<html><head>
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-15">
</head>
<body>
(apologise for multiple copies)<br>
<b><br>
1st International Workshop on Industrial Machine Learning</b><b><br>
</b>in conjunction with ICPR 2020<br>
January 11, 2020 - Milan, Italy<br>
<br>
Website: <u><a class="moz-txt-link-freetext" href="https://sites.google.com/view/iml2020">https://sites.google.com/view/iml2020</a></u><br>
<br>
Submission deadline: September 25th, 2020<br>
Submission server: <u><a class="moz-txt-link-freetext" href="https://cmt3.research.microsoft.com/IWIML2020">https://cmt3.research.microsoft.com/IWIML2020</a></u><br>
<br>
<br>
RATIONALE:<br>
==========<br>
With the advent of Industry 4.0 paradigm, data has become a valuable
resource, and very often an asset, for every manufacturer. Data from
the market, from machines, from warehouses and many other sources
are now cheaper than ever to be collected and stored. It has been
estimated that in 2020 we will have more than 50B devices connected
to the Industrial Internet of Things, generating more than 500ZB of
data. With such an amount of data, classical data analysis
approaches are not useful and only automated learning methods can be
applied to produce value, a market estimated in more than 200B$
worldwide. Through the use of machine learning techniques
manufacturers can use data to significantly impact their bottom line
by greatly improving production efficiency, product quality, and
employee safety.<br>
The introduction of ML in industry has many benefits that can result
in advantages well beyond efficiency improvements, opening doors to
new opportunities for both practitioners and researchers. Some
direct applications of ML in manufacturing include predictive
maintenance, supply chain management, logistics, quality control,
human-robot interaction, process monitoring, anomaly detection and
root cause analysis to name a few.<br>
<br>
SCOPE:<br>
======<br>
This workshop will draw attention to the importance of integrating
ML technologies and ML-based solutions into the manufacturing
domain, while addressing the challenges and barriers to meet the
specific needs of this sector. Workshop participants will have the
chance to discuss: <br>
- needs and barriers for ML in manufacturing<br>
- state-of-the-art in ML applications to manufacturing <br>
- future research opportunities in this domain<br>
<br>
CONTRIBUTIONS:<br>
==============<br>
This is an open call for papers, soliciting original contributions
considering recent findings in theory, methodologies, and
applications in the field of industrial machine learning. Position
papers presenting industrial use cases and discussing potential
solutions are welcome. Potential topics include, but are not limited
to:<br>
<ul>
<li>Robustness-oriented learning algorithms</li>
<li>Machine learning for robotics (e.g. learning from
demonstration)</li>
<li>Continuous and life-long learning for industrial applications</li>
<li>Transfer learning and domain adaptation</li>
<li>Anomaly detection and process monitoring</li>
<li>ML applications to Predictive Maintenance</li>
<li>ML applications to Supply Chain and Logistics</li>
<li>ML applications to Quality Control</li>
<li>ML for flexible manufacturing</li>
<li>Deep Learning for industrial applications</li>
<li>Learning from Big-Data</li>
<li>Inference in real-time applications</li>
<li>Machine Learning on Embedded and Edge computing hardware</li>
</ul>
All the contributions are expected to expose applications to the
industrial sector, possibly with real world case studies. Position
papers presenting new industrial systems and case studies, possibly
reporting preliminary validation studies, are also encouraged.<br>
<br>
SUBMISSION:<br>
===========<br>
Papers will be limited to 6 pages according to ICPR format (c.f.
Main conference authors guidelines). All papers will be reviewed by
at least two reviewers with double blind policy. Papers will be
selected based on relevance, significance and novelty of results,
technical merit, and clarity of presentation. Papers will be
published in ICPR proceedings.<br>
All the papers must be submitted using CMT server: <u><a class="moz-txt-link-freetext" href="https://cmt3.research.microsoft.com/IWIML2020">https://cmt3.research.microsoft.com/IWIML2020</a></u><br>
<br>
DATES:<br>
======<br>
<ul>
<li>Full Paper Submission: September 25, 2020</li>
<li>Notification of Acceptance: November 10, 2020</li>
<li>Camera-Ready Paper Due : November 15, 2020</li>
</ul>
In case of rejection from ICPR main conference, authors can submit
their work to the IML workshop by October 10, 2020. Authors should
address all ICPR reviewers' comments in the submitted paper and
submit the ICPR reviews as supplementary material.<br>
<br>
ORGANIZERS:<br>
===========<br>
Luigi Di Stefano (University of Bologna, Italy)<br>
Massimiliano Mancini (la Sapienza University, Italy) <br>
Vittorio Murino (University of Verona, Italy)<br>
Paolo Rota (University of Trento, Italy)<br>
Francesco Setti (University of Verona, Italy)<br>
<br>
CONTACTS:<br>
=========<br>
For any inquiry regarding the workshop please contact Francesco
Setti at <u><a class="moz-txt-link-abbreviated" href="mailto:francesco.setti@univr.it">francesco.setti@univr.it</a><br>
<br>
</u><br>
Please distribute this call to interested parties<br>
<br>
<pre class="moz-signature" cols="72">--
Francesco Setti, Ph.D.
Assistant Professor (RTD-a)
Department of Computer Science
University of Verona
Room 1.64 - Ca' Vignal 2
Strada le Grazie 15 - 37134 Verona (Italy)
Phone: +39 045 802 7804
Email: <a class="moz-txt-link-abbreviated" href="mailto:francesco.setti@univr.it">francesco.setti@univr.it</a>
Homepage: <a class="moz-txt-link-abbreviated" href="http://www.franzsetti.info">www.franzsetti.info</a>
</pre>
</body>
</html>