Digital Commons Network^™

Why Green Wavelength Is Closer To Blue Than To Red And How It Is Related To Computations: Information-Based Explanation, Victor L. Timchenko, Yury P. Kondratenko, Olga Kosheleva, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

In our previous papers, we analyzed the idea of using light signals of three basic color -- red, green, and blue -- to speed up computations, in particular fuzzy-related computations. A natural question is: why red, green, and blue? Why not select some other colors: e.g., from the wavelength viewpoint, green is much closer to blue than to green, so why not select colors whose distribution is more even? In this paper, we show that if we consider this problem from the information viewpoint, then the corresponding equal-information criterion indeed implies that the intermediate wavelength should be closer to the …

Training Neural Networks On Interval Data: Unexpected Results And Their Explanation, Edwin Tomy George, Vladik Kreinovich, Christoph Lauter, Martine Ceberio, Luc Jaulin

Departmental Technical Reports (CS)

In many practically useful numerical computations, training-and-then-using a neural network turned out to be a much faster alternative than running the original computations. When we applied a similar idea to take into account interval uncertainty, we encountered two unexpected results: (1) that while for numerical computations, it is usually better to represent an interval by its midpoint and half-width, for neural networks, it is more efficient to represent an interval by its endpoints, and (2) that while usually, it is better to train a neural network on the whole data processing algorithm, in our problems, it turned out to be …

The Impact Of Instrumental Attribution In Ai-Enabled Monitoring On Counterproductive Work Behavior, Qiang Zhang

Dissertations and Theses Collection (Open Access)

AI-enabled monitoring tools are theoretically expected to suppress unethical employee behavior. However, in practice, employees may perceive such monitoring as being driven by leaders' instrumental motives, primarily focused on personal performance evaluation and self-interest. This perception can foster feelings of job insecurity and moral disengagement, ultimately leading to counterproductive work behavior (CWB), which includes unethical employee behavior and turnover. These outcomes may undermine the intended effectiveness of AI-enabled monitoring tools. This study aims to explore the impact of Instrumental Attribution in AIenabled Monitoring (IAAIM) on CWB, specifically focusing on unethical employee behavior and turnover, through both theoretical and empirical lenses. …

Satirical Deepfakes, Surreal Dreamscapes & Nostalgic Pixels: The Rapid Evolution And Cultural Commentary Of Ai-Aesthetics, Andrew Smith, James Hutson

Faculty Scholarship

The rapid evolution of visual aesthetics driven by AI, shared globally through the internet and social media, has dramatically accelerated what once took centuries to develop. This article explores the unique visual tropes emerging from AI-generated content, characterized by surreal, uncanny, and often unsettling imagery. Examples range from the Dor Brothers' stylized narrative videos to horrifying depictions of transformations, such as people morphing into motorcycles. The article contextualizes this aesthetic within historical developments in creative experimentation, drawing parallels with David Bowie's unconventional approach to sound creation in the 1970s. It also considers how AI-driven art, free from copyright constraints in …

Ai Satire And Digital Dystopia: The Dor Brothers Crafting Imperfection And Political Commentary In Contemporary Video Art, James Hutson, Andrew Smith

Faculty Scholarship

The Dor Brothers' AI-generated video content exemplifies an inflection point in digital creativity, where technological limitations are repurposed as aesthetic tools. Drawing on recent interviews with Yonatan Dor, this article explores the innovative techniques of the brothers, such as masking visual imperfections with retro filters and embracing the unpredictability of AI outputs. Through generating numerous clips and meticulously editing selections, they create a unique aesthetic that juxtaposes surrealism with a gritty realism, often reminiscent of early CCTV or VHS footage. Their work not only transcends the typical "morphing face" trope of AI videos but also engages in satire, using deepfake-like …

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 …

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 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 …

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 …

La Vida: Towards A Motivated Goal Reasoning Agent, Ursula Addison

Dissertations, Theses, and Capstone Projects

An autonomous agent deployed to operate over extended horizons in uncertain environments will encounter situations for which it was not designed. A class of these situations involves an invalidation of agent goals and limited guidance in establishing a new set of goals to pursue. An agent will benefit from some mechanism that will allow it to pursue new goals under these circumstances such that the goals are broadly useful in its environment and take advantage of its existing skills while aligning with societal norms. We propose augmenting a goal reasoning agent, i.e., an agent that can deliberate on and self-select …

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 …

Unraveling The Dynamics Of Stable And Curious Audiences In Web Systems, Rodrigo Alves, Antoine Ledent, Renato Assunção, Pedro Vaz-De-Melo, Marius Kloft

Research Collection School Of Computing and Information Systems

We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time series data such as feeds or search queries. BPoP can distinguish between the slowly-varying regular activity of a stable audience and the bursty activity of a curious audience, often seen in viral threads. Our model consists of two hidden, interacting processes: a self-feeding process (SFP) that generates bursty behavior related to viral threads, and a non-homogeneous Poisson process (NHPP) with step function intensity that is influenced by the bursts from the SFP. The NHPP models the normal background behavior, driven solely by the overall popularity of the …

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 …

Certified Quantization Strategy Synthesis For Neural Networks, Yedi Zhang, Guangke Chen, Jun Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Quantization plays an important role in deploying neural networks on embedded, real-time systems with limited computing and storage resources (e.g., edge devices). It significantly reduces the model storage cost and improves inference efficiency by using fewer bits to represent the parameters. However, it was recently shown that critical properties may be broken after quantization, such as robustness and backdoor-freeness. In this work, we introduce the first method for synthesizing quantization strategies that verifiably maintain desired properties after quantization, leveraging a key insight that quantization leads to a data distribution shift in each layer. We propose to compute the preimage for …

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 …

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 …

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 …

The Impact Of Data Recovery Criteria, Data Backup Schedule And Data Backup Prosses On The Efficiency Of Data Recovery Management In Data Centers, Maen T. Alrashdan, Mutaz Abdel Wahed, Emran Aljarrah, Mohammad Tubishat, Malek Alzaqebah, Nader Aljawarneh

All Works

A large-scale cloud data center must have a low failure incidence rate and great service dependability and availability. However, due to several issues, such as hardware and software malfunctions that regularly cause task and job failure, large-scale cloud data centers still have high failure rates. These mistakes can have a substantial impact on cloud service dependability and need a large resource allocation to recover from failures. Therefore, it is important to have an efficient management of data recovery to protect organizations data from loss. This paper aims to study some factors that may improve the management of data recovery by …

Putting Gpt-4o To The Sword: A Comprehensive Evaluation Of Language, Vision, Speech, And Multimodal Proficiency, Sakib Shahriar, Brady D. Lund, Nishith Reddy Mannuru, Muhammad Arbab Arshad, Kadhim Hayawi, Ravi Varma Kumar Bevara, Aashrith Mannuru, Laiba Batool

All Works

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model’s language capability. Additionally, GPT-4o’s vision and speech capabilities are tested through image classification and object-recognition tasks, as well as accent classification. The multimodal evaluation assesses the model’s performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning …

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 …

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 …

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 …

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 …

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 …