*Apologies for any cross posting*
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CALL FOR TASK PARTICIPATION: NAILS (Neurally Augmented Image Labelling
Strategies) at NTCIR-13
http://ntcir-nails.computing.dcu.ie/
Registration deadline: June 30th (extended)
NTCIR-13 Conference: 5-8 Dec 2017 (NII, Tokyo, Japan)
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The goal of the NAILS (Neurally Augmented Image Labelling Strategies) task at
NTCIR-13 is to explore strategies for generating image annotations that use
features extracted from the EEG (Electroencephalography) readings of users
performing high-speed image search tasks. This is an emerging technique
that can potentially achieve high information throughput rates, however,
finding machine learning and feature processing strategies that work
optimally across different image-search task types and users can be
difficult.
NAILS aims to explore these challenges in the form of a collaborative
evaluation task where participating organisations are given access to a
dataset of EEG responses for users completing a variety of image search
tasks. Participating organisations will train machine-learning prediction
model(s) on the provided training set of EEG responses, and later benchmark
these model(s) on a test set (where the ground truth is kept by the
NAILS organisers).
Participating organisations will be able to submit test set predictions
multiple times in order to refine their approach before submitting their
paper for later presentation at NTCIR in NII, Tokyo, Japan on the 5-8
December 2017.
Other important details:
- Although this is a collaborative evaluation where participating
organisation's machine-learning strategies will be ranked in terms of
balanced accuracy, it is expected that many good signal
processing/machine-learning solutions that may perform suboptimally to
others in terms of accuracy alone may offer other advantages in terms of
speed, model complexity, neurophysiological interpretability and/or cross-
task/user applicability. *Contributions exploring such aspects of the
dataset and/or tasks are highly encouraged.*
- As we expect researchers from different research backgrounds who have
experience in machine learning who wish to work with the data, we provide
preprocessed data so that those who may be less familiar with working with
EEG (such as filtering/processing) may directly progress to applying
machine-learning techniques e.g. preprocessed multi-channel time-series and
wavelet-based feature vectors are available.
- We provide working code examples (python) of complete pipelines (from
training to prediction submission) that leverage existing common
machine-learning strategies
- This in an interdisciplinary research area involving areas such as IR
(Information Retrieval), BCI (Brain-computer Interfaces), ML (machine
learning), HCI (Human-computer Interaction) and signal processing.
For more information (and to find related publications of the dataset)
please visit:
http://ntcir-nails.computing.dcu.ie/
To get access to the data/participate in the evaluation, please complete
see the registration and user agreement forums:
http://research.nii.ac.jp/ntcir/ntcir-13/howto.html
http://research.nii.ac.jp/ntcir/ntcir-13/agrmnt.html
Important Dates:
30 June 2017 - Task Registration Due
1 June to 15 Sept 2017 - Formal Run
20 Sep 2017 - Evaluation Results Release
1 Oct 2017 - Paper Due
1 Nov 2017 - Camera ready due
5-8 Dec 2017 - NTCIR-13 in NII, Tokyo
Kind Regards,
Dr. Graham Healy,
The Insight Centre for Data Analytics,
School of Computing,
Dublin City University,
Glasnevin,
Dublin,
Ireland
Tel: 00 353 700 6840
e-mail: [log in to unmask] / [log in to unmask]
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