Ambientes virtuais tridimensionais imersivos são frequentemente utilizados como apoio às estratégias de treinamento ou educação conduzidas em ambientes reais. Técnicas estereoscópicas como as de anaglifos, obturação e polarização da luz são recursos que oferecem a sensação de imersão nesses ambientes virtuais. É crescente o interesse em pesquisas acerca de como os humanos percebem o espaço e interagem em ambientes virtuais, investigando como componentes de sistemas imersivos afetam a percepção humana. Essa percepção é importante para várias tarefas, como mover objetos e identificar estruturas, que requerem a interpretação e entendimento das informações do espaço. Apesar de tal interesse, não são encontrados na literatura modelos de mensuração de tal percepção. Este cenário constitui um desafio no que concerne à necessidade de criação de modelos para mensurar, independentemente da aplicação, a percepção da tridimensionalidade proporcionada pelas técnicas estereoscópicas citadas. Este projeto de mestrado define, implementa e valida um modelo extensível capaz de avaliar a percepção da tridimensionalidade em sistemas de realidade virtual estereoscópicos no contexto de ambientes que envolvem interação com manipulação de objetos. O escopo da presente pesquisa foi definido mediante a realização de uma Revisão Sistemática, que identificou métodos e parâmetros utilizados na avaliação de ambientes estereoscópicos. Para atingir os objetivos deste trabalho, foram conduzidos experimentos que serviram de base para a definição do modelo. Os resultados dos experimentos indicaram que o modelo elaborado é capaz de compor um veredicto sobre a percepção de tridimensionalidade fornecida por uma técnica, assim como auxiliar na tomada de decisões acerca da utilização de técnicas estereoscópicas em sistemas de RV / Three-Dimensional immersive virtual environments are often used as support for training strategies or education conducted in real environments. Stereoscopic techniques such as anaglyph, shutter glasses and polarized glasses can offer a sense of immersion in these virtual environments. There is a growing interest in research how humans perceive space and interact in virtual environments, investigating how components of immersive systems affect different understanding of space in virtual reality systems. This perception is important for various tasks, such as moving objects and identify structures that require interpretation and understanding of space information. This scenario is a challenge regarding the establishment of models to measure, independently of the application, the perception of three-dimensionality provided by the stereoscopic techniques cited. This master project defines and validates an extensible model able to evaluate the three-dimensionality perception in different virtual reality systems with stereoscopy that involves objects manipulation. The scope of this study was defined with a systematic review that identified methods and parameters used to assess stereoscopic environments. To achieve the objectives of this study, experiments was conducted as the basis to define the model. The results of the experiments indicated that the model developed is able to set a verdict about three-dimensionality perception provided by a stereoscopic technique and assist decision-making regarding the use of stereoscopic techniques in VR systems

The noise coming from wind power development can be an environmental impact forthe surrounding communities. It is well known that the main wind turbine noise iscaused by the movement of the turbine wings through the air. However, there areuncertainties about the importance of machinery sounds and possible variations amongwind turbines. A high resolution acquisition system was used to perform a fieldexperiment comparison of the noise spectra from some wind turbines at Laholm(Sweden). The results have shown different band spectra peaks associated to machinerysounds among wind turbines from the same model and also from those of differentmanufactures. Maintenance conditions of these wind turbines could explain thedifferences in intensity and frequency locations of the band spectra peaks found. Inorder to know the importance for human audition of these peaks, listening test or doseresponsestudies would be needed to provide relevant information in this regard. Themethodology developed in this study is suggested to be useful for identifying certainmachinery failures which could corrupt the noise sounds emitted at certain wind turbinelocations.

Context. Big data visualization is a vital part of today's technological advancement. It is about visualizing different variables on a graph, map, or other means often in real-time. Objectives. This study aims to determine what challenges there are for big data visualization, whether significant amounts of data impact the visualization, and finding existing solutions for the problems. Methods. Databases used in this systematic literature review include Inspec, IEEE Xplore, and BTH Summon. Papers are included in the review if certain criteria are upheld. Results. 6 solutions are found to reduce large data sets and reduce latency when viewing 2D and 3D graphs. Conclusions. In conclusion, many solutions exist in various forms to improve visualizing graphs of different dimensions. Future grows of data might change this though and might require new solutions of the growing data.

The aim of the diploma thesis is to analyze the current state of creation of management reporting, its contribution to a company and to create a proposal for improving and expanding awareness of reporting in a company.

Submitted thesis deals with the topic of synthetic hormones and human perception of this matter. The phenomenon of the endocrinal disruption that contains synthetic hormones as well is presented in the theoretical part. Then the attention is paid to the negative impacts on human body and on the environment as well as to low effectiveness of the wastewater treatment plant to deal with those pollutants. The practical part of the thesis contains survey among respondents of the Social and cultural department and Electronic culture and semiotics department on the Faculty of humanities of the Charles University. This survey deals with the awareness of this topic, consumer behaviour and opinions on the attention that is paid to this matter in the Czech Republic as well as on the ideal solution to this topic. Key words: hormones, environment, endocrine disruption, human perception

The challenges posed by climate change are a threat to human well-being as well as to natural ecosystems, but researchers indicate that awareness of and concern about climate change varies considerably. In this thesis, we investigate the socio-demographic factors associated with the perception of climate change, analysing four data sets collected from four surveys on urban middle-income households in Mexico. Our empirical strategy relies on the estimation of logit regressions on a binary variable defining whether respondents consider climate change as an important issue. Results indicate that age, gender, education, employment status, household size, and having in the household a member with a respiratory illness are important predictors that shape Mexicans’ perception of how important climate change is. It is important to know the determinants that effect climate change perception in order to develop sustainable policies to mitigate the risks of climate change.

<div>Face redaction is used to deidentify images of people. Most approaches depend on face detection, but automated algorithms are still not adequate for sensitive applications in which even one unredacted face could lead to irreversible harm. Human annotators can potentially provide the most accurate detection, but only trusted annotators should be allowed to see the faces of privacy-sensitive applications. Redacting more images than trusted annotators could accommodate requires a new approach. </div><div>This dissertation leverages the characteristics of human perception of faces in median-filtered images in a human computation algorithm to engage crowd workers to redact faces—without revealing the identities. IntoFocus, a system I developed, permits robust face redaction with probabilistic privacy guarantees. The system's design builds on an experiment that measured the filter levels and conditions where participants could detect and identify faces. </div><div> Pterodactyl is a system that focuses on increasing the productivity of crowd-based face redaction systems. It uses the AdaptiveFocus filter, a filter that combines human perception of faces in median filtered images with a convolutional neural network to estimate a median filter level for each region of the image to allow the faces to be detected and prevent them from being identified.</div>

This thesis is an attempt to improve visual object retrieval by allowing users to interact with the system. Our solution lies in constructing an interactive system that allows users to define their own visual concept from a concise set of visual patches given as input. These patches, which represent the most informative clues of a given visual category, are trained beforehand with a supervised learning algorithm in a discriminative manner. Then, and in order to specialize their models, users have the possibility to send their feedback on the model itself by choosing and weighting the patches they are confident of. The real challenge consists in how to generate concise and visually interpretable models. Our contribution relies on two points. First, in contrast to the state-of-the-art approaches that use bag-of-words, we propose embedding local visual features without any quantization, which means that each component of the high-dimensional feature vectors used to describe an image is associated to a unique and precisely localized image patch. Second, we suggest using regularization constraints in the loss function of our classifier to favor sparsity in the models produced. Sparsity is indeed preferable for concision (a reduced number of patches in the model) as well as for decreasing prediction time. To meet these objectives, we developed a multiple-instance learning scheme using a modified version of the BLasso algorithm. BLasso is a boosting-like procedure that behaves in the same way as Lasso (Least Absolute Shrinkage and Selection Operator). It efficiently regularizes the loss function with an additive L1-constraint by alternating between forward and backward steps at each iteration. The method we propose here is generic in the sense that it can be used with any local features or feature sets representing the content of an image region.

Background: Mental health can be divided into three groups: mental well-being, mental disorder, and mental illness, whereas the last one also includes neuropsychiatric illness. Nature has always been a central part for humanity, and it is well known that it’s a place that induces mental and physical health. Even though studies have shown that the city hustle is bad for brain activity and the mental health, humans mostly gather in the city. Being in the city leads to pondering and is not advantageous for the mental wellbeing. Aim: The aim is to study the experience of the impact of nature on the mental health. Method: A literature-based study was conducted on an analysis of qualitative research. Results: This study showed three main themes and six subthemes. The main themes were a strengthened self, changed feelings and to feel belonging. The subthemes were to handle life with nature as a resource, to be in the present, to feel freedom, to feel peaceful, to feel belonging to nature and to bring people together. Conclusion: Overall, nature has a positive impact on mental health, and it is a place where acceptance is central and gives a positive feeling.

L'objectif de cette thèse est d'améliorer la recherche d'objets visuels à l'aide de l'interactivité avec l'utilisateur. Notre solution est de construire un système intéractif permettant aux utilisateurs de définir leurs propres concepts visuels à partir de certains mots-clés visuels. Ces mots-clés visuels, qui en théorie représentent les mots visuels les plus informatifs liés à une catégorie d'objets, sont appris auparavant à l'aide d'un algorithme d'apprentissage supervisé et d'une manière discriminative. Le challenge est de construire des mots-clés visuels concis et interprétables. Notre contribution repose sur deux points. D'abord, contrairement aux approches existantes qui utilisent les sacs de mots, nous proposons d'employer les descripteurs locaux sans aucune quantification préalable. Deuxièmement, nous proposons d'ajouter une contrainte de régularisation à la fonction de perte de notre classifieur pour favoriser la parcimonie des modèles produits. La parcimonie est en effet préférable pour sa concision (nombre de mots visuels réduits) ainsi pour sa diminution du temps de prédiction. Afin d'atteindre ces objectifs, nous avons développé une méthode d'apprentissage à instances multiples utilisant une version modifiée de l'algorithme BLasso. Cet algorithme est une forme de boosting qui se comporte similairement au LASSO (Least Absolute Shrinkage and Selection Operator). Il régularise efficacement la fonction de perte avec une contrainte additive de type L1 et ceci en alternant entre des itérations en avant et en arrière. La méthode proposée est générique dans le sens où elle pourrait être utilisée avec divers descripteurs locaux voire un ensemble structuré de descripteurs locaux qui décrit une région locale de l'image. / This thesis is an attempt to improve visual object retrieval by allowing users to interact with the system. Our solution lies in constructing an interactive system that allows users to define their own visual concept from a concise set of visual patches given as input. These patches, which represent the most informative clues of a given visual category, are trained beforehand with a supervised learning algorithm in a discriminative manner. Then, and in order to specialize their models, users have the possibility to send their feedback on the model itself by choosing and weighting the patches they are confident of. The real challenge consists in how to generate concise and visually interpretable models. Our contribution relies on two points. First, in contrast to the state-of-the-art approaches that use bag-of-words, we propose embedding local visual features without any quantization, which means that each component of the high-dimensional feature vectors used to describe an image is associated to a unique and precisely localized image patch. Second, we suggest using regularization constraints in the loss function of our classifier to favor sparsity in the models produced. Sparsity is indeed preferable for concision (a reduced number of patches in the model) as well as for decreasing prediction time. To meet these objectives, we developed a multiple-instance learning scheme using a modified version of the BLasso algorithm. BLasso is a boosting-like procedure that behaves in the same way as Lasso (Least Absolute Shrinkage and Selection Operator). It efficiently regularizes the loss function with an additive L1-constraint by alternating between forward and backward steps at each iteration. The method we propose here is generic in the sense that it can be used with any local features or feature sets representing the content of an image region. / تعالج هذه الأطروحة مسألة البحث عن الأشياء في الصور الثابتة و هي محاولة لتحسين نتائج البحث المنتظرة عن طريق تفاعل المستخدم مع النظام . يتمثل الحل المقترح في تصميم نظام تفاعلي يتيح للمستخدم صياغة مفهومه المرئي عن طريق مجموعة مقتضبة من أجزاء صغيرة للصور هي عبارة عن كلمات مفاتيح قد تم تعلمها سابقا عن طريق تعلم آلي استنتاجي . يمكن للمستخدم حينئذ تخصيص أنموذجه أولا بالاختيار ثم بترجيح الأجزاء التي يراها مناسبة . يتمثل التحدي القائم في كيفية توليد نماذج مرئية مفهومة و مقتضبة . نكون قد ساهمنا في هذا المجال بنقطتين أساسيتين تتمثل الأولى في إدماج الواصفات المحلية للصور دون أي تكميم ، و بذلك يكون كل مكون من ناقلات الميزات ذات الأبعاد العالية مرتبط حصريا بمكان وحيد و محدد في الصورة . ثانيا ، نقترح إضافة قيود تسوية لدالة الخسارة من أجل التحصل على حلول متفرقة و مقتضبة . يساهم ذلك في تقلص عدد هذه الأجزاء المرئية و بالتالي في ربح إضافي لوقت التكهن . في إطار تحقيق الأهداف المرسومة ، قمنا بإعداد مشروع تعلم قائم على تعدد الأمثلة يرتكز أساسا على نسخة محورة لخوارزمية بلاسو . تجدر الإشارة في الأخير أنه يمكن توظيف هذا العمل باستخدام نوع أو عدة أنواع من الواصفات المحلية للصور.