Spoken language understanding (SLU) by using recurrent neural networks (RNN) achieves good performances for large training data sets, but collecting large training datasets is a challenge, especially for new voice applications.Therefore, the purpose of this study is to maximize SLU performances, especially for small training data sets.To this aim, we propose a novel CRF-based dialog act selector which chooses suitable dialog acts from outputs of RNN SLU and rule-based SLU.We evaluate the selector by using DSTC2 corpus when RNN SLU is trained by less than 1,000 training sentences.The evaluation demonstrates the selector achieves Micro F1 better than both RNN and rule-based SLUs.In addition, it shows the selector achieves better Macro F1 than RNN SLU and the same Macro F1 as rule-based SLU.Thus, we confirmed our method offers advantages in SLU performances for small training data sets.

SUMMARY: NGS-SNP is a collection of command-line scripts for providing rich annotations for SNPs identified by the sequencing of whole genomes from any organism with reference sequences in Ensembl. Included among the annotations, several of which are not available from any existing SNP annotation tools, are the results of detailed comparisons with orthologous sequences. These comparisons can, for example, identify SNPs that affect conserved residues, or alter residues or genes linked to phenotypes in another species. AVAILABILITY: NGS-SNP is available both as a set of scripts and as a virtual machine. The virtual machine consists of a Linux operating system with all the NGS-SNP dependencies pre-installed. The source code and virtual machine are freely available for download at http://stothard.afns.ualberta.ca/downloads/NGS-SNP/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper proposes novel and effective techniques to estimate a radius to answer k-nearest neighbor queries. The first technique targets datasets where it is possible to learn the distribution about the pairwise distances between the elements, generating a global estimation that applies to the whole dataset. The second technique targets datasets where the first technique cannot be employed, generating estimations that depend on where the query center is located. The proposed k-NNF() algorithm combines both techniques, achieving remarkable speedups. Experiments performed on both real and synthetic datasets have shown that the proposed algorithm can accelerate k-NN queries more than 26 times compared with the incremental algorithm and spends half of the total time compared with the traditional k-NN() algorithms.

Multimedia and complex data are usually queried by similarity predicates. Whereas there are many works dealing with algorithms to answer basic similarity predicates, there are not generic algorithms able to efficiently handle similarity complex queries combining several basic similarity predicates. In this work we propose a simple and effective set of algorithms that can be combined to answer complex similarity queries, and a set of algebraic rules useful to rewrite similarity query expressions into an adequate format for those algorithms. Those rules and algorithms allow relational database management systems to turn complex queries into efficient query execution plans. We present experiments that highlight interesting scenarios. They show that the proposed algorithms are orders of magnitude faster than the traditional similarity algorithms. Moreover, they are linearly scalable considering the database size.