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 article, 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.

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.

CyberPetri is a novel visualization technique that provides a flexible map of the network based on available characteristics, such as IP address, operating system, or service. Previous work introduced CyberPetri as a visualization feature in Ocelot, a network defense tool that helped security analysts understand and respond to an active defense scenario. In this paper we present a case study in which we use CyberPetri to support real-time situation awareness during the 2016 Cyber Defense Exercise.

Most cyber security research is focused on detecting network intrusions or anomalies through the use of automated methods, exploratory visual analytics systems, or real-time monitoring using dynamic visual representations. However, there has been minimal investigation of effective decision support systems for cyber analysts. This paper describes the user-centered design and development of a decision support visualization for active network defense. Ocelot helps the cyber analyst assess threats to a network and quarantine affected computers from the healthy parts of a network. The described web-based, functional visualization prototype integrates and visualizes multiple data sources through the use of a hybrid space partitioning tree and node link diagram. We describe our design process for requirements gathering and design feedback which included expert interviews, iterative design, and a user study.

The goal of the 1stWorkshop on Human-Centered Big Data Research was to explore the multi-disciplinary challenges of researching humans in Big Data environments. This paper summarizes the outcomes of the workshop and aims to define potential future work in this area.