Abstract: The present Master thesis broaches the issue of market segmentation and its importance for the Industry and Transportation department of 3M Česko. Market segmentation has been recognized to be a very important tool for strategic marketing planning but currently, its implementation at 3M in the Czech Republic is in an early stage and therefore rather unorganized and not yet framed by precise guidelines. The hypothesis is that linking together need-based and descriptive customer behavior characteristics would lead to more effective market segmentation within the 3M Industry and Transportation department. Therefore, the goal of this paper is to first of all test if the hypothesis is right and if so develop a segmentation method that combines descriptive as well as need-based segmentation criteria. It is to be shown that such an approach could improve segmentation efforts at 3M Česko and lead to more reliable results. The question that arises is whether 3M has understood the benefits market segmentation can bring for the company. And what has to be done in order to improve clustering of customers and to increase 3M's sales volume? Can segmentation take place on one level or is a multi-layer approach the better option? The present study tries to answer these questions by comparing theoretical findings from an extensive literature review with results obtained from semistructured interviews with 3M sales and marketing managers. Above all, one of the main points for improvement in the future is the collection of more market data and the combination of primary data with secondary information already available. The interviews have shown that even though managers are aware of the difficulty to understand 3M's complex and broad market, the feeling predominates that not enough effort is put into gathering additional information. Furthermore, it is recommended to perform descriptive segmentation through the usage of industry coding on a division-wide level, whereas need-based segmentation will be subject of each division individually. Moreover, keeping track of customer data in a standardized database is the key to success. The given recommendations are divided into actions that can be undertaken in the short, medium and long run in order to reach a department-wide effective and efficient use of the multi-layer segmentation in an organized and timely manner.

This master thesis focuses on proving the relevance and importance of body image in marketing. In theoretical part the thesis initially outlines basic marketing knowledge with special concentration on marketing research and segmentation. Next, sources of data and methodology used in the analytical part are described. In the analytical part itself the thesis firstly proves the relevance of body image as a segmentation variable on a few examples. First variable used for segmentation is the natural colour of hair, next variable used is the approach to own appearance and the last variable used is value orientation in context with body image. Finally this master thesis describes qualitative research on the topic influence of marketing on social perception of body image, that was carried out by the author.

<p>In order for agricultural retailers to remain successful in a volatile market, it is imperative that they understand the needs and buying behaviors of their producers. These producers can be divided into four buying segments: the Economic buyer, the Agronomic buyer, the Business buyer, and the Performance buyer by identifying similar buying characteristics. The retailer’s ability to correctly predict their producers into the correct buying segment would allow them to optimally market to individual producers offering a consistent value proposition across all farms. This research uses cluster analysis to segment the agricultural market, multinomial logistic regression models to extract the variables that determined cluster classifications, and accuracy measures from a multilevel confusion matrix to assess retailers’ ability to classify their producers into the correct buying segment. Retailers predicted 70% of their producers into the correct segment. However, the accuracies differed across each segment leaving opportunity for an inconsistent value proposition across all segments.  </p>

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With the desired deployment of Artificial Intelligence (AI), concerns over whether AI can “communicate” why it has made its decisions is of particular importance. In this thesis, we utilize predictive entropy (PE) as an surrogate for predictive uncertainty and report it for various test-time conditions that alter the testing distribution. This is done to evaluate the potential for PE to indicate when users should trust or dis- trust model predictions under dataset shift or out-of-distribution (OOD) conditions, two scenarios that are prevalent in real-world settings. Specifically, we trained an ensemble of three 2D-UNet architectures to segment synthetically damaged regions in fractional anisotropy scalar maps, a widely used diffusion metric to indicate mi- crostructural white-matter damage. Baseline ensemble statistics report that the true positive rate, false negative rate, false positive rate, true negative rate, Dice score, and precision are 0.91, 0.091, 0.23, 0.77, 0.85, and 0.80, respectively. Test-time PE was reported before and after the ensemble was exposed to increasing geometric distortions (OOD), adversarial examples (OOD), and decreasing signal-to-noise ratios (dataset shift). We observed that even though PE shows a strong negative correlation with model performance for increasing adversarial severity (ρAE = −1), this correlation is not seen under distortion or SNR conditions (ρD = −0.26, ρSNR = −0.30). However, the PE variability (PE-Std) between individual model predictions was shown to be a better indicator of uncertainty as strong negative correlations between model performance and PE-Std were seen during geometric distortions and adversarial ex- amples (ρD = −0.83, ρAE = −1). Unfortunately, PE fails to report large absolute uncertainties during these conditions, thus restricting the analysis to correlative relationships. Finally, determining an uncertainty threshold between “certain” and “uncertain” model predictions was seen to be heavily dependant on model calibra- tion. For augmentation conditions close to the training distribution, a single threshold could be hypothesized. However, caution must be taken if such a technique is clinically applied, as model miscalibration could nullify such a threshold for samples far from the distribution. To ensure that PE or PE-Std could be used more broadly for uncertainty estimation, further work must be completed. / Thesis / Master of Applied Science (MASc)

In this thesis, algorithms for image registration and segmentation are developed to locate and identify DU penetrators and associated metal projectile debris on or near the surface at the US DoD firing ranges and proving grounds. The proposed registration algorithm supports fusing the LWIR and visible images. Control points are indentified by area-base detection and followed by eliminating outliers. Associated with bilinear interpolation, the gravity centers of control points are used to estimate the transformation parameters. The segmentation with a statistical detector is developed to improve the fusion result. The power spectrum density is invoked to extract and identify the image properties, and the probability of each pixel classified as target further the decision. The final result is consistent with the true vision and carries distinguished target information. The combination of registration and segmentation approaches can effectively orientate and investigate the target area.

Temporal data is ubiquitous nowadays and can be easily found in many applications. Consider the extensively studied social media website Twitter. All the information can be associated with time stamps, and thus form different types of data sequences: a sequence of feature values of users who retweet a message, a sequence of tweets from a certain user, or a sequence of the evolving friendship networks. Mining these data sequences is an important task, which reveals patterns in the sequences, and it is a very challenging task as it usually requires different techniques for different sequences. The problem becomes even more complicated when the sequences are correlated. In this dissertation, we study the following two types of data sequences, and we show how to carefully exploit within-sequence and across-sequence correlations to develop more effective and scalable algorithms. 1. Multi-dimensional value sequences: We study sequences of multi-dimensional values, where each value is associated with a time stamp. Such value sequences arise in many domains such as epidemiology (medical records), social media (keyword trends), etc. Our goals are: for individual sequences, to find a segmentation of the sequence to capture where the pattern changes; for multiple correlated sequences, to use the correlations between sequences to further improve our segmentation; and to automatically find explanations of the segmentation results. 2. Social media post sequences: Driven by applications from popular social media websites such as Twitter and Weibo, we study the modeling of social media post sequences. Our goal is to understand how the posts (like tweets) are generated and how we can gain understanding of the users behind these posts. For individual social media post sequences, we study a prediction problem to find the users' latent state changes over the sequence. For dependent post sequences, we analyze the social influence among users, and how it affects users in generating posts and links. Our models and algorithms lead to useful discoveries, and they solve real problems in Epidemiology, Social Media and Critical Infrastructure Systems. Further, most of the algorithms and frameworks we propose can be extended to solve sequence mining problems in other domains as well. / Ph. D. / Temporal data is ubiquitous nowadays and can be easily found in many applications. Consider the extensively studied social media website Twitter. All the information can be associated with time stamps, and thus form different types of data sequences: a sequence of feature values of users who retweet a message, a sequence of tweets from a certain user, or a sequence of the evolving friendship networks. Mining these data sequences is an important task, which reveals patterns in the sequences, and helps downstream tasks like data compression and visualization. At the same time, it is a very challenging task as it usually requires different techniques for different sequences. The problem becomes even more complicated when the sequences are correlated. In this dissertation, we first study value sequences, where objects in the sequence are multidimensional data values, and move to text sequences, where each object in the sequence is a text document (like a tweet). For each of these data sequences, we study them either as independent individual sequences, or as a group of dependent sequences. We then show how to carefully exploit different types of correlations behind the sequences to develop more effective and scalable algorithms. Our models and algorithms lead to useful discoveries, and they solve real problems in Epidemiology, Social Media and Critical Infrastructure Systems. Further, most of the algorithms and frameworks we propose can be extended to solve sequence mining problems in other domains as well.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Este trabalho tem como objetivo desenvolver um método de segmentação de cenas em vídeos digitais que trate segmentos semânticamente complexos. Como prova de conceito, é apresentada uma abordagem multimodal que utiliza uma definição mais geral para cenas em telejornais, abrangendo tanto cenas onde âncoras aparecem quanto cenas onde nenhum âncora aparece. Desse modo, os resultados obtidos da técnica multimodal foram signifiativamente melhores quando comparados com os resultados obtidos das técnicas monomodais aplicadas em separado. Os testes foram executados em quatro grupos de telejornais brasileiros obtidos de duas emissoras de TV diferentes, cada qual contendo cinco edições, totalizando vinte telejornais / This work aims to develop a method for scene segmentation in digital video which deals with semantically complex segments. As proof of concept, we present a multimodal approach that uses a more general definition for TV news scenes, covering both: scenes where anchors appear on and scenes where no anchor appears. The results of the multimodal technique were significantly better when compared with the results from monomodal techniques applied separately. The tests were performed in four groups of Brazilian news programs obtained from two different television stations, containing five editions each, totaling twenty newscasts

This thesis presents and evaluates different methods to semantically segment 3D-models by rendered 2D-views. The 2D-views are segmented separately and then merged together. The thesis evaluates three different merge strategies, two different classification architectures, how many views should be rendered and how these rendered views should be arranged. The results are evaluated both quantitatively and qualitatively and then compared with the current classifier at Vricon presented in [30]. The conclusion of this thesis is that there is a performance gain to be had using this method. The best model was using two views and attains an accuracy of 90.89% which can be compared with 84.52% achieved by the single view network from [30]. The best nine view system achieved a 87.72%. The difference in accuracy between the two and the nine view system is attributed to the higher quality mesh on the sunny side of objects, which typically is the south side. The thesis provides a proof of concept and there are still many areas where the system can be improved. One of them being the extraction of training data which seemingly would have a huge impact on the performance.