Digital Commons Network^™

Explainable Multimodal Sentiment Analysis Of Social Media Visual Content For Child Safety, Yee Sen Tan, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Ensuring the safety and well-being of children is increasingly important, especially in a world where visual content is pervasive. This paper proposes a novel multimodal, multilingual, and multiclass sentiment analysis method for social media content, aimed at improving content moderation for child safety. Our approach integrates textual, visual, and audio data from videos, categorizing sentiment into four levels: positive, slightly negative, negative, and strongly negative, enabling granular detection of harmful content. To enhance explainability and trust, we also leverage interpretable mechanisms to analyze the contributions of each modality. Evaluation of our method demonstrates strong generalization across diverse video types, and …

Information-Theoretic Detection Of Unusual Source Code Changes, Adriano Torres, Markus Wagner, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

The code base of software projects evolves essentially through inserting and removing information to and from the source code. We can measure this evolution via the elements of information—tokens, words, nodes—of the respective representation of the code. In this work, we approach the measurement of the information content of the source code of open-source projects from an information-theoretic standpoint. Our focus is on the entropy of two fundamental representations of code: tokens and abstract syntax tree nodes, from which we derive definitions of textual and structural entropy. We proceed with an empirical assessment where we evaluate the evolution patterns of …

Leveraging Reviewer Experience In Code Review Comment Generation, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Michael W. Godfrey, Chunhua Liu, Wachiraphan Charoenwet

Research Collection School Of Computing and Information Systems

Modern code review is a ubiquitous software quality assurance process aimed at identifying and resolving potential issues (e.g., functional, evolvability) within newly written code. Despite its effectiveness, the process demands large amounts of effort from the human reviewers involved. To help alleviate this workload, researchers have trained various deep learning based language models to imitate human reviewers in providing natural language code reviews for submitted code. Formally, this automation task is known as code review comment generation. Prior work has demonstrated improvements in code review comment generation by leveraging machine learning techniques and neural models, such as transfer learning and …

Eformer: An Effective Edge-Based Transformer For Vehicle Routing Problems, Dian Meng, Zhiguang Cao, Yaoxin Wu, Yaqing Hou, Hongwei Ge, Qiang Zhang

Research Collection School Of Computing and Information Systems

Recent neural heuristics for the Vehicle Routing Problem (VRP) primarily rely on node coordinates as input, which may be less effective in practical scenarios where real cost metrics--such as edge-based distances--are more relevant. To address this limitation, we introduce EFormer, an Edge-based Transformer model that uses edge as the sole input for VRPs. Our approach employs a precoder module with a mixed-score attention mechanism to convert edge information into temporary node embeddings. We also present a parallel encoding strategy characterized by a graph encoder and a node encoder, each responsible for processing graph and node embeddings in distinct feature spaces, …

Connecting Giants: Synergistic Knowledge Transfer Of Large Multimodal Models For Few-Shot Learning, Hao Tang, Shengfeng He, Jing Qin

Research Collection School Of Computing and Information Systems

Few-shot learning (FSL) addresses the challenge of classifying novel classes with limited training samples. While some methods leverage semantic knowledge from smaller-scale models to mitigate data scarcity, these approaches often introduce noise and bias due to the data's inherent simplicity. In this paper, we propose a novel framework, Synergistic Knowledge Transfer (SYNTRANS), which effectively transfers diverse and complementary knowledge from large multimodal models to empower the off-the-shelf few-shot learner. Specifically, SYNTRANS employs CLIP as a robust teacher and uses a few-shot vision encoder as a weak student, distilling semantic-aligned visual knowledge via an unsupervised proxy task. Subsequently, a training-free synergistic …

Xfinbench: Benchmarking Llms In Complex Financial Problem Solving And Reasoning, Zhihan Zhang, Yixin Cao, Lizi Liao

Research Collection School Of Computing and Information Systems

Solving financial problems demands complex reasoning, multimodal data processing, and a broad technical understanding, presenting unique challenges for current large language models (LLMs). We introduce **XFinBench**, a novel benchmark with 4,235 examples designed to evaluate LLM’s ability in solving comple**X**, knowledge-intensive **Fin**ancial problems across diverse graduate-level finance topics with multi-modal context. We identify five core capabilities of LLMs using XFinBench, i.e., _terminology understanding_, _temporal reasoning_, _future forecasting_, _scenario planning_, and _numerical modelling_. Upon XFinBench, we conduct extensive experiments on 18 leading models. The result shows that o1 is the best-performing text-only model with an overall accuracy of 67.3%, but still …

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, …

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 …

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 …

August 2025 News Releases, University Of Montana--Missoula. Office Of University Relations

University of Montana News Releases, 1928, 1956-present

No abstract provided.

University Of Tennessee At Chattanooga Faculty Handbook, 2025 August 1, University Of Tennessee At Chattanooga. Office Of Academic Affairs, University Of Tennessee At Chattanooga. Faculty Senate

University of Tennessee at Chattanooga Faculty Handbooks

Governance, policies, guidelines, services, and resources managed by and for the faculty at the University of Tennessee at Chattanooga.

Islamism And Women’S Movements In Iran: Hijab As A Burden Of Representation, Samah Ahmed

International Studies (MA) Theses

Throughout history, Islamic discourse, political Islam, and the status of Muslim women have been subjects of study. Still, few research efforts have concentrated on the changes and dynamics between Islamic discourse and feminist resistance. Particularly in Iran, attention has primarily been given to the Islamism project, often treated as separate from social movements in post-Islamism. This thesis explores how social movements have used the symbolism of the veil in their resistance, focusing specifically on the women's social movement during the Islamic Revolution. Drawing on Assef Bayat's approach of Islamism and social movements and Gender and power dynamics theory, the main …

Misinformation, Disinformation, And The Erosion Of Institutional Trust: The Role Of Social Media In Democracy, Andres Murillo

International Studies (MA) Theses

In recent years, social media-driven misinformation and disinformation have helped erode public trust in democratic institutions. This thesis examines how constant exposure to false online content correlates with declining confidence in democracy and evaluates the roles of platforms, influencers, and mechanisms. Using a convergent mixed-methods design, this thesis pairs a quantitative survey with a qualitative analysis of three disinformation case studies: the 2020 U.S. election and the “Stop the Steal” movement, the Rohingya crisis in Myanmar crisis, and Russian denial of wartime atrocities in their invasion against Ukraine. Drawing on classical and modern propaganda theories (Lasswell, Herman & Chomsky, RAND’s …

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 …

A Qualitative Interpretative Meta-Analysis Of The Experiences Of Women Under Community Supervision, Gifty Kinful

Research Papers

Research reveals that women parolees or probationers encounter both challenges and support systems as they navigate their reintegration into the community. More specifically, most women returning from prison and those on probation are released into a society of stigma where access to housing, employment, education, transportation, and healthcare services is either severely limited or nonexistent. These serious obstacles can undermine their ability, motivation, and desire to succeed while on probation or parole. Support systems may be more or less available and effective. The qualitative literature on women under community supervision is large but to our knowledge, the diverse studies have …

Blackberry Long-Cane Cold Storage And Forcing Strategy Effects On Flower And Fruit Development, Leala Machesney, Ryan Dickson, Amanda Mcwhirt, Margaret L. Worthington, Stephanie E. Burnett

Horticulture Faculty Publications and Presentations

Blackberry (Rubus subg. Rubus) production using soilless culture and "long-cane" techniques offers growers the potential to achieve off-season harvests and increase yields. This study investigated the feasibility of achieving early spring harvests and high yields with four blackberry cultivars (Natchez, Ouachita, Prime-ArkVR Freedom, and Prime-ArkVR Traveler) grown using long-cane techniques and six different cold storage and forcing environment strategies. Long-cane blackberry plants were container-grown outdoors during 2020 and were overwintered either at an outdoor nursery or in a cold storage facility (4 degrees C). Dormant plants were then forced the following spring at the outdoor nursery location or in a …

Impaired Rest-Activity Rhythm Characteristics Predict Higher Risk Of Incident Type 2 Diabetes In Uk Biobank Participants, Chris Ho Ching Yeung, Alison K Wright, Daniel P Windred, Angus C Burns, Andrew J K Phillips, Sean W Cain, Martin K Rutter, Qian Xiao

Faculty, Staff and Student Publications

Objective: Circadian rhythms play a key role in metabolic health. Rest-activity rhythms, which are in part driven by circadian rhythms, may be associated with diabetes risk. There is a need for large prospective studies to comprehensively examine different rest-activity metrics to determine their relative strength in predicting risk of incident type 2 diabetes.

Research design and methods: In actigraphy data from 83,887 UK Biobank participants, we applied both parametric and nonparametric algorithms to derive 13 different metrics characterizing different aspects of rest-activity rhythm. Diabetes cases were identified using both self-reported data and health records. We used Cox proportional hazards models …

Linking Chronic Kidney Disease To Incident Heart Failure And Adverse Cardiac Remodeling Through The Plasma Proteome: The Aric Study, Leo F Buckley, Pranav Dorbala, Victoria Lamberson, Brian L Claggett, Yue Ren, Morgan E Grams, Josef Coresh, Kunihiro Matsushita, Ryan T Demmer, Ruth F Dubin, Rajat Deo, Peter Ganz, Christie M Ballantyne, Ron C Hoogeveen, Bing Yu, Amil M Shah

Faculty, Staff and Student Publications

Background: Investigation of circulating plasma proteins may reveal insights into molecular pathways that contribute to the development of heart failure (HF) among people with chronic kidney disease.

Objectives: The authors aimed to identify circulating kidney function-related plasma proteins that are associated with the risk of incident HF.

Methods: The authors studied participants of the ongoing longitudinal ARIC (Atherosclerosis Risk In Communities) study. Relative plasma concentrations of 4,697 unique proteins were measured with the use of an aptamer assay (Somalogic). Each protein was tested for associations with estimated glomerular filtration rate (eGFR), log-transformed urine albumin-to-creatinine ratio (UACR), and incident HF by …

Trends In Glucagon-Like Peptide 1 Receptor Agonist Prescribing Patterns, Maria Ukhanova, Joseph S Wozny, Chau N Truong, Lopita Ghosh, Trudy M Krause

Faculty, Staff and Student Publications

Objective: Obesity affects more than 40% of US adults, increasing risks for cardiovascular disease and type 2 diabetes. Glucagon-like peptide 1 receptor agonists (GLP-1 RAs), initially indicated for diabetes, show promise in weight loss but face coverage issues, high costs, and premature prescribing from physicians. Research is needed to assess prescribing patterns, especially in patients without diabetes.

Study design: We conducted a retrospective, population-based, observational study using the Merative MarketScan Commercial Database and the Merative MarketScan Medicare Supplemental Database, which capture person-specific clinical utilization and expenditures. We analyzed GLP-1 RA prescribing rates from 2018 to 2023, comparing semaglutide with other …

Racial Disparities In The Receipt Of Therapies For Cancer, Hypertension, And Diabetes, And In Mortality In A Large Population-Based Cohort Of Older Men With Prostate Cancer, Zhuoyun Li, Xianglin L Du

Faculty, Staff and Student Publications

Background: Racial disparities in treatment and mortality were widely studied among prostate cancer patients; however, few studies considered the prevalence of hypertension and diabetes and their treatment status.

Objectives: To determine whether there are racial discrepancies in the use of anti-cancer, antihypertensive, and antidiabetic drugs in a cohort of long-term survivors of prostate cancer diagnosed in 2007-2015; and to examine racial disparities in mortality after adjustments for their differences in those treatments.

Methods: We used the SEER (Surveillance, Epidemiology, and End Results)-Medicare linked database and included records for patients with prostate cancer at age 65 years or older diagnosed from …