Databases and Information Systems Commons^™

Elastic Scheduling For Graceful Degradation Of Mixed-Criticality Systems, Zhuoran Sun, Marion Sudvarg, Christopher Gill

Computer Science Faculty Research & Creative Works

Many mixed-criticality system models drop all jobs of low-criticality tasks when a criticality mode switch occurs, ensuring that high-criticality tasks still can meet their deadlines in the new mode. However, this means that even important low-criticality tasks are discarded, which may not be acceptable in some systems in practice. This paper addresses that distinction between criticality and importance through a new Inelastic Graceful Earliest Deadline First with Virtual Deadlines (IG-EDF-VD) scheme that upon a criticality mode switch only discards the least important low-criticality tasks necessary to ensure feasibility. Moreover, we consider elastic scheduling within our mixed-criticality model (EG-EDF-VD), using compression …

Towards Smart Farming: Image-Based Crop Health Assessment And Disease Diagnosis Using Deep Learning Techniques, Kristina Botova

Master's Theses or Doctor of Nursing Practice

Accurate crop monitoring is essential for optimizing agricultural productivity and ensuring food security. This study presents a comprehensive deep learning framework for image crop type recognition, health status prediction, and disease detection using multiple Convolutional Neural Network (CNN) models. The proposed approach uses open-source datasets consisting of five crop types (apple, corn, grape, potato, tomato), varying health conditions, and common diseases. By deploying specialized CNN architecture focused on each task, the system achieves a high accuracy of 99.25% in classifying crop types, identifying health status, and detecting specific diseases. Compared to a single CNN model, the use of the proposed …

Digital Infrastructure Development Through Digital Infrastructuring Work: An Institutional Work Perspective, Adrian Yeow, Wee-Kiat Lim, Samer Faraj

CCX Research

Being able to understand and characterize the digital infrastructure development (DID) process has become even more pressing today due to the rapid advent and implementation of new digital infrastructure (DI) in organizations as well as since the COVID-19 crisis. While information systems (IS) research has begun to recognize the institutional nature of such digital infrastructures, there remains a gap in our understanding of how such developments unfold from an institutional perspective. Through our field study of a digital infrastructure development project involving the implementation of an enterprise-wide electronic medical record system at a large US medical facility, we show how …

Enhanced Network Anomaly Detection Using Machine Learning Models, Ousmane Barry

CCAC Theses and Dissertations

This dissertation investigates enhanced network anomaly detection using Machine Learning (ML) models. The study addresses two distinct classification problems: binary classification and multiclass classification. In the binary classification task, network traffic data is categorized as either "normal" or "abnormal," where abnormal includes all non-normal traffic. Leveraging the balanced nature of the dataset, this study develops optimized models that achieve consistently high classification performance. Key metrics, including precision, recall, and F1 scores, are used to ensure robust evaluation and reliable detection across all classes.

For multiclass classification, only classes present in both training and test datasets are included to ensure meaningful …

A Survey Of Multilingual Large Language Models, Libo Qin, Qiguang Chen, Yuhang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che, Philip S. Yu

Research Collection School Of Computing and Information Systems

Multilingual large language models (MLLMs) leverage advanced large language models to process and respond to queries across multiple languages, achieving significant success in polyglot tasks. Despite these breakthroughs, a comprehensive survey summarizing existing approaches and recent developments remains absent. To this end, this paper presents a unified and thorough review of the field, highlighting recent progress and emerging trends in MLLM research. The contributions of this paper are as follows. (1) Extensive survey: to our knowledge, this is the pioneering thorough review of multilingual alignment in MLLMs. (2) Unified taxonomy: we provide a unified framework to summarize the current progress …

Empowering Crisis Information Extraction Through Actionability Event Schemata And Domain-Adaptive Pre-Training, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint

Research Collection School Of Computing and Information Systems

One of the persistent challenges in crisis detection is inferring actionable information to support emergency response. Existing methods focus on situational awareness but often lack actionable insights. This study proposes a holistic approach to implementing an actionability extraction system on social media, including requirement gathering, selection of machine learning tasks, data preparation, and integration with existing resources, providing guidance for governments, civil services, emergency workers, and researchers on supplementing existing channels with actionable information from social media. Our solution leverages an actionability schema and domain-adaptive pre-training, improving upon the state-of-the-art model by 5.5% and 10.1% in micro and macro F1 …

Defending Federated Recommender Systems Against Untargeted Attacks: A Contribution-Aware Robust Aggregation Scheme, Ruicheng Liang, Yuanchun Jiang, Feida Zhu, Ling Cheng, Huiwen Liu

Research Collection School Of Computing and Information Systems

Federated recommender systems (FedRSs) effectively tackle the tradeoff between recommendation accuracy and privacy preservation. However, recent studies have revealed severe vulnerabilities in FedRSs, particularly against untargeted attacks seeking to undermine their overall performance. Defense methods employed in traditional recommender systems are not applicable to FedRSs, and existing robust aggregation schemes for other federated learning-based applications have proven ineffective in FedRSs. Building on the observation that malicious clients contribute negatively to the training process, we design a novel contribution-aware robust aggregation scheme to defend FedRSs against untargeted attacks, named contribution-aware Bayesian knowledge distillation aggregation (ConDA), comprising two key components for the …

Qlorax: Heuristic-Guided Fine-Tuning Of Llama-2 For Domain Adaptation In Entrepreneurship, Gaurob Saha

Theses and Dissertations

This thesis presents a study on the fine-tuning of large language models (LLMs) for domain-specific applications using limited data. We fine-tuned the LLaMA-2 (7B) model on a curated entrepreneurial dataset containing 3,545 human-written question-answer pairs, of which 3,095 were used for training and 450 were reserved for evaluation. A complete fine-tuning and evaluation pipeline was developed, which included clustering human-written answers, generating centroid-based summaries for each cluster, and evaluating the model's generated responses through cosine similarity.

Training was carried out over five epochs, with model performance evaluated after each epoch. The fine-tuned model demonstrated strong semantic alignment with human-written content, …

Digital Platform Transitions In The Finance Industry: Three Essays, Cheryll-Ann Wilson

2025

This three-paper dissertation is motivated by an emerging dichotomy in the financial sector: an increasing use of an open-source digital platform—the Python platform—in an industry that historically has been wedded to proprietary systems.

Chapter 1 is a qualitative pilot study to ascertain which factors are likely to motivate investment professionals to select Python versus other tools and/or technologies. I find that efficiency and access to industry-specific libraries—notably Pandas and NumPy—are significant motivators in their selection of Python over Excel. Chapters 2 and 3 examine the issues through a sequential, exploratory mixed methods approach.

Chapter 2—the qualitative field study—investigates how and …

Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang

Computer Science Faculty Research & Creative Works

Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …

Leaf-Based Varietal Categorization Of Sweetpotato (Ipomoea Batatas L. Lam.), A Potentially Healthful Vegetable, Using Image Processing And K-Means Clustering, Shahidul Islam, Md Towfiqur Rahman, Md Hamidul Rahman, Abdul Momin

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Sweetpotato (Ipomoea batatas Lam) leaves contain higher concentrations of phenolic compounds, flavonoids, and carotenoids that are remarkable in health promotion. However, the nutrient content in sweetpotato leaves varies from variety to variety, and leaf shape and color are the key identifying factors for the varietal classification of sweetpotatoes. So, detecting sweetpotato leaves is essential for the in-situ identification of sweetpotato varieties and for developing intelligent agricultural systems. This study aimed to create a leaf-shape-based varietal classification technique for sweetpotato using image processing techniques coupled with a K-means clustering algorithm. 38 leaf images (RGB) of two sweetpotato cultivars were collected …

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Development And Application Of Computational Tools For Data-Driven Materials Science., Logan L. Lang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern materials science generates vast amounts of data from computational simulations and experiments, creating significant challenges for data processing and analysis. This thesis addresses these challenges through the development and application of computational tools within the framework of Material Data Science (MDS). Contributions span the four pillars of MDS: Material/Molecular Data, Algorithms, Databases, and High-Throughput Processes—with a primary focus on the Algorithm, Data, Database pillars.

For the Algorithm pillar, two Python libraries were developed to streamline common analysis tasks. PyProcar simplifies the post-processing and visualization of electronic structure data (band structures, density of states, Fermi surfaces) obtained from various Density …

University Of Akron Commuter Rideshare Service (Uacrs), Nicholas Szijarto

Williams Honors College, Honors Research Projects

At the University of Akron, a large number of students commute to and from the campus. A majority of them use their own personal vehicles every day. I propose a web-based application that would benefit the University by offering ridesharing services for commuting students and faculty. This would be made with an Angular frontend, a Spring Boot Java backend, and a MySQL database. I would hope to allow this application to have users register and then input their schedules of travel, including starting point, destination, and the time of this transfer. The program could suggest groups of students to travel …

Connecting Worlds: Travelmate’S Bidding System For Personalized Travel Experiences, Suman Khadka

Williams Honors College, Honors Research Projects

TravelMate is a web platform that connects travelers with local guides through a personalized trip posting system along with real time bidding from freelancer guides. This platform utilizes Next.js for a responsive frontend, and Supabase for managing database and backend RESTful API, using PostgREST - a thin API layer on top of Postgres. This platform is ideal for anyone looking to explore new places with a freelancer guide and gain cultural insights from a local expert who understands the place deeply.

Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions . Both aspects of user attention can be …

Learning To Rank Aspects And Opinions For Comparative Explanations, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Comparative recommendation explanations help to make sense of recommendations by comparing a recommended item along some aspects of interest with one or many items being considered. This work extends the notion of comparative explanations, by going beyond merely better/worse statements, to further incorporate aspect-level opinions for more informative comparisons. To enhance the quality of both the personalized recommendation and the explanation, we incorporate optimization objectives that preserve relative rankings of aspects and opinions, in addition to the classical rankings of overall preferences for items. We integrate the multiple ranking objectives and multi-tensor factorization together. Experiments on datasets of different domains …

Utilizing Biometrics And Blockchain For Enhanced Security Of Remote Patient Monitoring (Rpm) Data Sharing, Amaturrahman Raihanah Medlock

Dissertations, Master's Theses and Master's Reports

Advancements in Artificial Intelligence (AI) and Internet of Medical Things (IoMT) technologies have significantly revolutionized the conventional healthcare systems. Through the integration of smart devices, medical sensors, and communication technology, IoMT provides real-time patient’s monitoring data for healthcare providers, thus promoting accurate and timely clinical decisions for patient-centric care. The current healthcare sector is evolving to a connected ecosystem with connectivity and intelligence. While it also incurs increasing security and privacy concerns as integrating IoMT generated patient monitoring data into healthcare information systems. Both blockchain and biometrics are measures that have established reputable names in the security realm. When evaluating …

Data Privacy Regulations In The Gaming Industry: A Comparative Analysis Of Singapore, Macau, And Japan, Miloslava Plachkinova

Faculty Articles

This study explores the relatively under-researched area of comparing data privacy regulations and best practices across different countries, with a focus on the gaming industry. It provides an overview of general data privacy principles and existing global regulations, analyzing how gaming operators leverage personal data for competitive advantage. Specifically, the research examines the data privacy approaches and regulatory requirements in Singapore, Macau, and Japan, highlighting the cultural and historical contexts influencing these regulations. Through a comparative analysis, the article discusses the compliance needs for gaming operators in these jurisdictions.

A Global Application Programming Interface-Enabled Earthquake Ground Motion Relational Database For Engineering Applications, Tristan E. Buckreis, Chukwuebuka C. Nweke, Pengfei Wang, Scott J. Brandenberg, Maria E. Ramos-Sepúlveda, Rashid Shams, Shako Mohammed, Renmin Pretell, Silvia Mazzoni, Paolo Zimmaro, Jonathan P. Steward

Civil & Environmental Engineering Faculty Publications

We present a application programming interface (API)-enabled relational database of global earthquake ground motion intensity measures, associated metadata, and processed time-series data. Raw ground motion records were processed by the authors using either manual or semi-automated processing procedures, and every processed record has passed a quality review by a trained analyst. Computed intensity measures include peak acceleration and velocity, pseudo-spectral acceleration response spectra, cumulative absolute velocity, Arias Intensity, and Fourier amplitude spectra. The processed time-series data, associated metadata, and ground motion intensity measures were organized into a web-served relational database consisting of 32 tables connected by primary/foreign key pairs. Ground …

Coming Back Differently: An Exploratory Case Study Of Near Death Experiences Of Webpages, Lesley Frew, Michael L. Nelson, Michele Weigle

Computer Science Faculty Publications

In this case study, we use web archives to analyze 8,824 webpages that were taken offline and subsequently put back online, thus experiencing a “near death experience.” We enumerate the stages of a webpage’s near death experience, including the change from a successful HTTP status code to non-successful and back, the intermediate stage with markers such as an under construction banner, and an analysis of how the pages came back differently.

A Review On Knowledge And Information Extraction From Pdf Documents And Storage Approaches, Salvador D. Atagong, Henri Tonnang, Kennedy Senagi, Mark Wamalwa, Komi M. Agboka, John Odindi

All Peer-Reviewed Publications

Introduction: Automating the extraction of information from Portable Document Format (PDF) documents represents a major advancement in information extraction, with applications in various domains such as healthcare, law, or biochemistry. However, existing solutions face challenges related to accuracy, domain adaptability, and implementation complexity. Methods: A systematic review of the literature was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to examine approaches and trends in PDF information extraction and storage approaches. Results: The review revealed three dominant methodological categories: rule-based systems, statistical learning models, and neural network-based approaches. Key limitations include the rigidity of rule-based …

Analysis And Research On The Guiding Role Of Xi Jinping Thought On Socialism With Chinese Characteristics For A New Era In The Discipline Of Information Resources Management, Sanhong Deng, Yiqin Zhang, Hao Wang

Journal of Scientific Information Research

[Purpose/significance]This paper explores the guiding role of Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era in the development of the Information Resource Management discipline with Chinese characteristics, providing significant insights for the innovative advancement of China's Information Resource Management discipline and strengthening the discourse power of Chinese social sciences. [Method/process]This paper systematically reviews the core elements of the development philosophy of the Information Resource Management discipline within Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era from a holistic perspective,elucidates the logical system of the development of the discipline from the diverse perspectives …

Extraction Of Fine-Grained Research Methods In The Field Of Information Science, Jiayi Hao, Yuzhuo Wang, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Research methods in information science are one of the critical research directions in this field. Constructing a fine-grained research method corpus and extracting research method entities can help scholars quickly understand the research methods in this field, explore the evolution of methods and their future development trends, and lay the foundation for the service and application of the research method corpus in the subsequent digital wave. [Method/process]Firstly, based on academic articles published in the Journal of the China Society for Scientific and Technical Information from 2000 to 2023, this study randomly selected 50 articles and manually annotated the research methodology …

Interactive Video Search With Multi-Modal Llm Video Captioning, Yu-Tong Cheng, Jiaxin Wu, Zhixin Ma, Jiangshan He, Xiao-Yong Wei, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Cross-modal representation learning is essential for interactive text-to-video search tasks. However, the representation learning is limited by the size and quality of video-caption pairs. To improve the search accuracy, we propose to enlarge the size of available video-caption pairs by leveraging multi-model LLM on video captioning. Specifically, we use LLM to generate video captions for a large video collection (i.e., WebVid dataset) and use the generated video-caption pairs to pre-train a text-to-video search model. Additionally, we use LLM to generate fine-grained captions for test video collections to enable text-to-caption retrieval. Furthermore, we build a semantic overview of the retrieved rank …

Fedart: A Neural Model Integrating Federated Learning And Adaptive Resonance Theory, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed clients while preserving data privacy. However, prevailing FL approaches aggregate the clients’ local models into a global model through multi-round iterative parameter averaging. This leads to the undesirable bias of the aggregated model towards certain clients in the presence of heterogeneous data distributions among the clients. Moreover, such approaches are restricted to supervised classification tasks and do not support unsupervised clustering. To address these limitations, we propose a novel one-shot FL approach called Federated Adaptive Resonance Theory (FedART) which leverages self-organizing Adaptive Resonance Theory (ART) …

Impact Of Achievement-Oriented Gamification In Erp Systems: Examining Subjective And Objective User Outcomes, E. Adeborna, Fiona Fui-Hoon Nah, L. Motiwalla

Research Collection School Of Computing and Information Systems

This research explores the effect of gamification using achievement-oriented affordances in Enterprise Resource Planning (ERP) systems on subjective (behavioral intention) and objective (performance) outcomes. Drawing on the cognitive-affectiveconative (CAC) framework, a research model was developed to explain behavioral intention and tested in a pilot experiment with 63 participants. These participants completed a post-study questionnaire for assessing the impact of gamification on users’ behavioral intention that is mediated by CAC constructs: focused immersion, enjoyment, and selfrewarding experience. Preliminary results show that gamification enhances enjoyment and self-rewarding experience, which in turn positively influence and fully mediate behavioral intention. Objective performance outcomes were …

An Agent-Based Computational Finance Simulation Model To Study Market Efficiency, Wei Feng, Keng Siau, Wee-Yeap Lau, Lim-Thye Goh, Haonan Chen

Research Collection School Of Computing and Information Systems

The advancement of computational modeling, data systems, and digital infrastructure has enabled the rise of agent-based computational finance (ACF). This study models interactions among heterogeneous investors. By embedding behavioral logics such as environmental, social, and governance (ESG) preferences and volatility thresholds, the model captures microstructural dynamics under different trading rules. Using ACF, the authors compare transaction plus 0 day (T+0) to transaction plus 1 day (T+1). Results show that T+0 improves price discovery, deepens liquidity, and reduces transaction costs. From a computational perspective, this research contributes to ACF by showing how policy logic and investor heterogeneity can be encoded and …

Flowing Together Or Alone: Impact Of Collaboration In The Metaverse, Fiona Fui-Hoon Nah, Brenda Eschenbrenner, Langtao Chen

Research Collection School Of Computing and Information Systems

The metaverse is the next-generation Internet (Web3) that facilitates social connections and collaborations in a virtual world environment. Given the potential of the metaverse to provide more satisfying and effective means of remote collaborations, exploring the possibility of leveraging the metaverse for these endeavors is warranted. Therefore, an important question to address is whether greater engagement occurs when tasks are completed collaboratively versus individually in the metaverse. We address this question by drawing on flow and transportation theories to hypothesize the effect of carrying out a creative task in the metaverse collaboratively versus alone on one's cognitive absorption, a contextually …

Marrying Top-K With Skyline Queries: Operators With Relaxed Preference Input And Controllable Output Size, Kyriakos Mouratidis, Keming Li, Bo Tang

Research Collection School Of Computing and Information Systems

The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records' attributes (typically, using a top-k query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they …