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	<title>Learn About &#187; computer</title>
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	<link>http://learnaboutseek.com</link>
	<description>Home Technology</description>
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		<title>TED : Jeff Han (2006) Ideas Worth Sharing</title>
		<link>http://learnaboutseek.com/home-technology/ted-jeff-han-2006-ideas-worth-sharing</link>
		<comments>http://learnaboutseek.com/home-technology/ted-jeff-han-2006-ideas-worth-sharing#comments</comments>
		<pubDate>Mon, 10 May 2010 12:47:09 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[design]]></category>
		<category><![CDATA[Han]]></category>
		<category><![CDATA[human]]></category>
		<category><![CDATA[interface]]></category>
		<category><![CDATA[Jeff]]></category>
		<category><![CDATA[multi]]></category>
		<category><![CDATA[screen]]></category>
		<category><![CDATA[Ted]]></category>
		<category><![CDATA[TED2006]]></category>
		<category><![CDATA[Tedtalks]]></category>
		<category><![CDATA[touch]]></category>
		<category><![CDATA[user]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/ted-jeff-han-2006-ideas-worth-sharing</guid>
		<description><![CDATA[http://ted.com/tedtalks &#8211; Jeff Han is a research scientist for NYU&#8217;s Courant Institute of Mathematical Sciences, and the inventor of a MINORITY REPORT STYLE &#8220;interface-free&#8221; touch-driven computer screen (Recorded February 2006 in Monterey, CA. Duration: 09:32)
Duration : 0:9:31
[youtube 5JcSu7h-I40]
]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/5JcSu7h-I40/2.jpg" align="left">http://ted.com/tedtalks &#8211; Jeff Han is a research scientist for NYU&#8217;s Courant Institute of Mathematical Sciences, and the inventor of a MINORITY REPORT STYLE &#8220;interface-free&#8221; touch-driven computer screen (Recorded February 2006 in Monterey, CA. Duration: 09:32)</p>
<p>Duration : <b>0:9:31</b></p>
<p><span id="more-468"></span><br />[youtube 5JcSu7h-I40]</p>
]]></content:encoded>
			<wfw:commentRss>http://learnaboutseek.com/home-technology/ted-jeff-han-2006-ideas-worth-sharing/feed</wfw:commentRss>
		<slash:comments>25</slash:comments>
		</item>
		<item>
		<title>(hardstyle) Crixus &#8211; Computer Technology</title>
		<link>http://learnaboutseek.com/home-technology/hardstyle-crixus-computer-technology</link>
		<comments>http://learnaboutseek.com/home-technology/hardstyle-crixus-computer-technology#comments</comments>
		<pubDate>Fri, 07 May 2010 17:57:07 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[(hardstyle)]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[Crixus]]></category>
		<category><![CDATA[technology]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/hardstyle-crixus-computer-technology</guid>
		<description><![CDATA[(hardstyle) Crixus &#8211; Computer Technology
Duration : 0:4:15
[youtube 5uJtWM3d9gU]
]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/5uJtWM3d9gU/2.jpg" align="left">(hardstyle) Crixus &#8211; Computer Technology</p>
<p>Duration : <b>0:4:15</b></p>
<p><span id="more-460"></span><br />[youtube 5uJtWM3d9gU]</p>
]]></content:encoded>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Lecture 12 &#124; Machine Learning (Stanford)</title>
		<link>http://learnaboutseek.com/home-technology/lecture-12-machine-learning-stanford</link>
		<comments>http://learnaboutseek.com/home-technology/lecture-12-machine-learning-stanford#comments</comments>
		<pubDate>Thu, 06 May 2010 08:57:29 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[algorithm]]></category>
		<category><![CDATA[clustering]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[EM]]></category>
		<category><![CDATA[engineering]]></category>
		<category><![CDATA[expectation]]></category>
		<category><![CDATA[gaussians]]></category>
		<category><![CDATA[inequality]]></category>
		<category><![CDATA[Jensen's]]></category>
		<category><![CDATA[k-means]]></category>
		<category><![CDATA[learning]]></category>
		<category><![CDATA[math]]></category>
		<category><![CDATA[maximization]]></category>
		<category><![CDATA[mixture]]></category>
		<category><![CDATA[robotics]]></category>
		<category><![CDATA[SCIENCE]]></category>
		<category><![CDATA[technology]]></category>
		<category><![CDATA[unsupervised]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/lecture-12-machine-learning-stanford</guid>
		<description><![CDATA[Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng discusses unsupervised learning in the context of clustering, Jensen&#8217;s inequality, mixture of Gaussians, and expectation-maximization. 
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, [...]]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/ZZGTuAkF-Hw/2.jpg" align="left">Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng discusses unsupervised learning in the context of clustering, Jensen&#8217;s inequality, mixture of Gaussians, and expectation-maximization. </p>
<p>This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control.   Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.</p>
<p>Complete Playlist for the Course:<br />
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599</p>
<p>CS 229 Course Website:<br />
http://www.stanford.edu/class/cs229/</p>
<p>Stanford University:<br />
http://www.stanford.edu/</p>
<p>Stanford University Channel on YouTube:<br />
http://www.youtube.com/stanford</p>
<p>Duration : <b>1:14:23</b></p>
<p><span id="more-458"></span><br />[youtube ZZGTuAkF-Hw]</p>
]]></content:encoded>
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		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>Lecture 7 &#124; Machine Learning (Stanford)</title>
		<link>http://learnaboutseek.com/home-technology/lecture-7-machine-learning-stanford</link>
		<comments>http://learnaboutseek.com/home-technology/lecture-7-machine-learning-stanford#comments</comments>
		<pubDate>Mon, 03 May 2010 07:38:41 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[algorithm]]></category>
		<category><![CDATA[classifier]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[convex]]></category>
		<category><![CDATA[dual]]></category>
		<category><![CDATA[engineering]]></category>
		<category><![CDATA[kernels]]></category>
		<category><![CDATA[KKT]]></category>
		<category><![CDATA[learning]]></category>
		<category><![CDATA[margin]]></category>
		<category><![CDATA[math]]></category>
		<category><![CDATA[optimal]]></category>
		<category><![CDATA[optimization]]></category>
		<category><![CDATA[prime]]></category>
		<category><![CDATA[robotics]]></category>
		<category><![CDATA[SCIENCE]]></category>
		<category><![CDATA[SVM]]></category>
		<category><![CDATA[technology]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/lecture-7-machine-learning-stanford</guid>
		<description><![CDATA[Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals. 
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. [...]]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/s8B4A5ubw6c/2.jpg" align="left">Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals. </p>
<p>This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control.   Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.</p>
<p>Complete Playlist for the Course:<br />
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599</p>
<p>CS 229 Course Website:<br />
http://www.stanford.edu/class/cs229/</p>
<p>Stanford University:<br />
http://www.stanford.edu/</p>
<p>Stanford University Channel on YouTube:<br />
http://www.youtube.com/stanford</p>
<p>Duration : <b>1:15:45</b></p>
<p><span id="more-452"></span><br />[youtube s8B4A5ubw6c]</p>
]]></content:encoded>
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		<slash:comments>9</slash:comments>
		</item>
		<item>
		<title>Kwabena Boahen: Making a computer that works like the brain</title>
		<link>http://learnaboutseek.com/home-technology/kwabena-boahen-making-a-computer-that-works-like-the-brain</link>
		<comments>http://learnaboutseek.com/home-technology/kwabena-boahen-making-a-computer-that-works-like-the-brain#comments</comments>
		<pubDate>Sat, 01 May 2010 04:13:13 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[Boahen]]></category>
		<category><![CDATA[brain]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[Kwabena]]></category>
		<category><![CDATA[network]]></category>
		<category><![CDATA[neural]]></category>
		<category><![CDATA[neuron]]></category>
		<category><![CDATA[talks]]></category>
		<category><![CDATA[technology]]></category>
		<category><![CDATA[Ted]]></category>
		<category><![CDATA[Tedtalks]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/kwabena-boahen-making-a-computer-that-works-like-the-brain</guid>
		<description><![CDATA[http://www.ted.com Researcher Kwabena Boahen is looking for ways to mimic the brain&#8217;s supercomputing powers in silicon &#8212; because the messy, redundant processes inside our heads actually make for a small, light, superfast computer.
Duration : 0:17:49
[youtube nyLYQYHGbvI]
]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/nyLYQYHGbvI/2.jpg" align="left">http://www.ted.com Researcher Kwabena Boahen is looking for ways to mimic the brain&#8217;s supercomputing powers in silicon &#8212; because the messy, redundant processes inside our heads actually make for a small, light, superfast computer.</p>
<p>Duration : <b>0:17:49</b></p>
<p><span id="more-447"></span><br />[youtube nyLYQYHGbvI]</p>
]]></content:encoded>
			<wfw:commentRss>http://learnaboutseek.com/home-technology/kwabena-boahen-making-a-computer-that-works-like-the-brain/feed</wfw:commentRss>
		<slash:comments>25</slash:comments>
		</item>
		<item>
		<title>Lecture 4 &#124; Machine Learning (Stanford)</title>
		<link>http://learnaboutseek.com/home-technology/lecture-4-machine-learning-stanford</link>
		<comments>http://learnaboutseek.com/home-technology/lecture-4-machine-learning-stanford#comments</comments>
		<pubDate>Fri, 30 Apr 2010 04:53:36 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[engineering]]></category>
		<category><![CDATA[exponential]]></category>
		<category><![CDATA[family]]></category>
		<category><![CDATA[generalized]]></category>
		<category><![CDATA[linear]]></category>
		<category><![CDATA[logistic]]></category>
		<category><![CDATA[math]]></category>
		<category><![CDATA[method]]></category>
		<category><![CDATA[Model]]></category>
		<category><![CDATA[multinomial]]></category>
		<category><![CDATA[Newton]]></category>
		<category><![CDATA[regression]]></category>
		<category><![CDATA[robotics]]></category>
		<category><![CDATA[SCIENCE]]></category>
		<category><![CDATA[softmax]]></category>
		<category><![CDATA[technology]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/lecture-4-machine-learning-stanford</guid>
		<description><![CDATA[Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on Newton&#8217;s method, exponential families, and generalized linear models and how they relate to machine learning. 
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, [...]]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/nLKOQfKLUks/2.jpg" align="left">Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on Newton&#8217;s method, exponential families, and generalized linear models and how they relate to machine learning. </p>
<p>This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control.   Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.</p>
<p>Complete Playlist for the Course:<br />
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599</p>
<p>CS 229 Course Website:<br />
http://www.stanford.edu/class/cs229/</p>
<p>Stanford University:<br />
http://www.stanford.edu/</p>
<p>Stanford University Channel on YouTube:<br />
http://www.youtube.com/stanford</p>
<p>Duration : <b>1:13:7</b></p>
<p><span id="more-445"></span><br />[youtube nLKOQfKLUks]</p>
]]></content:encoded>
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		<slash:comments>12</slash:comments>
		</item>
		<item>
		<title>Computer Technology : How to Tell if Your Wireless Router Has Been Hacked</title>
		<link>http://learnaboutseek.com/home-technology/computer-technology-how-to-tell-if-your-wireless-router-has-been-hacked</link>
		<comments>http://learnaboutseek.com/home-technology/computer-technology-how-to-tell-if-your-wireless-router-has-been-hacked#comments</comments>
		<pubDate>Thu, 29 Apr 2010 06:43:12 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[drives]]></category>
		<category><![CDATA[files]]></category>
		<category><![CDATA[hard]]></category>
		<category><![CDATA[help]]></category>
		<category><![CDATA[operating]]></category>
		<category><![CDATA[restoring]]></category>
		<category><![CDATA[support]]></category>
		<category><![CDATA[systems]]></category>
		<category><![CDATA[tech]]></category>
		<category><![CDATA[technology]]></category>
		<category><![CDATA[tips]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/computer-technology-how-to-tell-if-your-wireless-router-has-been-hacked</guid>
		<description><![CDATA[When checking to see if a wireless router has been hacked, access the router by typing in 192.168.1.1 into the address bar. View a list of all of the clients who are accessing a router with help from a software developer in this free video on computers and wireless routers.
Duration : 0:3:2
[youtube aPcanGFV16A]
]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/aPcanGFV16A/2.jpg" align="left">When checking to see if a wireless router has been hacked, access the router by typing in 192.168.1.1 into the address bar. View a list of all of the clients who are accessing a router with help from a software developer in this free video on computers and wireless routers.</p>
<p>Duration : <b>0:3:2</b></p>
<p><span id="more-443"></span><br />[youtube aPcanGFV16A]</p>
]]></content:encoded>
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		<slash:comments>10</slash:comments>
		</item>
		<item>
		<title>GEO GEEK.COM THE  leading independent computer technology publication.wmv</title>
		<link>http://learnaboutseek.com/home-technology/geo-geek-com-the-leading-independent-computer-technology-publication-wmv</link>
		<comments>http://learnaboutseek.com/home-technology/geo-geek-com-the-leading-independent-computer-technology-publication-wmv#comments</comments>
		<pubDate>Wed, 28 Apr 2010 09:21:38 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[com]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[GEEK]]></category>
		<category><![CDATA[GEO]]></category>
		<category><![CDATA[independent]]></category>
		<category><![CDATA[leading]]></category>
		<category><![CDATA[publication]]></category>
		<category><![CDATA[technology]]></category>
		<category><![CDATA[the]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/geo-geek-com-the-leading-independent-computer-technology-publication-wmv</guid>
		<description><![CDATA[ 
Duration : 0:2:58
[youtube gd4N8vIwdRA]
]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/gd4N8vIwdRA/2.jpg" align="left"> </p>
<p>Duration : <b>0:2:58</b></p>
<p><span id="more-441"></span><br />[youtube gd4N8vIwdRA]</p>
]]></content:encoded>
			<wfw:commentRss>http://learnaboutseek.com/home-technology/geo-geek-com-the-leading-independent-computer-technology-publication-wmv/feed</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Lecture 3 &#124; Machine Learning (Stanford)</title>
		<link>http://learnaboutseek.com/home-technology/lecture-3-machine-learning-stanford</link>
		<comments>http://learnaboutseek.com/home-technology/lecture-3-machine-learning-stanford#comments</comments>
		<pubDate>Tue, 27 Apr 2010 11:01:15 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[algebra]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[digression]]></category>
		<category><![CDATA[distribution]]></category>
		<category><![CDATA[engineering]]></category>
		<category><![CDATA[Gaussian]]></category>
		<category><![CDATA[interpretation]]></category>
		<category><![CDATA[linear]]></category>
		<category><![CDATA[locally]]></category>
		<category><![CDATA[logistic]]></category>
		<category><![CDATA[math]]></category>
		<category><![CDATA[perceptron]]></category>
		<category><![CDATA[probabilistic]]></category>
		<category><![CDATA[regression]]></category>
		<category><![CDATA[robotics]]></category>
		<category><![CDATA[SCIENCE]]></category>
		<category><![CDATA[technology]]></category>
		<category><![CDATA[weighted]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/lecture-3-machine-learning-stanford</guid>
		<description><![CDATA[Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning. 
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, [...]]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/HZ4cvaztQEs/2.jpg" align="left">Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning. </p>
<p>This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control.   Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.</p>
<p>Complete Playlist for the Course:<br />
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599</p>
<p>CS 229 Course Website:<br />
http://www.stanford.edu/class/cs229/</p>
<p>Stanford University:<br />
http://www.stanford.edu/</p>
<p>Stanford University Channel on YouTube:<br />
http://www.youtube.com/stanford</p>
<p>Duration : <b>1:13:14</b></p>
<p><span id="more-439"></span><br />[youtube HZ4cvaztQEs]</p>
]]></content:encoded>
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		<slash:comments>11</slash:comments>
		</item>
		<item>
		<title>Man &amp; Computer &#8211; IBM 1965</title>
		<link>http://learnaboutseek.com/home-technology/man-computer-ibm-1965</link>
		<comments>http://learnaboutseek.com/home-technology/man-computer-ibm-1965#comments</comments>
		<pubDate>Mon, 26 Apr 2010 14:12:56 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[home technology]]></category>
		<category><![CDATA[computer]]></category>
		<category><![CDATA[documentary]]></category>
		<category><![CDATA[history]]></category>
		<category><![CDATA[IBM]]></category>
		<category><![CDATA[System/360]]></category>
		<category><![CDATA[technology]]></category>

		<guid isPermaLink="false">http://learnaboutseek.com/home-technology/man-computer-ibm-1965</guid>
		<description><![CDATA[The film Man &#38; Computer, made in 1965 by IBM&#8217;s UK branch, provides a basic understanding of computer operations. A large portion of the film shows the ways in which a computer can be simulated by five people using the standard office equipment of the day. The film employs a number of different techniques, including [...]]]></description>
			<content:encoded><![CDATA[<p><img src="http://i.ytimg.com/vi/BUCZJWo9MZo/2.jpg" align="left">The film Man &amp; Computer, made in 1965 by IBM&#8217;s UK branch, provides a basic understanding of computer operations. A large portion of the film shows the ways in which a computer can be simulated by five people using the standard office equipment of the day. The film employs a number of different techniques, including animations, and features a few brief scenes of an IBM System/360 in use—just months after the first machines were delivered.<br />
Starting in the 1940s, IBM became a major producer of films used for sales, training, documenting business processes, entertaining at company functions, and educating the public. Several IBM films were made by respected filmmakers and sometimes featured well-known actors.</p>
<p>Duration : <b>0:22:40</b></p>
<p><span id="more-437"></span><br />[youtube BUCZJWo9MZo]</p>
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