We propose a framework for quantitative security analysis of machine learning methods. Key issus of this framework are a formal specification of the deployed learning model and a...
Knowing which method parameters may be mutated during a method’s execution is useful for many software engineering tasks. We present an approach to discovering parameter referen...
Shay Artzi, Adam Kiezun, David Glasser, Michael D....
We present a novel experience based sampling or experiential sampling technique which has the ability to focus on the analysis’s task by making use of the contextual information...
Abstract. The rapidly emerging field of metagenomics seeks to examine the genomic content of communities of organisms to understand their roles and interactions in an ecosystem. I...
Gianluigi Folino, Fabio Gori, Mike S. M. Jetten, E...
We show that document image decoding (DID) supervised training algorithms, as a result of recent refinements, achieve high accuracy with low manual effort even under conditions o...