The goal of the diploma thesis is insight into the problematics of spectator's violence at football stadiums and elaboration of proposal of the precautions which would lead to elimination of football hooliganism. Theoretical part contains general characteristic of football hooliganism, history of destructive behaviour at football stadium and activities regarding ensuring security during football matches. Practical part includes marketing research, benchmarking and proposal of the precautions which would solve problematics of spectator's violence at football stadiums in the Czech Republic.

Magister Scientiae Dentium - MSc(Dent) / Autologous bone grafting in conjunction with dental implant therapy is a well-accepted procedure in oral and maxillofacial rehabilitation. A variety of intraoral donor sites, such as the mandibular symphysis, the mandibular ramus and the maxillary tuberosity have been used in oral and maxillofacial reconstruction. However these sites are associated with complications. In order to reduce these complications, the anterior palate has been proposed as a potential donor site. However, the scientific literature in this regard is sparse, and larger studies are required to investigate the clinical potential of this proposed site. Aim: To determine the volume and density of available bone in the anterior palate that may be used for bone harvesting using cone-beam computed tomography (CBCT) in a select South African population. Materials and methods: One hundred previously acquired CBCT scans taken at the Diagnostic and Radiology Department of Tygerberg Oral Health Centre were analyzed for the required data. These were all acquired from a single CBCT machine (Newtom VGI®, Verona, Italy). The study sample included 52 females and 48 males ranging from ages 20 years to 80 years. The CBCT scans were divided into 3 different age groups. The first age group was between the ages of 20 and 39 years, the second age group was from 40 to 59 years and the third age group was ≥ 60 years. The volume and density of the anterior palate of the different age groups were analyzed using specific criterion. CBCT specific software (Simplant Pro Crystal®) Dentsply implants, Mannheim, Germany was used to standardize the data collection. All data was stored in a Microsoft Excel spreadsheet (Microsoft Corporation, Washington, USA). Results: The mean volume of the anterior palate in this study was 2.11 ± 0.55 cm3, with a minimum volume of 1.04 cm3 and a maximum volume of 3.82 cm3. There was no significant difference in the volume and density of the anterior palate between different age groups and no significant difference in the volume between males and females (p value = 0.227). Conclusions: The anterior palate affords a considerable amount of bone volume which is similar or even more than other intraoral donor sites. The anterior palate is a potential donor site for bone harvesting and CBCT may be regarded as an ideal tool to analyze the amount of bone available for harvesting.

OBJETIVO: Apresentar os resultados da técnica de derivação urinária continente descrita por Macedo, em relação à continência, achados operatórios e complicações cirúrgicas. MÉTODOS: De janeiro de 2006 a novembro de 2016, 29 pacientes foram submetidos à técnica de Macedo. Dados demográficos, tempo de hospitalização, tempo cirúrgico, tempo de seguimento, taxa de continência, capacidade do reservatório e complicações pós-operatórias foram avaliados. RESULTADOS: Sessenta e nove por cento eram masculinos e a mediana de idade foi de 16,9 anos. A etiologia principal foi meningomielocele (69,1%). A média do tempo cirúrgico foi 4,2 h (DP 0,9 2,9-6,3). A mediana do tempo de internação foi 10 dias (IIQ: 11,3; 5-51). A média de seguimento foi 3,3 anos (DP 2,2 0,3 – 9,8). Procedimento no colo vesical foi realizado em 12 pacientes (41,3%). A taxa de continência do conduto cateterizável foi de 82,8%. A capacidade do reservatório aumentou de 134,4 para 364,4 ml (p <0.0001). A taxa de continência melhorou significativamente (20 vs. 74%, p <0.0001). Não houve mudança na taxa de filtração glomerular a longo prazo (143.1 vs. 147 ml/min, p = 0.45). Taxa de morbidade foi 58% (25 complicações em 17 pacientes), 72% ocorreram nos primeiros 60 dias e 60% foram classificadas Clavien-Dindo I ou II. CONCLUSÃO: Nossos resultados em relação a taxas de continência, tempo cirúrgico e complicações demonstram que a enterocistoplastia de Macedo é viável, reprodutível e com bons resultados. / OBJECTIVE: To present the results of technique of continent urinary diversion, described by Macedo, in relation to continence, operative findings and postoperative complications. METHODS: From January 2006 to November 2016, 29 patients were underwent to urinary diversion by Macedo’s technique. Patients demographics, hospitalization time, surgical time, follow up, continence rate, reservoir capacity and postoperative complications were evaluated. RESULTS: Sixty nine percent were male and the median age was 16.9 years. The main etiology was meningomyelocele (69.1%). The mean surgical time was 4.2 hours (SD 0.9 range 2.9-6.3). The median length of hospital stay was 10 days (IQR: 11.3 range 5-51). The mean follow up was 3.3 years (SD 2.2 range 0.3 - 9.8). Procedure in the bladder neck was performed in 12 patients (41.3%). The continence rate of the catheterizable conduit was 82.8%. The reservoir capacity increased from 134.4 to 364.4 ml (p <0.0001). The continence rate improved significantly (20 vs. 74%, p <0.0001). There was no change in glomerular filtration rates in the long term (143.1 vs. 147 ml/min, p = 0.45). Morbidity rate was 58% (25 complications in 17 patients), 72% occurred within the first 60 days and 60% were classified as Clavien-Dindo I or II. CONCLUSION: Our results regarding continence rates, surgical time and complications demonstrated that Macedo’s enterocystoplasty is feasible, reproducible and with good result.

In recent years, deep learning, a critical technology in computer vision, has achieved remarkable milestones in many fields, such as image classification and object detection. In particular, it has also been introduced to address the problem of violence detection, which is a big challenge considering the complexity to establish an exact definition for the phenomenon of violence. Thanks to the ever increasing development of new technologies for surveillance, we have nowadays access to an enormous database of videos that can be analyzed to find any abnormal behavior. However, by dealing with such huge amount of data it is unrealistic to manually examine all of them. Deep learning techniques, instead, can automatically study, learn and perform classification operations. In the context of violence detection, with the extraction of visual harmful patterns, it is possible to design various descriptors to represent features that can identify them. In this research we tackle the task of generating new augmented datasets in order to try to simplify the identification step performed by a violence detection technique in the field of Deep Learning. The novelty of this work is to introduce the usage of DensePose model to enrich the images in a dataset by highlighting (i.e. by identifying and segmenting) all the human beings present in them. With this approach we gained knowledge of how this algorithm performs on videos with a violent context and how the violent detection network benefit from this procedure. Performances have been evaluated from the point of view of segmentation accuracy and efficiency of the violence detection network, as well from the computational point of view. Results shows how the context of the scene is the major indicator that brings the DensePose model to correct segment human beings and how the context of violence does not seem to be the most suitable field for the application of this model since the common overlap of bodies (distinctive aspect of violence) acts as disadvantage for the segmentation. For this reason, the violence detection network does not exploit its full potential. Finally, we understood how such augmented datasets can boost up the training speed by reducing the time needed for the weights-update phase, making this procedure a helpful adds-on for implementations in different contexts where the identification of human beings still plays the major role.

abstract: In this dissertation two research questions in the field of applied experimental design were explored. First, methods for augmenting the three-level screening designs called Definitive Screening Designs (DSDs) were investigated. Second, schemes for strategic subdata selection for nonparametric predictive modeling with big data were developed. Under sparsity, the structure of DSDs can allow for the screening and optimization of a system in one step, but in non-sparse situations estimation of second-order models requires augmentation of the DSD. In this work, augmentation strategies for DSDs were considered, given the assumption that the correct form of the model for the response of interest is quadratic. Series of augmented designs were constructed and explored, and power calculations, model-robustness criteria, model-discrimination criteria, and simulation study results were used to identify the number of augmented runs necessary for (1) effectively identifying active model effects, and (2) precisely predicting a response of interest. When the goal is identification of active effects, it is shown that supersaturated designs are sufficient; when the goal is prediction, it is shown that little is gained by augmenting beyond the design that is saturated for the full quadratic model. Surprisingly, augmentation strategies based on the I-optimality criterion do not lead to better predictions than strategies based on the D-optimality criterion. Computational limitations can render standard statistical methods infeasible in the face of massive datasets, necessitating subsampling strategies. In the big data context, the primary objective is often prediction but the correct form of the model for the response of interest is likely unknown. Here, two new methods of subdata selection were proposed. The first is based on clustering, the second is based on space-filling designs, and both are free from model assumptions. The performance of the proposed methods was explored visually via low-dimensional simulated examples; via real data applications; and via large simulation studies. In all cases the proposed methods were compared to existing, widely used subdata selection methods. The conditions under which the proposed methods provide advantages over standard subdata selection strategies were identified. / Dissertation/Thesis / Doctoral Dissertation Statistics 2020

abstract: Generative Adversarial Networks are designed, in theory, to replicate the distribution of the data they are trained on. With real-world limitations, such as finite network capacity and training set size, they inevitably suffer a yet unavoidable technical failure: mode collapse. GAN-generated data is not nearly as diverse as the real-world data the network is trained on; this work shows that this effect is especially drastic when the training data is highly non-uniform. Specifically, GANs learn to exacerbate the social biases which exist in the training set along sensitive axes such as gender and race. In an age where many datasets are curated from web and social media data (which are almost never balanced), this has dangerous implications for downstream tasks using GAN-generated synthetic data, such as data augmentation for classification. This thesis presents an empirical demonstration of this phenomenon and illustrates its real-world ramifications. It starts by showing that when asked to sample images from an illustrative dataset of engineering faculty headshots from 47 U.S. universities, unfortunately skewed toward white males, a DCGAN’s generator “imagines” faces with light skin colors and masculine features. In addition, this work verifies that the generated distribution diverges more from the real-world distribution when the training data is non-uniform than when it is uniform. This work also shows that a conditional variant of GAN is not immune to exacerbating sensitive social biases. Finally, this work contributes a preliminary case study on Snapchat’s explosively popular GAN-enabled “My Twin” selfie lens, which consistently lightens the skin tone for women of color in an attempt to make faces more feminine. The results and discussion of the study are meant to caution machine learning practitioners who may unsuspectingly increase the biases in their applications. / Dissertation/Thesis / Masters Thesis Computer Science 2020

Machine learning techniques are being applied to many data-intensive problems because they can accurately provide classification of complex data using appropriate training. Often, the performance of machine learning can exceed the performance of traditional techniques because machine learning can take advantage of higher dimensionality than traditional algorithms. In this work, acoustic data sets taken using a rapid scanning technique on concrete bridge decks provided an opportunity to both apply machine learning algorithms to improve detection performance and also to investigate the ways that training of neural networks can be aided by data augmentation approaches. Early detection and repair can enhance safety and performance as well as reduce long-term maintenance costs of concrete bridges. In order to inspect for non-visible internal cracking (called delaminations) of concrete bridges, a rapid inspection method is needed. A six-channel acoustic impact-echo sounding apparatus is used to generate large acoustic data sets on concrete bridge decks at high speeds. A machine learning data processing architecture is described to accurately detect and map delaminations based on the acoustic responses. The machine learning approach achieves accurate results at speeds between 25 and 45 km/h across a bridge deck and successfully demonstrates the use of neural networks to analyze this type of acoustic data. In order to obtain excellent performance, model training generally requires large data sets. However, in many potentially interesting cases, such as bridge deck defect detection, acquiring enough data for training can be difficult. Data augmentation can be used to increase the effective size of the training data set. Acoustic signal data augmentation is demonstrated in conjunction with a machine learning model for acoustic defect detection on bridge decks. Four different augmentation methods are applied to data using two different augmentation strategies. This work demonstrates that a "goldilocks" data augmentation approach can be used to increase machine learning performance when only a limited data set is available. The major technical contributions of this work include application of machine learning to acoustic data sets relevant to bridge deck inspection, solving an important problem in the field of nondestructive evaluation, and a more generalized approach to data augmentation of limited acoustic data sets to expand the classes of acoustic problems that machine learning can successfully address.

La technologie d’édition CRISPR peut être définie comme un outil biologique qui par son efficacité, son extrême précision et la facilité de sa modélisation, permet aujourd’hui de modifier le génome des organismes vivants en général et celui de l’homme en particulier. Sa découverte en 2012 par les chercheuses française et américaine, E. Charpentier et J. Doudna, récompensées par le Prix Nobel de chimie 2020, permet des applications thérapeutiques, au niveau germinal, pour des maladies à transmission autosomique dominante. Jusqu’ici, aucune technologie antérieure d’édition génomique, ni aucun diagnostic anténatal (DPI, DPN), n’avait été capable de prévenir ces maladies. En novembre 2018, le chercheur chinois Hé Jiankui annonce avoir utilisé la technologie CRISPR pour éditer des embryons humains viables. Selon Jiankui cette tentative consiste à modifier génétiquement des embryons humains en FIV afin de prévenir « définitivement » l’infection au VIH des futurs bébés. Cette modification génétique est ainsi transmissible à leur descendance. A partir de là, il s’est ouvert un tournant décisif de l’édition du génome humain héritable. Celui-ci s’apparente, dans notre contexte marqué par la convergence des NBIC, à une perspective transhumaniste. Car à la vérité, CRISPR n’aurait pas fait que prévenir l’infection au VIH chez ces bébés, il aurait surtout amélioré un caractère génétique conférant à ces derniers une immunité à vie contre le VIH-SIDA, avec pour principal corollaire, que de telles modifications sont héritables. Cette application non thérapeutique controversée, nous a conduit à nous demander successivement s’il est souhaitable de se servir de la technologie d’édition du génome CRISPR-Cas9, pour corriger au niveau germinal ou embryonnaire, une anomalie génétique afin de préserver le futur enfant de certains handicaps qui pourraient mettre en péril sa santé ou alourdir sa vie ?Jusqu’où de telles modifications pourraient être jugées comme bénéfiques ou à risque pour l’enfant et qui en aurait l’ultime légitimité d’en juger ?Peut-on alors affirmer, qu’en regard de l’extrême étroitesse qui existe entre la finalité thérapeutique d’une modification génomique germinale ou embryonnaire et l’amélioration/l’augmentation génétique, le risque d’altération de la nature humaine et de fait, la sortie hors de l’espèce humaine devient inéluctable ? / Doctorat en Philosophie / info:eu-repo/semantics/nonPublished

1) real_time_object_detection.py: Python script for deploying trained deep neural network in live stream.<br>2) augmentation.py: Python script for augmenting Detector images.<div>3) tcp_send_command.py: Python script for sending system stop CPI command to Gateway as a CPI message.</div>

Population structure is an important field of study due to its importance in finding underlying genetics of various diseases.This is why this thesis has looked at a newly presented deep convolutional autoencoder that has been showing promising results when compared to the state-of-the-art method for quantifying genetic similarities within population structure. The main focus was to introduce data augmentation in the form of artificial diploid recombination to this autoencoder in an attempt to increase performance and robustness of the network structure.  The training data for the network consist of arrays containing information about single-nucleotide polymorphisms present in an individual. Each instance of augmented data was simulated by randomising cuts based on the distance between the polymorphisms, and then creating a new array by alternating between the arrays of two randomised original data instances. Several networks were then trained using this data augmentation. The performance of the trained networks was compared to networks trained on only original data using several metrics. Both groups of networks had similar performance for most metrics. The main difference was that networks trained on only original data had a low genotype concordance on simulated data. This indicates an underlying risk using the original networks, which can be overcome by introducing the artificial recombination.