Matching Items (94)
Description
The future will be replete with Artificial Intelligence (AI) based agents closely collaborating with humans. Although it is challenging to construct such systems for real-world conditions, the Intelligent Tutoring System (ITS) community has proposed several techniques to work closely with students. However, there is a need to extend these systems outside the controlled environment of the classroom. More recently, Human-Aware Planning (HAP) community has developed generalized AI techniques for collaborating with humans and providing personalized support or guidance to the collaborators. In this thesis, the take learning from the ITS community is extend to construct such human-aware systems for real-world domains and evaluate them with real stakeholders. First, the applicability of HAP to ITS is demonstrated, by modeling the behavior in a classroom and a state-of-the-art tutoring system called Dragoon. Then these techniques are extended to provide decision support to a human teammate and evaluate the effectiveness of the framework through ablation studies to support students in constructing their plan of study (\ipos). The results show that these techniques are helpful and can support users in their tasks. In the third section of the thesis, an ITS scenario of asking questions (or problems) in active environments is modeled by constructing questions to elicit a human teammate's model of understanding. The framework is evaluated through a user study, where the results show that the queries can be used for eliciting the human teammate's mental model.
ContributorsGrover, Sachin (Author) / Kambhampati, Subbarao (Thesis advisor) / Smith, David (Committee member) / Srivastava, Sidhharth (Committee member) / VanLehn, Kurt (Committee member) / Arizona State University (Publisher)
Created2022
Description
Written corrective feedback (WCF) has received considerable attention in secondlanguage (L2) writing research. The conducive role of WCF in developing L2 writing and
second language acquisition has been corroborated by a number of theoretical frameworks,
and the findings of empirical studies, meta-analyses, and research syntheses. WCF research
has predominantly addressed its effectiveness in improving learners’ syntactic, lexical, and
orthographic knowledge. This dissertation project extends the scope of this line of research
to formulaic aspects of language and investigates the relative effectiveness of WCF
targeting formulaic vs. non-formulaic constructions in L2 writing. The text-analytic
descriptive aspect of this research design aimed at investigating the extent of L2 learners’
non-target-like use of formulaic vs. non-formulaic forms in L2 writing and writing
teachers’ WCF treatment of non-target (non)formulaic language use. A total of 480 first
drafts of essays written by 33 advanced adult English-as-a-foreign language (EFL) learners
during one semester and 480 drafts of essays corrected through WCF by three EFL teachers
constituted the corpus in this study. Advancing the field of learner corpus research, the
findings demonstrated that whereas learners’ non-target formulaic forms outnumbered that
of non-formulaic ones in their writing assignments, all three teachers provided WCF more
often for erroneous use of non-formulaic forms. The quasi-experimental aspect of the
research design attempts to add new empirical evidence on the L2 learning potential of
accessing and processing WCF provided for formulaic vs. non-formulaic constructions in
L2 writing. To this end, a total of 66 EFL learners in a Test of English as a Foreign
Language preparation course participated in a pretest-posttest design, with 5 experimental groups (those who were provided with direct, indirect, direct plus metalinguistic, and
indirect plus metalinguistic WCF) and a control group (those who were not provided with
WCF). Maintaining a division between formulaic vs. non-formulaic forms, the findings
provide empirical evidence on the interactions between types of WCF, types of linguistic
targets, and the effectiveness of WCF in terms of enhancing L2 learners’ accuracy and
acquisition in their revised writing and new writings in the short and long term.
ContributorsGholami, Leila (Author) / Smith, David (Thesis advisor) / Matsuda, Paul K (Committee member) / James, Mark A (Committee member) / Arizona State University (Publisher)
Created2022
Description
Infrastructure systems are facing non-stationary challenges that stem from climate change and the increasingly complex interactions between the social, ecological, and technological systems (SETSs). It is crucial for transportation infrastructures—which enable residents to access opportunities and foster prosperity, quality of life, and social connections—to be resilient under these non-stationary challenges. Vulnerability assessment (VA) examines the potential consequences a system is likely to experience due to exposure to perturbation or stressors and lack of the capacity to adapt. Post-fire debris flow and heat represent particularly challenging problems for infrastructure and users in the arid U.S. West. Post-fire debris flow, which is manifested with heat and drought, produces powerful runoff threatening physical transportation infrastructures. And heat waves have devastating health effects on transportation infrastructure users, including increased mortality rates. VA anticipates the potential consequences of these perturbations and enables infrastructure stakeholders to improve the system's resilience. The current transportation climate VA—which only considers a single direct climate stressor on the infrastructure—falls short of addressing the wildfire and heat challenges. This work proposes advanced transportation climate VA methods to address the complex and multiple climate stressors and the vulnerability of infrastructure users. Two specific regions were chosen to carry out the progressive transportation climate VA: 1) the California transportation networks’ vulnerability to post-fire debris flows, and 2) the transportation infrastructure user’s vulnerability to heat exposure in Phoenix.
ContributorsLi, Rui (Author) / Chester, Mikhail V. (Thesis advisor) / Middel, Ariane (Committee member) / Hondula, David M. (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2022
Description
Transitioning into civilian life after military service is a challenging prospect. It can be difficult to find employment and maintain good mental health, and up to 70 percent of veterans experience homelessness or alcoholism. Upon discharge, many veterans pursue higher education as a way to reintegrate into civilian society. However, many studies have shown that veterans encounter multiple challenges during their attempt to reintegrate into civilian life, including anxiety, a lack of relevant skills, post-traumatic stress disorder (PTSD), and other issues that may lead to communication and interaction challenges in the higher education environment. Student veterans also face challenges in the lack of common language and culture clashes due to differences between military and college culture. This study used a mixed-methods approach to examine the challenges military veterans face related to language use in civilian life. The data was collected from 149 student veterans who completed a questionnaire and 11 student veterans who participated in interviews. Detailed analysis of collected data showed that student veterans experienced some challenges in language use, especially when they initially enrolled in their courses, but they seemed to have overcome challenges after spending time in the university setting. The veterans who had prior college education before joining the military seemed to have a slight advantage, having had experience using the academic language. The study also explored how student veterans chose to share their veteran status with other people in their university community. The findings showed that they strongly identified with their veteran identity and was comfortable sharing their status with others, but they also sometimes were reluctant to share their military experience in details because they were afraid that their peers would not understand.
ContributorsObaid, Naji (Author) / Matsuda, Aya (Thesis advisor) / Smith, David (Committee member) / James, Mark (Committee member) / Arizona State University (Publisher)
Created2022
Description
Studying the so-called ”hidden” phases of quantum materials—phases that do not exist under equilibrium conditions, but can be accessed with light—reveals new insights into the broader field of structural phase transitions. Using terahertz irradiation as well as hard x-ray probes made available by x-ray free electron lasers (XFELs) provides unique capabilities to study phonon dispersion in these materials. Here, we study the cubic peak of the quantum paraelectric strontium titanate (SrTiO3, STO) below the 110 K cubic-to-tetragonal tran- sition. Our results reveal a temperature and field strength dependence of the transverse acoustic mode in agreement with previous work on the avoided crossing occurring at finite wavevector, as well as evidence of anharmonic coupling between transverse optical phonons and a fully symmetric A1g phonon. These results elucidate previous optical studies on STO and hold promise for future studies on the hidden metastable phases of quantum materials.
ContributorsStanton, Jade (Author) / Teitelbaum, Samuel (Thesis director) / Smith, David (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2023-05
Description
Machine learning (ML) and deep learning (DL) has become an intrinsic part of multiple fields. The ability to solve complex problems makes machine learning a panacea. In the last few years, there has been an explosion of data generation, which has greatly improvised machine learning models. But this comes with a cost of high computation, which invariably increases power usage and cost of the hardware. In this thesis we explore applications of ML techniques, applied to two completely different fields - arts, media and theater and urban climate research using low-cost and low-powered edge devices. The multi-modal chatbot uses different machine learning techniques: natural language processing (NLP) and computer vision (CV) to understand inputs of the user and accordingly perform in the play and interact with the audience. This system is also equipped with other interactive hardware setups like movable LED systems, together they provide an experiential theatrical play tailored to each user. I will discuss how I used edge devices to achieve this AI system which has created a new genre in theatrical play. I will then discuss MaRTiny, which is an AI-based bio-meteorological system that calculates mean radiant temperature (MRT), which is an important parameter for urban climate research. It is also equipped with a vision system that performs different machine learning tasks like pedestrian and shade detection. The entire system costs around $200 which can potentially replace the existing setup worth $20,000. I will further discuss how I overcame the inaccuracies in MRT value caused by the system, using machine learning methods. These projects although belonging to two very different fields, are implemented using edge devices and use similar ML techniques. In this thesis I will detail out different techniques that are shared between these two projects and how they can be used in several other applications using edge devices.
ContributorsKulkarni, Karthik Kashinath (Author) / Jayasuriya, Suren (Thesis advisor) / Middel, Ariane (Thesis advisor) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this dissertation, the surface interactions of fluorine were studied during atomic layer deposition (ALD) and atomic layer etching (ALE) of wide band gap materials. To enable this research two high vacuum reactors were designed and constructed for thermal and plasma enhanced ALD and ALE, and they were equipped for in-situ process monitoring. Fluorine surface interactions were first studied in a comparison of thermal and plasma enhanced ALD (TALD and PEALD) of AlF3 thin films prepared using hydrogen fluoride (HF), trimethylaluminum (TMA), and H2-plasma. The ALD AlF3 films were compared ¬in-situ using ellipsometry and X-ray photoelectron spectroscopy (XPS). Ellipsometry showed a growth rate of 1.1 Å/ cycle and 0.7 Å/ cycle, at 100°C, for the TALD and PEALD AlF3 processes, respectively. XPS indicated the presence of Al-rich clusters within the PEALD film. The formation of the Al-rich clusters is thought to originate during the H2-plasma step of the PEALD process. The Al-rich clusters were not detected in the TALD AlF3 films. This study provided valuable insight on the role of fluorine in an ALD process.
Reactive ion etching is a common dry chemical etch process for fabricating GaN devices. However, the use of ions can induce various defects, which can degrade device performance. The development of low-damage post etch processes are essential for mitigating plasma induced damage. As such, two multistep ALE methods were implemented for GaN based on oxidation, fluorination, and ligand exchange. First, GaN surfaces were oxidized using either water vapor or O2-plasma exposures to produce a thin oxide layer. The oxide layer was addressed using alternating exposures of HF and TMG, which etch Ga2O3 films. Each ALE process was characterized using in-situ using ellipsometry and XPS and ex-situ transmission electron microscopy (TEM). XPS indicated F and O impurities remained on the etched surfaces. Ellipsometry and TEM showed a slight reduction in thickness. The very low ALE rate was interpreted as the inability of the Ga2O3 ALE process to fluorinate the ordered surface oxide on GaN (0001).
Overall, these results indicate HF is effective for the ALD of metal fluorides and the ALE of metal oxides.
ContributorsMessina, Daniel C (Author) / Nemanich, Robert J (Thesis advisor) / Goodnick, Stephen (Committee member) / Ponce, Fernando A (Committee member) / Smith, David (Committee member) / Arizona State University (Publisher)
Created2021
Description
Cities globally are experiencing substantial warming due to ongoing urbanization and climate change. However, existing efforts to mitigate urban heat focus mainly on new technologies, exacerbate social injustices, and ignore the need for a sustainability lens that considers environmental, social, and economic perspectives. Heat in urban areas is amplified and urgently needs to be considered as a critical sustainability issue that crosses disciplinary and sectoral (traditional) boundaries. The missing urgency is concerning because urban overheating is a multi-faceted threat to the well-being and performance of individuals as well as the energy efficiency and economy of cities. Urban heat consequences require transformation in ways of thinking by involving the best available knowledge engaging scientists, policymakers, and communities. To do so, effective heat mitigation planning requires a considerable amount of diverse knowledge sources, yet urban planners face multiple barriers to effective heat mitigation, including a lack of usable, policy-relevant science and governance structures. To address these issues, transdisciplinary approaches, such as co-production via partnerships and the creation of usable, policy-relevant science, are necessary to allow for sustainable and equitable heat mitigation that allow cities to work toward multiple Sustainable Development Goals (SDGs) using a systems approach. This dissertation presents three studies that contribute to a sustainability lens on urban heat, improve the holistic and multi-perspective understanding of heat mitigation strategies, provide contextual guidance for reflective pavement as a heat mitigation strategy, and evaluate a multilateral, sustainability-oriented, co-production partnership to foster heat resilience equitably in cities. Results show that science and city practice communicate differently about heat mitigation strategies while both avoid to communicate disservices and trade-offs. Additionally, performance evaluation of heat mitigation strategies for decision-making needs to consider multiple heat metrics, people, and background climate. Lastly, the partnership between science, city practice, and community needs to be evaluated to be accountable and provide a pathway of growth for all partners. The outcomes of this dissertation advance research and awareness of urban heat for science, practice, and community, and provide guidance to improve holistic and sustainable decision-making in cities and partnerships to address SDGs around urban heat.
ContributorsSchneider, Florian Arwed (Author) / Middel, Ariane (Thesis advisor) / Vanos, Jennifer K (Committee member) / Withycombe Keeler, Lauren (Committee member) / Arizona State University (Publisher)
Created2023
Description
Extreme heat and its human impacts are significant public health challenges that disproportionately affect certain populations. Often, people with the least resources to cope with the heat also live in the hottest regions of cities. Previous heat vulnerability research has predominantly been conducted at a coarse geographic scale, yet translating relationships measured at aggregated scales to the individual level can result in ecological fallacy. Prior work has also primarily studied the most severe health outcomes: hospitalization/emergency care and mortality. It is likely that magnitudes more people are experiencing negative health impacts from heat that do not necessarily result in medical care. Such less severe impacts are under-researched in the literature.This dissertation addresses these knowledge gaps by identifying how social characteristics and physical measurements of heat at the individual and household level act independently and in concert to influence human heat-related outcomes, especially less severe outcomes. In the first paper, meta-analysis was used to quantify the summary effects of vulnerability indicators on incidence of heat-related illness. More proximal vulnerability indicators (e.g., residential air conditioning use, indoor heat exposure, etc.) tended to have the strongest impact on odds of experiencing heat-related illness than more distal indicators. In the next paper, indoor air temperature observations were related to the social characteristics of the residents. The strongest predictor of indoor air temperature was the residents’ ideal thermally comfortable temperature, despite affordability. In the final paper, fine scale biometeorological observations of the outdoor thermal environment near residents’ homes were linked to their experience with heat-related illness. The outdoor thermal environment appeared to have a stronger, more consistent impact on heat-related illness among households in a lower income neighborhood compared to a higher income one. These findings affirm the value of employing residential heat mitigation solutions at the individual and household scale, indoors and outdoors. Across all chapters, the indoor thermal environment, and the ability to modify it, had a clear impact on residents’ comfort and health. Solutions that target the most proximal causal factors of heat-related illness will likely have the greatest impact on reducing the burden of heat on human health and well-being.
ContributorsWright, Mary K (Author) / Hondula, David M (Thesis advisor) / Larson, Kelli L (Committee member) / Middel, Ariane (Committee member) / Arizona State University (Publisher)
Created2023
Description
Adaptive capacity to climate change is the ability of a system to mitigate or take advantage of climate change effects. Research on adaptive capacity to climate change suffers fragmentation. This is partly because there is no clear consensus around precise definitions of adaptive capacity. The aim of this thesis is to place definitions of adaptive capacity into a formal framework. I formalize adaptive capacity as a computational model written in the Idris 2 programming language. The model uses types to constrain how the elements of the model fit together. To achieve this, I analyze nine existing definitions of adaptive capacity. The focus of the analysis was on important factors that affect definitions and shared elements of the definitions. The model is able to describe an adaptive capacity study and guide a user toward concepts lacking clarity in the study. This shows that the model is useful as a tool to think about adaptive capacity. In the future, one could refine the model by forming an ontology for adaptive capacity. One could also review the literature more systematically. Finally, one might consider turning to qualitative research methods for reviewing the literature.
ContributorsManuel, Jason (Author) / Bazzi, Rida (Thesis director) / Pavlic, Theodore (Committee member) / Middel, Ariane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05