Digital Commons Network^™

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 …

Rl4co: An Extensive Reinforcement Learning For Combinatorial Optimization Benchmark, Federico Berto, Et. Al

Research Collection School Of Computing and Information Systems

Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in …

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, …

Learning Frame-Level Classifiers For Video-Based Real-Time Assessment Of Stroke Rehabilitation Exercises From Weakly Annotated Datasets, Ana Rita Cóias, Min Hun Lee, Alexandre Bernardino, Asim Smailagic, Mariana Mateus, David Fernandes, Sofia Trapola

Research Collection School Of Computing and Information Systems

Autonomous rehabilitation support solutions, such as virtual coaches, should provide real-time feedback to improve motor function and maintain patient engagement. However, fully annotated dataset collection for real-time exercise assessment is time-consuming and costly, posing a barrier to evaluating proposed methods. In this work, we present a novel framework that learns a frame-level classifier using weakly annotated videos for real-time assessment of compensatory motions in stroke rehabilitation exercises by generating pseudo-labels at a frame level. We consider three approaches: 1) a baseline approach that uses a source dataset to train a frame-level classifier, 2) a transfer learning approach that uses target …

Causalabstain: Enhancing Multilingual Llms With Causal Reasoning For Trustworthy Abstention, Yuxi Sun, Aoqi Zuo, Wei Gao, Jing Ma

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) often exhibit knowledge disparities across languages. Encouraging LLMs to abstain when faced with knowledge gaps is a promising strategy to reduce hallucinations in multilingual settings. Current abstention strategies for multilingual scenarios primarily rely on generating feedback in various languages using LLMs and performing self-reflection. However, these methods can be adversely impacted by inaccuracies and biases in the generated feedback. To address this, from a causal perspective, we introduce CausalAbstain, a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones. Extensive experiments demonstrate that CausalAbstain effectively …

Memotune: A Measure And Moment-Driven Fine-Tuning Framework For Quantized Large Language Models, Yun Zhang, Xue Geng, Lizi Liao, Jintong Sun, Minghe Yu, Ge Yu

Research Collection School Of Computing and Information Systems

Quantizing large language models (LLMs) is essential for reducing memory and computational costs in natural language processing. Existing methods combine quantization with parameter-efficient fine-tuning but often fail to meet practical performance requirements. This paper introduces MeMoTune, a novel fine-tuning framework for quantized LLMs. By employing a measure and moment approach within a low-rank approximation framework in probability measure space, MeMoTune optimizes the objective function for superior fine-tuning results. The update process is further refined through scaled gradient, enhancing convergence efficiency and noise robustness. Experiments on tasks like text generation, summarization, and understanding show MeMoTune significantly outperforms state-of-the-art methods, e.g. fine-tuning …

Graph Positional Autoencoders As Self-Supervised Learners, Yang Liu, Deyu Bo, Wenxuan Cao, Yuan Fang, Yawen Li, Chuan Shi

Research Collection School Of Computing and Information Systems

Graph self-supervised learning seeks to learn effective graph representations without relying on labeled data. Among various approaches, graph autoencoders (GAEs) have gained significant attention for their efficiency and scalability. Typically, GAEs take incomplete graphs as input and predict missing elements, such as masked node features or edges. Although effective, our experimental investigation reveals that traditional feature or edge masking paradigms primarily capture low-frequency signals in the graph and fail to learn expressive structural information. To address these issues, we propose Graph Positional Autoencoders (GraphPAE), which employ a dual-path architecture to reconstruct both node features and positions. Specifically, the feature path …

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 …

Sifar: A Simple Faster Accelerated Variance‑Reduced Gradient Method, Zhize Li

Research Collection School Of Computing and Information Systems

In this paper, we propose a simple faster accelerated gradient method called SIFAR for solving the finite-sum optimization problems. Concretely, we consider both general convex and strongly convex settings: i) For general convex finite-sum problems, SIFAR improves previous state-of-the-art result given by Varag. In particular, for large-scale problems or the convergence error is not very small, SIFAR obtains the first optimal result O(n), matching the lower bound. ii) For strongly convex finite-sum problems, we also show that SIFAR can achieve the optimal convergence rate matching the lower bound. Besides, SIFAR enjoys a simpler loopless algorithmic structure while previous algorithms use …

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, …

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 …

Gnncontext: Gnn-Based Code Context Prediction For Programming Tasks, Xiaoye Zheng, Zhiyuan Wan, Shun Liu, Kaiwen Yang, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

A code context model comprises source code elements and their relations relevant to a programming task. The capture and use of code context models in software tools can benefit software development practices, such as code navigation and search. Prior research has explored approaches that leverage either the structural information of code or interaction histories of developers with integrated development environments to automate the construction of code context models. However, these approaches primarily capture shallow syntactic and lexical features of code elements, with limited ability to capture contextual and structural dependencies among neighboring code elements. In this paper, we propose GNNContext, …

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 …

Tetd: Trusted Execution In Trust Domains, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu

Research Collection School Of Computing and Information Systems

Intel TDX empowers cloud service providers to construct confidential virtual machines called trust domains (TDs) on x86 platforms. Similar to its counterparts from AMD and Arm, TDX's hardware based protection over integrity and secrecy of virtual machine memory and vCPU states inevitably hinders legitimate virtual machine management such as introspection. At the presence of compromised high-privileged software (e.g., the guest kernel), neither the cloud service provider nor the TD owner can securely carry out a task within the TD. To tackle this problem, we propose TETD, an in-TD trusted execution technique without trusting any TD system software. Our design does …

Oblivious Digital Tokens, Mihael Liskij, Xuhua Ding, Gene Tsudik, David A. Basin

Research Collection School Of Computing and Information Systems

A computing device typically identifies itself by exhibiting unique measurable behavior or by proving its knowledge of a secret. In both cases, the identifying device must reveal information to a verifier. Considerable research has focused on protecting identifying entities (provers) and reducing the amount of leaked data. However, little has been done to conceal the fact that the verification occurred.We show how this problem naturally arises in the context of digital emblems, which were recently proposed by the International Committee of the Red Cross to protect digital resources during cyber-conflicts. To address this new and important open problem, we define …

Consistent Client Simulation For Motivational Interviewing-Based Counseling, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Phey Ling Kit, Jenny Xiuhui Giam, John Pinto, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Simulating human clients in mental health counseling is crucial for training and evaluating counselors (both human or simulated) in a scalable manner. Nevertheless, past research on client simulation did not focus on complex conversation tasks such as mental health counseling. In these tasks, the challenge is to ensure that the client’s actions (i.e., interactions with the counselor) are consistent with with its stipulated profiles and negative behavior settings. In this paper, we propose a novel framework that supports consistent client simulation for mental health counseling. Our framework tracks the mental state of a simulated client, controls its state transitions, and …

Mixture Effects On Metal Accumulation In The Asian Clam, Corbicula Fluminea, Mark A. Huff

Electronic Theses and Dissertations

Field collected Corbicula fluminea were exposed to trace level copper, lead, and zinc with the aim of examining the preferential accumulation between the soft tissue and shell. Metal levels were based on those in a permitted industrial effluent. At 1X effluent and 2X effluent exposure levels, copper was found to preferentially accumulate in the clam soft tissue, lead was found to preferentially accumulate in the clam shell, and zinc was found to excrete back into the diluent after preferentially accumulating in the clam soft tissue. When exposed as a mixture at 1X effluent levels, copper and lead were found to …

Examining The Lived Experiences Of International Students At East Tennessee State University, John The Baptist Anodjo

Electronic Theses and Dissertations

This research investigates the lived experiences of international students at East Tennessee State University (ETSU), specifically examining how they navigate social, academic, and structural challenges. Through in-depth interviews with 20 international students from a variety of nationalities, I analyzed issues related to legal status, housing, food insecurity, healthcare, and social integration. I found that students encounter barriers due to immigration policies, financial constraints, and limited access to essential resources. Additionally, international students benefit significantly from support provided by campus services, community organizations, and their social networks, which play a vital role in their adjustment process. While institutional resources are beneficial …

Black Women In Information Technology Leadership Roles: Examining Their Experiences With Recruitment, Retention, And Advancement, Adepeju O. Adedeji

Electronic Theses and Dissertations

Black women account for only 3% of the information technology (IT) workforce in the United States, with a lower percentage in leadership roles. This qualitative phenomenological study explored the lived experiences of Black women who had attained leadership roles in the IT field. The researcher examined the opportunities and challenges Black women faced in their recruitment, retention, and advancement to leadership roles. The purpose of the study was to understand the strategies that aided Black women who experienced successful recruitment, advancement, and retention in IT to extract insights that can be leveraged for other Black women. The researcher conducted interviews …

Overcoming Second-Order Barriers To Teachers’ Technology Integration: A Study Of Positive Impacts, Crystal Ann Hume

Electronic Theses and Dissertations

This study explored K–12 public school teachers’ self-efficacy, value, and pedagogical beliefs about technology. The teachers worked in a medium-sized, semi-affluent public school district in North Texas. The study investigated second-order barriers to technology integration and what might positively impact teachers’ self-efficacy, value, and pedagogical beliefs. This study used a qualitative descriptive methodology to collect qualitative data about their beliefs by interviewing and observing K–12 classroom teachers who had completed a bachelor’s degree through a traditional teacher education program. Teachers with alternative certifications or noncertified classroom instructors were omitted. Findings revealed that external factors, failure, mistakes, confidence, and professional development …

Institutional Betrayal And Military Vaccine Mandates: An Inquiry Into Loss Of Health Autonomy Concerning Unlimited Liability Contractual Employment In The Covid-19 Era, Aaron G. Dehaven

Electronic Theses and Dissertations

The balance between government control and personal liberty is a growing concern in the modern world. Nowhere is this more evident than in discussions surrounding government policy in public health and individual health autonomy. The Covid-19 pandemic forced many institutions to change their standard operating procedures by adapting to rapidly changing health guidance instituted to protect public health. These changes highlighted the role of health autonomy and individuals’ rights to refuse medical treatments. The Department of Defense changed many aspects of how it operated during the pandemic, including the mandate of the Covid-19 vaccine for all service members. For various …

Perceived Leadership Behaviors And Workplace Motivation: The Predictive Role Of Generational Cohorts In The U.S. Construction Industry, Leslie D. Klinegardner

Electronic Theses and Dissertations

The purpose of this study was to examine the extent to which perceived leadership behaviors predicted workplace motivation among construction workers across generational cohorts in the Greater Houston, Texas area. Grounded in self-determination theory, the research explored how transformational, transactional, and passive-avoidant leadership behaviors predicted the satisfaction and frustration of psychological needs for autonomy, competence, and relatedness. The analysis also considered whether leadership behaviors and generational cohort served as significant predictors of workplace motivation outcomes. The study followed a quantitative, predictive, cross-sectional design using survey methodology. Data were collected using the Multifactor Leadership Questionnaire (MLQ) and the Basic Psychological Need …

A Global Movement Takes Shape – Physical Activity, Health, And The New Era Of Responsibility: “Global Alliance For Promotion Of Physical Activity”, J. M. Steinacker, W. Van Mechelen, C. Joisten, S. Zanuso, C. A. Jaworski, Y. P. Pitsiladis

Research outputs 2022 to 2026

No abstract provided.

Simmel’S Reification In The Digital Era: The Commodification Of Emotional Relationships In Rent-A-Partner Services On Social Media, Juliana Pramesti

Masyarakat: Jurnal Sosiologi

This study examines the commodification of emotional relationships in Indonesia through the rise of “rent-a-partner” services on social media platforms such as Instagram and TikTok. Existing scholarship on digital intimacy has largely focused on Western and East Asian contexts, particularly dating applications, offering limited insight into how commodified intimacy operates in non-Western settings shaped by distinct socio-cultural and moral frameworks. Addressing this gap, the study draws on Georg Simmel’s concept of reification to analyze how emotional intimacy is transformed into purchasable service packages within platform-mediated environments. Using a qualitative approach that combines netnographic observation and in-depth interviews, the study demonstrates …

Society For Healthcare Epidemiology Of America (Shea) Infectious Diseases Fellow Infection Prevention And Control And Healthcare Epidemiology Curriculum, Elise M Martin, Catherine Cichon, Rebecca Choudhury, Shandra R Day, Yasaman Fatemi, Vera P Luther, Terri Stillwell, Abby Sung

2020-Current year OA Pubs

With the rapid expansion of the Infection Prevention Control/Healthcare Epidemiology (IPC/HE) fields over recent decades, the pivotal roles of IPC/HE in hospital regulation, quality improvement, patient safety, and healthcare finances have become increasingly apparent. Consequently, the demand for effective IPC/HE leaders has surged.

Sugar Functionalized Collagen Material For Local Modulation Of Innate Immunity, John Otto Martinez, Silvia Minardi, Laura Pandolfi, Xing Wang, Ennio Tasciotti, Kavindra V Singh, Cesar A Arias, Bruna Corradetti

Faculty, Staff and Students Publications

Small alterations during the early stages of the innate immune response to an implant can drive large changes in adaptive immunity. Biomaterials for regenerative purposes can be engineered to modulate this immune response in beneficial ways. This study presents an innovative patch designed and functionalized to target the innate immunity at the implant site. Mannose moieties are incorporated into collagen patches, resulting in a technology called Local Immunotuning Patch (LIP), designed to directly interact with antigen presenting cells through their mannose receptor. In vitro, LIP shows anti-inflammatory effects on bone marrow-derived macrophages and inhibitory properties even on methicillin-resistant bacterial strains. …

Indoor Air Quality Conditions And Respiratory Virus Detections In Elementary Schools—Kansas City, Missouri, February–March 2023, Olivia M. Almendares, Jasmine D. Ruffin, Luke Gard, Rangaraj Selvarangan, Anjana Sasidharan, Dithi Banerjee, Nibha Sagar, Amanda Hayes, Sydnie Petty, Brian R. Lee, Chris T. Young, Janelle Potter, Shannon Tilsworth, Jennifer Goldman, Jennifer E. Schuster, Hannah L. Kirking

Manuscripts, Articles, Book Chapters and Other Papers

Background/Objectives: Respiratory viruses circulate year-round and can spread indoors via inhalation of airborne particles. Effective ventilation and filtration may reduce transmission, particularly in school settings where children and staff spend significant time. This study examines the impact of indoor air quality (IAQ) and ventilation in schools on respiratory virus detection.

Methods: During February 27 to March 30, 2023, we assessed the relationship between IAQ and respiratory viruses in two Kansas City, Missouri, elementary schools by collecting bioaerosol samples, student and staff nasal swabs, and monitoring IAQ across 10 rooms (six classrooms, two common spaces, and two nurses’ offices). We calculated …

Impact Of Biodegradation On The Mechanical And Fatigue Properties Of 3d-Printed Pla Bone Scaffolds, Hamed Bakhtiari, Alireza Nouri, Muhammad Aamir, Mohadeseh Najafi, Majid Tolouei-Rad

Research outputs 2022 to 2026

A proper degradation rate of bone scaffolds ensures optimal mechanical support and effective tissue regeneration. The present study examines the degradation effects of simulated body fluids (SBF) on the compressive and fatigue strength of 3D-printed PLA bone scaffolds. Scaffolds with varying surface-to-volume (S/V) ratios and identical porosity (60 %) were immersed in Hanks' solution for a maximum period of 30 days. Static and dynamic compression tests were performed at different immersion times to assess how S/V ratio influences the degradation process. CT images showed that scaffold pore structure remained interconnected after biodegradation, with no significant change in strut thickness or …

The Influence Of Surface Textural Properties And Adsorption Temperature On H2 Adsorption And Storage Performance Of Mil-88b(Fe) Mofs, Tawanda Matamba, Samira Sadeghi, Hussein Rasool Abid, Masoumeh Zargar, Alireza Keshavarz, Stefan Iglauer

Research outputs 2022 to 2026

To advance the establishment of a stable H2 economy we need to solve the issue regarding its storage. The physicochemical properties of Metal Organic Frameworks (MOFs) make them suitable for H2 storage. Therefore, here, we synthesized two Iron-based MOFs (12-MIL-88 B(Fe), 24-MIL-88 B(Fe) at different reaction times (12 h and 24 h), to investigate the relationship between adsorption temperature and pressure with the material's surface textural properties towards H2 storage (at 30 °C, 0 °C, −78.5 °C, and −196.15 °C and from vacuum pressure to 55 bar). Results showed that at 30 °C and 0 °C adsorption temperature, H2 adsorption …

Advancement Of Biocarbon Materials In Sustainable Thermal And Electrochemical Energy Storage With Future Outlooks, Md Shahriar Mohtasim, Barun K. Das

Research outputs 2022 to 2026

Biocarbon exhibits significant potential in thermal and electrochemical energy storage owing to its higher surface area, flexible porosity, and superior thermal stability, facilitating effective energy absorption, storage, and conversion. Additionally, its sustainable and cost-effective nature, derived from renewable biomass, aligns with the growing demand for eco-friendly solutions. There is not enough review work that offers comprehensive details on the unique properties of biocarbon (both animal and plant-derived), synthesis and characterization methods, the root level mechanism of biocarbon's interaction with phase change materials (PCMs) matrix, the generalization of thermal and electrochemical regulation with an emphasis on the benefits to the environment …