Empresa privada y participación digital: modelo de negocio y derecho de petición en Change.org

Esta investigación pretende aproximarse al papel de Change.org como plataforma de petición electrónica en España, donde no existen alternativas administradas por los poderes públicos. Mediante un análisis de contenido cualitativo y una entrevista semi-estructurada, investigamos el modelo de negocio de la página, con el objetivo de conocer su política de protección de datos, su sistema de verificación de los usuarios y, de forma más general, el marco legislativo en el que opera. Los resultados obtenidos muestran al proyecto alejado del derecho de petición español, con un sistema de testeo laxo y que basa sus beneficios en el coste por adquisición.

A multimodal genre recognition prototype

In this paper, a multimodal and interactive prototype to perform music genre classification is presented. The system is oriented to multi-part files in symbolic format but it can be adapted using a transcription system to transform audio content in music scores. This prototype uses different sources of information to give a possible answer to the user. It has been developed to allow a human expert to interact with the system to improve its results. In its current implementation, it offers a limited range of interaction and multimodality. Further development aimed at full interactivity and multimodal interactions is discussed.

Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lower the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show that this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.