Digital Commons Network^™

(Un)Likelihood Training For Interpretable Embedding, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan, Zhijian Hou

Research Collection School Of Computing and Information Systems

Cross-modal representation learning has become a new normal for bridging the semantic gap between text and visual data. Learning modality agnostic representations in a continuous latent space, however, is often treated as a black-box data-driven training process. It is well known that the effectiveness of representation learning depends heavily on the quality and scale of training data. For video representation learning, having a complete set of labels that annotate the full spectrum of video content for training is highly difficult, if not impossible. These issues, black-box training and dataset bias, make representation learning practically challenging to be deployed for video …

A Big Data Approach To Augmenting The Huff Model With Road Network And Mobility Data For Store Footfall Prediction, Ming Hui Tan, Kar Way Tan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Conventional methodologies for new retail store catchment area and footfall estimation rely on ground surveys which are costly and time-consuming. This study augments existing research in footfall estimation through the innovative integration of mobility data and road network to create population-weighted centroids and delineate residential neighbourhoods via a community detection algorithm. Our findings are then used to enhance Huff Model which is commonly used in site selection and footfall estimation. Our approach demonstrated the vast potential residing within big data where we harness the power of mobility data and road network information, offering a cost-effective and scalable alternative. It obviates …

How Helpful Do Novice Programmers Find The Feedback Of An Automated Repair Tool?, Oka Kurniawan, Christopher M. Poskitt, Ismam Al Hoque, Norman Tiong Seng Lee, Cyrille Jégourel, Nachamma Sockalingam

Research Collection School Of Computing and Information Systems

Immediate feedback has been shown to improve student learning. In programming courses, immediate, automated feedback is typically provided in the form of pre-defined test cases run by a submission platform. While these are excellent for highlighting the presence of logical errors, they do not provide novice programmers enough scaffolding to help them identify where an error is or how to fix it. To address this, several tools have been developed that provide richer feedback in the form of program repairs. Studies of such tools, however, tend to focus more on whether correct repairs can be generated, rather than how novices …

Class Participation: Using Technology To Enhance Efficiency And Fairness, Benjamin Gan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

Class participation can be considered as contribution to discussion, attendance, presentations, unsolicited responses, questions, comments, etc. What counts may vary across individual teachers. The more students participate, the less memorization they do, and the more they engage in higher levels of thinking, including interpretation, analysis, and synthesis. However, only a handful of students in many classrooms participate regularly, a phenomenon dubbed as "consolidation of responsibility". This study provides a literature review of inclass participation, as well as pedagogies and technologies that enhance participation. Pedagogies such as active learning, group learning, project-based learning and flipped classroom. Technologies to automate attendance taking, …

Sustainability Projects With A Community Partner: A Social Norm Nudging Effort, Benjamin Gan, Thomas Menkhoff, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

Singapore students from two inter-disciplinary courses worked with stakeholders of a local business association community partner on a series of sustainability topics to learn about climate change, its effects, and actions to mitigate them. They empathized with the association stakeholders, proposed a digital technology solution, tested their prototypes, and presented the final action plans. After the projects were completed, we found climate proficient (83%), motivated (83%), engaged (97%), and satisfied (70%) students; and two influencing predictors: interest/enjoyment and emotional engagement. The study results suggest that getting students interested and emotionally engaged in sustainability projects is an important first step towards …

Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

This paper explores ChatGPT’s potential in aiding government agencies, drawing from a case study based on a government agency in Singapore. While ChatGPT’s text generation abilities offer promise, it brings inherent challenges, including data opacity, potential misinformation, and occasional errors. These issues are especially critical in government decision-making.Public administration’s core values of transparency and accountability magnify these concerns. Ensuring AI alignment with these principles is imperative, given the potential repercussions on policy outcomes and citizen trust.AI explainability plays a central role in ChatGPT’s adoption within government agencies. To address these concerns, we propose strategies like prompt engineering, data governance, and …

Just Adjust One Prompt: Enhancing In-Context Dialogue Scoring Via Constructing The Optimal Subgraph Of Demonstrations And Prompts, Jiashu Pu, Ling Cheng, Lu Fan, Tangjie Lv, Rongsheng Zhang

Research Collection School Of Computing and Information Systems

The use of modern Large Language Models (LLMs) as chatbots still has some problems such as hallucinations and lack of empathy. Identifying these issues can help improve chatbot performance. The community has been continually iterating on reference-free dialogue evaluation methods based on large language models (LLMs) that can be readily applied. However, many of these LLM-based metrics require selecting specific datasets and developing specialized training tasks for different evaluation dimensions (e.g., coherence, informative). The developing step can be time-consuming and may need to be repeated for new evaluation dimensions. To enable efficient and flexible adaptation to diverse needs of dialogue …

Security Analysis Of Holystone Drones: Examining Attack Vectors And Data Extraction Techniques, Sandesh Ambadas More

Theses and Dissertations

In recent years, the surge in popularity of small-scale Unmanned Aerial Vehicles (UAVs), especially Holy Stone models, has raised significant security concerns. This study examines specific Holy Stone drone models, including the HS 175D, HS 430, HS 360S, and HS720, focusing on sub-250g drones exempt from FAA registration and those requiring registration and Remote ID. Despite advancements in drone technology, our research reveals persistent vulnerabilities that could be exploited by malicious actors for illicit purposes, posing a substantial security risk. Our comprehensive analysis involved simulated attacks in identifying and exploiting these vulnerabilities, leading to the successful acquisition of flight logs, …

Data-Driven Optimization Approaches For Dynamic Urban Logistics Operational Problems, Jingfeng Yang

Dissertations and Theses Collection (Open Access)

Given the rapid pace of urbanization, there is a pressing need to optimize urban logistics delivery operations for enhanced capacity and efficiency. Over recent decades, a multitude of optimization approaches have been put forth to address urban logistics challenges, encompassing routing and scheduling within both static and dynamic contexts. In light of the rising computational capabilities and the widespread adoption of machine learning in recent times, there is a growing body of research aimed at elucidating the seamless integration of data and machine learning within conventional urban logistics optimization models. Additionally, the ubiquitous utilization of smartphones and internet innovations presents …

Anxiety In International Graduate Students With U.S. Education, Aeshah Zarraa

Theses and Dissertations

Anxiety in international graduate students is a significant concern for most students planning to move out of their home country to pursue higher education in the United States. The primary purpose of this research is to identify the prime causes of anxiety triggers in the targetted Graduate students, thereby determining a step-wise methodology development to address the causes. The study initially identified a set of graduate students who were voluntarily willing to collaborate and address their concerns anonymously to identify the significant issues faced by current students and alumni of the Florida Institute of Technology, Melbourne, Florida. Several questions were …

Triple Helix: Ai-Artist-Audience Collaboration In A Performative Art Experience, Xuedan Zou

Dartmouth College Master’s Theses

Imagine an art exhibition that morphs its content according to the audience’s experience like a chameleon, reflecting the audience’s mind and culture and turning the artist’s exhibition into the viewer’s. But when the viewers leave, the work fades back to the creator’s original work and waits for the next audience. In this project, my team introduced an interactive exhibition called "Triple Helix," where audience members were provided the opportunity to alter the artworks created by the artist, thus imbuing them with their own perspectives. This interactive exhibition was held at three physical-locations and online, and a comprehensive user study was …

The Use Of Deception In Dementia-Care Robots: Should Robots Tell "White Lies" To Limit Emotional Distress?, Samuel R. Cox, Grace Cheong, Wei Tsang Ooi

ROSA Journal Articles and Publications

With projections of ageing populations and increasing rates of dementia, there is need for professional caregivers. Assistive robots have been proposed as a solution to this, as they can assist people both physically and socially. However, caregivers often need to use acts of deception (such as misdirection or white lies) in order to ensure necessary care is provided while limiting negative impacts on the cared-for such as emotional distress or loss of dignity. We discuss such use of deception, and contextualise their use within robotics.

Discourses Across Periods Of Time, Amanda Raquel Moreno

Theses

This literature review explores the revolutionary effect of generative artificial intelligence (AI) and virtual reality (VR) on digital art history, specifically concentrating on their capacity to enable dialogical exchanges with historical figures and deepen the understanding of artworks. This study considers the current state of research, detecting key methodologies, areas of improvement, and possible challenges and ethical concerns. The example historical figure used in this analysis is the iconic Mexican artist Frida Kahlo. Kahlo’s refusal to correspond to a specific artistic style makes her an ideal subject for generative AI and VR-based investigation, offering fresh insights into her work. The …

Responsibility Gaps And Black Box Healthcare Ai: Shared Responsibilization As A Solution, Benjamin H Lang, Sven Nyholm, Jennifer Blumenthal-Barby

Center for Medical Ethics and Health Policy Staff Publications

As sophisticated artificial intelligence software becomes more ubiquitously and more intimately integrated within domains of traditionally human endeavor, many are raising questions over how responsibility (be it moral, legal, or causal) can be understood for an AI’s actions or influence on an outcome. So called “responsibility gaps” occur whenever there exists an apparent chasm in the ordinary attribution of moral blame or responsibility when an AI automates physical or cognitive labor otherwise performed by human beings and commits an error. Healthcare administration is an industry ripe for responsibility gaps produced by these kinds of AI. The moral stakes of healthcare …

Mitigating Membership Inference Attacks Via Weighted Smoothing, Minghan Tan, Xiaofei Xie, Jun Sun, Tianhao Wang

Research Collection School Of Computing and Information Systems

Recent advancements in deep learning have spotlighted a crucial privacy vulnerability to membership inference attack (MIA), where adversaries can determine if specific data was present in a training set, thus potentially revealing sensitive information. In this paper, we introduce a technique, weighted smoothing (WS), to mitigate MIA risks. Our approach is anchored on the observation that training samples differ in their vulnerability to MIA, primarily based on their distance to clusters of similar samples. The intuition is clusters will make model predictions more confident and increase MIA risks. Thus WS strategically introduces noise to training samples, depending on whether they …

A Black-Box Attack On Code Models Via Representation Nearest Neighbor Search, Jie Zhang, Wei Ma, Qiang Hu, Shangqing Liu, Xiaofei Xie, Yves Le Traon, Yang Liu

Research Collection School Of Computing and Information Systems

Existing methods for generating adversarial code examples face several challenges: limted availability of substitute variables, high verification costs for these substitutes, and the creation of adversarial samples with noticeable perturbations. To address these concerns, our proposed approach, RNNS, uses a search seed based on historical attacks to find potential adversarial substitutes. Rather than directly using the discrete substitutes, they are mapped to a continuous vector space using a pre-trained variable name encoder. Based on the vector representation, RNNS predicts and selects better substitutes for attacks. We evaluated the performance of RNNS across six coding tasks encompassing three programming languages: Java, …

Weakly-Supervised Semantic Segmentation, Zhaozheng Chen

Dissertations and Theses Collection (Open Access)

Semantic segmentation is a fundamental task in computer vision that assigns a label to every pixel in an image based on the semantic meaning of the objects present. It demands a large amount of pixel-level labeled images for training deep models. Weakly-supervised semantic segmentation (WSSS) is a more feasible approach that uses only weak annotations to learn the segmentation task. Image-level label based WSSS is the most challenging and popular, where only the class label for the entire image is provided as supervision. To address this challenge, Class Activation Map (CAM) has emerged as a powerful technique in WSSS. CAM …

Supporting Software Engineers With Large Language Model-Based Automation, Ting Zhang

Dissertations and Theses Collection (Open Access)

In recent years, software engineering (SE) has witnessed significant growth, leading to the creation and sharing of an abundance of software artifacts such as source code, bug reports, and pull requests. Analyzing these artifacts is crucial for comprehending the sentiments of software developers and automating various SE tasks, ultimately leading to more human-centered automated SE and enhancing software development efficiency. However, the diverse and unstructured nature of software text poses a significant challenge to this analysis. In response, researchers have investigated a variety of approaches, including the utilization of natural language processing techniques. The advent of large language models (LLMs), …

Towards Securing Smart Contracts Systematically, Duy Tai Nguyen

Dissertations and Theses Collection (Open Access)

Smart contracts are a groundbreaking technique that allows users to programmatically modify the state of the blockchain. They are essentially self-enforcing programs that are deployed and executed on top of the blockchain. In recent years, we have witnessed various smart contract incidents that led to substantial financial losses and even business closures. These incidents mainly arise from design flaws in Solidity, a dominant programming language for writing smart contracts, which complicates the process of detecting and repairing vulnerabilities. Furthermore, there is a growing interest in attacking smart contracts by the attackers. This thesis is dedicated to developing effective methods to …

Technical Maturity And Network Effects Of Xf Artificial Intelligence Open Platform, Tao Jiang

Dissertations and Theses Collection (Open Access)

Studying the impact mechanism of the commercial value of artificial intelligence open technology platforms has theoretical and practical significance. This article aims to enrich and expand the theoretical research on technology maturity, value co creation, and network effects on open technology platforms at home and abroad through empirical research on artificial intelligence open technology platforms and ecology. This study takes XF's open technology platform case as the research object, and based on technology maturity theory, value co creation, and network effects theory, examines the network effect value creation mechanism of open technology platforms driven by technology maturity in three development …

Deep Isolation Forest For Anomaly Detection, Hongzuo Xu, Guansong Pang, Yijie Wang, Yongjun Wang

Research Collection School Of Computing and Information Systems

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation …

Neural Airport Ground Handling, Yaoxin Wu, Jianan Zhou, Yunwen Xia, Xianli Zhang, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, …

C³: Code Clone-Based Identification Of Duplicated Components, Yanming Yang, Ying Zou, Xing Hu, David Lo, Chao Ni, John C. Grundy, Xin: Xia

Research Collection School Of Computing and Information Systems

Reinventing the wheel is a detrimental programming practice in software development that frequently results in the introduction of duplicated components. This practice not only leads to increased maintenance and labor costs but also poses a higher risk of propagating bugs throughout the system. Despite numerous issues introduced by duplicated components in software, the identification of component-level clones remains a significant challenge that existing studies struggle to effectively tackle. Specifically, existing methods face two primary limitations that are challenging to overcome: 1) Measuring the similarity between different components presents a challenge due to the significant size differences among them; 2) Identifying …

Combat Covid-19 At National Level Using Risk Stratification With Appropriate Intervention, Xuan Jin, Kar Way Tan

Research Collection School Of Computing and Information Systems

In the national battle against COVID-19, harnessing population-level big data is imperative, enabling authorities to devise effective care policies, allocate healthcare resources efficiently, and enact targeted interventions. Singapore adopted the Home Recovery Programme (HRP) in September 2021, diverting low-risk COVID-19 patients to home care to ease hospital burdens amid high vaccination rates and mild symptoms. While a patient's suitability for HRP could be assessed using broad-based criteria, integrating machine learning (ML) model becomes invaluable for identifying high-risk patients prone to severe illness, facilitating early medical assessment. Most prior studies have traditionally depended on clinical and laboratory data, necessitating initial clinic …

Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

This paper explores ChatGPT’s potential in aiding government agencies, drawing from a case study based on a government agency in Singapore. While ChatGPT’s text generation abilities offer promise, it brings inherent challenges, including data opacity, potential misinformation, and occasional errors. These issues are especially critical in government decision-making.Public administration’s core values of transparency and accountability magnify these concerns. Ensuring AI alignment with these principles is imperative, given the potential repercussions on policy outcomes and citizen trust.AI explainability plays a central role in ChatGPT’s adoption within government agencies. To address these concerns, we propose strategies like prompt engineering, data governance, and …

Do Contributing Files Provide Information About Oss Newcomers' Onboarding Barriers?, Felipe Fronchetti, David Shepherd, Igor Wiese, Christoph Treude, Marco Gerosa, Igor Steinmacher

Research Collection School Of Computing and Information Systems

Effectively onboarding newcomers is essential for the success of open source projects. These projects often provide onboarding guidelines in their ‘CONTRIBUTING’ files (e.g., CONTRIBUTING.md on GitHub). These files explain, for example, how to find open tasks, implement solutions, and submit code for review. However, these files often do not follow a standard structure, can be too large, and miss barriers commonly found by newcomers. In this paper, we propose an automated approach to parse these CONTRIBUTING files and assess how they address onboarding barriers. We manually classified a sample of files according to a model of onboarding barriers from the …

Deeparc: Modularizing Neural Networks For The Model Maintenance, Xiaoning Ren, Yun Lin, Yinxing Xue, Ruofan Liu, Jun Sun, Zhiyong Feng, Jinsong Dong

Research Collection School Of Computing and Information Systems

Neural networks are an emerging data-driven programming paradigm widely used in many areas. Unlike traditional software systems consisting of decomposable modules, a neural network is usually delivered as a monolithic package, raising challenges for some maintenance tasks such as model restructure and re-adaption. In this work, we propose DeepArc, a novel modularization method for neural networks, to reduce the cost of model maintenance tasks. Specifically, DeepArc decomposes a neural network into several consecutive modules, each of which encapsulates consecutive layers with similar semantics. The network modularization facilitates practical tasks such as refactoring the model to preserve existing features (e.g., model …

Scalable Algorithm Design And Performance Analysis For Graph Motifs Discovery, Md Abdul Motaleb Faysal

UNLV Theses, Dissertations, Professional Papers, and Capstones

Discovering motifs or structural patterns, such as communities, is a significant graph application utilized for classifying groups in social and business networks, identifying similar proteins, detecting anomalous behavior in the cybersecurity domain, and finding critical entities in rumor propagation or infectious disease spreading. Existing state-of-the-art techniques for community discovery face challenges related to scalability, performance limitations, and methodological inaccuracies. The objective of this doctoral dissertation is to introduce novel parallel algorithms and propose high-performance computing architecture designs to address the performance constraints of current community detection approaches when processing large-scale social and biological data. This research focuses on two main …

Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman

UNLV Theses, Dissertations, Professional Papers, and Capstones

The integration of Advanced Driving Assistance Systems (ADAS) and autonomous driving functionalities into contemporary vehicles has notably surged, driven by the remarkable progress in artificial intelligence (AI). These AI systems, capable of learning from real-world data, now exhibit the capability to perceive their surroundings via a suite of sensors, create optimal routes from source to destination, and execute vehicle control akin to a human driver.

Within the context of this thesis, we undertake a comprehensive exploration of three distinct yet interrelated ADAS and Autonomy projects. Our central objective is the implementation of autonomous driving(AD) technology at UNLV campus, culminating in …

Perception Of Bias In Chatgpt: Analysis Of Social Media Data, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Ahmed El Noshokaty, Tareq Nasralah

Computer Information Systems Faculty Publications (Archived)

In this study, we aim to analyze the public perception of Twitter users with respect to the use of ChatGPT and the potential bias in its responses. Sentiment and emotion analysis were also analyzed. Analysis of 5,962 English tweets showed that Twitter users were concerned about six main types of biases, namely: political, ideological, data & algorithmic, gender, racial, cultural, and confirmation biases. Sentiment analysis showed that most of the users reflected a neutral sentiment, followed by negative and positive sentiment. Emotion analysis mainly reflected anger, disgust, and sadness with respect to bias concerns with ChatGPT use.