, Canada

  • Toshihiro Nakae, DENSO Corporation, Japan

  • Taro Sekiyama, National Institute of Informatics, Japan

  • Kohei Suenaga, Kyoto University, Japan

  • Mahito Sugiyama, National Institute of Informatics, Japan

  • Jun Sun, Singapore Management University, Singapore

  • Shinya Takamaeda-Yamazaki, The University of Tokyo, Japan

  • Naoyasu Ubayashi, Kyushu University, Japan

  • Xiaofei Xie, Nanyang Technological University, China

  • Yinxing Xue, University of Science and Technology of China, China

  • Jinqiu Yang, Concordia University, Japan

  • Norihiro Yoshida, Nagoya University, Japan

  • Jianjun Zhao, Kyushu University, Japan

  • Nobukazu Yoshioka (Waseda University, Japan)

  • Foutse Khomh (Polytechnique Montréal, Canada)

  • Lei Ma (University of Alberta, Canada / Kyushu University, Japan)

  • Hironori Washizaki (Waseda University, Japan)

  • Hironori Takeuchi (Musashi University, Japan)

  • Amel Bennaceur (The Open University, UK)

  • Hiroshi Maruyama (Preferred Networks, Inc. / Kao Corp., Japan)

  • David Lo (Singapore Management University, Singapore)

Previous workshops