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Guyon I.,ChaLearn | Athitsos V.,University of Texas at Arlington | Jangyodsuk P.,University of Texas at Arlington | Hamner B.,Kaggle | Escalante H.J.,National Institute of Astrophysics, Optics and Electronics
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops | Year: 2012

We organized a challenge on gesture recognition: http://gesture.chalearn. org. We made available a large database of 50,000 hand and arm gestures videorecorded with a Kinect™ camera providing both RGB and depth images. We used the Kaggle platform to automate submissions and entry evaluation. The focus of the challenge is on "one-shot-learning", which means training gesture classifiers from a single video clip example of each gesture. The data are split into subtasks, each using a small vocabulary of 8 to 12 gestures, related to a particular application domain: hand signals used by divers, finger codes to represent numerals, signals used by referees, marchalling signals to guide vehicles or aircrafts, etc. We limited the problem to single users for each task and to the recognition of short sequences of gestures punctuated by returning the hands to a resting position. This situation is encountered in computer interface applications, including robotics, education, and gaming. The challenge setting fosters progress in transfer learning by providing for training a large number of sub-tasks related to, but different from the tasks on which the competitors are tested. © 2012 IEEE. Source


Goldbloom A.,Kaggle
Proceedings - IEEE International Conference on Data Mining, ICDM | Year: 2010

Data prediction competitions facilitate a step change in the evolution of analytics outsourcing. They offer companies and researchers a cost effective way to harness the'cognitive surplus' of data scientists who are hungry for real-world data and motivated to excel whatever the prize. Competitions are effective because there are any number of techniques that can be applied to any modeling problem but we can't know in advance which will be most effective. By exposing the problem to a wide audience, competitions are an effective way to reach the frontier of what is possible from a given dataset. © 2010 IEEE. Source


Guyon I.,ChaLearn | Athitsos V.,University of Texas at Arlington | Jangyodsuk P.,University of Texas at Arlington | Escalante H.J.,National Institute of Astrophysics, Optics and Electronics | Hamner B.,Kaggle
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

The Kinect™ camera has revolutionized the field of computer vision by making available low cost 3D cameras recording both RGB and depth data, using a structured light infrared sensor. We recorded and made available a large database of 50,000 hand and arm gestures. With these data, we organized a challenge emphasizing the problem of learning from very few examples. The data are split into subtasks, each using a small vocabulary of 8 to 12 gestures, related to a particular application domain: hand signals used by divers, finger codes to represent numerals, signals used by referees, Marshalling signals to guide vehicles or aircrafts, etc. We limited the problem to single users for each task and to the recognition of short sequences of gestures punctuated by returning the hands to a resting position. This situation is encountered in computer interface applications, including robotics, education, and gaming. The challenge setting fosters progress in transfer learning by providing for training a large number of subtasks related to, but different from the tasks on which the competitors are tested. © 2013 Springer-Verlag. Source


Emam K.,Ottawa Health Research Institute | Emam K.,University of Ottawa | Arbuckle L.,Ottawa Health Research Institute | Koru G.,University of Maryland Baltimore County | And 6 more authors.
Journal of Medical Internet Research | Year: 2012

Background: There are many benefits to open datasets. However, privacy concerns have hampered the widespread creation of open health data. There is a dearth of documented methods and case studies for the creation of public-use health data. We describe a new methodology for creating a longitudinal public health dataset in the context of the Heritage Health Prize (HHP). The HHP is a global data mining competition to predict, by using claims data, the number of days patients will be hospitalized in a subsequent year. The winner will be the team or individual with the most accurate model past a threshold accuracy, and will receive a US s3 million cash prize. HHP began on April 4, 2011, and ends on April 3, 2013. Objective: To de-identify the claims data used in the HHP competition and ensure that it meets the requirements in the US Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. Methods: We defined a threshold risk consistent with the HIPAA Privacy Rule Safe Harbor standard for disclosing the competition dataset. Three plausible re-identification attacks that can be executed on these data were identified. For each attack the re-identification probability was evaluated. If it was deemed too high then a new de-identification algorithm was applied to reduce the risk to an acceptable level. We performed an actual evaluation of re-identification risk using simulated attacks and matching experiments to confirm the results of the de-identification and to test sensitivity to assumptions. The main metric used to evaluate re-identification risk was the probability that a record in the HHP data can be re-identified given an attempted attack. Results: An evaluation of the de-identified dataset estimated that the probability of re-identifying an individual was .0084, below the .05 probability threshold specified for the competition. The risk was robust to violations of our initial assumptions. Conclusions: It was possible to ensure that the probability of re-identification for a large longitudinal dataset was acceptably low when it was released for a global user community in support of an analytics competition. This is an example of, and methodology for, achieving open data principles for longitudinal health data. Source


Kitching T.D.,University College London | Kitching T.D.,University of Edinburgh | Rhodes J.,Jet Propulsion Laboratory | Rhodes J.,California Institute of Technology | And 14 more authors.
Astronomy and Computing | Year: 2015

In this paper we present results from the Mapping Dark Matter competition that expressed the weak lensing shape measurement task in its simplest form and as a result attracted over 700 submissions in 2 months and a factor of 3 improvement in shape measurement accuracy on high signal to noise galaxies, over previously published results, and a factor 10 improvement over methods tested on constant shear blind simulations. We also review weak lensing shape measurement challenges, including the Shear TEsting Programmes (STEP1 and STEP2) and the GRavitational lEnsing Accuracy Testing competitions (GREAT08 and GREAT10). © 2014 Elsevier B.V. Source

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