Tag Archives: books

What do you want your button to do?

Post Syndicated from Carrie Anne Philbin original https://www.raspberrypi.org/blog/button/

Here at Raspberry Pi, we know that getting physical with computing is often a catalyst for creativity. Building a simple circuit can open up a world of making possibilities! This ethos of tinkering and invention is also being used in the classroom to inspire a whole new generation of makers too, and here is why.

The all-important question

Physical computing provides a great opportunity for creative expression: the button press! By explaining how a button works, how to build one with a breadboard attached to computer, and how to program the button to work when it’s pressed, you can give learners young and old all the conceptual skills they need to build a thing that does something. But what do they want their button to do? Have you ever asked your students or children at home? I promise it will be one of the most mindblowing experiences you’ll have if you do.

A button. A harmless, little arcade button.

Looks harmless now, but put it into the hands of a child and see what happens!

Amy will want her button to take a photo, Charlie will want his button to play a sound, Tumi will want her button to explode TNT in Minecraft, Jack will want their button to fire confetti out of a cannon, and James Robinson will want his to trigger silly noises (doesn’t he always?)! Idea generation is the inherent gift that every child has in abundance. As educators and parents, we’re always looking to deeply engage our young people in the subject matter we’re teaching, and they are never more engaged than when they have an idea and want to implement it. Way back in 2012, I wanted my button to print geeky sayings:

Geek Gurl Diaries Raspberry Pi Thermal Printer Project Sneak Peek!

A sneak peek at the finished Geek Gurl Diaries ‘Box of Geek’. I’ve been busy making this for a few weeks with some help from friends. Tutorial to make your own box coming soon, so keep checking the Geek Gurl Diaries Twitter, facebook page and channel.

What are the challenges for this approach in education?

Allowing this kind of free-form creativity and tinkering in the classroom obviously has its challenges for teachers, especially those confined to rigid lesson structures, timings, and small classrooms. The most common worry I hear from teachers is “what if they ask a question I can’t answer?” Encouraging this sort of creative thinking makes that almost an inevitability. How can you facilitate roughly 30 different projects simultaneously? The answer is by using those other computational and transferable thinking skills:

  • Problem-solving
  • Iteration
  • Collaboration
  • Evaluation

Clearly specifying a problem, surveying the tools available to solve it (including online references and external advice), and then applying them to solve the problem is a hugely important skill, and this is a great opportunity to teach it.

A girl plays a button reaction game at a Raspberry Pi event

Press ALL the buttons!

Hands-off guidance

When we train teachers at Picademy, we group attendees around themes that have come out of the idea generation session. Together they collaborate on an achievable shared goal. One will often sketch something on a whiteboard, decomposing the problem into smaller parts; then the group will divide up the tasks. Each will look online or in books for tutorials to help them with their step. I’ve seen this behaviour in student groups too, and it’s very easy to facilitate. You don’t need to be the resident expert on every project that students want to work on.

The key is knowing where to guide students to find the answers they need. Curating online videos, blogs, tutorials, and articles in advance gives you the freedom and confidence to concentrate on what matters: the learning. We have a number of physical computing projects that use buttons, linked to our curriculum for learners to combine inputs and outputs to solve a problem. The WhooPi cushion and GPIO music box are two of my favourites.

A Raspberry Pi and button attached to a computer display

Outside of formal education, events such as Raspberry Jams, CoderDojos, CAS Hubs, and hackathons are ideal venues for seeking and receiving support and advice.

Cross-curricular participation

The rise of the global maker movement, I think, is in response to abstract concepts and disciplines. Children are taught lots of concepts in isolation that aren’t always relevant to their lives or immediate environment. Digital making provides a unique and exciting way of bridging different subject areas, allowing for cross-curricular participation. I’m not suggesting that educators should throw away all their schemes of work and leave the full direction of the computing curriculum to students. However, there’s huge value in exposing learners to the possibilities for creativity in computing. Creative freedom and expression guide learning, better preparing young people for the workplace of tomorrow.

So…what do you want your button to do?

Hello World

Learn more about today’s subject, and read further articles regarding computer science in education, in Hello World magazine issue 1.

Read Hello World issue 1 for more…

UK-based educators can subscribe to Hello World to receive a hard copy delivered for free to their doorstep, while the PDF is available for free to everyone via the Hello World website.

The post What do you want your button to do? appeared first on Raspberry Pi.

On Architecture and the State of the Art

Post Syndicated from Philip Fitzsimons original https://aws.amazon.com/blogs/architecture/on-architecture-and-the-state-of-the-art/

On the AWS Solutions Architecture team we know we’re following in the footsteps of other technical experts who pulled together the best practices of their eras. Around 22 BC the Roman Architect Vitruvius Pollio wrote On architecture (published as The Ten Books on Architecture), which became a seminal work on architectural theory. Vitruvius captured the best practices of his contemporaries and those who went before him (especially the Greek architects).

Closer to our time, in 1910, another technical expert, Henry Harrison Suplee, wrote Gas Turbine: progress in the design and construction of turbines operated by gases of combustion, from which we believe the phrase “state of the art” originates:

It has therefore been thought desirable to gather under one cover the most important papers which have appeared upon the subject of the gas turbine in England, France, Germany, and Switzerland, together with some account of the work in America, and to add to this such information upon actual experimental machines as can be secured.

In the present state of the art this is all that can be done, but it is believed that this will aid materially in the conduct of subsequent work, and place in the hands of the gas-power engineer a collection of material not generally accessible or available in convenient form.  

Source

Both authors wrote books that captured the current knowledge on design principles and best practices (in architecture and engineering) to improve awareness and adoption. Like these authors, we at AWS believe that capturing and sharing best practices leads to better outcomes. This follows a pattern we established internally, in our Principal Engineering community. In 2012 we started an initiative called “Well-Architected” to help share the best practices for architecting in the cloud with our customers.

Every year AWS Solution Architects dedicate hundreds of thousands of hours to helping customers build architectures that are cloud native. Through customer feedback, and real world experience we see what strategies, patterns, and approaches work for you.

“After our well-architected review and subsequent migration to the cloud, we saw the tremendous cost-savings potential of Amazon Web Services. By using the industry-standard service, we can invest the majority of our time and energy into enhancing our solutions. Thanks to (consulting partner) 1Strategy’s deep, technical AWS expertise and flexibility during our migration, we were able to leverage the strengths of AWS quickly.”

Paul Cooley, Chief Technology Officer for Imprev

This year we have again refreshed the AWS Well-Architected Framework, with a particular focus on Operational Excellence. Last year we announced the addition of Operational Excellence as a new pillar to AWS Well-Architected Framework. Having carried out thousands of reviews since then, we reexamined the pillar and are pleased to announce some significant changes. First, the pillar dives more deeply into people and process because this is an area where we see the most opportunities for teams to improve. Second, we’ve pivoted heavily to focusing on whether your team and your workload are ready for runtime operations. Key to this is ensuring that in the early phases of design that you think about how your architecture will be operated. Reflecting on this we realized that Operational Excellence should be the first pillar to support the “Architect for run-time operations” approach.

We’ve also added detail on how Amazon approaches technology architecture, covering topics such as our Principal Engineering Community and two-way doors and mechanisms. We refreshed the other pillars to reflect the evolution of AWS, and the best practices we are seeing in the field. We have also added detail on the review process, in the surprisingly named “The Review Process” section.

As part of refreshing the pillars are have also released a new Operational Excellence Pillar whitepaper, and have updated the whitepapers for all of the other pillars of the Framework. For example we have significantly updated the Reliability Pillar whitepaper to provide guidance on application design for high availability. New sections cover techniques for high availability including and beyond infrastructure implications, and considerations across the application lifecycle. This updated whitepaper also provides examples that show how to achieve availability goals in single and multi-region architectures.

You can find free training and all of the ”state of the art” whitepapers on the AWS Well-Architected homepage:

Philip Fitzsimons, Leader, AWS Well-Architected Team

UK Government Publishes Advice on ‘Illicit Streaming Devices’

Post Syndicated from Andy original https://torrentfreak.com/uk-government-publishes-advice-on-illicit-streaming-devices-171120/

With torrents and other methods of obtaining content simmering away in the background, unauthorized streaming is the now the method of choice for millions of pirates around the globe.

Previously accessible only via a desktop browser, streaming is now available on a wide range of devices, from tablets and phones through to dedicated set-top box. These, collectively, are now being branded Illicit Streaming Devices (ISD) by the entertainment industries.

It’s terminology the UK government’s Intellectual Property Office has adopted this morning. In a new public advisory, the IPO notes that illicit streaming is the watching of content without the copyright owner’s permission using a variety of devices.

“Illicit streaming devices are physical boxes that are connected to your TV or USB sticks that plug into the TV such as adapted Amazon Fire sticks and so called ‘Kodi’ boxes or Android TV boxes,” the IPO reports.

“These devices are legal when used to watch legitimate, free to air, content. They become illegal once they are adapted to stream illicit content, for example TV programmes, films and subscription sports channels without paying the appropriate subscriptions.”

The IPO notes that streaming devices usually need to be loaded with special software add-ons in order to view copyright-infringing content. However, there are now dedicated apps available to view movies and TV shows which can be loaded straight on to smartphones and tablets.

But how can people know if the device they have is an ISD or not? According to the IPO it’s all down to common sense. If people usually charge for the content you’re getting for free, it’s illegal.

“If you are watching television programmes, films or sporting events where you would normally be paying to view them and you have not paid, you are likely to be using an illicit streaming device (ISD) or app. This could include a film recently released in the cinema, a sporting event that is being broadcast by BT Sport or a television programme, like Game of Thrones, that is only available on Sky,” the IPO says.

In an effort to familiarize the public with some of the terminology used by ISD sellers on eBay, Amazon or Gumtree, for example, the IPO then wanders into a bit of a minefield that really needs much greater clarification.

First up, the government states that ISDs are often described online as being “Fully loaded”, which is a colloquial term for a device with addons already installed. Although they won’t all be infringing, it’s very often the case that the majority are intended to be, so no problems here.

However, the IPO then says that people should keep an eye out for the term ‘jail broken’, which many readers will understand to be the process some hardware devices, such as Apple products, are put through in order for third-party software to be run on them. On occasion, some ISD sellers do put this term on Android devices, for example, but it’s incorrect, in a tiny minority, and of course misleading.

The IPO also warns people against devices marketed as “Plug and Play” but again this is a dual-use term and shouldn’t put consumers off a purchase without a proper investigation. A search on eBay this morning for that exact term didn’t yield any ISDs at all, only games consoles that can be plugged in and played with a minimum of fuss.

“Subscription Gift”, on the other hand, almost certainly references an illicit IPTV or satellite card-sharing subscription and is rarely used for anything else. 100% illegal, no doubt.

The government continues by giving reasons why people should avoid ISDs, not least since their use deprives the content industries of valuable revenue.

“[The creative industries] provide employment for more than 1.9 million people and contributes £84.1 billion to our economy. Using illicit streaming devices is illegal,” the IPO writes.

“If you are not paying for this content you are depriving industry of the revenue it needs to fund the next generation of TV programmes, films and sporting events we all enjoy. Instead it provides funds for the organized criminals who sell or adapt these illicit devices.”

Then, in keeping with the danger-based narrative employed by the entertainment industries’ recently, the government also warns that ISDs can have a negative effect on child welfare, not to mention on physical safety in the home.

“These devices often lack parental controls. Using them could expose children or young people to explicit or age inappropriate content,” the IPO warns.

“Another important reason for consumers to avoid purchasing these streaming devices is from an electrical safety point of view. Where devices and their power cables have been tested, some have failed EU safety standards and have the potential to present a real danger to the public, causing a fire in your home or premises.”

While there can be no doubt whatsoever that failing EU electrical standards in any way is unacceptable for any device, the recent headlines stating that “Kodi Boxes Can Kill Their Owners” are sensational at best and don’t present the full picture.

As reported this weekend, simply not having a recognized branding on such devices means that they fail electrical standards, with non-genuine phone chargers presenting a greater risk around the UK.

Finally, the government offers some advice for people who either want to get off the ISD gravy train or ensure that others don’t benefit from it.

“These devices can be used legally by removing the software. If you are unsure get advice to help you use the device legally. If you wish to watch content that’s only available via subscription, such as sports, you should approach the relevant provider to find out about legal ways to watch,” the IPO advises.

Get it Right from a Genuine Site helps you get the music, TV, films, games, books, newspapers, magazines and sport that you love from genuine services.”

And, if the public thinks that people selling such devices deserve a visit from the authorities, people are asked to report them to the Crimestoppers charity via an anonymous hotline.

The government’s guidance is exactly what one might expect, given that the advisory is likely to have been strongly assisted by companies including the Federation Against Copyright Theft, Premier League, and Sky, who have taken the lead in this area during the past year or so.

The big question is, however, whether many people using these devices really believe that obtaining subscription TV, movies, and sports for next to free is 100% legal. If there are people out there they must be in the minority but at least the government itself is now putting them on the right path.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Multi-National Police Operation Shuts Down Pirate Forums

Post Syndicated from Andy original https://torrentfreak.com/multi-national-police-operation-shuts-down-pirate-forums-171110/

Once upon a time, large-scale raids on pirate operations were a regular occurrence, with news of such events making the headlines every few months. These days things have calmed down somewhat but reports coming out of Germany suggests that the war isn’t over yet.

According to a statement from German authorities, the Attorney General in Dresden and various cybercrime agencies teamed up this week to take down sites dedicated to sharing copyright protected material via the Usenet (newsgroups) system.

Huge amounts of infringing items were said to have been made available on a pair of indexing sites – 400,000 on Town.ag and 1,200,000 on Usenet-Town.com.

“Www.town.ag and www.usenet-town.com were two of the largest online portals that provided access to films, series, music, software, e-books, audiobooks, books, newspapers and magazines through systematic and unlawful copyright infringement,” the statement reads.

Visitors to these URLs are no longer greeted by the usual warez-fest, but by a seizure banner placed there by German authorities.

Seizure banner on Town.ag and Usenet-Town.com (translated)

Following an investigation carried out after complaints from rightsholders, 182 officers of various agencies raided homes and businesses Wednesday, each connected to a reported 26 suspects. In addition to searches of data centers located in Germany, servers in Spain, Netherlands, San Marino, Switzerland, and Canada were also targeted.

According to police the sites generated income from ‘sponsors’, netting their operators millions of euros in revenue. One of those appears to be Usenet reseller SSL-News, which displays the same seizure banner. Rightsholders claim that the Usenet portals have cost them many millions of euros in lost sales.

Arrest warrants were issued in Spain and Saxony against two German nationals, 39 and 31-years-old respectively. The man arrested in Spain is believed to be a ringleader and authorities there have been asked to extradite him to Germany.

At least 1,000 gigabytes of data were seized, with police scooping up numerous computers and other hardware for evidence. The true scale of material indexed is likely to be much larger, however.

Online chatter suggests that several other Usenet-related sites have also disappeared during the past day but whether that’s a direct result of the raids or down to precautionary measures taken by their operators isn’t yet clear.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Me on the Equifax Breach

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/11/me_on_the_equif.html

Testimony and Statement for the Record of Bruce Schneier
Fellow and Lecturer, Belfer Center for Science and International Affairs, Harvard Kennedy School
Fellow, Berkman Center for Internet and Society at Harvard Law School

Hearing on “Securing Consumers’ Credit Data in the Age of Digital Commerce”

Before the

Subcommittee on Digital Commerce and Consumer Protection
Committee on Energy and Commerce
United States House of Representatives

1 November 2017
2125 Rayburn House Office Building
Washington, DC 20515

Mister Chairman and Members of the Committee, thank you for the opportunity to testify today concerning the security of credit data. My name is Bruce Schneier, and I am a security technologist. For over 30 years I have studied the technologies of security and privacy. I have authored 13 books on these subjects, including Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World (Norton, 2015). My popular newsletter CryptoGram and my blog Schneier on Security are read by over 250,000 people.

Additionally, I am a Fellow and Lecturer at the Harvard Kennedy School of Government –where I teach Internet security policy — and a Fellow at the Berkman-Klein Center for Internet and Society at Harvard Law School. I am a board member of the Electronic Frontier Foundation, AccessNow, and the Tor Project; and an advisory board member of Electronic Privacy Information Center and VerifiedVoting.org. I am also a special advisor to IBM Security and the Chief Technology Officer of IBM Resilient.

I am here representing none of those organizations, and speak only for myself based on my own expertise and experience.

I have eleven main points:

1. The Equifax breach was a serious security breach that puts millions of Americans at risk.

Equifax reported that 145.5 million US customers, about 44% of the population, were impacted by the breach. (That’s the original 143 million plus the additional 2.5 million disclosed a month later.) The attackers got access to full names, Social Security numbers, birth dates, addresses, and driver’s license numbers.

This is exactly the sort of information criminals can use to impersonate victims to banks, credit card companies, insurance companies, cell phone companies and other businesses vulnerable to fraud. As a result, all 143 million US victims are at greater risk of identity theft, and will remain at risk for years to come. And those who suffer identify theft will have problems for months, if not years, as they work to clean up their name and credit rating.

2. Equifax was solely at fault.

This was not a sophisticated attack. The security breach was a result of a vulnerability in the software for their websites: a program called Apache Struts. The particular vulnerability was fixed by Apache in a security patch that was made available on March 6, 2017. This was not a minor vulnerability; the computer press at the time called it “critical.” Within days, it was being used by attackers to break into web servers. Equifax was notified by Apache, US CERT, and the Department of Homeland Security about the vulnerability, and was provided instructions to make the fix.

Two months later, Equifax had still failed to patch its systems. It eventually got around to it on July 29. The attackers used the vulnerability to access the company’s databases and steal consumer information on May 13, over two months after Equifax should have patched the vulnerability.

The company’s incident response after the breach was similarly damaging. It waited nearly six weeks before informing victims that their personal information had been stolen and they were at increased risk of identity theft. Equifax opened a website to help aid customers, but the poor security around that — the site was at a domain separate from the Equifax domain — invited fraudulent imitators and even more damage to victims. At one point, the official Equifax communications even directed people to that fraudulent site.

This is not the first time Equifax failed to take computer security seriously. It confessed to another data leak in January 2017. In May 2016, one of its websites was hacked, resulting in 430,000 people having their personal information stolen. Also in 2016, a security researcher found and reported a basic security vulnerability in its main website. And in 2014, the company reported yet another security breach of consumer information. There are more.

3. There are thousands of data brokers with similarly intimate information, similarly at risk.

Equifax is more than a credit reporting agency. It’s a data broker. It collects information about all of us, analyzes it all, and then sells those insights. It might be one of the biggest, but there are 2,500 to 4,000 other data brokers that are collecting, storing, and selling information about us — almost all of them companies you’ve never heard of and have no business relationship with.

The breadth and depth of information that data brokers have is astonishing. Data brokers collect and store billions of data elements covering nearly every US consumer. Just one of the data brokers studied holds information on more than 1.4 billion consumer transactions and 700 billion data elements, and another adds more than 3 billion new data points to its database each month.

These brokers collect demographic information: names, addresses, telephone numbers, e-mail addresses, gender, age, marital status, presence and ages of children in household, education level, profession, income level, political affiliation, cars driven, and information about homes and other property. They collect lists of things we’ve purchased, when we’ve purchased them, and how we paid for them. They keep track of deaths, divorces, and diseases in our families. They collect everything about what we do on the Internet.

4. These data brokers deliberately hide their actions, and make it difficult for consumers to learn about or control their data.

If there were a dozen people who stood behind us and took notes of everything we purchased, read, searched for, or said, we would be alarmed at the privacy invasion. But because these companies operate in secret, inside our browsers and financial transactions, we don’t see them and we don’t know they’re there.

Regarding Equifax, few consumers have any idea what the company knows about them, who they sell personal data to or why. If anyone knows about them at all, it’s about their business as a credit bureau, not their business as a data broker. Their website lists 57 different offerings for business: products for industries like automotive, education, health care, insurance, and restaurants.

In general, options to “opt-out” don’t work with data brokers. It’s a confusing process, and doesn’t result in your data being deleted. Data brokers will still collect data about consumers who opt out. It will still be in those companies’ databases, and will still be vulnerable. It just don’t be included individually when they sell data to their customers.

5. The existing regulatory structure is inadequate.

Right now, there is no way for consumers to protect themselves. Their data has been harvested and analyzed by these companies without their knowledge or consent. They cannot improve the security of their personal data, and have no control over how vulnerable it is. They only learn about data breaches when the companies announce them — which can be months after the breaches occur — and at that point the onus is on them to obtain credit monitoring services or credit freezes. And even those only protect consumers from some of the harms, and only those suffered after Equifax admitted to the breach.

Right now, the press is reporting “dozens” of lawsuits against Equifax from shareholders, consumers, and banks. Massachusetts has sued Equifax for violating state consumer protection and privacy laws. Other states may follow suit.

If any of these plaintiffs win in the court, it will be a rare victory for victims of privacy breaches against the companies that have our personal information. Current law is too narrowly focused on people who have suffered financial losses directly traceable to a specific breach. Proving this is difficult. If you are the victim of identity theft in the next month, is it because of Equifax or does the blame belong to another of the thousands of companies who have your personal data? As long as one can’t prove it one way or the other, data brokers remain blameless and liability free.

Additionally, much of this market in our personal data falls outside the protections of the Fair Credit Reporting Act. And in order for the Federal Trade Commission to levy a fine against Equifax, it needs to have a consent order and then a subsequent violation. Any fines will be limited to credit information, which is a small portion of the enormous amount of information these companies know about us. In reality, this is not an effective enforcement regime.

Although the FTC is investigating Equifax, it is unclear if it has a viable case.

6. The market cannot fix this because we are not the customers of data brokers.

The customers of these companies are people and organizations who want to buy information: banks looking to lend you money, landlords deciding whether to rent you an apartment, employers deciding whether to hire you, companies trying to figure out whether you’d be a profitable customer — everyone who wants to sell you something, even governments.

Markets work because buyers choose from a choice of sellers, and sellers compete for buyers. None of us are Equifax’s customers. None of us are the customers of any of these data brokers. We can’t refuse to do business with the companies. We can’t remove our data from their databases. With few limited exceptions, we can’t even see what data these companies have about us or correct any mistakes.

We are the product that these companies sell to their customers: those who want to use our personal information to understand us, categorize us, make decisions about us, and persuade us.

Worse, the financial markets reward bad security. Given the choice between increasing their cybersecurity budget by 5%, or saving that money and taking the chance, a rational CEO chooses to save the money. Wall Street rewards those whose balance sheets look good, not those who are secure. And if senior management gets unlucky and the a public breach happens, they end up okay. Equifax’s CEO didn’t get his $5.2 million severance pay, but he did keep his $18.4 million pension. Any company that spends more on security than absolutely necessary is immediately penalized by shareholders when its profits decrease.

Even the negative PR that Equifax is currently suffering will fade. Unless we expect data brokers to put public interest ahead of profits, the security of this industry will never improve without government regulation.

7. We need effective regulation of data brokers.

In 2014, the Federal Trade Commission recommended that Congress require data brokers be more transparent and give consumers more control over their personal information. That report contains good suggestions on how to regulate this industry.

First, Congress should help plaintiffs in data breach cases by authorizing and funding empirical research on the harm individuals receive from these breaches.

Specifically, Congress should move forward legislative proposals that establish a nationwide “credit freeze” — which is better described as changing the default for disclosure from opt-out to opt-in — and free lifetime credit monitoring services. By this I do not mean giving customers free credit-freeze options, a proposal by Senators Warren and Schatz, but that the default should be a credit freeze.

The credit card industry routinely notifies consumers when there are suspicious charges. It is obvious that credit reporting agencies should have a similar obligation to notify consumers when there is suspicious activity concerning their credit report.

On the technology side, more could be done to limit the amount of personal data companies are allowed to collect. Increasingly, privacy safeguards impose “data minimization” requirements to ensure that only the data that is actually needed is collected. On the other hand, Congress should not create a new national identifier to replace the Social Security Numbers. That would make the system of identification even more brittle. Better is to reduce dependence on systems of identification and to create contextual identification where necessary.

Finally, Congress needs to give the Federal Trade Commission the authority to set minimum security standards for data brokers and to give consumers more control over their personal information. This is essential as long as consumers are these companies’ products and not their customers.

8. Resist complaints from the industry that this is “too hard.”

The credit bureaus and data brokers, and their lobbyists and trade-association representatives, will claim that many of these measures are too hard. They’re not telling you the truth.

Take one example: credit freezes. This is an effective security measure that protects consumers, but the process of getting one and of temporarily unfreezing credit is made deliberately onerous by the credit bureaus. Why isn’t there a smartphone app that alerts me when someone wants to access my credit rating, and lets me freeze and unfreeze my credit at the touch of the screen? Too hard? Today, you can have an app on your phone that does something similar if you try to log into a computer network, or if someone tries to use your credit card at a physical location different from where you are.

Moreover, any credit bureau or data broker operating in Europe is already obligated to follow the more rigorous EU privacy laws. The EU General Data Protection Regulation will come into force, requiring even more security and privacy controls for companies collecting storing the personal data of EU citizens. Those companies have already demonstrated that they can comply with those more stringent regulations.

Credit bureaus, and data brokers in general, are deliberately not implementing these 21st-century security solutions, because they want their services to be as easy and useful as possible for their actual customers: those who are buying your information. Similarly, companies that use this personal information to open accounts are not implementing more stringent security because they want their services to be as easy-to-use and convenient as possible.

9. This has foreign trade implications.

The Canadian Broadcast Corporation reported that 100,000 Canadians had their data stolen in the Equifax breach. The British Broadcasting Corporation originally reported that 400,000 UK consumers were affected; Equifax has since revised that to 15.2 million.

Many American Internet companies have significant numbers of European users and customers, and rely on negotiated safe harbor agreements to legally collect and store personal data of EU citizens.

The European Union is in the middle of a massive regulatory shift in its privacy laws, and those agreements are coming under renewed scrutiny. Breaches such as Equifax give these European regulators a powerful argument that US privacy regulations are inadequate to protect their citizens’ data, and that they should require that data to remain in Europe. This could significantly harm American Internet companies.

10. This has national security implications.

Although it is still unknown who compromised the Equifax database, it could easily have been a foreign adversary that routinely attacks the servers of US companies and US federal agencies with the goal of exploiting security vulnerabilities and obtaining personal data.

When the Fair Credit Reporting Act was passed in 1970, the concern was that the credit bureaus might misuse our data. That is still a concern, but the world has changed since then. Credit bureaus and data brokers have far more intimate data about all of us. And it is valuable not only to companies wanting to advertise to us, but foreign governments as well. In 2015, the Chinese breached the database of the Office of Personal Management and stole the detailed security clearance information of 21 million Americans. North Korea routinely engages in cybercrime as way to fund its other activities. In a world where foreign governments use cyber capabilities to attack US assets, requiring data brokers to limit collection of personal data, securely store the data they collect, and delete data about consumers when it is no longer needed is a matter of national security.

11. We need to do something about it.

Yes, this breach is a huge black eye and a temporary stock dip for Equifax — this month. Soon, another company will have suffered a massive data breach and few will remember Equifax’s problem. Does anyone remember last year when Yahoo admitted that it exposed personal information of a billion users in 2013 and another half billion in 2014?

Unless Congress acts to protect consumer information in the digital age, these breaches will continue.

Thank you for the opportunity to testify today. I will be pleased to answer your questions.

Book Author Trolled Pirates With Fake Leak to Make a Point

Post Syndicated from Ernesto original https://torrentfreak.com/book-author-trolled-pirates-with-fake-leak-to-make-a-point-171104/

When it comes to how piracy affects sales, there are thousands of different opinions. This applies to music, movies, software and many other digital products, including ebooks.

When we interviewed Paulo Coelho nearly ten years ago, he pointed out how piracy helped him to sell more books. While a lot has changed since then, he still sees the benefits of piracy today.

However, for many other authors, piracy is a menace. They cringe at the sight of their book being shared online and believe that hurts their bottom line. This includes Maggie Stiefvater, who’s known for The Raven Cycle books, among others.

This week she responded to a tweet from a self-confessed pirate, stating that piracy got the box set of the Raven Cycle canceled. As is usual on social media, it quickly turned into a mess.

Instead of debating the controversial issue indefinitely in 140 character tweets, Stiefvater did what authors do best. She put her thoughts on paper. In a Tumblr post, she countered the belief that piracy doesn’t hurt authors and that pirates wouldn’t pay for a book anyway.

The story shared by Stiefvater isn’t hypothetical, it’s real-world experience. She had noticed that the third book in the Raven Cycle wasn’t doing as well as earlier editions. While this is not uncommon for a series, the sales drop was not equal across all formats, but mostly driven by a lack of eBook sales.

While her publisher wasn’t certain that piracy was to blame, Stiefvater was convinced it played an important role. After all, the interest in her book tours was growing and there was plenty of talk about the books online as well. So when the publisher said that the print run of her new book the Raven King would be cut in half compared to a previous release, she came up with a plan.

Instead of trying to take all pirated copies down following the new release, she created her own, with help from her brother. But one with a twist.

“It was impossible to take down every illegal pdf; I’d already seen that. So we were going to do the opposite. We created a pdf of the Raven King. It was the same length as the real book, but it was just the first four chapters over and over again,” Stiefvater writes.

“I knew we wouldn’t be able to hold the fort for long — real versions would slowly get passed around by hand through forum messaging — but I told my brother: I want to hold the fort for one week. Enough to prove a point. Enough to show everyone that this is no longer 2004. This is the smart phone generation, and a pirated book sometimes is a lost sale.”

And so it happened. When the book came out April last year, customized pirated copies were planted all over the Internet by the author’s brother. People were stumbling all over them, making it near impossible to find a real pirated copy.

“He uploaded dozens and dozens and dozens of these pdfs of The Raven King. You couldn’t throw a rock without hitting one of his pdfs. We sailed those epub seas with our own flag shredding the sky.”

This paid off. Many people could only find the “troll” copies and saw no other option than to buy the real deal.

“The effects were instant. The forums and sites exploded with bewildered activity. Fans asked if anyone had managed to find a link to a legit pdf. Dozens of posts appeared saying that since they hadn’t been able to find a pdf, they’d been forced to hit up Amazon and buy the book.”

As a result, the first print of the book sold out in two days. Stiefvater was on tour and at some stores she visited, the books were no longer available. The publisher had to print more and more until… the inevitable happened.

“Then the pdfs hit the forums and e-sales sagged and it was business as usual, but it didn’t matter: I’d proven the point. Piracy has consequences,” Stiefvater writes, summarizing the morale of her story.

While this is unlikely to change the minds of undeterred pirates, it might strike a chord with some people.

Of course Stiefvater’s anecdote is no better that Coelho’s, who argued the opposite in the past. Perhaps the real takeaway is that piracy doesn’t have any fixed effects and it certainly can’t be captured in oneliners either. It’s a complex puzzle of dozens of constantly changing factors, which will likely never be solved.

Maggie Stiefvater’s full Tumblr post is a recommended read and can be found here, or below.

http://maggie-stiefvater.tumblr.com/post/166952028861/ive-decided-to-tell-you-guys-a-story-about

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

‘Pirating’ Mainstream Media Outlets Haunted by Photographers in Court

Post Syndicated from Ernesto original https://torrentfreak.com/pirating-mainstream-media-outlets-haunted-by-photographers-in-court-171028/

When piracy hits the mainstream news, it’s often in relation to books, games, music, TV-shows and movies.

These industries grab headlines because of the major players that are involved, but they are not the only ones dealing with piracy.

In fact, photos are arguably the most commonly infringed works online. Not just by random users on Facebook, Twitter, and blogs, but also by major mainstream media outlets.

While most photographers spend little time battling piracy, more and more are willing to take the matter to federal court. This trend started two years ago by the Liebowitz Law Firm is paying off.

The lawsuits in question, filed on behalf of several independent photographers, are fairly straightforward.

A news site or other media outlet uses a photo to spice up an article but fails to pay the photographer. This is fairly common, even for major publications. The photographer then files a lawsuit demanding compensation.

However, before the case goes to trial both parties usually resolve their issues in a private settlement. Just this year alone, the Liebowitz Law Firm closed over 200 cases for its clients.

ABC, AOL, CBS Broadcasting, NBCUniversal, NPR, Time, Viacom, Warner Bros, Yahoo and Ziff Davis are just a few of the companies that have signed settlements recently.

One of the many settlements

While the court records don’t point out any winners, it’s safe to assume that many of these cases end favorably for the firm’s clients. Otherwise, they wouldn’t continue to file new ones.

TorrentFreak reached out to Richard Liebowitz, lead counsel in many of these cases. Unfortunately, he can’t share exact details as the settlements themselves are confidential.

The photographers don’t make millions through this scheme, but it appears to be an effective way to get paid a few thousand dollars. If one repeats that often enough, it should provide a decent income. And indeed, several have already filed over a dozen cases.

The practice is reminiscent of copyright trolling cases, with the exception that the accused are major companies instead of random citizens. And unlike the lawsuits movie companies file against BitTorrent users in the US, the evidence the photographers have is rock solid.

The evidence..

When a random copyright troll sues BitTorrent users, hoping to extract a settlement, they rely on indirect IP-addresses and geo-location evidence. The photographers, however, can show an actual screenshot of the infringing work in the mainstream news outlet.

That’s hard to ignore, to say the least, and based on the number of settlements it’s safe to argue that the media outlets prefer to settle instead of litigating the issue. It’s probably cheaper and avoids bad PR.

For the record, all photos used for this article are properly licensed or part of court documents, which are public domain.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

More Raspberry Pi labs in West Africa

Post Syndicated from Rachel Churcher original https://www.raspberrypi.org/blog/pi-based-ict-west-africa/

Back in May 2013, we heard from Dominique Laloux about an exciting project to bring Raspberry Pi labs to schools in rural West Africa. Until 2012, 75 percent of teachers there had never used a computer. The project has been very successful, and Dominique has been in touch again to bring us the latest news.

A view of the inside of the new Pi lab building

Preparing the new Pi labs building in Kuma Tokpli, Togo

Growing the project

Thanks to the continuing efforts of a dedicated team of teachers, parents and other supporters, the Centre Informatique de Kuma, now known as INITIC (from the French ‘INItiation aux TIC’), runs two Raspberry Pi labs in schools in Togo, and plans to open a third in December. The second lab was opened last year in Kpalimé, a town in the Plateaux Region in the west of the country.

Student using a Raspberry Pi computer

Using the new Raspberry Pi labs in Kpalimé, Togo

More than 400 students used the new lab intensively during the last school year. Dominique tells us more:

“The report made in early July by the seven teachers who accompanied the students was nothing short of amazing: the young people covered a very impressive number of concepts and skills, from the GUI and the file system, to a solid introduction to word processing and spreadsheets, and many other skills. The lab worked exactly as expected. Its 21 Raspberry Pis worked flawlessly, with the exception of a couple of SD cards that needed re-cloning, and a couple of old screens that needed to be replaced. All the Raspberry Pis worked without a glitch. They are so reliable!”

The teachers and students have enjoyed access to a range of software and resources, all running on Raspberry Pi 2s and 3s.

“Our current aim is to introduce the students to ICT using the Raspberry Pis, rather than introducing them to programming and electronics (a step that will certainly be considered later). We use Ubuntu Mate along with a large selection of applications, from LibreOffice, Firefox, GIMP, Audacity, and Calibre, to special maths, science, and geography applications. There are also special applications such as GnuCash and GanttProject, as well as logic games including PyChess. Since December, students also have access to a local server hosting Kiwix, Wiktionary (a local copy of Wikipedia in four languages), several hundred videos, and several thousand books. They really love it!”

Pi lab upgrade

This summer, INITIC upgraded the equipment in their Pi lab in Kuma Adamé, which has been running since 2014. 21 older model Raspberry Pis were replaced with Pi 2s and 3s, to bring this lab into line with the others, and encourage co-operation between the different locations.

“All 21 first-generation Raspberry Pis worked flawlessly for three years, despite the less-than-ideal conditions in which they were used — tropical conditions, dust, frequent power outages, etc. I brought them all back to Brussels, and they all still work fine. The rationale behind the upgrade was to bring more computing power to the lab, and also to have the same equipment in our two Raspberry Pi labs (and in other planned installations).”

Students and teachers using the upgraded Pi labs in Kuma Adamé

Students and teachers using the upgraded Pi lab in Kuma Adamé

An upgrade of the organisation’s first lab, installed in 2012 in Kuma Tokpli, will be completed in December. This lab currently uses ‘retired’ laptops, which will be replaced with Raspberry Pis and peripherals. INITIC, in partnership with the local community, is also constructing a new building to house the upgraded technology, and the organisation’s third Raspberry Pi lab.

Reliable tech

Dominique has been very impressed with the performance of the Raspberry Pis since 2014.

“Our experience of three years, in two very different contexts, clearly demonstrates that the Raspberry Pi is a very convincing alternative to more ‘conventional’ computers for introducing young students to ICT where resources are scarce. I wish I could convince more communities in the world to invest in such ‘low cost, low consumption, low maintenance’ infrastructure. It really works!”

He goes on to explain that:

“Our goal now is to build at least one new Raspberry Pi lab in another Togolese school each year. That will, of course, depend on how successful we are at gathering the funds necessary for each installation, but we are confident we can convince enough friends to give us the financial support needed for our action.”

A desk with Raspberry Pis and peripherals

Reliable Raspberry Pis in the labs at Kpalimé

Get involved

We are delighted to see the Raspberry Pi being used to bring information technology to new teachers, students, and communities in Togo – it’s wonderful to see this project becoming established and building on its achievements. The mission of the Raspberry Pi Foundation is to put the power of digital making into the hands of people all over the world. Therefore, projects like this, in which people use our tech to fulfil this mission in places with few resources, are wonderful to us.

More information about INITIC and its projects can be found on its website. If you are interested in helping the organisation to meet its goals, visit the How to help page. And if you are involved with a project like this, bringing ICT, computer science, and coding to new places, please tell us about it in the comments below.

The post More Raspberry Pi labs in West Africa appeared first on Raspberry Pi.

‘Pirate’ EBook Site Refuses Point Blank to Cooperate With BREIN

Post Syndicated from Andy original https://torrentfreak.com/pirate-ebook-site-refuses-point-blank-to-cooperate-with-brein-171015/

Dutch anti-piracy group BREIN is probably best known for its legal action against The Pirate Bay but the outfit also tackles many other forms of piracy.

A prime example is the case it pursued against a seller of fully-loaded Kodi boxes in the Netherlands. The subsequent landmark ruling from the European Court of Justice will reverberate around Europe for years to come.

Behind the scenes, however, BREIN persistently tries to take much smaller operations offline, and not without success. Earlier this year it revealed it had taken down 231 illegal sites and services includes 84 linking sites, 63 streaming portals, and 34 torrent sites. Some of these shut down completely and others were forced to leave their hosting providers.

Much of this work flies under the radar but some current action, against an eBook site, is now being thrust into the public eye.

For more than five years, EBoek.info (eBook) has serviced Internet users looking to obtain comic books in Dutch. The site informs TorrentFreak it provides a legitimate service, targeted at people who have purchased a hard copy but also want their comics in digital format.

“EBoek.info is a site about comic books in the Dutch language. Besides some general information about the books, people who have legally obtained a hard copy of the books can find a link to an NZB file which enables them to download a digital version of the books they already have,” site representative ‘Zala’ says.

For those out of the loop, NZB files are a bit like Usenet’s version of .torrent files. They contain no copyrighted content themselves but do provide software clients with information on where to find specific content, so it can be downloaded to a user’s machine.

“BREIN claims that this is illegal as it is impossible for us to verify if our visitor is telling the truth [about having purchased a copy],” Zala reveals.

Speaking with TorrentFreak, BREIN chief Tim Kuik says there’s no question that offering downloads like this is illegal.

“It is plain and simple: the site makes links to unauthorized digital copies available to the general public and therefore is infringing copyright. It is distribution of the content without authorization of the rights holder,” Kuik says.

“The unauthorized copies are not private copies. The private copy exception does not apply to this kind of distribution. The private copy has not been made by the owner of the book himself for his own use. Someone else made the digital copy and is making it available to anyone who wants to download it provided he makes the unverified claim that he has a legal copy. This harms the normal exploitation of the
content.”

Zala says that BREIN has been trying to take his site offline for many years but more recently, the platform has utilized the services of Cloudflare, partly as a form of shield. As readers may be aware, a site behind Cloudflare has its originating IP addresses hidden from the public, not to mention BREIN, who values that kind of information. According to the operator, however, BREIN managed to obtain the information from the CDN provider.

“BREIN has tried for years to take our site offline. Recently, however, Cloudflare was so friendly to give them our IP address,” Zala notes.

A text copy of an email reportedly sent by BREIN to EBoek’s web host and seen by TF appears to confirm that Cloudflare handed over the information as suggested. Among other things, the email has BREIN informing the host that “The IP we got back from Cloudflare is XXX.XXX.XX.33.”

This means that BREIN was able to place direct pressure on EBoek.info’s web host, so only time will tell if that bears any fruit for the anti-piracy group. In the meantime, however, EBoek has decided to go public over its battle with BREIN.

“We have received a request from Stichting BREIN via our hosting provider to take EBoek.info offline,” the site informed its users yesterday.

Interestingly, it also appears that BREIN doesn’t appreciate that the operators of EBoek have failed to make their identities publicly known on their platform.

“The site operates anonymously which also is unlawful. Consumer protection requires that the owner/operator of a site identifies himself,” Kuik says.

According to EBoek, the anti-piracy outfit told the site’s web host that as a “commercial online service”, EBoek is required under EU law to display its “correct and complete business information” including names, addresses, and other information. But perhaps unsurprisingly, the site doesn’t want to play ball.

“In my opinion, you are confusing us with Facebook. They are a foreign commercial company with a European branch in Ireland, and therefore are subject to Irish legislation,” Zala says in an open letter to BREIN.

“Eboek.info, on the other hand, is a foreign hobby club with no commercial purpose, whose administrators have no connection with any country in the European Union. As administrators, we follow the laws of our country of residence which do not oblige us to disclose our identity through our website.

“The fact that Eboek is visible in the Netherlands does not just mean that we are going to adapt to Dutch rules, just as we don’t adapt the site to the rules of Saudi Arabia or China or wherever we are available.”

In a further snub to the anti-piracy group, EBoek says that all visitors to the site have to communicate with its operators via its guestbook, which is publicly visible.

“We see no reason to make an exception for Stichting BREIN,” the site notes.

What makes the situation more complex is that EBoek isn’t refusing dialog completely. The site says it doesn’t want to talk to BREIN but will speak to BREIN’s customers – the publishers of the comic books in question – noting that to date no complaints from publishers have ever been received.

While the parties argue about lines of communication, BREIN insists that following this year’s European Court of Justice decision in the GS Media case, a link to a known infringing work represents copyright infringement. In this case, an NZB file – which links to a location on Usenet – would generally fit the bill.

But despite focusing on the Dutch market, the operators of EBoek say the ruling doesn’t apply to them as they’re outside of the ECJ’s jurisdiction and aren’t commercially motivated. Refusing point blank to take their site offline, EBoek’s operators say that BREIN can do its worst, nothing will have much effect.

“[W]hat’s the worst thing that can happen? That our web host hands [BREIN] our address and IP data. In that case, it will turn out that…we are actually far away,” Zala says.

“[In the case the site goes offline], we’ll just put a backup on another server and, in this case, won’t make use of the ‘services’ of Cloudflare, the provider that apparently put BREIN on the right track.”

The question of jurisdiction is indeed an interesting one, particularly given BREIN’s focus in the Netherlands. But Kuik is clear – it is the area where the content is made available that matters.

“The law of the country where the content is made available applies. In this case the EU and amongst others the Netherlands,” Kuik concludes.

To be continued…..

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

My Blogging

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/10/my_blogging.html

Blog regulars will notice that I haven’t been posting as much lately as I have in the past. There are two reasons. One, it feels harder to find things to write about. So often it’s the same stories over and over. I don’t like repeating myself. Two, I am busy writing a book. The title is still: Click Here to Kill Everybody: Peril and Promise in a Hyper-Connected World. The book is a year late, and as a very different table of contents than it had in 2016. I have been writing steadily since mid-August. The book is due to the publisher at the end of March 2018, and will be published in the beginning of September.

This is the current table of contents:

  • Introduction: Everything is Becoming a Computer
  • Part 1: The Trends
    • 1. Capitalism Continues to Drive the Internet
    • 2. Customer/User Control is Next
    • 3. Government Surveillance and Control is Also Increasing
    • 4. Cybercrime is More Profitable Than Ever
    • 5. Cyberwar is the New Normal
    • 6. Algorithms, Automation, and Autonomy Bring New Dangers
    • 7. What We Know About Computer Security
    • 8. Agile is Failing as a Security Paradigm
    • 9. Authentication and Identification are Getting Harder
    • 10. Risks are Becoming Catastrophic
  • Part 2: The Solutions
    • 11. We Need to Regulate the Internet of Things
    • 12. We Need to Defend Critical Infrastructure
    • 13. We Need to Prioritize Defense Over Offence
    • 14. We Need to Make Smarter Decisions About Connecting
    • 15. What’s Likely to Happen, and What We Can Do in Response
    • 16. Where Policy Can Go Wrong
  • Conclusion: Technology and Policy, Together

So that’s what’s been happening.

Predict Billboard Top 10 Hits Using RStudio, H2O and Amazon Athena

Post Syndicated from Gopal Wunnava original https://aws.amazon.com/blogs/big-data/predict-billboard-top-10-hits-using-rstudio-h2o-and-amazon-athena/

Success in the popular music industry is typically measured in terms of the number of Top 10 hits artists have to their credit. The music industry is a highly competitive multi-billion dollar business, and record labels incur various costs in exchange for a percentage of the profits from sales and concert tickets.

Predicting the success of an artist’s release in the popular music industry can be difficult. One release may be extremely popular, resulting in widespread play on TV, radio and social media, while another single may turn out quite unpopular, and therefore unprofitable. Record labels need to be selective in their decision making, and predictive analytics can help them with decision making around the type of songs and artists they need to promote.

In this walkthrough, you leverage H2O.ai, Amazon Athena, and RStudio to make predictions on whether a song might make it to the Top 10 Billboard charts. You explore the GLM, GBM, and deep learning modeling techniques using H2O’s rapid, distributed and easy-to-use open source parallel processing engine. RStudio is a popular IDE, licensed either commercially or under AGPLv3, for working with R. This is ideal if you don’t want to connect to a server via SSH and use code editors such as vi to do analytics. RStudio is available in a desktop version, or a server version that allows you to access R via a web browser. RStudio’s Notebooks feature is used to demonstrate the execution of code and output. In addition, this post showcases how you can leverage Athena for query and interactive analysis during the modeling phase. A working knowledge of statistics and machine learning would be helpful to interpret the analysis being performed in this post.

Walkthrough

Your goal is to predict whether a song will make it to the Top 10 Billboard charts. For this purpose, you will be using multiple modeling techniques―namely GLM, GBM and deep learning―and choose the model that is the best fit.

This solution involves the following steps:

  • Install and configure RStudio with Athena
  • Log in to RStudio
  • Install R packages
  • Connect to Athena
  • Create a dataset
  • Create models

Install and configure RStudio with Athena

Use the following AWS CloudFormation stack to install, configure, and connect RStudio on an Amazon EC2 instance with Athena.

Launching this stack creates all required resources and prerequisites:

  • Amazon EC2 instance with Amazon Linux (minimum size of t2.large is recommended)
  • Provisioning of the EC2 instance in an existing VPC and public subnet
  • Installation of Java 8
  • Assignment of an IAM role to the EC2 instance with the required permissions for accessing Athena and Amazon S3
  • Security group allowing access to the RStudio and SSH ports from the internet (I recommend restricting access to these ports)
  • S3 staging bucket required for Athena (referenced within RStudio as ATHENABUCKET)
  • RStudio username and password
  • Setup logs in Amazon CloudWatch Logs (if needed for additional troubleshooting)
  • Amazon EC2 Systems Manager agent, which makes it easy to manage and patch

All AWS resources are created in the US-East-1 Region. To avoid cross-region data transfer fees, launch the CloudFormation stack in the same region. To check the availability of Athena in other regions, see Region Table.

Log in to RStudio

The instance security group has been automatically configured to allow incoming connections on the RStudio port 8787 from any source internet address. You can edit the security group to restrict source IP access. If you have trouble connecting, ensure that port 8787 isn’t blocked by subnet network ACLS or by your outgoing proxy/firewall.

  1. In the CloudFormation stack, choose Outputs, Value, and then open the RStudio URL. You might need to wait for a few minutes until the instance has been launched.
  2. Log in to RStudio with the and password you provided during setup.

Install R packages

Next, install the required R packages from the RStudio console. You can download the R notebook file containing just the code.

#install pacman – a handy package manager for managing installs
if("pacman" %in% rownames(installed.packages()) == FALSE)
{install.packages("pacman")}  
library(pacman)
p_load(h2o,rJava,RJDBC,awsjavasdk)
h2o.init(nthreads = -1)
##  Connection successful!
## 
## R is connected to the H2O cluster: 
##     H2O cluster uptime:         2 hours 42 minutes 
##     H2O cluster version:        3.10.4.6 
##     H2O cluster version age:    4 months and 4 days !!! 
##     H2O cluster name:           H2O_started_from_R_rstudio_hjx881 
##     H2O cluster total nodes:    1 
##     H2O cluster total memory:   3.30 GB 
##     H2O cluster total cores:    4 
##     H2O cluster allowed cores:  4 
##     H2O cluster healthy:        TRUE 
##     H2O Connection ip:          localhost 
##     H2O Connection port:        54321 
##     H2O Connection proxy:       NA 
##     H2O Internal Security:      FALSE 
##     R Version:                  R version 3.3.3 (2017-03-06)
## Warning in h2o.clusterInfo(): 
## Your H2O cluster version is too old (4 months and 4 days)!
## Please download and install the latest version from http://h2o.ai/download/
#install aws sdk if not present (pre-requisite for using Athena with an IAM role)
if (!aws_sdk_present()) {
  install_aws_sdk()
}

load_sdk()
## NULL

Connect to Athena

Next, establish a connection to Athena from RStudio, using an IAM role associated with your EC2 instance. Use ATHENABUCKET to specify the S3 staging directory.

URL <- 'https://s3.amazonaws.com/athena-downloads/drivers/AthenaJDBC41-1.0.1.jar'
fil <- basename(URL)
#download the file into current working directory
if (!file.exists(fil)) download.file(URL, fil)
#verify that the file has been downloaded successfully
list.files()
## [1] "AthenaJDBC41-1.0.1.jar"
drv <- JDBC(driverClass="com.amazonaws.athena.jdbc.AthenaDriver", fil, identifier.quote="'")

con <- jdbcConnection <- dbConnect(drv, 'jdbc:awsathena://athena.us-east-1.amazonaws.com:443/',
                                   s3_staging_dir=Sys.getenv("ATHENABUCKET"),
                                   aws_credentials_provider_class="com.amazonaws.auth.DefaultAWSCredentialsProviderChain")

Verify the connection. The results returned depend on your specific Athena setup.

con
## <JDBCConnection>
dbListTables(con)
##  [1] "gdelt"               "wikistats"           "elb_logs_raw_native"
##  [4] "twitter"             "twitter2"            "usermovieratings"   
##  [7] "eventcodes"          "events"              "billboard"          
## [10] "billboardtop10"      "elb_logs"            "gdelthist"          
## [13] "gdeltmaster"         "twitter"             "twitter3"

Create a dataset

For this analysis, you use a sample dataset combining information from Billboard and Wikipedia with Echo Nest data in the Million Songs Dataset. Upload this dataset into your own S3 bucket. The table below provides a description of the fields used in this dataset.

Field Description
year Year that song was released
songtitle Title of the song
artistname Name of the song artist
songid Unique identifier for the song
artistid Unique identifier for the song artist
timesignature Variable estimating the time signature of the song
timesignature_confidence Confidence in the estimate for the timesignature
loudness Continuous variable indicating the average amplitude of the audio in decibels
tempo Variable indicating the estimated beats per minute of the song
tempo_confidence Confidence in the estimate for tempo
key Variable with twelve levels indicating the estimated key of the song (C, C#, B)
key_confidence Confidence in the estimate for key
energy Variable that represents the overall acoustic energy of the song, using a mix of features such as loudness
pitch Continuous variable that indicates the pitch of the song
timbre_0_min thru timbre_11_min Variables that indicate the minimum values over all segments for each of the twelve values in the timbre vector
timbre_0_max thru timbre_11_max Variables that indicate the maximum values over all segments for each of the twelve values in the timbre vector
top10 Indicator for whether or not the song made it to the Top 10 of the Billboard charts (1 if it was in the top 10, and 0 if not)

Create an Athena table based on the dataset

In the Athena console, select the default database, sampled, or create a new database.

Run the following create table statement.

create external table if not exists billboard
(
year int,
songtitle string,
artistname string,
songID string,
artistID string,
timesignature int,
timesignature_confidence double,
loudness double,
tempo double,
tempo_confidence double,
key int,
key_confidence double,
energy double,
pitch double,
timbre_0_min double,
timbre_0_max double,
timbre_1_min double,
timbre_1_max double,
timbre_2_min double,
timbre_2_max double,
timbre_3_min double,
timbre_3_max double,
timbre_4_min double,
timbre_4_max double,
timbre_5_min double,
timbre_5_max double,
timbre_6_min double,
timbre_6_max double,
timbre_7_min double,
timbre_7_max double,
timbre_8_min double,
timbre_8_max double,
timbre_9_min double,
timbre_9_max double,
timbre_10_min double,
timbre_10_max double,
timbre_11_min double,
timbre_11_max double,
Top10 int
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION 's3://aws-bigdata-blog/artifacts/predict-billboard/data'
;

Inspect the table definition for the ‘billboard’ table that you have created. If you chose a database other than sampledb, replace that value with your choice.

dbGetQuery(con, "show create table sampledb.billboard")
##                                      createtab_stmt
## 1       CREATE EXTERNAL TABLE `sampledb.billboard`(
## 2                                       `year` int,
## 3                               `songtitle` string,
## 4                              `artistname` string,
## 5                                  `songid` string,
## 6                                `artistid` string,
## 7                              `timesignature` int,
## 8                `timesignature_confidence` double,
## 9                                `loudness` double,
## 10                                  `tempo` double,
## 11                       `tempo_confidence` double,
## 12                                       `key` int,
## 13                         `key_confidence` double,
## 14                                 `energy` double,
## 15                                  `pitch` double,
## 16                           `timbre_0_min` double,
## 17                           `timbre_0_max` double,
## 18                           `timbre_1_min` double,
## 19                           `timbre_1_max` double,
## 20                           `timbre_2_min` double,
## 21                           `timbre_2_max` double,
## 22                           `timbre_3_min` double,
## 23                           `timbre_3_max` double,
## 24                           `timbre_4_min` double,
## 25                           `timbre_4_max` double,
## 26                           `timbre_5_min` double,
## 27                           `timbre_5_max` double,
## 28                           `timbre_6_min` double,
## 29                           `timbre_6_max` double,
## 30                           `timbre_7_min` double,
## 31                           `timbre_7_max` double,
## 32                           `timbre_8_min` double,
## 33                           `timbre_8_max` double,
## 34                           `timbre_9_min` double,
## 35                           `timbre_9_max` double,
## 36                          `timbre_10_min` double,
## 37                          `timbre_10_max` double,
## 38                          `timbre_11_min` double,
## 39                          `timbre_11_max` double,
## 40                                     `top10` int)
## 41                             ROW FORMAT DELIMITED 
## 42                         FIELDS TERMINATED BY ',' 
## 43                            STORED AS INPUTFORMAT 
## 44       'org.apache.hadoop.mapred.TextInputFormat' 
## 45                                     OUTPUTFORMAT 
## 46  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
## 47                                        LOCATION
## 48    's3://aws-bigdata-blog/artifacts/predict-billboard/data'
## 49                                  TBLPROPERTIES (
## 50            'transient_lastDdlTime'='1505484133')

Run a sample query

Next, run a sample query to obtain a list of all songs from Janet Jackson that made it to the Billboard Top 10 charts.

dbGetQuery(con, " SELECT songtitle,artistname,top10   FROM sampledb.billboard WHERE lower(artistname) =     'janet jackson' AND top10 = 1")
##                       songtitle    artistname top10
## 1                       Runaway Janet Jackson     1
## 2               Because Of Love Janet Jackson     1
## 3                         Again Janet Jackson     1
## 4                            If Janet Jackson     1
## 5  Love Will Never Do (Without You) Janet Jackson 1
## 6                     Black Cat Janet Jackson     1
## 7               Come Back To Me Janet Jackson     1
## 8                       Alright Janet Jackson     1
## 9                      Escapade Janet Jackson     1
## 10                Rhythm Nation Janet Jackson     1

Determine how many songs in this dataset are specifically from the year 2010.

dbGetQuery(con, " SELECT count(*)   FROM sampledb.billboard WHERE year = 2010")
##   _col0
## 1   373

The sample dataset provides certain song properties of interest that can be analyzed to gauge the impact to the song’s overall popularity. Look at one such property, timesignature, and determine the value that is the most frequent among songs in the database. Timesignature is a measure of the number of beats and the type of note involved.

Running the query directly may result in an error, as shown in the commented lines below. This error is a result of trying to retrieve a large result set over a JDBC connection, which can cause out-of-memory issues at the client level. To address this, reduce the fetch size and run again.

#t<-dbGetQuery(con, " SELECT timesignature FROM sampledb.billboard")
#Note:  Running the preceding query results in the following error: 
#Error in .jcall(rp, "I", "fetch", stride, block): java.sql.SQLException: The requested #fetchSize is more than the allowed value in Athena. Please reduce the fetchSize and try #again. Refer to the Athena documentation for valid fetchSize values.
# Use the dbSendQuery function, reduce the fetch size, and run again
r <- dbSendQuery(con, " SELECT timesignature     FROM sampledb.billboard")
dftimesignature<- fetch(r, n=-1, block=100)
dbClearResult(r)
## [1] TRUE
table(dftimesignature)
## dftimesignature
##    0    1    3    4    5    7 
##   10  143  503 6787  112   19
nrow(dftimesignature)
## [1] 7574

From the results, observe that 6787 songs have a timesignature of 4.

Next, determine the song with the highest tempo.

dbGetQuery(con, " SELECT songtitle,artistname,tempo   FROM sampledb.billboard WHERE tempo = (SELECT max(tempo) FROM sampledb.billboard) ")
##                   songtitle      artistname   tempo
## 1 Wanna Be Startin' Somethin' Michael Jackson 244.307

Create the training dataset

Your model needs to be trained such that it can learn and make accurate predictions. Split the data into training and test datasets, and create the training dataset first.  This dataset contains all observations from the year 2009 and earlier. You may face the same JDBC connection issue pointed out earlier, so this query uses a fetch size.

#BillboardTrain <- dbGetQuery(con, "SELECT * FROM sampledb.billboard WHERE year <= 2009")
#Running the preceding query results in the following error:-
#Error in .verify.JDBC.result(r, "Unable to retrieve JDBC result set for ", : Unable to retrieve #JDBC result set for SELECT * FROM sampledb.billboard WHERE year <= 2009 (Internal error)
#Follow the same approach as before to address this issue.

r <- dbSendQuery(con, "SELECT * FROM sampledb.billboard WHERE year <= 2009")
BillboardTrain <- fetch(r, n=-1, block=100)
dbClearResult(r)
## [1] TRUE
BillboardTrain[1:2,c(1:3,6:10)]
##   year           songtitle artistname timesignature
## 1 2009 The Awkward Goodbye    Athlete             3
## 2 2009        Rubik's Cube    Athlete             3
##   timesignature_confidence loudness   tempo tempo_confidence
## 1                    0.732   -6.320  89.614   0.652
## 2                    0.906   -9.541 117.742   0.542
nrow(BillboardTrain)
## [1] 7201

Create the test dataset

BillboardTest <- dbGetQuery(con, "SELECT * FROM sampledb.billboard where year = 2010")
BillboardTest[1:2,c(1:3,11:15)]
##   year              songtitle        artistname key
## 1 2010 This Is the House That Doubt Built A Day to Remember  11
## 2 2010        Sticks & Bricks A Day to Remember  10
##   key_confidence    energy pitch timbre_0_min
## 1          0.453 0.9666556 0.024        0.002
## 2          0.469 0.9847095 0.025        0.000
nrow(BillboardTest)
## [1] 373

Convert the training and test datasets into H2O dataframes

train.h2o <- as.h2o(BillboardTrain)
## 
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test.h2o <- as.h2o(BillboardTest)
## 
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Inspect the column names in your H2O dataframes.

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"

Create models

You need to designate the independent and dependent variables prior to applying your modeling algorithms. Because you’re trying to predict the ‘top10’ field, this would be your dependent variable and everything else would be independent.

Create your first model using GLM. Because GLM works best with numeric data, you create your model by dropping non-numeric variables. You only use the variables in the dataset that describe the numerical attributes of the song in the logistic regression model. You won’t use these variables:  “year”, “songtitle”, “artistname”, “songid”, or “artistid”.

y.dep <- 39
x.indep <- c(6:38)
x.indep
##  [1]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
## [24] 29 30 31 32 33 34 35 36 37 38

Create Model 1: All numeric variables

Create Model 1 with the training dataset, using GLM as the modeling algorithm and H2O’s built-in h2o.glm function.

modelh1 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
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Measure the performance of Model 1, using H2O’s built-in performance function.

h2o.performance(model=modelh1,newdata=test.h2o)
## H2OBinomialMetrics: glm
## 
## MSE:  0.09924684
## RMSE:  0.3150347
## LogLoss:  0.3220267
## Mean Per-Class Error:  0.2380168
## AUC:  0.8431394
## Gini:  0.6862787
## R^2:  0.254663
## Null Deviance:  326.0801
## Residual Deviance:  240.2319
## AIC:  308.2319
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0   1    Error     Rate
## 0      255  59 0.187898  =59/314
## 1       17  42 0.288136   =17/59
## Totals 272 101 0.203753  =76/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.192772 0.525000 100
## 2                       max f2  0.124912 0.650510 155
## 3                 max f0point5  0.416258 0.612903  23
## 4                 max accuracy  0.416258 0.879357  23
## 5                max precision  0.813396 1.000000   0
## 6                   max recall  0.037579 1.000000 282
## 7              max specificity  0.813396 1.000000   0
## 8             max absolute_mcc  0.416258 0.455251  23
## 9   max min_per_class_accuracy  0.161402 0.738854 125
## 10 max mean_per_class_accuracy  0.124912 0.765006 155
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or ` 
h2o.auc(h2o.performance(modelh1,test.h2o)) 
## [1] 0.8431394

The AUC metric provides insight into how well the classifier is able to separate the two classes. In this case, the value of 0.8431394 indicates that the classification is good. (A value of 0.5 indicates a worthless test, while a value of 1.0 indicates a perfect test.)

Next, inspect the coefficients of the variables in the dataset.

dfmodelh1 <- as.data.frame(h2o.varimp(modelh1))
dfmodelh1
##                       names coefficients sign
## 1              timbre_0_max  1.290938663  NEG
## 2                  loudness  1.262941934  POS
## 3                     pitch  0.616995941  NEG
## 4              timbre_1_min  0.422323735  POS
## 5              timbre_6_min  0.349016024  NEG
## 6                    energy  0.348092062  NEG
## 7             timbre_11_min  0.307331997  NEG
## 8              timbre_3_max  0.302225619  NEG
## 9             timbre_11_max  0.243632060  POS
## 10             timbre_4_min  0.224233951  POS
## 11             timbre_4_max  0.204134342  POS
## 12             timbre_5_min  0.199149324  NEG
## 13             timbre_0_min  0.195147119  POS
## 14 timesignature_confidence  0.179973904  POS
## 15         tempo_confidence  0.144242598  POS
## 16            timbre_10_max  0.137644568  POS
## 17             timbre_7_min  0.126995955  NEG
## 18            timbre_10_min  0.123851179  POS
## 19             timbre_7_max  0.100031481  NEG
## 20             timbre_2_min  0.096127636  NEG
## 21           key_confidence  0.083115820  POS
## 22             timbre_6_max  0.073712419  POS
## 23            timesignature  0.067241917  POS
## 24             timbre_8_min  0.061301881  POS
## 25             timbre_8_max  0.060041698  POS
## 26                      key  0.056158445  POS
## 27             timbre_3_min  0.050825116  POS
## 28             timbre_9_max  0.033733561  POS
## 29             timbre_2_max  0.030939072  POS
## 30             timbre_9_min  0.020708113  POS
## 31             timbre_1_max  0.014228818  NEG
## 32                    tempo  0.008199861  POS
## 33             timbre_5_max  0.004837870  POS
## 34                                    NA <NA>

Typically, songs with heavier instrumentation tend to be louder (have higher values in the variable “loudness”) and more energetic (have higher values in the variable “energy”). This knowledge is helpful for interpreting the modeling results.

You can make the following observations from the results:

  • The coefficient estimates for the confidence values associated with the time signature, key, and tempo variables are positive. This suggests that higher confidence leads to a higher predicted probability of a Top 10 hit.
  • The coefficient estimate for loudness is positive, meaning that mainstream listeners prefer louder songs with heavier instrumentation.
  • The coefficient estimate for energy is negative, meaning that mainstream listeners prefer songs that are less energetic, which are those songs with light instrumentation.

These coefficients lead to contradictory conclusions for Model 1. This could be due to multicollinearity issues. Inspect the correlation between the variables “loudness” and “energy” in the training set.

cor(train.h2o$loudness,train.h2o$energy)
## [1] 0.7399067

This number indicates that these two variables are highly correlated, and Model 1 does indeed suffer from multicollinearity. Typically, you associate a value of -1.0 to -0.5 or 1.0 to 0.5 to indicate strong correlation, and a value of 0.1 to 0.1 to indicate weak correlation. To avoid this correlation issue, omit one of these two variables and re-create the models.

You build two variations of the original model:

  • Model 2, in which you keep “energy” and omit “loudness”
  • Model 3, in which you keep “loudness” and omit “energy”

You compare these two models and choose the model with a better fit for this use case.

Create Model 2: Keep energy and omit loudness

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"
y.dep <- 39
x.indep <- c(6:7,9:38)
x.indep
##  [1]  6  7  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [24] 30 31 32 33 34 35 36 37 38
modelh2 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
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  |=================================================================| 100%

Measure the performance of Model 2.

h2o.performance(model=modelh2,newdata=test.h2o)
## H2OBinomialMetrics: glm
## 
## MSE:  0.09922606
## RMSE:  0.3150017
## LogLoss:  0.3228213
## Mean Per-Class Error:  0.2490554
## AUC:  0.8431933
## Gini:  0.6863867
## R^2:  0.2548191
## Null Deviance:  326.0801
## Residual Deviance:  240.8247
## AIC:  306.8247
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      280 34 0.108280  =34/314
## 1       23 36 0.389831   =23/59
## Totals 303 70 0.152815  =57/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.254391 0.558140  69
## 2                       max f2  0.113031 0.647208 157
## 3                 max f0point5  0.413999 0.596026  22
## 4                 max accuracy  0.446250 0.876676  18
## 5                max precision  0.811739 1.000000   0
## 6                   max recall  0.037682 1.000000 283
## 7              max specificity  0.811739 1.000000   0
## 8             max absolute_mcc  0.254391 0.469060  69
## 9   max min_per_class_accuracy  0.141051 0.716561 131
## 10 max mean_per_class_accuracy  0.113031 0.761821 157
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
dfmodelh2 <- as.data.frame(h2o.varimp(modelh2))
dfmodelh2
##                       names coefficients sign
## 1                     pitch  0.700331511  NEG
## 2              timbre_1_min  0.510270513  POS
## 3              timbre_0_max  0.402059546  NEG
## 4              timbre_6_min  0.333316236  NEG
## 5             timbre_11_min  0.331647383  NEG
## 6              timbre_3_max  0.252425901  NEG
## 7             timbre_11_max  0.227500308  POS
## 8              timbre_4_max  0.210663865  POS
## 9              timbre_0_min  0.208516163  POS
## 10             timbre_5_min  0.202748055  NEG
## 11             timbre_4_min  0.197246582  POS
## 12            timbre_10_max  0.172729619  POS
## 13         tempo_confidence  0.167523934  POS
## 14 timesignature_confidence  0.167398830  POS
## 15             timbre_7_min  0.142450727  NEG
## 16             timbre_8_max  0.093377516  POS
## 17            timbre_10_min  0.090333426  POS
## 18            timesignature  0.085851625  POS
## 19             timbre_7_max  0.083948442  NEG
## 20           key_confidence  0.079657073  POS
## 21             timbre_6_max  0.076426046  POS
## 22             timbre_2_min  0.071957831  NEG
## 23             timbre_9_max  0.071393189  POS
## 24             timbre_8_min  0.070225578  POS
## 25                      key  0.061394702  POS
## 26             timbre_3_min  0.048384697  POS
## 27             timbre_1_max  0.044721121  NEG
## 28                   energy  0.039698433  POS
## 29             timbre_5_max  0.039469064  POS
## 30             timbre_2_max  0.018461133  POS
## 31                    tempo  0.013279926  POS
## 32             timbre_9_min  0.005282143  NEG
## 33                                    NA <NA>

h2o.auc(h2o.performance(modelh2,test.h2o)) 
## [1] 0.8431933

You can make the following observations:

  • The AUC metric is 0.8431933.
  • Inspecting the coefficient of the variable energy, Model 2 suggests that songs with high energy levels tend to be more popular. This is as per expectation.
  • As H2O orders variables by significance, the variable energy is not significant in this model.

You can conclude that Model 2 is not ideal for this use , as energy is not significant.

CreateModel 3: Keep loudness but omit energy

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"
y.dep <- 39
x.indep <- c(6:12,14:38)
x.indep
##  [1]  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [24] 30 31 32 33 34 35 36 37 38
modelh3 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
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perfh3<-h2o.performance(model=modelh3,newdata=test.h2o)
perfh3
## H2OBinomialMetrics: glm
## 
## MSE:  0.0978859
## RMSE:  0.3128672
## LogLoss:  0.3178367
## Mean Per-Class Error:  0.264925
## AUC:  0.8492389
## Gini:  0.6984778
## R^2:  0.2648836
## Null Deviance:  326.0801
## Residual Deviance:  237.1062
## AIC:  303.1062
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      286 28 0.089172  =28/314
## 1       26 33 0.440678   =26/59
## Totals 312 61 0.144772  =54/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.273799 0.550000  60
## 2                       max f2  0.125503 0.663265 155
## 3                 max f0point5  0.435479 0.628931  24
## 4                 max accuracy  0.435479 0.882038  24
## 5                max precision  0.821606 1.000000   0
## 6                   max recall  0.038328 1.000000 280
## 7              max specificity  0.821606 1.000000   0
## 8             max absolute_mcc  0.435479 0.471426  24
## 9   max min_per_class_accuracy  0.173693 0.745763 120
## 10 max mean_per_class_accuracy  0.125503 0.775073 155
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
dfmodelh3 <- as.data.frame(h2o.varimp(modelh3))
dfmodelh3
##                       names coefficients sign
## 1              timbre_0_max 1.216621e+00  NEG
## 2                  loudness 9.780973e-01  POS
## 3                     pitch 7.249788e-01  NEG
## 4              timbre_1_min 3.891197e-01  POS
## 5              timbre_6_min 3.689193e-01  NEG
## 6             timbre_11_min 3.086673e-01  NEG
## 7              timbre_3_max 3.025593e-01  NEG
## 8             timbre_11_max 2.459081e-01  POS
## 9              timbre_4_min 2.379749e-01  POS
## 10             timbre_4_max 2.157627e-01  POS
## 11             timbre_0_min 1.859531e-01  POS
## 12             timbre_5_min 1.846128e-01  NEG
## 13 timesignature_confidence 1.729658e-01  POS
## 14             timbre_7_min 1.431871e-01  NEG
## 15            timbre_10_max 1.366703e-01  POS
## 16            timbre_10_min 1.215954e-01  POS
## 17         tempo_confidence 1.183698e-01  POS
## 18             timbre_2_min 1.019149e-01  NEG
## 19           key_confidence 9.109701e-02  POS
## 20             timbre_7_max 8.987908e-02  NEG
## 21             timbre_6_max 6.935132e-02  POS
## 22             timbre_8_max 6.878241e-02  POS
## 23            timesignature 6.120105e-02  POS
## 24                      key 5.814805e-02  POS
## 25             timbre_8_min 5.759228e-02  POS
## 26             timbre_1_max 2.930285e-02  NEG
## 27             timbre_9_max 2.843755e-02  POS
## 28             timbre_3_min 2.380245e-02  POS
## 29             timbre_2_max 1.917035e-02  POS
## 30             timbre_5_max 1.715813e-02  POS
## 31                    tempo 1.364418e-02  NEG
## 32             timbre_9_min 8.463143e-05  NEG
## 33                                    NA <NA>
h2o.sensitivity(perfh3,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.501855569251422. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.2033898
h2o.auc(perfh3)
## [1] 0.8492389

You can make the following observations:

  • The AUC metric is 0.8492389.
  • From the confusion matrix, the model correctly predicts that 33 songs will be top 10 hits (true positives). However, it has 26 false positives (songs that the model predicted would be Top 10 hits, but ended up not being Top 10 hits).
  • Loudness has a positive coefficient estimate, meaning that this model predicts that songs with heavier instrumentation tend to be more popular. This is the same conclusion from Model 2.
  • Loudness is significant in this model.

Overall, Model 3 predicts a higher number of top 10 hits with an accuracy rate that is acceptable. To choose the best fit for production runs, record labels should consider the following factors:

  • Desired model accuracy at a given threshold
  • Number of correct predictions for top10 hits
  • Tolerable number of false positives or false negatives

Next, make predictions using Model 3 on the test dataset.

predict.regh <- h2o.predict(modelh3, test.h2o)
## 
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print(predict.regh)
##   predict        p0          p1
## 1       0 0.9654739 0.034526052
## 2       0 0.9654748 0.034525236
## 3       0 0.9635547 0.036445318
## 4       0 0.9343579 0.065642149
## 5       0 0.9978334 0.002166601
## 6       0 0.9779949 0.022005078
## 
## [373 rows x 3 columns]
predict.regh$predict
##   predict
## 1       0
## 2       0
## 3       0
## 4       0
## 5       0
## 6       0
## 
## [373 rows x 1 column]
dpr<-as.data.frame(predict.regh)
#Rename the predicted column 
colnames(dpr)[colnames(dpr) == 'predict'] <- 'predict_top10'
table(dpr$predict_top10)
## 
##   0   1 
## 312  61

The first set of output results specifies the probabilities associated with each predicted observation.  For example, observation 1 is 96.54739% likely to not be a Top 10 hit, and 3.4526052% likely to be a Top 10 hit (predict=1 indicates Top 10 hit and predict=0 indicates not a Top 10 hit).  The second set of results list the actual predictions made.  From the third set of results, this model predicts that 61 songs will be top 10 hits.

Compute the baseline accuracy, by assuming that the baseline predicts the most frequent outcome, which is that most songs are not Top 10 hits.

table(BillboardTest$top10)
## 
##   0   1 
## 314  59

Now observe that the baseline model would get 314 observations correct, and 59 wrong, for an accuracy of 314/(314+59) = 0.8418231.

It seems that Model 3, with an accuracy of 0.8552, provides you with a small improvement over the baseline model. But is this model useful for record labels?

View the two models from an investment perspective:

  • A production company is interested in investing in songs that are more likely to make it to the Top 10. The company’s objective is to minimize the risk of financial losses attributed to investing in songs that end up unpopular.
  • How many songs does Model 3 correctly predict as a Top 10 hit in 2010? Looking at the confusion matrix, you see that it predicts 33 top 10 hits correctly at an optimal threshold, which is more than half the number
  • It will be more useful to the record label if you can provide the production company with a list of songs that are highly likely to end up in the Top 10.
  • The baseline model is not useful, as it simply does not label any song as a hit.

Considering the three models built so far, you can conclude that Model 3 proves to be the best investment choice for the record label.

GBM model

H2O provides you with the ability to explore other learning models, such as GBM and deep learning. Explore building a model using the GBM technique, using the built-in h2o.gbm function.

Before you do this, you need to convert the target variable to a factor for multinomial classification techniques.

train.h2o$top10=as.factor(train.h2o$top10)
gbm.modelh <- h2o.gbm(y=y.dep, x=x.indep, training_frame = train.h2o, ntrees = 500, max_depth = 4, learn_rate = 0.01, seed = 1122,distribution="multinomial")
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perf.gbmh<-h2o.performance(gbm.modelh,test.h2o)
perf.gbmh
## H2OBinomialMetrics: gbm
## 
## MSE:  0.09860778
## RMSE:  0.3140188
## LogLoss:  0.3206876
## Mean Per-Class Error:  0.2120263
## AUC:  0.8630573
## Gini:  0.7261146
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      266 48 0.152866  =48/314
## 1       16 43 0.271186   =16/59
## Totals 282 91 0.171582  =64/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                       metric threshold    value idx
## 1                     max f1  0.189757 0.573333  90
## 2                     max f2  0.130895 0.693717 145
## 3               max f0point5  0.327346 0.598802  26
## 4               max accuracy  0.442757 0.876676  14
## 5              max precision  0.802184 1.000000   0
## 6                 max recall  0.049990 1.000000 284
## 7            max specificity  0.802184 1.000000   0
## 8           max absolute_mcc  0.169135 0.496486 104
## 9 max min_per_class_accuracy  0.169135 0.796610 104
## 10 max mean_per_class_accuracy  0.169135 0.805948 104
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `
h2o.sensitivity(perf.gbmh,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.501205344484314. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.1355932
h2o.auc(perf.gbmh)
## [1] 0.8630573

This model correctly predicts 43 top 10 hits, which is 10 more than the number predicted by Model 3. Moreover, the AUC metric is higher than the one obtained from Model 3.

As seen above, H2O’s API provides the ability to obtain key statistical measures required to analyze the models easily, using several built-in functions. The record label can experiment with different parameters to arrive at the model that predicts the maximum number of Top 10 hits at the desired level of accuracy and threshold.

H2O also allows you to experiment with deep learning models. Deep learning models have the ability to learn features implicitly, but can be more expensive computationally.

Now, create a deep learning model with the h2o.deeplearning function, using the same training and test datasets created before. The time taken to run this model depends on the type of EC2 instance chosen for this purpose.  For models that require more computation, consider using accelerated computing instances such as the P2 instance type.

system.time(
  dlearning.modelh <- h2o.deeplearning(y = y.dep,
                                      x = x.indep,
                                      training_frame = train.h2o,
                                      epoch = 250,
                                      hidden = c(250,250),
                                      activation = "Rectifier",
                                      seed = 1122,
                                      distribution="multinomial"
  )
)
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##    user  system elapsed 
##   1.216   0.020 166.508
perf.dl<-h2o.performance(model=dlearning.modelh,newdata=test.h2o)
perf.dl
## H2OBinomialMetrics: deeplearning
## 
## MSE:  0.1678359
## RMSE:  0.4096778
## LogLoss:  1.86509
## Mean Per-Class Error:  0.3433013
## AUC:  0.7568822
## Gini:  0.5137644
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      290 24 0.076433  =24/314
## 1       36 23 0.610169   =36/59
## Totals 326 47 0.160858  =60/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                       metric threshold    value idx
## 1                     max f1  0.826267 0.433962  46
## 2                     max f2  0.000000 0.588235 239
## 3               max f0point5  0.999929 0.511811  16
## 4               max accuracy  0.999999 0.865952  10
## 5              max precision  1.000000 1.000000   0
## 6                 max recall  0.000000 1.000000 326
## 7            max specificity  1.000000 1.000000   0
## 8           max absolute_mcc  0.999929 0.363219  16
## 9 max min_per_class_accuracy  0.000004 0.662420 145
## 10 max mean_per_class_accuracy  0.000000 0.685334 224
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
h2o.sensitivity(perf.dl,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.496293348880151. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.3898305
h2o.auc(perf.dl)
## [1] 0.7568822

The AUC metric for this model is 0.7568822, which is less than what you got from the earlier models. I recommend further experimentation using different hyper parameters, such as the learning rate, epoch or the number of hidden layers.

H2O’s built-in functions provide many key statistical measures that can help measure model performance. Here are some of these key terms.

Metric Description
Sensitivity Measures the proportion of positives that have been correctly identified. It is also called the true positive rate, or recall.
Specificity Measures the proportion of negatives that have been correctly identified. It is also called the true negative rate.
Threshold Cutoff point that maximizes specificity and sensitivity. While the model may not provide the highest prediction at this point, it would not be biased towards positives or negatives.
Precision The fraction of the documents retrieved that are relevant to the information needed, for example, how many of the positively classified are relevant
AUC

Provides insight into how well the classifier is able to separate the two classes. The implicit goal is to deal with situations where the sample distribution is highly skewed, with a tendency to overfit to a single class.

0.90 – 1 = excellent (A)

0.8 – 0.9 = good (B)

0.7 – 0.8 = fair (C)

.6 – 0.7 = poor (D)

0.5 – 0.5 = fail (F)

Here’s a summary of the metrics generated from H2O’s built-in functions for the three models that produced useful results.

Metric Model 3 GBM Model Deep Learning Model

Accuracy

(max)

0.882038

(t=0.435479)

0.876676

(t=0.442757)

0.865952

(t=0.999999)

Precision

(max)

1.0

(t=0.821606)

1.0

(t=0802184)

1.0

(t=1.0)

Recall

(max)

1.0 1.0

1.0

(t=0)

Specificity

(max)

1.0 1.0

1.0

(t=1)

Sensitivity

 

0.2033898 0.1355932

0.3898305

(t=0.5)

AUC 0.8492389 0.8630573 0.756882

Note: ‘t’ denotes threshold.

Your options at this point could be narrowed down to Model 3 and the GBM model, based on the AUC and accuracy metrics observed earlier.  If the slightly lower accuracy of the GBM model is deemed acceptable, the record label can choose to go to production with the GBM model, as it can predict a higher number of Top 10 hits.  The AUC metric for the GBM model is also higher than that of Model 3.

Record labels can experiment with different learning techniques and parameters before arriving at a model that proves to be the best fit for their business. Because deep learning models can be computationally expensive, record labels can choose more powerful EC2 instances on AWS to run their experiments faster.

Conclusion

In this post, I showed how the popular music industry can use analytics to predict the type of songs that make the Top 10 Billboard charts. By running H2O’s scalable machine learning platform on AWS, data scientists can easily experiment with multiple modeling techniques and interactively query the data using Amazon Athena, without having to manage the underlying infrastructure. This helps record labels make critical decisions on the type of artists and songs to promote in a timely fashion, thereby increasing sales and revenue.

If you have questions or suggestions, please comment below.


Additional Reading

Learn how to build and explore a simple geospita simple GEOINT application using SparkR.


About the Authors

gopalGopal Wunnava is a Partner Solution Architect with the AWS GSI Team. He works with partners and customers on big data engagements, and is passionate about building analytical solutions that drive business capabilities and decision making. In his spare time, he loves all things sports and movies related and is fond of old classics like Asterix, Obelix comics and Hitchcock movies.

 

 

Bob Strahan, a Senior Consultant with AWS Professional Services, contributed to this post.

 

 

Friday Squid Blogging: Squid Empire Is a New Book

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/09/friday_squid_bl_594.html

Regularly I receive mail from people wanting to advertise on, write for, or sponsor posts on my blog. My rule is that I say no to everyone. There is no amount of money or free stuff that will get me to write about your security product or service.

With regard to squid, however, I have no such compunctions. Send me any sort of squid anything, and I am happy to write about it. Earlier this week, for example, I received two — not one — copies of the new book Squid Empire: The Rise and Fall of Cephalopods. I haven’t read it yet, but it looks good. It’s the story of prehistoric squid.

Here’s a review by someone who has read it.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

EU Piracy Report Suppression Raises Questions Over Transparency

Post Syndicated from Andy original https://torrentfreak.com/eu-piracy-report-suppression-raises-questions-transparency-170922/

Over the years, copyright holders have made hundreds of statements against piracy, mainly that it risks bringing industries to their knees through widespread and uncontrolled downloading from the Internet.

But while TV shows like Game of Thrones have been downloaded millions of times, the big question (one could argue the only really important question) is whether this activity actually affects sales. After all, if piracy has a massive negative effect on industry, something needs to be done. If it does not, why all the panic?

Quite clearly, the EU Commission wanted to find out the answer to this potential multi-billion dollar question when it made the decision to invest a staggering 360,000 euros in a dedicated study back in January 2014.

With a final title of ‘Estimating displacement rates of copyrighted content in the EU’, the completed study is an intimidating 307 pages deep. Shockingly, until this week, few people even knew it existed because, for reasons unknown, the EU Commission decided not to release it.

However, thanks to the sheer persistence of Member of the European Parliament Julia Reda, the public now has a copy and it contains quite a few interesting conclusions. But first, some background.

The study uses data from 2014 and covers four broad types of content: music,
audio-visual material, books and videogames. Unlike other reports, the study also considered live attendances of music and cinema visits in the key regions of Germany, UK, Spain, France, Poland and Sweden.

On average, 51% of adults and 72% of minors in the EU were found to have illegally downloaded or streamed any form of creative content, with Poland and Spain coming out as the worst offenders. However, here’s the kicker.

“In general, the results do not show robust statistical evidence of displacement of sales by online copyright infringements,” the study notes.

“That does not necessarily mean that piracy has no effect but only that the statistical analysis does not prove with sufficient reliability that there is an effect.”

For a study commissioned by the EU with huge sums of public money, this is a potentially damaging conclusion, not least for the countless industry bodies that lobby day in, day out, for tougher copyright law based on the “fact” that piracy is damaging to sales.

That being said, the study did find that certain sectors can be affected by piracy, notably recent top movies.

“The results show a displacement rate of 40 per cent which means that for every ten recent top films watched illegally, four fewer films are consumed legally,” the study notes.

“People do not watch many recent top films a second time but if it happens, displacement is lower: two legal consumptions are displaced by every ten illegal second views. This suggests that the displacement rate for older films is lower than the 40 per cent for recent top films. All in all, the estimated loss for recent top films is 5 per cent of current sales volumes.”

But while there is some negative effect on the movie industry, others can benefit. The study found that piracy had a slightly positive effect on the videogames industry, suggesting that those who play pirate games eventually become buyers of official content.

On top of displacement rates, the study also looked at the public’s willingness to pay for content, to assess whether price influences pirate consumption. Interestingly, the industry that had the most displaced sales – the movie industry – had the greatest number of people unhappy with its pricing model.

“Overall, the analysis indicates that for films and TV-series current prices are higher than 80 per cent of the illegal downloaders and streamers are willing to pay,” the study notes.

For other industries, where sales were not found to have been displaced or were positively affected by piracy, consumer satisfaction with pricing was greatest.

“For books, music and games, prices are at a level broadly corresponding to the
willingness to pay of illegal downloaders and streamers. This suggests that a
decrease in the price level would not change piracy rates for books, music and
games but that prices can have an effect on displacement rates for films and
TV-series,” the study concludes.

So, it appears that products that are priced fairly do not suffer significant displacement from piracy. Those that are priced too high, on the other hand, can expect to lose some sales.

Now that it’s been released, the findings of the study should help to paint a more comprehensive picture of the infringement climate in the EU, while laying to rest some of the wild claims of the copyright lobby. That being said, it shouldn’t have taken the toils of Julia Reda to bring them to light.

“This study may have remained buried in a drawer for several more years to come if it weren’t for an access to documents request I filed under the European Union’s Freedom of Information law on July 27, 2017, after having become aware of the public tender for this study dating back to 2013,” Reda explains.

“I would like to invite the Commission to become a provider of more solid and timely evidence to the copyright debate. Such data that is valuable both financially and in terms of its applicability should be available to everyone when it is financed by the European Union – it should not be gathering dust on a shelf until someone actively requests it.”

The full study can be downloaded here (pdf)

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Can an Army of Bitcoin “Bounty Hunters” Deter Pirates?

Post Syndicated from Ernesto original https://torrentfreak.com/can-an-army-of-bitcoin-bounty-hunters-deter-pirates-170917/

When we first heard of the idea to use Bitcoin bounties to track down pirated content online, we scratched our heads.

Snitching on copyright infringers is not a new concept, but the idea of instant cash rewards though cryptocurrency was quite novel.

In theory, it’s pretty straightforward. Content producers can add a unique identifying watermark into movies, eBooks, or other digital files before they’re circulated. When these somehow leak to the public, the bounty hunters use the watermark to claim their Bitcoin, alerting the owner in the process.

This helps to spot leaks early on, even on networks where automated tools don’t have access, and identify the source at the same time.

Two years have passed and it looks like the idea was no fluke. Custos, the South African company that owns the technology, has various copyright holders on board and recently announced a new partnership with book publisher Erudition Digital.

With help from anti-piracy outfit Digimarc, the companies will add identifying watermarks to eBook releases, counting on the bounty hunters to keep an eye out for leaks. These bounty hunters don’t have to be anti-piracy experts. On the contrary, pirates are more than welcome to help out.

“The Custos approach is revolutionary in that it attacks the economy of piracy by targeting uploaders rather than downloaders, turning downloaders into an early detection network,” the companies announced a few days ago.

“The result is pirates turn on one another, sowing seeds of distrust amongst their communities. As a result, the Custos system is capable of penetrating hard-to-reach places such as the dark web, peer-to-peer networks, and even email.”



Devon Weston, Director of Market Development for Digimarc Guardian, believes that this approach is the next level in anti-piracy efforts. It complements the automated detection tools that have been available in the past by providing access to hard-to-reach places.

“Together, this suite of products represents the next generation in technical measures against eBook piracy,” Weston commented on the partnership.

TorrentFreak reached out to Custos COO Fred Lutz to find out what progress the company has made in recent years. We were informed that they have been protecting thousands of copies every month, ranging from pre-release movie content to eBooks.

At the moment the company works with a selected group of “bounty hunters,” but they plan to open the extraction tool to the public in the near future, so everyone can join in.

“So far we have carefully seeded the free bounty extractor tool in relevant communities with great success. However, in the next phase, we will open the bounty hunting to the general public. We are just careful not to grow the bounty hunting community faster than the number of bounties in the wild require,” Lutz tells us.

The Bitcoin bounties themselves vary in size based on the specific use case. For a movie screener, they are typically anything between $10 and $50. However, for the most sensitive content, they can be $100 or more.

“We can also adjust the bounty over time based on the customer’s needs. A low-quality screener that was very sensitive prior to cinematic release does not require as large a bounty after cam-rips becomes available,” Lutz notes.

Thus far, roughly 50 Bitcoin bounties have been claimed. Some of these were planted by Custos themselves, as an incentive for the bounty hunters. Not a very high number, but that doesn’t mean that it’s not working.

“While this number might seem a bit small compared to the number of copies we protect, our aim is first and foremost not to detect leaks, but to pose a credible threat of quick detection and being caught.”

People who receive content protected by Custos are made aware of the watermarks, which may make them think twice about sharing it. If that’s the case, then it’s having effect without any bounties being claimed.

The question remains how many people will actively help to spot bounties. The success of the system largely depends on volunteers, and not all pirates are eager to rat on the people that provide free content.

On the other hand, there’s also room to abuse the system. In theory, people could claim the bounties on their own eBooks and claim that they’ve lost their e-reader. That would be fraud, of course, but since the bounties are in Bitcoin this isn’t easy to prove.

That brings us to the final question. What happens of a claimed bounty identifies a leaker? Custos admits that this alone isn’t enough evidence to pursue a legal case, but the measures that are taken in response are up to the copyright holders.

“A claim of a bounty is never a sufficient legal proof of piracy, instead, it is an invaluable first piece of evidence on which a legal case could be built if the client so requires. Legal prosecution is definitely not always the best approach to dealing with leaks,” Lutz says.

Time will tell if the Bitcoin bounty approach works…

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Make your own game with CoderDojo’s new book

Post Syndicated from Nuala McHale original https://www.raspberrypi.org/blog/coderdojo-nano/

The first official CoderDojo book, CoderDojo Nano: Build Your Own Website, was a resounding success: thousands of copies have been bought by aspiring CoderDojo Ninjas, and it‘s available in ten languages, including Bulgarian, Czech, Dutch, Lithuanian, Latvian, Portuguese, Spanish, and Slovakian. Now we are delighted to announce the release of the second book in our Create with Code trilogy, titled CoderDojo Nano: Make Your Own Game.

Cover of CoderDojo Nano Make your own game

The paperback book will be available in English from Thursday 7 September (with English flexibound and Dutch versions scheduled to follow in the coming months), enabling young people and adults to learn creative and fun coding skills!

What will you learn?

The new book explains the fundamentals of the JavaScript language in a clear, logical way while supporting you to create your very own computer game.

Pixel image of laptop displaying a jump-and-run game

You will learn how to animate characters, create a world for your game, and use the physics of movement within it. The book is full of clear step-by-step instructions and illustrated screenshots to make reviewing your code easy. Additionally, challenges and open-ended prompts at the end of each section will encourage you to get creative while making your game.

This book is the perfect first step towards understanding game development, particularly for those of you who do not (yet) have a local Dojo. Regardless of where you live, using our books you too can learn to ‘Create with Code’!

Tried and tested

As always, CoderDojo Ninjas from all around the world tested our book, and their reactions have been hugely positive. Here is a selection of their thoughts:

“The book is brilliant. The [game] is simple yet innovative. I personally love it, and want to get stuck in making it right away!”

“What I really like is that, unlike most books on coding, this one properly explains what’s happening, and what each piece of code does and where it comes from.”

“I found the book most enjoyable. The layout is great, with lots of colour, and I found the information very easy to follow. The Ninja Tips are a great help in case you get a bit stuck. I liked that the book represents a mix of boy and girl Ninjas — it really makes coding fun for all.”

“The book is a great guide for both beginners and people who want to do something creative with their knowledge of code. Even people who cannot go to a CoderDojo can learn code using this book!”

Writer Jurie Horneman

Author of CoderDojo Nano: Make Your Own Game Jurie Horneman has been working in the game development industry for more than 15 years.

stuffed toy rabbit wearing glasses

Jurie would get on well with Babbage, I think.

He shares how he got into coding, and what he has learnt while creating this awesome book:

“I’ve been designing and programming games since 1991, starting with ancient home computers, and now I’m working with PCs and consoles. As a game designer, it’s my job to teach players the rules of the game in a fun and playful manner — that gave me some useful experience for writing the book.

I believe that, if you want to understand something properly, you have to teach it to others. Therefore, writing this book was very educational for me, as I hope reading it will be for learners.”

Asked what his favorite thing about the book is, Jurie said he loves the incredible pixel art design: “The artist (Gary J Lucken, Army of Trolls) did a great job to help explain some of the abstract concepts in the book.”

Pixel image of a landscape with an East Asian temple on a lonely mountain

Gary’s art is also just gorgeous.

How can you get your copy?

You can pre-order CoderDojo Nano: Make Your Own Game here. Its initial pricing is £9.99 (around €11), and discounted copies with free international delivery are available here.

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Five Must-Watch Software Engineering Talks

Post Syndicated from Bozho original https://techblog.bozho.net/five-must-watch-software-engineering-talks/

We’ve all watched dozens of talks online. And we probably don’t remember many of them. But some do stick in our heads and we eventually watch them again (and again) because we know they are good and we want to remember the things that were said there. So I decided to compile a small list of talks that I find very insightful, useful and that have, in a way, shaped my software engineering practice or expanded my understanding of the software world.

1. How To Design A Good API and Why it Matters by Joshua Bloch – this is a must-watch (well, obviously all are). And don’t skip it because “you are not writing APIs” – everyone is writing APIs. Maybe not used by hundreds of other developers, but used by at least several, and that’s a good enough reason. Having watched this talk I ended up buying and reading one of the few software books that I have actually read end-to-end – “Effective Java” (the talk uses Java as an example, but the principles aren’t limited to Java)

2. How to write clean, testable code by Miško Hevery. Maybe there are tons of talks about testing code, maybe Uncle Bob has a more popular one, but I found this one particularly practical and the the point – that writing testable code is a skill, and that testable code is good code. (By the way, the speaker then wrote AngularJS)

3. Back to basics: the mess we’ve made of our fundamental data types by Jon Skeet. The title says it all, and it’s nice to be reminded of how fragile even the basics of programming languages are.

4. The Danger of Software Patents by Richard Stallman. That goes a little bit away from writing software, but puts software in legal context – how do legislation loopholes affect code reuse and business practices related it. It’s a bit long, but I think worth it.

5. Does my ESB look big in this? by Martin Fowler and Jim Webber. It’s about bloated enterprise architecture and how to actually do enterprise architecture without complex and expensive middleware. (Unfortunately it’s not on YouTube, so no embedding).

Although this is not a “ranking”, I’d like to add a few honourable mentions: The famous “WAT” lightning talk, showing some quirks of ruby and javascript, “The future of programming” by Bret Victor, “You suck at Excel” by Joel Spolsky, which isn’t really about creating software, but it’s cool. And a tiny shameless plug with my “Common sense driven development talk”

I hope the compilation is useful and enlightening. Enjoy.

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State of MAC address randomization

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/09/state-of-mac-address-randomization.html

tldr: I went to DragonCon, a conference of 85,000 people, so sniff WiFi packets and test how many phones now uses MAC address randomization. Almost all iPhones nowadays do, but it seems only a third of Android phones do.

Ten years ago at BlackHat, we presented the “data seepage” problem, how the broadcasts from your devices allow you to be tracked. Among the things we highlighted was how WiFi probes looking to connect to access-points expose the unique hardware address burned into the phone, the MAC address. This hardware address is unique to your phone, shared by no other device in the world. Evildoers, such as the NSA or GRU, could install passive listening devices in airports and train-stations around the world in order to track your movements. This could be done with $25 devices sprinkled around a few thousand places — within the budget of not only a police state, but also the average hacker.

In 2014, with the release of iOS 8, Apple addressed this problem by randomizing the MAC address. Every time you restart your phone, it picks a new, random, hardware address for connecting to WiFi. This causes a few problems: every time you restart your iOS devices, your home network sees a completely new device, which can fill up your router’s connection table. Since that table usually has at least 100 entries, this shouldn’t be a problem for your home, but corporations and other owners of big networks saw their connection tables suddenly get big with iOS 8.

In 2015, Google added the feature to Android as well. However, even though most Android phones today support this feature in theory, it’s usually not enabled.

Recently, I went to DragonCon in order to test out how well this works. DragonCon is a huge sci-fi/fantasy conference in Atlanta in August, second to San Diego’s ComicCon in popularity. It’s spread across several neighboring hotels in the downtown area. A lot of the traffic funnels through the Marriot Marquis hotel, which has a large open area where, from above, you can see thousands of people at a time.

And, with a laptop, see their broadcast packets.

So I went up on a higher floor and setup my laptop in order to capture “probe” broadcasts coming from phones, in order to record the hardware MAC addresses. I’ve done this in years past, before address randomization, in order to record the popularity of iPhones. The first three bytes of an old-style, non-randomized address, identifies the manufacturer. This time, I should see a lot fewer manufacturer IDs, and mostly just random addresses instead.

I recorded 9,095 unique probes over a couple hours. I’m not sure exactly how long — my laptop would go to sleep occasionally because of lack of activity on the keyboard. I should probably setup a Raspberry Pi somewhere next year to get a more consistent result.

A quick summary of the results are:

The 9,000 devices were split almost evenly between Apple and Android. Almost all of the Apple devices randomized their addresses. About a third of the Android devices randomized. (This assumes Android only randomizes the final 3 bytes of the address, and that Apple randomizes all 6 bytes — my assumption may be wrong).

A table of the major results are below. A little explanation:

  • The first item in the table is the number of phones that randomized the full 6 bytes of the MAC address. I’m guessing these are either mostly or all Apple iOS devices. They are nearly half of the total, or 4498 out of 9095 unique probes.
  • The second number is those that randomized the final 3 bytes of the MAC address, but left the first three bytes identifying themselves as Android devices. I’m guessing this represents all the Android devices that randomize. My guesses may be wrong, maybe some Androids randomize the full 6 bytes, which would get them counted in the first number.
  • The following numbers are phones from major Android manufacturers like Motorola, LG, HTC, Huawei, OnePlus, ZTE. Remember: the first 3 bytes of an un-randomized address identifies who made it. There are roughly 2500 of these devices.
  • There is a count for 309 Apple devices. These are either older iOS devices pre iOS 8, or which have turned off the feature (some corporations demand this), or which are actually MacBooks instead of phones.
  • The vendor of the access-points that Marriot uses is “Ruckus”. There have a lot of access-points in the hotel.
  • The “TCT mobile” entry is actually BlackBerry. Apparently, BlackBerry stopped making phones and instead just licenses the software/brand to other hardware makers. If you buy a BlackBerry from the phone store, it’s likely going to be a TCT phone instead.
  • I’m assuming the “Amazon” devices are Kindle ebooks.
  • Lastly, I’d like to point out the two records for “Ford”. I was capturing while walking out of the building, I think I got a few cars driving by.

(random)  4498
(Android)  1562
Samsung  646
Motorola  579
Murata  505
LG  412
Apple  309
HTC-phone  226
Huawei  66
Ruckus  60
OnePlus Tec  40
ZTE  23
TCT mobile  20
Amazon Tech  19
Nintendo  17
Intel  14
Microsoft  9
-hp-  8
BLU Product  8
Kyocera  8
AsusTek  6
Yulong Comp  6
Lite-On  4
Sony Mobile  4
Z-COM, INC.  4
ARRIS Group  2
AzureWave  2
Barnes&Nobl  2
Canon  2
Ford Motor  2
Foxconn  2
Google, Inc  2
Motorola (W  2
Sonos, Inc.  2
SparkLAN Co  2
Wi2Wi, Inc  2
Xiaomi Comm  2
Alps Electr  1
Askey  1
BlackBerry  1
Chi Mei Com  1
Clover Netw  1
CNet Techno  1
eSSys Co.,L  1
GoPro  1
InPro Comm  1
JJPlus Corp  1
Private  1
Quanta  1
Raspberry P  1
Roku, Inc.  1
Sonim Techn  1
Texas Instr  1
TP-LINK TEC  1
Vizio, Inc  1

[$] Business accounting with GnuCash

Post Syndicated from corbet original https://lwn.net/Articles/731126/rss

The first stop in the search for a free accounting system that can replace
QuickBooks is a familiar waypoint: the GnuCash application. GnuCash has been
around for many years and is known primarily as a personal-finance tool,
but it has acquired some business features as well. The question is: are
those business features solid enough to allow the program to serve as a
replacement for QuickBooks?

Rightscorp Bleeds Another Million, Borrows $200K From Customer BMG

Post Syndicated from Andy original https://torrentfreak.com/rightscorp-bleeds-another-million-borrows-200k-from-customer-bmg-170819/

Anti-piracy outfit Rightscorp is one of the many companies trying to turn Internet piracy into profit. The company has a somewhat novel approach but has difficulty balancing the books.

Essentially, Rightscorp operates like other so-called copyright-trolling operations, in that it monitors alleged offenders on BitTorrent networks, tracks them to their ISPs, then attempts to extract a cash settlement. Rightscorp does this by sending DMCA notices with settlement agreements attached, in the hope that at-this-point-anonymous Internet users break cover in panic. This can lead to a $20 or $30 ‘fine’ or in some cases dozens of multiples of that.

But despite settling hundreds of thousands of these cases, profit has thus far proven elusive, with the company hemorrhaging millions in losses. The company has just filed its results for the first half of 2017 and they contain more bad news.

In the six months ended June 2017, revenues obtained from copyright settlements reached just $138,514, that’s 35% down on the $214,326 generated in the same period last year. However, the company did manage to book $148,332 in “consulting revenue” in the first half of this year, a business area that generated no revenue in 2016.

Overall then, total revenue for the six month period was $286,846 – up from $214,326 last year. While that’s a better picture in its own right, Rightscorp has a lot of costs attached to its business.

After paying out $69,257 to copyright holders and absorbing $1,190,696 in general and administrative costs, among other things, the company’s total operating expenses topped out at $1,296,127 for the first six months of the year.

To make a long story short, the company made a net loss of $1,068,422, which was more than the $995,265 loss it made last year and despite improved revenues. The company ended June with just $1,725 in cash.

“These factors raise substantial doubt about the Company’s ability to continue as a going concern within one year after the date that the financial statements are issued,” the company’s latest statement reads.

This hanging-by-a-thread narrative has followed Rightscorp for the past few years but there’s information in the latest accounts which indicates how bad things were at the start of the year.

In January 2016, Rightscorp and several copyright holders, including Hollywood studio Warner Bros, agreed to settle a class-action lawsuit over intimidating robo-calls that were made to alleged infringers. The defendants agreed to set aside $450,000 to cover the costs, and it appears that Rightscorp was liable for at least $200,000 of that.

Rightscorp hasn’t exactly been flush with cash, so it was interesting to read that its main consumer piracy settlement client, music publisher BMG, actually stepped in to pay off the class-action settlement.

“At December 31, 2016, the Company had accrued $200,000 related to the settlement of a class action complaint. On January 7, 2017, BMG Rights Management (US) LLC (“BMG”) advanced the Company $200,000, which was used to pay off the settlement. The advance from BMG is to be applied to future billings from the Company to BMG for consulting services,” Rightscorp’s filing reads.

With Rightscorp’s future BMG revenue now being gobbled up by what appears to be loan repayments, it becomes difficult to see how the anti-piracy outfit can make enough money to pay off the $200,000 debt. However, its filing notes that on July 21, 2017, the company issued “an aggregate of 10,000,000 shares of common stock to an investor for a purchase price of $200,000.” While that amount matches the BMG debt, the filing doesn’t reveal who the investor is.

The filing also reveals that on July 31, Rightscorp entered into two agreements to provide services “to a holder of multiple copyrights.” The copyright holder isn’t named, but the deal reveals that it’s in Rightscorp’s best interests to get immediate payment from people to whom it sends cash settlement demands.

“[Rightscorp] will receive 50% of all gross proceeds of any settlement revenue received by the Client from pre-lawsuit ‘advisory notices,’ and 37.5% of all gross proceeds received by the Client from ‘final warning’ notices sent immediately prior to a lawsuit,” the filing notes.

Also of interest is that Rightscorp has offered not to work with any of the copyright holders’ direct competitors, providing certain thresholds are met – $10,000 revenue in the first month to $100,000 after 12 months. But there’s more to the deal.

Rightscorp will also provide a number of services to this client including detecting and verifying copyright works on P2P networks, providing information about infringers, plus reporting, litigation support, and copyright protection advisory services.

For this, Rightscorp will earn $10,000 for the first three months, rising to $85,000 per month after 16 months, valuable revenue for a company fighting for its life.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Community Profile: David Pride

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/community-profile-david-pride/

This column is from The MagPi issue 55. You can download a PDF of the full issue for free, or subscribe to receive the print edition in your mailbox or the digital edition on your tablet. All proceeds from the print and digital editions help the Raspberry Pi Foundation achieve its charitable goals.

David Pride’s experiences in computer education came slightly later in life. He admits to not being a grade-A student: he left school with few qualifications, unable to pursue further education at university. There was, however, a teacher who instilled in him a passion for computers and coding which would stick with him indefinitely.

David Pride The MagPi Raspberry Pi Community Profile

David joined us at the St James’s Palace community celebration, mingling with the likes of the Duke of York, plus organisers of Jams and clubs, such as Grace and Femi

Welcome to the Community

Twenty years later, back in 2012, David heard of the Raspberry Pi – a soon-to-be-released “new little marvel” that he instantly fell for, head first. Despite a lack of knowledge in Linux and Python, he experimented and had fun. He found a Raspberry Jam and, with it, Pi enthusiasts like Mike Horne and Peter Onion. The projects on display at the Jam were enough to push David further into the Raspberry Pi rabbit hole and, after working his way through several Python books, he began to take steps into the world of formal higher education.

David Pride The MagPi Raspberry Pi Community Profile

David’s determination to access and complete further education in computing has earned him a three-year PhD studentship. Not bad for a “lousy student”

Back to School

With a Mooc qualification from Rice University under his belt, he continued to improve upon his self-taught knowledge, and was fortunate enough to be accepted to study for a master’s degree in Computer Science at the University of Hertfordshire. With a distinction for his final dissertation, David completed the course with an overall distinction for his MSc, and was recently awarded a fully funded PhD studentship with The Open University’s Knowledge Media Institute.

David Pride The MagPi Raspberry Pi Community Profile

Self-playing xylophones, Wiimote air drums, Lego sorters, Pi Wars robots, and more. David is continually hacking toys, giving them new Pi-powered life

Maker of things

The portfolio of projects that helped him to achieve his many educational successes has provided regular retweet material for the Raspberry Pi Twitter account, and we’ve highlighted his fun, imaginative work on this blog before. His builds have travelled to a range of Jams and made their way to the Raspberry Pi and Code Club stands at the Bett Show, as well as to our birthday celebrations.

David Pride The MagPi Raspberry Pi Community Profile

“Pi & Chips – with a little extra source”

His website, the pun-tastic Pi and Chips, is home to the majority of his work; David also links to YouTube videos and walk-throughs of his projects, and relates his experiences at various events. If you’ve followed any of the action across the Raspberry Pi social media channels – or indeed read any previous issues of The MagPi magazine – you’ll no doubt have seen a couple of David’s projects.

David Pride The MagPi Raspberry Pi Community Profile 4-Bot

Many readers will have come across the wonderful 4-Bot before, and it has even made an appearance alongside David in a recent Bloomberg interview. Considering the trillions of possible game positions, David made a compromise and, if you’re lucky, you may just be able to beat it

The 4-Bot, a robotic second player for the family game Connect Four, allows people to go head to head with a Pi-powered robotic arm. Using a Python imaging library, the 4-Bot splits the game grid into 42 squares, and recognises them as being red, yellow, or empty by reading the RGB value of the space. Using the minimax algorithm, 4-Bot is able to play each move within 25 seconds. Believe us when we say that it’s not as easy to beat as you’d hope. Then there’s his more recent air drum kit, which uses an old toy found at a car boot sale together with a Wiimote to make a functional air drum that showcases David’s toy-hacking abilities… and his complete lack of rhythm. He does fare much better on his homemade laser harp, though!

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