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<span style="margin: 0px">Toronto, Canada, OCT 11-14, 2020<br>
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<div style="margin: 0px">Deadline: May 31st, 2020<br>
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<div style="margin: 0px">==COVID-19 UPDATE==<br>
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<div style="margin: 0px">We will be closely following the update of COVID-19 and organizing plan of SMC 2020.<br>
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<div style="margin: 0px">==Topical Collection==<br>
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<div style="margin: 0px">The selected papers are encouraged to submit their extended versions to the Topical Collection of the Neural Computing and Applications (Springer).<br>
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<div style="margin: 0px">https://www.springer.com/journal/521/updates/17925472<br>
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<div style="margin: 0px">Dear all,<br>
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<div style="margin: 0px">We would like to cordially invite you to submit a paper for the Special Session “Human-in-the-loop Machine Learning and Its Applications” in the IEEE International Conference on Systems, Man, and Cybernetics 2020, held on Oct 11-14,
2020, in Toronto Canada.<br>
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<div style="margin: 0px">Human-in-the-Loop (HIL) means including human feedback into the training loop of the machine learning models in order to facilitate the following requirements:<br>
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<div style="margin: 0px">1) to improve the quality of training and reduce/prevent the error of the model. When the testing error is larger than a certain threshold, the HIL learning model is able to obtain the new data-points from the users in an interactive
way.<br>
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<div style="margin: 0px">2) to incorporate the human user labelling to improve the pre-trained models. During the training of the state-of-the-art models, the quality of the training data-sets is extremely important. One solution to actively incorporate more
data is optimizing the models by including the human users’ feedback (e.g. rewards in RL) or new data-points (e.g. supervised learning) to adapt the pre-trained models in different environments.<br>
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<div style="margin: 0px">In the aforementioned requirements, humans are involved in the training process of the algorithms by continuously optimizing the model’s parameters, feeding the data or even adjusting the model itself by meta-learning. From the perspective
of algorithm design, a key problem to design a proper training with a human in the loop is how to leverage both active learning from a human and the optimization of the models. In other words, how can we design a proper query strategy depending on different
applications and scenarios?<br>
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<div style="margin: 0px">When properly implemented, the HIL is suitable to be applied in real-world applications where the data is sparse. The active learning mechanism built in the model can be helpful which could seek the human’s help in a form of supervised
or reinforcement learning. In this way, proper designs of interactive displays, machines and robots could be of help to obtain the human’s inputs.<br>
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<div style="margin: 0px">Specifically, we are particularly encouraging robotic applications and their experimental deployment using HIL algorithms. We believe that the HIL algorithms will be an effective method to make robotic platforms more adaptive and safer
to interact with. The workshop will offer the opportunity for researchers and practitioners in the diverse field where human reinforcement feedback would have a positive impact on the training processes. The inclusion of HIL would allow robots and machine
learning models to use both internal and external feedback to speed up the learning process and also improve its performance. In many ways, this could allow the models to learn through their own self-reflection as well as the external input from a human.<br>
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<div style="margin: 0px">Topics of interest include, but are not limited to:<br>
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<li>Human Guided Reinforcement Learning;<br>
</li><li>Human-robot Collaboration;<br>
</li><li>Human-robot Social Interaction;<br>
</li><li>Dialogue Systems with Human-in-the-loop;<br>
</li><li>Interpretable Machine Learning with Human-in-the-loop;<br>
</li><li>Active Learning and Continuous Learning;<br>
</li><li>Learning by Demonstration;<br>
</li><li>Human Factors in HCI/HRI, etc.</li></ul>
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<div style="margin: 0px">Important Dates:<br>
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<div style="margin: 0px">- Deadline for Submission: May 31, 2020 (extended)<br>
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<div style="margin: 0px">- Acceptance Notification: June 15, 2020<br>
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<div style="margin: 0px">- Conference Dates: Oct 11-14, 2020.<br>
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<div style="margin: 0px">Organizers:<br>
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<div style="margin: 0px">Joni Zhong, Nottingham Trent University, UK<br>
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<div style="margin: 0px">Mark Elshaw, Coventry University, UK<br>
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<div style="margin: 0px">Yanan Li, University of Sussex, UK<br>
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<div style="margin: 0px">Stefan Wermter, University of Hamburg, Germany<br>
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<span style="margin: 0px">Xiaofeng Liu, Hohai University, China</span></div>
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