Abstract
Heart rate variability (HRV) data display nonstationary characteristics, exhibit long-range correlations (memory) and instantaneous variability (volatility). Recently, we have proposed fractionally integrated autoregressive moving average (ARFIMA) models for a parametric alternative to the widely-used technique detrended fluctuation analysis, for long memory estimation in HRV. Usually, the volatility in HRV studies is assessed by recursive least squares. In this work, we propose an alternative approach based on ARFIMA models with generalized autoregressive conditionally heteroscedastic (GARCH) innovations. ARFIMA-GARCH models, combined with selective adaptive segmentation, may be used to capture and remove long-range correlation and estimate the conditional volatility in 24 hour HRV recordings. The ARFIMA-GARCH approach is applied to 24 hour HRV recordings from the Noltisalis database allowing to discriminate between the different groups.

Abstract
In this paper, an evaluation of several methods allowing the estimation of the Harmonic-to-Noise Ratio (HNR) of sustained vowels was conducted. The HNR estimation methods are mainly based on time, spectral, and cepstral signal representations. An algorithm was implemented for each method and was tested with synthesized voice sounds in order to evaluate their accuracy. Tests were also conducted with real pathological voice sounds in order to evaluate the behaviour of the different methods under real conditions. © 2008 Division of Signal Processin.

Abstract
The use of collaborative filtering (CF) recommenders on the Web is typically done in environments where data is constantly flowing. In this paper we propose an incremental version of item-based CF for implicit binary ratings, and compare it with a non-incremental one, as well as with an incremental user-based approach. We also study the usage of sparse matrices in these algorithms. We observe that recall and precision tend to improve when we continuously add information to the recommender model, and that the time spent for recommendation does not degrade. Time for updating the similarity matrix is relatively low and motivates the use of the item-based incremental approach. © 2008 IEEE.

Abstract
Traditionally, recommender systems for the Web deal with applications that have two types of entities/dimensions, users and items. With these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a direct method that enriches the information in the access logs with new dimensions. We empirically test this method with two recommender systems, an item-based collaborative filtering technique and association rules, on three data sets. Our results show that while collaborative filtering is not able to take advantage of the new dimensions added, association rules are capable of profiting from our direct method. © 2008 IEEE.

Abstract
Visualization in data mining is typically related to data exploration. In this chapter we present a methodology for the post processing and visualization of association rule models. One aim is to provide the user with a tool that enables the exploration of a large set of association rules. The method is inspired by the hypertext metaphor. The initial set of rules is dynamically divided into small comprehensible sets or pages, according to the interest of the user. From each set, the user can move to other sets by choosing one appropriate operator. The set of available operators transform sets of rules into sets of rules, allowing focusing on interesting regions of the rule space. Each set of rules can also be then seen with different graphical representations. The tool is web-based and dynamically generates SVG pages to represent graphics. Association rules are given in PMML format. © 2008 Springer-Verlag Berlin Heidelberg.

Abstract
In this paper we describe a platform that enables Web site automation and monitoring. The platform automatically gathers high quality site activity data, both from the server and client sides. Web adapters, such as rec-ommender systems, can be easily plugged into the platform, and take advantage of the up-to-date activity data. The platform also includes a module to support the editor of the site to monitor and assess the effects of automation. We illustrate the features of the platform on a case study, where we show how it can be used to gather information not only to model the behavior of users but also the impact of the personalization mechanism. Copyright © 2008, Association for the Advancement of Artificial Intelligence.