Digital Commons Network^™

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, …

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 …

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 …

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 …

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 …

Fingerprinting Voice Commands Of Vpn-Protected Smart Speakers, Xiaoguang Guo, Keyang Yu, Qi Li, Dong Chen

Computer Science Faculty Research and Publications

Extensive recent research has shown that it is surprisingly easy to infer Amazon Alexa voice commands over their network traffic data. To prevent these traffic analytics (TA)-based inference attacks, smart home owners are considering deploying virtual private networks (VPNs) to safeguard their smart speakers. In this work, we design a new machine learning-powered attack framework—VoiceAttack that could still accurately fingerprint voice commands on VPN-encrypted voice speaker network traffic. We evaluate VoiceAttack under 5 different real-world settings using Amazon Alexa and Google Home. Our results show that VoiceAttack could correctly infer voice command sentences with a Matthews Correlation Coefficient (MCC) of …

Phase 5 - Post Implementation, Anna Pugerud

Distinguished Student Scholarship Collection

This paper presents a post‑implementation analysis and system design plan for digitizing animal health records within the Animal Health Services (AHS) department. The project addresses inefficiencies, data inaccuracies, and physical storage limitations associated with paper‑based documentation by proposing the adoption of a Software as a Service (SaaS) digital records solution. Through stakeholder interviews, observations, and document analysis, the project identifies functional, non‑functional, and system requirements necessary to support streamlined reporting, treatment tracking, and regulatory compliance. The analysis outlines feasibility across technical, economic, and organizational dimensions, demonstrating that the proposed system can reduce documentation time, improve response times, and lower operational …

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 …

Next-Generation Democratic Cyber Statecraft - Balancing The Signal: Shutdown Shocks And Democratic Digital Governance, Scott M. Di Panni

School of Public Policy Capstones

This paper develops Next-Generation Democratic Cyber Statecraft (NG-DCS), a unified strategic doctrine for democratic governments to contest the cognitive domain against authoritarian adversaries. Drawing on twenty-six years of cross-national panel data (1999–2024) spanning 213 countries, game-theoretic modeling, and qualitative case analysis, the paper establishes three interconnected empirical and theoretical foundations. First, cross-national OLS regression across 160+ countries demonstrates that regime type is the dominant structural determinant of internet freedom (R²=0.615, β=2.513, p< 0.001), explaining more than twice the variance attributable to per-capita wealth (R²=0.268). Democratic governance, not economic development, produces open digital environments. Second, a two-way fixed effects (TWFE) difference-in-differences study exploiting government-ordered internet shutdowns as discrete policy interventions finds that digital restrictions causally degrade V-Dem governance quality by 0.21–0.38 standard deviations (p< 0.001 across all specifications). Treatment effects are immediate (β=−0.302 at k=0) and persist through five post-treatment years (β=−0.246 at k=+5), indicating structural rather than transitory governance damage. Parallel trends validation (p=0.352) and Callaway–Sant’Anna heterogeneity-robust estimation (ATT=−0.230, SE=0.077) support causal identification. Instrumental variable triangulation (2SLS β=−0.949, p=0.005) confirms that simultaneity was attenuating, not inflating, the primary estimates. Third, formal game-theoretic analysis reveals that the current U.S.–adversary equilibrium is (Restrain, Escalate)—the risk-dominant but Pareto-inferior outcome of a Stag Hunt structure. China, Russia, North Korea, and Venezuela each occupy structurally distinct positions (Stackelberg commitment, asymmetric two-level, autarky, and reactive trigger, respectively), requiring differentiated doctrinal responses rather than a uniform strategic playbook. Generative AI and algorithmic governance are shown to accelerate cognitive vulnerability by collapsing influence operation costs and exploiting engagement-optimized platform architectures that systematically degrade deliberative capacity in democratic populations.

Watch Me Watch: Reaction Videos As A Social Form Of Online Video Engagement, Rodney Okyere, Carlos Augusto Bautista Isaza, Jaehoon Pyon, Ihudiya Finda Ogbonnaya-Ogburu, Shuo Niu, Sang Wong Lee

Computer Science

Reaction videos (RVs) are surging in popularity, emerging as a distinctive facet of participatory culture on modern social video-sharing platforms such as YouTube, TikTok, and Twitch. This study aims to explore not only the motivations and engagement patterns of viewers towards RVs, but also the underlying nature of the virality and community-building phenomena that this sub-genre of video content fosters. We conducted 16 semi-structured interviews with individuals who identified as regular viewers of reaction videos to gain a deeper understanding of how they discover reaction videos (RQ1), the values that drive their motivations for viewing RVs (RQ2), and the ways …

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 …

Evaluating Predictive Structure In Penny Stocks Using Machine Learning And Statistical Methods, Susom Hait

Honors Theses

Market prediction attempts have primarily focused on large-cap stocks due to their stability and market consistency. As such, studies that use time-series techniques to predict large-cap stocks have produced consistent results. Despite the success of large-cap predictions, penny stocks have remained unexplored in modern academia due to their high volatility, low liquidity, and structural instability. Regardless, unexplored market potential and technological advancements underscore the need for preliminary research into penny stock forecasting. This study aims to determine whether meaningful predictive structures exist in time-series penny stock data. This study utilizes an incremental approach. Various penny stocks were selected, pooled, and …

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 …

Ethical Issues Of The Advancement Of Artificial Intelligence, Primo G. Moreno

Augustana Center for the Study of Ethics Essay Contest

Artificial intelligence has rapidly become integrated into our modern society, allowing for new streamlined methods to reign supreme. These methods raise concerns about control, privacy, and the shifting boundaries between human and machine decision-making. This paper examines how AI has developed, how it is currently being used, and the ethical dilemmas that arise as societies integrate it into systems such as healthcare, law enforcement, and businesses. Through the expansion of historical, technical, and philosophical perspectives, this paper argues that society must prioritize having set guidelines and systems for monitoring newly created AI systems. Without them, the implementation of AI models …

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) …

Finding Missing Input Validation In Tees Via Llm-Assisted Symbolic Execution, Chengyan Ma, Jieke Shi, Ruidong Han, Ye Liu, Yuqing Niu, David Lo

Research Collection School Of Computing and Information Systems

Trusted Execution Environments (TEEs) provide hardware-enforced isolation that protects sensitive code and data from untrusted software. Despite their strong security guarantees, analyzing TEE applications remains challenging due to the high cost and complexity of configuring complete TEE build and runtime environments, as well as the limited observability imposed by hardware isolation. This paper presents SymTEE, a novel large language model (LLM)-assisted symbolic execution framework for detecting missing input validation issues in TEE applications without requiring real TEE setups. SymTEE begins by leveraging Abstract Syntax Tree (AST) analysis to extract TEE code slices that may lack sufficient input validation, and then …

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 …

Developing A Multi-Band Gnss Remote Sensing Station To Measure Precipitable Water Vapor For Flash Flood Nowcasting, Madison Gleydura

Doctoral Dissertations and Master's Theses

Flash flood nowcasting in Central and Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at long range and signal blockage by mountains. GNSS-meteorology offers an established alternative for measuring precipitable water vapor and is currently integrated into several numerical weather models. Recent research demonstrates that commercial-grade GNSS receivers can produce tropospheric products comparable to those from geodetic-grade equipment. The gaps in mountain coverage can be addressed by developing a low-cost, self-contained embedded system that …

Data Tracking And Analytics Within Inventory Management: Coffee Shop And Retail Store Optimization, Mateo J. Moyon

Senior Theses

This thesis examines the application of inventory management theory in the small and medium-sized business context, with a specific focus on the food and beverage industry. Drawing on the foundational academic literature spanning from Harris’s EOQ formula in 1913 through stochastic inventory theory, ABC analysis, and just-in-time strategy, this paper establishes the mathematical and operational bases for modern inventory management practice. Although there are proven value to these frameworks, research demonstrates that small to medium sized businesses adopt inventory management systems at lower rates citing cost and implementation as barriers. This thesis argues that the emergence of low-cost inventory and …

Fallen Light (Video Game), Joshua Do

Presentations - 2026

Problem:

•People misunderstand Lucifer’s deception

•(shown through Temptation of Jesus in Matthew 4) •Lack of engaging ways to teach theological concepts   •Background: Lucifer corrupts not through force, but subtle self-elevation and doubt   Motivation:   •Just initially wanted to make a game of how sin entered the world for fun   •Just thought it was a cool idea in general.

Solution:

•Dialogue-driven narrative game   •6+ story chapters   •10+ regions   •Focused on spiritual conflict, deception, and discernment   •Uses event-based progression   •Presents Lucifer through subtle manipulation, by being a false light by choosing self-pleasure over what is right.

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 …

Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation, Naser Bader Alsaedi

Theses

Unmanned aerial vehicles (UAVs) are increasingly used in search‑and‑rescue (SAR) missions, yet many systems still rely on fragmented software where mission design, perception, and flight control are configured separately. This thesis examines whether a unified AI‑driven framework can reduce configuration effort and operator workload in UAV‑based SAR operations. The proposed system integrates natural‑language mission specification using a large language model (LLM) (LLaMA 3.1), autonomous coverage planning, YOLOv8‑based victim detection, and PX4/MAVSDK control within a single architecture. Operators describe missions through free‑form text or a graphical interface; the model converts these descriptions into structured mission parameters that are automatically planned and …

Trash Bot!, Owen Hoffman , ‘26, Ryder Maston , ‘26, Jack Nicholson , ‘26

Senior Theses, Projects, and Awards

Trash Bot! is the prototype of a smart and mobile waste receptacle for Swarthmore’s waste management system. With Swarthmore College committing to embed zero waste into campus culture and achieve a 90% diversion rate by 2035, flexible waste systems must be developed and improved. Trash Bot! seeks to improve the diversion rate and sort accuracy on campus by reducing the human error present in the current waste sorting process. Trash Bot! was also designed to seamlessly integrate with a classroom environment, maintaining safety standards and avoiding collisions. Trash Bot! uses computer vision coupled with a trained classifier algorithm to achieve …