Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

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

More information is available at Theses and Dissertations at University of Maryland Libraries.

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The "Extra Layer of Things": Everyday Information Management Strategies and Unmet Needs of Moms with ADHD
(2024) Walsh, Sheila Ann; St. Jean, Beth; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Mothers with ADHD need to manage their symptoms while balancing parenting responsibilities. Although technology is recommended to people with ADHD, there is limited related research in human-computer interaction (HCI). To help fill this gap, the author interviewed five mothers diagnosed with ADHD. The mothers, whose voices are largely unheard in HCI research, vividly describe their challenges managing everyday information and their attempts to adapt existing systems. The study uncovers a previously unrecognized tendency among moms with ADHD to frequently switch, and sometimes abandon, tools and systems. The study contributes to HCI by linking each finding to a design consideration. The study builds upon previous findings that neurodivergent individuals benefit from externalizing thoughts, providing new insights into how and why this occurs. These findings lay the groundwork for further HCI research and human-centered design initiatives to help parents with ADHD, and their families, thrive.
Developing and Measuring Latent Constructs in Text
(2024) Hoyle, Alexander Miserlis; Resnik, Philip; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Constructs---like inflation, populism, or paranoia---are of fundamental concern to social science. Constructs are the vocabulary over which theory operates, and so a central activity is the development and measurement of latent constructs from observable data. Although the social sciences comprise fields with different epistemological norms, they share a concern for valid operationalizations that transparently map between data and measure. Economists at the US Bureau of Labor Statistics, for example, follow a hundred-page handbook to sample the egg prices that constitute the Consumer Price Index; Clinical psychologists rely on suites of psychometric tests to diagnose schizophrenia. In many fields, this observable data takes the form of language: as a social phenomenon, language data can encode many of the latent social constructs that people care about. Commensurate with both increasing sophistication in language technologies and amounts of available data, there has thus emerged a "text-as-data" paradigm aimed at "amplifying and augmenting" the analyses that compose research. At the same time, Natural Language Processing (NLP), the field from which analysis tools originate, has often remained separate from real-world problems and guiding theories---as least when it comes to social science. Instead, it focuses on atomized tasks under the assumption that progress on low-level language aspects will generalize to higher-level problems that involve overlapping elements. This dissertation focuses on NLP methods and evaluations that facilitate the development and measurement of latent constructs from natural language, while remaining sensitive to social sciences' need for interpretability and validity.
Information Avoidance in the Archival Context
(2024) Beland II, Scott; St. Jean, Beth; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Information avoidance (IA) has been researched across several disciplines like psychology, economics, consumer health informatics, communications, and the information sciences, but the exploration of this phenomenon in archives is nearly non-existent. As information professionals, IA should be seen as a relevant concern to archivists as it may impact how people interact with archival materials, and more importantly how they may avoid certain materials, or the archives altogether. My study provides an extensive overview of IA in the archival context with a systematic literature review across disciplines and through qualitative interviews with 12 archivists across the United States of varying experience levels and from varying institution types. The aim is to explore how they think about IA in archives and how they may have experienced it in their work to answer the two research questions: 1) What abstract ideas do archivists have about IA as it relates to archives? 2) How do archivists experience IA in their daily work? Thematic analysis and synthesis grids were used to converge the transcripts into five key themes and findings about who is susceptible to IA, the contributing variables that impact and are impacted by IA, how IA manifests, real life applications of IA, and specific archival practices and concepts that impact and are impacted by IA in the context of archival work and research. Interpretations of this data resulted in theoretical models and implications that draw on existing understandings, as well as new understandings of IA that impact the information lifecycle of archival records and how people interact with them. These contributions to the archival and IA literatures can be used as a roadmap that will allow archivists to approach their work with a more mindful, and hopefully empathetic, ethic of care in handling information, understanding the costs and benefits of those decisions and actions, and better serving their patrons.
TRANSFORMING ENVIRONMENTAL EDUCATION: EXPLORING THE IMPACT OF DATA PHYSICALIZATION ON CHILDREN'S LEARNING
(2024) Lin, Yi-Hsieh; Aston, Jason; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This paper explores the integration of data physicalization in Education for SustainableDevelopment (ESD), focusing on its potential to enhance the learning experience for young audiences, particularly those aged 7-12. By examining current approaches in ESD and analyzing the impact of tangible data interactions on children's understanding and engagement with sustainability issues, the study underscores the importance of innovative educational methods. Preliminary findings indicate that data physicalization help enhance comprehension, engagement, and active learning among young learners. The research contributes to the discourse on effective ESD practices, advocating for the inclusion of data physicalization techniques in educational curriculums to better prepare youth for addressing global environmental challenges.
Values in American Hearing Healthcare
(2024) Menon, Katherine Noel; Hoover, Eric C; Hearing and Speech Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The long-term objective of this research is to create a more inclusive, patient-centered hearing healthcare system that aligns with all stakeholders’ diverse values and needs. This dissertation explores the values shaping hearing healthcare through three complementary studies. Chapter 2 analyzes the introduction of over-the-counter (OTC) hearing aids, revealing a values shift from traditional audiology’s focus on accuracy, safety, and subjective benefit to prioritizing access and affordability. Implementing an OTC service delivery model for hearing healthcare promoted values different from those of traditional audiology. Still, the creation of OTC offers affordances that enable us to create more patient-centered hearing healthcare systems to reflect stakeholders’ values. Chapter 3 validates a comprehensive list of values in audiology through a national survey of audiologists, confirming alignment with best-practice guidelines. Previous work developed a codebook of values based on textual documents representing best practices in traditional audiology, and it was essential to validate these findings by directly engaging with audiologists. Chapter 4 develops a codebook based on the values of individuals with hearing difficulties, categorizing their concerns into Material, Social, and Healthcare domains. Results from this study highlight the importance of considering the values of individuals with hearing loss, which encompasses not only the use of hearing aids and affordable hearing healthcare but also concerns regarding the effectiveness, usefulness, and social implications of hearing aids. Together, these studies underscore the balance between efforts to improve accessibility and the need to maintain patient-centered outcomes, suggesting that future research should focus on understanding how values intersect with the daily lives and decision-making processes of all people with difficulty hearing.
Systematic Analysis of Adversaries' Exploitations of the End-host
(2024) Avllazagaj, Erin; Dumitras, Tudor; Kwon, Yonghwi; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In the pipeline of a cyber attack, the malicious actor will first gain a foothold in the target system through a malware. The malware detection is still a challenging problem, as the malware authors are constantly evolving their techniques to evade detection. Therefore, it is important for us to understand why that is the case and what can the defenders do to improve the detection of the malware. In this thesis, I explore the behavior of the malware in the real users’ machines and how it changes across different executions. I show that the malware exhibits more variability than benign samples and that certain actions are often more prone to variability than others. This is the first study that quantitatively analyzes the behavior of the malware in the wildI leverage an observation from the first project, where variability in the malware samples happens due to running privilege escalation exploits. The variability in behavior is due to the fact that the malware sometimes runs in non-privileged mode and tries to run an exploit to escalate its privileges. For these reasons, I propose a new methodology to systematically discover sensitive memory corruption targets that cause privilege escalation. At last, I explore the sensitive memory corruption targets in the Linux kernel. Specifically, I propose a methodology to systematically discover sensitive fields in the Linux kernel that, when corrupted, lead the system into an exploitable state. This system, called SCAVY, is based on a novel definition of the exploitable state that allows the attacker to read and write into files and memory locations that they would normally. SCAVY explores the exploitable states based on the threat model of a local unprivileged attacker with the ability to issue system calls and with the capability to read/write into a limited location in the kernel memory. The framework revealed that there are 17 sensitive fields across 12 Linux kernel C structs that, when overwritten with the correct value, lead the system into an exploitable state. In this definition, unlike prior work, I consider the system to be in an exploitable state when the weird machine allows the attacker to read and/or write into files and memory locations that they would normally not be able to. This state can be used to write into sensitive files such as //etc//passwd where the exploit author can create a new root account on the vulnerable host and log in as that. Additionally, if the attacker can read unreadable files such as //etc//shadow they can leak passwords of root accounts, de-hash them and log in as the root account. I utilize these targets to develop 6 exploits for 5 CVE vulnerabilities. I also demonstrated the severity of these fields and the applicability of the exploitable state by exploiting CVE-2022-27666. I overwrote the f mapping pointer in struct file and caused a write into //etc//passwd. Unlike the original exploit, ours didn’t need to break KASLR, modify global variables or require support of FUSE-fs from the vulnerable host. This makes our methodology more extensible and more stable, since the exploit requires fewer corruption in the kernel memory and it doesn’t rely on the need to have the addresses of the kernel’s symbols for calculating the KASLR offset. Additionally, our exploit doesn’t modify global variables, which makes it more stable and less likely to crash the kernel, during its runtime. Our findings show that new memory corruption targets can change the security implications of vulnerabilities, urging researchers to proactively discover memory corruption targets.
When Good MT Goes Bad: Undestanding and Mitigating Misleading Machine Translations
(2024) Martindale, Marianna; Carpuat, Marine; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Machine Translation (MT) has long been viewed as a force multiplier, enabling monolingual users to assist in processing foreign language text. In ideal situations, Neural MT (NMT) provides unprecedented MT quality, potentially increasing productivity and user acceptance of the technology. However, outside of ideal circumstances, NMT introduces new types of errors that may be difficult for users who don't understand the source language to recognize, resulting in misleading output. This dissertation seeks to understand the prevalence, nature, and impact of potentially misleading output and whether a simple intervention can mitigate its effects on monolingual users. To understand the prevalence of misleading MT output, we conduct a study to quantify the potential impact of output that is fluent but not adequate, or ``fluently inadequate", by observing the relative frequency of these types of errors in two types of MT models, statistical and early neural models. We find that neural models were consistently more prone to this type of error than traditional statistical models. However, improving the overall quality of the MT system such as through domain adaptation reduces these errors. We examine the nature of misleading MT output by moving from an intrinsic feature (fluency) to a more user-centered feature, believability, defined as a monolingual user's perception of the likelihood that the meaning of the MT output matches the meaning of the input, without understanding the source. We find that fluency accounts for most believability judgments, but semantic features like plausibility also play a role. Finally, we turn to mitigating the impacts of potentially misleading NMT output. We propose two simple interventions to help users more effectively handle inadequate output: providing output from a second NMT system and providing output from a rule-based MT (RBMT) system. We test these interventions for one use case with a user study designed to mimic typical intelligence analysis triage workflows and with actual intelligence analysts as participants. We see significant increases in performance on relevance judgment tasks with output from two NMT systems and in performance on relevant entity identification tasks with the addition of RBMT output.
Value sets for the analysis of real-world patient data: Problems, theory, and solutions
(2024) Gold, Sigfried; Lutters, Wayne; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Observational, retrospective, in silico studies based on real-world data—that is, data for research collected from sources other than randomized clinical trials—cost a minute fraction of randomized clinical trials and are essential for clinical research, pharmacoepidemiology, clinical quality measurement, health system administration, value-based care, clinical guideline compliance, and public health surveillance. They offer an alternative when randomized trials cannot provide large enough patient cohorts or patients representative of real populations in terms of comorbidities, age range, disease severity, rare conditions.Improvements in the speed, frequency, and quality of research investigations using real-world data have accelerated with the emergence of distributed research networks based on common data models over the past ten years. Analyses of repositories of coded patient data involve data models, controlled medical vocabularies and ontologies, analytic protocols, implementations of query logic, value sets of vocabulary terms, and software platforms for developing and using these. These studies generally rely on clinical data represented using controlled medical vocabularies and ontologies—like ICD10, SNOMED, RxNorm, CPT, and LOINC—which catalogue and organize clinical phenomena such as conditions, treatments, and observations. Clinicians, researchers, and other medical staff collect patient data into electronic health records, registries, and claims databases with each phenomenon represented by a code, a concept identifier, from a medical vocabulary. Value sets are groupings of these identifiers that facilitate data collection, representation, harmonization, and analysis. Although medical vocabularies use hierarchical classification and other data structures to represent phenomena at different levels of granularity, value sets are needed for concepts that cover a number of codes. These lists of codes representing medical terms are a common feature of the cohort, phenotype, or other variable definitions that are used to specify patients with particular clinical conditions in analytic algorithms. Developing and validating original value sets is difficult to do well; it is a relatively small but ubiquitous part of real-world data analysis, it is time-consuming, and it requires a range of clinical, terminological, and informatics expertise. When a value set fails to match all the appropriate records or matches records that do not indicate the phenomenon of interest, study results are compromised. An inaccurate value set can lead to completely wrong study results. When value set inaccuracy causes more subtle errors in study results, conclusions may be incorrect without catching researchers’ attention. One hopes in this case that the researchers will notice a problem and track it down to a value set issue. Verifying or measuring value set accuracy is difficult and costly, often impractical, sometimes impossible. Literature recognizing the deleterious effects of value set quality on the reliability of observational research results frequently recommends public repositories where high-quality value sets for reuse can be stored, maintained, and refined by successive users. Though such repositories have been available for years and populated with hundreds or thousands of value sets, regular reuse has not been demonstrated. Value set quality has continued to be questioned in the literature, but the value of reuse has continued to be recommended and generally accepted at face value. The hope for value set repositories has been not only for researchers to have access to expertly designed value sets but for incremental refinement, that, over time, researchers will take advantage of others’ work, building on it where possible instead of repeating it, evaluating the accuracy of the value sets, and contributing their changes back to the repository. Rather than incremental improvement or indications of value sets being vetted and validated, what we see in repositories is proliferation and clutter: new value sets that may or may not have been vetted in any way and junk concept sets, created for some reason but never finished. We have found general agreement in our data that the presence of many alternative value sets for a given condition often leads value set developers to ignore all of them and start from scratch, as there is generally no easy way to tell which will be more appropriate for the researcher’s needs. And if they share their value set back to the repository, they further compound the problem, especially if they neglect to document the new value set's intention and provenance. The research offered here casts doubt on the value of reuse with currently available software and infrastructure for value set management. It is about understanding the challenges value sets present; understanding how they are made, used, and reused; and offering practice and software design recommendations to advance the ability of researchers to efficiently make or find accurate value sets for their studies, leveraging and adding to prior value set development efforts. This required field work, and, with my advisors, I conducted a qualitative study of professionals in the field: an observational user study with the aim of understanding and characterizing normative and real-world practices in value set construction and validation, with a particular focus on how researchers use the knowledge embedded in medical terminologies and ontologies to inform that work. I collected data through an online survey of RWD analysts and researchers interviews with a subset of survey participants, and observation of certain participants performing actual work to create value sets. We performed open coding and thematic analysis on interview and observation transcripts, interview notes, and open-ended question text from the surveys. The requirements, recommendations, and theoretical contributions in prior literature have not been sufficient to guide the design of software that could make effective leveraging of shared value sets a reality. This dissertation presents a conceptual framework, real-world experience, and deep, detailed account of the challenges to reuse, and makes up that deficit with a high-level requirements roadmap for improved value set creation tools. I argue, based on the evidence marshalled throughout, that there is one way to get researchers to reuse appropriate value sets or to follow best practices in determining whether a new one is absolutely needed creating their own and dedicate sufficient and appropriate effort to create them well and prepare them for reuse by others. That is, giving them software that pushes them to do these things, mostly by making it easy and obviously beneficial to do them. I offer a start in building such software with Value Set Hub, a platform for browsing, comparing, analyzing, and authoring value sets—a tool in which the presence of multiple, sometimes redundant, value sets for the same condition strengthens rather than stymies efforts to build on the work of prior value set developers. Particular innovations include the presentation of multiple value sets on the same screen for easy comparison, the display of compared value sets in the context of vocabulary hierarchies, the integration of these analytic features and value set authoring, and value set browsing features that encourage users to review existing value sets that may be relevant to their needs. Fitness-for-use is identified as the central challenge for value set developers and the strategies for addressing this challenge are categorized into two approaches: value-set-focused and code-focused. The concluding recommendations offer a roadmap for future work in building the next generation of value set repository platforms and authoring tools.
Understanding Sustainability Practices and Challenges in Making and Prototyping
(2024) Dhaygude, Mrunal Sanjay; Peng, Huaishu; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Democratization of prototyping technologies like 3D printers and laser cutters has led to more rapid prototyping practices for the reasons of research, product development and individual interests. While prototyping is becoming a much easier and faster process, there are many sustainability implications neglected. To investigate the current sustainability landscape within the realm of making, we conducted a comprehensive semi-structured interview study involving 15 participants, encompassing researchers, makerspace managers, entrepreneurs, and casual makers. In this paper, we present the findings from this study, shedding light on the challenges, knowledge gaps, motivations, and opportunities that influence sustainable making practices. We discuss potential future paradigms of HCI research to help resolve sustainability challenges in the maker community.
Studying the Effects of Colors Within Virtual Reality (VR) on Psychological and Physical Behavior
(2024) Fabian, Ciara Aliese; Aston, Jason; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Color theory is an important aspect of today's world, especially when consideringuser design, technology, and art. The primary objective of this thesis is to examine how the color groups, warm and cool, affect individuals psychologically and physiologically. While combining technological advancements, physiological methods, and psychological analyses, I will try to discover the emotional associations with specific color groups and determine the psychological and physiological impact of color groups on individuals. I hypothesize that warm colors will increase heart rate and skin conductance response, which will directly correlate to emotions of stress and excitement, and cool colors will decrease heart rate and skin conductance, which is associated with the emotions of calmness and positivity. This study demonstrated that the two-color groups exhibited a notable influence on heart rate. Using the skin conductance response method yielded unanticipated results in comparison to prior research. Prior studies have shown that there is a relationship between heart rate and skin conductance response, and therefore, if one increases, then the other should also increase. This study found that when the heart rate increased, many participants experienced a decrease in skin conductance response, showcasing a contrast in physiological reaction. Furthermore, the study demonstrated a correlation between physiological changes, such as heart rate variations, and corresponding changes in participants' psychological behavior.

Theses and Dissertations from UMD

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