Digital Commons Network^™

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

Attf-Gnn: An Attention-Based Multi-Omics Graph Neural Network With Modality Learning For Disease Subtyping, Sovon Chakraborty, Eleni Adam, Terry Stilwell, Harold Riethman, Desh Ranjan, Pratip Rana

Computer Science Faculty Publications

We propose AttF-GNN, an attention-based graph fusion strategy for diseases classification and subtyping. In multiomics analysis, not all types of molecular data are equally relevant for disease subtyping and considering all modalities equally may obscure discriminative signals and limit the effectiveness of predictive models by overlooking modality-specific contributions. Therefore, we design an attention-based multimodal GraphSAGE framework that can automatically emphasize the modalities providing the most relevant information for classification. At first, we have constructed three graphs using mRNA, RNA-seq and DNA methylation modalities, and train each omics with individual GraphSAGE encoders. Next, a unified intersection graph is formed using an …

Attention-Based Multi-Omics Fusion For Drug Synergy Prediction, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug combination therapy in disease management gained popularity in the last few decades. Computational modeling of such combinations is an active area of research in the drug discovery domain. While earlier approaches solely emphasized on the structural features of participating drugs for designing synergistic models, they lack other crucial factors directly linked with drug administration - omics expressions. As differential omics expression is a downstream consequence of the administered drug combinations, utilizing such expressions while designing synergistic models promises robust and dynamic modeling. In this work, we propose SynergyLM that fuses multi-omics features with drug embeddings to build an omics-aware …

Legal Ethics Of Ai Snake Oil: Navigating The Hype, Harm, And Hope Of Legal Ai, Drew Simshaw

Scholarly Works

This paper argues that the legal ethics of AI extend far beyond competence and hallucinations. It shows how AI often functions as a mirror, exposing deeper ethical questions about institutional incentives, lawyer wellbeing, access to justice, and AI's broader social and environmental impacts.

An Empirical Framework For Evaluating Semantic Preservation Using Hugging Face, Nan Jia, Anita Raja, Raffi Khatchadourian

Publications and Research

As machine learning (ML) becomes an integral part of high-autonomy systems, it is critical to ensure the trustworthiness of learning-enabled software systems (LESS). Yet, the nondeterministic and run-time-defined semantics of ML complicate traditional software refactoring. We define semantic preservation in LESS as the property that optimizations of intelligent components do not alter the system's overall functional behavior. This paper introduces an empirical framework to evaluate semantic preservation in LESS by mining model evolution data from HuggingFace. We extract commit histories, $\textit{Model Cards}$, and performance metrics from a large number of models. To establish baselines, we conducted case studies in three …

Evaluating Llms For Cpe Identification In Iot Reconnaissance, Christopher Davisson

EWU Masters Thesis Collection

Vulnerability identification during penetration testing relies on rigid string-matching to map network scan data to Common Platform Enumeration (CPE) identifiers and downstream Common Vulnerabilities and Exposures (CVEs). The approach frequently fails on physical Internet of Things (IoT) devices, which produce non-standard, irregular service banners that resist deterministic parsing. Large Language Models can reason through these fuzzy associations, but cloud-hosted models introduce cost, latency, and operational security concerns when processing reconnaissance data from live networks. This thesis asks whether locally-hosted open-weight Large Language Models (LLMs) can perform this task well enough to be useful, and how performance varies with model scale, …

Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

Electrical & Computer Engineering Faculty Publications

This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacoustic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential …

Digital Twin Technologies For Battery Systems: Advancements, Applications, And Future Directions, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

The relationships among deep learning, edge computing, artificial intelligence (AI), and the most recent advancements in digital twin (DT) technology for battery energy storage systems are discussed in this paper. The study highlights the need for improved cloud-edge coordination, AI model development, and stronger cybersecurity features by demonstrating real-world applications of digital twin technology in electric vehicles (EVs), aircraft, and grid storage. It also described DT-based structures for fault detection, real-time monitoring, and optimization through standardization and battery management system (BMS) fusion. Because DT-based solutions for distributed energy resources (DERs) offer improved energy management systems, various studies have been conducted …

An Explainable Cs-Mitigation Triangular (Ecsmt) Framework To Secure Graph Neural Networks, Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary …

Affordable Course Content And Open Education Resources For Undergraduate Courses Teaching Fundamentals Of Wireless Communications And Networking, Dimitrie C. Popescu, Otilia Popescu

Electrical & Computer Engineering Faculty Publications

Wireless communication systems and networks along with the services they provide have become an essential component of the modern 21st century society, fueling job growth in the wireless industry and increasing the need for engineers specialized in wireless communication systems. As a consequence, over the past two decades, undergraduate courses teaching fundamentals of wireless communication systems and networks have become common in electrical and computer engineering and technology programs. At the same time, the number of textbooks dedicated to wireless systems and networks published by mainstream publishers has also grown, with availability in various formats and offerings and a significant …

Automated Writer And Acquisition-Condition Classification Of Digitally Captured Handwriting Using Statistical Dynamic Features And Support Vector Machines, Long-Huang Tsai, Hsiang-Ju Lai, Wen-Chao Yang, Jiajun Jiang, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Digitally captured handwriting preserves pen trajectories and dynamic signals, but it also records hardware- and input-dependent properties that can confound forensic interpretation. This study revises a support vector machine (SVM) screening framework using 16,500 samples from 30 writers, 11 writing-content categories, and five acquisition conditions spanning three tablets and stylus or finger input. Twenty-four raw and derived time-series variables were summarized by maximum, minimum, mean, median, and standard deviation, yielding 120 features; the mode statistic was removed. Writing direction and angular velocity were recalculated with atan2-based vector formulas. Unavailable device/API channels were encoded as zero, and Z-score parameters were estimated …

Stabilized Multi-Objective Optimization For Camouflage Attack In Remote Sensing, Omid Rajabi Rostami, Kazi Aminul Islam, Liuwan Zhu, Rui Ning, Chungsheng Xin, Hongyi Wu, Jiang Li

Electrical & Computer Engineering Faculty Publications

We investigate the vulnerability of multi-task maritime surveillance systems in which object detection and semantic segmentation operate concurrently. We propose Stabilized Multi-Objective Optimization (SMO), a framework that jointly targets Mask R-CNN and U-Net with a single universal adversarial texture in a digital white-box setting, with additional cross-model transfer evaluation. SMO combines a logarithmic-inversion loss transformation, feature inconsistency, and a systematic comparison of six optimization strategies for seven heterogeneous objectives. The loss transformation controls loss-dependent gradient scaling, while a U-Net generator supplies a spatially coherent texture and a hull-aligned application function accommodates target geometry without explicit Expectation over Transformation (EoT) sampling. …

Autonomous Weapons And Strategic Stability, Chick Edmond

Political Science & Geography Faculty Publications

The increasing number of artificial intelligence (AI) elements within military systems has introduced new forms of security dilemmas related to speed, level of secrecy, and transfer of responsibility from humans to machines. This article addresses the question of whether or how AI enabled autonomous weapons can lead to greater levels of strategic instability. Three causal mechanisms were determined by this study to potentially create destabilizing effects due to the introduction of autonomy; the first mechanism is a reduction in time available for decision making. The second mechanism involves the creation of multiple pathways of escalation. The third mechanism is the …

Fairrfl: Fair And Robust Federated Learning In The Presence Of Selfish Clients, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) is a paradigm that enables collaborative machine learning without disclosing the local data of the participants. However, in real-world FL deployment scenarios, some unscrupolous clients may alter the training process to skew the global model towards their local optimum, unfairly prioritizing their data distribution. Their influence can degrade overall model performance for normal clients and reduce fairness in the system. We call this novel category of misbehaving clients 'selfish'. This work proposes a Fair and Robust strategy for aggregation in the Federated Learning (FL) server to mitigate the effect of Selfish clients (FairRFL). FairRFL incorporates a novel …

Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang

Computer Science Faculty Research & Creative Works

Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our …

Multimodal Representation Learning For Face Understanding: From Caption Supervision To Foundation Model Adaptation, Md Mahedi Hasan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The rapid advancement of intelligent surveillance systems and the increasing demand for reliable biometric identification in border security, public safety, and digital forensics require robust face understanding under unconstrained conditions, including low resolution, pose variation, and occlusion. While Vision Transformer (ViT)-based foundation models have greatly improved visual representation learning, their patch-based tokenization and lack of spatial inductive bias limit their ability to capture fine-grained details in low-resolution inputs. This dissertation investigates multimodal representation learning for face understanding through natural language supervision, large-scale face-caption pre-training, and parameter-efficient foundation model adaptation. It hypothesizes that textual supervision provides complementary semantic cues that improve …

Multi-Timescale Monitoring And Modeling For Resilient Smart Grids: Pmu Anomaly Detection And Battery Digital Twins, Muhammad Imran Hossain

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern power systems face failures at very different timescales. Cyber-physical disturbances may emerge within seconds, while battery degradation develops over hundreds of operating cycles. Both monitoring problems share the same underlying difficulty: power systems produce measurement data in abundance, but reliably labeled examples of abnormal or degraded operation are scarce. Rare grid events are difficult to label, and battery degradation data are heterogeneous across cells, cycling protocols, and chemistries. This thesis addresses these challenges through two complementary domain-informed learning frameworks that constrain representation learning using information specific to each physical problem.

First, at the system level, T-BiGAN, a Transformer-augmented bidirectional …

Reclaiming Agency: Ai Hallucinations And Translingual Interrogations In The City Tech Writing Center,, Joseph Franklin, Anna Laura Falvey

Publications and Research

No abstract provided.

Dataless Neural Networks For Boolean Satisfiability And Network Optimization, Andrew Evan Gautier

Graduate Theses, Dissertations, and Problem Reports (ETD)

Combinatorial optimization problems (COPs) require searching over a finite solution space subject to constraints, with the goal of satisfying an objective function. They arise in operations research, scheduling, resource allocation, circuit design, and many other fields. Many problems in combinatorial optimization (including satisfiability and network design) are NP-hard. Traditionally, researchers have built approximate solvers that return near- optimal solutions efficiently by developing increasingly sophisticated heuristics and meta- heuristics. Deep learning has provided new opportunities for improving combinatorial solvers by leveraging neural guidance to prune the search space. Traditional neural networks have distinct drawbacks in this context: separate training and …

Federated Learning In The Era Of Foundation Models, Guangyu Sun

Graduate Studies Theses and Dissertations 2026

The emergence of large-scale foundation models has reshaped computer vision and multi-modal learning, but their substantial computational and communication demands pose significant challenges for deployment in federated learning (FL) systems. Federated learning enables collaborative model training across decentralized clients without sharing raw data, offering strong privacy guarantees. However, adapting foundation models in FL remains difficult due to communication bottlenecks, heterogeneous data distributions, limited device resources, and scarce labeled data. This dissertation presents a unified line of research that addresses these challenges through four complementary frameworks, progressively advancing the efficiency, personalization, and practicality of foundation model adaptation in federated settings.

First, …

Watch Your Step, Mind The Crowd: Socially Compliant Navigation In Unstructured Environments, Jack Vice

Graduate Studies Theses and Dissertations 2026

Autonomous mobile robots increasingly operate in complex real-world environments characterized by uneven terrain, dynamic obstacles, and human presence. This dissertation addresses the fundamental challenge of developing adaptive navigation strategies that enable robots to safely and effectively traverse unstructured outdoor environments including construction sites, agricultural fields, and disaster areas while maintaining social compliance around people. We advance this capability through five complementary contributions that collectively target dynamic, unstructured, human-occupied environments. First, we demonstrate evolutionary optimization of legged mobility for uneven terrain. Second, we propose using a spatiotemporal autoencoder to learn the temporal dynamics of moving people and objects. Third, we introduce …

A Comprehensive Review Of Cyber Security And Current Practices In Global Mining Critical Infrastructure, Abu Barkat Ullah, Wanli Ma, Mohiuddin Ahmed, Bazlur Rashid, Munir Ahmad Saeed, Omer Arshad, Utkarsh Raghav

Research outputs 2022 to 2026

The purpose of the study is to explore the reasons behind the low uptake of Information Security Management Standards (ISMS), Asset Management, and Business Continuity Plans despite increasing cyber threats to the mining sector. Mining companies need to modernize and automate to keep up with the ‘Fourth Industrial Revolution’, driven by disruptive technology, forcing systems and technologies to become more integrated, increasing cyber attack threats. To address this, we conducted a literature review analyzing the mining industry across various regions. The research is based on a qualitative analysis of diversified literature. The results highlighted factors behind the low uptake of …

ℵ-Ipomdp: Mitigating Deception In A Cognitive Hierarchy With Off-Policy Counterfactual Anomaly Detection, Nitay Alon, Joseph M. Barnby, Stefan Sarkadi, Lion Schulz, Jeffrey S. Rosenschein, Peter Dayan

Research outputs 2022 to 2026

Social agents with finitely nested opponent models are vulnerable to manipulation by agents with deeper recursive capabilities. This imbalance, rooted in logic and the theory of recursive modelling frameworks, cannot be solved directly. We propose a computational framework called ℵ-IPOMDP, which augments the Bayesian inference of model-based RL agents with an anomaly detection algorithm and an out-of-belief policy. Our mechanism allows agents to realize that they are being deceived, even if they cannot understand how, and to deter opponents via a credible threat. We test this framework in both a mixed-motive and a zero-sum game. Our results demonstrate the ℵ-mechanism’s …

An Investigation Into The Mechanisms, Barriers, Degree And Sphere Of Risk Influence In Corporate Security, Nicola Lockhart

Theses: Doctorates and Masters

This study investigates the sphere of corporate security risk influence within organisations, addressing the conceptual and practical ambiguity surrounding the activity’s capacity to shape organisational decisions, behaviours, and risk priorities. While corporate security’s protective role is widely recognised, its broader organisational risk influence remains under-theorised. The study defines the sphere of risk influence as the range of organisational stakeholders and environments with which the corporate security activity interacts, and within which it may engage, persuade, and mobilise action. This sphere is analytically constituted through the intersection of three dimensions: the mechanisms through which influence is attempted, the barriers that constrain …

Development Of Virtual Reality Learning Environments In Science To Engage Secondary Students In Hazardous Activities, Luke Spartalis

Theses: Doctorates and Masters

This research examines the need for change in including hazardous activities in education. As different learning technologies develop, platforms that retain authentic outcomes via virtual reality are needed. The main objective of this research is to examine the inclusion of Virtual Reality Learning Environments (VRLEs) to determine their value in areas that include hazardous conditions. The research considered the ways that VR tools could be optimised to support these activities, as well as considering the challenges of VRLE implementation. The study examined whether VRLE’s allowed for authentic experiences to sufficiently drive an acceptance of VR to complement existing teaching and …

A Dataset-Centric Analysis Of Evaluation Protocols In Face Morph Attack Detection, Sheerko R. Hmasalah, Abdulbasit K. Al-Talabani, Aras T. Asaad

Polytechnic Journal

Face morph attack detection (MAD) is often evaluated using datasets collected from diverse sources, created for different purposes, and acquired under varying conditions. This heterogeneity raises questions about how evaluation protocols and dataset characteristics influence reported performance. This work adopts a dataset-centric perspective to analyze the effects of evaluation protocol and dataset composition, including visual quality and morph-to-genuine imbalance, on MAD performance. Experiments are conducted on eight datasets and four morphing techniques using a fixed pipeline evaluated across six backbones, including Vision Transformer (ViT) and five CNN architectures, and ten evaluation scenarios covering homogeneous and heterogeneous cross-morph generalization. Performance is …

When Helpfulness Becomes Harmful: Jailbreaking Llms For Malicious Code Generation, Noelle Capodieci

Electronic Theses & Dissertations (2024 - present)

Generative AI (GenAI) and Large Language Models (LLMs) have made large strides in coding task capabilities, with many software developers integrating agentic engineering into their workflow. While GenAI has largely benefited professional software engineers who can automate their work, it has also created room for those with little coding expertise to also create fully fledged programs and applications. It is commonly noted that GenAI is trained with two major goals in mind: to be as helpful as possible, and be as harmless as possible. There exist moments where helpfulness may be prioritized over harmlessness when these goals conflict. LLMs may …

Data Defines Success: Algorithm For Dataset Quality Assessment In Deep Learning For Malware Detection, Matei Ionescu

Theses and Dissertations

The field of artificial intelligence is based upon the premise of constructing architectures through which to propagate training data. However, the majority of existing research literature is focused on architecture. While necessary, the attention devoted to the architecture should not so precipitously exceed that of the data. It should be noted that this disparity is not without reasonable cause. Data quality is often exceedingly difficult to verify due to particularities of the field or subfield; LLM repositories of text are distinct from image recognition pictures of dog breeds which are distinct from EEG waveforms of human brains which are distinct …

Computational Methods For Single-Cell And Multi-Omic Data Integration And Regulatory Network Inference, Jianlan Ren

Dissertations

Single-cell and multi-omic technologies have transformed the dissection of cellular heterogeneity and regulatory dynamics in health and disease. However, the high dimensionality, technical variability, and biological complexity of these datasets present significant challenges for integration, annotation, and interpretation. In this dissertation, a suite of computational approaches is introduced to address key problems in single-cell and multi-omic data analysis through model-based innovations and applied statistical frameworks.

First, a constrained deep learning framework for single-cell data integration, label transfer, and clustering is proposed. By incorporating biologically motivated constraints into the training process, robust performance is achieved across simulated and benchmark datasets spanning …

Patterns Of Llm Weaponization: A Comparative Analysis Of Exploitation Incidents Across Commercial Ai Systems, George Antoniou

Faculty and Staff Publications & Presentations

This comparative study examines patterns of Large Language Model (LLM) weaponization through systematic analysis of four major exploitation incidents spanning from 2023-2025. While existing research focuses on isolated incidents or theoretical vulnerabilities, this study provides one of the first comprehensive comparative frameworks analyzing exploitation patterns across state-sponsored cyber-espionage (Anthropic Claude incident), academic security research (GPT- 4 autonomous privilege escalation), social engineering platforms (SpearBot phishing framework), and underground criminal commoditization (WormGPT/FraudGPT ecosystem). Through comparative analysis across eight dimensions: Adversary sophistication, target selection, exploitation techniques, autonomy levels, detection evasion, attribution challenges, defensive gaps, and capability democratization, this research identifies critical cross-case patterns …