Submitted by Cláudia Bueno (claudiamoura18@gmail.com) on 2015-10-28T20:28:40Z No. of bitstreams: 2 Dissertação - Mariana Rodrigues Gil Daher - 2014.pdf: 1461499 bytes, checksum: 8f2a5397278cfd4ed7d9b24f5840a0a8 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-29T10:18:41Z (GMT) No. of bitstreams: 2 Dissertação - Mariana Rodrigues Gil Daher - 2014.pdf: 1461499 bytes, checksum: 8f2a5397278cfd4ed7d9b24f5840a0a8 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-10-29T10:18:41Z (GMT). No. of bitstreams: 2 Dissertação - Mariana Rodrigues Gil Daher - 2014.pdf: 1461499 bytes, checksum: 8f2a5397278cfd4ed7d9b24f5840a0a8 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-02-14 / The aim of this study was to evaluate dental and facial anthropometric measures that may assist in planning aesthetic smile, especially in determining the width of the maxillary anterior teeth from the facial measurements. Volunteers who had their faces photographed in a standardized manner in postural position and during smiling were selected. Through a reading software images (ImageJ, USA) to measure calibration was made: interpupillary width (LIP); intercantal width (LIC); eye width (LO); face width (LF); interalar width at rest (LIAR); intercomissura width at rest (LICR); width interalar during smiling (LIAS); width intercomissura during smiling (LICs) and distance between the distal of canines during smiling (DCCaparente). Molding of the upper arch was performed to obtain plaster models, in which were measured the distance between the distal of canines obtained by the sum of the individual widths of the maxillary anterior teeth (DCCreal); between the cusp tips of canines (DCCcúspide); and the distance between the distal canine (DCCcurva). The 96 volunteers, 41 males and 55 females, had the measures compared by Student's t test, and statistically significant differences (p <0,001), except for LIC, with higher values for males. By means of the Pearson Correlation test correlations between facial and dental measurements were tested. Significant correlations (p <0,05) were found, however weak, for males: DCCcurva / LF (r = 0,32); DCCaparente / LF (r = 0,37); DCCaparente / LICR (r = 0,35); DCCaparente / LICs (r = 0,43); and for females: DCCaparente / LICR (r = 0,36). The measurements were divided by the dental facial measurements, and vice versa, in order to calculate reasons. The reasons DCCaparente: LICR (0,82) and LICR: DCCaparente (1,22) were statistically similar in males and females (p> 0,05). It was concluded that facial anthropometric measurements may aid in determining the width of the front teeth in front aspect photographs (DCCaparente), which indicates the existence of aesthetic harmony between the parts of the face and teeth. However, these ratios should not be used for exact, mathematical form, but as aids in planning aesthetic smile, since few significant correlations between measures were weak. / O objetivo deste trabalho foi avaliar medidas antropométricas faciais e dentais, que possam auxiliar no planejamento estético do sorriso, em especial na determinação da largura dos dentes anteriores superiores a partir das medidas faciais. Foram selecionados voluntários, os quais tiveram as faces fotografadas de forma padronizada em posição postural e durante o sorriso. Por meio de um software de leitura de imagens (ImageJ, EUA) foi feita a calibração das medidas: largura interpupilar (LIP); largura intercantal (LIC); largura do olho (LO); largura da face (LF); largura interalar em repouso (LIAR); largura intercomissura em repouso (LICR); largura interalar durante o sorriso (LIAS); largura intercomissura durante o sorriso (LICS) e distância entre as distais de caninos superiores durante o sorriso (DCCaparente). Moldagem do arco superior foi realizada para obtenção de modelos de gesso, nos quais foram aferidas a distância entre as distais de caninos obtida pela soma das larguras individuais dos dentes anteriores superiores (DCCreal); entre as pontas de cúspides de caninos (DCCcúspide); e distância entre as distais de caninos (DCCcurva). Os 96 voluntários, 41 do gênero masculino e 55 do gênero feminino, tiveram as medidas comparadas por meio do teste t de Student e apresentaram diferenças estatisticamente significativas (p<0,001), exceto para LIC, com valores maiores para o gênero masculino. Por meio do teste de Correlação de Pearson foram testadas correlações entre as medidas faciais e dentais. Foram encontradas correlações significativas (p<0,05), porém fracas, para o gênero masculino: DCCcurva/LF (r=0,32); DCCaparente/LF (r=0,37); DCCaparente/LICR (r=0,35); DCCaparente/LICS (r=0,43); e para o gênero feminino: DCCaparente/LICR (r=0,36). As medidas dentais foram divididas pelas medidas faciais, e vice-versa, a fim de calcular razões. As razões DCCaparente:LICR (0,82) e LICR:DCCaparente (1,22) foram estatisticamente semelhantes nos gêneros masculino e feminino (p>0,05). Concluiu-se que as medidas antropométricas faciais podem auxiliar na determinação da largura dos dentes anteriores sob aspecto frontal em fotografias (DCCaparente), o que indica a existência de harmonia estética entre os componentes da face e dentes. Contudo, estas proporções não devem ser empregadas de forma exata, matemática, e sim como auxiliares no planejamento estético do sorriso, uma vez que as poucas correlações significativas entre as medidas foram fracas.

This thesis explores and compares different methods of optimizing queries in Bayesian networks. Bayesian networks are graph-structured models that model probabilistic variables and their influences on each other; a query poses the question of what probabilities certain variables assume, given observed values on certain other variables. Bayesian inference (calculating these probabilities) is known to be NP-hard in general, but good algorithms exist in practice. Inference optimization traditionally concerns itself with finding and tweaking efficient algorithms, and leaves the choice of algorithms' parameters, as well as the construction of inference-friendly Bayesian network models, as an exercise to the end user. This thesis aims towards a more systematic approach to these topics: We try to optimize the structure of a given Bayesian network for inference, also taking into consideration what is known about the kind of queries that are posed. First, we implement several automatic model modifications that should help to make a model more suitable for inference. Examples of these are the conversion of definitions of conditional probability distributions from table form to noisy gates, and divorcing parents in the graph. Second, we introduce the concepts of usage profiles and query interfaces on Bayesian networks and try to take advantage of them. Finally, we conduct performance measurements of the different options available in the used library for Bayesian networks, to compare the effects of different options on speedup and stability, and to answer the question of which options and parameters represent the optimal choice to perform fast queries in the end product. The thesis gives an overview of what issues are important to consider when trying to optimize an application's query performance in Bayesian networks, and when trying to optimize Bayesian networks for queries. The project uses the SMILE library for Bayesian networks by the University of Pittsburgh, and includes a case study on script-generated Bayesian networks for troubleshooting by Scania AB.

Nowadays, financial derivatives play an increasingly important role in the global financial system, and options are popular structural financial derivatives, which attract much attention from academia and the industry. China Financial Futures Exchange (CFFEX) initiated the CSI 1000 index future and CSI 1000 index option in the Chinese market on July 22, 2022, which indicates a trend of acceleration in financial innovations in China’s financial market. This dissertation focuses on the volatility models in option pricing and modern numerical procedures that approximate option prices. In this dissertation, different stochastic volatility models, for example, the Black–Scholes model and the Heston stochastic volatility model, are introduced and applied to price in not only European options but also exotic options, which possess complicated payoff structures. Moreover, a comprehensive empirical analysis is conducted to demonstrate these option pricing algorithms based on the recent data of CSI 1000 index options in the Chinese market. / Business Administration/Finance

Yes / In this paper, we report the results of our recent research into the understanding of the exact distribution of a smile across the face, especially the distinction in the weight distribution of a smile between a genuine and a posed smile. To do this, we have developed a computational framework for the analysis of the dynamic motion of various parts of the face during a facial expression, in particular, for the smile expression. The heart of our dynamic smile analysis framework is the use of optical flow intensity variation across the face during a smile. This can be utilised to efficiently map the dynamic motion of individual regions of the face such as the mouth, cheeks and areas around the eyes. Thus, through our computational framework, we infer the exact distribution of weights of the smile across the face. Further, through the utilisation of two publicly available datasets, namely the CK+ dataset with 83 subjects expressing posed smiles and the MUG dataset with 35 subjects expressing genuine smiles, we show there is a far greater activity or weight distribution around the regions of the eyes in the case of a genuine smile. / Supported in part by the European Union's Horizon 2020 Programme H2020-MSCA-RISE-2017, under the project PDE-GIR with grant number 778035.

No description available.

No description available.

Yes / Firms widely use smiling models to create a positive background setting for advertisements. This study assesses the various effects of smiling in print advertisements across different stages of consumer decision-making, while also considering interaction effects between the genders of models and viewers. Empirical evidence comes from 175,647 consumer evaluations of 421 real advertisements across a broad spectrum of product categories (22). Beyond gender, a smiling model not only effects a positive attitude change but also influences a product's integration into a relevant set and a consumer's purchase intention. For female consumers, a smiling model of the same gender exerts a greater influence on positive brand attitude change and on purchase intention. Advertisers should avoid using non-smiling male models when targeting female consumers. In contrast, smiling models of both genders can positively influence male consumer reaction, while use of a female model should be avoided during the early stages.

Apresentamos duas abordagens para obter uma densidade de probabilidades para o preço futuro de um ativo: uma densidade preditiva, baseada em um modelo Bayesiano para série de tempo e uma densidade implícita, baseada na fórmula de precificação de opções de Black & Scholes. Considerando o modelo de Black & Scholes, derivamos as condições necessárias para obter a densidade implícita do preço do ativo na data de vencimento. Baseando-­se nas densidades de previsão, comparamos o modelo implícito com a abordagem histórica do modelo Bayesiano. A partir destas densidades, calculamos probabilidades de ordem e tomamos decisões de vender/comprar um ativo. Como exemplo, apresentamos como utilizar estas distribuições para construir uma fórmula de precificação. / We present two different approaches to obtain a probability density function for the stocks future price: a predictive distribution, based on a Bayesian time series model, and the implied distribution, based on Black & Scholes option pricing formula. Considering the Black & Scholes model, we derive the necessary conditions to obtain the implied distribution of the stock price on the exercise date. Based on predictive densities, we compare the market implied model (Black & Scholes) with a historical based approach (Bayesian time series model). After obtaining the density functions, it is simple to evaluate probabilities of one being bigger than the other and to make a decision of selling/buying a stock. Also, as an example, we present how to use these distributions to build an option pricing formula.

Face recognition is one of the most natural activities performed by the human beings. It has wide range of applications in the areas of Human Computer Interaction, Surveillance, Security etc. Face information of people can be obtained in a non-intrusive manner, without violating privacy. But, robust face recognition which is invariant under varying pose, illumination etc is still a challenging problem. The main aim of this thesis is to explore the usefulness of smile profile of human beings as an extra aid in recognizing people by faces. Smile profile of a person is the sequence of images captured by a camera when the person voluntarily smiles. Using sequence of images instead of a single image will increase the required computational resources significantly. The challenge here is to design a feature extraction technique from a smile sample, which is useful for authentication and is also efficient in terms of storage and computational aspects. There are some experimental evidences which support the claim that facial expressions have some person specific information. But, to the best of our knowledge, systematic study of a particular facial expression for biometrical purposes has not been done so far. The smile profile of human beings, which is captured under some reasonably controlled setup, is used for first time for face recognition purpose. As a first step, we applied two of the recent subspace based face classifiers on the smile samples. We were not able to obtain any conclusive results out of this experiment. Next we extracted features using only the difference vectors obtained from smile samples. The difference vectors depend only on the variations which occur in the corresponding smile profile. Hence any characterization we obtain from such features can be fully attributed to the smiling action. The feature extraction technique we employed is very much similar to PCA. The smile signature that we have obtained is named as Principal Direction of Change(PDC). PDC is a unit vector (in some high dimensional space) which represents the direction in which the major changes occurred during the smile. We obtained a reasonable recognition rate by applying Nearest Neighbor Classifier(NNC) on these features. In addition to that, these features turn out to be less sensitive to the speed of smiling action and minor variations in face detection and head orientation, while capturing the pattern of variations in various regions of face due to smiling action. Using set of experiments on PDC based features we establish that smile has some person specific characteristics. But the recognition rates of PDC based features are less than the recent conventional techniques. Next we have used PDC based features to aid a conventional face classifier. We have used smile signatures to reject some candidate faces. Our experiments show that, using smile signatures, we can reject some of the potential false candidate faces which would have been accepted by the conventional face classifier. Using this smile signature based rejection, the performance of the conventional classifier is improved significantly. This improvement suggests that, the biometric information available in smile profiles does not exist in still images. Hence the usefulness of smile profiles for biometric applications is established through this experimental investigation.

Submitted by Alison Vanceto (alison-vanceto@hotmail.com) on 2017-08-28T13:57:07Z No. of bitstreams: 1 DissNLO.pdf: 2139734 bytes, checksum: 9d9000013e5ab1fd3e860be06fc72737 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-09-06T13:18:03Z (GMT) No. of bitstreams: 1 DissNLO.pdf: 2139734 bytes, checksum: 9d9000013e5ab1fd3e860be06fc72737 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-09-06T13:18:12Z (GMT) No. of bitstreams: 1 DissNLO.pdf: 2139734 bytes, checksum: 9d9000013e5ab1fd3e860be06fc72737 (MD5) / Made available in DSpace on 2017-09-06T13:28:02Z (GMT). No. of bitstreams: 1 DissNLO.pdf: 2139734 bytes, checksum: 9d9000013e5ab1fd3e860be06fc72737 (MD5) Previous issue date: 2017-06-01 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / We present two different approaches to obtain a probability density function for the stock?s future price: a predictive distribution, based on a Bayesian time series model, and the implied distribution, based on Black & Scholes option pricing formula. Considering the Black & Scholes model, we derive the necessary conditions to obtain the implied distribution of the stock price on the exercise date. Based on predictive densities, we compare the market implied model (Black & Scholes) with a historical based approach (Bayesian time series model). After obtaining the density functions, it is simple to evaluate probabilities of one being bigger than the other and to make a decision of selling/buying a stock. Also, as an example, we present how to use these distributions to build an option pricing formula. / Apresentamos duas abordagens para obter uma densidade de probabilidades para o preço futuro de um ativo: uma densidade preditiva, baseada em um modelo Bayesiano para série de tempo e uma densidade implícita, baseada na fórmula de precificação de opções de Black & Scholes. Considerando o modelo de Black & Scholes, derivamos as condições necessárias para obter a densidade implícita do preço do ativo na data de vencimento. Baseando-se nas densidades de previsão, comparamos o modelo implícito com a abordagem histórica do modelo Bayesiano. A partir destas densidades, calculamos probabilidades de ordem e tomamos decisões de vender/comprar um ativo. Como exemplo, apresentamos como utilizar estas distribuições para construir uma fórmula de precificação.