An ML-Powered Human Behavior Management System
Overview & Synopsis
Our work aims to develop novel technologies for building an efficient data infrastructure as a backbone for a human behavior management system. Our infrastructure aims at facilitating behavior modeling, discovery, and exploitation, leading to two major outcomes: a behavior data management back-end and a high-level behavior specification API that supports mining, indexing and search, and AI-powered algorithms that provide the ability to extract insights on human behavior and to leverage data to advance human capital. We discuss the role of ML in populating and maintaining the back-end, and in exploiting it for human interest.
Indexed from repository: HAL Open Science
(
hal.science)
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Document Information
| Document Title | An ML-Powered Human Behavior Management System |
|---|---|
| Author / Organization | Sihem Amer-Yahia, Reynold Cheng, Mohamed Bouadi, Abdelouahab Chibah, Mohammadreza Esfandiari, Jiangping Zhou, Nan Zhang, Eric Lau, Yuguo Li, Xiaolin Han, Shivansh Mittal |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2020 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-205792 |
| Original Source Link | Visit Source Page on hal.science » |