Download Alone Together: Why We Expect More from Technology and Less by Sherry Turkle PDF

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By Sherry Turkle

Examine Facebook—it’s human touch, in simple terms more straightforward to have interaction with and more straightforward to prevent. constructing know-how gives you closeness. occasionally it offers, yet a lot of our sleek existence leaves us much less attached with humans and extra attached to simulations of them.
In Alone Together, MIT expertise and society professor Sherry Turkle explores the facility of our new instruments and toys to dramatically adjust our social lives. It’s a nuanced exploration of what we're having a look for—and sacrificing—in a global of digital partners and social networking instruments, and an issue that, regardless of the hand-waving of today’s self-described prophets of the longer term, it is going to be the following iteration who will chart the trail among isolation and connectivity.

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Of course, SVM is suitable not only for two-class classification but also for m-class classification, where m is more than two. For m-class classification, a simple and natural way is to use m hyper-planes generated by SVM, each of these hyper-planes is a decision rule for one category. Given C = {c1, …, cm}, CS = {cs1, …, csm}, where csi stands for a set of training documents belonging to ci. For ci ³ C, we set positive set ci + – + = csi and negative set ci = ­ csj (j ž i). Using ci and ci as input, we can generate a decision rule Ri for ci by SVM.

Christian) in each category were used as training examples and the last 300 documents were used as test examples. For this paper, we extracted documents from Reuters-21578, with assigning each document to only one category and adopted categories that contain more than 100 training documents. This extracted text collection is named Reuters_100. It is well known that it is not effective to use a small number of training documents for learning classification models. Note that for all test collections, we used a stop-list to remove common words, and the words were stemmed using Porter’s suffix-stripping algorithm [20].

The database is the MS SQL on Windows XP and 2000 server platform. R. S. Harinarayana, and M. Kumbar languages used are Javascript, ASP and HTML. Currently the Vidyanidhi database has upwards of 22,000 records of which 19,000 are for theses in English, 2,200 for theses in Hindi and 640 are for in Kannada. This paper reports the Vidyanidhi experiences in the design, development and implementation of UNICODE for Hindi and Kannada scripts in a database application. The implementation has been fairly successful in meeting the requirements of a database as well the Hindi and Kannada scripts, especially in respect of data entry, display and collation of the records.

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