Digital Commons Network^™

2025-05-01 Pac-3 Emeritus Status For Faculty - Revisal, Morehead State University. Faculty Senate.

Faculty Senate Records

Another proposed revisal for PAC-3 Emeritus / Emeriti status for faculty, submitted to the Faculty Senate on May 1, 2025.

2025-05-01 Proposed Uar Service Awards, Morehead State University. Faculty Senate.

Faculty Senate Records

A proposed UAR for annual service awards to full-time employees submitted to the Faculty Senate on May 1, 2025.

2025-05-01 Feedback On Proposed Uar On Emeriti Benefits, Morehead State University. Faculty Senate.

Faculty Senate Records

Feedback given on the proposed UAR on emeriti benefits submitted to the Faculty Senate on May 1, 2025.

A Radiologist's Guide To Hepatobiliary Tumor Board Imaging, Elliot Berger

Aurora GME

No abstract provided.

Imaging Of Nontraumatic Temporal Bone Emergencies, Jared Magnuson

Aurora GME

No abstract provided.

Multiple Sclerosis, Thang Nguyen

Aurora GME

No abstract provided.

Post-Cholecystectomy Complications - Case Based Review, Tisileli Tuifua

Aurora GME

No abstract provided.

Top-Of-License Practice For Registered Nurses: A Scoping Review, Jeanne Hlebichuk, Rachelle J. Lancaster, Vida Vizgirda, Sharon Quinlan

Nursing

Background:Registered nurses are vital to healthcare delivery, yet often perform non-nursing tasks, contributing to inefficiencies, dissatisfaction, and turnover. The concept "top-of-license" (TOL) nursing practice, introduced in 2013, remains inconsistently defined and applied.

Purpose:To explore how TOL nursing practice is defined and applied in existing literature.

Methods:A scoping review was conducted using Arksey and O'Malley's framework and PRISMA-ScR guidelines. Searches of 15 databases and gray literature (no start date-May 4, 2024) yielded 1,377 articles. Following screening, 45 articles met inclusion criteria.

Findings:Definitions and applications of TOL varied. Three themes emerged: (a) work-workforce efficiency, (b) benefits to patients-improving …

A Comprehensive Study Of Impacts Of “Q” Bus Rapid Transit System On Blackstone Avenue, Yertai Tanai, Kamil Ciftci

Mineta Transportation Institute

This study examines the dual impact of the Fresno Area Express (FAX) Q Line, a 15.7-mile Bus Rapid Transit (BRT) system launched in 2018, on the Fresno housing market and passenger satisfaction. The Q Line, designed to modernize public transit, features eco-friendly compressed natural gas (CNG) vehicles, real-time passenger information, and improved service efficiency. Housing market analysis focused on residential properties sold between 2012 and 2024, utilizing Geographic Information System(GIS) mapping to segment properties into three regions: the Q Line corridor, an outer buffer zone, and the rest of Fresno. Results indicate no statistically significant increase in property values within …

Assessing Perceived Safety Of Non Motorized Travel With Virtual Reality, Vahid Balali, Sahand Fathi

Mineta Transportation Institute

Cycling is increasingly advocated as a healthier and more sustainable mode of transportation, as reflected in both scholarly literature and policy initiatives. Nonetheless, the escalation in bicyclist crash fatalities underscores deficiencies in extant roadway designs that inadequately safeguard these vulnerable users. A persistent challenge in the examination of bicyclist safety, behavior, and comfort is the paucity of comprehensive cycling data. To enhance understanding of cyclists' behavioral and physiological responses safely and efficiently, this investigation utilizes a bicycle simulator within an immersive virtual environment (IVE). Off-the-shelf sensors are employed to evaluate cyclists' performance metrics (speed and lane position) and physiological responses …

Revolutionizing Higher Ed: Ai’S Impact On Faculty And Pedagogy, George Antoniou, David G. Wolf, Ellen Ramsey, Matteo Peroni, Saidi Porta, Philip L. Fazio, Kristen Migliano

Faculty and Staff Publications & Presentations

No abstract provided.

Reframing Group Visits: Motivating Student Ambassadors To Lead With Purpose, Ryan Keane

Faculty and Staff Publications & Presentations

Campus Visit Coordinator Ryan Keane presented an educational session. Keane's presentation discussed shifting student ambassadors' mindset to view group visits as a beneficial professional opportunity. He highlighted Lynn's initiative towards student leadership positions, skill development workshops with faculty, and a group tour certification process.

Sigscope: Detecting And Understanding Off‑Chain Message Signing‑Related Vulnerabilities In Decentralized Applications, Sajad Meisami, Hugo Dabadie, Song Li, Yuzhe Tang, Yue Duan

Research Collection School Of Computing and Information Systems

In Web 3.0, an emerging paradigm of building decentralized applications or DApps is off-chain message signing, which has advantages in performance, cost efficiency, and usability compared to conventional transaction-signing schemes. However, message signing burdens DApp developers with extra coding complexity and message designing, leading to new security risks.This paper presents the first systematic study to uncover and characterize the security issues in off-chain message signing schemes and the DApps built atop them. We present a holistic static-analysis framework, SigScope, that uniquely combines the insights extracted from DApp front-end code (HTML and Javascript) off-chain and back-end smart contracts on-chain. We evaluate …

Real-Time Rectifying Flight Control Misconfiguration Using Intelligent Agent, Ruidong Han, Shangzhi Xu, Juanru Li, Elisa Bertino, David Lo, Jianfeng Ma, Siqi Ma

Research Collection School Of Computing and Information Systems

Configurations are supported by most flight control systems, allowing users to control a flying drone adapted to complexities such as environmental changes or mission alterations. Such an advanced functionality also introduces a significant problem—misconfiguration settings. It may cause drone instability, threaten drone safety, and potentially lead to substantial financial loss. However, detecting and rectifying misconfigurations across different flight control systems is challenging because (1) (mis)configuration-related code snippets might be syntactically correct and thus hard to identify through traditional code analysis; (2) the response to each configuration varies under different flying scenarios.In this article, we propose and implement a novel rectification …

Hierarchical Frameworks For Scaling-Up Multi-Agent Coordination, Minghong Geng

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning has emerged as a powerful framework for developing collaborative behaviors in autonomous systems. However, existing MARL methods often struggle with scalability in terms of both the number of agents and decision-making horizons. My research focuses on developing hierarchicalapproaches to scale up MARL systems through two complementary directions: structural scaling by increasing the number of coordinated agents and temporal scaling by extending planning horizons. My initial work introduced HiSOMA, a hierarchical framework integrating self-organizing neural networks with MARL forlong-horizon planning, and MOSMAC, a benchmark for evaluating MARL methods on multi-objective MARL scenarios. Building on these foundations, my recent …

Relation Prediction In Knowledge Graphs: A Self-Organizing Neural Network Approach, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Knowledge graphs (KGs) in specialized domains frequently suffer from incomplete information. While current relation prediction methods for KG completion typically rely on neural network-based representation learning, we present KG2ART---a novel self-organizing neural network that employs a fundamentally different approach. Instead of learning distributed representations, KG2ART encodes relation triples of knowledge graphs explicitly and performs parallel inference over the graph structure through bidirectional interactions between bottom-up activations and top-down pattern matching. Our comprehensive evaluation across five diverse KGs (Nations, UMLS, Kinship, CoDEx-M, and a jet engine technical KG) demonstrates that KG2ART consistently outperforms state-of-the-art baselines (TuckER, ComplEX, RESCAL, ConvE, CompGCN) in …

Guest Editorial: When Multimedia Meets Food: Multimedia Computing For Food Data Analysis And Applications, Weiqing Min, Shuqiang Jiang, Petia Radeva, Vladimir Pavlovic, Chong-Wah Ngo, Kiyoharu Aizawa, Wanqing Li

Research Collection School Of Computing and Information Systems

Food is central in our life for its fundamental role in our survival, health, mood and culture. The deployment of various networks (e.g., IoT and mobile networks), devices (e.g., hyperspectral imaging devices, electronic nose/tongue), databases (e.g., nutrition tables and food compositional databases), recipe-sharing websites (e.g., Yummly and Meishijie) and social media (e.g., Twitter and Weibo) has generated unprecedented volumes of multi-modal food data. Such multi-source multi-modal food data provides new perspectives to analyze and understand food consumption via multimedia computing. Riding on the wave of AI, food-oriented multimedia computing integrates AI, multimedia technology and food science to enable a wide …

Creating Talking Points For Client Advisers At Banks To Promote Sustainable Investing, Wewe Zi Yi, Pradeep Varakantham, Alan Megargel

Research Collection School Of Computing and Information Systems

Environmental, social and governance (ESG) factors have become key nonfinancial factors for investors to evaluate companies with respect to understanding material risks and growth opportunities. While not mandatory, companies are providing ESG reports that outline progress in different ESG metrics (six broad metrics and 15 specific ones). Client advisers (CAs) read these reports to identify key metrics of interest to investors. Given the number of companies and investment products, however, it is not feasible for CAs to read all the reports, which can sometimes run into tens or hundreds of pages). The authors have developed multiple frameworks building on leading …

Quantitative Runtime Monitoring Of Ethereum Transaction Attacks, Xinyao Xu, Ziyu Mao, Jianzhong Su, Xingwei Lin, David Basin, Jun Sun, Jingyi Wang

Research Collection School Of Computing and Information Systems

The rapid growth of decentralized applications, while revolutionizing financial transactions, has created an attractive target for malicious attacks. Existing approaches to detecting attacks often rely on predefined rules or simplistic and overly-specialized models, which lack the flexibility to handle the wide spectrum of diverse and dynamically changing attack types. To address this challenge, we present a general and extensible framework, MoE (Monitoring Ethereum), that leverages runtime verification to detect a wide range of attacks on Ethereum. MoE features an expressive attack modeling language, based on Metric First-order Temporal Logic (MFOTL), that can formalize a wide range of attacks. We integrate …

Integrating Path Selection For Symbolic Execution And Variable Selection For Constraint Solving, Shunkai Zhu, Jun Sun, Jingyi Wang, Zhenbang Chen, Peng Cheng

Research Collection School Of Computing and Information Systems

Symbolic execution is a powerful technique that can accurately synthesize program inputs for program testing through constraint solving. Applying symbolic execution effectively means that we must solve two searching problems efficiently. One is to search through the many program paths and the other is, given a particular path condition, to search through the numerous variable assignments to identify one satisfying solution. With few exceptions, existing symbolic execution engines treat constraint solvers as black boxes. As a result, the two searches are completely separated, which results in much redundancy (i.e., the same variable assignments may be tried for solving many program …

Dissecting Global Search: A Simple Yet Effective Method To Boost Individual Discrimination Testing And Repair, Lili Quan, Tianlin Li, Xiaofei Xie, Zhenpeng Chen, Sen Chen, Lingxiao Jiang, Xiaohong Li

Research Collection School Of Computing and Information Systems

Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns. Among these unfair behaviors, individual discrimination-examining inequalities between instance pairs with identical profiles differing only in sensitive attributes such as gender, race, and age-is extremely socially impactful. Existing methods have made significant and commendable efforts in testing individual discrimination before deployment. However, their efficiency and effectiveness remain limited, particularly when evaluating relatively fairer models. It remains unclear which phase of the existing testing framework (global or local) is the primary bottleneck limiting performance. Facing the above issues, we first identify …

Scenario-Driven And Context-Aware Automated Accessibility Testing For Android Apps, Yuxin Zhang, Sen Chen, Xiaofei Xie, Zibo Liu, Lingling Fan

Research Collection School Of Computing and Information Systems

Mobile accessibility is increasingly important nowadays as it enables people with disabilities to use mobile applications to perform daily tasks. Ensuring mobile accessibility not only benefits those with disabilities but also enhances the user experience for all users, making applications more intuitive and user-friendly. Although numerous tools are available for testing and detecting accessibility issues in Android applications, a large number of false negatives and false positives persist due to limitations in the existing approaches, i.e., low coverage of UI scenarios and lack of consideration of runtime context. To address these problems, in this paper, we propose a scenario-driven exploration …

Sans: Efficient Densest Subgraph Discovery Over Relational Graphs Without Materialization, Yudong Niu, Yuchen Li, Jiaxin Jiang, Laks V. S. Lakshmanan

Research Collection School Of Computing and Information Systems

How can we efficiently identify the densest subgraph over relational graphs? Existing dense subgraph discovery (DSD) approaches assume that a relational graph H is already derived from a heterogeneous data source and they focus on efficient discovery of the densest subgraph on the materialized H. Unfortunately, materializing relational graphs can be resource-intensive, which thus limits the practical usefulness of existing algorithms over large datasets. To mitigate this, we propose a novel Summary-bAsed deNsest Subgraph discovery (SANS) system. Our unique summary-based peeling algorithm forms the core of SANS. Following the peeling paradigm, it utilizes summaries of each node's neighborhood to efficiently …

Robust Threshold Ecdsa With Online-Friendly Design In Three Rounds, Guofeng Tang, Haiyang Xue

Research Collection School Of Computing and Information Systems

Threshold signatures, especially ECDSA, enhance key protection by addressing the single-point-of-failure issue. Threshold signing can be divided into offline and online phases, based on whether the message is required. Schemes with low-cost online phases are referred to as “online-friendly”. Another critical aspect of threshold ECDSA for real-world applications is robustness, which guarantees the successful completion of each signing execution whenever a threshold number t of semi-honest participants is met, even in the presence of misbehaving signatories. The state-of-the-art online-friendly threshold ECDSA with-out robustness was developed by Doerner et al. in S&P'24, requiring only three rounds. Recent work by Wong et …

Gamba: Marry Gaussian Splatting With Mamba For Single-View 3d Reconstruction, Qiuhong Shen, Zike Wu, Xuanyu Yi, Pan Zhou, Hanwang Zhang, Shuicheng Yan, Xinchao Wang

Research Collection School Of Computing and Information Systems

We tackle the challenge of efficiently reconstructing a 3D asset from a single image at millisecond speed. Existing methods for single-image 3D reconstruction are primarily based on Score Distillation Sampling (SDS) with Neural 3D representations. Despite promising results, these approaches encounter practical limitations due to lengthy optimizations and significant memory consumption. In this work, we introduce Gamba, an end-to-end 3D reconstruction model from a single-view image, emphasizing two main insights: (1) Efficient Backbone Design: introducing a Mamba-based GambaFormer network to model 3D Gaussian Splatting (3DGS) reconstruction as sequential prediction with linear scalability of token length, thereby accommodating a substantial number …

Building Bridges Across Papua New Guinea’S Digital Divide In Growing The Ict Industry, Marc Cheong, Sankwi Abuzo, Hideaki Hata, Priscilla Kevin, Winifred Kula, Benson Mirou, Christoph Treude, Dong Wang, Raula Gaikovina Kula

Research Collection School Of Computing and Information Systems

Papua New Guinea (PNG) is an emerging tech society with an opportunity to overcome geographic and social boundaries, in order to engage with the global market. However, the current tech landscape, dominated by Big Tech in Silicon Valley and other multinational companies in the Global North, tends to overlook the requirements of emerging economies such as PNG. This is becoming more obvious as issues such as algorithmic bias (in tech product deployments) and the digital divide (as in the case of non-affordable commercial software) are affecting PNG users. The Open Source Software (OSS) movement, based on extant research, is seen …

Hierarchical Learning-Based Graph Partition For Large-Scale Vehicle Routing Problems, Yuxin Pan, Ruohong Liu, Yize Chen, Zhiguang Cao, Fangzhen Lin

Research Collection School Of Computing and Information Systems

Neural solvers based on the divide-and-conquer approach for Vehicle Routing Problems (VRPs) in general, and capacitated VRP (CVRP) in particular, integrates the global partition of an instance with local constructions for each subproblem to enhance generalization. However, during the global partition phase, misclusterings within subgraphs have a tendency to progressively compound throughout the multi-step decoding process of the learning-based partition policy. This suboptimal behavior in the global partition phase, in turn, may lead to a dramatic deterioration in the performance of the overall decomposition-based system, despite using optimal local constructions. To address these challenges, we propose a versatile Hierarchical Learning-based …

Unlocking The Planning Capabilities Of Llms Through Maximum Diversity Fine-Tuning, Wenjun Li, Changyu Chen, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have demonstrated impressive task-solving capabilities through prompting techniques and system designs, including solving planning tasks (e.g., math proofs, basic travel planning) when sufficient data is available online and used during pre-training. However, for planning tasks with limited prior data (e.g., blocks world, advanced travel planning), the performance of LLMs, including proprietary models like GPT and Gemini, is poor. This paper investigates the impact of fine-tuning on the planning capabilities of LLMs, revealing that LLMs can achieve strong performance in planning through substantial (tens of thousands of specific examples) fine-tuning. Yet, this process incurs high economic, time, …

Eduqate: Generating Adaptive Curricula Through Rmabs In Education Settings, Sidney Tio, Dexun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

There has been significant interest in the development of personalized and adaptive educational tools that cater to a student's individual learning progress. A crucial aspect in developing such tools is in exploring how mastery can be achieved across a diverse yet related range of content in an efficient manner. While Reinforcement Learning and Multi-armed Bandits have shown promise in educational settings, existing works often assume the independence of learning content, neglecting the prevalent interdependencies between such content. In response, we introduce Education Network Restless Multi-armed Bandits (EdNetRMABs), utilizing a network to represent the relationships between interdependent arms. Subsequently, we propose …

Towards Multimodal Empathetic Response Generation: A Rich Text-Speech-Vision Avatar-Based Benchmark, Han Zhang, Zixiang Meng, Meng Luo, Hong Han, Lizi Liao, Erik Cambria, Hao Fei

Research Collection School Of Computing and Information Systems

Empathetic Response Generation (ERG) is one of the key tasks of the affective computing area, which aims to produce emotionally nuanced and compassionate responses to user's queries. However, existing ERG research is predominantly confined to the singleton text modality, limiting its effectiveness since human emotions are inherently conveyed through multiple modalities. To combat this, we introduce an avatar-based Multimodal ERG (MERG) task, entailing rich text, speech, and facial vision information. We first present a large-scale high-quality benchmark dataset, AvaMERG, which extends traditional text ERG by incorporating authentic human speech audio and dynamic talking-face avatar videos, encompassing a diverse range of …