As expressões dramática e musical como veículo promotor do processo de ensino-aprendizagem da língua portuguesa no 1º ciclo do Ensino Básico

Mestrado (PES II), Educação Pré-Escolar e Ensino do 1º Ciclo do Ensino Básico, 1 de Julho de 2014, Universidade dos Açores.

LSP communication in a digital age: the outcomes of teaching and learning in tertiary education

This paper discusses the changes brought by the communication revolution in teaching and learning in the scope of LSP. Its aim is to provide an insight on how teaching which was bi-dimensional, turned into a multidimensional system, gathering other complementary resources that have transformed, in a incredibly short time, the ways we receive share and store information, for instance as professionals, and keep in touch with our peers. The increasing rise of electronic publications, the incredible boom of social and professional networks, search engines, blogs, list servs, forums, e-mail blasts, Facebook pages, YouTube contents, Tweets and Apps, have twisted the way information is conveyed. Classes ceased to be predictable and have been empowered by digital platforms, innumerous and different data repositories (TILDE, IATE, LINGUEE, and so many other terminological data banks) that have definitely transformed the academic world in general and tertiary education in particular. There is a bulk of information to be digested by students, who are no longer passive but instead responsible and active for their academic outcomes. The question is whether they possess the tools to select only what is accurate and important for a certain subject or assignment, due to that overflow? Due to the reduction of the number of course years in most degrees, after the implementation of Bologna and the shrinking of the curricula contents, have students the possibility of developing critical thinking? Both teaching and learning rely on digital resources to improve the speed of the spreading of knowledge. But have those changes been effective to promote really communication? Furthermore, with the increasing Apps that have already been developed and will continue to appear for learning foreign languages, for translation among others, will the students feel the need of learning them once they have those Apps. These are some the questions we would like to discuss in our paper.

Building a person home behavior model based on data from smart house sensors 2015

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Repairs of databases with null values

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications.

Disseny i implementació de la base de dades: Sistema de votació ciutadana a nivell Europeu a través d'Internet

La Unió Europea necessita i per tant ens encarrega, un disseny de base de dades per tald'emmagatzemar la informació de futures eleccions ciutadanes a través d'Internet.

Estudi de cloud cumputing i desenvolupament d'un gestor de currículums en eyeOS

El Cloud Computing o computació en el núvol és un conjunt de tecnologies que busca tenir tots els nostres arxius i informació a Internet sense dependre de tenir la capacitat suficient per emmagatzemar informació, tan sols d'oferir-nos el servei que volem en el moment que necessitem. En aquest projecte es detalla l'estudi de les diferents solucions cloud a nivell personal necessàries pel nostre dia a dia amb l'objectiu d'obtenir una eina que ens permeti posteriorment desenvolupar una petita aplicació per a gestionar els nostres currículums vitae.

Interference of age and repetition of the same noxious stimulus on hyperalgesia

Pain in animals has been recognized for less than one century. Several authors confirm that animals are capable to process, register and modulate nociceptive stimuli in a very similar way to human kind and there are several evidences registering the impact of pain sensation over vital systems interfering on disease outcome. Nevertheless, despite some evidences that animals, as human beings, can store information from past painful experiences less is known about how this so called pain memory works. The aims of this study were: to evaluate if the response to a painful stimuli differs during different stages of life and if repetition of a same acute stimuli in the same animal interferes with expression of hyperalgesia. Thus, 60 rats were selected and arranged in 3 equal groups: 3 months, 6 months, and 9 months of age. All animals were injected 5% formalin solution in the plantar face of hind paw under volatile general anesthesia. Von Frey filaments were applied at 1h, 24h and 48h after sensitization. Injection was repeated twice with a 30-day interval, each time in a different hind paw. Results showed that younger rats express lower hyperalgesia thresholds in the first stimulation compared to elder animals and that repetition of same stimulus diminishes hyperalgesia thresholds when it begins during infant period and augments hyperalgesia thresholds when it begins during elder ages.

On Recurrent and Deep Neural Networks

L'apprentissage profond est un domaine de recherche en forte croissance en apprentissage automatique qui est parvenu à des résultats impressionnants dans différentes tâches allant de la classification d'images à la parole, en passant par la modélisation du langage. Les réseaux de neurones récurrents, une sous-classe d'architecture profonde, s'avèrent particulièrement prometteurs. Les réseaux récurrents peuvent capter la structure temporelle dans les données. Ils ont potentiellement la capacité d'apprendre des corrélations entre des événements éloignés dans le temps et d'emmagasiner indéfiniment des informations dans leur mémoire interne. Dans ce travail, nous tentons d'abord de comprendre pourquoi la profondeur est utile. Similairement à d'autres travaux de la littérature, nos résultats démontrent que les modèles profonds peuvent être plus efficaces pour représenter certaines familles de fonctions comparativement aux modèles peu profonds. Contrairement à ces travaux, nous effectuons notre analyse théorique sur des réseaux profonds acycliques munis de fonctions d'activation linéaires par parties, puisque ce type de modèle est actuellement l'état de l'art dans différentes tâches de classification. La deuxième partie de cette thèse porte sur le processus d'apprentissage. Nous analysons quelques techniques d'optimisation proposées récemment, telles l'optimisation Hessian free, la descente de gradient naturel et la descente des sous-espaces de Krylov. Nous proposons le cadre théorique des méthodes à région de confiance généralisées et nous montrons que plusieurs de ces algorithmes développés récemment peuvent être vus dans cette perspective. Nous argumentons que certains membres de cette famille d'approches peuvent être mieux adaptés que d'autres à l'optimisation non convexe. La dernière partie de ce document se concentre sur les réseaux de neurones récurrents. Nous étudions d'abord le concept de mémoire et tentons de répondre aux questions suivantes: Les réseaux récurrents peuvent-ils démontrer une mémoire sans limite? Ce comportement peut-il être appris? Nous montrons que cela est possible si des indices sont fournis durant l'apprentissage. Ensuite, nous explorons deux problèmes spécifiques à l'entraînement des réseaux récurrents, à savoir la dissipation et l'explosion du gradient. Notre analyse se termine par une solution au problème d'explosion du gradient qui implique de borner la norme du gradient. Nous proposons également un terme de régularisation conçu spécifiquement pour réduire le problème de dissipation du gradient. Sur un ensemble de données synthétique, nous montrons empiriquement que ces mécanismes peuvent permettre aux réseaux récurrents d'apprendre de façon autonome à mémoriser des informations pour une période de temps indéfinie. Finalement, nous explorons la notion de profondeur dans les réseaux de neurones récurrents. Comparativement aux réseaux acycliques, la définition de profondeur dans les réseaux récurrents est souvent ambiguë. Nous proposons différentes façons d'ajouter de la profondeur dans les réseaux récurrents et nous évaluons empiriquement ces propositions.

Factores de riesgo relacionados con accidentalidad laboral en una empresa del sector de hidrocarburos, Colombia julio 2010- junio 2013

Objetivo. Para fortalecer las estrategias preventivas de forma integral, se pretende caracterizar la accidentalidad ocurrida en una empresa del sector de hidrocarburos e identificar los posibles factores de riesgo relacionados con estos eventos. Metodología: Se realizó un estudio descriptivo retrospectivo, utilizando las bases de datos SIGA y AUDICOMP del periodo Julio-2010 a Junio-2013, que almacenan información sobre accidentes laborales en trabajadores vinculados a Petrobras, Colombia. Nuestra variable resultado fue el número de accidentes laborales en función de la experiencia laboral y tiempo de contratación en la empresa, estratificada por características demográficas, propias del cargo ocupado y área anatómica lesionada. A las variables continuas se les calculo las medidas de tendencia central y dispersión y a las categóricas la proporción; se estimó el Odds Ratio (OR) de presentar un accidente en < 1 o entre 1-5 años de contratación. Resultados: se presentaron 457 accidentes, 96% (IC95% 94.2-97.8) fueron hombres, la década entre 25-34 años (36.8; IC95% 32.4-41.2) y el tipo de cargo obrero fueron los más frecuentes (35.3%; IC95% 30.9-39.6). Ser obrero (IC95% 2,11-2,65) y contar con experiencia laboral menor a un año (IC95% 1,78-2,33) fueron los principales factores relacionados con un accidente en < 1 año de contratación; el modelo con mayor AUC fue el de hombres entre 18-24 años de edad, contratados para laborar como obrero y con menos de un año de experiencia laboral (AUC 0,973; IC95% 0,865-0,995). Conclusiones. Los hombres entre 18-24 años de edad, contratados para laborar como obrero y con menos de un año de experiencia laboral, tenían mayor riesgo de presentar un accidente en menos de un año desde la contratación. El modelo propuesto ayudó a identificar a trabajadores con alta probabilidad de presentar un accidente en < 1 año desde la contratación.

CAFS in action

For those few readers who do not know, CAFS is a system developed by ICL to search through data at speeds of several million characters per second. Its full name is Content Addressable File Store Information Search Processor, CAFS-ISP or CAFS for short. It is an intelligent hardware-based searching engine, currently available with both ICL's 2966 family of computers and the recently announced Series 39, operating within the VME environment. It uses content addressing techniques to perform fast searches of data or text stored on discs: almost all fields are equally accessible as search keys. Software in the mainframe generates a search task; the CAFS hardware performs the search, and returns the hit records to the mainframe. Because special hardware is used, the searching process is very much more efficient than searching performed by any software method. Various software interfaces are available which allow CAFS to be used in many different situations. CAFS can be used with existing systems without significant change. It can be used to make online enquiries of mainframe files or databases or directly from user written high level language programs. These interfaces are outlined in the body of the report.

Using Neo4J geospatial data storage and integration

Online geographic-databases have been growing increasingly as they have become a crucial source of information for both social networks and safety-critical systems. Since the quality of such applications is largely related to the richness and completeness of their data, it becomes imperative to develop adaptable and persistent storage systems, able to make use of several sources of information as well as enabling the fastest possible response from them. This work will create a shared and extensible geographic model, able to retrieve and store information from the major spatial sources available. A geographic-based system also has very high requirements in terms of scalability, computational power and domain complexity, causing several difficulties for a traditional relational database as the number of results increases. NoSQL systems provide valuable advantages for this scenario, in particular graph databases which are capable of modeling vast amounts of inter-connected data while providing a very substantial increase of performance for several spatial requests, such as finding shortestpath routes and performing relationship lookups with high concurrency. In this work, we will analyze the current state of geographic information systems and develop a unified geographic model, named GeoPlace Explorer (GE). GE is able to import and store spatial data from several online sources at a symbolic level in both a relational and a graph databases, where several stress tests were performed in order to find the advantages and disadvantages of each database paradigm.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

Aprendizado não-supervisionado em redes neurais pulsadas de base radial: um estudo da capacidade de agrupamento para a classificação de pixels

Redes neurais pulsadas - redes que utilizam uma codificação temporal da informação - têm despontado como uma promissora abordagem dentro do paradigma conexionista, emergente da ciência cognitiva. Um desses novos modelos é a rede neural pulsada com função de base radial, que é capaz de armazenar informação nos tempos de atraso axonais dos neurônios. Um algoritmo de aprendizado foi aplicado com sucesso nesta rede pulsada, que se mostrou capaz de mapear uma seqüência de pulsos de entrada em uma seqüência de pulsos de saída. Mais recentemente, um método baseado no uso de campos receptivos gaussianos foi proposto para codificar dados constantes em uma seqüência de pulsos temporais. Este método tornou possível a essa rede lidar com dados computacionais. O processo de aprendizado desta nova rede não se encontra plenamente compreendido e investigações mais profundas são necessárias para situar este modelo dentro do contexto do aprendizado de máquinas e também para estabelecer as habilidades e limitações desta rede. Este trabalho apresenta uma investigação desse novo classificador e um estudo de sua capacidade de agrupar dados em três dimensões, particularmente procurando estabelecer seus domínios de aplicação e horizontes no campo da visão computacional.

Representação iterativa: um modelo para repositórios digitais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)