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 2020 

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

TBD

Presentation and Poster sessions will be included.
 
 

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

TBD​

  • Nobukazu Yoshioka (Waseda University, Japan)

  • Foutse Khomh (Polytechnique Montréal, Canada)

  • Lei Ma (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