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No / This paper describes a novel procedure for thinning binary text images by generating graphical representations of words within the image. A smoothed polygonal approximation of the boundaries of each word is first decomposed into a set of contiguous triangles. Each triangle is then classified into one of only three possible types from which a graph is generated that represents the topological features of the object. Joining graph points with straight lines generates a final polygon skeleton that, by construction, is one pixel wide and fully connected. Results of applying the procedure to thinning Arabic and English handwriting are presented. Comparisons of skeleton structure and execution time with results from alternative techniques are also presented. The procedure is considerably faster than the alternatives tested when the image resolution is greater than 600 dpi and the graphical representation often needed in subsequent recognition steps is available without further processing.

With the prevalence of large data stored in the cloud, including unstructured information in the form of text, there is now an increased emphasis on text mining. A broad range of techniques are now used for text mining, including algorithms adapted from machine learning, NLP, computational linguistics, and data mining. Applications are also multi-fold, including classification, clustering, segmentation, relationship discovery, and practically any task that discovers latent information from written natural language. Classical mining algorithms have traditionally focused on shallow representations such as bag-of-words and similar feature-based models. With the advent of modern high performance computing, deep sentence level linguistic analysis of large scale text corpora has become practical. In this dissertation, we evaluate the utility of dependency parses as textual features for different text mining applications. Dependency parsing is one form of syntactic parsing, based on the dependency grammar implicit in sentences. While dependency parsing has traditionally been used for text understanding, we investigate here its application to supply features for text mining applications. We specifically focus on three methods to construct textual features from dependency parses. First, we consider a dependency parse as a general feature akin to a traditional bag-of-words model. Second, we consider the dependency parse as the basis to build a feature graph representation. Finally, we use dependency parses in a supervised collocation mining method for feature selection. To investigate these three methods, several applications are studied, including: (i) movie spoiler detection, (ii) text segmentation, (iii) query expansion, and (iv) recommender systems. / Ph. D.

The application of feedback to enhance motivation is beneficial across various life contexts. While both feedback and motivation have been studied widely in psychological science, most of this research has used close-ended approaches to study feedback empirically, which limits the scope of investigation. The present study was one of the first applications of text-analysis to assess the impact of feedback on the recipient's motivation. A transformer machine-learning model was used to create a tool that can predict the average motivating influence of a particular feedback statement, as perceived by a recipient within an academic context. Feedback was defined and evaluated from the perspective of Feedback Intervention Theory (FIT). Both research hypotheses were supported, given that the model's motivation predictions were positively associated with the actual motivation scores of feedback statements, and the model was closer to estimating the true motivation scores than expected by chance. These findings, paired with additional exploratory analyses, demonstrated the utility and effectiveness of the model in predicting perceived student motivation from feedback statements. Thus, this research provided a reliable tool researchers and practitioners in academia could use to evaluate the motivating influence of feedback for students, and it might inspire future studies in this domain. / Doctor of Philosophy / The use of feedback to enhance motivation is beneficial across various life domains. While both feedback and motivation have been studied widely in psychological science, most of this research has used close-ended (not text-analytic) approaches to study feedback empirically, which limits the scope of investigation. The present study was one of the first applications of text-analysis to assess the impact of feedback on the recipient's motivation. A machine-learning model was used to create a tool that can predict the average motivating influence of a particular feedback statement, as perceived by a recipient within an academic context. Both research hypotheses were supported. The motivation predictions were positively associated with the actual motivation scores of feedback statements, and the model was closer to estimating the true motivation scores than would be expected by chance. These findings, paired with additional exploratory analyses, demonstrated the utility and effectiveness of the model in predicting perceived student motivation from feedback statements. Additionally, based on this study it is recommended that professors include specific behaviors to be modified when delivering feedback. Thus, this research provided a tool that researchers and practitioners in academia could use to evaluate the motivating influence of feedback for students, and it might certainly inspire future studies in this domain.

Into the Into of Earth Itself is a poetry collection that investigates the relationship between ecological violation and the violation of women, as well as toxicity and toxic masculinity. In doing so, it draws from the histories of two Pennsylvania towns: Palmerton and Centralia. The former is a Superfund site ravaged by zinc pollution and currently under threat of hydraulic fracturing and pipeline expansion. The latter is a nearby ghost town that was condemned and evacuated due to an underground mine fire, which will continue for another 200 years. The manuscript uses visual forms and digital text mining techniques to craft poetry about these extractive relationships to land and women. The speaker asks herself: As a woman, how have I also been mined and fracked by these same societal technologies? / Master of Fine Arts / Into the Into of Earth Itself is a poetry collection.

Målet med mitt arbete har varit att utvecklas som låtskrivare, främst inriktat på låttexter. Jag har gjort det genom att analysera 5 olika låttexter utifrån ett par förbestämda parametrar för att få en djupare förståelse för hur de teoretiska metoderna kan appliceras i praktiken. Jag har sedan skrivit 4 egna låttexter efter 4 olika tillvägagångssätt och sedan jämfört hur processen har sett ut i olika delar av låtskrivandet. I slutet av arbetet ansåg jag att det inte var särskilt stor skillnad på de olika tillvägagångssätten jag hade valt till just det här arbetet, men jag kom fram till att det förmodligen skulle vara större skillnad om jag hade valt andra tillvägagångssätt att jobba med.  Jag hoppas att det här arbetet kan ge inspiration till att verkligen tänka till på hur mycket påverkan en text kan ha när man skriver musik och att mitt arbete kan inspirera till en större djupdykning i just låttexter.

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.

Research across healthcare and organizational settings demonstrates the importance of social support to increase physical and mental well-being. However, the process of seeking social support is less well-understood than its outcomes. Specifically, research examining how people seek social support in natural settings is scarce. One natural setting increasingly used by people to seek support is the internet. In this online setting, people seek and provide social support verbally via social media platforms and messages. The present project seeks to further examine the nature of social support seeking in these online contexts by examining people’s language. This analysis includes discovering the common language features of social support seeking. By applying a data-driven content analysis approach, this research can examine the underlying themes present when seeking social support and build upon that insight to classify new instances of support seeking. These results would have important practical implications for occupational health. By identifying individuals who are seeking social support, future interventions will be able to take a more targeted approach in lending additional support to those individuals who have the greatest need. Subsequently, this application potentially provides the mental and physical health benefits of social support. Therefore, this research extends our knowledge of both the nature of support seeking and how to develop effective interventions. / M.S. / Research suggests that social support has important effects on our mental and physical health. To this point, though, the process of seeking social support has largely been neglected in research. Specifically, there hasn’t been much research on how social support is sought online. We know that people seek social support online by posting and messaging on social media. The present study seeks to examine the language of online support seeking—this way, we can understand what people tend to say when seeking support. The present study is concerned with the content of support seeking posts; by analyzing this content, we can understand themes that are prevalent in online support seeking. This allows us to better understand support seeking and, hopefully, better identify people in need of support. By identifying those people in need of support, we can ensure that their support needs are met and that they don’t suffer the health consequences related to a lack of social support. Therefore, this research extends our knowledge of social support seeking, both theoretically and practically.

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

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.