1st International Workshop on Industrial Machine Learning
ICPR 2020
January 10th, 2020 | Milan, Italy
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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.
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.
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:
needs and barriers for ML in manufacturing
state-of-the-art in ML applications to manufacturing
future research opportunities in this domain
Program
Program
ICPR registered people can access the proceedings of the workshop from the underline.io website (link).
Organizers
Organizers
Francesco Setti
University of Verona
Luigi Di Stefano
University of Bologna
Paolo Rota
University of Trento
Vittorio Murino
University of Verona
Massimiliano Mancini
University of Tübingen
Program Committee
Program Committee
Martino Alessandrini, Datalogic, Italy
Carlos Beltran, Istituto Italiano di Tecnologia, Italy
Michael Bortz, Fraunhofer-ITWM, Germany
Paolo Bosetti, University of Trento, Italy
Marco Carletti, Embedded Vision Systems, Italy
Nicolò Carissimi, Istituto Italiano di Tecnologia, Italy
Valerio Carpani, FIZYR, The Netherlands
Fabio Cermelli, Politecnico di Torino, Italy
Samrjit Chakraborty, University of North Carolina, USA
Chiara Corridori, Deltamax Automazione, Italy
Daniele De Gregorio, EyeCan.ai, Italy
Giacomo De Rossi, University of Verona, Italy
Ahmad Delforouzi, Fraunhofer, Germany
Siddhartha Dutta, Technische Universit{\"a}t Kaiserslautern, Germany
Dario Fontanel, Politecnico di Torino, Italy
Francesco Fornasa, Embedded Vision Systems, Italy
Emanuele Frontoni, Univ. Politecnica delle Marche, Italy
Giovanni Gualdi, Deep Vision Consulting, Italy
Florian Kleber, TU Wien, Austria
Donato Laico, SACMI, Italy
Adriano Mancini, Univ. Politecnica delle Marche, Italy
Matteo Moro, University of Trento, Italy
Danilo Pau, STMicroelectronics, Italy
Nicola Piccinelli, University of Verona, Italy
Paolo Piccinini, Marchesini Group, Italy
Andrea Pilzer, University of Trento, Italy
Matteo Poggi, University of Bologna, Italy
Fabio Regoli, Pirelli Tyres, Italy
Andrea Roberti, University of Verona, Italy
Luca Romeo, Univ. Politecnica delle Marche, Italy
Luca Rocchini, Datalogic, Italy
Cristiano Saltori, University of Trento, Italy
Andrea Simonelli, University of Trento, Italy
Levi Osterno Vasconcelos, Istituto Italiano di Tecnologia, Italy
Thorsten Wuest, West Virginia University, USA
Andrea Zignoli, University of Trento, Italy
Call for Papers
Call for Papers
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:
Robustness-oriented learning algorithms
Machine learning for robotics (e.g. learning from demonstration)
Continuous and life-long learning for industrial applications
Transfer learning and domain adaptation
Anomaly detection and process monitoring
ML applications to Predictive Maintenance
ML applications to Supply Chain and Logistics
ML applications to Quality Control
ML for flexible manufacturing
Deep Learning for industrial applications
Learning from Big-Data
Inference in real-time applications
Machine Learning on Embedded and Edge computing hardware
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.
A Best Paper Award will be assigned to the most relevant contribution. The decision will be taken by the Organizing Committee according to the reviewers’ feedback.
Sumbission
Sumbission
Papers will be limited to 8 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.
All the papers must be submitted using CMT submission server.
Important dates
Important dates
Full Paper Submission:
September 25, 2020DEADLINE EXTENDED:October 17, 2020Notification of Acceptance:
November10, 2020Camera-Ready Paper Due :
November 15, 2020
In case of rejection from ICPR main conference, authors can submit their work to the IML workshop by October 17, 2020. Authors should address all ICPR reviewers' comments in the submitted paper and submit the ICPR reviews as supplementary material.
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