Digital Commons Network^™

A Spoofing Speech Detection Method Combining Multi-Scale Features And Cross-Layer Identification, Hongyan Yuan, Linjuan Zhang, Baoning Niu, Xianrong Zheng

Information Technology & Decision Sciences Faculty Publications

Pre-trained self-supervised speech models can extract general acoustic features, providing feature inputs for various speech downstream tasks. Spoofing speech detection, which is a pressing issue in the age of generative AI, requires both global information and local features of speech. The multi-layer transformer structure in pre-trained speech models can effectively capture temporal information and global context in speech, but there is still room for improvement in handling local features. To address this issue, a speech spoofing detection method that integrates multi-scale features and cross-layer information is proposed. The method introduces a multi-scale feature adapter (MSFA), which enhances the model’s ability …

Synthetic Data Generation Of Health And Demographic Surveillance Systems Data: A Case Study In A Low- And Middle-Income Country, Dorcas G. Mwigereri, Nigel T. Kamotho, Akbar K. Waljee, Ryan T. Rego, Eileen M. Weinheimer-Haus, Farhana Alarakhiya, Anthony K. Ngugi, W. Nicholson Price, Ji Zhu, Stephen Peter Wong, Geoffrey H. Siwo

Articles

Objective: To evaluate effectiveness of open-source generative models in producing high-quality tabular synthetic data using a Health and Demographic Surveillance System (HDSS) dataset from rural Kenya, as a proof of concept in a low- and middle-income (LMIC) setting.

Materials and Methods: Three open-source models (CTGAN, TableGAN, and CopulaGAN) were used to generate synthetic data from the Kaloleni/ Rabai HDSS dataset. To assess the quality of the synthetic datasets generated by each model, we performed fidelity, utility, and privacy tests.

Results: CTGAN outperformed the other models, producing synthetic data that closely mirrored the statistical properties of the real dataset while preserving …

The Future Of Ai Regulation In Drug Development: A Comparative Analysis, Gabriela Lenarczyk, Timo Minssen, W. Nicholson Price Ii, Arti Rai

Articles

As artificial intelligence (AI) transforms drug development, regulatory frameworks are evolving to oversee its implementation, particularly at the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This paper makes three contributions to understanding emerging regulatory approaches. First, we offer a comparative analysis of how these agencies have responded to AI-driven advances, incorporating new US executive orders and the European Union (EU)’s AI Act. Second, we propose a novel analytical framework to understand regulatory divergence: the FDA’s flexible, dialog-driven model contrasts with the EMA’s structured, risk-tiered approach, reflecting broader institutional and political-economic differences. While the former encourages …

Development Of An Ecg-Based Deep Learning Model For Pediatric Congenital Heart Disease (Chd) Diagnosis, Annbar Mekouar

Selected Full-Text Master Theses 2021-

Congenital heart disease (CHD) stands as the leading congenital anomaly which affects pediatric populations throughout the world. The effectiveness of treatment depends on both early diagnosis and accurate identification but echocardiography requires manual interpretation which proves time-consuming and inconsistent especially when examining pediatric patients with their distinct cardiac systems. The research aims to create a deep learning-based diagnostic framework which uses ECG data to identify coronary artery disease subtypes in pediatric patients. The model uses high-quality datasets from Dr. Ignacio Lugones to extract R-R intervals and QRS durations through convolutional neural networks (CNNs). The system addresses pediatric-specific challenges while enhancing …

A Framework For Developing Collaborative Community Building Tools For Novice Computer Science Students, Daniel Olivares, Jakob Kubicki, Katie Imhof

Computer Science Faculty Scholarship

Students enrolled in introductory computer science courses tend towards individual work because of pedagogical practices discouraging collaboration and a focus on individual assignments. This can discourage new computer science students and may negatively affect persistence in computer science. In contrast, social learning theory research suggests a connection between student success and their level of involvement with peers, instructors, and in the greater learning community. Motivated by these contrasting conclusions, the research presented in this paper puts forth a framework based on social learning theories and teaching and learning methodologies to leverage social computing as a learning tool. This framework’s primary …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

5g-Practical Byzantine Fault Tolerance: An Improved Pbft Consensus Algorithm For The 5g Network, Xin Liu, Xing Fan, Baoning Niu, Xianrong Zheng

Information Technology & Decision Sciences Faculty Publications

The consensus algorithm is the core technology of blockchain systems to maintain data consistency, and its performance directly affects the efficiency and security of the whole system. Practical Byzantine Fault Tolerance (PBFT) plays a crucial role in blockchain consensus algorithms by providing a robust mechanism to achieve fault-tolerant and deterministic consensus in distributed networks. With the development of 5G network technology, its features of high bandwidth, low latency, and high reliability provide a new approach for consensus algorithm optimization. To take advantage of the features of the 5G network, this paper proposes 5G-PBFT, which is an improved practical Byzantine fault-tolerant …

A Systematic Review And Taxonomy For Privacy Breach Classification: Trends, Gaps, And Future Directions, Clint Fuchs, John Hastings

Research & Publications

In response to the rising frequency and complexity of data breaches and evolving global privacy regulations, this study presents a comprehensive examination of academic literature on the classification of privacy breaches and violations between 2010-2024. Through a systematic literature review, a corpus of screened studies was assembled and analyzed to identify primary research themes, emerging trends, and gaps in the field. A novel taxonomy is introduced to guide efforts by categorizing research efforts into seven domains: breach classification, report classification, breach detection, threat detection, breach prediction, risk analysis, and threat classification. An analysis reveals that breach classification and detection dominate …

Blockchain-Based Trust Model For Inter-Domain Routing, Qiong Yang, Li Ma, Sami Ullah, Shanshan Tu, Hisham Alasmary, Muhammad Waqas

Research outputs 2022 to 2026

Border Gateway Protocol (BGP), as the standard inter-domain routing protocol, is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems (AS). BGP nodes, communicating in a distributed dynamic environment, face several security challenges, with trust being one of the most important issues in inter-domain routing. Existing research, which performs trust evaluation when exchanging routing information to suppress malicious routing behavior, cannot meet the scalability requirements of BGP nodes. In this paper, we propose a blockchain-based trust model for inter-domain routing. Our model achieves scalability by allowing the master node of an AS alliance to transmit …

Novel Digital Twin Deployment Approaches: Local And Distributed Digital Twin, Shahid Rauf, Fazal Muhammad, Akhtar Badshah, Hisham Alasmary, Muhammad Waqas, Sheng Chen

Research outputs 2022 to 2026

The Digital Twin (DT) technology is considered as a backbone in the Industrial 4.0 revolution as it is playing a vital role in the digitization of various industries. A DT is a virtual representation of a physical entity, thus having the ability to simulate real data generated at physical space to optimize, estimate, control, monitor and forecast states/configurations. Despite enormous benefits, DT technology has several implementation challenges. Although deploying DT on edge or cloud platforms yields a plethora of services, its implementation in both spaces faces certain limitations. These limitations include latency, data communication overload, transmission energy consumption, privacy concerns, …

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel Stackelberg-game theoretic multilayer-online learning framework for cooperative control of nonlinear Physical Human-Robot Interaction (pHRI), where the human is modeled as the leader guiding a robot follower. This hierarchical interaction is captured as a dynamic Stackelberg game, with the human's intention estimated in real-time through online multilayer neural networks (MNNs). We introduce SVD-based weight update laws for actor-critic MNNs, which approximate value functions and control inputs for both human and robot, eliminating the need for predefined basis functions. In this framework, the human objective is first inferred and used to guide the robot actions by shaping …

Ethics Vs.. Regulation: Converging Frameworks For Trustworthy Human-Centered Ai In Biomedical Research, Tayo Obafemi-Ajayi, Tiffani J. Bright, Emily F. Wong, Donald Wunsch, Joan Peckham, Jason H. Moore

Electrical and Computer Engineering Faculty Research & Creative Works

The accelerating impact of AI in biomedical research is driving significant advances in precision medicine. As these systems increasingly shape health outcomes, the imperative to develop trustworthy, reliable, and ethically grounded AI becomes more pressing, particularly in addressing concerns related to data integrity, patient safety, and equitable outcomes. While the potential of AI to transform biomedical research is clear, its responsible integration depends on more than technological capability. Ensuring that these systems are aligned with societal values requires a dual commitment: the operationalization of ethical principles throughout the AI life cycle and the establishment of robust regulatory mechanisms. Ethics provides …

Genai’S Impact On Global It Management: A Multi-Expert Perspective And Research Agenda, Yogesh K. Dwivedi, Laurie Hughes, Mohammad S. Al-Ahmadi, Vincent Dutot, Syed Q. Ahmed, Shahriar Akter, Rahul De’, Keyao Li, Nitish Singh, Paul Walton

Research outputs 2022 to 2026

Generative AI (GenAI) is disrupting global IT management and challenging established practice. The increasing use of GenAI technology is redefining localization, transforming existing workforce roles, outsourcing strategy, and team dynamics. Simultaneously, GenAI’s security complexities have prompted the rethinking of existing risk frameworks to meet a new set of challenges from GenAI enhanced cyber threats. This article explores these complex and converging factors, providing a roadmap to address GenAI’s significant impact on global IT management. We advocate the responsible adoption of GenAI and importance of building resilient, value-driven, globally consistent IT ecosystems able to adapt to the significant challenges and opportunities …

Joint 3d Beamforming-And-Trajectory Design For Uav-Satellite Uplink Covert Communication, Jihong Yu, Yuting Cai, Shihao Yan, Yun Li, Jingjing Wang, Jiahao Liu, Jianping An

Research outputs 2022 to 2026

In this paper,we study uplink covert communication in a space-air system,where an unmanned aerial vehicle (UAV) transmits sensitive data to a Geosynchronous Earth Orbit (GEO) satellite while preventing the transmission action from being discovered by a warden. We derive the optimal decision threshold of the warden. We investigate the 3-dimensional (3D) beamformer and 3D trajectory design for the transmitter UAV against this optimum warden to maximize the covert transmission rate in the presence of imperfect channel state information and uncertain noise. Due to the non-convex structure and dependence between beamforming vectors and locations of the transmitter UAV,we develop a decoupling …

A Proof Of Np-Completeness For The K-Means Clustering Algorithm, Brooke C. Feinberg

Scripps Senior Theses

The k-means clustering algorithm is one of the most widely used clustering techniques in data analysis and machine learning, yet its exact computational complexity remains subject to ongoing theoretical investiga- tion. This work establishes the NP-completeness of k-means by proving (1) it is NP-hard and (2) it lies in NP. To demonstrate NP-hardness, we construct a series of polynomial-time reductions from well-known NP-complete problems. Specifically, we reduce 3sat to Vertex Cover, and then reduce Vertex Cover to k-means, thereby establishing the computational hardness of the k-means clustering problem. We then prove k-means is in NP, and thus conclude it is …

Utilizing Artificial Intelligence As A Strategic Risk Management Tool For Public Sector Operations And Auditing Processes, Oğuz Ümit Tamer, Bruce D. Mcdonald Iii, Farouk Hemici, Georgia Kontogeorga

School of Public Service Faculty Publications

Symbolizing a significant turning point in the historical landscape, AI is becoming an effective tool in today's public administration, not only for increasing capacity, quality, and speed in services, but also for strategic risk management. Regulators and algorithmic auditing play a central role in implementing fairness, transparency, and persistent controls against risks in AI systems. Discussing modern applications of AI, such as anomaly-based fraud detection, resource estimation, and continuous auditing, and their respective strengths and weaknesses, this study concludes that AI significantly enhances efficiency and oversight but also poses the risk of enshrining bias, opacity, and accountability gaps. By considering …

Improving Chatbot Interactions Through Ai-Driven Hate Speech Detection: Evolving To A Safer Digital Environment-Poster, Rata Gheorghita, Wellington Mariano

ICT

This project aims to explore how Machine Learning can contribute to a better digital interaction, mainly focusing on environments such as online chats, social media, and customer support as they are now an imperative part of daily communication. With this, concerns around hate speech in digital conversations is critical (Council of Europe, 2024). This study focus on the development of a Hate Speech Language Detection Chatbot using machine learning techniques. The key purpose of the chatbot is to monitor and detect harmful content in real time, reducing the need for manual intervention. The creation and implementation of such a tool …

On The Applicability Of Generating Pathological Speech Data Using Large Language Models, Caroline Hopkins

SURF Posters 2025

Pathological speech data is scarce in Speech-Language Pathology (SLP). Synthetic data, thus, is an appealing alternative. It extends the amount of usable data without the risk for privacy concerns that naturally occurring data may bring. In this work, a collection of Large-Language-Model-based methods for generating synthetic pathological speech data are studied. Human experts in SLP as judges delivered negative opinions on the quality of the synthetic data generated by a variety of prompt engineering methods. From the judgements, the resulting data was found to be weak in reflecting the characteristics of the target disorders. Further research will involve fine-tuning the …

Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced …

Watchdogs Or Lapdogs: How Audit Committees Influence Financial Reporting Quality And Cybersecurity, Yanru Yang

2025

With the increasing complexity of the business environment, the role of corporate governance and oversight is expanding continuously. Grounded in archival research, my dissertation consists of three studies that explore the features of audit committees in monitoring both financial reporting and broader areas, such as cybersecurity.

The first paper, co-authored with Gopal Krishnan and Wei Yu, examines the implications of audit committee (AC) director departure for financial reporting quality and audit risk. We find that the departures in the firms with most concerning AC departures are associated with a higher likelihood of a future “Big R” restatement announcement and auditor …

Digital Platform Transitions In The Finance Industry: Three Essays, Cheryll-Ann Wilson

2025

This three-paper dissertation is motivated by an emerging dichotomy in the financial sector: an increasing use of an open-source digital platform—the Python platform—in an industry that historically has been wedded to proprietary systems.

Chapter 1 is a qualitative pilot study to ascertain which factors are likely to motivate investment professionals to select Python versus other tools and/or technologies. I find that efficiency and access to industry-specific libraries—notably Pandas and NumPy—are significant motivators in their selection of Python over Excel. Chapters 2 and 3 examine the issues through a sequential, exploratory mixed methods approach.

Chapter 2—the qualitative field study—investigates how and …

Temporal Team Formation Games With Dynamic Preferences, Cameron Egbert

Theses and Dissertations--Computer Science

In the professional world, it is imperative for management entities to allocate their human resources to a work schedule, and such models of coalition formation games are well-studied. However, most existing literature only considers coalition formation in the context of a single moment in time, without accounting for changing preferences among individuals as they work together. The primary contribution of this thesis is a new team formation game that incorporates skill-based team formation and a dynamic variant of Additively Separable Hedonic Games. These Temporal Team Formation Games with Dynamic Preferences (TTFG-DPs) allow for two psychologically common preference dynamics: a preference …

Information-Theoretic Methods For Efficient Training And Robust Evaluations In Self-Supervised Learning, Oscar Skean

Theses and Dissertations--Computer Science

Self-supervised learning (SSL) has become a cornerstone of modern machine learning, offering a scalable alternative to costly human annotation by constructing pretext tasks directly from raw data. While SSL has delivered strong results across vision, language, and multimodal domains, two major limitations persist: (1) SSL methods are often significantly slower to train than supervised counterparts, and (2) evaluation protocols remain narrow, with most studies relying on linear probing accuracy on the pretraining dataset. . These challenges are particularly acute for large language models (LLMs), where training costs and interpretability of intermediate representations are critical concerns.

In this work, we propose …

Assessing Iot Intrusion Detection Computational Costs When Using A Convolutional Neural Network, Mathew Nicho, Brian Cusack, Christopher D. Mcdermott, Shini Girija

All Works

IoT systems face vulnerabilities due to their data processing requirements and resource constraints. With 13 billion connected devices globally, this research investigates the economic viability of AI-based intrusion detection systems (IDSs), specifically analyzing the automation costs of implementing a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) for classifying malicious sensor traffic. This study introduces an innovative framework that evaluates six distinct architectural components of CNN and LSTM: image input processing, convolutional layer operations, max pooling layer functionality, fully connected layer characteristics, softmax output activation, and class determination mechanisms. The framework employs six metrics: matrix size, feature vector number, …

Problem-Based Learning And Artificial Intelligence, Varna Taranikanti, Srineil Vuthaluru

Books and Book Chapters

In the current digital era, significant advancements in Artificial Intelligence (AI) have influenced various fields worldwide, particularly within the health professional education sector. This progress has also percolated into the medical education system, where Problem-Based Learning (PBL) is routinely used as a key pedagogical strategy. In PBL, students are able to fully exert their exploratory skills by solve problems through a student-centered learning approach. PBL also fosters critical thinking and encourages self-directed learning through the inquiry of clinical case scenarios. This chapter investigates the integration of AI, specifically tools like ChatGPT, into PBL frameworks to enhance learning outcomes for medical …

A Spine-Specific Lexicon For The Sentiment Analysis Of Interviews With Adult Spinal Deformity Patients Correlate With Sf-36, Sf-36, And Odi Scores: A Pilot Study Of 25 Patients, Ross Gore, Michael M. Safaee, Christopher J. Lynch, Christopher P. Ames

VMASC Publications

Classic health-related quality of life (HRQOL) metrics are cumbersome, time-intensive, and subject to biases based on the patient’s native language, educational level, and cultural values. Natural language processing (NLP) converts text into quantitative metrics. Sentiment analysis enables subject matter experts to construct domain-specific lexicons that assign a value of either negative (−1) or positive (1) to certain words. The growth of telehealth provides opportunities to apply sentiment analysis to transcripts of adult spinal deformity patients’ visits to derive a novel and less biased HRQOL metric. In this study, we demonstrate the feasibility of constructing a spine-specific lexicon for sentiment analysis …

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

The Bodhi App: A Post-Trauma Self-Regulation App For People With Intellectual And Developmental Disabilities, Liam Cannon

Open Access Master's Theses

People in the United States living with intellectual and developmental disabilities (I/DD) often experience trauma at a disproportionate rate. Due to societal and personal barriers, individuals with I/DD have problems with gaining access to therapy, thus often having to live with the negative effects of their untreated trauma. The effects of untreated trauma can cause severe impairment which affects a person’s quality of life and emotional, social, and physical development.

Post-trauma self-regulation is a way of doing activities to regulate one’s emotions to help cope with the negative effects of trauma. It has been shown as an effective method of …

A Happy Medium?: Using Image Generators To Explore Solution-Focused Art Therapy’S Miracle Question, Daniel A. Hernried

Art Therapy | Master's Theses

This mixed methods, randomized, single-session study tested whether integrating text-to-image generations into Solution-Focused Brief Art Therapy alters therapeutic rapport and short-term outcomes relative to traditional artmaking materials. Participants were assigned by coin flip to create using either a text-to-image generator or convention media (23 per group), completing immediate and three-day follow-ups. Alliance was measured using DREAM (Dimensions of Regard, Empathy, and Authenticity Metric), and problems were rated pre/post; groups did not differ significantly on DREAM total or facets, and both modalities produced reliable pre-to-post reductions in problem severity. At the same time, process differences were pronounced: the AI condition showed …

Scalable Parallel-In-Time Integration For Equations Of Motion, Nathan W. Chapman

All Master's Theses

Physical simulations always need to balance accuracy and run-time. This work implements the Parareal Algorithm using graphics processing units across a distributed system to accurately simulate time-dependent physics while attempting to minimize runtime. Data-transfer latency is identified as the primary bottleneck, for which mitigation methods are provided. Benchmarks comparing single-GPU and distributed implementations on a spectrum of coarse and fine discretizations are analyzed.