Community-Engaged Learning (CEL) provides students with an opportunity to engage with the community and develop academic skills as they reflect on social justice issues. We present the framework of a new CEL course that enables students to reflect on the inequities in tech through the process of teaching coding to youth from difficult circumstances such as those in the foster care and juvenile justice systems.

Implicit gender bias has costly and complex consequences for women in the workplace, with many women reporting gender microaggressions which result in them being overlooked or disrespected. We present an online desktop virtual environment that follows the story of a male or female self-avatar from the first-person perspective, who either experiences a positive or negative workplace scenario. The negative scenario included many examples from the taxonomy of gender microaggressions. Participants who experienced negative workplace experiences with a female self-avatar had significantly decreased levels of implicit gender bias compared to those who had a male self-avatar. There was evidence of empathy and perspective taking in the negative condition for the female self-avatar. Experiences of a positive workplace scenario showed no significant decreases in implicit gender bias regardless of self-avatar gender. We discuss the implications of these findings and make recommendations for virtual environment technologies and scenarios with respect to the reduction of implicit biases.

The scatterplot matrix (SPLOM) is a commonly used technique for visualizing multiclass multivariate data. However, multiclass SPLOMs have issues with overdraw (overlapping points), and most existing techniques for alleviating overdraw focus on individual scatterplots with a single class. This paper explores whether animation using flickering points is an effective way to alleviate overdraw in these multiclass SPLOMs. In a user study with 69 participants, we found that users not only performed better at identifying dense regions using animated SPLOMs, but also found them easier to interpret and preferred them to static SPLOMs. These results open up new directions for future work on alleviating overdraw for multiclass SPLOMs, and provide insights for applying animation to alleviate overdraw in other settings.

BACKGROUND: Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks. RESULTS: We developed an approach to "unbox" the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results. CONCLUSIONS: The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps.

With the increasing availability and popularity of visualization tools, it is easier than ever to create visual representations of data. The available tools and libraries work for a range of users from non-programmers to those with significant programming experience. A major challenge, however, is that a majority of users frequently stick with the default settings when using software.

We present an application of the generalized plot matrix (GPLOM) technique for visualizing a multidimensional dataset containing 202 YouTube videos from the "Statistics and Social Network of YouTube Videos" project. The web-based visualization provides users an easy to use interface to explore the data. Using our tool, we found several interesting relationships between the number of views, comments, ratings and stars. Visit git.io/vwDqw for a live demo.

In this short position paper, we explore three questions regarding cyber security visualization: (1) why cyber security visualization has not been more effective in the past, (2) how visualization can be utilized in cyber security, and (3) how to evaluate cyber security visualization.

A hive plot is a network layout algorithm that uses a parallel coordinate plot in which axes are radially arranged and node position is based on structural properties of that node [8]. We apply hive plots to message-passing communication networks formed by different high performance computing applications at Lawrence Berkeley National Laboratory. Hive plots have advantages over common network visualization methods, abandoning the popular "hairballs" of force-directed network layouts and opting instead for a tunable, repeatable, and interpretable view of network-theoretic properties. Small multiples of these hive plots, called hive panels, are analyzed to suggest which properties contribute to accurate classification of application behavior for anomaly detection in high performance computing environments.

Significant work on vulnerabilities focuses on buffer overflows, in which data exceeding the bounds of an array is loaded into the array. The loading continues past the array boundary, causing variables and state information located adjacent to the array to change. As the process is not programmed to check for these additional changes, the process acts incorrectly. The incorrect action often places the system in a nonsecure state. This work develops a taxonomy of buffer overflow vulnerabilities based upon characteristics, or preconditions that must hold for an exploitable buffer overflow to exist. We analyze several software and hardware countermeasures to validate the approach. We then discuss alternate approaches to ameliorating this vulnerability.

The insider threat has long been considered one of the most serious threats in computer security, and one of the most difficult to combat. But the problem has never been defined precisely, and that lack of precise definition inhibits solutions. This paper presents a precise definition of insider threat, and shows how the definition enables an analysis of the set of problems traditionally lumped into \the insider threat". It introduces a hierarchy of policy abstractions, and argues that the discrepancies between the different layers of abstraction expose the potential for insider threat. It also presents a methodology for analyzing the threat based upon our definitions. In the process, we introduce Attribute-Based Group Access Control, a generalization of the Role-Based Access Control model that allows any attributes to define a group. We apply this to the insider threat by defining groups based on access capabilities, and using that to identify users with a high level of threat with respect to high-risk resources.