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BiRD 2019 : Behavior analysis and Recognition for knowledge Discovery

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Link: http://bird2019.animal-behavior-challenge.org/
 
When Mar 11, 2019 - Mar 15, 2019
Where Kyoto, Japan
Submission Deadline Nov 10, 2018
Notification Due Dec 22, 2018
 

Call For Papers

- Affiliated to IEEE PerCom 2019
- March 11-15, 2019 (day to be confirmed)
- Kyoto,Japan

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About
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BiRD workshop aims to provide a forum for presenting and discussing research on the recognition and understanding of behavior data collected from various sources, such as humans, animals, and automobiles, towards scientific discovery (e.g., ecology and medical science) and/or actual business applications (e.g., industry, sports, healthcare, and stockbreeding).

In addition, we plan to invite biologists and neuroscientists who have collected and analyzed behavior data of various animals. While the biologists and neuroscientists have massive amounts of behavior data, they have limited opportunity to apply state-of-the-art behavior analysis techniques to their data. Therefore, this workshop provides a good opportunity for the PerCom researchers to apply the developed state-of-the-art methods to actual problems.

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Call for papers
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Due to the recent advances in sensing technologies, massive amounts of activity/behavior data is being collected from humans, animals, and automobiles. For example, activity and trajectory data of humans and automobiles can be easily collected by smart devices such as smartphones. In addition, small GPS and acceleration loggers enable us to collect behavior data from animals such as birds, bears, cattle, and turtles to better understand the ecology of the animals. Furthermore, activity recognition and indoor positioning techniques using the sensor data of humans have been actively studied in the PerCom community. However, computational methods for knowledge discovery in the sensory data and/or recognized behavior data have not yet been fully explored. Moreover, many pervasive computing researchers are eager to apply their state-of-the-art techniques to actual problems and contribute to scientific discovery. However, there are few academic conferences that mainly focus on knowledge discovery techniques for behavior/activity data, making it difficult for the researchers to develop methods that are actually used/needed.

This workshop solicits papers on the recognition and understanding of behavior data collected from various sources, such as humans, animals, and automobiles, towards scientific discovery (e.g., ecology and medical science) and/or actual business applications (e.g., industry, sports, healthcare, and stockbreeding). Because the organizers of this workshop are core members of an interdisciplinary project of engineer, computer science, biology, and neuroscience on behavior understanding (http://navi-science.org/english/) and one of the organizers is a biologist specialized in seabirds, we plan to invite biologists and neuroscientists who have collected and analyzed behavior data of various animals. While the biologists and neuroscientists have massive amounts of behavior data, they have limited limited opportunity to apply state-of-the-art behavior analysis techniques to their data. Therefore, this workshop provides a good opportunity for the PerCom researchers to apply the developed state-of-the-art methods to actual problems. In addition, we solicit poster papers that mainly focus on real data and discuss experience/limitations of conventional analysis methods applied to the data. The topics of interest include, but are not limited to:

- Data mining methods for behavior/activity data
- Trajectory mining
- Modeling behavior/activity
- Behavior/activity data collection for scientific discovery and/or actual business
- Data preparation and labeling for scientific discovery and/or actual business
- Knowledge extraction from massive amounts of behavior/activity data
- Behavior/activity monitoring and recognition systems for scientific discovery and/or actual business
- Applications of activity recognition and/or indoor/outdoor localization
- State-of-the-art indoor/outdoor localization techniques
- State-of-the-art behavior/activity recognition and understanding techniques
- Visualization of behavior/activity data for knowledge discovery
- Data collection from real world
- Limitation of conventional methods applied to real data
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Important dates
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Workshop paper submissions: November 10, 2018
Paper notifications: December 22, 2018
Camera ready: January 11, 2019

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Submission
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Authors are invited to submit full papers that are unpublished and not under review elsewhere. In addition, we solicit poster papers that mainly focus on real data and discuss experience/limitations of conventional analysis methods applied to the data.

The papers for the workshops should be at most 6 pages in the IEEE template. Poster papers must be stated as such. Papers can be submitted via the following site.
https://edas.info/newPaper.php?c=25231

Each accepted workshop paper requires a full PerCom registration (no registration is available for workshops only). For preparation of the camera ready paper, please refer to the instructions on the PerCom web site.
Workshop papers will be included and indexed in the IEEE digital libraries (Xplore).

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Organizers
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Takuya Maekawa (Osaka University)
Ken Yoda (Nagoya University)
Toru Tamaki (Hiroshima University)

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Program committee
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[Information science]
John Krumm (Microsoft Research)
Moustafa Youssef (E-JUST)
Jiannong Cao (The Hong Kong Polytechnic University)
Stephan Sigg (Aalto University)
Ichiro Takeuchi (Nagoya Institute of Technology)
Kazuya Murao (Ristumeikan University)
Joseph Korpela (Osaka University)
Teerawat Kumrai (Osaka University)
Daichi Amagata (Osaka University)
Yasushi Iwatani (Hirosaki University)
Hitoshi Habe (Kinki University)
Keiji Yanai (The University of Electro-Communications)
Masaki Onishi (AIST)
Shinsuke Kajioka (Nagoya Institute of Technology)

[Biology & neuroscience]
Kotaro Kimura (Nagoya City University)
Shizuko Hiryu (Doshisha University)
Susumu Takahashi (Doshisha University)
Hiroto Ogawa (Hokkaido University)

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