nomatic im l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Nomatic*IM PowerPoint Presentation
Download Presentation
Nomatic*IM

Loading in 2 Seconds...

play fullscreen
1 / 11

Nomatic*IM - PowerPoint PPT Presentation


  • 319 Views
  • Uploaded on

Nomatic*IM for presence Position-to-place problem At the campus Starbucks, I’m… reading drinking coffee drinking a Frappacino/latte/etc. doing homework at Starbucks at a coffee shop at UCI in Orange County in Irvine in the U.S. drinking coffee At Starbucks, drinking a Frappacino

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Nomatic*IM' - paul


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
nomatic im
Nomatic*IM
  • for presence
slide4

At the campus Starbucks, I’m…

  • reading
  • drinking coffee
  • drinking a Frappacino/latte/etc.
  • doing homework
  • at Starbucks
  • at a coffee shop
  • at UCI
  • in Orange County
  • in Irvine
  • in the U.S.
slide5

drinking coffee

At Starbucks,

drinking a Frappacino

at starbucks drinking coffee

drinking coffee

la la la la

at starbucks

having coffee

at a café reading

starbucks, drinking a delicious caramel frappacino with bill and george

at a coffee shop

doing homework

slide8
How
  • Gather sensor data.
    • Running applications, time of day, local wireless access points, connected displays, etc.
  • Build decision trees from that data.
  • Collect the data.
slide9
Why
  • We get situated labels.
  • We get labels broadly.
  • People will use it.
  • Privacy is possible.
future
Future
  • User studies.
  • Release (pending IRB approval).
  • Make it better.
slide11

Sam Kaufman

kaufmans@uci.edu

Donald J. Patterson

djp3@ics.uci.edu

Department of Informatics

Bren School of Information and Computer Sciences

University of California, Irvine