Digital Commons Network^™

From Holistic To Localized: Local Enhanced Adapters For Efficient Visual Instruction Fine-Tuning, Pengkun Jiao, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yugang Jiang

Research Collection School Of Computing and Information Systems

Efficient Visual Instruction Fine-Tuning (EVIT) seeks to adapt Multimodal Large Language Models (MLLMs) to downstream tasks with minimal computational overhead. However, as task diversity and complexity increase, EVIT faces significant challenges in resolving data conflicts. To address this limitation, we propose the Dual Low-Rank Adaptation (Dual-LoRA), a holistic-to-local framework that enhances the adapter’s capacity to address data conflict through dual structural optimization. Specifically, we utilize two subspaces: a skill space for stable, holistic knowledge retention, and a rank-rectified task space that locally activates the holistic knowledge. Additionally, we introduce Visual Cue Enhancement (VCE), a multi-level local feature aggregation module designed …

Exploring Object Status Recognition For Recipe Progress Tracking In Non-Visual Cooking, Franklin Mingzhe Li, Kaitlyn Ng, Bin Zhu, Patrick Carrington

Research Collection School Of Computing and Information Systems

Cooking plays a vital role in everyday independence and well-being, yet remains challenging for people with vision impairments due to limited support for tracking progress and receiving contextual feedback. Object status — the condition or transformation of ingredients and tools — offers a promising but underexplored foundation for context-aware cooking support. In this paper, we present OSCAR (Object Status Context Awareness for Recipes), a technical pipeline that explores the use of object status recognition to enable recipe progress tracking in non-visual cooking. OSCAR integrates recipe parsing, object status extraction, visual alignment with cooking steps, and time-causal modeling to support real-time …

Reproducibility Debt In Scientific Software, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Reproducibility Debt (RpD) refers to accumulated technical and organisational issues in scientific software that hinder the ability to reproduce research results. While reproducibility is essential to scientific integrity, RpD remains poorly defined and under-addressed. This study introduces a formal definition of RpD and investigates its causes, effects, and mitigation strategies using a mixed-methods approach involving a systematic literature review (214 papers), interviews (23 practitioners), and a global survey (59 participants). We identify seven categories of contributing issues, 75 causes, 110 effects, and 61 mitigation strategies. Findings are synthesised into a cause-effect model and supported by taxonomies of team roles and …

Viewsrd: 3d Visual Grounding Via Structured Multi-View Decomposition, Ronggang Huang, Haoxin Yang, Yan Cai, Xuemiao Xu, Huaidong Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

3Dvisual grounding aims to identify and localize objects in a 3Dspacebasedontextualdescriptions. However, existing methods struggle with disentangling targets from anchors in complex multi-anchor queries and resolving inconsisten cies in spatial descriptions caused by perspective variations. To tackle these challenges, we propose ViewSRD, a frame work that formulates 3D visual grounding as a structured multi-view decomposition process. First, the Simple Rela tion Decoupling (SRD) module restructures complex multi anchor queries into a set of targeted single-anchor state ments, generating a structured set of perspective-aware de scriptions that clarify positional relationships. These de composed representations serve as the foundation for the Multi-view …

Visual-Enhanced Multimodal Framework For Flexible Job Shop Scheduling Problem, Peng Zhao, Zhiguang Cao, Di Wang, Wen Song, Wei Pang, You Zhou, Yuan Jiang

Research Collection School Of Computing and Information Systems

Multimodal models leverage complementary information across modalities to enrich feature representations. While visual information shows potential in representing structure for some combinatorial optimization problems (COPs), its application to complex scheduling like the Flexible Job Shop Scheduling Problem (FJSP) remains underexplored. Current learning-based FJSP solvers predominantly rely on handcrafted state features. This dependence can lead to inconsistencies and may not fully capture the problem's intricate dynamics. Crucially, these methods overlook visual modalities. Visual representations offer a distinct advantage by inherently capturing the global topological structure and complex resource interactions within the FJSP state. Unlike localized handcrafted features, this holistic, structural view …

Mitigating Cross-Modal Representation Bias For Multicultural Image-To-Recipe Retrieval, Qing Wang, Chong-Wah Ngo, Yu Cao, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Existing approaches for image-to-recipe retrieval have the implicit assumption that a food image can fully capture the details textually documented in its recipe. However, a food image only reflects the visual outcome of a cooked dish and not the underlying cooking process. Consequently, learning cross-modal representations to bridge the modality gap between images and recipes tends to ignore subtle, recipe-specific details that are not visually apparent but are crucial for recipe retrieval. Specifically, the representations are biased to capture the dominant visual elements, resulting in difficulty in ranking similar recipes with subtle differences in use of ingredients and cooking methods. …

Art4math: Handwritten Mathematical Expression Recognition Via Multimodal Sketch Grounding, Yang Zhou, Jin Wang, Yuxiao Zhang, Kaixiang Huang, Guodong Lu, Jingru Yang, Shengfeng He

Research Collection School Of Computing and Information Systems

Handwritten Mathematical Expression Recognition (HMER) remains a challenging task due to the structural complexity of mathematical notation and the ambiguity of handwritten symbols-e.g., ''ρ'' vs. ''p'' or ''B'' vs. ''β''. While stroke-based models offer disambiguation via temporal cues, most existing methods are constrained by coarse modality fusion and a lack of fine-grained cross-modal alignment, further hindered by limited annotated data. We introduce Art for Math (Art4Math), a novel framework that leverages the structural richness of human sketches to enhance HMER through fine-grained, modality-aware learning. Art4Math follows a two-stage training paradigm: Art Grounding (A-Grd) and Math Decoding (M-Dec). In A-Grd, the …

Emoshortcuts: Emotionally Expressive Body Augmentation For Social Mixed Reality Avatars, Hyuna Seo, Youngki Lee, Rajesh Krishna Balan, Thivya Kandappu

Research Collection School Of Computing and Information Systems

We present EmoShortcuts1, a novel social Mixed Reality (MR) framework that enhances emotional expression by dynamically augmenting avatar body gestures to reflect users’ emotional states. While social MR enables immersive remote interactions through avatars, conveying emotions remains challenging due to limitations in head-mounted display (HMD) tracking (e.g., missing lower-body movements, such as stomping or defensive postures), and users’ tendency to deprioritize nonverbal expressions during multitasking. EmoShortcuts addresses these challenges by introducing an augmentation framework that generates expressive body gestures even when users’ physical movements are restricted. We conducted a formative study with 12 participants to identify key challenges in emotional …

Language-Driven 3d Human Pose Estimation In Multi-Person Scenarios: A New Dataset And Approach, Tingrui Shen, Bangzhen Liu, Zhirun Fan, Shiting Zhang, Weifeng Pan, Sun Fan, Dan Cao, Shengfeng He

Research Collection School Of Computing and Information Systems

In an NBA game scenario, consider the challenge of locating and analyzing the 3D poses of players performing a user-specified action, such as attempting a shot. Traditional 3D human pose estimation (3DHPE) methods often fall short in such complex, multi-person scenes due to their lack of semantic integration and reliance on isolated pose data. To address these limitations, we introduce Language-Driven 3D Human Pose Estimation (L3DHPE), a novel approach that extends 3DHPE to general multi-person contexts by incorporating detailed language descriptions. We present Panoptic-L3D, the first dataset designed for L3DHPE, featuring 3,838 linguistic annotations for 1,476 individuals across 588 videos, …

Advances In Iot, Ai, And Sensor‑Based Technologies For Disease Treatment, Health Promotion, Successful Ageing, And Ageing Well, Yuzhou Qian, Keng Siau

Research Collection School Of Computing and Information Systems

Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presents mounting concerns for healthcare systems and quality of life worldwide. By supporting continuous monitoring, personal care, and data-driven decision-making, IoT and AI are shifting healthcare delivery from a reactive approach to a proactive one. This paper presents a comprehensive overview of IoT-based systems with a particular focus on the Internet of Healthcare Things (IoHT) and their integration with AI, referred to as the Artificial Intelligence of …

Website Owner Identification Through Multi-Level Contrastive Representation Learning, Cheng Tu, Yunshan Ma, Yang Li, Min Zhang, Miao Hu, Fan Shi, Xiang Wang

Research Collection School Of Computing and Information Systems

Website owner identification aims to recognize the organization or individual who owns a given website that is served on the web. It is a crucial step for cyberspace surveying and mapping, playing a significant role in cyberspace administration and governance. Existing widely employed solutions for website owner identification mainly fall into two paradigms: (1) querying the public information databases such as WHOIS, which store the Internet resource’s registered users or assignees; and (2) directly extracting the organization or individual name of the website owner from the webpage using the technique of named entity recognition. However, the former is less reliable …

Unsupervised Visual Chain-Of-Thought Reasoning Via Preference Optimization, Kesen Zhao, Beier Zhu, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Chain-of-thought (CoT) reasoning greatly improves the interpretability and problem-solving abilities of multimodal large language models (MLLMs). However, existing ap proaches focus on text CoT, limiting their ability to lever age visual cues. Visual CoT remains underexplored, and the only work [35] is based on supervised fine-tuning that relies on extensive labeled bounding-box data and is hard to generalize to unseen cases. In this paper, we introduce Unsupervised Visual CoT (UV-CoT), a novel framework for image-level CoT reasoning via preference optimization. UV-CoTperforms preference comparisons between model generated bounding boxes (one is preferred and the other is dis-preferred), eliminating the need for …

Spd: Shallow Backdoor Protecting Deep Backdoor Against Backdoor Detection, Shunjie Yuan, Xinghua Li, Xuelin Cao, Haiyan Zhang, Mengyao Zhu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Backdoor attacks have revealed the vulnerability of deep neural networks (DNNs), which motivates the development of secure deep learning systems. However, existing backdoor attacks often fail to bypass backdoor detection and human visual inspection, resulting in the exposure of the backdoor implanted in DNNs, which can subsequently be significantly mitigated through pruning or fine-tuning on benign data. To address this issue, in this paper, we propose a novel backdoor attack called SPD (Shallow Protecting Deep), which consists of a deep backdoor in the frequency domain and a shallow backdoor in the pixel domain, where the shallow backdoor acts as a …

Morphology-Aware Hrv Estimation From Wrist Ppg In Sedentary Scenarios, Changshuo Hu, Hung Manh Pham, Dong Ma

Research Collection School Of Computing and Information Systems

Photoplethysmography (PPG) is widely used in wearable devices for non-invasive heart rate variability (HRV) monitoring. While most prior work focuses on mitigating motion artifacts, recent studies highlight that even subtle contact pressure variations can distort waveform morphology and lead to inaccurate HRV estimates. In this work, we propose a morphology-aware deep learning framework that conditions HRV estimation on beat-level waveform types. Our model jointly encodes the raw PPG waveform and a sequence of pressure-induced morphology labels using parallel encoders, integrates them via cross-attention, and predicts normal-to-normal (NN) intervals and beat count to support downstream HRV computation. Evaluated on the public …

Developing A Strong Cps Defender: An Evolutionary Approach, Qingyuan Hu, Christopher M. Poskitt, Jun Sun, Yuqi Chen

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) are used extensively in critical infrastructure, underscoring the need for anomaly detection systems that are able to catch even the most motivated attackers. Traditional anomaly detection techniques typically do `one-off' training on datasets crafted by experts or generated by fuzzers, potentially limiting their ability to generalize to unseen and more subtle attack strategies. Stopping at this point misses a key opportunity: a defender can actively challenge the attacker to find more nuanced attacks, which in turn can lead to more effective detection capabilities. Building on this concept, we propose Evo-Defender, an evolutionary framework that iteratively strengthens CPS …

Parent Perspectives On Contemporary Adolescent Concerns, Allyson Worthy

Theses and Dissertations

Adolescence is a critical developmental period that marks the transition between childhood and adulthood. Psychopathology is known to emerge during this time, in which many internalizing and externalizing disorders have their onset. The assessment of adolescent psychopathology involves use of questionnaire measures to obtain perspectives from the adolescent themselves, their parents, and their teachers. Parents have been found to be especially important informants of adolescent functioning. With new challenges that arise for present-day adolescents (e.g., social media use), parents can provide insight into the different areas that need to be evaluated when assessing domains of contemporary adolescents’ development and psychopathology. …

The Science Of Sound: Studying The Cognitive, Emotional, And Physiological Effects Of Frequency, Genre, And Music, Jett Yarborough

Senior Honors Theses

Music influences emotion, physiology, and cognition, yet little is known about how the frequency it is tuned to affects these influences. Prior research has shown that music tuned to 432 Hz can reduce stress, lower blood pressure, and improve sleep. My colleagues and I conducted two studies to investigate this. Our research found that music tuned to 432 Hz promotes a significant increase in memory retention and induces a state of focus and relaxation. These results suggest that shifting from the standard tuning frequency of 440 Hz to 432 Hz could have a profoundly positive impact on our daily lives. …

Artificial Intelligence And Medical Education (2013-2024): A Scopus-Based Bibliometric Analysis, Festus Mulakoli, Edward Misava

School of Nursing & Midwifery, East Africa

Background: Artificial Intelligence (AI) is transforming medical education by enabling personalised learning, adaptive feedback, simulation-based training, and automated assessments. While AI offers significant benefits, including curriculum optimisation and virtual tutoring, concerns around data privacy, access, and ethical implementation persist. Although bibliometric studies have explored AI in healthcare, comprehensive analyses of global collaboration and publication trends in AI-focused medical education remain limited. Aim: This study aims to analyse global research trends, key contributors, and thematic developments in the application of AI within medical education. Methods: A bibliometric analysis was conducted using the Scopus database. The search strategy included terms …

Integrating Ai And Generative Ai Across The Curriculum: A Look At Three Subject Areas, Angie Su, Victoria Brenes, Stephanie L. Nelson

FDLA Journal

This paper explores how artificial intelligence (AI) and generative AI (GenAI) models, such as ChatGPT and other GenAI tools, can effectively integrate into mathematics, computer science, and English classrooms. Students must understand foundational AI concepts and how these technologies function to facilitate meaningful engagement with AI and GenAI. This paper provides practical strategies for math educators to introduce AI algorithms that connect with students' existing knowledge and real-life experiences. Furthermore, it examines how GenAI can enhance learning, supporting students at each stage of coding, reading, and writing. The ethical considerations of GenAI usage, including potential overuse by students, are discussed. …

Internal Resonance Of A T-Shaped Electrostatic Levitation Actuator, Ali Eskandari, Mohammad Alzgool, Yu Tian, Mohammad I. Younis, Shahrzad Towfighian

Mechanical Engineering Faculty Scholarship

This study explores the internal resonance of a T-shaped beam subjected to electrostatic forces at the MEMS scale. The actuator's unique configuration includes a side substrate, a fixed bottom substrate, and a suspended beam, enabling out-of-plane movement when excited. The T-shaped beam is designed to have a commensurate ratio of 1:2 between the fifth and sixth modes. The mathematical model of the system is developed and the shooting method is used to solve the reduced order model. Theoretical and experimental results reveal the interaction of two modes. The results indicate that due to the exchange of energy between the two …

Tutoring Online During The Covid-19 Pandemic: Use Of Critical Incidents To Improve Practice, A.E. Dreyfuss, Jose Armando Sanchez Diaz, Barakat Adigun, Helen Baraki, Thalyia Thompson

Publications and Research

Developing expertise in the practice of online tutoring is presented by peer tutors who provided support to students with synchronous sessions with the onset of COVID-19. The experiences and learning of peer tutors were probed through the use of critical incidents (Flanagan, 1954), and how weekly training meetings provided support to them, creating a collaboration among the tutors and the training manager.

Academic Department Indicators: 2024-2025, Systems Intelligence And Data Analytics, University Of Nebraska At Omaha

Academic Department Indicators

The audience is the Academic Planning Council (APC), college and department heads, and faculty. The purpose is to report instructional productivity measures useful for making comparisons and observing trends. Selected departmental information in this report is submitted annually to the Delaware Study, a National Study of Instructional Costs and Productivity.

This Instructional Productivity information corresponds to the UNO state- and tuition-funded budget. The Supplemental Information at the end of the document provides counts and statistics for campus-wide programs embedded in most colleges. Only Dual Enrollment instruction is excluded from the productivity report since it is a fee-based instructional program for …

Impact Of Brain Breaks On Student Engagement In An Elementary Classroom, Kailey Hunt

Master's Theses & Capstone Projects

This capstone research project examines the relationship between scheduled brain breaks and student engagement in a third-grade classroom in Bondurant, Iowa. Since the return to in-person learning, classrooms have experienced notable declines in student engagement and motivation. The purpose of this study was to investigate whether incorporating structured brain breaks could provide students with opportunities to reset and reengage during instructional time. Using daily field notes and weekly student surveys, the researcher implemented two brain breaks per day, four days a week, over a four-week period, with 22 students. Findings indicate that short, structured breaks positively influenced student engagement, and …

Cooperative Learning And The Impact On Student Engagement, Erin Pint

Master's Theses & Capstone Projects

Cooperative learning is an essential instructional approach that supports student engagement. There is limited implementation of this learning method in the classroom which leads to students lacking motivation and opportunities to collaborate with their peers. Research has shown that when teachers incorporate cooperative learning experiences, students demonstrate increased participation which leads to deeper understanding of the content. This inquiry-based research study aims to determine whether cooperative learning can improve student engagement. The study utilized a qualitative approach, using both student and educator surveys as well as field notes. Findings revealed that most students prefer learning alongside at least one peer …

Effective Strategies For Creating Inclusive Ell Curricula In Multicultural Classrooms., Katie Struck

Master's Theses & Capstone Projects

This inquiry-based capstone project examines effective strategies for creating inclusive curricula for English Language Learners (ELLs) in multicultural classrooms, with a focus on leveraging students' first languages (L1) to reduce language barriers and promote motivation, cultural representation, and equitable academic outcomes. Amid the rapid growth of ELL populations in U.S. schools—from 8% to nearly 10% of public enrollment between 2000 and 2016—educators in regions like northwest Iowa face challenges in meeting diverse social and academic needs. A literature review synthesizes recent studies on supporting ELL demographics, preparing school staff, addressing barriers, and implementing evidence-based programs and practices. Conducted in a …

The Anchor: October 2025, Hope College

The Anchor: 2025

The Anchor began in 1887 and was first issued weekly in 1914. Covering national and campus news alike, Hope College’s student-run newspaper has grown over the years to encompass over two-dozen editors, reporters, and staff. For much of The Anchor's history, the latest issue was distributed across campus each Wednesday throughout the academic school year (with few exceptions). In recent years The Anchor moved to monthly print issues and a more frequently updated website. The Anchor is now published in print twice per academic semester. Occasionally, the volume and/or issue numbering is irregular.

Oer Financial Literacy Curriculum For Mathematics Educators And Georgia’S New Mandates, Marnie Phipps, Patty Condict, John Bragelman

Proceedings of the Annual Meeting of the Georgia Association of Mathematics Teacher Educators

In 2022, Georgia agencies added new financial literacy requirements for both high school students and teacher qualifications to enhance financial literacy across the state. In response, a group of faculty members in an education preparation program acquired funding and developed a financial literacy curriculum for secondary preservice teachers. The OER curriculum was implemented in a senior-level capstone course. This manuscript presents a pilot study of the curriculum, including data collection and analysis for the project.

Tools, Differentiation, And Professional Growth: What Preservice Teachers Gain From Attending A Mathematics Teaching Conference, Heidi Eisenreich, India Fleming

Proceedings of the Annual Meeting of the Georgia Association of Mathematics Teacher Educators

This study explores how Preservice Teachers’ (PSTs’) attendance at a professional mathematics teaching conference influences their development as future teachers. Fourteen University PSTs attended the Georgia Mathematics Conference (GMC), a two-and-a-half-day teaching conference for K–16 mathematics teachers and administrators. Using constant comparative analysis to code qualitative data, we analyzed responses from one question on tool usage in pre- and post- conference surveys. Initial findings indicate that conference attendance increased PSTs' commitment to ongoing professional development, while simultaneously helping them develop more nuanced understandings of the complexity of teaching.

The Distribution Of The Number Of Mutations In The Genealogy Of A Sample From A Single Population, Yun-Xin Fu

Faculty, Staff and Student Publications

The number K of mutations in the genealogy of a sample of n sequences from a single population is one essential summary statistic in molecular population genetics and is equal to the number of segregating sites in the sample under the infinite-sites model. Although its expectation and variance are the most widely utilized properties, its sampling formula (i.e., probability distribution) is the foundation for all explorations related to K. Despite existence of an analytic sampling formula, its numerical application is limited due to susceptibility to error propagation. This paper presents a new sampling formula for K in a random sample …

Optimization Of A Mobile Imaging System To Aid In Evaluating Patients With Oral Lesions In A Dental Care Setting, Ruchika Mitbander, Jennifer Carns, Richard A Schwarz, Sean Anderson, Geethanjali Vipulanandan, Miriam Hernandez, Amelia E Hartzell, Jun Jiao, Ilona Rutkowska, Safia Durab, Sahar Sadeghi, Ashlee D Clayton, Nancy Bass, Loganayaki Anandasivam, Rachel A Giese, Ann M Gillenwater, Nadarajah Vigneswaran, Rebecca Richards-Kortum

Faculty, Staff and Student Publications

Significance: Oral cancer is often diagnosed at an advanced stage even though the oral cavity is easily accessible for examination. There is a need for tools to support early detection and help non-expert clinicians make appropriate referral recommendations.

Aim: We aim to evaluate a mobile detection of oral cancer (mDOC) smartphone-based autofluorescence imaging system by collecting data in a representative, low-prevalence population and optimizing a referral algorithm suitable for deployment in community dental settings.

Approach: Data were collected from patients at a community dental clinic using the mDOC system, which captures white light and autofluorescence images. A multi-input deep learning …