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- Creators: Arizona State University
- Creators: Schumann, Robert, 1810-1856
Description
This mixed methods action research study closely examines what Club Advisors need in order to be successful in their role, develops an intervention that supports Club Advisors in skill-building along with Club Advisor’s self-motivation and the development of self-efficacy in their role. The purpose of this study was to understand what skills and motivations Club Advisors had and after an intervention occurred, observing whether their self-efficacy around club advising increased. While there has been growth in the area of student affairs and focus on student involvement outside of the classroom, there is currently limited research in the field of university Club Advising as the resources exist informally. The formal literature which does exist does not agree on what skills are needed to be a Club Advisor and does not bridge the gap between theory and practice. The lack of formal research on Club Advising impacts the student experience through Club Advisors not receiving the resources they need. Ensuring the Club Advisors who do volunteer their time are set-up to develop their students successfully requires additional research. This research study used surveys, interviews, memos, and workshop interventions to understand where Club Advisors were developmentally and how to develop them further. Club Advisors in the study wanted to use the resources and connect with others, but before this study did not know how or where to connect. Future cycles of research beyond this study would expand upon the findings and create a foundation for Club Advisor development.
ContributorsO'Brien, Jennifer (Author) / Chen, Ying-Chih (Thesis advisor) / Davis, Ben (Committee member) / Vela, Alicia (Committee member) / Arizona State University (Publisher)
Created2020
Description
The pervasive use of the Web has connected billions of people all around the globe and enabled them to obtain information at their fingertips. This results in tremendous amounts of user-generated data which makes users traceable and vulnerable to privacy leakage attacks. In general, there are two types of privacy leakage attacks for user-generated data, i.e., identity disclosure and private-attribute disclosure attacks. These attacks put users at potential risks ranging from persecution by governments to targeted frauds. Therefore, it is necessary for users to be able to safeguard their privacy without leaving their unnecessary traces of online activities. However, privacy protection comes at the cost of utility loss defined as the loss in quality of personalized services users receive. The reason is that this information of traces is crucial for online vendors to provide personalized services and a lack of it would result in deteriorating utility. This leads to a dilemma of privacy and utility.
Protecting users' privacy while preserving utility for user-generated data is a challenging task. The reason is that users generate different types of data such as Web browsing histories, user-item interactions, and textual information. This data is heterogeneous, unstructured, noisy, and inherently different from relational and tabular data and thus requires quantifying users' privacy and utility in each context separately. In this dissertation, I investigate four aspects of protecting user privacy for user-generated data. First, a novel adversarial technique is introduced to assay privacy risks in heterogeneous user-generated data. Second, a novel framework is proposed to boost users' privacy while retaining high utility for Web browsing histories. Third, a privacy-aware recommendation system is developed to protect privacy w.r.t. the rich user-item interaction data by recommending relevant and privacy-preserving items. Fourth, a privacy-preserving framework for text representation learning is presented to safeguard user-generated textual data as it can reveal private information.
Protecting users' privacy while preserving utility for user-generated data is a challenging task. The reason is that users generate different types of data such as Web browsing histories, user-item interactions, and textual information. This data is heterogeneous, unstructured, noisy, and inherently different from relational and tabular data and thus requires quantifying users' privacy and utility in each context separately. In this dissertation, I investigate four aspects of protecting user privacy for user-generated data. First, a novel adversarial technique is introduced to assay privacy risks in heterogeneous user-generated data. Second, a novel framework is proposed to boost users' privacy while retaining high utility for Web browsing histories. Third, a privacy-aware recommendation system is developed to protect privacy w.r.t. the rich user-item interaction data by recommending relevant and privacy-preserving items. Fourth, a privacy-preserving framework for text representation learning is presented to safeguard user-generated textual data as it can reveal private information.
ContributorsBeigi, Ghazaleh (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Tong, Hanghang (Committee member) / Eliassi-Rad, Tina (Committee member) / Arizona State University (Publisher)
Created2020
Description
The recent proliferation of online platforms has not only revolutionized the way people communicate and acquire information but has also led to propagation of malicious information (e.g., online human trafficking, spread of misinformation, etc.). Propagation of such information occurs at unprecedented scale that could ultimately pose imminent societal-significant threats to the public. To better understand the behavior and impact of the malicious actors and counter their activity, social media authorities need to deploy certain capabilities to reduce their threats. Due to the large volume of this data and limited manpower, the burden usually falls to automatic approaches to identify these malicious activities. However, this is a subtle task facing online platforms due to several challenges: (1) malicious users have strong incentives to disguise themselves as normal users (e.g., intentional misspellings, camouflaging, etc.), (2) malicious users are high likely to be key users in making harmful messages go viral and thus need to be detected at their early life span to stop their threats from reaching a vast audience, and (3) available data for training automatic approaches for detecting malicious users, are usually either highly imbalanced (i.e., higher number of normal users than malicious users) or comprise insufficient labeled data.
To address the above mentioned challenges, in this dissertation I investigate the propagation of online malicious information from two broad perspectives: (1) content posted by users and (2) information cascades formed by resharing mechanisms in social media. More specifically, first, non-parametric and semi-supervised learning algorithms are introduced to discern potential patterns of human trafficking activities that are of high interest to law enforcement. Second, a time-decay causality-based framework is introduced for early detection of “Pathogenic Social Media (PSM)” accounts (e.g., terrorist supporters). Third, due to the lack of sufficient annotated data for training PSM detection approaches, a semi-supervised causal framework is proposed that utilizes causal-related attributes from unlabeled instances to compensate for the lack of enough labeled data. Fourth, a feature-driven approach for PSM detection is introduced that leverages different sets of attributes from users’ causal activities, account-level and content-related information as well as those from URLs shared by users.
To address the above mentioned challenges, in this dissertation I investigate the propagation of online malicious information from two broad perspectives: (1) content posted by users and (2) information cascades formed by resharing mechanisms in social media. More specifically, first, non-parametric and semi-supervised learning algorithms are introduced to discern potential patterns of human trafficking activities that are of high interest to law enforcement. Second, a time-decay causality-based framework is introduced for early detection of “Pathogenic Social Media (PSM)” accounts (e.g., terrorist supporters). Third, due to the lack of sufficient annotated data for training PSM detection approaches, a semi-supervised causal framework is proposed that utilizes causal-related attributes from unlabeled instances to compensate for the lack of enough labeled data. Fourth, a feature-driven approach for PSM detection is introduced that leverages different sets of attributes from users’ causal activities, account-level and content-related information as well as those from URLs shared by users.
ContributorsAlvari, Hamidreza (Author) / Shakarian, Paulo (Thesis advisor) / Davulcu, Hasan (Committee member) / Tong, Hanghang (Committee member) / Ruston, Scott (Committee member) / Arizona State University (Publisher)
Created2020
Description
Modern Communication systems are progressively moving towards all-digital transmitters (ADTs) due to their high efficiency and potentially large frequency range. While significant work has been done on individual blocks within the ADT, there are few to no full systems designs at this point in time. The goal of this work is to provide a set of multiple novel block architectures which will allow for greater cohesion between the various ADT blocks. Furthermore, the design of these architectures are expected to focus on the practicalities of system design, such as regulatory compliance, which here to date has largely been neglected by the academic community. Amongst these techniques are a novel upconverted phase modulation, polyphase harmonic cancellation, and process voltage and temperature (PVT) invariant Delta Sigma phase interpolation. It will be shown in this work that the implementation of the aforementioned architectures allows ADTs to be designed with state of the art size, power, and accuracy levels, all while maintaining PVT insensitivity. Due to the significant performance enhancement over previously published works, this work presents the first feasible ADT architecture suitable for widespread commercial deployment.
ContributorsGrout, Kevin Samuel (Author) / Kitchen, Jennifer N (Thesis advisor) / Khalil, Waleed (Committee member) / Bakkaloglu, Bertan (Committee member) / Aberle, James T., 1961- (Committee member) / Garrity, Douglas (Committee member) / Arizona State University (Publisher)
Created2019
Description
This dissertation covers three primary topics and relates them in context. High frequency transformer design, microgrid modeling and control, and converter design as it pertains to the other topics are each investigated, establishing a summary of the state-of-the-art at the intersection of the three as a baseline. The culminating work produced by the confluence of these topics is a novel modular solid-state transformer (SST) design, featuring an array of dual active bridge (DAB) converters, each of which contains an optimized high-frequency transformer, and an array of grid-forming inverters (GFI) suitable for centralized control in a microgrid environment. While no hardware was produced for this design, detailed modeling and simulation has been completed, and results are contextualized by rigorous analysis and comparison with results from published literature. The main contributions to each topic are best presented by topic area. For transformers, contributions include collation and presentation of the best-known methods of minimum loss high-frequency transformer design and analysis, descriptions of the implementation of these methods into a unified design script as well as access to an example of such a script, and the derivation and presentation of novel tools for analysis of multi-winding and multi-frequency transformers. For microgrid modeling and control, contributions include the modeling and simulation validation of the GFI and SST designs via state space modeling in a multi-scale simulation framework, as well as demonstration of stable and effective participation of these models in a centralized control scheme under phase imbalance. For converters, the SST design, analysis, and simulation are the primary contributions, though several novel derivations and analysis tools are also presented for the asymmetric half bridge and DAB.
ContributorsMongrain, Robert Scott (Author) / Ayyanar, Raja (Thesis advisor) / Pan, George (Committee member) / Qin, Jiangchao (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Photocatalytic water splitting over suspended nanoparticles represents a potential solution for achieving CO2-neutral energy generation and storage. To design efficient photocatalysts, a fundamental understanding of the material’s structure, electronic properties, defects, and how these are controlled via synthesis is essential. Both bulk and nanoscale materials characterization, in addition to various performance metrics, can be combined to elucidate functionality at multiple length scales. In this work, two promising visible light harvesting systems are studied in detail: Pt-functionalized graphitic carbon nitrides (g-CNxHys) and TiO2-supported CeO2-x composites.
Electron energy-loss spectroscopy (EELS) is used to sense variations in the local concentration of amine moieties (defects believed to facilitate interfacial charge transfer) at the surface of a g-CNxHy flake. Using an aloof-beam configuration, spatial resolution is maximized while minimizing damage thus providing nanoscale vibrational fingerprints similar to infrared absorption spectra. Structural disorder in g-CNxHys is further studied using transmission electron microscopy at low electron fluence rates. In-plane structural fluctuations revealed variations in the local azimuthal orientation of the heptazine building blocks, allowing planar domain sizes to be related to the average polymer chain length. Furthermore, competing factors regulating photocatalytic performance in a series of Pt/g-CNxHys is elucidated. Increased polymer condensation in the g-CNxHy support enhances the rate of charge transfer to reactants owing to higher electronic mobility. However, active site densities are over 3x lower on the most condensed g-CNxHy which ultimately limits its H2 evolution rate (HER). Based on these findings, strategies to improve the cocatalyst configuration on intrinsically active supports are given.
In TiO2/CeO2-x photocatalysts, the effect of the support particle size on the bulk
anoscale properties and photocatalytic performance is investigated. Small anatase supports facilitate highly dispersed CeO2-x species, leading to increased visible light absorption and HERs resulting from a higher density of mixed metal oxide (MMO) interfaces with Ce3+ species. Using monochromated EELS, bandgap states associated with MMO interfaces are detected, revealing electronic transitions from 0.5 eV up to the bulk bandgap onset of anatase. Overall, the electron microscopy/spectroscopy techniques developed and applied herein sheds light onto the relevant defects and limiting processes operating within these photocatalyst systems thus suggesting rational design strategies.
Electron energy-loss spectroscopy (EELS) is used to sense variations in the local concentration of amine moieties (defects believed to facilitate interfacial charge transfer) at the surface of a g-CNxHy flake. Using an aloof-beam configuration, spatial resolution is maximized while minimizing damage thus providing nanoscale vibrational fingerprints similar to infrared absorption spectra. Structural disorder in g-CNxHys is further studied using transmission electron microscopy at low electron fluence rates. In-plane structural fluctuations revealed variations in the local azimuthal orientation of the heptazine building blocks, allowing planar domain sizes to be related to the average polymer chain length. Furthermore, competing factors regulating photocatalytic performance in a series of Pt/g-CNxHys is elucidated. Increased polymer condensation in the g-CNxHy support enhances the rate of charge transfer to reactants owing to higher electronic mobility. However, active site densities are over 3x lower on the most condensed g-CNxHy which ultimately limits its H2 evolution rate (HER). Based on these findings, strategies to improve the cocatalyst configuration on intrinsically active supports are given.
In TiO2/CeO2-x photocatalysts, the effect of the support particle size on the bulk
anoscale properties and photocatalytic performance is investigated. Small anatase supports facilitate highly dispersed CeO2-x species, leading to increased visible light absorption and HERs resulting from a higher density of mixed metal oxide (MMO) interfaces with Ce3+ species. Using monochromated EELS, bandgap states associated with MMO interfaces are detected, revealing electronic transitions from 0.5 eV up to the bulk bandgap onset of anatase. Overall, the electron microscopy/spectroscopy techniques developed and applied herein sheds light onto the relevant defects and limiting processes operating within these photocatalyst systems thus suggesting rational design strategies.
ContributorsHaiber, Diane Michelle (Author) / Crozier, Peter (Thesis advisor) / Chan, Candace (Committee member) / Liu, Jingyue (Committee member) / Treacy, Michael (Committee member) / Arizona State University (Publisher)
Created2019
Description
The ubiquity of single camera systems in society has made improving monocular depth estimation a topic of increasing interest in the broader computer vision community. Inspired by recent work in sparse-to-dense depth estimation, this thesis focuses on sparse patterns generated from feature detection based algorithms as opposed to regular grid sparse patterns used by previous work. This work focuses on using these feature-based sparse patterns to generate additional depth information by interpolating regions between clusters of samples that are in close proximity to each other. These interpolated sparse depths are used to enforce additional constraints on the network’s predictions. In addition to the improved depth prediction performance observed from incorporating the sparse sample information in the network compared to pure RGB-based methods, the experiments show that actively retraining a network on a small number of samples that deviate most from the interpolated sparse depths leads to better depth prediction overall.
This thesis also introduces a new metric, titled Edge, to quantify model performance in regions of an image that show the highest change in ground truth depth values along either the x-axis or the y-axis. Existing metrics in depth estimation like Root Mean Square Error(RMSE) and Mean Absolute Error(MAE) quantify model performance across the entire image and don’t focus on specific regions of an image that are hard to predict. To this end, the proposed Edge metric focuses specifically on these hard to classify regions. The experiments also show that using the Edge metric as a small addition to existing loss functions like L1 loss in current state-of-the-art methods leads to vastly improved performance in these hard to classify regions, while also improving performance across the board in every other metric.
This thesis also introduces a new metric, titled Edge, to quantify model performance in regions of an image that show the highest change in ground truth depth values along either the x-axis or the y-axis. Existing metrics in depth estimation like Root Mean Square Error(RMSE) and Mean Absolute Error(MAE) quantify model performance across the entire image and don’t focus on specific regions of an image that are hard to predict. To this end, the proposed Edge metric focuses specifically on these hard to classify regions. The experiments also show that using the Edge metric as a small addition to existing loss functions like L1 loss in current state-of-the-art methods leads to vastly improved performance in these hard to classify regions, while also improving performance across the board in every other metric.
ContributorsRai, Anshul (Author) / Yang, Yezhou (Thesis advisor) / Zhang, Wenlong (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2019
Description
Harm to patients remains high in US hospitals despite significant progress to improve the quality of care in our health systems. Leadership, a culture of patient safety, and a climate conducive to innovation in patient care are necessary to advance positive patient safety outcomes. Yet, little is known about how leadership can impact patient safety within a climate of innovation. This study examines the effects of transformational and transactional leadership (singularly and with transactional augmenting transformational leadership) as related to nurses’ perception of patient safety, how communication elements of a culture of patient safety may strengthen that relationship, and how the mediating role of team innovation climate may help explain the relationship between transformational and transactional leadership and nurses’ perception of patient safety. The variables were measured using three validated and reliable survey instruments: The Multifactor Leadership Questionnaire (MLQ Form 5X), the Team Climate Inventory-short (TCI), the Agency for Healthcare Research and Quality (AHRQ) Hospital Survey on Patient Safety Culture. A convenience sample of all staff registered nurses (N=952) from the single academic medical center with direct patient care responsibility was surveyed via e-mail for this research. A total of 210 surveys were returned, 157 met inclusion criteria for a response rate of 16%. Transformational leadership had a statistically significant relationship with patient safety perception, while the relationship of transactional leadership with patient safety perceptions was not significant. The results of the regression analysis that tested the effect of communication elements of a culture of patient safety on the relationship between transactional and transformational leadership and patient safety perception were not significant. Transformational leadership was significantly related with team innovation climate after controlling the effect of transactional leadership supporting the augmentation effect. Mediation analysis showed that team innovation climate had a significant mediating effect on the relationship between transformational leadership and patient safety perception. Team innovation climate had a significant mediating effect on the relationship between managers’ transformational leadership and patient safety perception after controlling for transactional leadership supporting the augmentation effect. This is the first study known to test the augmentation of transformational leadership related to patient safety and the role of team innovation climate.
ContributorsYounger, Samuel (Author) / Larkey, Linda (Thesis advisor) / Porter O'Grady, Timothy (Committee member) / Lamb, Gerri (Committee member) / Arizona State University (Publisher)
Created2019
Description
The presence of restorative justice (RJ) in the United States has grown steadily within the last five decades. The dynamics of RJ programs are meant to more holistically address the harms caused by crime in comparison to the traditional criminal justice system (CJS). Yet, evaluative research has provided inconsistent evidence of their effectiveness and the quality of empirical study has gone untested. The current study sought to fill the gaps within past research by examining how success has been measured, assessing the rigor of study methodology using the Maryland Scientific Methods Scale (SMS), and determining the impact of RJ programs on recidivism, victim satisfaction and restitution compliance using meta-analysis. A systematic search of past literature identified a sample of 121 studies whose dependent measures were coded, and methodological designs were rated using the SMS. Most studies failed to include community-based measures of success or measures which reflect the goals of RJ to undue harms and restore relationships. SMS scores were well distributed within the sample. Despite restricted sample sizes, meta-analyses used extracted data from 35 case-control, quasi-experimental and experimental studies to generate 43 unique treatment contrasts and 3 summary effects. Meta-analytic findings favored RJ treatment over CJS control groups across all dependent measures. Heterogeneity between subsequent arrest studies was scrutinized using subgroup analysis. The fewest subsequent arrests were associated with adult offenders, mandated participation, mediation and hybrid programs, and the most rigorous methodologies. Findings support continued efforts to improve the methodological rigor of evaluations, targeted focus on specific program types and delivery characteristics. Future meta-analyses would benefit from the inclusion of non-American RJ program evaluations to enlarge pooled sample populations and better detect moderating influences. Other suggestions for research design improvements include the use of more holistic and stakeholder-centric measures for success, use of continuous measures, and refined indicator variables for heterogeneity testing (e.g., crime type severity, characteristics of program fidelity). The author recommends continued use of these programs, specifically with adult offenders and incidents of serious crime toward a better understanding of the true impacts of RJ on stakeholders. More detailed results, study limitations and implications are discussed herein.
ContributorsErnest, Kyle (Author) / Fox, Kate (Thesis advisor) / Decker, Scott (Committee member) / Stolzenberg, Stacia (Committee member) / Arizona State University (Publisher)
Created2019
Description
Although researchers often conceptualize shyness as stable across different situations (e.g., Rubin, Coplan, & Bowker, 2009), evidence has suggested that shyness may consist of situation-specific components (e.g., Asendorpf, 1990a; 1990b; Gazelle & Faldowski, 2014; Xu & Farver, 2009). This study was aimed at developing a systematic measurement tool for situational shyness in adolescence, as well as examining the relations between situational shyness and other popular measures of shyness and between situational shyness and adjustment. A sample of Chinese adolescents (N = 492) from an urban school participated in the study during 7th (T1) and 8th (T2) grades. Adolescents self-reported their situational shyness using a new measure of hypothetical scenarios, as well as their general shyness, anxious shyness, regulated shyness, depressive symptoms, and loneliness. Peers reported adolescents’ general and conflicted shyness, and popularity and peer rejection. The school provided records of their academic achievement (exam scores).
Exploratory and confirmatory factor analyses of the situational shyness measure consistently supported that shyness in the hypothetical scenarios can be separated into three components: shyness with familiar peers, shyness with unfamiliar peers, and shyness in formal situations. These components had differential associations with other measures of shyness. Self-reported general and anxious shyness were related consistently to shyness with unfamiliar peers and in formal situations, and occasionally to shyness with familiar peers. Self-reported regulated shyness was not related to self-reported shyness in any situation. Peer-reported conflicted shyness was associated with shyness with familiar and unfamiliar peers, whereas peer-reported general shyness was associated with shyness with unfamiliar peers and in formal situations. Moreover, situational shyness showed differential relations to maladjustment. Shyness with familiar peers was associated positively with maladjustment in multiple domains, especially academic and peer difficulties. Shyness with unfamiliar peers and shyness in formal situations, in contrast, were associated primarily with internalizing problems. In addition, shyness with unfamiliar peers and in formal situations occasionally related to positive adjustment, suggesting shyness in specific situations may still be protective in contemporary urban China. The findings provided new evidence that the correlates of shyness depend on the situation in which shyness occurs, and may inform future intervention programs.
Exploratory and confirmatory factor analyses of the situational shyness measure consistently supported that shyness in the hypothetical scenarios can be separated into three components: shyness with familiar peers, shyness with unfamiliar peers, and shyness in formal situations. These components had differential associations with other measures of shyness. Self-reported general and anxious shyness were related consistently to shyness with unfamiliar peers and in formal situations, and occasionally to shyness with familiar peers. Self-reported regulated shyness was not related to self-reported shyness in any situation. Peer-reported conflicted shyness was associated with shyness with familiar and unfamiliar peers, whereas peer-reported general shyness was associated with shyness with unfamiliar peers and in formal situations. Moreover, situational shyness showed differential relations to maladjustment. Shyness with familiar peers was associated positively with maladjustment in multiple domains, especially academic and peer difficulties. Shyness with unfamiliar peers and shyness in formal situations, in contrast, were associated primarily with internalizing problems. In addition, shyness with unfamiliar peers and in formal situations occasionally related to positive adjustment, suggesting shyness in specific situations may still be protective in contemporary urban China. The findings provided new evidence that the correlates of shyness depend on the situation in which shyness occurs, and may inform future intervention programs.
ContributorsAn, Danming (Author) / Eggum-Wilkens, Natalie D (Thesis advisor) / Spinrad, Tracy L (Committee member) / Eisenberg, Nancy (Committee member) / Lemery-Chalfant, Kathryn (Committee member) / Arizona State University (Publisher)
Created2019