Publications

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Data privacy and security considerations for personal assistantsfor learning (PAL)

International Conference on Intelligent User Interfaces, Proceedings IUI

Raybourn, Elaine M.; Fabian, Nathan D.; Davis, Warren L.; Parks, Raymond C.; McClain, Jonathan T.; Trumbo, Derek T.; Regan, Damon; Durlach, Paula J.

A hypothetical scenario is utilized to explore privacy and security considerations for intelligent systems, such as a Personal Assistant for Learning (PAL). Two categories of potential concerns are addressed: factors facilitated by user models, and factors facilitated by systems. Among the strategies presented for risk mitigation is a call for ongoing, iterative dialog among privacy, security, and personalization researchers during all stages of development, testing, and deployment.

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Determining the optimal time on X-ray analysis for transportation security officers

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

Speed, Ann S.; Silva, Austin R.; Trumbo, Derek T.; Stracuzzi, David J.; Warrender, Christina E.; Trumbo, Michael; Divis, Kristin

The Transportation Security Administration has a large workforce of Transportation Security Officers, most of whom perform interrogation of x-ray images at the passenger checkpoint. To date, TSOs on the x-ray have been limited to a 30-min session at a time, however, it is unclear where this limit originated. The current paper outlines methods for empirically determining if that 30-min duty cycle is optimal and if there are differences between individual TSOs. This work can inform scheduling TSOs at the checkpoint and can also inform whether TSOs should continue to be cross-trained (i.e., performing all 6 checkpoint duties) or whether specialization makes more sense.

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Exploratory analysis of visual search data

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

Stracuzzi, David J.; Speed, Ann S.; Silva, Austin R.; Haass, Michael J.; Trumbo, Derek T.

Visual search data describe people’s performance on the common perceptual problem of identifying target objects in a complex scene. Technological advances in areas such as eye tracking now provide researchers with a wealth of data not previously available. The goal of this work is to support researchers in analyzing this complex and multimodal data and in developing new insights into visual search techniques. We discuss several methods drawn from the statistics and machine learning literature for integrating visual search data derived from multiple sources and performing exploratory data analysis. We ground our discussion in a specific task performed by officers at the Transportation Security Administration and consider the applicability, likely issues, and possible adaptations of several candidate analysis methods.

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Neurons to algorithms LDRD final report

Aimone, James B.; Warrender, Christina E.; Trumbo, Derek T.

Over the last three years the Neurons to Algorithms (N2A) LDRD project teams has built infrastructure to discover computational structures in the brain. This consists of a modeling language, a tool that enables model development and simulation in that language, and initial connections with the Neuroinformatics community, a group working toward similar goals. The approach of N2A is to express large complex systems like the brain as populations of a discrete part types that have specific structural relationships with each other, along with internal and structural dynamics. Such an evolving mathematical system may be able to capture the essence of neural processing, and ultimately of thought itself. This final report is a cover for the actual products of the project: the N2A Language Specification, the N2A Application, and a journal paper summarizing our methods.

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Human factors in the design of a search tool for a database of recorded human behavior

Advances in Cognitive Engineering and Neuroergonomics

Abbott, Robert G.; Forsythe, James C.; Glickman, Matthew R.; Trumbo, Derek T.

Many enterprises are becoming increasingly data-driven. For example, empirically collected data about customer behavior offers an alternative to more traditional, synthetic techniques such as surveys, focus groups, and subject-matter experts. In contrast, recordings of tactical training exercises for the US military are not broadly archived or available for analysis. There may be great opportunity for military training and planning to use analogous techniques, where tactical scenarios are systematically recorded, indexed, and archived. Such a system would provide information for all levels of analysis, including establishing benchmarks for individual performance, evaluating the relevance and impact of training protocols, and assessing the utility of proposed systems and conops. However, such a system also offers many challenges and risks, such as cost, security, privacy, and end-user accessibility. This paper examines the possible benefits and risks of such a system with some emphasis on our recent research to address end-user accessibility.

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17 Results
17 Results