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<span lang="EN-US" style="margin:0px"><a href="https://jonizhong.weebly.com/smc2020.html" id="LPlnk175165">https://jonizhong.weebly.com/smc2020.html</a></span></p>
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<a target="_blank" id="LPUrlAnchor239995" href="https://jonizhong.weebly.com/smc2020.html" style="text-decoration:none">SMC2020 - Cognitive Robotics, Machine Learning - J Zhong</a></div>
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==COVID-19 UPDATE== We will be closely following the update of COVID-19 and the organizing of SMC 2020. Important Dates: - Deadline for Submission: April 30, 2020 - Acceptance...</div>
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<span lang="EN-US" style="margin:0px">Toronto, Canada, OCT 11-14, 2020</span></p>
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<span lang="EN-US" style="margin:0px">Deadline: April 30, 2020</span></p>
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<span lang="EN-US" style="margin:0px">==COVID-19 UPDATE==</span></p>
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<span lang="EN-US" style="margin:0px">We will be closely following the update of COVID-19 and organizing plan of SMC 2020. </span></p>
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<span lang="EN-US" style="margin:0px">==Topical Collection==</span></p>
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<span lang="EN-US" style="margin:0px">The selected papers are encouraged to submit their extended versions to the Topical Collection of the Neural Computing and Applications (Springer).
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<span lang="EN-US" style="margin:0px"><a href="https://www.springer.com/journal/521/updates/17925472" id="LPlnk291738">https://www.springer.com/journal/521/updates/17925472</a><br>
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<span lang="EN-US" style="margin:0px"> </span></p>
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<span lang="EN-US" style="margin:0px">Dear all,</span></p>
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<span lang="EN-US" style="margin:0px"> </span></p>
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<span lang="EN-US" 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<span style="margin:0px"> <span style="margin:0px"> </span></span>IEEE International
Conference on Systems, Man, and Cybernetics 2020, held on Oct 11-14, 2020, in Toronto Canada.</span></p>
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<span lang="EN-US" style="margin:0px"> </span></p>
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<span lang="EN-US" 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:</span></p>
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<span lang="EN-US" 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.</span></p>
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<span lang="EN-US" style="margin:0px">2) to incorporate the human user labelling to improve the pre-trained models.<span style="margin:0px"> <span style="margin:0px"> </span></span>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.</span></p>
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<span lang="EN-US" style="margin:0px"><span style="margin:0px"> </span></span></p>
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<span lang="EN-US" 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?</span></p>
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<span lang="EN-US" style="margin:0px"><span style="margin:0px"> </span></span></p>
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<span lang="EN-US" 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.<span style="margin:0px"> <span style="margin:0px"> </span></span>In this way, proper designs of interactive displays, machines and robots could be of help to obtain the human’s inputs.</span></p>
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<span lang="EN-US" style="margin:0px"> </span></p>
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<span lang="EN-US" 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.<span style="margin:0px"> <span style="margin:0px"> </span></span>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.<span style="margin:0px"> <span style="margin:0px"> </span></span>In many ways, this
could allow the models to learn through their own self-reflection as well as the external input from a human.<span style="margin:0px"> </span></span></p>
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<span lang="EN-US" style="margin:0px"><span style="margin:0px"> </span></span></p>
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<span lang="EN-US" style="margin:0px">Topics of interest include, but are not limited to:</span></p>
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<span lang="EN-US" style="margin:0px">Human Guided Reinforcement Learning;</span></p>
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<span lang="EN-US" style="margin:0px">Human-robot Collaboration;</span></p>
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<span lang="EN-US" style="margin:0px">Human-robot Social Interaction;</span></p>
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<span lang="EN-US" style="margin:0px">Dialogue Systems with Human-in-the-loop;</span></p>
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<span lang="EN-US" style="margin:0px">Interpretable Machine Learning with Human-in-the-loop;</span></p>
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<span lang="EN-US" style="margin:0px">Active Learning and Continuous Learning;</span></p>
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<span lang="EN-US" style="margin:0px">Learning by Demonstration;</span></p>
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<span lang="EN-US" style="margin:0px">Human Factors in HCI/HRI, etc.</span></p>
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<span lang="EN-US" style="margin:0px"> </span></p>
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<span lang="EN-US" style="margin:0px">Important Dates:</span></p>
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<span lang="EN-US" style="margin:0px">-<span style="margin:0px"> <span style="margin:0px"> </span></span>Deadline for Submission: April 30, 2020</span></p>
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<span lang="EN-US" style="margin:0px">-<span style="margin:0px"> <span style="margin:0px"> </span></span>Acceptance Notification: May 10, 2020</span></p>
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<span lang="EN-US" style="margin:0px">-<span style="margin:0px"> <span style="margin:0px"> </span></span>Conference Dates: Oct 11-14, 2020.</span></p>
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<span lang="EN-US" style="margin:0px"> </span></p>
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<span lang="EN-US" style="margin:0px">Organizers:</span></p>
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<span lang="EN-US" style="margin:0px">Joni Zhong,<span style="margin:0px"> <span style="margin:0px"> </span></span>Nottingham Trent University, UK</span></p>
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<span lang="EN-US" style="margin:0px">Mark Elshaw, Coventry University, UK</span></p>
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<span lang="EN-US" style="margin:0px">Yanan Li, University of Sussex, UK</span></p>
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<span lang="EN-US" style="margin:0px">Stefan Wermter, University of Hamburg, Germany</span></p>
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<span lang="EN-US" style="margin:0px">Xiaofeng Liu, Hohai University, China</span></p>
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