Recently, broadcasted 3D video content has reached households with the first generation of 3DTV. However, few studies have been done to analyze the Quality of Experience (QoE) perceived by the end-users in this scenario. This paper studies the impact of transmission errors in 3DTV, considering that the video is delivered in side-by-side format over a conventional packet-based network. For this purpose, a novel evaluation methodology based on standard single stimulus methods and with the aim of keeping as close as possible the home environment viewing conditions has been proposed. The effects of packet losses in monoscopic and stereoscopic videos are compared from the results of subjective assessment tests. Other aspects were also measured concerning 3D content as naturalness, sense of presence and visual fatigue. The results show that although the final perceived QoE is acceptable, some errors cause important binocular rivalry, and therefore, substantial visual discomfort.

all the results. This metric is called mean opinion score (MOS) and it is the basis of most of the objective video quality metrics, which try to model video quality in a way which correlates as much as possible with MOS [2]. These kinds of solutions, however, are normally quite costly in terms of computing power required, and require measuring the video quality in the pixel domain, typically both before and after the degradation. Thus they are widely used in video codec calibration, but very limitedly in network monitoring.Multimedia quality of service is typically characterized by the Media Delivery Index (MDI) [10], which is a de facto standard in IPTV deployments. MDI is composed of two measurements: the packet loss rate (PLR), and the delay factor (DF), a measure of packet jitter. It is quite useful to model network issues and effective packet loss, but it assumes that all

This paper describes the subjective experiments and subsequent analysis carried out to validate the application of one of the most robust and influential video quality metrics, Video Multimethod Assessment Fusion (VMAF), to 360VR contents.

In this study, we focus on the egocentric segmentation of arms to improve self-perception in Augmented Virtuality (AV). The main contributions of this work are: i) a comprehensive survey of segmentation algorithms for AV; ii) an Egocentric Arm Segmentation Dataset (EgoArm), composed of more than 10, 000 images, demographically inclusive (variations of skin color, and gender), and open for research purposes. We also provide all details required for the automated generation of groundtruth and semi-synthetic images; iii) the proposal of a deep learning network to segment arms in AV; iv) a detailed quantitative and qualitative evaluation to showcase the usefulness of the deep network and EgoArm dataset, reporting results on different real egocentric hand datasets, including GTEA Gaze+, EDSH, EgoHands, Ego Youtube Hands, THU-Read, TEgO, FPAB, and Ego Gesture, which allow for direct comparisons with existing approaches using color or depth. Results confirm the suitability of the EgoArm dataset for this task, achieving improvements up to 40% with respect to the baseline network, depending on the particular dataset. Results also suggest that, while approaches based on color or depth can work under controlled conditions (lack of occlusion, uniform lighting, only objects of interest in the near range, controlled background, etc.), deep learning is more robust in real AV applications.

As a result of studies examining factors involved in the learning process, various structural models have been developed to explain the direct and indirect effects that occur between the variables in these models. The objective was to evaluate a structural model of cognitive and motivational variables predicting academic achievement, including general intelligence, academic self-concept, goal orientations, effort and learning strategies. The sample comprised of 341 Spanish students in the first year of compulsory secondary education. Different tests and questionnaires were used to evaluate each variable, and Structural Equation Modelling (SEM) was applied to contrast the relationships of the initial model. The model proposed had a satisfactory fit, and all the hypothesised relationships were significant. General intelligence was the variable most able to explain academic achievement. Also important was the direct influence of academic self-concept on achievement, goal orientations and effort, as well as the mediating ability of effort and learning strategies between academic goals and final achievement. Keywords: academic achievement, self-concept, intelligence, goal orientations, learning strategies.En el estudio de los factores que intervienen en el proceso de aprendizaje, se han desarrollado distintos modelos estructurales con el fin de ofrecer una explicación de los efectos directos e indirectos que se producen entre el conjunto de variables contempladas en los mismos. El objetivo de este trabajo fue contrastar un modelo estructural de variables cognitivo-motivacionales, predictoras del rendimiento académico, entre las que se incluyeron la inteligencia general, el autoconcepto académico, las orientaciones de meta, el esfuerzo y las estrategias de aprendizaje. La muestra estuvo compuesta por 341 alumnos españoles de primer curso de Educación Secundaria Obligatoria. Se emplearon distintas pruebas y cuestionarios para la evaluación de cada una de las variables y se aplicó SEM para contrastar las relaciones del modelo inicial. El modelo propuesto obtuvo un ajuste satisfactorio, siendo significativas todas relaciones hipotetizadas. La inteligencia general fue la variable con mayor poder explicativo sobre el rendimiento académico. También destacó la influencia directa del autoconcepto académico sobre el rendimiento, las orientaciones de meta y el esfuerzo, así como la capacidad mediadora del esfuerzo y de las estrategias de aprendizaje entre las metas académicas y el rendimiento final. Palabras clave: rendimiento academic, autoconcepto, inteligencia, orientaciones de meta, estrategias de aprendizaje.

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Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360°video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360°video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360°video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360°sequences. The following factors' influences were also analyzed: assessment methodology, sequence Manuscript received

Class identity is a key mechanism in the explanation of class-based collective action. For decades, this was particularly relevant in Latin America, where objective class inequality was persistent and there was a long history of collective action, originating in the workplace and expressed through unions and labor parties. Despite persistent inequalities in the region, since the 1990s scholars increasingly claimed that the relation between objective class position and subjective class identification weakened significantly, and that class dynamics centered on work were no longer central to explain group formation and collective action among the popular sectors. While in countries like Argentina scholars have explained these processes by focusing on the effects of the de-industrialization of the economy and the informalization of the job market, in Chile analysts have done so by emphasizing the growth of the service sector and the emergence of a middle-class society where ‘old-fashioned’ working-class identities have become irrelevant. This article questions these arguments based on a comparative analysis of the relationship between objective class position and subjective class identification in Argentina and Chile in 2009. The results show that class still matters. In both countries, people with a working-class position or a working-class trajectory are significantly more likely to uphold working-class identity than individuals with a privileged class position or trajectory. Surprisingly, the authors’ analysis also demonstrates that the overall rates of working-class identification are higher in Chile than in Argentina. The authors explain these unexpected results by looking at contemporary class-related phenomena (e.g. higher inequality and economic concentration in Chile) and longer-term class dynamics (particularly differences stemming from the ‘radical’ party–union configuration in Chile and the state-corporatist incorporation of labor in Argentina).