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6 Sure Ways To Spot A Fake Facebook Page

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ollowing the wrong facebook page is almost as dangerous as someone giving you the wrong directions to a destination. You never know you’re following the fake page when they look just like genuine and unsuspecting. They sometimes get you to do things that should immediately ring your inner ‘scam alert’ siren.

These pages are mostly set up by upcoming brands or some regular people just to double their following using familiar names that can do the magic for them. In order not to fall for such pages and the stuff they promote, note the following things about fake Facebook pages.

1. Check if the Facebook page has been verified

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Most Facebook pages ran by authentic people have been verified by Facebook. Verified pages have a blue badge with a white a tick sign right after the name. This is always visible even in search results.

2. Look at the Listing Category for the Page

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Another place where fake Facebook pages often show their true colours is in the page category listing. Sometimes a fake page of a celebrity/public figure will be categorized under ‘website.’

 

3. Fake pages often ask for donations.

Some miscreants hide behind fake pages to extort their unsuspecting followers. They mostly post pictures of sad-looking children and say they are orphans in need. However, there may be real NGOs out there seeking to help some underprivileged people, look before you pass your judgement.

 

4. Be on the lookout for scam posts

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Since the inception of social media, scamming is overly common nowadays. These pages, in order to gain large following, post pictures of luxurious products and ask their followers to share so they will be given one. In most cases, the winners are never announced and if they do, the profiles are fake as well.

 

5. Disconnected content

Do you really think a government official like Hon. Adwoa Safo would share contents like this?

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6. Check page information

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Certain categories require the person setting up the page to supply a lot of real information—like addresses and phone numbers that are easily checked. Fake pages won’t have this information to submit. In this case, checkout the information submitted in the photo above – it has “Waptrick.com” as their website but this is the official site of OMGVoice and WE DO NOT HAVE A BACKUP PAGE

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Apple’s 2019 iPhones to finally adopt USB-C, rumor says

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If you’ve been anxiously waiting for Apple to bring USB-C to its smartphones in some form, it looks like you’ll have to replenish your patience once more. According to a new rumor out of China, the company is only going to support the standard in the smartphones it releases next year.

Sources at analog IC vendors told Digitimes that Apple is busy revamping chargers and the related interfaces for the 2019 iPhones and iPads. The reason why you won’t see USB-C in this year’s models is that the company “is still in its redesign phase”.

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Apple iPhone X

At this point, it’s already pretty late in the 2018 iPhones’ development cycle, so it won’t be able to make the necessary changes in time, without possible production delays.

Oddly, the report doesn’t go into specifics regarding whether USB-C will be used on both ends of the iPhone cable, or just the one that goes into the charging brick (or a computer). It’s entirely plausible that Apple would switch that end from USB-A over to USB-C while leaving the Lightning port on the other end intact in order to continue reaping the benefits in accessory licensing.

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The US government is seriously underestimating how much Americans rely on gig work

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brooke cagle 609875 unsplash916635448 - The US government is seriously underestimating how much Americans rely on gig work

The US Bureau of Labor Statistics released its long-awaited 2017 Contingent Worker Supplement this morning—a huge event for us data nerds! It gives us a “current” (the data is from May 2017) view of gig work in the US, but there are a few problems with the findings.

Some background: The report gives an overview of people employed in what the BLS calls “alternative work arrangements,” which includes contingent, freelance, and contract work. The agency even has a handy video to explain the categories.

The results by the numbers: As of May 2017 …

  • 3.8 percent of US workers—5.9 millionpeople—held contingent jobs (jobs that are temporary or that workers do not expect to last).
  • About one-third of contingent workers were employed in education and health services.
  • 55 percent of them would have preferred a permanent job.
  • There were 10.6 million independent contractors, 2.6 million on-call workers, 1.4 million workers employed through temporary-help agencies, and 933,000workers provided by contract firms.
  • Two-thirds of independent contractors were men.

For comparison: The last equivalent BLS survey occurred back in 2005—long before Uber, and back when YouTube was still in its infancy. It showed that 7.4 percent of workers were employed as independent contractors. Surprisingly, that means the proportion of workers relying on independent consulting or freelancing for their main livelihood has dropped since the last survey.

Keep in mind: The data takes into account only people’s primary jobs. That disqualifies a large segment of the gig workforce, including people who supplement their regular income with freelance or contract work to get by. “This survey is anchored to methodologies that reflect a bygone era for the workforce,” says Stephane Kasriel, the CEO of Upwork, a website for freelancers.

Alastair Fitzpayne, executive director of the Aspen Institute’s future-of-work initiative, agrees that the report doesn’t paint the full picture. “We encourage BLS and other researchers to further explore how workers are supplementing their income with freelance or independent work as a complement to the data that was released today,” says Fitzpayne.

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Machine learning predicts World Cup winner

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The 2018 soccer World Cup kicks off in Russia on Thursday and is likely to be one of the most widely viewed sporting events in history, more popular even than the Olympics. So the potential winners are of significant interest.

One way to gauge likely outcomes is to look at bookmakers’ odds. These companies use professional statisticians to analyze extensive databases of results in a way that quantifies the probability of different outcomes of any possible match. In this way, bookmakers can offer odds on all the games that will kick off in the next few weeks, as well as odds on potential winners.


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An even better estimate comes from combing the odds from lots of different bookmakers. This approach suggests Brazil is the clear favorite to win the 2018 World Cup, with a probability of 16.6 percent, followed by Germany (12.8 percent) and Spain (12.5 percent).

But in recent years, researchers have developed machine-learning techniques that have the potential to outperform conventional statistical approaches. What do these new techniques predict as the likely outcome of the 2018 World Cup?

An answer comes from the work of Andreas Groll at the Technical University of Dortmund in Germany and a few colleagues. These guys use a combination of machine learning and conventional statistics, a method called a random-forest approach, to identify a different most likely winner.

First some background. The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data.

However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting.

The random-forest approach is different. Instead of calculating the outcome at every branch, the process calculates the outcome of random branches. And it does this many times, each time with a different set of randomly selected branches. The final result is the average of all these randomly constructed decision trees.

This approach has significant advantages. First, it does not suffer from the same overfitting problem that plagues ordinary decision trees. It also reveals which factors are most important in determining the outcome.

So if a particular decision tree includes lots of parameters, it becomes easy to see which ones have the biggest impact on the outcome and which do not. These less important factors can then be ignored in future.

Groll and co use exactly this approach to model the 2018 World Cup. They model the outcome of each game the teams are likely to play and use the results to construct the most probable course of the tournament.

Groll and co begin with a wide range of potential factors that might determine the outcome. These include economic factors such as a country’s GDP and population, FIFA’s ranking of national teams, and the properties of the teams themselves, such as their average age, the number of Champions League players they have, whether they have home advantage, and so on.

Interestingly, the random-forest approach allows Groll and co to include other ranking attempts, such as the rankings used by bookmakers.

Plugging all this into the model provides some interesting insights. For example, the most influential factors turn out to be the team rankings created by other methods, including those from bookmakers, FIFA, and others.

Other important factors include GDP and the number of Champions League players on the team. Unimportant factors include the country’s population, the nationality of the coach, and so on.

The predictions arrived at through this process differ from others in some important ways. For a start, the random-forest method picks out Spain as the most likely winner, with a probability of 17.8 percent.

However, a big factor in this prediction is the structure of the tournament itself. If Germany clears the group phase of the competition, it is more likely to face strong opposition in the 16-team knockout phase. Because of this, the random-forest method calculates Germany’s chances of reaching the quarter-finals as 58 percent. By contrast, Spain is unlikely to face strong opposition in the final 16 and so has a 73 percent chance of reaching the quarter-finals.

If both make the quarter-finals, they have a more or less equal chance of winning. “Spain is slightly favored over Germany mainly due to the fact that Germany has a comparatively high chance to drop out in the round-of-sixteen,” say Groll and co.

But there is an additional twist. The random-tree process makes it possible to simulate the entire tournament, and this produces a different result.

world cup 2018 - Machine learning predicts World Cup winner

Groll and co simulated the entire tournament 100,000 times. “According to the most probable tournament course, instead of the Spanish the German team would win the World Cup,” they say.

Of course, because of the huge number of permutations of games, this course is still extremely unlikely. Groll and co put the odds at about 1 in 100,000.

So there you have it. At the beginning of the tournament, Spain has the best chances of winning, according to Groll and co. But if Germany makes the quarter-finals, it then becomes the front-runner.

The tournament kicks off on Thursday, when the hosts, Russia, take on Saudi Arabia. Sadly, neither of these teams looks likely to make even the quarter-finals.

Ref: arxiv.org/abs/1806.03208 : Prediction Of The FIFA World Cup 2018 – A Random Forest Approach With An Emphasis On Estimated Team Ability Parameters

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