Learning facial-expression models with crowdsourcing

The computational power is increasing day by day. Despite that, there are some tasks that are still difficult or even impossible for a computer to perform. For example, while identifying a facial expression is easy for a human, for a computer it is an area in development. To tackle this and similar issues, crowdsourcing has grown as a way to use human computation in a large scale. Crowdsourcing is a novel approach to collect labels in a fast and cheap manner, by sourcing the labels from the crowds. However, these labels lack reliability since annotators are not guaranteed to have any expertise in the field. This fact has led to a new research area where we must create or adapt annotation models to handle these weaklylabeled data. Current techniques explore the annotators’ expertise and the task difficulty as variables that influences labels’ correction. Other specific aspects are also considered by noisy-labels analysis techniques. The main contribution of this thesis is the process to collect reliable crowdsourcing labels for a facial expressions dataset. This process consists in two steps: first, we design our crowdsourcing tasks to collect annotators labels; next, we infer the true label from the collected labels by applying state-of-art crowdsourcing algorithms. At the same time, a facial expression dataset is created, containing 40.000 images and respective labels. At the end, we publish the resulting dataset.

Real-time collaborative editing of OutSystems DSL models

Real-time collaborative editing systems are common nowadays, and their advantages are widely recognized. Examples of such systems include Google Docs, ShareLaTeX, among others. This thesis aims to adopt this paradigm in a software development environment. The OutSystems visual language lends itself very appropriate to this kind of collaboration, since the visual code enables a natural flow of knowledge between developers regarding the developed code. Furthermore, communication and coordination are simplified. This proposal explores the field of collaboration on a very structured and rigid model, where collaboration is made through the copy-modify-merge paradigm, in which a developer gets its own private copy from the shared repository, modifies it in isolation and later uploads his changes to be merged with modifications concurrently produced by other developers. To this end, we designed and implemented an extension to the OutSystems Platform, in order to enable real-time collaborative editing. The solution guarantees consistency among the artefacts distributed across several developers working on the same project. We believe that it is possible to achieve a much more intense collaboration over the same models with a low negative impact on the individual productivity of each developer.

A Classificação Internacional de Funcionalidade, Incapacidade e Saúde : um modelo multidimensional e multideterminado da funcionalidade em idosos assistidos na Rede

RESUMO: Os estudos sobre a funcionalidade da população idosa têm uma representação importante naquilo que é o atual conhecimento da demografia do mundo. Portugal posiciona-se e perspetiva-se como pertencendo aos países mais envelhecidos, possuindo uma rede de cuidados pós-agudos – a Rede Nacional de Cuidados Continuados Integrados (RNCCI)– que assiste uma parcela importante dessa população. Os aspetos conceptuais da funcionalidade de acordo com a OMS e operacionalizados pela Classificação Internacional de Funcionalidade (CIF), não mereceram até agora suficiente aplicabilidade no nosso país, inviabilizando a possibilidade de oferecermos contributos para a sua operacionalização. Da mesma forma, também os Core Sets da Classificação não têm sido sujeitos a processos de validação que contemplem amostras portuguesas, mantendo-se desconhecimento da especificidade dos fatores contextuais na nossa população. O presente estudo tem como objetivos conhecer a evolução da funcionalidade dos idosos assistidos na RNCCI na região do Algarve nas unidades de convalescença e média duração, validar o Core Set Geriátrico da OMS e propor uma versão abreviada da sua modalidade abrangente, no contexto destes cuidados. A amostra constituída por 451 idosos, dos quais 62,1% eram mulheres, revelou na pré-morbilidade níveis favoráveis de funcionalidade, com exceção para as Atividades Domésticas. Contudo, os mais idosos (≥ 85 anos), os indivíduos sem escolaridade, as mulheres e os viúvos/solteiros apresentaram mais casos desfavoráveis quando comparados com os seus pares. Na evolução da funcionalidade observámos melhorias significativas em todos os domínios avaliados, com diferenças relativamente à idade e à escolaridade; apesar dos resultados positivos os mais idosos e os indivíduos sem escolaridade apresentaram níveis inferiores de evolução. No entanto, a funcionalidade alcançada revelou ficar com resultados significativamente inferiores na comparação com aquela que os indivíduos possuíam na pré-morbilidade. Os modelos de regressão revelaram que as Funções Mentais, a Perceção do Estado de Saúde e a atividade Usar o Telefone, foram as variáveis que melhor explicaram os outcomes da funcionalidade alcançada. A validação do Core Set Geriátrico foi possível na maioria das categorias, sendo que foi no componente das Funções do Corpo onde esse processo revelou maior fragilidade. As Funções Neuromusculoesqueléticas e Relacionadas com o Movimento foram aquelas que registaram em ambos os momentos avaliativos frequências mais elevadas de deficiência, enquanto no componente Atividades & Participação isso ocorreu na atividade Utilização dos Movimentos Finos da Mão. Os capítulos Apoios e Relacionamentos e Atitudes foram considerados os Fatores Ambientais mais Facilitadores mas também com maior impacto Barreira. A proposta para o Core Set Geriátrico Abreviado resultou das categorias independentes que explicaram os modelos da funcionalidade alcançada e cujo resultado engloba um conjunto de 27 categorias, com um enfoque importante no componente Atividades/Participação de onde se destacam os domínios da Mobilidade e dos Auto Cuidados. A funcionalidade dos indivíduos e das populações deve ser considerada uma variável incontornável da Saúde Pública, cuja avaliação deve refletir uma abordagem biopsicossocial, apoiada na Classificação Internacional de Funcionalidade. A operacionalização da Classificação a partir dos Core Sets necessita de pesquisa mais aprofundada relativamente às caraterísticas psicométricas dos seus qualificadores e dos seus processos de validação.-----------ABSTRACT: The studies about the functioning of the elderly play an important role on what the present knowledge of the demography in the world is. Portugal figures high on the most aged countries, having a network of post-acute care - the National Network of Integrated Continuous Care (RNCCI) - which assists a large part of that population. The conceptual aspects of functioning according to WHO and operated by the International Classification of Functioning (ICF), have been insufficiently addressed concerning its adequate applicability in our country, hindering the contributions of its operation. In the same way, also the Core Sets of the Classification have not been subjected to validation procedures that include portuguese samples, keeping the unawareness of specificity of the contextual factors in our population. The objectives of the present study were to know the evolution of the functioning of the elderly assisted in the RNCCI in the Algarve region in units of convalescence and average duration, validate the WHO Geriatric Core Set and propose an abridged version of this comprehensive core set in this healthcare context. The sample was composed by 451 elderly people, of which 62.1% were women, they showed favourable levels in functioning in the pre-morbid state, except for Domestic Activities. However, the oldest (≥ 85 years), the individuals with no education, women and widowed/ unmarried showed more unfavourable cases when compared to their peers. In the evolution of functioning we observed significant improvements in all domains assessed, with diferences with respect to age and education. In spite of positive results, the oldest and the individuals with no education showed lower levels of evolution. However, the functioning achieved showed significantly lower results when compared to the those observed in pre-morbidity state. Regression models reveal that Mental Functions, the Perceived Health Status and the Use of the Phone activity, were the variables that better explain the functioning of the outcomes achieved. The validation of the Geriatric Core Set of ICF was possible in most categories, and Body Functions was the component where this process showed greatest weakness. Neuromusculoskeletal and Movement-Related Functions experienced in both evaluation times with higher rates of disability, while in the Activities & Participation component this occurred in the Fine Hand Use activity. The Support and Relationships and Attitudes chapters were considered the Environmental Factors most Facilitators but also with greater impact Barrier. The proposal for the Brief Geriatric Core Set has resulted from the independent categories that explained the regression models of functioning and includes a set of 27 categories, with na important emphasis on Activities & Participation component where we can highlight the areas of Mobility and Self Care domains. The functioning of individuals and populations should be considered as an unavoidable variable of Public Health, of which the assessment should reflect a biopsychosocial approach, based on the International Classification of Functioning. The operationalization of the Classification from the Core Sets requires further research regarding the psychometric characteristics of their qualifiers and their validation procedure.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

Simplified wind turbine models for wind energy integration into power systems

This paper analyses the boundaries of simplified wind turbine models used to represent the behavior of wind turbines in order to conduct power system stability studies. Based on experimental measurements, the response of recent simplified (also known as generic) wind turbine models that are currently being developed by the International Standard IEC 61400-27 is compared to complex detailed models elaborated by wind turbine manufacturers. This International Standard, whose Technical Committee was convened in October 2009, is focused on defining generic simulation models for both wind turbines (Part 1) and wind farms (Part 2). The results of this work provide an improved understanding of the usability of generic models for conducting power system simulations.

Development of 3D in vitro models for prediction of hepatic metabolism and toxicity

The development of human cell models that recapitulate hepatic functionality allows the study of metabolic pathways involved in toxicity and disease. The increased biological relevance, cost-effectiveness and high-throughput of cell models can contribute to increase the efficiency of drug development in the pharmaceutical industry. Recapitulation of liver functionality in vitro requires the development of advanced culture strategies to mimic in vivo complexity, such as 3D culture, co-cultures or biomaterials. However, complex 3D models are typically associated with poor robustness, limited scalability and compatibility with screening methods. In this work, several strategies were used to develop highly functional and reproducible spheroid-based in vitro models of human hepatocytes and HepaRG cells using stirred culture systems. In chapter 2, the isolation of human hepatocytes from resected liver tissue was implemented and a liver tissue perfusion method was optimized towards the improvement of hepatocyte isolation and aggregation efficiency, resulting in an isolation protocol compatible with 3D culture. In chapter 3, human hepatocytes were co-cultivated with mesenchymal stem cells (MSC) and the phenotype of both cell types was characterized, showing that MSC acquire a supportive stromal function and hepatocytes retain differentiated hepatic functions, stability of drug metabolism enzymes and higher viability in co-cultures. In chapter 4, a 3D alginate microencapsulation strategy for the differentiation of HepaRG cells was evaluated and compared with the standard 2D DMSO-dependent differentiation, yielding higher differentiation efficiency, comparable levels of drug metabolism activity and significantly improved biosynthetic activity. The work developed in this thesis provides novel strategies for 3D culture of human hepatic cell models, which are reproducible, scalable and compatible with screening platforms. The phenotypic and functional characterization of the in vitro systems performed contributes to the state of the art of human hepatic cell models and can be applied to the improvement of pre-clinical drug development efficiency of the process, model disease and ultimately, development of cell-based therapeutic strategies for liver failure.

Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.

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.

Flood resilient housing recovery models: a theoretical case study in Maldives

Natural disasters are events that cause general and widespread destruction of the built environment and are becoming increasingly recurrent. They are a product of vulnerability and community exposure to natural hazards, generating a multitude of social, economic and cultural issues of which the loss of housing and the subsequent need for shelter is one of its major consequences. Nowadays, numerous factors contribute to increased vulnerability and exposure to natural disasters such as climate change with its impacts felt across the globe and which is currently seen as a worldwide threat to the built environment. The abandonment of disaster-affected areas can also push populations to regions where natural hazards are felt more severely. Although several actors in the post-disaster scenario provide for shelter needs and recovery programs, housing is often inadequate and unable to resist the effects of future natural hazards. Resilient housing is commonly not addressed due to the urgency in sheltering affected populations. However, by neglecting risks of exposure in construction, houses become vulnerable and are likely to be damaged or destroyed in future natural hazard events. That being said it becomes fundamental to include resilience criteria, when it comes to housing, which in turn will allow new houses to better withstand the passage of time and natural disasters, in the safest way possible. This master thesis is intended to provide guiding principles to take towards housing recovery after natural disasters, particularly in the form of flood resilient construction, considering floods are responsible for the largest number of natural disasters. To this purpose, the main structures that house affected populations were identified and analyzed in depth. After assessing the risks and damages that flood events can cause in housing, a methodology was proposed for flood resilient housing models, in which there were identified key criteria that housing should meet. The same methodology is based in the US Federal Emergency Management Agency requirements and recommendations in accordance to specific flood zones. Finally, a case study in Maldives – one of the most vulnerable countries to sea level rise resulting from climate change – has been analyzed in light of housing recovery in a post-disaster induced scenario. This analysis was carried out by using the proposed methodology with the intent of assessing the resilience of the newly built housing to floods in the aftermath of the 2004 Indian Ocean Tsunami.

Epidemiological aspects of schistosomiasis in workers of the Movement of Landless Rural Workers

Introduction Schistosomiasis is endemic in 74 countries and is considered a serious public health problem in some locations. Methods A transverse study was performed of 13 landless settlements in southern Sergipe from February to December 2009. The study included 822 settlers, of whom 601 underwent stool testing. Results The prevalence of schistosomiasis in landless workers was 4.3%. The population has a low education level, and basic sanitation services are not available to all residents. Conclusions The prevalence of schistosomiasis was low in the population and among different settlements, possibly because of different forms of water use by the settlers.

The future of airline business models:

This research is titled “The Future of Airline Business Models: Which Will Win?” and it is part of the requirements for the award of a Masters in Management from NOVA BSE and another from Luiss Guido Carlo University. The purpose is to elaborate a complete market analysis of the European Air Transportation Industry in order to predict which Airlines, strategies and business models may be successful in the next years. First, an extensive literature review of the business model concept has been done. Then, a detailed overview of the main European Airlines and the strategies that they have been implementing so far has been developed. Finally, the research is illustrated with three case studies

Building 3D City Models: Testing and Comparing Laser Scanning and Low-Cost UAV Data Using FOSS Technologies

Contém resumo

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Damage propagation in composite Materials meso-mechanical models

Composite materials have a complex behavior, which is difficult to predict under different types of loads. In the course of this dissertation a methodology was developed to predict failure and damage propagation of composite material specimens. This methodology uses finite element numerical models created with Ansys and Matlab softwares. The methodology is able to perform an incremental-iterative analysis, which increases, gradually, the load applied to the specimen. Several structural failure phenomena are considered, such as fiber and/or matrix failure, delamination or shear plasticity. Failure criteria based on element stresses were implemented and a procedure to reduce the stiffness of the failed elements was prepared. The material used in this dissertation consist of a spread tow carbon fabric with a 0°/90° arrangement and the main numerical model analyzed is a 26-plies specimen under compression loads. Numerical results were compared with the results of specimens tested experimentally, whose mechanical properties are unknown, knowing only the geometry of the specimen. The material properties of the numerical model were adjusted in the course of this dissertation, in order to find the lowest difference between the numerical and experimental results with an error lower than 5% (it was performed the numerical model identification based on the experimental results).

Understanding multi-asset factor models: Factor exposure interpretation

Field lab: Business project