In the good ol’ days of spy vs. spy, the honeypot was a tried and true method espionage technique, laced with danger, intrigue, and sex. These days—as Australian soldiers have found out the hard way—all it takes to seduce your way to state secrets is a Facebook friend request and a Google image search for “hot chicks.”
As Australian news site News.com.au reports, a recent Aussie government look at the unhealthy intermingling of social media and the military, several of its soldiers have fallen victim to the oldest trick in the Facebook; someone pretending to be an attractive, flirtatious girl when in reality they’re not. Except instead of spammers, they get enemies of the state:
The review warns troops to beware of “fake profiles – media personnel and enemies create fake profiles to gather information. For example, the Taliban have used pictures of attractive women as the front of their Facebook profiles and have befriended soldiers.”
Why is that a problem, other than terrorists having access to your karaoke pics? Because soldier status updates can often include the kind of seemingly innocuous information that ends up giving away locations, statuses, and other sensitive details that could get people killed.
The report goes on to say that soldiers have been too trusting of Facebook’s default privacy settings, something which we’ve all fallen victim to at one point or another. Its just that the stakes for us normals aren’t anywhere near as high. But what’s the solution? Either to ban social media for troops altogether—as some have argued in favor of—or to insist on stricter guidelines and, especially, enforcement. Let’s hope the latter proves effective. It’s hard enough serving your country in a far-flung land without feeling even more cut off from the world than geography dictates. [News.com.au via Danger Room]
Google has already been working on patents that could pick out faces and song melodies in our YouTube clips. Now, it might just have the ultimate tool: the technique in a just-granted patent could pick out objects in a video, whether they’re living or not. Instead of asking the creator to label objects every time, Google proposes using a database of “feature vectors” such as color, movement, shape and texture to automatically identify subjects in the frame through their common traits — a cat’s ears and fast movement would separate it from the ball of yarn it’s attacking, for example. Movie makers themselves could provide a lot of the underlying material just by naming and tagging enough of their clips, with the more accurate labels helping to separate the wheat from the chaff if an automated visual ranking system falls short. The one mystery is what Google plans to do with its newfound observational skills, if anything, although the most logical step would be to fill in YouTube keywords without any user intervention — a potential time-saver when we’re uploading that twelfth consecutive pet video.
Filed under: Internet
Google lands patent for automatic object recognition in videos! , leaves no stone untagged originally appeared on Engadget on Tue, 28 Aug 2012 17:31:00 EDT. Please see our terms for use of feeds.
Mankind has been able to accomplish some pretty impressive things, but some of them were around long before we figured them out. Ants, for instance, hunt for food in a way that’s basically the same as the Internet’s Transmission Control Protocol (TCP), and they were doing it long before the Internet was around.
It all has to do with how harvester ants gather their food. The same way that TCP will throttle data transmission if initial packets indicate little bandwidth, harvester ants will send less foragers out for food if the initial ones take too long to come back with grub.
From Stanford News:
[The] rate at which harvester ants – which forage for seeds as individuals – leave the nest to search for food corresponds to food availability.
A forager won’t return to the nest until it finds food. If seeds are plentiful, foragers return faster, and more ants leave the nest to forage. If, however, ants begin returning empty handed, the search is slowed, and perhaps called off.
And that’s not where the similarities end either. Ants also use TCP’s slow start technique, by sending out a wave of foragers (packets) to figure out the relative amount of food (bandwidth) before scaling their numbers up or down. Likewise, the same way a connection will time out if the source stops sending packets, the ants will stop sending out new foragers if none return for 20 minutes.
Balaji Prabhakar, one of the researchers behind the discovery, says that if this behavior had been uncovered pre-Internet, it might have influenced its design. Even so, this foraging process has been seriously time-tested, and there still might be things we can learn from it. In the meantime, who knows what other algorithms might already be out there, quietly waiting to be discovered. [Stanford News]
One of the new iPad’s video features—along with 1080p recording and video stabilization—is temporal noise reduction. Apple claims it will improve the quality of footage in low-light conditions. OK, but what the hell is it?
It’s a clever technique…
There’s no getting around this: temporal noise reduction is tough to explain. That’s because it’s a complex process used to improve image and video rendering. This is very much a simplified explanation of what happens.
…that greatly reduces the noise of video…
When you record footage in low-light conditions, the resulting images are often noisy—speckled with pixelation that looks like a staticky TV screen. Why? Because there’s just not enough light hitting the sensor. In bright conditions, all the light provides a huge signal; noise—from electrical interference or imperfections in the detector—is still present, but it’s drowned out. In low light, the signals are much smaller which means that the noise is painfully apparent.
…by comparing what pixels actually move…
So, onto temporal noise reduction itself. Basically, it exploits the fact that with video there are two pools of data to use: each separate image, and the knowledge of how the frames change with time. Using that information, it’s possible to create an algorithm that can work out which pixels have changed between frames. But it’s also possible to work out which pixels are expected to change between frames. For instance, if a car’s moving from left to right in a frame, software can soon work out that pixels to the right should change dramatically.
…and guessing what is noise and what is actual detail…
By comparing what is expected to change between frames, and what actually does, it’s possible to make a very good educated guess as to which pixels are noisy and which aren’t. Then, the pixels that are deemed noisy can have a new value calculated for them based on their surrounding brothers.
…to make low-light video super-sharp.
So, the process manages to sneakily use data present in the video stream to attenuate the effects of noise and improve the image. It’s something that’s been used in 3D rendering for years, but it requires a fair amount of computational grunt. Clearly, the new iPad can handle that—and as a result, we’ll be fortunate enough to have better low-light video.
a great technique to use to see if your website design is too cluttered or busy is to shrink it down to a thumbnail (like below). You will quickly see that your eye is trying to find something to focus on in each case. If you can’t find the thing to focus on, then you need to go back and simplify the design. Only in rare and specific circumstances should your site deliberately have multiple points of focus. Even then, there should be a sequential order to what the user is led to see.
Dr. Augustine Fou is Digital Consigliere to marketing executives, advising them on digital strategy and Unified Marketing(tm). Dr Fou has over 17 years of in-the-trenches, hands-on experience, which enables him to provide objective, in-depth assessments of their current marketing programs and recommendations for improving business impact and ROI using digital insights.
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