Localization is a crucial topic in navigation, especially in autonomous vehicles navigation. It is usually done by using a global positioning system (GPS) sensor. Even though there have been many studies of vehicle localization in recent years, most of them combine GPS sensor with other sensors to get a more accurate result [1]. In this thesis, we propose a novel image-based vehicle localization by utilizing vision sensor and computer vision techniques to extract vehicle surrounding text landmarks and to locate the vehicle position. Firstly, we explore the feasibility of image-based vehicle localization by using text landmark of a position to locate vehicle position. A text landmark model, a location matching algorithm and a basic localization model are proposed, which allow a vehicle to find the best matching location in the database by cross-checking the text landmarks from query image and reference location images. Secondly, we propose two more robust localization models by applying vehicle moving distance and heading direction data as part of inputs, which significantly improve the localization accuracy. Finally, we simulate an experiment to evaluate our three different localization models and further prove the robustness of our model through experimental results. / Master of Science / In modern days, global positioning system (GPS) is the major approach to locate positions. However, GPS is not as reliable as we thought. Under some environmental situations, GPS cannot provide continuous navigation information. Besides, GPS signals can be jammed or spoofed by malicious attackers. In this thesis, we aim to explore how to locate the vehicle’s position without using GPS sensor. Here, we propose a novel image-based vehicle localization by utilizing vision sensor and computer vision techniques to extract vehicle surrounding text landmarks and to locate the vehicle position. Various tools and techniques are explored in the process of the research. With the explored result, we propose several localization models and simulate an experiment to prove the robustness of these models.

Unstructured data, which cannot be organized into predefined structures, such as texts, human behavior status, and system logs, often presented in a sequential format with inherent dependencies. Probabilistic model are commonly used to capture these dependencies in the data generation process through latent parameters and can naturally extend into hierarchical forms. However, these models rely on the correct specification of assumptions about the sequential data generation process, which often limits their scalable learning abilities. The emergence of neural network tools has enabled scalable learning for high-dimensional sequential data. From an algorithmic perspective, efforts are directed towards reducing dimensionality and representing unstructured data units as dense vectors in low-dimensional spaces, learned from unlabeled data, a practice often referred to as numerical embedding. While these representations offer measures of similarity, automated generalizations, and semantic understanding, they frequently lack the statistical foundations required for explicit inference. This dissertation aims to develop statistical inference techniques tailored for the analysis of unstructured sequential data, with their application in the field of transportation safety. The first part of dissertation presents a two-stage method. It adopts numerical embedding to map large-scale unannotated data into numerical vectors. Subsequently, a kernel test using maximum mean discrepancy is employed to detect abnormal segments within a given time period. Theoretical results showed that learning from numerical vectors is equivalent to learning directly through the raw data. A real-world example illustrates how driver mismatched visual behavior occurred during a lane change. The second part of the dissertation introduces a two-sample test for comparing text generation similarity. The hypothesis tested is whether the probabilistic mapping measures that generate textual data are identical for two groups of documents. The proposed test compares the likelihood of text documents, estimated through neural network-based language models under the autoregressive setup. The test statistic is derived from an estimation and inference framework that first approximates data likelihood with an estimation set before performing inference on the remaining part. The theoretical result indicates that the test statistic's asymptotic behavior approximates a normal distribution under mild conditions. Additionally, a multiple data-splitting strategy is utilized, combining p-values into a unified decision to enhance the test's power. The third part of the dissertation develops a method to measure differences in text generation between a benchmark dataset and a comparison dataset, focusing on word-level generation variations. This method uses the sliced-Wasserstein distance to compute the contextual discrepancy score. A resampling method establishes a threshold to screen the scores. Crash report narratives are analyzed to compare crashes involving vehicles equipped with level 2 advanced driver assistance systems and those involving human drivers. / Doctor of Philosophy / Unstructured data, such as texts, human behavior records, and system logs, cannot be neatly organized. This type of data often appears in sequences with natural connections. Traditional methods use models to understand these connections, but these models depend on specific assumptions, which can limit their effectiveness. New tools using neural networks have made it easier to work with large and complex data. These tools help simplify data by turning it into smaller, manageable pieces, a process known as numerical embedding. While this helps in understanding the data better, it often requires a statistical foundation for the proceeding inferential analysis. This dissertation aims to develop statistical inference techniques for analyzing unstructured sequential data, focusing on transportation safety. The first part of the dissertation introduces a two-step method. First, it transforms large-scale unorganized data into numerical vectors. Then, it uses a statistical test to detect unusual patterns over a period. For example, it can identify when a driver's visual behavior doesn't properly aligned with the driving attention demand during lane changes. The second part of the dissertation presents a method to compare the similarity of text generation. It tests whether the way texts are generated is the same for two groups of documents. This method uses neural network-based models to estimate the likelihood of text documents. Theoretical results show that as the more data observed, the distribution of the test statistic will get closer to the desired distribution under certain conditions. Additionally, combining multiple data splits improves the test's power. The third part of the dissertation constructs a score to measure differences in text generation processes, focusing on word-level differences. This score is based on a specific distance measure. To check if the difference is not a false discovery, a screening threshold is established using resampling technique. If the score exceeds the threshold, the difference is considered significant. An application of this method compares crash reports from vehicles with advanced driver assistance systems to those from human-driven vehicles.

Businesses have recognized that customers provide value to the firm beyond transactions, and leveraging this value through relationships in social media is a new area of interest for both academics and practitioners. Recent research has investigated how businesses can best manage their online presence on platforms not fully under their control, such as Facebook, YouTube, Instagram, TripAdvisor, and Yelp, among others. This dissertation extends the literature of customer engagement in social media through four contributions. First, we propose a framework that foregrounds the textual artifacts involved in online communication. Second, we develop a novel method for discovering the elements of successful Business to Customer (B2C) messages in online communities. Third, we propose a method, validated through experimentation, for finding critical product feedback in Customer to Customer (C2C) communications. Finally, we demonstrate that a set of novel numerical features can enhance the discovery of product defect mentions in C2C communications. We conclude by proposing a research agenda suggested by the framework that will further enhance our understanding of the complex customer interactions that characterize business in the era of social media. / Ph. D. / Businesses have recognized that customers provide value to the firm beyond transactions, and leveraging this value through relationships in social media is a new area of interest for both academics and practitioners. Recent research has investigated how businesses can best manage their online presence on platforms not fully under their control, such as Facebook, YouTube, Instagram, TripAdvisor, and Yelp, among others. This dissertation extends the literature of customer engagement in social media through four contributions. First, we propose a framework that foregrounds the textual artifacts involved in online communication. Second, we develop a novel method for discovering the elements of successful Business to Customer (B2C) messages in online communities. Third, we propose a method, validated through experimentation, for finding critical product feedback in Customer to Customer (C2C) communications. Finally, we demonstrate that a set of novel numerical features can enhance the discovery of product defect mentions in C2C communications. We conclude by proposing a research agenda suggested by the framework that will further enhance our understanding of the complex customer interactions that characterize business in the era of social media.

A Digital Library (DL) is an electronic information storage system focused on meeting the information seeking needs of its constituents. As modern DLs often stay in synchronization with the latest progress of technologies in all fields, interoperability among DLs is often hard to achieve. With the advent of the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) and Open Digital Libraries (ODL), lightweight protocols show a promising future in promoting DL interoperability. Furthermore, DL is envisaged as a network of independent components working collaboratively through simple standardized protocols. Prior work with ODL shows the feasibility of building componentized DLs with techniques that are a precursor to web services designs. In our study, we elaborate the feasibility to apply web services to DL design. DL services are modeled as a set of web services offering information dissemination through the Simple Object Access Protocol (SOAP). Additionally, a flexible DL user interface assembly framework is offered in order to build DLs with customizations and personalizations. Our hypothesis is proven and demonstrated in the PhysNet reengineering project. / Master of Science

This research comprises a comparative examination of the transformation of Afrikaans and Dutch youth narratives into drama texts. Attention has been paid to the story elements embodied in various narratives and dramas, and, in addition, to aspects related to narrative and drama. By means of the comparison of the constants and variants with regard to the four texts, the possibility of creating a transformation model has been examined. The transformation model derived from the research, is applicable, firstly, to the narratives and drama texts upon which this study has been based. It is therefore presented as a conception for the conversion of a narrative text into a drama text, but the uniqueness of each separate narrative is taken into consideration; hence the model is not prescriptive, and it is assumed that the model may be adjusted in line with each adaptation. The comparison is effected between Afrikaans and Dutch texts, in view of the existence in the Low Countries of an established culture of bookshops, publishers and theatrical companies, focused on youth literature and theatre. Some publishers and bookshops, moreover, exclusively publish and sell youth narratives and dramas. Theatre productions aimed at children and young adults are plentiful, and attract a large percentage of young people. Should the fact that some theatres specialise in youth theatre productions be taken into account, also, the contrast and the gaps pertaining to the Afrikaans literary system are marked. The research in respect of the transformation of prose texts into drama texts has identified those procedures employed to adapt the narrative aspects (narrator, focalization, character, event, time and space) in such a way that it is reconcilable with the unique nature of the dramatic aspects (didascalia, dialogue, character, action, time and space). By virtue of the transformation of youth narratives into drama texts (with the purpose of the eventual performance thereof), the adolescent reader is made aware in a different manner of the value of narrative. / Thesis (M.A. (Afrikaans and Dutch))--Potchefstroom University for Christian Higher Education, 2003

This research comprises a comparative examination of the transformation of Afrikaans and Dutch youth narratives into drama texts. Attention has been paid to the story elements embodied in various narratives and dramas, and, in addition, to aspects related to narrative and drama. By means of the comparison of the constants and variants with regard to the four texts, the possibility of creating a transformation model has been examined. The transformation model derived from the research, is applicable, firstly, to the narratives and drama texts upon which this study has been based. It is therefore presented as a conception for the conversion of a narrative text into a drama text, but the uniqueness of each separate narrative is taken into consideration; hence the model is not prescriptive, and it is assumed that the model may be adjusted in line with each adaptation. The comparison is effected between Afrikaans and Dutch texts, in view of the existence in the Low Countries of an established culture of bookshops, publishers and theatrical companies, focused on youth literature and theatre. Some publishers and bookshops, moreover, exclusively publish and sell youth narratives and dramas. Theatre productions aimed at children and young adults are plentiful, and attract a large percentage of young people. Should the fact that some theatres specialise in youth theatre productions be taken into account, also, the contrast and the gaps pertaining to the Afrikaans literary system are marked. The research in respect of the transformation of prose texts into drama texts has identified those procedures employed to adapt the narrative aspects (narrator, focalization, character, event, time and space) in such a way that it is reconcilable with the unique nature of the dramatic aspects (didascalia, dialogue, character, action, time and space). By virtue of the transformation of youth narratives into drama texts (with the purpose of the eventual performance thereof), the adolescent reader is made aware in a different manner of the value of narrative. / Thesis (M.A. (Afrikaans and Dutch))--Potchefstroom University for Christian Higher Education, 2003

This study investigates the use of different strategies for translating puns in Alice’s Adventures in Wonderland. The material chosen for this study consist of the two Swedish translations by Nonnen (1870/1984) and Westman (2009). Six puns were selected for the analysis which greatly relies on Delabastita’s (1996) eight strategies for translating puns, and Newmark’s (1988) translation methods. The analysis shows that Westman empathises with the readers of the TT while Nonnen empathises with the ST. This entails that Westman tends to use a more ‘free’ translation and is more inclined to adapt the ST puns to make them more visible for the readership of the TT. The priority for Nonnen, on the other hand, is to remain faithful to the contextual meaning of the ST. Paradoxically, to be faithful to the ST does not necessarily entail that the translator respects the semantic aspects of the ST, but that they adapt the culture of the ST to better fit the cultural and linguistic framework of the TL. Since Westman adapts the ST puns so that they are still recognised by the reader of the TT, her translation appears to be more suitable for the TL readership than Nonnen’s.

“The Ancient Egyptian Demonology Project: Second Millennium BCE” was intended and funded as a three-year project (2013-2016) to explore the world of Ancient Egyptian demons in the 2nd millennium BC. It intends to create a classification and ontology of benevolent and malevolent demons. Whereas ancient Egyptians did not use a specific term denoting “demons”, liminal beings known from various other cultures such as δαίμονες, ghosts, angels, Mischwesen, genies, etc., were nevertheless described in texts and illustrations. The project aims to collect philological, iconographical and archaeological evidence to understand the religious beliefs, practices, interactions and knowledge not only of the ancient Egyptians’ daily life but also their perception of the afterlife. Till today scholars, as well as interested laymen, have had no resource to consult for specific examples of those beings, except for rather general encyclopaedias that include all kinds of divine beings or the Iconography of Deities and Demons (IDD) project that is ongoing. Neither provides, however, a searchable platform for both texts and images. The database created by the Demonology Project: 2K is designed to remedy this gap. The idea is to provide scholars and the public with a database that allows statistical analyses and innovative data visualisation, accessible and augmentable from all over the world to stimulate the dialogue and open communication not only within Egyptology but also with neighbouring disciplines. For the time-span of the three year project a pilot database was planned as a foundation for further data-collection and analysis. The data that were chosen date to the 2nd Millennium BCE and originate from objects of daily life (headrests and ivory wands), as well as from objects related to the afterlife, (coffins and ‘Book of the Dead’ manuscripts). This material, connected by its religious purposes, nevertheless provides a cross-section through ancient Egyptian religious practice. The project is funded by the Leverhulme Trust and includes Kasia Szpakowska (director) who supervises the work of the two participating PhD students in Egyptology. The project does not include funds for computer scientists or specialists in digital humanities. Therefore, the database is designed, developed and input by the members of the team only. The focus of my presentation will be the structure of the database that faces the challenge to include both textual and iconographical evidence. I will explain the organisation of the data, search patterns and the opportunities of their visualisation and possible research outcome. Furthermore, I will discuss the potentials the database already possesses and might generate in the future for scholars and the public likewise. Since the evidence belongs to numerous collections from all over the world, I would like to address the problems of intellectual property and copyright with the solution we pursue for releasing the database for registered usage onto the internet.

Over the last decades, there have been remarkable shifts in the area of Information Retrieval (IR) as huge amount of information is increasingly accumulated on the Web. The gigantic information explosion increases the need for discovering new tools that retrieve meaningful knowledge from various complex information sources. Thus, techniques primarily used to search and extract important information from numerous database sources have been a key challenge in current IR systems. Topic modeling is one of the most recent techniquesthat discover hidden thematic structures from large data collections without human supervision. Several topic models have been proposed in various fields of study and have been utilized extensively for many applications. Latent Dirichlet Allocation (LDA) is the most well-known topic model that generates topics from large corpus of resources, such as text, images, and audio.It has been widely used in many areas in information retrieval and data mining, providing efficient way of identifying latent topics among document collections. However, LDA has a drawback that topic cohesion within a concept is attenuated when estimating infrequently occurring words. Moreover, LDAseems not to consider the meaning of words, but rather to infer hidden topics based on a statisticalapproach. However, LDA can cause either reduction in the quality of topic words or increase in loose relations between topics. In order to solve the previous problems, we propose a domain specific topic model that combines domain concepts with LDA. Two domain specific algorithms are suggested for solving the difficulties associated with LDA. The main strength of our proposed model comes from the fact that it narrows semantic concepts from broad domain knowledge to a specific one which solves the unknown domain problem. Our proposed model is extensively tested on various applications, query expansion, classification, and summarization, to demonstrate the effectiveness of the model. Experimental results show that the proposed model significantly increasesthe performance of applications.

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