Abstract
Current affective user experience studies require both laborious and time-consuming data analysis, as well as dedicated affective classification algorithms. Moreover, the high technical complexity and lack of general guidelines for developing these affective classification algorithms further limits the comparability of the obtained results. In this paper we target this issue by presenting a tool capable of automatically annotating and triangulating players' physiologically interpreted emotional reactions to in-game events. This tool was initially motivated by an experimental psychology study regarding the emotional habituation effects of audio-visual stimuli in digital games and we expect it to contribute in future similar studies by providing both a deeper and more objective analysis on the affective aspects of user experience. We also hope it will contribute towards the rapid implementation and accessibility of this type of studies by open-sourcing it. Throughout this paper we describe the development and benefits presented by our tool, which include: enabling researchers to conduct objective a posteriori analyses without disturbing the gameplay experience, automating the annotation and emotional response identification process, and formatted data exporting for further analysis in third-party statistical software applications.

Abstract
Three dimensional virtual worlds (3DVW) have experienced a large growth in number of users, and are being used for collaboration activities. In parallel, the research field of Computer Supported Cooperative Work (CSCW) has developed taxonomies to classify systems that support collaboration. However, the CSCW perspective presents a bias towards traditional user interface paradigms, whose affordances are quite distinct from those of 3DVW, which include features such as the spatial environment, embodiment, and their dynamics. These are features which are regarded as significant factors in the research field of Presence, and yet, in our opinion, are not well appreciated from the perspective of CSCW analysis. Because of this, we question of the ability of CSCW taxonomies to properly describe the collaboration characteristics of 3DVW. By "properly", we mean to say that 3DVW bring to fore collaboration characteristics that are in fact distinctive of them as collaboration tools, impacting collaboration in ways that are seldom found in usual groupware, and yet CSCW taxonomies do not distinguish them. We posit that these features should be contemplated in CSCW taxonomies and their usefulness taken into account in the development of future systems that aim to support collaboration.

Abstract
One of the current frontiers in the clinical management of Pectus Excavatum (PE) patients is the prediction of the surgical outcome prior to the intervention. This can be done through computerized simulation of the Nuss procedure, which requires an anatomically correct representation of the costal cartilage. To this end, we take advantage of the costal cartilage tubular structure to detect it through multi-scale vesselness filtering. This information is then used in an interactive 2D initialization procedure which uses anatomical maximum intensity projections of 3D vesselness feature images to efficiently initialize the 3D segmentation process. We identify the cartilage tissue centerlines in these projected 2D images using a livewire approach. We finally refine the 3D cartilage surface through region-based sparse field level-sets. We have tested the proposed algorithm in 6 noncontrast CT datasets from PE patients. A good segmentation performance was found against reference manual contouring, with an average Dice coefficient of 0.75 +/- 0.04 and an average mean surface distance of 1.69 +/- 0.30mm. The proposed method requires roughly 1 minute for the interactive initialization step, which can positively contribute to an extended use of this tool in clinical practice, since current manual delineation of the costal cartilage can take up to an hour.

Abstract
Humans exhibit their personality and their behavior through their daily actions. Moreover, these actions also show how behaviors differ between different scenarios or contexts. However, Human behavior is a complex issue as it results from the interaction of various internal and external factors such as personality, culture, education, social roles and social context, life experiences, among many others. This implies that a specific user may show different behaviors for a similar circumstance if one or more of these factors change. In past work we have addressed the development of behavior-based user identification based on keystroke and mouse dynamics. However, user states such as stress or fatigue significantly change interaction patterns, risking the accuracy of the identification. In this paper we address the effects of these variables on keystroke and mouse dynamics. We also show how, despite these effects, user identification can be successfully carried out, especially if task-specific information is considered.

Abstract
Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential ((pd)). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and (pd). A linear regression was defined using a parameterization dataset. The correlation analysis between (pd) and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r(2)) smaller than 0.67. However, the results of r(2) highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) showed the higher r(2) and stability index results. The equations obtained through the regression between measured (pd) ((pd_obs)) and optimal VARI and between (pd_obs) and optimal NDGI when using the parameterization dataset were adopted for predicting (pd) using a testing dataset. The comparison of (pd_obs) with (pd) predicted based on VARI led to R-2 = 0.79 and a regression coefficient b = 0.96. Similar R-2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating (pd), supporting vineyards irrigation management.

Abstract
Building on probabilistic models for interval-valued variables, parametric classification rules, based on Normal or Skew-Normal distributions, are derived for interval data. The performance of such rules is then compared with distancebased methods previously investigated. The results show that Gaussian parametric approaches outperform Skew-Normal parametric and distance-based ones in most conditions analyzed. In particular, with heterocedastic data a quadratic Gaussian rule always performs best. Moreover, restricted cases of the variance-covariance matrix lead to parsimonious rules which for small training samples in heterocedastic problems can outperform unrestricted quadratic rules, even in some cases where the model assumed by these rules is not true. These restrictions take into account the particular nature of interval data, where observations are defined by both MidPoints and Ranges, which may or may not be correlated. Under homocedastic conditions linear Gaussian rules are often the best rules, but distance-based methods may perform better in very specific conditions.