Computer Sciences Commons^™

Consumer Reaction To The Use Of Artificial Intelligence Chatbot On Distribution Of General Insurance In Singapore, Lai Hing Tan

Dissertations and Theses Collection (Open Access)

As technology rapidly permeates all aspects of our lives, it is not unusual to question and even challenge the rationale on why certain industries are slower to adapt to the new digital age. Insurance is a business that is under scrutiny given its traditional ways of selling and legacy challenges. Why is technology investment in insurance companies lagging others? One emerging technological disruption is artificial intelligence (AI). It is the science of designing and building intelligent systems that can complete tasks traditionally performed by humans. AI is expected to fundamentally transform today’s marketplace, for businesses and consumers alike. However, because …

Gaslight: Attacking Hard-Label Black-Box Classifiers Via Deep Reinforcement Learning, Rajat Sethi

All Theses

Through artificial intelligence, algorithms can classify arrays of data, such as images or videos, into a predefined set of categories. With enough labeled data, a classifier can analyze an input’s components and calculate confidence scores for each category. However, machine learning relies heavily on approximation, which allows attackers to exploit classifiers by providing adversarial
examples. Specifically, attackers can modify their input so that the victim classifier cannot correctly label it, while a human observer would be unable to notice the difference.
This thesis proposes Gaslight, a system that uses deep reinforcement learning to generate adversarial examples against a victim classifier. …

Assessing Key Factors Influencing Fire-Induced Spalling Of Concrete Using Explainable Artificial Intelligence (Xai), Mohammad Khaled Gazi Albashiti

All Theses

This thesis adopts eXplainable Artificial Intelligence (XAI) to identify the key factors influencing the fire-induced spalling of concrete and to extract new insights into the fire-induced spalling phenomenon. In this pursuit, an XAI model was developed, validated, and then augmented with two explainability measures, namely, Shapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The proposed XAI model not only can predict the fire-induced spalling with high accuracy (i.e., >92 %) but can also articulate the reasoning behind its predictions (as in, the proposed model can specify the rationale for each prediction instance); thus, providing us with valuable insights …

Individual Differences In Vulnerability To Phishing, Fake News, And Vishing, Jeff Black

All Theses

Digital deception, such as phishing emails, scam phone calls, and fake news, poses a threat to anyone using digital devices. Research on digital deception often points to individual differences like age, cognitive impulsivity, and digital literacy, but has only investigated different types of digital deception independent of each other. Therefore, it is unclear whether users vulnerable to one type of deception are also vulnerable to others, and why. The present research examined relationships between vulnerability to different types of deception, and how this vulnerability is associated with common individual differences like age, cognitive impulsivity, digital literacy, and gullibility, and exploratory …

Widget Detection-Based Testing For Industrial Mobile Games, Xiongfei Wu, Jiaming Ye, Ke Chen, Xiaofei Xie, Ruochen Huang, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

The fast advances in mobile hardware and widespread smartphone usage have fueled the growth of global mobile gaming in the past decade. As a result, the need for quality assurance of mobile gaming has become increasingly pressing. While general-purpose testing methods have been developed for mobile applications, they become struggling when being applied to mobile games due to the unique characteristics of mobile games, such as dynamic loading and stunning visual effects. There comes a growing industrial demand for automated testing techniques with high compatibility (compatible with various resolutions, and platforms) and non-intrusive characteristics (without packaging external modules into the …

Immersive Learning Environments For Computer Science Education, Dillon Buchanan

Electronic Theses and Dissertations

This master's thesis explores the effectiveness of an educational intervention using an interactive notebook to support and supplement instruction in a foundational-level programming course. A quantitative, quasi-experimental group comparison method was employed, where students were placed into either a control or a treatment group. Data was collected from assignment and final grades, as well as self-reported time spent using the notebook. Independent t-tests and correlation were used for data analysis. Results were inconclusive but did indicate that the intervention had a possible effect. Further studies may explore better efficacy, implementation, and satisfaction of interactive notebooks across a larger population and …

Re-Evaluating Natural Intelligence In The Face Of Chatgpt, Elvin T. Lim, Tze K Koh

Research Collection College of Integrative Studies

How will new technologies impact the nature of higher education? Before ChatGPT, the world witnessed major shifts led by innovations in information storage and transmission. Papyrus in ancient Egypt, the Gutenberg press in 15th-century Europe, and the internet in the 20th century were all milestones in the mass dissemination of knowledge.

A Framework For Identifying Malware Threat Distribution On The Dark Web, Shelby Caldwell

Graduate Theses and Dissertations (2019 - present)

The Dark Web is an ever-growing phenomenon that has not been deeply explored. It is no secret that in recent years, malware has become a powerful threat to technology users. The Dark Web is known for supporting anonymity and secure connections for private interactions. Over the years, it has become a rich environment for displaying trends, details, and indicators of emerging malware threats. Through the application of data science and open-source intelligence techniques, trends in malware distribution can be studied. In this research, we create a framework for helping identify malware threat distribution patterns. We examine this type of Dark …

Risk Assessment Framework For Evaluation Of Cybersecurity Threats And Vulnerabilities In Medical Devices, Maureen S. Van Devender

Graduate Theses and Dissertations (2019 - present)

Medical devices are vulnerable to cybersecurity exploitation and, while they can provide improvements to clinical care, they can put healthcare organizations and their patients at risk of adverse impacts. Evidence has shown that the proliferation of devices on medical networks present cybersecurity challenges for healthcare organizations due to their lack of built-in cybersecurity controls and the inability for organizations to implement security controls on them. The negative impacts of cybersecurity exploitation in healthcare can include the loss of patient confidentiality, risk to patient safety, negative financial consequences for the organization, and loss of business reputation. Assessing the risk of vulnerabilities …

Chicken Keypoint Estimation, Rohit Kala

Computer Science and Computer Engineering Undergraduate Honors Theses

Poultry is an important food source across the world. To facilitate the growth of the global population, we must also improve methods to oversee poultry with new and emerging technologies to improve the efficiency of poultry farms as well as the welfare of the birds. The technology we explore is Deep Learning methods and Computer Vision to help automate chicken monitoring using technologies such as Mask R-CNN to detect the posture of the chicken from an RGB camera. We use Meta Research's Detectron 2 to implement the Mask R-CNN model to train on our dataset created on videos of chickens …

More Than Meets The Eye: In-Store Retail Experiences With Augmented Reality Smart Glasses, Pauline Pfeifer, Tim Hilken, Jonas Heller, Saifeddin Alimamy, Roberta Di Palma

All Works

Augmented reality smart glasses (ARSGs) promise to enhance consumer experiences and decision-making when deployed as in-store retail technologies. However, research to date has not studied in-store use cases; instead, it has focused primarily on consumers' potential adoption of these devices for everyday use. Nor have prior studies compared ARSG uses with the now-common use of AR on touchscreen devices. The current research addresses these knowledge gaps by examining whether ARSGs outperform AR on touchscreen devices in the context of in-store retail experiences. Testing with an actual retail application (n = 308) shows that ARSGs are superior to AR on touchscreen …

Vanet Applications Under Loss Scenarios & Evolving Wireless Technology, Adil Alsuhaim

All Dissertations

In this work we study the impact of wireless network impairment on the performance of VANET applications such as Cooperative Adaptive Cruise Control (CACC), and other VANET applications that periodically broadcast messages. We also study the future of VANET application in light of the evolution of radio access technologies (RAT) that are used to exchange messages. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assume a fixed time headway value, and do not achieve string stability. In this work, we study the string stability of a …

Accessible Virtual Reality For Older Adults, Aaron Gluck

All Dissertations

Virtual reality (VR) has grown significantly since the commercial release of the Oculus Rift in March 2016. This growth accelerated during the COVID-19 pandemic, revolutionizing how individuals and businesses work, socialize, exercise, and stay entertained. However, commercial VR systems are not designed to be accessible to people with disabilities. While researchers have explored aspects of VR accessibility for people with disabilities, there is minimal research on accessible VR for older adults. Older adults (65+) self-report the highest rate of disabilities which may result in VR accessibility barriers, making this the ideal group to study the accessibility of VR.

This dissertation …

Results And Simulation Of Active Self-Assembly, Robert M. Alaniz

Theses and Dissertations

Self-assembly is the process by which simple elements in a system organize themselves into more complex structures based on a set of rules that govern their interactions. With many ways to create self-assembling systems, new models of abstraction have also arisen to handle specific mechanisms and procedures. We explore several open problems in the seeded model of Active Self-Assembly, Chemical Reaction Networks, and Surface Chemical Reaction Networks, proving new results while developing a robust simulation environment, AutoTile, to help build and test these results.

High Clearance Collision-Free Paths, Barun Thapa

UNLV Theses, Dissertations, Professional Papers, and Capstones

Path Planning is one of the widely investigated research areas in computational geometry and robotics. Given a set of polygonal obstacles inside a rectangular box, and start & goal points, the path planning problem is to construct a collision-free path connecting the start point to the goal point. We review existing well known algorithms for solving the path planning problem. We propose new approaches for constructing a collision-free path with high clearance from obstacles. The main idea of the proposed algorithm is the appropriate generation free-region nodes which can be processed to construct high clearance paths. Neighbors of free-region nodes …

Algorithmic Approaches To Battery Consolidation In A Smart Grid, Dara Nyknahad

UNLV Theses, Dissertations, Professional Papers, and Capstones

The concept of the battery exchange station (BES) as a part of the battery consolidation system (BCS) has certain criteria that make it a significant player in the better adaptation plan of electric vehicles (EVs) in the grid toward the smart grid. In this dissertation, we study the BES optimization problem as a promising approach for EV adaptation in a smart grid. We introduce the concept of BCS, which focuses on optimizing the incorporation and transaction of all of the components in a smart grid. Accordingly, we address three BES optimization problems in the BCS. In the first problem, we …

Dea2uth: A Decentralized Authentication And Authorization Scheme For Secure Private Data Transfer, Phillipe Austria

UNLV Theses, Dissertations, Professional Papers, and Capstones

The sharing of private information is a daunting, multifaceted, and expensive undertaking. Furthermore, identity management is an additional challenge that poses significant technological, operational, and legal obstacles. Present solutions and their accompanying infrastructures rely on centralized models that are susceptible to hacking and can hinder data control by the rightful owner. Consequently, blockchain technology has generated interest in the fields of identity and access control. This technology is viewed as a potential solution due to its ability to offer decentralization, transparency, provenance, security, and privacy benefits. Nevertheless, a completely decentralized and private solution that enables data owners to control their …

The Persuasive Design Of Ai-Synthesized Voices, Hannah H. Chang, Anirban Mukherjee

Research Collection Lee Kong Chian School Of Business

We investigate the impact of AI-based, machine-synthesized narrating voices on consumer cognitions and behavior in media-rich environment. Across four studies (plus pretests), we show that the design of AI voices systematically and predictably affects consumer cognition and behavior. Specifically, the designs of AI voices have differential effects in early versus later stages of consumer purchase journey. In situations where the consumers’ attention is already directed to the message, we find that marcomm with more AI voices generates a smaller proportion of favorable thoughts, which leads to a lower purchase likelihood. These results support our conceptualization that hearing more AI voices …

Algorithms, Leadership, And Morality: Why A Mere Human Effect Drives The Preference For Human Over Algorithmic Leadership, Jack Mcguire, David De Cremer

Research Collection Lee Kong Chian School Of Business

Algorithms are increasingly making decisions in organizations that carry moral consequences and such decisions are considered to be ordinarily made by leaders. An important consideration to be made by organizations is therefore whether adopting algorithms in this domain will be accepted by employees and whether this practice will harm their reputation. Considering this emergent phenomenon, we set out to examine employees’ perceptions about (a) algorithmic decision-making systems employed to occupy leadership roles and make moral decisions in organizations, and (b) the reputation of organizations that employ such systems. Furthermore, we examine the extent to which the decision agent needs to …

Multi-Lingual Multi-Partite Product Title Matching, Huan Lin Tay, Wei Jie Tay, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In a globalized marketplace, one could access products or services from almost anywhere. However, resolving which product in one language corresponds to another product in a different language remains an under-explored problem. We explore this from two perspectives. First, given two products of different languages, how to assess their similarity that could signal a potential match. Second, given products from various languages, how to arrive at a multi-partite clustering that respects cardinality constraints efficiently. We describe algorithms for each perspective and integrate them into a promising solution validated on real-world datasets.

Resale Hdb Price Prediction Considering Covid-19 Through Sentiment Analysis, Srinaath Anbu Durai, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or …

Msrl-Net: A Multi-Level Semantic Relation-Enhanced Learning Network For Aspect-Based Sentiment Analysis, Zhenda Hu, Zhaoxia Wang, Yinglin Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of a given text towards several specific aspects. For implementing the ABSA, one way is to convert the original problem into a sentence semantic matching task, using pre-trained language models, such as BERT. However, for such a task, the intra- and inter-semantic relations among input sentence pairs are often not considered. Specifically, the semantic information and guidance of relations revealed in the labels, such as positive, negative and neutral, have not been completely exploited. To address this issue, we introduce a self-supervised sentence pair relation classification task and propose a …

Generation-Based Code Review Automation: How Far Are We?, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Code review is an effective software quality assurance activity; however, it is labor-intensive and time-consuming. Thus, a number of generation-based automatic code review (ACR) approaches have been proposed recently, which leverage deep learning techniques to automate various activities in the code review process (e.g., code revision generation and review comment generation).We find the previous works carry three main limitations. First, the ACR approaches have been shown to be beneficial in each work, but those methods are not comprehensively compared with each other to show their superiority over their peer ACR approaches. Second, general-purpose pre-trained models such as CodeT5 are proven …

Niche: A Curated Dataset Of Engineered Machine Learning Projects In Python, Ratnadira Widyasari, Zhou Yang, Ferdian Thung, Sheng Qin Sim, Fiona Wee, Camellia Lok, Jack Phan, Haodi Qi, Constance Tan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This …

Picaso: Enhancing Api Recommendations With Relevant Stack Overflow Posts, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo

Research Collection School Of Computing and Information Systems

While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowadays, API has become imperative in making software developers' life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of open-source libraries to choose from, choosing the right APIs is not a simple task. Previous studies on API recommendation leverage natural language (query) to identify which API would be suitable for the given task. However, these studies only consider one source of input, i.e., GitHub or …

Exploring A Gradient-Based Explainable Ai Technique For Time-Series Data: A Case Study Of Assessing Stroke Rehabilitation Exercises, Min Hun Lee, Yi Jing Choy

Research Collection School Of Computing and Information Systems

Explainable artificial intelligence (AI) techniques are increasingly being explored to provide insights into why AI and machine learning (ML) models provide a certain outcome in various applications. However, there has been limited exploration of explainable AI techniques on time-series data, especially in the healthcare context. In this paper, we describe a threshold-based method that utilizes a weakly supervised model and a gradient-based explainable AI technique (i.e. saliency map) and explore its feasibility to identify salient frames of time-series data. Using the dataset from 15 post-stroke survivors performing three upper-limb exercises and labels on whether a compensatory motion is observed or …

Trustworthy And Synergistic Artificial Intelligence For Software Engineering: Vision And Roadmaps, David Lo

Research Collection School Of Computing and Information Systems

For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engiueering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to …

She Elicits Requirements And He Tests: Software Engineering Gender Bias In Large Language Models, Christoph Treude, Hideaki Hata

Research Collection School Of Computing and Information Systems

Implicit gender bias in software development is a well-documented issue, such as the association of technical roles with men. To address this bias, it is important to understand it in more detail. This study uses data mining techniques to investigate the extent to which 56 tasks related to software development, such as assigning GitHub issues and testing, are affected by implicit gender bias embedded in large language models. We systematically translated each task from English into a genderless language and back, and investigated the pronouns associated with each task. Based on translating each task 100 times in different permutations, we …

Overcoming Challenges In Devops Education Through Teaching Methods, Samuel Ferino, Marcelo Fernandes, Elder Cirilo, Lucas Agnez, Bruno Batista, Uirá Kulesza, Eduardo Aranha, Christoph Treude

Research Collection School Of Computing and Information Systems

DevOps is a set of practices that deals with coordination between development and operation teams and ensures rapid and reliable new software releases that are essential in industry. DevOps education assumes the vital task of preparing new professionals in these practices using appropriate teaching methods. However, there are insufficient studies investigating teaching methods in DevOps. We performed an analysis based on interviews to identify teaching methods and their relationship with DevOps educational challenges. Our findings show that project-based learning and collaborative learning are emerging as the most relevant teaching methods.

Message From The Chairs: Techdebt 2023, Christoph Treude, Yuanfang Cai, Xin Xia, Zadia Codabux, Hideaki Hata, Florian Deissenboeck, Rodrigo Spinola

Research Collection School Of Computing and Information Systems

Welcome to the 6th ACM/IEEE International Conference on Technical Debt, TechDebt 2023, co-located with the International Conference on Software Engineering (ICSE) 2023, in the beautiful city of Melbourne, Australia. After several years of virtual and hybrid conferences, TechDebt 2023 marks the first predominantly in-person edition of the conference series since the onset of the Covid-19 pandemic.