8 resultados para Fieldwork Learning Framework


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Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial Para obtenção do grau de Mestre em Engenharia Informática

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Tese de doutoramento em Ciências da Educação, área de Teoria Curricular e Ensino das Ciências

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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão de Sistemas Ambientais

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Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e Formação

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Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

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The Intel R Xeon PhiTM is the first processor based on Intel’s MIC (Many Integrated Cores) architecture. It is a co-processor specially tailored for data-parallel computations, whose basic architectural design is similar to the ones of GPUs (Graphics Processing Units), leveraging the use of many integrated low computational cores to perform parallel computations. The main novelty of the MIC architecture, relatively to GPUs, is its compatibility with the Intel x86 architecture. This enables the use of many of the tools commonly available for the parallel programming of x86-based architectures, which may lead to a smaller learning curve. However, programming the Xeon Phi still entails aspects intrinsic to accelerator-based computing, in general, and to the MIC architecture, in particular. In this thesis we advocate the use of algorithmic skeletons for programming the Xeon Phi. Algorithmic skeletons abstract the complexity inherent to parallel programming, hiding details such as resource management, parallel decomposition, inter-execution flow communication, thus removing these concerns from the programmer’s mind. In this context, the goal of the thesis is to lay the foundations for the development of a simple but powerful and efficient skeleton framework for the programming of the Xeon Phi processor. For this purpose we build upon Marrow, an existing framework for the orchestration of OpenCLTM computations in multi-GPU and CPU environments. We extend Marrow to execute both OpenCL and C++ parallel computations on the Xeon Phi. We evaluate the newly developed framework, several well-known benchmarks, like Saxpy and N-Body, will be used to compare, not only its performance to the existing framework when executing on the co-processor, but also to assess the performance on the Xeon Phi versus a multi-GPU environment.

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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.

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This thesis is a case study on Corporate Governance and Business Ethics, using the Portuguese Corporate Law as a general setting. The thesis was conducted in Portugal with illustrations on past cases under the Business Judgment Rule of the State of Delaware, U.SA along with illustrations on current cases in Portugal under the Portuguese Judicial setting, along with a comparative analysis between both. A debate is being considered among scholars and executives; a debate on best practices within corporate governance and corporate law, associated with recent discoveries of unlawful investments that lead to the bankruptcy of leading institutions and an aggravation of the crisis in Portugal. The study aimed at learning possible reasons and causes for the current situation of the country’s corporations along with attempts to discover the best way to move forward. From the interviews and analysis conducted, this paper concluded that the corporate governance structure and legal frameworks in Portugal were not the sole influencers behind the actions and decisions of Corporate Executives, nor were they the main triggers for the recent corporate mishaps. But it is rather a combination of different factors that played a significant role, such as cultural and ethical aspects, individual personalities, and others all of which created gray areas beyond the legal structure, which in turn accelerated and aggravated the corporate governance crisis in the country.