Digital Commons Network^™

Taclr: A Scalable And Efficient Retrieval-Based Method For Industrial Product Attribute Value Identification, Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen

Research Collection School Of Computing and Information Systems

Product Attribute Value Identification (PAVI) involves identifying attribute values from product profiles, a key task for improving product search, recommendation, and business analytics on e-commerce platforms. However, existing PAVI methods face critical challenges, such as inferring implicit values, handling outof-distribution (OOD) values, and producing normalized outputs. To address these limitations, we introduce Taxonomy-Aware Contrastive Learning Retrieval (TACLR), the first retrieval-based method for PAVI. TACLR formulates PAVI as an information retrieval task by encoding product profiles and candidate values into embeddings and retrieving values based on their similarity. It leverages contrastive training with taxonomy-aware hard negative sampling and employs adaptive inference …

Starpose: 3d Human Pose Estimation Via Spatial-Temporal Autoregressive Diffusion, Haoxin Yang, Weihong Chen, Xuemiao Xu, Cheng Xu, Peng Xiao, Cuifeng Sun, Shaoyu Huang, Shengfeng He

Research Collection School Of Computing and Information Systems

Monocular 3D human pose estimation remains a challenging task due to inherent depth ambiguities and occlusions. Compared to traditional methods based on Transformers or Convolutional Neural Networks (CNNs), recent diffusionbased approaches have shown superior performance, leveraging their probabilistic nature and high-fidelity generation capabilities. However, these methods often fail to account for the spatial and temporal correlations across predicted frames, resulting in limited temporal consistency and inferior accuracy in predicted 3D pose sequences. To address these shortcomings, this paper proposes StarPose, an autoregressive diffusion framework that effectively incorporates historical 3D pose predictions and spatialtemporal physical guidance to significantly enhance both the …

From Risk To Resilience: Towards Assessing And Mitigating The Risk Of Data Reconstruction Attacks In Federated Learning, Xiangrui Xu, Zhize Li, Yufei Han, Bin Wang, Jiqiang Liu, Wei Wang

Research Collection School Of Computing and Information Systems

Data Reconstruction Attacks (DRA) pose a significant threat to Federated Learning (FL) systems by enabling adversaries to infer sensitive training data from local clients. Despite extensive research, the question of how to characterize and assess the risk of DRAs in FL systems remains unresolved due to the lack of a theoretically-grounded risk quantification framework. In this work, we address this gap by introducing Invertibility Loss (InvLoss) to quantify the maximum achievable effectiveness of DRAs for a given data instance and FL model. We derive a tight and computable upper bound for InvLoss and explore its implications from three perspectives. First, …

L2m2: A Hierarchical Framework Integrating Large Language Model And Multi‑Agent Reinforcement Learning, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Lin Li, Xin Zhao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning (MARL) has demonstrated remarkable success in collaborative tasks, yet faces significant challenges in scaling to complex scenarios requiring sustained planning and coordination across long horizons. While hierarchical approaches help decompose these tasks, they typically rely on hand-crafted subtasks and domain-specific knowledge, limiting their generalizability. We present L2M2, a novel hierarchical framework that leverages large language models (LLMs) for high-level strategic planning and MARL for low-level execution. L2M2 enables zero-shot planning that supports both end-to-end training and direct integration with pre-trained MARL models. Experiments in the VMAS environment demonstrate that L2M2's LLM-guided MARL achieves superior performance while requiring …

Freqllm: Frequency-Aware Large Language Models For Time Series Forecasting, Shunan Wang, Min Gao, Zongwei Wang, Yibing Bai, Feng Jiang, Guansong Pang

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently shown promise in Time Series Forecasting (TSF) by effectively capturing intricate time-domain dependencies. However, our preliminary experiments reveal that standard LLM-based approaches often fail to capture global correlations, limiting predictive performance. We found that embedding frequency-domain signals smooths weight distributions and enhances structured correlations by clearly separating global trends (low-frequency components) from local variations (high-frequency components). Building on these insights, we propose FreqLLM, a novel framework that integrates frequency-domain semantic alignment into LLMs to refine prompts for improved time series analysis. By bridging the gap between frequency signals and textual embeddings, FreqLLM effectively captures …

Fine‑Tuning Multimodal Large Language Models For Product Bundling, Xiaohao Liu, Jie Wu, Zhulin Tao, Yunshan Ma, Yinwei Wei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent advances in product bundling have leveraged multimodal information through sophisticated encoders, but remain constrained by limited semantic understanding and a narrow scope of knowledge. Therefore, some attempts employ In-context Learning (ICL) to explore the potential of large language models (LLMs) for their extensive knowledge and complex reasoning abilities. However, these efforts are inadequate in understanding mulitmodal data and exploiting LLMs' knowledge for product bundling. To bridge the gap, we introduce Bundle-MLLM, a novel framework that fine-tunes LLMs through a hybrid item tokenization approach within a well-designed optimization strategy. Specifically, we integrate textual, media, and relational data into a unified …

Anomalygfm: Graph Foundation Model For Zero/Few-Shot Anomaly Detection, Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular and heterogeneous abnormality patterns in graphs from different domains. To address this challenge, we propose AnomalyGFM, a GAD-oriented graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD in diverse graph datasets. …

Affinitytune: A Prompt-Tuning Framework For Few-Shot Anomaly Detection On Graphs, Jingyan Chen, Guanghui Zhu, Guansong Pang, Chunfeng Yuan, Yihua Huang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) is a critical task with applications in domains such as networking, finance, and bioinformatics. % However, the scarcity of labeled anomalies and the limitations of unsupervised methods hinder effective detection. % While semi-supervised and few-shot learning approaches offer improvements, they struggle with knowledge transfer and rely heavily on labeled data. % Recent advancements in prompt tuning on graphs provide a promising direction, but their application to heterophilous graphs in anomaly detection remains underexplored. % In this work, we propose AffinityTune, a novel framework for few-shot graph anomaly detection based on prompt tuning. % Our approach introduces …

Llm2rec: Large Language Models Are Powerful Embedding Models For Sequential Recommendation, Yingzhi He, Xiaohao Liu, An Zhang, Yunshan Ma, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns. However, these embeddings depend solely on past interactions, lacking transferable knowledge to generalize to unseen domains. Recent advances in large language models (LLMs) have motivated text-based recommendation approaches that derive item representations from textual descriptions. While these methods enhance generalization, they fail to encode CF signals-i.e., latent item correlations and preference patterns-crucial for effective recommendation. We argue that an ideal embedding model …

Collisionrepair: First‑Aid And Automated Patching For Storage Collision Vulnerabilities In Smart Contracts, Yu Pan, Wanjing Han, Yue Duan, Mu Zhang

Research Collection School Of Computing and Information Systems

Storage collision vulnerabilities, a significant security risk in upgradeable smart contracts, often arise when a user-facing proxy contract and a backend logic contract share storage space. While static analysis techniques can detect such issues, they often over-approximate program states, leading to false positives and requiring developers to manually verify each issue, giving attackers time to exploit any overlooked vulnerabilities. To address this, we propose COLLISIONREPAIR, an automated patching technique for mitigating storage collision risks. COLLISIONREPAIR monitors storage access sequences between proxy and logic contracts by defining an "ownership" property for storage locations. It then replays historical transactions to recover existing …

Prism: To Fortify Widget Based User‑App Data Exchanges Using Android Virtualization Framework, Yingtat Ng, Zhe Chen, Haiqing Qiu, Xuhua Ding

Research Collection School Of Computing and Information Systems

We present Prism, an UI hardening technique for an Android app to safeguard its widgets against a corrupted kernel. Prism ensures secure interface rendering and allows for visual authentication, which developers could use to enable user intent confidentiality protection. Our design leverages the recent Android Virtualization Framework with minimal changes to the existing UI framework and graphics subsystem. It is much easier to deploy and use Prism on Android phones than TrustZone-based secure UI schemes, because the apps are not admitted to the Secure World and retain their full rights to manage and control their own interfaces. We have implemented …

Achilles: A Formal Framework Of Leaking Secrets From Signature Schemes Via Rowhammer, Junkai Liang, Zhi Zhang, Xin Zhang, Qingni Sheng, Yansong Gao, Xinliang Yuan, Haiyang Xue, Pengfei Wu, Zhonghai. Wu

Research Collection School Of Computing and Information Systems

Signature schemes are a fundamental component of cyber-security infrastructure. While they are designed to be mathematically secure against cryptographic attacks, they are vulnerable to Rowhammer fault-injection attacks. Since all existing attacks are ad-hoc in that they target individual parameters of specific signature schemes, it remains unclear about the impact of Rowhammer on signature schemes as a whole.In this paper, we present Achilles, a formal framework that aids in leaking secrets in various real-world signature schemes via Rowhammer. Particularly, Achilles can be used to find potentially more vulnerable parameters in schemes that have been studied before and also new schemes that …

Improved Secure Two-Party Computation From A Geometric Perspective, Hao Guo, Liqiang Peng, Haiyang Xue, Li Peng, Weiran Liu, Zhe Liu, Lei. Hu

Research Collection School Of Computing and Information Systems

Multiplication and other non-linear operations are widely recognized as the most costly components of secure two-party computation (2PC) based on linear secret sharing. Moreover, the comparison protocol (or Wrap protocol) is essential for various operations such as truncation, signed extension, and signed non-uniform multiplication. This paper aims to optimize these protocols by avoiding invoking the costly comparison protocol, thereby improving their efficiency.We propose a novel approach to study 2PC from a geometric perspective. Specifically, we interpret the two shares of a secret as the horizontal and vertical coordinates of a point in a Cartesian coordinate system, with the secret itself …

Exploring Rehabilitation Therapists' Knowledge And Perspectives On The Use Of Artificial Intelligence And Machine Learning For Persons Poststroke, Hannah Clark, Mia Delvecchio, Min Hun Lee, Elena D. Brown, Kaia Mikula, Robert Halyama, Kasey Stepansky

Research Collection School Of Computing and Information Systems

Research Objectives: The use of technology such as robotics, gaming systems, self-monitoring apps, or other sensor-based devices in standard practice is infrequent. Due to the rapid development of artificial intelligence (AI) and machine learning (ML) applications, it is important to look at how therapists perceive AI/ML, and design applications with potential barriers in mind. to support future integration into practice. The purpose of this research project is to gain rehabilitation therapists’ perspectives on AI/ML in post-stroke assessment and intervention.Design: This ongoing study uses a mixed methods design with surveys and focus groups. Participants engaged in a 30-minute webinar to learn …

Advancing Real-World Implementation Of The Well Optimized Linear Finder (Wolf) High-Speed Atmospheric Turbulence Compensation Method, Timothy Evan Coon

Theses and Dissertations

This dissertation advances the real-world implementation of the Well Optimized Linear Finder (WOLF) method for high-speed Atmospheric Turbulence Compensation (ATC). Atmospheric turbulence introduces phase aberrations into optical wavefronts and degrades image quality in terrestrial imaging systems. Traditional phase diversity methods are computationally intensive and poorly suited to real-time operation. The WOLF method addresses these limitations through a novel, point-wise formulation of the optical transfer function (OTF) as a structured autocorrelation of the generalized pupil function (GPF). This formulation enables the estimation of phase aberrations at individual spatial coordinates with distributed computational complexity.

The research begins by developing a MATLAB-based simulation …

Investigating Global Lightning Observed From Ground And Space, And Its Relationship To Solar Activity, Megan Diane Mark

Theses and Dissertations

The extremes of lightning, specifically lightning with long-lasting continuing currents and lightning with extremely high peak currents, are investigated from both ground- and space-based observations. Additionally, the potential solar influence on lightning is investigated on large spatial scales.

Continuing currents may occur following the impulsive flow of current during a cloud-to-ground (CG) return stroke and are usually low amplitude (from a few amperes to a few kiloamperes) and long duration (several to hundreds of milliseconds). Remotely estimating their duration from the electromagnetic fields measured by existing ground-based lightning locating systems (LLSs) is not possible, but some space-based lightning detection systems …

Layer-Wise Prediction Of Overhang-Related Geometric Deviation In Metal Additive Manufacturing With Conditional Generative Adversarial Networks, Himal Sapkota

Theses

Ensuring dimensional precision in parts with complex overhangs is a significant concern in Metal Additive Manufacturing (MAM), as undetected geometric deviations can compromise functionality and reliability. This research introduces a Conditional Generative Adversarial Network (cGAN), specifically the Pix2Pix framework, to predict layer-wise geometric deviations in Laser Powder Bed Fusion (LPBF) printed parts with overhang geometries using paired 2D CAD slices and corresponding X-ray Computed Tomography (XCT) based ground truth images. A key innovation is using RGB color-coded CAD slices to encode overhang angle information, enhancing feature distinction and prediction accuracy compared to non-color-coded inputs. Eighteen Pix2Pix models were trained across …

Ai-Enhanced Structured Literacy Intervention For Secondary Students: A Case Study Of Science Of Reading, Jennifer Bird

Teaching & Learning Faculty Publications

This study examines the effectiveness of Lexia PowerUp, an AI-powered literacy program, for sixth-grade students requiring Tier 3 reading intervention. Seven sixth-grade students (six boys, one girl; five African American, two Caucasian; all qualifying for free/reduced lunch) participated in a six-month intervention combining 50 minutes of daily small-group instruction with individualized Lexia PowerUp usage. Researchers measured progress through Achieve 3000 Lexile assessments and Lexia PowerUp performance data across three skill strands: Word Study, Grammar, and Comprehension. All participants demonstrated Lexile level improvements from beginning-of-year to mid-year assessments, though students remained below sixth-grade benchmarks (925-1070L). Analysis of Lexia PowerUp progression showed …

Autoregressive Temporal Modeling For Advanced Tracking-By-Diffusion, Pha Nguyen, Rishi Madhok, Bhiksha Raj, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Object tracking is a widely studied computer vision task with video and instance analysis applications. While paradigms such as tracking-by-regression,-detection,-attention have advanced the field, generative modeling offers new potential. Although some studies explore the generative process in instance-based understanding tasks, they rely on prediction refinement in the coordinate space rather than the visual domain. Instead, this paper presents Tracking-by-Diffusion, a novel paradigm for object tracking in video, leveraging visual generative models via the perspective of autoregressive models. This paradigm demonstrates broad applicability across point, box, and mask modalities while uniquely enabling textual guidance. We present DIFTracker, a framework that utilizes …

From Storm To Strength: Katrina's Legacy Of Health, Healing, And Hope, Lsu Health Sciences Center - New Orleans

LSUHSC Hurricane Katrina Archive: Reports & Presentations

This is the schedule for "From Storm to Strength" Katrina Symposium organized by LSU Health New Orleans to commemorate the 20th Anniversary of Hurricane Katrina.

A speaker panel session was held each day from Monday, August 24, 2025 to Thursday, August 28, 2025 for LSU Health faculty, staff and guests to share their remembrances of the hurricane and its impact on the school, on the City, and on their lives.

Computational Frameworks To Unravel The Immune Landscape, Shan He

Dissertations and Theses (Open Access)

Recent advances in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, face challenges such as resistance and immune-related adverse events, partly due to our limited understanding of the immune signaling pathways. While high- throughput genomic data provide unprecedented resolution into these immune pathways, their full potential is limited by the lack of well-annotated, context-specific immune gene sets. To address this need, I developed a workflow to construct immune gene sets by integrating RNA- seq datasets and performing decomposition. Using this approach, I constructed 28 immune- specific gene sets from 83 bulk RNA-seq datasets and 12 Natural Killer (NK) …

Insights Into The Radiation Chemistry Of Flash Radiation Therapy, Alan E. Lopez Hernandez

Dissertations and Theses (Open Access)

Purpose: FLASH radiation therapy (FLASH-RT) is an emerging modality that delivers radiation at ultra-high dose rates (UHDR) and has shown consistent normal tissue sparing while preserving tumor control compared to conventional RT—a phenomenon termed the FLASH effect. Despite promising results, the mechanisms behind the FLASH effect remain unclear. Many hypotheses point to radiolytic interactions, but experimental validation is limited. This work aims to develop a robust experimental platform to evaluate the relationship between radiolytic species production and physical beam parameters relevant to FLASH-RT, alongside dosimetry tools for reliable UHDR beam characterization.

Methods: A beam collector (BC) Faraday cup detector was …

Facilitating The Clinical Translation Of Flash Radiotherapy Through Dosimetry Development And Beam Parameter Optimization, Kevin Liu

Dissertations and Theses (Open Access)

Advisory Professor: Emil Schüler, PhD

Radiation therapy (RT) is a crucial component of curative cancer therapy, with a majority of cancer patients in the United States receiving RT as part of treatment. The goal of RT is to maximize the therapeutic index in curing disease while minimizing any associated normal-tissue complications¹. Recently, ultra-high dose-rate (UHDR) RT (mean dose rates ≥40 Gy/s for a total duration of ≤200 ms) has been reported to selectively spare normal tissues and organs while maintaining an isoeffective tumoricidal effect compared to conventional (CONV) dose rate RT in a variety of in vivo preclinical …

Predicting Genetic Interactions Using Functional Interaction Networks, Iulia Veronica Gheorghe

Dissertations and Theses (Open Access)

Mapping genetic interactions is central to understanding cellular systems and identifying therapeutic vulnerabilities, particularly in the context of cancer. Among these interactions, synthetic lethality, where simultaneous loss of two genes is lethal but loss of either alone is tolerated, offers a powerful framework for selectively targeting tumor-specific dependencies. In model organisms like S. cerevisiae, comprehensive double-knockout screens have revealed detailed genetic interaction maps, enabling systems-level insights into pathway structure, gene function, and cellular organization. Replicating this achievement in human cells, however, is complicated by the scale and complexity of the human genome. Recent advances in genome-wide CRISPR knockout screening have …

Executive Committee - Agenda, 08/01/2025, Academic Senate

Academic Senate Agendas

No abstract provided.

Drug Delivery, Disulfide Crosslinked Hydrogel Coating For Polydimethylsiloxane (Pdms) Implant Materials, Paul A. Contos, Paul Anthony Contos

Master's Theses

Though polydimethylsiloxane (PDMS) is often used in medical devices due to its desirable mechanical properties and biocompatibility, its surface is often prone to bacterial adhesion. This is especially a problem for catheters, where bacteria can adhere and form biofilms that can in turn result in Urinary Tract Infections (UTI). In order to deter the presence of UTI causing bacteria on PDMS catheters, methods were developed to formulate a disulfide crosslinked hydrogel coating and create a methodology to allow adhesion to a PDMS substrate. The hydrogel coating discussed in this paper will be synthesized by chemically crosslinking stimuli-responsive vesicles, that carry …

Internal Funding Newsletter, Academic Year 2024-2025, Office Of Research And Creative Activity, University Of Nebraska At Omaha

Internal Funding Newsletters

The University of Nebraska at Omaha is committed to educating people of the world and the Office of Research and Creative Activity is proud to contribute to UNO?s exceptional education, groundbreaking research, and the life-long success of students and alumni. UNO innovates for the public good through pragmatic and impactful research and discovery. This year's programs provided over $750,000 in awards for students and faculty. These projects included recording a new album of music for a saxophone, flute, and piano trio, understanding college campus bicycle theft, studying student perceptions of the difficulty of organic chemistry concepts, evaluating a therapy program …

Revolutionizing Digital Privacy Education For Older Adults: Enhanced Interventions And Ai-Assisted Learning Strategies, Heba Aly

All Dissertations

As older adults increasingly engage with digital platforms, they face unique privacy risks stemming from limited digital literacy, reduced trust in AI technologies, and constrained access—especially in rural or underserved communities. While digital tools offer benefits like social connection and information access, current privacy education efforts often neglect the needs of older adults. This dissertation addresses this gap by developing, testing, and refining digital privacy education interventions tailored for older adults, with a focus on trust, personalization, and AI-assisted learning.

Study 1 evaluates multiple instructional modalities across age groups, revealing older adults prefer structured videos and interactive tutorials, while younger …

A Study On The Propagation And Exploitation Of Structured Light In Underwater Turbulence, Jaxon P. Wiley

All Dissertations

The development and optimization of optical systems will play a pivotal role in the continued exploration and exploitation of the world’s underwater environments. These systems offer advantages in many sectors, and includes applications in areas such as high-speed communication, advanced sensing and imaging, and environmental characterization and monitoring. Underwater environments offer a plethora of challenges, however, and mitigating these obstacles remains an arduous task. In this work, the inherent advantages of structured light are leveraged to optimize optical system performance through non-ideal underwater conditions. Additionally, fundamental relationships between the generation of specified structured modes and their interactions with complex environments …

Multiple View Neural Regression Of A Facial Shape Model, Xiang Li

All Dissertations

Creating re-topologized 3D facial meshes is a critical step in high-quality facial animation pipelines, yet it remains a labor-intensive and time-consuming task. Traditional approaches typically rely on multiview stereo reconstruction and specialized photometric environments to acquire accurate geometric and reflectance data under controlled conditions. This dissertation presents work toward more efficient capture of production-ready meshes including (1) developmental aspects of VarIS, a custom-designed light sphere capable of capturing high-resolution stereo geometry and reflectance maps—including diffuse, specular, and normal components under programmable illumination; (2) a study of the effects of camera parameters on automatic 2D and 3D landmarking methods, (3) methods …