Submitted by Adriana Alves Rodrigues (aalves@espm.br) on 2018-10-09T12:07:43Z No. of bitstreams: 1 MPCC - MARYANNE AKEMI SAKAI.pdf: 1800094 bytes, checksum: 4cb5c8618e0dbdbec0c30d59dc1f6b16 (MD5) / Approved for entry into archive by Adriana Alves Rodrigues (aalves@espm.br) on 2018-10-09T12:08:17Z (GMT) No. of bitstreams: 1 MPCC - MARYANNE AKEMI SAKAI.pdf: 1800094 bytes, checksum: 4cb5c8618e0dbdbec0c30d59dc1f6b16 (MD5) / Approved for entry into archive by Debora Cristina Bonfim Aquarone (deborabonfim@espm.br) on 2018-10-09T12:11:44Z (GMT) No. of bitstreams: 1 MPCC - MARYANNE AKEMI SAKAI.pdf: 1800094 bytes, checksum: 4cb5c8618e0dbdbec0c30d59dc1f6b16 (MD5) / Made available in DSpace on 2018-10-09T12:11:54Z (GMT). No. of bitstreams: 1 MPCC - MARYANNE AKEMI SAKAI.pdf: 1800094 bytes, checksum: 4cb5c8618e0dbdbec0c30d59dc1f6b16 (MD5) Previous issue date: 2018-03-19 / Companies have a wide range of information from their customers, such as their registration and last purchase data. For a service provider in a market like Pet Shops, know your customer and know what the variables are. which most impact on your purchases is of paramount importance. The study gain additional gains when improving the purchasing model through variables consumer attitudes. The RFM analysis (frequency, frequency and monetary value) contributed clients to be grouped according to frequency, frequency and value transaction. The methodology was complemented with semi-structured interviews, 140 respondents, and multivariate linear regression models. The object of the study was the Pet Shop located in São Paulo. Five regression models were used to verify the incremental gains with the incorporation of attitudinal variables. The model that obtained R2 (68.13%) was the one that considered as a response variable the mean value of transaction of the year 2017 and as explanatory variables the quintiles of recency, frequency, of value, in addition to incomplete higher education, pet be considered as a child or as a member family, amount of other pets and cluster of clients that need to be remembered. As As a result of the research, it is noted that the perception of the family vis-a-vis the pet prevailing in the decision to purchase services and products, making variables such as distance from the residence to the Pet Shop or household income become secondary when determining the attitudinal variables that most influence the purchase decision. Understand each client and how it relates to the pet allows you to increase your transaction value because the client seeks above all the welfare of your pet. / As empresas possuem uma grande variedade de informação de seus clientes, como seu cadastro e dados de últimas compras. Para uma empresa prestadora de serviços presente em um mercado pulverizado como o de Pet Shops, conhecer o seu cliente e saber quais são as variáveis que mais impactam em suas compras é de suma importância. O estudo verifica se é possível auferir ganhos adicionais quando se aprimora o modelo de compras por meio de variáveis atitudinais do consumidor. A análise RFM (recência, frequência e valor monetário) contribuiu para que os clientes fossem agrupados conforme os padrões de recência, frequência e valor de transação. A metodologia foi complementada com entrevistas semiestruturadas, survey com 140 respondentes, e modelos de regressão linear multivariada. O objeto do estudo foi a loja de Pet Shop localizada em São Paulo. Foram conduzidos 5 modelos de regressão para se verificar os ganhos incrementais com a incorporação de variáveis atitudinais. O modelo que obteve o R2 mais alto (68,13%) foi aquele que contemplava como variável resposta o valor médio de transação do ano de 2017 e como variáveis explicativas os quintis da recência, da frequência, do valor, além de ensino superior incompleto, pet ser considerado como filho ou como membro da família, quantidade de outros pets e cluster de clientes que precisam ser lembrados. Como resultado da pesquisa, nota-se que a percepção da família frente ao pet exerce papel preponderante na decisão de compra de serviços e produtos, fazendo com que variáveis como distância da residência ao Pet Shop ou renda domiciliar tornem-se secundárias ao se determinar as variáveis atitudinais que mais influenciam na decisão de compra. Entender cada cliente e como ele se relaciona com o pet permite aumentar o seu valor de transação, pois o cliente busca acima de tudo o bem-estar de seu pet.

The need for a consistent and accurate production of long-term nearshore wave record is discussed. With multiple decades of offshore hindcasts and long, continuous data sets available, it is possible to create a nearshore synthetic wave record. The Brevard County coastline offers an area with a high quality 62-year long offshore hindcast, as well as an 11-year long and nearly continuous high-resolution nearshore wave record to compare with model performance. This thesis presents the steps in the development and performance of the synthetic nearshore wave record produced. A novel approach was used to compare, validate and calibrate this type of data which included using quantile-quantile plots and bin-averaged scatter plots. In a comparison between two reputable deep-water hindcasts (MSC50 and Wavewatch III), it was found that Wavewatch III significantly underpredicts wave heights in the higher range (>8m). At the nearshore STWAVE proves to be a simple, robust and fast way to create a nearshore wave record. Root mean squared error (0.272m-0.317m) and modified index of agreement (0.697-0.646) values for significant wave height show promising results for overall model performance with the currently available hindcast. Possible future improvements could be made by modifying the offshore hindcast to have finer grid resolution and further studying different friction models for the nearshore wave transformation model. Overall, the use of the MSC50 hindcast, to drive STWAVE at the nearshore, exhibits good agreement with ADCP data and analysis for significant wave heights can be used with confidence. Currently, no long-term trends can be resolved with the available record at the location used herein, yet more years of data/hindcasts in the future could provide more evident trends in wave climate change.

<p dir="ltr">Deep Neural Networks (DNNs) successfully perform object detection tasks, and the Con- volutional Neural Network (CNN) backbone is a commonly used feature extractor before secondary tasks such as detection, classification, or segmentation. In a DNN model, the relationship between the features learned by the model from the training data and the features leveraged by the model during test and deployment has motivated the area of feature interpretability studies. The work presented here applies equally to white-box and black-box models and to any DNN architecture. The metrics developed do not require any information beyond the feature vector generated by the feature extraction backbone. These methods are therefore the first methods capable of estimating black-box model robustness in terms of latent space complexity and the first methods capable of examining feature representations in the latent space of black box models.</p><p dir="ltr">This work contributes the following four novel methodologies and results. First, a method for quantifying the invariance and/or equivariance of a model using the training data shows that the representation of a feature in the model impacts model performance. Second, a method for quantifying an observed domain gap in a dataset using the latent feature vectors of an object detection model is paired with pixel-level augmentation techniques to close the gap between real and synthetic data. This results in an improvement in the model’s F1 score on a test set of outliers from 0.5 to 0.9. Third, a method for visualizing and quantifying similarities of the latent manifolds of two black-box models is used to correlate similar feature representation with increase success in the transferability of gradient-based attacks. Finally, a method for examining the global complexity of decision boundaries in black-box models is presented, where more complex decision boundaries are shown to correlate with increased model robustness to gradient-based and random attacks.</p>