Magnitudes of numbers influence numerical inequality judgments of people. Do symbols representing numbers also affect numerical inequality judgments? To answer the question, I manipulated digit similarity in two-digit number comparison tasks. During the experiment, the participants took part in two comparison tasks – the judging-larger task and the judging-smaller task. Given pairs of two-digit numbers, the participants were required to make numerical inequality judgments (judging larger or judging smaller). To investigate the effect of digit similarity, two kinds of number pairs were used. Two-digit number pairs consisting of same-digits numbers (e.g., 21 – 12) and two-digit number pairs consisting of different-digits numbers (e.g., 21 – 30) were presented at random. The participants needed more time to compare the same-digits number pairs than the different-digits pairs. The result was independent of the findings in number comparison studies such as the numerical-distance effect (Moyer & Landauer, 1967) and the unit-decade compatibility effect (Nuerk, Weger, & Willmes, 2001). The present study poses challenge to the current theories of two-digit number comparison. / Thesis (Master, Psychology) -- Queen's University, 2009-09-02 11:59:06.647

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The purpose of this thesis is to evaluate the differences between 9-ending on promotion prices used by Thai retailers and Swedish retailers. The quantitative research approach is the chosen method in order to evaluate the differences between 9-ending on promotion prices used by Thai retailers and Swedish retailers. The collection of data will be mainly concentrated on primary data through brochures from retailers in both countries. Evaluation of the price endings for advertised products in the brochures from Thai and Swedish retailers founded the distinctions between the use of 9-ending prices in many ways such as the way the retailers used left and right digits for price endings, the way they set the prices compared between the same product category and the same product sub-category.

The principle problem of the present research was to determine the relative effects of two major variables, nature of verbal reinforcement and achievement history, upon the performance of elementary school children on a relatively simple learning task.

This dissertation is devoted to the measure theoretical aspects of the theory of automata and groups generated by them. It consists of two main parts. In the first part we study the action of automata on Bernoulli measures. We describe how a finite-state automorphism of a regular rooted tree changes the Bernoulli measure on the boundary of the tree. It turns out, that a finite-state automorphism of polynomial growth, as defined by Sidki, preserves a measure class of a Bernoulli measure, and we write down the explicit formula for its Radon-Nikodim derivative. On the other hand the image of the Bernoulli measure under the action of a strongly connected finite-state automorphism is singular to the measure itself. The second part is devoted to introduction of measure into the theory of limit spaces of Nekrashevysh. Let G be a group and φ : H → G be a contracting homomorphism from a subgroup H < G of finite index. Nekrashevych associated with the pair (G, φ) the limit dynamical system (JG, s) and the limit G-space XG together with the covering ∪g∈GT · g by the tile T. We develop the theory of selfsimilar measures m on these limit spaces. It is shown that (JG, s,m) is conjugate to the one-sided Bernoulli shift. Using sofic subshifts we prove that the tile T has integer measure and we give an algorithmic way to compute it. In addition we give an algorithm to find the measure of the intersection of tiles T ∩ (T · g) for g ∈ G. We present applications to the evaluation of the Lebesgue measure of integral self-affine tiles. Previously the main tools in the theory of self-similar fractals were tools from measure theory and analysis. The methods developed in this disseration provide a new way to investigate self-similar and self-affine fractals, using combinatorics and group theory.

archives@tulane.edu / Introduction: Mass distribution of insecticide-treated nets (ITNs) is a core malaria prevention strategy that has proven to be efficacious and cost-effective in low- and middle-income countries (LMIC). Monitoring ITN coverage, use, and access has relied on household surveys which are expensive and time consuming. Recently, mobile phone survey (MPS) methodologies have emerged as a comparatively inexpensive alternative to large scale population-based household surveys and are becoming increasingly attractive considering the rapid growth trend of mobile phone ownership in LMIC. The overall research objective of the current body of work is to determine if interactive voice response (IVR) MPS can serve to rapidly and reliably monitor ITN indicators in LMIC. Methods: Data collection used either household surveys or IVR MPS – all of which included a module on bed net ownership, access, and use. The first study aim analyzed data from the last five nationally representative household surveys conducted in Tanzania in order to assess and quantify the potential for bias as a result of using MPS over traditional household surveys in estimating bed net coverage indicators. The conceptual design compares surveyed households reporting mobile phone ownership, and thus the potential for participation in an MPS, against all other households regardless of mobile phone ownership over the course of a 10-year period. The second study aim was designed as an individual-level test of inter-rater reliability of bed net indicator estimates between a face-to-face household survey and a follow-up IVR MPS to these same households. The third study aim was designed as a population-level test of external validity comparing ITN coverage indicator results from a nationally representative random-digit dial (RDD) IVR MPS and the malaria module from a nationally representative household survey. Results: Household mobile phone ownership increased by over 50 percentage points from 28.1% in 2007-08 to 81.5% in 2017. In more recent years, survey results show that bias in measuring ITN coverage indicators is minimal under a scenario that compares estimates calculated from DHS surveys for all households against those households reporting mobile phone ownership. For the four ITN coverage indicators assessed using the 2017 MIS data, national-level measures of bias did not exceed a 2.5-percentage point difference for mobile phone-owning households compared to the overall sample of households. Further, regional measures of bias for these same indicators rarely exceeded ± 3-percentage points in 2017. The second study aim, which compared bed net indicator estimates between the small-scale a household survey and a follow-up MPS, found that agreement between survey modalities was variable depending on the indicator, but was highest for household ownership of at least one bed net of any type (Gwet’s AC1 = 0.8). There was low agreement for indicators calculated from counts reflected in the low concurrent validity of key data elements used to calculate bed net use and access indicators. The third study aim comparing bed net indicator estimates from a national household and IVR RDD survey found that the external validity was variable but, in general, the RDD MPS tended to underestimate bed net indicators at the national level. Differences in bed net indicator estimates ranged from 3 to 23-percentage points but overall, it appeared that indicators non-specific to net treatment status demonstrated less bias in measurement through the RDD MPS when compared against the nationally representative household survey. Conclusions: According to estimates, mobile phone ownership has increased drastically in Tanzania since 2007 suggesting that MPS could presently be used to track population-level indicators of ITN coverage, among others. The IVR MPS methodology we applied has the potential to serve as a mechanism that can accurately estimate certain bed net indicators – primarily those that would make use of data elements derived from binary response options. Their use could be scaled to much larger RDD surveys to collect discrete packets of information. At a total cost of approximately US$22,000 (2017 USD) to obtain nationally and regionally representative bed net indicator estimates, the cost-for-information benefit is promising, but more research needs to be done to optimize question sets in order to ensure RDD survey results are able to repeatedly track with face-to-face household survey results. / 1 / Matt Worges

L’objectif de ce travail de thèse est d’amener des éléments de réflexions méthodologiques autour du Napping®. Le premier chapitre de ce manuscrit rappelle aux lecteurs ce qu’est le Napping®, à travers une description de la tâche et des données qu’elle fournit, telles qu’elles ont été imaginées initialement par Jérôme Pagès (2005). C’est dans ce même chapitre que le lecteur peut réaliser à quel point il est important de prendre en compte le comportement du sujet lors de la tâche, tant pour la compréhension des données que pour leur traitement. Le deuxième chapitre de ce manuscrit propose un modèle du comportement du sujet dans le cas où le nombre de stimuli est relativement élevé, une situation couramment rencontrée en pratique.Ce modèle et ses limites amènent le lecteur au chapitre 3, dans lequel il est présenté une méthode originale de recueil de données de Napping® appelée digit-tracking. Cette méthode permet d’observer le comportement du sujet au cours du temps par l’enregistrement du positionnement relatif des stimuli tout au long de la tâche et non plus seulement lors de l’étape finale. Enfin le dernier chapitre du manuscrit présente l’outil qui a été développé au cours de cette thèse et qui permet de recueillir des données de Napping® au cours du temps. Cet outil est une plateforme collaborative, appelée Holos, couplée à un logiciel de recueil de données sur tablette tactile, qui permet aux chercheurs de partager une partie ou l’ensemble de leurs résultats. / The objective of this dissertation aims to bring the methodological reflection elements around the Napping® method. Chapter 1 provides the concept of Napping® via a description of its procedure and data format, as it was originally conceived by Jérôme Pagès (2005). This chapter also discussed the importance of taking into account the subject's behaviour during the task for a better understanding of the data collected and their analysis. Chapter 2 proposes a model of the subject's behaviour in which the number of stimuli is relatively high, a situation commonly encountered in practice. This model and its limitations lead to Chapter 3, where a new technique for collecting Napping® data, named digit-tracking, is presented.This new technique of data collection allows to observe the subject’s behaviour over time by recording the relative positioning of all stimuli throughout the task rather than solely her/his final configuration. Finally, Chapter 4 presents a tool devoted to collecting Napping® data over time. This tool is a collaborative platform that works together with an android application for collecting data on tactile tablet devices – Holos environment, which enables researchers to share totally or partially their study resources.

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Context: The main purpose of this thesis is to build an automatic handwritten digit recognition method for the recognition of connected handwritten digit strings. To accomplish the recognition task, first, the digits were segmented into individual digits. Then, a digit recognition module is employed to classify each segmented digit completing the handwritten digit string recognition task. In this study, different machine learning methods, which are SVM, ANN and CNN architectures are used to achieve high performance on the digit string recognition problem. In these methods, images of digit strings are trained with the SVM, ANN and CNN model with HOG feature vectors and Deep learning methods structure by sliding a fixed size window through the images labeling each sub-image as a part of a digit or not. After the completion of the segmentation, to achieve the complete recognition of handwritten digits.Objective: The main purpose of this thesis is to find out the recognition performance of the methods. In order to analyze the performance of the methods, data is needed to be used for training using machine learning methods. Then digit data is tested on the desired machine learning technique. In this thesis, the following methods are performed: Implementation of HOG Feature extraction method with SVM Implementation of HOG Feature extraction method with ANN Implementation of Deep Learning methods with CNN Methods: This research will be carried out using two methods. The first research method is the ¨Literature Review¨ and the second ¨Experiment¨. Initially, a literature review is conducted to get a clear knowledge on the algorithms and techniques which will be used to answer the first research question i.e., to know which type of data is required for the machine learning methods and the data analysis is performed. Later on, with the knowledge of RQ1, Experimentation is conducted to answer the RQ2, RQ3, RQ4. Quantitative data is used to perform the experimentation because qualitative data which obtains from case-study and survey cannot be used for this experiment method as it contains non-numerical data. In this research, an experiment is conducted to find the best suitable machine learning method from the existing methods. As mentioned above in the objectives, an experiment is conducted using SVM, ANN, and CNN. By considering the results obtained from the experiment, a comparison is made on the metrics considered which results in CNN as the best method suitable for Documents Images. Results: Compare the results for SVM, ANN with HOG Feature extraction and the CNN method by using segmented results. Based on the Experiment results it is found that SVM and ANN have some drawbacks like low accuracy and low performance in the recognition of documented images. So, the other method i.e., CNN has greater performance with high accuracy. The following are the results of the recognition rates of each method. SVM performance - 39% ANN performance - 37% CNN performance - 71%. Conclusion: This research concentrates on providing an efficient method for recognition of automatic handwritten digits recognition. Here a sample training data is treated with existing machine learning and deep learning methods like SVM, ANN, and CNN. By the results obtained from the experimentation, it clearly is shown that the CNN method is much efficient with 71% performance when compared to ANN and SVM methods. Keywords: Handwritten Digit Recognition, Handwritten Digit Segmentation, Handwritten Digit Classification, Machine Learning Methods, Deep Learning, Image processing on document images, Support Vector Machine, Conventional Neural Networks, Artificial Neural Networks

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