Beyond Artificial Reality-Reference-Cited by-同舟云学术

Beyond Artificial Reality

Published:2020-03-05 Issue:1 Volume:20 Page:1-21
ISSN:1533-5399
Container-title:ACM Transactions on Internet Technology
language:en
Short-container-title:ACM Trans. Internet Technol.

Author:

Pu Calton¹,Suprem Abhijit¹,Lima Rodrigo Alves¹,Musaev Aibek²,Wang De³,Irani Danesh⁴,Webb Steve⁵,Ferreira Joao Eduardo⁶

Affiliation:

1. School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA

2. Department of Computer Science, University of Alabama, Tuscaloosa, AL, USA

3. Sunmi US Inc, USA

4. Google, USA

5. Web Gnomes, USA

6. Department of Computer Science, University of Sao Paulo, Sao Paulo, Brazil

Abstract

With billions of active social media accounts and millions of live video cameras, live new big data offer many opportunities for smart applications. However, the main consumers of the new big data have been humans. We envision the research on live knowledge , to automatically acquire real-time, validated, and actionable information. Live knowledge presents two significant and diverging technical challenges: big noise and concept drift. We describe the EBKA (evidence-based knowledge acquisition) approach, illustrated by the LITMUS landslide information system. LITMUS achieves both high accuracy and wide coverage, demonstrating the feasibility and promise of EBKA approach to achieve live knowledge.

Funder

CRISP

CNS

National Science Foundation CISE's SAVI/RCN

SaTC

REU

gifts, grants, or contracts from Fujitsu, HP, Intel, and Georgia Tech Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

Link

https://dl.acm.org/doi/pdf/10.1145/3374214

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