Human-Centred Machine Learning
Workshop Summaries
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Gillies, Marco
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Fiebrink, Rebecca
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Tanaka, Atau
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Garcia, Jérémie
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Bevilacqua, Frédéric
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Heloir, Alexis
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Nunnari, Fabrizio
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Mackay, Wendy
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Amershi, Saleema
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Lee, Bongshin
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d'Alessandro, Nicolas
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Tilmanne, Joëlle
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Kulesza, Todd
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Caramiaux, Baptiste
Extended Abstracts of the ACM CHI'16 Conference on Human Factors in
Computing Systems
2016-05-07
v.2
p.3558-3565
© Copyright 2016 ACM
Summary: Machine learning is one of the most important and successful techniques in
contemporary computer science. It involves the statistical inference of models
(such as classifiers) from data. It is often conceived in a very impersonal
way, with algorithms working autonomously on passively collected data. However,
this viewpoint hides considerable human work of tuning the algorithms,
gathering the data, and even deciding what should be modeled in the first
place. Examining machine learning from a human-centered perspective includes
explicitly recognising this human work, as well as reframing machine learning
workflows based on situated human working practices, and exploring the
co-adaptation of humans and systems. A human-centered understanding of machine
learning in human context can lead not only to more usable machine learning
tools, but to new ways of framing learning computationally. This workshop will
bring together researchers to discuss these issues and suggest future research
questions aimed at creating a human-centered approach to machine learning.
Stylistic Walk Synthesis Based on Fourier Decomposition
Gaming Technologies
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Tilmanne, Joelle
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Dutoit, Thierry
Proceedings of the 2013 International Conference on INtelligent TEchnologies
for interactive enterTAINment
2013-07-03
p.71-79
Keywords: motion capture; synthesis; Fourier transform
© Copyright 2013 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Summary: We present a stylistic walk modeling and synthesis method based on frequency
analysis of motion capture data. We observe that two peaks corresponding to the
walk cycle fundamental frequency and its first harmonic can easily be found for
most walk styles in the Fourier transform. Hence a second order Fourier series
efficiently represents most styles, as assessed in the subjective user
evaluation procedure, even though it results in a strong filtering of the
original signals and hence a strong smoothing of the resulting motion
sequences.
The Attentive Machine: Be Different!
Demos
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Leroy, Julien
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Riche, Nicolas
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Zajega, François
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Mancas, Matei
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Tilmanne, Joelle
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Gosselin, Bernard
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Dutoit, Thierry
Proceedings of the 2011 International Conference on INtelligent TEchnologies
for interactive enterTAINment
2011-05-25
p.249-251
© Copyright 2011 Springer-Verlag
Summary: We will demonstrate an intelligent Machine which is capable to choose within
a small group of people (typically 3 people) the one it will interact with.
Depending on people behavior, this person may change. The participants can thus
compete to be chosen by the Machine. We use the Kinect sensor to capture both
classical 2D video and depth map of the participants. Video-projection and
audio feedback are provided to the participants.
