Digital Commons Network^™

Anxiety And Depression In Today's Youth: A Current Look Into Assessment And Treatment., Meredith J. Scafe, Meghan Kanya, Meaghan Flynn, Ram Chettiar

Manuscripts, Articles, Book Chapters and Other Papers

Following the COVID-19 pandemic, the American Academy of Pediatrics (AAP), the American Academy of Child and Adolescent Psychiatry, and the Children's Hospital Association declared a national emergency in child and adolescent mental health. Rates of anxiety and depression in youth continue at unprecedented levels, contributing to rising numbers of suicide attempts and lowered school attendance. Though many medical providers are trained to assess and provide recommendations for anxiety and depression, many report feeling ill-equipped to address these concerns in a timely, feasible, and effective manner. We review the existing literature on screening for anxiety and depression in the medical setting …

A Brief Guide To Statistical Analysis Of Grouped Data In Preclinical Research, Colby J Vorland, Lilian Golzarri-Arroyo, David B Allison

Children’s Nutrition Research Center Staff Publications

Clustering and nesting (C&N) arise in many preclinical studies such as when animals are group-housed, share litters, or in cell culture. Ignoring C&N undermines the validity of analyses. We explain how C&N arise and valid designs and analyses.

To Pause With A Cliffhanger Or A Temporary Closure? The Differential Impact Of Serial Versus Episodic Narratives On Children's Physical Activity Behaviors, Amy Shirong Lu, Melanie C Green, Caio Victor Sousa, Jungyun Hwang, I-Min Lee, Debbe Thompson, Tom Baranowski

Children’s Nutrition Research Center Staff Publications

Research has supported the effectiveness of narratives for promoting health behavior, but different narrative presentation formats (serial vs. episodic) have seldom been compared. Suspense theories suggest that serial narratives, which do not provide a full resolution at the end of an episode, may create higher motivation for continued engagement with a story. Forty-four 8 to 12-year-old children were randomly assigned to watch an animation series designed for an existing active video game in which the plot was delivered either continuously across multiple episodes (serial) or in multiple yet relatively independent self-contained episodes (episodic). Controlling for social desirability, children who watched …

Mitigating Regression Faults Induced By Feature Evolution In Deep Learning Systems, Hanmo Yu, Zan Wang, Xuyang Chen, Junjie Chen, Jun Sun, Shuang Liu, Zishuo Dong

Research Collection School Of Computing and Information Systems

Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To …

An On-The-Fly Synthesis Framework For Ltl Over Finite Traces, Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Vardi

Research Collection School Of Computing and Information Systems

We present an on-the-fly synthesis framework for Linear Temporal Logic over finite traces (LTLf) based on top-down deterministic automata construction. Existing approaches rely on constructing a complete Deterministic Finite Automaton (DFA) corresponding to the LTLf specification, a process with doubly exponential complexity relative to formula size in the worst case. In this case, the synthesis cannot be conducted until the entire DFA is constructed. This inefficiency is the main bottleneck of existing approaches. To address this challenge, we first present a method for converting LTLf into Transition-based DFA (TDFA) by directly leveraging LTLf semantics, incorporating intermediate results as direct components …

Efficient Prompt Tuning For Hierarchical Ingredient Recognition, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Fine-grained ingredient recognition presents a significant challenge due to the diverse appearances of ingredients, resulting from different cutting and cooking methods. While existing approaches have shown promising results, they still require extensive training costs and focus solely on fine-grained ingredient recognition. In this paper, we address these limitations by introducing an efficient prompt-tuning framework that adapts pretrained visual-language models (VLMs), such as CLIP, to the ingredient recognition task without requiring full model finetuning. Additionally, we introduce three-level ingredient hierarchies to enhance both training performance and evaluation robustness. Specifically, we propose a hierarchical ingredient recognition task, designed to evaluate model performance …

Dual-Target Disjointed Cross-Domain Recommendation Mediated Via Latent User Preferences, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Users often navigate multiple platforms online, each characterized by its own set of scarce data. Recommender systems face a significant challenge in such fragmented environments. This paper proposes a novel approach to enhance recommendation systems by leveraging connections across distinct yet conceptually similar datasets from multiple platforms. We introduce a unique scenario of dual-target overlapping-free cross-platform recommendation, presenting a bridging mechanism to mutually improve across platforms and learn latent user preferences. Our approach addresses the data sparsity prevalent in each platform and enhances recommendation quality by harnessing redundant, rich, and similar domain data. Experiments validate the effectiveness of our method, …

Foodlmm: A Versatile Food Assistant Using Large Multi-Modal Model, Yuehao Yin, Huiyan Qi, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities, including food recognition, ingredient recognition, recipe generation, nutrition estimation, food segmentation and multi-round conversation. To facilitate FoodLMM to deal with tasks beyond pure text output, we introduce a series of novel task-specific tokens and heads, enabling the model to predict food nutritional values and multiple segmentation masks. We adopt a two-stage training strategy. In the first stage, we utilize multiple …

Unveiling Knowledge Boundary Of Large Language Models For Trustworthy Information Access, Yang Deng, Moxin Li, Liang Pang, Wenxuan Zhang, Wai Lam

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have emerged as powerful tools for generating content and facilitating information seeking across diverse domains. While their integration into conversational systems opens new avenues for interactive information-seeking experiences, their effectiveness is constrained by their knowledge boundaries—the limits of what they know and their ability to provide reliable, truthful, and contextually appropriate information. Understanding these boundaries is essential for maximizing the utility of LLMs for real-time information seeking while ensuring their reliability and trustworthiness. In this tutorial, we will explore the taxonomy of knowledge boundary in LLMs, addressing their handling of uncertainty, response calibration, and mitigation of …

Hps: Hard Preference Sampling For Human Preference Alignment, Xiandong Zou, Wanyu Lin, Yuchen Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Aligning Large Language Model (LLM) responses with human preferences is vital for building safe and controllable AI systems. While preference optimization methods based on PlackettLuce (PL) and Bradley-Terry (BT) models have shown promise, they face challenges such as poor handling of harmful content, inefficient use of dispreferred responses, and, specifically for PL, high computational costs. To address these issues, we propose Hard Preference Sampling (HPS), a novel framework for robust and efficient human preference alignment. HPS introduces a training loss that prioritizes the most preferred response while rejecting all dispreferred and harmful ones. It emphasizes “hard” dispreferred responses — those …

Repairing Adversarial Texts Through Perturbation, Guoliang Dong, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay, Xinyu Wang, Ting Dai, Jie Shi, Jin Song Dong

Research Collection School Of Computing and Information Systems

It is known that neural networks are subject to attacks through adversarial perturbations. Worse yet, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training. Multiple approaches have been developed to detect and reject such adversarial inputs. Rejecting suspicious inputs however may not be always feasible or ideal. First, normal inputs may be rejected due to false alarms generated by the detection algorithm. Second, denial-of-service attacks may be conducted by feeding such systems with adversarial inputs. To address this, in this work, we focus on the text domain and …

Sanitizable Cross-Domain Access Control With Policy-Driven Dynamic Authorization, Jianfei Sun, Guowen Xu, Hongwei Li, Tianwei Zhang, Cong Wu, Xuehuan Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The increasing demand for secure and efficient data sharing has underscored the importance of developing robust cryptographic schemes. However, many existing endeavors have overlooked the following critical issues: (1) unauthorized access resulting from malicious information leakage by senders; (2) absence of constraints on write and read permissions for participants; (3) and inflexibility of strategies to dynamically designate ciphertexts to multiple recipients. In this paper, we present SCPA, a cross-domain access control scheme imbued with sanitization features and propelled by policy-driven dynamic authorization, tailored for cloud-based data sharing. This scheme not only facilitates access controls, including regulations for no-read and no-write …

Understanding The Bad Development Practices Of Android Custom Permissions In The Wild, Xiaohan Zhang, Zhiyuan Yu, Xinghua Li, Cen Zhang, Cong Sun, Ning Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Android system provides application developers with the ability to define custom permissions, which serve to moderate the sharing of resources and interactions with other applications. However, poor development practices of developers can render the permission mechanism ineffective, weakening the system protection. This paper presents a comprehensive examination of the problematic practices surrounding custom permissions employed by developers, referred to as Bad Practices of Custom Permissions (BPCP issues). To accomplish this, we conducted an empirical study and identified nine common BPCP issue patterns that can lead to various adverse consequences, such as installation failures, crashes, or even component hijacking. To automatically …

Runtime Anomaly Detection For Drones: An Integrated Rule-Mining And Unsupervised Learning Approach, Ivan Wei Han Tan, Wei Minn, Christopher M. Poskitt, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have witnessed a remarkable surge in popularity due to their versatile applications. These cyber-physical systems depend on multiple sensor inputs, such as cameras, GPS receivers, accelerometers, and gyroscopes, with faults potentially leading to physical instability and serious safety concerns. To mitigate such risks, anomaly detection has emerged as a crucial safeguarding mechanism, capable of identifying the physical manifestations of emerging issues and allowing operators to take preemptive action at runtime. Recent anomaly detection methods based on LSTM neural networks have shown promising results, but three challenges persist: the need for models …

Diversity Optimization For Travelling Salesman Problem Via Deep Reinforcement Learning, Qi Li, Zhiguang Cao, Yining Ma, Yaoxin Wu, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Existing neural methods for the Travelling Salesman Problem (TSP) mostly aim at finding a single optimal solution. To discover diverse yet high-quality solutions for Multi-Solution TSP (MSTSP), we propose a novel deep reinforcement learning based neural solver, which is primarily featured by an encoder-decoder structured policy. Concretely, on the one hand, a Relativization Filter (RF) is designed to enhance the robustness of the encoder to affine transformations of the instances, so as to potentially improve the quality of the found solutions. On the other hand, a Multi-Attentive Adaptive Active Search (MA3S) is tailored to allow the decoders to strike a …

Robust Relevance Feedback For Interactive Known-Item Video Search, Zhixin Ma, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Known-item search (KIS) involves only a single search target, making relevance feedback-typically a powerful technique for efficiently identifying multiple positive examples to infer user intent-inapplicable. PicHunter addresses this issue by asking users to select the top-k most similar examples to the unique search target from a displayed set. Under ideal conditions, when the user's perception aligns closely with the machine's perception of similarity, consistent and precise judgments can elevate the target to the top position within a few iterations. However, in practical scenarios, expecting users to provide consistent judgments is often unrealistic, especially when the underlying embedding features used for …

Cradle: Empowering Foundation Agents Towards General Computer Control, Weihao Tan, Et. Al.

Research Collection School Of Computing and Information Systems

Despite their success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encapsulations of environments with manually designed observation and action spaces. To handle this issue, we propose the General Computer Control (GCC) setting to restrict foundation agents to interact with software through the most unified and standardized interface, i.e., using screenshots as input and keyboard and mouse actions as output. We introduce Cradle, a modular and flexible LMM-powered framework, as a preliminary attempt towards GCC. Enhanced by six key modules, Information Gathering, Self-Reflection, Task Inference, Skill Curation, Action …

Fashiondpo: Fine‑Tune Fashion Outfit Generation Model Using Direct Preference Optimization, Mingzhe Yu, Yunshan Ma, Lei Wu, Changshuo Wang, Xue Li, Lei Meng

Research Collection School Of Computing and Information Systems

Personalized outfit generation aims to construct a set of compatible and personalized fashion items as an outfit. Recently, generative AI models have received widespread attention, as they can generate fashion items for users to complete an incomplete outfit or create a complete outfit. However, they have limitations in terms of lacking diversity and relying on the supervised learning paradigm. Recognizing this gap, we propose a novel framework FashionDPO, which fine-tunes the fashion outfit generation model using direct preference optimization. This framework aims to provide a general fine-tuning approach to fashion generative models, refining a pre-trained fashion outfit generation model using …

Explaining Explanations: An Empirical Study Of Explanations In Code Reviews, Ratnadira Widyasari, Ting Zhang, Abir Bouraffa, Walid Maalej, David Lo

Research Collection School Of Computing and Information Systems

Code reviews are central for software quality assurance. Ideally, reviewers should explain their feedback to enable authors of code changes to understand the feedback and act accordingly. Different developers might need different explanations in different contexts. Therefore, assisting this process first requires understanding the types of explanations reviewers usually provide. The goal of this article is to study the types of explanations used in code reviews and explore the potential of Large Language Models (LLMs), specifically ChatGPT, in generating these specific types. We extracted 793 code review comments from Gerrit and manually labeled them based on whether they contained a …

How Are We Detecting Inconsistent Method Names? An Empirical Study From Code Review Perspective, Kisub Kim, Xin Zhou, Dongsun Kim, Julia Lawall, Kui Liu, Tegawendé F. Bissyandé, Jacques Klein, Jaekwon Lee, David Lo

Research Collection School Of Computing and Information Systems

Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication or a lack of familiarity with conventions within the software development lifecycle. To address this issue, much research effort has been invested into building automatic tools that can check for method name inconsistency and recommend consistent names. However, existing datasets generally do not provide precise details about why a method name was deemed improper and required to be changed. Such information can give useful hints on how to improve the recommendation of adequate method …

Lianas Reduce Tree Height With Negative Consequences For Carbon Storage And Growth Estimates, Xingyan Cao, Frieke Van Coillie, Sruthi M. Krishna Moorthy, Kasper Coppieters, Barbara D'Hont, Stefan A. Schnitzer, Geertje M. F. Van Der Heijden, Hans Verbeeck, Félicien Meunier

Biological Sciences Faculty Research and Publications

1. Current evidence suggests that liana (woody vine) competition with trees could be threatening the carbon sink by reducing carbon uptake and storage in tropical forests. Previous studies investigating forest demography in liana removal experiments have, however, assumed similar allometries for trees, regardless of the amount of lianas they support. Yet, recent observations suggest that liana load may alter tree shape and structure, including height, which could result in an underestimation of the liana effects on forest carbon stocks and tree growth.
2. In this study, we used terrestrial laser scanning (TLS) in a liana removal experiment in Gigante Peninsula, Panama, collected …

Carolyn Nadeau, Carolyn Nadeau, Nathalie Romero '25, Posse Legacy Project, Illinois Wesleyan University

All oral histories

Interview of Carolyn Nadeau on July 1, 2025. Carolyn Nadeau is a Posse 4 mentor and Byron S. Tucci Professor of Spanish at Illinois Wesleyan University.

A Wearable-Based Approach To Spinal Posture Tracking And Assessment, Sydney Marie Sherman, Noah Jeffery

Biomedical Engineering: Graduate Reports and Projects

This paper describes the design, manufacturing, and testing of a novel spinal posture tracking wearable device with embedded sensors. Currently on the market, most of the spinal posture tracking devices only focus on one segment of the spine and therefore do not track the entirety of human posture. The objective for this project was to create a wearable device in the form of a “smart shirt” that offers insights into posture trends throughout the day by tracking the entire spine. To begin the project, multiple designs were developed for the sensor array and wearable materials. These concepts were compared against …

Interview Of Naomi Cahn, June Carbone & Nancy Levit, Naomi Cahn, June Carbone, Nancy Levit, Claire Hill, Matthew T. Bodie

Minnesota Journal of Law & Inequality

No abstract provided.

The Homo Economicus Model Of Work Describes Men More Than Women, But Only In Weird Cultures, Thomas Talhelm, Danila Medvedev, Yin Li

Minnesota Journal of Law & Inequality

No abstract provided.

An Economic, Psychological, And Linguistic Explanation Of (Some Reasons) Why Women Don’T Get A Fair Shake, Claire A. Hill

Minnesota Journal of Law & Inequality

No abstract provided.

Conversations Abroad: The Effect Of Interlocutor Type, Relationship Quality, And L2 Proficiency On Interaction In Naturalistic Settings, Tripp Strawbridge

Modern Languages & Literature

Study abroad (SA) is touted for providing language learners with regular exposure to a second language (L2) in naturalistic settings. However, few studies have examined how interaction occurs in situ. This study analyzed 13 hours of naturalistic dyadic conversations self-recorded by 15 US-based undergraduate sojourners studying abroad for one semester in Spain. Conversations were analyzed for interaction metrics previously posited as relevant to L2 acquisition in SA: speaking time, negotiation of meaning, lexical assistance, and corrective feedback. The study then analyzed how these variables were conditioned by interlocutor identity (host family [HF] member, local native speaker [NS] peer, and nonnative …

Design, Modeling, And Testing Of A Novel Inductor For Electric Vehicles: Iron Nitride Soft Magnetic Composites, Macen Wahoske, Jesse Alan Winkel

Materials Engineering

This multi-year project focuses on the design, modeling, and testing of novel inductors for electric vehicles (EVs) using iron nitride (IN) soft magnetic composites (SMCs). This work is a continuation of previous research, with the overarching goal of enhancing EV efficiency by developing inductor cores with improved magnetic properties, leveraging IN's competitive magnetic characteristics.

This year, the project concentrated on refining fabrication processes to optimize relative permeability (μ_r) and effective IN concentration. Investigations included various mixing vessels, the application of vibration during powder settling, and different pressing methods and pressures across initial IN concentrations of 75, 80, and …

The Ghannouchi Test: The Compatibility Of Religious Ideas And Secular Democracy, Andrew P. Sheppe

Dartmouth College Master’s Theses

Secular, democratic societies and governments evolved out of religious societies and defined themselves partially in opposition to religious authority. This historical relationship has led to a debate about the compatibility between religious ideas and modern, secular society. Ernst-Wolfgang Böckenförde posited that secular, democratic societies still depend on religious foundations. Jürgen Habermas disagrees, claiming that processes of rational dialogue can provide sufficient support.

Two prominent twentieth century theologians, Reinhold Niebuhr and Abraham Joshua Heschel demonstrate that democratic systems are enriched and protected by theistic ideas, thus supporting Böckenförde’s dictum. However, they both recognize the importance of epistemic humility and a …

Rattler Python, Samer Jabor

Systems Manuals - 2026

The Rattler Python project is an interactive game-based learning system that intends to teach the basic concepts of Python programming through guided instruction, gameplay challenges, and review-based assessments. The document contains a proposal for this system consisting of problem definition, background research, existing solutions, and the proposed product, together with the system scope, assumptions, and the organization of the remainder of this document.