A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Developing a knowledge management system using an ontological approach in global organisation

This paper introduces an ontology-based knowledge model for knowledge management. This model can facilitate knowledge discovery that provides users with insight for decision making. The users requiring the insight normally play different roles with different requirements in an organisation. To meet the requirements, insights are created by purposely aggregated transnational data. This involves a semantic data integration process. In this paper, we present a knowledge management system which is capable of representing knowledge requirements in a domain context and enabling the semantic data integration through ontology modeling. The knowledge domain context of United Bible Societies is used to illustrate the features of the knowledge management capabilities.

Corporeidade e educação: sinfonia de saberes na educação musical

This study investigates the implications between the musical theory and pedagogical practice based on a study that questions the reasons why some students feel incapable of learning the music language, as well as, if the musical codes are truly so difficult to be apprehended by them. To answer these questions takes itself as reference, the classes I have minister while teaching the disciplines: Music Workshop and Music Language I in the Art Department, Scenic Art Course at the Federal University of Rio Grande do Norte. I have searched the knowledge that constitutes the teacher formation based on the union between the pedagogical efficiency and sensibility, searching in the Corporality the theoretical port for my investigation. This way, advocating is a methodological principle for musical education originated from the experience of knowledge: creating, playing, feeling, thinking, and the interaction among them, conducting the students not only to music learning, but to a process of human formation. It adopts itself as methodological resource, amongst the qualitative methods, some of the techniques that are associated with the ethnographic research, for having as its main objective, to study the meaning of the actions and events of the investigated group. The analysis of the data leads to the conclusion that when the teacher displays his or her pedagogical knowledge in an environment constructed with affectivity, in a playful and pleasant form, the assimilation and construction of the musical concepts happen naturally and efficiently, surpassing the taboo that music learning is only possible to the especially well endowed people for music. Very aware that the scientific debate is important for the strengthening of formative programs involved in the growth and consolidation of the musical teaching and learning area, it is expected to promote this research discussions and reflections in the general educational field with this research, as well as, to contribute significantly to the specific growth of musical education

Professores das séries iniciais do ensino fundamental e as feiras de ciências

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

O significado da liderança para o aluno de graduação em enfermagem

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Poéticas e marginalidade: experiência no Projeto Cidadãos Cantantes

Pós-graduação em Psicologia - FCLAS

Evaluating Integration. Research contributions for a gender sensitive migration policy: Research contributions for a gender sensitive migration policy

The integration of academic and non-academic knowledge is a key concern for researchers who aim at bridging the gap between research and policy. Researchers involved in the sustainability-oriented NCCR North-South programme have made the experience that linking different types of knowledge requires time and effort, and that methodologies are still lacking. One programme component was created at the inception of this transdisciplinary research programme to support exchange between researchers, development practitioners and policymakers. After 8 years of research, the programme is assessing whether research has indeed enabled a continuous communication across and beyond academic boundaries and has effected changes in the public policies of poor countries. In a first review of the data, we selected two case studies explicitly addressing the lives of women. In both cases – one in Pakistan, the other in Nepal – the dialogue between researchers and development practitioners contributed to important policy changes for female migration. In both countries, outmigration has become an increasingly important livelihood strategy. National migration policies are gendered, limiting the international migration of women. In Nepal, women were not allowed to migrate to specific countries such as the Gulf States or Malaysia. This was done in the name of positive discrimination, to protect women from potential exploitation and harassment in domestic work. However, women continued to migrate in many other and often illegal and more risky ways, increasing their vulnerability. In Pakistan, female labour migration was not allowed at all and male migration increased the vulnerability of the families remaining back home. Researchers and development practitioners in Nepal and Pakistan brought women’s shared experience of and exposure to the mechanisms of male domination into the public debate, and addressed the discriminating laws. Now, for the first time in Pakistan, the new draft policy currently under discussion would enable broadly-based female labour migration. What can we learn from the two case studies with regard to ways of relating experience- and research-based knowledge? The paper offers insights into the sequence of interactions between researchers, local people, development practitioners, and policy-makers, which eventually contributed to the formulation of a rights-based migration policy. The reflection aims at exploring the gendered dimension of ways to co-produce and share knowledge for development across boundaries. Above all, it should help researchers to better tighten the links between the spheres of research and policy in future.

Knowledge transfer in software-maintenance offshore outsourcing

Software-maintenance offshore outsourcing (SMOO) projects have been plagued by tedious knowledge transfer during the service transition to the vendor. Vendor engineers risk being over-strained by the high amounts of novel information, resulting in extra costs that may erode the business case behind offshoring. Although stakeholders may desire to avoid these extra costs by implementing appropriate knowledge transfer practices, little is known on how effective knowledge transfer can be designed and managed in light of the high cognitive loads in SMOO transitions. The dissertation at hand addresses this research gap by presenting and integrating four studies. The studies draw on cognitive load theory, attributional theory, and control theory and they apply qualitative, quantitative, and simulation methods to qualitative data from eight in-depth longitudinal cases. The results suggest that the choice of appropriate learning tasks may be more central to knowledge transfer than the amount of information shared with vendor engineers. Moreover, because vendor staff may not be able to and not dare to effectively self-manage learn-ing tasks during early transition, client-driven controls may be initially required and subsequently faded out. Collectively, the results call for people-based rather than codification-based knowledge management strategies in at least moderately specific and complex software environments.

Using modular neural networks to model self-consciousness and self-representation for artificial entities

Self-consciousness implies not only self or group recognition, but also real knowledge of one’s own identity. Self-consciousness is only possible if an individual is intelligent enough to formulate an abstract self-representation. Moreover, it necessarily entails the capability of referencing and using this elf-representation in connection with other cognitive features, such as inference, and the anticipation of the consequences of both one’s own and other individuals’ acts. In this paper, a cognitive architecture for self-consciousness is proposed. This cognitive architecture includes several modules: abstraction, self-representation, other individuals'representation, decision and action modules. It includes a learning process of self-representation by direct (self-experience based) and observational learning (based on the observation of other individuals). For model implementation a new approach is taken using Modular Artificial Neural Networks (MANN). For model testing, a virtual environment has been implemented. This virtual environment can be described as a holonic system or holarchy, meaning that it is composed of autonomous entities that behave both as a whole and as part of a greater whole. The system is composed of a certain number of holons interacting. These holons are equipped with cognitive features, such as sensory perception, and a simplified model of personality and self-representation. We explain holons’ cognitive architecture that enables dynamic self-representation. We analyse the effect of holon interaction, focusing on the evolution of the holon’s abstract self-representation. Finally, the results are explained and analysed and conclusions drawn.

MedVir: 3D visual interface applied to gene profile analysis

The use of data mining techniques for the gene profile discovery of diseases, such as cancer, is becoming usual in many researches. These techniques do not usually analyze the relationships between genes in depth, depending on the different variety of manifestations of the disease (related to patients). This kind of analysis takes a considerable amount of time and is not always the focus of the research. However, it is crucial in order to generate personalized treatments to fight the disease. Thus, this research focuses on finding a mechanism for gene profile analysis to be used by the medical and biologist experts. Results: In this research, the MedVir framework is proposed. It is an intuitive mechanism based on the visualization of medical data such as gene profiles, patients, clinical data, etc. MedVir, which is based on an Evolutionary Optimization technique, is a Dimensionality Reduction (DR) approach that presents the data in a three dimensional space. Furthermore, thanks to Virtual Reality technology, MedVir allows the expert to interact with the data in order to tailor it to the experience and knowledge of the expert.

Diseño y banco de test VHDL de un procesador que realice la ICT para HEVC

Se va a realizar un estudio de la codificación de imágenes sobre el estándar HEVC (high-effiency video coding). El proyecto se va a centrar en el codificador híbrido, más concretamente sobre la aplicación de la transformada inversa del coseno que se realiza tanto en codificador como en el descodificador. La necesidad de codificar vídeo surge por la aparición de la secuencia de imágenes como señales digitales. El problema principal que tiene el vídeo es la cantidad de bits que aparecen al realizar la codificación. Como consecuencia del aumento de la calidad de las imágenes, se produce un crecimiento exponencial de la cantidad de información a codificar. La utilización de las transformadas al procesamiento digital de imágenes ha aumentado a lo largo de los años. La transformada inversa del coseno se ha convertido en el método más utilizado en el campo de la codificación de imágenes y video. Las ventajas de la transformada inversa del coseno permiten obtener altos índices de compresión a muy bajo coste. La teoría de las transformadas ha mejorado el procesamiento de imágenes. En la codificación por transformada, una imagen se divide en bloques y se identifica cada imagen a un conjunto de coeficientes. Esta codificación se aprovecha de las dependencias estadísticas de las imágenes para reducir la cantidad de datos. El proyecto realiza un estudio de la evolución a lo largo de los años de los distintos estándares de codificación de video. Se analiza el codificador híbrido con más profundidad así como el estándar HEVC. El objetivo final que busca este proyecto fin de carrera es la realización del núcleo de un procesador específico para la ejecución de la transformada inversa del coseno en un descodificador de vídeo compatible con el estándar HEVC. Es objetivo se logra siguiendo una serie de etapas, en las que se va añadiendo requisitos. Este sistema permite al diseñador hardware ir adquiriendo una experiencia y un conocimiento más profundo de la arquitectura final. ABSTRACT. A study about the codification of images based on the standard HEVC (high-efficiency video coding) will be developed. The project will be based on the hybrid encoder, in particular, on the application of the inverse cosine transform, which is used for the encoder as well as for the decoder. The necessity of encoding video arises because of the appearance of the sequence of images as digital signals. The main problem that video faces is the amount of bits that appear when making the codification. As a consequence of the increase of the quality of the images, an exponential growth on the quantity of information that should be encoded happens. The usage of transforms to the digital processing of images has increased along the years. The inverse cosine transform has become the most used method in the field of codification of images and video. The advantages of the inverse cosine transform allow to obtain high levels of comprehension at a very low price. The theory of the transforms has improved the processing of images. In the codification by transform, an image is divided in blocks and each image is identified to a set of coefficients. This codification takes advantage of the statistic dependence of the images to reduce the amount of data. The project develops a study of the evolution along the years of the different standards in video codification. In addition, the hybrid encoder and the standard HEVC are analyzed more in depth. The final objective of this end of degree project is the realization of the nucleus from a specific processor for the execution of the inverse cosine transform in a decoder of video that is compatible with the standard HEVC. This objective is reached following a series of stages, in which requirements are added. This system allows the hardware designer to acquire a deeper experience and knowledge of the final architecture.

Experience-dependent corticofugal adjustment of midbrain frequency map in bat auditory system

Recent studies of corticofugal modulation of auditory information processing indicate that cortical neurons mediate both a highly focused positive feedback to subcortical neurons “matched” in tuning to a particular acoustic parameter and a widespread lateral inhibition to “unmatched” subcortical neurons. This cortical function for the adjustment and improvement of subcortical information processing is called egocentric selection. Egocentric selection enhances the neural representation of frequently occurring signals in the central auditory system. For our present studies performed with the big brown bat (Eptesicus fuscus), we hypothesized that egocentric selection adjusts the frequency map of the inferior colliculus (IC) according to auditory experience based on associative learning. To test this hypothesis, we delivered acoustic stimuli paired with electric leg stimulation to the bat, because such paired stimuli allowed the animal to learn that the acoustic stimulus was behaviorally important and to make behavioral and neural adjustments based on the acquired importance of the acoustic stimulus. We found that acoustic stimulation alone evokes a change in the frequency map of the IC; that this change in the IC becomes greater when the acoustic stimulation is made behaviorally relevant by pairing it with electrical stimulation; that the collicular change is mediated by the corticofugal system; and that the IC itself can sustain the change evoked by the corticofugal system for some time. Our data support the hypothesis.

MDWEnet: a practical approach to achieving interoperability of model-driven web engineering methods

Current model-driven Web Engineering approaches (such as OO-H, UWE or WebML) provide a set of methods and supporting tools for a systematic design and development of Web applications. Each method addresses different concerns using separate models (content, navigation, presentation, business logic, etc.), and provide model compilers that produce most of the logic and Web pages of the application from these models. However, these proposals also have some limitations, especially for exchanging models or representing further modeling concerns, such as architectural styles, technology independence, or distribution. A possible solution to these issues is provided by making model-driven Web Engineering proposals interoperate, being able to complement each other, and to exchange models between the different tools. MDWEnet is a recent initiative started by a small group of researchers working on model-driven Web Engineering (MDWE). Its goal is to improve current practices and tools for the model-driven development of Web applications for better interoperability. The proposal is based on the strengths of current model-driven Web Engineering methods, and the existing experience and knowledge in the field. This paper presents the background, motivation, scope, and objectives of MDWEnet. Furthermore, it reports on the MDWEnet results and achievements so far, and its future plan of actions.

Origins and early development of human body knowledge

As a knowable object, the human body is highly complex. Evidence from several converging lines of research, including psychological studies, neuroimaging and clinical neuropsychology, indicates that human body knowledge is widely distributed in the adult brain, and is instantiated in at least three partially independent levels of representation. Sensori-motor body knowledge is responsible for on-line control and movement of one's own body and may also contribute to the perception of others' moving bodies; visuo-spatial body knowledge specifies detailed structural descriptions of the spatial attributes of the human body; and lexical-semantic body knowledge contains language-based knowledge about the human body. In the first chapter of this Monograph, we outline the evidence for these three hypothesized levels of human body knowledge, then review relevant literature on infants' and young children's human body knowledge in terms of the three-level framework. In Chapters II and III, we report two complimentary series of studies that specifically investigate the emergence of visuospatial body knowledge in infancy. Our technique is to compare infants' responses to typical and scrambled human bodies, in order to evaluate when and how infants acquire knowledge about the canonical spatial layout of the human body. Data from a series of visual habituation studies indicate that infants first discriminate scrambled from typical human body pictures at 15 to 18 months of age. Data from object examination studies similarly indicate that infants are sensitive to violations of three-dimensional human body stimuli starting at 15-18 months of age. The overall pattern of data supports several conclusions about the early development of human body knowledge: (a) detailed visuo-spatial knowledge about the human body is first evident in the second year of life, (b) visuo-spatial knowledge of human faces and human bodies are at least partially independent in infancy and (c) infants' initial visuo-spatial human body representations appear to be highly schematic, becoming more detailed and specific with development. In the final chapter, we explore these conclusions and discuss how levels of body knowledge may interact in early development.