Estudio perceptual sobre cuestiones académicas y su relación con el rendimiento escolar.

Averiguar en qué medida influyen en el rendimiento académico, las percepciones elaboradas por los chicos acerca del interés, actitudes y expectativas mostradas por los padres hacia sus estudios. Esta cuestión se estudia atendiendo al diverso origen social y nivel cultural de las familias. Se divide la ciudad de Oviedo en dos zonas: alta y baja. Para la muestra se toman 97 alumnos de los colegios de la zona baja y 73 de dos colegios de la zona alta, todos ellos en séptimo de EGB en el curso 84-85. En total se trabajó con una muestra de 170 alumnos y sus respectivos padres. Se utiliza como variable dependiente el rendimiento escolar. Las variables independientes se agrupan en tres grandes bloques: interés, actitudes y expectativas. En el cuestionario sobre cada variable de las que integran los bloques mencionados se establecen dos preguntas que dan lugar a dos subvariables: la percepción elaborada por el niño acerca de la conducta de sus padres en un determinado tema; y lo que él considera oportuno sobre ese mismo tema. De cada par de subvariables se obtiene una nueva: la diferencia entre ellas, que permite medir el grado de acuerdo entre ambas percepciones. Calificaciones de Lengua y Matemáticas para la medida del rendimiento escolar. Cuestionario elaborado para esta investigación en el que se incluyen preguntas sobre percepción de intereses, actitudes y expectativas en los padres y las propias del niño. Correlación múltiple entre variables independientes de cada factor y las dependientes y a partir de ellas se elaboran las consiguientes ecuaciones de predicción tanto del rendimiento en Lengua como del rendimiento en Matemáticas el estudio de estas cuestiones se realiza tanto a nivel global y teniendo en cuenta las categorías culturales de pertenencia. Los valores del coeficiente de regresión entre el factor interés y el rendimiento en las materias de Lengua y Matemáticas ascienden a medida que se eleva el nivel cultural del padre y lo mismo ocurre con el factor actitudes y el factor expectativas. El rendimiento escolar es diferencial de unos a otros estratos sociales siendo más elevado en aquellos niños con padres de mayor nivel cultural. El rendimiento académico aumenta cuando los chicos tienen sus propios criterios es decir a medida que los desacuerdos en las percepciones elaboradas acerca de sus padres y las propias son amplios y negativos. Existe un alto grado de acuerdo entre las percepciones elaboradas por el chico respecto a la conducta paterna en determinados problemas escolares y la propia sobre los mismos problemas, lo cual demuestra la importancia de las actitudes, intereses y expectativas de los padres respecto a la actividad escolar ya que éstas mediatizan las de sus hijos si los padres se encuentran en una línea de acercamiento a las cuestiones escolares, los niños se comportan también según esa línea, favoreciéndose la consecución de un buen rendimiento.

Using Analogy to Acquire Commonsense Knowledge from Human Contributors

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain of natural language processing, it will provide the world knowledge much needed for semantic processing of natural language. To acquire knowledge from contributors not trained in knowledge engineering, I take the following four steps: (i) develop a knowledge representation (KR) model for simple assertions in natural language, (ii) introduce cumulative analogy, a class of nearest-neighbor based analogical reasoning algorithms over this representation, (iii) argue that cumulative analogy is well suited for knowledge acquisition (KA) based on a theoretical analysis of effectiveness of KA with this approach, and (iv) test the KR model and the effectiveness of the cumulative analogy algorithms empirically. To investigate effectiveness of cumulative analogy for KA empirically, Learner, an open source system for KA by cumulative analogy has been implemented, deployed, and evaluated. (The site "1001 Questions," is available at http://teach-computers.org/learner.html). Learner acquires assertion-level knowledge by constructing shallow semantic analogies between a KA topic and its nearest neighbors and posing these analogies as natural language questions to human contributors. Suppose, for example, that based on the knowledge about "newspapers" already present in the knowledge base, Learner judges "newspaper" to be similar to "book" and "magazine." Further suppose that assertions "books contain information" and "magazines contain information" are also already in the knowledge base. Then Learner will use cumulative analogy from the similar topics to ask humans whether "newspapers contain information." Because similarity between topics is computed based on what is already known about them, Learner exhibits bootstrapping behavior --- the quality of its questions improves as it gathers more knowledge. By summing evidence for and against posing any given question, Learner also exhibits noise tolerance, limiting the effect of incorrect similarities. The KA power of shallow semantic analogy from nearest neighbors is one of the main findings of this thesis. I perform an analysis of commonsense knowledge collected by another research effort that did not rely on analogical reasoning and demonstrate that indeed there is sufficient amount of correlation in the knowledge base to motivate using cumulative analogy from nearest neighbors as a KA method. Empirically, evaluating the percentages of questions answered affirmatively, negatively and judged to be nonsensical in the cumulative analogy case compares favorably with the baseline, no-similarity case that relies on random objects rather than nearest neighbors. Of the questions generated by cumulative analogy, contributors answered 45% affirmatively, 28% negatively and marked 13% as nonsensical; in the control, no-similarity case 8% of questions were answered affirmatively, 60% negatively and 26% were marked as nonsensical.

From First Contact to Close Encounters: A Developmentally Deep Perceptual System for a Humanoid Robot

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply 'pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively. The motivation for this work is simple. Training on large corpora of annotated real-world data has proven crucial for creating robust solutions to perceptual problems such as speech recognition and face detection. But the powerful tools used during training of such systems are typically stripped away at deployment. Ideally they should remain, particularly for unstable tasks such as object detection, where the set of objects needed in a task tomorrow might be different from the set of objects needed today. The key limiting factor is access to training data, but as this thesis shows, that need not be a problem on a robotic platform that can actively probe its environment, and carry out experiments to resolve ambiguity. This work is an instance of a general approach to learning a new perceptual judgment: find special situations in which the perceptual judgment is easy and study these situations to find correlated features that can be observed more generally.

Learning Commonsense Categorical Knowledge in a Thread Memory System

If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that we humans have about the world. This endeavor suggests steps such as identifying the kinds of knowledge people commonly have about the world, constructing suitable knowledge representations, and exploring the mechanisms that people use to make judgments about the everyday world. In this work, I contribute to these goals by proposing an architecture for a system that can learn commonsense knowledge about the properties and behavior of objects in the world. The architecture described here augments previous machine learning systems in four ways: (1) it relies on a seven dimensional notion of context, built from information recently given to the system, to learn and reason about objects' properties; (2) it has multiple methods that it can use to reason about objects, so that when one method fails, it can fall back on others; (3) it illustrates the usefulness of reasoning about objects by thinking about their similarity to other, better known objects, and by inferring properties of objects from the categories that they belong to; and (4) it represents an attempt to build an autonomous learner and reasoner, that sets its own goals for learning about the world and deduces new facts by reflecting on its acquired knowledge. This thesis describes this architecture, as well as a first implementation, that can learn from sentences such as ``A blue bird flew to the tree'' and ``The small bird flew to the cage'' that birds can fly. One of the main contributions of this work lies in suggesting a further set of salient ideas about how we can build broader purpose commonsense artificial learners and reasoners.

Perceptual Evaluation of Video-Realistic Speech

abstract With many visual speech animation techniques now available, there is a clear need for systematic perceptual evaluation schemes. We describe here our scheme and its application to a new video-realistic (potentially indistinguishable from real recorded video) visual-speech animation system, called Mary 101. Two types of experiments were performed: a) distinguishing visually between real and synthetic image- sequences of the same utterances, ("Turing tests") and b) gauging visual speech recognition by comparing lip-reading performance of the real and synthetic image-sequences of the same utterances ("Intelligibility tests"). Subjects that were presented randomly with either real or synthetic image-sequences could not tell the synthetic from the real sequences above chance level. The same subjects when asked to lip-read the utterances from the same image-sequences recognized speech from real image-sequences significantly better than from synthetic ones. However, performance for both, real and synthetic, were at levels suggested in the literature on lip-reading. We conclude from the two experiments that the animation of Mary 101 is adequate for providing a percept of a talking head. However, additional effort is required to improve the animation for lip-reading purposes like rehabilitation and language learning. In addition, these two tasks could be considered as explicit and implicit perceptual discrimination tasks. In the explicit task (a), each stimulus is classified directly as a synthetic or real image-sequence by detecting a possible difference between the synthetic and the real image-sequences. The implicit perceptual discrimination task (b) consists of a comparison between visual recognition of speech of real and synthetic image-sequences. Our results suggest that implicit perceptual discrimination is a more sensitive method for discrimination between synthetic and real image-sequences than explicit perceptual discrimination.

Towards a knowledge economy

This paper provides a preliminary formulation of a new currency based on knowledge. Through a literature review of alternative currencies, various properties and benefits are selected that we hope will enable such a currency to be created. Nowadays not only money but also knowledge is necessary to do business. For instance, knowledge about markets and consumers is highly valuable but difficult to achieve, and even more difficult to store, transport or trade. The basic premise of this proposal is a knowledge measurement pattern that is formulated as a new alternative social currency. Therefore, it is an additional means of contributing to the worldwide evolution of a knowledge society. It is intended as a currency to facilitate the conservation and storage of knowledge, and its organization and categorization, but mainly its exploitation and transference

Knowledge integration within an applied mobile robot summer course

This paper presents the use of a mobile robot platform as an innovative educational tool in order to promote and integrate different curriculum knowledge. Hence, it is presented the acquired experience within a summer course named ldquoapplied mobile roboticsrdquo. The main aim of the course is to integrate different subjects as electronics, programming, architecture, perception systems, communications, control and trajectory planning by using the educational open mobile robot platform PRIM. The summer course is addressed to a wide range of student profiles. However, it is of special interests to the students of electrical and computer engineering around their final academic year. The summer course consists of the theoretical and laboratory sessions, related to the following topics: design & programming of electronic devices, modelling and control systems, trajectory planning and control, and computer vision systems. Therefore, the clues for achieving a renewed path of progress in robotics are the integration of several knowledgeable fields, such as computing, communications, and control sciences, in order to perform a higher level reasoning and use decision tools with strong theoretical base

Adding new components to the knowledge plane GMPLS over WDM networks

A recent study defines a new network plane: the knowledge plane. The incorporation of the knowledge plane over the network allows having more accurate information of the current and future network states. In this paper, the introduction and management of the network reliability information in the knowledge plane is proposed in order to improve the quality of service with protection routing algorithms in GMPLS over WDM networks. Different experiments prove the efficiency and scalability of the proposed scheme in terms of the percentage of resources used to protect the network

Context constraints, prior vocabulary knowledge and on-line inferences in reading

Resumen tomado de la publicaci??n

Knowledge and question asking

Resumen tomado de la publicaci??n

Talk the talk: Learner-generated podcasts as catalysts for knowledge creation

Instead of using the technology for the mere recording and dissemination of lectures and other instructor-centred information, the project reported on in this article focused on enabling students to create their own podcasts for distribution to their peers. The article describes how engaging in the podcasting exercise promoted collaborative knowledge building among the student-producers, as evidenced through focus-group interviewing and an analysis of the products of their shared dialogue and reflection.

Acknowledging knowledge: an academic guide to referencing

This guide explains why referencing in essays is so important, and provides clear examples of exactly how to reference a wide variety of sources from books to YouTube clips

The Spectrum of knowledge - starting off

A few files for background reading

Knowledge Technologies

An overview of COMP3028 Knowledge Technologies