FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images

Feature selection is used in machine learning to improve predictions, decrease computation time, reduce noise, and tune models based on limited sample data. In this article, we present FeatureExplorer, a visual analytics system that supports the dynamic evaluation of regression models and importance of feature subsets through the interactive selection of features in high-dimensional feature spaces typical of hyperspectral images. The interactive system allows users to iteratively refine and diagnose the model by selecting features based on their domain knowledge, interchangeable (correlated) features, feature importance, and the resulting model performance.Comment: To appear in IEEE VIS 2019 Short Paper

Data Analytics and Visualization for Virtual Simulation

Healthcare organizations attract a diversity of caregivers and patients by providing essential care. While interacting with people of various races, ethnicity, and economical background, caregivers need to be empathetic and compassionate. Proper training and exposure are needed to understand the patient’s background and handle different situations and provide the best care for the patient. With social determinants of health (SDOH) as the basis, the thesis focuses on providing exposure through “Wright LIFE (Lifelike Immersion for Equity) - A simulation-based training tool” to two such scenarios covering patients from the LGBTQIA+ community & autism spectrum disorder (ASD). This interactive tool helps to create mindfulness about the social and economic disparities faced by the patients through realistic and captivating gameplay. Though the primary focus of the “Wright LIFE” application is “Digital Learning”, it would help to understand how effective the application is in terms of improving the provider\u27s abilities. Through statistical evidence, the tool can be improved, which in turn will improve the user experience. For this analysis, during the simulation, we also focus on collecting the data gathered from the participants through surveys. The simulation includes different questionnaires where participants can provide feedback at various stages within the simulation. This then allows for a comparison between the participants’ responses to see the rate of improvement as a result of the simulation. To analyze the data from the participant\u27s responses, data analysis, and visualization tools help to represent the data using charts, infographics, animations, and many more to assist this in this analytic process. The analysis of the data can help to understand the trend of the participants’ responses to the questionnaire. The goal of the questionnaire is to collect participants’ responses to assess anxiety, frustration, and compassion levels pre- and post-simulation. A comparative analysis is then performed. This analysis shows that the provider’s anxiety and frustration decreased after the simulation whereas the compassion increased. This is an indication that the simulation can improve the provider’s experience while working with patients with biases. The data also helped to identify the users who actively participated in the survey based on demographic data like gender, profession, experience, and age. “A picture is worth a thousand words”. Through visualization, we can bring the data to life and provide a clear idea of what the data represents by giving visual context. Tableau is used for visualizing the survey data collected from the “SDOH” simulation consisting of responses from the providers before and after the interaction with the patients. The visualizations transform the raw data into simple and informative graphs to understand the behavioral trends and to check how the providers respond to the stories in the simulations. This allows us to determine the effectiveness of the simulation more efficiently

Cognitive Activity Support Tools: Design of the Visual Interface

This dissertation is broadly concerned with interactive computational tools that support the performance of complex cognitive activities, examples of which are analytical reasoning, decision making, problem solving, sense making, forecasting, and learning. Examples of tools that support such activities are visualization-based tools in the areas of: education, information visualization, personal information management, statistics, and health informatics. Such tools enable access to information and data and, through interaction, enable a human-information discourse. In a more specific sense, this dissertation is concerned with the design of the visual interface of these tools. This dissertation presents a large and comprehensive theoretical framework to support research and design. Issues treated herein include interaction design and patterns of interaction for cognitive and epistemic support; analysis of the essential properties of interactive visual representations and their influences on cognitive and perceptual processes; an analysis of the structural components of interaction and how different operational forms of interaction components affect the performance of cognitive activities; an examination of how the information-processing load should be distributed between humans and tools during the performance of complex cognitive activities; and a categorization of common visualizations according to their structure and function, and a discussion of the cognitive utility of each category. This dissertation also includes a chapter that describes the design of a cognitive activity support tool, as guided by the theoretical contributions that comprise the rest of the dissertation. Those that may find this dissertation useful include researchers and practitioners in the areas of data and information visualization, visual analytics, medical and health informatics, data science, journalism, educational technology, and digital games

INFORMATION VISUALIZATION DESIGN FOR MULTIDIMENSIONAL DATA: INTEGRATING THE RANK-BY-FEATURE FRAMEWORK WITH HIERARCHICAL CLUSTERING

Interactive exploration of multidimensional data sets is challenging because: (1) it is difficult to comprehend patterns in more than three dimensions, and (2) current systems are often a patchwork of graphical and statistical methods leaving many researchers uncertain about how to explore their data in an orderly manner. This dissertation offers a set of principles and a novel rank-by-feature framework that could enable users to better understand multidimensional and multivariate data by systematically studying distributions in one (1D) or two dimensions (2D), and then discovering relationships, clusters, gaps, outliers, and other features. Users of this rank-by-feature framework can view graphical presentations (histograms, boxplots, and scatterplots), and then choose a feature detection criterion to rank 1D or 2D axis-parallel projections. By combining information visualization techniques (overview, coordination, and dynamic query) with summaries and statistical methods, users can systematically examine the most important 1D and 2D axis-parallel projections. This research provides a number of valuable contributions: Graphics, Ranking, and Interaction for Discovery (GRID) principles- a set of principles for exploratory analysis of multidimensional data, which are summarized as: (1) study 1D, study 2D, then find features (2) ranking guides insight, statistics confirm. GRID principles help users organize their discovery process in an orderly manner so as to produce more thorough analyses and extract deeper insights in any multidimensional data application. Rank-by-feature framework - a user interface framework based on the GRID principles. Interactive information visualization techniques are combined with statistical methods and data mining algorithms to enable users to orderly examine multidimensional data sets using 1D and 2D projections. The design and implementation of the Hierarchical Clustering Explorer (HCE), an information visualization tool available at www.cs.umd.edu/hcil/hce. HCE implements the rank-by-feature framework and supports interactive exploration of hierarchical clustering results to reveal one of the important features - clusters. Validation through case studies and user surveys: Case studies with motivated experts in three research fields and a user survey via emails to a wide range of HCE users demonstrated the efficacy of HCE and the rank-by-feature framework. These studies also revealed potential improvement opportunities in terms of design and implementation

An Investigation of Cognitive Implications in the Design of Computer Games

Computer games have been touted for their ability to engage players in cognitive activities (e.g., decision making, learning, planning, problem solving). By ‘computer game’ we mean any game that uses computational technology as its platform, regardless of the actual hardware or software; games on personal computers, tablets, game consoles, cellphones, or specialized equipment can all be called computer games. However, there remains much uncertainty regarding how to design computer games so that they support, facilitate, and promote the reflective, effortful, and conscious performance of cognitive activities. The goal of this dissertation is to relieve some of this uncertainty, so that the design of such computer games can become more systematic and less ad hoc. By understanding how different components of a computer game influence the resulting cognitive system, we can more consciously and systematically design computer games for the desired cognitive support. This dissertation synthesizes concepts from cognitive science, information science, learning science, human-computer interaction, and game design to create a conceptual design framework. The framework particularly focuses on the design of: gameplay, the player-game joint cognitive system, the interaction that mediates gameplay and the cognitive system, and the components of this interaction. Furthermore, this dissertation also includes a process by which researchers can explore the relationship between components of a computer game and the resulting cognitive system in a consistent, controlled, and precise manner. Using this process, three separate studies were conducted to provide empirical support for different aspects of the framework; these studies investigated how the design of rules, visual interface, and the core mechanic influence the resulting cognitive system. Overall then, the conceptual framework and three empirical studies presented in this dissertation provide designers with a greater understanding of how to systematically design computer games to provide the desired support for any cognitive activity