Representation and Detection of Shapes in Images

We present a set of techniques that can be used to represent and detect shapes in images. Our methods revolve around a particular shape representation based on the description of objects using triangulated polygons. This representation is similar to the medial axis transform and has important properties from a computational perspective. The first problem we consider is the detection of non-rigid objects in images using deformable models. We present an efficient algorithm to solve this problem in a wide range of situations, and show examples in both natural and medical images. We also consider the problem of learning an accurate non-rigid shape model for a class of objects from examples. We show how to learn good models while constraining them to the form required by the detection algorithm. Finally, we consider the problem of low-level image segmentation and grouping. We describe a stochastic grammar that generates arbitrary triangulated polygons while capturing Gestalt principles of shape regularity. This grammar is used as a prior model over random shapes in a low level algorithm that detects objects in images.

Active Learning with Statistical Models

For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.

Can you tell if they're learning?

The proliferation of Web-based learning objects makes finding and evaluating online resources problematic. While established Learning Analytics methods use Web interaction to evaluate learner engagement, there is uncertainty regarding the appropriateness of these measures. In this paper we propose a method for evaluating pedagogical activity in Web-based comments using a pedagogical framework, and present a preliminary study that assigns a Pedagogical Value (PV) to comments. This has value as it categorises discussion in terms of pedagogical activity rather than Web interaction. Results show that PV is distinct from typical interactional measures; there are negative or insignificant correlations with established Learning Analytics methods, but strong correlations with relevant linguistic indicators of learning, suggesting that the use of pedagogical frameworks may produce more accurate indicators than interaction analysis, and that linguistic rather than interaction analysis has the potential to automatically identify learning behaviour.

El trabajo en equipo en el Proceso Administrativo y de Compras en OPL Carga de Bucaramanga

El proceso administrativo y de compras de OPL Carga tiene algunas falencias entre ellas: fallas en la Comunicación entre el personal operativo, no se realizan llamadas internas usando con frecuencia el email, produciendo la saturación de solicitudes las cuales terminan sin ser resueltas en cuanto a roles se refiere, no hay enfoque de procesos en vista que no se tiene claras las tareas de cada cargo, adicionalmente no hay claridad en los subprocesos, perjudicando el proceso con el aumento de costos, pérdida de tiempo, las responsabilidades de los funcionario no todas las veces se ejecutan en el tiempo asignado, el liderazgo compartido presenta ambigüedades. Objetivos: Definir el trabajo en equipo en el proceso administrativo y de compras en OPL carga de Bucaramanga. La investigación que a realizar es de tipo descriptivo, busca descubrir las falencias o características que permiten diseñar y desarrollar un modelo de solución para los problemas del equipo de OPL Carga S.A.S. Materiales y métodos: La investigación efectuada es de tipo descriptivo, el objetivo es definir el modelo del trabajo en equipo y describir las falencias en el proceso administrativo y de compras en OPL carga de Bucaramanga, que permitan obtener un diagnóstico integral que conlleve a la implementación de estrategias de solución. Resultados: Se identificaron las falencias en los siguientes aspectos: Variable comunicación, rendimiento, destrezas complementarias, propósito significativo y meta específicas de los funcionarios en OPL carga sección administrativa. Conclusiones: El modelo de trabajo en equipo que OPL aplica es jerárquico, en el que se ofrece estabilidad, seguridad, se toman decisiones en forma piramidal, mediante la planeación de tareas, la colaboración, igualdad y respeto por los miembros, trabajando en pro de la solución de problemas. Se construyó un plano conceptual que permitió exponer la interpretación que la estudiante tiene de las teorías, investigaciones y antecedentes válidos para la comprensión del problema investigado. Área comunicacional: Coordinar acciones tendientes para que los funcionarios respondan a tiempo los emails atenientes a su trabajo. Área condiciones de trabajo: Clarificar y diseñar las reglas de comportamiento al interior de los equipos de trabajo que redunden en el mejoramiento del mismo y la búsqueda de soluciones oportunas. Área metas específicas: Procurar mediante auditorías el cumplimiento de las metas y objetivos propuestos por cada equipo de trabajo.

Estrategias para desarrollar curr??culos basados en la formulaci??n de problemas y organizados en base a proyectos

Resumen del autor

Educar las emociones: una reflexión sobre los programas de educación emocional en la primera infancia a través de la lente de la Psicología Positiva

La primera infancia es la etapa comprendida entre los 0 y los 6 años, y es el momento en el cual el cerebro tiene mayor plasticidad, por lo que en la actualidad se cree que la educación debe iniciar en este momento de la vida. La educación inicial ha venido dejando de lado el interés por los procesos cognitivos y ha puesto su foco de atención en el desarrollo social y emocional de los niños. El objetivo del presente trabajo es describir las características de los programas de educación emocional en la primera infancia a través de la lente de la Psicología Positiva utilizando una metodología cualitativa documental y específicamente el estado del arte. Los resultados obtenidos se dividen en corte empírico y corte teórico; para los de corte empírico se encontró que si bien el target que se pretende impactar son los niños en la primera infancia, varios programas le apuntan a trabajar como primera instancia con los cuidadores de los niños. Por otro lado como variables más comunes se manejaban las habilidades interpersonales y la resolución de problemas. Para los de corte teórico se encontró que los programas se orientan bajo una pedagogía dinámica y que muchos se centran en enfoques orientados a la promoción de habilidades sociales.

Example uses of a learning object

A self study course for learning to program using the C programming language has been developed. A Learning Object approach was used in the design of the course. One of the benefits of the Learning Object approach is that the learning material can be reused for different purposes. 'Me course developed is designed so that learners can choose the pedagogical approach most suited to their personal learning requirements. For all learning approaches a set of common Assessment Learning Objects (ALOs or tests) have been created. The design of formative assessments with ALOs can be carried out by the Instructional Designer grouping ALOs to correspond to a specific assessment intention. The course is non-credit earning, so there is no summative assessment, all assessment is formative. In this paper examples of ALOs and their uses is presented together with their uses as decided by the Instructional Designer and learner. Personalisation of the formative assessment of skills can be decided by the Instructional Designer or the learner using a repository of pre-designed ALOs. The process of combining ALOs can be carried out manually or in a semi-automated way using metadata that describes the ALO and the skill it is designed to assess.

Ethnographic methodologies for construction research: knowing, practice and interventions

Ethnographic methodologies developed in social anthropology and sociology hold considerable promise for addressing practical, problem-based research concerned with the construction site. The extended researcher-engagement characteristic of ethnography reveals rich insights, yet is infrequently used to understand how workplace realities are lived out on construction sites. Moreover, studies that do employ these methods are rarely reported within construction research journals. This paper argues that recent innovations in ethnographic methodologies offer new routes to: posing questions; understanding workplace socialities (i.e. the qualities of the social relationships that develop on construction sites); learning about forms, uses and communication of knowledge on construction sites; and turning these into meaningful recommendations. This argument is supported by examples from an interdisciplinary ethnography concerning migrant workers and communications on UK construction sites. The presented research seeks to understand how construction workers communicate with managers and each other and how they stay safe on site, with the objective of informing site health-and-safety strategies and the production and evaluation of training and other materials.

Tensor regression based on linked multiway parameter analysis

Classical regression methods take vectors as covariates and estimate the corresponding vectors of regression parameters. When addressing regression problems on covariates of more complex form such as multi-dimensional arrays (i.e. tensors), traditional computational models can be severely compromised by ultrahigh dimensionality as well as complex structure. By exploiting the special structure of tensor covariates, the tensor regression model provides a promising solution to reduce the model’s dimensionality to a manageable level, thus leading to efficient estimation. Most of the existing tensor-based methods independently estimate each individual regression problem based on tensor decomposition which allows the simultaneous projections of an input tensor to more than one direction along each mode. As a matter of fact, multi-dimensional data are collected under the same or very similar conditions, so that data share some common latent components but can also have their own independent parameters for each regression task. Therefore, it is beneficial to analyse regression parameters among all the regressions in a linked way. In this paper, we propose a tensor regression model based on Tucker Decomposition, which identifies not only the common components of parameters across all the regression tasks, but also independent factors contributing to each particular regression task simultaneously. Under this paradigm, the number of independent parameters along each mode is constrained by a sparsity-preserving regulariser. Linked multiway parameter analysis and sparsity modeling further reduce the total number of parameters, with lower memory cost than their tensor-based counterparts. The effectiveness of the new method is demonstrated on real data sets.

Effectiveness of mobile apps in teaching field-based identification skills

It has been suggested that few students graduate with the skills required for many ecological careers, as field-based learning is said to be in decline in academic institutions. Here, we asked if mobile technology could improve field-based learning, using ability to identify birds as the study metric. We divided a class of ninety-one undergraduate students into two groups for field-based sessions where they were taught bird identification skills. The first group has access to a traditional identification book and the second group were provided with an identification app. We found no difference between the groups in the ability of students to identify birds after three field sessions. Furthermore, we found that students using the traditional book were significantly more likely to identify novel species. Therefore, we find no evidence that mobile technology improved students’ ability to retain what they experienced in the field; indeed, there is evidence that traditional field guides were more useful to students as they attempted to identify new species. Nevertheless, students felt positively about using their own smartphone devices for learning, highlighting that while apps did not lead to an improvement in bird identification ability, they gave greater accessibility to relevant information outside allocated teaching times.

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They Should be able to recognize human beings and each other, and to engage in social, interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction. behavior control and learning from environment. Learning processes described oil Science of Behavior Analysis may lead to the development of promising methods and Structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation. are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction. (C) 2009 Elsevier Inc. All rights reserved.

Science is Primary - Children Thinking and Learning in theChemistry Laboratory

The goal of primary science education is to foster children’s interest, develop positive science attitudes and promote science process skills development. Learning by playing and discovering provides several opportunities for children to inquiry and understand science based on the first–hand experience. The current research was conducted in the children’s laboratory in Heureka, the Finnish science centre. Young children (aged 7 years) which came from 4 international schools did a set of chemistry experiments in the laboratory. From the results of the cognitive test, the pre-test, the post-test, supported by observation and interview, we could make the conclusion that children enjoyed studying in the laboratory. Chemistry science was interesting and fascinating for young children; no major gender differences were found between boys and girls learning in the science laboratory. Lab work not only encouraged children to explore and investigate science, but also stimulated children’s cognitive development.

Discourse strategies and power roles in student-led distance learning

The assertion of identity and power via computer-mediated communication in the context of distance or web-based learning presents challenges to both teachers and students. When regular, face-to-face classroom interaction is replaced by online chat or group discussion forums, participants must avail themselves of new techniques and tactics for contributing to and furthering interaction, discussion, and learning. During student-only chat sessions, the absence of teacher-led, face-to-face classroom activities requires the students to assume leadership roles and responsibilities normally associated with the teacher. This situation raises the questions of who teaches and who learns; how students discursively negotiate power roles; and whether power emerges as a function of displayed expertise and knowledge or rather the use of authoritative language. This descriptive study represents an examination of a corpus of task-based discussion logs among Vietnamese students of distance learning courses in English linguistics. The data reveal recurring discourse strategies for 1) negotiating the progression of the discussion sessions, 2) asserting and questioning knowledge, and 3) assuming or delegating responsibility. Power is defined ad hoc as the ability to successfully perform these strategies. The data analysis contributes to a better understanding of how working methods and materials can be tailored to students in distance learning courses, and how such students can be empowered by being afforded opportunities and effectively encouraged to assert their knowledge and authority.

A heuristic algorithm based on multi-assignment procedures for nurse scheduling

This paper tackles a Nurse Scheduling Problem which consists of generating work schedules for a set of nurses while considering their shift preferences and other requirements. The objective is to maximize the satisfaction of nurses' preferences and minimize the violation of soft constraints. This paper presents a new deterministic heuristic algorithm, called MAPA (multi-assignment problem-based algorithm), which is based on successive resolutions of the assignment problem. The algorithm has two phases: a constructive phase and an improvement phase. The constructive phase builds a full schedule by solving successive assignment problems, one for each day in the planning period. The improvement phase uses a couple of procedures that re-solve assignment problems to produce a better schedule. Given the deterministic nature of this algorithm, the same schedule is obtained each time that the algorithm is applied to the same problem instance. The performance of MAPA is benchmarked against published results for almost 250,000 instances from the NSPLib dataset. In most cases, particularly on large instances of the problem, the results produced by MAPA are better when compared to best-known solutions from the literature. The experiments reported here also show that the MAPA algorithm finds more feasible solutions compared with other algorithms in the literature, which suggest that this proposed approach is effective and robust. © 2013 Springer Science+Business Media New York.