Comments for Design Perceptive http://www.designperceptive.com function follows form; form follows design Mon, 07 Sep 2009 15:32:16 +0000 hourly 1 http://wordpress.org/?v=3.1 Comment on A Computational Creativity Project at AAAI.UTA by tim http://www.designperceptive.com/2009/09/01/computational-creativity-idea/comment-page-1/#comment-5 tim Mon, 07 Sep 2009 15:32:16 +0000 http://www.designperceptive.com/?p=167#comment-5 Outstanding! My friend and Sr. Design teammate, Brandon Skinner might be interested in putting together the controller board for this. I know he's got the skills to do it. Outstanding! My friend and Sr. Design teammate, Brandon Skinner might be interested in putting together the controller board for this. I know he’s got the skills to do it.

]]> Comment on Zen Home by tim http://www.designperceptive.com/2009/08/14/zen-home/comment-page-1/#comment-4 tim Sun, 16 Aug 2009 23:59:40 +0000 http://www.designperceptive.com/?p=146#comment-4 I've found that results through Google Lattitude may be good enough for my purposes. I found 100% accuracy between classification of being home or not. Out of 144 samples taken at 10-minute intervals over a 24-hour period, during which I was home for 10 hours and out of town for the remaining 14, the system recorded me home. If I extend the sample to the full data set (midnight on the 14th to 1pm on the 16th), then I found small discrepancies. I setup the program to record only the City and State. As I live close to the border between the cities Duncanville and Cedar Hill, I found the resolution of the GPS in my phone to cause some issue. If the phone can only pinpoint its area to a 1400 foot radius, then it may report me in Cedar Hill. To correct this, I may have to use the GPS's lattitudinal and longitudinal coordinates and calibrate. I’ve found that results through Google Lattitude may be good enough for my purposes. I found 100% accuracy between classification of being home or not. Out of 144 samples taken at 10-minute intervals over a 24-hour period, during which I was home for 10 hours and out of town for the remaining 14, the system recorded me home. If I extend the sample to the full data set (midnight on the 14th to 1pm on the 16th), then I found small discrepancies.

I setup the program to record only the City and State. As I live close to the border between the cities Duncanville and Cedar Hill, I found the resolution of the GPS in my phone to cause some issue. If the phone can only pinpoint its area to a 1400 foot radius, then it may report me in Cedar Hill. To correct this, I may have to use the GPS’s lattitudinal and longitudinal coordinates and calibrate.

]]> Comment on Experiment in Context-Aware Computing by tim http://www.designperceptive.com/2009/01/09/experiment-in-context-aware-computing/comment-page-1/#comment-3 tim Thu, 12 Feb 2009 14:59:47 +0000 http://www.designperceptive.com/?p=27#comment-3 Wow! Collecting data by this manner is intrusive! Scratch this. We'll find a better way. Wow! Collecting data by this manner is intrusive! Scratch this. We’ll find a better way.

]]> Comment on Vocabulary Behavior and the Locality Principle by tim http://www.designperceptive.com/2008/11/30/vocabulary-behavior-and-the-locality-principle/comment-page-1/#comment-2 tim Mon, 08 Dec 2008 15:41:22 +0000 http://www.designperceptive.com/?p=6#comment-2 Zipf's law states that given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table. Thus the most frequent word will occur approximately twice as often as the second most frequent word, which occurs twice as often as the fourth most frequent word, etc. For example, in the Brown Corpus "the" is the most frequently occurring word, and by itself accounts for nearly 7% of all word occurrences (69971 out of slightly over 1 million). True to Zipf's Law, the second-place word "of" accounts for slightly over 3.5% of words (36411 occurrences), followed by "and" (28852). Only 135 vocabulary items are needed to account for half the Brown Corpus. - Wikipedia on Zipf's Law. Zipf’s law states that given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table. Thus the most frequent word will occur approximately twice as often as the second most frequent word, which occurs twice as often as the fourth most frequent word, etc. For example, in the Brown Corpus “the” is the most frequently occurring word, and by itself accounts for nearly 7% of all word occurrences (69971 out of slightly over 1 million). True to Zipf’s Law, the second-place word “of” accounts for slightly over 3.5% of words (36411 occurrences), followed by “and” (28852). Only 135 vocabulary items are needed to account for half the Brown Corpus. – Wikipedia on Zipf’s Law.

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