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DTSTART;TZID=America/New_York:20221013T144200
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UID:HFESAM_HFES 66th International Annual Meeting_sess176_LECT528@linkling
 s.com
SUMMARY:The DataScope: A Mixed-Initiative Architecture for Data Labeling
DESCRIPTION:Lecture\n\nThe DataScope: A Mixed-Initiative Architecture for 
 Data Labeling\n\nAlsaid, Lee\n\nMachine learning promises many advantages,
  but achieving these promises requires methods that smooth the interaction
  between humans and machine learning. Machine learning systems require met
 iculous training on large, labeled datasets. Labeling data is a tedious ex
 pensive process, and many times requires complex human-judgment skills. Mi
 xed-initiative designs divide the labor between the artificial agent and t
 he human to make the task at hand more efficient and effective. This paper
  proposes a mixed-initiative method for efficient coding of video and imag
 e data in general and emotion data in particular. We integrate an unsuperv
 ised dimensionality reduction algorithm and the R Shiny platform to develo
 p an interactive method that leverages human expertise to label the data m
 ore efficiently and effectively. The method, through the interactive web t
 ool, allows the user to explore the data interactively, examine similariti
 es and dissimilarities in the data, and label clusters of many images and 
 video frames at once. The combination of the unsupervised learning algorit
 hm and the R Shiny platform enables interactive exploration and annotation
  of high-dimensional, complicated data. This method can be used to annotat
 e large data sets faster and can advance research in machine vision.\n\nTr
 ack: Human AI Robot Teaming (HART), Human Performance Modeling
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