Graphics and Human Computer Interfaces Commons^™

Programming By Voice, Sadia Nowrin

Dissertations, Master's Theses and Master's Reports

Programmers typically rely on a keyboard and mouse for input, which poses significant challenges for individuals with motor impairments, limiting their ability
to effectively input programs. Voice-based programming offers a promising alternative,
enabling a more inclusive and accessible programming environment. Insights from interviews with motor-impaired programmers revealed that memorizing unnatural commands in existing voice-based programming systems led to frustration. In this work, we explore how programmers naturally speak a single line of code and present a comprehensive methodology for a voice programming system aimed at making programming more accessible for diverse users. To achieve this, we adopted a two-step pipeline. …

The Impact Of Eye-Tracking On Mixed Reality Typing, Cecilia Schmitz

Dissertations, Master's Theses and Master's Reports

Accuracy and speed are pivotal when typing. We hypothesized that the lack of tactile feedback on midair mixed reality keyboards may adversely impact typing performance, especially when ten fingers are used to type. We examined the differences in performance when participants typed on a virtual keyboard using just their index fingers versus all ten fingers. The keyboard was deterministic (without auto-correct), relied only on the headset's egocentric cameras for sensing, and included symbol keys. We used a novel eye-tracking technique to mitigate accidental key presses. The technique was successful at reducing error rates, though participants still typed faster using their …

Scene Understanding And Spatial Analysis Using Scene Graph Enhanced By Hall's Proxemics Zones In Smart Homes, Debaleen Das Spandan

Graduate Theses/Dissertations

Voice-controlled smart assistants have received widespread popularity. It plays a pivotal role in smart homes by providing a natural and convenient interface for interacting with smart devices. However, these assistants are unable to serve persons with physical disabilities and speech impairments. Therefore, non-verbal communication methods, such as eye tracking, gesture recognition, and context awareness can complement and overcome some of these limitations to enhance user experience in smart homes. To address this issue, I am investigating non-verbal communication methods to make smart home technology more accessible and intuitive. In this research, I focus on proxemics, i.e., the study of distance …

‘I Know It When I See It’– Developing Quality Schedules Considering Subjective Or Unspecified Criteria, Douglas L. Moody

Publications and Research

Most timetabling problems have a given objective function to measure the quality of a solution. However, users may have a “I know it when I see it” recognition of a quality schedule, without specifying the complete basis for their judgment. In this situation, the objective function cannot be exclusively used as a solution quality measurement. This work presents an AI based approach to aid in categorizing the solution’s quality when the users have not explicitly defined all factors used in their criteria.

Poster, Performed: Understanding Public Opinions Of Authorship In Generative Artificial Intelligence Models Via Analogy, Wylie Z. Kasai

Dartmouth College Master’s Theses

Over the last decade, generative artificial intelligence models have advanced significantly and provided the public with several tools to create new works of art. However, the true authorship of these works has been debated due to their training on web-scraped data. Serving as an analogy to these larger models, Poster, Performed is an interactive artificial intelligence exhibition project that uses image assets submitted by the public to create poster compositions with custom image processing algorithms. During the course of a four-day exhibition, visitors were asked to identify the exhibition’s primary artist from five options: (1) participants who submitted image assets, …

Breaking Down Computer Networking Instructional Videos: Automatic Summarization With Video Attributes And Language Models, Totok Sukardiyono, Muhammad Irfan Luthfi, Nisa Dwi Septiyanti

Elinvo (Electronics, Informatics, and Vocational Education)

Instructional videos have become a popular tool for teaching complex topics in computer networking. However, these videos can often be lengthy and time-consuming, making it difficult for learners to obtain the key information they need. In this study, we propose an approach that leverages automatic summarization and language models to generate concise and informative summaries of instructional videos. To enhance the performance of the summarization algorithm, we also incorporate video attributes that provide contextual information about the video content. Using a dataset of computer networking tutorials, we evaluate the effectiveness of the proposed method and show that it significantly improves …

Differences In Software Usability Level Based On User Background, Abdur Rohman Sholeh, Agung Fatwanto

Elinvo (Electronics, Informatics, and Vocational Education)

The development of software must consider usability as one of its key success indicators. Relatively few studies discuss the factors influencing usability, including users' backgrounds. The purpose of this research was to investigate the impact of user background, specifically gender, class (year of college admission), and frequency of use on the rating of usability. This research utilized a descriptive quantitative method with instruments: the usability matrix of the Computer System Usability Questionnaire (CSUQ), the System Usability Scale (SUS), the Usability Metric for User Experience (UMUX), and the Net Promoter Score (NPS). The research object was the Learning Management System (LMS) …

Usability Of Mobile Application For Implementing Genetic Counselling Intervention Among Thalassemia Patients And Caregivers: A Case Study Of Cyber Gen, Henri Setiawan, Nur Hidayat, Atun Farihatun, Marlina Indriastuti, Rudi Kurniawan, Andan Firmansyah, Esti Andarini, Yudisa Diaz Lutfi Sandi

Elinvo (Electronics, Informatics, and Vocational Education)

Utilization of communication and information technology has been widely used in the health sector, especially nursing. As one of the nursing interventions for thalassemia patients and caregivers, genetic counseling is not only done face to face but can use android-based telenursing facilities through the complete features available in the Cyber Gen application. This study aims to measure the usability level of Cyber Gen application as an indirect genetic counseling medium for thalassemia patients. This application was developed with four main services: basic information about diseases, consultation rooms, social support, and direct surveys. This application is built using the Flutter Framework, …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam

SMU Data Science Review

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was …

Adaptable Object And Animation System For Game Development, Isaiah Turner

Masters Theses & Specialist Projects

In contemporary times, video games have swiftly evolved into a prominent medium, excelling in both entertainment and narrative delivery, positioning themselves as significant rivals to traditional forms such as film and theater. The burgeoning popularity of gaming has led to a surge in aspiring game developers seeking to craft their own creations, driven by both commercial aspirations and personal passion. However, a common challenge faced by these individuals involves the considerable time investment required to acquire essential skills and establish a foundational framework for their projects. Accessible game development engines that offer a diverse range of fundamental features play a …

Clueless: Revolutionizing Sustainable Fashion And Combating Overconsumption, Tanya Ravichandran

Graphic Communication

“Clueless” revolutionizes sustainable fashion by combating wardrobe overconsumption and the industry’s carbon footprint, using AI to suggest personalized outfits from existing wardrobes tailored to weather and wear history. It enhances user engagement through features like outfit ‘shuffle’ and provides insights into wardrobe utilization and carbon impact.

It’s more than an app; it’s a step towards a greener wardrobe and a healthier planet.

Video Sentiment Analysis For Child Safety, Yee Sen Tan, Nicole Anne Huiying Teo, Ezekiel En Zhe Ghe, Jolie Zhi Yi Fong, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

The proliferation of online video content underscores the critical need for effective sentiment analysis, particularly in safeguarding children from potentially harmful material. This research addresses this concern by presenting a multimodal analysis method for assessing video sentiment, categorizing it as either positive (child-friendly) or negative (potentially harmful). This method leverages three key components: text analysis, facial expression analysis, and audio analysis, including music mood analysis, resulting in a comprehensive sentiment assessment. Our evaluation results validate the effectiveness of this approach, making significant contributions to the field of video sentiment analysis and bolstering child safety measures. This research serves as a …

Mermaid: A Dataset And Framework For Multimodal Meme Semantic Understanding, Shaun Toh, Adriel Kuek, Wen Haw Chong, Roy Ka Wei Lee

Research Collection School Of Computing and Information Systems

Memes are widely used to convey cultural and societal issues and have a significant impact on public opinion. However, little work has been done on understanding and explaining the semantics expressed in multimodal memes. To fill this research gap, we introduce MERMAID, a dataset consisting of 3,633 memes annotated with their entities and relations, and propose a novel MERF pipeline that extracts entities and their relationships in memes. Our framework combines state-of-the-art techniques from natural language processing and computer vision to extract text and image features and infer relationships between entities in memes. We evaluate the proposed framework on a …

Developing Detection And Mapping Of Roads Within Various Forms Of Media Using Opencv, Jordan C. Lyle

Computer Science and Computer Engineering Undergraduate Honors Theses

OpenCV, and Computer Vision in general, has been a Computer Science topic that has interested me for a long time while completing my Bachelor’s degree at the University of Arkansas. As a result of this, I ended up choosing to utilize OpenCV in order to complete the task of detecting road-lines and mapping roads when given a wide variety of images. The purpose of my Honors research and this thesis is to detail the process of creating an algorithm to detect the road-lines such that the results are effective and instantaneous, as well as detail how Computer Vision can be …

Depwignn: A Depth-Wise Graph Neural Network For Multi-Hop Spatial Reasoning In Text, Shuaiyi Li, Yang Deng, Wai Lam

Research Collection School Of Computing and Information Systems

Spatial reasoning in text plays a crucial role in various real-world applications. Existing approaches for spatial reasoning typically infer spatial relations from pure text, which overlook the gap between natural language and symbolic structures. Graph neural networks (GNNs) have showcased exceptional proficiency in inducing and aggregating symbolic structures. However, classical GNNs face challenges in handling multi-hop spatial reasoning due to the over-smoothing issue, i.e., the performance decreases substantially as the number of graph layers increases. To cope with these challenges, we propose a novel Depth-Wise Graph Neural Network (DepWiGNN). Specifically, we design a novel node memory scheme and aggregate the …

Unifying Text, Tables, And Images For Multimodal Question Answering, Haohao Luo, Ying Shen, Yang Deng

Research Collection School Of Computing and Information Systems

Multimodal question answering (MMQA), which aims to derive the answer from multiple knowledge modalities (e.g., text, tables, and images), has received increasing attention due to its board applications. Current approaches to MMQA often rely on single-modal or bi-modal QA models, which limits their ability to effectively integrate information across all modalities and leverage the power of pre-trained language models. To address these limitations, we propose a novel framework called UniMMQA, which unifies three different input modalities into a text-to-text format by employing position-enhanced table linearization and diversified image captioning techniques. Additionally, we enhance cross-modal reasoning by incorporating a multimodal rationale …

Self-Supervised Pseudo Multi-Class Pre-Training For Unsupervised Anomaly Detection And Segmentation In Medical Images, Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be applied in disease screening problems because the training sets available for those problems usually contain only normal images. However, the exclusive reliance on normal images may result in the learning of ineffective low-dimensional image representations that are not sensitive enough to detect and segment unseen abnormal lesions of varying size, appearance, and shape. Pre-training UAD methods with self-supervised learning, based on computer …

Leveraging Artificial Intelligence For Team Cognition In Human-Ai Teams, Beau Schelble

All Dissertations

Advances in artificial intelligence (AI) technologies have enabled AI to be applied across a wide variety of new fields like cryptography, art, and data analysis. Several of these fields are social in nature, including decision-making and teaming, which introduces a new set of challenges for AI research. While each of these fields has its unique challenges, the area of human-AI teaming is beset with many that center around the expectations and abilities of AI teammates. One such challenge is understanding team cognition in these human-AI teams and AI teammates' ability to contribute towards, support, and encourage it. Team cognition is …

Data-Centric Image Super-Resolution In Magnetic Resonance Imaging: Challenges And Opportunities, Mamata Shrestha

Graduate Theses and Dissertations

Super-resolution has emerged as a crucial research topic in the field of Magnetic Resonance Imaging (MRI) where it plays an important role in understanding and analysis of complex, qualitative, and quantitative characteristics of tissues at high resolutions. Deep learning techniques have been successful in achieving state-of-the-art results for super-resolution. These deep learning-based methods heavily rely on a substantial amount of data. Additionally, they require a pair of low-resolution and high-resolution images for supervised training which is often unavailable. Particularly in MRI super-resolution, it is often impossible to have low-resolution and high-resolution training image pairs. To overcome this, existing methods for …

Enhanced Privacy-Enabled Face Recognition Using Κ-Identity Optimization, Ryan Karl

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

Facial recognition is becoming more and more prevalent in the daily lives of the common person. Law enforcement utilizes facial recognition to find and track suspects. The newest smartphones have the ability to unlock using the user's face. Some door locks utilize facial recognition to allow correct users to enter restricted spaces. The list of applications that use facial recognition will only increase as hardware becomes more cost-effective and more computationally powerful. As this technology becomes more prevalent in our lives, it is important to understand and protect the data provided to these companies. Any data transmitted should be encrypted …

Graph Contrastive Learning With Stable And Scalable Spectral Encoding, Deyu Bo, Yuan Fang, Yang Liu, Chuan Shi

Research Collection School Of Computing and Information Systems

Graph contrastive learning (GCL) aims to learn representations by capturing the agreements between different graph views. Traditional GCL methods generate views in the spatial domain, but it has been recently discovered that the spectral domain also plays a vital role in complementing spatial views. However, existing spectral-based graph views either ignore the eigenvectors that encode valuable positional information, or suffer from high complexity when trying to address the instability of spectral features. To tackle these challenges, we first design an informative, stable, and scalable spectral encoder, termed EigenMLP, to learn effective representations from the spectral features. Theoretically, EigenMLP is invariant …

Mrim: Lightweight Saliency-Based Mixed-Resolution Imaging For Low-Power Pervasive Vision, Jiyan Wu, Vithurson Subasharan, Minh Anh Tuan Tran, Kasun Pramuditha Gamlath, Archan Misra

Research Collection School Of Computing and Information Systems

While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes …

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 Propagation And Execution Of Malware In Images, Piper Hall

Cybersecurity Undergraduate Research Showcase

Malware has become increasingly prolific and severe in its consequences as information systems mature and users become more reliant on computing in their daily lives. As cybercrime becomes more complex in its strategies, an often-overlooked manner of propagation is through images. In recent years, several high-profile vulnerabilities in image libraries have opened the door for threat actors to steal money and information from unsuspecting users. This paper will explore the mechanisms by which these exploits function and how they can be avoided.

Performative Mixing For Immersive Audio, Brian A. Elizondo

LSU Doctoral Dissertations

Immersive multichannel audio can be produced with specialized setups of loudspeakers, often surrounding the audience. These setups can feature as few as four loudspeakers or more than 300. Performative mixing in these environments requires a bespoke solution offering intuitive gestural control. Beyond the usual faders for gain control, advancements in multichannel sound demand interfaces capable of quickly positioning sounds between channels. The Quad Cartesian Positioner is such a solution in the form of a Eurorack module for surround mixing for use in live or studio performances.

Diffusion/mixing methods for live multichannel immersive music often rely on the repurposing of hardware …

User Feedback On Celebratory Technology Model For Reducing Stigma, Evelyn Lawrie, Daniel Dinh, Sav Avalos, Jack De Bruyn, Spencer Au, Christian Lopez, Ray Tan, Cyrus Fa'amafoe

Student Scholar Symposium Abstracts and Posters

Social stigma is a complex manifestation that affects humanity, particularly individuals with disabilities and other marginalized groups, including those with physical, cognitive, and emotional conditions. Society often judges these individuals' interactions with the world, and many technologies designed to assist those with disabilities attempt to change their daily interactions and behaviors. Nonetheless, when the emphasis is placed on validating disabled identities, there is a potential for it to be seen as "inspiration porn." This approach might inadvertently reduce inclusivity and do little to challenge negative stereotypes; it can also lead to the objectification of individuals with disabilities. Therefore, this project …

Vrmovian - An Immersive Data Annotation Tool For Visual Analysis Of Human Interactions In Vr, Isaac Browen

Student Scholar Symposium Abstracts and Posters

Understanding human behavior in virtual reality (VR) is a key component for developing intelligent systems to enhance human focused VR experiences. The ability to annotate human motion data proves to be a very useful way to analyze and understand human behavior. However, due to the complexity and multi-dimensionality of human activity data, it is necessary to develop software that can display the data in a comprehensible way and can support intuitive data annotation for developing machine learning models able recognize and assist human motions in VR (e.g., remote physical therapy). Although past research has been done to improve VR data …

Bridging Domain Gaps For Cross-Spectrum And Long-Range Face Recognition Using Domain Adaptive Machine Learning, Cedric Armel Nimpa Fondje

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Face recognition technology has witnessed significant advancements in recent decades, enabling its widespread adoption in various applications such as security, surveillance, and biometrics applications. However, one of the primary challenges faced by existing face recognition systems is their limited performance when presented with images from different modalities or domains( such as infrared to visible, long range to close range, nighttime to daytime, profile to f rontal, etc.) Additionally, advancements in camera sensors, analytics beyond the visible spectrum, and the increasing size of cross-modal datasets have led to a particular interest in cross-modal learning for face recognition in the biometrics and …

Enhancing Search Engine Results: A Comparative Study Of Graph And Timeline Visualizations For Semantic And Temporal Relationship Discovery, Muhammad Shahiq Qureshi

Electronic Theses and Dissertations

In today’s digital age, search engines have become indispensable tools for finding information among the corpus of billions of webpages. The standard that most search engines follow is to display search results in a list-based format arranged according to a ranking algorithm. Although this format is good for presenting the most relevant results to users, it fails to represent the underlying relations between different results. These relations, among others, can generally be of either a temporal or semantic nature. A user who wants to explore the results that are connected by those relations would have to make a manual effort …