Software Engineering Commons^™

Delidar: Decoupling Lidars For Pervasive Spatial Computing, Kanatta Gamage Ramesh Darshana Rathnayake, Razat Sutradhar, Abbaas A. M. Nishar, Weerakoon Dulaj S., Ashwin Ashok, Archan Misra

Research Collection School Of Computing and Information Systems

Unbounded proliferation of LiDAR-equipped pervasive devices generates two challenges: (a) mutual interference among emitters and (b) significantly higher sensing energy overhead. We propose a fundamentally different approach for LiDAR sensing, in indoor spaces, that decouples the sensor’s emitter and receiver components. Our proposed approach, called DeLiDAR, centralizes the emitter functionality in one or more stationary nodes that continually emit pulses; this decoupling allows each mobile LiDAR sensor to be an ultra-low power, pure receiver unit consisting solely of passive multiple photodiodes. We explain how the emitter can utilize VLC-based encoding of its pulses to convey parameter settings that allow a …

Computational Representation, Analysis And Verification Of Requirements In Engineering Design And Systems Engineering, Chandan Kumar Sahu

All Dissertations

Systems are developed to satisfy a set of requirements derived from stakeholders’ needs, defining the problem space for which the system is created as a feasible solution. The system design process begins with eliciting these requirements and concludes with validating whether the created system meets them. Requirements engineering (RE) encompasses elicitation, representation, analysis, documentation, verification, and validation. However, challenges in RE, such as imprecision in natural language (NL), proprietary restrictions, and a lack of standardized quality metrics, hinder the creation of well-formed and comprehensive requirements. These challenges complicate formalization and analysis of requirements.

This dissertation addresses these challenges by proposing …

Agchain: A Blockchain-Based Gateway For Trustworthy App Delegation From Mobile App Markets, Mengjie Chen, Xiao Yi, Daoyuan Wu, Jianliang Xu, Yingjiu Li, Debin Gao

Research Collection School Of Computing and Information Systems

The popularity of smartphones has led to the growth of mobile app markets, creating a need for enhanced transparency, global access, and secure downloading. This paper introduces AGChain, a blockchain-based gateway that enables trustworthy app delegation within existing markets. AGChain ensures that markets can continue providing services while users benefit from permanent, distributed, and secure app delegation. During its development, we address two key challenges: significantly reducing smart contract gas costs and enabling fully distributed IPFS-based file storage. Additionally, we tackle three system issues related to security and sustainability. We have implemented a prototype of AGChain on Ethereum and Polygon …

Divlog: Log Parsing With Prompt Enhanced In-Context Learning, Junjielong Xu, Ruichun Yang, Yintong Huo, Chengyu Zhang, Pinjia He

Research Collection School Of Computing and Information Systems

Log parsing, which involves log template extraction from semistructured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing supervised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose DivLog, an effective log parsing …

Lilac: Log Parsing Using Llms With Adaptive Parsing Cache, Zhihan Jiang, Jinyang Liu, Zhuangbin Chen, Yichen Li, Junjie Huang, Yintong Huo, Pinjia He, Jiazhen Gu, R. Michael Lyu

Research Collection School Of Computing and Information Systems

Log parsing transforms log messages into structured formats, serving as the prerequisite step for various log analysis tasks. Although a variety of log parsing approaches have been proposed, their performance on complicated log data remains compromised due to the use of human-crafted rules or learning-based models with limited training data. The recent emergence of powerful large language models (LLMs) demonstrates their vast pre-trained knowledge related to code and logging, making it promising to apply LLMs for log parsing. However, their lack of specialized log parsing capabilities currently hinders their parsing accuracy. Moreover, the inherent inconsistent answers, as well as the …

Abstracttrace: The Use Of Execution Traces To Cluster, Classify, Prioritize, And Optimize A Bloated Test Suite, Ziad A. Al-Sharif, Clinton L. Jeffrey

Engineering, Computing and Mathematical Sciences Faculty Articles

Due to the incremental and iterative nature of the software testing process, a test suite may become bloated with redundant, overlapping, and similar test cases. This paper aims to optimize a bloated test suite by employing an execution trace that encodes runtime events into a sequence of characters forming a string. A dataset of strings, each of which represents the code coverage and execution behavior of a test case, is analyzed to identify similarities between test cases. This facilitates the de-bloating process by providing a formal mechanism to identify, remove, and reduce extra test cases without compromising software quality. This …

Development Of A Web-Based Information System For Student Leave Permission At Dar Al-Raudhah Islamic Boarding School: Iso Quality Standards Analysis, Bonita Destiana, Priyanto Priyanto, Rahmatul Irfan, Muhammad Gus Khamim, Muhammad Yusuf Ridlo, Muhammad Iqbal

Elinvo (Electronics, Informatics, and Vocational Education)

Dar Al-Raudhah Entrepreneur, Islamic Boarding School, has adopted digital technology by upgrading hardware and software also investing in reliable internet infrastructure. However, this school still faces issues with students’ leave permission process due to reliance on manual bookkeeping and Excel, which leads to potential errors. Based on those problems, this research aims to create a web-based student leave permission system called SIPERSAN. The SIPERSAN system was developed with a Waterfall development model, which includes requirements analysis, design, implementation, testing, and deployment. The database is managed with MySQL, and the system is developed using PHP with the Laravel framework. Based on …

Improving Students’ Cognitive Abilities In Remote Learning Environment Using Brain Computer Interface And Eye-Tracking, Nuraini Jamil

Thesis/ Dissertation Defenses

Attention and cognitive engagement are crucial factors in remote learning environments, where the absence of physical presence often diminishes learning outcomes. Traditional methods for assessing these cognitive states, such as observation and self-reporting, are limited by subjectivity and inefficiency. Automated solutions, particularly those based on biometric data like EEG and eye-tracking, offer a more accurate and scalable alternative. However, developing robust systems that leverage biometric data in real-time presents significant challenges. These include handling large volumes of complex data, ensuring low-latency processing, and adapting machine learning models to diverse learning environments and individual cognitive states. Additionally, the integration of neurofeedback …

Effect Of Virtual Reality Technology On Ce/Cs Based Laboratories Education – A, Mariam Abdulla Al Nuaimi

Thesis/ Dissertation Defenses

Virtual reality (VR) is becoming increasingly popular in different fields, as institutions strive to incorporate technology into the education process. This thesis explores the effect of the use of VR on the users learning experience, and whether gamification, and human-computer interaction (HCI) affect the VR experience in a positive way. The main goal of this thesis is to explore the VR environment in STEM courses/Labs and investigate its effect on learning advanced topics. Specifically, we developed Digital Design & Computer Organization Lab (CS/CE Laboratory) as a VR environment to research this topic. We set and conducted experiments, surveyed participating students …

Hybridizing Reinforcement Learning With Metaheuristics For Improved Traffic Signal Control And Optimization In Urban Transportation Networks, Jiyana Nikhil Jaisinghani

Thesis/ Dissertation Defenses

Managing road traffic in metropolitan cities is a crucial aspect of Intelligent Transportation Systems (ITS). The rapid growth of population and vehicles has led to increasing traffic congestion, which negatively affects travel times, fuel consumption, and air quality in urban areas. Intersections and their traffic lights are key contributors to this congestion, making efficient and adaptable Traffic Signal Control (TSC) and Traffic Signal Scheduling (TSS) essential. TSC manages traffic flow at intersections, while TSS optimizes the timing and sequencing of traffic signals. The techniques, Reinforcement Learning (RL) and Metaheuristic Optimization (MO), have shown promising results in addressing traffic control challenges …

Simplify Workflows: Ai As A Coding Companion, Tiffany Garrett

Library Scholarship

Artificial Intelligence is on everyone’s minds and has been the topic of the past two Matheson Lectures. But, what is the role of academic health sciences libraries? Moving the theoretical into practical, seven of our colleagues will present real-life case studies. What worked - what didn’t - what would they do differently?

This entry is from one of those case presentations on how a librarian at Roseman University of Health Sciences used AI to complete simple computer programming projects that optimized a few library workflows.

Student Perceptions Of A Novel No-Cost Mobile Application For Ophthalmic History And Physical Examination, Soryan Kumar, Anagha Lokhande, Spandana Jarmale, Arnav Kumar, Samantha Rosenthal, Grayson W. Armstrong, Michael Migliori, Jamie Schaefer

Journal of Academic Ophthalmology

Background: Mobile applications have shown promise in enhancing medical trainee performance. In ophthalmology, a comprehensive mobile app can streamline the trainee education process by providing guidance for patient intake. Language barriers pose additional challenges impacting the quality of care for Spanish-speaking patients; literature has documented the adverse impacts of inadequate translation on quality of medical care for both trainees and patients. We aim to develop a free mobile application to guide medical trainees through the ophthalmic patient intake process and assist with Spanish-language translation.

Methods: We developed EyeCheck as a free mobile application for ophthalmology trainee education with …

Permission Recommendation For Android Applications: Leveraging Natural Language Processing On App Descriptions, Saeed Salem Al Shebli

Thesis/ Dissertation Defenses

This study develops an NLP-based system to recommend essential permissions for Android apps by analyzing app descriptions. It leverages advanced models, including LSTM and ensemble techniques, to align permissions with app functionality while minimizing unnecessary requests.

Turning 50 Hours Into 5 Minutes: Automating Work With Custom Tools, Aidan La Penta

Honors Student Research

This project streamlines a critical business task for the Kutztown Honors Program by automating the extraction of information from student transcript PDFs. Using custom software written in Python, the program parses 400 pages of data in 70 seconds to significantly reduce the hours of manual effort previously required. By leveraging skills from the CSIT curriculum, this project represents an innovative approach for a CSIT student to support a university department through custom software solutions. The project enhances operational efficiency for the Honors Program and demonstrates the practical application of computer science to solve real-world problems within the wider academic community.

Elevating Automated Software Maintenance Tasks With Large Language Models, Xin Zhou

Dissertations and Theses Collection (Open Access)

Software engineering involves many tasks across different phases such as requirements, design, implementation, testing, and maintenance. Among them, software maintenance is a crucial phase, typically accounting for more than half of the software life cycle's duration.
To boost developer productivity, in recent years, numerous research endeavors in software engineering have sought to automate certain software maintenance tasks through the application of machine learning techniques.
Since 2020, the emergence of advanced Large Language Models (LLMs) of code has opened new avenues for enhancing automated solutions in software maintenance.
This dissertation presents a series of works aimed at advancing automated solutions for …

Defending Large Language Models Against Jailbreak Attacks Via Layer-Specific Editing, Wei Zhao, Zhe Li, Yige Li, Jun Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly being adopted in a wide range of realworld applications. Despite their impressive performance, recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts even when aligned via Reinforcement Learning from Human Feedback or supervised fine-tuning. While existing defense methods focus on either detecting harmful prompts or reducing the likelihood of harmful responses through various means, defending LLMs against jailbreak attacks based on the inner mechanisms of LLMs remains largely unexplored. In this work, we investigate how LLMs respond to harmful prompts and propose a novel defense method termed Layer-specific Editing (LED) …

Revisiting The Conflict-Resolving Problem From A Semantic Perspective, Jinhao Dong, Jun Sun, Yun Lin, Yedi Zhang, Murong Ma, Jin Song Dong, Dan Hao

Research Collection School Of Computing and Information Systems

Collaborative software development significantly enhances development productivity by enabling multiple contributors to work concurrently on different branches. Despite these advantages, such collaboration often increases the likelihood of causing conflicts. Resolving these conflicts brings huge challenges, primarily due to the necessity of comprehending the differences between conflicting versions. Researchers have explored various automatic conflict resolution techniques, including unstructured, structured, and learning-based approaches. However, these techniques are mostly heuristic-based or black-box in nature, which means they do not attempt to solve the root cause of the conflicts, i.e., the existence of different program behaviors exhibited by the conflicting versions.In this work, we …

Ai-Powered Pedagogy: Revolutionizing Students’ Learning Experiences Through Integration Of Ai Technologies, Amna Awad Alsaedi

Theses

This research examines the integration of Artificial Intelligence (AI) within the educational sector with the aim of enhancing student learning outcomes. AI offers tailored learning experiences, interactive educational content, prompt feedback, and access to diverse learning resources. Nonetheless, challenges including addiction, costliness, privacy infringement, bias, ethical dilemmas, market competition, and moral considerations require resolution. Research particularly delves into the utilization of chatbots, and algorithms designed to simulate human interactions and generate text resembling human speech. Educational applications powered by AI have the potential to heighten student engagement, comprehension, and academic performance by substituting traditional assignments with concise, information-rich lessons. Furthermore, …

Contactless And Scalable Approaches For Human Health And Performance Sensing, Ngoc Doan Thu Tran

Dissertations and Theses Collection (Open Access)

Human health and performance sensing has been extensively studied, from physiology to mental health and movement analytics. However, typical approaches rely on invasive and contact sensors or require professional practitioners, limiting their scalability. For example, the gold standard for measuring heart rate is through an electrocardiogram (ECG), which requires multiple probes attached to the skin and is impractical for individuals with skin issues. Additionally, it typically needs to be performed in a hospital setting under the supervision of a trained cardiac physiologist. Depression detection often relies on the expertise of psychologists or psychiatrists. However, there is a shortage of these …

Lr-Auth: Towards Practical Implementation Of Implicit User Authentication On Earbuds, Changshuo Hu, Xiao Ma, Xinger Huang, Yiran Shen, Dong Ma

Research Collection School Of Computing and Information Systems

The increasing use of earbuds in applications like immersive entertainment and health monitoring necessitates effective implicit user authentication systems to preserve the privacy of sensitive data and provide personalized experiences. Existing approaches, which leverage physiological cues (e.g., jawbone structure) and behavioral cues (e.g., gait), face challenges such as limited usability, high delay and energy overhead, and significant computational demands, rendering them impractical for resource-constrained earbuds. To address these issues, we present LR-Auth, a lightweight, user-friendly implicit authentication system designed for various earbud usage scenarios. LR-Auth utilizes the modulation of sound frequencies by the user's unique occluded ear canal, generating user-specific …