Physical Sciences and Mathematics Commons^™

Super Lidar Intensity For Robotic Perception, Wei Gao, Jie Zhang, Mingle Zhao, Zhiyuan Zhang, Shu Kong, Maani Ghaffari, Dezhen Song, Chengzhong Xu, Hui Kong

Research Collection School Of Computing and Information Systems

Conventionally, human intuition defines vision as a modality of passive optical sensing, relying on ambient light to perceive the environment. However, active optical sensing, which involves emitting and receiving signals, offers unique advantages by capturing both radiometric and geometric properties of the environment, independent of external illumination conditions. This work focuses on advancing active optical sensing using Light Detection and Ranging (LiDAR), which captures intensity data, enabling the estimation of surface reflectance that remains invariant under varying illumination. Such properties are crucial for robotic perception tasks, including detection, recognition, segmentation, and Simultaneous Localization and Mapping (SLAM). A key challenge with …

Learning Feature Inversion For Multi-Class Anomaly Detection Under General-Purpose Coco-Ad Benchmark, Jiangning Zhang, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Zhucun Xue, Yong Liu, Guansong Pang, Dacheng Tao

Research Collection School Of Computing and Information Systems

Anomaly detection (AD) is often focused on detecting anomaly areas for industrial quality inspection and medical lesion examination. However, due to the specific scenario targets, the data scale for AD is relatively small, and evaluation metrics are still deficient compared to classic vision tasks, such as object detection and semantic segmentation. To fill these gaps, this work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. This enables fair evaluation and sustainable development for different methods on this challenging benchmark. Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which …

Developing Blockchain-Based Transparent E-Commerce Solutions For Danish Smes To Promote Sustainable Design Products, Somnath Mazumdar, Robert John Kauffman, Thomas Jensen, Raghava Rao Mukkamala, Jan Damsgaard

Research Collection School Of Computing and Information Systems

Typically, a firm's objectives include establishing consumer confidence, preserving its brand image, and developing a profitable business strategy. Consumers now place greater emphasis on the sustainability and transparency of their purchases. Given environmental and economic limitations, firms are often compelled to implement sustainable production methods. This is especially a struggle for small- and medium-sized enterprises (SMEs) with new technology, as it can increase their risk of failure. This has led to a problem for consumers, who must cross-check the sustainability-related claims of the firms they buy from. This is challenging because of limited process trace data and restricted enforcement capabilities. …

Hypersiniel: Guaranteed Output Delivery Comes (Almost) Free In Private Delegation Of Zksnarks, Yunbo Yang, Yuejia Cheng, Junkai Liang, Kailun Wang, Xuanming Liu, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Meng Hao, Guomin Yang, Deng, Robert H., Kui Ren

Research Collection School Of Computing and Information Systems

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness.Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to …

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) …

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 …

Deep Learning Based Approaches For Low Cost Defense Detection, Adele J. Noel-Rickert

All NMU Master's Theses

Pulmonary fibrosis is a progressive interstitial lung disease characterized by the accumulation of fibrotic tissue within the lungs, leading to impaired respiratory function and reduced quality of life. Early detection is important for disease management; however, accurate diagnosis often relies on high-resolution computed tomography (CT), which may not be accessible in all clinical settings. Chest radiography provides a lower-cost and widely available imaging modality, but interpretation of chest X-rays for fibrotic disease can be challenging due to subtle radiographic patterns and overlapping anatomical structures. This thesis investigates the use of multimodal deep learning techniques to assist in pul- monary fibrosis …

Evaluation Of Seal!Nd: School Year 2024-2025, Shawnda Schroeder, Rashid Ahmad

Indigenous Health Faculty Publications

Dr. Schroeder and Rashid worked with the team at the Oral Health Program to collect data on the clinical reach of the School-based Sealant program (SEAL!ND), the referral practices, dental sealant cost savings, and perceptions of school personnel regarding their experiences with the program. SEAL!ND continues to make a measurable difference in improving children’s oral health by increasing access to preventive care, reducing barriers for families, and supporting schools in meeting the needs of their students. The program’s impact is reflected not only in the thousands of services provided each year, but also in the strong satisfaction and gratitude expressed …

Monitoring Terrestrial Ecosystem Productivity Using Hyperspectral Satellite Data, Serge Tuyambaze

School of Natural Resources: Dissertations, Theses, and Student Research

Accurate estimation of terrestrial Gross Primary Productivity (GPP) is critical for quantifying global carbon sequestration and understanding the terrestrial carbon cycle. While eddy covariance (EC) towers provide standard flux measurements, upscaling these measurements to continuous global coverage remains challenging due to the sparse distribution of flux towers and their small footprints. Remote sensing (RS), especially coupled with eddy covariance data, has been used to estimate GPP at the global scale. Different RS derived productivity models have been developed, including vegetation indices, light use efficiency (LUE) models, solar-induced fluorescence (SIF), dynamic global vegetation models (DGVM), and machine learning. One widely adapted …

Impact Of In Situ Observations From Three Torus Cases On Model Analyses And Forecasts Of Severe Convection, Robert M. Szot

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

The quality of forecasts of severe deep convection in high-resolution numerical weather prediction is dependent on the representativeness of the model initial conditions. This representativeness may be negatively impacted by limited observation availability and by the presence of mesoscale heterogeneities that were not detected by conventional observations assimilated into convection-allowing models. By assimilating storm-scale observations from the Targeted Observation by Radars and Uncrewed Aircraft Systems (UAS) of Supercells (TORUS) campaign into an ensemble styled after the Warn-on-Forecast System, this study aims to investigate if data from field work platforms can improve the quality of initial conditions in the ensemble, potentially …

Explainable Artificial Intelligence In The Image Domain And Its Applications To The Medical Field, Mirtha Lucas

Theses and Dissertations from DePaul University

This dissertation investigates the development of Explainable Artificial Intelligence (XAI) methods for deep learning models in the image domain, with a particular focus on medical imaging applications. Although neural networks achieve high predictive performance, their lack of interpretability limits their adoption in critical domains such as healthcare, where transparency and trust are essential. This work addresses this challenge by proposing novel approaches that improve the interpretability and reliability of model predictions.   A primary contribution is the introduction of Riemann–Stieltjes Integrated Grad-CAM (RSI Grad-CAM), a gradient-based attribution method that generates more relevant and spatially localized saliency maps. The method is evaluated …

Continuum Observations Of Water Maser Sites, Aman Khan

Theses and Dissertations from DePaul University

Stars form in cores deep within cold (10 K) clouds of hydrogen gas. As material is accreted onto the central object inside these cores (the protostar), powerful outflows that channel mass away from the protostar are also launched. The launching mechanism for these outflows is still not fully understood. This thesis presents observations of the 22 GHz continuum emission from two star-forming regions, NGC 7129 FIRS2 and IC 1396-n. The immediate aim of these observations is to find where this continuum emission is coming from, particularly if it is free-free emission from a jet or outflow, and the long term …

Factors Associated With Lapses In Care Among People Living With Hiv In South Carolina, Xueying Yang Ph.D., Fanghui Shi, Shujie Chen, Gavi Samuel, Bankole Olatosi Ph.D., Sharon Weissman, Xiaoming Li Ph.D., Jiajia Zhang Ph.D.

Faculty Publications

Utilizing statewide electronic health records (EHR) data, this study aims to assess the factors determining the occurrence of lapses in HIV care among people with HIV (PWH) in South Carolina (SC). All adult (≥ 18 years old) PWH who were diagnosed with HIV between 2006 and 2018 with at least two HIV care encounters and at least 1-year follow-up record were included in the analysis. The outcome, a lapse in care, was defined as a repeated measure of HIV care encounter that occurs over a year following the previous visit. Generalized Estimation Equation models were employed. The study cohort had …

Bridging Draft Policy Misalignment: Group Tree Optimization For Speculative Decoding, Shijing Hu, Jingyang Li, Zhihui Lu, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates large language model (LLM) inference by letting a lightweight draft model propose multiple tokens that the target model verifies in parallel. Yet existing training objectives optimize only a single greedy draft path, while decoding follows a tree policy that re-ranks and verifies multiple branches. This draft policy misalignment limits achievable speedups. We introduce Group Tree Optimization (GTO), which aligns training with the decoding-time tree policy through two components: (i) Draft Tree Reward, a sampling-free objective equal to the expected acceptance length of the draft tree under the target model, directly measuring decoding performance; (ii) Group-based Draft Policy …

Real-Time Motion-Controllable Autoregressive Video Diffusion, Kesen Zhao, Jiaxin Shi, Beier Zhu, Junbao Zhou, Xiaolong Shen, Yuan Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) approaches. Existing AR video diffusion models are limited to simple control signals or text-to-video generation, and often suffer from quality degradation and motion artifacts in few-step generation. To address these challenges, we propose AR-Drag, the first RL-enhanced few-step AR video diffusion model for real-time image-to-video generation with diverse motion control. We first fine-tune a base I2V model to support basic motion control, then further improve it via reinforcement learning with a trajectory-based reward model. Our design preserves the …

Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where “prompt” plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: (1) temporal lag of training data, and (2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power …

Structural Silence: When Ai Infrastructure Fails Speakers Of Underrepresented Languages, Avijit Roy, Proma Roy

Publications and Research

Artificial intelligence tools for education and language support are increasingly framed as scalable responses to access gaps in under-resourced communities. Yet the infrastructure underlying these tools—training corpora, tokenization schemes, evaluation benchmarks, and deployment architectures—encodes a set of assumptions that systematically disadvantages speakers of underrepresented languages before a single model is trained. This paper examines those assumptions through the lens of Bengali, one of the world’s most widely spoken languages with roughly 285 million speakers (Ethnologue, 2025; International Communication and Leadership School, 2026), and the structural barriers that emerge when attempting to build AI-assisted educational tools for Bengali-speaking learners in low-connectivity …

Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – First Quarter 2025, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Silver Bow Creek Conservation Area Materials Management Plan Revision 1, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Machine Learning And Multisensor Data Fusion For Forest Above Ground Biomass Estimation In Arkansas, Abdullah Al Saim, Mohamed Aly

Geosciences Faculty Publications and Presentations

Forests are essential for biodiversity conservation, climate change, natural education, scientific research, and carbon sequestration. This study uses machine learning-based Random Forest (RF) regression to estimate the Above Ground Biomass (AGB) of the Ozark and Ouachita forests at a 10-meter resolution by combining data from Sentinel-2, Sentinel-1, and GEDI (Global Ecosystem Dynamics Investigation) on Google Earth Engine. The RF model included 34 out of 154 variables representing topographical, spectral, and textural factors demonstrating strong correlations with measured biomass. The RF model showed strong performance with R-squared and RMSE values of 0.95 and 18.46 for the training dataset and 0.75 and …

Upgrades To Carvers Bay Water System To Improve Safety And Water Quality, Samantha Stollenmaier

Goal 6: Clean Water and Sanitation

No abstract provided.

Freedom Readers And Sustainable Development Goal 4: Advancing Literacy In Low-Income Communities Of South Carolina, Madison Hayes

Goal 4: Quality Education

No abstract provided.

Leveraging Section 208 Water Quality Planning To Mitigate Pfas Contamination In Watersheds Across The Waccamaw Region, Anna Dimatteo

Goal 6: Clean Water and Sanitation

No abstract provided.

Implementation And Costs Of A Food Insecurity Resource Navigation Program For Primary Care Patients With Diabetes And Hypertension In South Carolina, Deeksha Gupta, Darin Thomas, Stella Coker Watson Self Ph.D., Ms, Edward A. Frongillo Jr. Ph.D., Alain H. Litwin, Joseph A. Ewing, Lynnette Ramos-Gonzalez, Lynnette Ramos-Gonzalez

Faculty Publications

Objective

To examine food insecurity resource navigation program costs and how navigation intensity relates to clinical outcomes, healthcare costs, and quality of life (QOL) for diabetes and/or hypertension patients.

Methods

This retrospective study included patients receiving resource navigation (July 12, 2021-December 31, 2022 with twelve-month follow-up) across three primary care practices in South Carolina's largest health system. Participants were 18+ years old (from electronic medical records/Epic), had food insecurity (from Hunger Vital Sign™), and diabetes and/or hypertension (from Epic registries). Matched controls came from food insecurity screening-only practices. Patients in each group (n= 219) had diabetes (9.13%), hypertension …

Draft Final 2025 Bpsou Subdrain Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System – Third Quarter 2025, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft 2025 Butte Priority Soils Operable Unit Residential Metals Abatement Program Construction Completion Report, Abby Peltomaa

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Fluid-Mediated Particle Transport: Revisiting Snowstorm Simulations In 2d With An Updated Pressure Calculation Algorithm, E. Maldonado, Michael W. Roth

The Compass: Earth Science Journal of Sigma Gamma Epsilon

We develop and present computer simulations of snowfall and snowfall management near buildings using deflection fins. Our approach first entails a numerical solution to the Navier – Stokes fluid dynamics equations in order to determine wind velocity profiles with and without deflection fins. Subsequent to finding the velocity field, snow particles are introduced and are subject to Newton’s Laws in order to allow transport, sticking to surfaces, landing and ultimately accumulation. We revisit previous work, presenting and discussing program construction and validation, along with cursory systematic studies of varying geometrical parameters in the simulation. The utility of the simulations is …

The Compass - Volume 95 Issue 1 - Complete Issue, Scott R. Beason

The Compass: Earth Science Journal of Sigma Gamma Epsilon

No abstract provided.

Construction Of A Deffi Sprayer: Using 3d Printer Methodology And Techniques For Cost-Effective Analysis Using Desi Mass Spectrometry Imaging, Ryan Karst, Savvy Stevens, Luke Amos, Logan Koester, Ethan Newbold, Karson Whitaker

Symposium Projects

Using 3D Printer Methodology and Techniques for Cost-Effective Analysis using DESI Mass Spectrometry Imaging