46,058 research outputs found
BEAT: An Open-Source Web-Based Open-Science Platform
With the increased interest in computational sciences, machine learning (ML),
pattern recognition (PR) and big data, governmental agencies, academia and
manufacturers are overwhelmed by the constant influx of new algorithms and
techniques promising improved performance, generalization and robustness.
Sadly, result reproducibility is often an overlooked feature accompanying
original research publications, competitions and benchmark evaluations. The
main reasons behind such a gap arise from natural complications in research and
development in this area: the distribution of data may be a sensitive issue;
software frameworks are difficult to install and maintain; Test protocols may
involve a potentially large set of intricate steps which are difficult to
handle. Given the raising complexity of research challenges and the constant
increase in data volume, the conditions for achieving reproducible research in
the domain are also increasingly difficult to meet.
To bridge this gap, we built an open platform for research in computational
sciences related to pattern recognition and machine learning, to help on the
development, reproducibility and certification of results obtained in the
field. By making use of such a system, academic, governmental or industrial
organizations enable users to easily and socially develop processing
toolchains, re-use data, algorithms, workflows and compare results from
distinct algorithms and/or parameterizations with minimal effort. This article
presents such a platform and discusses some of its key features, uses and
limitations. We overview a currently operational prototype and provide design
insights.Comment: References to papers published on the platform incorporate
Spartan Daily, November 26, 1968
Volume 56, Issue 43https://scholarworks.sjsu.edu/spartandaily/5151/thumbnail.jp
- …