Digital Commons Network^™

Sound Decisions: The Impact Of Music Genre In Fashion Advertising On Consumer Perceptions And Purchase Intention, Cassandra Thomas

Graduate Theses and Dissertations

This study examines how music genre, brand type, and the perceived congruency between the two influences consumer perceptions of fashion advertising. Guided by Congruity Theory and Attitude Theory, this study employed a 3x3 experimental design in which 252 participants viewed one of nine advertisement conditions varying by music genre (pop, hip-hop, electronic) and brand type (activewear, fast fashion, luxury). A series of one-way ANOVAs, bivariate correlations, and regression analyses were conducted to interpret the effects of music genre, brand type, and perceived congruency on attitudes toward 1) the advertisement and brand, 2) credibility of the advertisement and brand, and 3) …

A Security Worth Having: The Pursuit Of Happiness Through Reading Burney, Radcliffe, And Austen, Samantha Meeks

Graduate Theses and Dissertations

This dissertation explores the works of three female authors of female protagonists in the long 18th century: Frances Burney’s Camilla and Evelina; Anne Radcliffe’s The Mysteries of Udolpho and The Italian; and Jane Austen’s Northanger Abbey, Sense and Sensibility, and Pride and Prejudice. By following the paths to security the female protagonists of these novels fashion, and identifying the elements of Stoicism and related Hellenism and the aspects of cybernetics, this dissertation delves into the choices female protagonists make along the way regarding their security and how such choices lead to agency and self-fashioning for these women. Emotional, economic, and …

Nanoparticle Iron Based Catalysts For Clean Water And Other Applications, Shelby Lynn Foster

Graduate Theses and Dissertations

This dissertation explores the development, characterization, and application of iron-based catalysts for environmental remediation and energy generation. The research focuses on Fe-based nanomaterials for water purification, electrochemical ammonia production, and ethanol oxidation enhancement. The first section of the dissertation investigates the removal of water contaminants using nickel-iron (NiFe) bimetallic nanoparticles. Various NiFe ratios were synthesized and tested for their efficiency in degrading the azo dye (Orange G), with lower Ni content formulations achieving 80–99% dye removal within 60 minutes. Results show that changing the molar ratio of nickel to iron caused different removal rates, as well as the extent of …

Understanding The Factors Affecting Social Acceptance Of Solar Energy Technologies, Jane Osei, Kerry Brown, Mehran Nejati

Research outputs 2022 to 2026

This study investigates the key factors influencing the social acceptance of solar energy technologies, aiming to develop a policy and practice framework from a socio-political perspective. While previous research has identified various factors, it has often overlooked the role of policy interventions. By employing Transition Management theory, this study maps these factors and emphasizes the importance of learning and policy adaptation in the energy transition process. Solar energy, known for its cost-effectiveness and potential to stabilize electricity costs, remains relatively unfamiliar to many at the local level despite its global technological maturity. This research consolidates fragmented findings from 46 articles …

From Imaging To Impact: A Multi-Disciplinary T32 Program Framework And Initial Insights For Training In Image-Guided Cancer Therapy, Kari J Brewer Savannah, Stephen Y Lai, Clifton D Fuller, Kristy K Brock

Faculty, Staff and Student Publications

As multi-disciplinary image-guided assessment and therapies become increasingly important for precision oncology, the need for comprehensive training opportunities that effectively prepare the next generation of clinician-scientists and scientists is evident. We present an innovative, evidence-based research training framework that integrates the following critical features: 1) intentional and personalized mentoring and career development, 2) immersive research in image-guided cancer therapy, 3) strategic mentored clinical exposure, 4) structured didactic learning and professional development opportunities, and 5) mentored grant writing. Designed to foster scientific rigor, translational thinking, and multi-disciplinary collaboration, the Image-Guided Cancer Therapy T32 Training Program (IGCT-T32) aims to prepare postdoctoral fellows …

How Do Self-Employment Rates Change Across The Lifespan?, Cal J. Halvorsen, Bruna C. Lopez

Harvey A. Friedman Center for Aging

People pursue self-employment for a variety of reasons, including autonomy, the ability to pursue their own goals, and income. Yet for many older people who are not yet eligible for Social Security retirement benefits, which can begin at age 62, or Medicare, the U.S. health insurance program that generally begins at age 65, self-employment is also associated with less health insurance and retirement savings program coverage. 1,2 3,4

This research brief asks, What is the proportion of Americans who are self-employed, and how do these rates change throughout the life course?

A Review Of The United States' Long Term War Support Capabilities In The Indo-Pacific Command Region, Brian J. Mullin Jr.

Theses and Dissertations

This study examines U.S. maritime transportation readiness in the Indo-Pacific, highlighting fleet age, mariner shortages, shipyard decline, and port vulnerabilities. It also considers contested logistics and technological threats. Recommendations include fleet recapitalization, mariner pipeline growth, port diversification, and defensive upgrades. The study concludes that secure sea line assumptions are outdated and calls for greater resilience, with follow-on efficiency analysis proposed for ports and ships.

Genetic Variability In Biomass Partitioning And Surface Residue Carbon-Nitrogen Ratios In Soybean, Joscif G. Raigne, Race H. Higgins, Elvis F. Elli, Sotirios V. Archontoulis, Somak Dutta, Fernando C. Miguez, Asheesh K. Singh

Crop, Soil and Environmental Sciences Faculty Publications and Presentations

The drive to increase seed yield in soybean [Glycine max (L.) Merr.] has traditionally overshadowed the exploration of biomass partitioning and the compositional characteristics of plant residue traits such as leaves, petioles, stems, and pods. The exploration of biomass partitioning and the compositional characteristics of plant residue traits in soybean provide insights into plant nutrient allocation strategies that can be utilized to increase crop productivity and improve management practices for maximizing yields and sustainability. Recognizing this gap, our study aimed to investigate the variability in these traits across 32 genetically diverse soybean genotypes cultivated over 2 years in central Iowa. …

Older Adults And Financial Exploitation, Aaron Li, Natalie Galucia, Cal J. Halvorsen

Harvey A. Friedman Center for Aging

The National Adult Protective Services Association defines elder financial exploitation as misuse, mishandling, or exploitation of an older adult’s property, possession, or assets. These actions often occur without consent and are typically carried out through false pretense, undue influence, coercion, or manipulation.

Ageism In Employment, Aaron Li, Natalie Galucia, Cal J. Halvorsen

Harvey A. Friedman Center for Aging

The World Health Organization defines ageism as stereotypes, prejudice, and discrimination directed at individuals based on their age. As people remain in the labor force longer, they are increasingly exposed to ageist attitudes and discrimination. Ageism often manifests through negative assumptions about older workers’ abilities, adaptability, and value in the workplace. Older workers are frequently perceived as less competent, resistant to new technology, inflexible, or physically and mentally less capable.

Mental Health Challenges In Later Life, Sophie Mauk, Michele Dinman, Natalie Galucia, Cal J. Halvorsen

Harvey A. Friedman Center for Aging

Although older adults are less likely to be diagnosed with mental health challenges than younger adults, Americans over the age of 65 have experienced the greatest growth in new diagnoses — an increase of 57% between 2019 and 2023. By 2023, about 1 in 7 adults age 51 and older had received mental health diagnoses, including major depressive, generalized anxiety, adjustment, bipolar, and mood disorders. However, older adults are less likely to receive treatment than younger people. At the same time, older adults may also misuse substances such as alcohol, tobacco, cannabis, and prescription and nonprescription medications. Americans age 75 …

Neurologists' Perspectives On Sudep Communication: A Comparative Study Across Five European Countries, Rohit Shankar

Peninsula Medical School

Objectives: The leading cause of epilepsy mortality is Sudden unexpected death in epilepsy (SUDEP). National and international guidelines recommend that people with epilepsy are counseled on SUDEP risk at the point of diagnosis or at the earliest opportunity. This study compares neurologist views from five European countries on attitudes and relevance of SUDEP communication. Methods: A cross-sectional online survey of 17 Likert questions using validated themes was circulated via different ILAE and/or equivalent organizations using a non-discriminatory exponential snowballing technique, leading to a non-probability sampling. Data were analyzed using descriptive statistics, Kruskal–Wallis, Chi-square, and Fisher's exact tests. Significance was accepted …

Learning Orientation Field For Osm-Guided Autonomous Navigation, Yuming Huang, Wei Gao, Zhiyuan Zhang, Maani Ghaffari, Dezhen Song, Cheng-Zhong Xu, Hui Kong

Research Collection School Of Computing and Information Systems

OpenStreetMap (OSM) has gained popularity recently in autonomous navigation due to its public accessibility, lower maintenance costs, and broader geographical coverage. However, existing methods often struggle with noisy OSM data and incomplete sensor observations, leading to inaccuracies in trajectory planning. These challenges are particularly evident in complex driving scenarios, such as at intersections or facing occlusions. To address these challenges, we propose a robust and explainable two-stage framework to learn an Orientation Field (OrField) for robot navigation by integrating LiDAR scans and OSM routes. In the first stage, we introduce a novel representation, OrField, which can provide orientations for each …

Probabilistic Modeling, Learnability And Uncertainty Estimation For Interaction Prediction In Movie Rating Datasets, Jennifer Poernomo, Nicole Gabrielle Lee Tan, Rodrigo Alves, Antoine Ledent

Research Collection School Of Computing and Information Systems

In this paper, we examine the hypothesis that the interactions recorded in many Recommendation Systems datasets are distributed according to a low-rank distribution, i.e. a mixture of factorizable distributions. Surprisingly, we find that on several popular datasets, a simple non-negative matrix factorization method equals or outperforms more modern methods such as LightGCN, which indicates that the sampling distribution over interactions is indeed low-rank. Furthermore, we mathematically prove that low-rank distributions are learnable with a sparse number of observations (where m/n and r refer to the number of users/items and the non-negative rank respectively) both in terms of the total variation …

Privacy-Preserving Ridge Regression Over Encrypted Data Under Multiple Keys, Yanling Li, Junzuo Lai, Meng Sun, Beibei Song, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the increase of private data being collected by data owners, it has been a trend for data owners to store the data on cloud computing platforms. The huge amounts of data in cloud servers bring fresh development opportunities to machine learning, which is applied to build a high-quality machine learning model based on a large training dataset. However, to ensure the privacy of data and facilitate retrieval, data owners often upload encrypted data under their public keys. But it creates new challenges for machine learning to learn a predictive model over these encrypted data under different keys. Most existing …

An Efficient Security-Enhanced Accountable Access Control For Named Data Networking, Jianfei Sun, Yuxian Li, Xuehuan Yang, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Named Data Networking (NDN) is embraced as the crucial implementation of Information-Centric Networking (ICN), enhancing content distribution and caching efficiency through edge routers. However, existing NDN architectures face significant security and privacy challenges, including: (a) a lack of secure and efficient access control; (b) inadequate support for flexible and selective content management by content publishers; (c) insufficient implementation of accountability and privilege revocation mechanisms. To handle these challenges, we propose ESAS, the first-ever Efficient Security-enhanced Accountable Access Control Scheme for NDN. Specifically, our ESAS incorporates anonymous authentication using group signatures at network routers to prevent unauthorized access, employs key-aggregation-based access …

Educator Perceptions Of Devops Teaching Recommendations And Their Alignment With Common Challenges, Marcelo Romulo Fernandes, Pablo Paiva, Samuel Lucas De Moura Ferino, Roberta Coelho, Christoph Treude, Eduardo Aranha, Uirá Kulesza

Research Collection School Of Computing and Information Systems

DevOps education presents unique pedagogical challenges due to the diversity of tools, rapid technological change, and the multidisciplinary nature of the field. Although previous work has proposed recommendations to address these challenges, it is unclear how educators perceive these recommendations and whether they align with the challenges encountered in practice. In this paper, we present a quantitative and qualitative methods study involving 11 DevOps educators who interacted with Improve, a tool that presents a curated set of educational challenges and recommendations derived from previous literature. Educators indicated which recommendations they already use, which they intend to use, and which challenges …

Studying Satd In Drone Systems With Human-Ai Collaboration, Leevi Rantala, Lwin Khin Shar, Mäntylä Mika V., Wei Minn, Naing Tun Yan

Research Collection School Of Computing and Information Systems

Background: Self-Admitted Technical Debt (SATD) refers to sub-optimal solutions that developers acknowledge within the source code. SATD research originated on Java projects but is expanding to other domains. We focus on SATD in drones, which are used for various critical tasks.Aims: The primary objective is to investigate SATD in drone systems. The second aim is to explore the integration of AI and human collaboration for SATD labelling and classification.Method: We conducted a sample study of SATD comments in drone systems (14 open source, 4 SDKs) to analyse the quantity and types of SATD comments present. Our study incorporates collaboration between …

Large Lithium-Ion Battery Model For Secure Shared E-Bike Battery In Smart Cities, Donghui Ding, Zhao Li, Linhao Luo, Ming Jin, Bin Zhu, Yichen Zhong, Junhao Hu, Peng Cai, Huiqi Hu

Research Collection School Of Computing and Information Systems

Electric bikes powered by lithium-ion batteries are increasingly used in smart cities to promote sustainable mobility and efficient delivery services. However, limited battery range and slow plug-in charging remain key challenges. Shared electric bike battery systems, facilitated by battery swapping stations, offer a promising solution by enabling quick and efficient battery replacements. However, their success hinges on accurate anomaly detection, battery health estimation and remain range prediction. These tasks remain challenging due to data scarcity, battery diversity and environmental variability. Here we show that a large-scale lithium-ion battery model trained on over ten million battery time series data enables robust …

Stylegan-∞: Extending Stylegan To Arbitrary-Ratio Translation With Stylebook, Yihua Dai, Tianyi Xiang, Bailin Deng, Yong Du, Hongmin Cai, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Although pre-trained large-scale generative models StyleGAN series have proven to be effective in various editing and translation tasks, they are limited to pre-defined fixed aspect ratio. To overcome this limitation, we propose StyleGAN-∞, a model that enables pre-trained StyleGAN to perform arbitrary-ratio conditional synthesis. Our key insight is to distill the expressive StyleGAN features into a StyleBook, such that an arbitrary-ratio condition can be translated to other forms by properly assembling pre-defined StyleBook vectors. To learn and leverage the StyleBook, we employ a network with three distinct stages, each corresponding to StyleBook extraction, StyleBook correspondence learning, and arbitrary-ratio synthesis. Extensive …

Stable And Fair Cost Allocation In Platform-Enabled Lcl Consolidation, Pang Jin Tan, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Many logistics platforms enable collaboration between agents to reduce costs, but determining fair pricing remains challenging when agents have pre-existing partnerships. This paper introduces a cooperative game theory framework to model platform-mediated collaboration, modeling the platform as an additional player. We present a novel characteristic function that distinguishes between partial collaborations (existing relationships) and full collaborations (platform-enabled). Using Shapley value, we derive fair cost allocations and platform charges that reflect each participant's contribution. We address stability concerns through an optimization model that minimizes platform subsidies while preventing profitable deviations. The framework is demonstrated through an application in freight forwarding for …

Preference-Based Deep Reinforcement Learning For Historical Route Estimation, Boshen Pan, Yaoxin Wu, Zhiguang Cao, Yaqing Hou, Guangyu Zou, Qiang Zhang

Research Collection School Of Computing and Information Systems

Recent Deep Reinforcement Learning (DRL) techniques have advanced solutions to Vehicle Routing Problems (VRPs). However, many of these methods focus exclusively on optimizing distance-oriented objectives (i.e., minimizing route length), often overlooking the implicit drivers' preferences for routes. These preferences, which are crucial in practice, are challenging to model using traditional DRL approaches. To address this gap, we propose a preference-based DRL method characterized by its reward design and optimization objective, which is specialized to learn historical route preferences. Our experiments demonstrate that the method aligns generated solutions more closely with human preferences. Moreover, it exhibits strong generalization performance across a …

Dgl: Dynamic Global-Local Information Aggregation For Scalable Vrp Generalization With Self-Improvement Learning, Yubin Xiao, Yuesong Wu, Rui Cao, Di Wang, Zhiguang Cao, Xuan Wu, Peng Zhao, Yuanshu Li, You Zhou, Yuan Jiang

Research Collection School Of Computing and Information Systems

The Vehicle Routing Problem (VRP) is a critical combinatorial optimization problem with wide-reaching real-world applications, particularly in logistics, transportation. While neural network-based VRP solvers have shown impressive results on test instances similar to training data, their performance often degrades when faced with varying scales and unseen distributions, limiting their practical applicability. To overcome these limitations, we introduce DGL (Dynamic Global-Local Information Aggregation), a novel model that combines global and local information to effectively solve VRPs. DGL dynamically adjusts local node selections within a localized range, capturing local invariance across problems of different scales and distributions, thereby enhancing generalization. At the …

Coupling Category Alignment For Graph Domain Adaptation, Nan Yin, Xiao Teng, Zhiguang Cao, Mengzhu Wang

Research Collection School Of Computing and Information Systems

Graph domain adaptation (GDA), which transfers knowledge from a labeled source domain to an unlabeled target graph domain, attracts considerable attention in numerous fields. However, existing methods commonly employ message-passing neural networks (MPNNs) to learn domain-invariant representations by aligning the entire domain distribution, inadvertently neglecting category-level distribution alignment and potentially causing category confusion. To address the problem, we propose an effective framework named Coupling Category Alignment (CoCA) for GDA, which effectively addresses the category alignment issue with theoretical guarantees. CoCA incorporates a graph convolutional network branch and a graph kernel network branch, which explore graph topology in implicit and explicit …

Map As A By-Product: Collective Landmark Mapping From Imu Data And User-Provided Texts In Situated Tasks, Ryo Yonetani, Kotaro Hara

Research Collection School Of Computing and Information Systems

This paper presents Collective Landmark Mapper, a novel map-as-a-by-product system for generating semantic landmark maps of indoor environments. Consider users engaged in situated tasks that require them to navigate these environments and regularly take notes on their smartphones. Collective Landmark Mapper exploits the smartphone's IMU data and the user's free text input during these tasks to identify a set of landmarks encountered by the user. The identified landmarks are then aggregated across multiple users to generate a unified map representing the positions and semantic information of all landmarks. In developing the proposed system, we focused specifically on retail applications and …

Towards Multimodal Emotional Support Conversation Systems, Yuqi Chu, Lizi Liao, Zhiyuan Zhou, Chong-Wah Ngo, Richang Hong

Research Collection School Of Computing and Information Systems

The integration of conversational artificial intelligence (AI) into mental health care promises a new horizon for therapist-client interactions, aiming to closely emulate the depth and nuance of human conversations. Despite the potential, the current landscape of conversational AI is markedly limited by its reliance on single-modal data, constraining the systems’ ability to empathize and provide effective emotional support. This limitation stems from a paucity of resources that encapsulate the multimodal nature of human communication essential for therapeutic counseling. To address this gap, we introduce the Multimodal Emotional Support Conversation (MESC) dataset, a first-of-its-kind resource enriched with comprehensive annotations across text, …

Conv4rec: A 1‑By‑1 Convolutional Autoencoder For User Profiling Through Joint Analysis Of Implicit And Explicit Feedbacks, Antoine Ledent, Petr Kasalický, Rodrigo Alves, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility to learn a set of associations and combinations between different interaction types in a way that carries over to each user and item. Second, our model is able to learn jointly from both the explicit ratings and the implicit information in the sampling pattern (which we refer to as ”implicit feedback”). It can also make separate predictions for the probability of consuming content and the likelihood of granting it a high rating …

Rethinking Cognitive Complexity For Unit Tests: Toward A Readability-Aware Metric Grounded In Developer Perception, Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang, Xin Zhou, Anil Koyuncu, Jacques Klein, David Lo, Tegawendé F. Bissyandé

Research Collection School Of Computing and Information Systems

Automatically generated unit tests-from searchbased tools like EvoSuite or LLMs-vary significantly in structure and readability. Yet most evaluations rely on metrics like Cyclomatic Complexity and Cognitive Complexity, designed for functional code rather than test code. Recent studies have shown that SonarSource's Cognitive Complexity metric assigns nearzero scores to LLM-generated tests, yet its behavior on EvoSuitegenerated tests and its applicability to test-specific code structures remain unexplored. We introduce CCTR, a Test-Aware Cognitive Complexity metric tailored for unit tests. CCTR integrates structural and semantic features like assertion density, annotation roles, and test composition patterns-dimensions ignored by traditional complexity models but critical for …

Flow, Immersion, And Presence: Creating Virtual Reality And Engagement In The Era Of Ubiquitous And Intelligent Technologies, Yi Maggie Guo, Fiona Fui-Hoon Nah, Nannan Xi, Marshall Scott Poole

Research Collection School Of Computing and Information Systems

Researchers use the concepts of flow, immersion, and presence to explain the usage of and engagement (e.g., cognitive absorption) with information technology. In this special issue, we showcase four papers on empirical investigations of flow and immersion, their antecedents, and their outcomes. These papers address research questions that range from investigating the antecedents and consequences of immersion in head-mounted displays of virtual reality, designing for the flow experience in extended reality, studying factors influencing user engagement in the metaverse, and identifying adverse effects of work-related flow. We also provide directions and suggestions for future research.

Improving Co-Decoding Based Security Hardening Of Code Llms Leveraging Knowledge Distillation, Dong Li, Shanfu Shu, Meng Yan, Zhongxin Liu, Chao Liu, Xiaohong Zhang, David Lo

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have been widely adopted by developers in software development. However, the massive pretraining code data is not rigorously filtered, allowing LLMs to learn unsafe coding patterns. Several prior studies have demonstrated that code LLMs tend to generate code with potential vulnerabilities. The widespread adoption of intelligent programming assistants poses a significant threat to the software development process. Existing approaches to mitigating this risk primarily involve constructing secure data that are free of vulnerabilities and then retraining or fine-tuning the models. However, such an effort is resource intensive and requires significant manual supervision. When the model parameters …