Abstract
Dealing with increasing amounts of data creates the need to deal with redundant, inconsistent and/or complementary repositories which may be different in their data models and/or in their schema. Current data cleaning techniques developed to tackle data quality problems are just suitable for scenarios were all repositories share the same model and schema. Recently, an ontology-based methodology was proposed to overcome this limitation. In this paper, this methodology is briefly described and applied to a real scenario in the health domain with data quality problems. © 2016 ACM.

Abstract

Abstract
SCIP is a solver for a wide variety of mathematical optimization problems. It is written in C and extendable due to its plug-in based design. However, dealing with all C specifics when extending SCIP can be detrimental to development and testing of new ideas. This paper attempts to provide a remedy by introducing PySCIPOPT, a Python interface to SCIP that enables users to write new SCIP code entirely in Python. We demonstrate how to intuitively model mixed-integer linear and quadratic optimization problems and moreover provide examples on how new Python plug-ins can be added to SCIP.

Abstract
In this paper we address the transformation of whispered speech into natural voiced speech. Representative state-of-the-art solutions are first reviewed as well as a baseline algorithm. For the most part, these solutions fall in the realm of voice conversion strategies since the output signal is obtained as a projection of an input signal. In this paper, we propose a different approach that addresses flexible parametric synthesis of the voiced signal component, as well as its implantation on the whispered signal, in a linguistically consistent way and while trying to convey idiosyncratic information. The most critical functions of phonetic segmentation, spectral envelope estimation, arbitrary periodic wave shape synthesis, and F0 modulation, are described and their operation illustrated with examples.

Abstract
In this work we propose a method for maximization of the efficiency of an underwater wireless power transfer system that has to cope with load changes, quality factor and coupling coefficient deviations. By means of 3D electromagnetic simulation and numerical computation, parameter analysis is accomplished using different compensation methods, namely series-series, series-parallel and parallel-parallel. Moreover, a linear load profile is assessed as a proof of concept applicable to more complex load behaviours. For this linear load variation a maximum measured average efficiency of 82% was obtained throughout the entire battery state of charge. Electronics and full system considerations are also presented. Finally, a good agreement between theoretical predictions of the proposed method, simulation assessment and measurement results was verified.

Abstract
We develop a general framework for compressive linear-projection measurements with side information. Side information is an additional signal correlated with the signal of interest. We investigate the impact of side information on classification and signal recovery from low-dimensional measurements. Motivated by real applications, two special cases of the general model are studied. In the first, a joint Gaussian mixture model is manifested on the signal and side information. The second example again employs a Gaussian mixture model for the signal, with side information drawn from a mixture in the exponential family. Theoretical results on recovery and classification accuracy are derived. The presence of side information is shown to yield improved performance, both theoretically and experimentally. © 2016 IEEE.