Digital Commons Network^™

Enhancing Urban Energy Modeling: A Case Study Of Data Acquisition, Enrichment, And Evaluation In Berlin, Felix Rehmann, Martín Mosteiro-Romero, Clayton Miller, Rita Streblow

Research Collection College of Integrative Studies

Urban Building Energy Modeling (UBEM) has become a critical tool for developing local heating and cooling plans, as required by the European Union. Despite growing interest, the reproducibility and reliability of UBEM studies remain limited due to data scarcity and workflow complexity. This paper presents a comprehensive framework to evaluate the data pipeline in UBEM, with a particular focus on data acquisition, enrichment, simulation, calibration, and information application. The approach applies three distinct UBEM workflows (CityEnergyAnalyst, DistrictGenerator, and SimStadt) to the Mierendorffinsel district in Berlin, Germany. We compare the based on quantitative performance metrics and qualitative framework criteria. The results …

Effectiveness Of Ai-Powered Language Learning Tools In The Workplace, Iswariya Baskar

Electronic Theses and Dissertations

This dissertation explored the experiences of multilingual employees with AI-powered language learning tools in financial and technology firms across the United States. As multilingual communication becomes increasingly vital in the workplace, the integration of artificial intelligence (AI) offers promising support in language acquisition and cross-cultural collaboration. The purpose of this basic qualitative study was to explore how AI-powered language learning tools influence the acquisition and retention of foreign languages of employees within financial and technology firms, how these tools strengthen professional identity and interpersonal communication, and how code-switching facilitated by AI impacts user experience and workplace engagement. Drawing from the …

Seeing Culture: A Benchmark For Visual Reasoning And Grounding, Burak Satar, Zhixin Ma, Patrick Amadeus Irrawan, Wilfried Ariel Mulyawan, Jing Jiang, Ee-Peng Lim, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Multimodal vision-language models (VLMs) have made substantial progress in various tasks that require a combined understanding of visual and textual content, particularly in cultural understanding tasks, with the emergence of new cultural datasets. However, these datasets frequently fall short of providing cultural reasoning while underrepresenting many cultures.In this paper, we introduce the Seeing Culture Benchmark (SCB), focusing on cultural reasoning with a novel approach that requires VLMs to reason on culturally rich images in two stages: i) selecting the correct visual option with multiple-choice visual question answering (VQA), and ii) segmenting the relevant cultural artifact as evidence of reasoning. Visual …

Seeing Is Fixing: Cross-Modal Reasoning With Multimodal Llms For Visual Software Issue Fixing, Kai Huang, Jian Zhang, Xiaofei Xie, Chunyang Chen

Research Collection School Of Computing and Information Systems

Large language model (LLM)-based automated program repair (APR) techniques have shown promising results in resolving real-world github issue tasks. Existing APR systems are primarily evaluated in unimodal settings (e.g., SWE-bench), relying solely on textual issue descriptions and source code. However, these autonomous systems struggle to resolve multimodal problem scenarios (e.g., SWE-bench M) due to limitations in interpreting and leveraging visual information. In multimodal scenarios, LLMs need to rely on visual information in the graphical user interface (GUI) to understand bugs and generate fixes. To bridge this gap, we propose GUIRepair, a cross-modal reasoning approach for resolving multimodal issue scenarios by …

From Personas To Talks: Revisiting The Impact Of Personas On Llm-Synthesized Emotional Support Conversations, Shenghan Wu, Yimo Zhu, Wynne Hsu, Mong-Li Lee, Yang Deng

Research Collection School Of Computing and Information Systems

The rapid advancement of Large Language Models (LLMs) has revolutionized the generation of emotional support conversations (ESC), offering scalable solutions with reduced costs and enhanced data privacy. This paper explores the role of personas in the creation of ESC by LLMs. Our research utilizes established psychological frameworks to measure and infuse persona traits into LLMs, which then generate dialogues in the emotional support scenario. We conduct extensive evaluations to understand the stability of persona traits in dialogues, examining shifts in traits post-generation and their impact on dialogue quality and strategy distribution. Experimental results reveal several notable findings: 1) LLMs can …

Adasteer: Your Aligned Llm Is Inherently An Adaptive Jailbreak Defender, Weixiang Zhao, Jiahe Guo, Yulin Hu, Yang Deng, An Zhang, Xingyu Sui, Xinyang Han, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Research Collection School Of Computing and Information Systems

Despite extensive efforts in safety alignment, large language models (LLMs) remain vulnerable to jailbreak attacks. Activation steering offers a training-free defense method but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs. To address this, we propose AdaSteer, an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics. We identify two key properties: Rejection Law (R-Law), which shows that stronger steering is needed for jailbreak inputs opposing the rejection direction, and Harmfulness Law (H-Law), which differentiates adversarial and benign inputs. AdaSteer steers input representations along both the Rejection Direction …

Chain Of Strategy Optimization Makes Large Language Models Better Emotional Supporter, Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu

Research Collection School Of Computing and Information Systems

The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they face two key challenges: (1) low strategy selection accuracy, and (2) preference bias, limiting their adaptability to users’ emotional needs. Existing supervised fine-tuning (SFT) struggles to address these issues, as it rigidly trains models on single gold-standard responses without modeling nuanced strategy trade-offs. To overcome these limitations, we propose a novel two-stage framework that optimizes strategy selection preferences at each dialogue turn. We first leverage Monte Carlo Tree Search to construct ESC-Pro, a high-quality …

Exploring Autonomous Agents: A Closer Look At Why They Fail When Completing Tasks, Ruofan Lu, Yichen Li, Yintong Huo

Research Collection School Of Computing and Information Systems

Autonomous agent systems powered by Large Language Models (LLMs) have demonstrated promising capabilities in automating complex tasks. However, current evaluations largely rely on success rates without systematically analyzing the interactions, communication mechanisms, and failure causes within these systems. To bridge this gap, we present a benchmark of 34 representative programmable tasks designed to rigorously assess autonomous agents. Using this benchmark, we evaluate three popular open-source agent frameworks combined with two LLM backbones, observing a task completion rate of approximately 50%. Through in-depth failure analysis, we develop a three-tier taxonomy of failure causes aligned with task phases, highlighting planning errors, task …

Envisioning Future Interactive Web Development: Editing Webpage With Natural Language, Truong Hai Dang, Jingyu Xiao, Yintong Huo

Research Collection School Of Computing and Information Systems

The evolution of web applications relies on iterative code modifications, a process that is traditionally manual and time-consuming. While Large Language Models (LLMs) can generate UI code, their ability to edit existing code from new design requirements (e.g., ”center the logo”) remains a challenge. This is largely due to the absence of large-scale, high-quality tuning data to align model performance with human expectations. In this paper, we introduce a novel, automated data generation pipeline that uses LLMs to synthesize a high-quality fine-tuning dataset for web editing, named Instruct4Edit. Our approach generates diverse instructions, applies the corresponding code modifications, and performs …

Crisis Observatory: Extracting Credible Signals During A Crisis In The Age Of Llms, Kuan-Chieh Lo, Pranav Maneriker, Sriram Sai Ganesh, Dominik Winecki, Kelly Garrett, Ayaz Hyder, Arnab Nandi, Valerie Shalin, Shannon A. Bowen Ph.D., Amit Sheth, Srinivasan Parthasarathy

Publications

Systems for crisis response have required several different models for the analysis of unstructured text, such as identifying needs, locations, topics, routing, and matching of needs with available responders. Large Language Models (LLMs) have replaced task-specific models across various language processing tasks. However, LLMs are known to be limited by their training data, collected before the crisis. In this demo, we explore the use of LLMs for crisis response scenarios with rapidly evolving information environments. We show how the augmentation of these models with external reliable sources of crisis-specific information can help build adaptive systems for response. The demonstration video …

Cerumen Impaction, Children's Mercy Kansas City

Clinical Pathways

No abstract provided.

Longitudinal And Cross-Sectional Predictors Of Sleep Disturbance In A Treatment Follow-Up Sample With Tourette’S Disorder, Maya S. Tooker, Kathryn E. Barber, Joseph F. Mcguire, Flint M. Espil, Jordan T. Stiede, Jennifer S. Schild, Shannon M. Bennett, Matthew W. Specht, John T. Walkup, Douglas W. Woods, John Piacentini, Emily J. Ricketts

Psychology Faculty Research and Publications

Background: Sleep disturbance is common in individuals with Tourette’s disorder (TD). Tic symptoms, medication, functional impairment, and psychiatric comorbidity frequently contribute to sleep disturbance in children and adults with TD. However, long-term predictors of sleep disturbance in TD are not known. This study examined longitudinal and cross-sectional predictors of sleep disturbance in a treatment follow-up sample with TD.

Methods: Eighty subjects who completed a 10-week randomized controlled trial of behavior therapy for tics in childhood (M_age = 11.47, SD = 2.42 years) participate in follow-up evaluation on average, 11.17 (SD = 1.25) years after post-treatment assessment ( …

Student Engagement In Early Education Classrooms: A Mixed Methods Study Examining Student And Teacher Perspectives, Jill Ann Derosa

Theses & Dissertations

Student disengagement is a persistent issue in U.S. public schools and has been linked to negative outcomes for students. When students are engaged in learning, they are more likely to achieve academic success and socioemotional well-being in school. This study evaluated multiple perspectives on student engagement to gain a deeper understanding of how students and teachers in one school are making sense of engagement in their classrooms. Using a mixed-methods approach, I employed student surveys and conducted focus groups with both students and teachers to gather data on participants’ perceptions of student engagement. The findings from this study highlight that …

Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Accurately identifying crop diseases plays a crucial role in advancing intelligent and modern agricultural production. Deep learning techniques have performed robust performance in classifying plant disease images. However, current studies face the challenge that many plant disease datasets are generated in controlled environments, leading to reduced model performance in real-world agricultural settings. This paper aims to provide a lightweight model that can accurately classify plant diseases in natural environments. Specifically, this paper investigates the Dual-Attention Multi-Scale Lightweight Network (DAMSLNet), which combines dual-attention-based multi-scale feature extraction and deep information fusion, to classify plant diseases. At the front end, the model employs …

Usefulness And Diminishing Returns: Evaluating Social Information In Recommender Systems, Qing Meng, Huiyu Min, Ming Shan Hee, Roy Ka-Wei Lee, Bing Tian Dai, Shuai Xu

Research Collection School Of Computing and Information Systems

Social recommendation, which leverages users’ social information to predict users’ preferences, is a popular branch of recommender systems. Many existing studies have attempted to advance the performance of collaborative filtering methods by leveraging the user-user matrix to enhance user embedding learning with user’s social connections. While the existing social recommender systems have demonstrated good performance in various recommendation tasks, the extent of social information usefulness in recommender systems remains unclear. This paper addresses the research gap by designing experiments to answer three research questions: (i) How useful is social information in varying user-item data sparsity? (ii) How much social information …

Context-Aware Hierarchical Taxonomy Generation For Scientific Papers Via Llm-Guided Multi-Aspect Clustering, Kun Zhu, Lizi Liao, Yuxuan Gu, Lei Huang, Xiaocheng Feng, Bing Qin

Research Collection School Of Computing and Information Systems

The rapid growth of scientific literature demands efficient methods to organize and synthesize research findings. Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models (LLMs), often lack coherence and granularity. We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering. Our method leverages LLMs to identify key aspects of each paper (e.g., methodology, dataset, evaluation) and generates aspect-specific paper summaries, which are then encoded and clustered along each aspect to form a coherent hierarchy. In addition, we introduce a new evaluation benchmark of 156 expert-crafted taxonomies encompassing 11.6k …

Why Stop At One Error? Benchmarking Llms As Data Science Code Debuggers For Multi-Hop And Multi-Bug Errors, Zhiyu Yang, Shuo Wang, Yukun Yan, Yang Deng

Research Collection School Of Computing and Information Systems

LLMs are transforming software development, yet current code generation and code repair benchmarks mainly assess syntactic and functional correctness in simple, single-error cases. LLMs’ capabilities to autonomously find and fix runtime logical errors in complex data science code remain largely unexplored. To address this gap, we introduce DSDBench: the Data Science Debugging Benchmark, the first benchmark for systematic evaluation of LLMs on multi-hop error tracing and multi-bug detection in data science code debugging. DSDBench adapts datasets from existing data science task benchmarks, such as DABench and MatPlotBench, featuring realistic data science debugging tasks with automatically synthesized multi-hop, multi-bug code snippets. …

Temporal And Heterogeneous Graph Neural Network For Remaining Useful Life Prediction, Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu

Research Collection School Of Computing and Information Systems

Predicting remaining useful life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time-series sensory data from such systems, deep learning (DL) models have risen to prominence at identifying complex, nonlinear temporal dependencies in these data. In addition to the temporal dependencies of individual sensors, spatial dependencies emerge as important correlations among these sensors, which can be naturally modeled by a temporal graph that describes time-varying spatial relationships. However, the majority of existing studies have relied on capturing discrete snapshots of this temporal …

Does Generative Ai Facilitate Investor Trading? Early Evidence From Chatgpt Outages, Qiang Cheng, Pengkai Lin, Yue Zhao

Research Collection School Of Accountancy

In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading …

Commentary: Delayed Climate Reporting Requirements Threaten Singapore’S Sustainability Leadership, Jiwei Wang, Holly I. Yang, Liandong Zhang

Research Collection School Of Accountancy

In a commentary by SMU Professor of Accounting (Practice) and Director of Master of Professional Accounting (MPA) and Master of Science in Accounting (MSA) programmes Wang Jiwei, SMU Associate Professor of Accounting, Lee Kong Chian Fellow and Co-Director (Academic Research) of the School of Accountancy Research Centre (SOAR) Holly Yang, and SMU Dean of School of Accountancy and Lee Kong Chian Professor of Accounting Zhang Liandong, they suggested that delaying climate disclosure requirements for small and mid-sized enterprises (SMEs) from 2025 to 2030 could weaken Singapore’s sustainable finance leadership, despite regulators saying the extension would help smaller firms build environmental, …

Discussion Of "Csr And Negative Corporate Events: The Moderating Role Of Managerial Overconfidence", Hye Sun Chang

Research Collection School Of Accountancy

Corporate social responsibility (CSR) refers to the notion that firms are accountable not only to shareholders but also to a broader set of stakeholders including customers, employees, creditors, and the communities in which they operate. Although CSR has long been part of corporate discourse, its prominence has grown with the rise of environmental, social, and governance (ESG) concerns. Today’s consumers and stakeholders are increasingly attentive to issues such as climate change, economic inequality, labor rights, and social justice, prompting firms to align their strategies with evolving societal expectations. Building on this increasingly salient backdrop, Chu et al. (2026), hereafter CCT, …

A Comparison Of Comparisons: Evidence From An International Comparative Study Of 'Smart Cities', Kevin Ward, Teresa Abbruzzese, Tim Bunnell, Paolo Cardullo, I-Chun Catherine Chang, Byron Miller, Ramon Ribera-Fumaz, Haeran Shin, Zachary Spicer, Orlando Woods

Research Collection College of Integrative Studies

Every year the list lengthens of cities with some sort of ‘smart city’ public policy. In some, it emerges as the latest in a long line of urban digital and information communication policies. In others, the introduction of the notion of the ‘smart city’ marks a departure from past approaches to public policy. Additionally, the more studies emerge of actual smart city policies, then the less definitional agreement there seems to be. Nevertheless, that we have witnessed in the last two decades the ‘repeated instance’ of smart cities emerging in cities around the world seems incontrovertible. Like so much urban …

Remaking The Smart City Through The Covid-19 Pandemic: Seoul, Singapore, Taipei, Orlando Woods, I-Chun Catherine Chang, Haeran Shin

Research Collection College of Integrative Studies

The COVID-19 pandemic has proven to be a catalysing force for urban innovation in general, and for smart city agendas specifically, throughout the world. From the development of contact tracing technologies and the enforcement of quarantine and safe distancing measures, to the dissemination of public health information and advancement of telehealth rollouts, the promises of “smart” technologies to reveal and govern patterns of socio-spatial contact and mobility can be found at the core of effective city management during the pandemic. Arguably nowhere is this truer than in the cities of Seoul, Singapore and Taipei, where the Asian developmental state was …

Global Cooling Watch 2025, Omar Abdelaziz, Ray Gluckman, Ian Hamilton, Radhika Khosla, Lily Riahi

Research Collection College of Integrative Studies

The second edition of UNEP’s Global Cooling Watch Report takes a deep dive into one of the decade’s most urgent challenges: surging heat, soaring cooling demand, and stark inequalities in access. Produced by the UNEP-led Cool Coalition, the report provides a comprehensive assessment of the rapidly growing global demand for cooling and the need for climate-friendly solutions to the issue. The 2025 report builds on the first Global Cooling Watch (2023), which established the Sustainable Cooling Pathway framework. The new edition deepens analysis of intensifying extreme heat and its effect on cooling demand, improved assessment of passive cooling, and coverage …

A Fake Friend? Ai Companions Are Exactly That, Seow Hon Tan

Research Collection Yong Pung How School Of Law

In a commentary, SMU Associate Professor of Law Tan Seow Hon discussed how AI companions, which promise emotionally intelligent companionship, have blurred the line between human and machine relationships by mimicking empathy, memory, and affection. She suggested that while such technologies may ease loneliness, they risk fostering narcissism, diminishing real human connection, and replacing authentic friendship with comforting illusions that erode the capacity for love and community.

From Great “Liberator” To “Landlord Seeking Rent”: The Implications Of U.S. Reciprocal Tariffs Policy In Asia And Beyond, Henry S. Gao

Research Collection Yong Pung How School Of Law

The post-war international economic order was, to a large extent, underwritten by U.S. leadership. Nowhere was this more visible than in Asia, where the United States not only financed post-colonial development but also provided open access to its huge market, laying the foundation for export-led growth across the region. It underpinned regional stability through a blend of military, diplomatic, and economic engagement, including costly interventions in the Korean and Vietnam wars. That legacy, however, was fundamentally disrupted on April 2, 2025, when the Trump administration unveiled sweeping tariffs targeting key Asian economies. As Singapore’s defense minister wryly observed, the United …

Nationalism And Anglo-American Neo-Colonialism In Southeast Asia, 1945-1965, Wen-Qing (Wei Wenqing) Ngoei

Research Collection College of Integrative Studies

It is tempting to treat the United States’ ill-fated military intervention in Vietnam in the 1960s as the desperate final gasp of a moribund Western imperial system. Certainly, scholars have described Washington’s military debacle in Vietnam as the end of America’s “short-lived empire” in Southeast Asia, emblematic of the broader, decisive triumph of Southeast Asian nationalism over western colonialism. This reading of Southeast Asian countries attaining formal independence after 1945, with tacit assumptions that colonialism and nationalism exist in binary opposition, tends to ossify when the region’s history is viewed through the lens of U.S. defeat in Vietnam.

In contrast, …

Seeing Beyond The State: Private Sector-Led Smart City Governance In Bsd City, Indonesia, Prerona Das, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

In recent decades new cities have sprung up at a rapid pace, aided by private sector funding (Hogan et al., 2012). Within the realm of smart city development, private sector influence over urban governance has substantially increased, particularly through Public-Private Partnerships (PPPs). However, in Indonesia, there is an emerging trend of the private sector exerting significant control over ‘independent’ greenfield smart cities beyond state-led frameworks. This paper develops the idea of seeing beyond the state which involves looking past conventional state-centered approaches to urban governance. It considers instead how power and control are exercised outside or beyond the usual boundaries …

Scenarios For Csrd Scope Amendments - Advancing Reporting Scope While Reducing Further Burden, Andreas Rasche, Theodor Florian Cojoianu, Andreas G. F. Hoepner, Fabiola Schneider

Research Collection College of Integrative Studies

This paper presents calculations related to the CSRD scope criteria which are modified through the Omnibus I Simplification Package. We show how many companies would be in scope of CSRD when considering the criteria suggested by (a) the Commission’s Omnibus proposal, (b) the position adopted by the Council of the EU, and (c) the European Peoples Party (EPP) proposal in the European Parliament. For each of these three “cases” we show the impact of lowering or increasing the employee-related threshold. 1.    Our calculations demonstrate that the number of in-scope companies varies significantly when modifying the employee threshold. We conclude from …

A Dataset Exploring Urban Comfort Through Novel Wearables And Environmental Surveys, Patrick Chwalek, Sailin Zhong, Nathan Perry, Tianqi Liu, Clayton Miller, Seiied Hamed Alavi, Denis Lalanne, A. Joseph Paradiso

Research Collection College of Integrative Studies

This study presents a comprehensive dataset capturing indoor environmental parameters, physiological responses, and subjective perceptions across three global cities. Utilizing wearable sensors, including smart eyeglasses, and a modified Cozie app, environmental and physiological data were collected, along with pre-screening, onboarding, and recurring surveys. Peripheral cues facilitated participant engagement with micro-EMA surveys, minimizing disruption over a 5-day collection period. The dataset offers insights into urban comfort dynamics, highlighting the interplay between environmental conditions, physiological responses, and subjective perceptions. Researchers can utilize this dataset to deepen their understanding of indoor environmental quality and inform the design of healthier built environments. Access to …