Databases and Information Systems Commons^™

Unearthing The Past: A Comprehensive Study Of Natural And Anthropogenic Changes At An Archaeological Site Through Hydrogeologic Connectivity Utilizing Gis, Mehlich Ii Phosphorus Extractant, And Ph, Dana L. F. Herren

Theses

This thesis aims to thoroughly analyze the Mehlich II Phosphorus Extractant and pH levels at the Bains Gap Village Site in Anniston, AL., while examining the impact of various environmental factors and human activities on them. Phosphorus is often used in archaeology as an indicator of human activity. Soil core samples were collected to analyze anomalies in phosphorus levels.

To establish any relationships, phosphorus and pH levels from soil cores were correlated with findings from past excavation units and features. The potential effects of hydrogeologic connectivity on soil phosphorus and pH levels were investigated. Geospatial technologies were used to manage …

Continual Normalization: Rethinking Batch Normalization For Online Continual Learning, Quang Pham, Chenghao Liu, Steven Hoi

Research Collection School Of Computing and Information Systems

Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of continual learning data, especially in the online setting, amplify the discrepancy between training and testing in BN and hinder the performance of older tasks. In this work, we study the cross-task normalization effect of BN in online continual learning where BN normalizes the testing data using moments biased towards the current task, resulting in higher catastrophic forgetting. This limitation motivates us to propose a simple yet effective method that we call Continual Normalization (CN) to facilitate training …

Flgan: Gan-Based Unbiased Federated Learning Under Non-Iid Settings, Zhuoran Ma, Yang Liu, Yinbin Miao, Guowen Xu, Ximeng Liu, Jianfeng Ma, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, called FlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically, FlGan first …

Elevating Academic Administration: A Comprehensive Faculty Dashboard For Tracking Student Evaluations And Research, Musa M. Azeem

Senior Theses

The USC Faculty Dashboard is a web application designed to revolutionize how department heads, professors, and instructors monitor progress and make decisions, providing a centralized hub for efficient data storage and analysis. Currently, there’s a gap in tools tailored for department heads to concisely manage the performance of their department, which our platform aims to fill. The USC Faculty Dashboard offers easy access to upload and view student evaluation and research information, empowering department heads to evaluate the performance of faculty members and seamlessly track their research grants, publications, and expenditures. Furthermore, professors and instructors gain personalized performance analysis tools, …

The Impact Of Government Data Open Platform Construction On Promoting The Development Of Digital Government: An Econometric Analysis Based On Psm Model, Taitian Mao, Tang Gan, Jinliang Chen

Journal of Scientific Information Research

[Purpose/significance]Open government data is the direction of the digital development of government, and exploring the impact of the Open Government Data Platform （OGDP） on the development of digital government is of guiding significance in advancing the goal of modernizing the national governance system and governance capacity. [Method/process]Based on the establishment or not of OGDP, this paper uses the propensity score matching method to investigate the causal relationship between OGDP construction and digital government development by using the cross-sectional data of 101 prefecture-level cities across China in 2019 as the research sample for empirical analysis. [Result/conclusion]Overcoming sample selection bias and eliminating …

Delivered Mobile App, Juni Ejere

Posters - 2024

This platform is a mail delivery notification system to assist school mailrooms, specifically the St. Mary’s University mailroom, in notifying users of the mailroom when packages have arrived, creating one location where all mail information can be found and overall improving the efficiency of the mailroom. Students and faculty at St. Mary’s University have access to a mail room to receive packages. This is very useful, especially for those who stay on campus or who want an external location to receive mail. Now that recipients receive an email when a package is delivered, the main concern of picking up a …

Test Optimization In Dnn Testing: A Survey, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain.

Exploring The Potential Of Chatgpt In Automated Code Refinement: An Empirical Study, Guo Qi, Junming Cao, Xiaofei Xie, Shangqing Liu, Xiaohong Li, Bihuan Chen, Xin Peng

Research Collection School Of Computing and Information Systems

Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can significantly impact the development process. Recently, ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks, suggesting its potential to automate code review processes. However, it is still unclear how well ChatGPT performs in code review tasks. To fill this gap, in this paper, we conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks, specifically focusing on automated code refinement based on given …

Development Of An Explainable Artificial Intelligence Model For Asian Vascular Wound Images, Zhiwen Joseph Lo, Malcolm Han Wen Mak, Shanying Liang, Yam Meng Chan, Cheng Cheng Goh, Tina Peiting Lai, Audrey Hui Min Tan, Patrick Thng, Patrick Thng, Tillman Weyde, Sylvia Smit

Research Collection School Of Computing and Information Systems

Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into …

Adaptive Content-Aware Influence Maximization Via Online Learning To Rank, Konstantinos Theocharidis, Panagiotis Karras, Manolis Terrovitis, Spiros Skiadopoulos, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a …

Filter-Based Stance Network For Rumor Verification, Jun Li, Yi Bin, Yunshan Ma, Yang Yang, Zi Huang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Rumor verification on social media aims to identify the truth value of a rumor, which is important to decreasethe detrimental public effects. A rumor might arouse heated discussions and replies, conveying differentstances of users that could be helpful in identifying the rumor. Thus, several works have been proposedto verify a rumor by modelling its entire stance sequence in the time domain. However, these works ignorethat such a stance sequence could be decomposed into controversies with different intensities, which could beused to cluster the stance sequences with the same consensus. In addition, the existing stance extractors fail toconsider both the impact …

Impact Of Government Outsourcing Contracts On High-Tech Vendors: An Empirical Study, Yi Dong, Nan Hu, Yonghua Ji, Chenkai Ni, Jing Xie

Research Collection School Of Computing and Information Systems

Outsourcing is an important strategic decision of high-tech firms. However, while the research has extensively studied the implications of outsourcing to high-tech clients, its impact on high-tech vendors remains underexplored. This study empirically estimates the impact of government outsourcing contracts on high-tech vendors. Employing the earnings-return analyses framework, we find that, for high-tech vendors engaged in government outsourcing contracts, the stock market places a higher value on each unit of unexpected earnings compared to other firms. Additionally, this impact becomes stronger for contracts with longer terms, for contracts outsourced by the U.S. government or by countries with better political and …

Multi-Modality Transformer For E-Commerce: Inferring User Purchase Intention To Bridge The Query-Product Gap, Srivatsa Mallapragada

Dissertations

The rapid growth of e-commerce has necessitated the development of sophisticated product retrieval systems that can effectively match user queries with relevant products. However, the semantic gap between queries and products remains a significant challenge, as traditional retrieval methods often fail to capture the nuances of user purchase intentions. E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge that are untapped in the current product search algorithms. This dissertation presents learning strategies that leverage the query-product transaction logs to enrich the pipeline of our proposed multi-modal transformer model, which transforms initial user queries into pseudo …

Home Is Where The Work Is: How Biases In Managers’ Resource Allocation Decisions Affect Task Performance In Remote Work Environments, Richard D. Mautz Iii

USF Tampa Graduate Theses and Dissertations

As the use of remote and hybrid work arrangements continues to grow, it is important to understand how these arrangements can yield performance. In this paper, I conduct two studies to examine how the remote work environment affects managers’ task assignment decisions across different task types and how those decisions affect workers’ task performance. First, I survey managers, in both a cross-section of industries and specifically in accounting, to study the effect of remote work on their task assignment decisions. Consistent with prior literature and economic theory, I predict and find that managers are more inclined to assign generative tasks …

Symptom Detection With Text Messag Log Distributions For Holistic Depression And Anxiety Screening, Ml Tlachac, Michael Heinz, Miranda Reisch, Samuel S. Ogden

Information Systems and Analytics Department Faculty Journal Articles

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are both heterogeneous in their clinical presentations, manifesting with unique symptom profiles. Despite this, prior digital phenotype research has primarily focused on disorder-level detection rather than symptom-level detection. In this research, we predict the existence of individual symptoms of MDD and GAD with SMS log metadata, and ensemble these symptom-level classifiers to screen for depression and anxiety, thus accounting for disorder heterogeneity. Further, we collect an additional dataset of retrospectively harvested SMS logs to augment an existing dataset collected after COVID-19 altered communication patterns, and propose two new types of distribution …

Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan

Research Collection School Of Computing and Information Systems

Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number …

Community Similarity Based On User Profile Joins, Konstantinos Theocharidis, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Similarity joins on multidimensional data are crucial operators for recommendation purposes. The classic ��-join problem finds all pairs of points within �� distance to each other among two ��-dimensional datasets. In this paper, we consider a novel and alternative version of ��-join named community similarity based on user profile joins (CSJ). The aim of CSJ problem is, given two communities having a set of ��-dimensional users, to find how similar are the communities by matching every single pair of users (a user can be matched with at most one other user) having an absolute difference of at most �� per …

Hypergraphs With Attention On Reviews For Explainable Recommendation, Theis E. Jendal, Trung Hoang Le, Hady Wirawan Lauw, Matteo Lissandrini, Peter Dolog, Katja Hose

Research Collection School Of Computing and Information Systems

Given a recommender system based on reviews, the challenges are how to effectively represent the review data and how to explain the produced recommendations. We propose a novel review-specific Hypergraph (HG) model, and further introduce a model-agnostic explainability module. The HG model captures high-order connections between users, items, aspects, and opinions while maintaining information about the review. The explainability module can use the HG model to explain a prediction generated by any model. We propose a path-restricted review-selection method biased by the user preference for item reviews and propose a novel explanation method based on a review graph. Experiments on …

Pa2blo: Low-Power, Personalized Audio Badge, Hemanth Sabbella, Dulaj Sanjaya Weerakoon, Manoj Gulati, Archan Misra

Research Collection School Of Computing and Information Systems

We present the hardware design and software pipeline for an ultra-low power device, in the form factor of a wearable badge, that supports energy efficient sensing, processing and wireless transfer of human voice commands and interactions. The proposed system, called PA2BLO, is envisioned to support both: (a) real-time, scalable, authorized voice based interaction and control of devices and appliances, and (b) longitudinal, low-power logging of natural voice interactions. PA2BLO in-troduces two key novel capabilities. First, it includes a low power, low-complexity voice authentication module that is able to reliably authenticate an authorized user only using low sampling rate (500 Hz) …

Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao

Research Collection School Of Computing and Information Systems

Lexically constrained text generation (CTG) is to generate text that contains given constrained keywords. However, the text diversity of existing models is still unsatisfactory. In this paper, we propose a lightweight dynamic refinement strategy that aims at increasing the randomness of inference to improve generation richness and diversity while maintaining a high level of fluidity and integrity. Our basic idea is to enlarge the number and length of candidate sentences in each iteration, and choose the best for subsequent refinement. On the one hand, different from previous works, which carefully insert one token between two words per action, we insert …