Digital Commons Network^™

Proceedings Of The 2024 Child Language Acquisition Symposium For Indigenous Communities, Tamera C. Yazzie, Bethany G. Lycan, Alec J. Goldberg, Kayleigh L. Russell, Melvatha R. Chee

Indigenous Child Language Research Center

The proceedings of the inaugural Child Language Acquisition Symposium for Indigenous Communities, which took place at the University of New Mexico on May 17-18, 2024.

2024 Indigenous Child Language Research Center Handbook, Tamera C. Yazzie, Bethany G. Lycan, Ryan D. Smith, Cormac League, Melvatha R. Chee

Indigenous Child Language Research Center

This handbook is continuously being developed by the Indigenous Child Language Research Center at UNM to provide Indigenous communities with a description of our organization and our research methodologies to aid in their own child language research endeavors.

Triadic Temporal-Semantic Alignment For Weakly-Supervised Video Moment Retrieval, Jin Liu, Jialong Xie, Fengyu Zhou, Shengfeng He

Research Collection School Of Computing and Information Systems

Video Moment Retrieval (VMR) aims to identify specific event moments within untrimmed videos based on natural language queries. Existing VMR methods have been criticized for relying heavily on moment annotation bias rather than true multi-modal alignment reasoning. Weakly supervised VMR approaches inherently overcome this issue by training without precise temporal location information. However, they struggle with fine-grained semantic alignment and often yield multiple speculative predictions with prolonged video spans. In this paper, we take a step forward in the context of weakly supervised VMR by proposing a triadic temporalsemantic alignment model. Our proposed approach augments weak supervision by comprehensively addressing …

An Aggregate Matching And Pick-Up Model For Mobility-On-Demand Services, Xinwei Li, Jintao Ke, Hai Yang, Hai Wang, Yaqian Zhou

Research Collection School Of Computing and Information Systems

This paper presents an Aggregate Matching and Pick-up (AMP) model to delineate the matching and pick-up processes in mobility-on-demand (MoD) service markets by explicitly considering the matching mechanisms in terms of matching intervals and matching radii. With passenger demand rate, vehicle fleet size and matching strategies as inputs, the AMP model can well approximate drivers’ idle time and passengers’ waiting time for matching and pick-up by considering batch matching in a stationary state. Properties of the AMP model are then analyzed, including the relationship between passengers’ waiting time and drivers’ idle time, and their changes with market thickness, which is …

Unsupervised Modality Adaptation With Text-To-Image Diffusion Models For Semantic Segmentation, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Bo Li, Yang Tang, Pan Zhou

Research Collection School Of Computing and Information Systems

Despite their success, unsupervised domain adaptation methods for semantic segmentation primarily focus on adaptation between image domains and do not utilize other abundant visual modalities like depth, infrared and event. This limitation hinders their performance and restricts their application in real-world multimodal scenarios. To address this issue, we propose Modality Adaptation with text-toimage Diffusion Models (MADM) for semantic segmentation task which utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model’s cross-modality capabilities. Specifically, MADM comprises two key complementary components to tackle major challenges. First, due to the large modality gap, using one modal data to generate …

3d Snapshot: Invertible Embedding Of 3d Neural Representations In A Single Image, Yuqin Lu, Bailin Deng, Zhixuan Zhong, Tianle Zhang, Yuhui Quan, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method …

Mimicking To Dominate: Imitation Learning Strategies For Success In Multiagent Competitive Games, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

Training agents in multi-agent games presents significant challenges due to their intricate nature. These challenges are exacerbated by dynamics influenced not only by the environment but also by strategies of opponents. Existing methods often struggle with slow convergence and instability. To address these challenges, we harness the potential of imitation learning (IL) to comprehend and anticipate actions of the opponents, aiming to mitigate uncertainties with respect to the game dynamics. Our key contributions include: (i) a new multi-agent IL model for predicting next moves of the opponents --- our model works with hidden actions of opponents and local observations; (ii) …

Sampdetox : Black-Box Backdoor Defense Via Perturbation-Based Sample Detoxification, Yanxin Yang, Chentao Jia, Dengke Yan, Ming Hu, Tianlin Li, Xiaofei Xie, Xian Wei, Mingsong Chen

Research Collection School Of Computing and Information Systems

The advancement of Machine Learning has enabled the widespread deployment of Machine Learning as a Service (MLaaS) applications. However, the untrustworthy nature of third-party ML services poses backdoor threats. Existing defenses in MLaaS are limited by their reliance on training samples or white-box model analysis, highlighting the need for a black-box backdoor purification method. In our paper, we attempt to use diffusion models for purification by introducing noise in a forward diffusion process to destroy backdoors and recover clean samples through a reverse generative process. However, since a higher noise also destroys the semantics of the original samples, it still …

Learning To Handle Complex Constraints For Vehicle Routing Problems, Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive …

Self-Supervised Fine-Tuning For Neural Expert Finding, Budhitama Subagdja, Dan Sanchari, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Expert finding systems allow ones to find individuals who have expertise in specific fields or domains. Traditional expert finding are mostly based on topic modeling or keyword search methods that are limited in their capability to encode contextual knowledge from natural language. To address the limitation, this paper presents Neural Expert Finder (NEF), a novel method that takes a transfer learning approach based on transformer encoder networks to leverage the rich seman-tic and syntactic patterns of language encoded in pre-trained language models (PLMs). We propose a self-supervised learning approach utilizing contrastive training using both positive and automatically generated negative samples …

Long-Tailed Out-Of-Distribution Detection Via Normalized Outlier Distribution Adaptation, Wenjun Miao, Guansong Pang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Onekeychallenge in Out-of-Distribution (OOD) detection is the absence of groundtruth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to train OOD detectors. However, we find empirically that the outlier samples often present a distribution shift compared to the true OOD samples, especially in LongTailed Recognition (LTR) scenarios, where ID classes are heavily imbalanced, i.e., the true OOD samples exhibit very different probability distribution to the head and tailed ID classes from the outliers. In this work, we propose a novel approach, namely normalized outlier …

Relationship Between Nursing Care Provided For Children Postextubated From Mechanical Ventilation And Their Reventilation., Marwa G. Abouheiba, Magda E. Youssef

Nursing

Background: Respiratory failure requires mechanical ventilation to maintain gas exchange. Extubation means endotracheal tube removal, where ventilated children maintain an efficient gas exchange without ventilatory support. If extubation fails, the children will need reventilation. Therefore, after extubation, children must receive proper nursing care to prevent reventilation. Aim: This study aims to identify the relationship between nursing care provided for children post-extubated from mechanical ventilation and their reventilation. Settings: The study was conducted at the Pediatric Intensive Care Unit of Alexandria University Children’s Hospital at El-Shatby. Subjects: A convenient sample of 50 extubated children and all nurses provided care to these …

A Report Summarizing Hsc Faculty Participants: Ithaka S+R Coordinated Study Making Ai Generative For Higher Education, Laura J. Hall, Robyn Gleasner

Publications

This report presents findings from the University of New Mexico Health Sciences Center’s (HSC) participation in the ITHAKA S+R study Making AI Generative for Higher Education. Seven faculty members from HSC disciplines including internal medicine, pathology, psychiatry, nursing, radiology, and population health were interviewed to explore their perspectives on generative AI (GenAI) in teaching and learning, research and scholarship, and institutional support. A unique theme of “practice” emerged, reflecting how GenAI is influencing clinical care, bench-to-bedside translation, and the preparation of students for future professional practice. The report highlights faculty experiences, ethical concerns, and support needs, offering insight into how …

Divlog: Log Parsing With Prompt Enhanced In-Context Learning, Junjielong Xu, Ruichun Yang, Yintong Huo, Chengyu Zhang, Pinjia He

Research Collection School Of Computing and Information Systems

Log parsing, which involves log template extraction from semistructured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing supervised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose DivLog, an effective log parsing …

Exploring The Cognitive Sense Of Self In Ai: Ethical Frameworks And Technological Advances For Enhanced Decision-Making, Emily Barnes, James Hutson

Faculty Scholarship

The burgeoning field of Artificial Intelligence (AI) increasingly focuses on developing systems capable of self-awareness, merging technological innovation with deep ethical and philosophical considerations. This article explores the cognitive sense of self within AI, examining mechanisms through which AI systems may mirror human-like consciousness and self-perception. Despite significant advances, substantial gaps remain in the understanding and practical implementation of self-aware characteristics in AI, particularly in applying theoretical models and ethical frameworks to real-world scenarios. There is a pressing need for comprehensive research to explore these theoretical underpinnings and translate them into operational systems capable of ethical and adaptable behaviors. This …

Radial, Vortex, And Spiral Solutions To The Nonlinear Schrödinger Equation And Other Reaction--Diffusion Systems, James Redmon Cummins

Masters Theses and Doctoral Dissertations

This dissertation explores various solutions to nonlinear reaction-diffusion systems, focusing primarily on the nonlinear Schrödinger equation. Three types of solutions are investigated: radial solutions (with no angular dependence), vortex solutions (with angular dependence but no radial phase dependence), and spiral solutions (which have radial phase dependence). The study considers free particles without potential and trapped particles inside the cylindrical potential with an impenetrable barrier. We show that spiral solutions to the nonlinear Schrödinger equation only exist for a radially constant phase by considering a related system of nonlinear ordinary differential equations. This system is shown to be a special case …

Learning To Represent Temporal Dynamics And Generative Factors For Intelligent Visual Navigation, Sahand Khoshdel

All Theses

Visual navigation systems are crucial in various applications, including autonomous driving, unmanned aerial systems (UAS), and industrial automation. For these systems to operate efficiently in dynamic environments, they must not only interpret complex surroundings but also anticipate changes over time. Temporal prediction—forecasting environmental changes like moving obstacles or shifting lighting conditions—enables navigation systems to act proactively, enhancing both safety and performance. This dissertation investigates representation learning methods both as a backbone feature extractor for RL agents as well as a proxy for systems oriented for Explainable AI (XAI). Two main projects are presented as case studies to achieve the aforementioned …

The Impact Of In Ovo Administration Of Probiotic Lactobacilli On The Gut Health And Immune Competence Of Broiler Chickens, Shreeya Sharma

All Theses

Due to the incomplete development of their immune system, newly hatched chicks are vulnerable to a wide range of pathogenic microorganisms, including enteric bacterial pathogens during the early stage of life. To boost their immune system during this critical period, numerous studies suggest early feeding, either in ovo or immediately post-hatch, of nutrients and immunomodulators such as prebiotics, probiotics, phytobiotics, Toll-like receptors (TLRs) ligands, vitamins and minerals. Probiotics, defined as live, non-pathogenic microorganisms that confer beneficial effects on the host, have been extensively studied for their effects on poultry health. Supplementation of probiotics to chickens has demonstrated a broad spectrum …

Let's Get The Ball Rolling: Dynamic Affordances For An Object Retrieval Task In Virtual Reality, Elenah Rosopa

All Theses

Virtual reality (VR) is widely used in domains such as training, rehabilitation, entertainment, and gaming. For users to perceive affordances in VR, the virtual environment must provide sufficient perceptual information. Recent studies have focused on affordance perception—individuals' ability to discern action-relevant information—primarily in static environments. This study explored dynamic affordances by manipulating the size, speed, and gap width of a virtual target. Participants performed an object retrieval task, and performance was measured by the percentage of targets retrieved and the percentage of errors on each trial. Participants were more successful in retrieving larger targets, contrary to the expectation that larger …

Toward A Globally Lunar Calendar: A Machine Learning-Driven Approach For Crescent Moon Visibility Prediction, Samia Loucif, Murad Al-Rajab, Raed Abu Zitar, Mahmoud Rezk

All Works

This paper presents a comprehensive approach to harmonizing lunar calendars across different global regions, addressing the long-standing challenge of variations in new crescent Moon sightings that mark the beginning of lunar months. We propose a machine learning (ML)-based framework to predict the visibility of the new crescent Moon, representing a significant advancement toward a globally unified lunar calendar. Our study utilized a dataset covering various countries globally, making it the first to analyze all 12 lunar months over a span of 13 years. We applied a wide array of ML algorithms and techniques. These techniques included feature selection, hyperparameter tuning, …

User Acceptance Of Ai Voice Assistants In Jordan's Telecom Industry, Mousa Al-Kfairy, Dheya Mustafa, Ahmed Al-Adaileh, Samah Zriqat, Obsa Sendaba

All Works

Purpose: This study aims to understand factors influencing consumer acceptance of artificial intelligence (AI) voice assistants used in customer support within telecom companies in Jordan. Methodology: A survey was conducted involving 248 individuals who have experience with telecom support services. To evaluate consumer acceptance, the study incorporates the Unified Theory of Acceptance and Use of Technology (UTAUT) framework and extends it with attributes specific to AI, such as Perceived Reliability, Voice Quality, and Quality of Information. Advanced statistical methods, including structural equation modeling with SPSS AMOS 28 and SmartPLS, were utilized to analyze the collected data. Findings: The results revealed …

Chatgpt In Higher Education - A Student's Perspective, Ahmed Shuhaiber, Mohammad Amin Kuhail, Sinan Salman

All Works

The purpose of this study is to assess the impact of factors influencing students' adoption of ChatGPT within the context of higher education. With the rapid expansion of its user base and its increasing utilization across various fields, there is a pressing need to comprehensively explore students' interactions and experiences with this innovative technology, a topic largely unaddressed in existing literature. This paper aims to identify the factors contributing to ChatGPT's rapid proliferation and to highlight its potential for reshaping higher education. To achieve our research objective, we extend the Unified Theory of Acceptance and Use of Technology (UTAUT2) with …

Suboptimal Attainment Of Global Goals Of Human Rights, Universal Health Coverage And Sustainable Development Goals In Gaza During 2023–2024, Mohammed Alkhaldi, Bhavana Akilan, Maha Hoteit, Mohamed Elhadidy, Suzanne Morrison

All Works

The Israel military occupation, ongoing for over 75 years, has profoundly impacted the health and well-being of Palestinians. Despite longstanding calls for Universal Health Coverage (UHC) and sustainable development, the response of global health systems and organizations to crises such as the recent large-scale military assault on Gaza in October 2023 has been inadequate. There is a critical need to examine why these global health approaches have failed and how they can be restructured to address the unique challenges in Gaza effectively. This analysis aims to analyze the shortcomings of global health strategies in the context of the Gaza crisis …

Before Ministry, Education Is Important (Bmei): A Comprehensive Enquiry About The Impact Of Education On Ministry, Mcclintock Acsmhour

Electronic Theses and Dissertations

Ministry has a variety of fields ranging from churches, social nonprofit organizations, and government ministries in agriculture, health, education, lands, and many more. While this study did not focus on other ministries apart from Christian non profit ministries like church, to some extent, all leaders in these ministries avow that one requires some level of education to be in leading positions in these ministries. In other words, the qualified individual must have been trained in a particular field within the ministry of interest. This indicates the importance of education in ministry. Therefore, this study was conducted to uncover the impact …

Using Nano-Ities For Detection Of Metal Ions In Complex Aqueous Solutions, Mohammed Muzammil Nishar Ahmed

Theses and Dissertations

Recent urbanization has induced a rapid increase in heavy metal exposure to humans. The exploitation of heavy metals for economic gains coupled with poor and economically infeasible recycling methods have led to poor management of heavy metal wastes, which has opened up several pathways for heavy metals to enter the human body. Such heavy metals target many vital organs, but the early symptoms are common and difficult to diagnose. Severe symptoms appear in later stages and are very expensive to treat and the damage caused to the health are not typically reversable in such later stages. To counter this issue, …

Learning-Based Feature Identification For Rendezvous And Capture Of Non-Cooperative Space Objects, Trupti Mahendrakar

Theses and Dissertations

In recent years, On-Orbit Servicing (OOS) and Active Debris Removal (ADR) have attracted increasing interest due to growing concerns about space debris. This debris poses a significant risk to spacecraft, as collisions can catastrophically end missions and potentially trigger a cascading chain reaction of more debris. Space debris ranges from tiny paint chips to large non-functional spacecraft and even launch vehicle components. One way to mitigate the formation of additional space debris is to reduce the total number of large non-cooperative resident space objects present in operational orbits. This can be achieved through two approaches: de-orbiting these objects as part …

A Comparative Analysis Of Early Algebraic Thinking Activities From U.S. And Singapore Primary Textbooks, Christian Joel Hernandez

Theses and Dissertations

Many studies highlight the challenges students face when transitioning to algebra at the secondary level. Introducing algebraic concepts and fostering algebraic thinking at the primary level can help mitigate these difficulties. Prior to formal algebra instruction, early algebra can be cultivated as a mode of thinking known as algebraic thinking. Several international curricula, such as Singapore Math, incorporate early algebraic thinking into the early stages of schooling. Singapore Math, renowned for its high performance in international assessments, has been widely adopted by schools seeking to replicate its success.

This study compares two primary-level mathematics curricula—CCSSM-aligned textbooks and Singapore Math—specifically focusing …

Machine Learning Methods For Quantification Of Glacier Variations Through Satellite Imagery, Robert D. Breininger

Theses and Dissertations

Glaciers around the world have experienced a trend of recession within the past century. Quantification of glacier variations using satellite imagery is of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. The potential methods to quantify glacier variations with increasing complexity include detecting the terminus location, quantifying the glacier surface area, and measuring glacier volume. Although there are methods in literature designed purposefully for glacier area segmentation that have achieved acceptable results, they are often localized to the region where their training data were acquired and further rely on training sets …

Morphometric Based Roughness Estimates For Wind Loading Applications In The Coastal Transition Zone, Hadley Rose Besing

Theses and Dissertations

This project was developed to support the Florida Tech Wireless Sensor Network System (WSNS) hurricane research team in determining wind loads during high-impact wind events. The WSNS team objectives included estimating the critical pressure (𝐶_p) from observed 3-s gusts during high impact weather events (tropical cyclones). The field work was conducted at a single-story residential home in Satellite Beach, FL approximately 400 m from the coast. During data collection periods, more than 1-million 20-second LiDAR wind profiles were observed.

The bulk roughness at the test residence is characterized utilizing an existing morphometric technique with geometry that is derived …

Understanding The Relationship Between Humble Leadership, Reflecting Dialogue, Psychological Safety And Job Satisfaction: A Study Of Japanese Correctional Officers, Yayoi Kimura

Theses

This study investigates the relationships between humble leadership, reflecting dialogue, psychological safety, and job satisfaction among correctional officers in Japanese correctional institutions. Correctional officers face high levels of stress due to their demanding roles in rehabilitating and managing inmates, yet the impact of organizational factors on their job satisfaction remains underexplored. As Japan undergoes significant changes in its penal system, understanding the factors that contribute to a supportive work environment is crucial. First, this study examines the relationship between psychological safety and job satisfaction among correctional officers. Second, it explores whether facilities that practice reflecting dialogue and humble leadership experience …