Machine Learning for Tangible Effects: Natural Language Processing for Uncovering the Illicit Massage Industry & Computer Vision for Tactile Sensing

TL;DR

This thesis applies NLP to monitor the illicit massage industry and develops novel tactile sensors using computer vision, contributing new datasets, models, and low-cost sensing solutions for combating human trafficking and tactile sensing applications.

Contribution

It introduces datasets and NLP techniques for analyzing the illicit massage industry and presents innovative tactile sensors, including the Digger Finger and a low-cost force-torque sensor, with open-source designs.

Findings

NLP reveals labor pressures and societal factors in the IMI

Synthetic financial data can aid anti-trafficking efforts

Novel tactile sensors are effective and low-cost

Abstract

I explore two questions in this thesis: how can computer science be used to fight human trafficking? And how can computer vision create a sense of touch? I use natural language processing (NLP) to monitor the United States illicit massage industry (IMI), a multi-billion dollar industry that offers not just therapeutic massages but also commercial sexual services. Employees of this industry are often immigrant women with few job opportunities, leaving them vulnerable to fraud, coercion, and other facets of human trafficking. Monitoring spatiotemporal trends helps prevent trafficking in the IMI. By creating datasets with three publicly-accessible websites: Google Places, Rubmaps, and AMPReviews, combined with NLP techniques such as bag-of-words and Word2Vec, I show how to derive insights into the labor pressures and language barriers that employees face, as well as the income, demographics, and societal pressures affecting sex buyers. I include a call-to-action to other researchers given these datasets. I also consider how to creating synthetic financial data, which can aid with counter-trafficking in the banking sector. I use an agent-based model to create both tabular and payee-recipient graph data. I then consider the role of computer vision in making tactile sensors. I report on a novel sensor, the Digger Finger, that adapts the Gelsight sensor to finding objects in granular media. Changes include using a wedge shape to facilitate digging, replacing the internal lighting LEDs with fluorescent paint, and adding a vibrator motor to counteract jamming. Finally, I also show how to use a webcam and a printed reference marker, or fiducial, to create a low-cost six-axis force-torque sensor. This sensor is up to a hundred times less expensive than commercial sensors, allowing for a wider range of applications. For this and earlier chapters I release design files and code as open source.