Abstract
We present a system for a dynamic graphical representation of the interactions captured in educational online environments. The system goes beyond interaction between students and teachers, also addressing resource usage or any other entity for which it is possible to create a relation which binds two entities. By defining these relationships between pairs of entities in an online learning environment (Moodle, in our case) our tool creates a graph, where it is possible to apply techniques of social network analysis. This system brings up new possibilities for e-learning as a tool capable of helping the teacher assorting and illustrating the degree of participation and to find the implicit relations between participants, or participants and resources or events. © 2017 Association for Computing Machinery.

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Abstract
In this paper, a mixed-integer linear programing (MILP) model for characterizing the traffic behavior of plug-in electric vehicles (PEVs) in an urban environment is proposed. The paper proposes the procedure to mathematically model the uncertain behavior of the PEVS. As the PEV's traffic pattern affects the potential of PEV parking lot (PL) in terms of available capacity and state of charge, the characterized PEVs are employed to provide the proper pricing schemes for the PEVs in PL. The results discuss the mutual effect of PEVs behavior on the profit of PL, as well as the effects of PL's tariffs on motivating PEVs to provide more flexibility for the PL. The results show that with a proper pricing scheme both parties (i.e., PEV owners and PL) can earn higher profits, while the charging requirements are met based on the preferences of the owners.

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This article addresses the problem of creating interactive mixed reality applications where virtual objects interact in images of real world scenarios. This is relevant to create games and architectural or space planning applications that interact with visual elements in the images such as walls, floors and empty spaces. These scenarios are intended to be captured by the users with regular cameras or using previously taken photographs. Introducing virtual objects in photographs presents several challenges, such as pose estimation and the creation of a visually correct interaction between virtual objects and the boundaries of the scene. The two main research questions addressed in this article include, the study of the feasibility of creating interactive augmented reality (AR) applications where virtual objects interact in a real world scenario using the image detected high-level features and, also, verifying if untrained users are capable and motivated enough to perform AR initialization steps. The proposed system detects the scene automatically from an image with additional features obtained using basic annotations from the user. This operation is significantly simple to accommodate the needs of non-expert users. The system analyzes one or more photos captured by the user and detects high-level features such as vanishing points, floor and scene orientation. Using these features it will be possible to create mixed and augmented reality applications where the user interactively introduces virtual objects that blend with the picture in real time and respond to the physical environment. To validate the solution several system tests are described and compared using available external image datasets.

Abstract
Clinical decisions are sometimes based on a variety of patient's information such as: age, weight or information extracted from image exams, among others. Depending on the nature of the disease or anatomy, clinicians can base their decisions on different image exams like mammographies, positron emission tomography scans or magnetic resonance images. However, the analysis of those exams is far from a trivial task. Over the years, the use of image descriptors-computational algorithms that present a summarized description of image regions-became an important tool to assist the clinician in such tasks. This paper presents an overview of the use of image descriptors in healthcare contexts, attending to different image exams. In the making of this review, we analyzed over 70 studies related to the application of image descriptors of different natures-e.g., intensity, texture, shape-in medical image analysis. Four imaging modalities are featured: mammography, PET, CT and MRI. Pathologies typically covered by these modalities are addressed: breast masses and microcalcifications in mammograms, head and neck cancer and Alzheimer's disease in the case of PET images, lung nodules regarding CTs and multiple sclerosis and brain tumors in the MRI section.