Computer Sciences Commons^™

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, …

Graph Traverse Reference Network For Sign Language Corpus Retrieval In The Wild, Kun Hu, Fengxiang He, Adam Schembri, Zhiyong Wang

Research outputs 2022 to 2026

Sign languages are the primary languages of the deaf community as well as hearing individuals who are unable to speak, which engage the visual-manual modality to convey meanings. In recent years, there has been an explosive growth of sign language videos available from video streaming and social media service platforms. Given the size of these corpora, sign language users often face significant challenges in effectively acquiring the information they need. Therefore, we propose a novel deep learning architecture, namely Graph Traverse Reference Network (GTRN), allowing visual signing queries to retrieve relevant sign language videos (documents) from a large corpus. GTRN …

A Survey On Security Applications With Smartnics: Taxonomy, Implementations, Challenges, And Future Trends, Serigo Elizalde, Ali Alsabeh, Ali Mazloum, Samia Choeiri, Elie Kfoury, Jose Gomez, Jorge Crichigno

Faculty Publications

Over the last decade, network applications have grown exponentially, demanding high-speed interconnects. Unfortunately, chip manufacturers are approaching the upper limits of silicon-based computing with slow improvements in computational performance and energy efficiency. This trend has forced the industry to shift paradigms, moving from monolithic architectures to heterogeneous, domain-specific designs. Moreover, the ever-evolving threats compromise digital services and demand more scalable and flexible solutions to ensure service continuity in production networks. Smart Network Interface Cards (SmartNICs) are a product of this new paradigm, integrating domain-specific engines and general-purpose cores to offload various network infrastructure tasks, including those related to security. This …

Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Kabe Aberle, Nadia Kako, Kateri Mcrae, Brooke Agulnek, Sky Palmon, Yasmine Ramirez, Francisca Aguirre Beltran, Ashley Juarez, Bridget Kim, Tessa Appel, Sterling Kerr, Spencer Ingley, Gabe Meyer, Robin Tinghitella, Dale Broder, Lily Baeza, Chloe Beers, Julia Coakley, Whitney Kelsey, Sydney Gainforth, Gabi Wing, Audrey Martin, Aaliyah Amore Berry, Brooke Watley, Kiruthika Venkatesan, Rachel Bienstock, Annabella Brotherston, Madison Bryant, Mia Burgener, Emma P. Lieb, Rachel A. Johnson, Jennifer L. Hoffman, Kania Campbell, Kiena Campbell, Courtney Cassidy, Sage Krzyzkowski, Maddox Jones, Skylar Abookire, Luke Hawkins, Sunnah Yoon, Andrea Chu, Yan Qin, Nyah Cubbison, Brian Gearity, Daniel Mcintosh, Mariely Cruz, Edward Garrido, Grady Dionne, Nicole Doris, Lyndsie Salvagio, Ann-Charlotte Granholm-Bentley, Anna Dymov, Hannah Eckert, Gabrielle Welsh, Erica Larson, Charlie Ernst, Anna Zhou, Sarah Watamura, Larissa Fedorovich-Klein, Georgie Fields, Kimberly A. Guevara, Aven Mccall, Ben Peltier, Feruz Yahia, Patrick Flores, Jadyn Floyd, Sophia Forcier, J. Von R. Monteza, Peter Sokol-Hessner, Gwendolyn Geiger, Scott Nichols, Camryn Gunter, Kendal Hengst, Charlie Bednarz, Issy Garside, Addison Baker, Rachel Mina, Brooke Hermanson, Amanda Klingler, William Highfill, Sydney Jaques, Kerstin Lewey, Allison Grossery, Daniel Linseman, Ethan Lim, Jagger Livengood, Owen Mantelli, Gabby Pappas, Abby Mcdonald, Madeleine Dierking, Eve Miller, Emma Loeber, Anna Marlow, Michael Kerwin, Ella Mathews, Hillary Hamann, Khadija Mohamed, Vivian Nguyen, Gabri Notov, Ifunayachi Ogbonna-Ukuku, Sunil Kumar, Charles Baysah, Sarah Olson, Don Sullivan, Anna Paradiso, Jay Parrish, Mira Pronobis, Alisha Pravasi, Kerstin Haring, Diego Ramirez, Christopher Reardon, Juliana Ramirez, Casey Doherty, Ella Kestner, Teagan Weindel, Cate Billings, Pablo Torre-Walter, Lucy Rand, Samantha Reynolds, Mark Siemens, Khadeeja Rashid, Laine Satterlee, Piper Heilbronner, Lily Pound, Ben Whitehurst, Anna Respet, Lizzie Lesoing, Sydney Hertel, Aya Saad-Masri, Brooke Ballenger, Max Proske, Hannah Rosenberg, Ellia Nakahara, Sophia Espinoza, Ivan Woolhouse, Simon Ruland, Gorkem Er, Timothy Sweeny, Melaku Saketa, Michela Schenk, Maren Lynch, Madi Hamm, Grace Schroeder, Michelle Rozenman, Rana Seif, Jackson Hall, Marisela Simental, Daniel Paredes, Aaron Mena, Preston Spaan, Evelyn Stovin, David Andrew Swartz, Anh Tran, Daniel Pittman, Luke Farchione, Emily Boyer, Ukari Verner, Lacey Conrad, Jonathan Velotta, James Weiner, Jagger Gossett, Noah Sherry, Sam Proud, Ben Block, Avi Narayana, Zoey Weiss, Alyssa Wilson, Gabrielle Walsh, David Zonana, Keely Wright, Kena Riveria, Lillybelle Deer, Jena Doom, Elysia Davis, Isabelle Yaremenko, Caitlyn Young

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Research Showcase.

Coordinating Instruments For Multi-Messenger Astrophysics, Daisy Wang, Ye Htet, Marion Sudvarg, Roger Chamberlain, Jeremy Buhler, James Buckley

Computer Science Faculty Research & Creative Works

In multi-messenger astrophysics, signals of multiple types (e.g., gravitational waves, neutrinos, electromagnetic waves) are combined in an effort to learn more about the observed phenomena of interest. The Advanced Particle-astrophyics Telescope (APT) is a mission concept for a space-borne instrument that detects gammaray bursts (GRBs) omnidirectionally, facilitating multi-messenger observations by identifying and localizing celestial events of interest. Here, we describe the on-instrument computations for APT and its Antarctic Demonstrator (ADAPT) as well as techniques for follow-up observations of transient events.

Emergent Collective Reproduction Via Evolving Neuronal Flocks, Nam H. Le, Michael Levin, Richard A. Watson, Josh Bongard, Christopher L. Buckley

Northeast Journal of Complex Systems (NEJCS)

This study advances the understanding of evolutionary transitions in individuality (ETIs) through a novel artificial life framework, VitaNova, which integrates self-organization and natural selection to simulate the emergence of complex, reproductive groups. By dynamically modeling individual agents within an environment shaped by predators and spatial constraints, VitaNova reveals mechanisms by which simple agents evolve into cohesive units exhibiting collective reproduction. The findings highlight the synergy between self-organized behaviors and adaptive evolutionary strategies as fundamental drivers of ETIs. This approach deepens our understanding of higher-order biological individuality and offers a new empirical pathway for investigating ETIs, extending current theoretical frameworks.

Positive And Negative Parity States Of 226-236th Isotopes, Ali A. Mezban, Falih H. Al-Khudair

Karbala International Journal of Modern Science

The level scheme of the ^226-236Th nuclei is analyzed using the Interacting Boson Model (IBM-1). The ground state band (g. s.-band), beta band (β-band), gamma band (γ-band) have been described in the sd model space. To calculate the negative parity energy levels, the model space is extended to sdf by adding the f-boson (L=3). A sequence of states in energy bands up to 3.5 MeV has been obtained. The decay of negative states below 1 MeV built on an octupole vibration modes is investigated. The transition probabilities in-band and inter-band are calculated using the wave function of the …

Adapting A Segmentation Foundation Model For Medical Image Classification, Pengfei Gu, Haoteng Tang, Islam A. Ebeid, Jose Angel Nunez, Fabian Vazquez Jr., Diego Adame

Computer Science Faculty Publications

Recent advancements in foundation models, such as the Segment Anything Model (SAM), have shown strong performance in various vision tasks, particularly image segmentation, due to their impressive zero-shot segmentation capabilities. However, effectively adapting such models for medical image classification is still a less explored topic. In this paper, we introduce a new framework to adapt SAM for medical image classification. First, we utilize the SAM image encoder as a feature extractor to capture segmentation-based features that convey important spatial and contextual details of the image, while freezing its weights to avoid unnecessary overhead during training. Next, we propose a novel …

8-Page Zine Extra Credit Assignment, Ricaute Rogers

Open Educational Resources

This is an 8-page minizine extra credit assignment that put into practice the concepts learned in CIS100.

An Action Research Study On Ai Video Vs. Written Feedback: Enhancing Undergraduate Academic Writing And Critical Thinking, Sandra Baroudi, Nida Mubeen, Suha Karaki

All Works

This action research explores how AI-assisted feedback formats influence students’ academic writing and critical thinking, a gap particularly relevant in the digital learning landscape. This study involved 40 undergraduate Emirati students divided into two groups, receiving either AI-generated video or written feedback across three assignments. Using a quantitative design, data were gathered using standardized critical thinking and academic writing skills rubrics. Additionally, students completed post-surveys to capture their perceptions, engagement levels and use of AI tools in the learning process. Results showed written feedback significantly improvemed critical thinking (M = 2.80, SD = 0.75) compared to video feedback (M = …

Implementing A Sustainability Approach By Converting Plastic Bottle Waste From A Mischievous Substance To A Beneficial Material By Means Of Zero Residue Level Concept, Younus Mohammed Abed, Fatima Allawi, Shahad Mudhafar Ali, Salem Jawad Alhamd, Thekra Atta Ibrahim, Mohammed Nsaif Abbas

Karbala International Journal of Modern Science

Plastic waste represents a serious environmental and health hazard due to its non-biodegradability and toxic leachates. This study investigates the potential conversion of plastic waste into a sustainable raw material for producing a useful substance. This investigation focuses on repurposing PET beverage bottles—a major contributor to microplastic pollution—into high-value activated carbon (AC) for water treatment, aligning with circular economy principles. The chemical preparation method for AC using phosphoric acid as an activating agent was evaluated. Results demonstrated the successful conversion of 68% of plastic waste into AC under the following conditions: pyrolysis temperature of 700°C, pyrolysis time of 7 hours, …

Native Plants For Green Roofs In The Midwest, Katerina Mcsweyn

Rose-Hulman Undergraduate Research Publications

No abstract provided.

Pure And Strong Nash Equilibrium Computation In Compactly Representable Aggregate Games, Jared Soundy, Mohammad T. Irfan, Hau Chan

Research & Publications

Aggregate games model interdependent decision making when an agent’s utility depends on their own choice and the aggregation of everyone's choices. We define a compactly representable subclass of aggregate games we call additive aggregate games, which encompasses popular games like congestion games, anonymous games, Schelling games, etc. We study computational questions on pure Nash equilibrium (PNE) and pure strong Nash equilibrium (SNE). We show that PNE existence is NP-complete for very simple cases of additive aggregate games. We devise an efficient algorithmic scheme for deciding the existence of a PNE and computing one (if it exists) for bounded aggregate space. …

Machine Learning For Digital Biomarker-Based Detection Of Cognitive Decline, Seng Khoon The

Dissertations and Theses Collection (Open Access)

Dementia is a neurodegenerative disease with a prevalence rate expected to triple by 2050, posing a significant challenge for health services. To impede the increasing prevalence, medical professionals and scientists are actively investigating technology to detect cognitive decline at a reversible stage known as Mild Cognitive Impairment (MCI). Digital biomarker technology is an emerging pragmatic approach to permit objective, ecologically valid, and long-term continuous measurement of cognitive health status, rendering it as one of the promising technologies for early MCI detection. Despite its potential, it is nontrivial to encode, extract and combine predictive information from these digital biomarker technologies; advanced …