机器学习用于有形效果：用于揭露非法按摩业的自然语言处理与用于触觉感知的计算机视觉
Machine Learning for Tangible Effects: Natural Language Processing for Uncovering the Illicit Massage Industry & Computer Vision for Tactile Sensing
AI总结：
本文利用NLP技术监控美国非法按摩业并生成合成金融数据以打击人口贩卖，同时开发改造型触觉传感器Digger Finger和低成本六轴力矩传感器，实现计算机视觉在触觉感知中的应用。
摘要：
我在本论文中探讨两个问题：如何利用计算机科学来打击人口贩卖？以及如何利用计算机视觉创造触觉？\n我使用自然语言处理（NLP）来监控美国非法按摩业（IMI），这是一个价值数十亿美元的行业，不仅提供保健按摩，还提供商业性服务。该行业的员工通常是缺乏就业机会的移民女性，这使得她们容易受到欺诈、胁迫和人口贩卖其他方面的影响。监控时空趋势有助于防止IMI中的人口贩卖。通过利用三个可公开访问的网站（Google Places、Rubmaps和AMPReviews）创建数据集，结合词袋和Word2Vec等NLP技术，我展示了如何深入了解员工面临的劳动压力和语言障碍，以及影响性买家的收入、人口统计数据和社会压力。鉴于这些数据集，我向其他研究人员发出了行动呼吁。我还考虑了如何创建合成金融数据，以协助银行业反人口贩卖工作。我使用基于智能体的模型创建了表格数据和收款人-收款方图数据。\n随后，我探讨了计算机视觉在制造触觉传感器中的作用。我报告了一种新型传感器Digger Finger，它对Gelsight传感器进行了改造，使其能够在颗粒介质中寻找物体。改造包括使用楔形形状以方便挖掘，用荧光漆替换内部照明LED，以及添加振动马达以防止卡死。最后，我还展示了如何使用网络摄像头和打印的参考标记（或基准标记）来创建低成本的六轴力矩传感器。该传感器比商用传感器便宜多达一百倍，从而允许更广泛的应用。对于本章及前面的章节，我将设计文件和代码作为开源发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
