An elevator configuration task, the VT task, is modelled within DESIRE as a design task. DESIRE is a framework within which complex reasoning tasks are modelled as compositional architectures. Compositional architectures are based on a task decomposition, acquired during task analysis. An earlier developed generic task model of design, based on a logical analysis and synthesis of task models de...

Unlike early versions of MPEG, MPEG-7 is shifting from a data-compression standard to a multimedia content description standard. As a result, it will have much broader implications for the human-computer interaction research. There are countless ways that the MPEG-7 metadata description framework can enhance users’ experiences in accessing multimedia content. In this paper, we will report the a...

We study the continuous evaluation of conjunctive triple pattern queries over RDF data stored in distributed hash tables. In a continuous query scenario network nodes subscribe with long-standing queries and receive answers whenever RDF triples satisfying their queries are published. We present two novel query processing algorithms for this scenario and analyze their properties formally. Our pe...

XGR (XML Data Grid) and BabelPeers are both data management systems based on distributed hash tables (DHT) that use the Pastry DHT to store data and meta data. XGR is based on the XML data model; BabelPeers uses the Resource Description Framework (RDF) for its data. XGR and BabelPeers have different but complementary functionality. On the one hand, XGR focuses on document-based storage of XML d...

Humans are able to come up with plans to achieve their goals, and to adapt these plans to changes in their environment, finding fixes, alternatives and taking advantages of opportunities without much deliberation. For example, they may use a tea kettle to water the plants, or a mug instead of a glass. Despite decades of research, artificial agents are not as robust or as flexible. In this work,...

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Question answering (QA) on table data is a challenging information retrieval task. This paper describes a QA system for tables created with both machine learning and heuristic table extraction methods. Errors were analyzed in order to improve the system using government statistical data. We also apply these improvements on another type of table data set and show the experimental results.

Imagine that you have created an introduction capable of contextualizing the subject in a clear and orderly way and that you have, therefore, raised in the reader the uncontrollable urge to read your article. Of course, the reader appears to be hooked. But beware. The reader seems to be a slippery fish that, even when apparently hooked, can escape at any time. Say that you went further and offe...