Databases and Information Systems Commons^™

Language Embeddings Meet Shallow Autoencoders, Rodrigo Alves, Vojtěch Vančura, Pavel Kordík, Antoine Ledent

Research Collection School Of Computing and Information Systems

Shallow autoencoders are appealing recommenders due to their simplicity, scalability, and competitive retrieval quality, but they struggle in strict cold-start settings where new items have no interactions. We propose an inductive shallow autoencoder that leverages item side information (language embeddings) by fixing the decoder to item features and learning only an encoder in the same semantic space. To prevent trivial self-reconstruction without enforcing a hard zero diagonal, we introduce diagonal gating: a leave-one-item-out objective that blocks the self-copy shortcut only for the item being updated while retaining context from the rest of the user history. An alternating-style optimization trains the …

Ppg-Sport: A Dataset For Reliable Heart Rate Monitoring From Wrist Ppg Under Dynamic Sports Conditions, Changshuo Hu, Hung Manh Pham, Yiming Zhang, Guanru Yan, Xiao Ma, Yuezhong Wu, Thivya Kandappu, Archan Misra, Dong Ma

Research Collection School Of Computing and Information Systems

Photoplethysmography (PPG) has become a cornerstone of physiological sensing in wearable devices, enabling non-invasive monitoring of heart rate and related biomarkers. However, its reliability deteriorates sharply under dynamic, high-intensity, or non-periodic motions such as those in sports, where existing datasets fail to capture realistic wrist dynamics. To address this gap, we introduce PPG-Sport, the first large-scale dataset designed for heart rate monitoring from wrist-worn PPG under real sports conditions. The PPG-Sport dataset includes synchronized PPG, inertial measurement unit (IMU), and electrocardiography (ECG) recordings from both wrists of 30 participants across six representative activities: stationary, walking, running, badminton, table tennis, and …

Vehicle-Based Multi-Services For Future Smart Cities, Hao Sun, Jinhua Zhao, Hai Yang, Shenhao Wang, Hamsa Balakrishnan, Thomas W. Malone, Hai Wang

Research Collection School Of Computing and Information Systems

Vehicles are crucial for sustaining socioeconomic activity and improving quality of life in modern cities by offering diverse services. These include passenger mobility, goods delivery, information acquisition, and acting as mobile servers such as food trucks and mobile lockers. At the same time, they also contribute to traffic congestion and air pollution. This tension fosters the rise of urban resource-conserving and sustainable service solutions. In this article, we introduce the concept of “Vehicle-Based Multi-Services” (VeMuS), in which a single vehicle offers multiple services simultaneously. Drawing on practical use cases, we examine service classification and integration for vehicles and the potential …

Task Complexity Matters: An Empirical Study Of Reasoning In Llms For Sentiment Analysis, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this claim through a comprehensive evaluation of 504 configurations across seven model families—including adaptive, conditional, and reinforcement learning-based reasoning architectures—on sentiment analysis datasets of varying granularity (binary, five-class, and 27-class emotion). Our findings reveal that reasoning effectiveness is strongly task-dependent, challenging prevailing assumptions: (1) Reasoning shows task-complexity dependence—binary classification degrades up to -19.9 F1% points (pp), while 27-class emotion recognition gains up to  +16.0 pp; (2) Distilled reasoning variants underperform base models by 3–18 pp on simpler tasks, …

A Novel Hierarchical Multi-Agent System For Payments Using Llms, Donghao Huang, Joon Kiat Chua, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language model (LLM) agents, such as OpenAI’s Operator and Claude’s Computer Use, can automate workflows but unable to handle payment tasks. Existing agentic solutions have gained significant attention; however, even the latest approaches face challenges in implementing end-to-end agentic payment workflows. To address this gap, this research proposes the Hierarchical Multi-Agent System for Payments (HMASP), which provides an end-to-end agentic method for completing payment workflows. The proposed HMASP leverages either open-weight or proprietary LLMs and employs a modular architecture consisting of the Conversational Payment Agent (CPA - first agent level), Supervisor agents (second agent level), Routing agents (third agent …

Happycal: Designing Text And Image-Based Supports For Savouring Positive Work Experiences, Molly Stewart, Minghao Cai, Anthony Tang, Sam Liu, Chris Mosunic, Sowmya Somanath

Research Collection School Of Computing and Information Systems

Savouring positive work experiences can promote positive affect and well-being at work, yet there is limited guidance on how digital applications can support workers to engage in savouring. We developed HappyCal, a work-focused savouring application offering two forms of savouring support: text-based, a common modality in workplace reflection tools, and images, a largely unexplored approach in work-related savouring. We conducted an exploratory qualitative study where participants (N=36) used HappyCal over five days and engaged in savouring through either a text-only modality (n=17) or text input paired with image output (n=19). We found that (1) participants in both groups reported heightened …

“Alexa, Do Not Say That In Front Of My Boss!” A Cross-Cultural Comparison Of User And Ai Preferences For Privacy-Aware Smart Speaker Interactions Across Contexts, Lynne Warin, Emily Aurelia, Anthony Tang, Emily Aurelia, Delphine Reinhardt

Research Collection School Of Computing and Information Systems

Due to their limited ability to reason about the social context in which they are used, smart speakers pose significant privacy risks by responding in ways that may violate people's implicit social boundaries. We conducted a cross-cultural vignette study (N = 944) in Germany and Singapore to investigate how situational factors—specifically social context (bystander relationships and closeness), physical context (location), and interaction context (topic and deceptive intent)—regulate user preferences for smart speaker responses. Our results demonstrate that these factors are superior predictors of response preferences than dispositional user traits (i.e., intrinsic personal traits). We identify two distinct social dynamics: a …

Political Inconsistency Detection Across Legislative Speech And Public Communications, Scott M. Pramuk

Master's Theses

Political actors communicate about legislation across multiple contexts, including committee hearings, recorded votes, and public-facing press releases. Differences between these forms of communication can provide useful signals for journalists and researchers seeking to understand how legislators present policy positions to different audiences.

This thesis extends the Digital Democracy Project, a legislative transparency initiative that provides access to California state legislative hearing transcripts, voting records, and related legislative data. Specifically, this work incorporates publicly accessible, legislator-authored news releases into the Digital Democracy Database and develops a pipeline for analyzing legislative communication across multiple sources. The system collects news releases from California …

Seizing Strategic High Ground Of Space Computing Power: Global Competition Landscape And China’S Path, Yan Chen, Wenbin Song, Ping Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The deep integration of artificial intelligence and commercial aerospace is accelerating the transformation of space computing power from conceptual exploration to engineering verification, becoming a key direction for building an integrated space-air-ground information infrastructure. This study delves into its strategic value, global landscape, industrial chain bottlenecks, and advancement paths. The research reveals that the core value of space computing power does not lie in replacing ground data centers, but rather in focusing on network coverage blind spots, data transmission limitations, and high-timeliness scenarios, providing a new supply model of “in-orbit computing + space-ground collaboration”. Currently, the world has entered a …

A Gis Analysis Of The Relationship Between Ndvi Value, Slope, And Aspect Of The Terrain Of The Prairie Restoration Project, Basil Lund

2026 Symposium

The Prairie Restoration Project (PRP) encompasses 115 acres of land on the west side of the EWU campus. The goal of the PRP is to restore what was once a section of wheat farm back to its original Palouse prairie state. Here, I am using publicly available satellite data to examine some potential variables affecting the health of wheat plants planted in the years 2009 to 2017. I have used 4-band aerial imagery to create a map of NDVI values within the bounds of the PRP, which visualizes the near-infrared wavelengths that are reflected by healthy, chlorophyll-rich plants. I have …

Labeling And Describing Objects In A Photogrammetry-Based 3d Environment Using Computer Vision And Artificial Intelligence, Colin Donald Moschella

Capstone Projects

Cataloging and digitizing the objects inside a building manually is a task that is often impractical at scale. This project therefore automates the process, using a custom-made system. Using a photogrammetry-based 3D reconstruction of a room, this system is applied to sequences of 2D images used to make the 3D models. The system applies object detection, image segmentation, and image-text models to identify and describe objects, using CNN based models such as YOLO and OpenCLIP. Each analyzed object is then stored in a structured database with spatial coordinates from the 3D scanning, descriptive attributes from the image-text models, and other …

Development And Launch Of Abn: An Adventist Freelance Web Application, Abishur Moses-Pakkianathan

MS in Computer Science Project Reports

The Seventh-day Adventist community often relies on personal networks and word-of-mouth for Adventist business services. Many of these businesses meet and provide services to their clients primarily through church connections and community recommendations. As the community and businesses grow, traditional networking methods become increasingly difficult to maintain. General marketplaces lack the trust framework shared by Adventists, and faith-based directories are often static or outdated without booking capabilities. Our solution provides a reliable and modern platform for Adventists to create service listings, receive and manage bookings, and connect with faith-aligned clients.

Developing Narrative-Based Stem Learning Tool For K-6 Visually Impaired Students, Daniel Tsivkovski, Dylan Ravel, Jeffrey Kraskouskas, Brandon Foley, Maryam Etezad, Franceli Cibrian, Rajeev Joshi, Ariel Han

Student Scholar Symposium Abstracts and Posters

This research develops a free, accessible web application that enables K-6 students who are blind or visually impaired (BVI) to learn STEM concepts using refreshable braille displays. Currently, most online learning tools are not designed for BVI students, creating a significant educational barrier.

The application interfaces with commercial braille displays and uses narrative-based learning to make STEM content approachable and engaging. By presenting material as personalized interactive stories generated with the help of Artificial Intellligence (AI), students can connect with concepts while developing braille reading skills. The curriculum design prioritizes accessibility through the Accessible Rich Internet Applications (ARIA) standards and …

Radar Spoofing Attacks And Their Impact On Sensor Fusion-Based Perception For Autonomous Vehicles, Ahmad Mohammed Bara

Student Theses

Safe autonomous vehicle (AV) operation depends on robust perception of the surrounding environment. While multimodal sensing—integrating cameras, LiDAR, and radar—provides comprehensive environmental awareness, radar’s robustness under adverse conditions has made it a critical component of modern perception pipelines. However, the security vulnerabilities of radar within these fusion architectures remain largely unexplored. This work presents an end-to-end analysis of radar spoofing attacks on learning-based radar–camera fusion systems. Using a simulation framework grounded in reflect-array attack models, we inject physically plausible perturbations into radar measurements by altering depth by and velocity, while maintaining crossmodal consistency. Evaluation on the nuScenes dataset shows that …

Research On Smart Intelligence Service System Of National Defense Science And Technology In Complex Information Environment, Yang Yang, Keping Wang, Huawei Sun

Journal of Scientific Information Research

[Purpose/significance] National defense science and technology intelligence serves as a powerful guarantee for promoting national defense science and techndogy innovation and development. Exploring optimization paths for defense science and technology intelligence service systems holds practical significance in developing new quality combat capabilities and safeguarding national security and stability. [Method/process] Through literature review and summarization, this study clarifies the impact of complex information environments on intelligent defense science and technology intelligence services, as well as the core tasks of intelligent intelligence services. Based on activity theory, the study deconstructs system elements and employs systems engineering principles to construct an intelligent defense …

Sql Query Optimization - Human Vs. Chatgpt, Hailey Dennis

All Graduate Reports and Creative Projects, Fall 2023 to Present

Large Language Models (LLMs) such as ChatGPT have become ubiquitous tools for working professionals in the software industry. Many engineers are finding new ways to increase productivity by offloading tasks onto LLMs, while others are finding it difficult to trust code produced artificially, even after review. Taking a look at both perspectives, this study aims to compare a human’s ability to optimize SQL queries to that of an LLM and assess the experience using both methods.

Manual query optimization is a tedious task that relies heavily on statistics, heuristics, and good intuition. The SQL developer must search for the optimal …

A Study Of Visualized Diagnostics In Early Stage Digital Twin Implementation Of An Industrial Control System, Michael R. Kinzel

All-Inclusive List of Electronic Theses and Dissertations

Industrial Control Systems (ICS) are used for process control in almost all industries. An ICS combines Operational Technologies (OT) with Information Technologies (IT) to allow human supervision of a process through surveillance of process variables and manipulation of controlling elements such as valves to maintain stable process conditions. ICSs have been in-service for several decades and may remain operational past their technological service life. Organizational personnel interact with the ICS through visual displays that both indicate the process variables and also the controlling elements. The Human Machine Interface (HMI) allows visibility of the process and the ability to manipulate controlling …

The Application Of Natural Language Processing Towards Auditing Of Unstructured Data: A Design Science Approach, Dennis K. Amoatey

Electronic Theses and Dissertations

Financial auditors must manually review large volumes of unstructured text that may include contracts, internal policies, footnotes, and journal entry descriptions. This time-intensive process introduces risk of human error and inconsistency. Despite advances in automation, no systematic approach exists for applying Natural Language Processing (NLP) to this problem at scale. Using a design science approach, this study develops a framework that demonstrates how NLP techniques can be incorporated across key phases in the audit process, including planning, internal controls evaluation, evidence gathering, and reporting. Initial evaluation through expert feedback had a mix of responses. While some argued difficulty with data …

Market Reactions To Deceptive Language In Fake News: Implications From Language Expectancy Theory And Transfer Learning, Ka Chung Ng, Ping Fan Ke, Ping Fan, Mike So, Tam, Kar Yan

Research Collection School Of Computing and Information Systems

The advent of generative artificial intelligence (AI) has heightened the proliferation of fake news. A key challenge is the limited real-world data to investigate the societal impact of fake news produced by generative AI. In this paper, we examine stock market reactions to financial news articles that exhibit stylometric similarity to human-crafted and AI-crafted fake financial news. Grounded in language expectancy theory, we employ a style-based transfer learning model, pre-trained to recognizing deceptive language employed in various types of fake news intricacies. We then apply this model to a comprehensive dataset of financial news, assigning a “veracity style score” to …

Fighting Against Recruitment Scams: Theory-Driven Supervised Learning And Empirical Analysis For Digital Fraudulent Recruitment Posting Behavior, Tom (Tianteng) Wang, David (Jingjun) Xu, Keng Siau, Zhongju (John) Zhang

Research Collection School Of Computing and Information Systems

The number of recruitment postings on digital recruitment hiring platforms has increased since the COVID-19 pandemic. However, the weak surveillance and operations of these platforms, combined with the fact that most job seekers have relatively low vigilance and a strong desire for recruitment offers, enable scammers to easily deceive job seekers for their money and confidential information. In this work, we combine prevailing text mining techniques (i.e., ChatGPT with prompting engineering and supervised machine learning) with interpersonal deception theory (IDT) from social science to design an interpretable IT system to predict fraudulent recruitment postings on digital recruitment-hiring platforms. We compare …