<article>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#article09_11_30_1629242</id>
	<title>Genetic Algorithm Helps Identify Criminals</title>
	<author>ScuttleMonkey</author>
	<datestamp>1259569320000</datestamp>
	<htmltext><a href="http://poncacityweloveyou.com/" rel="nofollow">Ponca City, We love you</a> writes to tell us that a <a href="http://www.sciencedaily.com/releases/2009/10/091005161328.htm">new software approach to police sketch artists</a> is finding surprising success in a trial run of 15 police departments in the UK and a few other sites.  The software borrows principles from evolution with an <a href="http://www.forensicmag.com/articles.asp?pid=212">interactive genetic algorithm</a> that progressively changes as witnesses try to remember specific details.  Current field trials are reporting an increase in successful identification by as much as double conventional methods.  A <a href="http://www.youtube.com/watch?v=NKujX52iHXw">short video</a> with a few working shots of the new "EFIT-V" system is also available on YouTube. <i>"[Researcher Christopher Solomon]'s software generates its own faces that progressively evolve to match the witness' memories. The witness starts with a general description such as 'I remember a young white male with dark hair.' Nine different computer-generated faces that roughly fit the description are generated, and the witness identifies the best and worst matches. The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features, based on what it learned from the rejected faces.  'Over a number of generations, the computer can learn what face you're looking for,' says Solomon.  The mathematics underlying the software is borrowed from Solomon's experience using optics to image turbulence in the atmosphere in the 1990s."</i></htmltext>
<tokenext>Ponca City , We love you writes to tell us that a new software approach to police sketch artists is finding surprising success in a trial run of 15 police departments in the UK and a few other sites .
The software borrows principles from evolution with an interactive genetic algorithm that progressively changes as witnesses try to remember specific details .
Current field trials are reporting an increase in successful identification by as much as double conventional methods .
A short video with a few working shots of the new " EFIT-V " system is also available on YouTube .
" [ Researcher Christopher Solomon ] 's software generates its own faces that progressively evolve to match the witness ' memories .
The witness starts with a general description such as 'I remember a young white male with dark hair .
' Nine different computer-generated faces that roughly fit the description are generated , and the witness identifies the best and worst matches .
The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features , based on what it learned from the rejected faces .
'Over a number of generations , the computer can learn what face you 're looking for, ' says Solomon .
The mathematics underlying the software is borrowed from Solomon 's experience using optics to image turbulence in the atmosphere in the 1990s .
"</tokentext>
<sentencetext>Ponca City, We love you writes to tell us that a new software approach to police sketch artists is finding surprising success in a trial run of 15 police departments in the UK and a few other sites.
The software borrows principles from evolution with an interactive genetic algorithm that progressively changes as witnesses try to remember specific details.
Current field trials are reporting an increase in successful identification by as much as double conventional methods.
A short video with a few working shots of the new "EFIT-V" system is also available on YouTube.
"[Researcher Christopher Solomon]'s software generates its own faces that progressively evolve to match the witness' memories.
The witness starts with a general description such as 'I remember a young white male with dark hair.
' Nine different computer-generated faces that roughly fit the description are generated, and the witness identifies the best and worst matches.
The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features, based on what it learned from the rejected faces.
'Over a number of generations, the computer can learn what face you're looking for,' says Solomon.
The mathematics underlying the software is borrowed from Solomon's experience using optics to image turbulence in the atmosphere in the 1990s.
"</sentencetext>
</article>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30277722</id>
	<title>Re:GA vs. Hillclimbing</title>
	<author>ascari</author>
	<datestamp>1259588340000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>After watching the simulation of six hillclimbers "breeding" for the fourth time the witness cried "Yes, it's him! It's him! Just get me out of here!"</htmltext>
<tokenext>After watching the simulation of six hillclimbers " breeding " for the fourth time the witness cried " Yes , it 's him !
It 's him !
Just get me out of here !
"</tokentext>
<sentencetext>After watching the simulation of six hillclimbers "breeding" for the fourth time the witness cried "Yes, it's him!
It's him!
Just get me out of here!
"</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30275264</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30275600</id>
	<title>Re:This is actually very cool...</title>
	<author>vlm</author>
	<datestamp>1259579100000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>Imagine, a site that you can go to and evolve the face of the woman of your dreams?</p></div><p>Just the face?  Thats all?  Try again.</p><p>Your project has already been done, anyway.</p><p><a href="http://en.wikipedia.org/wiki/Averageness" title="wikipedia.org">http://en.wikipedia.org/wiki/Averageness</a> [wikipedia.org]</p><p>Weirdly enough, the difference between the 1.0 and 10.0 womens face seems to be little more than body fat percentage.  Actually, IRL for the whole body, isn't the difference between 1.0 and 10.0 little more than body fat percentage?</p></div>
	</htmltext>
<tokenext>Imagine , a site that you can go to and evolve the face of the woman of your dreams ? Just the face ?
Thats all ?
Try again.Your project has already been done , anyway.http : //en.wikipedia.org/wiki/Averageness [ wikipedia.org ] Weirdly enough , the difference between the 1.0 and 10.0 womens face seems to be little more than body fat percentage .
Actually , IRL for the whole body , is n't the difference between 1.0 and 10.0 little more than body fat percentage ?</tokentext>
<sentencetext>Imagine, a site that you can go to and evolve the face of the woman of your dreams?Just the face?
Thats all?
Try again.Your project has already been done, anyway.http://en.wikipedia.org/wiki/Averageness [wikipedia.org]Weirdly enough, the difference between the 1.0 and 10.0 womens face seems to be little more than body fat percentage.
Actually, IRL for the whole body, isn't the difference between 1.0 and 10.0 little more than body fat percentage?
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274526</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274242</id>
	<title>Does it swim?</title>
	<author>bugnuts</author>
	<datestamp>1259573760000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>4</modscore>
	<htmltext><p>Yes: Is it a frog?<br>No: Please enter the type of animal.</p><p>This article reminds me of the old Animal game, where it does a binary search for whatever type of animal you're thinking.  It's been expanded to handle all types of nouns, with a 15-questions interface that is uncanny.</p><p>For another computer-generated facial reconstruction test, take a look at the <a href="http://rogeralsing.com/2008/12/07/genetic-programming-evolution-of-mona-lisa/" title="rogeralsing.com">mona lisa.</a> [rogeralsing.com]</p></htmltext>
<tokenext>Yes : Is it a frog ? No : Please enter the type of animal.This article reminds me of the old Animal game , where it does a binary search for whatever type of animal you 're thinking .
It 's been expanded to handle all types of nouns , with a 15-questions interface that is uncanny.For another computer-generated facial reconstruction test , take a look at the mona lisa .
[ rogeralsing.com ]</tokentext>
<sentencetext>Yes: Is it a frog?No: Please enter the type of animal.This article reminds me of the old Animal game, where it does a binary search for whatever type of animal you're thinking.
It's been expanded to handle all types of nouns, with a 15-questions interface that is uncanny.For another computer-generated facial reconstruction test, take a look at the mona lisa.
[rogeralsing.com]</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274178</id>
	<title>History Lesson?</title>
	<author>Deflagro</author>
	<datestamp>1259573520000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Didn't they used to do this like 100 yrs ago?  "See he looks like an animal, therefore he must be criminal"  I vaguely remember seeing something along the lines of that being a prosecution's argument and it being accepted.</p><p>It's funny that they may have been onto something?<nobr> <wbr></nobr>:P</p></htmltext>
<tokenext>Did n't they used to do this like 100 yrs ago ?
" See he looks like an animal , therefore he must be criminal " I vaguely remember seeing something along the lines of that being a prosecution 's argument and it being accepted.It 's funny that they may have been onto something ?
: P</tokentext>
<sentencetext>Didn't they used to do this like 100 yrs ago?
"See he looks like an animal, therefore he must be criminal"  I vaguely remember seeing something along the lines of that being a prosecution's argument and it being accepted.It's funny that they may have been onto something?
:P</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30276404</id>
	<title>Re:Does it swim?</title>
	<author>jockeys</author>
	<datestamp>1259581860000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Alsing's program is very cool, and I've had a lot of fun playing with it, but it is NOT genetic... it's hillclimbing.</htmltext>
<tokenext>Alsing 's program is very cool , and I 've had a lot of fun playing with it , but it is NOT genetic... it 's hillclimbing .</tokentext>
<sentencetext>Alsing's program is very cool, and I've had a lot of fun playing with it, but it is NOT genetic... it's hillclimbing.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274242</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274644</id>
	<title>Finally...</title>
	<author>ground.zero.612</author>
	<datestamp>1259575260000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>A wizard for designing a criminal suspect on the fly!</p><p>I understand the use of the term genetic in the case of this algorithm, but I can't help but wonder about obligatory Minority Report hypotheticals. In the USA your DNA is been stolen by the Government at birth; apparently they've been doing this since the 1970's. After the Feds work with it, it is then "anonymized" and sold to third parties such as medical research facilities and insurance agencies.</p><p>

Intertwining these technologies leads this avid conspiracy theorist to fanciful visions of a future where one is not guilty because some mutant fortune tellers can see your future crimes, but instead because the computer simply says so.</p></htmltext>
<tokenext>A wizard for designing a criminal suspect on the fly ! I understand the use of the term genetic in the case of this algorithm , but I ca n't help but wonder about obligatory Minority Report hypotheticals .
In the USA your DNA is been stolen by the Government at birth ; apparently they 've been doing this since the 1970 's .
After the Feds work with it , it is then " anonymized " and sold to third parties such as medical research facilities and insurance agencies .
Intertwining these technologies leads this avid conspiracy theorist to fanciful visions of a future where one is not guilty because some mutant fortune tellers can see your future crimes , but instead because the computer simply says so .</tokentext>
<sentencetext>A wizard for designing a criminal suspect on the fly!I understand the use of the term genetic in the case of this algorithm, but I can't help but wonder about obligatory Minority Report hypotheticals.
In the USA your DNA is been stolen by the Government at birth; apparently they've been doing this since the 1970's.
After the Feds work with it, it is then "anonymized" and sold to third parties such as medical research facilities and insurance agencies.
Intertwining these technologies leads this avid conspiracy theorist to fanciful visions of a future where one is not guilty because some mutant fortune tellers can see your future crimes, but instead because the computer simply says so.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274584</id>
	<title>Re:How do you measure success?</title>
	<author>Anonymous</author>
	<datestamp>1259575080000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>They won't present the sketch as evidence to the jury.  They will call the witness and ask him to identify the suspect.  They will be able to do other things like take fingerprints and DNA samples from the scene and match them to the suspect.</p></htmltext>
<tokenext>They wo n't present the sketch as evidence to the jury .
They will call the witness and ask him to identify the suspect .
They will be able to do other things like take fingerprints and DNA samples from the scene and match them to the suspect .</tokentext>
<sentencetext>They won't present the sketch as evidence to the jury.
They will call the witness and ask him to identify the suspect.
They will be able to do other things like take fingerprints and DNA samples from the scene and match them to the suspect.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274394</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274280</id>
	<title>Re:What's genetic about that?</title>
	<author>Kjella</author>
	<datestamp>1259573940000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>From wikipedia:</p><p><div class="quote"><p>A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.</p></div><p>I'm guessing the main feature here is crossover. Basically you have the eyebrows that look like the suspect in one, the jawline in another and it'll probably show you a crossover with both. With a little bit of memory of what features you've already voted for and against in the past you can "assemble" a face by parts adjusting the different features as you see them coming together. It doesn't really sound too complex to me, but making it actually work is a lot of effort.</p></div>
	</htmltext>
<tokenext>From wikipedia : A genetic algorithm ( GA ) is a search technique used in computing to find exact or approximate solutions to optimization and search problems .
Genetic algorithms are categorized as global search heuristics .
Genetic algorithms are a particular class of evolutionary algorithms ( EA ) that use techniques inspired by evolutionary biology such as inheritance , mutation , selection , and crossover.I 'm guessing the main feature here is crossover .
Basically you have the eyebrows that look like the suspect in one , the jawline in another and it 'll probably show you a crossover with both .
With a little bit of memory of what features you 've already voted for and against in the past you can " assemble " a face by parts adjusting the different features as you see them coming together .
It does n't really sound too complex to me , but making it actually work is a lot of effort .</tokentext>
<sentencetext>From wikipedia:A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems.
Genetic algorithms are categorized as global search heuristics.
Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.I'm guessing the main feature here is crossover.
Basically you have the eyebrows that look like the suspect in one, the jawline in another and it'll probably show you a crossover with both.
With a little bit of memory of what features you've already voted for and against in the past you can "assemble" a face by parts adjusting the different features as you see them coming together.
It doesn't really sound too complex to me, but making it actually work is a lot of effort.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274056</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274340</id>
	<title>Perfect tool... to throw investigators off</title>
	<author>Anonymous</author>
	<datestamp>1259574120000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>This is the perfect tool to throw someone off.

</p><p>Commit a crime but "become a victim".  Falsely describe who you saw and bam, they're in jail and you are "free".

</p><p>"Yes ossifer, the man that robbed that liquor store was a black man, about 6-1/2 feet tall, dingy yellow/blonde short hair, lots of tattoos on his body, earrings.  Just looking at him, I think he plays basketball.".  A short time later after the call goes out, some cops arrest Dennis Rodman for a crime he didn't commit.</p></htmltext>
<tokenext>This is the perfect tool to throw someone off .
Commit a crime but " become a victim " .
Falsely describe who you saw and bam , they 're in jail and you are " free " .
" Yes ossifer , the man that robbed that liquor store was a black man , about 6-1/2 feet tall , dingy yellow/blonde short hair , lots of tattoos on his body , earrings .
Just looking at him , I think he plays basketball. " .
A short time later after the call goes out , some cops arrest Dennis Rodman for a crime he did n't commit .</tokentext>
<sentencetext>This is the perfect tool to throw someone off.
Commit a crime but "become a victim".
Falsely describe who you saw and bam, they're in jail and you are "free".
"Yes ossifer, the man that robbed that liquor store was a black man, about 6-1/2 feet tall, dingy yellow/blonde short hair, lots of tattoos on his body, earrings.
Just looking at him, I think he plays basketball.".
A short time later after the call goes out, some cops arrest Dennis Rodman for a crime he didn't commit.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274386</id>
	<title>How valid does it turn out to be?</title>
	<author>Anonymous</author>
	<datestamp>1259574360000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext>There's a fair amount of research on the performance of memory and how our recall of events and things is affected by the very act of being questioned about and actively recalling those memories. Before I relied on this for much of anything, I'd want to see some pretty well controlled studies on just how accurate it is.

For example, they should put the test subjects under some kind of stress, have them look at the person they will have to describe and have sketched, then put them in front of the software (do a control group using traditional sketch-artist techniques, while you're at it. You should be able to do an objective evaluation of the accuracy of the sketch by mathematically comparing it (using existing algorithms developed for facial recognition) to determine just how close the resemblance is.</htmltext>
<tokenext>There 's a fair amount of research on the performance of memory and how our recall of events and things is affected by the very act of being questioned about and actively recalling those memories .
Before I relied on this for much of anything , I 'd want to see some pretty well controlled studies on just how accurate it is .
For example , they should put the test subjects under some kind of stress , have them look at the person they will have to describe and have sketched , then put them in front of the software ( do a control group using traditional sketch-artist techniques , while you 're at it .
You should be able to do an objective evaluation of the accuracy of the sketch by mathematically comparing it ( using existing algorithms developed for facial recognition ) to determine just how close the resemblance is .</tokentext>
<sentencetext>There's a fair amount of research on the performance of memory and how our recall of events and things is affected by the very act of being questioned about and actively recalling those memories.
Before I relied on this for much of anything, I'd want to see some pretty well controlled studies on just how accurate it is.
For example, they should put the test subjects under some kind of stress, have them look at the person they will have to describe and have sketched, then put them in front of the software (do a control group using traditional sketch-artist techniques, while you're at it.
You should be able to do an objective evaluation of the accuracy of the sketch by mathematically comparing it (using existing algorithms developed for facial recognition) to determine just how close the resemblance is.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274176</id>
	<title>GA vs. Hillclimbing</title>
	<author>jockeys</author>
	<datestamp>1259573460000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>4</modscore>
	<htmltext>it seems to me that if you pick the best face from each "generation" and then randomly modify it and pick the best from the next generation, you are merely hillclimbing:
<br>http://en.wikipedia.org/wiki/Hill\_climbing and not using a proper GA.  This seems to be something that the EigenFit package does.
<br> <br>TFA says that up to six faces may be "bred" together resulting in a new generation, which would indeed be genetic, so the EvoFit package seems to be genuinely genetic.
<br> <br>TFA is unsurprisingly short on details, but it seems to me that EigenFit is using hillclimbing (at least partially) while EvoFit is using shotgun-genetic.</htmltext>
<tokenext>it seems to me that if you pick the best face from each " generation " and then randomly modify it and pick the best from the next generation , you are merely hillclimbing : http : //en.wikipedia.org/wiki/Hill \ _climbing and not using a proper GA. This seems to be something that the EigenFit package does .
TFA says that up to six faces may be " bred " together resulting in a new generation , which would indeed be genetic , so the EvoFit package seems to be genuinely genetic .
TFA is unsurprisingly short on details , but it seems to me that EigenFit is using hillclimbing ( at least partially ) while EvoFit is using shotgun-genetic .</tokentext>
<sentencetext>it seems to me that if you pick the best face from each "generation" and then randomly modify it and pick the best from the next generation, you are merely hillclimbing:
http://en.wikipedia.org/wiki/Hill\_climbing and not using a proper GA.  This seems to be something that the EigenFit package does.
TFA says that up to six faces may be "bred" together resulting in a new generation, which would indeed be genetic, so the EvoFit package seems to be genuinely genetic.
TFA is unsurprisingly short on details, but it seems to me that EigenFit is using hillclimbing (at least partially) while EvoFit is using shotgun-genetic.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30275514</id>
	<title>Porky's</title>
	<author>t0qer</author>
	<datestamp>1259578800000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>I couldn't help it, this story made me think of an epic scene from the 1982 movie porky's.  In the movie a few young men are looking at girls through a peep hole in the girls locker room shower.  One young man sticks his talleywhacker through the hole and almost has it torn off by the lesbian'ish PE teacher.  Anyways, here's a synopsis of the following scene, courtesy of imdb.</p><p>Balbricker: Now, Mr. Carter. I know this is completely unorthodox. But I think this is the only way to find that boy. Now that penis had a mole on it - I'd recognize that penis anywhere. In spite of the juvenile snickers of some, this is a serious matter. That seducer and despoiler must be stopped; he's extremely dangerous. And, Mr. Carter, I'm certain that everyone in this room knows who that is. He's a contemptible little pervert who...<br>Mr. Carter: Miss Balbricker!<br>Balbricker: Well, I'm sorry, but I've got him now, and I'm not going to let him slip through my fingers again. Now, all I'm asking is that you give me five boys for a few minutes. The coaches can be present - Tommy Turner and any four boys you see fit to choose and we... and we... can put a stop to this menace. And it is a menace.<br>[pause]<br>Balbricker: Well, what are you gonna do about it?<br>Mr. Carter: Five young boys in the nude, a police line-up so that you can identify his tallywhacker. Please, please can we call it a "tallywhacker"? Penis is so ppp... penis is so personal.<br>Balbricker: We can put hoods over their heads to avoid embarrassment. Now listen: we have got to do it, as distasteful as it is. I know it's him. That<br>[pause]<br>Balbricker: tallywhacker had a mole on it. And that mole is the key to it.<br>Mr. Carter: Miss Balbricker, do you realize the difficulty of your request? Now, I would be very happy to, uh, to apprehend the young man myself. But can you imagine what the board of education would say if you were granted a line-up in order to examine their private pa... their private parts for an incriminating mole?<br>Balbricker: But Mr. Carter.<br>Coach Brakett: Mr. Carter, I think I have a way out of this. We, uh, call the police, and we have 'em send over one of their sketch artists. And Miss Balbricker can give a description. We can put up "Wanted" posters all over school..."Have you seen this prick? Report immediately to Beulah Balbricker. Do not attempt to apprehend this prick, as it is armed and dangerous. It was last seen hanging out in the girls' locker room at Angel Beach High School."</p></htmltext>
<tokenext>I could n't help it , this story made me think of an epic scene from the 1982 movie porky 's .
In the movie a few young men are looking at girls through a peep hole in the girls locker room shower .
One young man sticks his talleywhacker through the hole and almost has it torn off by the lesbian'ish PE teacher .
Anyways , here 's a synopsis of the following scene , courtesy of imdb.Balbricker : Now , Mr. Carter. I know this is completely unorthodox .
But I think this is the only way to find that boy .
Now that penis had a mole on it - I 'd recognize that penis anywhere .
In spite of the juvenile snickers of some , this is a serious matter .
That seducer and despoiler must be stopped ; he 's extremely dangerous .
And , Mr. Carter , I 'm certain that everyone in this room knows who that is .
He 's a contemptible little pervert who...Mr. Carter : Miss Balbricker ! Balbricker : Well , I 'm sorry , but I 've got him now , and I 'm not going to let him slip through my fingers again .
Now , all I 'm asking is that you give me five boys for a few minutes .
The coaches can be present - Tommy Turner and any four boys you see fit to choose and we... and we... can put a stop to this menace .
And it is a menace .
[ pause ] Balbricker : Well , what are you gon na do about it ? Mr .
Carter : Five young boys in the nude , a police line-up so that you can identify his tallywhacker .
Please , please can we call it a " tallywhacker " ?
Penis is so ppp... penis is so personal.Balbricker : We can put hoods over their heads to avoid embarrassment .
Now listen : we have got to do it , as distasteful as it is .
I know it 's him .
That [ pause ] Balbricker : tallywhacker had a mole on it .
And that mole is the key to it.Mr .
Carter : Miss Balbricker , do you realize the difficulty of your request ?
Now , I would be very happy to , uh , to apprehend the young man myself .
But can you imagine what the board of education would say if you were granted a line-up in order to examine their private pa... their private parts for an incriminating mole ? Balbricker : But Mr. Carter.Coach Brakett : Mr. Carter , I think I have a way out of this .
We , uh , call the police , and we have 'em send over one of their sketch artists .
And Miss Balbricker can give a description .
We can put up " Wanted " posters all over school... " Have you seen this prick ?
Report immediately to Beulah Balbricker .
Do not attempt to apprehend this prick , as it is armed and dangerous .
It was last seen hanging out in the girls ' locker room at Angel Beach High School .
"</tokentext>
<sentencetext>I couldn't help it, this story made me think of an epic scene from the 1982 movie porky's.
In the movie a few young men are looking at girls through a peep hole in the girls locker room shower.
One young man sticks his talleywhacker through the hole and almost has it torn off by the lesbian'ish PE teacher.
Anyways, here's a synopsis of the following scene, courtesy of imdb.Balbricker: Now, Mr. Carter. I know this is completely unorthodox.
But I think this is the only way to find that boy.
Now that penis had a mole on it - I'd recognize that penis anywhere.
In spite of the juvenile snickers of some, this is a serious matter.
That seducer and despoiler must be stopped; he's extremely dangerous.
And, Mr. Carter, I'm certain that everyone in this room knows who that is.
He's a contemptible little pervert who...Mr. Carter: Miss Balbricker!Balbricker: Well, I'm sorry, but I've got him now, and I'm not going to let him slip through my fingers again.
Now, all I'm asking is that you give me five boys for a few minutes.
The coaches can be present - Tommy Turner and any four boys you see fit to choose and we... and we... can put a stop to this menace.
And it is a menace.
[pause]Balbricker: Well, what are you gonna do about it?Mr.
Carter: Five young boys in the nude, a police line-up so that you can identify his tallywhacker.
Please, please can we call it a "tallywhacker"?
Penis is so ppp... penis is so personal.Balbricker: We can put hoods over their heads to avoid embarrassment.
Now listen: we have got to do it, as distasteful as it is.
I know it's him.
That[pause]Balbricker: tallywhacker had a mole on it.
And that mole is the key to it.Mr.
Carter: Miss Balbricker, do you realize the difficulty of your request?
Now, I would be very happy to, uh, to apprehend the young man myself.
But can you imagine what the board of education would say if you were granted a line-up in order to examine their private pa... their private parts for an incriminating mole?Balbricker: But Mr. Carter.Coach Brakett: Mr. Carter, I think I have a way out of this.
We, uh, call the police, and we have 'em send over one of their sketch artists.
And Miss Balbricker can give a description.
We can put up "Wanted" posters all over school..."Have you seen this prick?
Report immediately to Beulah Balbricker.
Do not attempt to apprehend this prick, as it is armed and dangerous.
It was last seen hanging out in the girls' locker room at Angel Beach High School.
"</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274182</id>
	<title>Re:What's genetic about that?</title>
	<author>Anonymous</author>
	<datestamp>1259573520000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>1</modscore>
	<htmltext><p><a href="http://en.wikipedia.org/wiki/Genetic\_algorithm" title="wikipedia.org" rel="nofollow">"Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover."</a> [wikipedia.org]</p></htmltext>
<tokenext>" Genetic algorithms are a particular class of evolutionary algorithms ( EA ) that use techniques inspired by evolutionary biology such as inheritance , mutation , selection , and crossover .
" [ wikipedia.org ]</tokentext>
<sentencetext>"Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.
" [wikipedia.org]</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274056</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30276908</id>
	<title>Re:It's about time for GP</title>
	<author>Delkster</author>
	<datestamp>1259583960000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>I think GAs have definitely had a time when they were popular at least as an idea, mostly sometime in the early 90's or so, and there was quite a bit of research into applying them to various problems. They haven't always turned out to perform very well, though. Quite a few attempts have been made towards using GAs as a heuristic to traditional NP-hard combinatorial problems, for example, and while there has been some success, quite often other heuristics have beaten GAs.</p><p>My impression of the beauty of GAs in general isn't quite as positive as yours. The idea certainly is aesthetically pleasing, and you can, in theory, try to apply a GA to pretty much any optimization problem, but how well GAs work really depends a lot on the problem: the very nature of the problem (does it fulfill the building block hypothesis, or whatever magic is that makes GAs work for <i>some</i> problems?), what kind of a landscape the search space provides, what kinds of cases of the problem are more likely in your application, etc. That's not including all the nontrivial problem-specific tweaking that will be needed in a practical application of a GA, such as how to encode or represent the solutions (has a big effect on how much good genetic crossover does).</p><p>I'd rather say that GAs have worked well for some specific problems, and some new specific applications will probably still emerge, but I'm not sure they will ever become very generally applicable. They had a chance, but it turned out that they mostly work just for some particular problems, not others, and nobody seems to really know very well why.</p></htmltext>
<tokenext>I think GAs have definitely had a time when they were popular at least as an idea , mostly sometime in the early 90 's or so , and there was quite a bit of research into applying them to various problems .
They have n't always turned out to perform very well , though .
Quite a few attempts have been made towards using GAs as a heuristic to traditional NP-hard combinatorial problems , for example , and while there has been some success , quite often other heuristics have beaten GAs.My impression of the beauty of GAs in general is n't quite as positive as yours .
The idea certainly is aesthetically pleasing , and you can , in theory , try to apply a GA to pretty much any optimization problem , but how well GAs work really depends a lot on the problem : the very nature of the problem ( does it fulfill the building block hypothesis , or whatever magic is that makes GAs work for some problems ?
) , what kind of a landscape the search space provides , what kinds of cases of the problem are more likely in your application , etc .
That 's not including all the nontrivial problem-specific tweaking that will be needed in a practical application of a GA , such as how to encode or represent the solutions ( has a big effect on how much good genetic crossover does ) .I 'd rather say that GAs have worked well for some specific problems , and some new specific applications will probably still emerge , but I 'm not sure they will ever become very generally applicable .
They had a chance , but it turned out that they mostly work just for some particular problems , not others , and nobody seems to really know very well why .</tokentext>
<sentencetext>I think GAs have definitely had a time when they were popular at least as an idea, mostly sometime in the early 90's or so, and there was quite a bit of research into applying them to various problems.
They haven't always turned out to perform very well, though.
Quite a few attempts have been made towards using GAs as a heuristic to traditional NP-hard combinatorial problems, for example, and while there has been some success, quite often other heuristics have beaten GAs.My impression of the beauty of GAs in general isn't quite as positive as yours.
The idea certainly is aesthetically pleasing, and you can, in theory, try to apply a GA to pretty much any optimization problem, but how well GAs work really depends a lot on the problem: the very nature of the problem (does it fulfill the building block hypothesis, or whatever magic is that makes GAs work for some problems?
), what kind of a landscape the search space provides, what kinds of cases of the problem are more likely in your application, etc.
That's not including all the nontrivial problem-specific tweaking that will be needed in a practical application of a GA, such as how to encode or represent the solutions (has a big effect on how much good genetic crossover does).I'd rather say that GAs have worked well for some specific problems, and some new specific applications will probably still emerge, but I'm not sure they will ever become very generally applicable.
They had a chance, but it turned out that they mostly work just for some particular problems, not others, and nobody seems to really know very well why.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274502</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30277096</id>
	<title>Vaguely remember?</title>
	<author>flyingfsck</author>
	<datestamp>1259584800000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Damn, you are that old?  No wonder that you only vaguely remember it, but my goodness, you must be the oldest Slashdotter.</htmltext>
<tokenext>Damn , you are that old ?
No wonder that you only vaguely remember it , but my goodness , you must be the oldest Slashdotter .</tokentext>
<sentencetext>Damn, you are that old?
No wonder that you only vaguely remember it, but my goodness, you must be the oldest Slashdotter.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274178</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30275392</id>
	<title>Yes, but is it better than...</title>
	<author>Smivs</author>
	<datestamp>1259578440000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p> <a href="http://news.sky.com/skynews/Home/World-News/Bolivia-Worst-E-Fit-Sketch-Helps-Police-Arrest-Suspect-In-Taxi-Driver-Rafael-Vargas-Murder/Article/200911415465584?lid=ARTICLE\_15465584\_Bolivia:WorstE-FitSketchHelpsPoliceArrestSuspectInTaxiDriverRafaelVargasMurder&amp;lpos=searchresults" title="sky.com">this e-fit</a> [sky.com] that helped the Bolivian Police track down a murder suspect</p></htmltext>
<tokenext>this e-fit [ sky.com ] that helped the Bolivian Police track down a murder suspect</tokentext>
<sentencetext> this e-fit [sky.com] that helped the Bolivian Police track down a murder suspect</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274198</id>
	<title>The scene opens with...</title>
	<author>Anonymous</author>
	<datestamp>1259573640000</datestamp>
	<modclass>Funny</modclass>
	<modscore>3</modscore>
	<htmltext><p>...a police artist sitting at a sketch pad drawing a helical structure. He glances back at a witness sitting across the desk. After drawing two intertwined double-helices, he begins filling in base pairs like the rungs of a ladder. He draws Guanine joining a Cytosine. And just as he finishes the Adenine joining a Thiamine the witness screams "That's the guy!"</p></htmltext>
<tokenext>...a police artist sitting at a sketch pad drawing a helical structure .
He glances back at a witness sitting across the desk .
After drawing two intertwined double-helices , he begins filling in base pairs like the rungs of a ladder .
He draws Guanine joining a Cytosine .
And just as he finishes the Adenine joining a Thiamine the witness screams " That 's the guy !
"</tokentext>
<sentencetext>...a police artist sitting at a sketch pad drawing a helical structure.
He glances back at a witness sitting across the desk.
After drawing two intertwined double-helices, he begins filling in base pairs like the rungs of a ladder.
He draws Guanine joining a Cytosine.
And just as he finishes the Adenine joining a Thiamine the witness screams "That's the guy!
"</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274104</id>
	<title>Impossible.</title>
	<author>Luke727</author>
	<datestamp>1259573220000</datestamp>
	<modclass>Troll</modclass>
	<modscore>-1</modscore>
	<htmltext><p>Most criminals are young black men who all look the same.</p></htmltext>
<tokenext>Most criminals are young black men who all look the same .</tokentext>
<sentencetext>Most criminals are young black men who all look the same.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30277262</id>
	<title>Re:GA vs. Hillclimbing</title>
	<author>Tablizer</author>
	<datestamp>1259585580000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><blockquote><div><p>it seems to me that if you pick the best face from each "generation" and then randomly modify it and pick the best from the next generation, you are merely hillclimbing</p></div></blockquote><p>How is natural selection *not* hill-climbing? I agree that the hopped valleys may be much deeper in real evolution, but it's merely a matter of degree, not existence of.<br>
&nbsp; &nbsp; &nbsp; &nbsp;</p></div>
	</htmltext>
<tokenext>it seems to me that if you pick the best face from each " generation " and then randomly modify it and pick the best from the next generation , you are merely hillclimbingHow is natural selection * not * hill-climbing ?
I agree that the hopped valleys may be much deeper in real evolution , but it 's merely a matter of degree , not existence of .
       </tokentext>
<sentencetext>it seems to me that if you pick the best face from each "generation" and then randomly modify it and pick the best from the next generation, you are merely hillclimbingHow is natural selection *not* hill-climbing?
I agree that the hopped valleys may be much deeper in real evolution, but it's merely a matter of degree, not existence of.
       
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274176</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274394</id>
	<title>How do you measure success?</title>
	<author>Hatta</author>
	<datestamp>1259574360000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>How do they know if this thing actually works?  If they're using the computer generated sketch to finger a suspect, and then presenting that sketch as evidence to a jury who convicts, and then using that conviction as evidence of the algorithms accuracy that's just circular reasoning.</p><p>The memory is not an immutable thing.  It's quite possible that in the process of generating the sketch you are leading the witness on, even implanting memories.  So what happens if you generate a sketch that doesn't look like the actual criminal, and present that to a jury and get a conviction.  Is that going to be counted as a success?</p></htmltext>
<tokenext>How do they know if this thing actually works ?
If they 're using the computer generated sketch to finger a suspect , and then presenting that sketch as evidence to a jury who convicts , and then using that conviction as evidence of the algorithms accuracy that 's just circular reasoning.The memory is not an immutable thing .
It 's quite possible that in the process of generating the sketch you are leading the witness on , even implanting memories .
So what happens if you generate a sketch that does n't look like the actual criminal , and present that to a jury and get a conviction .
Is that going to be counted as a success ?</tokentext>
<sentencetext>How do they know if this thing actually works?
If they're using the computer generated sketch to finger a suspect, and then presenting that sketch as evidence to a jury who convicts, and then using that conviction as evidence of the algorithms accuracy that's just circular reasoning.The memory is not an immutable thing.
It's quite possible that in the process of generating the sketch you are leading the witness on, even implanting memories.
So what happens if you generate a sketch that doesn't look like the actual criminal, and present that to a jury and get a conviction.
Is that going to be counted as a success?</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274324</id>
	<title>Re:History Lesson?</title>
	<author>TheThiefMaster</author>
	<datestamp>1259574060000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Um, no?</p><p>This is about an eyewitness trying to create an image of the criminal for police to track down, by choosing between multiple different images based on their last choice. Instead of the traditional approach of choosing eyes, hair, lips, nose etc individually and then assembling the final image, the witness guides the system towards the result.</p><p>It has nothing to do with deciding that someone is a criminal because of their genetics. The title could be taken to mean that, but the summary is pretty clear.</p></htmltext>
<tokenext>Um , no ? This is about an eyewitness trying to create an image of the criminal for police to track down , by choosing between multiple different images based on their last choice .
Instead of the traditional approach of choosing eyes , hair , lips , nose etc individually and then assembling the final image , the witness guides the system towards the result.It has nothing to do with deciding that someone is a criminal because of their genetics .
The title could be taken to mean that , but the summary is pretty clear .</tokentext>
<sentencetext>Um, no?This is about an eyewitness trying to create an image of the criminal for police to track down, by choosing between multiple different images based on their last choice.
Instead of the traditional approach of choosing eyes, hair, lips, nose etc individually and then assembling the final image, the witness guides the system towards the result.It has nothing to do with deciding that someone is a criminal because of their genetics.
The title could be taken to mean that, but the summary is pretty clear.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274178</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274370</id>
	<title>Re:History Lesson?</title>
	<author>omnichad</author>
	<datestamp>1259574240000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>At least read the summary.  Helps identify as in helps create a sketch.</p></htmltext>
<tokenext>At least read the summary .
Helps identify as in helps create a sketch .</tokentext>
<sentencetext>At least read the summary.
Helps identify as in helps create a sketch.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274178</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30277382</id>
	<title>Re:It's about time for GP</title>
	<author>Tablizer</author>
	<datestamp>1259586300000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><blockquote><div><p>essentially breathe life into binary data, <b>becoming a God</b>, and allowing 'your people' to evolve into a solution to your problem.</p></div></blockquote><p>Sergeant: "Odd, why do all the people we arrest look like Jesus?"<br>
&nbsp; &nbsp;</p></div>
	</htmltext>
<tokenext>essentially breathe life into binary data , becoming a God , and allowing 'your people ' to evolve into a solution to your problem.Sergeant : " Odd , why do all the people we arrest look like Jesus ?
"    </tokentext>
<sentencetext>essentially breathe life into binary data, becoming a God, and allowing 'your people' to evolve into a solution to your problem.Sergeant: "Odd, why do all the people we arrest look like Jesus?
"
   
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274502</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30275482</id>
	<title>Re:How valid does it turn out to be?</title>
	<author>vlm</author>
	<datestamp>1259578740000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>Before I relied on this for much of anything, I'd want to see some pretty well controlled studies on just how accurate it is. For example, they should put the test subjects under some kind of stress,</p></div><p>That is an interesting, but tedious way to test the overall system on average across all users.  I think I could test the hillclimber algorithm itself, on an individual user, by making the algorithm converge intentionally slowly, simulated annealing style.  Then pester the person with lots of very slowly converging and even occasionally diverging sets of faces, and see how "consistent" the persons answers are compared to their final answer.  Does the user always select the bushy eyebrows, or only about 50\% of the time, etc?  Perhaps even assign a numerical or verbal value to their consistency as part of the final report each time the system is used.</p></div>
	</htmltext>
<tokenext>Before I relied on this for much of anything , I 'd want to see some pretty well controlled studies on just how accurate it is .
For example , they should put the test subjects under some kind of stress,That is an interesting , but tedious way to test the overall system on average across all users .
I think I could test the hillclimber algorithm itself , on an individual user , by making the algorithm converge intentionally slowly , simulated annealing style .
Then pester the person with lots of very slowly converging and even occasionally diverging sets of faces , and see how " consistent " the persons answers are compared to their final answer .
Does the user always select the bushy eyebrows , or only about 50 \ % of the time , etc ?
Perhaps even assign a numerical or verbal value to their consistency as part of the final report each time the system is used .</tokentext>
<sentencetext>Before I relied on this for much of anything, I'd want to see some pretty well controlled studies on just how accurate it is.
For example, they should put the test subjects under some kind of stress,That is an interesting, but tedious way to test the overall system on average across all users.
I think I could test the hillclimber algorithm itself, on an individual user, by making the algorithm converge intentionally slowly, simulated annealing style.
Then pester the person with lots of very slowly converging and even occasionally diverging sets of faces, and see how "consistent" the persons answers are compared to their final answer.
Does the user always select the bushy eyebrows, or only about 50\% of the time, etc?
Perhaps even assign a numerical or verbal value to their consistency as part of the final report each time the system is used.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274386</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274056</id>
	<title>What's genetic about that?</title>
	<author>Anonymous</author>
	<datestamp>1259573100000</datestamp>
	<modclass>Offtopic</modclass>
	<modscore>0</modscore>
	<htmltext>I understand it's an evolutionary algorithm, but it has nothing to do with DNA.</htmltext>
<tokenext>I understand it 's an evolutionary algorithm , but it has nothing to do with DNA .</tokentext>
<sentencetext>I understand it's an evolutionary algorithm, but it has nothing to do with DNA.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30275214</id>
	<title>Re:How do you measure success?</title>
	<author>CraftyJack</author>
	<datestamp>1259577780000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>How do they know if this thing actually works?</p></div><p>Especially if no one has actually *seen* Keyser Soze.  And like that, poof. He's gone.</p></div>
	</htmltext>
<tokenext>How do they know if this thing actually works ? Especially if no one has actually * seen * Keyser Soze .
And like that , poof .
He 's gone .</tokentext>
<sentencetext>How do they know if this thing actually works?Especially if no one has actually *seen* Keyser Soze.
And like that, poof.
He's gone.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274394</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274634</id>
	<title>Re:Perfect tool... to throw investigators off</title>
	<author>Sir\_Lewk</author>
	<datestamp>1259575260000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>And this is different from any other sort of eye witness accounts how exactly?</p></htmltext>
<tokenext>And this is different from any other sort of eye witness accounts how exactly ?</tokentext>
<sentencetext>And this is different from any other sort of eye witness accounts how exactly?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274340</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30280882</id>
	<title>EFIT-V</title>
	<author>mayhem79</author>
	<datestamp>1259663340000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>My name is Dr Matthew Maylin, I developed this software at the university of Kent during my PhD, and continue to develope it as the sole software engineer for the company.

The algorithm used is an evolutionary algorithm, implementing random mutations, but no cross-over or mutation. Although the user does have the option to 'bred'/combine certain faces within the software.

The method uses a statistical model of the human face (Cootes et el 2001), and at anyone time is restricted to a sample to a single ethnicity and gender. The are however, many databases of face statistcs that are used.

Here is a movie of the process: <a href="http://www.visionmetric.com/images/stories/EFIT-V\_demo.htm" title="visionmetric.com" rel="nofollow">http://www.visionmetric.com/images/stories/EFIT-V\_demo.htm</a> [visionmetric.com]

I believe here is the original slashdot post: <a href="http://slashdot.org/articles/04/05/17/1042231.shtml?tid=133&amp;tid=152&amp;tid=185&amp;tid=186" title="slashdot.org" rel="nofollow">http://slashdot.org/articles/04/05/17/1042231.shtml?tid=133&amp;tid=152&amp;tid=185&amp;tid=186</a> [slashdot.org]

There have been many controlled studies on this system, it has been trialled by the forces in the UK over 3 years ago, and now is actively sold across the world. Psychological studies have been made by Dr Graham Pike (seen in the video) at the Open University.

The quality of the images vary and are ultimately limited by the users ability to recall the face - some users are better than others - but generally composites are produced more quickly - to a higher quality - than 'jigsaw' based methods.</htmltext>
<tokenext>My name is Dr Matthew Maylin , I developed this software at the university of Kent during my PhD , and continue to develope it as the sole software engineer for the company .
The algorithm used is an evolutionary algorithm , implementing random mutations , but no cross-over or mutation .
Although the user does have the option to 'bred'/combine certain faces within the software .
The method uses a statistical model of the human face ( Cootes et el 2001 ) , and at anyone time is restricted to a sample to a single ethnicity and gender .
The are however , many databases of face statistcs that are used .
Here is a movie of the process : http : //www.visionmetric.com/images/stories/EFIT-V \ _demo.htm [ visionmetric.com ] I believe here is the original slashdot post : http : //slashdot.org/articles/04/05/17/1042231.shtml ? tid = 133&amp;tid = 152&amp;tid = 185&amp;tid = 186 [ slashdot.org ] There have been many controlled studies on this system , it has been trialled by the forces in the UK over 3 years ago , and now is actively sold across the world .
Psychological studies have been made by Dr Graham Pike ( seen in the video ) at the Open University .
The quality of the images vary and are ultimately limited by the users ability to recall the face - some users are better than others - but generally composites are produced more quickly - to a higher quality - than 'jigsaw ' based methods .</tokentext>
<sentencetext>My name is Dr Matthew Maylin, I developed this software at the university of Kent during my PhD, and continue to develope it as the sole software engineer for the company.
The algorithm used is an evolutionary algorithm, implementing random mutations, but no cross-over or mutation.
Although the user does have the option to 'bred'/combine certain faces within the software.
The method uses a statistical model of the human face (Cootes et el 2001), and at anyone time is restricted to a sample to a single ethnicity and gender.
The are however, many databases of face statistcs that are used.
Here is a movie of the process: http://www.visionmetric.com/images/stories/EFIT-V\_demo.htm [visionmetric.com]

I believe here is the original slashdot post: http://slashdot.org/articles/04/05/17/1042231.shtml?tid=133&amp;tid=152&amp;tid=185&amp;tid=186 [slashdot.org]

There have been many controlled studies on this system, it has been trialled by the forces in the UK over 3 years ago, and now is actively sold across the world.
Psychological studies have been made by Dr Graham Pike (seen in the video) at the Open University.
The quality of the images vary and are ultimately limited by the users ability to recall the face - some users are better than others - but generally composites are produced more quickly - to a higher quality - than 'jigsaw' based methods.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274526</id>
	<title>This is actually very cool...</title>
	<author>jarrowwx</author>
	<datestamp>1259574840000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>3</modscore>
	<htmltext><p>This technology, at its core, is a little bit like <a href="picbreeder.org" title="slashdot.org" rel="nofollow">PicBreeder</a> [slashdot.org].  It doesn't include the complexification, but the principle is the same.</p><p>There is an argument about 'leading the witness' being bandied about as if that makes this thing useless.  If you read the articles, they talk about that, and they show that it is no worse than any existing techniques, gets good results, and works for people that can't work with sketch artists.</p><p>The reality is, this technology has applications beyond what it is being used for.</p><ul>
<li>Imagine, a site that you can go to and evolve the face of the woman of your dreams?</li>
<li>Or the face of a character in the book you are writing.</li>
<li>Or an avatar for the video game you are playing.</li>
<li>Or use the basic tech to create random faces for the crowd for an animated movie.</li>
</ul><p>

Personally, I would *LOVE* to be able to tinker with technology like this.</p></htmltext>
<tokenext>This technology , at its core , is a little bit like PicBreeder [ slashdot.org ] .
It does n't include the complexification , but the principle is the same.There is an argument about 'leading the witness ' being bandied about as if that makes this thing useless .
If you read the articles , they talk about that , and they show that it is no worse than any existing techniques , gets good results , and works for people that ca n't work with sketch artists.The reality is , this technology has applications beyond what it is being used for .
Imagine , a site that you can go to and evolve the face of the woman of your dreams ?
Or the face of a character in the book you are writing .
Or an avatar for the video game you are playing .
Or use the basic tech to create random faces for the crowd for an animated movie .
Personally , I would * LOVE * to be able to tinker with technology like this .</tokentext>
<sentencetext>This technology, at its core, is a little bit like PicBreeder [slashdot.org].
It doesn't include the complexification, but the principle is the same.There is an argument about 'leading the witness' being bandied about as if that makes this thing useless.
If you read the articles, they talk about that, and they show that it is no worse than any existing techniques, gets good results, and works for people that can't work with sketch artists.The reality is, this technology has applications beyond what it is being used for.
Imagine, a site that you can go to and evolve the face of the woman of your dreams?
Or the face of a character in the book you are writing.
Or an avatar for the video game you are playing.
Or use the basic tech to create random faces for the crowd for an animated movie.
Personally, I would *LOVE* to be able to tinker with technology like this.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274502</id>
	<title>It's about time for GP</title>
	<author>Kingrames</author>
	<datestamp>1259574780000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>2</modscore>
	<htmltext>I got the opportunity to do a genetic algorithm at my university for one of my projects, and I'm surprised that only now is this tech becoming slightly popular.<br> <br>

You take a fistful of bad answers to a problem, throw 'em in a breeding pit, and let 'em go at it.
<br>
you essentially breathe life into binary data, becoming a God, and allowing 'your people' to evolve into a solution to your problem.
<br>
I suppose you could call yourself an 'Intelligent Designer', but that lacks panache.</htmltext>
<tokenext>I got the opportunity to do a genetic algorithm at my university for one of my projects , and I 'm surprised that only now is this tech becoming slightly popular .
You take a fistful of bad answers to a problem , throw 'em in a breeding pit , and let 'em go at it .
you essentially breathe life into binary data , becoming a God , and allowing 'your people ' to evolve into a solution to your problem .
I suppose you could call yourself an 'Intelligent Designer ' , but that lacks panache .</tokentext>
<sentencetext>I got the opportunity to do a genetic algorithm at my university for one of my projects, and I'm surprised that only now is this tech becoming slightly popular.
You take a fistful of bad answers to a problem, throw 'em in a breeding pit, and let 'em go at it.
you essentially breathe life into binary data, becoming a God, and allowing 'your people' to evolve into a solution to your problem.
I suppose you could call yourself an 'Intelligent Designer', but that lacks panache.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30277328</id>
	<title>Re:How do you measure success?</title>
	<author>Tablizer</author>
	<datestamp>1259586000000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>I imagine they use test cases, not (just) actual cases. For example, 40 people see the same candidate in person. Then the group is split up into 2 groups of 20. One group does the traditional method and the other does the GA method to derive 40 sketches total. Then an independent panel of judges who don't know about the GA software rate the sketch matches to the original candidate, and the total for each group's sketches is compared.</p></htmltext>
<tokenext>I imagine they use test cases , not ( just ) actual cases .
For example , 40 people see the same candidate in person .
Then the group is split up into 2 groups of 20 .
One group does the traditional method and the other does the GA method to derive 40 sketches total .
Then an independent panel of judges who do n't know about the GA software rate the sketch matches to the original candidate , and the total for each group 's sketches is compared .</tokentext>
<sentencetext>I imagine they use test cases, not (just) actual cases.
For example, 40 people see the same candidate in person.
Then the group is split up into 2 groups of 20.
One group does the traditional method and the other does the GA method to derive 40 sketches total.
Then an independent panel of judges who don't know about the GA software rate the sketch matches to the original candidate, and the total for each group's sketches is compared.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274394</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274268</id>
	<title>You're going to want to read the article</title>
	<author>Wrexs0ul</author>
	<datestamp>1259573880000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>This is about evolving an image from generic features to a specific person by having the viewer rate a series of generated faces from best-to-worst matching.</p><p>Not that this is any better. At best it's leading a witness because it promotes guessing, at worst I feed source imagery of stereotypical "bad guys" and voila: every Snidley Whiplash lookalike in the country is running for the hills.</p><p>-Matt</p></htmltext>
<tokenext>This is about evolving an image from generic features to a specific person by having the viewer rate a series of generated faces from best-to-worst matching.Not that this is any better .
At best it 's leading a witness because it promotes guessing , at worst I feed source imagery of stereotypical " bad guys " and voila : every Snidley Whiplash lookalike in the country is running for the hills.-Matt</tokentext>
<sentencetext>This is about evolving an image from generic features to a specific person by having the viewer rate a series of generated faces from best-to-worst matching.Not that this is any better.
At best it's leading a witness because it promotes guessing, at worst I feed source imagery of stereotypical "bad guys" and voila: every Snidley Whiplash lookalike in the country is running for the hills.-Matt</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274178</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274842</id>
	<title>Re:Does it swim?</title>
	<author>Kozz</author>
	<datestamp>1259576160000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>3</modscore>
	<htmltext><p>Algorithm wonks please correct me if I've got it all wrong, but... I believe a binary search is only performed on a sorted list of items.  What you're describing sounds more like a well-trained decision tree.</p><p>In a similar manner there's pages out there in the triple-dub that ask you questions in an attempt to guess what fictional tv/movie character you're thinking of.  It is trained by the very people who are "playing" the game so that at the end, if the program did not guess correctly, you can enter your answer.  And provided you haven't been giving bogus data, you're helping to provide training data which makes the decision tree even stronger.</p></htmltext>
<tokenext>Algorithm wonks please correct me if I 've got it all wrong , but... I believe a binary search is only performed on a sorted list of items .
What you 're describing sounds more like a well-trained decision tree.In a similar manner there 's pages out there in the triple-dub that ask you questions in an attempt to guess what fictional tv/movie character you 're thinking of .
It is trained by the very people who are " playing " the game so that at the end , if the program did not guess correctly , you can enter your answer .
And provided you have n't been giving bogus data , you 're helping to provide training data which makes the decision tree even stronger .</tokentext>
<sentencetext>Algorithm wonks please correct me if I've got it all wrong, but... I believe a binary search is only performed on a sorted list of items.
What you're describing sounds more like a well-trained decision tree.In a similar manner there's pages out there in the triple-dub that ask you questions in an attempt to guess what fictional tv/movie character you're thinking of.
It is trained by the very people who are "playing" the game so that at the end, if the program did not guess correctly, you can enter your answer.
And provided you haven't been giving bogus data, you're helping to provide training data which makes the decision tree even stronger.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274242</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30275264</id>
	<title>Re:GA vs. Hillclimbing</title>
	<author>vlm</author>
	<datestamp>1259578080000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>TFA says that up to six faces may be "bred" together resulting in a new generation, which would indeed be genetic, so the EvoFit package seems to be genuinely genetic.</p></div><p>Worst case scenario, by breeding faces together, they may only mean six simultaneous hillclimber algorithms, one for the chin, one for the eyes, snout, eyebrows, cheeks, lips, completely independent hillclimbers, one for each region...</p></div>
	</htmltext>
<tokenext>TFA says that up to six faces may be " bred " together resulting in a new generation , which would indeed be genetic , so the EvoFit package seems to be genuinely genetic.Worst case scenario , by breeding faces together , they may only mean six simultaneous hillclimber algorithms , one for the chin , one for the eyes , snout , eyebrows , cheeks , lips , completely independent hillclimbers , one for each region.. .</tokentext>
<sentencetext>TFA says that up to six faces may be "bred" together resulting in a new generation, which would indeed be genetic, so the EvoFit package seems to be genuinely genetic.Worst case scenario, by breeding faces together, they may only mean six simultaneous hillclimber algorithms, one for the chin, one for the eyes, snout, eyebrows, cheeks, lips, completely independent hillclimbers, one for each region...
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274176</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30276604</id>
	<title>Re:It's about time for GP</title>
	<author>Anonymous</author>
	<datestamp>1259582640000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Genetic Algorithms are for situations where you don't otherwise know how to optimize, because the problem is just too complex.  And when that's the situation, then great, use it.  But a lot of the time, people have a good handle on things -- or at least think they do.  I see GA as an solution of last resort.  If you have to use GA, maybe you don't know your problem well enough.  Or maybe you're really working on something difficult, and let's face it: that's a situation <em>most</em> programmers aren't in.</p></htmltext>
<tokenext>Genetic Algorithms are for situations where you do n't otherwise know how to optimize , because the problem is just too complex .
And when that 's the situation , then great , use it .
But a lot of the time , people have a good handle on things -- or at least think they do .
I see GA as an solution of last resort .
If you have to use GA , maybe you do n't know your problem well enough .
Or maybe you 're really working on something difficult , and let 's face it : that 's a situation most programmers are n't in .</tokentext>
<sentencetext>Genetic Algorithms are for situations where you don't otherwise know how to optimize, because the problem is just too complex.
And when that's the situation, then great, use it.
But a lot of the time, people have a good handle on things -- or at least think they do.
I see GA as an solution of last resort.
If you have to use GA, maybe you don't know your problem well enough.
Or maybe you're really working on something difficult, and let's face it: that's a situation most programmers aren't in.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274502</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30275044</id>
	<title>Only 9 choices?</title>
	<author>Anonymous</author>
	<datestamp>1259577120000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><blockquote><div><p>Nine different computer-generated faces that roughly fit the description are generated, and the witness identifies the best and worst matches. The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features, based on what it learned from the rejected faces. "Over a number of generations, the computer can learn what face you're looking for," says Solomon. The mathematics underlying the software is borrowed from Solomon's experience using optics to image turbulence in the atmosphere in the 1990s.</p></div></blockquote><p>

The wii gives me a lot more than 9 choices when I make a new Mii, and I don't have to use any math or borrowing<nobr> <wbr></nobr>...</p></div>
	</htmltext>
<tokenext>Nine different computer-generated faces that roughly fit the description are generated , and the witness identifies the best and worst matches .
The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features , based on what it learned from the rejected faces .
" Over a number of generations , the computer can learn what face you 're looking for , " says Solomon .
The mathematics underlying the software is borrowed from Solomon 's experience using optics to image turbulence in the atmosphere in the 1990s .
The wii gives me a lot more than 9 choices when I make a new Mii , and I do n't have to use any math or borrowing .. .</tokentext>
<sentencetext>Nine different computer-generated faces that roughly fit the description are generated, and the witness identifies the best and worst matches.
The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features, based on what it learned from the rejected faces.
"Over a number of generations, the computer can learn what face you're looking for," says Solomon.
The mathematics underlying the software is borrowed from Solomon's experience using optics to image turbulence in the atmosphere in the 1990s.
The wii gives me a lot more than 9 choices when I make a new Mii, and I don't have to use any math or borrowing ...
	</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274576</id>
	<title>Re:History Lesson?</title>
	<author>Anonymous</author>
	<datestamp>1259575080000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Lol. You didn't even read the summary.</p></htmltext>
<tokenext>Lol .
You did n't even read the summary .</tokentext>
<sentencetext>Lol.
You didn't even read the summary.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274178</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30274912</id>
	<title>Mii Channel</title>
	<author>FiloEleven</author>
	<datestamp>1259576460000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>Nine different computer-generated faces that roughly fit the description are generated, and the witness identifies the best and worst matches. The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features, based on what it learned from the rejected faces.</p> </div><p>I immediately thought of the Mii Channel on Wii when I read this.  One of the ways to create a Mii is to start with a bunch of randomized faces and pick the one that looks the most like you (or whoever you're modeling).  From there, it generates 9 variations of that face for you to choose from.  This system is obviously more advanced, but the basic idea is the same.</p></div>
	</htmltext>
<tokenext>Nine different computer-generated faces that roughly fit the description are generated , and the witness identifies the best and worst matches .
The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features , based on what it learned from the rejected faces .
I immediately thought of the Mii Channel on Wii when I read this .
One of the ways to create a Mii is to start with a bunch of randomized faces and pick the one that looks the most like you ( or whoever you 're modeling ) .
From there , it generates 9 variations of that face for you to choose from .
This system is obviously more advanced , but the basic idea is the same .</tokentext>
<sentencetext>Nine different computer-generated faces that roughly fit the description are generated, and the witness identifies the best and worst matches.
The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features, based on what it learned from the rejected faces.
I immediately thought of the Mii Channel on Wii when I read this.
One of the ways to create a Mii is to start with a bunch of randomized faces and pick the one that looks the most like you (or whoever you're modeling).
From there, it generates 9 variations of that face for you to choose from.
This system is obviously more advanced, but the basic idea is the same.
	</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment09_11_30_1629242.30276574</id>
	<title>Oh great, here we go...</title>
	<author>Anonymous</author>
	<datestamp>1259582520000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Welcome to the digital age of racial profiling.</p></htmltext>
<tokenext>Welcome to the digital age of racial profiling .</tokentext>
<sentencetext>Welcome to the digital age of racial profiling.</sentencetext>
</comment>
<thread>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#thread_09_11_30_1629242_14</id>
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	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#thread_09_11_30_1629242_2</id>
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	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#thread_09_11_30_1629242_3</id>
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	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#thread_09_11_30_1629242_7</id>
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	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#thread_09_11_30_1629242_11</id>
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	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#thread_09_11_30_1629242_4</id>
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	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#thread_09_11_30_1629242_15</id>
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