Ring Graphs in Virtual Reality: Exploring a New and Novel Method for Node Placement and Link Visibility in VR-Based Graph Analysis
M. Sorokin, G. Stetsyuk, A. Busch, R. Gupta, S. Khuller, B. Russin, and C. L. Paul.
ACM SIGGRAPH Asia Posters,
2018.
conference, workshop
vr, virtual reality, visualization, graph
We present a new and novel graph visualization technique designed specifically for virtual reality (VR). Ring graphs organize graph nodes by categorical attributes along a ring that are placed in a sphere layout. Links between nodes are drawn within the rings using an edge bundling technique. This 3D placement of data takes advantage of the stereoscopic environment that VR offers. We conducted a user study that compared our ring visualization to a traditional node-based graph visualization and found that our ring graph method had higher usability, both in terms of accuracy in completing a set of tasks as well as lower task completion time.
Toward Integrated Tactical Operations for Red/Blue Network Defense Teams
J. M. Haney, and C. L. Paul.
Journal of Sensitive Cybersecurity Research and Engineering,
2018.
(Expanded WSIW 2018 workshop paper)
journal
cyber, human factors, case study
Toward Integrated Tactical Operations for Red/Blue Cyber Defense Teams
J. M. Haney, and C. L. Paul.
SOUPS Workshop on Security Information Workers,
2018.
conference, workshop
cyber, human factors, case study
Red and Blue cyberdefense teams provide valuable cybersecurity assessment services to help prevent and defend against malicious intruders. Through interviews, we investigated the methods, tools, and challenges of two specific U.S. Government Department of Defense Red and Blue teams and how they work together during integrated operations. We found examples of successful integration, as well as opportunities for enhanced, shared situation awareness. Based on these findings, we discuss design implications for tools that can facilitate situation awareness among multiple cyberdefense teams by supporting data fusion, change detection, network mapping, and access tracking.
Cyber Operations Stress Survey (COSS): Studying fatigue, frustration, and cognitive workload in cybersecurity operations
J. Dykstra, and C. L. Paul.
USENIX Workshop on Cyber Security Experimentation and Test,
2018.
conference, workshop
cyber, stress, human factors
Operator stress is a common, persistent, and disabling effect of cyber operations and an important risk factor for performance, safety, and employee burnout. We designed the Cyber Operations Stress Survey (COSS) as a low-cost method for studying fatigue, frustration, and cognitive workload in real-time tactical cyber operations. The combination of pre- and post-operational measures with well validated factors from the NASA Task Load Index and additional contextual factors provide a quick, easy, and valuable assessment of cognitive stress. We report on our experiences developing and fielding the survey instrument, validation, and describe the use and results of the COSS in four studies of cyber operations across the National Security Agency.
Human Machine Teaming Panel Discussion
J. Crouser, K. Thompson, C.L. Paul, K. Cook, S. Szymczak.
Laboratory for Analytic Sciences Research Symposium,
Panel,
2018.
AI, ML, Artificial Intelligence, Machine Learning, Analysis, Human-Machine Teaming, HCI
Four Perspectives on Human Bias in Visual Analytics
E. Wall, L. Blaha, C. L. Paul, K. Cook, and A. Endert.
Cognitive Biases in Visualizations,
G. Ellis (eds),
Springer,
29-42,
2018.
(Expanded DECISIVE 2017 workshop paper)
book chapter
cognitive bias, visualization
Visual analytic systems, especially mixed-initiative systems, can steer analytical models and adapt views by making inferences from users’ behavioral patterns with the system. Because such systems rely on incorporating implicit and explicit user feedback, they are particularly susceptible to the injection and propagation of human biases. To ultimately guard against the potentially negative effects of systems biased by human users, we must first qualify what we mean by the term bias. Thus, in this chapter we describe four different perspectives on human bias that are particularly relevant to visual analytics. We discuss the interplay of human and computer system biases, particularly their roles in mixed-initiative systems. Given that the term bias is used to describe several different concepts, our goal is to facilitate a common language in research and development efforts by encouraging researchers to mindfully choose the perspective(s) considered in their work.
Enhancing Deep Learning with Visual Interactions
E. Krokos, H.C. Chen, J. Chang, B. Nebesh, C. L. Paul, K. Whitley, and A. Varshney.
ACM Transactions on Interactive Intelligent Systems,
2018.
(Accepted, in press)
journal
machine learning, deep learning, visualization, interaction
Deep learning has emerged as a powerful tool for feature-driven labeling of datasets. However, for it to be effective, it requires a large and finely-labeled training dataset. Precisely labeling a large training dataset is expensive, time consuming, and error-prone. In this paper, we present a visually-driven deep learning approach that starts with a coarsely-labeled training dataset, and iteratively refines the labeling through intuitive interactions that leverage the latent structures of the dataset. Our approach can be used to (a) alleviate the burden of intensive manual labeling that captures the fine nuances in a high-dimensional dataset by simple visual interactions, (b) replace a complicated (and therefore difficult to design) labeling algorithm by a simpler (but coarse) labeling algorithm supplemented by user interaction to refine the labeling, or (c) use low-dimensional features (such as the RGB colors) for coarse labeling and turn to higher-dimensional latent structures, that are progressively revealed by deep learning, for fine labeling. We validate our approach through use cases on three high-dimensional datasets and a user study.
Size Matters: The Effects of Interactive Display Size on Interaction Zones
C. L. Paul, and L. Bradel.
ACM International Conference on Advanced Visual Interfaces,
2018.
(Article #41)
conference, workshop
visualization, interaction, display
The goal of our research was to understand the effects of display size on interaction zones as it applies to interactive systems. Interaction zone models for interactive displays are often static and do not consider the size of the display in their definition. As the interactive display ecosystem becomes more size diverse, current models for interaction are limited in their applicability. This paper describes the results of an exploratory study in which participants interacted with and discussed expectations with interactive displays ranging from personal to wall-sized. Our approach was open-ended rather than grounded in existing interaction zone models in order to explore potential differences in interaction zones and distances. We found that the existence of different interaction zones and the distance at which these zones are relevant are dependent on display size. In discussion of the results, we explore implications of our findings and offer guidelines for the design of interactive display systems.
Stress and Hacking: Understanding Cognitive Stress in Tactical Cyber Operations
C. L. Paul, and J. Dykstra.
Black Hat USA,
Presentation,
2018.
(50 minute briefing)
cyber, stress, human factors
Hacking is a high-risk, high-reward, with a high-cost to human capital. In this session, we will talk about the effects of human factors in cyber operations and why you should care about them. Specifically, we will focus on results of research at the National Security Agency that studied the effects of cognitive stress on tactical cyber operators. A key motivation for this work was the intuition that cognitive stress may negatively affect operational security, work performance, and employee satisfaction. Operator fatigue, frustration, and cognitive workload increases significantly over the course of a tactical cyber operation. Fatigue and frustration are correlated, and as one increases so does the other. The longer the operation, the greater the mental demand, physical demand, time pressure, frustration, and overall effort needed to complete the operation. Operations longer than 5 hours have 10% greater increases in fatigue and frustration compared to shorter operations. We found no link of performance to operation length; that is, from the operator's perspective longer operations did not result in higher success. Knowing how these factors affect cyber operations has helped us make more informed decisions about mission policy and workforce health. We hope that by sharing this with the greater Black Hat community, they will also be able to learn from our study and improve their own cybersecurity operations.
TexTonic: Interactive Visual Data Exploration for Very Large Text Collections
C. L. Paul, J. Chang, A. Endert, R. Burtner, N. Cramer, D. Gillen, S. Hampton, R. Perko, and K. Cook.
Information Visualization,
2018.
(Online first: 12 July 2018)
journal
big data, visualization, machine learning, interaction
TexTonic is a visual analytic system for interactive exploration of very large unstructured text collections. TexTonic visualizes hierarchical clusters of representative terms, snippets, and documents in a single, multi-scale spatial layout. Exploration is supported by interacting with the visualization and directly manipulating the terms in the visualization using semantic interactions. These semantic interactions steer the underlying analytic model by translating user interactions within the visualization to contextual updates to the supporting data model. The combination of semantic interactions and information visualization at multiple levels of the data hierarchy helps users manage information overload so that they can more effectively explore very large text collections. In this article, we describe TexTonic's data processing and analytic pipeline, user interface and interaction design principles, and results of a user study conducted mid-development with experienced data analysts. We also discuss the implications TexTonic could have on visual exploration and discovery tasks.