Graphics and Human Computer Interfaces Commons^™

Stumbling Into Virtual Worlds. How Resolution Affects Users’ Immersion In Virtual Reality And Implications For Virtual Reality In Therapeutic Applications, Brianna Martinson

Undergraduate Honors Theses

Studies of how users experience Virtual Reality (VR) have thus far failed to address the extent to which rendering resolution and rendering frame rate affect users’ sense of immersion in VR, including applications of VR involving simulators, treatments for psychological and mental disorders, explorations of new and nonexistent structures, and ways to better understand the human body in medical applications.

This study investigated if rendering resolution affected users’ sense of immersion in VR. This was conducted by comparing the responses of two groups, relative to two measures of participant immersion: (a) participant’s sense of presence and (b) participant’s sense of …

Changeling Vr, Elouise Oyzon

Frameless

Changeling VR is an interactive virtual reality narrative game. As we progress through the game, each level is seen through the Point of View of a different character whose emotional core is expressed through different aesthetics, mechanics and interactions.

Rethinking The Design Of Online Professor Reputation Systems, Haley Tatum

LSU Master's Theses

Online Professor Reputation (OPR) systems, such as RateMyProfessors.com (RMP), are frequently used by college students to post and access peer evaluations of their pro- fessors. However, recent evidence has shown that these platforms suffer from major bias problems. Failing to address bias in online professor ratings not only leads to negative expectations and experiences in class, but also poor performance on exams. To address these concerns, in this thesis, we study bias in OPR systems from a software design point of view. At the first phase of our analysis, we conduct a systematic literature review of 23 interdisciplinary studies on …

Few-Shot Object Detection Via Baby Learning, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Khanh-Duy Nguyen, Vinh-Tiep Nguyen, Thanh Duc Ngo, Thanh-Toan Do, Tam Nguyen

Computer Science Faculty Publications

Few-shot learning is proposed to overcome the problem of scarce training data in novel classes. Recently, few-shot learning has been well adopted in various computer vision tasks such as object recognition and object detection. However, the state-of-the-art (SOTA) methods have less attention to effectively reuse the information from previous stages. In this paper, we propose a new framework of few-shot learning for object detection. In particular, we adopt Baby Learning mechanism along with the multiple receptive fields to effectively utilize the former knowledge in novel domain. The propoed framework imitates the learning process of a baby through visual cues. The …

Vietnamese Document Analysis: Dataset, Method And Benchmark Suite, Khang Nguyen, An Nguyen, Nguyen D. Vo, Tam Nguyen

Computer Science Faculty Publications

Document image understanding is increasingly useful since the number of digital documents is increasing day-by-day and the need for automation is increasing. Object detection plays a significant role in detecting vital objects and layouts in document images and contributes to providing a clearer understanding of the documents. Nonetheless, previous research mainly focuses on English document images, and studies on Vietnamese document images are limited. In this study, we extensively benchmark state-of-the-art object detectors and analyze the performance of each method on Vietnamese document images. Moreover, we also investigate the effectiveness of four different loss functions on the experimental object detection …

Realtime Event Detection In Sports Sensor Data With Machine Learning, Mallory Cashman

Honors Theses and Capstones

Machine learning models can be trained to classify time series based sports motion data, without reliance on assumptions about the capabilities of the users or sensors. This can be applied to predict the count of occurrences of an event in a time period. The experiment for this research uses lacrosse data, collected in partnership with SPAITR - a UNH undergraduate startup developing motion tracking devices for lacrosse. Decision Tree and Support Vector Machine (SVM) models are trained and perform with high success rates. These models improve upon previous work in human motion event detection and can be used a reference …

Interpretable Machine Learning For Self-Service High-Risk Decision Making, Charles Recaido

All Master's Theses

This research contributes to interpretable machine learning via visual knowledge discovery in General Line Coordinates (GLC). The concepts of hyperblocks as interpretable dataset units and GLC are combined to create a visual self-service machine learning model. Two variants of GLC known as Dynamic Scaffold Coordinates (DSC) are proposed. DSC1 and DSC2 can map in a lossless manner multiple dataset attributes to a single two-dimensional (X, Y) Cartesian plane using a dynamic scaffolding graph construction algorithm.

Hyperblock analysis is used to determine visually appealing dataset attribute orders and to reduce line occlusion. It is shown that hyperblocks can generalize decision tree …

Evaluation Of Gpu Acceleration For Wrf–Sfire, Joshua Benz

Master's Projects

WRF–SFIRE is an open source, atmospheric–wildfire model that couples the WRF model with the level set fire spread model to simulate wildfires in real time. This model has many applications and more scientific questions can be asked and answered if the model can be run faster. Nvidia has put a lot of effort into easing the barrier of entry for accelerating applications with their tools to be run on GPUs. Various physical simulations have been successfully ported to utilize GPUs and have benefited from the speed increase. In this research, we take a look at WRF-SFIRE and try to use …

Let's Read: Designing A Smart Display Application To Support Codas When Learning Spoken Language, Katie Rodeghiero, Yingying Yuki Chen, Annika M. Hettmann, Franceli L. Cibrian

Engineering Faculty Articles and Research

Hearing children of Deaf adults (CODAs) face many challenges including having difficulty learning spoken languages, experiencing social judgment, and encountering greater responsibilities at home. In this paper, we present a proposal for a smart display application called Let's Read that aims to support CODAs when learning spoken language. We conducted a qualitative analysis using online community content in English to develop the first version of the prototype. Then, we conducted a heuristic evaluation to improve the proposed prototype. As future work, we plan to use this prototype to conduct participatory design sessions with Deaf adults and CODAs to evaluate the …

Feel And Touch: A Haptic Mobile Game To Assess Tactile Processing, Ivonne Monarca, Monica Tentori, Franceli L. Cibrian

Engineering Faculty Articles and Research

Haptic interfaces have great potential for assessing the tactile processing of children with Autism Spectrum Disorder (ASD), an area that has been under-explored due to the lack of tools to assess it. Until now, haptic interfaces for children have mostly been used as a teaching or therapeutic tool, so there are still open questions about how they could be used to assess tactile processing of children with ASD. This article presents the design process that led to the development of Feel and Touch, a mobile game augmented with vibrotactile stimuli to assess tactile processing. Our feasibility evaluation, with 5 children …

Facilitating Heuristic Evaluation For Novice Evaluators, Anas Abulfaraj

College of Computing and Digital Media Dissertations

Heuristic evaluation (HE) is one of the most widely used usability evaluation methods. The reason for its popularity is that it is a discount method, meaning that it does not require substantial time or resources, and it is simple, as evaluators can evaluate a system guided by a set of usability heuristics. Despite its simplicity, a major problem with HE is that there is a significant gap in the quality of results produced by expert and novice evaluators. This gap has made some scholars question the usefulness of the method as they claim that the evaluation results are a product …

Masked Face Analysis Via Multi-Task Deep Learning, Vatsa S. Patel, Zhongliang Nie, Trung-Nghia Le, Tam Van Nguyen

Computer Science Faculty Publications

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to …

Verification Of Piecewise Deep Neural Networks: A Star Set Approach With Zonotope Pre-Filter, Hoang-Dung Tran, Neelanjana Pal, Diego Manzanas Lopez, Patrick Musau, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson

Computer Science Faculty Publications

Verification has emerged as a means to provide formal guarantees on learning-based systems incorporating neural network before using them in safety-critical applications. This paper proposes a new verification approach for deep neural networks (DNNs) with piecewise linear activation functions using reachability analysis. The core of our approach is a collection of reachability algorithms using star sets (or shortly, stars), an effective symbolic representation of high-dimensional polytopes. The star-based reachability algorithms compute the output reachable sets of a network with a given input set before using them for verification. For a neural network with piecewise linear activation functions, our approach can …

Olympic Games Event Recognition Via Transfer Learning With Photobombing Guided Data Augmentation, Yousef I. Mohamad, Samah S. Baraheem, Tam Van Nguyen

Computer Science Faculty Publications

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport …

R2u3d: Recurrent Residual 3d U-Net For Lung Segmentation, Dhaval D. Kadia, Md Zahangir Alom, Ranga Burada, Tam Nguyen, Vijayan K. Asari

Computer Science Faculty Publications

3D Lung segmentation is essential since it processes the volumetric information of the lungs, removes the unnecessary areas of the scan, and segments the actual area of the lungs in a 3D volume. Recently, the deep learning model, such as U-Net outperforms other network architectures for biomedical image segmentation. In this paper, we propose a novel model, namely, Recurrent Residual 3D U-Net (R(2)U3D), for the 3D lung segmentation task. In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net. It helps learn spatial dependencies in 3D and increases the propagation of 3D volumetric …

Interactive Virtual Reality Reading Experience, Nathaniel Shetler

Williams Honors College, Honors Research Projects

The project is an interactive virtual reality reading experience. The user is able to read a book or story in VR. When certain achievements are reached, such as finishing a chapter, the user is given the opportunity to transport to the environment that they are reading about. This gives the user a great opportunity to interact and learn hands-on with the material they are reading about. For example, if the user is reading about World War I, they will be given the opportunity to transport to the battlefields/trenches in Europe.

Divide And Slide: Layer-Wise Refinement For Output Range Analysis Of Deep Neural Networks, Chao Huang, Jiameng Fan, Xin Chen, Wenchao Li, Qi Zhu

Computer Science Faculty Publications

In this article, we present a layer-wise refinement method for neural network output range analysis. While approaches such as nonlinear programming (NLP) can directly model the high nonlinearity brought by neural networks in output range analysis, they are known to be difficult to solve in general. We propose to use a convex polygonal relaxation (overapproximation) of the activation functions to cope with the nonlinearity. This allows us to encode the relaxed problem into a mixed-integer linear program (MILP), and control the tightness of the relaxation by adjusting the number of segments in the polygon. Starting with a segment number of …

Espade: An Efficient And Semantically Secure Shortest Path Discovery For Outsourced Location-Based Services, Bharath K. Samanthula, Divyadharshini Karthikeyan, Boxiang Dong, K. Anitha Kumari

Department of Computer Science Faculty Scholarship and Creative Works

With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. …

Exploring Attacks And Defenses In Additive Manufacturing Processes: Implications In Cyber-Physical Security, Nicholas Deily

McKelvey School of Engineering Graduate Student Theses & Dissertations

Many industries are rapidly adopting additive manufacturing (AM) because of the added versatility this technology offers over traditional manufacturing techniques. But with AM, there comes a unique set of security challenges that must be addressed. In particular, the issue of part verification is critically important given the growing reliance of safety-critical systems on 3D printed parts. In this thesis, the current state of part verification technologies will be examined in the con- text of AM-specific geometric-modification attacks, and an automated tool for 3D printed part verification will be presented. This work will cover: 1) the impacts of malicious attacks on …

Storage Management Strategy In Mobile Phones For Photo Crowdsensing, En Wang, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, Weibin Meng

Department of Computer Science Faculty Scholarship and Creative Works

In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to …

Building Something With The Raspberry Pi, Richard Kordel

Harrisburg University Presidential Research Grants

In 2017 Ryan Korn and I submitted a grant proposal in the annual Harrisburg University President’s Grant process. Our proposal was to partner with a local high school to install a classroom of 20 Raspberry Pi’s, along with the requisite peripherals. In that classroom students would be challenged to design something that combined programming with physical computing. In our presentation to the school we suggested that this project would give students the opportunity to be “amazing.”

As part of the grant, the top three students would be given scholarships to HU and the top five finalists would all be permitted …

Nnv: The Neural Network Verification Tool For Deep Neural Networks And Learning-Enabled Cyber-Physical Systems, Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson

Computer Science Faculty Publications

This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS). The crux of NNV is a collection of reachability algorithms that make use of a variety of set representations, such as polyhedra, star sets, zonotopes, and abstract-domain representations. NNV supports both exact (sound and complete) and over-approximate (sound) reachability algorithms for verifying safety and robustness properties of feed-forward neural networks (FFNNs) with various activation functions. For learning-enabled CPS, such as closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis schemes for linear …

Security Camera Using Raspberry Pi, Tejendra Khatri

Student Academic Conference

Making a security camera using raspberry pi utilizing OpenCV for facial recognition, upper body recognition or full-body recognition

Where Did The Time Go?, John C. Viaud, Bilal Abdulmajid, Vitali Surmach, Jia Yanxia

Capstone Showcase

Study shows that most people spend a full quarter of their active hours on their mobile device which can take a serious toll on our productivity as well as our mental and physical well-being. We created an Android app that is able to track and visualize phone usage patterns to help user establish awareness of how much and in what ways they use their Android devices. Unlike currently existing phone time management apps, such as Apple’s ScreenTime and Google’s new Digital Wellbeing, our app provides functionalities to encourage off-phone time and personal goal management.

Android Game, Ryan Weston

Williams Honors College, Honors Research Projects

The purpose of this project was to create an endless runner game for Android coded in Java and XML and developed in Android Studio. In the game, the player controls a frog that jumps from lily pad to lily pad to avoid logs moving toward the player. The player must also maneuver the lily pads as they can randomly disappear. There are three difficulties in the game that vary the disappearance rate of lily pads as well as the frequency and acceleration rate of the log obstacles. The game also has a scoring system and saves the high score locally …

Feature Extraction And Description For Retinal Fundus Image Registration, Ramli Roziana

Student Works (2020-2029)

Retinal fundus image registration (RIR) is performed to align two or more fundus images. A general framework of a feature-based RIR technique comprises of preprocessing, feature extraction, feature descriptor, matching and estimating geometrical transformation. The RIR is mainly performed for super-resolution, image mosaicking and longitudinal study applications to assist diagnosis and monitoring retinal diseases. Registering image pair from these applications involve a combination of challenges such as overlapping area and rotation between images. The challenges of the overlapping area and rotation can be addressed at feature extraction and feature descriptor stages of the feature-based RIR technique, respectively. To address the …

Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor

School of Computing: Dissertations, Theses, and Student Research

Formal concept analysis (FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. It has been used in various domains such as data mining, machine learning, semantic web, Sciences, for the purpose of data analysis and Ontology over the last few decades. Various extensions of FCA are being researched to expand it's scope over more departments. In this thesis,we review the theory of Formal Concept Analysis (FCA) and its extension Fuzzy FCA. Many studies to use FCA in data mining and text learning have been pursued. We extend these studies to include …

Improving Medication Information Presentation Through Interactive Visualization In Mobile Apps: Human Factors Design, Don Roosan, Yan Li, Anandi Law, Huy Truong, Mazharul Karim, Jay Chok, Moom Roosan

Pharmacy Faculty Articles and Research

Background: Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications.

Objective: The …

Vrsensory: Designing Inclusive Virtual Games With Neurodiverse Children, Ben Wasserman, Derek Prate, Bryce Purnell, Alex Muse, Kaitlyn Abdo, Kendra Day, Louanne Boyd

Engineering Faculty Articles and Research

We explore virtual environments and accompanying interaction styles to enable inclusive play. In designing games for three neurodiverse children, we explore how designing for sensory diversity can be understood through a formal game design framework. Our process reveals that by using sensory processing needs as requirements we can make sensory and social accessible play spaces. We contribute empirical findings for accommodating sensory differences for neurodiverse children in a way that supports inclusive play. Specifically, we detail the sensory driven design choices that not only support the enjoyability of the leisure activities, but that also support the social inclusion of sensory-diverse …

Reachnn: Reachability Analysis Of Neural-Network Controlled Systems, Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, Qi Zhu

Computer Science Faculty Publications

Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for …