Digital Commons Network^™

Supporting Satisfaction, Satisfying Support: Bidirectional Associations Of Social Support And Life Satisfaction, Jonathan Louis Jie Sheng Chia, Andree Hartanto, William Tov

Research Collection School of Social Sciences

Existing literature has explored the impact of social support (SS) on life satisfaction (LS). However, the reciprocal relationship—that is, the influence of LS on SS—remains understudied, despite extant theoretical support. In addressing this gap, the present study employed bivariate latent growth modeling to examine bidirectional associations between SS and LS trajectories among 8,449 middle-aged and older adults in Singapore, over seven waves spanning almost 6 years. Results provided evidence supporting the notion of bidirectional associations. Specifically, baseline SS positively predicted subsequent changes in LS, and baseline LS positively predicted subsequent changes in SS. Findings underscore the potential for interventions and …

The Return Of Chinese Emigrants: Legal Barriers And Local Practices, Jiaqi M. Liu

Research Collection School of Social Sciences

While China has observed an increasing influx of foreign nationals, a notable phenomenon involves Chinese emigrants opting for return migration to reestablish themselves in their country of origin. The process of emigrants returning to China, referred to as “return and resettlement” (huiguo dingju 回国定居) in official terminology, however, is not automatic. Emigrant returnees must fulfill a set of selective and documentary criteria, implying that only economically affluent and duly documented migrants are permitted to resettle in China. Through a comprehensive review of national policies and an analysis of local practices in a diaspora hometown, this chapter argues that the hukou …

Recurrent Autoregressive Linear Model For Next-Basket Recommendation, Tereza Zmeskalova, Antoine Ledent, Martin Spisak, Pavel Kordik, Rodrigo Alves

Research Collection School Of Computing and Information Systems

Next-basket recommendation aims to predict the (sets of) items that a user is most likely to purchase during their next visit, capturing both short-term sequential patterns and long-term user preferences. However, effectively modeling these dynamics remains a challenge for traditional methods, which often struggle with interpretability and computational efficiency, particularly when dealing with intricate temporal dependencies and inter-item relationships. In this paper, we propose ReALM, a Recurrent Autoregressive Linear Model that explicitly captures temporal item-to-item dependencies across multiple time steps. By leveraging a recurrent loss function and a closed-form optimization solution, our approach offers both interpretability and scalability while maintaining …

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 …

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 …

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 …

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.

Vibemus: Proactive Agentic System For Music Personalization, Zhiliang Guo, Teng Tu, Yunshan Ma, Xun Yang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) enable diverse forms of AI-assisted creation, yet they often struggle to bridge the preference-articulation gap: users may provide incomplete or vague intentions or lack the vocabulary to specify what they want, yielding outputs misaligned with true preferences. To address this gap and facilitate music creation in a vibe-centric environment, we introduce VibeMus, a proactive agentic system built on open-source components. The system engages in multi-turn dialogue to progressively determine the music’s emotion, genre, lyrics, and other aspects before generation. Simulated evaluations show that proactive clarification improves alignment with users’ intended nuances. Our approach is training-free, leveraging …

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 …

Exploring Parameter-Efficient Fine-Tuning Techniques For Code Generation With Large Language Models, Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, Houari A. Sahraoui

Research Collection School Of Computing and Information Systems

Large language models (LLMs) demonstrate impressive capabilities to generate accurate code snippets given natural language intents in a zero-shot manner, i.e., without the need for specific fine-tuning. While prior studies have highlighted the advantages of fine-tuning LLMs, this process incurs high computational costs, making it impractical in resource-scarce environments, particularly for models with billions of parameters. To address these challenges, previous research explored in-context learning (ICL) and retrieval-augmented generation (RAG) as strategies to guide the LLM generative process with task-specific prompt examples. However, ICL and RAG introduce inconveniences, such as the need for designing contextually relevant prompts and the absence …

September 2025 News Releases, University Of Montana--Missoula. Office Of University Relations

University of Montana News Releases, 1928, 1956-present

No abstract provided.

The Moderating Role Of Trait Online Disinhibition In Exacerbating The Online Victim-Bully Cycle, Sandeeshwara K Tennakoon Appuhamillage Kasturiratna, Andree Hartanto

Research Collection School of Social Sciences

The growth of the internet and social media as a communication platform has increased the incidence of cyberbullying victimisation. While there has been increasing research interest on the antecedents of cyberbullying perpetration, little is known on why some individuals who themself experience cyber victimisation are more likely to become cyberbullying perpetrators. Considering the large number of individuals who experience cyberbullying victimisation in the current, highly digitalised world, this trend of victims turning into bullies poses a worrisome possibility and deserves research attention. One promising individual difference that could potentially moderate the relationship between cyberbullying victimisation can lead to cyberbullying perpetration …

Bgen 361.01: Principles Of Business Law, Michael V. Harrington

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Csd 330.01: Anatomy And Physiology Of The Speech And Swallowing Mechanisms, Trudi F. Mingus

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Phar 560.00: Pharmaceutical Care Lab Iv, Jenner Minto, Kimberly A. Madson

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Pt 526.01: Foundational Skills And Intervention I, Jaclyn C. Carson

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Anty 101h.50: Anthropology And The Human Experience, Erin D. Rosenkrance

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Ahrc 252.01: Respiratory Care Review, Paul J. Crockford

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Enst 480.01: Food Justice And Sustainability, Margiana Petersen-Rockney

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Enst 513.01: Natural Resources Conflict Resolution, Travis D. Anklam, Shawn M. Johnson

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Ptrm 310.01: Natural Resource Interpretation And Communication, Elena A. Bigart

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Comx 480.01: Health Communication, Heather L. Voorhees

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Phl 101l.01: Introduction To Philosophy, Jeffrey E. Stephenson

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Psci 330.01: Japanese Politics & Society, Kristin Vekasi

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Psyx 330.01: Child Development, Rachel L. Severson

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Soci 391.01: Guns In America, Madison B. Gerdes

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Behavior Change Communication Strategies On Human Immunodeficiency Virus /Acquired Immunodeficiency Syndrome Risk Reduction For Long-Distance Truckers In Kenya, Cyrus Mutie, John Gachohi, Kawira Kithuci, Grace Mbuthia

School of Nursing & Midwifery, East Africa

Background Long-distance truckers (LDTs) not only experience heightened risk of human immunodeficiency virus (HIV) infections due to risky sexual networks, but are also hard to reach with consistent risk reduction messages due to their often disruptive work schedules. Besides, evidence of the existing behavior change communication (BCC) strategies to enhance HIV/ acquired immunodeficiency syndrome (AIDS) risk reduction is limited. Thus, the study sought to explore the most effective and preferred BCC strategies for adoption among LDTs in Kenya.

Methods and Methodology Nine key informants and 18 in-depth interviews were used to gather qualitative data from purposively sampled participants at Kenya's …