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IEEE HMData 2020 : Fourth IEEE Workshop on Human-in-the-Loop Methods and Future of Work in BigData


When Dec 10, 2020 - Dec 10, 2020
Where Online
Submission Deadline Oct 1, 2020
Notification Due Nov 2, 2020
Final Version Due Nov 13, 2020
Categories    big data   human computer interaction   crowdsourcing   collaboration

Call For Papers

The fourth IEEE Workshop on
Human-in-the-Loop Methods and Future of Work
in BigData (HMData 2020) co-located with IEEE Bigdata 2020 (online)
Dec. 10th (Planned)

HMData workshop, which originally began as the "Human-Machine
collaboration in BigData" workshop, will investigate the
opportunities and challenges in human machine collaboration in
work with bigdata, which are described by two terms: Human-in-
the-Loop Methods and Future of Work. Human-in-the-Loop is a
term focusing on the employer's viewpoint while Future of Work
focuses more on worker's viewpoint, in both of which the division
of labor among humans and machines is a key issue. This area is
likely to be heavily AI driven, and we intend to invite papers
covering the following aspects, (a) Capturing human capabilities
through intelligent models and how to adapt them through changing
perceptions, needs, and skills. (2) High level tools that provide
the ability for all stakeholders in the new ecosystem, including
regulators for policies and AI workers, to specify their
requirements. (3) system design and engineering of job platforms
for collection, storage, retrieval, and analysis of data deluge
about workers, jobs, and their activities. (4) Benchmarking and
the development of appropriate metrics to measure system
performance as well as human aspects, such as satisfaction,
capital advancement, and equity.

We welcome any interesting ideas and results on any relevant
topics, but this year, we also encourage submitting papers on
new projects inspired by the COVID-19 crisis, such as those on
human-in-the-loop solutions in the pandemic, those on re-evaluating
how we organize labor and how we share work with machines in the
future. To make the workshop an attractive place for those people,
we solicit practitioner papers as well as research papers, in order
to facilitate discussion among researchers who know solutions and
practitioners who know problems. We also would like to make the
place valuable for young researchers. All papers accepted for the
workshop will be included in the Workshop Proceedings published by
the IEEE Computer Society Press, made available at the Conference.


This workshop covers a wide range of topics of human-machine
collaboration in work with bigdata. Keywords include: crowdsourcing,
collaborative recommendation, crowdsensing, workflow model for
humans and machines, incentives, human-assisted bigdata analysis,
bigdata-human interaction, human-machine collaboration in real-world
applications (such as natural disaster response, education, and citizen
science), and ELSI in Human-in-the-loop systems and Future of Work.

We expect submissions to address some of the following issues:
- capturing human characteristics and capabilities,
- stakeholder requirement specification,
- social processes around the human-in-the-loop systems,
- platforms and ecosystems,
- computation capabilities, and
- benchmarks and metrics for human-in-the-loop systems and Future of Work


Kurt Luther (Virginia Tech)

Bio: Dr. Kurt Luther is an associate professor of computer science and
(by courtesy) history at Virginia Tech, based in the Washington, D.C.
area. He directs the Crowd Intelligence Lab, creating new ways for
experts to leverage the complementary strengths of crowdsourced human
intelligence and artificial intelligence (AI) in domains like journalism,
national security, and history. His current research focuses on
supporting open source intelligence (OSINT) investigations, combating
disinformation and misinformation, and identifying unknown people and
places in historical and modern photos. Dr. Luther has been honored with
the National Science Foundation CAREER Award, the Virginia Tech College
of Engineering Outstanding New Assistant Professor Award, and the Purdue
Polytechnic Institute Outstanding Technology Alumni Award. His papers
have received the ACM CSCW Best Paper Award, the AAAI HCOMP Notable Paper
Award, and the ACM IUI Best Paper Award. His software has won the
Microsoft Cloud AI Research Challenge Grand Prize and two HCOMP Best
Demo Awards. His research has been funded by DOD, Google, NEH, NHPRC,
NIH, and NSF; and featured in The Atlantic, CNN, NPR, Smithsonian, and
TIME. He is a member of AAAI and a senior member of ACM. Previously,
Dr. Luther was a postdoctoral fellow in the Human-Computer Interaction
Institute at Carnegie Mellon University. He received his Ph.D. in
human-centered computing from Georgia Tech, where he was a James D. Foley
Scholar. He received his B.S. in computer graphics technology, with
honors and highest distinction, from Purdue University. He also completed
internships at IBM Research, Microsoft Research, and YouTube/Google.
Important Dates (Tentative)
Oct 1 (Thu), 2020: Due date for workshop papers submission
Nov 2 (Mon), 2020: Notification of paper acceptance to authors
Nov 13 (Fri), 2020: Camera-ready of accepted papers
Dec 10-13(Thu-Sun), 2020: Workshops
All submissions must be submitted electronically through CyberChair.
Please prefix your submission category such as [Research Paper] to
the Title of Paper field in the submission page.
For example, if you would like to submit a project-in-progress paper
"Crowd-centric Approach to Digital Archive Maintenance," you have
to put "[project-in-progress paper] Crowd-centric Approach to Digital
Archive Maintenance" into the Title of Paper field.

All papers accepted for the workshop will be included in the Workshop
Proceedings published by the IEEE Computer Society Press, made
available at the Conference.
Submission Categories

Research Papers (*) (long presentation): They report significant and
original results relevant to the scope of this workshop. We solicit
innovative or thought-provoking work but they do not necessarily
have to reach the level of completion. The expected length is between
4 and 6 pages. The maximum length is 10 pages, though the paper
should be commensurate with the size of the contribution.

Practitioner papers (*)(long presentation): They present interesting
problems that require human-in-the-loop solutions in a variety of
application domains, or present the interesting results of applying
existing human-in-the-loop solutions to their domains. The expected
length is between 4 and 6 pages. The maximum length is 10 pages,
though the paper should be commensurate with the size of the contribution.

Project-in-progress papers (short presentation): They present the goals,
challenges, and preliminary results of research or real-world projects
in progress. The maximum length is 3 pages.

(*) Some of the papers submitted to the research or practitioner paper
categories may be accepted as project-in-progress papers and allotted
to short presentation slots.

Papers should be formatted to IEEE Computer Society Proceedings
Manuscript Formatting Guidelines in the IEEE Bigdata 2020 CFP page

Senjuti Basu Roy (NJIT)
Alex Quinn (Purdue University)
Atsuyuki Morihsima (Univesity of Tsukuba)

Program Committee


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