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The blue social bookmark and publication sharing system.
- Gephi is an open-source software for visualizing and analyzing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and spee...Gephi is an open-source software for visualizing and analyzing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and speed up the exploration. Use Gephi to explore, analyse, spatialise, filter, cluterize, manipulate and export all types of graphs.
- Django-filter can be used for generating interfaces similar to the Django admin's ``list_filter`` interface. It has an API very similar to Django's ``Model...Django-filter can be used for generating interfaces similar to the Django admin's ``list_filter`` interface. It has an API very similar to Django's ``ModelForms``. For example if you had a Product model you could have a filterset for it with the code::
- Django-filter can be used for generating interfaces similar to the Django admin's ``list_filter`` interface. It has an API very similar to Django's ``Model...Django-filter can be used for generating interfaces similar to the Django admin's ``list_filter`` interface. It has an API very similar to Django's ``ModelForms``. For example if you had a Product model you could have a filterset for it with the code::
- Browsing and filtering in social web feeds; summarization, recommendation and personalization of messages; An extension could be a semantic enabled filteri...Browsing and filtering in social web feeds; summarization, recommendation and personalization of messages; An extension could be a semantic enabled filtering
- A comprehensive blog post on how web software can leverage collective intelligence to particular effect.
- An introductory, comprehensive article on Collaborative Filtering. It's a bit old, but still is useful and gives some insights on the topic.
- If it's Safari, the PAC command PROXY 127.0.0.1:8228 is placed in a variable (assuming that GlimmerBlocker runs on your local machine on port 8228 -- so th...If it's Safari, the PAC command PROXY 127.0.0.1:8228 is placed in a variable (assuming that GlimmerBlocker runs on your local machine on port 8228 -- so the request will be directed to GlimmerBlocker). If it's any other application, it will get a direct connection to the internet without any proxy (assuming that you don't have any proxy -- otherwise you have to include a PROXY xxx.xxx.xxx.xxx:xxxx line, too) Now in the Network System Preferences panel, go to the Advanced options of your Ethernet or AirPort, and then to the Proxies tab. In pre-10.6, you have to choose the PAC method from a pull-down menu; in 10.6, you have to check Automatic proxy configuration and uncheck all other checkboxes. For
- If it's Safari, the PAC command PROXY 127.0.0.1:8228 is placed in a variable (assuming that GlimmerBlocker runs on your local machine on port 8228 -- so th...If it's Safari, the PAC command PROXY 127.0.0.1:8228 is placed in a variable (assuming that GlimmerBlocker runs on your local machine on port 8228 -- so the request will be directed to GlimmerBlocker). If it's any other application, it will get a direct connection to the internet without any proxy (assuming that you don't have any proxy -- otherwise you have to include a PROXY xxx.xxx.xxx.xxx:xxxx line, too) Now in the Network System Preferences panel, go to the Advanced options of your Ethernet or AirPort, and then to the Proxies tab. In pre-10.6, you have to choose the PAC method from a pull-down menu; in 10.6, you have to check Automatic proxy configuration and uncheck all other checkboxes. For
- In this week's screencast, we'll learn how to implement quick and dirty filtering without a database. By applying some classes and a touch of jQuery, we ca...In this week's screencast, we'll learn how to implement quick and dirty filtering without a database. By applying some classes and a touch of jQuery, we can implement a nice little system very quickly.
- In this week's screencast, we'll learn how to implement quick and dirty filtering without a database. By applying some classes and a touch of jQuery, we ca...In this week's screencast, we'll learn how to implement quick and dirty filtering without a database. By applying some classes and a touch of jQuery, we can implement a nice little system very quickly.
- We investigate the statistical filtering of phishing emails, where a classifier is trained on characteristic features of existing emails and subsequentl...We investigate the statistical filtering of phishing emails, where a classifier is trained on characteristic features of existing emails and subsequently is able to identify new phishing emails with different contents. We propose advanced email features generated by adaptively trained Dynamic Markov Chains and by novel latent Class-Topic Models. On a publicly available test corpus classifiers using these features are able to reduce the number of misclassified emails by two thirds compared to previous work. Using a recently proposed more expressive evaluation method we show that these results are statistically significant. In addition we successfully tested our approach on a non-public email corpus with a real-life composition.
- We have developed a systems that enables the detection of certain common salting tricks that are employed by criminals. Salting is the intentional addit...We have developed a systems that enables the detection of certain common salting tricks that are employed by criminals. Salting is the intentional addition or distortion of content. In this paper we describe a framework to identify email messages that might contain new, previously unseen tricks. To this end, we compare the simulated perceived email message text generated by our hidden salting simulation system to the OCRed text we obtain from the rendered email message. We present robust text comparison techniques and train a classifier based on the differences of these two texts. In simulations we show that we can detect suspicious emails with a high level of accuracy.
- author: Ole Winther, Technical University of Denmark
- Proceedings of the SIGCHI conference on Human factors in computing systems (2003)
- Adv. in Artif. Intell. (January 2009)
- Proceedings of the 1994 ACM Conference on Computer supported cooperative work, page 175--186. New York, NY, USA, ACM, (1994)
- 10. European Academy of Management Conference EURAM 2010, 10, Rome, Italy, (2010)197 45-10 .
- Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation, (2011)
- Journal of Machine Learning Research (June 2009)
- RecSys'10 : proceedings of the 4th ACM Conference on Recommender Systems, 26-30(10):8 (September 2010)
- Proceedings of the ECML/PKDD 2011, (2011)
- Proceedings of the ECML/PKDD 2011, (2011)
- SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, page 532--539. New York, NY, USA, ACM, (2009)
- Proceedings of the SIGCHI conference on Human factors in computing systems (2003)
- Proceedings of the 19th international conference on World wide web, page 1029--1038. New York, NY, USA, ACM, (2010)
- Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation, page 14--18. New York, NY, USA, ACM, (2011)
- Commun. ACM (March 1997)
- WWW '07: Proceedings of the 16th international conference on World Wide Web, page 271--280. New York, NY, USA, ACM, (2007)
- ACM Trans. Inf. Syst. (January 2004)
- (2004)
- Encyclopedia of Data Warehousing and Mining, Idea Group, (2005)
- De Gruyter - Saur, Berlin, (2010)
- Requir. Eng. 12(1):23-40 (2007)
