This thesis presents a novel Dynamic Gesture Language Recognition (DGLR) system for human-robot and inter-robot communication. We developed and implemented an experimental setup consisting of a humanoid robot/android able to recognize and execute in real time all the arm gestures of the Dynamic Gesture Language (DGL) in similar way as humans do. Our DGLR system comprises two main subsystems: an image processing (IP) module and a linguistic recognition system (LRS) module. The IP module enables recognizing individual DGL gestures. In this module, we use the bag-of-features (BOFs) and a local part model approach for dynamic gesture recognition from images. Dynamic gesture classification is conducted using the BOFs and nonlinear support-vector-machine (SVM) methods. The multiscale local part model preserves the temporal context. The IP module was tested using two databases, one consisting of images of a human performing a series of dynamic arm gestures under different environmental conditions and a second database consisting of images of an android performing the same series of arm gestures. The linguistic recognition system (LRS) module uses a novel formal grammar approach to accept DGL-wise valid sequences of dynamic gestures and reject invalid ones. LRS consists of two subsystems: one using a Linear Formal Grammar (LFG) to derive the valid sequence of dynamic gestures and another using a Stochastic Linear Formal Grammar (SLFG) to occasionally recover gestures that were unrecognized by the IP module. Experimental results have shown that the DGLR system had a slightly better overall performance when recognizing gestures made by a human subject (98.92% recognition rate) than those made by the android (97.42% recognition rate).

The aim of this thesis is to analyse the inviolability of the diplomatic bag and its abuse. The first chapter introduces the current work of the International Law Commission relating to subsequent conduct of treaties. Second chapter defines diplomatic privileges and immunities, three theories of diplomatic immunity and its historical background. Third chapter elaborately analyses treatment of diplomatic bag in Vienna Convention on Diplomatic Relations. Fourth chapter presents an overview of cases of abuse of the diplomatic bag or violation of its inviolability. Finally it introduces options, how states can fight against such abuses.

Dans cette thèse, on s'intéresse à des algorithmes d'analyse de données utilisant des marches aléatoires sur des graphes de voisinage, ou graphes géométriques aléatoires, construits à partir des données. On sait que les marches aléatoires sur ces graphes sont des approximations d'objets continus appelés processus de diffusion. Dans un premier temps, nous utilisons ce résultat pour proposer un nouvel algorithme de partitionnement de données flou de type recherche de modes. Dans cet algorithme, on définit les paquets en utilisant les propriétés d'un certain processus de diffusion que l'on approche par une marche aléatoire sur un graphe de voisinage. Après avoir prouvé la convergence de notre algorithme, nous étudions ses performances empiriques sur plusieurs jeux de données. Nous nous intéressons ensuite à la convergence des mesures stationnaires des marches aléatoires sur des graphes géométriques aléatoires vers la mesure stationnaire du processus de diffusion limite. En utilisant une approche basée sur la méthode de Stein, nous arrivons à quantifier cette convergence. Notre résultat s'applique en fait dans un cadre plus général que les marches aléatoires sur les graphes de voisinage et nous l'utilisons pour prouver d'autres résultats : par exemple, nous arrivons à obtenir des vitesses de convergence pour le théorème central limite. Dans la dernière partie de cette thèse, nous utilisons un concept de topologie algébrique appelé homologie persistante afin d'améliorer l'étape de "pooling" dans l'approche "sac-de-mots" pour la reconnaissance de formes 3D. / In this thesis, we study data analysis algorithms using random walks on neighborhood graphs, or random geometric graphs. It is known random walks on such graphs approximate continuous objects called diffusion processes. In the first part of this thesis, we use this approximation result to propose a new soft clustering algorithm based on the mode seeking framework. For our algorithm, we want to define clusters using the properties of a diffusion process. Since we do not have access to this continuous process, our algorithm uses a random walk on a random geometric graph instead. After proving the consistency of our algorithm, we evaluate its efficiency on both real and synthetic data. We then deal tackle the issue of the convergence of invariant measures of random walks on random geometric graphs. As these random walks converge to a diffusion process, we can expect their invariant measures to converge to the invariant measure of this diffusion process. Using an approach based on Stein's method, we manage to obtain quantitfy this convergence. Moreover, the method we use is more general and can be used to obtain other results such as convergence rates for the Central Limit Theorem. In the last part of this thesis, we use the concept of persistent homology, a concept of algebraic topology, to improve the pooling step of the bag-of-words approach for 3D shapes.

The study of RNA-binding proteins has recently increased in importance due to discoveries of their larger role in cellular processes. One study currently conducted at Umeå University involves constructing a model that will be able to improve our knowledge about T-cells by explaining how these cells work in different diseases. But before this model can become a reality, Umeå Univerity needs to investigate the relation between RNA and RNA-binding proteins and find proteins of which highly contribute to the activity of the RNA-binding proteins. To do so, they have decided to use four penalized regression Machine Learning models to analyse protein sequences from CD4 cells. These models consist of a ridge penalized model, an elastic net model, a neural network model, and a Bayesian model. The results show that the models have a number of RNA-binding protein sequences in common which they list as highly decisive in their predictions.

Bakgrund: Dagligen drabbas mer än 25 personer av hjärtstopp utanför sjukhus där ungefär 500 av dessa räddas årligen. Luftvägshanteringen är en av de viktigaste faktorerna under hjärt-och lungräddning och en obehandlad hypoxi i samband med hjärtstopp ökar risken för att patienten kan erhålla neurologiska skador. Enligt Erikssons omvårdnadsteori kan olika former av lidande upplevas, men när kroppen, själen och anden är i balans uppnås hälsa. För att hantera luftvägen kan ambulanspersonal använda sig utav mask-och blåsa eller larynxmask. Endotrakeal intubation är även ett alternativ, men kräver särskild kompetens i Sverige vilken främst specialistsjuksköterska inom anestesisjukvård innehar. Studier visar på en låg procentuell framgång för antal lyckade försök gällande utövandet av endotrakeal intubation prehospitalt och att larynxmask numera används i stället av ambulanspersonalenför att den kan appliceras snabbt och enkelt. Syfte: Syftet var att belysa om förekommande luftvägshjälpmedel vid hjärtstopp utanför sjukhus kan påverka patientens utfall vid återkomst av spontan cirkulation. Metod: Litteraturöversikt med systematisk ansats. Cinahl plus och PubMed har använts som databassökning. Totalt 15 artiklar av kvantitativ metod inkluderades. Artiklarna har därefter analyserats genom integrerad analys. Resultat: De signifikanta huvudfynden som framkom med var att luftvägshantering med mask-och blåsa påvisade en hög prevalens för gynnsamt neurologiskt utfall och överlevnad medan endotrakeal intubation påvisade en högre prevalens för återgång av spontan cirkulation. Slutsats: Utifrån resultatet visade sig användning av mask-och blåsa ge mest utdelning för att uppnå ett gynnsamt neurologiskt utfall och ökad chans till överlevnad för patienten. Däremot framkom det att användning av endotrakealtub vid prehospitalt hjärtstopp medförde störst chans till återkomst av spontan cirkulation. Av de tre förekommande luftvägshjälpmedlen att använda sig av vid prehospitalt hjärtstopp kan det förekomma skillnader i utfallet för patienten. Det förekommer däremot inte tillräckligt med stora skillnader och resultatet bördärmed tolkas med försiktighet då det anses behövas fler studier inom området. / Background: Every day more than 25 people suffer from out-of hospital cardiac arrest, of which approximately 500 rescued annually. Airway management is one of the most important factors in cardiopulmonary resuscitation and an untreated hypoxia in conjunction with cardiac arrest increases the patient’s risk of receiving neurological damage. According to Eriksson's nursing theory, various forms of suffering can be experienced, and a state of health can only be achieved when the body, soul and spirit are in balance. To manage the airway, the ambulance clinician can use a bag-valve mask or laryngeal mask. Endotracheal intubation is also an alternative, but in Sweden, it requires specific competence that mainly specialist nurses in anesthesia care possess. Studies shows a low success rate regarding the practice of performing a prehospital endotracheal intubation and that laryngeal mask nowadays more used instead by ambulance staff because it’s applied quickly and easily. Aim: The purpose was to shed light on whether the available respiratory aids in out-of-hospital cardiac arrest can affect the patient outcomes on the return of spontaneous circulation. Method: Literature overview with systematic approach. Cinahl plus and PubMed has been used as database search. A total of 15 articles of quantitative method were included. The articles were analyzed through integrated analysis. Results: The significant main findings that emerged were that airway management with bag-valve mask correlated with a high prevalence for favorable neurological outcome and survival while endotracheal intubation showed a higher prevalence for return of spontaneous circulation. Conclusion: Based on the results, the use of bag-valve mask found to be the best option to achieve a favorable neurological outcome and increased chance of survival for the patient. However, the use of endotracheal tube in out-ofhospital cardiac arrest for increasing the chance of the patient regaining return of spontaneous circulation. Out of the three available airway aids to use in out-of-hospital cardiac arrest, there may be differences in the outcome for the patient. However, there are not enough significant differences, and the result therefore should be interpreted with caution as it is considered that more studies in the subject required.

Les documents visuels comprenant des images et des vidéos sont en croissance rapide sur Internet et dans nos collections personnelles. Cela nécessite une analyse automatique du contenu visuel qui fait appel à la conception de méthodes intelligentes pour correctement indexer, rechercher et récupérer des images et des vidéos. Cette thèse vise à améliorer la détection automatique des concepts dans les vidéos sur Internet. Nos contributions portent sur des différents niveaux dans le cadre de détection de concept et peuvent être divisés en trois parties principales. La première partie se focalise sur l’amélioration du modèle de représentation des vidéos « Bag-of-Words (BOW) » en proposant un nouveau mécanisme de construction qui utilise des étiquettes de concepts et une autre technique qui ajoute un raffinement à la signature BOW basée sur la distribution de ses éléments. Nous élaborons ensuite des méthodes pour intégrer des entités semblables et dissemblables pour construire des modèles de reconnaissance améliorés dans la deuxième partie. A ce stade-là, nous observons l’information potentielle que les concepts partagent et construisons des modèles pour les méta-concepts dont sont dérivés les résultats spécifiques de concepts. Cela améliore la reconnaissance des concepts qui ont peu d’exemples annotés. Enfin, nous concevons certaines méthodes d'apprentissage semi-supervisé pour bénéficier de la quantité importante de données non étiquetées. Nous proposons des techniques pour améliorer l'algorithme de cotraining avec une sélection optimale des classifieurs utilisés. / Visual material comprising images and videos is growing ever so rapidly over the internet and in our personal collections. This necessitates automatic understanding of the visual content which calls for the conception of intelligent methods to correctly index, search and retrieve images and videos. This thesis aims at improving the automatic detection of concepts in the internet videos by exploring all the available information and putting the most beneficial out of it to good use. Our contributions address various levels of the concept detection framework and can be divided into three main parts. The first part improves the Bag of Words (BOW) video representation model by proposing a novel BOW construction mechanism using concept labels and by including a refinement to the BOW signature based on the distribution of its elements. We then devise methods to incorporate knowledge from similar and dissimilar entities to build improved recognition models in the second part. Here we look at the potential information that the concepts share and build models for meta-concepts from which concept specific results are derived. This improves recognition for concepts lacking labeled examples. Lastly we contrive certain semi-supervised learning methods to get the best of the substantial amount of unlabeled data. We propose techniques to improve the semi-supervised cotraining algorithm with optimal view selection.

Indiana University-Purdue University Indianapolis (IUPUI) / Acquired Immunodeficiency Syndrome (AIDS), as it would later be known, began to appear in the United States in 1981. Medical professionals from around the country began to track a mysterious set of illnesses that were affecting previously healthy people, most of who were homosexual men. As the disease spread, it was clear that homosexual men were being most affected. There was no cure to this illness which was quickly killing those infected. In October 1981, the Indianapolis Bag Ladies, a group of gay men, began as a simple Halloween Bus Tour around the city. Coby Palmer, Gary Johnson, and Ed Walsh teamed up by renting three charter busses for their new “Bag Ladies Bus.” Their campy drag involved multiple costume changes that required them to tote bags around, thus earning their name. By 1982, the Bag Ladies knew they needed to do more than have a party. The second bus tour was all about collecting money and creating a “war chest” for the gay community of Indianapolis in case AIDS made its way to the city. In doing this, they became one of the first grassroots HIV/AIDS support groups in the United States. After over 38 years of continued efforts, the Indianapolis Bag Ladies have impacted the Indianapolis LGBTQ communities through a variety of programs that expanded beyond the original bus tour. This thesis explores and analyzes these efforts which include Nurse Safe Sexx, a safe sex campaign; the Damien Center, a HIV/AIDS health clinic; and the Buddy House and Buddy Support Program, two programs connecting people with AIDS to support programs. The final chapter of this thesis expands on the discussion through a public program hosted by the Indiana Historical Society and demonstrates how programs surrounding these topics can be successful for museums and participants.

[pt] Hoje em dia, grande parte do conhecimento acumulado está armazenada em forma de dados. Dentre as ferramentas capazes de atuar como modelos representativos de sistemas reais, os Sistemas de Inferência Fuzzy têm se destacado pela capacidade de fornecer modelos precisos e, ao mesmo tempo, interpretáveis. A interpretabilidade é obtida a partir de regras linguísticas, que podem ser extraídas de bases de dados bases históricas e que permitem ao usuário compreender a relação entre as variáveis do problema. Entretanto, tais sistemas sofrem com a maldição da dimensionalidade ao lidar com problemas complexos, isto é, com um grande número de variáveis de entrada ou padrões, gerando problemas de escalabilidade. Esta dissertação apresenta um novo algoritmo de geração automática de regras, denominado RandomFIS, especificamente para problemas de classificação, capaz de lidar com grandes bases de dados tanto em termos de número de variáveis de entrada (atributos) quanto em termos de padrões (instâncias). O modelo RandomFIS utiliza os conceitos de seleção de variáveis (Random Subspace) e Bag of Little Bootstrap (BLB), que é uma versão escalável do Bootstrapping, criando uma estrutura de comitê de classificadores. O RandomFIS é avaliado em várias bases benchmark, demostrando ser um modelo robusto que mantém a interpretabilidade e apresenta boa acurácia mesmo em problemas envolvendo grandes bases de dados. / [en] Nowadays, much of the accumulated knowledge is stored as data. Among the tools capable of acting as representative models of real systems, Fuzzy Inference Systems are recognized by their ability to provide accurate and at the same time interpretable models. Interpretability is obtained from linguistic rules, which can be extracted from historical databases. These rules allow the end user to understand the relationship between variables in a specific problem. However, such systems experience the curse of dimensionality when handling complex problems, i.e. with a large number of input variables or patterns in the dataset, giving origin to scalability issues. This dissertation presents a new algorithm for automatic generation of fuzzy rules, called RandomFIS, specifically for classification problems, which is able to handle large databases both in terms of number of input variables (attributes) and in terms of patterns (instances). The RandomFIS model makes use of feature selection concepts (Random Subspace) and Bag of Little Bootstrap (BLB), which is a scalable version of Bootstrapping, creating a classifier committee structure. RandomFIS is tested in several benchmark datasets and shows to be a robust model that maintains interpretability and good accuracy even in problems involving large databases.

Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12- month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1– 3.5% and of the more stable substrates by 3.8–10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4–2.2% and that of low-quality litter by 0.9–1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate.

Background: Movies have become an important marketing and advertising tool that can influence consumer behaviour and trends. Reading film reviews is an im- important part of watching a movie, as it can help viewers gain a general under- standing of the film. And also, provide filmmakers with feedback on how their work is being received. Sentiment analysis is a method of determining whether a review has positive or negative sentiment, and this study investigates a machine learning method for classifying sentiment from film reviews. Objectives: This thesis aims to perform comparative sentiment analysis on textual IMDb movie reviews using lexicon-based and BERT neural network models. Later different performance evaluation metrics are used to identify the most effective learning model. Methods: This thesis employs a quantitative research technique, with data analysed using traditional machine learning. The labelled data set comes from an online website called Kaggle (https://www.kaggle.com/datasets), which contains movie review information. Algorithms like the lexicon-based approach and the BERT neural networks are trained using the chosen IMDb movie reviews data set. To discover which model performs the best at predicting the sentiment analysis, the constructed models will be assessed on the test set using evaluation metrics such as accuracy, precision, recall and F1 score. Results: From the conducted experimentation the BERT neural network model is the most efficient algorithm in classifying the IMDb movie reviews into positive and negative sentiments. This model achieved the highest accuracy score of 90.67% over the trained data set, followed by the BoW model with an accuracy of 79.15%, whereas the TF-IDF model has 78.98% accuracy. BERT model has the better precision and recall with 0.88 and 0.92 respectively, followed by both BoW and TF-IDF models. The BoW model has a precision and recall of 0.79 and the TF-IDF has a precision of 0.79 and a recall of 0.78. And also the BERT model has the highest F1 score of 0.88, followed by the BoW model having a F1 score of 0.79 whereas, TF-IDF has 0.78. Conclusions: Among the two models evaluated, the lexicon-based approach and the BERT transformer neural network, the BERT neural network is the most efficient, having a good performance score based on the measured performance criteria.