INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 932 authors who have published 2618 publications receiving 37658 citations.

A comparison of ray pointing techniques for very large displays

Abstract: Ray-pointing techniques are often advocated as a way for people to interact with very large displays from several meters away. We are interested in two factors that can affect ray pointing: the particular technique's control type, and parallax. Consequently, we tested four ray pointing variants on a wall display that covers a large part of the user's field of view. Tasks included horizontal and vertical targeting, and tracing. Our results show that (a) techniques based on 'rotational control' perform better for targeting tasks, and (b) techniques with low parallax are best for tracing tasks. We also show that a Fitts's law analysis based on angles (as opposed to linear distances) better approximates people's ray pointing performance.

Sudoku as a SAT Problem.

Abstract: Sudoku is a very simple and well-known puzzle that has achieved international popularity in the recent past. This paper addresses the problem of encoding Sudoku puzzles into conjunctive normal form (CNF), and subsequently solving them using polynomial-time propositional satisfiability (SAT) inference techniques. We introduce two straightforward SAT encodings for Sudoku: the minimal encoding and the extended encoding. The minimal encoding suffices to characterize Sudoku puzzles, whereas the extended encoding adds redundant clauses to the minimal encoding. Experimental results demonstrate that, for thousands of very hard puzzles, inference techniques struggle to solve these puzzles when using the minimal encoding. However, using the extended encoding, unit propagation is able to solve about half of our set of puzzles. Nonetheless, for some puzzles more sophisticated inference techniques are required.

Misep—linear and nonlinear ICA based on mutual information

Abstract: Linear Independent Components Analysis (ICA) has become an important signal processing and data analysis technique, the typical application being blind source separation in a wide range of signals, such as biomedical, acoustical and astrophysical ones. Nonlinear ICA is less developed, but has the potential to become at least as powerful.This paper presents MISEP, an ICA technique for linear and nonlinear mixtures, which is based on the minimization of the mutual information of the estimated components. MISEP is a generalization of the popular INFOMAX technique, which is extended in two ways: (1) to deal with nonlinear mixtures, and (2) to be able to adapt to the actual statistical distributions of the sources, by dynamically estimating the nonlinearities to be used at the outputs. The resulting MISEP method optimizes a network with a specialized architecture, with a single objective function: the output entropy.The paper also briefly discusses the issue of nonlinear source separation. Examples of linear and nonlinear source separation performed by MISEP are presented.

RWTH-PHOENIX-Weather: A Large Vocabulary Sign Language Recognition and Translation Corpus

Abstract: This paper introduces the RWTH-PHOENIX-Weather corpus, a video-based, large vocabulary corpus of German Sign Language suitable for statistical sign language recognition and translation. In contrastto most available sign language data collections, the RWTH-PHOENIX-Weather corpus has not been recorded for linguistic research but for the use in statistical pattern recognition. The corpus contains weather forecasts recorded from German public TV which are manually annotated using glosses distinguishing sign variants, and time boundaries have been marked on the sentence and the gloss level. Further, the spoken German weather forecast has been transcribed in a semi-automatic fashion using a state-of-the-art automatic speech recognition system. Moreover, an additional translation of the glosses into spoken German has been created to capture allowable translation variability. In addition to the corpus, experimental baseline results for hand and head tracking, statistical sign language recognition and translation are presented.