Digital Commons Network^™

The Kruger Collection Reimagined: A Case Study In 3d Scanning And Interactive Exhibit Design, Annissa Davis

Department of Anthropology: Theses and Student Research

This thesis examines the use of 3D modeling in museum exhibition to create exploratory exhibits that facilitate unique relationships between the visitors and the collection beyond what is provided by the collection’s in person counterparts. Typical use of 3D modeling in museums is currently often representative rather than exploratory. By employing a Digital Humanities lens to approach the development of a digital exhibition utilizing 3D technology and interactive elements created in a video game engine (Unity), this thesis project evaluates these potential new relationships. Using the Eloise Kruger Collection of Miniatures as a case study, the following text details the …

Vr Circuit Simulation With Advanced Visualization For Enhancing Comprehension In Electrical Engineering, Elliott Wolbach

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

As technology advances, the field of electrical and computer engineering continuously demands innovative tools and methodologies to facilitate effective learning and comprehension of fundamental concepts. Through a comprehensive literature review, it was discovered that there was a gap in the current research on using VR technology to effectively visualize and comprehend non-observable electrical characteristics of electronic circuits. This thesis explores the integration of Virtual Reality (VR) technology and real-time electronic circuit simulation with enhanced visualization of non-observable concepts such as voltage distribution and current flow within these circuits. The primary objective is to develop an immersive educational platform that makes …

Towards Explainable Harmful Meme Detection Through Multimodal Debate Between Large Language Models, Hongzhan Lin, Ziyang Luo, Wei Gao, Jing Ma, Bo Wang, Ruichao Yang

Research Collection School Of Computing and Information Systems

The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to the implicit meaning embedded in memes, which is not explicitly conveyed through the surface text and image. However, existing harmful meme detection methods do not present readable explanations that unveil such implicit meaning to support their detection decisions. In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. Specifically, inspired by the powerful capacity of Large Language Models …

Navigating Real-World Challenges: A Quadruped Robot Guiding System For Visually Impaired People In Diverse Environments, Shaojun Cai, Ashwin Ram, Zhengtai Gou, Mohd Alqama Wasim Shaikh, Yu-An Chen, Yingjia Wan, Kotaro Hara, Shengdong Zhao, David Hsu

Research Collection School Of Computing and Information Systems

Blind and Visually Impaired (BVI) people find challenges in navigating unfamiliar environments, even using assistive tools such as white canes or smart devices. Increasingly affordable quadruped robots offer us opportunities to design autonomous guides that could improve how BVI people find ways around unfamiliar environments and maneuver therein. In this work, we designed RDog, a quadruped robot guiding system that supports BVI individuals’ navigation and obstacle avoidance in indoor and outdoor environments. RDog combines an advanced mapping and navigation system to guide users with force feedback and preemptive voice feedback. Using this robot as an evaluation apparatus, we conducted experiments …

Enabling Roll-Up And Drill-Down Operations In News Exploration With Knowledge Graphs For Due Diligence And Risk Management, Sha Wang, Yuchen Li, Hanhua Xiao, Zhifeng Bao, Yanfei Dong

Research Collection School Of Computing and Information Systems

Efficient news exploration is crucial in real-world applications, particularly within the financial sector, where numerous control and risk assessment tasks rely on the analysis of public news reports. The current processes in this domain predominantly rely on manual efforts, often involving keyword-based searches and the compilation of extensive keyword lists. In this paper, we introduce NCEXPLORER, a framework designed with OLAP-like operations to enhance the news exploration experience. NCEXPLORER empowers users to use roll-up operations for a broader content overview and drill-down operations for detailed insights. These operations are achieved through integration with external knowledge graphs (KGs), encompassing both fact-based …

Factored Mdp Based Moving Target Defense With Dynamic Threat Modeling, Megha Bose, Praveen Paruchuri, Akshat Kumar

Research Collection School Of Computing and Information Systems

Moving Target Defense (MTD) has emerged as a proactive defense framework to counteract ever-changing cyber threats. Existing approaches often make assumptions about attacker-side knowledge and behavior, potentially resulting in suboptimal defense. This paper introduces a novel MTD approach, leveraging a Markov Decision Process (MDP) model that eliminates the need for prior knowledge about attacker intentions or payoffs. Our framework seamlessly integrates real-time attacker responses into the defender's MDP using a dynamic Bayesian network. We use a factored MDP model to enable a more comprehensive and realistic representation of the system having multiple switchable aspects and also accommodate incremental updates of …

Rule-Guided Counterfactual Explainable Recommendation, Yinwei Wei, Xiaoyang Qu, Xiang Wang, Yunshan Ma, Liqiang Nie, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

To empower the trust of current recommender systems, the counterfactual explanation (CE) method is adopted to generate the counterfactual instance for each input and take their changes causing the different outcomes as the explanation. Although promising results have been achieved by existing CE-based methods, we propose to generate the attribute-oriented counterfactual explanation. Different from them, we aim to generate the counterfactual instance by performing the intervention on the attributes, and then build an attribute-oriented counterfactual explainable recommender system. Considering the correlation and categorical values of attributes, how to efficiently generate the reliable counterfactual instances on the attributes challenges us. To …

Learning To Generate Explainable Stock Predictions Using Self‑Reflective Large Language Models, Kelvin J.L. Koa, Yunshan Ma, Ritchie Ng, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) present a solution to this problem, given their known capabilities to generate human-readable explanations for their decision-making process. However, the task of stock prediction remains challenging for LLMs, as it requires the ability to weigh the varying impacts of chaotic social texts on stock prices. The problem gets progressively harder with the introduction of the explanation component, which requires LLMs to explain verbally why certain factors are more important …

Fashionregen: Llm‑Empowered Fashion Report Generation, Yujuan Ding, Yunshan Ma, Wenqi Fan, Yige Yao, Tat‑Seng Chua, Qing Li

Research Collection School Of Computing and Information Systems

Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports. It is traditionally performed by fashion professionals based on their expertise and experience, which requires high labour cost and may also produce biased results for relying heavily on a small group of people. In this paper, to tackle the Fashion Report Generation (FashionReGen) task, we propose an intelligent Fashion Analyzing and Reporting system based the advanced Large Language Models (LLMs), debbed as GPT-FAR. Specifically, it tries to deliver FashionReGen based on effective …

Large Language Model Powered Agents In The Web, Yang Deng, An Zhang, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Web applications serve as vital interfaces for users to access information, perform various tasks, and engage with content. Traditional web designs have predominantly focused on user interfaces and static experiences. With the advent of large language models (LLMs), there’s a paradigm shift as we integrate LLM-powered agents into these platforms. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions in web applications. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various web applications, such as …

The Impact Of Ai On Ux: Challenges And Opportunities, Susan Stephanie Wells

Theses

Integrating artificial intelligence (AI) in user experience (UX) design is reshaping the field of UX, offering new opportunities and challenges for designers. This thesis project explores the multifaceted relationship between AI and UX design, focusing on the challenges, opportunities, and skills demanded of UX designers in the age of AI. Through a review of academic research and real-world experiences, this project studies the impact of AI on web design processes, UX testing, and data analysis. Key findings highlight the transformative potential of AI in enhancing user experiences, from suggesting website structures to facilitating UX testing and data analysis.

Comparative analysis …

The Role Of Student Motivation In Integrating Ai Into Web Design Education: A Longitudinal Study, Jason Lively, James Hutson

Faculty Scholarship

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, …

Ethical Imperatives And Challenges: Review Of The Use Of Machine Learning For Predictive Analytics In Higher Education, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The escalating integration of machine learning (ML) in higher education necessitates a critical examination of its ethical implications. This article conducts a comprehensive review of the application of ML for predictive analytics within higher education institutions (HEIs), emphasizing the technology's potential to enhance student outcomes and operational efficiency. The study identifies significant ethical concerns, such as data privacy, informed consent, transparency, and accountability, that arise from the use of ML. Through a detailed analysis of current practices, this review underscores the need for HEIs to develop robust ethical frameworks and technological infrastructures to navigate these challenges effectively. The findings reveal …

Evaluation Of Cybersecurity In Remote Working Settings For Mobile Network Operators, Victoria Mahabi

Tanzania Journal of Engineering and Technology (TJET)

Cybersecurity has increasingly been a primary concern to people as technology advances and allows them to work remotely. This study thus evaluated the cybersecurity posture for organisations that have opted for remote working culture, whereas emerging cyber threats, practices to combat them, and appropriate guidelines for managing cyber threats were discussed. The study used a descriptive design with a quantitative approach from 118 information technology personnel working for Tanzania's three major mobile network operators (MNOs). SPSS analysed the collected data. The study revealed that predominant cyber-threats affecting MNOs in remote working include human errors, phishing attacks, malicious domains, denial of …

Osint Research Studios: A Flexible Crowdsourcing Framework To Scale Up Open Source Intelligence Investigations, A. Mukhopadhyay, Sukrit Venkatagiri, K. Luther

Computer Science Faculty Works

Open Source Intelligence (OSINT) investigations, which rely entirely on publicly available data such as social media, play an increasingly important role in solving crimes and holding governments accountable. The growing volume of data and complex nature of tasks, however, means there is a pressing need to scale and speed up OSINT investigations. Expert-led crowdsourcing approaches show promise, but tend to either focus on narrow tasks or domains, or require resource-intense, long-term relationships between expert investigators and crowds. We address this gap by providing a flexible framework that enables investigators across domains to enlist crowdsourced support for discovery and verification of …

Priming Through Persuasion: Towards Secure Password Behavior, Rizu Paudel, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

Users tend to create weak passwords even for the important accounts. The prior research shed light on user’s insecure password behavior, and why the interventions, including requirement specification (e.g., password composition policies) and feedback systems (e.g., password meters) fail in practice. To this end, we propose and evaluate the concept: priming-through-persuasion in the realm of secure password creation. In particular, we created visual designs, aimed at priming users about the repercussions of weak passwords before their password creation. We base our designs on two forms of persuasion methods: pathos and logos. Pathos appeals to people’s emotion in order to persuade …

“It’S Hard For Him To Make Choices Sometimes And He Needs Guidance”: Re-Orienting Parental Control For Children, Prakriti Dumaru, Hanieh Atashpanjeh, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

With access to devices and online platforms at an increasingly younger age, it is essential to protect children from potential online risks. In our studies, we particularly focused the parents who have a child under 13 years of age. To this end, deriving from developmental psychology, we base our low-fidelity prototype design (termed as ‘treatment’) on a set of design principles focusing on open communication, instilling self-regulation, and availing granularity along with designs of existing Google’s parental control as a baseline. First, we conducted semi-structured interviews with 21 parents to understand their perceptions, including suggestions for design improvement within our …

A Transcendental Phenomenological Study Of Multimedia-Based Pedagogy: Experiences Of Elementary School Teachers In Developing Cognitively Stimulating Instruction, Siyanbola Enitan Balogun

Doctoral Dissertations and Projects

The purpose of this transcendental phenomenological study is to understand the lived experiences of elementary school teachers in developing multimedia-based teaching materials that stimulate cognition and aid in improving learners' comprehension and academic achievements for elementary students in the urban city of Lagos, Nigeria. The central question guiding this study is: What are elementary teachers' experiences incorporating and designing multimedia-based pedagogy to create cognitively stimulating instructional material for their learning environment? A transcendental phenomenological study was conducted at two elementary schools—Camden Elementary School (CES) and Goshen Elementary School (GES). The study utilized Mayer's cognitive theory of multimedia learning (CTML) as …

Gmr-29 Identification Of Ai-Generated Images, Chris Foster, Joshua Brock, Harini Kottala, Srilatha Korrapati

C-Day Computing Showcase

With the quick rise of Artificial Intelligence (AI), generative AI models have greatly increased the volume and velocity of data creation. Among that data, AI-generated images have become a highly discussed topic, especially when discussing the potential dangers of these AI models. Due to these dangers, being able to distinguish AI-generated art from human-made art is becoming a necessity. Additionally, as these AI-models improve, it is becoming increasingly difficult for humans to determine whether art is AI-generated or human-made. This paper proposes the further exploration of the effectiveness of a current state of the art AI-image identification model.

Gpr-16 Attention Driven Framework For Detecting Mental Illness Causes From Social Media, Abm Adnan Azmee, Dinesh Chowdary Attota, Francis E Nweke

C-Day Computing Showcase

Mental health is a critical aspect of our overall well-being. Mental illness refers to conditions that impact an individual's psychological state, resulting in considerable distress, and limitations in functioning day-to-day tasks. Due to the progress of technology, social media has merged as the platform, for individuals to share their thoughts and emotions. The psychological state of individuals can be accessed with the help of data from these platforms. However, it is challenging for conventional machine learning models to analyze the diverse linguistic contexts of social media data. In this work, we propose a novel attention-driven deep framework to overcome these …

Artificial Intelligence And Film: A Journey In Public Perception From 1960 To The Present Day, Kayla Anderson, Andrew Roggeman, Joseph Fuller

Celebrating Scholarship and Creativity Day (2018-)

An analysis of accomplishments in film from the 1960s-2020s that feature Artificial Intelligence to give a full picture of how public perception has changed towards these technologies over time, supplemented by historical and technological context.

Designing For Deployable, Secure, And Generic Machine Learning Systems, Li-Yun Wang

Dissertations and Theses

Machine learning systems have catalyzed numerous image-centric applications owing to the significant achievements of machine learning algorithms and models. While these systems have showcased the efficacy of machine learning models, certain challenges persist, such as machine learning system design and security vulnerabilities inherent in deep neural networks. Moreover, the deployment of deep neural network models remains a significant hurdle. This dissertation introduces a multimedia prototyping framework tailored for visual analytical applications, improving the reusability of video analysis software tools with minimal performance overhead. Furthermore, we present novel image-processing techniques designed to bolster the robustness of deep neural networks and propose …

Hello, World., Elliot Cetinski, Evan Chartock, Olivia Cross, Kiran Drew, Kaya Eller, Ben Little, Joey Nolan, Spencer Toth, Sophie Wahl-Taylor, Sadie Walker, Destiny Young, Annie Zulick

Theater and Dance Presentations

This project works to theatrically represent the current state of Artificial Intelligence (AI), as well as its benefits and drawbacks, in the style of the Living Newspaper. Originating from a Great Depression-era job program, the Living Newspaper sought to take headlines and present them onstage for a poignant and contemporary social critique. This work does the same, melding different angles of the AI debate into a single production that emphasizes the rapidly progressing state of modern AI technology and the need for humans to consider the impacts such technologies will have. Furthermore, it asks the audience to question their position …

React Native Photo & Video Streaming/Processing Api Integration, Anamaria Oharciuc, William Carr, Sushanth Ambati, Ryan Blaisdell, Kyle Reed, Jack F. Myers

STEM Student Research Symposium Posters

RunSignup is a software company specializing in event management technology. Event organizers, known as Race Directors, utilize their platform to create and manage events, organize media albums, stream their events to the public, and more. In order to allow Race Directors to complete these actions in real-time at their events, RunSignup asked our team to develop a mobile app. With the app, Race Directors would be able to upload and stream photos to the event’s photo albums as soon as they are taken, and they could livestream the event to YouTube directly from their mobile device. Even if the device …

Bookworm: A Home Library Aid, Johnny Quach

Student Academic Conference

This project involved the design and development of a website intended to allow users to create a personal inventory of their purchased media with a primary focus on books. The goal of the project was to create a website that is both user-friendly, convenient, and both personally and professionally useful. The project began with an analysis of existing personal inventory websites and apps which identified many different aspects that are included in the finished website, as well as aspects that fell short. Utilizing Python and Flask, the design includes a number of easy-to-use features, such as the ability to create …

Drone-Based Bug Detection In Orchards With Nets: A Novel Orienteering Approach, Francesco Betti Sorbelli, Federico Coró, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti

Computer Science Faculty Research & Creative Works

The Use of Drones for Collecting Information and Detecting Bugs in Orchards Covered by Nets is a Challenging Problem. the Nets Help in Reducing Pest Damage, But They Also Constrain the Drone's Flight Path, Making It Longer and More Complex. to Address This Issue, We Model the Orchard as an Aisle-Graph, a Regular Data Structure that Represents Consecutive Aisles Where Trees Are Arranged in Straight Lines. the Drone Flies Close to the Trees and Takes Pictures at Specific Positions for Monitoring the Presence of Bugs, But its Energy is Limited, So It Can Only Visit a Subset of Positions. to …

Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre

Whittier Scholars Program

The introduction of PoetHQ, a mobile application, offers an economical strategy for colleges, potentially ushering in significant cost savings. These savings could be redirected towards enhancing academic programs and services, enriching the educational landscape for students. PoetHQ aims to democratize access to crucial software, effectively removing financial barriers and facilitating a richer educational experience. By providing an efficient software solution that reduces organizational overhead while maximizing accessibility for students, the project highlights the essential role of equitable education and resource optimization within academic institutions.

Social Order In Age Of Artificial Intelligence, Yongnian Zheng

Bulletin of Chinese Academy of Sciences (Chinese Version)

While the rapid development of artificial intelligence is empowering society, it is also posing serious threats to the continuation of the traditional social order. The "de-intellectualization" function of AI has given rise to the phenomenon of "artificial ignorance", referring to the self-inflicted intellectual harm caused by the uncontrolled and excessive misuse of AI-related tools, thus causing a profound impact on the social order. The technical structural characteristics and governance structural characteristics of AI contributed to the emergence of the phenomenon of "artificial ignorance". The technical structural characteristics of AI can be summarized as highly concentrated capabilities, highly centralized control, highly …

Ai-Powered Learning: Blending Ai With Active Learning In The Information Literacy Classroom, Kevin J. Reagan, Wilhelmina Randtke

Georgia International Conference on Information Literacy

In 2016, the ACRL Framework for Information Literacy in Higher Education launched in response to more voluminous, less-vetted online information, including misinformation and content farms. Subsequently, the ACRL Framework has been widely adopted, and numerous high-quality lesson plans and resources for teaching the frames already exist, including published lesson plans and textbooks. Now, generative AI tools, such as ChatGPT and other chat bots present new challenges for information literacy educators. For instance, in addition to teaching students how to identify issues such as fake news, the information literacy professional has to address topics such as ethical AI use, AI hallucination …

Use Of Deep Learning In Content-Based Image Retrieval (Cbir), Angelina Das

ATU Scholars Symposium

In the world of computer vision and data retrieval, a crucial task is finding images within a database based on their visual content. This is known as content-based image retrieval (CBIR). As the number of digital images explodes across fields like online shopping, healthcare, and social media, the need for powerful and precise CBIR systems becomes ever more critical. Early CBIR methods depended on features crafted by hand, like color distributions, texture descriptions, and shape characteristics. However, these techniques often have difficulty capturing the true meaning of an image and might not handle very large datasets effectively. With the rise …