for reconstructing missing provenance. Unfortunately, there is currently no agreed upon evaluation framework for testing these methods. Moreover, there is a paucity of datasets that these methods can be applied to. To begin to address this gap, we present a survey of existing benchmark corpora from other computer

Summary. We propose Dagger, a lightweight system to dynamically vet sens-itive behaviors in Android apps. Dagger avoids costly instrumentation of virtual machines or modifications to the Android kernel. Instead, Dagger reconstructs the program semantics by tracking provenance relationships

interference by using different items on every trial and reconstruction of order as the retention measure; in a reconstruction test the just-presented items are given back in random order and the task is to place the items into their original presentation order. Under these conditions very little evidence

. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement

Abstract—We leverage semantic technologies and Cytoscape.js to create a provenance-aware, probabilistic analysis platform for systems biology and evaluate its usefulness in discovering links between drugs and diseases. In our efforts to create a system-atic approach to discovering new uses

results on several publicly available benchmark corpora. 1

of various source corpora, could not encompass all the motion sequences that took place in the context of different interactions. Nevertheless, it has proven useful to treat socially meaningful actions as communicative acts within the contexts of 1) religion, 2) law, 3) ceremonial and 4) etiquette.

Abstract — Face recognition can be studied as an associative memory (AM) problem and kernel-based AM models have been proven efficient. In this paper, a hierarchical Kernel Associative Memory (KAM) face recognition scheme with a multiscale Gabor transform, is proposed. The pyramidal multiscale

) benchmarks and two non-English corpora, we demonstrate that our overcomplete representation is robust in characterizing various speakers, no matter whether their utterances have been used in training our DNA, and highly insensitive to text and languages spoken. Extensive comparative studies suggest that our

the interactions occurring among genes, starting from gene expression steady state data. Results: The algorithm was first validated on synthetic and real benchmarks. It was then applied to the reconstruction of the core of the amino acids metabolism in Bifidobacterium longum, an essential, yet poorly known player