Ms. Sunshine Stacy Profile

Sunshine Stacy

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Proceedings Article | 12 April 2021 Poster + Presentation + Paper

Semantic lifting of integrated messages (SLIM)

Patrick Fisher, Nicholas Del Rio, Sunshine Stacy

Proceedings Volume 11746, 117462X (2021) https://doi.org/10.1117/12.2586131

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The Joint Force’s ability to integrate data and systems at speed and scale is of utmost importance to maintain a competitive edge in the data-driven arena of 21st century warfare. The current methods employed by developers to find system integration points, however, are ad-hoc, predominantly manual, and too costly – consider the explosion in volume and heterogeneity of the underlying databases that support modern-day operations. In this work, we present a data archeology and alignment workbench, known as Semantic Lifting of Integrated Messages (SLIM), that allows developers to quickly discover and document commonalities among disparate data. Our novel workbench uses background knowledge to constrain the geometric orientation of unfamiliar data in ways that are meaningful to developers. To accomplish this, SLIM uses a variety of Machine Learning algorithms to unify and characterize the meaning of data, and a dashboard of linked visualizations to convey semantic commonalities from different perspectives and dimensions. Additionally, the workbench maintains a web of execution provenance that developers can use to trace and probe results in order to assess relevancy and meaningfulness. As a demonstration, we describe two studies that use our process to quickly find non-trivial commonalities among multiple datasets, including the Universal Command and Control Interface (UCI) and an ontology about the arrival and departure of vehicles.

Proceedings Article | 12 April 2021 Poster + Presentation + Paper

Rigorous evaluation of vector-associated model performance (REVAMP)

Sunshine Stacy, Patrick Fisher, Nicholas Del Rio

Proceedings Volume 11746, 117462W (2021) https://doi.org/10.1117/12.2586124

KEYWORDS: Systems modeling, Performance modeling, Algorithm development, Vector spaces, Mathematical modeling, Machine learning, Defense technologies, Data modeling, Algorithms

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DevOps engineers take many factors into account when assessing the suitability of AI/ML algorithms for operations. They rely on documentation about data requirements, parameter settings, theoretical limits, and operating characteristics to anticipate how well an algorithm will perform in the field prior to writing a single line of code. However, the USAF currently lacks code quality and documentation standards for AI/ML, which forces downstream consumers to make assumptions about the behavior of algorithms that often lead to unexpected results and wasted efforts. Therefore, we present a preliminary set of criteria by which to judge the maturity of word embedding algorithms in terms of reproducibility, testability, and documentation quality. We hope the criteria will grow into a set of quality requirements that govern how the DoD procures AI/ML capabilities, and eventually motivate the need for an ML development maturity model. Our nascent evaluation criteria surfaced during our own struggles with trying to replicate and compare the performance between a well-known word embedding algorithm (Word2Vec) and a custom graph embedding algorithm (IRI2Vec), procured specifically for the purpose of connecting missions across ATOs. We walk through two case studies in which we applied our evaluation criteria to assess the maturity of both Word2Vec and IRI2Vec

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