Abstract
In spite of significant advances in image segmentation techniques, evaluation of these methods thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images that axe evaluated by some method, or it is otherwise left to subjective evaluation by the reader. We propose a new approach for evaluation of segmentation that takes into account not only the accuracy of the boundary localization of the created segments but also the under-segmentation and over-segmentation effects, regardless to the number of regions in each partition. In addition, it takes into account the way humans perceive visual information. This new metric can be applied both to automatically provide a ranking among different segmentation algorithms and to find an optimal set of input parameters of a given algorithm.

Abstract

Abstract
This paper describes a rule learning method that obtains models biased towards a particular class of regression tasks. These tasks have as main distinguishing feature the fact that the main goal is to be accurate at predicting rare extreme values of the continuous target variable. Many real-world applications from scientific areas like ecology, meteorology, finance,etc., share this objective. Most existing approaches to regression problems search for the model parameters that optimize a given average error estimator (e.g. mean squared error). This means that they are biased towards achieving a good performance on the most common cases. The motivation for our work is the claim that being accurate at a small set of rare cases requires different error metrics. Moreover, given the nature and relevance of this type of applications an interpretable model is usually of key importance to domain experts, as predicting these rare events is normally associated with costly decisions. Our proposed system (R-PREV) obtains a set of interpretable regression rules derived from a set of bagged regression trees using evaluation metrics that bias the resulting models to predict accurately rare extreme values. We provide an experimental evaluation of our method confirming the advantages of our proposal in terms of accuracy in predicting rare extreme values.

Abstract
Modelling extreme data is very important in several application domains, like for instance finance, meteorology, ecology, etc.. This paper addresses the problem of predicting extreme values of a continuous variable. The main distinguishing feature of our target applications resides on the fact that these values are rare. Any prediction model is obtained by some sort of search process guided by a pre-specified evaluation criterion. In this work we argue against the use of standard criteria for evaluating regression models in the context of our target applications. We propose. a new predictive performance metric for this class of problems that our experiments show to perform better in distinguishing models that are more accurate at rare extreme values. This new evaluation metric could be used as the basis for developing better models in terms of rare extreme values prediction.

Abstract
This paper presents a case in the development of a knowledge community support system in the context of an industrial association group in the construction sector. This system is a result of the Know-Construct project which aims at providing association sponsored SME communities of the construction sector with a sophisticated information management platform and community building tools for knowledge sharing. The paper begins by characterizing the so-called construction industry knowledge community. The Know-Construct system concept and the its general architecture are described, focusing on the semantic resources, in particular the ontologies structure. The final part of the paper depicts the approach to the actual introduction of the system in the community. An action-research approach was planned to obtain research results regarding the social acceptance of semantic resources such as the ontologies and technical classifications used in system.

Abstract
In the Arabidopsis root, asymmetric stem-cell divisions produce daughters that form the different root cell types. Here we report the establishment of a confocal tracking system that allows the analysis of numbers and orientations of cell divisions in root stem cells. The system provides direct evidence that stem cells have lower division rates than cells in the proximal meristem. It also allows tracking of cell division timing, which we have used to analyse the synchronization of root cap divisions. Finally, it gives new insights into lateral root cap formation: epidermal stem-cell daughters can rotate the orientation of the division plane like the stem cell.