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- Creators: Arizona State University
Description
Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learners' behavior and assessing learners' performance for personalization. Hands-on labs are a critical learning approach for cybersecurity education. It provides real-world complex problem scenarios and helps learners develop a deeper understanding of knowledge and concepts while solving real-world problems. But there are unique challenges when using hands-on labs for cybersecurity education. Existing hands-on lab exercises materials are usually managed in a problem-centric fashion, while it lacks a coherent way to manage existing labs and provide productive lab exercising plans for cybersecurity learners. To solve these challenges, a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment is established. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. A knowledge graph in the cybersecurity domain is also constructed using Natural language processing (NLP) technologies including word embedding and hyperlink-based concept mining. This knowledge graph is then utilized during the regular learning process to build a personalized lab recommendation system by suggesting relevant labs based on students' past learning history to maximize their learning outcomes. To evaluate ThoTh Lab, several in-class experiments were carried out in cybersecurity classes for both graduate and undergraduate students at Arizona State University and data was collected over several semesters. The case studies show that, by leveraging the personalized lab platform, students tend to be more absorbed in a lab project, show more interest in the cybersecurity area, spend more effort on the project and gain enhanced learning outcomes.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Li, Baoxin (Committee member) / Zhao, Ming (Committee member) / Hsiao, Sharon (Committee member) / Arizona State University (Publisher)
Created2021
Description
The presence of strategic agents can pose unique challenges to data collection and distributed learning. This dissertation first explores the social network dimension of data collection markets, and then focuses on how the strategic agents can be efficiently and effectively incentivized to cooperate in distributed machine learning frameworks.
The first problem explores the impact of social learning in collecting and trading unverifiable information where a data collector purchases data from users through a payment mechanism. Each user starts with a personal signal which represents the knowledge about the underlying state the data collector desires to learn. Through social interactions, each user also acquires additional information from his neighbors in the social network. It is revealed that both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation for a given total payment budget. In the second half, a federated learning scheme to train a global learning model with strategic agents, who are not bound to contribute their resources unconditionally, is considered. Since the agents are not obliged to provide their true stochastic gradient updates and the server is not capable of directly validating the authenticity of reported updates, the learning process may reach a noncooperative equilibrium. First, the actions of the agents are assumed to be binary: cooperative or defective. If the cooperative action is taken, the agent sends a privacy-preserved version of stochastic gradient signal. If the defective action is taken, the agent sends an arbitrary uninformative noise signal. Furthermore, this setup is extended into the scenarios with more general actions spaces where the quality of the stochastic gradient updates have a range of discrete levels. The proposed methodology evaluates each agent's stochastic gradient according to a reference gradient estimate which is constructed from the gradients provided by other agents, and rewards the agent based on that evaluation.
ContributorsAkbay, Abdullah Basar (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Committee member) / Kosut, Oliver (Committee member) / Ewaisha, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
Description
Understanding the consequences of changes in social networks is an important an-
thropological research goal. This dissertation looks at the role of data-driven social
networks on infectious disease transmission and evolution. The dissertation has two
projects. The first project is an examination of the effects of the superspreading
phenomenon, wherein a relatively few individuals are responsible for a dispropor-
tionate number of secondary cases, on the patterns of an infectious disease. The
second project examines the timing of the initial introduction of tuberculosis (TB) to
the human population. The results suggest that TB has a long evolutionary history
with hunter-gatherers. Both of these projects demonstrate the consequences of social
networks for infectious disease transmission and evolution.
The introductory chapter provides a review of social network-based studies in an-
thropology and epidemiology. Particular emphasis is paid to the concept and models
of superspreading and why to consider it, as this is central to the discussion in chapter
2. The introductory chapter also reviews relevant epidemic mathematical modeling
studies.
In chapter 2, social networks are connected with superspreading events, followed
by an investigation of how social networks can provide greater understanding of in-
fectious disease transmission through mathematical models. Using the example of
SARS, the research shows how heterogeneity in transmission rate impacts super-
spreading which, in turn, can change epidemiological inference on model parameters
for an epidemic.
Chapter 3 uses a different mathematical model to investigate the evolution of TB
in hunter-gatherers. The underlying question is the timing of the introduction of TB
to the human population. Chapter 3 finds that TB’s long latent period is consistent
with the evolutionary pressure which would be exerted by transmission on a hunter-
igatherer social network. Evidence of a long coevolution with humans indicates an
early introduction of TB to the human population.
Both of the projects in this dissertation are demonstrations of the impact of var-
ious characteristics and types of social networks on infectious disease transmission
dynamics. The projects together force epidemiologists to think about networks and
their context in nontraditional ways.
thropological research goal. This dissertation looks at the role of data-driven social
networks on infectious disease transmission and evolution. The dissertation has two
projects. The first project is an examination of the effects of the superspreading
phenomenon, wherein a relatively few individuals are responsible for a dispropor-
tionate number of secondary cases, on the patterns of an infectious disease. The
second project examines the timing of the initial introduction of tuberculosis (TB) to
the human population. The results suggest that TB has a long evolutionary history
with hunter-gatherers. Both of these projects demonstrate the consequences of social
networks for infectious disease transmission and evolution.
The introductory chapter provides a review of social network-based studies in an-
thropology and epidemiology. Particular emphasis is paid to the concept and models
of superspreading and why to consider it, as this is central to the discussion in chapter
2. The introductory chapter also reviews relevant epidemic mathematical modeling
studies.
In chapter 2, social networks are connected with superspreading events, followed
by an investigation of how social networks can provide greater understanding of in-
fectious disease transmission through mathematical models. Using the example of
SARS, the research shows how heterogeneity in transmission rate impacts super-
spreading which, in turn, can change epidemiological inference on model parameters
for an epidemic.
Chapter 3 uses a different mathematical model to investigate the evolution of TB
in hunter-gatherers. The underlying question is the timing of the introduction of TB
to the human population. Chapter 3 finds that TB’s long latent period is consistent
with the evolutionary pressure which would be exerted by transmission on a hunter-
igatherer social network. Evidence of a long coevolution with humans indicates an
early introduction of TB to the human population.
Both of the projects in this dissertation are demonstrations of the impact of var-
ious characteristics and types of social networks on infectious disease transmission
dynamics. The projects together force epidemiologists to think about networks and
their context in nontraditional ways.
ContributorsNesse, Hans P (Author) / Hurtado, Ana Magdalena (Thesis advisor) / Castillo-Chavez, Carlos (Committee member) / Mubayi, Anuj (Committee member) / Arizona State University (Publisher)
Created2019
Description
While prior diversity management research has extensively focused on having a representative workforce in public organizations, recent discussions on racism and social equity have shed light on the importance of an inclusive work environment, where individuals feel integrated into organizations and involved in organizational processes. Perceived inclusion in the workplace, defined as the extent to which individuals perceive they are part of significant organizational processes, is the core theme of this dissertation. This study focuses on the perceived inclusion of academic scientists in the US. Inclusion of Scholars of Color (SOCs) and women in science is of particular importance given the low representation and retention of SOCs and women as well prolonged marginalization in academic science.This dissertation aims to understand what shapes perceived inclusion in the workplace by looking at how the demographic and social compositions of one’s social environment shape individuals’ perceptions of workplace inclusion. Focusing on race and gender, the dissertation recognizes the relative and contingent relationships among individuals and networks that affect perceived inclusion. To investigate, I ask two key questions, each focusing on different social structures and their interplay:
1. How do different aspects of social structure in networks (demographics, social network structural characteristics, social network compositional characteristics) influence perceived inclusion in the workplace?
2. How do individuals’ demographic attributes shape the impacts of social structures on workplace inclusion?
To explore these questions, I draw from social identity theories, focusing on intergroup relations, and social capital theory to develop hypotheses. To investigate how social network structures shape inclusion in the workplace, I use a 2011 National Science Foundation-funded national survey of Science, Technology, Engineering, and Mathematics (STEM) faculty in four science fields (biology, biochemistry, civil engineering, and mathematics) at diverse types of higher education institutions. I find that perceived inclusion is a function of social network structure, but the effects depend on the demographic characteristics of the individual and the network. I conclude this study with a discussion about the implications of findings for future research and diversity and inclusion policies.
ContributorsJung, Heyjie (Author) / Welch, Eric W (Thesis advisor) / Corley, Elizabeth A (Committee member) / Stritch, Justin M (Committee member) / Melkers, Julia (Committee member) / Arizona State University (Publisher)
Created2022
Description
Emerging body movement detection and gesture recognition software have opened a gateway of possibilities to make technology more intuitive, engaging, and accessible for people. A vast areaof natural user interfaces is leveraging body motion tracking and gesture recognition technologies and a human’s readily expressive body to extend interactions with software beyond mouse clicks and scrolls. However, these interfaces have been limited by hardware and software expenses, high development time and costs, and learning curves. This paper explores different approaches to providing both software developers and designers with easier ways to incorporate computer vision-based body and gesture detection solutions into the development of embodied experiences without suppressing creativity. Gesture.js is a JavaScript framework as a service (FaaS) that is both a thin library on top of the Document Object Model (DOM) consisting of a collection of tools for developing embodied-enabled applications on the web and a landmark computation and processing application programming interface. It wraps MediaPipe, an open-source collection of machine-learning solutions that perform inference over arbitrary sensory data, and additional landmark processing frameworks such as KalidoKit, a 3D model rigging solution, and ports the necessary information through either an object-oriented or an API-oriented implementation. It also comes with its web-based graphical interface for easy connection between Gesture.js and other application clients with little to no JavaScript code. This thesis also details a collection of example applications that demonstrate the usability, capacity, and potential of this framework.
ContributorsFowler, Azaria (Author) / Gowda, Tejaswi (Thesis advisor) / Kuznetsov, Anastasia (Committee member) / Kobayashi, Yoshihiro (Committee member) / Arizona State University (Publisher)
Created2022
Description
This descriptive research used social network analysis to explore the influence of relationships and communication among hospital nursing (RN, LPN, CNA) and discharge planning staff on adherence to evidence-based practices (EBP) for reducing preventable hospital readmissions. Although previous studies have shown that nurses are a valued source of research information for each other, there have been few studies concerning the role that staff relationships and communication play in adherence to evidence-based practice. The investigator developed the Relational Model of Communication and Adherence to EBP from diffusion of innovation theory, social network theories, relational coordination theory, and quality improvement literature.
The study sample consisted of 10 adult-medical surgical units, five home care agencies and six long-term care facilities. A total of 273 hospital nursing and discharge planning staff and 69 post-acute staff participated. Hospital staff completed a survey about communication patterns for patient care and patient discharge and about communication quality on the unit. Hospital and post-acute care staff completed surveys about relationship quality and demographic characteristics. Evidence-based practice adherence rates for risk assessment, medication reconciliation, and discharge summary were measured as documented in the electronic medical record.
Social network analysis was used to analyze the communication patterns for patient care communication at the unit. These findings were correlated with (1) aggregate responses for communication quality, (2) aggregate responses for relationship quality, and (3) EBP adherence. Statistically significant relationships were found between communication patterns, and communication quality and relationship quality. There were
ii
two significant relationships between communication quality, and EBP adherence. Limitations in response rates and missing data prevented the analysis of all of the hypothesized relationships.
The findings from this study provide empirical support for the role of social networks and relationships among staff in adoption of, and adherence to, EBP. Social network theory and social network analysis, especially the concept of knowledge sharing, provide ways to understand and leverage the influence of peer relationships. Future studies are needed to better understand the contribution that relationships among staff (social networks) have in the adoption of and adherence to EBP among nursing staff. Further model development and multilevel studies are
The study sample consisted of 10 adult-medical surgical units, five home care agencies and six long-term care facilities. A total of 273 hospital nursing and discharge planning staff and 69 post-acute staff participated. Hospital staff completed a survey about communication patterns for patient care and patient discharge and about communication quality on the unit. Hospital and post-acute care staff completed surveys about relationship quality and demographic characteristics. Evidence-based practice adherence rates for risk assessment, medication reconciliation, and discharge summary were measured as documented in the electronic medical record.
Social network analysis was used to analyze the communication patterns for patient care communication at the unit. These findings were correlated with (1) aggregate responses for communication quality, (2) aggregate responses for relationship quality, and (3) EBP adherence. Statistically significant relationships were found between communication patterns, and communication quality and relationship quality. There were
ii
two significant relationships between communication quality, and EBP adherence. Limitations in response rates and missing data prevented the analysis of all of the hypothesized relationships.
The findings from this study provide empirical support for the role of social networks and relationships among staff in adoption of, and adherence to, EBP. Social network theory and social network analysis, especially the concept of knowledge sharing, provide ways to understand and leverage the influence of peer relationships. Future studies are needed to better understand the contribution that relationships among staff (social networks) have in the adoption of and adherence to EBP among nursing staff. Further model development and multilevel studies are
ContributorsSolomons, Nan M (Author) / Lamb, Gerri (Thesis advisor) / Verran, Joyce (Committee member) / Marek, Karen (Committee member) / Arizona State University (Publisher)
Created2016
Description
Software-as-a-Service (SaaS) has received significant attention in recent years as major computer companies such as Google, Microsoft, Amazon, and Salesforce are adopting this new approach to develop software and systems. Cloud computing is a computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and virtualized manner. Computer Simulations are widely utilized to analyze the behaviors of software and test them before fully implementations. Simulation can further benefit SaaS application in a cost-effective way taking the advantages of cloud such as customizability, configurability and multi-tendency.
This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability.
The following chapters are provided in the architecture:
Multi-Tenancy Simulation Software-as-a-Service.
Policy Specification for MTA simulation environment.
Model Driven PaaS Based SaaS modeling.
Dynamic analysis and dynamic calibration for timing analysis.
Event-driven Service-Oriented Simulation Framework.
LTBD: A Triage Solution for SaaS.
This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability.
The following chapters are provided in the architecture:
Multi-Tenancy Simulation Software-as-a-Service.
Policy Specification for MTA simulation environment.
Model Driven PaaS Based SaaS modeling.
Dynamic analysis and dynamic calibration for timing analysis.
Event-driven Service-Oriented Simulation Framework.
LTBD: A Triage Solution for SaaS.
ContributorsLi, Wu (Author) / Tsai, Wei-Tek (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Ye, Jieping (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2015
Description
Network analysis is a key conceptual orientation and analytical tool in the social sciences that emphasizes the embeddedness of individual behavior within a larger web of social relations. The network approach is used to better understand the cause and consequence of social interactions which cannot be treated as independent. The relational nature of network data and models, however, amplify the methodological concerns associated with inaccurate or missing data. This dissertation addresses such concerns via three projects. As a motivating substantive example, Project 1 examines factors associated with the selection of interaction partners by students at a large urban high school implementing a reform which, like many organizational improvement initiatives, is associated with a theory of change that posits changes to the structuring of social interactions as a central causal pathway to improved outcomes. A distinctive aspect of the data used in Project 1 is that it was a complete egocentric network census – in addition to being asked about their own relationships, students were asked about the relationships between alters that they nominated in the self-report. This enables two unique examinations of methodological challenges in network survey data collection: Project 2 examines the factors related to how well survey respondents assess the strength of social connections between others, finding that "informant" competence corresponds positively with their social proximity to target dyad as well as their centrality in the network. Project 3 explores using such third-party reports to augment network imputation methods, and finds that incorporating third-party reports into model-based methods provides a significant boost in imputation accuracy. Together these findings provide important implications for collecting and extrapolating data in research contexts where a complete social network census is highly desirable but infeasible.
ContributorsBates, Jordan T (Author) / Maroulis, Spiro J (Thesis advisor) / Kang, Yun (Thesis advisor) / Frank, Kenneth A. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Theories related to social identity provide insight on how gender may be meaningful in organizations. This dissertation examines how psychosocial outcomes for science, technology, engineering, and mathematics (STEM) faculty are influenced by the proportion of women in productivity, support, and advice networks in gendered academic institutions. Psychosocial outcomes are defined as the psychological and social perspectives of the organizational environment. Gendered aspects in organizations are of theoretical importance because they provide opportunities to investigate how STEM faculty attain psychosocial outcomes. An underlying argument in gender literature is that women, compared to men, are more likely to provide emotional support. As women’s presence in STEM departments increases, STEM faculty are likely to rely on women to provide emotional support which may influence psychosocial outcomes of the work environment.
Universities are considered to be gendered organizational environments, where masculine and feminine characteristics are evident within their processes, practices, images, and through distribution of power. Universities are broadly categorized as two types: research focused and teaching focused universities. Both university types are deeply involved with the education of students but promotional standards for faculty members and the primary focus of these universities is dictated by the categorization of research versus teaching. University structuring is gendered, making them an ideal setting to investigate questions related to identity and psychosocial outcomes. Drawing from gendered theory, social identity theory, social network theory, and social capital theory, I ask the following research question: Does the proportion of women in informal networks influence psychosocial outcomes within gendered university settings?
To examine how psychosocial outcomes are influenced by informal networks, I use survey data from a 2011 National Science Foundation funded national survey of STEM faculty across universities in the United States (U.S.). I find that psychosocial outcomes vary by university type, faculty gender, and a high proportion of women in three types of academic informal networks. I conclude with a discussion about what the results mean for practice and future research.
Universities are considered to be gendered organizational environments, where masculine and feminine characteristics are evident within their processes, practices, images, and through distribution of power. Universities are broadly categorized as two types: research focused and teaching focused universities. Both university types are deeply involved with the education of students but promotional standards for faculty members and the primary focus of these universities is dictated by the categorization of research versus teaching. University structuring is gendered, making them an ideal setting to investigate questions related to identity and psychosocial outcomes. Drawing from gendered theory, social identity theory, social network theory, and social capital theory, I ask the following research question: Does the proportion of women in informal networks influence psychosocial outcomes within gendered university settings?
To examine how psychosocial outcomes are influenced by informal networks, I use survey data from a 2011 National Science Foundation funded national survey of STEM faculty across universities in the United States (U.S.). I find that psychosocial outcomes vary by university type, faculty gender, and a high proportion of women in three types of academic informal networks. I conclude with a discussion about what the results mean for practice and future research.
ContributorsCamarena, Leonor (Author) / Feeney, Mary K. (Thesis advisor) / Bozeman, Barry (Committee member) / Stritch, Justin (Committee member) / Welch, Eric (Committee member) / Arizona State University (Publisher)
Created2020
Description
Emerging trends in cyber system security breaches in critical cloud infrastructures show that attackers have abundant resources (human and computing power), expertise and support of large organizations and possible foreign governments. In order to greatly improve the protection of critical cloud infrastructures, incorporation of human behavior is needed to predict potential security breaches in critical cloud infrastructures. To achieve such prediction, it is envisioned to develop a probabilistic modeling approach with the capability of accurately capturing system-wide causal relationship among the observed operational behaviors in the critical cloud infrastructure and accurately capturing probabilistic human (users’) behaviors on subsystems as the subsystems are directly interacting with humans. In our conceptual approach, the system-wide causal relationship can be captured by the Bayesian network, and the probabilistic human behavior in the subsystems can be captured by the Markov Decision Processes. The interactions between the dynamically changing state graphs of Markov Decision Processes and the dynamic causal relationships in Bayesian network are key components in such probabilistic modelling applications. In this thesis, two techniques are presented for supporting the above vision to prediction of potential security breaches in critical cloud infrastructures. The first technique is for evaluation of the conformance of the Bayesian network with the multiple MDPs. The second technique is to evaluate the dynamically changing Bayesian network structure for conformance with the rules of the Bayesian network using a graph checker algorithm. A case study and its simulation are presented to show how the two techniques support the specific parts in our conceptual approach to predicting system-wide security breaches in critical cloud infrastructures.
ContributorsNagaraja, Vinjith (Author) / Yau, Stephen S. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015