[robotics-worldwide] [WCCI/IJCNN 2018][meetings] 2nd CFP: Special Session on Neural Models for Behavior IJCNN 2018

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[robotics-worldwide] [WCCI/IJCNN 2018][meetings] 2nd CFP: Special Session on Neural Models for Behavior IJCNN 2018

Pablo Barros
2nd Call for papers: special session on “Neural Models for Behavior
Recognition” at IJCNN 2018 - https://urldefense.proofpoint.com/v2/url?u=http-3A__www.ecomp.poli.br_-7Ewcci2018_&d=DwIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=Y2ZJ5_n398cJ3loPgqnsHlhLYO-fGRt29Hw4IKWWzl8&s=MBirShFCRdfEK7B5GZxhXMqixp30KWxTfd7vJLvAUHQ&e=

International Joint Conference on Neural Networks, hosted at IEEE World
Congress on Computational Intelligence (IEEE WCCI 2018)
8-13 July 2018, Rio de Janeiro, Brazil


Special Session on Neural Models for Behavior Recognition

Organized by: Pablo Barros (Universität Hamburg, Germany), Bruno
Fernandes (Universidade de Pernambuco, Brazil) and Stefan Wermter
(Universität Hamburg, Germany)


Categorizing and, to a certain degree, understanding human behavior is
an important skill for autonomous systems. A robot able to identify
whether a person needs help, an autonomous car to perceive whether a
pedestrian is crossing the road, or a camera to localize someone in
danger need a robust and reliable human behavior understanding. Although
many different solutions have appeared in the past years, most of them
do not take longer contextual information into consideration, usually
only relying on instantaneous or short-context scenarios which limits
the applicability of such systems in a real-world scenario. The
potential of having neural models that take into consideration
contextual information is substantial and it would be beneficial to
develop this technology further for this field.

To have a system which is capable of learning, understanding and
generalizing human behavior within a longer contextual scene is a
challenging task. With recent neural computing technologies, such as
training and understanding deep neural networks, life-long learning and
unsupervised approaches, to name a few, we believe that autonomous
systems which are capable of learning from long-context human behavior
are feasible. This kind of processing would provide contextual
information which would be beneficial to understanding and acting in
real-world scenarios.


We invite papers on both practical and theoretical issues about neural
learning for behavior recognition. In particular, topics of interest
include, but are not limited to:


- Gesture processing, recognition and/or generation
- Models for emotional behavior analysis and/or understanding
- Speech based sentiment analysis and/or dialog processing
- Action recognition and/or generation
- Crossmodal human behavior learning
- Temporal architectures for human behavior learning
- Emotional feedback and modulation in decision-making processes
- Life-long learning for human behavior processing



Submitted papers will be reviewed according to the IJCNN reviewing
process and will be evaluated on their scientific value: originality,
correctness, and writing style.
More details:
https://urldefense.proofpoint.com/v2/url?u=http-3A__www.ecomp.poli.br_-7Ewcci2018_submissions_-23papersubmission&d=DwIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=Y2ZJ5_n398cJ3loPgqnsHlhLYO-fGRt29Hw4IKWWzl8&s=BPFOXqkZvjo2QrJs-Vs2DsAiBvNJ4ykNfp5dHZ_o0ZA&e=


IMPORTANT DATES:

    Paper submission: 15th January 2018
    Paper acceptance: 15th March 2018
    Final paper submission: 1st May 2018
    Early registration: 1st May 2018
    IEEE WCCI 2018 conference: 8-13 July 2018


Dr. rer. nat. Pablo Barros
Knowledge Technology
Department of Informatics
University of Hamburg
barros at informatik.uni-hamburg.de
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.inf.uni-2Dhamburg.de_en_inst_ab_wtm_people_barros.html&d=DwIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=Y2ZJ5_n398cJ3loPgqnsHlhLYO-fGRt29Hw4IKWWzl8&s=H6zomoJ6mnnRNqBDI1bAtrt9px5o8_IDHEXrNZlPWlk&e=
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.informatik.uni-2Dhamburg.de_wtm_&d=DwIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=Y2ZJ5_n398cJ3loPgqnsHlhLYO-fGRt29Hw4IKWWzl8&s=a27Tzao1yPa0Xai-LAdQ4ubqYn_wQA6lxfN3IZFV_lM&e=


--
Dr.rer.nat. Pablo Barros
Research Associate
Knowledge Technology
Department of Informatics
University of Hamburg
Vogt-Koelln-Str. 30
22527 Hamburg, Germany
Phone: +49 40 42883 2535
Fax: +49 40 42883 2515
barros at informatik.uni-hamburg.de
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.inf.uni-2Dhamburg.de_en_inst_ab_wtm_people_barros.html&d=DwIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=Y2ZJ5_n398cJ3loPgqnsHlhLYO-fGRt29Hw4IKWWzl8&s=H6zomoJ6mnnRNqBDI1bAtrt9px5o8_IDHEXrNZlPWlk&e=
www.knowledge-technology.info

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