Graphics and Human Computer Interfaces Commons^™

Covariance Pooling For Facial Expression Recognition, D. Acharya, Zhiwu Huang, D. Paudel, Gool L. Van

Research Collection School Of Computing and Information Systems

Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks. We believe that second-order statistics such as covariance is better able to capture such distortions in regional facial features. In this work, we explore the benefits of using a manifold network structure for covariance pooling to improve facial expression recognition. In particular, we first employ such kind of manifold networks in conjunction with traditional convolutional networks for spatial pooling within individual image feature maps in an end-to-end deep learning manner. By doing so, we are able to achieve a recognition accuracy of 58.14% on the validation set …

Deep Adversarial Subspace Clustering, Pan Zhou, Yunqing Hou, Jiashi Feng

Research Collection School Of Computing and Information Systems

Most existing subspace clustering methods hinge on self-expression of handcrafted representations and are unaware of potential clustering errors. Thus they perform unsatisfactorily on real data with complex underlying subspaces. To solve this issue, we propose a novel deep adversarial subspace clustering (DASC) model, which learns more favorable sample representations by deep learning for subspace clustering, and more importantly introduces adversarial learning to supervise sample representation learning and subspace clustering. Specifically, DASC consists of a subspace clustering generator and a quality-verifying discriminator, which learn against each other. The generator produces subspace estimation and sample clustering. The discriminator evaluates current clustering performance …

Dimensionality's Blessing: Clustering Images By Underlying Distribution, Wen-Yan Lin, Jian-Huang Lai, Siying Liu, Yasuyuki Matsushita

Research Collection School Of Computing and Information Systems

Many high dimensional vector distances tend to a constant. This is typically considered a negative “contrastloss” phenomenon that hinders clustering and other machine learning techniques. We reinterpret “contrast-loss” as a blessing. Re-deriving “contrast-loss” using the law of large numbers, we show it results in a distribution’s instances concentrating on a thin “hyper-shell”. The hollow center means apparently chaotically overlapping distributions are actually intrinsically separable. We use this to develop distribution-clustering, an elegant algorithm for grouping of data points by their (unknown) underlying distribution. Distribution-clustering, creates notably clean clusters from raw unlabeled data, estimates the number of clusters for itself and …

Scale Impacts Elicited Gestures For Manipulating Holograms: Implications For Ar Gesture Design, Tran Pham, Jo Vermeulen, Anthony Tang, Lindsay Macdonald

Research Collection School Of Computing and Information Systems

Because gesture design for augmented reality (AR) remains idiosyncratic, people cannot necessarily use gestures learned in one AR application in another. To design discoverable gestures, we need to understand what gestures people expect to use. We explore how the scale of AR affects the gestures people expect to use to interact with 3D holograms. Using an elicitation study, we asked participants to generate gestures in response to holographic task referents, where we varied the scale of holograms from desktop-scale to room-scale objects. We found that the scale of objects and scenes in the AR experience moderates the generated gestures. Most …

Jupyterlab_Voyager: A Data Visualization Enhancement In Jupyterlab, Ji Zhang

Master's Theses

With the emergence of big data, scientific data analysis and visualization (DAV) tools are critical components of the data science software ecosystem; the usability of these tools is becoming extremely important to facilitate next-generation scientific discoveries. JupyterLab has been considered as one of the best polyglot, web-based, open-source data science tools. As the next phase of extensible interface for the classic iPython Notebooks, this tool supports interactive data science and scientific computing across multiple programming languages with great performances. Despite these advantages, previous heuristics evaluation studies have shown that JupyterLab has some significant flaws in the data visualization side. The …

Tessellated Voxelization For Global Illumination Using Voxel Cone Tracing, Sam Thomas Freed

Master's Theses

Modeling believable lighting is a crucial component of computer graphics applications, including games and modeling programs. Physically accurate lighting is complex and is not currently feasible to compute in real-time situations. Therefore, much research is focused on investigating efficient ways to approximate light behavior within these real-time constraints.

In this thesis, we implement a general purpose algorithm for real-time applications to approximate indirect lighting. Based on voxel cone tracing, we use a filtered representation of a scene to efficiently sample ambient light at each point in the scene. We present an approach to scene voxelization using hardware tessellation and compare …

Real-Time Object Removal In Augmented Reality, Tyler Dahl

Master's Theses

Diminished reality, as a sub-topic of augmented reality where digital information is overlaid on an environment, is the perceived removal of an object from an environment. Previous approaches to diminished reality used digital replacement techniques, inpainting, and multi-view homographies. However, few used a virtual representation of the real environment, limiting their domains to planar environments.

This thesis provides a framework to achieve real-time diminished reality on an augmented reality headset. Using state-of-the-art hardware, we combine a virtual representation of the real environment with inpainting to remove existing objects from complex environments.

Our work is found to be competitive with previous …

Effect Of Temporality, Physical Activity, And Cognitive Load On Spatiotemporal Vibrotactile Pattern Recognition, Qin Chen, Simon T. Perrault, Quentin Xavier Louis Roy, Lonce Wyse

Research Collection School Of Computing and Information Systems

Previous research demonstrated the ability for users to accurately recognize Spatiotemporal Vibrotactile Patterns (SVP): sequences of vibrations on different motors occurring either sequentially or simultaneously. However, the experiments were only run in a lab setting and the ability for users to recognize SVP in a real-world environment remains unclear. In this paper, we investigate how several factors may affect recognition: (1) physical activity (running), (2) cognitive task (i.e. primary task, typing), (3) distribution of the vibration motors across body parts and (4) temporality of the patterns. Our results suggest that physical activity has very little impact, specifically compared to cognitive …

Movespace: On-Body Athletic Interaction For Running And Cycling, Velko Vechev, Alexandru Dancu, Simon T. Perrault, Quentin Xavier Louis Roy, Morten Fjeld, Shengdong Zhao

Research Collection School Of Computing and Information Systems

Wearables are increasingly used during training to quantify performance and provide valuable real-time information. However, interacting with these devices in motion may disrupt the movements of the activity. We propose a method of interaction involving tapping specific locations on the body, identify candidate locations for running and cycling, and compare them in a series of controlled experiments with athletes. A purpose-built prototype measures speed of interaction and gives feedback cues for athletes to report the physical effects on the activity itself. Our results suggest that specific locations are faster and have minimal disruption to movement, even under induced fatigue conditions. …

A Critical Analysis Of Mystery In Videogames, Ali Alkhafaji

College of Computing and Digital Media Dissertations

Historically, videogame research has focused on how different videogame attributes (like challenge, fantasy, control, goals, etc.) impact the player experience. This type of research is important because it can provide insight into how to design more enjoyable videogames. However, very little exists within the current literature that focuses on mystery and its impact on the player experience. This dissertation is concerned with providing the research community with a better understanding of how mystery manifests in videogames and consequently impacts the player experience, specifically curiosity and motivation. To this end, the research questions are: 1. How do players experience mystery in …

Designing Smart Applications Using Ar (Augmented Reality), Kimberly A. De La Santa

Publications and Research

Augmented Reality is rapidly developing in popularity because it brings elements of the virtual world, into our real world. Augmented Reality (AR) is a variation of Virtual Reality (VR). VR technologies immerses a user inside an imaginary environment. While immersed, the user cannot see the real world around them. In contrast, AR allows the user to see the real world, with virtual objects and information intertwined. Therefore, AR supplements reality and enhances the things we see, hear, and feel. This research project will implement a Web page that gives the user the opportunity to experiment with AR.

Usability Of Sound-Driven User Interfaces, Zachary T. Roth, Dale R. Thompson

Computer Science and Computer Engineering Undergraduate Honors Theses

The model for interacting with computing devices remains primarily focused on visual design. However, sound has a unique set of advantages. In this work, an experiment was devised where participants were tasked with identifying elements in an audio-only computing environment. The interaction relied on mouse movement and button presses for navigation. Experiment trials consisted of variations in sound duration, volume, and distinctness according to both experiment progress and user behavior. Participant interactions with the system were tracked to examine the usability of the interface. Preliminary results indicated the majority of participants mastered every provided test, but the total time spent …

Dynamic 3d Network Data Visualization, Brok Stafford

Computer Science and Computer Engineering Undergraduate Honors Theses

Monitoring network traffic has always been an arduous and tedious task because of the complexity and sheer volume of network data that is being consistently generated. In addition, network growth and new technologies are rapidly increasing these levels of complexity and volume. An effective technique in understanding and managing a large dataset, such as network traffic, is data visualization. There are several tools that attempt to turn network traffic into visual stimuli. Many of these do so in 2D space and those that are 3D lack the ability to display network patterns effectively. Existing 3D network visualization tools lack user …

Cslc Tutoring Portal, Brian Hodges

Theses/Capstones/Creative Projects

A web portal designed for the Computer Science Learning Center to track students requesting help

Designing Interactive Virtual Environments With Feedback In Health Applications., Yi Li

Electronic Theses and Dissertations

One of the most important factors to influence user experience in human-computer interaction is the user emotional reaction. Interactive environments including serious games that are responsive to user emotions improve their effectiveness and user satisfactions. Testing and training for user emotional competence is meaningful in healthcare field, which has motivated us to analyze immersive affective games using emotional feedbacks. In this dissertation, a systematic model of designing interactive environment is presented, which consists of three essential modules: affect modeling, affect recognition, and affect control. In order to collect data for analysis and construct these modules, a series of experiments were …

Improving Swarm Performance By Applying Machine Learning To A New Dynamic Survey, John Taylor Jackson

Master's Theses

A company, Unanimous AI, has created a software platform that allows individuals to come together as a group or a human swarm to make decisions. These human swarms amplify the decision-making capabilities of both the individuals and the group. One way Unanimous AI increases the swarm’s collective decision-making capabilities is by limiting the swarm to more informed individuals on the given topic. The previous way Unanimous AI selected users to enter the swarm was improved upon by a new methodology that is detailed in this study. This new methodology implements a new type of survey that collects data that is …

Neural Correlates Of States Of User Experience In Gaming Using Eeg And Predictive Analytics, Chandana Mallapragada, Fiona Fui-Hoon Nah, Keng Siau, Langtao Chen, Tejaswini Yelamanchili

Research Collection School Of Computing and Information Systems

In this research, we will analyze EEG signals to obtain neural correlate classifications of user experience by applying predictive analytics. Boredom, flow, and anxiety are three states experienced by users interacting with a computer-based system. A within-subjects experiment was used to collect EEG data for these three states and a baseline. We will apply predictive analytics including linear regression, support vector machine, and neural networks to analyze and classify the EEG data for these three states of user experience.

A Proposed Approach To Hybrid Software-Hardware Application Design For Enhanced Application Performance, Alex Shipman

Graduate Theses and Dissertations

One important aspect of many commercial computer systems is their performance; therefore, system designers seek to improve the performance next-generation systems with respect to previous generations. This could mean improved computational performance, reduced power consumption leading to better battery life in mobile devices, smaller form factors, or improvements in many areas. In terms of increased system speed and computation performance, processor manufacturers have been able to increase the clock frequency of processors up to a point, but now it is more common to seek performance gains through increased parallelism (such as a processor having more processor cores on a single …

A Continuous Space Generative Model, Erzen Komoni

Graduate Theses and Dissertations

Generative models are a class of machine learning models capable of producing digital images with plausibly realistic properties. They are useful in such applications as visualizing designs, rendering game scenes, and improving images at higher magnifications. Unfortunately, existing generative models generate only images with a discrete predetermined resolution. This paper presents the Continuous Space Generative Model (CSGM), a novel generative model capable of generating images as a continuous function, rather than as a discrete set of pixel values. Like generative adversarial networks, CSGM trains by alternating between generative and discriminative steps. But unlike generative adversarial networks, CSGM uses only one …

Improving Asynchronous Advantage Actor Critic With A More Intelligent Exploration Strategy, James B. Holliday

Graduate Theses and Dissertations

We propose a simple and efficient modification to the Asynchronous Advantage Actor Critic (A3C)

algorithm that improves training. In 2016 Google’s DeepMind set a new standard for state-of-theart

reinforcement learning performance with the introduction of the A3C algorithm. The goal of

this research is to show that A3C can be improved by the use of a new novel exploration strategy we

call “Follow then Forage Exploration” (FFE). FFE forces the agents to follow the best known path

at the beginning of a training episode and then later in the episode the agent is forced to “forage”

and explores randomly. In …