Graphics and Human Computer Interfaces Commons^™

Visual And Spatial Audio Mismatching In Virtual Environments, Zachary Lawrence Garris

Theses and Dissertations

This paper explores how vision affects spatial audio perception in virtual reality. We created four virtual environments with different reverb and room sizes, and recorded binaural clicks in each one. We conducted two experiments: one where participants judged the audio-visual match, and another where they pointed to the click direction. We found that vision influences spatial audio perception and that congruent audio-visual cues improve accuracy. We suggest some implications for virtual reality design and evaluation.

Hyperbolic Graph Topic Modeling Network With Continuously Updated Topic Tree, Ce Zhang, Rex Ying, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Connectivity across documents often exhibits a hierarchical network structure. Hyperbolic Graph Neural Networks (HGNNs) have shown promise in preserving network hierarchy. However, they do not model the notion of topics, thus document representations lack semantic interpretability. On the other hand, a corpus of documents usually has high variability in degrees of topic specificity. For example, some documents contain general content (e.g., sports), while others focus on specific themes (e.g., basketball and swimming). Topic models indeed model latent topics for semantic interpretability, but most assume a flat topic structure and ignore such semantic hierarchy. Given these two challenges, we propose a …

All Hands On Deck: Choosing Virtual End Effector Representations To Improve Near Field Object Manipulation Interactions In Extended Reality, Roshan Venkatakrishnan

All Dissertations

Extended reality, or "XR", is the adopted umbrella term that is heavily gaining traction to collectively describe Virtual reality (VR), Augmented reality (AR), and Mixed reality (MR) technologies. Together, these technologies extend the reality that we experience either by creating a fully immersive experience like in VR or by blending in the virtual and "real" worlds like in AR and MR.

The sustained success of XR in the workplace largely hinges on its ability to facilitate efficient user interactions. Similar to interacting with objects in the real world, users in XR typically interact with virtual integrants like objects, menus, windows, …

The Effects Of Primary And Secondary Task Workloads On Cybersickness In Immersive Virtual Active Exploration Experiences, Rohith Venkatakrishnan

All Dissertations

Virtual reality (VR) technology promises to transform humanity. The technology enables users to explore and interact with computer-generated environments that can be simulated to approximate or deviate from reality. This creates an endless number of ways to propitiously apply the technology in our lives. It follows that large technological conglomerates are pushing for the widespread adoption of VR, financing the creation of the Metaverse - a hypothetical representation of the next iteration of the internet.

Even with VR technology's continuous growth, its widespread adoption remains long overdue. This can largely be attributed to an affliction called cybersickness, an analog to …

Understanding The Role Of Interactivity And Explanation In Adaptive Experiences, Lijie Guo

All Dissertations

Adaptive experiences have been an active area of research in the past few decades, accompanied by advances in technology such as machine learning and artificial intelligence. Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest and resources in developing and improving these research focuses. Even though the research on adaptive experiences has been dynamic and rapidly evolving, achieving a high level of user engagement in adaptive experiences remains a challenge. %????? This dissertation aims to uncover ways to engage users in adaptive experiences by incorporating …

Anatoview: Using Interactive 3d Visualizations With Augmented Reality Support For Laypersons’ Medical Education In Informed Consent Processes, Michelle Chen

Dartmouth College Master’s Theses

AnatoView is an interactive multimedia educational application that visualizes medical procedures in three-dimensional (3D), augmented reality (AR) space. By providing visual and spatial information of medical procedures, AnatoView acts as a learning supplement for laypersons/patients in informed consent (IC) processes — wherein instructional content is traditionally limited to purely spoken explanations that lead to poor patient comprehension. We design a mixed study and conduct a randomized, controlled trial with 15 laypersons as participants: administering a traditional IC process to a control group, and an IC process supplemented by the use of AnatoView to experimental groups. As a primary outcome, medical …

Fine-Grained Domain Adaptive Crowd Counting Via Point-Derived Segmentation, Yongtuo Liu, Dan Xu, Sucheng Ren, Hanjie Wu, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle domain-invariant crowd and domain-specific background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a …

Socialz: Multi-Feature Social Fuzz Testing, Francisco Zanartu, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others. However, bugs and glitches, even minor ones, can cause anything from frustrating problems to serious data leaks that can have farreaching impacts on millions of users. To mitigate these risks, fuzz testing, a method of testing with randomised inputs, can provide increased confidence in the correct functioning of a social network. However, implementing traditional fuzz testing methods can be prohibitively difficult or impractical for programmers outside of the network’s development team. To tackle this challenge, we present …

Contrastive Video Question Answering Via Video Graph Transformer, Junbin Xiao Xiao, Pan Zhou, Angela Yao, Yicong Li, Richang Hong, Shuicheng Yan, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT’s uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning. 2) It designs separate video and text transformers for contrastive learning between the video and text to perform QA, instead of multi-modal transformer for answer classification. Fine-grained video-text communication is done by additional cross-modal interaction modules. 3) It is optimized by the joint fully- and self-supervised contrastive objectives between the …

Robust And Parallel Segmentation Model (Rpsm) For Early Detection Of Skin Cancer Disease Using Heterogeneous Distributions, Nancy Zreika, Ali El-Zaart, Abdallah El Chakik

BAU Journal - Science and Technology

Melanoma is the most common dangerous type of skin cancer; however, it is preventable if it is diagnosed early. Diagnosis of Melanoma would be improved if an accurate skin image segmentation model is available. Many computer vision methods have been investigated, yet the problem of finding a consistent and robust model that extracts the best threshold value, persists. This paper suggests a novel image segmentation approach using a multilevel cross entropy thresholding algorithm based on heterogeneous distributions. The proposed strategy searches the problem space by segmenting the image into several levels, and applying for each level one of the three …

Deep-Learning Realtime Upsampling Techniques In Video Games, Biruk Mengistu

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper addresses the challenge of keeping up with the ever-increasing graphical complexity of video games and introduces a deep-learning approach to mitigating it. As games get more and more demanding in terms of their graphics, it becomes increasingly difficult to maintain high-quality images while also ensuring good performance. This is where deep learning super sampling (DLSS) comes in. The paper explains how DLSS works, including the use of convolutional autoencoder neural networks and various other techniques and technologies. It also covers how the network is trained and optimized, as well as how it incorporates temporal antialiasing and frame generation …

How Photorealistic Images Are Generated, Nahom Ketema

University Honors Theses

The field of computer graphics looks into how computers can be used to generate images. From using some trigonometry to plot 3D objects to using rays to calculate the lighting of an object, there are a variety of ways that we can use to draw objects onto a screen. For this thesis, we will be looking at a few of those methods to determine how photorealistic images are generated.

Interstice, Shravan Rao

Masters Theses

When I was about three years old, I distinctly remember being too small to see what was on top of the table. A couple of years later, when I could see those objects, I thought the world around me had grown smaller. In a way, it did, as I experienced, lived, captured, remembered, and shared the space repeatedly. This sense of the world shrinking was exaggerated during the Covid-19 pandemic, allowing new behaviours and modes of interaction to emerge. Continually shaping our modern lives, virtual technologies redefine how we access and share information and stories or even explore new places. …

Gnnlens: A Visual Analytics Approach For Prediction Error Diagnosis Of Graph Neural Networks., Zhihua Jin, Yong Wang, Qianwen Wang, Yao Ming, Tengfei Ma, Huamin Qu

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis …

Scanet: Self-Paced Semi-Curricular Attention Network For Non-Homogeneous Image Dehazing, Yu Guo, Yuan Gao, Ryan Wen Liu, Yuxu Lu, Jingxiang Qu, Shengfeng He, Ren Wenqi

Research Collection School Of Computing and Information Systems

The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details. Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner. The crucial challenge of non-homogeneous dehazing is to effectively extract the non-uniform distribution features and reconstruct the details of hazy areas with high quality. In this paper, we propose a novel self-paced semi-curricular attention network, called SCANet, for non-homogeneous image dehazing that focuses on enhancing haze-occluded regions. Our approach consists of an attention generator network and a scene re-construction network. We use the luminance …

Towards A Smaller Student: Capacity Dynamic Distillation For Efficient Image Retrieval, Yi Xie, Huaidong Zhang, Xuemiao Xu, Jianqing Zhu, Shengfeng He

Research Collection School Of Computing and Information Systems

Previous Knowledge Distillation based efficient image retrieval methods employ a lightweight network as the student model for fast inference. However, the lightweight student model lacks adequate representation capacity for effective knowledge imitation during the most critical early training period, causing final performance degeneration. To tackle this issue, we propose a Capacity Dynamic Distillation framework, which constructs a student model with editable representation capacity. Specifically, the employed student model is initially a heavy model to fruitfully learn distilled knowledge in the early training epochs, and the student model is gradually compressed during the training. To dynamically adjust the model capacity, our …

Venus: A Geometrical Representation For Quantum State Visualization, Shaolun Ruan, Ribo Yuan, Qiang Guan, Yanna Lin, Ying Mao, Weiwen Jiang, Zhepeng Wang, Wei Xu, Yong Wang

Research Collection School Of Computing and Information Systems

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum …

Generalizing Graph Neural Networks Across Graphs, Time, And Tasks, Zhihao Wen

Dissertations and Theses Collection (Open Access)

Graph-structured data are ubiquitous across numerous real-world contexts, encompassing social networks, commercial graphs, bibliographic networks, and biological systems. Delving into the analysis of these graphs can yield significant understanding pertaining to their corresponding application fields.Graph representation learning offers a potent solution to graph analytics challenges by transforming a graph into a low-dimensional space while preserving its information to the greatest extent possible. This conversion into low-dimensional vectors enables the efficient computation of subsequent graph algorithms. The majority of prior research has concentrated on deriving node representations from a single, static graph. However, numerous real-world situations demand rapid generation of representations …

Curricular Contrastive Regularization For Physics-Aware Single Image Dehazing, Yu Zheng, Jiahui Zhan, Shengfeng He, Yong Du

Research Collection School Of Computing and Information Systems

Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are non-consensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-constricted. Moreover, the interpretability of deep dehazing models is underexplored towards the physics of the hazing process. In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one. Our negatives, which provide better lower-bound constraints, can be assembled from 1) the hazy …

An Investigation Into Machine Learning Techniques For Designing Dynamic Difficulty Agents In Real-Time Games, Ryan Adare Dunagan

Electronic Theses and Dissertations

Video games are an incredibly popular pastime enjoyed by people of all ages world wide. Many different kinds of games exist, but most games feature some elements of the player overcoming some challenge, usually through gameplay. These challenges are insurmountable for some people and may turn them off to video games as a pastime. Games can be made more accessible to players of little skill and/or experience through the use of Dynamic Difficulty Adjustment (DDA) systems that adjust the difficulty of the game in response to the player’s performance. This research seeks to establish the effectiveness of machine learning techniques …