UMBC ebiquity
2011 April

Archive for April, 2011

New Journal of Web Semantics preprint server

April 12th, 2011, by Tim Finin, posted in AI, Semantic Web

The new Journal of Web Semantics preprint server is now online. Final drafts of accepted papers will be added to the preprint server as papers are accepted for publication, making a preprint available as soon as possible.

We are loading papers from back issues into the preprint server as time permits. The preprint server is based on the Open Journal Systems software and hosted by Gesis, the Leibniz Institute for the Social Sciences.

After drafts are on the preprint server, they enter Elsevier’s production pipeline in which they are professionally copy edited, formatted for the journal, and proofed by the authors. The result is assigned a DOI and put online as a JWS article in press available to to individual and institutional subscribers. When the article is assigned to an issue and printed, the final copy will be available online to subscribers in Elsevier’s Science Direct system.

We would like to thank the people who helped stand up the new preprint server, including Ute Koch of Gesis, Kaixuan Wang of the University of Manchester, and Silke Werger of the University of Koblenz and Landau.

New frontiers in spam: the Kindle Swindle

April 6th, 2011, by Tim Finin, posted in Machine Learning

Publishing trends has a good post describing a new variation on spam: creating low-quality ebooks from plagiarized or public-domain content and selling them in ebook markets like Amazon’s Kindle store. If you want to MAKE.MONEY.FAST there are people willing to help:

Automatically detecting these spam ebooks might be a good machine learning project. One problem is that to use features of the ebook itself (e.g., poor formatting) might require purchasing it. But there are sure to be many useful features that the ebook store provides that might support an effective classifier.

(h/t Bruce Schneier)

DARPA uses computer game to learn anti-submarine warfare tactics

April 5th, 2011, by Tim Finin, posted in AI, GAIM, Machine Learning

DARPA is developing a new component to track “quiet submarines” to be part of the Navy’s Anti Submarine Warfare toolkit and is using a software game to collect effective strategies for its use.

“Before autonomous software is developed for ACTUV’s computers, DARPA needs to determine what approaches and methods are most effective. To gather information from a broad spectrum of users, ACTUV has been integrated into the Dangerous Waters™ game. DARPA is offering this new ACTUV Tactics Simulator for free public download.

This software has been written to simulate actual evasion techniques used by submarines, challenging each player to track them successfully. Your tracking vessel is not the only ship at sea, so you’ll need to safely navigate among commercial shipping traffic as you attempt to track the submarine, whose driver has some tricks up his sleeve. You will earn points as you complete mission objectives, and will have the opportunity to see how you rank against the competition on DARPA’s leaderboard page. You can also share your experiences and insights from playing the simulator with others.”

This is a kind of crowdsourcing — leveraging the experiences of a large number of people playing a game. Applying various kinds of machine learning algorithms to the simulator data could be an effective way to train an autonomous tool for this task.

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