Digital Commons Network^™

The Cake Is A Lie: Hid Wireless Adapter*, Andrew J. Patton, Benjamin Chant

Campus Research Month

This project explores converting wired Human Interface Devices (HID) into wireless devices by creating an adapter. Devices without wireless chips or dongles are hindered when flexibility is required, creating electrical waste. Our solution consists of a Transmitter (TX) and Receiver (RX) device pair and is designed to wirelessly bridge USB input from an HID device to a target host.

Towards Smaller Artificial Neural Network Using Mean Compression*, Michael D. Burks, Matthew K. Chuhng

Campus Research Month

Artificial Neural Networks (ANNs) require substantial memory and computational resources, limiting their deployment on resource-constrained devices. Our contribution is a compression method using Mean Compression (MC) to reduce ANN size while preserving functionality and accuracy. MC consolidates connections with similar edge weights into meta-nodes with averaged values. Unlike traditional pruning that only removes connections among neurons, MC restructures networks by recomputing weights and creating meta-nodes. Additionally, unlike fixed pruning thresholds, MC uses flexible weight range patterns. Applied to multilayer perceptron (MLP), ANNs are made more accessible for deployment on constrained devices as proven in several experiments. Specifically, across five classification …

Code Visualizer: An Interactive Code Visualization Tool, Tadd Trumbull

Campus Research Month

Code Visualizer is a web-based, interactive algorithm visualization tool designed to help introductory computer science students develop a deeper understanding of array searching and sorting algorithms. Code Visualizer presents a step-by-step simulation environment built on a restricted Python subset, which allows students to observe array traversal, index manipulation, and algorithmic operations in real time. The tool features two learning modes: View Mode and Predict Mode. The underlying architecture utilizes a behavioral software design pattern called command pattern.

Spatial Future Ahead! Augmented Reality And Anticipated Life Consequences, Sergio Barta, Reto Felix, Chris Hinsch, Mahdokht Kalantari, Nina Krey, Philipp A. Rauschnabel

Marketing Faculty Publications

Purpose: This study explores how initial exposure to immersive spatial computing experiences using AR headsets generates lasting inspiration and shapes consumers expected long-term life consequences (i.e., enhancement of reality, perceived substitutability and social impact).

Design/methodology/approach: The study uses a time-lagged research design based on 148 first-time users of spatial computing devices (AR headsets). Respondents were interviewed once shortly after being exposed to AR and a few days later. Data is analyzed using partial least squares structural equation modeling (PLS-SEM).

Findings: Users' immediate “inspired-by” experiences predict increased “inspired-to” intentions days later. Such inspiration translates into anticipated consequences such as virtually customizing …

The Intricate Dance Of Emotions And Psychophysiology: Unveiling The Secrets Of Microexpressions, Jaiteg Singh, Deepika Sharma, Babar Shah, Sukhjit Singh Sehra, Farman Ali, Irfan Hussain

All Works

Background: Emotion recognition plays a pivotal role in behavioral analysis, mental health assessment, and human-computer interaction. Micro-expressions, which are brief and involuntary facial movements, offer valuable insights into concealed emotions. However, validating micro-expressions remains a challenge due to their subtlety and short duration. This study aims to enhance the validation and classification of micro-expressions by integrating electromyogram (EMG) signals with facial action units (AUs). Methods: EMG data was collected using the EMG Muscle Sensor Module V3.0, interfaced with an Arduino Mega 2560 microcontroller. To ensure signal clarity, various data filtration techniques were applied to eliminate noise, motion artifacts, and baseline …

Adopting Artificial Intelligence: Cross-Sector Analysis Of Ai Adoption Risks, Brandon Saari, Yona Berger, Yanett Munoz, Alex Agnick, Paul Wagner, Robert J. Honomichl

Journal of Cybersecurity Education, Research and Practice

Artificial intelligence (AI) is rapidly being adopted across public and private sectors. This offers significant gains in efficiency, decision making, and access to information. At the same time, AI introduces complex risks related to cybersecurity, privacy, bias, transparency, accountability, and equity that existing governance and security frameworks do not fully address. This paper presents a cross-sector literature review and comparative analysis of AI adoption risks and mitigation strategies across four critical domains: the federal government, libraries, K–12 education, and healthcare. Drawing on peer-reviewed research, institutional frameworks, and policy guidance, the study identifies sector-specific challenges alongside shared systemic gaps, including insufficient …

Innovations And Applications Of Virtual Private Networks And Sustainable Security In Society 5.0 Libraries, Stella Chinnaya Nduka Dr., Adeyinka Tella Prof, Petros Dlamini Dr

Journal of Cybersecurity Education, Research and Practice

In order to improve digital resilience, privacy, and access equity in contemporary library environments, this study investigates the role of Virtual Private Networks (VPNs) in fostering sustainable cybersecurity within the framework of Society 5.0 libraries. It does this by looking at the latest developments, applications, difficulties, moral dilemmas, and tactical methods associated with VPN deployment. Using peer-reviewed journal articles, conference proceedings, white papers, and policy documents published between 2010 and 2024, a methodical approach to literature review was used. The literature that bridges the fields of cybersecurity, library science, and Society 5.0 concepts was the main focus of the review. …

Cloud-Based Lightweight Spatiotemporal Deep Learning Sign Language Recognition Model With Digital Twin Avatar, Mohammed Zuhair Abduljabbar

Thesis/ Dissertation Defenses

This dissertation proposes a full framework for sign language recognition (SLR). This study introduces Meta AI concept and a cloud-based framework that integrates a spatial detection and recognition model for sign language alphabet, and a spatiotemporal Transformer model for word-level SLR. The framework design also integrates a 3D digital twin that acts as a sign language interpreter on real time enhancing user friendliness and human computer interaction with the system. The major focus is finding the balance between performance accuracy and computation complexity efficiency starting with the framework communication between AI agents and the SLR models and digital twin efficiency. …

Dna Barcoding Of Eight Freshwater Fish Species (Order Siluriformes) From The River Ravi, Pakistan, Hafiz Muhammad Ashraf, Hafiz Abdullah Shakir, Muhammad Irfan, Shaukat Ali, Chaman Ara, Noor Khan, Muhammad Khan, Abdul Qadir, Muhammad Zafar Saleem

Karbala International Journal of Modern Science

Pakistan's freshwater bodies host significant fish species, but molecular identification is still in its infancy, necessitating the use of morphological and molecular DNA barcoding techniques. The study confirmed the species of the order Siluriformes found in the River Ravi, Pakistan, using DNA extracted from muscle tissue, sequenced for the COI gene, and obtained accession and Barcode Index Numbers from GenBank and BOLD databases. In the BLAST search, identities ranged from 99.23–100% and 99.52–100% in the GenBank and BOLD databases, respectively. The study revealed that the Kimura 2-Parameter genetic distance increased from lower to higher taxonomic levels: within species (0.00%) < within genus (15.17%) < within family (19.77%). The study estimated average nucleotide differences (104.964) and nucleotide diversity (0.16099) but found Tajima's neutrality test to be statistically insignificant. The neighbor-joining tree displayed closely linked species under a node, whereas divergent species were grouped under distinct nodes. The COI gene-based DNA barcoding aids in Pakistan's fish resource inventory, monitoring, and management, providing valuable input for traditional methods.

Eco-Assisted Ultrasonic Synthesis, Characterization, And Electrical Conductivity Study Of Polyaniline/ Silica@Fe3o4 Q1 Nanocomposites, Ali Salim Shaway, Kholoud Dham Khamkheem, Jawad Kadhim Abaies, Athra G. Sager

Karbala International Journal of Modern Science

وفي هذه الدراسة الصوتية، أصبحت السيليكا المُنتجة بأكسيد الحديد (Fe₃O₄) ومركبات نانوية قائمة على البولي أنيلين مُطعّم بنسبة 5-15% من السيليكا المُعزز للحديد (Fe₃O₄). وأكملت نهائيا ماء أنيلين والمركبات العلمية المتنوعة الارتباط باستخدام تقنيات FT-IR وUV-Vis وXRD وSEM وTEM. وأحدثت نتائج XRD أن حجم جسيمات البولينج أنيلين الناي (PANI) يبلغ 55.5. مكثف هذا الحجم إلى 45.2 عند تطعيم بولي أنيلين بنسبة 15% وزناً من السيليكا المُغلفة بأكسيد الحديد (Fe₃O₄) مُحضّرة بطريقة جيدة بشكل جيد، مما يميل إلى التمدد بشكل ثابت في تشكيل المركب المتباين المنظم. ضاقت النطاق البصري من 3.2 إلى 2.5 إلكترون فولت مع زيادة نسبة المطعّم، مما يشير …

Threat-Analysis Oriented Digital Twinning Of Ml-Powered Future Autonomous Weapon Systems, Thomas Neubert

Doctoral Dissertations and Master's Theses

Warfare is undergoing a rapid transformation with the integration of artificial intelligence (AI) and machine learning (ML) into autonomous weapon systems (AWS) for perception, decision support, and control. As these systems become more software-defined, their cyber attack surface expands across sensing, communications, autonomy logic, and human-machine interfaces. As human oversight diminishes, ensuring the cybersecurity, resilience, and reliability of these systems becomes critical to mission success. This thesis investigates how a digital twin-driven threat modeling framework that integrates system-centric analysis with adversary-informed methodologies can support structured cybersecurity vulnerability evaluation and defensive strategy development associated with ML-powered AWS. First, the study analyzes …

The Expanding Digital Border: Ai, Surveillance, And The Fight For Justice, James Chesser

Immigration and Human Rights Law Review

As artificial intelligence transforms the mechanisms of immigration control, the modern border has become a digital filter—one governed less by geography and more by code. This Article examines the legal, technical, and ethical implications of AI-driven systems now central to global border enforcement, including biometric surveillance, algorithmic risk scoring, and predictive profiling. It explores how states use these technologies not only to manage irregular migration, but to compete for global talent—constructing migration regimes that reward capital and compliance while eroding transparency, due process, and equality.

Through an international and comparative lens, the piece highlights the expansion of algorithmic decision-making across …

Adaptive Parallel Downloader For Large Genomic Datasets, Rasman Mubtasim Swargo

Miners Solving for Tomorrow Research Conference

Modern next-generation sequencing (NGS) projects routinely generate terabytes of data that researchers download from public repositories such as SRA and ENA. Existing download tools typically employ static concurrency settings, leading to inefficient bandwidth utilization and prolonged download times under dynamic network conditions. We introduce FastBioDL, a parallel downloader for large biological datasets with an adaptive concurrency controller. FastBioDL frames downloading as an online optimization problem, using a utility function and gradient descent to adjust the number of concurrent socket streams during runtime. This approach maximizes throughput while minimizing resource overhead. Evaluations on public genomic datasets show that FastBioDL achieves up …

Thermal Mirage: Towards Robust Thermal Perception Via Gan-Guided Diffusion, Nuzaer Omar

Miners Solving for Tomorrow Research Conference

Thermal object detection systems are critical for safety sensitive applications due to their reliability under adverse conditions. However, existing robustness evaluations in thermal domain primarily focuses on physical or sensor-level perturbations, overlooking vulnerabilities from semantically realistic scene and object manipulations. We introduce Thermal Mirage, a generative framework that leverages GAN-guided diffusion to expose weaknesses in thermal detectors through controlled object and context level perturbations. Our approach learns class-conditional thermal priors via a GAN and uses diffusion to transform object appearances into ambiguous or low-saliency patterns. Simultaneously, a context module degrades scene conditions by simulating harsher night environments. Integrated with a …

Robust Federated Learning With Strategic Adversaries, Manoj Twarakavi

Miners Solving for Tomorrow Research Conference

Federated Learning (FL) leverages the intelligence of untrusted distributed devices through collaborative training. This makes the training process susceptible to malicious behavior. Existing defense mechanisms largely consider an adversary who attacks a proactive FL server without any adaptability. However, they overlook the presence of a strategic adversary. To address this challenge, our work proposes a Robust Game-theoretic framework where the adversary is both strategic and is equipped with the capability of performing large-scale poisoning attacks.

Introducing Gridtrees For Streaming, Scalable Hierarchical Data Visualization, Nathan Tibbetts

Miners Solving for Tomorrow Research Conference

File browsing in the consumer sphere has seen very little advancement in recent years, although attempts have been made to improve upon it. With the goal of multi-level visual file browsing in mind, we present a prototype GridTree, a dynamic, recursive, stable, spatial layout data structure based on subdividing grids, represented as a hierarchy of maps whose granularity increases with depth. We motivate this work with characteristics we have identified as important for viability of a multi-level file-browser, which have become our design goals. We touch on the underlying logic of a GridTree and identify its complexity. We briefly discuss …

Distributed Control Plane For Cross-Silo Federated Learning, Rabin Pandey

Miners Solving for Tomorrow Research Conference

Cross-silo federated learning (FL) trains shared models across geographically distributed institutions without centralizing raw data, but its reliance on wide-area networks makes round completion time highly sensitive to heterogeneous link conditions, congestion, and straggler clients. Existing SDN-based FL frameworks address this through centralized traffic engineering, an approach that breaks down when silos span independent administrative domains where no single entity can maintain complete, timely network knowledge. We replace the centralized control plane with a fully distributed overlay in which each silo gateway operates as an equal peer, continuously probing local links, exchanging EWMA-smoothed metrics via bounded gossip, and computing least-cost …

Robustness Of Fuzzy Artmap To Adversarial Attacks And Progressive Adversarial Training For Streaming Learning, Shane Cairns

Miners Solving for Tomorrow Research Conference

Incremental learners deployed on streaming data must remain robust to evolving adversarial perturbations, yet most adversarial-robustness studies assume offline multi-epoch training with repeated access to historical data. We investigate adversarial robustness in Fuzzy ARTMAP, a prototype-based Adaptive Resonance Theory model that supports single-pass learning without replay. We propose WB-Softmax, a differentiable relaxation that aggregates category-level activations into class-level scores for gradient-based attacks. WB-Softmax PGD achieves 89–100% attack success on vanilla models, exceeding transfer and query-based baselines. We then study adversarial training under true streaming constraints by comparing offline versus online adversarial example generation and standard versus selective updates. Offline adversarial …

Anti-Inflammatory And Insulin-Modulating Effects Of Tithonia-Curcuma-Moringa (Tcm) Formulation In Type 2 Diabetes: An Experimental Study In Mice, Rif’Atul Hawani Burhan, Nabila Shafa Yumna Salsabila, Devita Zulfa Pratama, La Tazkia Aulia Wibowo, Yulian Ervin Maulana, Moh Dliyauddin, Noviana Dwi Lestari, Agung Pramana Warih Marhendra, Aris Soewondo, Hideo Tsuboi, Muhaimin Rifa’I

Karbala International Journal of Modern Science

Type 2 diabetes is a chronic metabolic disorder characterized by hyperglycemia and chronic inflammation. This study investigated the anti-inflammatory and insulin-modulating effects of Tithonia-Curcuma-Moringa (TCM) formulation in type 2 diabetes model. Male BALB/c mice were divided into six groups: normal control, type 2 diabetes control, metformin treatment, and three TCM formulation treatment groups with three different dose combinations. Type 2 diabetes was induced using a high-fat diet combined with streptozotocin injection, supported by sucrose administration. After 21 days of treatment, the spleen and pancreas were isolated for flow cytometry analysis. The results showed that the expression levels of …

Bridging The Cybersecurity Education Gap: The Role Of Open Educational Resources In Supporting Rural Cybersecurity Programs, Brittni Hardie

Theses and Dissertations

This study examines the intersection of cybersecurity education, open educational resources (OER), and rural higher education through a systematic review of current literature and an exploratory survey of rural community college faculty. The purpose of this research, consistent with the approved Institutional Review Board (IRB) protocol, was to understand how OER can be leveraged to design and deliver an affordable, high-quality System Security course within a rural higher-education environment. As cybersecurity workforce shortages continue to grow across the United States, rural institutions face persistent challenges in sustaining high-quality programs due to financial constraints, limited faculty capacity, and rapidly evolving curriculum …

Elucidating Complex H Behavior In Amorphous Oxide Semiconductors By Machine Learning, Lucas Ethington

Miners Solving for Tomorrow Research Conference

In this project, the disordered oxide structures will be mapped by a machine-learning algorithm (e.g., HDBSCAN) to identify characteristic behaviors of the proton across different material densities and defect types. This should help identify the under-coordinated, highly distorted, weakly-bonded, and dynamically unstable atoms to predict the most probable H locations. This fast and accurate prediction of energetically favorable H distribution will enable a reliable and fast screening of a large number of AOSs with variable cation and/or anion compositions. The approach will help find AOSs with suppressed numbers of M-OH defects (that form deep electron traps, limiting the number of …

Wheel-Spoke Encoding: A Product Quantization-Compatible Radial Encoding Scheme For Convex Polygonal Data, Maris Reinkemeyer

Miners Solving for Tomorrow Research Conference

Product quantization has historically been unapplied to GIS datasets, likely due to a mismatch between quantization’s input precondition of fixed, equal length vectors and GIS data’s inherent variability in the number of data points. Therefore, any quantization-compatible encoding method must operate independently of the data points within GIS records. The challenge is to balance the guarantee of producing fixed, equal length vectors with the preservation of semantic meaning present in the raw data. Here, we develop a radial polygon encoding method that achieves this balance while providing acceptable recall in a time complexity of O(nrv), where n is the number …

Dynamical Transition From A Two-Dimensional Soliton To A Rogue Wave In Quantum Droplets, Punit Sesha Sai Turlapati

Miners Solving for Tomorrow Research Conference

We investigate the nonequilibrium dynamics of two-dimensional quantum droplets: ultracold self-bound many-body states stabilized by the interplay of mean-field attractive interactions and repulsive quantum fluctuations. Flat-top ground state droplets are subject to an external potential, an attractive well and a repulsive barrier. Under the influence of the attractive well, we observe signatures of a Townes soliton formation, which for increasing strength of the well transitions into a two-dimensional rogue wave structure, a time-periodic highly localized configuration with amplitude three times larger than the background. The barrier instead favors a dynamical splitting of the droplet. We have developed a parallelized simulation …

Lay Summarization For Medical Patents, Manav Raja Vinotha

Miners Solving for Tomorrow Research Conference

Technical documents, such as medical patents, are often inaccessible to non-expert audiences due to specialized terminology. This paper presents a multi-agent system for automated lay summarization that optimizes both quality and computational efficiency through strategic task decomposition. The proposed pipeline utilizes four specialized stages: autonomous medical entity retrieval from the Unified Medical Language System (UMLS), context distillation into lay conceptualizations, and a structured writer-critic loop for iterative refinement. By distributing cognitive load across specialized agents, this architecture effectively leverages smaller, cost-effective language models while maintaining the performance of SOTA LLMs. Evaluated against single-agent baselines using an LLM as a Judge …

Analysis Of Autonomous Vehicle Survivability To 5g Communication Errors, Sydney Clark

Miners Solving for Tomorrow Research Conference

Autonomous vehicles rely on low-latency, high-reliability data exchange for real-time perception and control. Disruptions such as packet loss, latency variation, protocol-level errors, and malicious interference can pose significant safety risks to both passengers and surrounding environments. This project aims to evaluate, quantify, and predict the survivability of autonomous vehicle systems to communication errors, with focus on 5G network environments. The impact of these communication impairments on vehicle stability and control will be investigated through high-fidelity cyber-physical simulation of the vehicle and its surrounding environment. Experiments designed to capture varying network conditions will be used to assess a broad range of …

A Wearable Mxene-Based Sweat Sensor For Real-Time Monitoring Of Inflammatory Biomarkers, Ariel Pilger

Miners Solving for Tomorrow Research Conference

Many conventional biosensing approaches rely on invasive sampling or bulky benchtop instrumentation, limiting their use in continuous and portable applications. This project focuses on the development of wearable sweat-based biosensors that enable non-invasive, continuous, and portable monitoring of physical, chemical, and biological markers. The system will be designed to target markers present in sweat and transduce the biochemical interactions into measurable electrical signals. These signals will be processed through integrated electronics to produce clear, interpretable outputs for users and medical professionals. Supporting circuitry including filters, amplifiers, and an independent power supply will be implemented as necessary to ensure signal accuracy, …

Ai Models As Cultural Beings: Investigating Ai Cultural Biases And The Impact Of Cultural Alignment On Human-Ai Creative Collaboration, Choon Ngee Tan, Meng Han, Roy Y. J. Chua, Chi-Ying Cheng

Research Collection Lee Kong Chian School Of Business

Existing research on AI cultural biases predominantly focuses on Western models, overlooking critical gaps in non-Western models. We conduct a comparative analysis of AI models – ChatGPT (U.S. developed) and ErnieBot (China developed) – from different cultures to investigate how corresponding cultural biases manifest in their outputs. Additionally, we examine how cultural alignment between human users and AI models impacts their collaborative creative performance and the underlying psychological mechanisms. In Study 1, multi-choice prompt with zero-shot technique was used to evaluate cultural biases in four widely used AI models – ChatGPT-3.5/4, ErnieBot-3.5/4 – comparing their responses to established cultural psychometric …

Conflogger: Enhance Systems’ Configuration Diagnosability Through Configuration Logging, Shiwen Shan, Yintong Huo, Yuxin Su, Zhining Wang, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Modern configurable systems offer customization via intricate configuration spaces, yet such flexibility introduces pervasive configuration-related issues such as misconfigurations and latent softwarebugs. Existing diagnosability supports focus on post-failure analysis of software behavior to identify configuration issues, but none of these approaches look into whether the software clue sufficient failure information for diagnosis. To fill in the blank, we propose the idea of configuration logging to enhance existing logging practices at the source code level. We develop ConfLogger, the first tool that unifies configuration-aware static taint analysis with LLM-based log generation to enhance software configuration diagnosability. Specifically, our method 1) identifies …

Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) can exhibit concept-conditioned semantic divergence: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present RAVEN (Response Anomaly Vigilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling semantic entropy over paraphrastic samples with cross-model disagreement. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using …

Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking …