Matching Items (132)
Description
Global violent conflict has become an increasing problem in recent decades, especially in the African continent. Civil wars, terrorism, riots, and political violence has wrought havoc not only on civilian lives, but also on economic foundations. Trade networks are a way to measure these economic foundations. To summarize trade networks clustering coefficient as well as trade quantity/value summation measures are used. To understand effects of global trade on violent conflict, Pearson product-moment correlations are utilized. This work details a comparison of African national economies and violent conflict events using clustering coefficient, trade summation measures and Pearson correlation coefficient.
ContributorsKadambi, Sagarika Sanjay (Author) / Maciejewski, Ross (Thesis director) / Shutters, Shade (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This thesis offers a look into color theory and how it applies to commonly-used electronics, with computers being the main focus. This is done by employing research in user interface design, color theory, Brief Implicit Association Task validity, and Mechanical Turk participant validity. This study utilizes a recent modification of the more widely known implicit association task and takes advantage of MTurk's pool of subjects for its' data. Via a BIAT, implicit associations between red or blue colored computer images and "analytic" or "creative" synonyms were examined. No significant associations were found, despite strong background research. These findings suggest that further research is needed in this area before broader conclusions can be made.
ContributorsMorris, Taylor Monroe (Author) / Branaghan, Russel (Thesis director) / Song, Hyunjin (Committee member) / Human Systems Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Minimally invasive surgery is a surgical technique that is known for its reduced
patient recovery time. It is a surgical procedure done by using long reached tools and an
endoscopic camera to operate on the body though small incisions made near the point of
operation while viewing the live camera feed on a nearby display screen. Multiple camera
views are used in various industries such as surveillance and professional gaming to
allow users a spatial awareness advantage as to what is happening in the 3D space that is
presented to them on 2D displays. The concept has not effectively broken into the
medical industry yet. This thesis tests a multi-view camera system in which three cameras
are inserted into a laparoscopic surgical training box along with two surgical instruments,
to determine the system impact on spatial cognition, perceived cognitive workload, and
the overall time needed to complete the task, compared to one camera viewing the
traditional set up. The task is a non-medical task and is one of five typically used to train
surgeons’ motor skills when initially learning minimally invasive surgical procedures.
The task is a peg transfer and will be conducted by 30 people who are randomly assigned
to one of two conditions; one display and three displays. The results indicated that when
three displays were present the overall time initially using them to complete a task was
slower; the task was perceived to be completed more easily and with less strain; and
participants had a slightly higher performance rate.
patient recovery time. It is a surgical procedure done by using long reached tools and an
endoscopic camera to operate on the body though small incisions made near the point of
operation while viewing the live camera feed on a nearby display screen. Multiple camera
views are used in various industries such as surveillance and professional gaming to
allow users a spatial awareness advantage as to what is happening in the 3D space that is
presented to them on 2D displays. The concept has not effectively broken into the
medical industry yet. This thesis tests a multi-view camera system in which three cameras
are inserted into a laparoscopic surgical training box along with two surgical instruments,
to determine the system impact on spatial cognition, perceived cognitive workload, and
the overall time needed to complete the task, compared to one camera viewing the
traditional set up. The task is a non-medical task and is one of five typically used to train
surgeons’ motor skills when initially learning minimally invasive surgical procedures.
The task is a peg transfer and will be conducted by 30 people who are randomly assigned
to one of two conditions; one display and three displays. The results indicated that when
three displays were present the overall time initially using them to complete a task was
slower; the task was perceived to be completed more easily and with less strain; and
participants had a slightly higher performance rate.
ContributorsSchroll, Katelyn (Author) / Cooke, Nancy J. (Thesis advisor) / Chiou, Erin (Committee member) / Craig, Scotty (Committee member) / Arizona State University (Publisher)
Created2019
Description
The focus of my honors thesis is to find ways to use deep learning in tandem with tools in statistical mechanics to derive new ways to solve problems in biophysics. More specifically, I’ve been interested in finding transition pathways between two known states of a biomolecule. This is because understanding the mechanisms in which proteins fold and ligands bind is crucial to creating new medicines and understanding biological processes. In this thesis, I work with individuals in the Singharoy lab to develop a formulation to utilize reinforcement learning and sampling-based robotics planning to derive low free energy transition pathways between two known states. Our formulation uses Jarzynski’s equality and the stiff-spring approximation to obtain point estimates of energy, and construct an informed path search with atomistic resolution. At the core of this framework, is our first ever attempt we use a policy driven adaptive steered molecular dynamics (SMD) to control our molecular dynamics simulations. We show that both the reinforcement learning (RL) and robotics planning realization of the RL-guided framework can solve for pathways on toy analytical surfaces and alanine dipeptide.
ContributorsHo, Nicholas (Author) / Maciejewski, Ross (Thesis director) / Singharoy, Abhishek (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description
Team communication facilitates team coordination strategies and situations, and how teammates perceive one another. In human-machine teams, these perceptions affect how people trust and anthropomorphize their machine counterparts, which in turn affects future team communication, forming a feedback loop. This thesis investigates how personifying and objectifying contents in human-machine team communication relate to team performance and perceptions in a simulated remotely piloted aircraft system task environment. A total of 46 participants grouped into teams of two were assigned unique roles and teamed with a synthetic pilot agent that in reality was a trained confederate following a script. Quantities of verbal personifications and objectifications were compared to questionnaire responses about participants’ perceived trust and anthropomorphism of the synthetic pilot, as well as team performance. It was hypothesized that verbal personifications would positively correlate with reflective trust, anthropomorphism, and team performance, and that verbal objectifications would negatively correlate with the same measures. It was also predicted that verbal personifications would decrease over time as human teammates interact more with the machine teammate, and that verbal objectifications would increase. Verbal personifications were not found to be correlated with trust and anthropomorphism outside of perceptions related to gender, albeit patterns of change in the navigator’s personifications coincided with a co-calibration of trust among the navigator and the photographer. Results supported the prediction that verbal objectifications are negatively correlated with trust and anthropomorphism of a teammate. Significant relationships between verbal personifications and objectifications and team performance were not found. This study provides support to the notion that people verbally personify machines to ease communication when necessary, and that the same processes that underlie tendencies to personify machines may be reciprocally related to those that influence team trust. Overall, this study provides evidence that personifying and objectifying language in human-machine team communication is a viable candidate for measuring the perceptions and states of teams, even in highly restricted communication environments.
ContributorsCohen, Myke C. (Author) / Cooke, Nancy J. (Thesis advisor) / Chiou, Erin K. (Committee member) / Amazeen, Polemnia G. (Committee member) / Arizona State University (Publisher)
Created2022
Description
Molecular Dynamics (MD) simulations are ubiquitous throughout the physical sci-ences; they are critical in understanding how particle structures evolve over time
given a particular energy function. A software package called ParSplice introduced a
new method to generate these simulations in parallel that has significantly inflated
their length. Typically, simulations are short discrete Markov chains, only captur-
ing a few microseconds of a particle’s behavior and containing tens of thousands of
transitions between states; in contrast, a typical ParSplice simulation can be as long
as a few milliseconds, containing tens of millions of transitions. Naturally, sifting
through data of this size is impossible by hand, and there are a number of visualiza-
tion systems that provide comprehensive and intuitive analyses of particle structures
throughout MD simulations. However, no visual analytics systems have been built
that can manage the simulations that ParSplice produces. To analyze these large
data-sets, I built a visual analytics system that provides multiple coordinated views
that simultaneously describe the data temporally, within its structural context, and
based on its properties. The system provides fluid and powerful user interactions
regardless of the size of the data, allowing the user to drill down into the data-set to
get detailed insights, as well as run and save various calculations, most notably the
Nudged Elastic Band method. The system also allows the comparison of multiple
trajectories, revealing more information about the general behavior of particles at different temperatures, energy states etc.
ContributorsHnatyshyn, Rostyslav (Author) / Maciejewski, Ross (Thesis advisor) / Bryan, Chris (Committee member) / Ahrens, James (Committee member) / Arizona State University (Publisher)
Created2022
Description
It is difficult to imagine a society that does not utilize teams. At the same time, teams need to evolve to meet today’s challenges of the ever-increasing proliferation of data and complexity. It may be useful to add artificial intelligent (AI) agents to team up with humans. Then, as AI agents are integrated into the team, the first study asks what roles can AI agents take? The first study investigates this issue by asking whether an AI agent can take the role of a facilitator and in turn, improve planning outcomes by facilitating team processes. Results indicate that the human facilitator was significantly better than the AI facilitator at reducing cognitive biases such as groupthink, anchoring, and information pooling, as well as increasing decision quality and score. Additionally, participants viewed the AI facilitator negatively and ignored its inputs compared to the human facilitator. Yet, participants in the AI Facilitator condition performed significantly better than participants in the No Facilitator condition, illustrating that having an AI facilitator was better than having no facilitator at all. The second study explores whether artificial social intelligence (ASI) agents can take the role of advisors and subsequently improve team processes and mission outcome during a simulated search-and-rescue mission. The results of this study indicate that although ASI advisors can successfully advise teams, they also use a significantly greater number of taskwork interventions than teamwork interventions. Additionally, this study served to identify what the ASI advisors got right compared to the human advisor and vice versa. Implications and future directions are discussed.
ContributorsBuchanan, Verica (Author) / Cooke, Nancy J. (Thesis advisor) / Gutzwiller, Robert S. (Committee member) / Roscoe, Rod D. (Committee member) / Arizona State University (Publisher)
Created2023
Description
Machine learning models are increasingly being deployed in real-world applications where their predictions are used to make critical decisions in a variety of domains. The proliferation of such models has led to a burgeoning need to ensure the reliability and safety of these models, given the potential negative consequences of model vulnerabilities. The complexity of machine learning models, along with the extensive data sets they analyze, can result in unpredictable and unintended outcomes. Model vulnerabilities may manifest due to errors in data input, algorithm design, or model deployment, which can have significant implications for both individuals and society. To prevent such negative outcomes, it is imperative to identify model vulnerabilities at an early stage in the development process. This will aid in guaranteeing the integrity, dependability, and safety of the models, thus mitigating potential risks and enabling the full potential of these technologies to be realized. However, enumerating vulnerabilities can be challenging due to the complexity of the real-world environment. Visual analytics, situated at the intersection of human-computer interaction, computer graphics, and artificial intelligence, offers a promising approach for achieving high interpretability of complex black-box models, thus reducing the cost of obtaining insights into potential vulnerabilities of models. This research is devoted to designing novel visual analytics methods to support the identification and analysis of model vulnerabilities. Specifically, generalizable visual analytics frameworks are instantiated to explore vulnerabilities in machine learning models concerning security (adversarial attacks and data perturbation) and fairness (algorithmic bias). In the end, a visual analytics approach is proposed to enable domain experts to explain and diagnose the model improvement of addressing identified vulnerabilities of machine learning models in a human-in-the-loop fashion. The proposed methods hold the potential to enhance the security and fairness of machine learning models deployed in critical real-world applications.
ContributorsXie, Tiankai (Author) / Maciejewski, Ross (Thesis advisor) / Liu, Huan (Committee member) / Bryan, Chris (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2023
Description
The implementation of chatbots in customer service is widely prevalent in today’s world with insufficient research to appropriately refine all of their conversational abilities. Chatbots are favored for their ability to handle simple and typical requests made by users, but chatbots have proven to be prone to conversational breakdowns. The study researched how the use of repair strategies to combat conversational breakdowns in a simple versus complex task setting affected user experience. Thirty participants were collected and organized into six different groups in a two by three between subjects factorial design. Participants were assigned one of two tasks (simple or complex) and one of three repair strategies (repeat, confirmation, or options). A Wizard-of-Oz approach was used to simulate a chatbot that participants interacted with to complete a task in a hypothetical setting. Participants completed the task with this researcher-controlled chatbot as it intentionally failed the conversation multiple times, only to repair it with a repair strategy. Participants recorded their user experience regarding the chatbot afterwards. An Analysis of Covariance statistical test was run with task duration being a covariate variable. Findings indicate that the simple task difficulty was significant in improving the user experience that participants recorded whereas the particular repair strategy had no effect on the user experience. This indicates that simpler tasks lead to improved positive user experience and the more time that is spent on a task, the less positive the user experience. Overall, results associated with the effects of task difficulty and repair strategies on user experience were only partially consistent with previous literature.
ContributorsRios, Aaron (Author) / Cooke, Nancy J. (Thesis advisor) / Gutzwiller, Robert S. (Committee member) / Chiou, Erin K. (Committee member) / Arizona State University (Publisher)
Created2022
Description
What makes a human, artificial intelligence, and robot team (HART) succeed despite unforeseen challenges in a complex sociotechnical world? Are there personalities that are better suited for HARTs facing the unexpected? Only recently has resilience been considered specifically at the team level, and few studies have addressed team resilience for HARTs. Team resilience here is defined as the ability of a team to reorganize team processes to rebound or morph to overcome an unforeseen challenge. A distinction from the individual, group, or organizational aspects of resilience for teams is how team resilience trades off with team interdependent capacity. The following study collected data from 28 teams comprised of two human participants (recruited from a university populace) and a synthetic teammate (played by an experienced experimenter). Each team completed a series of six reconnaissance missions presented to them in a Minecraft world. The research aim was to identify how to better integrate synthetic teammates for high-risk, high-stress dynamic operations to boost HART performance and HART resilience. All team communications were orally over Zoom. The primary manipulation was the communication given by the synthetic teammate (between-subjects, Task or Task+): Task only communicated the essentials, and Task+ offered clear and concise communications of its own capabilities and limitations. Performance and resilience were measured using a primary mission task score (based upon how many tasks teams completed), time-based measures (such as how long it took to recognize a problem or reorder team processes), and a subjective team resilience score (calculated from participant responses to a survey prompt). The research findings suggest the clear and concise reminders from Task+ enhanced HART performance and HART resilience during high-stress missions in which the teams were challenged by novel events. An exploratory study regarding what personalities may correlate with these improved performance metrics indicated that the Big Five trait taxonomies of extraversion and conscientiousness were positively correlated, whereas neuroticism was negatively correlated with higher HART performance and HART resilience. Future integration of synthetic teammates must consider the types of communications that will be offered to maximize HART performance and HART resilience.
ContributorsGraham, Hudson D. (Author) / Cooke, Nancy J. (Thesis advisor) / Gray, Robert (Committee member) / Holder, Eric (Committee member) / Arizona State University (Publisher)
Created2023