Europe divided over robot ‘personhood’

While autonomous robots with humanlike, all-encompassing capabilities are still decades away, European lawmakers, legal experts and manufacturers are already locked in a high-stakes debate about their legal status: whether it’s these machines or human beings who should bear ultimate responsibility for their actions.

The battle goes back to a paragraph of text, buried deep in a European Parliament report from early 2017, which suggests that self-learning robots could be granted “electronic personalities.” Such a status could allow robots to be insured individually and be held liable for damages if they go rogue and start hurting people or damaging property.

Those pushing for such a legal change, including some manufacturers and their affiliates, say the proposal is common sense. Legal personhood would not make robots virtual people who can get married and benefit from human rights, they say; it would merely put them on par with corporations, which already have status as “legal persons,” and are treated as such by courts around the world.

Source: Europe divided over robot ‘personhood’ – POLITICO

Tried checking under the sofa? Indian BTC exchange Coinsecure finds itself $3.5m lighter

Indian Bitcoin exchange Coinsecure has mislaid 438.318 BTC belonging to its customers.

In a statement by parent firm Secure Bitcoin Traders Pvt, posted late on Thursday, the biz said its chief security officer had extracted a bunch of Bitcoin to distribute to punters – and discovered the funds were “lost in the process.”

The vanished Bitcoin stash was worth £2,493,590 ($3,547,745) at the time of publication, and apparently departed Coinsecure’s secure coin servers on April 9.

Earlier this week, folks began to smell a rat as the site went down for an unexpected nap that day:

Things proceeded to become more alarming for worried customers as Coinsecure stopped accepting deposits due to “backend updates.”

We’re told chief security officer Dr Amitabh Saxena and chief exec Mohit Kalra should have been the only ones with access to the wallet’s private keys. Here’s a crime report the biz filled out and submitted to Indian authorities:

Coinsecure FIR

With Bitcoin values tumbling after historic highs, it seems the quickest way to lose your cryptocurrency is to, er, deposit it somewhere.

Source: Tried checking under the sofa? Indian BTC exchange Coinsecure finds itself $3.5m lighter • The Register

Google uses AI to seperate out audio from a single person in a high noise rate video

People are remarkably good at focusing their attention on a particular person in a noisy environment, mentally “muting” all other voices and sounds. Known as the cocktail party effect, this capability comes natural to us humans. However, automatic speech separation — separating an audio signal into its individual speech sources — while a well-studied problem, remains a significant challenge for computers. In “Looking to Listen at the Cocktail Party”, we present a deep learning audio-visual model for isolating a single speech signal from a mixture of sounds such as other voices and background noise. In this work, we are able to computationally produce videos in which speech of specific people is enhanced while all other sounds are suppressed. Our method works on ordinary videos with a single audio track, and all that is required from the user is to select the face of the person in the video they want to hear, or to have such a person be selected algorithmically based on context. We believe this capability can have a wide range of applications, from speech enhancement and recognition in videos, through video conferencing, to improved hearing aids, especially in situations where there are multiple people speaking.

A unique aspect of our technique is in combining both the auditory and visual signals of an input video to separate the speech. Intuitively, movements of a person’s mouth, for example, should correlate with the sounds produced as that person is speaking, which in turn can help identify which parts of the audio correspond to that person. The visual signal not only improves the speech separation quality significantly in cases of mixed speech (compared to speech separation using audio alone, as we demonstrate in our paper), but, importantly, it also associates the separated, clean speech tracks with the visible speakers in the video.

The input to our method is a video with one or more people speaking, where the speech of interest is interfered by other speakers and/or background noise. The output is a decomposition of the input audio track into clean speech tracks, one for each person detected in the video.

An Audio-Visual Speech Separation Model To generate training examples, we started by gathering a large collection of 100,000 high-quality videos of lectures and talks from YouTube. From these videos, we extracted segments with a clean speech (e.g. no mixed music, audience sounds or other speakers) and with a single speaker visible in the video frames. This resulted in roughly 2000 hours of video clips, each of a single person visible to the camera and talking with no background interference. We then used this clean data to generate “synthetic cocktail parties” — mixtures of face videos and their corresponding speech from separate video sources, along with non-speech background noise we obtained from AudioSet. Using this data, we were able to train a multi-stream convolutional neural network-based model to split the synthetic cocktail mixture into separate audio streams for each speaker in the video. The input to the network are visual features extracted from the face thumbnails of detected speakers in each frame, and a spectrogram representation of the video’s soundtrack. During training, the network learns (separate) encodings for the visual and auditory signals, then it fuses them together to form a joint audio-visual representation. With that joint representation, the network learns to output a time-frequency mask for each speaker. The output masks are multiplied by the noisy input spectrogram and converted back to a time-domain waveform to obtain an isolated, clean speech signal for each speaker. For full details, see our paper.

Our multi-stream, neural network-based model architecture.

Here are some more speech separation and enhancement results by our method, playing first the input video with mixed or noisy speech, then our results. Sound by others than the selected speakers can be entirely suppressed or suppressed to the desired level.

Application to Speech Recognition Our method can also potentially be used as a pre-process for speech recognition and automatic video captioning. Handling overlapping speakers is a known challenge for automatic captioning systems, and separating the audio to the different sources could help in presenting more accurate and easy-to-read captions.

You can similarly see and compare the captions before and after speech separation in all the other videos in this post and on our website, by turning on closed captions in the YouTube player when playing the videos (“cc” button at the lower right corner of the player). On our project web page you can find more results, as well as comparisons with state-of-the-art audio-only speech separation and with other recent audio-visual speech separation work. Indeed, with recent advances in deep learning, there is a clear growing interest in the academic community in audio-visual analysis. For example, independently and concurrently to our work, this work from UC Berkeley explored a self-supervised approach for separating speech of on/off-screen speakers, and this work from MIT addressed the problem of separating the sound of multiple on-screen objects (e.g., musical instruments), while locating the image regions from which the sound originates. We envision a wide range of applications for this technology. We are currently exploring opportunities for incorporating it into various Google products. Stay tuned!

Source: Research Blog: Looking to Listen: Audio-Visual Speech Separation

Watch artificial intelligence create a 3D model of a person—from just a few seconds of video

Transporting yourself into a video game, body and all, just got easier. Artificial intelligence has been used to create 3D models of people’s bodies for virtual reality avatars, surveillance, visualizing fashion, or movies. But it typically requires special camera equipment to detect depth or to view someone from multiple angles. A new algorithm creates 3D models using standard video footage from one angle.

The system has three stages. First, it analyzes a video a few seconds long of someone moving—preferably turning 360° to show all sides—and for each frame creates a silhouette separating the person from the background. Based on machine learning techniques—in which computers learn a task from many examples—it roughly estimates the 3D body shape and location of joints. In the second stage, it “unposes” the virtual human created from each frame, making them all stand with arms out in a T shape, and combines information about the T-posed people into one, more accurate model. Finally, in the third stage, it applies color and texture to the model based on recorded hair, clothing, and skin.

The researchers tested the method with a variety of body shapes, clothing, and backgrounds and found that it had an average accuracy within 5 millimeters, they will report in June at the Computer Vision and Pattern Recognition conference in Salt Lake City. The system can also reproduce the folding and wrinkles of fabric, but it struggles with skirts and long hair. With a model of you, the researchers can change your weight, clothing, and pose—and even make you perform a perfect pirouette. No practice necessary.

Source: Watch artificial intelligence create a 3D model of a person—from just a few seconds of video | Science | AAAS

Whois is dead as Europe hands DNS overlord ICANN its arse :(

The Whois public database of domain name registration details is dead.

In a letter [PDF] sent this week to DNS overseer ICANN, Europe’s data protection authorities have effectively killed off the current service, noting that it breaks the law and so will be illegal come 25 May, when GDPR comes into force.

The letter also has harsh words for ICANN’s proposed interim solution, criticizing its vagueness and noting it needs to include explicit wording about what can be done with registrant data, as well as introduce auditing and compliance functions to make sure the data isn’t being abused.

ICANN now has a little over a month to come up with a replacement to the decades-old service that covers millions of domain names and lists the personal contact details of domain registrants, including their name, email and telephone number.

ICANN has already acknowledged it has no chance of doing so: a blog post by the company in response to the letter warns that without being granted a special temporary exemption from the law, the system will fracture.

“Unless there is a moratorium, we may no longer be able to give instructions to the contracted parties through our agreements to maintain Whois,” it warns. “Without resolution of these issues, the Whois system will become fragmented.”

We spoke with the president of ICANN’s Global Domains Division, Akram Atallah, and he told us that while there was “general agreement that having every thing public is not the right way to go”, he was hopeful that the letter would not result in the Whois service being turned off completely while a replacement was developed.

Source: Whois is dead as Europe hands DNS overlord ICANN its arse • The Register

It’s an important and useful tool – hopefully they will resolve this one way or another.

Orkut Hello: The Man Behind Orkut Says His ‘Hello’ Platform Doesn’t Sell User Data

In 2004, one of the world’s most popular social networks, Orkut, was founded by a former Google employee named Orkut Büyükkökten. Later that year, a Harvard University student named Mark Zuckerberg launched ‘the Facebook’, which over the course of a year became ubiquitous in Ivy League universities and was eventually called Facebook.com.

Orkut was shut down by Google in 2014, but in its heyday, the network had hit 300 million users around the world. Facebook took five years to achieve that feat. At a time when the #DeleteFacebook movement is gaining traction worldwide in light of the Cambridge Analytica scandal, Orkut has made a comeback

“Hello.com is a spiritual successor of Orkut.com,” Büyükkökten told BloombergQuint. “The most important thing about Orkut was communities, because they brought people together around topics and things that interested them and provided a safe place for people to exchange ideas and share genuine passions and feelings. We have built the entire ‘Hello’ experience around communities and passions and see it as Orkut 2.0.”

Orkut has decided to make a comeback when Mark Zuckerberg, founder and CEO of Facebook, has been questioned by U.S. congressmen and senators about its policies and data collection and usage practices. That came after the Cambridge Analytica data leak which impacted nearly 87 million users, including Zuckerberg himself.

“People have lost trust in social networks and the main reason is social media services today don’t put the users first. They put advertisers, brands, third parties, shareholders before the users,” Büyükkökten said. “They are also not transparent about practices. The privacy policy and terms of services are more like black boxes. How many users actually read them?”

Büyükkökten said users need to be educated about these things and user consent is imperative in such situations when data is shared by such platforms. “On Hello, we do not share data with third parties. We have our own registration and login and so the data doesn’t follow you anywhere,”he said. “You don’t need to sell user data in order to be profitable or make money.”

Source: Orkut Hello: The Man Behind Orkut Says His ‘Hello’ Platform Doesn’t Sell User Data – Bloomberg Quint

I am very curious what his business model is then

Do you have a browser based bitcoin wallet? Check you’re not hacked if it’s JavaScript based

A significant number of past and current cryptocurrency products
contain a JavaScript class named SecureRandom(), containing both
entropy collection and a PRNG. The entropy collection and the RNG
itself are both deficient to the degree that key material can be
recovered by a third party with medium complexity. There are a
substantial number of variations of this SecureRandom() class in
various pieces of software, some with bugs fixed, some with additional
bugs added. Products that aren't today vulnerable due to moving to
other libraries may be using old keys that have been previously
compromised by usage of SecureRandom().

Source: [bitcoin-dev] KETAMINE: Multiple vulnerabilities in SecureRandom(), numerous cryptocurrency products affected.

Cops Around the Country Can Now Unlock iPhones, Records Show

Police forces and federal agencies around the country have bought relatively cheap tools to unlock up-to-date iPhones and bypass their encryption, according to a Motherboard investigation based on several caches of internal agency documents, online records, and conversations with law enforcement officials. Many of the documents were obtained by Motherboard using public records requests.

 

The news highlights the going dark debate, in which law enforcement officials say they cannot access evidence against criminals. But easy access to iPhone hacking tools also hamstrings the FBI’s argument for introducing backdoors into consumer devices so authorities can more readily access their contents.

“It demonstrates that even state and local police do have access to this data in many situations,” Matthew Green, an assistant professor and cryptographer at the Johns Hopkins Information Security Institute, told Motherboard in a Twitter message. “This seems to contradict what the FBI is saying about their inability to access these phones.”

As part of the investigation, Motherboard found:

[…]

The GrayKey itself is a small, 4×4 inches box with two lightning cables for connecting iPhones, according to photographs published by cybersecurity firm Malwarebytes. The device comes in two versions: a $15,000 one which requires online connectivity and allows 300 unlocks (or $50 per phone), and and an offline, $30,000 version which can crack as many iPhones as the customer wants. Marketing material seen by Forbes says GrayKey can unlock devices running iterations of Apple’s latest mobile operating system iOS 11, including on the iPhone X, Apple’s most recent phone.

The issue GrayKey overcomes is that iPhones encrypt user data by default. Those in physical possession normally cannot access the phone’s data, such as contact list, saved messages, or photos, without first unlocking the phone with a passcode or fingerprint. Malwarebytes’ post says GrayKey can unlock an iPhone in around two hours, or three days or longer for 6 digit passcodes.

Source: Cops Around the Country Can Now Unlock iPhones, Records Show – Motherboard

India completes its GPS alternative, for the second time

India has successfully conducted the satellite launch needed to re-construct its Indian Regional Navigation Satellite System (IRNSS).

The Indian Space Research Organisation’s Polar Satellite Launch Vehicle PSLV-C41 ascended on Thursday, April 12th. Atop the craft was a satellite designated IRNSS-1L, the last of seven satellites in India’s constellation of navigational craft.

India understands that satellite navigation services have become an assumed resource for all manner of applications, but that relying on another nation’s network is fraught with danger in the event of war or other disputes. Like Russia, China and the European Union, India has therefore decided it needs a satnav system of its own.

[…]

ndia’s already completed the network once before: in April 2016 we covered the launch of IRNSS-G, which at the time was the seventh satellite in the constellation. But just three months later, the first satellite in the fleet broke: IRNSS-1A’s atomic clocks clocked off, leaving India with insufficient satellites to deliver its hoped-for 10-metre accuracy over land.

A replacement satellite, IRNSS-1H, failed to reach its desired orbit in August 2017.

Much rejoicing has therefore followed IRNSS-1L’s success, including the following prime-ministerial Tweet.

India’s said IRNSS has only regional ambitions: its seven satellites cover India and about 1,500km beyond the nation’s borders. But that’s enough distance to help India launch missiles, like its 5,000-km-range Agni-5, deep into Pakistan, China or Russia. Don’t forget: India is a nuclear power! The nation’s suggested it might add some more sats to the service, which would likely extend its range and enhance its accuracy.

Component-makers have already started making receivers capable of linking to INRSS satellites and other similar services, so there’s a decent chance your smartphone will be able to talk to India’s satellites should you visit the region.

Source: India completes its GPS alternative, for the second time • The Register

This AI Can Automatically Animate New Flintstones Cartoons

Researchers have successfully trained artificial intelligence to generate new clips of the prehistoric animated series based on nothing but random text descriptions of what’s happening in a scene.

A team of researchers from the Allen Institute for Artificial Intelligence, and the University of Illinois Urbana-Champaign, trained an AI by feeding it over 25,000 three-second clips of the cartoon, which hasn’t seen any new episodes in over 50 years. Most AI experiments as of late have involved generating freaky images based on what was learned, but this time the researchers included detailed descriptions and annotations of what appeared, and what was happening, in every clip the AI ingested.

As a result, the new Flintstones animations generated by the Allen Institute’s AI aren’t just random collages of chopped up cartoons. Instead, the researchers are able to feed the AI a very specific description of a scene, and it outputs a short clip featuring the characters, props, and locations specified—most of the time.

The quality of the animations that are generated is awful at best; no one’s going to be fooled into thinking these are the Hanna-Barbera originals. But seeing an AI generate a cartoon, featuring iconic characters, all by itself, is a fascinating sneak peek at how some films and TV shows might be made one day.

Source: This AI Can Automatically Animate New Flintstones Cartoons

Properly random random number generator generated

From dice to modern electronic circuits, there have been many attempts to build better devices to generate random numbers. Randomness is fundamental to security and cryptographic systems and to safeguarding privacy. A key challenge with random-number generators is that it is hard to ensure that their outputs are unpredictable1,2,3. For a random-number generator based on a physical process, such as a noisy classical system or an elementary quantum measurement, a detailed model that describes the underlying physics is necessary to assert unpredictability. Imperfections in the model compromise the integrity of the device. However, it is possible to exploit the phenomenon of quantum non-locality with a loophole-free Bell test to build a random-number generator that can produce output that is unpredictable to any adversary that is limited only by general physical principles, such as special relativity1,2,3,4,5,6,7,8,9,10,11. With recent technological developments, it is now possible to carry out such a loophole-free Bell test12,13,14,22. Here we present certified randomness obtained from a photonic Bell experiment and extract 1,024 random bits that are uniformly distributed to within 10−12. These random bits could not have been predicted according to any physical theory that prohibits faster-than-light (superluminal) signalling and that allows independent measurement choices. To certify and quantify the randomness, we describe a protocol that is optimized for devices that are characterized by a low per-trial violation of Bell inequalities. Future random-number generators based on loophole-free Bell tests may have a role in increasing the security and trust of our cryptographic systems and infrastructure.

Source: Experimentally generated randomness certified by the impossibility of superluminal signals | Nature

Data exfiltrators send info over PCs’ power supply cables

If you want your computer to be really secure, disconnect its power cable.

So says Mordechai Guri and his team of side-channel sleuths at the Ben-Gurion University of the Negev.

The crew have penned a paper titled PowerHammer: Exfiltrating Data from Air-Gapped Computers through Power Lines that explains how attackers could install malware that regulates CPU utilisation and creates fluctuations in the current flow that could modulate and encode data. The variations would be “propagated through the power lines” to the outside world.

PowerHammer attack

Put the receiver near the user for highest speed, behind the panel for greatest secrecy

Depending on the attacker’s approach, data could be exfiltrated at between 10 and 1,000 bits-per-second. The higher speed would work if attackers can get at the cable connected to the computer’s power supply. The slower speed works if attackers can only access a building’s electrical services panel.

The PowerHammer malware spikes the CPU utilisation by choosing cores that aren’t currently in use by user operations (to make it less noticeable).

Guri and his pals use frequency shift keying to encode data onto the line.

After that, it’s pretty simple, because all the attacker needs is to decide where to put the receiver current clamp: near the target machine if you can get away with it, behind the switchboard if you have to.

Source: Data exfiltrators send info over PCs’ power supply cables • The Register

FDA approves AI-powered software to detect diabetic retinopathy

30.3 million Americans have diabetes according to a 2015 CDC study. An additional 84.1 million have prediabetes, which often leads to the full disease within five years. It’s important to detect diabetes early to avoid health complications like heart disease, stroke, amputation of extremities and vision loss. Technology increasingly plays an important role in early detection, too. In that vein, the US Food and Drug Administration (FDA) has just approved an AI-powered device that can be used by non-specialists to detect diabetic retinopathy in adults with diabetes.

Diabetic retinopathy occurs when the high levels of blood sugar in the bloodstream cause damage to your retina’s blood vessels. It’s the most common cause of vision loss, according to the FDA. The approval comes for a device called IDx-DR, a software program that uses an AI algorithm to analyze images of the eye that can be taken in a regular doctor’s office with a special camera, the Topcon NW400.

The photos are then uploaded to a server that runs IDx-DR, which can then tell the doctor if there is a more than mild level of diabetic retinopathy present. If not, it will advise a re-screen in 12 months. The device and software can be used by health care providers who don’t normally provide eye care services. The FDA warns that you shouldn’t be screened with the device if you have had laser treatment, eye surgery or injections, as well as those with other conditions, like persistent vision loss, blurred vision, floaters, previously diagnosed macular edema and more.

Source: FDA approves AI-powered software to detect diabetic retinopathy

After Millions of Trials, These Simulated Humans Learned to Do Perfect Backflips and Cartwheels

Using well-established machine learning techniques, researchers from University of California, Berkeley have taught simulated humanoids to perform over 25 natural motions, from somersaults and cartwheels through to high leg kicks and breakdancing. The technique could lead to more realistic video gameplay and more agile robots.

[…]

UC Berkeley graduate student Xue Bin “Jason” Peng, along with his colleagues, have combined two techniques—motion-capture technology and deep-reinforcement computer learning—to create something completely new: a system that teaches simulated humanoids how to perform complex physical tasks in a highly realistic manner. Learning from scratch, and with limited human intervention, the digital characters learned how to kick, jump, and flip their way to success. What’s more, they even learned how to interact with objects in their environment, such as barriers placed in their way or objects hurled directly at them.

[…]

The new system, dubbed DeepMimic, works a bit differently. Instead of pushing the simulated character towards a specific end goal, such as walking, DeepMimic uses motion-capture clips to “show” the AI what the end goal is supposed to look like. In experiments, Bin’s team took motion-capture data from more than 25 different physical skills, from running and throwing to jumping and backflips, to “define the desired style and appearance” of the skill, as Peng explained at the Berkeley Artificial Intelligence Research (BAIR) blog.

Results didn’t happen overnight. The virtual characters tripped, stumbled, and fell flat on their faces repeatedly until they finally got the movements right. It took about a month of simulated “practice” for each skill to develop, as the humanoids went through literally millions of trials trying to nail the perfect backflip or flying leg kick. But with each failure came an adjustment that took it closer to the desired goal.

Bots trained across a wide variety of skills.
GIF: Berkeley Artificial Intelligence Research

Using this technique, the researchers were able to produce agents who behaved in a highly realistic, natural manner. Impressively, the bots were also able to manage never-before-seen conditions, such as challenging terrain or obstacles. This was an added bonus of the reinforcement learning, and not something the researchers had to work on specifically.

“We present a conceptually simple [reinforcement learning] framework that enables simulated characters to learn highly dynamic and acrobatic skills from reference motion clips, which can be provided in the form of mocap data [i.e. motion capture] recorded from human subjects,” writes Peng. “Given a single demonstration of a skill, such as a spin-kick or a backflip, our character is able to learn a robust policy to imitate the skill in simulation. Our policies produce motions that are nearly indistinguishable from mocap,” adding that “We’re moving toward a virtual stuntman.”

Simulated dragon.
GIF: Berkeley Artificial Intelligence Research

Not to be outdone, the researchers used DeepMimic to create realistic movements from simulated lions, dinosaurs, and mythical beasts. They even created a virtual version of ATLAS, the humanoid robot voted most likely to destroy humanity. This platform could conceivably be used to produce more realistic computer animation, but also for virtual testing of robots.

Source: After Millions of Trials, These Simulated Humans Learned to Do Perfect Backflips and Cartwheels

Facebook admits: Apps were given users’ permission to go into their inboxes

Facebook has admitted that some apps had access to users’ private messages, thanks to a policy that allowed devs to request mailbox permissions.

The revelation came as current Facebook users found out whether they or their friends had used the “This Is Your Digital Life” app that allowed academic Aleksandr Kogan to collect data on users and their friends.

Users whose friends had been suckered in by the quiz were told that as a result, their public profile, Page likes, birthday and current city were “likely shared” with the app.

So far, so expected. But, the notification went on:

A small number of people who logged into “This Is Your Digital Life” also shared their own News Feed, timeline, posts and messages which may have included post and messages from you. They may also have shared your hometown.

That’s because, back in 2014 when the app was in use, developers using Facebook’s Graph API to get data off the platform could ask for read_mailbox permission, allowing them access to a person’s inbox.

That was just one of a series of extended permissions granted to devs under v1.0 of the Graph API, which was first introduced in 2010.

Following pressure from privacy activists – but much to the disappointment of developers – Facebook shut that tap off for most permissions in April 2015, although the changelog shows that read_mailbox wasn’t deprecated until 6 October 2015.

Facebook confirmed to The Register that this access had been requested by the app and that a small number of people had granted it permission.

“In 2014, Facebook’s platform policy allowed developers to request mailbox permissions but only if the person explicitly gave consent for this to happen,” a spokesborg told us.

“According to our records only a very small number of people explicitly opted into sharing this information. The feature was turned off in 2015.”

Source: Facebook admits: Apps were given users’ permission to go into their inboxes • The Register

How to Check if Cambridge Analytica Had Your Facebook Data

Facebook launched a tool yesterday that you can use to find out whether you or your friends shared information with Cambridge Analytica, the Trump-affiliated company that harvested data from a Facebook app to support the then-candidate’s efforts in the 2016 presidential election.

If you were affected directly—and you have plenty of company, if so—you should have already received a little notification from Facebook. If you missed that in your News Feed (or you’ve already sworn off Facebook, but want to check and see if your information was compromised), Facebook also has a handy little Cambridge Analytica tool you can use.

The problem? While the tool can tell you if you or your friends shared your information via the spammy “This is Your Digital Life” app, it won’t tell you who among your friends was foolish enough to give up your information to a third party. You have lost your ability to publicly shame them, yell at them, or go over to where they live (or fire up a remote desktop session) to teach them how to … not do that ever again.

So, what can you do now?

Even though your past Facebook data might already be out there in the digital ether somewhere, you can now start locking down your information a bit more. Once you’re done checking the Cambridge Analytica tool, go here (Facebook’s Settings page). Click on Apps and Websites. Up until recently, Facebook had a setting (under “Apps Others Use”) that you could use to restrict the information that your friends could share about you to apps they were using. Now, you’ll see this message instead:

“These outdated settings have been removed because they applied to an older version of our platform that no longer exists.

To see or change the info you currently share with apps and websites, review the ones listed above, under ‘Logged in with Facebook.’”

Sounds ominous, right? Well, according to Facebook, these settings haven’t really done much of anything for years, anyway. As a Facebook spokesperson recently told Wired:

“These controls were built before we made significant changes to how developers build apps on Facebook. At the time, the Apps Others Use functionality allowed people to control what information could be shared to developers. We changed our systems years ago so that people could not share friends’ information with developers unless each friend also had explicitly granted permission to the developer.”

Instead, take a little time to review (again) the apps you’ve allowed to access your Facebook information. If you’re not using the app anymore, or if it sounds a little fishy, remove it—heck, remove as many apps as you can in one go.

Source: How to Check if Cambridge Analytica Had Your Facebook Data

3D-printed public housing unveiled in France

NANTES, France (Reuters) – Researchers have unveiled what they billed as the world’s first 3D-printed house to serve as a home in the French city of Nantes, with the first tenants due to move in by June.

Academics at the University of Nantes who led the project said it was the first house built in situ for human habitation using a robot 3D-printer.

The robot, known as BatiPrint3D, uses a special polymer material that should keep the building insulated effectively for a century.

It took BatiPrint3D around 18 days to complete its part of the work on the house – creating hollow walls that were subsequently filled with concrete for insulation.

“Is this the future? It’s a solution and a constructive principle that is interesting because we create the house directly on site and in addition thanks to the robot, we are able to create walls with complex shapes,” said Benoit Furet, a professor who worked on the project.

The 95 square meter (1000 square feet), five-room house will be allocated to a local family which qualifies for social housing, authorities said.

The Y-shaped home is equipped with multiple sensors that monitor air quality, humidity and temperature, as well as equipment to evaluate and analyze the thermal properties of the building.

Researchers believe this technology will enable tenants to save on energy costs.

Authorities in Nantes are planning further 3D-printed building projects, including a public reception building and a housing estate.

Source: 3D-printed public housing unveiled in France

CubeYou: Cambridge-like app collected data on millions from Facebook

Facebook is suspending a data analytics firm called CubeYou from the platform after CNBC notified the company that CubeYou was collecting information about users through quizzes.

CubeYou misleadingly labeled its quizzes “for non-profit academic research,” then shared user information with marketers. The scenario is eerily similar to how Cambridge Analytica received unauthorized access to data from as many as 87 million Facebook user accounts to target political marketing.

CubeYou, whose CEO denies any deception, sold data that had been collected by researchers working with the Psychometrics Lab at Cambridge University, similar to how Cambridge Analytica used information it obtained from other professors at the school for political marketing.

The CubeYou discovery suggests that collecting data from quizzes and using it for marketing purposes was far from an isolated incident. Moreover, the fact that CubeYou was able to mislabel the purpose of the quizzes — and that Facebook did nothing to stop it until CNBC pointed out the problem — suggests the platform has little control over this activity.

[…]

CubeYou boasts on its website that it uses census data and various web and social apps on Facebook and Twitter to collect personal information. CubeYou then contracts with advertising agencies that want to target certain types of Facebook users for ad campaigns.

CubeYou’s site says it has access to personally identifiable information (PII) such as first names, last names, emails, phone numbers, IP addresses, mobile IDs and browser fingerprints.

On a cached version of its website from March 19, it also said it keeps age, gender, location, work and education, and family and relationship information. It also has likes, follows, shares, posts, likes to posts, comments to posts, check-ins and mentions of brands/celebrities in a post. Interactions with companies are tracked back to 2012 and are updated weekly, the site said.

Source: CubeYou Cambridge-like app collected data on millions from Facebook

$0.75 – about how much Cambridge Analytica paid per voter in bid to micro-target their minds, internal docs reveal

Cambridge Analytica bought psychological profiles on individual US voters, costing roughly 75 cents to $5 apiece, each crafted using personal information plundered from millions of Facebook accounts, according to revealed internal documents.

Over the course of the past two weeks, whistleblower Chris Wylie has made a series of claims against his former employer, Cambridge Analytica, and its parent organizations SCL Elections and SCL Group.

He has alleged CA drafted in university academic Dr Aleksander Kogan to help micro-target voters using their personal information harvested from Facebook, and that the Vote Leave campaign in the UK’s Brexit referendum “cheated” election spending limits by funneling money to Canadian political ad campaign biz AggregateIQ through a number of smaller groups.

Cambridge Analytica has denied using Facebook-sourced information in its work for Donald Trump’s US election campaign, and dubbed the allegations against it as “completely unfounded conspiracy theories.”

A set of internal CA files released Thursday by Britain’s House of Commons’ Digital, Culture, Media and Sport Select Committee includes contracts and email exchanges, plus micro-targeting strategies and case studies boasting of the organization’s influence in previous international campaigns.

Among them is a contract, dated June 4, 2014, revealing a deal struck between SCL Elections and Kogan’s biz Global Science Research, referred to as GS in the documents. It showed that Kogan was commissioned by SCL to build up psychological profiles of people, using data slurped from their Facebook accounts by a quiz app, and match them to voter records obtained by SCL.

The app was built by GS, installed by some 270,000 people, and was granted access to their social network accounts and those of their friends, up to 50 million of them. The information was sold to Cambridge Analytica by GS.

[…]

GS’s fee was a nominal £3.14, and up to $5 per person during the trial stage. The maximum payment would have been $150,000 for 30,000 records.

The price tag for the full sample was to be established after the trial, the document stated, but the total fee was not to exceed $0.75 per matched record. The total cost of the full sample stage would have been up to $1.5m for all two million matches. Wylie claimed roughly $1m was spent in the end.

[…]

Elsewhere in the cache are documents relating to the relationship between AggregateIQ and SCL.

One file laid out an AIQ contract to develop a platform called Ripon – which SCL and later CA is said to have used for micro-targeting political campaigns – in the run-up to the 2014 US mid-term elections. Although this document wasn’t signed, it indicated the first payment to AIQ was made on April 7, 2014: a handsome sum of $25,000 (CA$27,000, £18,000).

[…]

A separate contract showed the two companies had worked together before this. It is dated November 25, 2013, and set out a deal in wbhich AIQ would “assist” SCL by creating a constituent relationship management (CRM) system and help with the “acquisition of online data” for a political campaign in Trinidad and Tobago.

The payment for this work was $50,000, followed by three further installments of $50,000. The document is signed by AIQ cofounders: president Zackary Massingham, and chief operating officer Jeff Silvester. Project deliverables include data mapping, and use of behavioral datasets of qualified sources of data “that illustrate browsing activity, online behaviour and social contributions.”

A large section in the document, under the main heading for CRM deliverables, between sections labelled “reports” and “markup and CMS integration design / HTML markup,” is heavily redacted.

The document dump also revealed discussions between Rebekah Mercer, daughter of billionaire CA backer Robert Mercer, and Trump strategist Steve Bannon, about how to manage the involvement of UK-based Cambridge Analytica – a foreign company – with American elections and US election law, as well as praise for SCL from the UK’s Ministry of Defence.

Source: $0.75 – about how much Cambridge Analytica paid per voter in bid to micro-target their minds, internal docs reveal • The Register

Under Armour Data Breach: 150 Million MyFitnessPal Accounts Hacked

Under Armour Inc., joining a growing list of corporate victims of hacker attacks, said about 150 million user accounts tied to its MyFitnessPal nutrition-tracking app were breached earlier this year.

An unauthorized party stole data from the accounts in late February, Under Armour said on Thursday. It became aware of the breach earlier this week and took steps to alert users about the incident, the company said.

Shares of Under Armour fell as much as 4.6 percent to $15.59 in late trading following the announcement. The stock had been up 13 percent this year through Thursday’s close.

The data didn’t include payment-card information or government-issued identifiers, including Social Security numbers and driver’s license numbers. Still, user names, email addresses and password data were taken. And the sheer scope of the attack — affecting a user base that’s bigger than the population of Japan — would make it one of the larger breaches on record.

Source: Under Armour Data Breach: 150 Million MyFitnessPal Accounts Hacked | Fortune

Cambridge Analytica’s daddy biz SCL had ‘routine access’ to UK secrets

Cambridge Analytica’s parent biz had “routine access to UK secret information” as part of training it offered to the UK’s psyops group, according to documents released today.

A letter, published as part of a cache handed over to MPs by whisteblower Chris Wylie, details work that Strategic Communications Laboratories (SCL) carried out for the 15 (UK) Psychological Operations Group.

Dated 11 January 2012, it said that the group – which has since been subsumed into the unit 77 Brigade – received training from SCL, first as part of a commission and then on a continued basis without additional cost to the Ministry of Defence.

The author’s name is redacted, but it stated that SCL were a “UK List ‘X’ accredited company cleared to routine access to UK secret information”.

It said that five training staff from SCL provided the group with measurement of effect training over the course of two weeks, with students including Defence Science and Technology Ltd scientists, deploying military officers and senior soldiers.

It said that, because of SCL’s clearance, the final part of the package “was a classified case study from current operations in Helmand, Afghanistan”.

The author commented: “Such contemporary realism added enormous value to the course.”

The letter went on to say that, since delivery, SCL has continued to support the group “without additional charge to the MoD”, which involved “further testing of the trained product on operations in Libya and Afghanistan”.

Finally, the document’s author offered their recommendation for the service provided by SCL.

It said that, although the MoD is “officially disbarred from offering commercial endorsement”, the author would have “no hesitation in inviting SCL to tender for further contracts of this nature”.

They added: “Indeed it is my personal view that there are very few, if any, other commercial organisations that can deliver proven training and education of this very specialist nature.”

Source: Cambridge Analytica’s daddy biz had ‘routine access’ to UK secrets • The Register

Grindr’s API Surrendered Location Data to a Third-Party Website—Even After Users Opted Out

A website that allowed Gindr’s gay-dating app users to see who blocked them on the service says that by using the company’s API it was able to view unread messages, email addresses, deleted photos, and—perhaps most troubling—location data, according to a report published Wednesday.

The website, C*ckblocked, boasts of being the “first and only way to see who blocked you on Grindr.” The website’s owner, Trever Faden, told NBC that, by using Grindr’s API, he was able to access a wealth of personal information, including the location data of users—even for those who had opted to hide their locations.

“One could, without too much difficulty or even a huge amount of technological skill, easily pinpoint a user’s exact location,” Faden told NBC. But before he could access this information, Grindr users first had to supply C*ckblocked with their usernames and passwords, meaning that they voluntarily surrendered access to their accounts.

Grindr said that, once notified by Faden, it moved quickly to resolve the issue. The API that allowed C*ckblocked to function was patched on March 23rd, according to the website.

Source: Grindr’s API Surrendered Location Data to a Third-Party Website—Even After Users Opted Out

SpyParty – A Subtle Game About Human Behavior

SpyParty is a tense competitive spy game set at a high society party. It’s about subtle behavior, perception, and deception, instead of guns, car chases, and explosions. One player is the Spy, trying to accomplish missions while blending into the crowd. The other player is the Sniper, who has one bullet with which to find and terminate the Spy!

Source: SpyParty – A Subtle Game About Human Behavior

Mozilla launches Facebook container extension

This extension helps you control more of your web activity from Facebook by isolating your identity into a separate container. This makes it harder for Facebook to track your activity on other websites via third-party cookies.

Rather than stop using a service you find valuable and miss out on those adorable photos of your nephew, we think you should have tools to limit what data others can collect about you. That includes us: Mozilla does not collect data from your use of the Facebook Container extension. We only know the number of times the extension is installed or removed.

When you install this extension it will delete your Facebook cookies and log you out of Facebook. The next time you visit Facebook it will open in a new blue-colored browser tab (aka “container tab”). In that tab you can login to Facebook and use it like you normally would. If you click on a non-Facebook link or navigate to a non-Facebook website in the URL bar, these pages will load outside of the container.

Source: Facebook Container Extension: Take control of how you’re being tracked | The Firefox Frontier

The Interstitium Is Important, But Don’t Call It An Organ (Yet)

In brief: It’s called the interstitium, or a layer of fluid-filled pockets hemmed in by collagen and it can be found all over our bodies, from skin to muscles to our digestive system. The interstitium likely acts as a kind of shock absorber for the rest of our interior bits and bobs and the workings of the fluid itself could help explain everything from tumor growth to how cells move within our bodies. The authors stop short of saying “new organ,” but the word is certainly on everyone’s lips.

Is it just me, or are you feeling a bit of deja vu?

Well, maybe it’s just me, but that’s because I’ve been in this situation before. You see, just over a year ago, researchers announced that they’d discovered a different “new” organ — the mesentery. That particular collection of bodily tissue is a fan-shaped fold that helps hold our guts in place. It had been known about for centuries, but only recently discovered to be large and important enough to justify calling it an organ. It was to be the body’s 79th, but that number is entirely arbitrary.

As we discovered here at Discover, the definition of an organ is hardly settled (and we’re aware of what a church organ is, thankyouverymuch). As became apparent during the whole mesentery craze, there’s no real definition for what an organ actually is. And the human body doesn’t have 79 organs, or 80 organs, or 1,000 organs, because that number can change drastically depending on the definition. And you can bet scientists debate what an organ actually is.

“It’s a silly number,” said Paul Neumann, a professor of medicine at Dalhousie University in Canada and member of the Federative International Programme for Anatomical Terminology, in a Discover article from last year. “If a bone is an organ, there’s 206 organs right there. No two anatomists will agree on a list of organs in the body”

Calling the interstitium a new organ, then, is a bit of a stretch. It’s there, it’s certainly important, but we need a better idea of what an organ is before we can start labeling things as such.

There is a definition of sorts, but it’s got more wiggle room than your large intestine. An organ is composed of two or more tissues, is self-contained and performs a specific function, according to most definitions you get by Googling “what is an organ?” But there’s no governing body that explicitly determines what an organ is, and there’s no official definition. Things like skin, nipples, eyeballs, mesenteries and more have crossed into organ-dom and back throughout history as anatomists debated the definition.

Source: The Interstitium Is Important, But Don’t Call It An Organ (Yet)

 
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