Digital Commons Network^™

Artificial Intelligence And Digital Technologies In Finance: A Comprehensive Review, Soudeh Pazouki, Mohamad Jamshidi, Mirarmia Jalali, Arya Tafreshi

Finance Faculty Publications

This study explores the transformative impact of artificial intelligence (AI) and digital technologies on the financial technology (FinTech) industry, highlighting their role in fostering business growth, operational efficiency, and enhanced customer engagement. AI-driven strategies have unlocked new avenues for streamlining workflows, boosting productivity, and expanding financial inclusion by reaching underrepresented populations. However, these advancements also pose challenges, including navigating complex regulatory frameworks and adapting to the rapidly evolving technological landscape. This paper delves into the macroeconomic effects of AI, examining its influence on labor markets, consumer behavior, and organizational success. Furthermore, the paper discusses blockchain applications and their potential to …

Augmenting, Not Replacing: The Role Of Llms In Human-Centric Formal Re: Supplemental Material, Sonora Halili, Paola Spoletini, Alicia M. Grubb

Computer Science: Faculty Publications

This repository contains the supplemental information for the RE'25 paper entitled "Augmenting, Not Replacing: The Role of LLMs in Human-Centric Formal RE" and the Smith College Departmental Honors Thesis entitled "The LTL Whisperer: Prompting AI to Explain Temporal Logic: Supplemental Information". This work investigates how and to what extent generative AI with large language models (LLMs) can assist practitioners and novices in interpreting formal requirements expressed in Linear Temporal Logic (LTL).

A View Of The Restaurant Script Through The Lens Of Hierarchical Planning, Jamie C. Macbeth, Mark Roberts, Boming Zhang, Sharmin Badhan, Molly Neu, Tanush Garg, Yining Hua, Manushaqe Muco, Mackie Zhou

Computer Science: Faculty Publications

The flexible nature of human cognition and of the structures it uses is well known, as is the difficulty of building cognitive systems that exhibit transfer and use the same structures for radically different tasks. In this paper, we perform a close examination of Schank-Abelsonian scripts, picking apart the goal- and plan- oriented nature of low-level acts and high-level reasoning inherent in them. We then view scripts through the lens of hierarchical planning systems and construct the well-known restaurant script as a hierarchical goal network planning domain. These are evidence in support of a claim that some, if not all, …

Evaluating Annual Precipitation Extremes In The Us Great Plains Using Prism Data, Sumiaya Amin Preota, Gaurav Talukdar, Sam Zipper

School of Earth, Environmental, & Marine Sciences Faculty Publications

Understanding hydroclimatic extremes in the U.S. Great Plains (USGP) is crucial for effective water resource management, resiliency of agricultural systems, and mitigation of climate change impacts. This study examines changing hydroclimatic conditions in the USGP, with a focus on annual-resolution precipitation trends and extremes over the past 119 years (1904–2022) using precipitation data from the gridded PRISM climate dataset. We categorized annual-scale precipitation totals into six categories of hydroclimatic extremes: (1) isolated wet extremes, (2) isolated dry extremes, (3) dry-to-dry recurring extremes, (4) wet-to-wet recurring extremes, (5) dry-to-wet whiplash extremes, and (6) wet-to-dry whiplash extremes. “Recurring” and “whiplash” are both …

Implementing A Chatbot To Promote Hereditary Breast & Ovarian Cancer Genetic Screening In Women's Health: Identifying Barriers And Facilitators To Screening Adoption, Easton N. Wollney, Shireen Madani Sims, Luisel J. Ricks-Santi, Elizabeth Eddy, Daniel Wiesman, Carla L. Fisher

Department of Biomedical and Translational Sciences Faculty Publications

Background

To promote genetic screening among women at risk for hereditary breast and ovarian cancer (HBOC), the American College of Obstetricians and Gynecologists recommends that risk assessment be integrated into practice. Chatbots like the Genetic Information Assistant (Gia®) are increasingly implemented to expand access to hereditary genetic screening. Factors that impact chatbot implementation for HBOC risk screening and women's uptake are not fully realized. To refine implementation strategies prior to full scale implementation, we sought to identify women's perceived facilitators/barriers to adopting Gia screening in a rural population within a large healthcare system in the southern United States.

Methods

We …

Harnessing The Power Of Gradient-Based Simulations For Multi-Objective Optimization In Particle Accelerators, Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch

Data Science Faculty Publications

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Data Science Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

An Overview Of The Special Issue, Rachel Gibson, Trent Buskirk

Data Science Faculty Publications

[Introduction] Across the quantitative social sciences, researchers increasingly face significant challenges and opportunities prompted by the arrival of new sources of very rich, highly granular, and often unstructured digital data. While traditional methods such as surveys and content analysis tools remain indispensable for measuring individual attitudes, behaviors, demographic characteristics, and media messaging online, they often struggle to capture the complex multimodal information streams and metadata generated by social media platforms, mobile devices, sensors, and tracking applications. Collecting and analyzing these diverse new forms of content, dynamic moment-to-moment behaviors, and naturally occurring interactions has become a pressing and exciting research task-one …

The Stochastic Occupation Kernel (Sock) Method For Learning Stochastic Differential Equations, M. L. Wells, Kamel Lahouel, Bruno Jedynak

Mathematics and Statistics Faculty Publications and Presentations

We present a novel kernel-based method for learning multivariate stochastic differential equations (SDEs). The method follows a two-step procedure: we first estimate the drift term function, then the (matrix-valued) diffusion function given the drift. Occupation kernels are integral functionals on a reproducing kernel Hilbert space (RKHS) that aggregate information over a trajectory. Our approach leverages vector-valued occupation kernels for estimating the drift component of the stochastic process. For diffusion estimation, we extend this framework by introducing operator-valued occupation kernels, enabling the estimation of an auxiliary matrix-valued function as a positive semi-definite operator, from which we readily derive the diffusion estimate. …

Optical Coherence Tomography Harmonization With Anatomy-Guided Latent Metric Schrödinger Bridges, Shuwen Wei, Blake E. Dewey, Samuel W. Remedios, Bruno Jedynak, Shimeng Wang, Peter Calabresi, Shiv Saidha, Aaron Carass, Multiple Additional Authors

Mathematics and Statistics Faculty Publications and Presentations

Medical image harmonization aims to reduce the differences in appearance caused by scanner hardware variations to allow for consistent and reliable comparisons across devices. Harmonization based on paired images from different devices has limited applicability in real-world clinical settings. On the other hand, unpaired harmonization typically does not guarantee anatomy consistency, which is problematic because anatomical information preservation is paramount. The Schrödinger bridge framework has achieved state-of-the-art style transfer performance with natural images by matching distributions of unpaired images, but this approach can also introduce anatomy changes when applied to medical images. We show that such changes occur because the …

Unsupervised Oct Image Interpolation Using Deformable Registration And Generative Models, Shuwen Wei, Samuel W. Remedios, Bruno Jedynak, Peter Calabresi, Shiv Saidha, Aaron Carass, Multiple Additional Authors

Mathematics and Statistics Faculty Publications and Presentations

Optical coherence tomography (OCT) images are often acquired as highly anisotropic volumes, where the scanning step is dense along the fast axis but sparse along the slow axis. This affects image analysis, such as image registration for longitudinal alignment. To create more isotropic volumes, bicubic interpolation can be used along the slow axis, but it generally produces blurry features. Registration-based interpolation can reduce blurriness, but often fails to generate realistic OCT images. Deep generative models can sample realistic images, but lack the structural consistency constraints required for interpolation. In this paper, we propose an unsupervised image interpolation method that combines …

The Hidden Cost Of Venture Capital, Emilie Aguirre

Faculty Scholarship

Founders, employees, consumers, and even funders increasingly expect businesses to pursue social goals alongside financial performance. Yet even the most committed firms have found it difficult to maintain social performance over time. Scholars in economics, management, and law have put forth several explanations for this “mission drift,” including inappropriate governance, poor management, lack of genuine commitment, and threat of takeovers. Puzzlingly, research to date primarily focuses on later-stage firms, even though the events and decisions that take place in a firm’s early stages can critically impact retention of its social performance.

Drawing from over five years of qualitative field research …

Llm-Driven Apt Detection For 6g Wireless Networks: A Systematic Review And Taxonomy, Muhammed Golec, Yaser Khamayseh, Suhib Bani Melhem, Abdulmalik Alwarafy

All Works

Sixth Generation (6G) wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and AI-assisted orchestration capabilities they promise, they are vulnerable to stealthy and long-term Advanced Persistent Threats (APTs). Large Language Models (LLMs) stand out as an ideal candidate to fill this gap with their high success in semantic reasoning and threat intelligence. This paper presents the first systematic review and taxonomy for LLM-assisted APT detection in 6G networks. It …

Streamlining Digital Elevation Model Construction From Historical Aerial Photographs: The Impact Of Reference Elevation Data On Spatial Accuracy, Xin Hong, Christopher H. Roosevelt

All Works

This study proposes a streamlined workflow for producing historical digital elevation models (hDEMs) from scanned 1950s aerial photographs using structure-from-motion and multi-view-stereo (SfM-MVS) techniques along with co-registration methods. We also conducted a sensitivity analysis to assess the impact of DEM references with varying spatial resolutions on the SfM-MVS process and co-registration accuracy. The DEM references included a 30 m SRTM DEM (low resolution), a 12 m TanDEM-X DEM (medium resolution), and a 5 m DEM provided by the General Directorate of Mapping of the Ministry of National Defense, Republic of T & uuml;rkiye (high resolution). Results indicate that higher resolution …

Ai Innovations In Rppg Systems For Driver Monitoring: Comprehensive Systematic Review And Future Prospects, Soha G. Ahmed, Katrien Verbert, Nazar Zaki, Ashraf Khalil, Hamad Aljassmi, Fady Alnajjar

All Works

Advanced technologies, notably camera-based systems using remote photoplethysmography (rPPG), are increasingly used in automotive safety to non-invasively monitor driver well-being and fatigue by measuring physiological metrics like heart and respiration rates. This review examines recent advancements in machine learning algorithms and signal processing for rPPG in driver monitoring. A literature search up to April 2, 2024, across major databases, identified 344 studies; 29 were analyzed in depth, focusing on: 1) rPPG signal extraction and heart rate estimation, where deep learning improved accuracy; 2) fatigue detection, showing benefits of multimodal data fusion; 3) mental state monitoring, with machine learning classifying cognitive …

Explicit Inverse Of Symmetric, Tridiagonal Near Toeplitz Matrices With Strictly Diagonally Dominant Toeplitz Part, Bakytzhan Kurmanbek, Yogi Erlangga, Yerlan Amanbek

All Works

Let T n = tridiag ( - 1 , b , - 1 ) , an n x n symmetric, strictly diagonally dominant tridiagonal matrix ( divided by b divided by > 2 ). This article investigates tridiagonal near-Toeplitz matrices T n & colone; [ t i , j ] , obtained by perturbing the ( 1 , 1 ) and ( n , n ) entry of T n . Let t 1 , 1 = t n , n = b not equal b . We derive exact inverses of T n . Furthermore, we demonstrate that these results …

Designing Ai To Foster Acceptance: Do Freedom To Choose And Social Proof Impact Ai Attitudes Among British And Arab Populations?, Sameha Alshakhsi, Mohamed Basel Almourad, Areej Babkir, Dena Al-Thani, Ala Yankouskaya, Christian Montag, Raian Ali

All Works

This study examines the impact of two key AI modalities–freedom of choice (FoC) and social proof (SP)–on public attitudes toward AI, focusing on cultural differences between UK and Arab participants. FoC refers to the option of selecting a non-AI, possibly human, alternative, while SP means knowing that others have used AI without issues. Four scenarios were designed, combining the presence or absence of these modalities. The context was a customer service chatbot for a telecommunications company, familiar to all participants. A total of 639 participants (316 British and 323 Arab) were introduced to the modalities and then the scenarios in …

Certain Knowledge Of Administrative Decisions Issued By Artificial Intelligence Systems In The Public Sector: A Comparative Analysis, Nayel Musa Alomran, Odai Mohammad Alheilat

All Works

This paper aims to elucidate the matter of certain knowledge pertaining to administrative decisions, those issued by artificial intelligence. It also clarifies the position of the administrative judiciary with regard to the adoption of this presumption and through a comparative analysis of administrative judicial applications in Jor-dan, Egypt, and Morocco. The text addresses the most significant evidence for achieving certain knowledge of an administrative decision – and thereby initiating the appeal period against the appellant. The question at hand is whether the administrative judiciary applies the traditional theory of certain knowledge to its counterpart issued by artificial intelligence systems, especially …

Performance Based Scheduling In Distributed Mixed Criticality Systems, Amjad Ali, Saud Wasly, Asad Masood Khattak, Ihsan Ali, Shahid Iqbal, Bashir Hayat

All Works

With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores …

Enhancing Teachers’ Design Thinking Mindsets Through Reflective Practice: Cultivating Innovation In An Upskilling Steam Training Program, Areej Elsayary

All Works

Promoting innovation in education through enhanced design thinking (DT) mindsets among teachers is a central goal. This study examines the use of reflective practice within an upskilling STEAM training program to achieve this enhancement. The upskilling training initiative examined in this research encompasses a ten-week instructional session characterized by a hybrid learning methodology under the guidance of an instructor. An explanatory mixed method design is employed, starting with a quantitative method (teachers’ survey) followed by a qualitative method (focus group discussion). The participants were in-service K-12 teachers (n = 55) from charity schools across Northern Emirates in the United Arab …

Structural Optimization And Stability Enhancement Of Uba(2) And Ntl9 Using The Computational Tool: Emcast, Bahar Ghazi Esfahani

Graduate Student Theses, Dissertations, & Professional Papers

This study enhances protein stability prediction by refining EmCAST (Empirical C-Alpha Stability Tool), a computational tool used to identify stabilizing mutations in protein domains. We applied EmCAST to two model proteins: the α-helical ubiquitin-associated domain 2 (UBA(2)) and the α/β N-terminal domain of ribosomal protein L9 (NTL9). Protein stability is critical for proper function, and mutations can lead to misfolding and disease. To validate EmCAST’s predictions, guanidine hydrochloride-induced denaturation was monitored using circular dichroism (CD) spectroscopy for both wild-type and mutant variants. Results guided refinements to EmCAST’s algorithm, incorporating new experimental datasets to improve accuracy. Additionally, we examined the assumption …

Kinetic Grain Growth In Firn Induced By Meltwater Infiltration On The Greenland Ice Sheet, Kirsten Leigh Gehl

Graduate Student Theses, Dissertations, & Professional Papers

The microstructure of polar firn governs its porosity, permeability, and compaction rate, and is therefore important to studies of surface elevation change, heat and gas exchange, and meltwater infiltration on ice sheets. Previous work on high-elevation, dry-firn has identified two distinct atmospheric drivers of kinetic grain growth in deposited snow, but both mechanisms result in near-surface mm-scale layers. In this work, we show that meltwater infiltration through surface wetting fronts and deep preferential flow in the Greenland Ice Sheet (GrIS) percolation zone leads to the formation and preservation of centi- to decimeter scale layers of faceted firn, ranging from faceted …

The Relationship Between Principals’ Transformational Leadership And Teachers’ Organizational Citizenship Behavior Within Public Primary And Middle Schools In Baotou, Inner Mongolia, China, Chuxuan Yin

Graduate Student Theses, Dissertations, & Professional Papers

This non-experimental causal-comparative study was designed to explore the relationship between principals’ transformational leadership and teachers’ organizational citizenship behavior within public primary and middle schools in Baotou, Inner Mongolia, China, as well as the differences in principals’ transformational leadership and teachers’ organizational citizenship behavior based on demographic characteristics. A sample of 142 principals and 675 teachers was recruited. Data collection utilized two validated instruments: the Transformational Leadership Questionnaire for evaluating principals’ transformational leadership level, and the Organizational Citizenship Behavior Scale for Primary and Middle School Teachers for measuring teachers’ organizational citizenship behavior level. Both descriptive statistics (frequency distribution, mean, standard …

Modeling Neighborhoods As Fuel For Wildfire, Bryce Alan Young

Graduate Student Theses, Dissertations, & Professional Papers

Wildfire models drive billions of dollars in risk mitigation efforts. However, the modeling community currently lacks a representative fuelscape on which to base simulations of fire spread in the built environment and the wildland-urban interface (WUI) where vegetation and structures act together as fuel for wildfire. This thesis advances wildfire risk modeling by addressing the underdeveloped representation of the built environment in existing frameworks. By identifying inconsistencies in how structure and defensible space features are defined and used across empirical studies, predictive indices, and fire spread models, this research lays the groundwork for standardized modeling approaches and feature selection (Chapter …

Worn History: The Biography And Value Transformation Of Emma Sansaver's Dress, Lauren I. Stgeorge

Graduate Student Theses, Dissertations, & Professional Papers

This thesis examines the life of Emma Sansaver’s beaded buckskin dress as both a material object and a cultural witness. Worn during her time at the Fort Shaw Government Industrial Indian Boarding School and the 1904 St. Louis World’s Fair, the dress intersects with assimilationist policy, Indigenous resilience, public spectacle, and personal memory. Unlike the regalia of her teammates, Emma’s dress was preserved, first by Emma herself, then by her descendants, and continues to be cared for and displayed in educational and institutional settings today.

Using the theoretical frameworks of Arjun Appadurai and Igor Kopytoff, this project approaches the dress …

Fluvial Geomorphic Resilience In Alluvial Mountain Rivers To Large Floods, Riley T. Henson

Graduate Student Theses, Dissertations, & Professional Papers

As hydroclimatic extremes intensify, large floods increasingly reshape river systems and threaten infrastructure. Fluvial geomorphic resilience, defined as a river’s resistance to, and recovery from, flood disturbance, offers a useful framework for understanding river response and guiding management. We assessed resilience at two scales: (1) a detailed geomorphic and hydraulic analysis of the June 2022 flood on East Rosebud Creek, Montana, which was part of broader flooding in the Greater Yellowstone Ecosystem, and (2) a broad-scale comparison of 12 flood-impacted alluvial mountain rivers across North America and Europe. We quantified resistance using active channel width ratios (W_R) and …

Data-Driven Melt Pool Monitoring And Defect Prediction In Lpbf Of Ti-6al-4v Alloy, Marshima Mohd Rosli, Ali Rachini, Musaria Karim Mahmood

Babylonian Journal of Mechanical Engineering

Real-time process monitoring is essential for achieving consistent part quality in Laser Powder Bed Fusion (LPBF), yet robust and practical defect prediction frameworks remain underdeveloped. This study presents a machine learning-based approach for predicting unstable melt pool conditions, leveraging an open dataset of melt pool variability measurements from Ti-6Al-4V builds on an EOS M290 system. The framework utilizes fundamental process parameters—laser velocity, power, scan orientation, and track location—combined with statistical melt pool geometry features to train a Random Forest classifier. The model achieved an accuracy of 99.79% in distinguishing between stable and unstable melt pool states, with balanced sensitivity and …

A Numerical Study Of Time-Dependent Casson Nanofluid Flow With Convection And Joule Heating Over A Stretching Surface, M Nazim Tufail, Nadia Ayub, Y Khan, S Ilyas

Babylonian Journal of Mechanical Engineering

This study focuses on the behavior of Casson nanofluid under mixed convection, Joule heating, and mass transfer effects over a stretching surface. The governing equations for the conservation of momentum, mass, energy, and species transfer are simplified using a stream function. These equations are then transformed from non-linear partial differential equations (PDEs) into non-linear ordinary differential equations (ODEs) through similarity transformations. The resulting ordinary differential equations are solved numerically using the bvp4c method. Subsequently, the influence of various physical parameters on the velocity, temperature, and concentration profiles is analyzed. It is observed that the Casson fluid parameter enhances the velocity …

Realization Of Water Tidal Datum Levels From Observed Sea Level Data In Damietta Harbor, Egypt, Mohamed M. Helmy, Amr N. Shokry, Mohamed M. Youssef, Ahmed M. Khedr

Mansoura Engineering Journal

Sea level is an essential oceanographic parameter in hydrographic surveying that is always necessary for the realization of Chart Datum (CD). The International Hydrographic Organization (IHO) defines sea levels as those expected to occur under typical meteorological conditions, regardless of any possible combination of astronomical conditions. The current research focuses on analysing the observed sea level in Damietta Harbor, using two sets of sea-level records obtained from two sensors: a pressure sensor and a radar sensor. These records were collected from a tide gauge station located at Damietta Harbor from July 1, 2019, to March 2, 2020. Furthermore, harmonic analysis …

Mathematical Modeling For Pile Yarn Length And Crimp Ratio In Woven Terry Fabric, Elham E. Elemam, Hamdy A.A. Ebraheem, Ahmed S. Eldeeb, Rehab Abd Elkhalek

Mansoura Engineering Journal

In this research, a mathematical model for predicting the consumed length and crimp ratio of pile warp yarns in woven terry towel fabrics was developed. The mathematical formulas interrelate the construction parameters of the woven terry fabric such as weave structure, weft count, weft density, counts of pile and ground warp yarns and loop length with the consumed length and crimp ratio of pile warp yarns. Experimental verification was conducted on nine terry cotton fabric samples produced with three different levels of weft density, weft count and loop length. Results showed that theoretical values highly corresponded to the measured values …