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<a href="https://www.springer.com/journal/521/updates/17925472" id="LPlnk">https://www.springer.com/journal/521/updates/17925472</a><br>
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<b style="color: rgb(32, 31, 30); font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-size: 15px; background-color: rgb(255, 255, 255)">Call for Papers: NCAA <span class="x_gmail-il" style="margin: 0px"><span data-markjs="true" class="markd024ylqv4" data-ogac="" data-ogab="" data-ogsc="" data-ogsb="" style="margin: 0px">Special</span></span> <span class="x_gmail-il" style="margin: 0px"><span data-markjs="true" class="mark1rm66xg88" data-ogac="" data-ogab="" data-ogsc="" data-ogsb="" style="margin: 0px">Issue</span></span> on
human-in-the-loop machine learning and its applications</b>
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Deadline for submission: Dec 31, 2020</div>
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Topical Collection on Human-in-the-loop Machine Learning and its Applications. Aims, Scope and Objective. Human-in-the-Loop (HIL) means including human feedback into ...</div>
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Dear Colleagues,</div>
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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: 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. In some situations, a large error produced by the model should be avoided. For instance, reinforcement learning alone
is not sufficient to achieve safety if there exists an exploration policy in robot manipulation, by which some unexpected actions may be generated. In such scenarios, the data-points from human guidance are crucial during both robot’s safe execution as well
as model optimization. 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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This special issue will also offer the opportunity for researchers and practitioners in the diverse fields of robotics to showcase its solutions and applications 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.
<div> </div>
<div>Specifically, as a following-up journal publication of the special session in HIL machine learning in IEEE SMC 2020, the extended versions of the accepted paper are mostly welcomed.
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<div>Topics of interest include, but are not limited to:</div>
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<div>Human Guided Reinforcement Learning</div>
<div>Human-robot Collaboration</div>
<div>Human-robot Social Interaction</div>
<div>Dialogue Systems with Human-in-the-loop</div>
<div>Interpretable Machine Learning with Human-in-the-loop</div>
<div>Active Learning and Continuous Learning</div>
<div>Learning by Demonstration</div>
<div>Human Factors in HCI/HRI</div>
<div>etc.</div>
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Deadlines</div>
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<b>Deadline for submissions: 31st December 2020</b></div>
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Deadline for review: 28th February 2021</div>
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Decisions: 20th March 2021</div>
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Deadline for revised version by authors: 20th April 2021</div>
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Deadline for 2nd review: 10th May 2021</div>
<span style="font-family: Calibri, Helvetica, sans-serif; background-color: rgb(255, 255, 255); display: inline !important">Final decisions: 20th May 2021</span><br>
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<div>Guest Editors</div>
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<div>Dr. Joni Zhong (Lead Guest Editor), Nottingham Trent University, UK, joni.zhong@ieee.org</div>
<div>Dr. Mark Elshaw, Coventry University, UK, mark.elshaw@coventry.ac.uk</div>
<div>Dr. Yanan Li, Sussex University, UK, yl557@sussex.ac.uk</div>
<div>Prof. Dr. Stefan Wermter, University of Hamburg, Germany, wermter@informatik.uni-hamburg.de</div>
<div>Prof. Xiaofeng Liu, Hohai University, China, xfliu@hhu.edu.cn</div>
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