Digital Commons Network^™

Ai Exposure And The Future Of Work: Tasks, Skill Demand, And Education, Erik Vasilauskas, Michael Horrigan

External Papers and Reports

No abstract provided.

Ai Method For Classification Of Diagnosis Of Near-Infrared Breast Lesion Images, Kaiquan Chen, Fangyang Shen, Honggang Wang, Zhengchao Dong, Jizhong Xiao, Ming Ma, Afroza Aktar, Christopher Chow, Wenxiong Zhang

Publications and Research

In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to generate standard samples from these scanned breast lesion images, a process that depends heavily on physicians with clinical expertise. On average, a single physician can annotate only approximately ten samples per working day. As a result, this process is time-consuming and labor-intensive, and the collected samples often suffer from low accuracy, large variability, and limited diagnostic reliability. Several AI-based annotation tools, such as QuPath, HALO AI™, and X-AnyLabeling, …

Towards Physics-Informed Neural Networks For Simulating Multiphase Geothermal Convection​*, Daniel C. Patton, Andrew Harrison Eno

Campus Research Month

Water and steam flow through porous rock, transferring heat via conduction and buoyancy-driven convection caused by density differences. Traditional numerical methods (finite-volume/finite-element) model this well but can become memory-intensive and unstable for long, high-detail simulations. This work demonstrates a Physics-Informed Neural Network (PINN) using a finite-difference approach within the NVIDIA PhysicsNeMo framework to simulate magma chambers in 2D. Tested on the Rio Pisco pluton in Peru, results are compared with the USGS HYDROTHERM model. PINNs learn from physical laws, offering accurate, flexible solutions with less data and development effort.

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 …

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 …

A View Under The Hood: Duquesne Kline's Law And Computing Program, Wesley M. Oliver, Katherine L.W. Norton, Martin Mckown, David Horrigan

West Virginia Law Review

No abstract provided.

The Energy And Environmental Footprint Of Ai, Michael P. Vandenbergh, Ethan I. Thorpe, Jonathan M. Gilligan

Michigan Journal of Environmental & Administrative Law

Artificial intelligence (AI) has the potential to create major economic and social benefits, but also to rapidly escalate electricity demand and its associated environmental impacts. Information availability has been a cornerstone of environmental law for half a century, and this Article argues that providing information to individual, corporate, and other users about the electricity demand and environmental impacts of AI can reduce those impacts without delaying development of the technology. Little is known about how different large language models (LLMs) compare on these metrics, though. To address whether users have access to the information necessary to address this shortcoming, the …

Match-A-Fit, Brianna Mendoza, Adan Diaz De Leon, Pedro Jacobo, Juan Marco Saca Dada

Posters - 2026

Welcome to Match-a-Fit! Match-a-Fit is an iOS application that allows the user to create a digital closet by uploading images of their clothing items. With AI, the program can generate outfits based on the digital closet, the time, and the occasion. Match-a-Fit’s purpose is designed to help users who struggle to get ready, run out of time, or can’t decide on an outfit, by easily generating outfit options based on the occasion.

A.I.R.E., Laurene Robinson

Presentations - 2026

•Cybersecurity analysts rely on reverse engineering to understand suspicious software. •Ghidra can surface decompiled code, but it does not fully explain function purpose, behavioral meaning, or analyst priority. •When symbols are stripped and context is weak, analysts must still reconstruct intent manually from low-level output. •That process is Time-consuming , complex and , operationally costly

A.I.R.E. - Ai-Assisted Reverse Engineering, Laurene Robinson

Posters - 2026

Reverse engineering plays a vital role in cybersecurity by helping analysts examine unknown binaries, investigate malware, identify vulnerabilities, and better protect sensitive systems. However, once a program is compiled and stripped, the meaningful names that describe its behavior are lost, leaving behind generic function labels like FUN_00401a30. Analysts must then manually interpret decompiled code, trace call chains, and infer program behavior function by function, which is slow and mentally demanding on large binaries. To address this challenge, this project introduces A.I.R.E., a local Ghidra extension that extracts contextual evidence from stripped functions and uses a locally hosted language model to …

Be Responsible In Your Answers! Monitoring Out-Of-Domain Behaviors In Domain-Specific Llms, Boquan Li, Chenzhe Lou, Zhe Ren, Peixin Zhang, Zirui Fu, Jun Sun, Yaowen Zheng

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have accelerated the rapid development of chatbot web applications in various domains, such as coding, biomedicine and psychology. Compared to general LLMs like ChatGPT, domain-specific LLMs require a greater sense of responsibility. For instance, if a programming LLM casually answers medical or psychological questions, it not only misleads the public but also poses legal risks. This highlights new demands for monitoring and preventing such irresponsible behaviors. Existing efforts attempt to monitor LLMs from multiple aspects, such as lying, jailbreaks, and toxic content, while overlooking out-of-domain behaviors. In this work, we propose an innovative LLM domain monitoring …

Architecture-Agnostic Test-Time Adaptation Via Backprop-Free Embedding Alignment, Xiao Ma, Young D. Kwon, Pan Zhou, Dong Ma

PhD Student’s Publications Collection

Test-Time Adaptation (TTA) adapts a deployed model during online inference to mitigate the impact of domain shift. While achieving strong accuracy, most existing methods rely on backpropagation, which is memory and computation intensive, making them unsuitable for resource-constrained devices. Recent attempts to reduce this overhead often suffer from high latency or are tied to specific architectures such as ViT-only or CNN-only. In this work, we revisit domain shift from an embedding perspective. Our analysis reveals that domain shift induces three distinct structural changes in the embedding space: translation (mean shift), scaling (variance shift), and rotation (covariance shift). Based on this …

Scalable Multi-Task Low-Rank Model Adaptation, Zichen Tian, Antoine Ledent, Qianru Sun

PhD Student’s Publications Collection

Scaling multi-task low-rank adaptation (LoRA) to a large number of tasks induces catastrophic performance degradation, such as an accuracy drop from 88.2% to 2.0% on DOTA when scaling from 5 to 15 tasks. This failure is due to parameter and representation misalignment. We find that existing solutions, like regularization and dynamic routing, fail at scale because they are constrained by a fundamental trade-off: strengthening regularization to reduce inter-task conflict inadvertently suppresses the essential feature discrimination required for effective routing. In this work, we identify two root causes for this trade-off. First, uniform regularization disrupts inter-task knowledge sharing: shared underlying knowledge …

Deep Learning-Based Automated Pneumonia Detection From Chest X-Rays: A Comparative Study Of Custom Cnn And Transfer Learning Architectures, Ahmed Sajim

Honors Theses

Pneumonia is a leading global cause of mortality, claiming approximately 2.5 million lives an-nually and placing exceptional diagnostic pressure on radiologists in resource-limited settings. Manual interpretation of chest X-ray (CXR) images is time-consuming, subject to inter-observer variability, and limited by radiologist availability. This thesis presents a systematic investiga-tion into deep learning-based automated pneumonia detection comparing five convolutional neural network (CNN) architectures: a custom-designed 2D CNN and four pretrained transfer learning models—ResNet, DenseNet, MobileNet, and VGG19.

A targeted data augmentation pipeline addresses the severe class imbalance in the Kag-gle Chest X-Ray Pneumonia dataset, expanding the Normal class from 1,583 to 9,495 …

Semantic Entanglement In Vector-Based Retrieval: A Formal Framework And Context-Conditioned Disentanglement Pipeline For Agentic Rag Systems, Nick Loghmani

iSchool - All Scholarship

Retrieval-Augmented Generation (RAG) systems deployed in agentic environments depend on the geometric properties of vector representations to retrieve contextually appropriate evidence for autonomous reasoning. When source documents conflate multiple topics within contiguous text regions, standard vectorization pipelines produce embedding spaces in which semantically distinct content occupies overlapping geometric neighborhoods — a condition we term semantic entanglement. This paper formalizes semantic entanglement as a model-relative measure of cross-topic overlap, defines an Entanglement Index (EI) as a quantitative proxy, and argues that higher EI is associated with reduced attainable Top-K retrieval precision under cosine similarity retrieval. We introduce the Semantic Disentanglement …

Penforge: On-The-Fly Expert Agent Construction For Automated Penetration Testing, Huihui Huang, Jieke Shi, Junkai Chen, Ting Zhang, Yikun Li, Chengran Yang, Eng Lieh Ouh, Lwin Khin Shar, David Lo

Research Collection School Of Computing and Information Systems

Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with Large Language Model (LLM)-powered agents, but existing approaches either rely on a single generic agent that struggles in complex scenarios or narrowly specialized agents that cannot adapt to diverse vulnerability types. We therefore introduce PenForge, a framework that dynamically constructs expert agents during testing rather than relying on those prepared beforehand. By integrating automated reconnaissance of potential attack surfaces with agents instantiated on the fly for context-aware exploitation, PenForge achieves a 30.0% exploit success rate (12/40) …

Stacked From One: Multi-Scale Self-Injection For Context Window Extension, Wei Han, Pan Zhou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

The limited context window of contemporary large language models (LLMs) remains a primary bottleneck for their broader application across diverse domains. Although continual pre-training on long-context data offers a straightforward solution, it incurs prohibitive data acquisition and computational costs. To address this challenge, we propose SHAREDLLM, a novel framework based on multi-grained context compression and query-aware information acquisition. SHAREDLLM comprises two stacked short-context LLMs: a lower model serving as a compressor and an upper model acting as a decoder. The lower model compresses long inputs into compact, multi-grained representations, which are then forwarded to the upper model for context-aware processing. …

Thinktank-Me: A Multi-Expert Framework For Middle East Event Forecasting, Haoxuan Li, He Chang, Yunshan Ma, Yi Bin, Yang Yang, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM-based approaches employ single-model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank-ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real-world strategic decision-making. To facilitate expert specialization and rigorous evaluation, we construct POLECAT-FOR-ME, a Middle East–focused event forecasting benchmark. Experimental results demonstrate the superiority of multi-expert collaboration in handling complex temporal geopolitical forecasting …

Discrete Diffusion For Bundle Construction, Teng Tu, Ai Li, Yunshan Ma, Shuo Xu, Xiaohao Liu, Haokai Ma, Liang Pang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

As a central task in product bundling, bundle construction aims to select a subset of items from large item catalogs to build an entire bundle or, more practically, complete a partial bundle. Existing methods often rely on the sequential construction paradigm that predicts items one at a time, nevertheless, this paradigm is fundamentally unsuitable for the essentially unordered bundles. In contrast, non-sequential methods model a bundle as a set, but still face two dimensionality curses: the combinatorial space grows exponentially with both bundle length and catalog size. Accordingly, we identify two technical challenges: 1) how to effectively and efficiently model …

Columnas: The Honors Program Newsletter At Bentley University, Amanda Li, Wilson Jan, Michael Raphael, Alexandra Rieckehoff, Karina Wu, Michael Shehata, Nilufar Noorian, Eloise Weintraub

Honors Program

INSIDE THE MODERN WORLD

Page 2: Stepping Out by Amanda Li

Page 3: Inside the Corporate Slop Bowl by Wilson Jan

Page 4: The Silencing: An Evaluation of the Global Attacks on the Right to Protest by Michael Raphael

THE SOUND OF CHANGE

Page 5: The Social, Cultural, and Economic Impact of Bad Bunny by Alexandra Rieckehoff

Page 6: Streaming Changed Music, But Is It Fair to Artists? by Karina Wu

Page 7: Feeling the Music: How Haptic Wearables Are Changing the Way We Experience Sound by Michael Shehata

SHIFTING SYSTEMS

Page 8: The Story Behind Davos, One of the …

Storycomposerai: Supporting Human-Ai Story Co-Creation Through Decomposition And Linking, Shuo Niu, Dylan Clements, Marina Margalit Nemanov, Hyungsin Kim

Computer Science

GenAI's ability to produce text and images is increasingly incorporated into human-AI co-creation tasks such as storytelling and video editing. However, integrating GenAI into these tasks requires enabling users to retain control over editing individual story elements while ensuring that generated visuals remain coherent with the storyline and consistent across multiple AI-generated outputs. This work examines a paradigm of creative decomposition and linking, which allows creators to clearly communicate creative intent by prompting GenAI to tailor specific story elements, such as storylines, personas, locations, and scenes, while maintaining coherence among them. We implement and evaluate StoryComposerAI, a system that exemplifies …

Scenarioxp: A Complete Scenario-Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Doctoral Dissertations and Master's Theses

Today is an age of exciting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

Study Of Output And Behavior Of Llms Using Confidence Framing In Prompt Engineering, Micah Parrilla

Doctoral Dissertations and Master's Theses

While prompt engineering is pivotal for shaping Large Language Model (LLM) outputs, the impact of confidence framing on behavioral calibration remains underexplored. This study investigates the ways in which psychological framing, utilizing techniques such as capability praise, role amplification, and doubt induction, affects linguistic tone, objective accuracy, and internal calibration. A 1,080-trial experimental matrix evaluated six diverse models across factual, logical, coding, and cyber security domains. Analysis using the Kruskal-Wallis H-test revealed highly significant behavioral shifts across all measured dimensions, providing conclusive evidence that the applied frames exert a substantial influence on model performance.

The findings identify a distinct cognitive …

Sentra, David Aguilar

Posters - 2026

In the fast-paced space of event organization, fostering continuous collaboration among participants is essential. However, organizers often lose valuable time monitoring multiple, disconnected systems once an event is underway. Enter Sentra: an all-in-one Discord bot tailored specifically for weekend events like hackathons. Sentra bridges the gap between participants and organizers by consolidating seamless team matchmaking, robust support ticketing, and automated AI moderation into a single, unified interface.

Synapse, Nicolas Diaz, Alexander Murphy, Sonia Cerrillo, Naomi Ramirez, Jesse Kemmer

Posters - 2026

People tend to accumulate a great deal of notes throughout their lives with no coherent way to organize them. Even with the built-in notes app, the notes eventually accumulate until it becomes borderline impossible to find what is needed. Our proposed solution is Synapse, an LLM powered notes app with a tagging system that allows notes to be sorted by topic. The LLM will be able to read the user's notes and recommend tags

Ai Dependence And Its Impact On Human Decision-Making Quality And Supply Chain Efficiency, Jesus Salazar, Leonardo Fabbri, Axel Villegas

Posters - 2026

• Artificial Intelligence (AI) is transforming supply chain management by enabling:
  • Data-driven decision-making
  • Improved forecasting accuracy
  • Enhanced operational efficiency (Choudhary et al., 2023; Ivanov & Dolgui, 2021)
• AI applications such as predictive analytics support:
  • Inventory optimization, Logistics planning
  • Procurement decisions in real time
• However, increasing reliance on AI introduces risks:
  • Automation bias (over-trusting AI outputs)
  • Reduced human critical thinking
  • Overdependence on algorithmic recommendations (Raisch & Krakowski, 2021)
• This study examines the dual impact of AI dependence on:
  • Decision-making quality
  • Supply chain efficiency
• Objective:
  • Identify whether AI improves performance or reduces human effectiveness
  • Determine the optimal balance between AI support and human …

From Attention To Reasoning: Beyond Accuracy In Multimodal Ai, Wayner Barrios

Dartmouth College Ph.D Dissertations

Multimodal large language models have achieved impressive performance on vision-language benchmarks by integrating visual encoders with large language models. Yet a critical gap persists between benchmark accuracy and genuine multimodal understanding: current evaluation frameworks assess performance by final answers alone, rewarding confident predictions while leaving systematic reasoning failures undetected.

This thesis addresses this gap through a unified framework that progresses from understanding to reasoning, using video as the most comprehensive multimodal testbed. Video inherently combines vision, audio, and language with temporal dynamics and massive token redundancy; techniques developed for video's comprehensive challenges transfer naturally to simpler multimodal tasks.

On understanding …

A Real Account Of Deep Fakes, Benjamin L.W Sobel

Michigan Law Review

Laws regulating pornographic deepfakes are written to prohibit “digital forgeries,” “false” images, or media “indistinguishable” from “authentic” recordings. Yet the typical anti-deepfake law covers materials that aren’t forgeries, aren’t false, and that reasonable observers can easily distinguish from authentic recordings. Though drafted as if they regulate statements of fact, anti-deepfake laws actually target certain outrageous depictions per se—and rightly so, because pornographic deepfakes cause harm irrespective of their truth or falsity. However, the inapposite language of facts results in statutes with crucial ambiguities. Moreover, because anti-deepfake laws ban outrageous depictions irrespective of the factual assertions they make, they differ fundamentally …

Enhancing Financial Audit Operations Through Ai Anomaly Detection, Nadya Cousin, Rebekah Garza, Talisa Gomez

Posters - 2026

❖ Financial auditing plays a critical role in ensuring accuracy, regulatory compliance, and fraud detection in financial reporting

❖ Traditional audit approaches rely heavily on sampling and manual review processes, limiting their ability to scale with increasing data complexity

❖ The rapid growth of high-volume, high-velocity financial data (big data) has exposed significant limitations in traditional auditing, including:

• Incomplete data coverage
• Delayed anomaly detection
• Increased risk of material misstatements

❖ These limitations create a need for scalable, automated, and data-driven audit solutions

❖ Artificial Intelligence (AI), particularly anomaly detection models, enables:

•  Full-population testing
•  Real-time pattern recognition
•  Proactive risk identification