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Exploring Explicit Uncertainty for Binary Analysis (EUBA)

Leger, Michelle A.; Darling, Michael C.; Jones, Stephen T.; Matzen, Laura E.; Stracuzzi, David J.; Wilson, Andrew T.; Bueno, Denis B.; Christentsen, Matthew C.; Ginaldi, Melissa J.; Hannasch, David A.; Heidbrink, Scott H.; Howell, Breannan C.; Leger, Chris; Reedy, Geoffrey E.; Rogers, Alisa N.; Williams, Jack A.

Reverse engineering (RE) analysts struggle to address critical questions about the safety of binary code accurately and promptly, and their supporting program analysis tools are simply wrong sometimes. The analysis tools have to approximate in order to provide any information at all, but this means that they introduce uncertainty into their results. And those uncertainties chain from analysis to analysis. We hypothesize that exposing sources, impacts, and control of uncertainty to human binary analysts will allow the analysts to approach their hardest problems with high-powered analytic techniques that they know when to trust. Combining expertise in binary analysis algorithms, human cognition, uncertainty quantification, verification and validation, and visualization, we pursue research that should benefit binary software analysis efforts across the board. We find a strong analogy between RE and exploratory data analysis (EDA); we begin to characterize sources and types of uncertainty found in practice in RE (both in the process and in supporting analyses); we explore a domain-specific focus on uncertainty in pointer analysis, showing that more precise models do help analysts answer small information flow questions faster and more accurately; and we test a general population with domain-general sudoku problems, showing that adding "knobs" to an analysis does not significantly slow down performance. This document describes our explorations in uncertainty in binary analysis.

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Large-Scale Trajectory Analysis via Feature Vectors

Rintoul, Mark D.; Jones, Jessica L.; Newton, Benjamin D.; Wisniewski, Kyra L.; Wilson, Andrew T.; Ginaldi, Melissa J.; Waddell, Cleveland A.; Goss, Kenneth G.; Ward, Katrina J.

The explosion of both sensors and GPS-enabled devices has resulted in position/time data being the next big frontier for data analytics. However, many of the problems associated with large numbers of trajectories do not necessarily have an analog with many of the historic big-data applications such as text and image analysis. Modern trajectory analytics exploits much of the cutting-edge research in machine-learning, statistics, computational geometry and other disciplines. We will show that for doing trajectory analytics at scale, it is necessary to fundamentally change the way the information is represented through a feature-vector approach. We then demonstrate the ability to solve large trajectory analytics problems using this representation.

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Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations

IEEE Transactions on Visualization and Computer Graphics

Matzen, Laura E.; Haass, Michael J.; Divis, Kristin; Wang, Zhiyuan; Wilson, Andrew T.

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.

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Modeling human comprehension of data visualizations

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Haass, Michael J.; Wilson, Andrew T.; Matzen, Laura E.; Divis, Kristin

A critical challenge in data science is conveying the meaning of data to human decision makers. While working with visualizations, decision makers are engaged in a visual search for information to support their reasoning process. As sensors proliferate and high performance computing becomes increasingly accessible, the volume of data decision makers must contend with is growing continuously and driving the need for more efficient and effective data visualizations. Consequently, researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles to assess the effectiveness of data visualizations. In this paper, we compare the performance of three different saliency models across a common set of data visualizations. This comparison establishes a performance baseline for assessment of new data visualization saliency models.

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Time series discord detection in medical data using a parallel relational database

Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Woodbridge, Diane W.; Wilson, Andrew T.; Rintoul, Mark D.; Goldstein, Richard H.

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients' emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

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Results 1–25 of 66
Results 1–25 of 66