Usability guidelines for user interface (UI) design formulated by Nielsen and other authorities were used to redesign the web interface for a web based distance education course. The website's original UI, as judged by a group of web design professionals, violated standard guidelines for usability, readability, learnability, consistency, efficiency of use and flexibility. The original and redesigned UIs were tested using thinking aloud testing, a user satisfaction survey, and usability inspection. The results of user testing and heuristic evaluation suggest that the use of these guidelines in website redesign had a positive impact on user performance. A more conclusive result would have required a larger sample size and a redesigned testing protocol that varied the order in which participants worked with the old and redesigned interfaces.

Individuals who suffer from severe motor disabilities face the possibility of the loss of speech. A Brain-Computer Interface (BCI) can provide a means for communication through non-muscular control. Current BCI systems use characters that flash from gray to white (GW), making adjacent character difficult to distinguish from the target. The current study implements two types of color stimulus (grey to color [GC] and color intensification [CI]) and I hypotheses that color stimuli will; (1) reduce distraction of nontargets (2) enhance target response (3) reduce eye strain. Online results (n=21) show that GC has increased information transfer rate over CI. Mean amplitude revealed that GC had earlier positive latency than GW and greater negative amplitude than CI, suggesting a faster perceptual process for GC. Offline performance of individual optimal channels revealed significant improvement over online standardized channels. Results suggest the importance of a color stimulus for enhanced response and ease of use.

Several studies have applied recommender technologies to support requirements engineering activities. As in other application areas of recommender systems (RS), many studies have focused on the algorithms’ prediction accuracy, while there have been limited discussions around users’ interactions with the systems. Since recommender systems are designed to aid users in information retrieval, they should be assessed not just as recommendation algorithms but also from the users’ perspective. In contrast to accuracy measures, user-related issues can only be effectively investigated via empirical studies involving real users. Furthermore, researchers are becoming increasingly aware that the effectiveness of the systems goes beyond recommendation accuracy, as many factors can be relevant to their adoption besides accuracy. To better understand recommender systems in RE, it has become necessary to explore them from users’ perspectives. Consequently, this research evaluates a feature recommender system from users’ perspectives adopting the “Recommender systems’ Quality of user experience” (ResQue) model - a user-centered evaluation model from the RS field. This was done by designing a content-based feature recommender system and then assessing it from the users’ view point. A between-subjects user study was conducted involving two groups of participants, an experimental and a control group. The experimental group interacted with the feature recommender system while developing a list of software requirements for a software product (an antivirus software). In contrast, the control group performed the same task without receiving support from the recommender. After completing the task, both groups completed a post-task evaluation questionnaire, including questions about their experiences and opinions about the task they completed. In addition, participants in the experimental group rated their perceptions of various aspects of the recommender; question items were adapted from the ResQue questionnaire. Users’ subjective evaluation of the recommender was investigated using the ResQue constructs - perceived system qualities, user beliefs, user attitudes, and behavioral intentions. Additionally, the impact of recommendations on the requirements elicitation process was assessed in terms of the process and outcome level measures. Possible qualitative differences were also examined. Users' preferences were identified, and possible HCI issues requiring attention in recommender systems used in RE are discussed.

In computer graphics applications, the choice and implementation of a rendering technique is crucial when targeting real-time performance. Traditionally, rasterization-based approaches have dominated the real-time sector. Other algorithms were simply too slow to compete on consumer graphics hardware. With the addition of hardware support for ray-intersection calculations on modern GPUs, hybrid ray tracing/rasterization and purely ray tracing approaches have become possible in real-time as well. Industry real-time graphics applications, namely games, have been exploring these different rendering techniques with great levels of success. The addition of ray tracing into the graphics developer’s toolkit has without a doubt increased what level of graphical fidelity is achievable in real-time. In this thesis, three rendering techniques are implemented in a custom rendering engine built on the Vulkan® Explicit API. Each technique represents a different family of modern real-time rendering algorithms. A largely rasterization-based method, a hybrid ray tracing/rasterization method, and a method solely using ray tracing. Both the hybrid and ray tracing exclusive approach rely on the ReSTIR algorithm for lighting calculations. Analysis of the performance and render quality of these approaches reveals the trade-offs incurred by each approach, alongside the performance viability of each in a real-time setting.

Accurately quantifying the human perception of color is an unsolved prob- lem. There are dozens of numerical systems for quantifying colors and how we as humans perceive them, but as a whole, they are far from perfect. The ability to accurately measure color for reproduction and verification is critical to indus- tries that work with textiles, paints, food and beverages, displays, and media compression algorithms. Because the science of color deals with the body, mind, and the subjective study of perception, building models of color requires largely empirical data over pure analytical science. Much of this data is extremely dated, from small and/or homogeneous data sets, and is hard to compare. While these studies have somewhat advanced our understanding of color adequately, mak- ing significant, further progress without improved datasets has proven dicult if not impossible. I propose new methods of crowdsourcing color experiments through color-accurate mobile devices to help develop a massive, global set of color perception data to aid in creating a more accurate model of human color perception.

Desktops, laptops, smartphones, tablets, and the Kinect, oh my! With so many devices available to the average consumer, the limitations and pitfalls of each interface are becoming more apparent. Swimming in devices, users often have to stop and think about how to interact with each device to accomplish the current tasks at hand. The goal of this thesis is to minimize user cognitive effort in handling multiple devices by creating a context aware hybrid interface. The context aware system will be explored through the hybridization of gesture and touch interfaces using a multi-touch coffee table and the next-generation Microsoft Kinect. Coupling gesture and touch interfaces creates a novel multimodal interface that can leverage the benefits of both gestures and touch. The hybrid interface is able to utilize the more intuitive and dynamic use of gestures, while maintaining the precision of a tactile touch interface. Joining these two interfaces in an intuitive and context aware way will open up a new avenue for design and innovation.

Digital painting is the field of software designed to provide artists a virtual medium to emulate the experience and results of physical drawing. Several hardware and software components come together to form a whole workflow, ranging from the physical input devices, to the stroking process, to the texture content authorship. This thesis explores an artist-friendly approach to synthesize the textures that give life to digital brush strokes. Most painting software provides a limited library of predefined brush textures. They aim to offer styles approximating physical media like paintbrushes, pencils, markers, and airbrushes. Often these are static bitmap textures that are stamped onto the canvas at repeating intervals, causing discernible repetition artifacts. When more variety is desired, artists often download commercially available brush packs that expand the library of styles. However, included and supplemental brush packs are not easily artist-customizable. In recent years, a separate field of digital art tooling has seen the popular growth of node-based procedural content generation. 3D models, shaders, and materials are commonly authored by artists using functions that can be linked together in a visual programming environment called a node graph. In this work, the feasibility is tested of using a node graph to procedurally generate highly customizable brush textures. The system synthesizes textures that adapt to parameters like pen pressure and stretch along the full length of each brush stroke instead of stamping repetitively. The result is a more flexible and artist-friendly way to define, share, and tweak brush textures used in digital painting.

This work proposes a series of driver assistance technologies that enable blind persons to safely and independently operate an automobile on standard public roads. Such technology could additionally benefit sighted drivers by augmenting vision with suggestive cues during normal and low-visibility driving conditions. This work presents a non-visual human-computer interface system with passive and adaptive controlling software to realize this type of driver assistance technology. The research and development behind this work was made possible through the Blind Driver Challenge® initiative taken by the National Federation of the Blind. The instructional technologies proposed in this work enable blind drivers to operate an automobile through the provision of steering wheel angle and speed cues to the driver in a non-visual method. This paradigm imposes four principal functionality requirements: Perception, Motion Planning, Reference Transformations, and Communication. The Reference Transformation and Communication requirements are the focus of this work and convert motion planning trajectories into a series of non-visual stimuli that can be communicated to the human driver. This work proposes two separate algorithms to perform the necessary reference transformations described above. The first algorithm, called the Passive Non-Visual Interface Driver, converts the planned trajectory data into a form that can be understood and reliably interacted with by the blind driver. This passive algorithm performs the transformations through a method that is independent of the driver. The second algorithm, called the Adaptive Non-Visual Interface Driver, performs similar trajectory data conversions through methods that adapt to each particular driver. This algorithm uses Model Predictive Control supplemented with Artificial Neural Network driver models to generate non-visual stimuli that are predicted to induce optimal performance from the driver. The driver models are trained online and in real-time with a rapid training approach to continually adapt to changes in the driver's dynamics over time. The communication of calculated non-visual stimuli is subsequently performed through a Non-Visual Interface System proposed by this work. This system is comprised of two non-visual human computer interfaces that communicate driving information through haptic stimuli. The DriveGrip interface is pair of vibro-tactile gloves that communicate steering information through the driver's hands and fingers. The SpeedStrip interface is a vibro-tactile cushion fitted on the driver's seat that communicates speed information through the driver's legs and back. The two interfaces work simultaneously to provide a continuous stream of directions to the driver as he or she navigates the vehicle. / Ph. D.

The term e-Government refers to providing citizens a series of services that can be conveniently conducted over the Internet. However, the potential to redefine and transform e-Government increasingly relies on citizens successfully establishing and managing a user account profile online. E-Government has not adequately addressed user-centric designs for social inclusion of all citizens on e-Government websites. There is a lack of research on the usability of user account management, and a clear lack of innovation in incorporating user-friendly authentication interfaces to accommodate a diverse user population given the wealth of existing research in web authentication techniques within Identity Management. The problem is e-Government has no standardized approach to evaluate and compare the usability of user account interfaces to accommodate a diverse user population and encourage improvements in making user account interfaces more user-friendly and accessible to citizens online. This study proposed extending a well-established usability evaluation methodology called GOMS to evaluate e-Government security interfaces for usability. GOMS, which comprises of Goals, Operations, Methods, and Selection, was used to compare the task time users took to complete similar goals on different websites. GOMS was extended to include Security Cases, which are security related goals users desire to accomplish along with the selected link and trail necessary to satisfy those goals. An observational study was conducted to capture the task time 31 users took to complete similar Security Cases on three popular e-Government websites (DMV.CA.gov, HealthCare.gov, and USPS.com). The study initially defined a catalog of six Security Cases specific to user account management and then established benchmark time predictions for each of the Security Cases using CogTool. The six Security Cases selected were as follows: Registration, Login, Change Settings, Forgot Password, Change Password, and Logout. The task time to complete each of the six Security Case on the three websites, along with statistical analysis and CogTool’s benchmark time predications, were used to quantify and compare the usability of these three websites. In order to capture demographic data and assess participant’s satisfaction using the website, the study conducted a post evaluation survey using the System Usability Scale (SUS). The survey captured age, gender, education, user satisfaction, and computer/security knowledge for each participant to assess design considerations to accommodate a diverse population. Finally, a library of Security Cases was established to compare and highlight the more effective user account interface designs on the three selected e-Government websites. This study found task time data from similar Security Cases could be categorized and used to successfully compare and highlight more effective user account interface designs. The study revealed gender and education had no distinctions in task time when performing user account management related tasks. The study also revealed seniors took significantly longer than any other age group to complete complex user account management interfaces. Additionally, CogTool did not prove to be effective in establishing reliable task time predictions to establish as benchmarks. The study concluded the GOMS method could successfully be used to establish a set of task time metrics in a catalog of Security Cases that can be used to evaluate and compare the usability of user account interfaces to accommodate a diverse user population on e-Government websites. Future usability research should be conducted to evaluate if there is a performance relationship between age and security interface complexity. Future research should also further evaluate GOMS as a viable methodology to evaluate other security interfaces not limited to e-Government and expand upon the library of Security Cases to highlight effective security interfaces designs on other websites to accommodate a diverse user population.

Mobile technologies present an opportunity for scholars and practitioners to extend the application of instructional design theories and models to a mobile learning environment. The goal was to examine mobile learning design and development issues, validate and extend the instructional design theory, Component Display Theory (CDT), to the development of mobile learning activities, and recommend guiding principles for mobile learning system development. Using a formative research approach, which focuses on improving design theory for instructional practices and processes, CDT was used to design a tutorial mobile application targeting faculty professional development. This design instance was formatively evaluated to determine how CDT can be used to guide the design and development of a mobile learning environment; the key processes that are pertinent to translating instructional design plans into mobile learning lessons; and the challenges and issues in designing instruction for a mobile learning environment. The findings resulted in the identification of variables and factors related to the instructional strategies, design variables, and the learning system that affected the application of the CDT. Recommendations and further research opportunities are presented to increase practitioner use of the theory and to address learner and organizational readiness. This research contributes to the field of instructional design and development by examining how underlying theories, principles, and frameworks can be applied to the design and development of mobile learning systems.