Graphics and Human Computer Interfaces Commons^™

Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two key components: ScratchNet and PatchNet. In detail, ScratchNet employs the parallel Multi-scale Partial Convolution Module to effectively repair scratches, learning from multi-scale local receptive fields. In contrast, the patch-misses necessitate the network to emphasize global information. To this end, we incorporate a transformer-based encoder and decoder …

Multi-Script Handwriting Identification By Fragmenting Strokes, Joshua Jude Thomas

<strong> Theses and Dissertations </strong>

This study tests the effectiveness of Multi-Script Handwriting Identification after simplifying character strokes, by segmenting them into sub-parts. Character simplification is performed through splitting the character by branching-points and end-points, a process called stroke fragmentation in this study. The resulting sub-parts of the character are called stroke fragments and are evaluated individually to identify the writer. This process shares similarities with the concept of stroke decomposition in Optical Character Recognition which attempts to recognize characters through the writing strokes that make them up. The main idea of this study is that the characters of different writing‑scripts (English, Chinese, etc.) may …

Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang

Research Collection School Of Computing and Information Systems

Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …

Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availability of task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the …

Binder, Tyler A. Peaster, Lindsey M. Davenport, Madelyn Little, Alex Bales

ATU Research Symposium

Binder is a mobile application that aims to introduce readers to a book recommendation service that appeals to devoted and casual readers. The main goal of Binder is to enrich book selection and reading experience. This project was created in response to deficiencies in the mobile space for book suggestions, library management, and reading personalization. The tools we used to create the project include Visual Studio, .Net Maui Framework, C#, XAML, CSS, MongoDB, NoSQL, Git, GitHub, and Figma. The project’s selection of books were sourced from the Google Books repository. Binder aims to provide an intuitive interface that allows users …

Factors Influencing The Perceptions Of Human-Computer Interaction Curriculum Developers In Higher Education Institutions During Curriculum Design And Delivery, Cynthia Augustine, Salah Kabanda

The African Journal of Information Systems

Computer science (CS) and information systems students seeking to work as software developers upon graduating are often required to create software that has a sound user experience (UX) and meets the needs of its users. This includes addressing unique user, context, and infrastructural requirements. This study sought to identify the factors that influence the perceptions of human-computer interaction (HCI) curriculum developers in higher education institutions (HEIs) in developing economies of Africa when it comes to curriculum design and delivery. A qualitative enquiry was conducted and consisted of fourteen interviews with HCI curriculum developers and UX practitioners in four African countries. …

Immersive Japanese Language Learning Web Application Using Spaced Repetition, Active Recall, And An Artificial Intelligent Conversational Chat Agent Both In Voice And In Text, Marc Butler

MS in Computer Science Project Reports

In the last two decades various human language learning applications, spaced repetition software, online dictionaries, and artificial intelligent chat agents have been developed. However, there is no solution to cohesively combine these technologies into a comprehensive language learning application including skills such as speaking, typing, listening, and reading. Our contribution is to provide an immersive language learning web application to the end user which combines spaced repetition, a study technique used to review information at systematic intervals, and active recall, the process of purposely retrieving information from memory during a review session, with an artificial intelligent conversational chat agent both …

Image De‑Photobombing Benchmark, Vatsa S. Patel, Kunal Agrawal, Samah Baraheem, Amira Yousif, Tam Nguyen

Computer Science Faculty Publications

Removing photobombing elements from images is a challenging task that requires sophisticated image inpainting techniques. Despite the availability of various methods, their effectiveness depends on the complexity of the image and the nature of the distracting element. To address this issue, we conducted a benchmark study to evaluate 10 state-of-the-art photobombing removal methods on a dataset of over 300 images. Our study focused on identifying the most effective image inpainting techniques for removing unwanted regions from images. We annotated the photobombed regions that require removal and evaluated the performance of each method using peak signal-to-noise ratio (PSNR), structural similarity index …

Terry Riley's "In C" For Mobile Ensemble, David B. Wetzel, Griffin Moe, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

This workshop presents a mobile-friendly Web Audio application for a “technology ensemble play-along” of Terry Riley’s 1964 composition In C. Attendees will join in a reading of In C using available web-enabled devices as musical instruments. We hope to demonstrate an accessible music-technology experience that relies on face-to-face interaction within a shared space. In this all-electronic implementation, no special musical or technical expertise is required.

Accepted for presentation and publication at WAC 2024.

Transiam: Aggregating Multi-Modal Visual Features With Locality For Medical Image Segmentation, Xuejian Li, Shiqiang Ma, Junhai Xu, Jijun Tang, Shengfeng He, Fei Guo

Research Collection School Of Computing and Information Systems

Automatic segmentation of medical images plays an important role in the diagnosis of diseases. On single-modal data, convolutional neural networks have demonstrated satisfactory performance. However, multi-modal data encompasses a greater amount of information rather than single-modal data. Multi-modal data can be effectively used to improve the segmentation accuracy of regions of interest by analyzing both spatial and temporal information. In this study, we propose a dual-path segmentation model for multi-modal medical images, named TranSiam. Taking into account that there is a significant diversity between the different modalities, TranSiam employs two parallel CNNs to extract the features which are specific to …

Catnet: Cross-Modal Fusion For Audio-Visual Speech Recognition, Xingmei Wang, Jianchen Mi, Boquan Li, Yixu Zhao, Jiaxiang Meng

Research Collection School Of Computing and Information Systems

Automatic speech recognition (ASR) is a typical pattern recognition technology that converts human speeches into texts. With the aid of advanced deep learning models, the performance of speech recognition is significantly improved. Especially, the emerging Audio–Visual Speech Recognition (AVSR) methods achieve satisfactory performance by combining audio-modal and visual-modal information. However, various complex environments, especially noises, limit the effectiveness of existing methods. In response to the noisy problem, in this paper, we propose a novel cross-modal audio–visual speech recognition model, named CATNet. First, we devise a cross-modal bidirectional fusion model to analyze the close relationship between audio and visual modalities. Second, …

What Does One Billion Dollars Look Like?: Visualizing Extreme Wealth, William Mahoney Luckman

Dissertations, Theses, and Capstone Projects

The word “billion” is a mathematical abstraction related to “big,” but it is difficult to understand the vast difference in value between one million and one billion; even harder to understand the vast difference in purchasing power between one billion dollars, and the average U.S. yearly income. Perhaps most difficult to conceive of is what that purchasing power and huge mass of capital translates to in terms of power. This project blends design, text, facts, and figures into an interactive narrative website that helps the user better understand their position in relation to extreme wealth: https://whatdoesonebilliondollarslooklike.website/

The site incorporates …

Foodmask: Real-Time Food Instance Counting, Segmentation And Recognition, Huu-Thanh Nguyen, Yu Cao, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Food computing has long been studied and deployed to several applications. Understanding a food image at the instance level, including recognition, counting and segmentation, is essential to quantifying nutrition and calorie consumption. Nevertheless, existing techniques are limited to either category-specific instance detection, which does not reflect precisely the instance size at the pixel level, or category-agnostic instance segmentation, which is insufficient for dish recognition. This paper presents a compact and fast multi-task network, namely FoodMask, for clustering-based food instance counting, segmentation and recognition. The network learns a semantic space simultaneously encoding food category distribution and instance height at pixel basis. …

Hgprompt: Bridging Homogeneous And Heterogeneous Graphs For Few-Shot Prompt Learning, Xingtong Yu, Yuan Fang, Zemin Liu, Xinming Zhang

Research Collection School Of Computing and Information Systems

Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their performance in an end-to-end supervised framework greatly depends on the availability of task-specific supervision. To reduce the labeling cost, pre-training on selfsupervised pretext tasks has become a popular paradigm, but there is often a gap between the pre-trained model and downstream tasks, stemming from the divergence in their objectives. To bridge the gap, prompt learning has risen as a promising direction especially in few-shot settings, without the need to fully fine-tune the pre-trained model. While there has been …

Digitizing Delphi: Educating Audiences Through Virtual Reconstruction, Kate Koury

The Journal of Purdue Undergraduate Research

Implementing a 3D model into a virtual space allows the general public to engage critically with archaeological processes. There are many unseen decisions that go into reconstructing an ancient temple. Analysis of available materials and techniques, predictions of how objects were used, decisions of what sources to reference, puzzle piecing broken remains together, and even educated guesses used to fill gaps in information often go unobserved by the public. This work will educate users about those choices by allowing the side-by-side comparison of conflicting theories on the reconstruction of the Tholos at Delphi, which is an ideal site because of …

Piecing Together Performance: Collaborative, Participatory Research-Through-Design For Better Diversity In Games, Daniel L. Gardner, Louanne Boyd, Reginald T. Gardner

Engineering Faculty Articles and Research

Digital games are a multi-billion-dollar industry whose production and consumption extend globally. Representation in games is an increasingly important topic. As those who create and consume the medium grow ever more diverse, it is essential that player or user-experience research, usability, and any consideration of how people interface with their technology is exercised through inclusive and intersectional lenses. Previous research has identified how character configuration interfaces preface white-male defaults [39, 40, 67]. This study relies on 1-on-1 play-interviews where diverse participants attempt to create “themselves” in a series of games and on group design activities to explore how participants may …

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, …

(Meta-)Physical Artworks: Digital Augmentation In Art Observation, Macy A. Toppan

Dartmouth College Master’s Theses

Augmented art— the subgenre of art that incorporates physical and digital artwork— is a rapidly growing field driven by advancing technology and a new generation for whom that tech is a given. Yet the presence of media like augmented and virtual reality in exhibition remains a controversial subject. Rather than focusing on the many theoretical debates about whether digital pieces can qualify as "good" art, we study it in practice through the eyes of the casual art observer. This paper highlights the audience in a within-participant study that asked viewers to take in a physical sculpture intentionally built with virtual …

Virtual Reality & Pilot Training: Existing Technologies, Challenges & Opportunities, Tim Marron M.S., Niall Dungan Bsc, Captain, Brian Mac Namee Phd, Anna Donnla O'Hagan Phd

Journal of Aviation/Aerospace Education & Research

The introduction of virtual reality (VR) to flying training has recently gained much attention, with numerous VR companies, such as Loft Dynamics and VRpilot, looking to enhance the training process. Such a considerable change to how pilots are trained is a subject that warrants careful consideration. Examining the effect that VR has on learning in other areas gives us an idea of how VR can be suitably applied to flying training. Some of the benefits offered by VR include increased safety, decreased costs, and increased environmental sustainability. Nevertheless, some challenges ahead for developers to consider are negative transfer of learning, …

An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban

Faculty Publications

Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective …

Escape The Planet: Revolutionizing Game Design With Novel Oop Techniques, Qusai Kamal Fannoun

All Graduate Theses, Dissertations, and Other Capstone Projects

Mobile devices are continuously evolving and greater computing power and graphics capabilities are being introduced every year. As a result, there is an increasing demand for challenging and engaging mobile games that leverage these advanced features. This project explores best design practices using the development of Escape the Planet, which is an intricate maze game for mobile devices in which players navigate using a spaceship that is trapped in a hostile planet’s maze while avoiding obstacles and enemy attacks. The goal is to safely guide the spaceship out of the maze without colliding into walls or taking bullets from defensive …

Learning An Interpretable Stylized Subspace For 3d-Aware Animatable Artforms, Chenxi Zheng, Bangzhen Liu, Xuemiao Xu, Huaidong Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

Throughout history, static paintings have captivated viewers within display frames, yet the possibility of making these masterpieces vividly interactive remains intriguing. This research paper introduces 3DArtmator, a novel approach that aims to represent artforms in a highly interpretable stylized space, enabling 3D-aware animatable reconstruction and editing. Our rationale is to transfer the interpretability and 3D controllability of the latent space in a 3D-aware GAN to a stylized sub-space of a customized GAN, revitalizing the original artforms. To this end, the proposed two-stage optimization framework of 3DArtmator begins with discovering an anchor in the original latent space that accurately mimics the …

Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to …

Trust: The Feature That Vending Machines And Atms Share, But Simplygo Lacks, Sun Sun Lim

Research Collection College of Integrative Studies

The article discussed the intricacies of trust in the SimplyGo debacle and highlighted how the design of physical interfaces like vending machines and ATMs and digital interfaces from apps like Grab, Parking.sg and ShopBack have critical features to instil trust. People need to be reassured that their transactions have proceeded as they should, and thay have not been short-changed.

Efficient Unsupervised Video Hashing With Contextual Modeling And Structural Controlling, Jingru Duan, Yanbin Hao, Bin Zhu, Lechao Cheng, Pengyuan Zhou, Xiang Wang

Research Collection School Of Computing and Information Systems

The most important effect of the video hashing technique is to support fast retrieval, which is benefiting from the high efficiency of binary calculation. Current video hash approaches are thus mainly targeted at learning compact binary codes to represent video content accurately. However, they may overlook the generation efficiency for hash codes, i.e., designing lightweight neural networks. This paper proposes an method, which is not only for computing compact hash codes but also for designing a lightweight deep model. Specifically, we present an MLP-based model, where the video tensor is split into several groups and multiple axial contexts are explored …

Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao

Research Collection School Of Computing and Information Systems

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) …

Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He

Research Collection School Of Computing and Information Systems

Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with highcontrast crowd counts as training guidance. To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem. In particular, we tailor a Siamese Ranking Network that predicts the potential scores of two images indicating the ordering of the counts. Hence, the ultimate goal is to assign appropriate …

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, …

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) …