Digital Commons Network^™

Rode: Linear Rectified Mixture Of Diverse Experts For Food Large Multi-Modal Models, Pengkun Jiao, Xinlan Wu, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang

Research Collection School Of Computing and Information Systems

Large Multi-modal Models (LMMs) have significantly advanced a variety of vision-language tasks. The scalability and availability of high-quality training data play a pivotal role in the success of LMMs. In the realm of food, while comprehensive food datasets such as Recipe1M offer an abundance of ingredient and recipe information, they often fall short of providing ample data for nutritional analysis. The Recipe1M+ dataset, despite offering a subset for nutritional evaluation, is limited in the scale and accuracy of nutrition information. To bridge this gap, we introduce Uni-Food, a unified food dataset that comprises over 100,000 images with various food labels, …

Benchmarking Gaslighting Negation Attacks Against Multimodal Large Language Models, Bin Zhu, Yinxuan Gui, Huiyan Qi, Jingjing Chen, Chong-Wah Ngo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Multimodal Large Language Models (MLLMs) have exhibited remarkable advancements in integrating different modalities, excelling in complex understanding and generation tasks. Despite their success, MLLMs remain vulnerable to conversational adversarial inputs. In this paper, we systematically study gaslighting negation attacks—a phenomenon where models, despite initially providing correct answers, are persuaded by user-provided negations to reverse their outputs, often fabricating justifications. We conduct extensive evaluations of state-of-the-art MLLMs across diverse benchmarks and observe substantial performance drops when negation is introduced. Notably, we introduce the first benchmark GaslightingBench, specifically designed to evaluate the vulnerability of MLLMs to negation arguments. GaslightingBench consists of multiple-choice …

Sam3-Litetext: An Anatomical Study Of The Sam3 Text Encoder For Efficient Vision-Language Segmentation, Chengxi Zeng, Yuxuan Jiang, Ge Gao, Shuai Wang, Duolikun Danier, Bin Zhu, Stevan Rudinac, David Bull, Fan Zhang

Research Collection School Of Computing and Information Systems

Vision-language segmentation models such as SAM3 enable flexible, prompt-driven visual grounding, but inherit large, general-purpose text encoders originally designed for open-ended language understanding. In practice, segmentation prompts are short, structured, and semantically constrained, leading to substantial over-provisioning in text encoder capacity and persistent computational and memory overhead. In this paper, we perform a large-scale anatomical analysis of text prompting in vision–language segmentation, covering 404,796 real prompts across multiple benchmarks. Our analysis reveals severe redundancy: most context windows are underutilized, vocabulary usage is highly sparse, and text embeddings lie on a low-dimensional manifold despite high-dimensional representations. Motivated by these findings, we …

Frozen Lvlms For Micro-Video Recommendation: A Systematic Study Of Feature Extraction And Fusion, Huatuan Sun, Yunshan Ma, Changguang Wu, Yanxin Zhang, Pengfei Wang, Xiaoyu Du

Research Collection School Of Computing and Information Systems

Frozen Large Video Language Models (LVLMs) are increasingly employed in micro-video recommendation (MVR) due to their strong multimodal understanding. However, existing apporches typically deploy LVLMs as fixed black-box feature extractors without systematically comparing alternative representation strategies. To address this gap, we present the first systematic empirical study on various feature extraction paradigms and integration strategies, along with hierarchical representations from frozen LVLMs for MVR. Extensive experiments on representative LVLMs reveal that hidden states from multiple decoder layers provide richer and more effective representations for MVR. Guided by this insight, we propose the Dual Feature Fusion (DFF) Framework, a lightweight approach …

Co-Designing With Autistic Livestreamers: Care, Constraints, And Trade-Offs In Livestreaming, Terrance Mok, Anthony Tang, Lora Oehlberg

Research Collection School Of Computing and Information Systems

Autistic livestreamers use platforms like Twitch for social connection, self-expression, and community, but these spaces also impose ongoing social and emotional demands. Prior work has documented these experiences, but less is known about what autistic creators themselves envision for the tools and platforms they use. We address this gap through a Research through Design (RtD) co-design study with three autistic Twitch streamers, using speculative artefacts as discussion prompts to explore how participants reasoned about potential livestreaming technologies. Across three co-design activities, we identify three overarching tensions shaping autistic streaming practice: Expression versus Misinterpretation and Harm; Public Participation versus Control and …

Group Conversational Agents: A Review Of Designs That Support And Shape Group Interaction, Shunyi Yeo, Tianyi Zhang, Scott Bateman, Gary Hsieh, Young-Ho Kim, Simon Tangi Perrault, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Conversational agents that participate in or mediate group interaction introduce challenges that extend beyond supporting individual users, raising new questions about how agents participate in and influence groups. To characterise this emerging design space, we present a systematic review of 53 peer-reviewed studies on group conversational agents (GCAs). We analyse how GCAs intervene in group-level processes, including participation regulation, conflict mediation, task alignment, and execution support. Using concepts from group research as an analytic lens, we organise prior GCA work around recurring group interactional challenges (orientation, conflict, alignment, and execution), and examine the roles agents are designed to play in …

Language Embeddings Meet Shallow Autoencoders, Rodrigo Alves, Vojtěch Vančura, Pavel Kordík, Antoine Ledent

Research Collection School Of Computing and Information Systems

Shallow autoencoders are appealing recommenders due to their simplicity, scalability, and competitive retrieval quality, but they struggle in strict cold-start settings where new items have no interactions. We propose an inductive shallow autoencoder that leverages item side information (language embeddings) by fixing the decoder to item features and learning only an encoder in the same semantic space. To prevent trivial self-reconstruction without enforcing a hard zero diagonal, we introduce diagonal gating: a leave-one-item-out objective that blocks the self-copy shortcut only for the item being updated while retaining context from the rest of the user history. An alternating-style optimization trains the …

Ppg-Sport: A Dataset For Reliable Heart Rate Monitoring From Wrist Ppg Under Dynamic Sports Conditions, Changshuo Hu, Hung Manh Pham, Yiming Zhang, Guanru Yan, Xiao Ma, Yuezhong Wu, Thivya Kandappu, Archan Misra, Dong Ma

Research Collection School Of Computing and Information Systems

Photoplethysmography (PPG) has become a cornerstone of physiological sensing in wearable devices, enabling non-invasive monitoring of heart rate and related biomarkers. However, its reliability deteriorates sharply under dynamic, high-intensity, or non-periodic motions such as those in sports, where existing datasets fail to capture realistic wrist dynamics. To address this gap, we introduce PPG-Sport, the first large-scale dataset designed for heart rate monitoring from wrist-worn PPG under real sports conditions. The PPG-Sport dataset includes synchronized PPG, inertial measurement unit (IMU), and electrocardiography (ECG) recordings from both wrists of 30 participants across six representative activities: stationary, walking, running, badminton, table tennis, and …

A Survey Of Earable Technology: Trends, Tools, And The Road Ahead, Changshuo Hu, Qiang Yang, Yang Liu, Tobias Röddiger, Kayla-Jade Butkow, Mathias Ciliberto, Adam Luke Pullin, Jake Stuchbury-Wass, Mahbub Hassan, Cecilia Mascolo, Dong Ma

Research Collection School Of Computing and Information Systems

Earable devices, wearables positioned in or around the ear, are undergoing a rapid transformation from audio-centric accessories into multifunctional systems for interaction, contextual awareness, and health monitoring. This evolution is driven by commercial trends emphasizing sensor integration and by a surge of academic interest exploring novel sensing capabilities. Building on the foundation established by earlier surveys, this work presents a timely and comprehensive review of earable research published since 2022. We attempt to answer three core questions: (1) how has earable research evolved in recent years, (2) what enabling resources are now available, and (3) what opportunities remain for future …

Anatomical Domain Shifts: Test-Time Heterogeneous Adaptation For 3d Human Pose Prediction, Qiongjie Cui, Pan Zhou, Jingjing Chen, Na Zhao

Research Collection School Of Computing and Information Systems

The research frontier in human pose prediction (HPP) is advancing toward continual test-time adaptation (TTA), where models must self-adapt to dynamic test distributions. To date, the homeostatic continual TTA remains the sole viable solution, which isolates the model parameters and update domain-sensitive ones. Despite mitigating full-body domain gaps, human anatomical heterogeneity (domain shifts often localize to specific regions) is ignored. This anatomical-agnostic approach forces uniform parameter adaptation across kinematically distinct segments, causing: over-adaptation of stable regions and under-adaptation of shift-prone articulations. To address it, we introduce TT-HA, a novel Test-Time Heterogeneous Adaptation that implicitly estimates domain changes for anatomical segments, …

History To Future: Evolving Agent With Experience And Thought For Zero-Shot Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Zihan Wang, Guo-Sen Xie, Yang Yang, Jinshan Pan, Qianru Sun, Xiangbo Shu

Research Collection School Of Computing and Information Systems

Vision-and-Language Navigation in Continuous Environment (VLN-CE) requires an agent to follow language instructions to navigate the target destination. With the advancement of large language models (LLMs), recent efforts have explored adapting them for zero-shot VLN-CE, offering a promising solution in addressing the drawbacks of poor generalization in the training-based paradigm. However, existing LLM-based works primarily perform naive reasoning for decision-making and lack feedback, e.g., reviewing historical errors and predicting future potentials. Consequently, it may suffer from continuous failure for those initial error tasks. In this paper, we rethink LLM-based zero-shot VLN-CE and propose a new paradigm, named EvoNav, to improve …

Adaptive Outlier Detection Over Data Stream, Rui Zhu, Mingyuan Jiang, Xiaochun Yang, Baihua Zheng, Bin Wang, Tao Qiu

Research Collection School Of Computing and Information Systems

Continuous distance-based outlier detection in streaming data poses significant challenges and has a wide range of practical applications. Traditional threshold-based methods perform well under stable streaming conditions, where fixed parameters remain effective. However, they often struggle with dynamic data distributions and high stream speeds, leading to suboptimal performance, limited control over the number of returned outliers, and failure to meet real-time detection requirements. To address these issues, this paper introduces a novel Recall and Proportion-Aware Outlier Detection (RPA-OD) query. In RPA-OD, ρ defines a distance relaxation that enables real-time outlier detection. Specifically, objects with fewer than k neighbors within the …

“Alexa, Do Not Say That In Front Of My Boss!” A Cross-Cultural Comparison Of User And Ai Preferences For Privacy-Aware Smart Speaker Interactions Across Contexts, Lynne Warin, Emily Aurelia, Anthony Tang, Emily Aurelia, Delphine Reinhardt

Research Collection School Of Computing and Information Systems

Due to their limited ability to reason about the social context in which they are used, smart speakers pose significant privacy risks by responding in ways that may violate people's implicit social boundaries. We conducted a cross-cultural vignette study (N = 944) in Germany and Singapore to investigate how situational factors—specifically social context (bystander relationships and closeness), physical context (location), and interaction context (topic and deceptive intent)—regulate user preferences for smart speaker responses. Our results demonstrate that these factors are superior predictors of response preferences than dispositional user traits (i.e., intrinsic personal traits). We identify two distinct social dynamics: a …

Cfalr: Collaborative Filtering-Augmented Large Language Model For Personalized Fashion Outfit Recommendation, Yujuan Ding, Junrong Liao, Yunshan Ma, Yi Bin, Wenqi Fan, Tat-Seng Chua, Qing Li

Research Collection School Of Computing and Information Systems

Personalized outfit recommendation poses a significant challenge in e-commerce and social media platforms, requiring systems that balance user preferences with aesthetic compatibility. Collaborative filtering (CF) provides a traditional solution for this, but it struggles with data-sparse scenarios and complex user-item-outfit relationships. Meanwhile, existing template-based approaches are constrained by rigid pre-designed structures. To bridge these research gaps, we introduce CFALR (Collaborative Filtering-Augmented Large Language Model for Recommendation), a novel framework that synergizes collaborative filtering with large language models for personalized outfit recommendation. Specifically, CFALR describes user-outfit interactions in natural language and leverages LLMs to capture fashion semantics while employing CF-enhanced embeddings …

How Do Machine Learning Models Change?, Joel Castaño, Rafael Cabañas, Antonio Salmerón, David Lo, Silverio Martínez-Fernández

Research Collection School Of Computing and Information Systems

The proliferation of Machine Learning (ML) models and their open source implementations has transformed AI research and applications. Platforms like Hugging Face (HF) enable this evolving ecosystem, yet a large-scale longitudinal study of how these models change is lacking. This study addresses this gap by analyzing over 680,000 commits from 100,000 models and 2,251 releases from 202 of these models on HF using repository mining and longitudinal methods. We apply an extended ML change taxonomy to classify commits and use Bayesian networks to model temporal patterns in commit and release activities. Our findings show that commit activities align with established …

From Institutions To Families: Assessing The Progress Of Egypt’S National Strategy For Alternative Child Care Reform, Yasmine M. El Hagry

Theses and Dissertations

This study serves as a critical mid-term review of the Egyptian National Strategy for Alternative Care for Children and Youth without Parental Care (NACS) (2021-2030). The 10-year strategy overall goal is to shift from institutional care, such as orphanages, to family-based care, such as kafala, the Islamic alternative to legal adoption, for children deprived of parental care. While the global literature strongly advocates deinstitutionalization, empirical research on the operational risks and unintended harms that can arise during such complex transformations remains scarce.

Adapting the International Civil Service Effectiveness (InCiSE) Framework, this research uses a qualitative approach to diagnose the …

Submittal Of Mixed Waste Landfill Annual Long-Term Monitoring & Maintenance Report, April 2025-March 2026 For Sandia National Laboratories/New Mexico, Epa Id Number Nm5890110518, June 2026, Sandia National Laboratories/Nm

Facilities-Units

The Department of Energy/National Nuclear Security Administration (DOE/NNSA) and Sandia National Laboratories /New Mexico (SNL/NM) personnel submitted the Mixed Waste Landfill (MWL) Annual Long-Term Monitoring and Maintenance Report for the April 2025 through March 2026 reporting period to the New Mexico Environment Department (NMED) in accordance with the MWL Long-Term Monitoring and Maintenance Plan (LTMMP) Section 4.8.1. The LTMMP became effective on January 8, 2014.

The MWL is a 2.6-acre Solid Waste Management Unit (SWMU 76) located in the north-central part of Technical Area III at SNL/NM that has undergone corrective action. As of March 13, 2016, the NMED February …

Update: Colorado River Basin Storage Continues Slide Toward System Crash, Anne Castle, Jack Schmidt, Eric Kuhn, Kathryn Sorensen, Katherine Tara

The Traveling Wilburys of the Colorado River

If the Colorado River Basin (Basin) experiences another dry year, similar to Water Year 2025, it is likely that reasonably accessible storage in Lake Powell and Lake Mead would be mostly depleted, even if consumptive uses and losses are at or near historic lows. Run-of-the-river operations would shortly ensue. This would be an outcome with devastating consequences. In contrast, if next year is very wet, similar to Water Year 2023, the Basin’s largest federal reservoirs would recover somewhat, but would provide only about two years of cushion before we find ourselves again in the same position we are in today, …

Repurposing Electrical Waste Materials For Developing Corrosion-Resistant Medium Entropy Alloys, Iman El-Mahallawi, Shaza Raji Eng, Shimaa El-Hadad Prof., Mahmoud Tash Prof., Lamiaa Zaki Dr.

Mechanical Engineering

Reusing electrical waste as a sustainable metal source is a promising approach for producing next-generation alloys. In this work, vacuum arc melting (VAM) was used to produce three aluminum-based medium-entropy alloys (AlCuFeCrSi MEAs) from recycled electrical waste. The alloys contained Al in the range 36-46%; Cu in the range 28-35%; Fe in the range 11-18%; Cr in the range 5-6%; and Si in the range 3-6%, with variations in elemental composition resulting from the use of different recovered electrical waste components. Their corrosion performance was evaluated at room temperature, in a 3.5% NaCl solution with and without 1 g/L Mn …

Modeling Generative Ai Adoption In Higher Education: An Integrated Tam–Tpb–Sdt Framework With Sem Validation, Dina Tbaishat, Omar Alfandi, Faten Hamad, Syed Muhammad Salman Bukhari, Suha Al Muhaissen

All Works

This study investigates the determinants of university students' adoption of generative artificial intelligence (GAI) tools in higher education. Integrating the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and Self-Determination Theory (SDT), it develops and tests a complete model that captures cognitive, social, and motivational influences on adoption. A cross-sectional survey was conducted among 517 undergraduate and postgraduate students at Jordanian universities. The data were analyzed using structural equation modeling (SEM) with a two-step approach: confirmatory factor analysis (CFA) to validate the measurement model, followed by SEM to test the hypothesized structural relationships. Reliability, validity, measurement invariance across …

Co-Editors’ Corner, Raven Jones, Tanya Upthegrove Gregory, Alexandra Sánchez

Language Arts Journal of Michigan

No abstract provided.

Bayesian Optimization Of Laser Wakefield Acceleration Via Spectral Pulse Shaping, B. Z. Djordjević, C. Benedetti, Asa D. Mcnaughton, R. Lehe, H.-E. Tsai, S. C. Wilks, B. A. Reagan, G. J. Williams, J. Van Tilborg, C. B. Schroeder

Physics Faculty Publications and Presentations

In this paper, we investigate the effect of spectral pulse shaping of the laser driver on the performance of channel-guided, laser–plasma accelerators. The study was carried out with the assistance of Bayesian optimization using particle-in-cell simulations. We used a realistic plasma profile based on a novel optical-field-ionized channel technique with ionization injection and low, on-axis plasma densities to maximize the energy gain of the electron bunch trailing the laser. Spectral shaping allows us to modify the temporal profile of the laser driver while keeping the laser energy constant, affecting the acceleration and injection processes. In addition, we consider how modifying …

U.S. Men's Views About Women's And Men's Agency In The Abortion Decision-Making Process, Xiana Bueno, Lucretia Mena-Melendez, Brandon L. Crawford, Ronna C. Turner, Wen-Juo Lo, Kristen N. Jozkowski

Counseling, Leadership, and Research Methods Faculty Publications and Presentations

Research examining men's public opinion on women's and men's roles in abortion is limited but increasingly important as access to abortion has shifted dramatically in the U.S. In this study, we examined U.S. men's views of women's and men's agency in abortion decision-making. We explored 72 in-depth interviews on abortion attitudes conducted with men in 2021 and identified three main narratives. First, most men indicated that abortion is exclusively a women's decision. Several men noted wanting men's opinions to at least be heard in the decision-making process, while still maintaining that women should be the decision-makers. We identified subtle nuances, …

Stock Market Analysis And Volatility Behavior During The Covid-19 Pandemic, Manoj Venkatachalaiah, Soon Leong Yeap, Salman Ahmed Lnu, Sangwhan Cha

Harrisburg University Other Works

This report outlines the structural design, cloud implementation, and analytical findings of a scalable Big Data architecture deployed on Google Cloud Platform (GCP). The primary objective is to investigate the macroeconomic and microeconomic disruption caused by the COVID-19 pandemic on global equities, focusing on two dominant digital business models: online retail/cloud computing (Amazon, Inc. - AMZN) and subscription-based digital streaming entertainment (Netflix, Inc. - NFLX). Through a serverless orchestration pipeline leveraging GCP Cloud Run, automated workflows fetched and blended high-velocity epidemiological metrics alongside daily financial asset layers. Data transformations and parallel analytical calculations were executed utilizing Apache Beam pipelines inside …

Human Stem Cell-Based Embryo Models: Innovation, Ethics, And Policy, Alfonso Martinez Arias, Thorold Theunissen, Et Al.

2020-Current year OA Pubs

The aim of this White Paper is to establish a foundational framework for research, technological development, and regulation in the emerging field of stem cell-based embryo models (SCBEMs). These models, generated from Pluripotent Stem Cells, are designed to recapitulate essential events in early stages of human development. They have the potential to illuminate the early stages of embryo development and implantation and hold promise as an avenue to address global health challenges, including infertility and pregnancy loss, congenital, neonatal and adult conditions, and the need for organ transplants. While SCBEMs are not a substitute for human embryos, their tractability for …

Artificial Personhood: The Implications Of Recognizing The Legal Personhood Of Artificialintelligence, Daniel C. Borges

Vanderbilt Journal of Entertainment & Technology Law

Artificial intelligence (AI) is here in a big way. As society grapples with the implications of this profound new technology, the question of whether AI entities should be granted independent legal recognition, akin to human personhood, has emerged as a pivotal topic in jurisprudence. This Article explores the foundations, evolution, and implications of AI personhood, addressing philosophical perspectives, the development of personhood as a legal fiction (with specific emphasis on corporate personhood), and the need for AI personhood to counter the risks posed by rapidly developing AI technology. It defines the "AI person" as one whose legal recognition furthers a …

Who's Afraid Of Copyright And Ai?, Deborah R. Gerhardt

Vanderbilt Journal of Entertainment & Technology Law

Copyright and AI need not be a copyright no man’s land. Recent decisions by the Copyright Office have gotten so much media attention that many creators are unsure if they can secure copyright protection if they use AI as a creative tool. The proposed creative control paradigm can help us all tackle the challenge of determining when works created with the assistance of AI are copyrightable. Instead of relying on long-established nuanced precedent for protecting works generated with new technology, the Copyright Office asserts that any content generated with assistance from AI tools is unprotectable. This position is radically inconsistent …

Open Secrets, Robin Feldman Distinguished Professor Of Law, Vaughn Goehrig -- Center For Innovation

Vanderbilt Journal of Entertainment & Technology Law

Patents and trade secrets were never intended to meet, but in the world of biologic drugs, they’ve become as thick as thieves. Patent law’s disclosure requirement has demanded far too little of biologics manufacturers—allowing them to keep vital manufacturing details under trade secret lock and key. This intellectual property patchwork has been devastating for drug affordability: Because the most critical manufacturing information remains hidden, makers of follow-on biologics (called biosimilars) are often stuck reinventing the wheel, even after the original patents have expired. This Article makes two principal contributions to illuminating this problem. First, it presents, for the first time …

The Impact Of Religious Behaviors, Islamophobia, Gender Roles, Language Proficiency, And Education Levels On Mental Illness Stigma And Help-Seeking Behaviors Among Arab Women In The U.S., Rima Muhsen

Dissertations

The official U.S. census does not provide data on the number of Arab Americans in the United States, which hinders studies that focus on this population group to explore their needs and improve access to mental health services. Arab women in America represent a subgroup that has received insufficient attention from researchers, particularly regarding culturally sensitive topics such as mental health problems and their associated stigma. The influence of cultural, social, and religious factors on self-stigma associated with mental illness has not been adequately studied among this marginalized group. While previous research has suggested a relationship between self-stigma, social stigma …

Well-Being, Resilience, And Trauma During Migration: A Mixed Methods Study Of Venezuelan Migrants In Transit, Margaret S. Port

Dissertations

A substantial body of research has examined the psychological impact of experiences preceding and following migration, yet comparatively little attention has been given to the psychological toll of the migration journey itself. Moreover, due to the relative novelty of the Venezuelan migrant crisis and the community’s arrival in the United States, the psychological impact of Venezuelan migrants’ unique migration experience has been underexamined. This study utilized an explanatory sequential mixed methods design to investigate the relationships between well-being, resilience, and trauma among Venezuelan migrants in Chicago, Illinois, while also exploring their lived experiences of the migration journey. Study participants (n …