Abstract
The aim of the present work is the recognition of human face visual information, in order to automatically control the access to restricted areas, granting access to authorized "clients" and barring the entrance to "impostors". The vision system assembled performed the image acquisition, processing and recognition by first creating a database with a single view of each "client" and then by using multiple test images of each individual candidate to access. To get the test images, a video sequence was captured during the individual's approach path to the camera. Because subjects presented themselves in a random pose before the camera, the synthesis of frontal views was incorporated, by using a view-morphing method. ne modelling and the recognition were handled through the use of ICA methods. The identification of valid "clients" was fully successful. In order to check the rejection of "impostors", a leave-one-out test was performed which gave promising results.

Abstract
Cardiotocography is widely used, all over the world, for fetal heart rate and uterine contractions monitoring before (antepartum) and during (intrapartum) labor, regarding the detection of fetuses in danger of death or permanent damage. However, analysis of cardiotocogram tracings remains a large and unsolved issue. State-of-the-art monitoring systems provide quantitative parameters that are difficult to assess by the human eye. These systems also trigger alerts for changes in the behavior of the signals. However, they usually take up to 10 min to detect these different behaviors. Previous work using machine learning for concept drift detection has successfully achieved faster results in the detection of such events. Our aim is to extend the monitoring system with memory-less fading statistics, which have been successfully applied in drift detection and statistical tests, to improve detection of alarming events.

Abstract
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that continuously evolve over time, run in resource-aware environments, and detect and react to changes in the environment generating data. One important issue, not yet conveniently addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. In this paper we propose a general framework for assessing the quality of streaming learning algorithms. We defend the use of Predictive Sequential error estimates over a sliding window to assess performance of learning algorithms that learn from open-ended data streams in non-stationary environments. This paper studies properties of convergence and methods to comparatively assess algorithms performance. Copyright 2009 ACM.

Abstract
This paper addresses the space-time change detection problem in climate data over the Iberian Peninsula using a 50 years dataset. The data were analyzed concerning the temporal and geographical information, using the following methodology: information about space-time drifts in climate data was obtained by applying a change detection algorithm on all the temporal data available for each physical location considered in this study; the performance and the robustness of this algorithm were then assessed by the McNemar nonparametric statistical test on cluster structures; geographical correlations were inferred using visualization tools and graphical representations of data. Most of the space-temporal drifts detected by the algorithm were confirmed by the results of the McNemar test and are in accordance with visual and graphical representations, supporting the advantage of using inter-disciplinary methods. This analysis also shows that there are locations which do not reveal any change along all the observed years.

Abstract
In the clinical practice the concerns about the administration of hypnotics and analgesics for minimally invasive diagnostics and therapeutic procedures have enormously increased in the past years. The automatic detection of changes in the signals used to evaluate the depth of anesthesia is hence of foremost importance in order to decide how to adapt the doses of hypnotics and analgesics that should be administered to patients. The aim of this work is to online detect drifts in the referred depth of anesthesia signals of patients undergoing general anesthesia. The performance of the proposed method is illustrated using BIS records previously collected from patients subject to abdominal surgery. The results show that the drifts detected by the proposed method are in accordance with the actions of the clinicians in terms of times where a change in the hypnotic or analgesic rates had occurred. This detection was performed under the presence of noise and sensor faults. The presented algorithm was also online validated. The results encourage the inclusion of the proposed algorithm in a decision support system based on depth of anesthesia signals.

Abstract
Concept drift in data is usually considered only as abrupt or gradual thus referring to the speed of change. Such simple distinguishing by speed is sufficient for most of the problems, but there might be situations for which a finer representation would be of use. This paper studies further the phenomenon of concept drift and introduces a simple measure which is relevant to the speed and amount of change between different concepts.