Coaching académico a través de las mentorías entre iguales

En este trabajo se describe una nueva experiencia llevada a cabo al añadir tareas de coaching académico dentro de nuestro programa de mentorías entre iguales. Las tareas de coaching académico consisten principalmente en una serie de entrevistas entre el mentor (estudiante de los últimos cursos) y los estudiantes de nuevo ingreso en el grado en Ingeniería Informática. Estas entrevistas se basan en cuestionarios. Esos cuestionarios son diseñados por una comisión de profesores, y además de recoger información, el punto donde se pretende incidir es la toma de conscienccia del estudiante novel, introduciendo metodología de coaching basada en el planteamiento de objetivos y un plan de acción. De esta forma, las preguntas las realiza el mentor y las contestan los estudiantes de nuevo ingreso. En la primera reunión el cuestionario se centra en mejorar la motivación y la autoconfianza del estudiante novel, descubrir sus fortalezas e intereses, determinar áreas para mejorar y finalmente diseñar un plan de acción. Las reuniones posteriores se centran en el seguimiento y revisión de estas estrategias. Esta iniciativa fue motivada por la necesidad de contar con una herramienta más efectiva para orientar, ayudar y acompañar al estudiante de nuevo ingreso en sus primeros pasos en su nueva vida universitaria, pero teniendo en cuenta que el principal objetivo es que el estudiante novel identifique e implemente sus propias soluciones para alcanzar con éxito sus retos. Esta propuesta ha tenido una gran acogida por los mentores y por los alumnos de nuevo ingreso, con un alto número de estudiantes inscritos con una participación activa y comprometida. El resultado esperado con esta propuesta de coaching académico es: mejorar la capacidad de autoconfianza y el rendimiento académico del estudiante novel, y por otro lado, potenciar la competencia de liderazgo del mentor.This paper describes a new experience carried out by adding academic coaching tasks in our peer mentoring program. The academic coaching tasks mainly consist in a series of conversations between the mentor (senior student) and the incoming students in the Computer Science Engineering Degree. These conversations are based on questionnaires. These questionnaires are designed by a teacher team and, besides colleting information, their main aim is to train, through introducing coaching methodology based on identifying a career goal and designing an action plan. In this way, the questions are asked by the mentor and answered by the incoming students. In the first meeting, the questionnaire is focused on improving the motivation and self-confidence of the novel student, to discover his/her strengths and interests, to determine areas of improvement and finally to draw an action plan. In later meetings, the focus is a monitoring and revision of these strategies. This initiative was motivated by the need to have a more efficient tool in order to orientate, help and support novel students in their first steps in their new university life, but taking into account that the main aim is that the novel student identify and implement his/her own solutions to achieve successfully his/her challenging issues. This proposal has had a warm welcome by mentor and incoming students, with a high number of enrolled students taking active participation. The goal of this academic coaching is twofold. On the one hand, to improve the self-confidence skills and academic performance of the novel students. On the other hand, to strengthen the leadership skills of the mentor students

Dynamic stereoscopic selective visual attention (dssva): integrating motion and shape with depth in video segmentation

Depth inclusion as an important parameter for dynamic selective visual attention is presented in this article. The model introduced in this paper is based on two previously developed models, dynamic selective visual attention and visual stereoscopy, giving rise to the so-called dynamic stereoscopic selective visual attention method. The three models are based on the accumulative computation problem-solving method. This paper shows how software reusability enables enhancing results in vision research (video segmentation) by integrating earlier works. In this article, the first results obtained for synthetic sequences are included to show the effectiveness of the integration of motion and shape features with depth parameter in video segmentation

Revisiting algorithmic lateral inhibition and accumulative computation

Certainly, one of the prominent ideas of Professor Mira was that it is absolutely mandatory to specify the mechanisms and/or processes underlying each task and inference mentioned in an architecture in order to make operational that architecture. The conjecture of the last fifteen years of joint research of Professor Mira and our team at University of Castilla-La Mancha has been that any bottom-up organization may be made operational using two biologically inspired methods called ?algorithmic lateral inhibition?, a generalization of lateral inhibition anatomical circuits, and ?accumulative computation?, a working memory related to the temporal evolution of the membrane potential. This paper is dedicated to the computational formulations of both methods, which have led to quite efficient solutions of problems related to motion-based computer vision

Real-time motion detection by lateral inhibition in accumulative computation.

Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. In the few last years, the neurally inspired lateral inhibition in accumulative computation (LIAC) method and its application to the motion detection task have been introduced. The article shows how to implement the tasks directly related to LIAC in motion detection by means of a formal model described as finite state machines. This paper introduces two steps towards that direction: (a) A simplification of the general LIAC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation of such a designed LIAC module, as well as an 8×8 LIAC module, has been tested on several video sequences, providing promising performance results

Skeleton simplification by key points identification

The current skeletonisation algorithms, based on thinning, extract the morphological features of an object in an image but the skeletonized objects are coarsely presented. This paper proposes an algorithm which goes beyond that approach by changing the coarse line segments into perfect ?straight? line segments, obtaining points, angles, line segment size and proportions. Our technique is applied in the post-processing phase of the skeleton, which improves it no matter which skeletonisation technique is used, as long as the structure is made with one-pixel width continuous line segments. This proposal is a first step towards human activity recognition through the analysis of human poses represented by their skeletons

Motion-based stereovision model with potential utility in robot navigation.

Autonomous robot guidance in dynamic environments requires, on the one hand, the study of relative motion of the objects of the environment with respect to the robot, and on the other hand, the analysis of the depth towards those objects. In this paper, a stereo vision method, which combines both topics with potential utility in robot navigation, is proposed. The goal of the stereo vision model is to calculate depth of surrounding objects by measuring the disparity between the two-dimensional imaged positions of the object points in a stereo pair of images. The simulated robot guidance algorithm proposed starts from the motion analysis that occurs in the scene and then establishes correspondences and analyzes the depth of the objects. Once these steps have been performed, the next step is to induce the robot to take the direction where objects are more distant in order to avoid obstacles

Video sequence motion tracking by fuzzification techniques

In this paper a method for moving objects segmentation and tracking from the so-called permanency matrix is introduced. Our motion-based algorithms enable to obtain the shapes of moving objects in video sequences starting from those image pixels where a change in their grey levels is detected between two consecutive frames by means of the permanency values. In the segmentation phase matching between objects along the image sequence is performed by using fuzzy bi-dimensional rectangular regions. The tracking phase performs the association between the various fuzzy regions in all the images through time. Finally, the analysis phase describes motion through a long video sequence. Segmentation, tracking an analysis phases are enhanced through the use of fuzzy logic techniques, which enable to work with the uncertainty of the permanency values due to image noise inherent to computer vision

Parametric improvement of lateral interaction in accumulative computation in motion-based segmentation

Segmentation of moving objects is an essential component of any vision system. However, its accomplishment is hard due to some challenges such as the occlusion treatment or the detection of objects with deformable appearance. In this paper an artificial neuronal network approach for moving object segmentation, called lateral interaction in accumulative computation (LIAC), which uses accumulative computation and recurrent lateral interaction is revisited. Although the results reported for this approach so far may be considered relevant, the problems faced each time (environment, objects of interest, etc.) make that the system outcome varies. Hence, our aim is to improve segmentation provided by LIAC in a double sense: by removing the detected objects not matching some size or compactness constraints, and by learning suitable parameters that improve the segmentation behavior through a genetic algorithm

A historical perspective of algorithmic lateral inhibition and accumulative computation in computer vision

Certainly, one of the prominent ideas of Professor José Mira was that it is absolutely mandatory to specify the mechanisms and/or processes underlying each task and inference mentioned in an architecture in order to make operational that architecture. The conjecture of the last fifteen years of joint research has been that any bottom-up organization may be made operational using two biologically inspired methods called ?algorithmic lateral inhibition?, a generalization of lateral inhibition anatomical circuits, and ?accumulative computation?, a working memory related to the temporal evolution of the membrane potential. This paper is dedicated to the computational formulation of both methods. Finally, all of the works of our group related to this methodological approximation are mentioned and summarized, showing that all of them support the validity of this approximation

Real-time accumulative computation motion detectors

The neurally inspired accumulative computation (AC) method and its application to motion detection have been introduced in the past years. This paper revisits the fact that many researchers have explored the relationship between neural networks and finite state machines. Indeed, finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The article shows how to reach real-time performance after using a model described as a finite state machine. This paper introduces two steps towards that direction: (a) A simplification of the general AC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation in FPGA of such a designed AC module, as well as an 8-AC motion detector, providing promising performance results. We also offer two case studies of the use of AC motion detectors in surveillance applications, namely infrared-based people segmentation and color-based people tracking, respectively