Tag Archives: got

Pirate Party Wins Big in Czech Parliament Elections

Post Syndicated from Ernesto original https://torrentfreak.com/pirate-party-wins-big-in-czech-parliament-elections-171023/

The Czech Pirates have made quite a name for themselves in recent years.

The political party previously took on a local anti-piracy outfit by launching their own movie download sites, making the point that linking is not a crime.

The bold move resulted in a criminal investigation, but the case was eventually dropped after it was deemed that the Pirates acted in accordance with EU law.

In the political arena, the Czech Pirate Party booked several successes as well. In Parliamentary elections, however, the party never managed to beat the required threshold. Until this weekend.

With 10.79% of the total vote, the Pirates won 22 seats in the national parliament. Not just that, they also became the third largest political party in the country, where more than 30 parties participated in the elections.

The Czech Republic becomes the fourth country where a Pirate Party is represented in the national parliament, following Sweden, Germany, and Iceland, which is quite an achievement.

“It is the best result of any Pirate Party in history and gives us a great mandate to transform the dynamics of Czech politics. At the same time, we understand this as a huge responsibility towards the voters and the Pirate movement as a whole,” Tomáš Vymazal, one of the new Members of Parliament, tells TorrentFreak.

The Pirates (photo via)

While there were some celebrations after the election result came in, the Czech Pirate Party is moving full steam ahead. The twenty-two newly elected members have already held their first meeting, discussing how to get the most out of their negotiations with other parties.

“The negotiation team has been established and the club’s chairman was elected. We’ll now need to set up our offices, hire assistants and distribute specific responsibilities among the club,” Vymazal says.

“One of the first issues we will open up a discussion about how parliament will be fixing an historic anti-corruption bill.”

The bill in question makes sure that every contract the state or a state-owned business enters into is put on the record. However, the previous parliament introduced several exceptions and as a result, many of the money flows remain hidden from the public.

Like other Pirate parties, the Czech branch is by no means a single issue outfit. The party has a broad vision which it distilled to a twenty point program. In addition to fighting corruption, this includes tax reform and increasing teachers’ salaries, for example.

More classical pirate themes are also on the agenda of course. The Pirate Party wants to overhaul the country’s copyright legislation, stop internet censorship, and put an end to cell phone tracking. In addition, the use of medical marijuana should be allowed.

With the backing of hundreds of thousands of Czechs, these and other issues will certainly be on the political agenda during the years to come. It’s now up to the Pirates to make them a reality.

“We must do a very good job to successfully establish the Pirate Party in Czech politics and deliver on the promises we made to the voters,” Vymazal says.

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

[$] A look at the 4.14 development cycle

Post Syndicated from corbet original https://lwn.net/Articles/736578/rss

The 4.14 kernel, due in the first half of November, is moving into the
relatively slow part of the development cycle as of this writing. The time
is thus ripe for a look at the changes that went into this kernel cycle and
how they got there. While 4.14 is a fairly typical kernel development
cycle, there are a couple of aspects that stand out this time around.

Epic Games Sues Man Over Bitcoin Mining Fortnite ‘Cheat’

Post Syndicated from Ernesto original https://torrentfreak.com/epic-games-sues-man-over-bitcoin-mining-fortnite-cheat-171019/

A few weeks ago, Epic Games released Fortnite’s free-to-play “Battle Royale” game mode for the PC and other platforms, generating massive interest among gamers.

The release also attracted attention from thousands of cheaters, many of whom were subsequently banned. In addition, Epic Games went a step further by taking several cheaters to court over copyright infringement.

This week the North Carolina-based game developer continued its a war against cheaters. In a new lawsuit, it targets two other cheaters who promoted their hacks through YouTube videos.

One of the defendants is a Swedish resident, Mr. Josefson. He created a cheat and promoted it in various videos, adding instructions on how to download and install it. In common with the previous defendants, he is being sued for copyright infringement.

The second cheater listed in the complaint, a Russian man named Mr. Yakovenko, is more unique. This man also promoted his Fortnite cheats through a series of YouTube videos, but they weren’t very effective.

When Epic downloaded the ‘cheat’ to see how it works, all they got was a Bitcoin miner.

“Epic downloaded the purported cheat from the links provided in Yakovenko’s YouTube videos. While the ‘cheat’ does not appear to be a functional Fortnite cheat, it functions as a bitcoin miner that infects the user’s computer with a virus that causes the user’s computer to mine bitcoin for the benefit of an unknown third party,” the complaint reads.

Epic ‘cheat’

Despite the non-working cheat, Epic Games maintains that Yakovenko created a cheat for Fortnite’s Battle Royale game mode, pointing to a YouTube video he posted last month.

“The First Yakovenko video and associated post contained instructions on how to download and install the cheat and showed full screen gameplay using the purported cheat,” the complaint reads.

All the videos have since been removed following takedown notices from Epic. Through the lawsuit, the game developer now hopes to get compensation for the damages it suffered.

In addition to the copyright infringement claims the two men are also accused of trademark infringement, unfair competition, and breach of contract.

There’s little doubt that Epic Games is doing its best to hold cheaters accountable. However, the problem is not easy to contain. A simple search for Fortnite Hack or Fortnite Cheat still yields tens of thousands of results, with new videos being added continuously.

A copy of the full complaint against Josefson and Yakovenko is available here (pdf).

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

Tips to Secure Your Network in the Wake of KRACK (Linux.com)

Post Syndicated from corbet original https://lwn.net/Articles/736798/rss

Konstantin Ryabitsev argues
on Linux.com that WiFi security is only a part of the problem.
Wi-Fi is merely the first link in a long chain of communication
happening over channels that we should not trust. If I were to guess, the
Wi-Fi router you’re using has probably not received a security update since
the day it got put together. Worse, it probably came with default or easily
guessable administrative credentials that were never changed. Unless you
set up and configured that router yourself and you can remember the last
time you updated its firmware, you should assume that it is now controlled
by someone else and cannot be trusted.

Weekly roundup: Nothing in particular

Post Syndicated from Eevee original https://eev.ee/dev/2017/10/17/weekly-roundup-nothing-in-particular/

  • fox flux: I’m taking a little time to clean up the guts of my “engine”, which has now been copy-pasted between half a dozen games and moderately modified in all of them.

  • blog: I wrote about how I’ve gotten Unity to do 2D platforming.

  • art: Doodling.

  • misc: Wrote half of a small Discord bot. Started on a new small game.

How to Compete with Giants

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/how-to-compete-with-giants/

How to Compete with Giants

This post by Backblaze’s CEO and co-founder Gleb Budman is the sixth in a series about entrepreneurship. You can choose posts in the series from the list below:

  1. How Backblaze got Started: The Problem, The Solution, and the Stuff In-Between
  2. Building a Competitive Moat: Turning Challenges Into Advantages
  3. From Idea to Launch: Getting Your First Customers
  4. How to Get Your First 1,000 Customers
  5. Surviving Your First Year
  6. How to Compete with Giants

Use the Join button above to receive notification of new posts in this series.

Perhaps your business is competing in a brand new space free from established competitors. Most of us, though, start companies that compete with existing offerings from large, established companies. You need to come up with a better mousetrap — not the first mousetrap.

That’s the challenge Backblaze faced. In this post, I’d like to share some of the lessons I learned from that experience.

Backblaze vs. Giants

Competing with established companies that are orders of magnitude larger can be daunting. How can you succeed?

I’ll set the stage by offering a few sets of giants we compete with:

  • When we started Backblaze, we offered online backup in a market where companies had been offering “online backup” for at least a decade, and even the newer entrants had raised tens of millions of dollars.
  • When we built our storage servers, the alternatives were EMC, NetApp, and Dell — each of which had a market cap of over $10 billion.
  • When we introduced our cloud storage offering, B2, our direct competitors were Amazon, Google, and Microsoft. You might have heard of them.

What did we learn by competing with these giants on a bootstrapped budget? Let’s take a look.

Determine What Success Means

For a long time Apple considered Apple TV to be a hobby, not a real product worth focusing on, because it did not generate a billion in revenue. For a $10 billion per year revenue company, a new business that generates $50 million won’t move the needle and often isn’t worth putting focus on. However, for a startup, getting to $50 million in revenue can be the start of a wildly successful business.

Lesson Learned: Don’t let the giants set your success metrics.

The Advantages Startups Have

The giants have a lot of advantages: more money, people, scale, resources, access, etc. Following their playbook and attacking head-on means you’re simply outgunned. Common paths to failure are trying to build more features, enter more markets, outspend on marketing, and other similar approaches where scale and resources are the primary determinants of success.

But being a startup affords many advantages most giants would salivate over. As a nimble startup you can leverage those to succeed. Let’s breakdown nine competitive advantages we’ve used that you can too.

1. Drive Focus

It’s hard to build a $10 billion revenue business doing just one thing, and most giants have a broad portfolio of businesses, numerous products for each, and targeting a variety of customer segments in multiple markets. That adds complexity and distributes management attention.

Startups get the benefit of having everyone in the company be extremely focused, often on a singular mission, product, customer segment, and market. While our competitors sell everything from advertising to Zantac, and are investing in groceries and shipping, Backblaze has focused exclusively on cloud storage. This means all of our best people (i.e. everyone) is focused on our cloud storage business. Where is all of your focus going?

Lesson Learned: Align everyone in your company to a singular focus to dramatically out-perform larger teams.

2. Use Lack-of-Scale as an Advantage

You may have heard Paul Graham say “Do things that don’t scale.” There are a host of things you can do specifically because you don’t have the same scale as the giants. Use that as an advantage.

When we look for data center space, we have more options than our largest competitors because there are simply more spaces available with room for 100 cabinets than for 1,000 cabinets. With some searching, we can find data center space that is better/cheaper.

When a flood in Thailand destroyed factories, causing the world’s supply of hard drives to plummet and prices to triple, we started drive farming. The giants certainly couldn’t. It was a bit crazy, but it let us keep prices unchanged for our customers.

Our Chief Cloud Officer, Tim, used to work at Adobe. Because of their size, any new product needed to always launch in a multitude of languages and in global markets. Once launched, they had scale. But getting any new product launched was incredibly challenging.

Lesson Learned: Use lack-of-scale to exploit opportunities that are closed to giants.

3. Build a Better Product

This one is probably obvious. If you’re going to provide the same product, at the same price, to the same customers — why do it? Remember that better does not always mean more features. Here’s one way we built a better product that didn’t require being a bigger company.

All online backup services required customers to choose what to include in their backup. We found that this was complicated for users since they often didn’t know what needed to be backed up. We flipped the model to back up everything and allow users to exclude if they wanted to, but it was not required. This reduced the number of features/options, while making it easier and better for the user.

This didn’t require the resources of a huge company; it just required understanding customers a bit deeper and thinking about the solution differently. Building a better product is the most classic startup competitive advantage.

Lesson Learned: Dig deep with your customers to understand and deliver a better mousetrap.

4. Provide Better Service

How can you provide better service? Use your advantages. Escalations from your customer care folks to engineering can go through fewer hoops. Fixing an issue and shipping can be quicker. Access to real answers on Twitter or Facebook can be more effective.

A strategic decision we made was to have all customer support people as full-time employees in our headquarters. This ensures they are in close contact to the whole company for feedback to quickly go both ways.

Having a smaller team and fewer layers enables faster internal communication, which increases customer happiness. And the option to do things that don’t scale — such as help a customer in a unique situation — can go a long way in building customer loyalty.

Lesson Learned: Service your customers better by establishing clear internal communications.

5. Remove The Unnecessary

After determining that the industry standard EMC/NetApp/Dell storage servers would be too expensive to build our own cloud storage upon, we decided to build our own infrastructure. Many said we were crazy to compete with these multi-billion dollar companies and that it would be impossible to build a lower cost storage server. However, not only did it prove to not be impossible — it wasn’t even that hard.

One key trick? Remove the unnecessary. While EMC and others built servers to sell to other companies for a wide variety of use cases, Backblaze needed servers that only Backblaze would run, and for a single use case. As a result we could tailor the servers for our needs by removing redundancy from each server (since we would run redundant servers), and using lower-performance components (since we would get high-performance by running parallel servers).

What do your customers and use cases not need? This can trim costs and complexity while often improving the product for your use case.

Lesson Learned: Don’t think “what can we add” to what the giants offer — think “what can we remove.”

6. Be Easy

How many times have you visited a large company website, particularly one that’s not consumer-focused, only to leave saying, “Huh? I don’t understand what you do.” Keeping your website clear, and your product and pricing simple, will dramatically increase conversion and customer satisfaction. If you’re able to make it 2x easier and thus increasing your conversion by 2x, you’ve just allowed yourself to spend ½ as much acquiring a customer.

Providing unlimited data backup wasn’t specifically about providing more storage — it was about making it easier. Since users didn’t know how much data they needed to back up, charging per gigabyte meant they wouldn’t know the cost. Providing unlimited data backup meant they could just relax.

Customers love easy — and being smaller makes easy easier to deliver. Use that as an advantage in your website, marketing materials, pricing, product, and in every other customer interaction.

Lesson Learned: Ease-of-use isn’t a slogan: it’s a competitive advantage. Treat it as seriously as any other feature of your product

7. Don’t Be Afraid of Risk

Obviously unnecessary risks are unnecessary, and some risks aren’t worth taking. However, large companies that have given guidance to Wall Street with a $0.01 range on their earning-per-share are inherently going to be very risk-averse. Use risk-tolerance to open up opportunities, and adjust your tolerance level as you scale. In your first year, there are likely an infinite number of ways your business may vaporize; don’t be too worried about taking a risk that might have a 20% downside when the upside is hockey stick growth.

Using consumer-grade hard drives in our servers may have caused pain and suffering for us years down-the-line, but they were priced at approximately 50% of enterprise drives. Giants wouldn’t have considered the option. Turns out, the consumer drives performed great for us.

Lesson Learned: Use calculated risks as an advantage.

8. Be Open

The larger a company grows, the more it wants to hide information. Some of this is driven by regulatory requirements as a public company. But most of this is cultural. Sharing something might cause a problem, so let’s not. All external communication is treated as a critical press release, with rounds and rounds of editing by multiple teams and approvals. However, customers are often desperate for information. Moreover, sharing information builds trust, understanding, and advocates.

I started blogging at Backblaze before we launched. When we blogged about our Storage Pod and open-sourced the design, many thought we were crazy to share this information. But it was transformative for us, establishing Backblaze as a tech thought leader in storage and giving people a sense of how we were able to provide our service at such a low cost.

Over the years we’ve developed a culture of being open internally and externally, on our blog and with the press, and in communities such as Hacker News and Reddit. Often we’ve been asked, “why would you share that!?” — but it’s the continual openness that builds trust. And that culture of openness is incredibly challenging for the giants.

Lesson Learned: Overshare to build trust and brand where giants won’t.

9. Be Human

As companies scale, typically a smaller percent of founders and executives interact with customers. The people who build the company become more hidden, the language feels “corporate,” and customers start to feel they’re interacting with the cliche “faceless, nameless corporation.” Use your humanity to your advantage. From day one the Backblaze About page listed all the founders, and my email address. While contacting us shouldn’t be the first path for a customer support question, I wanted it to be clear that we stand behind the service we offer; if we’re doing something wrong — I want to know it.

To scale it’s important to have processes and procedures, but sometimes a situation falls outside of a well-established process. While we want our employees to follow processes, they’re still encouraged to be human and “try to do the right thing.” How to you strike this balance? Simon Sinek gives a good talk about it: make your employees feel safe. If employees feel safe they’ll be human.

If your customer is a consumer, they’ll appreciate being treated as a human. Even if your customer is a corporation, the purchasing decision-makers are still people.

Lesson Learned: Being human is the ultimate antithesis to the faceless corporation.

Build Culture to Sustain Your Advantages at Scale

Presumably the goal is not to always be competing with giants, but to one day become a giant. Does this mean you’ll lose all of these advantages? Some, yes — but not all. Some of these advantages are cultural, and if you build these into the culture from the beginning, and fight to keep them as you scale, you can keep them as you become a giant.

Tesla still comes across as human, with Elon Musk frequently interacting with people on Twitter. Apple continues to provide great service through their Genius Bar. And, worst case, if you lose these at scale, you’ll still have the other advantages of being a giant such as money, people, scale, resources, and access.

Of course, some new startup will be gunning for you with grand ambitions, so just be sure not to get complacent. 😉

The post How to Compete with Giants appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Manufacturing Astro Pi case replicas

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/astro-pi-case-guest-post/

Tim Rowledge produces and sells wonderful replicas of the cases which our Astro Pis live in aboard the International Space Station. Here is the story of how he came to do this. Over to you, Tim!

When the Astro Pi case was first revealed a couple of years ago, the collective outpouring of ‘Squee!’ it elicited may have been heard on board the ISS itself. People wanted to buy it or build it at home, and someone wanted to know whether it would blend. (There’s always one.)

The complete Astro Pi

The Sense HAT and its Pi tucked snugly in the original Astro Pi flight case — gorgeous, isn’t it?

Replicating the Astro Pi case

Some months later the STL files for printing your own Astro Pi case were released, and people jumped at the chance to use them. Soon reports appeared saying you had to make quite a few attempts before getting a good print — normal for any complex 3D-printing project. A fellow member of my local makerspace successfully made a couple of cases, but it took a lot of time, filament, and post-print finishing work. And of course, a plastic Astro Pi case simply doesn’t look or feel like the original made of machined aluminium — or ‘aluminum’, as they tend to say over here in North America.

Batch of tops of Astro Pi case replicas by Tim Rowledge

A batch of tops designed by Tim

I wanted to build an Astro Pi case which would more closely match the original. Fortunately, someone else at my makerspace happens to have some serious CNC machining equipment at his small manufacturing company. Therefore, I focused on creating a case design that could be produced with his three-axis device. This meant simplifying some parts to avoid expensive, slow, complex multi-fixture work. It took us a while, but we ended up with a design we can efficiently make using his machine.

Lasered Astro Pi case replica by Tim Rowledge

Tim’s first lasered case

And the resulting case looks really, really like the original — in fact, upon receiving one of the final prototypes, Eben commented:

“I have to say, at first glance they look spectacular: unless you hold them side by side with the originals, it’s hard to pinpoint what’s changed. I’m looking forward to seeing one built up and then seeing them in the wild.”

Inside the Astro Pi case

Making just the bare case is nice, but there are other parts required to recreate a complete Astro Pi unit. Thus I got my local electronics company to design a small HAT to provide much the same support the mezzanine board offers: an RTC and nice, clean connections to the six buttons. We also added well-labelled, grouped pads for all the other GPIO lines, along with space for an ADC. If you’re making your own Astro Pi replica, you might like the Switchboard.

The electronics supply industry just loves to offer *some* of what you need, so that one supplier never has everything: we had to obtain the required stand-offs, screws, spacers, and JST wires from assorted other sources. Jeff at my nearby Industrial Paint & Plastics took on the laser engraving of our cases, leaving out copyrighted logos etcetera.

Lasering the top of an Astro Pi case replica by Tim Rowledge

Lasering the top of a case

Get your own Astro Pi case

Should you like to buy one of our Astro Pi case kits, pop over to www.astropicase.com, and we’ll get it on its way to you pronto. If you’re an institutional or corporate customer, the fully built option might make more sense for you — ordering the Pi and other components, and having a staff member assemble it all, may well be more work than is sensible.

Astro Pi case replica Tim Rowledge

Tim’s first full Astro Pi case replica, complete with shiny APEM buttons

To put the kit together yourself, all you need to do is add a Pi, Sense HAT, Camera Module, and RTC battery, and choose your buttons. An illustrated manual explains the process step by step. Our version of the Astro Pi case uses the same APEM buttons as the units in orbit, and whilst they are expensive, just clicking them is a source of great joy. It comes in a nice travel case too.

Tim Rowledge holding up a PCB

This is Tim. Thanks, Tim!

Take part in Astro Pi

If having an Astro Pi replica is not enough for you, this is your chance: the 2017-18 Astro Pi challenge is open! Do you know a teenager who might be keen to design a experiment to run on the Astro Pis in space? Are you one yourself? You have until 29 October to send us your Mission Space Lab entry and become part of the next generation of space scientists? Head over to the Astro Pi website to find out more.

The post Manufacturing Astro Pi case replicas 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.

Coaxing 2D platforming out of Unity

Post Syndicated from Eevee original https://eev.ee/blog/2017/10/13/coaxing-2d-platforming-out-of-unity/

An anonymous donor asked a question that I can’t even begin to figure out how to answer, but they also said anything else is fine, so here’s anything else.

I’ve been avoiding writing about game physics, since I want to save it for ✨ the book I’m writing ✨, but that book will almost certainly not touch on Unity. Here, then, is a brief run through some of the brick walls I ran into while trying to convince Unity to do 2D platforming.

This is fairly high-level — there are no blocks of code or helpful diagrams. I’m just getting this out of my head because it’s interesting. If you want more gritty details, I guess you’ll have to wait for ✨ the book ✨.

The setup

I hadn’t used Unity before. I hadn’t even used a “real” physics engine before. My games so far have mostly used LÖVE, a Lua-based engine. LÖVE includes box2d bindings, but for various reasons (not all of them good), I opted to avoid them and instead write my own physics completely from scratch. (How, you ask? ✨ Book ✨!)

I was invited to work on a Unity project, Chaos Composer, that someone else had already started. It had basic movement already implemented; I taught myself Unity’s physics system by hacking on it. It’s entirely possible that none of this is actually the best way to do anything, since I was really trying to reproduce my own homegrown stuff in Unity, but it’s the best I’ve managed to come up with.

Two recurring snags were that you can’t ask Unity to do multiple physics updates in a row, and sometimes getting the information I wanted was difficult. Working with my own code spoiled me a little, since I could invoke it at any time and ask it anything I wanted; Unity, on the other hand, is someone else’s black box with a rigid interface on top.

Also, wow, Googling for a lot of this was not quite as helpful as expected. A lot of what’s out there is just the first thing that works, and often that’s pretty hacky and imposes severe limits on the game design (e.g., “this won’t work with slopes”). Basic movement and collision are the first thing you do, which seems to me like the worst time to be locking yourself out of a lot of design options. I tried very (very, very, very) hard to minimize those kinds of constraints.

Problem 1: Movement

When I showed up, movement was already working. Problem solved!

Like any good programmer, I immediately set out to un-solve it. Given a “real” physics engine like Unity prominently features, you have two options: ⓐ treat the player as a physics object, or ⓑ don’t. The existing code went with option ⓑ, like I’d done myself with LÖVE, and like I’d seen countless people advise. Using a physics sim makes for bad platforming.

But… why? I believed it, but I couldn’t concretely defend it. I had to know for myself. So I started a blank project, drew some physics boxes, and wrote a dozen-line player controller.

Ah! Immediate enlightenment.

If the player was sliding down a wall, and I tried to move them into the wall, they would simply freeze in midair until I let go of the movement key. The trouble is that the physics sim works in terms of forces — moving the player involves giving them a nudge in some direction, like a giant invisible hand pushing them around the level. Surprise! If you press a real object against a real wall with your real hand, you’ll see the same effect — friction will cancel out gravity, and the object will stay in midair..

Platformer movement, as it turns out, doesn’t make any goddamn physical sense. What is air control? What are you pushing against? Nothing, really; we just have it because it’s nice to play with, because not having it is a nightmare.

I looked to see if there were any common solutions to this, and I only really found one: make all your walls frictionless.

Game development is full of hacks like this, and I… don’t like them. I can accept that minor hacks are necessary sometimes, but this one makes an early and widespread change to a fundamental system to “fix” something that was wrong in the first place. It also imposes an “invisible” requirement, something I try to avoid at all costs — if you forget to make a particular wall frictionless, you’ll never know unless you happen to try sliding down it.

And so, I swiftly returned to the existing code. It wasn’t too different from what I’d come up with for LÖVE: it applied gravity by hand, tracked the player’s velocity, computed the intended movement each frame, and moved by that amount. The interesting thing was that it used MovePosition, which schedules a movement for the next physics update and stops the movement if the player hits something solid.

It’s kind of a nice hybrid approach, actually; all the “physics” for conscious actors is done by hand, but the physics engine is still used for collision detection. It’s also used for collision rejection — if the player manages to wedge themselves several pixels into a solid object, for example, the physics engine will try to gently nudge them back out of it with no extra effort required on my part. I still haven’t figured out how to get that to work with my homegrown stuff, which is built to prevent overlap rather than to jiggle things out of it.

But wait, what about…

Our player is a dynamic body with rotation lock and no gravity. Why not just use a kinematic body?

I must be missing something, because I do not understand the point of kinematic bodies. I ran into this with Godot, too, which documented them the same way: as intended for use as players and other manually-moved objects. But by default, they don’t even collide with other kinematic bodies or static geometry. What? There’s a checkbox to turn this on, which I enabled, but then I found out that MovePosition doesn’t stop kinematic bodies when they hit something, so I would’ve had to cast along the intended path of movement to figure out when to stop, thus duplicating the same work the physics engine was about to do.

But that’s impossible anyway! Static geometry generally wants to be made of edge colliders, right? They don’t care about concave/convex. Imagine the player is standing on the ground near a wall and tries to move towards the wall. Both the ground and the wall are different edges from the same edge collider.

If you try to cast the player’s hitbox horizontally, parallel to the ground, you’ll only get one collision: the existing collision with the ground. Casting doesn’t distinguish between touching and hitting. And because Unity only reports one collision per collider, and because the ground will always show up first, you will never find out about the impending wall collision.

So you’re forced to either use raycasts for collision detection or decomposed polygons for world geometry, both of which are slightly worse tools for no real gain.

I ended up sticking with a dynamic body.


Oh, one other thing that doesn’t really fit anywhere else: keep track of units! If you’re adding something called “velocity” directly to something called “position”, something has gone very wrong. Acceleration is distance per time squared; velocity is distance per time; position is distance. You must multiply or divide by time to convert between them.

I never even, say, add a constant directly to position every frame; I always phrase it as velocity and multiply by Δt. It keeps the units consistent: time is always in seconds, not in tics.

Problem 2: Slopes

Ah, now we start to get off in the weeds.

A sort of pre-problem here was detecting whether we’re on a slope, which means detecting the ground. The codebase originally used a manual physics query of the area around the player’s feet to check for the ground, which seems to be somewhat common, but that can’t tell me the angle of the detected ground. (It’s also kind of error-prone, since “around the player’s feet” has to be specified by hand and may not stay correct through animations or changes in the hitbox.)

I replaced that with what I’d eventually settled on in LÖVE: detect the ground by detecting collisions, and looking at the normal of the collision. A normal is a vector that points straight out from a surface, so if you’re standing on the ground, the normal points straight up; if you’re on a 10° incline, the normal points 10° away from straight up.

Not all collisions are with the ground, of course, so I assumed something is ground if the normal pointed away from gravity. (I like this definition more than “points upwards”, because it avoids assuming anything about the direction of gravity, which leaves some interesting doors open for later on.) That’s easily detected by taking the dot product — if it’s negative, the collision was with the ground, and I now have the normal of the ground.

Actually doing this in practice was slightly tricky. With my LÖVE engine, I could cram this right into the middle of collision resolution. With Unity, not quite so much. I went through a couple iterations before I really grasped Unity’s execution order, which I guess I will have to briefly recap for this to make sense.

Unity essentially has two update cycles. It performs physics updates at fixed intervals for consistency, and updates everything else just before rendering. Within a single frame, Unity does as many fixed physics updates as it has spare time for (which might be zero, one, or more), then does a regular update, then renders. User code can implement either or both of Update, which runs during a regular update, and FixedUpdate, which runs just before Unity does a physics pass.

So my solution was:

  • At the very end of FixedUpdate, clear the actor’s “on ground” flag and ground normal.

  • During OnCollisionEnter2D and OnCollisionStay2D (which are called from within a physics pass), if there’s a collision that looks like it’s with the ground, set the “on ground” flag and ground normal. (If there are multiple ground collisions, well, good luck figuring out the best way to resolve that! At the moment I’m just taking the first and hoping for the best.)

That means there’s a brief window between the end of FixedUpdate and Unity’s physics pass during which a grounded actor might mistakenly believe it’s not on the ground, which is a bit of a shame, but there are very few good reasons for anything to be happening in that window.

Okay! Now we can do slopes.

Just kidding! First we have to do sliding.

When I first looked at this code, it didn’t apply gravity while the player was on the ground. I think I may have had some problems with detecting the ground as result, since the player was no longer pushing down against it? Either way, it seemed like a silly special case, so I made gravity always apply.

Lo! I was a fool. The player could no longer move.

Why? Because MovePosition does exactly what it promises. If the player collides with something, they’ll stop moving. Applying gravity means that the player is trying to move diagonally downwards into the ground, and so MovePosition stops them immediately.

Hence, sliding. I don’t want the player to actually try to move into the ground. I want them to move the unblocked part of that movement. For flat ground, that means the horizontal part, which is pretty much the same as discarding gravity. For sloped ground, it’s a bit more complicated!

Okay but actually it’s less complicated than you’d think. It can be done with some cross products fairly easily, but Unity makes it even easier with a couple casts. There’s a Vector3.ProjectOnPlane function that projects an arbitrary vector on a plane given by its normal — exactly the thing I want! So I apply that to the attempted movement before passing it along to MovePosition. I do the same thing with the current velocity, to prevent the player from accelerating infinitely downwards while standing on flat ground.

One other thing: I don’t actually use the detected ground normal for this. The player might be touching two ground surfaces at the same time, and I’d want to project on both of them. Instead, I use the player body’s GetContacts method, which returns contact points (and normals!) for everything the player is currently touching. I believe those contact points are tracked by the physics engine anyway, so asking for them doesn’t require any actual physics work.

(Looking at the code I have, I notice that I still only perform the slide for surfaces facing upwards — but I’d want to slide against sloped ceilings, too. Why did I do this? Maybe I should remove that.)

(Also, I’m pretty sure projecting a vector on a plane is non-commutative, which raises the question of which order the projections should happen in and what difference it makes. I don’t have a good answer.)

(I note that my LÖVE setup does something slightly different: it just tries whatever the movement ought to be, and if there’s a collision, then it projects — and tries again with the remaining movement. But I can’t ask Unity to do multiple moves in one physics update, alas.)

Okay! Now, slopes. But actually, with the above work done, slopes are most of the way there already.

One obvious problem is that the player tries to move horizontally even when on a slope, and the easy fix is to change their movement from speed * Vector2.right to speed * new Vector2(ground.y, -ground.x) while on the ground. That’s the ground normal rotated a quarter-turn clockwise, so for flat ground it still points to the right, and in general it points rightwards along the ground. (Note that it assumes the ground normal is a unit vector, but as far as I’m aware, that’s true for all the normals Unity gives you.)

Another issue is that if the player stands motionless on a slope, gravity will cause them to slowly slide down it — because the movement from gravity will be projected onto the slope, and unlike flat ground, the result is no longer zero. For conscious actors only, I counter this by adding the opposite factor to the player’s velocity as part of adding in their walking speed. This matches how the real world works, to some extent: when you’re standing on a hill, you’re exerting some small amount of effort just to stay in place.

(Note that slope resistance is not the same as friction. Okay, yes, in the real world, virtually all resistance to movement happens as a result of friction, but bracing yourself against the ground isn’t the same as being passively resisted.)

From here there are a lot of things you can do, depending on how you think slopes should be handled. You could make the player unable to walk up slopes that are too steep. You could make walking down a slope faster than walking up it. You could make jumping go along the ground normal, rather than straight up. You could raise the player’s max allowed speed while running downhill. Whatever you want, really. Armed with a normal and awareness of dot products, you can do whatever you want.

But first you might want to fix a few aggravating side effects.

Problem 3: Ground adherence

I don’t know if there’s a better name for this. I rarely even see anyone talk about it, which surprises me; it seems like it should be a very common problem.

The problem is: if the player runs up a slope which then abruptly changes to flat ground, their momentum will carry them into the air. For very fast players going off the top of very steep slopes, this makes sense, but it becomes visible even for relatively gentle slopes. It was a mild nightmare in the original release of our game Lunar Depot 38, which has very “rough” ground made up of lots of shallow slopes — so the player is very frequently slightly off the ground, which meant they couldn’t jump, for seemingly no reason. (I even had code to fix this, but I disabled it because of a silly visual side effect that I never got around to fixing.)

Anyway! The reason this is a problem is that game protagonists are generally not boxes sliding around — they have legs. We don’t go flying off the top of real-world hilltops because we put our foot down until it touches the ground.

Simulating this footfall is surprisingly fiddly to get right, especially with someone else’s physics engine. It’s made somewhat easier by Cast, which casts the entire hitbox — no matter what shape it is — in a particular direction, as if it had moved, and tells you all the hypothetical collisions in order.

So I cast the player in the direction of gravity by some distance. If the cast hits something solid with a ground-like collision normal, then the player must be close to the ground, and I move them down to touch it (and set that ground as the new ground normal).

There are some wrinkles.

Wrinkle 1: I only want to do this if the player is off the ground now, but was on the ground last frame, and is not deliberately moving upwards. That latter condition means I want to skip this logic if the player jumps, for example, but also if the player is thrust upwards by a spring or abducted by a UFO or whatever. As long as external code goes through some interface and doesn’t mess with the player’s velocity directly, that shouldn’t be too hard to track.

Wrinkle 2: When does this logic run? It needs to happen after the player moves, which means after a Unity physics pass… but there’s no callback for that point in time. I ended up running it at the beginning of FixedUpdate and the beginning of Update — since I definitely want to do it before rendering happens! That means it’ll sometimes happen twice between physics updates. (I could carefully juggle a flag to skip the second run, but I… didn’t do that. Yet?)

Wrinkle 3: I can’t move the player with MovePosition! Remember, MovePosition schedules a movement, it doesn’t actually perform one; that means if it’s called twice before the physics pass, the first call is effectively ignored. I can’t easily combine the drop with the player’s regular movement, for various fiddly reasons. I ended up doing it “by hand” using transform.Translate, which I think was the “old way” to do manual movement before MovePosition existed. I’m not totally sure if it activates triggers? For that matter, I’m not sure it even notices collisions — but since I did a full-body Cast, there shouldn’t be any anyway.

Wrinkle 4: What, exactly, is “some distance”? I’ve yet to find a satisfying answer for this. It seems like it ought to be based on the player’s current speed and the slope of the ground they’re moving along, but every time I’ve done that math, I’ve gotten totally ludicrous answers that sometimes exceed the size of a tile. But maybe that’s not wrong? Play around, I guess, and think about when the effect should “break” and the player should go flying off the top of a hill.

Wrinkle 5: It’s possible that the player will launch off a slope, hit something, and then be adhered to the ground where they wouldn’t have hit it. I don’t much like this edge case, but I don’t see a way around it either.

This problem is surprisingly awkward for how simple it sounds, and the solution isn’t entirely satisfying. Oh, well; the results are much nicer than the solution. As an added bonus, this also fixes occasional problems with running down a hill and becoming detached from the ground due to precision issues or whathaveyou.

Problem 4: One-way platforms

Ah, what a nightmare.

It took me ages just to figure out how to define one-way platforms. Only block when the player is moving downwards? Nope. Only block when the player is above the platform? Nuh-uh.

Well, okay, yes, those approaches might work for convex players and flat platforms. But what about… sloped, one-way platforms? There’s no reason you shouldn’t be able to have those. If Super Mario World can do it, surely Unity can do it almost 30 years later.

The trick is, again, to look at the collision normal. If it faces away from gravity, the player is hitting a ground-like surface, so the platform should block them. Otherwise (or if the player overlaps the platform), it shouldn’t.

Here’s the catch: Unity doesn’t have conditional collision. I can’t decide, on the fly, whether a collision should block or not. In fact, I think that by the time I get a callback like OnCollisionEnter2D, the physics pass is already over.

I could go the other way and use triggers (which are non-blocking), but then I have the opposite problem: I can’t stop the player on the fly. I could move them back to where they hit the trigger, but I envision all kinds of problems as a result. What if they were moving fast enough to activate something on the other side of the platform? What if something else moved to where I’m trying to shove them back to in the meantime? How does this interact with ground detection and listing contacts, which would rightly ignore a trigger as non-blocking?

I beat my head against this for a while, but the inability to respond to collision conditionally was a huge roadblock. It’s all the more infuriating a problem, because Unity ships with a one-way platform modifier thing. Unfortunately, it seems to have been implemented by someone who has never played a platformer. It’s literally one-way — the player is only allowed to move straight upwards through it, not in from the sides. It also tries to block the player if they’re moving downwards while inside the platform, which invokes clumsy rejection behavior. And this all seems to be built into the physics engine itself somehow, so I can’t simply copy whatever they did.

Eventually, I settled on the following. After calculating attempted movement (including sliding), just at the end of FixedUpdate, I do a Cast along the movement vector. I’m not thrilled about having to duplicate the physics engine’s own work, but I do filter to only things on a “one-way platform” physics layer, which should at least help. For each object the cast hits, I use Physics2D.IgnoreCollision to either ignore or un-ignore the collision between the player and the platform, depending on whether the collision was ground-like or not.

(A lot of people suggested turning off collision between layers, but that can’t possibly work — the player might be standing on one platform while inside another, and anyway, this should work for all actors!)

Again, wrinkles! But fewer this time. Actually, maybe just one: handling the case where the player already overlaps the platform. I can’t just check for that with e.g. OverlapCollider, because that doesn’t distinguish between overlapping and merely touching.

I came up with a fairly simple fix: if I was going to un-ignore the collision (i.e. make the platform block), and the cast distance is reported as zero (either already touching or overlapping), I simply do nothing instead. If I’m standing on the platform, I must have already set it blocking when I was approaching it from the top anyway; if I’m overlapping it, I must have already set it non-blocking to get here in the first place.

I can imagine a few cases where this might go wrong. Moving platforms, especially, are going to cause some interesting issues. But this is the best I can do with what I know, and it seems to work well enough so far.

Oh, and our player can deliberately drop down through platforms, which was easy enough to implement; I just decide the platform is always passable while some button is held down.

Problem 5: Pushers and carriers

I haven’t gotten to this yet! Oh boy, can’t wait. I implemented it in LÖVE, but my way was hilariously invasive; I’m hoping that having a physics engine that supports a handwaved “this pushes that” will help. Of course, you also have to worry about sticking to platforms, for which the recommended solution is apparently to parent the cargo to the platform, which sounds goofy to me? I guess I’ll find out when I throw myself at it later.

Overall result

I ended up with a fairly pleasant-feeling system that supports slopes and one-way platforms and whatnot, with all the same pieces as I came up with for LÖVE. The code somehow ended up as less of a mess, too, but it probably helps that I’ve been down this rabbit hole once before and kinda knew what I was aiming for this time.

Animation of a character running smoothly along the top of an irregular dinosaur skeleton

Sorry that I don’t have a big block of code for you to copy-paste into your project. I don’t think there are nearly enough narrative discussions of these fundamentals, though, so hopefully this is useful to someone. If not, well, look forward to ✨ my book, that I am writing ✨!

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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  |=================================================================| 100%

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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  |=====                                                            |   8%
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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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  |                                                                       
  |=======                                                          |  10%
  |                                                                       
  |=================================================================| 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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  |========                                                         |  12%
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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.

 

 

The Evil Within 2 Used Denuvo, Then Dumped it Before Launch

Post Syndicated from Andy original https://torrentfreak.com/the-evil-within-2-used-denuvo-then-dumped-it-before-launch-171013/

At the end of September we reported on a nightmare scenario for videogame anti-tamper technology Denuvo.

With cracking groups chipping away at the system for the past few months, progressing in leaps and bounds, the race to the bottom was almost complete. After aiming to hold off pirates for the first few lucrative weeks and months after launch, the Denuvo-protected Total War: Warhammer 2 fell to pirates in a matter of hours.

In the less than two weeks that have passed since, things haven’t improved much. By most measurements, in fact, the situation appears to have gotten worse.

On Wednesday, action role-playing game Middle Earth: Shadow of War was cracked a day after launch. While this didn’t beat the record set by Warhammer 2, the scene was given an unexpected gift.

Instead of the crack appearing courtesy of scene groups STEAMPUNKS or CPY, which has largely been the tradition thus far this year, old favorite CODEX stepped up to the mark with their own efforts. This means there are now close to half a dozen entities with the ability to defeat Denuvo, which isn’t a good look for the anti-piracy outfit.

A CODEX crack for Denuvo, from nowhere

Needless to say, this development was met with absolute glee by pirates, who forgave the additional day taken to crack the game in order to welcome CODEX into the anti-Denuvo club. But while this is bad news for the anti-tamper technology, there could be a worse enemy crossing the horizon – no confidence.

This Tuesday, DSO Gaming reported that it had received a review copy of Bethesda’s then-upcoming survival horror game, The Evil Within 2. The site, which is often a reliable source for Denuvo-related news, confirmed that the code was indeed protected by Denuvo.

“Another upcoming title that will be using Denuvo is The Evil Within 2,” the site reported. “Bethesda has provided us with a review code for The Evil Within 2. As such, we can confirm that Denuvo is present in it.”

As you read this, October 13, 2017, The Evil Within 2 is enjoying its official worldwide launch. Early yesterday afternoon, however, the title leaked early onto the Internet, courtesy of cracking group CODEX.

At first view, it looked like CODEX had cracked Denuvo before the game’s official launch but the reality was somewhat different after the dust had settled. For reasons best known to developer Bethesda, Denuvo was completely absent from the title. As shown by the title’s NFO (information) file, the only protection present was that provided by Steam.

Denuvo? What Denuvo?

This raises a number of scenarios, none of them good for Denuvo.

One possibility is that all along Bethesda never intended to use Denuvo on the final release. Exactly why we’ll likely never know, but the theory doesn’t really gel with them including it in the review code reviewed by DSO Gaming earlier this week.

The other proposition is that Bethesda witnessed the fiasco around Denuvo’s ‘protection’ in recent days and decided not to invest in something that wasn’t going to provide value for money.

Of course, these theories are going to be pretty difficult to confirm. Denuvo are a pretty confident bunch when things are going their way but they go suspiciously quiet when the tide is turning. Equally, developers tend to keep quiet about their anti-piracy strategies too.

The bottom line though is that if the protection really works and turns in valuable cash, why wouldn’t Bethesda use it as they have done on previous titles including Doom and Prey?

With that question apparently answering itself at the moment, all eyes now turn to Denuvo. Although it has a history of being one of the most successful anti-piracy systems overall, it has taken a massive battering in recent times. Will it recover? Only time will tell but at the moment things couldn’t get much worse.

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

Introducing Gluon: a new library for machine learning from AWS and Microsoft

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/introducing-gluon-a-new-library-for-machine-learning-from-aws-and-microsoft/

Post by Dr. Matt Wood

Today, AWS and Microsoft announced Gluon, a new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance.

Gluon Logo

Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components. Developers who are new to machine learning will find this interface more familiar to traditional code, since machine learning models can be defined and manipulated just like any other data structure. More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed.

Gluon is available in Apache MXNet today, a forthcoming Microsoft Cognitive Toolkit release, and in more frameworks over time.

Neural Networks vs Developers
Machine learning with neural networks (including ‘deep learning’) has three main components: data for training; a neural network model, and an algorithm which trains the neural network. You can think of the neural network in a similar way to a directed graph; it has a series of inputs (which represent the data), which connect to a series of outputs (the prediction), through a series of connected layers and weights. During training, the algorithm adjusts the weights in the network based on the error in the network output. This is the process by which the network learns; it is a memory and compute intensive process which can take days.

Deep learning frameworks such as Caffe2, Cognitive Toolkit, TensorFlow, and Apache MXNet are, in part, an answer to the question ‘how can we speed this process up? Just like query optimizers in databases, the more a training engine knows about the network and the algorithm, the more optimizations it can make to the training process (for example, it can infer what needs to be re-computed on the graph based on what else has changed, and skip the unaffected weights to speed things up). These frameworks also provide parallelization to distribute the computation process, and reduce the overall training time.

However, in order to achieve these optimizations, most frameworks require the developer to do some extra work: specifically, by providing a formal definition of the network graph, up-front, and then ‘freezing’ the graph, and just adjusting the weights.

The network definition, which can be large and complex with millions of connections, usually has to be constructed by hand. Not only are deep learning networks unwieldy, but they can be difficult to debug and it’s hard to re-use the code between projects.

The result of this complexity can be difficult for beginners and is a time-consuming task for more experienced researchers. At AWS, we’ve been experimenting with some ideas in MXNet around new, flexible, more approachable ways to define and train neural networks. Microsoft is also a contributor to the open source MXNet project, and were interested in some of these same ideas. Based on this, we got talking, and found we had a similar vision: to use these techniques to reduce the complexity of machine learning, making it accessible to more developers.

Enter Gluon: dynamic graphs, rapid iteration, scalable training
Gluon introduces four key innovations.

  1. Friendly API: Gluon networks can be defined using a simple, clear, concise code – this is easier for developers to learn, and much easier to understand than some of the more arcane and formal ways of defining networks and their associated weighted scoring functions.
  2. Dynamic networks: the network definition in Gluon is dynamic: it can bend and flex just like any other data structure. This is in contrast to the more common, formal, symbolic definition of a network which the deep learning framework has to effectively carve into stone in order to be able to effectively optimizing computation during training. Dynamic networks are easier to manage, and with Gluon, developers can easily ‘hybridize’ between these fast symbolic representations and the more friendly, dynamic ‘imperative’ definitions of the network and algorithms.
  3. The algorithm can define the network: the model and the training algorithm are brought much closer together. Instead of separate definitions, the algorithm can adjust the network dynamically during definition and training. Not only does this mean that developers can use standard programming loops, and conditionals to create these networks, but researchers can now define even more sophisticated algorithms and models which were not possible before. They are all easier to create, change, and debug.
  4. High performance operators for training: which makes it possible to have a friendly, concise API and dynamic graphs, without sacrificing training speed. This is a huge step forward in machine learning. Some frameworks bring a friendly API or dynamic graphs to deep learning, but these previous methods all incur a cost in terms of training speed. As with other areas of software, abstraction can slow down computation since it needs to be negotiated and interpreted at run time. Gluon can efficiently blend together a concise API with the formal definition under the hood, without the developer having to know about the specific details or to accommodate the compiler optimizations manually.

The team here at AWS, and our collaborators at Microsoft, couldn’t be more excited to bring these improvements to developers through Gluon. We’re already seeing quite a bit of excitement from developers and researchers alike.

Getting started with Gluon
Gluon is available today in Apache MXNet, with support coming for the Microsoft Cognitive Toolkit in a future release. We’re also publishing the front-end interface and the low-level API specifications so it can be included in other frameworks in the fullness of time.

You can get started with Gluon today. Fire up the AWS Deep Learning AMI with a single click and jump into one of 50 fully worked, notebook examples. If you’re a contributor to a machine learning framework, check out the interface specs on GitHub.

-Dr. Matt Wood

DevOps Cafe Episode 76 – Randy Shoup

Post Syndicated from DevOpsCafeAdmin original http://devopscafe.org/show/2017/10/11/devops-cafe-episode-76-randy-shoup.html

Technical talent is obviously in his jeans (pun intended) 

John and Damon chat with Randy Shoup (Stitch Fix) about what he’s learned building high-scale systems and teams through multiple generations of technology and practices… and how he is doing it again today.

  

Direct download

Follow John Willis on Twitter: @botchagalupe
Follow Damon Edwards on Twitter: @damonedwards 
Follow Randy Shoup on Twitter: @randyshoup

Notes:

 

Please tweet or leave comments or questions below and we’ll read them on the show!

"Responsible encryption" fallacies

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/10/responsible-encryption-fallacies.html

Deputy Attorney General Rod Rosenstein gave a speech recently calling for “Responsible Encryption” (aka. “Crypto Backdoors”). It’s full of dangerous ideas that need to be debunked.

The importance of law enforcement

The first third of the speech talks about the importance of law enforcement, as if it’s the only thing standing between us and chaos. It cites the 2016 Mirai attacks as an example of the chaos that will only get worse without stricter law enforcement.

But the Mira case demonstrated the opposite, how law enforcement is not needed. They made no arrests in the case. A year later, they still haven’t a clue who did it.

Conversely, we technologists have fixed the major infrastructure issues. Specifically, those affected by the DNS outage have moved to multiple DNS providers, including a high-capacity DNS provider like Google and Amazon who can handle such large attacks easily.

In other words, we the people fixed the major Mirai problem, and law-enforcement didn’t.

Moreover, instead being a solution to cyber threats, law enforcement has become a threat itself. The DNC didn’t have the FBI investigate the attacks from Russia likely because they didn’t want the FBI reading all their files, finding wrongdoing by the DNC. It’s not that they did anything actually wrong, but it’s more like that famous quote from Richelieu “Give me six words written by the most honest of men and I’ll find something to hang him by”. Give all your internal emails over to the FBI and I’m certain they’ll find something to hang you by, if they want.
Or consider the case of Andrew Auernheimer. He found AT&T’s website made public user accounts of the first iPad, so he copied some down and posted them to a news site. AT&T had denied the problem, so making the problem public was the only way to force them to fix it. Such access to the website was legal, because AT&T had made the data public. However, prosecutors disagreed. In order to protect the powerful, they twisted and perverted the law to put Auernheimer in jail.

It’s not that law enforcement is bad, it’s that it’s not the unalloyed good Rosenstein imagines. When law enforcement becomes the thing Rosenstein describes, it means we live in a police state.

Where law enforcement can’t go

Rosenstein repeats the frequent claim in the encryption debate:

Our society has never had a system where evidence of criminal wrongdoing was totally impervious to detection

Of course our society has places “impervious to detection”, protected by both legal and natural barriers.

An example of a legal barrier is how spouses can’t be forced to testify against each other. This barrier is impervious.

A better example, though, is how so much of government, intelligence, the military, and law enforcement itself is impervious. If prosecutors could gather evidence everywhere, then why isn’t Rosenstein prosecuting those guilty of CIA torture?

Oh, you say, government is a special exception. If that were the case, then why did Rosenstein dedicate a precious third of his speech discussing the “rule of law” and how it applies to everyone, “protecting people from abuse by the government”. It obviously doesn’t, there’s one rule of government and a different rule for the people, and the rule for government means there’s lots of places law enforcement can’t go to gather evidence.

Likewise, the crypto backdoor Rosenstein is demanding for citizens doesn’t apply to the President, Congress, the NSA, the Army, or Rosenstein himself.

Then there are the natural barriers. The police can’t read your mind. They can only get the evidence that is there, like partial fingerprints, which are far less reliable than full fingerprints. They can’t go backwards in time.

I mention this because encryption is a natural barrier. It’s their job to overcome this barrier if they can, to crack crypto and so forth. It’s not our job to do it for them.

It’s like the camera that increasingly comes with TVs for video conferencing, or the microphone on Alexa-style devices that are always recording. This suddenly creates evidence that the police want our help in gathering, such as having the camera turned on all the time, recording to disk, in case the police later gets a warrant, to peer backward in time what happened in our living rooms. The “nothing is impervious” argument applies here as well. And it’s equally bogus here. By not helping police by not recording our activities, we aren’t somehow breaking some long standing tradit

And this is the scary part. It’s not that we are breaking some ancient tradition that there’s no place the police can’t go (with a warrant). Instead, crypto backdoors breaking the tradition that never before have I been forced to help them eavesdrop on me, even before I’m a suspect, even before any crime has been committed. Sure, laws like CALEA force the phone companies to help the police against wrongdoers — but here Rosenstein is insisting I help the police against myself.

Balance between privacy and public safety

Rosenstein repeats the frequent claim that encryption upsets the balance between privacy/safety:

Warrant-proof encryption defeats the constitutional balance by elevating privacy above public safety.

This is laughable, because technology has swung the balance alarmingly in favor of law enforcement. Far from “Going Dark” as his side claims, the problem we are confronted with is “Going Light”, where the police state monitors our every action.

You are surrounded by recording devices. If you walk down the street in town, outdoor surveillance cameras feed police facial recognition systems. If you drive, automated license plate readers can track your route. If you make a phone call or use a credit card, the police get a record of the transaction. If you stay in a hotel, they demand your ID, for law enforcement purposes.

And that’s their stuff, which is nothing compared to your stuff. You are never far from a recording device you own, such as your mobile phone, TV, Alexa/Siri/OkGoogle device, laptop. Modern cars from the last few years increasingly have always-on cell connections and data recorders that record your every action (and location).

Even if you hike out into the country, when you get back, the FBI can subpoena your GPS device to track down your hidden weapon’s cache, or grab the photos from your camera.

And this is all offline. So much of what we do is now online. Of the photographs you own, fewer than 1% are printed out, the rest are on your computer or backed up to the cloud.

Your phone is also a GPS recorder of your exact position all the time, which if the government wins the Carpenter case, they police can grab without a warrant. Tagging all citizens with a recording device of their position is not “balance” but the premise for a novel more dystopic than 1984.

If suspected of a crime, which would you rather the police searched? Your person, houses, papers, and physical effects? Or your mobile phone, computer, email, and online/cloud accounts?

The balance of privacy and safety has swung so far in favor of law enforcement that rather than debating whether they should have crypto backdoors, we should be debating how to add more privacy protections.

“But it’s not conclusive”

Rosenstein defends the “going light” (“Golden Age of Surveillance”) by pointing out it’s not always enough for conviction. Nothing gives a conviction better than a person’s own words admitting to the crime that were captured by surveillance. This other data, while copious, often fails to convince a jury beyond a reasonable doubt.
This is nonsense. Police got along well enough before the digital age, before such widespread messaging. They solved terrorist and child abduction cases just fine in the 1980s. Sure, somebody’s GPS location isn’t by itself enough — until you go there and find all the buried bodies, which leads to a conviction. “Going dark” imagines that somehow, the evidence they’ve been gathering for centuries is going away. It isn’t. It’s still here, and matches up with even more digital evidence.
Conversely, a person’s own words are not as conclusive as you think. There’s always missing context. We quickly get back to the Richelieu “six words” problem, where captured communications are twisted to convict people, with defense lawyers trying to untwist them.

Rosenstein’s claim may be true, that a lot of criminals will go free because the other electronic data isn’t convincing enough. But I’d need to see that claim backed up with hard studies, not thrown out for emotional impact.

Terrorists and child molesters

You can always tell the lack of seriousness of law enforcement when they bring up terrorists and child molesters.
To be fair, sometimes we do need to talk about terrorists. There are things unique to terrorism where me may need to give government explicit powers to address those unique concerns. For example, the NSA buys mobile phone 0day exploits in order to hack terrorist leaders in tribal areas. This is a good thing.
But when terrorists use encryption the same way everyone else does, then it’s not a unique reason to sacrifice our freedoms to give the police extra powers. Either it’s a good idea for all crimes or no crimes — there’s nothing particular about terrorism that makes it an exceptional crime. Dead people are dead. Any rational view of the problem relegates terrorism to be a minor problem. More citizens have died since September 8, 2001 from their own furniture than from terrorism. According to studies, the hot water from the tap is more of a threat to you than terrorists.
Yes, government should do what they can to protect us from terrorists, but no, it’s not so bad of a threat that requires the imposition of a military/police state. When people use terrorism to justify their actions, it’s because they trying to form a military/police state.
A similar argument works with child porn. Here’s the thing: the pervs aren’t exchanging child porn using the services Rosenstein wants to backdoor, like Apple’s Facetime or Facebook’s WhatsApp. Instead, they are exchanging child porn using custom services they build themselves.
Again, I’m (mostly) on the side of the FBI. I support their idea of buying 0day exploits in order to hack the web browsers of visitors to the secret “PlayPen” site. This is something that’s narrow to this problem and doesn’t endanger the innocent. On the other hand, their calls for crypto backdoors endangers the innocent while doing effectively nothing to address child porn.
Terrorists and child molesters are a clichéd, non-serious excuse to appeal to our emotions to give up our rights. We should not give in to such emotions.

Definition of “backdoor”

Rosenstein claims that we shouldn’t call backdoors “backdoors”:

No one calls any of those functions [like key recovery] a “back door.”  In fact, those capabilities are marketed and sought out by many users.

He’s partly right in that we rarely refer to PGP’s key escrow feature as a “backdoor”.

But that’s because the term “backdoor” refers less to how it’s done and more to who is doing it. If I set up a recovery password with Apple, I’m the one doing it to myself, so we don’t call it a backdoor. If it’s the police, spies, hackers, or criminals, then we call it a “backdoor” — even it’s identical technology.

Wikipedia uses the key escrow feature of the 1990s Clipper Chip as a prime example of what everyone means by “backdoor“. By “no one”, Rosenstein is including Wikipedia, which is obviously incorrect.

Though in truth, it’s not going to be the same technology. The needs of law enforcement are different than my personal key escrow/backup needs. In particular, there are unsolvable problems, such as a backdoor that works for the “legitimate” law enforcement in the United States but not for the “illegitimate” police states like Russia and China.

I feel for Rosenstein, because the term “backdoor” does have a pejorative connotation, which can be considered unfair. But that’s like saying the word “murder” is a pejorative term for killing people, or “torture” is a pejorative term for torture. The bad connotation exists because we don’t like government surveillance. I mean, honestly calling this feature “government surveillance feature” is likewise pejorative, and likewise exactly what it is that we are talking about.

Providers

Rosenstein focuses his arguments on “providers”, like Snapchat or Apple. But this isn’t the question.

The question is whether a “provider” like Telegram, a Russian company beyond US law, provides this feature. Or, by extension, whether individuals should be free to install whatever software they want, regardless of provider.

Telegram is a Russian company that provides end-to-end encryption. Anybody can download their software in order to communicate so that American law enforcement can’t eavesdrop. They aren’t going to put in a backdoor for the U.S. If we succeed in putting backdoors in Apple and WhatsApp, all this means is that criminals are going to install Telegram.

If the, for some reason, the US is able to convince all such providers (including Telegram) to install a backdoor, then it still doesn’t solve the problem, as uses can just build their own end-to-end encryption app that has no provider. It’s like email: some use the major providers like GMail, others setup their own email server.

Ultimately, this means that any law mandating “crypto backdoors” is going to target users not providers. Rosenstein tries to make a comparison with what plain-old telephone companies have to do under old laws like CALEA, but that’s not what’s happening here. Instead, for such rules to have any effect, they have to punish users for what they install, not providers.

This continues the argument I made above. Government backdoors is not something that forces Internet services to eavesdrop on us — it forces us to help the government spy on ourselves.
Rosenstein tries to address this by pointing out that it’s still a win if major providers like Apple and Facetime are forced to add backdoors, because they are the most popular, and some terrorists/criminals won’t move to alternate platforms. This is false. People with good intentions, who are unfairly targeted by a police state, the ones where police abuse is rampant, are the ones who use the backdoored products. Those with bad intentions, who know they are guilty, will move to the safe products. Indeed, Telegram is already popular among terrorists because they believe American services are already all backdoored. 
Rosenstein is essentially demanding the innocent get backdoored while the guilty don’t. This seems backwards. This is backwards.

Apple is morally weak

The reason I’m writing this post is because Rosenstein makes a few claims that cannot be ignored. One of them is how he describes Apple’s response to government insistence on weakening encryption doing the opposite, strengthening encryption. He reasons this happens because:

Of course they [Apple] do. They are in the business of selling products and making money. 

We [the DoJ] use a different measure of success. We are in the business of preventing crime and saving lives. 

He swells in importance. His condescending tone ennobles himself while debasing others. But this isn’t how things work. He’s not some white knight above the peasantry, protecting us. He’s a beat cop, a civil servant, who serves us.

A better phrasing would have been:

They are in the business of giving customers what they want.

We are in the business of giving voters what they want.

Both sides are doing the same, giving people what they want. Yes, voters want safety, but they also want privacy. Rosenstein imagines that he’s free to ignore our demands for privacy as long has he’s fulfilling his duty to protect us. He has explicitly rejected what people want, “we use a different measure of success”. He imagines it’s his job to tell us where the balance between privacy and safety lies. That’s not his job, that’s our job. We, the people (and our representatives), make that decision, and it’s his job is to do what he’s told. His measure of success is how well he fulfills our wishes, not how well he satisfies his imagined criteria.

That’s why those of us on this side of the debate doubt the good intentions of those like Rosenstein. He criticizes Apple for wanting to protect our rights/freedoms, and declare they measure success differently.

They are willing to be vile

Rosenstein makes this argument:

Companies are willing to make accommodations when required by the government. Recent media reports suggest that a major American technology company developed a tool to suppress online posts in certain geographic areas in order to embrace a foreign government’s censorship policies. 

Let me translate this for you:

Companies are willing to acquiesce to vile requests made by police-states. Therefore, they should acquiesce to our vile police-state requests.

It’s Rosenstein who is admitting here is that his requests are those of a police-state.

Constitutional Rights

Rosenstein says:

There is no constitutional right to sell warrant-proof encryption.

Maybe. It’s something the courts will have to decide. There are many 1st, 2nd, 3rd, 4th, and 5th Amendment issues here.
The reason we have the Bill of Rights is because of the abuses of the British Government. For example, they quartered troops in our homes, as a way of punishing us, and as a way of forcing us to help in our own oppression. The troops weren’t there to defend us against the French, but to defend us against ourselves, to shoot us if we got out of line.

And that’s what crypto backdoors do. We are forced to be agents of our own oppression. The principles enumerated by Rosenstein apply to a wide range of even additional surveillance. With little change to his speech, it can equally argue why the constant TV video surveillance from 1984 should be made law.

Let’s go back and look at Apple. It is not some base company exploiting consumers for profit. Apple doesn’t have guns, they cannot make people buy their product. If Apple doesn’t provide customers what they want, then customers vote with their feet, and go buy an Android phone. Apple isn’t providing encryption/security in order to make a profit — it’s giving customers what they want in order to stay in business.
Conversely, if we citizens don’t like what the government does, tough luck, they’ve got the guns to enforce their edicts. We can’t easily vote with our feet and walk to another country. A “democracy” is far less democratic than capitalism. Apple is a minority, selling phones to 45% of the population, and that’s fine, the minority get the phones they want. In a Democracy, where citizens vote on the issue, those 45% are screwed, as the 55% impose their will unwanted onto the remainder.

That’s why we have the Bill of Rights, to protect the 49% against abuse by the 51%. Regardless whether the Supreme Court agrees the current Constitution, it is the sort right that might exist regardless of what the Constitution says. 

Obliged to speak the truth

Here is the another part of his speech that I feel cannot be ignored. We have to discuss this:

Those of us who swear to protect the rule of law have a different motivation.  We are obliged to speak the truth.

The truth is that “going dark” threatens to disable law enforcement and enable criminals and terrorists to operate with impunity.

This is not true. Sure, he’s obliged to say the absolute truth, in court. He’s also obliged to be truthful in general about facts in his personal life, such as not lying on his tax return (the sort of thing that can get lawyers disbarred).

But he’s not obliged to tell his spouse his honest opinion whether that new outfit makes them look fat. Likewise, Rosenstein knows his opinion on public policy doesn’t fall into this category. He can say with impunity that either global warming doesn’t exist, or that it’ll cause a biblical deluge within 5 years. Both are factually untrue, but it’s not going to get him fired.

And this particular claim is also exaggerated bunk. While everyone agrees encryption makes law enforcement’s job harder than with backdoors, nobody honestly believes it can “disable” law enforcement. While everyone agrees that encryption helps terrorists, nobody believes it can enable them to act with “impunity”.

I feel bad here. It’s a terrible thing to question your opponent’s character this way. But Rosenstein made this unavoidable when he clearly, with no ambiguity, put his integrity as Deputy Attorney General on the line behind the statement that “going dark threatens to disable law enforcement and enable criminals and terrorists to operate with impunity”. I feel it’s a bald face lie, but you don’t need to take my word for it. Read his own words yourself and judge his integrity.

Conclusion

Rosenstein’s speech includes repeated references to ideas like “oath”, “honor”, and “duty”. It reminds me of Col. Jessup’s speech in the movie “A Few Good Men”.

If you’ll recall, it was rousing speech, “you want me on that wall” and “you use words like honor as a punchline”. Of course, since he was violating his oath and sending two privates to death row in order to avoid being held accountable, it was Jessup himself who was crapping on the concepts of “honor”, “oath”, and “duty”.

And so is Rosenstein. He imagines himself on that wall, doing albeit terrible things, justified by his duty to protect citizens. He imagines that it’s he who is honorable, while the rest of us not, even has he utters bald faced lies to further his own power and authority.

We activists oppose crypto backdoors not because we lack honor, or because we are criminals, or because we support terrorists and child molesters. It’s because we value privacy and government officials who get corrupted by power. It’s not that we fear Trump becoming a dictator, it’s that we fear bureaucrats at Rosenstein’s level becoming drunk on authority — which Rosenstein demonstrably has. His speech is a long train of corrupt ideas pursuing the same object of despotism — a despotism we oppose.

In other words, we oppose crypto backdoors because it’s not a tool of law enforcement, but a tool of despotism.

Sweden Supreme Court: Don’t Presume Prison Sentences For Pirates

Post Syndicated from Andy original https://torrentfreak.com/sweden-supreme-court-dont-presume-prison-sentences-for-pirates-171010/

The trend over the past several years is for prosecutors to present copyright infringement offenses as serious crimes, often tantamount to those involving theft of physical goods.

This has resulted in many cases across the United States and Europe where those accused of distributing or assisting in the distribution of copyrighted content face the possibility of custodial sentences. Over in Sweden, prosecutors have homed in on one historical case in order to see where the boundaries lie.

Originally launched as Swepirate, ‘Biosalongen‘ (Screening Room) was shut down by local authorities in early 2013. A 50-year-old man said to have been the main administrator of the private tracker was arrested and charged with sharing at least 125 TV shows and movies via the site, including Rocky, Alien and Star Trek.

After the man initially pleaded not guilty, the case went to trial and a subsequent appeal. In the summer of 2015 the Court of Appeal in Gothenburg sentenced him to eight months in prison for copyright infringement offenses.

The former administrator, referenced in court papers as ‘BH’, felt that the punishment was too harsh, filing a claim with the Supreme Court in an effort to have the sentence dismissed.

Prosecutor My Hedström also wanted the Supreme Court to hear the case, seeking clarity on sentencing for these kinds of offenses. Are fines and suspended sentences appropriate or is imprisonment the way to deal with pirates, as most copyright holders demand?

The Supreme Court has now handed down its decision, upholding an earlier ruling of probation and clarifying that copyright infringement is not an offense where a custodial sentence should be presumed.

“Whether a crime should be punished by imprisonment is generally determined based on its penal value,” a summary from International Law Office reads.

“If the penal value is less than one year, imprisonment should be a last resort. However, certain crimes are considered of such a nature that the penalty should be a prison sentence based on general preventive grounds, even if the penal value is less than one year.”

In the Swepirate/Biosalongen/Screening Room case, the Court of Appeal found that BH’s copyright infringement had a penal value of six months, so there was no presumption for a custodial sentence based on the penal value alone.

Furthermore, the Supreme Court found that there are no legislative indications that copyright infringement should be penalized via a term of imprisonment. In reaching this decision the Court referenced a previous trademark case, noting that trademark
infringement and copyright infringement are similar offenses.

In the trademark case, it was found that there should be no presumption of imprisonment. The Court found that since it is a closely related crime, copyright infringement offenses should be treated in the same manner.

According to an analysis of the ruling by Henrik Wistam and Siri Alvsing at the Lindahl lawfirm, the decision by the Supreme Court represents a change from previous case law concerning penalties for illegal file-sharing.

The pair highlight the now-infamous case of The Pirate Bay, where three defendants – Peter Sunde, Fredrik Neij and Carl Lundström – were sentenced to prison terms of eight, ten and four months respectively.

“In 2010 the Svea Court of Appeal concluded that the penalty for such crimes should be imprisonment. The Supreme Court did not grant leave to appeal,” they note.

“The Supreme Court has now aligned the view on the severity of IP infringements. This is a welcome development, although rights holders may have benefited from a stricter view and a development in the opposite direction.

The full ruling is available here (pdf, Swedish)

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

PureVPN Logs Helped FBI Net Alleged Cyberstalker

Post Syndicated from Andy original https://torrentfreak.com/purevpn-logs-helped-fbi-net-alleged-cyberstalker-171009/

Last Thursday, Ryan S. Lin, 24, of Newton, Massachusetts, was arrested on suspicion of conducting “an extensive cyberstalking campaign” against his former roommate, a 24-year-old Massachusetts woman, as well as her family members and friends.

According to the Department of Justice, Lin’s “multi-faceted campaign of computer hacking and cyberstalking” began in April 2016 when he began hacking into the victim’s online accounts, obtaining personal photographs, sensitive information about her medical and sexual histories, and other private details.

It’s alleged that after obtaining the above material, Lin distributed it to hundreds of others. It’s claimed he created fake online profiles showing the victim’s home address while soliciting sexual activity. This caused men to show up at her home.

“Mr. Lin allegedly carried out a relentless cyber stalking campaign against a young woman in a chilling effort to violate her privacy and threaten those around her,” said Acting United States Attorney William D. Weinreb.

“While using anonymizing services and other online tools to avoid attribution, Mr. Lin harassed the victim, her family, friends, co-workers and roommates, and then targeted local schools and institutions in her community. Mr. Lin will now face the consequences of his crimes.”

While Lin awaits his ultimate fate (he appeared in U.S. District Court in Boston Friday), the allegation he used anonymization tools to hide himself online but still managed to get caught raises a number of questions. An affidavit submitted by Special Agent Jeffrey Williams in support of the criminal complaint against Lin provides most of the answers.

Describing Lin’s actions against the victim as “doxing”, Williams begins by noting that while Lin was the initial aggressor, the fact he made the information so widely available raises the possibility that other people got involved with malicious acts later on. Nevertheless, Lin remains the investigation’s prime suspect.

According to the affidavit, Lin is computer savvy having majored in computer science. He allegedly utilized a number of methods to hide his identity and IP address, including TOR, Virtual Private Network (VPN) services and email providers that “do not maintain logs or other records.”

But if that genuinely is the case, how was Lin caught?

First up, it’s worth noting that plenty of Lin’s aggressive and stalking behaviors towards the victim were demonstrated in a physical sense, offline. In that respect, it appears the authorities already had him as the prime suspect and worked back from there.

In one instance, the FBI examined a computer that had been used by Lin at a former workplace. Although Windows had been reinstalled, the FBI managed to find Google Chrome data which indicated Lin had viewed articles about bomb threats he allegedly made. They were also able to determine he’d accessed the victim’s Gmail account and additional data suggested that he’d used a VPN service.

“Artifacts indicated that PureVPN, a VPN service that was used repeatedly in the cyberstalking scheme, was installed on the computer,” the affidavit reads.

From here the Special Agent’s report reveals that the FBI received cooperation from Hong Kong-based PureVPN.

“Significantly, PureVPN was able to determine that their service was accessed by the same customer from two originating IP addresses: the RCN IP address from the home Lin was living in at the time, and the software company where Lin was employed at the time,” the agent’s affidavit reads.

Needless to say, while this information will prove useful to the FBI’s prosecution of Lin, it’s also likely to turn into a huge headache for the VPN provider. The company claims zero-logging, which clearly isn’t the case.

“PureVPN operates a self-managed VPN network that currently stands at 750+ Servers in 141 Countries. But is this enough to ensure complete security?” the company’s marketing statement reads.

“That’s why PureVPN has launched advanced features to add proactive, preventive and complete security. There are no third-parties involved and NO logs of your activities.”

PureVPN privacy graphic

However, if one drills down into the PureVPN privacy policy proper, one sees the following:

Our servers automatically record the time at which you connect to any of our servers. From here on forward, we do not keep any records of anything that could associate any specific activity to a specific user. The time when a successful connection is made with our servers is counted as a ‘connection’ and the total bandwidth used during this connection is called ‘bandwidth’. Connection and bandwidth are kept in record to maintain the quality of our service. This helps us understand the flow of traffic to specific servers so we could optimize them better.

This seems to match what the FBI says – almost. While it says it doesn’t log, PureVPN admits to keeping records of when a user connects to the service and for how long. The FBI clearly states that the service also captures the user’s IP address too. In fact, it appears that PureVPN also logged the IP address belonging to another VPN service (WANSecurity) that was allegedly used by Lin to connect to PureVPN.

That record also helped to complete another circle of evidence. IP addresses used by
Kansas-based WANSecurity and Secure Internet LLC (servers operated by PureVPN) were allegedly used to access Gmail accounts known to be under Lin’s control.

Somewhat ironically, this summer Lin took to Twitter to criticize VPN provider IPVanish (which is not involved in the case) over its no-logging claims.

“There is no such thing as a VPN that doesn’t keep logs,” Lin said. “If they can limit your connections or track bandwidth usage, they keep logs.”

Or, in the case of PureVPN, if they log a connection time and a source IP address, that could be enough to raise the suspicions of the FBI and boost what already appears to be a pretty strong case.

If convicted, Lin faces up to five years in prison and three years of supervised release.

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

Weekly roundup: Slow start

Post Syndicated from Eevee original https://eev.ee/dev/2017/10/08/weekly-roundup-slow-start/

Getting back up to speed, finishing getting my computer back how it was, etc. Also we got a SNES Classic and Stardew Valley so, those have been things. But between all that, I somehow found time to do a microscopic amount of actual work!

  • art: Sketched some stuff! It wasn’t very good. Need to do this more often.

  • fox flux: Finally, after a great many attempts, I drew a pixel art bush I’m fairly happy with. And yet, I can already see ways to improve it! But hey I’m learning stuff and that’s really cool. I’ve been working on a much larger pixel art forest background, too, which is proving a little harder to figure out.

  • blog: After a long period of silence, I wrote about how JavaScript has gotten a bit better lately. More words to come, probably!

I’ve got some high aspirations for the month, so I’m gonna get to it and definitely not go visit my video game chickens.

JavaScript got better while I wasn’t looking

Post Syndicated from Eevee original https://eev.ee/blog/2017/10/07/javascript-got-better-while-i-wasnt-looking/

IndustrialRobot has generously donated in order to inquire:

In the last few years there seems to have been a lot of activity with adding emojis to Unicode. Has there been an equal effort to add ‘real’ languages/glyph systems/etc?

And as always, if you don’t have anything to say on that topic, feel free to choose your own. :p

Yes.

I mean, each release of Unicode lists major new additions right at the top — Unicode 10, Unicode 9, Unicode 8, etc. They also keep fastidious notes, so you can also dig into how and why these new scripts came from, by reading e.g. the proposal for the addition of Zanabazar Square. I don’t think I have much to add here; I’m not a real linguist, I only play one on TV.

So with that out of the way, here’s something completely different!

A brief history of JavaScript

JavaScript was created in seven days, about eight thousand years ago. It was pretty rough, and it stayed rough for most of its life. But that was fine, because no one used it for anything besides having a trail of sparkles follow your mouse on their Xanga profile.

Then people discovered you could actually do a handful of useful things with JavaScript, and it saw a sharp uptick in usage. Alas, it stayed pretty rough. So we came up with polyfills and jQuerys and all kinds of miscellaneous things that tried to smooth over the rough parts, to varying degrees of success.

And… that’s it. That’s pretty much how things stayed for a while.


I have complicated feelings about JavaScript. I don’t hate it… but I certainly don’t enjoy it, either. It has some pretty neat ideas, like prototypical inheritance and “everything is a value”, but it buries them under a pile of annoying quirks and a woefully inadequate standard library. The DOM APIs don’t make things much better — they seem to be designed as though the target language were Java, rarely taking advantage of any interesting JavaScript features. And the places where the APIs overlap with the language are a hilarious mess: I have to check documentation every single time I use any API that returns a set of things, because there are at least three totally different conventions for handling that and I can’t keep them straight.

The funny thing is that I’ve been fairly happy to work with Lua, even though it shares most of the same obvious quirks as JavaScript. Both languages are weakly typed; both treat nonexistent variables and keys as simply false values, rather than errors; both have a single data structure that doubles as both a list and a map; both use 64-bit floating-point as their only numeric type (though Lua added integers very recently); both lack a standard object model; both have very tiny standard libraries. Hell, Lua doesn’t even have exceptions, not really — you have to fake them in much the same style as Perl.

And yet none of this bothers me nearly as much in Lua. The differences between the languages are very subtle, but combined they make a huge impact.

  • Lua has separate operators for addition and concatenation, so + is never ambiguous. It also has printf-style string formatting in the standard library.

  • Lua’s method calls are syntactic sugar: foo:bar() just means foo.bar(foo). Lua doesn’t even have a special this or self value; the invocant just becomes the first argument. In contrast, JavaScript invokes some hand-waved magic to set its contextual this variable, which has led to no end of confusion.

  • Lua has an iteration protocol, as well as built-in iterators for dealing with list-style or map-style data. JavaScript has a special dedicated Array type and clumsy built-in iteration syntax.

  • Lua has operator overloading and (surprisingly flexible) module importing.

  • Lua allows the keys of a map to be any value (though non-scalars are always compared by identity). JavaScript implicitly converts keys to strings — and since there’s no operator overloading, there’s no way to natively fix this.

These are fairly minor differences, in the grand scheme of language design. And almost every feature in Lua is implemented in a ridiculously simple way; in fact the entire language is described in complete detail in a single web page. So writing JavaScript is always frustrating for me: the language is so close to being much more ergonomic, and yet, it isn’t.

Or, so I thought. As it turns out, while I’ve been off doing other stuff for a few years, browser vendors have been implementing all this pie-in-the-sky stuff from “ES5” and “ES6”, whatever those are. People even upgrade their browsers now. Lo and behold, the last time I went to write JavaScript, I found out that a number of papercuts had actually been solved, and the solutions were sufficiently widely available that I could actually use them in web code.

The weird thing is that I do hear a lot about JavaScript, but the feature I’ve seen raved the most about by far is probably… built-in types for working with arrays of bytes? That’s cool and all, but not exactly the most pressing concern for me.

Anyway, if you also haven’t been keeping tabs on the world of JavaScript, here are some things we missed.

let

MDN docs — supported in Firefox 44, Chrome 41, IE 11, Safari 10

I’m pretty sure I first saw let over a decade ago. Firefox has supported it for ages, but you actually had to opt in by specifying JavaScript version 1.7. Remember JavaScript versions? You know, from back in the days when people actually suggested you write stuff like this:

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<SCRIPT LANGUAGE="JavaScript1.2" TYPE="text/javascript">

Yikes.

Anyway, so, let declares a variable — but scoped to the immediately containing block, unlike var, which scopes to the innermost function. The trouble with var was that it was very easy to make misleading:

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// foo exists here
while (true) {
    var foo = ...;
    ...
}
// foo exists here too

If you reused the same temporary variable name in a different block, or if you expected to be shadowing an outer foo, or if you were trying to do something with creating closures in a loop, this would cause you some trouble.

But no more, because let actually scopes the way it looks like it should, the way variable declarations do in C and friends. As an added bonus, if you refer to a variable declared with let outside of where it’s valid, you’ll get a ReferenceError instead of a silent undefined value. Hooray!

There’s one other interesting quirk to let that I can’t find explicitly documented. Consider:

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let closures = [];
for (let i = 0; i < 4; i++) {
    closures.push(function() { console.log(i); });
}
for (let j = 0; j < closures.length; j++) {
    closures[j]();
}

If this code had used var i, then it would print 4 four times, because the function-scoped var i means each closure is sharing the same i, whose final value is 4. With let, the output is 0 1 2 3, as you might expect, because each run through the loop gets its own i.

But wait, hang on.

The semantics of a C-style for are that the first expression is only evaluated once, at the very beginning. So there’s only one let i. In fact, it makes no sense for each run through the loop to have a distinct i, because the whole idea of the loop is to modify i each time with i++.

I assume this is simply a special case, since it’s what everyone expects. We expect it so much that I can’t find anyone pointing out that the usual explanation for why it works makes no sense. It has the interesting side effect that for no longer de-sugars perfectly to a while, since this will print all 4s:

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closures = [];
let i = 0;
while (i < 4) {
    closures.push(function() { console.log(i); });
    i++;
}
for (let j = 0; j < closures.length; j++) {
    closures[j]();
}

This isn’t a problem — I’m glad let works this way! — it just stands out to me as interesting. Lua doesn’t need a special case here, since it uses an iterator protocol that produces values rather than mutating a visible state variable, so there’s no problem with having the loop variable be truly distinct on each run through the loop.

Classes

MDN docs — supported in Firefox 45, Chrome 42, Safari 9, Edge 13

Prototypical inheritance is pretty cool. The way JavaScript presents it is a little bit opaque, unfortunately, which seems to confuse a lot of people. JavaScript gives you enough functionality to make it work, and even makes it sound like a first-class feature with a property outright called prototype… but to actually use it, you have to do a bunch of weird stuff that doesn’t much look like constructing an object or type.

The funny thing is, people with almost any background get along with Python just fine, and Python uses prototypical inheritance! Nobody ever seems to notice this, because Python tucks it neatly behind a class block that works enough like a Java-style class. (Python also handles inheritance without using the prototype, so it’s a little different… but I digress. Maybe in another post.)

The point is, there’s nothing fundamentally wrong with how JavaScript handles objects; the ergonomics are just terrible.

Lo! They finally added a class keyword. Or, rather, they finally made the class keyword do something; it’s been reserved this entire time.

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class Vector {
    constructor(x, y) {
        this.x = x;
        this.y = y;
    }

    get magnitude() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    }

    dot(other) {
        return this.x * other.x + this.y * other.y;
    }
}

This is all just sugar for existing features: creating a Vector function to act as the constructor, assigning a function to Vector.prototype.dot, and whatever it is you do to make a property. (Oh, there are properties. I’ll get to that in a bit.)

The class block can be used as an expression, with or without a name. It also supports prototypical inheritance with an extends clause and has a super pseudo-value for superclass calls.

It’s a little weird that the inside of the class block has its own special syntax, with function omitted and whatnot, but honestly you’d have a hard time making a class block without special syntax.

One severe omission here is that you can’t declare values inside the block, i.e. you can’t just drop a bar = 3; in there if you want all your objects to share a default attribute. The workaround is to just do this.bar = 3; inside the constructor, but I find that unsatisfying, since it defeats half the point of using prototypes.

Properties

MDN docs — supported in Firefox 4, Chrome 5, IE 9, Safari 5.1

JavaScript historically didn’t have a way to intercept attribute access, which is a travesty. And by “intercept attribute access”, I mean that you couldn’t design a value foo such that evaluating foo.bar runs some code you wrote.

Exciting news: now it does. Or, rather, you can intercept specific attributes, like in the class example above. The above magnitude definition is equivalent to:

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Object.defineProperty(Vector.prototype, 'magnitude', {
    configurable: true,
    enumerable: true,
    get: function() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    },
});

Beautiful.

And what even are these configurable and enumerable things? It seems that every single key on every single object now has its own set of three Boolean twiddles:

  • configurable means the property itself can be reconfigured with another call to Object.defineProperty.
  • enumerable means the property appears in for..in or Object.keys().
  • writable means the property value can be changed, which only applies to properties with real values rather than accessor functions.

The incredibly wild thing is that for properties defined by Object.defineProperty, configurable and enumerable default to false, meaning that by default accessor properties are immutable and invisible. Super weird.

Nice to have, though. And luckily, it turns out the same syntax as in class also works in object literals.

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Vector.prototype = {
    get magnitude() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    },
    ...
};

Alas, I’m not aware of a way to intercept arbitrary attribute access.

Another feature along the same lines is Object.seal(), which marks all of an object’s properties as non-configurable and prevents any new properties from being added to the object. The object is still mutable, but its “shape” can’t be changed. And of course you can just make the object completely immutable if you want, via setting all its properties non-writable, or just using Object.freeze().

I have mixed feelings about the ability to irrevocably change something about a dynamic runtime. It would certainly solve some gripes of former Haskell-minded colleagues, and I don’t have any compelling argument against it, but it feels like it violates some unwritten contract about dynamic languages — surely any structural change made by user code should also be able to be undone by user code?

Slurpy arguments

MDN docs — supported in Firefox 15, Chrome 47, Edge 12, Safari 10

Officially this feature is called “rest parameters”, but that’s a terrible name, no one cares about “arguments” vs “parameters”, and “slurpy” is a good word. Bless you, Perl.

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function foo(a, b, ...args) {
    // ...
}

Now you can call foo with as many arguments as you want, and every argument after the second will be collected in args as a regular array.

You can also do the reverse with the spread operator:

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let args = [];
args.push(1);
args.push(2);
args.push(3);
foo(...args);

It even works in array literals, even multiple times:

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let args2 = [...args, ...args];
console.log(args2);  // [1, 2, 3, 1, 2, 3]

Apparently there’s also a proposal for allowing the same thing with objects inside object literals.

Default arguments

MDN docs — supported in Firefox 15, Chrome 49, Edge 14, Safari 10

Yes, arguments can have defaults now. It’s more like Sass than Python — default expressions are evaluated once per call, and later default expressions can refer to earlier arguments. I don’t know how I feel about that but whatever.

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function foo(n = 1, m = n + 1, list = []) {
    ...
}

Also, unlike Python, you can have an argument with a default and follow it with an argument without a default, since the default default (!) is and always has been defined as undefined. Er, let me just write it out.

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function bar(a = 5, b) {
    ...
}

Arrow functions

MDN docs — supported in Firefox 22, Chrome 45, Edge 12, Safari 10

Perhaps the most humble improvement is the arrow function. It’s a slightly shorter way to write an anonymous function.

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(a, b, c) => { ... }
a => { ... }
() => { ... }

An arrow function does not set this or some other magical values, so you can safely use an arrow function as a quick closure inside a method without having to rebind this. Hooray!

Otherwise, arrow functions act pretty much like regular functions; you can even use all the features of regular function signatures.

Arrow functions are particularly nice in combination with all the combinator-style array functions that were added a while ago, like Array.forEach.

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[7, 8, 9].forEach(value => {
    console.log(value);
});

Symbol

MDN docs — supported in Firefox 36, Chrome 38, Edge 12, Safari 9

This isn’t quite what I’d call an exciting feature, but it’s necessary for explaining the next one. It’s actually… extremely weird.

symbol is a new kind of primitive (like number and string), not an object (like, er, Number and String). A symbol is created with Symbol('foo'). No, not new Symbol('foo'); that throws a TypeError, for, uh, some reason.

The only point of a symbol is as a unique key. You see, symbols have one very special property: they can be used as object keys, and will not be stringified. Remember, only strings can be keys in JavaScript — even the indices of an array are, semantically speaking, still strings. Symbols are a new exception to this rule.

Also, like other objects, two symbols don’t compare equal to each other: Symbol('foo') != Symbol('foo').

The result is that symbols solve one of the problems that plauges most object systems, something I’ve talked about before: interfaces. Since an interface might be implemented by any arbitrary type, and any arbitrary type might want to implement any number of arbitrary interfaces, all the method names on an interface are effectively part of a single global namespace.

I think I need to take a moment to justify that. If you have IFoo and IBar, both with a method called method, and you want to implement both on the same type… you have a problem. Because most object systems consider “interface” to mean “I have a method called method, with no way to say which interface’s method you mean. This is a hard problem to avoid, because IFoo and IBar might not even come from the same library. Occasionally languages offer a clumsy way to “rename” one method or the other, but the most common approach seems to be for interface designers to avoid names that sound “too common”. You end up with redundant mouthfuls like IFoo.foo_method.

This incredibly sucks, and the only languages I’m aware of that avoid the problem are the ML family and Rust. In Rust, you define all the methods for a particular trait (interface) in a separate block, away from the type’s “own” methods. It’s pretty slick. You can still do obj.method(), and as long as there’s only one method among all the available traits, you’ll get that one. If not, there’s syntax for explicitly saying which trait you mean, which I can’t remember because I’ve never had to use it.

Symbols are JavaScript’s answer to this problem. If you want to define some interface, you can name its methods with symbols, which are guaranteed to be unique. You just have to make sure you keep the symbol around somewhere accessible so other people can actually use it. (Or… not?)

The interesting thing is that JavaScript now has several of its own symbols built in, allowing user objects to implement features that were previously reserved for built-in types. For example, you can use the Symbol.hasInstance symbol — which is simply where the language is storing an existing symbol and is not the same as Symbol('hasInstance')! — to override instanceof:

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// oh my god don't do this though
class EvenNumber {
    static [Symbol.hasInstance](obj) {
        return obj % 2 == 0;
    }
}
console.log(2 instanceof EvenNumber);  // true
console.log(3 instanceof EvenNumber);  // false

Oh, and those brackets around Symbol.hasInstance are a sort of reverse-quoting — they indicate an expression to use where the language would normally expect a literal identifier. I think they work as object keys, too, and maybe some other places.

The equivalent in Python is to implement a method called __instancecheck__, a name which is not special in any way except that Python has reserved all method names of the form __foo__. That’s great for Python, but doesn’t really help user code. JavaScript has actually outclassed (ho ho) Python here.

Of course, obj[BobNamespace.some_method]() is not the prettiest way to call an interface method, so it’s not perfect. I imagine this would be best implemented in user code by exposing a polymorphic function, similar to how Python’s len(obj) pretty much just calls obj.__len__().

I only bring this up because it’s the plumbing behind one of the most incredible things in JavaScript that I didn’t even know about until I started writing this post. I’m so excited oh my gosh. Are you ready? It’s:

Iteration protocol

MDN docs — supported in Firefox 27, Chrome 39, Safari 10; still experimental in Edge

Yes! Amazing! JavaScript has first-class support for iteration! I can’t even believe this.

It works pretty much how you’d expect, or at least, how I’d expect. You give your object a method called Symbol.iterator, and that returns an iterator.

What’s an iterator? It’s an object with a next() method that returns the next value and whether the iterator is exhausted.

Wait, wait, wait a second. Hang on. The method is called next? Really? You didn’t go for Symbol.next? Python 2 did exactly the same thing, then realized its mistake and changed it to __next__ in Python 3. Why did you do this?

Well, anyway. My go-to test of an iterator protocol is how hard it is to write an equivalent to Python’s enumerate(), which takes a list and iterates over its values and their indices. In Python it looks like this:

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for i, value in enumerate(['one', 'two', 'three']):
    print(i, value)
# 0 one
# 1 two
# 2 three

It’s super nice to have, and I’m always amazed when languages with “strong” “support” for iteration don’t have it. Like, C# doesn’t. So if you want to iterate over a list but also need indices, you need to fall back to a C-style for loop. And if you want to iterate over a lazy or arbitrary iterable but also need indices, you need to track it yourself with a counter. Ridiculous.

Here’s my attempt at building it in JavaScript.

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function enumerate(iterable) {
    // Return a new iter*able* object with a Symbol.iterator method that
    // returns an iterator.
    return {
        [Symbol.iterator]: function() {
            let iterator = iterable[Symbol.iterator]();
            let i = 0;

            return {
                next: function() {
                    let nextval = iterator.next();
                    if (! nextval.done) {
                        nextval.value = [i, nextval.value];
                        i++;
                    }
                    return nextval;
                },
            };
        },
    };
}
for (let [i, value] of enumerate(['one', 'two', 'three'])) {
    console.log(i, value);
}
// 0 one
// 1 two
// 2 three

Incidentally, for..of (which iterates over a sequence, unlike for..in which iterates over keys — obviously) is finally supported in Edge 12. Hallelujah.

Oh, and let [i, value] is destructuring assignment, which is also a thing now and works with objects as well. You can even use the splat operator with it! Like Python! (And you can use it in function signatures! Like Python! Wait, no, Python decided that was terrible and removed it in 3…)

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let [x, y, ...others] = ['apple', 'orange', 'cherry', 'banana'];

It’s a Halloween miracle. 🎃

Generators

MDN docs — supported in Firefox 26, Chrome 39, Edge 13, Safari 10

That’s right, JavaScript has goddamn generators now. It’s basically just copying Python and adding a lot of superfluous punctuation everywhere. Not that I’m complaining.

Also, generators are themselves iterable, so I’m going to cut to the chase and rewrite my enumerate() with a generator.

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function enumerate(iterable) {
    return {
        [Symbol.iterator]: function*() {
            let i = 0;
            for (let value of iterable) {
                yield [i, value];
                i++;
            }
        },
    };
}
for (let [i, value] of enumerate(['one', 'two', 'three'])) {
    console.log(i, value);
}
// 0 one
// 1 two
// 2 three

Amazing. function* is a pretty strange choice of syntax, but whatever? I guess it also lets them make yield only act as a keyword inside a generator, for ultimate backwards compatibility.

JavaScript generators support everything Python generators do: yield* yields every item from a subsequence, like Python’s yield from; generators can return final values; you can pass values back into the generator if you iterate it by hand. No, really, I wasn’t kidding, it’s basically just copying Python. It’s great. You could now built asyncio in JavaScript!

In fact, they did that! JavaScript now has async and await. An async function returns a Promise, which is also a built-in type now. Amazing.

Sets and maps

MDN docs for MapMDN docs for Set — supported in Firefox 13, Chrome 38, IE 11, Safari 7.1

I did not save the best for last. This is much less exciting than generators. But still exciting.

The only data structure in JavaScript is the object, a map where the strings are keys. (Or now, also symbols, I guess.) That means you can’t readily use custom values as keys, nor simulate a set of arbitrary objects. And you have to worry about people mucking with Object.prototype, yikes.

But now, there’s Map and Set! Wow.

Unfortunately, because JavaScript, Map couldn’t use the indexing operators without losing the ability to have methods, so you have to use a boring old method-based API. But Map has convenient methods that plain objects don’t, like entries() to iterate over pairs of keys and values. In fact, you can use a map with for..of to get key/value pairs. So that’s nice.

Perhaps more interesting, there’s also now a WeakMap and WeakSet, where the keys are weak references. I don’t think JavaScript had any way to do weak references before this, so that’s pretty slick. There’s no obvious way to hold a weak value, but I guess you could substitute a WeakSet with only one item.

Template literals

MDN docs — supported in Firefox 34, Chrome 41, Edge 12, Safari 9

Template literals are JavaScript’s answer to string interpolation, which has historically been a huge pain in the ass because it doesn’t even have string formatting in the standard library.

They’re just strings delimited by backticks instead of quotes. They can span multiple lines and contain expressions.

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console.log(`one plus
two is ${1 + 2}`);

Someone decided it would be a good idea to allow nesting more sets of backticks inside a ${} expression, so, good luck to syntax highlighters.

However, someone also had the most incredible idea ever, which was to add syntax allowing user code to do the interpolation — so you can do custom escaping, when absolutely necessary, which is virtually never, because “escaping” means you’re building a structured format by slopping strings together willy-nilly instead of using some API that works with the structure.

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// OF COURSE, YOU SHOULDN'T BE DOING THIS ANYWAY; YOU SHOULD BUILD HTML WITH
// THE DOM API AND USE .textContent FOR LITERAL TEXT.  BUT AS AN EXAMPLE:
function html(literals, ...values) {
    let ret = [];
    literals.forEach((literal, i) => {
        if (i > 0) {
            // Is there seriously still not a built-in function for doing this?
            // Well, probably because you SHOULDN'T BE DOING IT
            ret.push(values[i - 1]
                .replace(/&/g, '&amp;')
                .replace(/</g, '&lt;')
                .replace(/>/g, '&gt;')
                .replace(/"/g, '&quot;')
                .replace(/'/g, '&apos;'));
        }
        ret.push(literal);
    });
    return ret.join('');
}
let username = 'Bob<script>';
let result = html`<b>Hello, ${username}!</b>`;
console.log(result);
// <b>Hello, Bob&lt;script&gt;!</b>

It’s a shame this feature is in JavaScript, the language where you are least likely to need it.

Trailing commas

Remember how you couldn’t do this for ages, because ass-old IE considered it a syntax error and would reject the entire script?

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{
    a: 'one',
    b: 'two',
    c: 'three',  // <- THIS GUY RIGHT HERE
}

Well now it’s part of the goddamn spec and if there’s anything in this post you can rely on, it’s this. In fact you can use AS MANY GODDAMN TRAILING COMMAS AS YOU WANT. But only in arrays.

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[1, 2, 3,,,,,,,,,,,,,,,,,,,,,,,,,]

Apparently that has the bizarre side effect of reserving extra space at the end of the array, without putting values there.

And more, probably

Like strict mode, which makes a few silent “errors” be actual errors, forces you to declare variables (no implicit globals!), and forbids the completely bozotic with block.

Or String.trim(), which trims whitespace off of strings.

Or… Math.sign()? That’s new? Seriously? Well, okay.

Or the Proxy type, which lets you customize indexing and assignment and calling. Oh. I guess that is possible, though this is a pretty weird way to do it; why not just use symbol-named methods?

You can write Unicode escapes for astral plane characters in strings (or identifiers!), as \u{XXXXXXXX}.

There’s a const now? I extremely don’t care, just name it in all caps and don’t reassign it, come on.

There’s also a mountain of other minor things, which you can peruse at your leisure via MDN or the ECMAScript compatibility tables (note the links at the top, too).

That’s all I’ve got. I still wouldn’t say I’m a big fan of JavaScript, but it’s definitely making an effort to clean up some goofy inconsistencies and solve common problems. I think I could even write some without yelling on Twitter about it now.

On the other hand, if you’re still stuck supporting IE 10 for some reason… well, er, my condolences.

timeShift(GrafanaBuzz, 1w) Issue 16

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/10/06/timeshiftgrafanabuzz-1w-issue-16/

Welcome to another issue of TimeShift. In addition to the roundup of articles and plugin updates, we had a big announcement this week – Early Bird tickets to GrafanaCon EU are now available! We’re also accepting CFPs through the end of October, so if you have a topic in mind, don’t wait until the last minute, please send it our way. Speakers who are selected will receive a comped ticket to the conference.


Early Bird Tickets Now Available

We’ve released a limited number of Early Bird tickets before General Admission tickets are available. Take advantage of this discount before they’re sold out!

Get Your Early Bird Ticket Now

Interested in speaking at GrafanaCon? We’re looking for technical and non-tecnical talks of all sizes. Submit a CFP Now.


From the Blogosphere

Get insights into your Azure Cosmos DB: partition heatmaps, OMS, and More: Microsoft recently announced the ability to access a subset of Azure Cosmos DB metrics via Azure Monitor API. Grafana Labs built an Azure Monitor Plugin for Grafana 4.5 to visualize the data.

How to monitor Docker for Mac/Windows: Brian was tired of guessing about the performance of his development machines and test environment. Here, he shows how to monitor Docker with Prometheus to get a better understanding of a dev environment in his quest to monitor all the things.

Prometheus and Grafana to Monitor 10,000 servers: This article covers enokido’s process of choosing a monitoring platform. He identifies three possible solutions, outlines the pros and cons of each, and discusses why he chose Prometheus.

GitLab Monitoring: It’s fascinating to see Grafana dashboards with production data from companies around the world. For instance, we’ve previously highlighted the huge number of dashboards Wikimedia publicly shares. This week, we found that GitLab also has public dashboards to explore.

Monitoring a Docker Swarm Cluster with cAdvisor, InfluxDB and Grafana | The Laboratory: It’s important to know the state of your applications in a scalable environment such as Docker Swarm. This video covers an overview of Docker, VM’s vs. containers, orchestration and how to monitor Docker Swarm.

Introducing Telemetry: Actionable Time Series Data from Counters: Learn how to use counters from mulitple disparate sources, devices, operating systems, and applications to generate actionable time series data.

ofp_sniffer Branch 1.2 (docker/influxdb/grafana) Upcoming Features: This video demo shows off some of the upcoming features for OFP_Sniffer, an OpenFlow sniffer to help network troubleshooting in production networks.


Grafana Plugins

Plugin authors add new features and bugfixes all the time, so it’s important to always keep your plugins up to date. To update plugins from on-prem Grafana, use the Grafana-cli tool, if you are using Hosted Grafana, you can update with 1 click! If you have questions or need help, hit up our community site, where the Grafana team and members of the community are happy to help.

UPDATED PLUGIN

PNP for Nagios Data Source – The latest release for the PNP data source has some fixes and adds a mathematical factor option.

Update

UPDATED PLUGIN

Google Calendar Data Source – This week, there was a small bug fix for the Google Calendar annotations data source.

Update

UPDATED PLUGIN

BT Plugins – Our friends at BT have been busy. All of the BT plugins in our catalog received and update this week. The plugins are the Status Dot Panel, the Peak Report Panel, the Trend Box Panel and the Alarm Box Panel.

Changes include:

  • Custom dashboard links now work in Internet Explorer.
  • The Peak Report panel no longer supports click-to-sort.
  • The Status Dot panel tooltips now look like Grafana tooltips.


This week’s MVC (Most Valuable Contributor)

Each week we highlight some of the important contributions from our amazing open source community. This week, we’d like to recognize a contributor who did a lot of work to improve Prometheus support.

pdoan017
Thanks to Alin Sinpaleanfor his Prometheus PR – that aligns the step and interval parameters. Alin got a lot of feedback from the Prometheus community and spent a lot of time and energy explaining, debating and iterating before the PR was ready.
Thank you!


Grafana Labs is Hiring!

We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

Check out our Open Positions


Tweet of the Week

We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

Wow – Excited to be a part of exploring data to find out how Mexico City is evolving.

We Need Your Help!

Do you have a graph that you love because the data is beautiful or because the graph provides interesting information? Please get in touch. Tweet or send us an email with a screenshot, and we’ll tell you about this fun experiment.

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