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.

Using Ontologies for Enterprise Architecture Integration and Analysis

Abstract: Enterprise architecture facilitates the alignment between different domains, such as business, applications and information technology. These domains must be described with description languages that best address the concerns of its stakeholders. However, current model-based enterprise architecture techniques are unable to integrate multiple descriptions languages either due to the lack of suitable extension mechanisms or because they lack the means to maintain the coherence, consistency and traceability between the representations of the multiple domains of the enterprise. On the other hand, enterprise architecture models are often designed and used for communication and not for automated analysis of its contents. Model analysis is a valuable tool for assessing the qualities of a model, such as conformance and completeness, and also for supporting decision making. This paper addresses these two issues found in model-based enterprise architecture: (1) the integration of domain description languages, and (2) the automated analysis of models. This proposal uses ontology engineering techniques to specify and integrate the different domains and reasoning and querying as a means to analyse the models. The utility of the proposal is shown through an evaluation scenario that involve the analysis of an enterprise architecture model that spans multiple domains.

Blind People Interacting with Large Touch Surfaces: Strategies for One-handed and Two-handed Exploration

Abstract: Interaction with large touch surfaces is still a relatively infant domain, particularly when looking at the accessibility solutions offered to blind users. Their smaller mobile counterparts are shipped with built-in accessibility features, enabling non-visual exploration of linearized screen content. However, it is unknown how well these solutions perform in large interactive surfaces that use more complex spatial content layouts. We report on a user study with 14 blind participants performing common touchscreen interactions using one and two-hand exploration. We investigate the exploration strategies applied by blind users when interacting with a tabletop. We identified six basic strategies that were commonly adopted and should be considered in future designs. We finish with implications for the design of accessible large touch interfaces.

Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series

Abstract: Functional connectivity (FC) describes the statistical dependence between brain regions in resting-state fMRI studies and is usually estimated as the Pearson correlation of time courses. Clustering reveals densely coupled sets of regions constituting a set of resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs lasting many minutes but appear to fluctuate on shorter time scales. Here, we propose a new approach to track these temporal fluctuations. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair/edge, and reveals fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging sessions and disclose fine-scale profiles of the time-varying levels of expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

Computational Modelling of Culture and Affect

Abstract: This article discusses work on implementing emotional and cultural models into synthetic graphical characters. An architecture, FAtiMA, implemented first in the antibullying application FearNot! and then extended as FAtiMA-PSI in the cultural-sensitivity application ORIENT, is discussed. We discuss the modelling relationships between culture, social interaction, and cognitive appraisal. Integrating a lower level homeostatically based model is also considered as a means of handling some of the limitations of a purely symbolic approach. Evaluation to date is summarised and future directions discussed.

Improving ASR error detection with non-decoder based features.

Abstract: This study reports error detection experiments in large vocabulary automatic speech recognition (ASR) systems, by using statistical classifiers We explored new features gathered from other knowledge sources than the decoder itself: a binary feature that compares outputs from two different ASR systems (word by word), a feature based on the number of hits of the hypothesized bigrams, obtained by queries entered into a very popular Web search engine, and finally a feature related to automatically infered topics at sentence and word levels Experiments were conducted on a European Portuguese broadcast news corpus The combination of baseline decoder-based features and two of these additional features led to significant improvements, from 1387% to 1216% classification error rate (CER) with a maximum entropy model, and from 1401% to 1239% CER with linear-chain conditional random fields, comparing to a baseline using only decoder-based features