In this paper we describe our work on the formulation of a document-oriented paradigm for improving the construction and maintenance of content-intensive applications (i.e. applications that make intensive use of the information provided by the experts in a given domain: the contents). According to this paradigm, the development of a content-intensive application must be the result of close col...

The ability to find short representations, i.e. compress data, is crucial for many intelligent systems. We present a theory of incremental compression showing that arbitrary data strings, can be described by set features, compressed searching those features incrementally, which results in partition the information content string into complete pairwise independent pieces. description length this...

Recent studies have shown that incremental systems are perceived as more reactive, natural, and easier to use than non-incremental systems. However, previous work on incremental NLG has not employed recent advances in statistical optimisation using machine learning. This paper combines the two approaches, showing how the update, revoke and purge operations typically used in incremental approach...

Much research has investigated the differences between option implied volatilities and econometric model-based forecasts in terms of forecast accuracy and relative informational content. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing to generate forecasts. Therefore, implied volatility has the potential to reflect in...

Recent studies show that visual search often involves a combination of both parallel and serial search strategies. Consequently, computational models and theoretical accounts of visual search processing have evolved from traditional parallel or serial descriptions to a continuum from ”efficient” to ”inefficient.” In our first experiment, we demonstrate with various control conditions that searc...