Rik Van de Walle

TL;DR: A feature learning model for condition monitoring based on convolutional neural networks is proposed to autonomously learn useful features for bearing fault detection from the data itself and significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier.

TL;DR: The rml mapping language is introduced, a generic language based on an extension over r2rml, the w3c standard for mapping relational databases into rdf, which becomes source-agnostic and extensible, while facilitating the denition of mappings of multiple heterogeneous sources.

TL;DR: This paper proposes a novel method for unsupervised and content-based hashtag recommendation for tweets that relies on Latent Dirichlet Allocation (LDA) to model the underlying topic assignment of language classified tweets.

TL;DR: A semisupervised system that detects 10 types of named entities that achieved the fourth position in the final ranking, without using any kind of hand-crafted features such as lexical features or gazetteers.

TL;DR: A novel subjective quality assessment methodology based on full-length movies is proposed that enables audiovisual quality assessment in the same environments and under the same conditions users typically watch television.