IBM PS/1 model 2121 (1991)
IoT vending machines and lightbulbs corralled into massive botnet - htxt.africa
htxt.africaIoT vending machines and lightbulbs corralled into massive botnet
htxt.africa
Unbeknownst the university, these systems had been compromised and corralled into a botnet that was eating up university resources. Neither the university nor the incident commander who dealt with the situation were named. “My phone lit up with a call …
University attacked by its own vending machines, smart light bulbs & 5000 IoT devicesNetwork World
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(Source: amargedom, via privilegejunkie)
(Source: fuzzyghost, via optimorph)
(Source: aliensfromhell, via echoesfromtheruins)
Remastering Classic Films in Tensorflow with Pix2Pix
Medium article from Arthur Juliani details his experiment with the Pix2Pix neural network image generating framework for the purpose of colourizing black and white film:
I have been working with a Generative Adversarial Network called Pix2Pix for the past few days, and want to share the fruits of the project. This framework comes from the paper “Image-to-Image Translation with Conditional Adversarial Networks” recently out of Berkeley. Unlike vanilla GANs, which take noise inputs and produce images, Pix2Pix learns to take an image and translate it into another image using an adversarial framework. Examples of this include turning street maps into aerial photography, drawings into photographs, and day photos into night photos. Theoretically, translation is possible between any two images which maintain the same structure.
What struck me as a possible and exciting usage was the capacity to colorize black and white photos, as well as fill in the missing gaps in images. Taken together these two capacities could be used to perform a sort of remastering of films from the 1950s and earlier. Films shot in black and white, and at a 4:3 aspect ratio could particularly benefit from this process. This “remastering” would both colorize and extend the aspect ratio to the more familiar 16:9.
(Source: hackernoon.com, via fleshcoatedtechnology)
(Source: fuzzyghost)
