It has previously been determined that using 3 or 4 points on a categorized response scale will fail to produce a continuous distribution of scores. However, there is no evidence, thus far, revealing the number of scale points that may indeed possess an approximate or sufficiently continuous distribution. This study provides the evidence to suggest the level of categorization in discrete scales that makes them directly comparable to continuous scales in terms of their measurement properties. To do this, we first introduced a novel procedure for simulating discretely scaled data that was both informed and validated through the principles of the Classical True Score Model. Second, we employed a measurement invariance (MI) approach to confirmatory factor analysis (CFA) in order to directly compare the measurement quality of continuously scaled factor models to that of discretely scaled models. The simulated design conditions of the study varied with respect to item-specific variance (low, moderate, high), random error variance (none, moderate, high), and discrete scale categorization (number of scale points ranged from 3 to 101). A population analogue approach was taken with respect to sample size (N = 10,000). We concluded that there are conditions under which response scales with 11 to 15 scale points can reproduce the measurement properties of a continuous scale. Using response scales with more than 15 points may be, for the most part, unnecessary. Scales having from 3 to 10 points introduce a significant level of measurement error, and caution should be taken when employing such scales. The implications of this research and future directions are discussed. / Education, Faculty of / Educational and Counselling Psychology, and Special Education (ECPS), Department of / Graduate

This dissertation thesis deals with similarity measures for nominal data in hierarchical clustering, which can cope with variables with more than two categories, and which aspire to replace the simple matching approach standardly used in this area. These similarity measures take into account additional characteristics of a dataset, such as frequency distribution of categories or number of categories of a given variable. The thesis recognizes three main aims. The first one is an examination and clustering performance evaluation of selected similarity measures for nominal data in hierarchical clustering of objects and variables. To achieve this goal, four experiments dealing both with the object and variable clustering were performed. They examine the clustering quality of the examined similarity measures for nominal data in comparison with the commonly used similarity measures using a binary transformation, and moreover, with several alternative methods for nominal data clustering. The comparison and evaluation are performed on real and generated datasets. Outputs of these experiments lead to knowledge, which similarity measures can generally be used, which ones perform well in a particular situation, and which ones are not recommended to use for an object or variable clustering. The second aim is to propose a theory-based similarity measure, evaluate its properties, and compare it with the other examined similarity measures. Based on this aim, two novel similarity measures, Variable Entropy and Variable Mutability are proposed; especially, the former one performs very well in datasets with a lower number of variables. The third aim of this thesis is to provide a convenient software implementation based on the examined similarity measures for nominal data, which covers the whole clustering process from a computation of a proximity matrix to evaluation of resulting clusters. This goal was also achieved by creating the nomclust package for the software R, which covers this issue, and which is freely available.

Untersuchungsgegenstand der vorliegenden Arbeit ist die Schnittstelle zwischen Verkehrsprozess und Informationsprozess in Systemen für die fahrzeuggenerierte Verkehrsdatengewinnung. Dabei konzentrieren sich die Untersuchungen auf die originäre Größe Geschwindigkeit. Das wesentliche Ziel der theoretischen und praktischen Untersuchungen bildet die qualifizierte Bestimmung makroskopischer Kenngrößen des Verkehrsflusses aus mikroskopischen Einzelfahrzeugdaten. Einen Schwerpunkt der Arbeit bildet die Analyse von mikroskopischen Einzelfahrzeugdaten mit Hilfe von informationsentropischen und spektralen Betrachtungen. Diese Untersuchungen erfolgen mit dem Ziel, eine optimale Nutzung der limitierten Übertragungs- und Verarbeitungskapazität in realen FCD-Systemen zu ermöglichen, theoretische Grenzerte abzuleiten und in der Praxis verwendete Parameter von FCD-Systemen theoretisch zu begründen. Ausgehend von empirischen und theoretischen Untersuchungen wird die Entropie der Informationsquelle "Geschwindigkeitsganglinie" bestimmt. Es wird gezeigt, dass Geschwindigkeitsganglinien als Markov-Quellen modelliert werden können. Aus der Entropiedynamik von Geschwindigeitsganglinien wird eine optimale Größe für den Erfassungstakt abgeleitet. Eine Analyse der spektralen Eigenschaften von Geschwindigkeitsverläufen zeigt, dass zwischen den Spektren von Geschwindigkeitsganglinien und dem Verkehrszustand Zusammenhänge bestehen. Geschwindigkeitsganglinien besitzen Tiefpasscharakter. Für die Berechnung der Tiefpassgrenzfrequenzen von empirischen Geschwindigkeitsganglinien wird ein Leistungskriterium eingeführt. Ausgehend von den derart bestimmten empirischen Tiefpassgrenzfrequenzen kann ein optimaler Erfassungstakt ermittelt werden, dessen Größe näherungsweise mit dem aus der Entropiedynamik abgeleiteten Erfassungstakt übereinstimmt. Ein einfacher Indikator für die Dynamik von Geschwindigkeitsverläufen ist der Variationskoeffizient der Einzelfahrzeuggeschwindigkeit. Es wird gezeigt, dass die Gewinnung und Übertragung von Variationskoeffizienten der Einzelfahrzeuggeschwindigkeiten in FCD-Systemen sinnvoll ist. In der Arbeit erfolgt eine theoretische Begründung des erforderlichen Ausrüstungsgrades in FCD-Systemen. Die Beurteilung der Leistungsfähigkeit von FCD-Systemen erfolgt dabei auf der Grundlage einer Konfidenzschätzung für die Zufallsgröße Reisegeschwindigkeit. Das verwendete Verfahren ist geeignet, die Leistungsfähigkeit von FCD-Systemen in unterschiedlichen Szenarien (Stadt-, Landstraßen-, Autobahnverkehr) zu vergleichen. Es wird gezeigt, dass FC-Daten in bestimmten Szenarien (insbesondere Stadtverkehr) zwingend einer Fusion mit anderen Verkehrsdaten bedürfen. Für die statistische Dimensionierung und Auswertung eines FCD-Systems ist der Variationskoeffizient der mittleren Reisegeschwindigkeiten der Fahrzeuge eines Fahrzeugkollektivs (kollektiver Variationskoeffizient) ein wesentlicher Parameter. Es wird gezeigt, dass der kollektive Variationskoeffizient in der Regel nicht nur vom Verkehrszustand, sondern auch von der räumlichen und zeitlichen Strukturierung des Beobachtungsgebietes abhängig ist. Für die näherungsweise Bestimmung des kollektiven Variationskoeffizienten werden Modelle abgeleitet und verifiziert.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an artificial neural network (NN) model to predict fuel consumption in heavy vehicles. The model uses predictors derived from vehicle speed, mass, and road grade. These variables are readily available from telematics devices that are becoming an integral part of connected vehicles. The model predictors are aggregated over a fixed distance traveled (i.e., window) instead of fixed time interval. It was found that 1km windows is most appropriate for the vocations studied in this thesis. Two vocations were studied, refuse and delivery trucks. The proposed NN model was compared to two traditional models. The first is a parametric model similar to one found in the literature. The second is a linear regression model that uses the same features developed for the NN model. The confidence level of the models using these three methods were calculated in order to evaluate the models variances. It was found that the NN models produce lower point-wise error. However, the stability of the models are not as high as regression models. In order to improve the variance of the NN models, an ensemble based on the average of 5-fold models was created. Finally, the confidence level of each model is analyzed in order to understand how much error is expected from each model. The mean training error was used to correct the ensemble predictions for five K-Fold models. The ensemble K-fold model predictions are more reliable than the single NN and has lower confidence interval than both the parametric and regression models.

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Recently, a variety of machine learning techniques have been applied to different healthcare sectors, and the results appear to be promising. One such sector is healthcare planning, in which patient data is used to produce statistical models for predicting the load on different units of the healthcare system. This research introduces an attempt to design and implement a privacy-preserving synthetic data generation method adapted explicitly to patients’ health data and for healthcare planning. A Privacy-preserving Conditional Generative Adversarial Network (PPCGAN) is used to generate synthetic data of Healthcare events, where a well-designed noise is added to the gradients in the training process. The concept of differential privacy is used to ensure that adversaries cannot reveal the exact training samples from the trained model. Notably, the goal is to produce digital patients and model their journey through the healthcare system. / Nyligen har en mängd olika maskininlärningstekniker tillämpats på olika hälso- och sjukvårdssektorer, och resultaten verkar lovande. En sådan sektor är vårdplanering, där patientdata används för att ta fram statistiska modeller för att förutsäga belastningen på olika enheter i sjukvården. Denna forskning introducerar ett försök att utforma och implementera en sekretessbevarande syntetisk datagenereringsmetod som uttryckligen anpassas till patienters hälsodata och för vårdplanering. Ett sekretessbevarande villkorligt generativt kontradiktoriskt nätverk (PPCGAN) används för att generera syntetisk data från hälsovårdshändelser, där ett väl utformat brus läggs till gradienterna i träningsprocessen. Begreppet differentiell integritet används för att säkerställa att motståndare inte kan avslöja de exakta träningsproven från den tränade modellen. Målet är särskilt att producera digitala patienter och modellera deras resa genom sjukvården.

[pt] A fisioterapia visa melhorar a funcionalidade física das pessoas, procurando atenuar as incapacidades causadas por alguma lesão, distúrbio ou doença. Nesse contexto, diversas tecnologias computacionais têm sido desenvolvidas com o intuito de apoiar o processo de reabilitação, como as tecnologias adaptáveis para o usuário final. Essas tecnologias possibilitam ao fisioterapeuta adequar aplicações e criarem atividades com características personalizadas de acordo com as preferências e necessidades de cada paciente. Nesta tese é proposta uma abordagem de baixo custo baseada no aprendizado de máquina interativo (iML - Interactive Machine Learning) que visa auxiliar os fisioterapeutas a criarem atividades personalizadas para seus pacientes de forma fácil e sem a necessidade de codificação de software, a partir de apenas alguns exemplos em vídeo RGB (capturadas por uma câmera de vídeo digital) Para tal, aproveitamos a estimativa de pose baseada em aprendizado profundo para rastrear, em tempo real, as articulações-chave do corpo humano a partir de dados da imagem. Esses dados são processados como séries temporais por meio do algoritmo Dynamic Time Warping em conjunto com com o algoritmo K-Nearest Neighbors para criar um modelo de aprendizado de máquina. Adicionalmente, usamos um algoritmo de detecção de anomalias com o intuito de avaliar automaticamente os movimentos. A arquitetura de nossa abordagem possui dois módulos: um para o fisioterapeuta apresentar exemplos personalizados a partir dos quais o sistema cria um modelo para reconhecer esses movimentos; outro para o paciente executar os movimentos personalizados enquanto o sistema avalia o paciente. Avaliamos a usabilidade de nosso sistema com fisioterapeutas de cinco clínicas de reabilitação. Além disso, especialistas avaliaram clinicamente nosso modelo de aprendizado de máquina. Os resultados indicam que a nossa abordagem contribui para avaliar automaticamente os movimentos dos pacientes sem monitoramento direto do fisioterapeuta, além de reduzir o tempo necessário do especialista para treinar um sistema adaptável. / [en] Physiotherapy aims to improve the physical functionality of people, seeking to mitigate the disabilities caused by any injury, disorder or disease. In this context, several computational technologies have been developed in order to support the rehabilitation process, such as the end-user adaptable technologies. These technologies allow the physiotherapist to adapt applications and create activities with personalized characteristics according to the preferences and needs of each patient. This thesis proposes a low-cost approach based on interactive machine learning (iML) that aims to help physiotherapists to create personalized activities for their patients easily and without the need for software coding, from just a few examples in RGB video (captured by a digital video camera). To this end, we take advantage of pose estimation based on deep learning to track, in real time, the key joints of the human body from image data. This data is processed as time series using the Dynamic Time Warping algorithm in conjunction with the K-Nearest Neighbors algorithm to create a machine learning model. Additionally, we use an anomaly detection algorithm in order to automatically assess movements. The architecture of our approach has two modules: one for the physiotherapist to present personalized examples from which the system creates a model to recognize these movements; another to the patient performs personalized movements while the system evaluates the patient. We assessed the usability of our system with physiotherapists from five rehabilitation clinics. In addition, experts have clinically evaluated our machine learning model. The results indicate that our approach contributes to automatically assessing patients movements without direct monitoring by the physiotherapist, in addition to reducing the specialist s time required to train an adaptable system.

This study investigates the usability of the South African Super 14 Rugby franchise web sites. Web site usability is a measure of a web site user’s experience when visiting a web site. A web site user’s experience will determine how well a web site’s goals are achieved. The relevant web site goals are, having as many visitors as possible, both unique visitors and repeat visitors, and ensuring that those visitors stay on the web site for as long as possible. This study uses data generation method triangulation to enhance the validity of the findings. The data generation methods are an e-mail questionnaire survey and an expert group consensus method called the Delphi Method. This study shows that within each web site and across all five web sites, there is poor usability consistency. Management guidelines and recommendations for improvements to these web sites are presented, so that the web site goals can be achieved. / Computer Science / M.Sc. (Information Systems)

This study investigates the usability of the South African Super 14 Rugby franchise web sites. Web site usability is a measure of a web site user’s experience when visiting a web site. A web site user’s experience will determine how well a web site’s goals are achieved. The relevant web site goals are, having as many visitors as possible, both unique visitors and repeat visitors, and ensuring that those visitors stay on the web site for as long as possible. This study uses data generation method triangulation to enhance the validity of the findings. The data generation methods are an e-mail questionnaire survey and an expert group consensus method called the Delphi Method. This study shows that within each web site and across all five web sites, there is poor usability consistency. Management guidelines and recommendations for improvements to these web sites are presented, so that the web site goals can be achieved. / Computer Science / M.Sc. (Information Systems)