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Researchers in the humanities are among the many who are now exploring the world of big data. They have begun to use programming languages like Python or R and their corresponding libraries to manipulate large data sets and discover brand new insights. One of the major hurdles that still exists is incorporating visualizations of this data into their projects. Visualization libraries can be difficult to learn how to use, even for those with formal training. Yet these visualizations are crucial for recognizing themes and communicating results to not only other researchers, but also the general public. This paper focuses on producing meaningful visualizations of data using machine learning. We allow the user to visually specify their code requirements in order to lower the barrier for humanities researchers to learn how to program visualizations. We use a hybrid model, combining a neural network and optical character recognition to generate the code to create the visualization.
Project X is a multi-megawatt proton facility being developed to support intensity frontier research in elementary particle physics, with possible applications to nuclear physics and nuclear energy research, at Fermilab. A Functional Requirements Spe
Ensuring correctness of cyber-physical systems (CPS) is an extremely challenging task that is in practice often addressed with simulation based testing. Formal specification languages, such as Signal Temporal Logic (STL), are used to mathematically e
What makes two images similar? We propose new approaches to generate model-agnostic explanations for image similarity, search, and retrieval. In particular, we extend Class Activation Maps (CAMs), Additive Shapley Explanations (SHAP), and Locally Int
This paper presents a new state space generation approach for dynamic fault trees (DFTs) together with a technique to synthesise failures rates in DFTs. Our state space generation technique aggressively exploits the DFT structure --- detecting symmet
In this paper, we discuss an approach to system requirements engineering, which is based on using models of the responsibilities assigned to agents in a multi-agency system of systems. The responsibility models serve as a basis for identifying the st