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How Organizations Protect Against Email Phishing and How To White-list Your Domain

To-Dos for  Postmasters:

Domain owners can publish a policy telling Gmail and other participating email providers how to handle messages that are sent from your domain but aren’t authenticated. By defining a policy, you can help combat phishing to protect users and your reputation.

Email Phishing and White-listing

To-Dos:

Domain owners can publish a policy telling Gmail and other participating email providers how to handle messages that are sent from your domain but aren’t authenticated. By defining a policy, you can help combat phishing to protect users and your reputation.

Email Phishing and White-listing

To-Dos:

Domain owners can publish a policy telling Gmail and other participating email providers how to handle messages that are sent from your domain but aren’t authenticated. By defining a policy, you can help combat phishing to protect users and your reputation.

How big IT companies detecting fake news at its source

Detecting fake news at its source

Machine learning system aims to determine if an information outlet is accurate or biased.

Adam Conner-Simons | CSAIL October 4, 2018

Lately the fact-checking world has been in a bit of a crisis. Sites like Politifact and Snopes have traditionally focused on specific claims, which is admirable but tedious; by the time they’ve gotten through verifying or debunking a fact, there’s a good chance it’s already traveled across the globe and back again.

Social media companies have also had mixed results limiting the spread of propaganda and misinformation. Facebook plans to have 20,000 human moderators by the end of the year, and is putting significant resources into developing its own fake-news-detecting algorithms.

Researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI) believe that the best approach is to focus not only on individual claims, but on the news sources themselves. Using this tack, they’ve demonstrated a new system that uses machine learning to determine if a source is accurate or politically biased.

“If a website has published fake news before, there’s a good chance they’ll do it again,” says postdoc Ramy Baly, the lead author on a new paper about the system. “By automatically scraping data about these sites, the hope is that our system can help figure out which ones are likely to do it in the first place.”

Baly says the system needs only about 150 articles to reliably detect if a news source can be trusted — meaning that an approach like theirs could be used to help stamp out new fake-news outlets before the stories spread too widely.

The system is a collaboration between computer scientists at MIT CSAIL and QCRI, which is part of the Hamad Bin Khalifa University in Qatar. Researchers first took data from Media Bias/Fact Check (MBFC), a website with human fact-checkers who analyze the accuracy and biases of more than 2,000 news sites; from MSNBC and Fox News; and from low-traffic content farms.

They then fed those data to a machine learning algorithm, and programmed it to classify news sites the same way as MBFC. When given a new news outlet, the system was then 65 percent accurate at detecting whether it has a high, low or medium level of factuality, and roughly 70 percent accurate at detecting if it is left-leaning, right-leaning, or moderate.

The team determined that the most reliable ways to detect both fake news and biased reporting were to look at the common linguistic features across the source’s stories, including sentiment, complexity, and structure.

For example, fake-news outlets were found to be more likely to use language that is hyperbolic, subjective, and emotional. In terms of bias, left-leaning outlets were more likely to have language that related to concepts of harm/care and fairness/reciprocity, compared to other qualities such as loyalty, authority, and sanctity. (These qualities represent a popular theory — that there are five major moral foundations — in social psychology.)

Co-author Preslav Nakov, a senior scientist at QCRI, says that the system also found correlations with an outlet’s Wikipedia page, which it assessed for general — longer is more credible — as well as target words such as “extreme” or  “conspiracy theory.” It even found correlations with the text structure of a source’s URLs: Those that had lots of special characters and complicated subdirectories, for example, were associated with less reliable sources.

“Since it is much easier to obtain ground truth on sources [than on articles], this method is able to provide direct and accurate predictions regarding the type of content distributed by these sources,” says Sibel Adali, a professor of computer science at Rensselaer Polytechnic Institute who was not involved in the project.

Nakov is quick to caution that the system is still a work in progress, and that, even with improvements in accuracy, it would work best in conjunction with traditional fact-checkers.

“If outlets report differently on a particular topic, a site like Politifact could instantly look at our fake news scores for those outlets to determine how much validity to give to different perspectives,” says Nakov.

Baly and Nakov co-wrote the new paper with MIT Senior Research Scientist James Glass alongside graduate students Dimitar Alexandrov and Georgi Karadzhov of Sofia University. The team will present the work later this month at the 2018 Empirical Methods in Natural Language Processing (EMNLP) conference in Brussels, Belgium.

The researchers also created a new open-source dataset of more than 1,000 news sources, annotated with factuality and bias scores, that is the world’s largest database of its kind. As next steps, the team will be exploring whether the English-trained system can be adapted to other languages, as well as to go beyond the traditional left/right bias to explore region-specific biases (like the Muslim world’s division between religious and secular).

“This direction of research can shed light on what untrustworthy websites look like and the kind of content they tend to share, which would be very useful for both web designers and the wider public,” says Andreas Vlachos, a senior lecturer at the University of Cambridge who was not involved in the project.

Nakov says that QCRI also has plans to roll out an app that helps users step out of their political bubbles, responding to specific news items by offering users a collection of articles that span the political spectrum.

“It’s interesting to think about new ways to present the news to people,” says Nakov. “Tools like this could help people give a bit more thought to issues and explore other perspectives that they might not have otherwise considered."

Fwd: 'It's like a final': Atletico meet Juve in a clash of high stakes

https://www.channelnewsasia.com/news/sport/it-s-like-a-final-atletico-meet-juve-in-a-clash-of-high-stakes-11257302

Elon Musk unveils his company’s first tunnel in Hawthorne, and it’s not a smooth ride

Elon Musk unveils his company’s first tunnel in Hawthorne, and it’s not a smooth ride

Billionaire Elon Musk envisions a world where commuting in Los Angeles is as easy as pointing a self-driving car toward an elevator platform embedded in a city street, sinking into a tunnel and zipping seamlessly beneath the traffic at speeds of up to 150 mph.

So far, his company’s progress toward this goal has been a bumpier ride.

On Tuesday night, in a parking lot next to SpaceX, Musk’s Boring Co. unveiled its first tunnel — a 1.14-mile route that runs 20 to 40 feet beneath the streets of Hawthorne, through a neighborhood sandwiched between the 105 Freeway and Hawthorne Municipal Airport.

Musk had promised modified “but fully autonomous” vehicles at the unveiling, but the reality was more modest: a Tesla Model X that reached a top speed of 53 mph, manually driven by an employee who previously drove in the Indianapolis 500.

The trip through the tunnel took about two minutes, illuminated by the car’s headlights and a strip of blue neon lights tacked to the ceiling. The Model X rolled on two molded concrete shelves along the wall, which were so uneven in places that it felt like riding on a dirt road.

The car emerged from the tunnel on an elevator erected inside a round shaft lined with corrugated metal. The shaft, named O’Leary Station in memory of a longtime SpaceX employee who died, is at the site of a shuttered cabinetry store on Prairie Avenue.

“We kind of ran out of time,” Musk said, attributing the rough ride to problems with a paving machine. “The bumpiness will not be there down the road. It will be as smooth as glass. This is just a prototype. That’s why it's just a little rough around the edges.”

Building the 1.14-mile tunnel took about 18 months and cost about $10 million, Musk said. The figure does not include the costs of research, development or equipment, the company said, and it is not clear whether it includes the money spent on property acquisition or labor.

Still, the $10 million is orders of magnitude lower than a typical subway project, Musk said. Part of Boring Co.’s goal, he said, is to create a tunneling process that will be 15 times faster than the “next best” option.

Elon Musk tunnel under Hawthrone
Elon Musk, co-founder and chief executive officer of Tesla Inc., arrives in a modified Tesla Model X electric vehicle during an unveiling event for the Boring Co. Hawthorne test tunnel in Hawthorne. (Robyn Beck / Pool Photo)

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So far, the company has used the tunnel exclusively for research, searching for ways to make tunneling faster and cheaper. But Musk said he hoped the route would “ultimately be part of a much larger network in greater Los Angeles.”

A map the company published last year showed a network of stations, including stops at the Getty Center, Union Station and Los Angeles International Airport. Musk also has announced plans for a 3.6-mile tunnel between Dodger Stadium and a Metro Red Line station.

He said the tunnels would be restricted to autonomous, electric cars, but not just Teslas. There will also be vehicles circulating for people on foot or with bicycles.

If work goes well, Musk said, the company could have the whole system running by 2028, when Los Angeles hosts the Olympic Games.

“Ten years sounds like infinity,” he said. “I damn well hope we’d have that thing done.”

Musk said he has spent about $40 million of his own money on the fledgling Boring Co., which was started after he tweeted that traffic was driving him “nuts” and that he was going to “just start digging” to escape it.

The company previously said its planned urban transportation network, called the Loop, would whisk cars and pods through multiple levels of tunnels on autonomous, electric platforms called skates.

But Musk told reporters that Boring officials have abandoned the concept of the skate, saying it was “far more complex” than his new plan: guide wheels that can be attached to the front tires of autonomous, electric cars, steadying the vehicles as they move forward through tunnels.

The company modeled that idea on Tuesday, attaching horizontal wheels to the Model X’s front wheels.

Musk played a simulation showing the wheels folding neatly underneath the car’s undercarriage when not in use. Adding them during assembly or after-market would cost $200 to $300, he said, and would not interfere with the vehicle’s normal operation.

Musk said his first ride in the tunnel was bumpy but “epic.”

By 6 p.m., hundreds of chattering people had lined up to enter the invitation-only event. Many were Tesla owners, some wearing Tesla hats, T-shirts and fleece jackets.

“I’m intrigued,” said Kash Jayawardena, 36, of Venice Beach, who owns a Model S, as he stood in line. He came to the event with his friend Tito Vecchione, 39, who said he was looking forward to hearing more but was concerned that Musk's vision did not encourage people to get out of their cars.

Musk has said the company could build several layers of tunnels to accommodate as many people as necessary. And because the tunnels won’t run above ground, they won’t divide communities like a freeway project would, he said.

Inside, on the construction site near the tunnel’s entrance, Boring Co. had hung long, black curtains to mask a boxy construction trailer, and laid down plush gray carpet to disguise the asphalt — kept tidy by cleaners who stepped in with vacuums after employees and journalists walked past.

They started digging in the SpaceX parking lot, Musk said, “so I could see it from my desk, so I could see if we were making progress or not.”

The first tunneling machine is called Godot, after the play by Samuel Beckett, “because we kept waiting for it,” Musk said. “It took a lot of time to get it active because we didn’t know how to work the thing.”

The company’s next machine, which Musk says will run more efficiently, will be named Line Storm, a reference to a Robert Frost poem.