Computer Sciences Commons^™

Review Of Averting The Digital Dark Age: The Future Of Digital Literary Heritage, Rebecca L. Hastings

Journal of Contemporary Archival Studies

In Averting the Digital Dark Age: How Archivists, Librarians, and Technologists Built the Web a Memory, Ian Milligan explores the efforts of technologists and information professionals to develop means of preserving information shared on the World Wide Web. The web lacks a built-in archiving function, raising fears of a “digital dark age,” an unprecedented gap in the historical record as information sharing via the web increases. Milligan tracks the development of web preservation debates and activities from the anxiety and turmoil of the early to mid-1990s to the coming of age of web preservation in the wake of the …

Using Linear Programming And Game Theory To Optimize The Relation Between Us And China, Junhao Su

Student Works

This paper develops an optimization model to analyze U.S.–China bilateral trade dynamics and competition in artificial intelligence (AI). First, grounded in WTO tariff limits, we formulate a linear programming model to maximize the combined trade volume and conduct a comprehensive sensitivity analysis on tariff parameters. Second, we integrate zero‑sum and non‑zero‑sum game‑theoretic frameworks to identify the Nash equilibria governing both trade negotiations and technological rivalry. The model is implemented in Python using PuLP and is empirically validated with real‑world tariff data to highlight the policy relevance of the optimal solutions. Our results reveal a high concordance between the zero‑sum game …

Integrating Artificial Intelligence And Machine Learning Technologies Into Common Operating Picture And Course Of Action Development, C. Anthony Pfaff, Christopher John Hickey

Books, Monographs & Collaborative Studies

C. Anthony Pfaff and Christopher John Hickey, Principal Investigators

©2025 C. Anthony Pfaff. All rights reserved.

Integrating Artificial Intelligence and Machine Learning Technologies into Common Operating Picture and Course of Action Development explores the potential of artificial intelligence (AI) and machine learning to revolutionize military planning processes by enhancing situational awareness and expediting course of action development within the Joint planning process. The study delves into technical, organizational, and resource considerations that are critical for AI integration. In addition, the study highlights the importance of clean, structured data in training AI systems, addresses challenges in data collection across varying formats …

Creativity And Curb Cuts: Experiences In Our First Offering Of A Front End Development And Accessibility Focused Cs Course, Briana C. Bettin, Tony Garnett, Alex Gore, Andrea Llanas

Michigan Tech Publications

Students learn an abundance of technical skills while obtaining a computer science degree. The ability to develop meaningful front end user interfaces is often considered the domain of only ''more artistic'' CS students. However, for users to effectively engage with any piece of software, functional user interfaces are critical. Moreover, even among students who have front end skills, semantic and accessible design is all too often less considered. The first author piloted a ''Front End Development and Accessibility'' course this past Fall. This course teaches basic skills of front end with web and leverages key accessibility standards via WCAG. This …

"Chatgpt Told Me To Say It": Ai Chatbots And Class Participation Apprehension In University Students, Daisuke Akiba

Publications and Research

The growing prevalence of AI chatbots in everyday life has prompted educators to explore their potential applications in promoting student success, including support for classroom engagement and communication. This exploratory study emerged from semester-long observations of class participation apprehensions in an introductory educational psychology course, examining how chatbots might scaffold students toward active and independent classroom contribution. Four students experiencing situational participation anxiety voluntarily participated in a pilot intervention using AI chatbots as virtual peer partners. Following comprehensive training in AI use and prompt design given to the entire class, participants employed systematic consultation frameworks for managing classroom discourse trepidations. …

Seeking Structure In Complex Systems: From Feature Analysis To Space-Time Causal Discovery With Earth Science Applications, Jeffrey J. Nichol

Computer Science ETDs

Complex systems are difficult to study because of their many interacting parts, emergent phenomena, and feedback loops. These systems underpin all life on Earth. We need improved tools for seeking an understanding of them. This body of research presents my investigations into data-driven methods for understanding complex systems, including my invention of a novel causal discovery meta-algorithm for space-time gridded data. I demonstrated machine learning feature importance and causal discovery capabilities for comparing simulated and observed climate data. I developed a new benchmark for modeling space-time dynamics of locally driven phenomena and examined a prominent causal discovery algorithm. Finding that …

Qder: Query-Specific Document And Entity Representations For Multi-Vector Document Re-Ranking, Shubham Chatterjee, Jeff Dalton

Computer Science Faculty Research & Creative Works

Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. QDER's key innovation lies in its modeling of query-document relationships: rather than computing similarity scores on aggregated embeddings, we maintain individual token and entity representations throughout the ranking process, performing aggregation only at the final scoring stage-an approach we call "late aggregation." We first transform these fine-grained representations through learned attention patterns, then apply carefully chosen mathematical operations for precise matches. …

Understanding The Roots Of Swarm Intelligence In Defence To Find The Path Forward: A Scientometric Study Of Autonomous Systems, Anton Klarin, Pi-Shen Seet, Janice Jones, Michael N. Johnstone, Helen Cripps, Jalleh Sharafizad, Tony Marceddo

Research outputs 2022 to 2026

Swarm intelligence, inspired by the decentralised, adaptive and self-synchronising behaviours of natural swarms, is a pivotal component of autonomous systems, enhancing efficiency, robustness and scalability. The research in this area is nascent and interdisciplinary. To drive this important research forward, it is necessary to adopt a systems perspective on what is available in the current literature. This chapter offers a comprehensive systems perspective of the integration of swarm intelligence within the broader domain of automation, emphasising its application in the defence sector. A systems perspective of an interdisciplinary field is afforded through scientometrics. Using VOSviewer algorithms, we analysed 1706 publications …

Role Of Staphylococcus Aureus And Streptococcus Pyogenes Biofilms On The Alternation Of Cellular Immunity In Pediat-Ric Tonsillitis Patients, Shayma Ali Hussein, Taban Kamal Rasheed

Karbala International Journal of Modern Science

This study examines the effect of Staphylococcus aureus and Streptococcus pyogenes biofilms on cellular hematological parameters and distribution and phenotyping of cellular immunity in mucosal tissue of tonsils. Thirty healthy controls and fifty pediatric tonsillitis patients participated in the research. Thirty isolated S. aureus and S. pyogenes were tested for biofilm-forming capability (BFC). Hematological parameters were assessed before tonsillectomy, and 9 tonsil samples were evaluated using hematoxylin and eosin stain to investigate the histopathological alterations. Immunohistochemistry (IHC) staining was carried out for detecting dendritic cells (CD1a), neutrophils (CD15), macrophages (CD68), helper T cells (CD4), and cytotoxic T cells (CD8). Hematological …

Neurophysiology And Endocrine Responses To Hunger And Satiety Mechanisms: The Brain-Gut Crosstalk, Nour Shakir Rezaieg, Muthanna M. Awad

Karbala International Journal of Modern Science

Background: Obesity is a main public health problem which substantially increases the risk of many diseases. The complex neural circuitry controls energy homeostasis and food consumption by the incorporation of hormonal and neural signals. Circulating hormones, in specific the gut hormones, have been found to be very important in appetite regulation. These hormones transfer energy situation signs to the brain throughout three principle paths: the circulation system, activation of the vagus nerve, and direct modification of main brain regions such as the hypothalamus and brainstem. The control of food eating is not exclusively dependent on the homeostatic processes, rather it …

A Novel Chaotic Dna-Based Image Cryptosystem Leveraging Euclidean Division, Dynamic Josephus Traversal, And Reservoir Computing, Ahmed Kareem Shibeeb, Salah Albermany, Sadiq A. Mehdi

Karbala International Journal of Modern Science

This study presents a new chaotic DNA-based image cryptosystem that combines Euclidean division, dynamic Josephus traversal (DJT), and reservoir computing to address the weaknesses of current methods. Old chaotic DNA cryptosystems usually have problems such as using the same keys for different messages, simple DNA processes, and being vulnerable to attacks where the attacker can choose the input or try many options. The cryptosystem in this study uses a 7D hyperchaotic system that starts with keys created from SHA-512 hashes to produce changing keystreams based on the plaintext, making it very strong against such attacks. The proposed cryptosystem uses a …

Type-2 Neutrosophic Set With Mcdm Methodology For Fire Safety Estimation In Healthcare Services, Eman Sayed

Neutrosophic Systems with Applications

Fire safety represents a critical priority in healthcare facilities, where complex infrastructures and the vulnerability of patients present significant challenges to evacuation and emergency response. Traditional fire risk assessment methods often fall short in addressing the linguistic variability, uncertainty, inconsistency, and indeterminacy inherent in expert evaluations. While fuzzy and Neutrosophic approaches have been applied in broader healthcare decision-making contexts, no existing study has utilized Type-2 Neutrosophic Numbers Sets (T2NNs) for prioritizing hospital departments based on fire risk. To address this gap, this study introduces a novel multi-criteria decision-making (MCDM) framework that integrates T2NNs for expert modeling, the Entropy method for …

Location Selection Of Migrating Beetles Under Neutrosophic Model With Sensitivity And Comparative Analysis, Rabih Sbera, Ahmed A El-Douh, Darin Shafek, Tareef S. Alkellezli

Neutrosophic Systems with Applications

Location Selection of Migrating Beetles has different criteria to select the best location. So, multi-criteria decision making (MCDM) is used to deal with different and numerous criteria in this study. This study proposes an MCDM methodology to rank the locations and select the best criterion. The average method is used to compute the criteria weights. The locations are ranked using the root assessment method (RAM). This study uses eight criteria and 20 locations. We use the single valued neutrosophic numbers (SVNNs) to overcome uncertainty and vague information. The RAM methodology is used under the SVNNs. The results show that Availability …

Risk Management Of The Open Data Services Industry In Digital Transformation Under Neutrosophic Sets, Fadhl Ehsan Hadi, Mohammed Musa Mohammed, Noorhan Waleed Abdullah, Baraa Hasan Hadi

Neutrosophic Systems with Applications

The open data services industry is very important for artificial intelligence and digital transformations. The open data services industry has different risks and challenges, so this study proposed a multi-criteria decision making (MCDM) approach for risk management in open data services industry. This study uses the average method to compute the criteria weights and the WASPAS method to rank the alternatives. The triangular neutrosophic set (TNS) is used in this study to overcome uncertainty and vague information. It has three membership functions such as truth, indeterminacy, and falsity. This study uses nine criteria and 18 risks to be evaluated. The …

Type-2 Neutrosophic Numbers For Artificial Intelligence Software Choice For Cybersecurity Testing, O.M. Akash, We’Am Adel Talafha, Mamdouh Gomaa

Neutrosophic Systems with Applications

The choice of artificial intelligence (AI) software for cybersecurity testing is a multi-criteria decision-making approach (MCDM) due to it including different criteria. Evaluation decision making problems include uncertainty and vague information. So, the neutrosophic set is used in this study to overcome this uncertainty and vague information. It has three functions such as truth, indeterminacy, and falsity functions. Type-2 neutrosophic numbers is a type of neutrosophic set that includes nine membership functions. This study uses the average method of computing the criteria weights. The CoCoSo method is used to rank alternatives. Six experts and decision makers created the decision makers …

Neutrosophic Numbers For Selection Best Strategy For Dual Supply Chains With Green And Non-Green Products, Nada A. Nabeeh, Waleed Abd Elkhalik, Gawaher Soliman Hussein

Neutrosophic Systems with Applications

Dual supply chains with green and non-green products are an important aspect of supply chain management, which enable companies to balance traditional operations with ethical and eco-friendly practices to reduce carbon emissions. This study proposed a novel approach that combines the Probabilistic Simplified Neutrosophic Set (PSNS) with the Ranking of Alternatives Method (RAM) for the selection of best strategy selection for dual supply chains with green and non-green products. The novel approach uses PSNS as a representation of uncertainty, by incorporating probabilistic degrees of truth, indeterminacy, and falsity, which occurred in real life situations. Furthermore, RAM illustrates efficient ranking and …

Cilia In The Brain Display Region-Dependent Oscillations Of Length And Orientation, Roudabeh Vakil Monfared, Sherif Abdelkarim, Pieter Derdeyn, Kiki Chen, Hanting Wu, Kenneth Leong, Tiffany Chang, Justine Lee, Sara Versales, Surya M. Nauli, Kevin Beier, Pierre Baldi, Amal Alachkar

Pharmacy Faculty Articles and Research

In this study, we conducted high-throughput spatiotemporal analysis of primary cilia length and orientation across 22 mouse brain regions. We developed automated image analysis algorithms, which enabled us to examine over 10 million individual cilia, generating the largest spatiotemporal atlas of cilia. We found that cilia length and orientation display substantial variations across different brain regions and exhibit fluctuations over a 24-h period, with region-specific peaks during light-dark phases. Our analysis revealed unique orientation patterns of cilia, suggesting that cilia orientation within the brain is not random but follows specific patterns. Using BioCycle, we identified rhythmic fluctuations in cilia length …

Detecting Misuse Of Security Apis: A Systematic Review, Zahra Mousavi, Chadni Islam, Muhammad Ali Babar, Alsharif Abuadbba, Kristen Moore

Research outputs 2022 to 2026

Security Application Programming Interfaces (APIs) are crucial for ensuring software security. However, their misuse introduces vulnerabilities, potentially leading to severe data breaches and substantial financial loss. Complex API design, inadequate documentation, and insufficient security training often lead to unintentional misuse by developers. The software security community has devised and evaluated several approaches to detecting security API misuse to help developers and organizations. This study rigorously reviews the literature on detecting misuse of security APIs to gain a comprehensive understanding of this critical domain. Our goal is to identify and analyze security API misuses, the detection approaches developed, and the evaluation …

Modern Procedural Terrain Generation Techniques And Their Background, Hunter A. Barton

2025 Symposium

Procedural terrain generation has become a staple in many digital environments, enabling the automated creation of large-scale and realistic landscapes for applications such as video games and movies. This paper provides an in-depth look at smooth noise functions and their use for terrain generation, as well as an overview of some more modern methods of generation. A method utilizing machine learning stlye transfer was reproduced for this paper with some alterations to improve visualization and realism.

Discrete Time Series Forecasting In Non-Invasive Monitoring Of Managed Honey Bee Colonies: Part Ii: Are Hive Weight And In-Hive Temperature Seasonal And Colony-Specific, Vladimir A. Kulyukin, Aleksey V. Kulyukin, William G. Meikle

Computer Science Faculty and Staff Publications

We explored the stationarity, trend, and seasonality of the hive weight and in-hive temperature of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, Arizona, USA. The hives were monitored with electronic scales and in-hive temperature sensors from June to October 2022. The weight and temperature were recorded every five minutes around the clock. The collected data were curated into 2160 timestamped weight and 2160 timestamped temperature observations. We performed a systematic autoregressive integrated moving average (ARIMA) time series analysis to answer three fundamental questions: (a) Does …

A Participatory Approach To Deploy Responsible Artificial Intelligence For Diabetes Prediction And Prevention, Laura C. Rosella, James Shaw, Shion Guha, Ibukun-Oluwa Omolade Abejirinde, Jennifer L. Gibson, Lorraine Lipscombe, Kathy Kornas, Remziye Zaim, Victoria Chui, Ijeoma Uchenna Itanyi

Health Services and Informatics Research

Artificial intelligence (AI) technologies have the potential to improve healthcare and public health. Although there has been success in AI for research uses, little progress has been made in implementing health-related AI technologies in health systems. Responsible AI for health systems requires engagement and co-design with health system partners, policymakers, and the community. Deploying responsible AI requires engaging stakeholders, particularly those affected by the technology. This commentary presents the importance of participatory approaches for responsible AI implementation. In this commentary, we discuss the planned use of participatory approaches to responsibly deploying validated machine learning models, with a specific case example …

In The Shadow Of Prompts: Adversarial Attacks And Model Cloning In Large Language Models, Kanchon Gharami

Doctoral Dissertations and Master's Theses

Large-language models (LLMs) already power mission critical tasks such as command-and-control chat, satellite ground-station automation, military analytics, and cyber-defense. Since most of these services are offered through application programming interfaces (APIs) that still expose full or top-k logits and lack mature safeguards, they present a serious, often overlooked attack surface. Earlier work has shown how to rebuild the output projection layer or distill surface behavior, but no attack has produced a deployable clone within a tight query budget. In this thesis, we address this problem by presenting a practical pipeline for cloning LLMs under constrained settings. The approach first estimates …

The Impact Of Entrepreneurial Orientation On Innovation Performance: The Role Of Knowledge Sharing As A Mediating Factor, Dhia Qasim, Ahmed Shuhaiber, Zainab Rawshdeh

All Works

Innovation is critical for enhancing business products and processes, leading to improved overall performance for firms. Knowledge sharing (KS) plays a crucial role in fostering innovation within firms. Literature has addressed entrepreneurial orientation (EO) and innovation performance (IP) in firms; however, extant literature has not considered the influence of EO on IP in emerging economies. Thus, based on the EO theory, this paper develops a theoretical framework to investigate the influence of EO antecedents on IP within the mediating role of KS. Data were collected from three national telecom companies in Jordan, and 215 responses were analyzed using partial least …

Can We Discover Physical Models Using Machine Learning? A Case Study Of Galaxy Sizes, Festa Buçinca-Çupallari, Ariyeh Maller, Viviana Acquaviva, Austen Gabrielpillai, Rachel S. Somerville

Publications and Research

We explore the ability of machine learning methods to discover underlying equations of physics by searching for the equations governing galaxy size in a semianalytic model. This case study allows us to evaluate the process as we know the ground truth. We find that we fail to find an equation to predict galaxy size on the entire data set, but are successful when we separate out disk galaxies where we expect the physics driving galaxy size to be different than in bulge-dominated systems. We are also able to find an equation for bulge size, but not without adding an additional …

Exact Sampling Of The Six-Vertex Model Using Coupling From The Past, Malaeka Amir

DePaul Discoveries

This paper aims to explore the six-vertex model through simulations designed to investigate the behavior of configurations under specific domain wall boundary conditions. To generate random configurations, we employ the Markov Chain Monte Carlo method while addressing the challenge of mixing times by utilizing the Coupling from the Past (CFTP) algorithm. Implemented in Python, our approach leverages CFTP to ensure exact sampling, avoiding the uncertainty of convergence in traditional Monte Carlo methods. We explore the monotonicity property within this framework and prove that it is only maintained by the steps of this algorithm for very particular values of the parameters.

Benford's Law In Basic Rnn And Long Short-Term Memory And Their Associations, Farshad Ghassemi Toosi

Department of Computer Science Publications

Benford's Law describes the distribution of numerical patterns, specifically focusing on the frequency of the leading digit in a set of natural numbers. It divides these numbers into nine groups based on their first digit, with the largest category comprising numbers beginning with 1, followed by those starting with 2, and so on. Each neuron within a neural network (NN) is associated with a numerical value called a weight, which is updated according to specific functions. This research examines the Degree of Benford's Law Existence (DBLE) across two language model methodologies: (1) recurrent neural networks (RNNs) and (2) long short-term …

The Impact Of Artificial Intelligence As An Intervening Variable Between The Digital Government Strategy And Competency Development "An Applied Study At Sharjah Police Sciences Academy ", Elsayed Kamal Risha, Abd Al-Rahman Al-Naqbi

Journal of Police and Legal Sciences

The study aimed to determine the impact of the digital government strategy on competencies development, through artificial intelligence as an intervening variable, and to achieve the objectives, the study relied on the quantitative approach and the questionnaire was used as the main tool for collecting data. The study community represented officers, non-commissioned officers and individuals at the Sharjah Academy for Police Sciences, and the study sample amounted to 30 affiliates, i.e. the total number of employees in the Competency Development Department at the Academy. The study reached a set of results, the most prominent of which are:

- The existence …

Legislative And Security Confrontation Of Crimes Artificial Intelligence In The State Of Kuwait (An Analytical Study), Rashid Mohammed Al Marri

Journal of Police and Legal Sciences

The study aimed to demonstrate the mechanisms of legislative and security confrontation of artificial intelligence crimes. The use of technologies associated with artificial intelligence may go beyond the imposed limits, whether by exploiting it through its program developers & specialists to commit crimes in the cyber field, or it may be with the growing capabilities of artificial intelligence to make decisions in the field. Many behaviors occur automatically. Our research also aims to study the criminal responsibility for these crimes, & determine it in order to hold the real perpetrator accountable in accordance with the legal rules in force to …

Bridging Classical Rhetoric And Ai: A Systematic Framework For Developing Authorial Voice Through Large Language Models, Daniel Plate

Theses

This project addresses critical gaps in AI-assisted writing by developing the first systematic framework that integrates classical rhetorical principles with modern large language model capabilities for authorial voice development. The primary focus is on creating reliable methods for stylistic control through strategic AI collaboration rather than ad hoc prompting approaches. The project develops a comprehensive coding system for analyzing prose style, creates ten distinct authorial personas, and establishes a dual curation methodology that structures both stylistic analysis and content preparation. Implementation through the AI Writing Guide website provides practical tools including prompt templates, annotated examples, and instructional materials that demonstrate …

Domain-Adaptive Diagnosis Of Lewy Body Disease With Transferability Aware Transformer, Xiaowei Yu, Jing Zhang, Tong Chen, Yan Zhuang, Minheng Chen, Chao Cao, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Lewy Body Disease (LBD) is a common yet understudied form of dementia that imposes a significant burden on public health. It shares clinical similarities with Alzheimer’s disease (AD), as both progress through stages of normal cognition, mild cognitive impairment, and dementia. A major obstacle in LBD diagnosis is data scarcity, which limits the effectiveness of deep learning. In contrast, AD datasets are more abundant, offering potential for knowledge transfer. However, LBD and AD data are typically collected from different sites using different machines and protocols, resulting in a distinct domain shift. To effectively leverage AD data while mitigating domain shift, …