Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Change Detection: Theoretical and Applied Approaches for Providing Updates Related to a Topic of Interest(2024) Rogers, Kristine M.; Oard, Douglas; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The type of user studied in this dissertation has built up expertise on a topic of interest to them, and regularly invests time to find updates on that topic. This research area—referred to within this dissertation as "change detection"—includes the user's process of identifying what has changed as well as internalizing the changes into their mental model. For these users who follow a specific topic over time, how might a system organize information to enable them to update their mental model quickly? Current information retrieval systems are largely not optimized for addressing the long-term change detection needs of users. This dissertation focuses on approaches for enhancing the change detection process, including for short documents (e.g., social media) as well as longer documents (e.g., news articles). This mixed methods exploration of change detection consists of four sections. First, this dissertation introduces a new theory: the Group-Pile-Arrange (GPA) Change Detection Theory. This theory is about organizing documents relevant to a topic of interest in order to accelerate an individual's ability to identify changes and update their mental model. The three components of this theory include: 1. Group the documents by theme; 2. Pile the grouped documents into an order; and 3. Arrange the piles in a meaningful way for the user. These steps could be applied in a range of ways, including using approaches driven by people (e.g., a research librarian providing information), computers (e.g., an information retrieval system), or a hybrid of the two. The second section of this dissertation includes the results of a survey on users' sort order preferences in social media. For this study, change detection was compared with three other use cases: following an event while it happens (experiential), running a search within social media, and browsing social media posts. Respondents recognized the change detection use case, with 66% of the respondents indicating that they perform change detection tasks on social media sites. When engaged in change detection tasks, these respondents showed a strong preference for posts to be clustered and presented in reverse chronological order, in alignment with the "group" and "pile" components of the GPA Change Detection Theory. These organization preferences were distinct from the other studied use cases. To further understand users' goals and preferences related to change detection, the third section of this dissertation includes the design and prototype implementation of a change detection system called Daybreak. The Daybreak system presents news articles relevant to a user's topic of interest and allows them to tag articles and apply tag labels. Based on these tags and tag labels, the system retrieves new results, groups them into subtopic clusters based on the user's tags, enables generation of chronological or relevance-based piles of documents, and arranges the piles by subtopic importance; for this study, rarity was used as a proxy for subtopic importance. The Daybreak system was used for a qualitative user study, using the framework method for analyzing and interpreting results. In this study, fifteen participants engaged in a change detection scenario across five simulated "days." The participants heavily leveraged the Daybreak system's clustering function when viewing results; there was a weak preference for chronological sorting of documents, compared to relevance ranking. The participants did not view rarity as an effective proxy for subtopic importance; instead, they preferred approaches that enabled them to indicate which subtopics were of greatest interest, such as pinning certain subtopics. The fourth and final component of this dissertation research describes an evaluation approach for comparing arrangements of subtopic clusters (piles). This evaluation approach uses Spearman's rank correlation coefficient to compare a user's ideal subtopic ordering with a variety of system-generated orderings. This includes a sample evaluation using data from the Daybreak user study to demonstrate how a formal evaluation would work. Based on the results of these four dissertation research components, it appears that the GPA Change Detection Theory provides a useful framework for organizing information for individuals engaged in change detection tasks. This research provides insights into users' change detection needs and behaviors that could be helpful for building or extending systems attempting to address this use case.Item SEARCHING HETEROGENEOUS DOCUMENT IMAGE COLLECTIONS(2015) Jain, Rajiv; Doermann, David; Jacobs, David; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A decrease in data storage costs and widespread use of scanning devices has led to massive quantities of scanned digital documents in corporations, organizations, and governments around the world. Automatically processing these large heterogeneous collections can be difficult due to considerable variation in resolution, quality, font, layout, noise, and content. In order to make this data available to a wide audience, methods for efficient retrieval and analysis from large collections of document images remain an open and important area of research. In this proposal, we present research in three areas that augment the current state of the art in the retrieval and analysis of large heterogeneous document image collections. First, we explore an efficient approach to document image retrieval, which allows users to perform retrieval against large image collections in a query-by-example manner. Our approach is compared to text retrieval of OCR on a collection of 7 million document images collected from lawsuits against tobacco companies. Next, we present research in document verification and change detection, where one may want to quickly determine if two document images contain any differences (document verification) and if so, to determine precisely what and where changes have occurred (change detection). A motivating example is legal contracts, where scanned images are often e-mailed back and forth and small changes can have severe ramifications. Finally, approaches useful for exploiting the biometric properties of handwriting in order to perform writer identification and retrieval in document images are examined.Item Distribution and Dynamics of the Evergreen Understory Layer in Central Appalachian Highland Forests(2004-07-13) Chastain, Robert Arthur; Townsend, Philip A; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Evergreen understory layer communities dominated by Rhododendron maximum L. and/or Kalmia latifolia L. may exert significant controls on regeneration of overstory trees, carbon sequestration, and nutrient retention in central Appalachian forests, but their distribution and ecological importance are poorly understood at the regional scale. The distribution, temporal dynamics, and biomass of the evergreen understory layer were examined in the Ridge and Valley and Allegheny Plateau physiographic provinces in the mid-Atlantic Highlands using plot data, remote sensing, dendrochronology, and modeling. First, leaf-off satellite remote sensing and topographic data were applied to map the spatial extent and distribution of R. maximum and K. latifolia with better than 80 percent accuracy. Second, plot data were used to determine the relevant environmental factors and species associations related to the distributions of K. latifolia and R. maximum and assess their influence on forest vertical structure. Cluster analysis and ordination revealed that topo-edaphic gradients and intensity of gypsy moth defoliation were associated with differences in the distribution of these two shrub species within and between the two study areas, and midstory volume was significantly lower where evergreen understory coverage was continuous. Third, variation in K. latifolia and R. maximum growth rates were examined using remote sensing change detection and dendrochronology, and trends were compared to the timing of climatic fluctuations and gypsy moth defoliation of canopy trees. Remote sensing showed that patterns of evergreen understory growth vigor correlated with both defoliation and topographically mediated drought stress. Dendrochronology revealed considerable within-site variability among individual shrubs. However, both releases and suppressions in growth were associated with the timing of gypsy moth defoliation for K. latifolia in both provinces and for R. maximum in the Allegheny Plateau. Finally, carbon sequestration and nutrient storage impacts of these species were estimated by modeling their current aboveground biomass and ecosystem storage influences on several test watersheds. The inclusion of K. latifolia and R. maximum in the ecosystem model NuCSS indicated increases of up to 4825 kg/ha of carbon and 224 kg/ha of nitrogen storage, including notable increases of carbon and nitrogen in the forest floor and soil pools.