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.

Factores sociodemográficos na saúde oral : influências nos comportamentos de rotinas de saúde oral, frequências de idas a consultas, auto avaliação do estado de saúde oral e presença de dor em jovens de 12 anos

RESUMO - Introdução: A saúde oral é uma componente essencial na saúde geral e no bem-estar dos indivíduos. Sabe-se que os problemas de saúde oral afectam predominantemente os elementos de níveis socioeconómicos mais baixos, evidenciando a influência dos determinantes sociais da saúde na saúde oral das populações. Os objectivos deste estudo são caracterizar os comportamentos de rotinas diárias de higiene oral, frequências de idas a consultas de saúde oral, auto-avaliação do estado de saúde oral e percepção de dor na cavidade oral em crianças de 12 anos em Portugal e analisar a associação entre estes e os factores sociodemográficos. Métodos: Foi realizado um estudo observacional, transversal e analítico, abrangendo 1309 jovens e baseado em informação recolhida no III Estudo Nacional de Prevalência de Doenças Orais (ENPDO). Para além das estatísticas descritivas usuais, as estatísticas inferenciais basearam-se predominantemente em modelos de regressão logística binária. Resultados: Dos participantes, 70.6% (n=924) escova “duas ou mais vezes por dia” com associação com todas as variáveis sociodemográficas. Na análise multivariada, o género masculino (OR=2.088; IC95%: 1.574-2.770, em relação ao género feminino), a área de residência predominantemente rural ou mediamente urbana (OR= 1.800; IC95%: 2.587; OR=1.516; IC95%: 1.093-2.103, em relação a zonas predominantemente urbanas), a escolaridade da mãe ser o ensino básico (OR= 2.112; IC95%: 1.408-3.168, em relação ao ensino superior) e a actividade laboral do pai ser desempregado (OR= 1.938; IC95%: 1.280-2.934, em relação a ser trabalhador) foram as variáveis com mais impacto para a adopção de comportamentos de escovagem potencialmente inadequados (p<0.05). A maioria dos inquiridos (94.2%; n=1247) já tinham ido a uma consulta de saúde oral e 74.5% (n=860) nos últimos 12 meses, 95.5% (n=1250) encontram-se satisfeitos com a saúde oral e 44.5% (n=578) afirma ter tido algum tipo de dor na cavidade oral nos últimos 12 meses. Conclusão: Os resultados obtidos estão de acordo com a literatura em termos de factores de associação. Desta forma, a saúde oral nos jovens de 12 anos em Portugal, nos diversos contextos aqui analisados, pode ser considerada como satisfatória. A única excepção relevante é a componente da dor, com valores alarmantes embora de natureza mais subjectiva. A influência dos factores sociodemográficos sugere que futuras abordagens para a promoção da saúde oral tenham em conta os determinantes de saúde no delineamento de estratégias quer a nível individual quer a nível comunitário.

Flexible multivariate GARCH modeling with an application to international stock markets

This paper offers a new approach to estimating time-varying covariance matrices in the framework of the diagonal-vech version of the multivariate GARCH(1,1) model. Our method is numerically feasible for large-scale problems, produces positive semidefinite conditional covariance matrices, and does not impose unrealistic a priori restrictions. We provide an empirical application in the context of international stock markets, comparing the nev^ estimator with a number of existing ones.

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.

Experimental and modeling studies on reverse electrodialysis for sustainable power generation

A potentially renewable and sustainable source of energy is the chemical energy associated with solvation of salts. Mixing of two aqueous streams with different saline concentrations is spontaneous and releases energy. The global theoretically obtainable power from salinity gradient energy due to World’s rivers discharge into the oceans has been estimated to be within the range of 1.4-2.6 TW. Reverse electrodialysis (RED) is one of the emerging, membrane-based, technologies for harvesting the salinity gradient energy. A common RED stack is composed by alternately-arranged cation- and anion-exchange membranes, stacked between two electrodes. The compartments between the membranes are alternately fed with concentrated (e.g., sea water) and dilute (e.g., river water) saline solutions. Migration of the respective counter-ions through the membranes leads to ionic current between the electrodes, where an appropriate redox pair converts the chemical salinity gradient energy into electrical energy. Given the importance of the need for new sources of energy for power generation, the present study aims at better understanding and solving current challenges, associated with the RED stack design, fluid dynamics, ionic mass transfer and long-term RED stack performance with natural saline solutions as feedwaters. Chronopotentiometry was used to determinate diffusion boundary layer (DBL) thickness from diffusion relaxation data and the flow entrance effects on mass transfer were found to avail a power generation increase in RED stacks. Increasing the linear flow velocity also leads to a decrease of DBL thickness but on the cost of a higher pressure drop. Pressure drop inside RED stacks was successfully simulated by the developed mathematical model, in which contribution of several pressure drops, that until now have not been considered, was included. The effect of each pressure drop on the RED stack performance was identified and rationalized and guidelines for planning and/or optimization of RED stacks were derived. The design of new profiled membranes, with a chevron corrugation structure, was proposed using computational fluid dynamics (CFD) modeling. The performance of the suggested corrugation geometry was compared with the already existing ones, as well as with the use of conductive and non-conductive spacers. According to the estimations, use of chevron structures grants the highest net power density values, at the best compromise between the mass transfer coefficient and the pressure drop values. Finally, long-term experiments with natural waters were performed, during which fouling was experienced. For the first time, 2D fluorescence spectroscopy was used to monitor RED stack performance, with a dedicated focus on following fouling on ion-exchange membrane surfaces. To extract relevant information from fluorescence spectra, parallel factor analysis (PARAFAC) was performed. Moreover, the information obtained was then used to predict net power density, stack electric resistance and pressure drop by multivariate statistical models based on projection to latent structures (PLS) modeling. The use in such models of 2D fluorescence data, containing hidden, but extractable by PARAFAC, information about fouling on membrane surfaces, considerably improved the models fitting to the experimental data.

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.

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

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

Orthogonal models, structure crossing, nesting and inference

We intend to study the algebraic structure of the simple orthogonal models to use them, through binary operations as building blocks in the construction of more complex orthogonal models. We start by presenting some matrix results considering Commutative Jordan Algebras of symmetric matrices, CJAs. Next, we use these results to study the algebraic structure of orthogonal models, obtained by crossing and nesting simpler ones. Then, we study the normal models with OBS, which can also be orthogonal models. We intend to study normal models with OBS (Orthogonal Block Structure), NOBS (Normal Orthogonal Block Structure), obtaining condition for having complete and suffcient statistics, having UMVUE, is unbiased estimators with minimal covariance matrices whatever the variance components. Lastly, see ([Pereira et al. (2014)]), we study the algebraic structure of orthogonal models, mixed models whose variance covariance matrices are all positive semi definite, linear combinations of known orthogonal pairwise orthogonal projection matrices, OPOPM, and whose least square estimators, LSE, of estimable vectors are best linear unbiased estimator, BLUE, whatever the variance components, so they are uniformly BLUE, UBLUE. From the results of the algebraic structure we will get explicit expressions for the LSE of these models.

Impacto da crise no volume e perfil de internamento de doentes com VIH-SIDA em Portugal Continental : análise temporal 2001-2012

RESUMO - Contexto: O início da crise económica em Portugal no ano de 2009 colocou o país numa grave recessão económica aliada a diversas medidas de austeridade. Como consequência assistiu-se, ao nível nacional, uma diminuição do PIB, aumento do desemprego e assim como uma série de restrições orçamentais em várias áreas, nomeadamente a da saúde. Apesar de existir inúmeros estudos que avaliaram o impacto das recessões económicas na saúde os resultados são controversos e não existe um consenso quanto a esta associação. No que se refere às doenças infeciosas o número de estudos é bastante mais reduzido. O objetivo deste estudo foi o de analisar o impacto da crise atual no volume e perfil de internamento de doentes com VIH/SIDA, de forma a complementar a escassa evidência existente neste domínio. Metodologia: Foram analisados 53,296 episódios de internamento nos hospitais do SNS entre o ano de 2001 e 2012, cujo diagnóstico principal é a infeção pelo VIH/SIDA. Considerou-se o ano de 2009 como o ano inicial da crise. Através de regressões multivariadas avaliou-se o impacto da crise no volume de doentes internados, duração de internamento, número de co-morbilidades, risco de ser admitido via urgência e risco de mortalidade no internamento. Adicionalmente repetiu-se a análise por região NUTS II de Portugal Continental (Norte, Alentejo, LVT, Centro e Algarve). Resultados: A crise não teve impacto no volume de doentes internados. No entanto, após o ano de 2009, registou-se uma diminuição de 5.6% na duração de internamento; um aumento de 1.6% no número de co-morbilidades; um aumento de 11.1% no risco de ser admitido via urgência e um aumento de 8.6% do risco de mortalidade no internamento. As análises por região permitiram verificar que as regiões mais afetadas pela crise foram a região LVT e a região Norte. Conclusão: A crise em Portugal não teve impacto na incidência de internamentos por VIH/SIDA. Porém o aumento do número de co-morbilidades, do risco de ser admitido via urgência e do risco de mortalidade no internamento parece refletir um agravamento da severidade dos casos após o ano de 2009. Adicionalmente a diminuição da duração de internamento com o efeito da crise poderá refletir tanto aumento da eficiência dos cuidados prestados ou ao contrário, uma diminuição da sua qualidade.