Tuesday, June 01, 2010

Onion Futures Act

I'm not making this up:
http://en.wikipedia.org/wiki/Onion_Futures_Act


This law is notable as the first and only ban on the trading of futures contracts of a specific commodity in United States history, and as a unique modern case with which to study the effects of the existence of an active futures market on commodity prices.


I'm sure a more skilled comedian could come up with a good joke about The Onion, or build on the famous Simpson's reference.

Forbes on Disclosed to Death

Forbes Magazine has a nice article arguing that more disclosure isn't necessarily better, pointing out the complexity, the difficulty in making choices, and the legalese. My favorite passage:


One study found that despite the [Miranda] warning the overwhelming majority of suspects (78% to 96%) waive their rights ... "Next to the warning label on cigarette packs, Miranda is the most widely ignored piece of official advice in our society."

Four papers accepted to Ubicomp 2010

Our group had a good year for Ubicomp with four papers accepted, all on various aspects of privacy, location, and social networking.

Jialiu Lin, Guang Xiang, Jason Hong, Norman Sadeh. Modeling People’s Place Naming Preferences in Location Sharing.
This paper looks at how people name places when sharing with others.

Eran Toch et al. Empirical Models of Privacy in Location Sharing.
This paper examines what location information people share with others, using models of how public a place is, and how mobile that individual is.

Karen Tang, Jialiu Lin, Jason Hong, Norman Sadeh. Rethinking Location Sharing: Exploring the Implications of Social-Driven vs. Purpose-Driven Location Sharing.
Here, we examine the difference between two different kinds of location sharing. One is purpose-driven ("where are you now?"), the other is social-driven ("hey, I'm in Paris now").

Justin Cranshaw, Eran Toch, Jason Hong, Niki Kittur, Norman Sadeh. Bridging the Gap Between Physical Location and Online Social Networks.
Using co-location data, we apply machine learning models to make inferences about one's social network.