Computer Sciences Commons^™

Bridging Disciplines With Ai-Powered Coding: Empowering Non-Stem Students To Build Advanced Apis In The Humanities, Daniel Plate, James Hutson

Faculty Scholarship

The integration of AI-powered coding assistants, such as Cursor AI, GitHub Copilot, and Replit’s Ghostwriter AI, represents a transformative shift in programming education, particularly for non-STEM students. These tools democratize coding by enabling natural language code generation, intelligent error correction, and context-aware assistance within familiar coding environments. This article explores how these technologies empower educators across disciplines to introduce basic and advanced coding concepts to humanities students, a demographic traditionally underserved in programming education. By leveraging AI, instructors can teach non-STEM students the foundational principles of coding and guide them through the development of sophisticated projects, such as building APIs …

Contemplating Existence: Ai And The Meaning Of Life, Emily Barnes, James Hutson

Faculty Scholarship

This article explores the intersection of artificial intelligence (AI) with existential philosophy, examining how AI technologies influence human conceptualizations of purpose and meaning. Despite rapid advancements in AI, the domain's implications for existential thought remain underexplored. By integrating interdisciplinary perspectives from psychology, philosophy, and AI ethics, this study elucidates how AI can shape, challenge, or enhance our understanding of life's purpose. It investigates theoretical frameworks and practical implementations of AI engaging in existential questions, analyzing both the capabilities and limitations of AI systems such as ChatGPT in simulating human existential thought. The ethical implications of AI's role in existential inquiries …

Advancing Robust Autonomous System Localization: Labeling Optimizations For Convolutional Neural Networks, Jeffrey L. Choate

Theses and Dissertations

AAR is increasingly critical as aircraft autonomy advances, particularly for the Global Strike mission of the USAF, enhancing operational range and endurance. Traditional methods relying on GPS and custom communication links are limited in GPS-denied environments. This dissertation advances a single camera method to estimate object pose across three interconnected studies. The system trains a CNN on synthetic imagery to predict bboxes for object components, Solve-PnP algorithm finds the 6DoF pose, then employs novel pseudo-labeling on real-world images. These findings are pivotal for the AAR community and contribute to robotics, computer vision, and CNN research. By enabling robust GPS-free autonomous …

An Artificial Intelligence Report Card For Judicial Review, Zoe E. Niesel

Michigan Journal of Environmental & Administrative Law

The rapid advancement of technology, including artificial intelligence (AI), is creating new challenges for judicial review under the Administrative Procedure Act (APA). In late 2023, federal administrative agencies publicly disclosed over 700 use cases of AI that employ sophisticated techniques like machine learning and natural language processing. While the APA's flexible judicial review framework certainly allows agencies to utilize new technologies, the APA also requires explainability of agency decisions; thus, agencies must be able to articulate the reasoning and methodology behind AI-enabled decisions for the purpose of judicial review. This Article examines APA judicial review as it applies to agency …

Layered Visualization Of Argumentation Frameworks, Yilin Xia, Daphne Oderkerken, Shaun Bowers, Bertram Ludäscher

Computer Science Faculty Scholarship

We propose a new layered visualization in PyArg for grounded labelings of abstract argumentation frameworks. Argument nodes are colored according to their label (IN, OUT, or UNDEC) and have a new length annotation, which is derived from provenance subgraphs. New edge annotations explain an attack-edge’s role in determining the value (label) of nodes in an argumentation framework.

Fake Base Station Detection And Link Routing Defense, Sourav Purification, Jinoh Kim, Jonghyun Kim, Sang-Yoon Chang

Faculty Publications

Fake base stations comprise a critical security issue in mobile networking. A fake base station exploits vulnerabilities in the broadcast message announcing a base station’s presence, which is called SIB1 in 4G LTE and 5G NR, to get user equipment to connect to the fake base station. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discovered that a fake base station can disable the victim user equipment’s connectivity for an indefinite period of time, which we validated using our threat prototype against current 4G/5G practices. We designed and built a …

Enhancing History Education With Google Notebooklm: Case Study Of Mary Easton Sibley’S Diary For Multimedia Content And Podcast Creation, Paul Huffman, James Hutson

Faculty Scholarship

This article explores new features of Google’s NotebookLM, an AI-powered tool designed for advanced document analysis and educational content generation. Tested on the 92-page transcribed diary of Mary Easton Sibley, the founder of Lindenwood University, NotebookLM effectively generated FAQs, a study guide, a table of contents, a briefing document, and an audio overview in podcast format. By transforming static historical documents into dynamic learning materials, the document-based AI model provides a user-friendly interface for educators and students, especially those without experience in audio editing or podcasting. While successful in creating study guides and audio formats, the tool faced challenges in …

A Staged Approach Using Machine Learning And Uncertainty Quantification To Predict The Risk Of Hip Fracture, Anjum Shaik, Kristoffer A. Larsen, Nancy E. Lane, Chen Zhao, Kuan Jui Su, Joyce H. Keyak, Qing Tian, Qiuying Sha, Hui Shen, Hong Wen Deng, Weihua Zhou

Michigan Tech Publications

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. The study cohort included 547 patients, with 94 experiencing hip fracture. To assess the risk of hip fracture, clinical variables and clinical variables combined with hip DXA …

Developing Empathetic Ai: Exploring The Potential Of Artificial Intelligence To Understand And Simulate Family Dynamics And Cultural Identity, Emily Barnes, James Hutson

Faculty Scholarship

The rapid advancement of Artificial Intelligence (AI) has significantly impacted various domains. Yet, the exploration of AI's potential to develop a deep understanding of family culture and identity remains underexplored. This study introduces the concept of "a love of grandma and apple pie" to symbolize the potential of various AI to internalize and appreciate familial relationships, cultural traditions, and personal identity. The proposed study would investigate how an advanced deep learning model, trained on diverse unstructured datasets—including multimedia data from 100 families-could learn and reflect human-like emotions, values, and cultural understanding. Utilizing Convolutional Neural Networks (CNNs) for visual data processing …

Technoculture And Language Models In Archaeology: Reconstructing And Preserving Cultural Narratives Through Digital Humanities, James Hutson

Faculty Scholarship

Technoculture, which examines the intersection of culture and technology, has increasingly permeated archaeological practice, transforming both scholarly research and public engagement [1-3]. The introduction of digital tools such as virtual reality (VR), geographic information systems (GIS), and large language models (LLMs) has democratized access to archaeological knowledge, enabling communities to engage more actively with their cultural heritage [4-6]. This short article explores the mutual influence of technocultural studies and AI technologies on archaeology, with a focus on the preservation and reconstruction of cultural narratives through digital means.

The first aspect of this intersection lies in how technocultural tools are creating …

Dirt/Μ: Automated Extraction Of Root Hair Traits Using Combinatorial Optimization, Peter Pietrzyk, Neen Phan-Udom, Chartinun Chutoe, Lise Pingault, Ankita Roy, Marc Libault, Patompong Johns Saengwilai, Alexander Bucksch

Department of Entomology: Faculty Publications

As with phenotyping of any microscopic appendages, such as cilia or antennae, phenotyping of root hairs has been a challenge due to their complex intersecting arrangements in two-dimensional images and the technical limitations of automated measurements. Digital Imaging of Root Traits at Microscale (DIRT/μ) is a newly developed algorithm that addresses this issue by computationally resolving intersections and extracting individual root hairs from two-dimensional microscopy images. This solution enables automatic and precise trait measurements of individual root hairs. DIRT/μ rigorously defines a set of rules to resolve intersecting root hairs and minimizes a newly designed cost function to combinatorically identify …

Paper-Recorded Ecg Digitization Method With Automatic Reference Voltage Selection For Telemonitoring And Diagnosis, Liang Hung Wang, Chao Xin Xie, Tao Yang, Hong Xin Tan, Ming Hui Fan, I. Chun Kuo, Zne Jung Lee, Tsung Yi Chen, Pao Cheng Huang, Shih Lun Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed …

Advancing Affective Computing: Emotion Recognition And Tracking Across Diverse Contexts (Varied Environments), Shao Liu

Dissertations, Theses, and Capstone Projects

Affective Computing (AC) is an interdisciplinary field that recognizes, interprets, and processes human emotions. Emotions are complex, involving consciousness, physical sensations, and behavioral expressions, and are significant in various domains like mental health, human-computer interaction, and social security. Real-world applications of AC include monitoring drivers’ emotional states to improve road safety and understanding the emotions expressed by artists in visual arts. Traditional methods relying on facial expressions often fall short due to the nuanced nature of emotions, which vary across individuals, cultures, and contexts. Accurate AC systems require sophisticated, multimodal models to handle these variations and external factors like noise …

Pulsey: Stellar Pulsation Models In Python, Andrew Ayala

Dissertations, Theses, and Capstone Projects

The era of the Kepler/K2 and TESS space-telescope missions has inundated the astrophysics community with photometric data for millions of stars with continuous observation and very high cadence. This data confirms that nearly every observed stellar source exhibits an oscillating luminosity, with stellar pulsation being the driving mechanism behind a large fraction of the cause. The result has been an explosion in the field of asteroseismology, where the recorded luminosity variations, or "light curves", of pulsating stars to probe their interior structures. By modeling observed light curves, we can constrain values of the physical stellar parameters producing them. The brightness …

Dynamic Difficulty Adjustment For Combat Systems In Role-Playing Genre Video Games, Cheuk Man Chan

Dissertations, Theses, and Capstone Projects

Static difficulty adjustment has been applied to video games since their inception. However, dynamic difficulty adjustment did not become a topic of interest in either the academic fields or the industry until the turn of the century with sufficient advancement in processing power of computers and console systems. Amongst the work done in this area, most of the focus has either been placed on the action/adventure or the strategy game genre. However, there are only a limited number of studies regarding the role playing game genre which, by the nature of such games, generates a massive amount of data regarding …

A Machine Learning Approach To Discovering Physical Models Of Galaxy Formation, Festa Bucinca

Dissertations, Theses, and Capstone Projects

Galaxies are the breathtakingly beautiful starry islands of the Universe. The process of galaxy formation involves the transformation from simple initial conditions in the early Universe to the complex galaxy structures we observe today. Spanning an immense spatial range and tremendous time scales - from the vastness of the Universe to the scale of individual stars - the physics of galaxy formation is both complex and crucial for understanding the Universe we live in. However, despite significant advancements, our theoretical understanding of galaxy formation remains incomplete.

In the era of big data available from hydrodynamical simulations and observations, Machine Learning …

Cluster-Wide Task Slowdown Detection In Cloud System, Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, Shuiguang Deng

Research Collection School Of Computing and Information Systems

Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, considering millions of concurrent tasks in large-scale cloud computing clusters, it becomes impractical and inefficient. Moreover, single-task slowdowns are very common and do not necessarily indicate a malfunction of a cluster due to its violent fluctuation nature in a virtual environment. Thus, we shift our attention to cluster-wide task slowdowns by utilizing the duration time distribution of tasks across a cluster, so that …

Pias: Privacy-Preserving Incentive Announcement System Based On Blockchain For Internet Of Vehicles, Yonghua Zhan, Yang Yang, Hongju Cheng, Xiangyang Luo, Zhuangshuang Guan, Robert H. Deng

Research Collection School Of Computing and Information Systems

More vehicles are connecting to the Internet of Things (IoT), transforming Vehicle Ad hoc Networks (VANETs) into the Internet of Vehicles (IoV), providing a more environmentally friendly and safer driving experience. Vehicular announcement networks show promise in vehicular communication applications. However, two major issues arise when establishing such a system. Firstly, user privacy cannot be guaranteed when messages are forwarded anonymously, thus the reliability of these messages is in question. Secondly, users often lack interest in responding to announcements. To address these problems, we introduce a Blockchain-based incentive announcement system called PIAS. This system enables anonymous message commitment in a …

Artificial Intelligence In Orthopaedic Education: A Comparative Analysis Of Chatgpt And Bing Ai’S Orthopaedic In-Training Examination Performance, Clark Chen, Vivek Biololikar, Duncan Vannest, James Raphael, Gene Shaffer

Einstein Health Papers

Background: This study evaluated the performance of generative artificial intelligence (AI) models on the Orthopaedic In-Training Examination (OITE), an annual exam administered to U.S. orthopaedic residency programs. Methods: ChatGPT 3.5 and Bing AI GPT 4.0 were evaluated on standardised sets of multiple-choice questions drawn from the American Academy of Orthopaedic Surgeons OITE online question bank spanning 5 years (2018–2022). A total of 1165 questions were posed to each AI system. The performance of both systems was standardised using the latest versions of ChatGPT 3.5 and Bing AI GPT 4.0. Historical data of resident scores taken from the annual OITE technical …

Editorial: Dsaa 2023 Journal Track On Theoretical And Practical Data Science And Analytics., Bin Yang, Feida Zhu, Wei Wei

Research Collection School Of Computing and Information Systems

This special issue of the International Journal of Data Science and Analytics includes the DSAA 2023 Journal Track papers, which cover advances in both theoretical and practical aspects of data science and analytics, with a particular focus on trustworthy data science and analytics. The track contains nine papers, all of which underwent rigorous review by the guest editors and invited reviewers.

Climate Change Prediction Model Using Mcdm Technique Based On Neutrosophic Soft Functions With Aggregate Operators, Kainat Muniba, Muhammad Naveed Jafar, Asma Riffat, Jawaria Mukhtar, Adeel Saleem

Neutrosophic Systems with Applications

The increasing impact of climate change necessitates innovative approaches in modeling and prediction to mitigate its adverse effects. This paper introduces a novel methodology integrating Neutrosophic Soft Functions (NSFs) into climate change prediction frameworks. NSFs, a hybrid of Neutrosophic Set Theory and Soft Set Theory, provide a flexible framework for handling uncertain and imprecise information inherent in climate data. This study explores the application of NSFs in capturing the complex interplay of various climatic variables, including temperature, precipitation, humidity, and atmospheric pressure, thereby enhancing the accuracy and reliability of climate change predictions. By incorporating NSFs into existing predictive models, such …

Exploring The Potential Of Neutrosophic Topological Spaces In Computer Science, A. A. Salama, Huda E. Khalid, Ahmed K. Essa, Ahmed G. Mabrouk

Neutrosophic Systems with Applications

Neutrosophic topological spaces (NTS) offer a novel framework for uncertainty modeling by incorporating degrees of truth, indeterminacy, and falsity. This paper investigates the potential applications of NTS in computer science. We provide background on neutrosophic sets and their extension to topological spaces. We then explore how NTS could be used for uncertainty modeling in data analysis (e.g., handling noisy data in sensor networks), pattern recognition (e.g., improving image classification with imprecise features), and information retrieval (e.g., enhancing search results by considering relevance uncertainty). We discuss the challenges associated with applying NTS and highlight promising areas for future research, such as …

Multi-Modal Alignment Via Hyperbolic Geometry, Suyu Liu

Dissertations and Theses Collection (Open Access)

Strong capabilities of generalization to unseen domains are vital for deep neural networks. While existing methods have shown promising results without source domain access, they mostly rely on models that are extensively pre-trained on source domains or overlook the intricate hierarchical structures inherent in visual and textual features. These limitations may have bad impacts on performances, especially on datasets with many classes. To overcome this, in this paper we propose a novel approach that projects the model onto hyperbolic geometry and employs geometric optimal transport to align cross-modal features in an unsupervised manner. Unlike Euclidean geometry, hyperbolic geometry is characterized …

Quality Assurance In Software Engineering: A Journey Towards Explainable Automated Solutions, Ratnadira Widyasari

Dissertations and Theses Collection (Open Access)

In today's digital era, the pervasive influence of software on daily life underscores the necessity for high-quality and reliable systems. Software failures can result in substantial harm and financial losses, highlighting the pivotal role of Software Quality Assurance (SQA). While automated SQA techniques have evolved to aid developers in ensuring software quality, the necessity for explainability in these automated solutions has become equally important. For example, in automated fault localization, only identifying suspicious locations is insufficient; it is essential to provide reasoning on why these locations are suspicious. This dissertation presents a series of interconnected studies aimed at developing explainable …

Neutrosophic Model For Measuring And Evaluating The Role Of Digital Transformation In Improving Sustainable Performance Using The Balanced Scorecard In Egyptian Universities, A. A. Salama, Osama Mohamed Mobarez, Mohamed Hamed Elfar, Rafif Alhabib

Neutrosophic Systems with Applications

This paper proposes a neutrosophic model for measuring and evaluating the role of digital transformation in improving sustainable performance using the balanced scorecard in Egyptian universities. The model takes into account uncertainty, ambiguity, and incompleteness in the data. The model first calculates the neutrosophic measures of digital transformation and sustainable performance for each university. Then, it uses neutrosophic logic to evaluate the causal relationship between digital transformation and sustainable performance. The results of the analysis can used to identify the digital transformation indicators that have the greatest impact on sustainable performance. This information can then be used to develop strategies …

Two-Dimensional Quantum Material Identification Via Self-Attention And Soft-Labeling In Deep Learning, Xuan Bac Nguyen, Apoorva Bisht, Benjamin Thompson, Hugh O.H. Churchill, Khoa Luu, Samee U. Khan

Electrical Engineering and Computer Science Faculty Publications and Presentations

Detecting two-dimensional (2D) materials in silicon chips presents a significant challenge in the field of quantum machines due to the difficulty of data collection. Specifically, among thousands of flakes, not all flakes are useful or well-annotated, resulting in noisy and hard samples within the dataset, which challenges the deep neural network (DNN) to learn. To address this problem, we propose a novel method for identifying quantum 2D flakes even when there is a high rate of missing annotations in the input images. In particular, we first propose a new mechanism for automatically detecting false negative flakes that are missing annotations. …

Integrating Blockchain Technology Into The Software Development Life Cycle To Satisfy The Software Bill Of Materials Requirement For Government Software Systems, Walter T. Scott Ii

Theses and Dissertations

This thesis explores the integration of Blockchain Technology (BT) into the Software Development Life Cycle (SDLC) to satisfy the Software Bill of Materials (SBOM) requirement for government software systems. This study begins by synthesizing a standard SDLC definition from various government and industry references, which may provide the foundation for future efforts to standardize software development practices across the government software development community. This study proceeds to define working definitions for the software supply chain (SSC) and software supply chain management (SCM) before introducing and detailing the SBOM requirement as well as providing an overview of prior research regarding SBOMs …

Autonomous Experimentation For Accelerated Calibration Of Fused Deposition Modeling 3d Printers, Graig S. Ganitano

Theses and Dissertations

Additive Manufacturing (AM), also known as 3D printing, has emerged as a key component of Industry 4.0, enabling reduced cost, quick production, greater sustainability, and increased design complexity compared to its traditional manufacturing counterpart. Currently, Fused Deposition Modeling (FDM) technology dominates the AM market with respect to the number of 3D printers in use. However, the FDM process is sensitive to changes in system configuration, especially the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters, acting as a barrier to the technology.

To enable greater accessibility …

Machine Visual Perception For Autonomous Docking Maneuvers, Derek B. Worth

Theses and Dissertations

This dissertation presents a novel approach to autonomous docking using machine learning for visual perception, particularly during probe and drogue aerial refueling. Autonomous vehicles have become pervasive in both civilian and defense sectors, and their ability to interact with their surroundings and each other autonomously is critical for future operations. Traditional methods relying on signals or inertial sensors face significant limitations such as interference, jamming, and drift. This research focuses on developing a computer vision-based solution to overcome these limitations. A novel pipeline, termed relative vectoring, is introduced, which utilizes dual object detection and machine learning to estimate relative positions …

A Comparison Of Four Approaches To Modeling Information Insufficiency, Pengya Ai, Sonny Rosenthal

Research Collection College of Integrative Studies

Information insufficiency, or the disparity between the level of knowledge needed to confidently judge an issue and the perceived level of current knowledge, is a key motivator of risk information seeking and processing. This study compared 4 approaches to modeling information insufficiency within the planned risk information seeking model. These approaches included the raw difference score, regression approach, partial variance score, and direct measure. Statistical modeling used data from large samples in Singapore (n = 2,124) and the United States (n = 2,125). The results of ordinary least squares regression analysis and structural equation modeling pointed to several issues. First, …