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.

The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functions, to demonstrate their efficacy on real-world datasets and draw useful conclusions about their behavior. These methods include simple aggregation, meta-learning, cluster-based meta-learning, and ensemble selection using heterogeneous classifiers trained on resampled data to improve the diversity of their predictions. We present a detailed analysis of these methods across 4 genomics datasets and find the best of these methods offer statistically significant improvements over the state of the art in their respective domains. In addition, we establish a novel connection between ensemble selection and meta-learning, demonstrating how both of these disparate methods establish a balance between ensemble diversity and performance.

A system is described which integrates multiple hardware interfaces and software packages in order to control the parameters of a guitar synthesizer in real time. An interactive genetic algorithm is developed in order to create and explore parameter settings, and a mobile device wirelessly sets the fitness values. The synthesizer parameters are represented as genes within an individual, and individuals dynamically interact within a population as the user rates the resulting sounds by changing orientation.

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.

We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream "teacher" and then pass samples from the model to their downstream "student". It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process using structured populations with memory. We examine the diffusion and fixation properties of several drift processes and propose applications to learning, inference, and evolution. We also demonstrate how the organization of drift process space controls fidelity, facilitates innovations, and leads to information loss in sequential learning with and without memory.

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.