Entire DC Network | Open Access Articles | Digital Commons Network™

Draft Final 2025 Bpsou Subdrain Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Draft Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Sister Dump (Bres No. 38) Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Logistics Center: Sustainable Design Build, Dylon Moala, Daryn Nguyen, Andrew Sabuda, Gage Urbach, Nicolae Vesca

Civil, Environmental and Sustainable Engineering Senior Theses

The Cal Centre Distribution Center is a proposed 4,000,000 square foot, four (4) warehouses total, e-commerce warehouse and logistics facility located along Interstate 5 (the I-5) in Kern County, California. The site occupies a very strategic location for overall distribution logistics of the Western United States being close to the Port of Los Angeles allowing for 65,000,000 people to be reached within a two (2) day truck turn. The site also addresses the local economic needs of Kern County and the greater Bakersfield area. Ranking as one of the highest functional unemployment rates among cities, this site will provide approximately …

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Conflogger: Enhance Systems’ Configuration Diagnosability Through Configuration Logging, Shiwen Shan, Yintong Huo, Yuxin Su, Zhining Wang, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Modern configurable systems offer customization via intricate configuration spaces, yet such flexibility introduces pervasive configuration-related issues such as misconfigurations and latent softwarebugs. Existing diagnosability supports focus on post-failure analysis of software behavior to identify configuration issues, but none of these approaches look into whether the software clue sufficient failure information for diagnosis. To fill in the blank, we propose the idea of configuration logging to enhance existing logging practices at the source code level. We develop ConfLogger, the first tool that unifies configuration-aware static taint analysis with LLM-based log generation to enhance software configuration diagnosability. Specifically, our method 1) identifies …

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Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) can exhibit concept-conditioned semantic divergence: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present RAVEN (Response Anomaly Vigilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling semantic entropy over paraphrastic samples with cross-model disagreement. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using …

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Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking …

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Reducing Class-Wise Performance Disparity Via Margin Regularization, Beier Zhu, Kesen Zhao, Jiequan Cui, Qianru Sun, Yuan Zhou, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data—posing concerns for reliable deployment. While prior efforts have explored empirical remedies, a theoretical understanding of such performance disparities in classification remains limited. In this work, we present Margin Regularization for performance disparity Reduction (MR2 ), a theoretically principled regularization for classification by dynamically adjusting margins in both the logit and representation spaces. Our analysis establishes a margin-based, class-sensitive generalization bound that reveals how per-class feature variability contributes to error, motivating the use of larger margins for “hard” classes. Guided by this insight, MR2 optimizes …

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Real-Time Motion-Controllable Autoregressive Video Diffusion, Kesen Zhao, Jiaxin Shi, Beier Zhu, Junbao Zhou, Xiaolong Shen, Yuan Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) approaches. Existing AR video diffusion models are limited to simple control signals or text-to-video generation, and often suffer from quality degradation and motion artifacts in few-step generation. To address these challenges, we propose AR-Drag, the first RL-enhanced few-step AR video diffusion model for real-time image-to-video generation with diverse motion control. We first fine-tune a base I2V model to support basic motion control, then further improve it via reinforcement learning with a trajectory-based reward model. Our design preserves the …

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Llmqua: Practical Backdoor Injection On Large Language Model Quantization, Xiangxiang Chen, Peixin Zhang, Jun Sun, Jin Song Dong, Wenhai Wang, Jingyi Wang

Research Collection School Of Computing and Information Systems

Quantization is widely used to enable local deployment of large language models (LLMs) on resource-constrained devices. Recent work (e.g., QuRA) shows quantization can be exploited via rounding manipulation to implant backdoors. However, such an attack has been evaluated only on small models and does not directly apply to LLMs due to three key constraints: (1) limited poisoning data from small, task-agnostic calibration sets; (2) layer-wise quantization restricting adversarial access to global representations; and (3) lack of gradient access in quantization pipelines, blocking gradient-based attacks.We propose LLMQuA, a practical quantization-phase backdoor attack tailored to the LLM setting. LLMQuA (i) injects backdoors …

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Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where “prompt” plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: (1) temporal lag of training data, and (2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power …

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Who You Explain To Matters: Learning By Explaining To Conversational Agents With Different Pedagogical Roles, Zhengtao Xu, Junti Zhang, Anthony Tang, Yi-Chieh Lee

Research Collection School Of Computing and Information Systems

Conversational agents are increasingly used in education for learning support. An application is “learning by explaining”, where learners explain their understanding to an agent. However, existing research focuses on single roles, leaving it unclear how different pedagogical roles influence learners’ interaction patterns, learning outcomes and experiences. We conducted a between-subjects study (N=96) comparing agents with three pedagogical roles (Tutee, Peer, Challenger) and a control condition while learning an economics concept. We found that different pedagogical roles shaped learning dynamics, including interaction patterns and experiences. Specifically, the Tutee agent elicited the most cognitive investment but led to high pressure. The Peer …

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Reasoning On Time-Series For Financial Technical Analysis, Kelvin J. L. Koa, Jan Chen, Yunshan Ma, Huanhuan Zheng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), a novel framework that combine verbal and latent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over time-series, we convert stock price data into textual annotations and …

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Defining Her2 Associated Proteogenomic Features In Breast Cancer And Extending To Gynecologic Cancers, Maya Anand

Theses

HER2 amplification is a well-established driver of breast cancer and serves as the primary basis for clinical classification and treatment selection. However, this framework assumes that HER2-driven tumor biology is defined solely by ERBB2 amplification or overexpression. The goal of this study was to evaluate whether HER2-associated signaling is represented as a pathway-level activation state and whether this framework could help identify tumors with clinically relevant HER2 activity beyond current routine classification methods. HER2-associated transcriptional programs were identified across three independent breast cancer cohorts, resulting in conserved gene sets (P76 and P25). Amplification-independent HER2 activation was assessed using the HER2 …

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Nlp Crowdsourcing For Predominantly Oral Languages: The Case Of Bambara, Allahsera Auguste Tapo

Theses

Predominantly oral languages (POLs) face a significant "digital divide," as they are often excluded from the benefits of modern natural language processing (NLP) technologies, due to a lack of extensive, readily available machine learning (ML) datasets. We investigate methods to overcome this data scarcity for Bambara, a Manding language, spoken primarily in Mali, with a rich oral tradition but limited digital presence. The research leverages crowdsourcing and community engagement to build high-quality ML ready dataset resources. Key contributions include methods for automatic speech recognition (ASR) and machine translation (MT) dataset collection and curation and for educational resource creation. Our findings …

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Dissent By Design: Graphic Communication And The Continuity Of Resistance As A Civic Practice In The United States, Ryan "Rain" Milligan

Theses

Graphic communication has played a role in shaping civic life in the United States throughout much of its history, influencing how individuals recognize injustice, understand systems of power, and participate in collective action. Typically associated with moments of protest or political upheaval, printed materials such as pamphlets, broadsides, newspapers, and posters have also functioned as everyday tools that structure public engagement. This project examines these materials through the framework of agitation, education, and organization, considering how graphic communication operates across different historical contexts to provoke recognition, make complex ideas accessible, and coordinate collective effort. Drawing from a range of historical …

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Navigating The Ai Classroom: Integrating Ai In Education For Enhanced Learning And Career Readiness, Katryna M. Johnson

Journal of Applied Marketing Theory

This study employs an innovative approach by integrating AI and human endeavors in the research process. The examination provides a literature review and theoretical frameworks for integrating AI in education, focusing on three key stakeholder groups: students, faculty, and institutions. The analysis explores the benefits and challenges of AI in the classroom from each group’s perspective. The paper emphasizes hands-on AI experience for marketing students to remain competitive. The paper provides practical tips and suggestions for using AI in marketing classes and creating an institutional environment to support long-term AI growth. Additionally, the study presents widely used AI tools in …

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Clusters Of Student Perceptions Of Ai-Assisted Grading: Implications For Fair And Transparent Assessment Practice In Higher Education, Roberto Bello

Journal of Applied Marketing Theory

As artificial intelligence (AI) tools become increasingly embedded in assessment and feedback systems, understanding how students perceive these technologies is vital for maintaining trust and fairness in higher education. This study investigates how students experience AI-assisted grading through an extended Technology Acceptance Model (TAM) that incorporates fairness, transparency, and trust as pedagogically relevant constructs. Survey data from undergraduate marketing students (N = 142) were analyzed using cluster analysis, revealing three distinct perception profiles: Enthusiasts, Pragmatists, and Skeptics. These clusters differ significantly in their willingness to rely on AI feedback, perceived fairness of algorithmic grading, and expectations of …

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Examining The Relationship Of Work Stressors And Resilience Among Inpatient Oncology Nurses: A Cross-Sectional Study:, Blanca Melida Vasquez-Clarfield

Theses & Dissertations

Burnout among inpatient oncology nurses remains a concern in a specialty marked by high patient acuity, complex care regimens, emotional intensity, and repeated exposure to suffering and death. Resilience is often discussed as a protective resource in nursing, yet less is known about how personal resilience and work resilience relate to work stress and burnout within inpatient oncology practice. This study examined the relationships among perceived work stress, personal resilience, work resilience, and burnout among registered nurses working in inpatient oncology units. It also explored whether selected nurse characteristics were associated with these variables and how nurses described workplace stressors, …

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Learning Feature Inversion For Multi-Class Anomaly Detection Under General-Purpose Coco-Ad Benchmark, Jiangning Zhang, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Zhucun Xue, Yong Liu, Guansong Pang, Dacheng Tao

Research Collection School Of Computing and Information Systems

Anomaly detection (AD) is often focused on detecting anomaly areas for industrial quality inspection and medical lesion examination. However, due to the specific scenario targets, the data scale for AD is relatively small, and evaluation metrics are still deficient compared to classic vision tasks, such as object detection and semantic segmentation. To fill these gaps, this work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. This enables fair evaluation and sustainable development for different methods on this challenging benchmark. Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which …

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Semat: Semantic Enhanced Natural Image Interactive Matting, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Qianru Sun, Yang Tang, Bo Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. It selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 36,980 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen …

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Visual Loop: Bridging The Cognitive Gap In Software Development Through Visual-Ai Collaboration, Luis Filipe Fernandes Gomes, Xin Zhou, David Lo, Rui Abreu

Research Collection School Of Computing and Information Systems

Software development remains predominantly text-centric, despite decades of evidence showing that developers think and communicate visually. While sketches and diagrams externalize developers’ mental models, they remain disconnected from source code and quickly become outdated. Recent advances in foundation models, capable of both code and visual reasoning, create an opportunity to unify these representations. In this vision paper, we introduce Visual Loop, a continuous visual development environment that keeps code and informal sketches in bidirectional synchronization. Our prototype connects a code editor with a tablet-based visualization workspace, allowing developers to explore, annotate, and modify systems through freehand sketches interpreted by multimodal …

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Mistargeted Advertising: The Impact Of Human Vs. Ai Error On Consumer Brand Perception, Abdulela Alshehri, Umair Usman

AMTP Proceedings 2026

Artificial intelligence (AI) has revolutionized personalized advertising by enabling precise targeting based on consumer profiles, yet mistargeted ads—those falsely implying eligibility for benefits like premium credit cards—can trigger frustration, disappointment, and negative brand perceptions. This study examines how consumers respond to such errors when attributed to a human employee, an AI system, or an AI system with human oversight. Grounded in attribution theory (Folkes, 1988; Weiner, 2000), it tests perceived group homogeneity (Longoni et al., 2022) and negative self-perception (Grewal et al., 2019) as mediators, with belief in a just world (BJW; White et al., 2012) as a moderator. The …

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Bias In Generative Ai: Amplified Stereotypes And Their Impact On Decision-Making, Jaymo Kim, Mi Zhou, Vibhanshu Abhishek, Tim Derdenger, Kannan Srinivasan

AMTP Proceedings 2026

This study examines bias in generative AI through an analysis of approximately 8,000 occupational portraits created by Midjourney, Stable Diffusion, and DALL·E 2. We document significant underrepresentation of women and Black individuals compared to real-world benchmarks. The research identifies two primary manifestations of bias: systematic gender and racial disparities, and subtle biases in facial expressions that influence perceptions of competence and trustworthiness. Through an iterative "Creative Lab" involving a fictional brand, we employ a three-phase experimental design to test whether AI disclosure labels—as proposed in the AI Disclosure Act of 2023—can mitigate the impact of these biases on consumer evaluations. …

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Conversation Analysis Of Public-Servant And Persons-Of-Interest Face-To-Face Talk Via Llm‑Supported Ai Script Mapping Tools, Arch G. Woodside, Suresh Sood

AMTP Proceedings 2026

Professional public servants (PSs) including police officers, postal workers, and government tax office and DMV employees interact face-to-face with citizens/persons of interest (POIs), in encounters that are frequently recorded using mobile phones and publicized on platforms such as YouTube.com. Building on our observations about recurrent PS–POI scripts and LLM-supported analysis, we develop five grounded propositions linking enacted scripts to existing theory. The propositions focus on describing early turn-taking asymmetry, the importance of sequential detail beyond-shallow analysis, the predictive role of script recurrence, the value of rich, script-based role-play, and the potential of narrative-intelligence databases for institutional learning. Instead of treating …

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Can Financial Literacy Enhance Esg Disclosures?, Xiaoran Jia, Kiridaran Kanagaretnam, Kiat Bee Jimmy Lee, Chee Yeow Lim

Research Collection School Of Accountancy

Using an international sample of firms and two country-level measures of financial literacy, we find robust evidence of a positive relation between financial literacy and firms' Environmental, Social and Governance (ESG) disclosures. In cross-sectional analyses, we find that the effect of financial literacy in enhancing ESG disclosures is more prominent in poorer information environments, weaker legal institutions and weaker ESG reporting environments. Lastly, we find that financial literacy also enhances ESG performance. Our study contributes to and extends the literature by providing strong evidence that citizens' financial literacy enhances firms' ESG disclosure and the associated ESG performance.

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Sequencing Dengue Control Policy In Singapore: An Evolutionary Perspective For Policy Design, Ishani Mukherjee, Panchali Guha

Research Collection School of Social Sciences

About half the global population is now at risk of contracting dengue, a mosquito-borne viral infection that can cause severe morbidity and fatalities. Effective dengue control depends on controlling the mosquito vector, but finding the right mix of vector control policies has proved challenging. Using a content analysis of 208 Hansard records from parliamentary proceedings in Singapore, where dengue outbreaks have significantly increased in both frequency and magnitude since the early 1990s, we trace the processual evolution of Singapore’s anti-dengue policy mix from 1960 to 2023 and conclude that the evolution of dengue control policies is consistent with a customized …

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Negotiating At A Distance: The Impact Of Communication Media And Negotiator Traits, Dorcas Quek Anderson, Tra My Ngo

Research Collection Yong Pung How School Of Law

Purpose – Prior research has yet to provide a coherent theoretical framework explaining how communication media hinder or advance negotiation success, and many dated studies are unlikely to be relevant. This study aims to examine the impact of four communication media on negotiation outcomes. It also examines the potential moderating effects of the following negotiator characteristics: conflict management style, personality traits and indirect communication style.Design/methodology/approach – A total of 400 participants formed 200 dyads to negotiate a mixed- motive relational conflict through face-to-face (FTF) interaction, videoconferencing, audio call or synchronous text messaging. Linear mixed regression was used to assess the …

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Variation In Corneal Biomechanical Properties Following Continuous Cross-Linking Treatment, Chenhao Zhao, Xinyu Yao, Yewei Zhao, Hongjiang Wu, Xuanya Tong, Daniela Oehring, Lan Yang, Xiaofei Zhou, Ying Li, Yanjie Shen, Yufeng Ye, Shihao Chen, Jia Qu, Qinmei Wang, Ahmed Elsheikh, Fangjun Bao

School of Health Professions

Background: This retrospective study aimed to evaluate the efficacy of transepithelial accelerated corneal cross-linking (transEpi ACXL) when applied separately or combined with phototherapeutic keratectomy (transPTK ACXL) in keratoconic eyes using biomechanical parameters. Methods: The study included eyes with progressive keratoconus treated with either continuous transEpi ACXL or transPTK ACXL with 6 months follow-up. The following parameters were assessed preoperatively and 6 months postoperatively: biomechanically corrected intraocular pressure (bIOP), maximum and mean keratometry (K _max and K _m), minimum and central corneal thickness (MCT and CCT), corneal coma, best-corrected visual acuity (BCVA), deformation amplitude ratio at 2 mm nasal/temporal (DAR2), …

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Weather Rescue At Sea: Recovering Historical Weather Observations From 19th Century British Naval Ships, Praveen Teleti, Ed Hawkins, Clive Wilkinson

Research Collection College of Integrative Studies

Ship logbooks represent a critical source of historical meteorological data, providing valuable observations of barometric pressure, air temperature, sea surface temperature, wind force and direction, and other variables. Substantial quantities of these records are unavailable to climate science as they have not yet been transcribed. We present ‘Weather Rescue at Sea’, a citizen-science project which transcribed millions of weather observations contained in 19th Century UK Royal Navy ship logbooks. We describe the logbook structure and weather observation-taking instructions and discuss significant challenges with the translation of handwritten text into accurate data due to errors arising from ambiguous handwriting, historical terminology, …

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On Autopilot? An Empirical Study Of Human-Ai Teaming And Review Practices In Open Source, Haoyu Gao, Peerachai Banyongrakkul, Hao Guan, Mansooreh Zahedi, Christoph Treude

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) increasingly automate software engineering tasks. While recent studies highlight the accelerated adoption of “AI as a teammate” in Open Source Software (OSS), developer interaction patterns remain under-explored. In this work, we investigated project-level guidelines and developers’ interactions with AI-assisted pull requests (PRs) by expanding the AIDev dataset to include finer-grained contributor code ownership and a comparative baseline of human-created PRs. We found that over 67.5% of AI-co-authored PRs originate from contributors without prior code ownership. Despite this, the majority of repositories lack guidelines for AI-coding agent usage. Notably, we observed a distinct interaction pattern: AI-co-authored PRs …

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Full-Text Articles in Entire DC Network

Draft Final 2025 Bpsou Subdrain Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

Draft Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Sister Dump (Bres No. 38) Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

Logistics Center: Sustainable Design Build, Dylon Moala, Daryn Nguyen, Andrew Sabuda, Gage Urbach, Nicolae Vesca

Civil, Environmental and Sustainable Engineering Senior Theses

Conflogger: Enhance Systems’ Configuration Diagnosability Through Configuration Logging, Shiwen Shan, Yintong Huo, Yuxin Su, Zhining Wang, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Reducing Class-Wise Performance Disparity Via Margin Regularization, Beier Zhu, Kesen Zhao, Jiequan Cui, Qianru Sun, Yuan Zhou, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Real-Time Motion-Controllable Autoregressive Video Diffusion, Kesen Zhao, Jiaxin Shi, Beier Zhu, Junbao Zhou, Xiaolong Shen, Yuan Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Llmqua: Practical Backdoor Injection On Large Language Model Quantization, Xiangxiang Chen, Peixin Zhang, Jun Sun, Jin Song Dong, Wenhai Wang, Jingyi Wang

Research Collection School Of Computing and Information Systems

Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Who You Explain To Matters: Learning By Explaining To Conversational Agents With Different Pedagogical Roles, Zhengtao Xu, Junti Zhang, Anthony Tang, Yi-Chieh Lee

Research Collection School Of Computing and Information Systems

Reasoning On Time-Series For Financial Technical Analysis, Kelvin J. L. Koa, Jan Chen, Yunshan Ma, Huanhuan Zheng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Defining Her2 Associated Proteogenomic Features In Breast Cancer And Extending To Gynecologic Cancers, Maya Anand

Theses

Nlp Crowdsourcing For Predominantly Oral Languages: The Case Of Bambara, Allahsera Auguste Tapo

Theses

Dissent By Design: Graphic Communication And The Continuity Of Resistance As A Civic Practice In The United States, Ryan "Rain" Milligan

Theses

Navigating The Ai Classroom: Integrating Ai In Education For Enhanced Learning And Career Readiness, Katryna M. Johnson

Journal of Applied Marketing Theory

Clusters Of Student Perceptions Of Ai-Assisted Grading: Implications For Fair And Transparent Assessment Practice In Higher Education, Roberto Bello

Journal of Applied Marketing Theory

Examining The Relationship Of Work Stressors And Resilience Among Inpatient Oncology Nurses: A Cross-Sectional Study:, Blanca Melida Vasquez-Clarfield

Theses & Dissertations

Learning Feature Inversion For Multi-Class Anomaly Detection Under General-Purpose Coco-Ad Benchmark, Jiangning Zhang, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Zhucun Xue, Yong Liu, Guansong Pang, Dacheng Tao

Research Collection School Of Computing and Information Systems

Semat: Semantic Enhanced Natural Image Interactive Matting, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Qianru Sun, Yang Tang, Bo Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Visual Loop: Bridging The Cognitive Gap In Software Development Through Visual-Ai Collaboration, Luis Filipe Fernandes Gomes, Xin Zhou, David Lo, Rui Abreu

Research Collection School Of Computing and Information Systems

Mistargeted Advertising: The Impact Of Human Vs. Ai Error On Consumer Brand Perception, Abdulela Alshehri, Umair Usman

AMTP Proceedings 2026

Bias In Generative Ai: Amplified Stereotypes And Their Impact On Decision-Making, Jaymo Kim, Mi Zhou, Vibhanshu Abhishek, Tim Derdenger, Kannan Srinivasan

AMTP Proceedings 2026

Conversation Analysis Of Public-Servant And Persons-Of-Interest Face-To-Face Talk Via Llm‑Supported Ai Script Mapping Tools, Arch G. Woodside, Suresh Sood

AMTP Proceedings 2026

Can Financial Literacy Enhance Esg Disclosures?, Xiaoran Jia, Kiridaran Kanagaretnam, Kiat Bee Jimmy Lee, Chee Yeow Lim

Research Collection School Of Accountancy

Sequencing Dengue Control Policy In Singapore: An Evolutionary Perspective For Policy Design, Ishani Mukherjee, Panchali Guha

Research Collection School of Social Sciences

Negotiating At A Distance: The Impact Of Communication Media And Negotiator Traits, Dorcas Quek Anderson, Tra My Ngo

Research Collection Yong Pung How School Of Law

Variation In Corneal Biomechanical Properties Following Continuous Cross-Linking Treatment, Chenhao Zhao, Xinyu Yao, Yewei Zhao, Hongjiang Wu, Xuanya Tong, Daniela Oehring, Lan Yang, Xiaofei Zhou, Ying Li, Yanjie Shen, Yufeng Ye, Shihao Chen, Jia Qu, Qinmei Wang, Ahmed Elsheikh, Fangjun Bao

School of Health Professions

Weather Rescue At Sea: Recovering Historical Weather Observations From 19th Century British Naval Ships, Praveen Teleti, Ed Hawkins, Clive Wilkinson

Research Collection College of Integrative Studies

On Autopilot? An Empirical Study Of Human-Ai Teaming And Review Practices In Open Source, Haoyu Gao, Peerachai Banyongrakkul, Hao Guan, Mansooreh Zahedi, Christoph Treude

Research Collection School Of Computing and Information Systems