Abstract
Purpose - The purpose of this paper is to create a movement within the service research community that aspires to help the billions of impoverished people across the world achieve better service from each other, from their communities, from corporations, from their governments, and from nongovernmental organizations. The authors believe every human being is worthy of being served properly. To achieve this purpose, understanding and learning from this huge low-income segment of society known as the base of the pyramid (BoP) is essential. There are myths about the BoP that need to be dispelled and there is a fundamental lack of service research on this important problem. Design/methodology/approach - The existence of an extensive BoP literature combined with service research priorities has called attention to drafting research agendas. Human service systems are explored historically and systems theory provides a perspective for understanding and reducing poverty. Transformative service research, service design research, and community action research are presented to illustrate three research approaches that can contribute to understanding and then better serving the needs of the neglected billions of humanity. Findings - First, the authors present a practical and meaningful call to action by making the case for the service research community to contribute to poverty alleviation with the creation of fresh ideas and research agendas. Second, the authors describe the ample opportunity for conducting service research in and with the BoP and thereby expanding service knowledge about the BoP. Third, the authors suggest a number of approaches for service researchers to join this new movement and help improve the well-being of billions of impoverished people. Social implications - Most existing service research comes from highly developed Anglo-Saxon countries and concerns the service problems of customers in affluent societies. Therefore, there is a fundamental lack of service research at the BoP. The social implications are truly global. Poverty is a global service system problem that can be reduced. Effective poverty alleviation solutions in one part of the world can be adapted to other parts of the world. Originality/value - This paper is a new and very original call to action to the service research community. First, with the exception of a few previous manuscripts calling for research on the BoP, this is the first time a collaborative effort has been made to start systematically changing this knowledge gap. Second, the service research community has never worked on a project of this magnitude. The authors hope to offer a role model to other academic communities as to how to marshal their resources to have a collective, positive impact on the well-being of the world's impoverished.

Abstract
Multimedia content consumption is very popular nowadays. However, not every content can be consumed in its original format: the combination of content, transport and access networks, consumption device and usage environment characteristics may all pose restrictions to that purpose. One way to provide the best possible quality to the user is to adapt the content according to these restrictions as well as user preferences. This adaptation stage can be best executed if knowledge about the content is known a-priori. In order to provide this knowledge we classify the content based on metrics to define its temporal and spatial complexity. The temporal complexity classification is based on the Motion Vectors of the predictive encoded frames and on the difference between frames. The spatial complexity classification is based on different implementations of an edge detection algorithm and an image activity measure.

Abstract
Exceptional Preferences Mining (EPM) is a crossover between two subfields of datamining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where the preference relations between subsets of the labels significantly deviate from the norm; a variant of Subgroup Discovery, with rankings as the (complex) target concept. We employ three quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes 'exceptional' varies with the quality measure: the first gauges exceptional overall ranking behavior, the second indicates whether a particular label stands out from the rest, and the third highlights subgroups featuring unusual pairwise label ranking behavior. As proof of concept, we explore five datasets. The results confirm that the new task EPM can deliver interesting knowledge. The results also illustrate how the visualization of the preferences in a Preference Matrix can aid in interpreting exceptional preference subgroups.

Abstract
The research proposes a novel technological solution for marker-based human motion capture called WirelessSyncroVision (WSV). The WSV is formed by two main modules: the visual node (WSV-V) which is based on a stereoscopic vision system and the marker node (WSV-M) that is constituted by a 6-DOF active marker. The solution synchronizes the acquisition of images in remote muti-cameras with the ON period of the active marker. This increases the robustness of the stereoscopic system to illumination changes, which is extremely relevant for programming industrial robotic-arms using a human demonstrator programming by demonstration (PbD). In addition, the research presents a robust method named Adaptive and Robust Synchronization (ARS), that is designed for temporal alignment of remote devices using a wireless network. The algorithm models the phase difference as a function of time, measuring the parameters that must be known to predict the synchronization instant between the active marker and the remote cameras. Results demonstrate that the ARS creates a balance between the real-time capability and the performance estimation of the phase difference. Therefore, this research proposes an elegant solution to synchronize image acquisition systems in real-time that is easy to implement with low operational costs; however, the major advantage of the WSV is related to its high level of flexibility since it can be extended toward to other devices besides the PbD, for instance, motion capture, motion analysis, and remote sensoring systems.

Abstract
This paper offers a characterization of fundamental limits on the classification and reconstruction of high-dimensional signals from low-dimensional features, in the presence of side information. We consider a scenario where a decoder has access both to linear features of the signal of interest and to linear features of the side information signal; while the side information may be in a compressed form, the objective is recovery or classification of the primary signal, not the side information. The signal of interest and the side information are each assumed to have (distinct) latent discrete labels; conditioned on these two labels, the signal of interest and side information are drawn from a multivariate Gaussian distribution that correlates the two. With joint probabilities on the latent labels, the overall signal-(side information) representation is defined by a Gaussian mixture model. By considering bounds to the misclassification probability associated with the recovery of the underlying signal label, and bounds to the reconstruction error associated with the recovery of the signal of interest itself, we then provide sharp sufficient and/or necessary conditions for these quantities to approach zero when the covariance matrices of the Gaussians are nearly low rank. These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are the function of the number of linear features extracted from signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay. Moreover, on assuming that the signal of interest and the side information obey such an approximately low-rank model, we derive the expansions of the reconstruction error as a function of the deviation from an exactly low-rank model; such expansions also allow the identification of operational regimes, where the impact of side information on signal reconstruction is most relevant. Our framework, which offers a principled mechanism to integrate side information in high-dimensional data problems, is also tested in the context of imaging applications. In particular, we report state-of-theart results in compressive hyperspectral imaging applications, where the accompanying side information is a conventional digital photograph.

Abstract
A curvature sensor based on a Fabry-Perot interferometer is proposed. A capillary tube of silica is fusion spliced between two single mode fibers, producing a Fabry-Perot cavity. The light propagates in air, when passing through the capillary tube. Two different cavities are subjected to curvature and temperature. The cavity with shorter length shows insensitivity to both measurands. The larger cavity shows two operating regions for curvature measurement, where a linear response is shown, with a maximum sensitivity of 18.77pm/m(-1) for the high curvature radius range. When subjected to temperature, the sensing head produces a similar response for different curvature radius, with a sensitivity of 0.87pm/degrees C.