Digital Commons Network^™

23. Cake-Making Analogy For Setting Generative Ai Guidelines/Ethics, Maha Bali

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

This is a lesson plan that offers metaphor as an innovative approach to teaching about the ethical use of generative Al. The cake-making analogy equates different ways of acquiring a cake (baking from scratch, using a readymade mix from a box, buying from a bakery or buying preserved cake from a supermarket) with varying degrees of reliance on Al as a shortcut for tasks or assignments. The lesson invites participants (who may be students or teachers) to critically consider the implications of each mode, examining factors such as quality, time, cost, and personal investment. This analogy is then applied to …

6. More Is Less?: Using Generative Ai For Idea Generation And Diversification In Early Writing Processes, Franziska Tsufim, Lainie Pomerleau

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

As writing teachers, we are strong proponents of process writing. At the same time, we are aware that early process work, especially in a group setting, can be time consuming and anxiety-inducing. Students may also self-censor when sharing work with peers especially if they are not confident in their ideas. Drawing on the process of nominal, electronic brainstorming, we created two different prompts that allowed students to incorporate generative Al into their idea generation process. This first activity improves the efficiency of individual idea generation, while the second exercise helps increase student confidence in their ideas in collaborative brainstorming situations. …

28. Creating Constitutions With Chatgpt, Julia M. Gossard

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

This chapter explores how students used ChatGPT to construct constitutions in a course on the history of the Age of Revolutions. Student responses, the product, and instructor implications and critiques are included.

19. Wrestling With A.I., Catherine J. Denial

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

Generative AI has been sold to us at speed, promising quick resolutions to writing problems for students and demanding nimble responses from faculty. This essay suggests that instead of surrendering to a manufactured sense of urgency we take the time to fully grapple with the meaning of generative AI, and to respond to its challenges. By thinking through the ethical dimensions of AI in the classroom, and by gradually changing our assessment practices, we place humans back at the center of our common educational experiences.

14. Indigenous Futures In Generative Artificial Intelligence: The Paradox Of Participation, Rogelio E. Cardona-Rivera, J. Kaleo Alladin, Breanne K. Litts, Melissa Tehee

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

As we work toward expanding and diversifying accurate representations of Indigenous peoples in classrooms, we must also consider the role of Native people in the construction of these technologies. Indigenous communities face the following paradox in the Generative Al space: in wanting to be represented by the Generative Al by sharing data representations their ways of knowing and being, they lose the agency to exert their rhetorical, technological, and data sovereignty over whatever is shared into the Generative Al system. Alternatively, not participating in the Generative Al space continues to perpetuate Western-centric biases and systemic racism built into the existing …

Part Iii: Section 3: Race And Indigenous Studies, Beth Buyserie, Travis N. Thurston

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

No abstract provided.

Target Size Estimation Through Sensor Fusion, Morgan Davidson, Cory Brinck, Kori Moore, Tanner Chase

Space Dynamics Laboratory Publications

Target size is an important quantity in both classification and multi-sensor target tracking applications, but it is often an unknown. In some imaging applications, target size is assumed based on classification results. This assumed size is sometimes used in the synthesis of range measurements from monocular imagery, based on the apparent target size that is observed. Range-from-apparent-size measurements may be the only range measurements available during times when sensors that produce range information (such as radar) are not available. This range-from-apparent-size approach is typically plagued by range bias in the resulting measurements due to several factors. In this work, we …

Small Satellite Formation Control For A Fixed Thrust Propulsion System, John Akagi, Tyson K. Smith

Space Dynamics Laboratory Publications

This paper develops a model predictive control based formation flying algorithm for small spacecraft with a fixed thrust magnitude constraint that models the limitations of thrusters which have little to no ability to scale the thrust magnitude. The control instead calculates the optimal thrust duration which results in a mixed integer linear program (MILP). A guidance trajectory and control deadband are implemented to mitigate the increased computation time and possibility of control chattering that arise from the MILP formulation. A three spacecraft formation is tested in a series of simulations with varying dead- band widths and the performance in terms …

Orbit Determination Demonstration Using Onboard One-Way Radiometrics From The Iris Radio On The Capstone Mission, Todd A. Ely, Anthony Zara, Dana Sorensen, Zaid J. Towfic, Connor Ott, Alec Forsman, John D. Baker

Space Dynamics Laboratory Publications

Onboard one-way radiometric tracking from an Iris Radio paired with a Chip Scale Atomic Clock (CSAC) on NASA’s CAPSTONE mission has been collected and demonstrated to be able to determine its near rectilinear halo orbit. This is a first-ever demonstration of one-way radiometric tracking originating from the DSN and being collected onboard a spacecraft. For CAPSTONE, the tracking data from the Iris/CSAC pair are shown to have sufficient accuracy to enable future autonomous radio navigation for missions with modest navigation requirements. This work will compare the space-based data with prior ground-based test results, as well as assess CAPSTONE’s orbit solution …

Phase Space Analysis Of Two-Wavelength Interferometry, Robert H. Leonard, Spencer E. Olson

Space Dynamics Laboratory Publications

Multiple wavelength phase shifting interferometry is widely used to extend the unambiguous range ( UR ) beyond that of a single wavelength. Towards this end, many algorithms have been developed to calculate the optical path difference (OPD) from the phase measurements of multiple wavelengths. These algorithms fail when phase error exceeds a specific threshold. In this paper, we examine this failure condition. We introduce a “phase-space” view of multi-wavelength algorithms and demonstrate how this view may be used to understand an algorithm’s robustness to phase measurement error. In particular, we show that the robustness of the synthetic wavelength algorithm deteriorates …

The Cypress Dome 2024, Issue 35, The Cypress Dome Society

The Cypress Dome

Featuring the work of Gian Arellano, K.A. Basilisk, Sophia Cardenal, Tara Crowe, Nicole Del Prete, Danielle Deptula Pokrandt, Ashley Francke, Lais Guimarães, S. Kali, Samantha Lefebvre, Erik Leon, Emily Liu, Farah Mara Shah, Aja Moncrieffe, Juan Morillo, Mariana Nagy, Daniel Otero, Kaitlyn Pottinger, Max Rainey, Nathaniel Ratón, Bella Riley Love, Lydia Robbins, Ambar Santiago, Nishtha Tikalal, and Peyton Worsham.

Founded in 1989 and named after a natural feature of our campus, The Cypress Dome is the University of Central Florida’s undergraduate literary journal. It is edited, designed, and produced by a selected editorial team of students from our literary organization, …

Antitrust Regulation Of Copyright Markets, Jacob Noti-Victor, Xiyin Tang

Articles

Late last year, a federal court sided with the Department of Justice and blocked the planned merger of book publishers Simon & Schuster and Penguin Random House. The decision was a rare collision between antitrust law and the deeply consolidated copyright content industries. Over the course of the past decade, acquisitions and mergers in the recording, music publishing, and audiovisual space have left just a handful of juggernaut content producers in their wake. Moreover, new technology companies that have entered the content-creation and distribution markets have begun to leverage their scale to further their own industry consolidation.

This Article examines …

Classification Of Sow Postures Using Convolutional Neural Network And Depth Images, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

The United States swine industry reports an average preweaning mortality of approximately 16% where approximately 6% of them are attributed to piglets overlayed by sows. Detecting postural transitions and estimating sows’ time budgets for different postures are valuable information for breeders and engineering design of farrowing facilities to eventually reduce piglet death. Computer vision tools can help monitor changes in animal posture accurately and efficiently. To create a more robust system and eliminate varying lighting issues within a day including daytime/ nighttime differences, there is an advantage to using depth cameras over digital cameras. In this study, a computer vision …

Machine Learning-Based Disease Classification In Tomato (Solanum Lycopersicum) Plants = Klasifikasi Penyakit Berbasis Pembelajaran Mesin Pada Tanaman Tomat (Solanum Lycopersicum), Md Towfiqur Rahman, Sudipto Dhar Dipto, Israt Jahan June, Abdul Momin, Muhammad Rashed Al Mamun

Department of Agricultural and Biological Systems Engineering: Faculty Publications

English abstract

In Bangladesh, tomato cultivation faces significant challenges due to its susceptibility to various microorganisms, parasites, and bacterial infections. Typically, the early symptoms of these diseases first appear in roots and leaves, complicating timely detection. This study addresses the challenge of timely and accurate detection of diseases in tomato plants, crucial for effective plant protection management. Conventional manual inspection methods are time-consuming and subjective, resulting in delays in implementing necessary protection measures. Therefore, an image processing technique and machine learning algorithms were used for rapid and robust detection of diseases in tomato plant leaves, aiming to streamline the detection …

Behave Yourself! Behavioral Indicators Of Trust In Human-Agent Teams, Kendall Carmody, Vivek Sharm, Arianna Addis, Daniel Nguyen, Cherrise Ficke, Amanda Thayer, Jessica Wildman, Meredith Carroll

Aeronautics Faculty Publications

As advancements in artificial intelligence accelerate, there is a rise in the complexity and number of autonomous agents placed in human-agent teams (HATs). With this expansion, it is important to understand how trust in agent teammates evolves and is influenced by contextual events. In support of this, significant research has focused on the factors that influence human trust in an agent and elements that negatively impact this trust. In this research, human trust in agent teammates is typically measured via self-report surveys. Although a reliable format, surveys are not without limitations, as they can be disruptive and lack the temporal …

From City Hall To Twitter: Navigating Political Context In Us Mayors' Online Engagement, Minshuai Ding

Bureau of Sociological Research: Faculty Publications

Twitter has become an indispensable tool for politicians and officials, including US mayors as heads of local governments, to engage with constituents in real time and convey their political agendas. However, there is limited research on the relationship between the political context associated with the role of mayor and their ways of communication on Twitter. This study explores the Twitter usage patterns of mayors of the 100 largest US cities in the light of two unique political contexts: partisan affiliation and form of municipal government. By conducting a twofold statistical analysis, this study found significant differences in Twitter usage patterns …

Inaugural Future Of Bse Days: Workshop Report, Mark Stone

Department of Agricultural and Biological Systems Engineering: Presentations and White Papers

The inaugural Future of BSE Days (FBD) event, held on May 2-3, 2024, brought together the Biological Systems Engineering (BSE) community to discuss and shape the future of work and learning within the field. The event was designed to foster collaboration between faculty, students, industry leaders, and alumni, with the goal of reimagining the department’s educational approach, enhancing industry partnerships, and preparing students for the challenges of tomorrow. Over the course of two days, participants engaged in insightful discussions, workshops, and presentations that highlighted both the strengths of the department and opportunities for growth.

The workshop resulted in several key …

Biogeographic Affiliation And Centers Of Richness As Predictors Of Elevational Range-Size Patterns For Malesian Flora, Melissa Whitman, Sabrina E. Russo

School of Biological Sciences: Faculty Publications

Our goal was to interrogate the idea that “mountain passes are higher in the tropics” by investigating ecological and biogeographic drivers of elevational range-sizes patterns among equatorial flora. We used herbarium records for 60 species-rich plant families, representing 18 535 species total, to estimate distributions over a 4500 m elevational gradient. For each family, we estimated the change in average range-sizes with increasing elevation (i.e. Rapoport's rule, abbreviated as ERR) and quantified 15 metrics of familial richness distribution, evolutionary age, and biogeographic affiliation. We visualized covariation across families using phylogenetic principal components analysis (pPCA). We then evaluated how family-level ERR …

Large Language Models Struggle In Token-Level Clinical Named Entity Recognition, Qiuhao Lu, Rui Li, Andrew Wen, Jinlian Wang, Liwei Wang, Hongfang Liu

Faculty, Staff and Student Publications

Large Language Models (LLMs) have revolutionized various sectors, including healthcare where they are employed in diverse applications. Their utility is particularly significant in the context of rare diseases, where data scarcity, complexity, and specificity pose considerable challenges. In the clinical domain, Named Entity Recognition (NER) stands out as an essential task and it plays a crucial role in extracting relevant information from clinical texts. Despite the promise of LLMs, current research mostly concentrates on document-level NER, identifying entities in a more general context across entire documents, without extracting their precise location. Additionally, efforts have been directed towards adapting ChatGPTfor token-level …

Decoding And Generating Poetry: Using Feminist Theory And Digital Media To Demonstrate Maya Angelou's Writing As Activism, Emery Beckman

Graduate Thesis and Dissertation post-2024

This thesis will demonstrate how rhetorical strategies can present social change through descriptive elements of persuasion. Using foundational rhetorical concepts, including ethos, logos, and pathos, it will connect concerns of inequality to the work of Maya Angelou, who can validate the impact intersectionality has within the use of rhetoric. Having been a victim of racism, sexual abuse, and economic disadvantages, she used her voice to break the silence for women. This thesis will showcase the impact of rhetorical techniques through digital media by creating a website featuring visual data and mapping by decoding Maya Angelou's work to demonstrate the power …

External Boundary Conditions For Solidification Process Modeling Of Equiaxed Investment Castings, Weston Olson

Graduate Thesis and Dissertation post-2024

The equiaxed investment casting process is a multi-physics problem which requires knowledge from engineers who have expertise in materials, metallurgy, fluid dynamics, thermodynamics, and heat transfer. Process modeling is a tool used by foundries to help predict casting defects such as shrinkage porosity, hot tears, dimensional distortion, and poor grain structure. ProCAST is the numerical simulation suite used in this dissertation and is considered an industry standard due to its extensive proven track record. The reliability of these predictions is strongly dependent on the accuracy of the thermal boundary conditions set in the model. This dissertation consists of two studies. …

Design And Optimization Of Temperature-Stable Mems Resonators For Timing Applications Based On Thin-Film Lithium Tantalate, Yasaman Majd

Graduate Thesis and Dissertation post-2024

All radio devices require a highly accurate frequency reference with superb temperature stability. While quartz-based oscillators meet accuracy demands, they cannot be easily miniaturized for higher frequencies. Silicon-based MEMS resonators offer a promising alternative due to their small size, and low power consumption, though they suffer from significant temperature drift.

This research proposes a silicon-based MEMS device that exhibits low-frequency variations across wide temperature ranges, high-quality factors, and low insertion loss, meeting modern communication system requirements. Two main research avenues are explored.

Firstly, the thermal behavior of S₀ Lamb wave X-cut Lithium Tantalate (LT) resonators is investigated, demonstrating a …

Comprehensive Analytics Of Automated Vehicles’ Effectiveness, Benefits, And Crash Severity, Shengxuan Ding

Graduate Thesis and Dissertation post-2024

Automated Vehicles (AV) are increasingly becoming a reality, offering major road safety improvements and advancements through the development of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). Although research on AVs is progressing, several key gaps remain. There is a lack of sufficient matched data between AV and HDV crashes to compare their differences, along with a need for more detailed datasets to analyze ADS and ADAS crash severity and assess ADAS safety effectiveness. First, the research investigates overall trends within the AV dataset by utilizing 1,099 ADS, 1,001 ADAS and 35,113 HDV crash data. including ADS (Society …

A Framework For Improving Employee Experience In The Hospitality Industry, Scott Hall

Graduate Thesis and Dissertation post-2024

This dissertation explores the unique aspects of employee experience within the hospitality industry. By conducting in-depth interviews with 40 employees, both within and outside of the hospitality sector, this research aims to identify the factors that distinguish the employee experience in hospitality and to lay the groundwork for future improvements in this field.

The study employs a thematic analysis to uncover key themes that define the hospitality employee experience. The findings reveal that job satisfaction in hospitality is significantly influenced by factors such as workplace culture, career development opportunities, and the intrinsic satisfaction derived from serving guests. Moreover, the research …

Shock Tube Measurements Of Sustainable Aviation Fuel Sooting Tendencies For Supersonic Commercial Transport, Koby D. Rouviere

Graduate Thesis and Dissertation post-2024

Sustainable Aviation fuels (SAFs) have been launched to the forefront of the aviation industry as the desire to push the boundaries of flight continues to grow. The Concorde, a symbol of cutting-edge aerospace technology, was decommissioned due to its extreme inefficiency, as it formed high levels of soot, carbon monoxide (CO), and ethylene (C₂H₄), among other emissions. The UCF shock tube experiments aim to investigate the variation between sustainable aviation fuel and traditional commercial jet fuel under supersonic conditions similar to those seen on the Concorde. The test mixtures were formulated using Jet-A, Gevo’s Alcohol-to-Jet sustainable fuel, a 50/50 blend …

Adaptive Anomaly Prediction Models, Ashkan Farhangi

Graduate Thesis and Dissertation post-2024

Anomalies are rare in nature. This rarity makes it difficult for models to provide accurate and reliable predictions. Deep learning models typically excel at identifying underlying patterns from abundant data through supervised learning mechanisms but struggle with anomalies due to their limited representation. This results in a significant portion of errors arising from these rare and poorly represented events. Here, we present various methods and frameworks to develop the specialized ability of models to better detect and predict anomalies. Additionally, we improve the interpretability of these models by enhancing their anomaly awareness, leading to stronger performance on real-world datasets that …

Corporate Restructuring Spin-Off In A Technical Project-Based Organization, Stephen J. Davis

Graduate Thesis and Dissertation post-2024

A technical project-based organization (TPBO) is a type of organizational setup that uses project management as the main economic driver. An TPBO tends to be adept at accepting change due to its inherent trait of constant change within the organization as resources move from project-to-project accepting scope changes along the way. Regardless of its ability to accept more change, every organization goes through a lifecycle. At some point, the organization must implement a change in the life cycle to avoid failure. A corporate restructuring spin-off is one way an organization can avoid organizational failure. Although the tool is used in …

Agile Healthcare: The Effects Of Information Technology Ambidexterity And Human Resource Practices On Workforce Agility And Job Performance, Jorge Flavio Sarmiento Falla

Graduate Thesis and Dissertation post-2024

Healthcare organizations grapple with uncertainties and rapid changes, necessitating agility within their workforce. However, empirical studies on healthcare agility are notably scarce, impeding efforts to foster an agile workforce. Through a systematic literature review, human-related dimensions and information technology aspects were found to be relevant to healthcare agility. This study addresses the impact of Information Technology Ambidexterity (ITA) and Human Resource Practices (HRP) on establishing Workforce Agility (WA). Subsequently, the study also addresses the importance of WA for the job performance of healthcare professionals. Lastly, it evaluates the mediation effect of WA on the impact of both ITA and HRP …

Architectural And Training Regime Modifications To Transformer Models For Low Data Applications, Azwad Tamir

Graduate Thesis and Dissertation post-2024

A significant catalyst of the recent Artificial Intelligence and Machine Learning revolution is attributed to large language models (LLMs), which include many prominent AI systems such as ChatGPT, Bard, and Gemini. The majority of high-performance LLM models are based on the transformer architecture and the primary factor behind the recent enhancement in the performance of these models is attributed to the increase of layer quantity and dimensionality, resulting in an exponential rise in the total number of learnable parameters, necessitating substantial data for effective training. This may pose a significant barrier in certain application domains, such as bioinformatics, pharmaceutical research, …

Ammonia For Aviation, Brandon Cotto

Graduate Thesis and Dissertation post-2024

Aviation’s share of total global carbon emissions has been on the rise as both the decarbonization of other industries and the number of flights has increased. Ammonia as a jet fuel has been receiving attention as a potential carbon free energy storage pathway, both as a fuel and as a carrier of hydrogen. Utilizing ammonia as a fuel requires energy intensive processes making this pathway a compromise specifically intended for power generation purposes that cannot directly use sustainable energy. It is pertinent to understand the cost of realizing ammonia’s advantages as a fuel in the hard to decarbonize industry of …