Desarrollo de una aplicación usando LEAP

[ES]Hoy en día existen diferentes alternativas para interactuar con los ordenadores. Sin embargo, las más extendidas y utilizadas son el teclado y el ratón. En ambos casos resulta necesario que las manos del usuario entren en contacto con algún dispositivo, ya sea un teclado físico o un ratón. En determinadas circunstancias en las que la higiene de las manos es un factor importante, este hecho puede suponer un inconveniente. En este proyecto de fin de grado se ha desarrollado KVLeap, una aplicación de escritorio para los sistemas Windows, que usando el controlador Leap Motion, un dispositivo que detecta y rastrea la posición y los movimientos de las manos en el aire, permite interactuar con un ordenador sin que las manos del usuario tengan que entrar en contacto con ningún dispositivo.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Frame-level features conveying phonetic information for language and speaker recognition

150 p.

Leukemia-Associated Mutations in Nucleophosmin Alter Recognition by CRM1: Molecular Basis of Aberrant Transport

Nucleophosmin (NPM) is a nucleocytoplasmic shuttling protein, normally enriched in nucleoli, that performs several activities related to cell growth. NPM mutations are characteristic of a subtype of acute myeloid leukemia (AML), where mutant NPM seems to play an oncogenic role. AML-associated NPM mutants exhibit altered subcellular traffic, being aberrantly located in the cytoplasm of leukoblasts. Exacerbated export of AML variants of NPM is mediated by the nuclear export receptor CRM1, and due, in part, to a mutationally acquired novel nuclear export signal (NES). To gain insight on the molecular basis of NPM transport in physiological and pathological conditions, we have evaluated the export efficiency of NPM in cells, and present new data indicating that, in normal conditions, wild type NPM is weakly exported by CRM1. On the other hand, we have found that AML-associated NPM mutants efficiently form complexes with CRM1HA (a mutant CRM1 with higher affinity for NESs), and we have quantitatively analyzed CRM1HA interaction with the NES motifs of these mutants, using fluorescence anisotropy and isothermal titration calorimetry. We have observed that the affinity of CRM1HA for these NESs is similar, which may help to explain the transport properties of the mutants. We also describe NPM recognition by the import machinery. Our combined cellular and biophysical studies shed further light on the determinants of NPM traffic, and how it is dramatically altered by AML-related mutations.