Digital Commons Network^™

Modeling, Characterization, And Machine Learning Algorithm For Rectangular Choke Horn Antennas, Ibrahim N. Alquaydheb, Saleh A. Alfawaz, Amirreza Ghadimi Avval, Sara Ghayouraneh, Samir M. El-Ghazaly

Electrical Engineering and Computer Science Faculty Publications and Presentations

In this work, we present the design and modeling of a new type of choke horn antenna. It incorporates a rectangular waveguide and a rectangular choke acting as a parasitic element. The four-sided geometry of the antenna is applicable to systems that utilize rectangular waveguides. Also, it can overcome the need for rectangular-to-circular transition of transmission line or mode conversion. The main objective of this paper is to develop a model that calculates the far field radiation characteristics of the proposed antenna (analytical part) and to incorporate a finite element method (FEM) solver that adds to the theoretical solution (empirical …

Improving Ethics Surrounding Collegiate-Level Hacking Education: Comprehensive Implementation Plan And Affiliation With Peer-Led Initiatives, Shannon O. Morgan

ALL - Honors Theses

The purpose of this research study is to analyze the ethical ramifications of hacking education on the collegiate level in relation to cybersecurity majors and minors. Educators and university officials must take action to prevent the misuse of the information, skills, and knowledge gained from being a student in a cyber-related course. As the dependence on technology rises, it is crucial that future technology professionals are effectively trained and taught the ins and outs of the field to better protect users from becoming a victim of a cyberattack. The outcome of this study is to compile a comprehensive list of …

The Social Pot: A Social Media Application, Reid Long

Honors Projects

The Social Pot is a web application that allows a user to post to Instagram and X simultaneously from one place. The user creates a Social Pot Account and from there can set their Instagram username and password within the home page. Once the user attempts to post, it will redirect them to login to X which once successful will make the tweet. Used the API 'instagram-private-api'. User needed to give access to my X Project which in turn gave an Auth token (via X redirect URL). The auth token was then sent to my endpoint in order to get …

A Deep Dive On The Groundbreak Role Of Role Playing Games, Lawton Fong

ART 108: Introduction to Games Studies

Role-playing games (RPGs) have revolutionized the gaming industry, fundamentally altering the way we engage with and perceive games. RPGs are defined by their emphasis on narrative-driven gameplay, character development, and immersive worlds. Originating from tabletop games like Dungeons & Dragons (D&D), RPGs have evolved into complex digital experiences that allow players to interact with richly detailed virtual environments and storylines. This genre's growth has been propelled by advances in technology, expanding from simple text-based adventures to fully realized 3D worlds. Overall, role-playing games have significantly influenced the gaming landscape and transformed our perceptions of games as interactive experiences by advancing …

Towards Low-Resource Rumor Detection: Unified Contrastive Transfer With Propagation Structure, Hongzhan Lin, Jing Ma, Ruichao Yang, Zhiwei Yang, Mingfei Cheng

Research Collection School Of Computing and Information Systems

The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of substantial training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a simple yet effective framework with unified contrastive transfer learning, to detect rumors by adapting the features learned from well-resourced rumor data to that …

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 …

Environmental, Social, And Governance (Esg) And Artificial Intelligence In Finance: State-Of-The-Art And Research Takeaways, Tristan Lim

Research Collection School Of Computing and Information Systems

The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical …

Redriver: Runtime Enforcement For Autonomous Vehicles, Yang Sun, Christopher M. Poskitt, Xiaodong Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Autonomous driving systems (ADSs) integrate sensing, perception, drive control, and several other critical tasks in autonomous vehicles, motivating research into techniques for assessing their safety. While there are several approaches for testing and analysing them in high-fidelity simulators, ADSs may still encounter additional critical scenarios beyond those covered once they are deployed on real roads. An additional level of confidence can be established by monitoring and enforcing critical properties when the ADS is running. Existing work, however, is only able to monitor simple safety properties (e.g., avoidance of collisions) and is limited to blunt enforcement mechanisms such as hitting the …

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 …

Unleashing The Power Of Clippy In Real-World Rust Projects, Chunmiao Li, Yijun Yu, Haitao Wu, Luca Carlig, Shijie Nie, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

The error messages generated by the Rust compiler (rustc) are useful for developers to identify and diagnose suspicious code segments. Complementing the compiler, linters can also play an important role in promoting the adherence to certain coding style conventions and best practices. Prominent linters utilized in the Rust ecosystem include Clippy [1] and Rustfmt [2]. Among them, the Rust community particularly emphasizes on the importance of heeding the warnings provided by Clippy to mitigate common errors and promote the adoption of idiomatic conventions. Clippy provides a set of more than 600 lints in addition to the built-in rustc lints. These …

My Github Sponsors Profile Is Live!": Investigating The Impact Of Twitter/X Mentions On Github Sponsors, Youmei Fan, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

GitHub Sponsors was launched in 2019, enabling donations to opensource software developers to provide financial support, as per GitHub’s slogan: “Invest in the projects you depend on”. However, a 2022 study on GitHub Sponsors found that only two-fifths of developers who were seeking sponsorship received a donation. The study found that, other than internal actions (such as offering perks to sponsors), developers had advertised their GitHub Sponsors profiles on social media, such as Twitter (also known as X). Therefore, in this work, we investigate the impact of tweets that contain links to GitHub Sponsors profiles on sponsorship, as well as …

Concretely Mapped Symbolic Memory Locations For Memory Error Detection, Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang

Research Collection School Of Computing and Information Systems

Memory allocation is a fundamental operation for managing memory objects in many programming languages. Misusing allocated memory objects (e.g., buffer overflow and use-after-free) can lead to catastrophic consequences. Symbolic execution-based approaches are often used to detect such memory errors, leveraging their capabilities in automatic path exploration and test case generation. However, existing symbolic execution engines face significant limitations in modeling dynamic memory layouts. These engines either represent memory object locations as concrete addresses, limiting analyses to specific address layouts and missing errors that occur at special addresses, or represent locations as simple symbolic variables without sufficient constraints, resulting in memory …

Code Search Is All You Need? Improving Code Suggestions With Code Search, Junkai Chen, Xing Hu, Zhenhao Li, Cuiyun Gao, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retrieve similar code snippets from the code base or leverage deep learning models to provide code suggestions. However, how to effectively enhance the code suggestions using code retrieval has not been systematically investigated. In this paper, we study and explore a retrieval-augmented framework for code suggestions. Specifically, our framework leverages different retrieval approaches and search strategies to search similar code snippets. Then the retrieved code is used to further enhance the performance of language …

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, …

Large Language Model For Vulnerability Detection: Emerging Results And Future Directions, Xin Zhou, Ting Zhang, David Lo

Research Collection School Of Computing and Information Systems

Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the effectiveness of LLMs in detecting software vulnerabilities is largely unexplored. This paper aims to bridge this gap by exploring how LLMs perform with various prompts, particularly focusing on two state-of-the-art LLMs: GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves competitive performance with the prior state-of-the-art vulnerability detection approach and GPT-4 consistently outperformed the state-of-the-art.

Ppt4j: Patch Presence Test For Java Binaries, Zhiyuan Pan, Xing Hu, Xin Xia, Xian Zhan, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results.In this paper, we propose a new patch presence test framework named Ppt4J …

On Adaptivity And Randomness For Streaming Algorithms, Manuel Stoeckl

Dartmouth College Ph.D Dissertations

A streaming algorithm has a limited amount of memory and reads a long sequence (data stream) of input elements, one by one, and computes an output depending on the input. Such algorithms may be used in an online fashion, producing a sequence of intermediate outputs corresponding to the prefixes of the data stream. Adversarially robust streaming algorithms are required to give correct outputs with a desired probability even when the data stream is adaptively generated by an adversary that can see all intermediate outputs of the algorithm. This thesis binds together research on a variety of problems related to the …

Global Challenges In Accessing Mental Health Services And Addressing The Impact Of Alzheimer’S Disease And Depression, Padmapriya Velupillai Meikandan

Master's Theses (2009 -)

This research project focuses on developing a quantum sensing system that can detect biomarkers associated with health disorders, like Alzheimer’s and depression. Our goal is to create a sensitive and highly selective quantum sensing device using a diamond nitrogen vacancy (NV) center. To train and test our quantum machine learning algorithms we will preprocess data from the available Human Connectome Project dataset. This dataset forms the basis of our quantum-based methods. The core of our project revolves around developing quantum machine learning algorithms that utilize techniques such as Support Vector Machines and neural networks to diagnose health disorders using data …

Advanced Machine Learning Approaches For Predicting Mental Health Disorders Following Long Covid Diagnosis, Manoj Purohit

Master's Theses (2009 -)

After the global spread of COVID-19, the enduring effects of Long COVID and its health implications have emerged as a significant global issue, affecting people worldwide. The lingering symptoms post a COVID-19 infection can significantly affect individuals who had previously contracted the virus, exerting considerable influence over their mental well-being. Prolonged recuperation associated with Long COVID has been connected with the emergence of symptoms such as depression and anxiety, all of which can have adverse effects on emotional health. This project delves into an in-depth analysis of healthcare data pertaining to Long COVID from the Froedtert Health (FH) Medical System …

Optimal Selection And Investment-Allocation Decisions For Sustainable Supplier Development Practices, Chunguang Bai, Kannan Govindan, Dileep G. Dhavale

School of Business

Organization’s sustainability performance is influenced by its suppliers’ sustainability performance. This relationship makes sustainable supplier development a strategic competitive option for a buyer or focal organization. When considering sustainable supplier development practices (SSDPs) adoption, organizations have to balance and consider their limited financial resources and operational constraints. It becomes necessary to both select the best SSDPs set and investment allocation among the selected SSDP set such that the organization can maximize overall sustainability performance level. In this paper, an integrated formal modeling methodology using DEMATEL, the NK model, and multi-objective linear programming model is used support this objective. The proposed …

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 …

Exploring Quaternion Neural Network Loss Surfaces, Jeremiah Bill, Bruce A. Cox

Faculty Publications

This paper explores the superior performance of quaternion multi-layer perceptron (QMLP) neural networks over real-valued multi-layer perceptron (MLP) neural networks, a phenomenon that has been empirically observed but not thoroughly investigated. The study utilizes loss surface visualization and projection techniques to examine quaternion-based optimization loss surfaces for the first time. The primary contribution of this research is the statistical evidence that QMLP models yield smoother loss surfaces than real-valued neural networks, which are measured and compared using a robust quantitative measure of loss surface “goodness” based on estimates of surface curvature. Extensive computational testing validates the effectiveness of these surface …

A Literature Review On The Use Of Ai Technology For Medical Diagnosis, Olivia Maddock

Senior Honors Projects

The integration of technology like artificial intelligence (AI) in medical diagnosis offers a unique solution to the growing demands of healthcare providers across all fields of medicine. The purpose of the literature review is to examine current and future applications of artificial intelligence in healthcare, as well as associated challenges to implementing AI in medical decision-making and care access. The literature review was organized into sections examining current applications, limitations, and future directions. From the literature review conducted, I found that AI technology like machine learning (ML) and deep learning (DL) have the potential to optimize fields like medical diagnostics, …

Tea Shop Manager, Ruben Becerril

Posters - 2024

Tea Shop Manager is meant to be a business simulator game. Business is a subject that is difficult to learn especially as a child who may not understand how the world works yet. Business is all about its application to the real world, as a result the game will:

• Bring aspects of business, simplify it, and then apply it to a semi-realistic setting such as managing a shop.

• Concepts and skills will be taught through the user’s interaction with the game.

• Show statistics based on management performance.

Objective.Gg: Uniting Scholastic Esports, Douglas Beirne

Posters - 2024

Objective.gg is a startup recruitment platform within the scholastic esports scene that seeks to unite esports prospects with collegiate esports programs in an effective manner. Objective.gg seeks to accomplish its mission by operating an online platform that allows prospects and collegiate coaches to create their own profiles and connect with one another, building a community in the process. This platform will begin with a free tier, but additional features will be offered through a subscription-based model with two additional pricing tiers to choose from. The online platform will be supported by free online and paid in-person tournaments for Objective.gg platform …

Atlas, James P . Clark

Posters - 2024

• Problem: Hard for new gym goers to know how to achieve their goals.
• Motivation: I had a hard time figuring out how to achieve the goals I wanted in the gym. A lot of it was just not knowing what to do or how to do it.
• Solution: I want to create an application that will display some basic information, such as maintenance calories and current weight. It will be able to assist with calculations that need to take place to achieve a certain fitness goal.

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 …

Exploring Neural Networks For Developing A Chess Learning Platform With Integrated Ai Agent, Lauren Escobedo

Posters - 2024

Chess is a highly strategic, complex, and long-form game that has been popular for many centuries. Due to the aforementioned complexities of this game, new players often have a hard time learning how to effectively and successfully play. With the recent developments in machine learning algorithms, new opportunities arise to create artificially intelligent tutors - not only for chess, but for all subjects. This project aims to develop a product which investigates the integration of an artificially intelligent “coach”, named Chesster, to train the player, which is trained on a neural network machine learning algorithm.

Nookipedia, Shyann Francis

Posters - 2024

Animal Crossing involves managing around 9000 items within a gameplay environment. This abundance of items makes it difficult for players to effectively track progress and navigate through their possessions. The primary motivation of Nookipedia is to enhance overall gameplay experience by streamlining achievement management, thereby reducing unnecessary interactions with non-playable characters (NPCs) and enabling players to focus more on core game activities. By improving inventory organization and reducing clutter, the aim is to create a smoother and more immersive gaming experience for players.

Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy

Computer Science Faculty Research & Creative Works

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed …