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Falcon-AO, a prominent component of Falcon, is an automatic ontology matching system that helps actualize interoperability between (Semantic) Web applications that use different but related ontologies. Recently, it has become a very practical and popular choice for matching Web ontologies expressed by RDF(S) and OWL. Falcon-AO is implemented in Java, and presently, it is an open source project under the Apache 2.0 license.

I chose to use a 110A because I got a good deal on eBay. 110Bs are the most popular just because they have a couple minor improvements over the 110A. Some people convert the 'lesser' models as well. Here is my 150 next to my 110A. See the obvious difference in the lens quality.

Here’s how to get your blog (or any other RSS or Atom feed) sent to popular microblogging platforms:

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Here’s how to get your blog (or any other RSS or Atom feed) sent to popular microblogging platforms:

1. Decide which network(s) you want to post to

2. Login to twitterfeed using your OpenID

3. Provide us with the URL for your blog's RSS feed, and how often we should post on your behalf

Uploading tourist photos is a popular activity on photo sharing platforms. These photographs and their associated metadata (tags, geo-tags, and temporal information) should be useful for mining information about the sites visited. However, user-supplied metadata are often noisy and efficient filtering methods are needed before extracting useful knowledge. We focus here on exploiting temporal information, associated with tourist sites that appear in Flickr. From automatically filtered sets of geo-tagged photos, we deduce answers to questions like "how long does it take to visit a tourist attraction?" or "what can I visit in one day in this city?" Our method is evaluated and validated by comparing the automatically obtained visit duration times to manual estimations.

or the last few years we have studied the diffusion of private information about users as they visit various Web sites triggering data gathering aggregation by third parties. This paper reports on our longitudinal study consisting of multiple snapshots of our examination of such diffusion over four years. We examine the various technical ways by which third-party aggregators acquire data and the depth of userrelated information acquired. We study techniques for protecting against this privacy diffusion as well as limitations of such techniques. We introduce the concept of secondary privacy damage. Our results show increasing aggregation of user-related data by a steadily decreasing number of entities. A handful of companies are able to track users' movement across almost all of the popular Web sites. Virtually all the protection techniques have significant limitations highlighting the seriousness of the problem and the need for alternate solutions.

SuRF is an Object Oriented - RDF Mapper based on the popular rdflib python library. It exposes the RDF triple sets as sets of resources and seamlessly integrates them into the Object Oriented paradigm of python in a similar manner as ActiveRDF does for ruby

Microframeworks let you build an entire web application in a single file, usually with only one import statement. They are becoming increasingly popular for building small, self-contained applications that perform only one task—Service Oriented Architecture reborn as a combination of the Unix development philosophy and RESTful API design.

The Photos application is one of Facebook’s most popular features. Up to date, users have uploaded over 15 billion photos which makes Facebook the biggest photo sharing website. For each uploaded photo, Facebook generates and stores four images of different sizes, which translates to a total of 60 billion images and 1.5PB of storage. The current growth rate is 220 million new photos per week, which translates to 25TB of additional storage consumed weekly. At the peak there are 550,000 images served per second. These numbers pose a significant challenge for the Facebook photo storage infrastructure.

Using geotag data attached to 35 million photos uploaded to Flickr, David Crandall and colleagues at Cornell University in Ithaca, New York, created accurate global and city maps and identified popular snapping sites.

Eating local food when it’s in season is an increasingly popular goal amongst people who are interested in eating the tastiest, healthiest food while also being good to the environment. Knowing what’s available in your area at a given time of year is often difficult to determine, so we’ve taken on the task of collecting data from a variety of sources and presenting it all in the most understandable of ways.