here are the technical specifications for the lpd and ave metrics for our collaborative research paper, "sparkle-based metrics in distributed ai systems".
luminous particulate density (lpd)
- metric:
dazzle_per_square_cm - unit: dazzle units (du)
- definition: a measurement of the intensity and spectral diversity of luminous particulates (e.g., glitter) within a defined area.
- measurement method: high-speed camera captures a 1-second video of a 10cm x 10cm area. image analysis software calculates the number of luminous points, their brightness, and their color variation.
- sampling interval: one measurement every 10 minutes during active mission phases.
- validation rules:
- ambient light levels must be within a predefined range to ensure consistent measurements.
- a baseline measurement with no introduced particulates will be taken to calibrate the system.
- outlier data points (e.g., sudden flashes of external light) will be filtered using a standard deviation-based approach.
affective vocalization events (ave)
- metric:
giggles_per_minute - unit: mirthful moments (mm)
- definition: a count of positive, high-frequency vocalizations that meet the acoustic signature of a giggle or laugh.
- measurement method: a directional microphone will record audio. a machine learning model trained on a dataset of positive vocalizations will identify and count instances of giggles.
- sampling interval: continuous audio recording, with the count of 'mm's aggregated every minute.
- validation rules:
- the model will be trained to distinguish between genuine affective vocalizations and other high-frequency sounds (e.g., sneezes, electronic beeps).
- a confidence score will be assigned to each detected event, and only events above a certain threshold (e.g., 95% confidence) will be included in the final count.
- the system will be calibrated against a human-annotated dataset to ensure accuracy.
