Digital Commons Network^™

Contribution Of Lagoon-Rearing Juvenile Chinook Salmon (Oncorhynchus Tshawytscha) To The Adult Spawning Population In The Mattole River, Emma Held

Cal Poly Humboldt theses and projects

Bar-built estuaries are rivermouth confluences characterized by periods of disconnection between the river and the ocean due to a sandbar that builds across the mouth. Sandbar timing characteristics vary between years and between systems, but when closed, a lake-like lagoon forms in the estuary footprint. The Mattole River has a bar-built estuary that forms in late spring or early summer, often overlapping with juvenile Chinook salmon outmigration. Juveniles that do not leave the river before the bar closes must remain in the lagoon until the mouth opens in fall. The purpose of this thesis was to understand if lagoon-rearing is …

Symmetry In Affiliative Interactions Of North American River Otters (Lontra Canadensis) In A Northern California Estuary, Charlotte E. Norman

Cal Poly Humboldt theses and projects

Patterns of participation in affiliative behaviors may reflect underlying social dynamics. Two affiliative behaviors, allogrooming and social play, were assessed for their symmetry, or relative participation of participants, in a wild group of North American river otters. Behavioral observations were conducted over five months at the Little River estuary in northern California using video recordings and all-occurrence sampling. Observations included all-adult groups, a family group consisting of a breeding female and two pups, and an extended family group with one or two helpers, indicative of cooperative breeding. Individuals engaged in allogrooming and social play were classified by their group membership, …

A Narrative Analysis Of Equity-Focused Cs Educators: A Multiple Case Illustration, Aleata Hubbard, Jen Tsan

Journal of Computer Science Integration

Background and Context. Equity-focused CS teaching is often prescribed as a solution for addressing disparities in K–12 CS participation and disengaging student experiences. However, adopting such an orientation can be challenging as it pushes educators to shift their teaching beliefs, identity, and practices. Objective. This study examines how the personal and professional experiences of four high school CS teachers influenced their paths to becoming equity-oriented practitioners. We illustrate variation in these developmental trajectories and advance theory building around equity-focused CS teacher identity development. Method. We utilized a multiple-case narrative inquiry approach, collecting data through in-depth, semi-structured interviews that included a …

Does Volunteering Give An Extra Health Boost? Volunteering And Hypertension Risk Among Insured And Uninsured U.S. Adults In Late Midlife, Byeongju Ryu, Seoyoun Kim, Koichiro Shiba, Cal J. Halvorsen

Brown School Faculty Publications

Introduction: Hypertension is a major predictor of cardiovascular disease, a leading cause of death in the U.S. Growing evidence suggests that formal volunteering in later life may reduce hypertension risk; however, it remains unclear whether these benefits extend to adults in late midlife and whether they differ by health insurance status.

Methods: This prospective cohort study analyzed data from the 2012–2018 Health and Retirement Study. The analytic sample included 4,169 respondents between the ages of 50 and 64. Because blood pressure was measured in half the sample in alternating waves, data were pooled across waves. To address selection into volunteering, …

Humanizing The Base Of The Pyramid: Exploring The Influence Of Resource (Dis)Advantages On Value Creation, Ted London, Lisa Jones Christensen, Tyson Mackey, Alison Mackey, Heather Esper, Andrew Grogan-Kaylor

Faculty Publications

Base of the pyramid (BoP) scholarship emphasizes the potential for enterprises to generate profits by enhancing the wellbeing of the impoverished, often by selling socially-beneficial products (SBPs). Yet, many such BoP enterprises have experienced mixed results. We argue that this may stem from insufficient attention to how BoP consumers value SBPs and other products. We frame SBPs as a type of human capital investment and thus use human capital theory to study how the consumption of SBPs does or does not create value for consumers. Specifically, we use a novel data set from rural India to measure value creation in …

Retrenchment, Segregation, And Public Education: A Five-Year Analysis Of State Exclusionary School Discipline Legislation, Thalia Gonzalez, Will Martel

Faculty Scholarship

Notwithstanding the decision in Brown v. Board of Education formally ending educational segregation and more than two decades of state reform, the legislative landscape protecting minoritized children from the structural violence of exclusionary school discipline has dramatically shifted in less than five years. From the passage of state “Teacher’s Bills of Rights” to mandatory expulsions, rescissions of prior protections, and new categories of removal, a retrenchment of anti-inclusion legislation—and its corollary, educational carcerality— has occurred. While studies of other forms of legislative retrenchment in K–12 public schools, such as anti-Critical Race Theory and Don’t Say Gay laws, literacy bans, and …

Open Secrets, Robin C. Feldman, Vaughn Goehrig

Faculty Scholarship

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 …

Contingent Value Of Coopetition In Business Clusters From A Resource-Based View: A Moderated-Mediation Model, George Vandyck, Christopher Agyapong Siaw, Jonas Aryee, Shikhar Bhaskar, Chiwuokem Nwoko, Rahul Govind

Plymouth Business School

This study examines how perceived cluster resource attractiveness (PCRA) shapes coopetition among co-located firms and how coopetition translates those resource perceptions into knowledge sharing and innovation. Building on the resource-based view, we model coopetition as the mechanism through which managers leverage valuable, unique, and complementary cluster resources into firm outcomes, and we theorize that engagement mode in cluster activities conditions this translation. Using survey data from 221 firms in a business cluster, we estimate a moderated-mediation model with regression and corroborate the results with a latent-variable SEM robustness check. Findings show that PCRA increases coopetition, and coopetition in turn enhances …

“We’Re All In The Struggle Together”: The Experiences Of First-Generation Graduate Students During Covid-19, Rebecca Stallworth, Danielle E. Maurici-Pollock, Jade Obler, Rachel A. Starr

Publications

This study explores the experiences of first-generation graduate students (FGGS) during the COVID-19 pandemic, specifically the positive and negative impacts of the pandemic on their learning experiences and their implications for the design of online graduate programs that foster a sense of belonging.

Cultural Humility In The Context Of Muslim Patients​, Farah Alqawasmi

HSC Education Days

No abstract provided.

It’S Showtime! Concussion Management Strategies For Clinicians Working With Performing Artists, Allyssa Memmini, Kristen Schuyten, Paola Savvidou

HSC Education Days

No abstract provided.

Prevention Beyond Deterrence, Benjamin A. Barsky

Faculty Scholarship

This Article reconceptualizes preventive justice—the public safety paradigm that seeks to prevent harm before it occurs. Scholars have long documented how cities have advanced this paradigm through largely punitive measures, notably variants of broken windows policing, which posit that aggressive misdemeanor enforcement deters more serious crime. Yet in the aftermath of the 2020 George Floyd protests, and as underscored recently in City of Grants Pass v. Johnson, these measures have faced a legitimacy crisis—prompting calls for nonpunitive responses to nonviolent incidents.

This Article establishes a preventive justice approach that advances health and safety without emphasizing crime deterrence. It draws on …

"Junk Science" Or Admissible? The Role Of Psychological Profiling In Serial Homicide Trials, Alice Gnesin

Florida Atlantic University Undergraduate Law Journal

In many homicide trials, juries have to consider more than just forensic and testimonial evidence. Sometimes, a perpetrator’s actions can appear illogical or unexplainable, and in this case, juries have to examine and consider the mental state, possible motivations, and behavioral patterns of the perpetrator. Psychological profiling, also known as behavioral analysis, has been used in criminal investigations for years. But even after all these years, many still question its legitimacy. This article examines the effectiveness of behavioral analysis when used as expert testimony in serial homicide cases through the evidentiary standards under Frye v. United States, Daubert v. Merrell …

When Parenting Becomes Production: Family Vlogging, Child Labor, And The Law's Silence, Morgan Robinson

Florida Atlantic University Undergraduate Law Journal

The rise of family vlogging and the ability to earn revenue from the internet through monetization programs have ushered in a new era of child stars. So-called “kidfluencers” are frequently under the scrutiny of their parents, sponsors, and those watching behind a screen. These child influencers currently lack federal protections that ensure their well-being and safety. Family vlogging has been proven to place a financial, mental, and sometimes, physical burden on children involved. Federal regulations have been encouraged by experts and survivors of child internet exploitation but have not been passed. This paper will examine the impact of family vlogging …

Automatic Generation Of Introductory Programming Exercises With Large Language Models, Nguyen Binh Duong Ta, Hua Gia Phuc Nguyen, Gottipati Swapna

Research Collection School Of Computing and Information Systems

Despite recent advances in code generation made possible by large language models (LLMs), programming is still an essential skill that computing students need to master now and in the foreseeable future. In learning programming, frequent practices with exercises set at an appropriate difficulty and knowledge level is of crucial importance for students. However, it’s not a trivial task for instructors to create many good quality exercises customized for each student. Programming problems found on Internet sources such as LeetCode are mostly too challenging for novice programmers with no prior coding knowledge. Recent work in AI-enabled education has been leveraging LLMs …

Elephant Economics And Agrarian Transitions: Conditions For Benefiting From Elephant Tourism Market In An Upland Karen (Paganyaw) Village, Xiaoyun Neo, Aswin Baharuddin, Nur Fadilah Sunardi

Forest and Society

In rural, upland northern Thailand, Indigenous Karen (Paganyaw) communities have long practiced diversified subsistence cultivation and forest-based livelihoods. Yet state conservation policies and tourism-led development are reshaping these agrarian systems and livelihoods. This paper explores how one Karen (Paganyaw) community in Chiang Mai province reconfigures its livelihood strategies amid the decline of swidden cultivation and the rise of elephant tourism as a new rural economy. Beyond subsistence cultivation, villagers are seeking to diversify their households and personal income by participating in this new economic sector. We argue that the transition out of small-scale agriculture into tourism is not a neutral …

The Disenchanted Grove: Postcolonial Modernity And The Decline Of Ri’I Bamboo Governance In Flores, Indonesia, Handoyo Handoyo, Puji Hastuti, Lukas Rumboko Wibowo, Budiyanto Dwi Prasetyo, Surati Surati, Kresno Agus Hendarto, Lila Juniyanti, Dewi Ratna Kurnia Sari, Desmiwati Desmiwati, Sanusi Sanusi, Ane Dwi Septina, Ramawati Ramawati

Forest and Society

This article explores the erosion of ri’i, a traditional ecological governance system practiced by the Ngadha people in Flores, Indonesia, in the face of modernization and postcolonial development. Based on long-term ethnographic fieldwork and interviews with cultural elders, ritual practitioners, and local authorities, the study documents how ri’i—a ritual-based regulatory system for bamboo resource management—has been progressively dismantled through the combined pressures of technocratic governance, land commodification, and shifts toward individualism. Empirically, the main drivers identified are state land certification (including Sertifikat Hak Milik, SHM) and village modernization campaigns, which reconfigure communal tenure and everyday governance practices. Once embedded …

Generalization Bounds For Semi‑Supervised Matrix Completion With Distributional Side Information, Antoine Ledent, Mun Chong Soo, Minh Hieu Nong

Research Collection School Of Computing and Information Systems

We study a matrix completion problem where both the ground truth R matrix and the unknown sampling distribution P over observed entries are low-rank matrices, and share a common subspace. We assume that a large amount M of unlabeled data drawn from the sampling distribution P is available, together with a small amount N of labeled data drawn from the same distribution and noisy estimates of the corresponding ground truth entries. This setting is inspired by recommender systems scenarios where the unlabeled data corresponds to ‘implicit feedback’ (consisting in interactions such as purchase, click, etc. ) and the labeled data …

Can We Read Ai’S Mind? A Quest For Transparency, Santhosh Kumar Ravindran, Estera Kot, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Although Artificial Intelligence (AI) systems are playing an increasing role in critical domains such as healthcare, finance, and autonomous systems, their decision-making processes remain largely opaque. This paper examines the challenges of AI transparency, addressing the “black box” problem using Explainable AI (XAI) techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It also examines the ethical, regulatory, and societal implications of AI opacity and proposes a Comprehensive AI Observability (CAO) Framework that integrates deep explainability, provenance tracking, and real-time monitoring to enhance AI accountability. By bridging technical solutions with governance structures, this research emphasizes the …

Actor-Critic For Continuous Action Chunks: A Reinforcement Learning Framework For Long-Horizon Robotic Manipulation With Sparse Reward, Jiarui Yang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Existing reinforcement learning (RL) methods struggle with long-horizon robotic manipulation tasks, particularly those involving sparse rewards. While action chunking is a promising paradigm for robotic manipulation, using RL to directly learn continuous action chunks in a stable and data-efficient manner remains a critical challenge. This paper introduces AC3 (Actor-Critic for Continuous Chunks), a novel RL framework that learns to generate high-dimensional, continuous action sequences. To make this learning process stable and dataefficient, AC3 incorporates targeted stabilization mechanisms for both the actor and the critic. First, to ensure reliable policy improvement, the actor is trained with an asymmetric update rule, learning …

Interpretable Machine Learning For In-Home Mild Cognitive Impairment Detection, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

This paper introduces a novel system for in-home cognitive health assessment using ambient sensors and a machine learning technology that can robustly detect mild cognitive impairment (MCI) despite limited available data. The learned model can explain the aspects of individuals’ daily lives led to the prediction, while reliably predicting MCI, providing more insights to healthcare workers for further clinical interventions. We developed the robust transparent machine learning model, based on fusion adaptive resonance theory (Fusion ART) neural network to learn individuals’ daily patterns of activity from continuous sensor data in terms of a suite of digital biomarkers reflecting four key …

Ai Systems For Physicians: A Review From Socio-Technical And Human-Computer Interaction Perspectives, Wu Jiaqi Young, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

The adoption of artificial intelligence (AI) in healthcare is accelerating, yet successful implementations of physician-facing AI systems remain limited and uneven. This paper presents a literature review of 40 peer-reviewed studies published between November 2022 and November 2024, spanning clinical, technical, and human-computer interaction (HCI) domains. Anchored in a socio-technical perspective, the review examines our existing understanding of how technical design, user expertise, and organizational factors shape the effectiveness of AI systems in real-world clinical settings. Our analysis identifies two meta-themes: (1) context as a dynamic, multi-level influence that actively reshapes AI system behavior, and (2) trust as an emergent …

Leveraging Large Language Models For Career Mobility Analysis: A Study Of Gender, Race, And Job Change Using Us Online Resume Profiles, Palakorn Achananuparp, Ye Xu, Yao Lu, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are associated with upward mobility. This study addresses key research questions of how the job changes affect their upward career mobility, and how the outcomes of upward career mobility differ by gender and race. We address data challenges – such as missing demographic attributes, missing wage data, and noisy occupation labels – through various data processing and Artificial Intelligence (AI) methods. In particular, we develop a large language models (LLMs) based occupation classification method known …

Thinkmatter: Panoramic-Aware Instructional Semantics For Monocular Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Hao Zhao, Bin Zhu, Qianru Sun, Xiangbo Shu

Research Collection School Of Computing and Information Systems

Vision-and-Language Navigation in continuous environments (VLN-CE) requires an embodied robot to navigate the target destination following the natural language instruction. Most existing methods use panoramic RGB-D cameras for 360° observation of environments. However, these methods struggle in real-world applications because of the higher cost of panoramic RGB-D cameras. This paper studies a low-cost and practical VLN-CE setting, e.g., using monocular cameras of limited field of view, which means “Look Less” for visual observations and environment semantics. In this paper, we propose a ThinkMatter framework for monocular VLN-CE, where we motivate monocular robots to “Think More” by 1) generating novel views …

Generalized Visual Relation Detection With Diffusion Models, Kaifeng Gao, Siqi Chen, Hanwang Zhang, Jun Xiao, Yueting Zhuang, Qianru Sun

Research Collection School Of Computing and Information Systems

Visual relation detection (VRD) aims to identify relationships (or interactions) between object pairs in an image. Although recent VRD models have achieved impressive performance, they are all restricted to pre-defined relation categories, while failing to consider the semantic ambiguity characteristic of visual relations. Unlike objects, the appearance of visual relations is always subtle and can be described by multiple predicate words from different perspectives, e.g., “ride” can be depicted as “race” and “sit on”, from the sports and spatial position views, respectively. To this end, we propose to model visual relations as continuous embeddings, and design diffusion models to achieve …

Memoryart: Enhancing Llms Via Multi-Memory Models With Adaptive Resonance Theory For Healthcare Agents, Renke Dai, Hebin Hu, Jiahui Zhang, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Though promising in healthcare consultation applications, large language models (LLMs) face critical limitations in retaining and utilizing long-term memory across multiturn interactions. In particular, existing memory enhancing paradigms are constrained by limited context windows and embedding-based retrieval, often failing to maintain task relevance and still suffering from memory prototype collapse in multi-turn healthcare consultation. To address these challenges, we propose a cognitively-inspired memory framework named MemoryART, which is grounded in Adaptive Resonance Theory (ART)—a cognitive and learning theory of how humans and animals adapt to dynamic environments. MemoryART employs three memory modules—working memory, episodic memory, and semantic memory to support …

Gui Test Migration Via Abstraction And Concretization, Yakun Zhang, Chen Liu, Xiaofei Xie, Yun Lin, Jin Song Dong, Dan Hao, Lu Zhang

Research Collection School Of Computing and Information Systems

GUI test migration aims to produce test cases with events and assertions to test specific functionalities of a target app. Existing migration approaches typically focus on the widget-mapping paradigm that maps widgets from source apps to target apps. However, since different apps may implement the same functionality in different ways, direct mapping may result in incomplete or buggy test cases, thus significantly impacting the effectiveness of testing the target functionality and the practical applicability of migration approaches.In this article, we propose a new migration paradigm (i.e., the abstraction-concretization paradigm) that first abstracts the test logic for the target functionality and …

A General Algorithm For Assortment Optimization Under Random Utility Choice Models, Tien Mai, Andrea Lodi

Research Collection School Of Computing and Information Systems

This work concerns the assortment optimization problem that refers to selecting a subset of items that maximizes the expected revenue in the presence of the substitution behavior of consumers specified by a random utility choice model. The key challenge lies in the computational difficulty of finding the best subset solution, which often requires exhaustive search. The literature on constrained assortment optimization lacks a practically efficient method that is general to deal with different types of customer choice models (e.g., the multinomial logit, mixed logit or general multivariate extreme value models). In this work, we propose a new approach that allows …

Dual-Lora And Quality-Enhanced Pseudo Replay For Multimodal Continual Food Learning, Xinlan Wu, Bin Zhu, Feng Han, Pengkun Jiao, Jingling Chen

Research Collection School Of Computing and Information Systems

Food analysis has become increasingly critical for health-related tasks such as personalized nutrition and chronic disease prevention. However, existing large multimodal models (LMMs) in food analysis suffer from catastrophic forgetting when learning new tasks, requiring costly retraining from scratch. To address this, we propose a novel continual learning framework for multimodal food learning, integrating a Dual-LoRA architecture with Quality-Enhanced Pseudo Replay. We introduce two complementary low-rank adapters for each task: a specialized LoRA that learns task-specific knowledge with orthogonal constraints to previous tasks’ subspaces, and a cooperative LoRA that consolidates shared knowledge across tasks via pseudo replay. To improve the …

Benchmarking Gaslighting Negation Attacks Against Reasoning Models, Bin Zhu, Hailong Yin, Jingjing Chen, Yu Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in reasoning-centric models promise improved robustness through mechanisms such as chain-of-thought prompting and test-time scaling. However, their ability to withstand gaslighting negation attacks—adversarial prompts that confidently deny correct answers—remains underexplored. In this paper, we conduct a systematic evaluation of three state-of-the-art reasoning models, i.e., OpenAI’s o4-mini, Claude-3.7-Sonnet and Gemini-2.5-Flash, across three multimodal benchmarks: MMMU, MathVista, and CharXiv. Our evaluation reveals significant accuracy drops (25–29% on average) following gaslighting negation attacks, indicating that even top-tier reasoning models struggle to preserve correct answers under manipulative user feedback. Built upon the insights of the evaluation and to further probe this vulnerability, …