By Ben Shneiderman (auth.), Aijun An, Pawan Lingras, Sheila Petty, Runhe Huang (eds.)
This ebook constitutes the refereed complaints of the sixth foreign convention on energetic Media know-how, AMT 2010, held in Toronto, Canada, in August 2010. The fifty two revised complete papers and a couple of invited talks have been conscientiously reviewed and chosen for inclusion of the publication. they're grouped in topcial sections on energetic computers and clever interfaces; adaptive internet sysetms and knowledge foraging brokers; AMT for semantic net and internet 2.0; facts mining, ontology mining and net reasoning; e-commerce and internet companies; leisure and social functions of energetic media; review of lively media and AMT established platforms; clever info retrieval; laptop studying and human-centered robotics; multi-agent structures; multi-modal processing, detection, reputation, and expression research; semantic computing for energetic media and AMT-based platforms; shrewdpermanent electronic media; Web-based social networks; and internet mining, knowledge net and net intelligence.
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Additional info for Active Media Technology: 6th International Conference, AMT 2010, Toronto, Canada, August 28-30, 2010. Proceedings
In order to enable these buttons, the learner should input plaintext and a key into their respective textboxes. The key can be entered in two formats. The first one is in a binary representation with length equals to 64 bits. The other format is text with a length of at least eight characters. Fig. 1. DES interface before the encryption in details After this preparation, the learner can do the encryption by one of two ways: 1. 2. Encrypt the whole plaintext altogether: This is the usual way. By clicking on "Encrypt" button, the learner gets the resulting ciphertext, see Fig.
By incorporating with an eﬃcient user interest proﬁle algorithm, the bookmarks which match the user better are expected to be re-ranked upward. The topic generation and evaluation is conducted in the following steps . For each document d in the testing set: 1) we generate a topic description using the top k most popular tags associated to the document; 2) we query the topic on a Web search engine and return the top R documents as the topic’s result list; 3) if document d is found in the result list, we apply the user interest proﬁle modeling algorithm to each document in the result list; and 4) we calculate the original ranking position γ1 and the new ranking position γ2 of d and compute Mean Reciprocal Ranking (MRR), which is the average of reciprocal ranking positions in the result list, for all documents in the testing set.
The automatic mode system is especially for technical users who have IT knowledge and who can access Internet. User information is retrieved from Internet rather than inquired him, for each available website source, when user has a web site or may have a Facebook account / any public profile application. This information can be gathered from based on tags. Fig. 1. System Information Flow IG saves users attributes like gender, age, address, facebook name, height, and some other. Website or Facebook username can be used to implicitly capture user’s data and some fields like favorite celebrity and profession.
Active Media Technology: 6th International Conference, AMT 2010, Toronto, Canada, August 28-30, 2010. Proceedings by Ben Shneiderman (auth.), Aijun An, Pawan Lingras, Sheila Petty, Runhe Huang (eds.)