Deep learning training typically starts with a random sampling initialization approach to set
the weights of trainable layers. Therefore, different and/or uncontrolled weight initialization …

Provenance, ie, the origin or source of something, is becoming an important concern, since it
offers the means to verify data products, to infer their quality, to analyse the processes that …

Until not long ago, manually capturing and storing provenance from scientific experiments
were constant concerns for scientists. With the advent of computational experiments …

The purpose of this article is to highlight a main issue regarding reliable audit evidence
derived from Big Data—that of secure data provenance. Traditionally, audit evidence …

Many scientific workflows are data intensive: large volumes of intermediate datasets are
generated during their execution. Some valuable intermediate datasets need to be stored for …

ABSTRACT A key advantage of scientific workflow systems over traditional scripting
approaches is their ability to automatically record data and process dependencies …

Many scientific workflows are data intensive where a large volume of intermediate data is
generated during their execution. Some valuable intermediate data need to be stored for …

Event data are often dirty owing to various recording conventions or simply system errors.
These errors may cause many serious damages to real applications, such as inaccurate …

With the increasing popularity of scientific workflows, public repositories are gaining
importance as a means to share, find, and reuse such workflows. As the sizes of these …

Many scientific workflows are data intensive where large volumes of intermediate data are
generated during their execution. Some valuable intermediate data need to be stored for …