Digital Commons Network^™

Rhetorical Physics And The Laws Of Language: A Comparative Analysis Of President Trump And President Macron At The United Nations 2025, David Edmund Allen

Research outputs 2022 to 2026

The paper features a textual analysis of the UN speeches made by the US President Donald Trump and by France’s President Macron. President Trump’s speech was made in the absence of a teleprompter and lasted almost an hour. President Macron’s speech was representative of a more standard diplomatic speech. Both speeches are analyzed using a variety of models and techniques. The analysis incorporates an exploration of Manin’s link of Zipf’s law to concepts from information theory and algorithmic complexity, Tao’s concepts applied to power laws, ‘E pluribus unum’, out of many, one, plus a completely new application of Villermaux’s universal …

Do I Fit In Here? How Immigrants’ Endorsement Of Social Markers Is Related To Their Wellbeing In Three Migrant Receiving Societies, Justine Dandy, Chan Hoong Leong, Adam Komisarof, Travis Lim, Krystal Perkins

Research outputs 2022 to 2026

Social markers of acceptance are the attributes seen as important for immigrants to achieve or display in order to be accepted similarly to a local-born citizen. In research on constructions of national identity, factor analysis reveals a culture-specific, two-factor structure akin to the ethnic–civic distinction observed. In this paper, we extend this work by examining the relationship between social markers of acceptance and immigrants’ well-being in three national contexts: Australia, Japan, and the U.S. Specifically, in study 1 we examined the factor structure of social markers in non-immigrant samples in each context (Australia, n = 1357; Japan, n = 2000; …

Variational Speculative Decoding: Rethinking Draft Training From Token Likelihood To Sequence Acceptance, Xiandong Zou, Jianshu Li, Jing Huang, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persists: while existing methods optimize single greedy trajectories, decoding involves verifying and ranking multiple sampled draft paths. We propose Variational Speculative Decoding (VSD), formulating draft training as variational inference over latent proposals (draft paths). VSD maximizes the marginal probability of target-model acceptance, yielding an ELBO that promotes high-quality latent proposals while minimizing divergence from the target distribution. To enhance quality and reduce variance, we incorporate a path-level utility and optimize via an Expectation-Maximization procedure. The E-step draws MCMC samples from an oracle-filtered posterior, while the M-step maximizes weighted likelihood using …

Itimo: An Llm-Empowered Synthesis Dataset For Travel Itinerary Modification, Zhuoxuan Huang, Yunshan Ma, Hongyu Zhang, Hua Ma, Zhu Sun

Research Collection School Of Computing and Information Systems

Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored due to the scarcity of shape need-to-modify itinerary data. To bridge this gap, we formally define the itinerary modification task and propose a general pipeline to construct the corresponding dataset, namely iTIMO. This pipeline frames the generation of shape need-to-modify itinerary data as an intent-driven perturbation task. It instructs large language models to perturb real-world itineraries using three operations: REPLACE, ADD, and DELETE. Each perturbation is grounded in three intents: …

Evaluating Special Education Teachers’ Competence In Inclusive Education: A Study Of Behavior Management, Level Of Confidence, And Training Needs, Kourtney Guthrie

Electronic Theses and Dissertations

The purpose of this mixed-methods study is to examine the competence of special education teachers in rural East Texas, focusing on their experiences, perceptions, and professional development needs related to behavior management and inclusive education. Quantitative data were gathered through questionnaires given to special education teachers and elaborated upon through focus group interviews, where themes emerged. An explanatory sequential mixed-methods design was used to develop a better understanding of teachers’ preparedness and the problems involved in the implementation of inclusive practices. The findings may inform more effective teacher training, policy-making, and support systems in rural educational settings by highlighting professional …

Air: Improving Agent Safety Through Incident Response, Zibo Xiao, Jun Sun, Junjie Chen

Research Collection School Of Computing and Information Systems

Large Language Model (LLM) agents are increasingly deployed in practice across a wide range of autonomous applications. Yet current safety mechanisms for LLM agents focus almost exclusively on preventing failures in advance, providing limited capabilities for responding to, containing, or recovering from incidents after they inevitably arise. In this work, we introduce AIR, the first incident response framework for LLM agent systems. AIR defines a domain-specific language for managing the incident response lifecycle autonomously in LLM agent systems, and integrates it into the agent's execution loop to (1) detect incidents via semantic checks grounded in the current environment state and …

Train In Vain: Functionality-Preserving Poisoning To Prevent Unauthorized Use Of Code Datasets, Yuan Xiao, Yuchen Chen, Jiaming Wang, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen

Research Collection School Of Computing and Information Systems

The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage. Dataset poisoning offers a proactive defense by reducing the utility of such unauthorized training. However, existing poisoning methods often require full-dataset poisoning and introduce transformations that break code compilability. In this paper, we introduce FunPoison, a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. FunPoison leverages reusable statement-level templates with automatic repair and conservative safety checking to ensure side-effect freedom, while a type-aware synthesis module preserves type correctness, suppresses static-analysis warnings, and …

Dual-Diffusional Generative Fashion Recommendation, Mingzhe Yu, Lei Wu, Qianru Sun, Yunshan Ma

Research Collection School Of Computing and Information Systems

Personalized generative recommender systems have emerged as a promising solution for fashion recommendation. However, existing methods primarily rely on implicit visual embeddings from historical interactions, which often contain preference-irrelevant information and result in insufficient user behavior modeling. Moreover, these models typically generate only item images, providing limited interpretability. To address these limitations, we propose DualFashion, a Dual-Diffusional Generative Fashion Recommendation Architecture that jointly models image and text modalities for personalized and explainable recommendation. DualFashion adopts a dual-diffusion Transformer with image and text branches, where structured attribute-level captions and visual outfit information are jointly used as conditioning signals to model user …

Generation-Augmented Video Corpus Moment Retrieval, Mingjin Kuai, Qianyin Xiao, Juncheng Li, Jin Peng, Lizi Liao, Wei Ji

Research Collection School Of Computing and Information Systems

Video Corpus Moment Retrieval (VCMR) requires models to efficiently retrieve and precisely locate specific moments relevant to natural language queries within a massive, untrimmed video corpus. However, existing discriminative approaches typically rely on shallow visual-textual feature matching mechanisms, which often struggle to capture fine-grained semantic differences. To address this limitation, we propose Video-GAR, a novel framework that reframes the conventional retrieval task from superficial matching to generative understanding, positing that the capability for query reconstruction evidences deep semantic comprehension. Specifically, Video-GAR orchestrates three synergistic components: To overcome the computational efficiency bottleneck, we construct a Bi-Mamba backbone that leverages the linear …

Examining The Relationship Between Trauma-Informed Safety Drill Practices And School-Level Chronic Absenteeism In Maine Secondary Schools, Tina Nightingale

Dissertations

School safety practices can influence students' perceptions of safety, emotional well-being, and engagement in the educational environment; however, limited research has examined whether trauma-informed safety drill practices are associated with measurable educational outcomes. The purpose of this quantitative, non-experimental, correlational study was to examine the relationship between trauma-informed safety drill practices and school-level chronic absenteeism in Maine middle and high schools. Guided by trauma-informed care, adolescent neurodevelopment, and Protection Motivation Theory, the study investigated whether higher levels of trauma-informed implementation of safety drills, as measured by the Trauma-Informed Safety Drill Practices Index (TISDPI), were associated with lower rates of chronic …

Centering Culture, Language, And Identity In A History Of Mathematics Course For Preservice Teachers, Luis Miguel Fernández, Tenchita Alzaga Elizondo

School of Mathematical & Statistical Sciences Faculty Publications

This article presents the redesign of an undergraduate History of Mathematics course for preservice teachers at a Hispanic-Serving Institution. Grounded in Community- and Family-Centered Pedagogy (CFCP) and Marshall’s framework for culturally sustaining mathematics teaching, the course intentionally moved beyond Eurocentric narratives to center students’ linguistic, familial, and cultural resources as integral to mathematical meaning-making. Two core assignments anchored the redesign: (1) evolving Padlet reflections that connected global mathematical histories to students’ family and community practices, and (2) bilingual Cuentos Matemáticos that wove mathematical concepts with personal and cultural narratives in English and Spanish. Selected student reflections illustrate how these experiences …

Universal Rules Of The Russian Sound Verbs’ Synthesis: Representation In A Matrix Of Forms, Irina V. Ivliyeva

Arts, Languages and Philosophy Faculty Research & Creative Works

English Abstract:

The article is devoted to the construction of a system of universal rules for the synthesis of modified verbs of the Russian language and ways of presenting them in a matrix table of forms. The aim of the study is to identify parameters of the sound process relevant for predicting the composition of modified sound verbs’ nests, and in developing a system of rules of word-formation synthesis. The research material consisted of 152 base verbs of sound in the Russian language and their 880 modified derivatives.

Methods of lexical-semantic, word-formation (WF) and component analysis are used in the …

Conceptualizations Of "Public Health" And Government Action Among U.S. Adults In 2025., Lauren Yan, Emily Cramer, Peter J. Winch, Emily A. Hurley

Manuscripts, Articles, Book Chapters and Other Papers

INTRODUCTION: Understanding how Americans think about public health and the role of government institutions is crucial for informing effective health communication in a polarized political climate.

METHODS: We deployed an open-ended national survey of 1004 adults in October 2025 to assess public perceptions of these topics.

RESULTS: Respondents associated the term "public health" with health for everyone (44%) and health of everyone (23%), alongside healthcare (20%) and other themes. The most common perceived government benefit was facilitating access to healthcare (39%)-particularly by addressing cost (16%). Among perceived government harms, more Republicans than Democrats cited overregulation (16% vs 8%, P < 0.01), limiting …