LDA-TM : A two-step approach to twitter topic data clustering

The Twitter System is the biggest social network in the world, and everyday millions of tweets are posted and talked about, expressing various views and opinions. A large variety of research activities have been conducted to study how the opinions can be clustered and analyzed, so that some tendencies can be uncovered. Due to the inherent weaknesses of the tweets - very short texts and very informal styles of writing - it is rather hard to make an investigation of tweet data analysis giving results with good performance and accuracy. In this paper, we intend to attack the problem from another aspect - using a two-layer structure to analyze the twitter data: LDA with topic map modelling. The experimental results demonstrate that this approach shows a progress in twitter data analysis. However, more experiments with this method are expected in order to ensure that the accurate analytic results can be maintained.

A Reliable Downscaling of ECG Signals for the Detection of T wave Heterogeneity Features

In cardiovascular disease the definition and the detection of the ECG parameters related to repolarization dynamics in post MI patients is still a crucial unmet need. In addition, the use of a 3D sensor in the implantable medical devices would be a crucial mean in the assessment or prediction of Heart Failure status, but the inclusion of such feature is limited by hardware and firmware constraints. The aim of this thesis is the definition of a reliable surrogate of the 500 Hz ECG signal to reach the aforementioned objective. To evaluate the worsening of reliability due to sampling frequency reduction on delineation performance, the signals have been consecutively down sampled by a factor 2, 4, 8 thus obtaining the ECG signals sampled at 250, 125 and 62.5 Hz, respectively. The final goal is the feasibility assessment of the detection of the fiducial points in order to translate those parameters into meaningful clinical parameter for Heart Failure prediction, such as T waves intervals heterogeneity and variability of areas under T waves. An experimental setting for data collection on healthy volunteers has been set up at the Bakken Research Center in Maastricht. A 16 – channel ambulatory system, provided by TMSI, has recorded the standard 12 – Leads ECG, two 3D accelerometers and a respiration sensor. The collection platform has been set up by the TMSI property software Polybench, the data analysis of such signals has been performed with Matlab. The main results of this study show that the 125 Hz sampling rate has demonstrated to be a good candidate for a reliable detection of fiducial points. T wave intervals proved to be consistently stable, even at 62.5 Hz. Further studies would be needed to provide a better comparison between sampling at 250 Hz and 125 Hz for areas under the T waves.

Data-Adaptive Modeling using Convex Regression

Thesis (Ph.D.)--University of Washington, 2016-08

“It’s the People That Drive It”: Motivating and Supporting Collaborative Data Use Practices in Three Teacher Education Programs

Thesis (Ph.D.)--University of Washington, 2016-08

Caractérisation des propriétés mécaniques de la pâte de carbone à 150°C dans le but d’optimiser la mise en forme des anodes utilisées dans les cuves Hall-Héroult

Les anodes de carbone sont des éléments consommables servant d’électrode dans la réaction électrochimique d’une cuve Hall-Héroult. Ces dernières sont produites massivement via une chaine de production dont la mise en forme est une des étapes critiques puisqu’elle définit une partie de leur qualité. Le procédé de mise en forme actuel n’est pas pleinement optimisé. Des gradients de densité importants à l’intérieur des anodes diminuent leur performance dans les cuves d’électrolyse. Encore aujourd’hui, les anodes de carbone sont produites avec comme seuls critères de qualité leur densité globale et leurs propriétés mécaniques finales. La manufacture d’anodes est optimisée de façon empirique directement sur la chaine de production. Cependant, la qualité d’une anode se résume en une conductivité électrique uniforme afin de minimiser les concentrations de courant qui ont plusieurs effets néfastes sur leur performance et sur les coûts de production d’aluminium. Cette thèse est basée sur l’hypothèse que la conductivité électrique de l’anode n’est influencée que par sa densité considérant une composition chimique uniforme. L’objectif est de caractériser les paramètres d’un modèle afin de nourrir une loi constitutive qui permettra de modéliser la mise en forme des blocs anodiques. L’utilisation de la modélisation numérique permet d’analyser le comportement de la pâte lors de sa mise en forme. Ainsi, il devient possible de prédire les gradients de densité à l’intérieur des anodes et d’optimiser les paramètres de mise en forme pour en améliorer leur qualité. Le modèle sélectionné est basé sur les propriétés mécaniques et tribologiques réelles de la pâte. La thèse débute avec une étude comportementale qui a pour objectif d’améliorer la compréhension des comportements constitutifs de la pâte observés lors d’essais de pressage préliminaires. Cette étude est basée sur des essais de pressage de pâte de carbone chaude produite dans un moule rigide et sur des essais de pressage d’agrégats secs à l’intérieur du même moule instrumenté d’un piézoélectrique permettant d’enregistrer les émissions acoustiques. Cette analyse a précédé la caractérisation des propriétés de la pâte afin de mieux interpréter son comportement mécanique étant donné la nature complexe de ce matériau carboné dont les propriétés mécaniques sont évolutives en fonction de la masse volumique. Un premier montage expérimental a été spécifiquement développé afin de caractériser le module de Young et le coefficient de Poisson de la pâte. Ce même montage a également servi dans la caractérisation de la viscosité (comportement temporel) de la pâte. Il n’existe aucun essai adapté pour caractériser ces propriétés pour ce type de matériau chauffé à 150°C. Un moule à paroi déformable instrumenté de jauges de déformation a été utilisé pour réaliser les essais. Un second montage a été développé pour caractériser les coefficients de friction statique et cinétique de la pâte aussi chauffée à 150°C. Le modèle a été exploité afin de caractériser les propriétés mécaniques de la pâte par identification inverse et pour simuler la mise en forme d’anodes de laboratoire. Les propriétés mécaniques de la pâte obtenues par la caractérisation expérimentale ont été comparées à celles obtenues par la méthode d’identification inverse. Les cartographies tirées des simulations ont également été comparées aux cartographies des anodes pressées en laboratoire. La tomodensitométrie a été utilisée pour produire ces dernières cartographies de densité. Les résultats des simulations confirment qu’il y a un potentiel majeur à l’utilisation de la modélisation numérique comme outil d’optimisation du procédé de mise en forme de la pâte de carbone. La modélisation numérique permet d’évaluer l’influence de chacun des paramètres de mise en forme sans interrompre la production et/ou d’implanter des changements coûteux dans la ligne de production. Cet outil permet donc d’explorer des avenues telles la modulation des paramètres fréquentiels, la modification de la distribution initiale de la pâte dans le moule, la possibilité de mouler l’anode inversée (upside down), etc. afin d’optimiser le processus de mise en forme et d’augmenter la qualité des anodes.

How do Young People with ADHD Perceive their Condition: An Interpretative Phenomenological Analysis

Attention Deficit Hyperactivity Disorder (ADHD) is one the most prevalent of childhood diagnoses. There is limited research available from the perspective of the child or young person with ADHD. The current research explored how young people perceive ADHD. A secondary aim of the study was to explore to what extent they identify with ADHD. Five participants took part in this study. Their views were explored using semi-structured interviews guided by methods from Personal Construct Psychology. The data was analysed using Interpretative Phenomenological Analysis (IPA). Data analysis suggests that the young people’s views of ADHD are complex and, at times, contradictory. Four super-ordinate themes were identified: What is ADHD?, The role and impact of others on the experience of ADHD, Identity conflict and My relationship with ADHD. The young people’s contradictory views on ADHD are reflective of portrayals of ADHD in the media. A power imbalance was also identified where the young people perceive that they play a passive role in the management of their treatment. Finally, the young people’s accounts revealed a variety of approaches taken to make sense of their condition.

Multi-Disciplinary Applications of Oceanographic Geophysical Data Collection

Thesis (Ph.D.)--University of Washington, 2016-08

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

Statistical modeling of protein lysate array data

The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational modifications and protein pathway relationships. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly due to two challenges: the increasing dimension of the parameter space and the need to account for dependence in the data. Each chapter of this thesis addresses one of these issues. In Chapter 1, an introduction to the protein lysate array quantification is presented, followed by the motivations and goals for this thesis work. In Chapter 2, we develop a multi-step procedure for the Sigmoidal models, ensuring consistent estimation of the concentration level with full asymptotic efficiency. The results obtained in this chapter justify inferential procedures based on large-sample approximations. Simulation studies and real data analysis are used to illustrate the performance of the proposed method in finite-samples. The multi-step procedure is simpler in both theory and computation than the single-step least squares method that has been used in current practice. In Chapter 3, we introduce a new model to account for the dependence structure of the errors by a nonlinear mixed effects model. We consider a method to approximate the maximum likelihood estimator of all the parameters. Using the simulation studies on various error structures, we show that for data with non-i.i.d. errors the proposed method leads to more accurate estimates and better confidence intervals than the existing single-step least squares method.

Data-Aware Scheduling in Datacenters

Datacenters have emerged as the dominant form of computing infrastructure over the last two decades. The tremendous increase in the requirements of data analysis has led to a proportional increase in power consumption and datacenters are now one of the fastest growing electricity consumers in the United States. Another rising concern is the loss of throughput due to network congestion. Scheduling models that do not explicitly account for data placement may lead to a transfer of large amounts of data over the network causing unacceptable delays. In this dissertation, we study different scheduling models that are inspired by the dual objectives of minimizing energy costs and network congestion in a datacenter. As datacenters are equipped to handle peak workloads, the average server utilization in most datacenters is very low. As a result, one can achieve huge energy savings by selectively shutting down machines when demand is low. In this dissertation, we introduce the network-aware machine activation problem to find a schedule that simultaneously minimizes the number of machines necessary and the congestion incurred in the network. Our model significantly generalizes well-studied combinatorial optimization problems such as hard-capacitated hypergraph covering and is thus strongly NP-hard. As a result, we focus on finding good approximation algorithms. Data-parallel computation frameworks such as MapReduce have popularized the design of applications that require a large amount of communication between different machines. Efficient scheduling of these communication demands is essential to guarantee efficient execution of the different applications. In the second part of the thesis, we study the approximability of the co-flow scheduling problem that has been recently introduced to capture these application-level demands. Finally, we also study the question, "In what order should one process jobs?'' Often, precedence constraints specify a partial order over the set of jobs and the objective is to find suitable schedules that satisfy the partial order. However, in the presence of hard deadline constraints, it may be impossible to find a schedule that satisfies all precedence constraints. In this thesis we formalize different variants of job scheduling with soft precedence constraints and conduct the first systematic study of these problems.

Usefulness of social tagging in organizing and providing access to the web: An analysis of indexing consistency and quality

This dissertation research points out major challenging problems with current Knowledge Organization (KO) systems, such as subject gateways or web directories: (1) the current systems use traditional knowledge organization systems based on controlled vocabulary which is not very well suited to web resources, and (2) information is organized by professionals not by users, which means it does not reflect intuitively and instantaneously expressed users’ current needs. In order to explore users’ needs, I examined social tags which are user-generated uncontrolled vocabulary. As investment in professionally-developed subject gateways and web directories diminishes (support for both BUBL and Intute, examined in this study, is being discontinued), understanding characteristics of social tagging becomes even more critical. Several researchers have discussed social tagging behavior and its usefulness for classification or retrieval; however, further research is needed to qualitatively and quantitatively investigate social tagging in order to verify its quality and benefit. This research particularly examined the indexing consistency of social tagging in comparison to professional indexing to examine the quality and efficacy of tagging. The data analysis was divided into three phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of tag attributes. Most indexing consistency studies have been conducted with a small number of professional indexers, and they tended to exclude users. Furthermore, the studies mainly have focused on physical library collections. This dissertation research bridged these gaps by (1) extending the scope of resources to various web documents indexed by users and (2) employing the Information Retrieval (IR) Vector Space Model (VSM) - based indexing consistency method since it is suitable for dealing with a large number of indexers. As a second phase, an analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. Finally, to investigate tagging pattern and behaviors, a content analysis on tag attributes was conducted based on the FRBR model. The findings revealed that there was greater consistency over all subjects among taggers compared to that for two groups of professionals. The analysis of tagging exhaustivity and tag specificity in relation to tagging effectiveness was conducted to ameliorate difficulties associated with limitations in the analysis of indexing consistency based on only the quantitative measures of vocabulary matching. Examination of exhaustivity and specificity of social tags provided insights into particular characteristics of tagging behavior and its variation across subjects. To further investigate the quality of tags, a Latent Semantic Analysis (LSA) was conducted to determine to what extent tags are conceptually related to professionals’ keywords and it was found that tags of higher specificity tended to have a higher semantic relatedness to professionals’ keywords. This leads to the conclusion that the term’s power as a differentiator is related to its semantic relatedness to documents. The findings on tag attributes identified the important bibliographic attributes of tags beyond describing subjects or topics of a document. The findings also showed that tags have essential attributes matching those defined in FRBR. Furthermore, in terms of specific subject areas, the findings originally identified that taggers exhibited different tagging behaviors representing distinctive features and tendencies on web documents characterizing digital heterogeneous media resources. These results have led to the conclusion that there should be an increased awareness of diverse user needs by subject in order to improve metadata in practical applications. This dissertation research is the first necessary step to utilize social tagging in digital information organization by verifying the quality and efficacy of social tagging. This dissertation research combined both quantitative (statistics) and qualitative (content analysis using FRBR) approaches to vocabulary analysis of tags which provided a more complete examination of the quality of tags. Through the detailed analysis of tag properties undertaken in this dissertation, we have a clearer understanding of the extent to which social tagging can be used to replace (and in some cases to improve upon) professional indexing.

The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms

The dendritic cell algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a ‘context aware’ detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a self-organizing map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique.

Towards a Workflow Management Approach for Health Monitoring of Bridges

Part 14: Interoperability and Integration