Tag Archives: million

Google Asked to Remove 3 Billion “Pirate” Search Results

Post Syndicated from Ernesto original https://torrentfreak.com/google-asked-to-remove-3-billion-pirate-search-results-171018/

Copyright holders continue to flood Google with DMCA takedown requests, asking the company to remove “pirate links” from its search results.

In recent years the number of reported URLs has exploded, surging to unprecedented heights.

Since Google first started to report the volume of takedown requests in its Transparency Report, the company has been asked to remove more than three billion allegedly infringing search results.

The frequency at which these URLs are reported has increased over the years and at the moment roughly three million ‘pirate’ URLs are submitted per day.

The URLs are sent in by major rightsholders including members of the BPI, RIAA, and various major Hollywood studios. They target a wide variety of sites, over 1.3 million, but a few dozen ‘repeat offenders’ are causing the most trouble.

File-hosting service 4shared.com currently tops the list of most-targeted domains with 66 million URLs, followed by the now-defunct MP3 download site MP3toys.xyz and Rapidgator.net, with 51 and 28 million URLs respectively.

3 billion URLs

Interestingly, the high volume of takedown notices is used as an argument for and against the DMCA process.

While Google believes that the millions of reported URLs per day are a sign that the DMCA takedown process is working correctly, rightsholders believe the volumes are indicative of an unbeatable game of whack-a-mole.

According to some copyright holders, the takedown efforts do little to seriously combat piracy. Various industry groups have therefore asked governments and lawmakers for broad revisions.

Among other things they want advanced technologies and processes to ensure that infringing content doesn’t reappear elsewhere once it’s removed, a so-called “notice and stay down” approach. In addition, Google has often been asked to demote pirate links in search results.

UK music industry group BPI, who are responsible for more than 10% of all the takedown requests on Google, sees the new milestone as an indicator of how much effort its anti-piracy activities take.

“This 3 billion figure shows how hard the creative sector has to work to police its content online and how much time and resource this takes. The BPI is the world’s largest remover of illegal music links from Google, one third of which are on behalf of independent record labels,” Geoff Taylor, BPI’s Chief Executive, informs TF.

However, there is also some progress to report. Earlier this year BPI announced a voluntary partnership with Google and Bing to demote pirate content faster and more effectively for US visitors.

“We now have a voluntary code of practice in place in the UK, facilitated by Government, that requires Google and Bing to work together with the BPI and other creator organizations to develop lasting solutions to the problem of illegal sites gaining popularity in search listings,” Taylor notes.

According to BPI, both Google and Bing have shown that changes to their algorithms can be effective in demoting the worst pirate sites from the top search results and they hope others will follow suit.

“Other intermediaries should follow this lead and take more responsibility to work with creators to reduce the proliferation of illegal links and disrupt the ability of illegal sites to capture consumers and build black market businesses that take money away from creators.”

Agreement or not, there are still plenty of pirate links in search results, so the BPI is still sending out millions of takedown requests per month.

We asked Google for a comment on the new milestone but at the time of writing, we have yet to hear back. In any event, the issue is bound to remain a hot topic during the months and years to come.

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

What’s new in HiveMQ 3.3

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/whats-new-in-hivemq-3-3

We are pleased to announce the release of HiveMQ 3.3. This version of HiveMQ is the most advanced and user friendly version of HiveMQ ever. A broker is the heart of every MQTT deployment and it’s key to monitor and understand how healthy your system and your connected clients are. Version 3.3 of HiveMQ focuses on observability, usability and advanced administration features and introduces a brand new Web UI. This version is a drop-in replacement for HiveMQ 3.2 and of course supports rolling upgrades for zero-downtime.

HiveMQ 3.3 brings many features that your users, administrators and plugin developers are going to love. These are the highlights:

Web UI

Web UI
The new HiveMQ version has a built-in Web UI for advanced analysis and administrative tasks. A powerful dashboard shows important data about the health of the broker cluster and an overview of the whole MQTT deployment.
With the new Web UI, administrators are able to drill down to specific client information and can perform administrative actions like disconnecting a client. Advanced analytics functionality allows indetifying clients with irregular behavior. It’s easy to identify message-dropping clients as HiveMQ shows detailed statistics of such misbehaving MQTT participants.
Of course all Web UI features work at scale with more than a million connected MQTT clients. Learn more about the Web UI in the documentation.

Time To Live

TTL
HiveMQ introduces Time to Live (TTL) on various levels of the MQTT lifecycle. Automatic cleanup of expired messages is as well supported as the wiping of abandoned persistent MQTT sessions. In particular, version 3.3 implements the following TTL features:

  • MQTT client session expiration
  • Retained Message expiration
  • MQTT PUBLISH message expiration

Configuring a TTL for MQTT client sessions and retained messages allows freeing system resources without manual administrative intervention as soon as the data is not needed anymore.
Beside global configuration, MQTT PUBLISHES can have individual TTLs based on application specific characteristics. It’s a breeze to change the TTL of particular messages with the HiveMQ plugin system. As soon as a message TTL expires, the broker won’t send out the message anymore, even if the message was previously queued or in-flight. This can save precious bandwidth for mobile connections as unnecessary traffic is avoided for expired messages.

Trace Recordings

Trace Recordings
Debugging specific MQTT clients or groups of MQTT clients can be challenging at scale. HiveMQ 3.3 introduces an innovative Trace Recording mechanism that allows creating detailed recordings of all client interactions with given filters.
It’s possible to filter based on client identifiers, MQTT message types and topics. And the best of all: You can use regular expressions to select multiple MQTT clients at once as well as topics with complex structures. Getting detailed information about the behavior of specific MQTT clients for debugging complex issues was never easier.

Native SSL

Native SSL
The new native SSL integration of HiveMQ brings a performance boost of more than 40% for SSL Handshakes (in terms of CPU usage) by utilizing an integration with BoringSSL. BoringSSL is Google’s fork of OpenSSL which is also used in Google Chrome and Android. Besides the compute and huge memory optimizations (saves up to 60% Java Heap), additional secure state-of-the-art cipher suites are supported by HiveMQ which are not directly available for Java (like ChaCha20-Poly1305).
Most HiveMQ deployments on Linux systems are expected to see decreased CPU load on TLS handshakes with the native SSL integration and huge memory improvements.

New Plugin System Features

New Plugin System Features
The popular and powerful plugin system has received additional services and callbacks which are useful for many existing and future plugins.
Plugin developers can now use a ConnectionAttributeStore and a SessionAttributeStore for storing arbitrary data for the lifetime of a single MQTT connection of a client or for the whole session of a client. The new ClientGroupService allows grouping different MQTT client identifiers by the same key, so it’s easy to address multiple MQTT clients (with the same group) at once.

A new callback was introduced which notifies a plugin when a HiveMQ instance is ready, which means the instance is part of the cluster and all listeners were started successfully. Developers can now react when a MQTT client session is ready and usable in the cluster with a dedicated callback.

Some use cases require modifying a MQTT PUBLISH packet before it’s sent out to a client. This is now possible with a new callback that was introduced for modifying a PUBLISH before sending it out to a individual client.
The offline queue size for persistent clients is now also configurable for individual clients as well as the queue discard strategy.

Additional Features

Additional Features
HiveMQ 3.3 has many additional features designed for power users and professional MQTT deployments. The new version also has the following highlights:

  • OCSP Stapling
  • Event Log for MQTT client connects, disconnects and unusual events (e.g. discarded message due to slow consumption on the client side
  • Throttling of concurrent TLS handshakes
  • Connect Packet overload protection
  • Configuration of Socket send and receive buffer sizes
  • Global System Information like the HiveMQ Home folder can now be set via Environment Variables without changing the run script
  • The internal HTTP server of HiveMQ is now exposed to the holistic monitoring subsystem
  • Many additional useful metrics were exposed to HiveMQ’s monitoring subsystem

 

In order to upgrade to HiveMQ 3.3 from HiveMQ 3.2 or older versions, take a look at our Upgrade Guide.
Don’t forget to learn more about all the new features with our HiveMQ User Guide.

Download HiveMQ 3.3 now

Google Asked to Delist Pirate Movie Sites, ISPs Asked to Block Them

Post Syndicated from Andy original https://torrentfreak.com/google-asked-to-delist-pirate-movie-sites-isps-asked-to-block-them-171018/

After seizing several servers operated by popular private music tracker What.cd, last November French police went after a much bigger target.

Boasting millions of regular visitors, Zone-Telechargement (Zone-Download) was ranked the 11th most-visited website in the whole of the country. The site offered direct downloads of a wide variety of pirated content, including films, series, games, and music. Until the French Gendarmerie shut it down, that is.

After being founded in 2011 and enjoying huge growth following the 2012 raids against Megaupload, the Zone-Telechargement ‘brand’ was still popular with French users, despite the closure of the platform. It, therefore, came as no surprise that the site was quickly cloned by an unknown party and relaunched as Zone-Telechargement.ws.

The site has been doing extremely well following its makeover. To the annoyance of copyright holders, SimilarWeb reports the platform as France’s 37th most popular site with around 58 million visitors per month. That’s a huge achievement in less than 12 months.

Now, however, the site is receiving more unwanted attention. PCInpact says it has received information that several movie-focused organizations including the French National Film Center are requesting tough action against the site.

The National Federation of Film Distributors, the Video Publishing Union, the Association of Independent Producers and the Producers Union are all demanding the blocking of Zone-Telechargement by several local ISPs, alongside its delisting from search results.

The publication mentions four Internet service providers – Free, Numericable, Bouygues Telecom, and Orange – plus Google on the search engine front. At this stage, other search companies, such as Microsoft’s Bing, are not reported as part of the action.

In addition to Zone-Telechargement, several other ‘pirate’ sites (Papystreaming.org, Sokrostream.cc and Zonetelechargement.su, another site playing on the popular brand) are included in the legal process. All are described as “structurally infringing” by the complaining movie outfits, PCInpact notes.

The legal proceedings against the sites are based in Article 336-2 of the Intellectual Property Code. It’s ground already trodden by movie companies who following a 2011 complaint, achieved victory in 2013 against several Allostreaming-linked sites.

In that case, the High Court of Paris ordered ISPs, several of which appear in the current action, to “implement all appropriate means including blocking” to prevent access to the infringing sites.

The Court also ordered Google, Microsoft, and Yahoo to “take all necessary measures to prevent the occurrence on their services of any results referring to any of the sites” on their platforms.

Also of interest is that the action targets a service called DL-Protecte.com, which according to local anti-piracy agency HADOPI, makes it difficult for rightsholders to locate infringing content while at the same time generates more revenue for pirate sites.

A judgment is expected in “several months.”

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

ACME Support in Apache HTTP Server Project

Post Syndicated from ris original https://lwn.net/Articles/736668/rss

Let’s Encrypt has announced
that Automatic Certificate Management Environment (ACME) protocol support
is being integrated into the Apache HTTP Server (httpd). “ACME support being built in to one of the world’s most popular Web servers, Apache httpd, is great because it means that deploying HTTPS will be even easier for millions of websites. It’s a huge step towards delivering the ideal certificate issuance and management experience to as many people as possible.

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.

ACME Support in Apache HTTP Server Project

Post Syndicated from Let's Encrypt - Free SSL/TLS Certificates original https://letsencrypt.org//2017/10/17/acme-support-in-apache-httpd.html

We’re excited that support for getting and managing TLS certificates via the ACME protocol is coming to the Apache HTTP Server Project (httpd). ACME is the protocol used by Let’s Encrypt, and hopefully other Certificate Authorities in the future. We anticipate this feature will significantly aid the adoption of HTTPS for new and existing websites.

We created Let’s Encrypt in order to make getting and managing TLS certificates as simple as possible. For Let’s Encrypt subscribers, this usually means obtaining an ACME client and executing some simple commands. Ultimately though, we’d like for most Let’s Encrypt subscribers to have ACME clients built in to their server software so that obtaining an additional piece of software is not necessary. The less work people have to do to deploy HTTPS the better!

ACME support being built in to one of the world’s most popular Web servers, Apache httpd, is great because it means that deploying HTTPS will be even easier for millions of websites. It’s a huge step towards delivering the ideal certificate issuance and management experience to as many people as possible.

The Apache httpd ACME module is called mod_md. It’s currently in the development version of httpd and a plan is being formulated to backport it to an httpd 2.4.x stable release. The mod_md code is also available on GitHub.

It’s also worth mentioning that the development version of Apache httpd now includes support for an SSLPolicy directive. Properly configuring TLS has traditionally involved making a large number of complex choices. With the SSLPolicy directive, admins simply select a modern, intermediate, or old TLS configuration, and sensible choices will be made for them.

Development of mod_md and the SSLPolicy directive has been funded by Mozilla and carried out primarily by Stefan Eissing of greenbytes. Thank you Mozilla and Stefan!

Let’s Encrypt is currently providing certificates for more than 55 million websites. We look forward to being able to serve even more websites as efforts like this make deploying HTTPS with Let’s Encrypt even easier. If you’re as excited about the potential for a 100% HTTPS Web as we are, please consider getting involved, making a donation, or sponsoring Let’s Encrypt.

Spinrilla Wants RIAA Case Thrown Out Over ‘Lies’ About ‘Hidden’ Piracy Data

Post Syndicated from Ernesto original https://torrentfreak.com/spinrilla-wants-riaa-case-thrown-out-over-lies-about-hidden-piracy-data-171016/

Earlier this year, a group of well-known labels targeted Spinrilla, a popular hip-hop mixtape site and app which serves millions of users.

The coalition of record labels, including Sony Music, Warner Bros. Records, and Universal Music Group, filed a lawsuit against the service over alleged copyright infringements.

While the discovery process is still ongoing, Spinrilla recently informed the court that the record labels have “just about derailed” the entire case. The company has submitted a motion for sanctions, which is currently sealed, but additional information submitted to the court this week reveals what’s going on.

When the labels filed their original complaint they listed 210 tracks, without providing the allegedly infringing URLs. These weren’t shared during the early stages of the discovery process either, forcing the site to manually search for potentially infringing links.

Then, early October, Spinrilla received a massive spreadsheet with over 2,000 tracks, including the infringing URLs. This data came from the RIAA and supported the long list of infringements in the amended complaint submitted around the same time.

The spreadsheet would have made the discovery process much easier for Spinrilla. In a supplemental brief supporting a motion for sanctions, Spinrilla accuses the labels of hiding the piracy data from them and lying about it, “derailing” the case in the process.

“Significantly, Plaintiffs used that lie to convince the Court they should be allowed to add about 1,900 allegedly infringed sound recordings to their original list of 210. Later, Plaintiffs repeated that lie to convince the Court to give them time to add even more sound recordings to their list.”

vbcn

Spinrilla says they were forced to go down an expensive and unnecessary rabbit hole to find the infringing files, even though the RIAA data was available all along.

“By hiding and lying about the RIAA data, Plaintiffs forced Defendants to spend precious time and money fumbling through discovery. Not knowing that Plaintiffs had the RIAA data,” the company writes.

The hip-hop mixtape site argues that the alleged wrongdoing is severe enough to have the entire complaint dismissed, as the ultimate sanction.

“It is without exaggeration to say that by hiding the RIAA spreadsheets and that underlying data, Defendants have been severely prejudiced. The Complaint should be dismissed with prejudice and, if it is, Plaintiffs can only blame themselves,” Spinrilla concludes.

The stakes are certainly high in this case. With well over 2,000 infringing tracks listed in the amended complaint, the hip-hop mixtape site faces statutory damages as high as $300 million, at least in theory.

Spinrilla’s supplement brief in further support of the motion for sanctions is available here (pdf).

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

Millions of high-security crypto keys crippled by newly discovered flaw (Ars Technica)

Post Syndicated from jake original https://lwn.net/Articles/736520/rss

Ars Technica is reporting on a flaw in the RSA library developed by Infineon that drastically reduces the amount of work needed to discover a private key from its corresponding public key. This flaw, dubbed “ROCA”, mainly affects key pairs that have been generated on keycards. “While all keys generated with the library are much weaker than they should be, it’s not currently practical to factorize all of them. For example, 3072-bit and 4096-bit keys aren’t practically factorable. But oddly enough, the theoretically stronger, longer 4096-bit key is much weaker than the 3072-bit key and may fall within the reach of a practical (although costly) factorization if the researchers’ method improves.

To spare time and cost, attackers can first test a public key to see if it’s vulnerable to the attack. The test is inexpensive, requires less than 1 millisecond, and its creators believe it produces practically zero false positives and zero false negatives. The fingerprinting allows attackers to expend effort only on keys that are practically factorizable. The researchers have already used the method successfully to identify weak keys, and they have provided a tool here to test if a given key was generated using the faulty library. A blog post with more details is here.”

New KRACK Attack Against Wi-Fi Encryption

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

Mathy Vanhoef has just published a devastating attack against WPA2, the 14-year-old encryption protocol used by pretty much all wi-fi systems. Its an interesting attack, where the attacker forces the protocol to reuse a key. The authors call this attack KRACK, for Key Reinstallation Attacks

This is yet another of a series of marketed attacks; with a cool name, a website, and a logo. The Q&A on the website answers a lot of questions about the attack and its implications. And lots of good information in this ArsTechnica article.

There is an academic paper, too:

“Key Reinstallation Attacks: Forcing Nonce Reuse in WPA2,” by Mathy Vanhoef and Frank Piessens.

Abstract: We introduce the key reinstallation attack. This attack abuses design or implementation flaws in cryptographic protocols to reinstall an already-in-use key. This resets the key’s associated parameters such as transmit nonces and receive replay counters. Several types of cryptographic Wi-Fi handshakes are affected by the attack. All protected Wi-Fi networks use the 4-way handshake to generate a fresh session key. So far, this 14-year-old handshake has remained free from attacks, and is even proven secure. However, we show that the 4-way handshake is vulnerable to a key reinstallation attack. Here, the adversary tricks a victim into reinstalling an already-in-use key. This is achieved by manipulating and replaying handshake messages. When reinstalling the key, associated parameters such as the incremental transmit packet number (nonce) and receive packet number (replay counter) are reset to their initial value. Our key reinstallation attack also breaks the PeerKey, group key, and Fast BSS Transition (FT) handshake. The impact depends on the handshake being attacked, and the data-confidentiality protocol in use. Simplified, against AES-CCMP an adversary can replay and decrypt (but not forge) packets. This makes it possible to hijack TCP streams and inject malicious data into them. Against WPA-TKIP and GCMP the impact is catastrophic: packets can be replayed, decrypted, and forged. Because GCMP uses the same authentication key in both communication directions, it is especially affected.

Finally, we confirmed our findings in practice, and found that every Wi-Fi device is vulnerable to some variant of our attacks. Notably, our attack is exceptionally devastating against Android 6.0: it forces the client into using a predictable all-zero encryption key.

I’m just reading about this now, and will post more information
as I learn it.

EDITED TO ADD: More news.

EDITED TO ADD: This meets my definition of brilliant. The attack is blindingly obvious once it’s pointed out, but for over a decade no one noticed it.

EDITED TO ADD: Matthew Green has a blog post on what went wrong. The vulnerability is in the interaction between two protocols. At a meta level, he blames the opaque IEEE standards process:

One of the problems with IEEE is that the standards are highly complex and get made via a closed-door process of private meetings. More importantly, even after the fact, they’re hard for ordinary security researchers to access. Go ahead and google for the IETF TLS or IPSec specifications — you’ll find detailed protocol documentation at the top of your Google results. Now go try to Google for the 802.11i standards. I wish you luck.

The IEEE has been making a few small steps to ease this problem, but they’re hyper-timid incrementalist bullshit. There’s an IEEE program called GET that allows researchers to access certain standards (including 802.11) for free, but only after they’ve been public for six months — coincidentally, about the same time it takes for vendors to bake them irrevocably into their hardware and software.

This whole process is dumb and — in this specific case — probably just cost industry tens of millions of dollars. It should stop.

Nicholas Weaver explains why most people shouldn’t worry about this:

So unless your Wi-Fi password looks something like a cat’s hairball (e.g. “:SNEIufeli7rc” — which is not guessable with a few million tries by a computer), a local attacker had the capability to determine the password, decrypt all the traffic, and join the network before KRACK.

KRACK is, however, relevant for enterprise Wi-Fi networks: networks where you needed to accept a cryptographic certificate to join initially and have to provide both a username and password. KRACK represents a new vulnerability for these networks. Depending on some esoteric details, the attacker can decrypt encrypted traffic and, in some cases, inject traffic onto the network.

But in none of these cases can the attacker join the network completely. And the most significant of these attacks affects Linux devices and Android phones, they don’t affect Macs, iPhones, or Windows systems. Even when feasible, these attacks require physical proximity: An attacker on the other side of the planet can’t exploit KRACK, only an attacker in the parking lot can.

Netflix Expands Content Protection Team to Reduce Piracy

Post Syndicated from Ernesto original https://torrentfreak.com/netflix-expands-content-protection-team-to-reduce-piracy-171015/

There is little doubt that, in the United States and many other countries, Netflix has become the standard for watching movies on the Internet.

Despite the widespread availability, however, Netflix originals are widely pirated. Episodes from House of Cards, Narcos, and Orange is the New Black are downloaded and streamed millions of times through unauthorized platforms.

The streaming giant is obviously not happy with this situation and has ramped up its anti-piracy efforts in recent years. Since last year the company has sent out over a million takedown requests to Google alone and this volume continues to expand.

This growth coincides with an expansion of the company’s internal anti-piracy division. A new job posting shows that Netflix is expanding this team with a Copyright and Content Protection Coordinator. The ultimate goal is to reduce piracy to a fringe activity.

“The growing Global Copyright & Content Protection Group is looking to expand its team with the addition of a coordinator,” the job listing reads.

“He or she will be tasked with supporting the Netflix Global Copyright & Content Protection Group in its internal tactical take down efforts with the goal of reducing online piracy to a socially unacceptable fringe activity.”

Among other things, the new coordinator will evaluate new technological solutions to tackle piracy online.

More old-fashioned takedown efforts are also part of the job. This includes monitoring well-known content platforms, search engines and social network sites for pirated content.

“Day to day scanning of Facebook, YouTube, Twitter, Periscope, Google Search, Bing Search, VK, DailyMotion and all other platforms (including live platforms) used for piracy,” is listed as one of the main responsibilities.

Netflix’ Copyright and Content Protection Coordinator Job

The coordinator is further tasked with managing Facebook’s Rights Manager and YouTube’s Content-ID system, to prevent circumvention of these piracy filters. Experience with fingerprinting technologies and other anti-piracy tools will be helpful in this regard.

Netflix doesn’t do all the copyright enforcement on its own though. The company works together with other media giants in the recently launched “Alliance for Creativity and Entertainment” that is spearheaded by the MPAA.

In addition, the company also uses the takedown services of external anti-piracy outfits to target more traditional infringement sources, such as cyberlockers and piracy streaming sites. The coordinator has to keep an eye on these as well.

“Liaise with our vendors on manual takedown requests on linking sites and hosting sites and gathering data on pirate streaming sites, cyberlockers and usenet platforms.”

The above shows that Netflix is doing its best to prevent piracy from getting out of hand. It’s definitely taking the issue more seriously than a few years ago when the company didn’t have much original content.

The switch from being merely a distribution platform to becoming a major content producer and copyright holder has changed the stakes. Netflix hasn’t won the war on piracy, it’s just getting started.

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

Hollywood Giants Sue Kodi-powered ‘TickBox TV’ Over Piracy

Post Syndicated from Ernesto original https://torrentfreak.com/hollywood-giants-sue-kodi-powered-tickbox-tv-over-piracy-171014/

Online streaming piracy is booming and many people use dedicated media players to bring this content to their regular TVs.

The bare hardware is not illegal and neither is media player software such as Kodi. When these devices are loaded with copyright-infringing addons, however, they turn into an unprecedented piracy threat.

It becomes even more problematic when the sellers of these devices market their products as pirate tools. This is exactly what TickBox TV does, according to Hollywood’s major movie studios, Netflix, and Amazon.

TickBox is a Georgia-based provider of set-top boxes that allow users to stream a variety of popular media. The company’s devices use the Kodi media player and come with instructions on how to add various add-ons.

In a complaint filed in a California federal court yesterday, Universal, Columbia Pictures, Disney, 20th Century Fox, Paramount Pictures, Warner Bros, Amazon, and Netflix accuse Tickbox of inducing and contributing to copyright infringement.

“TickBox sells ‘TickBox TV,’ a computer hardware device that TickBox urges its customers to use as a tool for the mass infringement of Plaintiffs’ copyrighted motion pictures and television shows,” the complaint, picked up by THR, reads.

While the device itself does not host any infringing content, users are informed where they can find it.

The movie and TV studios stress that Tickbox’s marketing highlights its infringing uses with statements such as “if you’re tired of wasting money with online streaming services like Netflix, Hulu or Amazon Prime.”

Sick of paying high monthly fees?

“TickBox promotes the use of TickBox TV for overwhelmingly, if not exclusively, infringing purposes, and that is how its customers use TickBox TV. TickBox advertises TickBox TV as a substitute for authorized and legitimate distribution channels such as cable television or video-on-demand services like Amazon Prime and Netflix,” the studios’ lawyers write.

The complaint explains in detail how TickBox works. When users first boot up their device they are prompted to download the “TickBox TV Player” software. This comes with an instruction video guiding people to infringing streams.

“The TickBox TV instructional video urges the customer to use the ‘Select Your Theme’ button on the start-up menu for downloading addons. The ‘Themes’ are curated collections of popular addons that link to unauthorized streams of motion pictures and television shows.”

“Some of the most popular addons currently distributed — which are available through TickBox TV — are titled ‘Elysium,’ ‘Bob,’ and ‘Covenant’,” the complaint adds, showing screenshots of the interface.

Covenant

The movie and TV studios, which are the founding members of the recently launched ACE anti-piracy initiative, want TickBox to stop selling their devices. In addition, they demand compensation for the damages they’ve suffered. Requesting the maximum statutory damages of $150,000 per copyright infringement, this can run into the millions.

The involvement of Amazon, albeit the content division, is notable since the online store itself sells dozens of similar streaming devices, some of which even list “infringing” addons.

The TickBox lawsuit is the first case in the United States where a group of major Hollywood players is targeting a streaming device. Earlier this year various Hollywood insiders voiced concerns about the piracy streaming epidemic and if this case goes their way, it probably won’t be the last.

A copy of the full complaint is available here (pdf)

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

Popular Zer0day Torrent Tracker Taken Offline By Mass Copyright Complaint

Post Syndicated from Andy original https://torrentfreak.com/popular-zer0day-torrent-tracker-taken-offline-by-mass-copyright-complaint-171014/

In January 2016, a BitTorrent enthusiast decided to launch a stand-alone tracker, purely for fun.

The Zer0day platform, which hosts no torrents, is a tracker in the purest sense, directing traffic between peers, no matter what content is involved and no matter where people are in the world.

With this type of tracker in short supply, it was soon utilized by The Pirate Bay and the now-defunct ExtraTorrent. By August 2016, it was tracking almost four million peers and a million torrents, a considerable contribution to the BitTorrent ecosystem.

After handling many ups and downs associated with a service of this type, the tracker eventually made it to the end of 2016 intact. This year it grew further still and by the end of September was tracking an impressive 5.5 million peers spread over 1.2 million torrents. Soon after, however, the tracker disappeared from the Internet without warning.

In an effort to find out what had happened, TorrentFreak contacted Zer0day’s operator who told us a familiar story. Without any warning at all, the site’s host pulled the plug on the service, despite having been paid 180 euros for hosting just a week earlier.

“We’re hereby informing you of the termination of your dedicated server due to a breach of our terms of service,” the host informed Zer0day.

“Hosting trackers on our servers that distribute infringing and copyrighted content is prohibited. This server was found to distribute such content. Should we identify additional similar activity in your services, we will be forced to close your account.”

While hosts tend not to worry too much about what their customers are doing, this one had just received a particularly lengthy complaint. Sent by the head of anti-piracy at French collecting society SCPP, it laid out the group’s problems with the Zer0day tracker.

“SCPP has been responsible for the collective management and protection of sound recordings and music videos producers’ rights since 1985. SCPP counts more than 2,600 members including the majority of independent French producers, in addition to independent European producers, and the major international companies: Sony, Universal and Warner,” the complaints reads.

“SCPP administers a catalog of 7,200,000 sound tracks and 77,000 music videos. SCPP is empowered by its members to take legal action in order to put an end to any infringements of the producers’ rights set out in Article L335-4 of the French Intellectual Property Code…..punishable by a three-year prison sentence or a fine of €300,000.”

Noting that it works on behalf of a number of labels and distributors including BMG, Sony Music, Universal Music, Warner Music and others, SCPP listed countless dozens of albums under its protection, each allegedly tracked by the Zer0day platform.

“It has come to our attention that these music albums are illegally being communicated to the public (made available for download) by various users of the BitTorrent-Network,” the complaint reads.

Noting that Zer0day is involved in the process, the anti-piracy outfit presented dozens of hash codes relating to protected works, demanding that the site stop facilitation of infringement on each and every one of them.

“We have proof that your tracker udp://tracker.zer0day.to:1337/announce provided peers of the BitTorrent-Network with information regarding these torrents, to be specific IP Addresses of peers that were offering without authorization the full albums for download, and that this information enabled peers to download files that contain the sound recordings to which our members producers have the exclusive rights.

“These sound recordings are thus being illegally communicated to the public, and your tracker is enabling the seeders to do so.”

Rather than take the hashes down from the tracker, SCPP actually demanded that Zer0day create a permanent blacklist within 24 hours, to ensure the corresponding torrents wouldn’t be tracked again.

“You should understand that this letter constitutes a notice to you that you may be liable for the infringing activity occurring on your service. In addition, if you ignore this notice, you may also be liable for any resulting infringement,” the complaint added.

But despite all the threats, SCPP didn’t receive the response they’d demanded since the operator of the site refused to take any action.

“Obviously, ‘info hashes’ are not copyrightable nor point to specific copyrighted content, or even have any meaning. Further, I cannot verify that request strings parameters (‘info hashes’) you sent me contain copyrighted material,” he told SCPP.

“Like the website says; for content removal kindly ask the indexing site to remove the listing and the .torrent file. Also, tracker software does not have an option to block request strings parameters (‘info hashes’).”

The net effect of non-compliance with SCPP was fairly dramatic and swift. Zer0day’s host took down the whole tracker instead and currently it remains offline. Whether it reappears depends on the site’s operator finding a suitable web host, but at the moment he says he has no idea where one will appear from.

“Currently I’m searching for some virtual private server as a temporary home for the tracker,” he concludes.

As mentioned in an earlier article detailing the problems sites like Zer0day.to face, trackers aren’t absolutely essential for the functioning of BitTorrent transfers. Nevertheless, their existence certainly improves matters for file-sharers so when they go down, millions can be affected.

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

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

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

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

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

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

Walkthrough

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

This solution involves the following steps:

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

Install and configure RStudio with Athena

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

Launching this stack creates all required resources and prerequisites:

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

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

Log in to RStudio

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

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

Install R packages

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

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

load_sdk()
## NULL

Connect to Athena

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

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

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

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

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

Create a dataset

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

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

Create an Athena table based on the dataset

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

Run the following create table statement.

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

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

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

Run a sample query

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

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

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

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

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

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

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

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

Next, determine the song with the highest tempo.

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

Create the training dataset

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

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

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

Create the test dataset

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

Convert the training and test datasets into H2O dataframes

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

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

Create models

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

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

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

Create Model 1: All numeric variables

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

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

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

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

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

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

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

You can make the following observations from the results:

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

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

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

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

You build two variations of the original model:

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

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

Create Model 2: Keep energy and omit loudness

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"
y.dep <- 39
x.indep <- c(6:7,9:38)
x.indep
##  [1]  6  7  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [24] 30 31 32 33 34 35 36 37 38
modelh2 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
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Measure the performance of Model 2.

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

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

You can make the following observations:

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

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

CreateModel 3: Keep loudness but omit energy

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

You can make the following observations:

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

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

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

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

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

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

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

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

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

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

View the two models from an investment perspective:

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

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

GBM model

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

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

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

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

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

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

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

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

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

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

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

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

0.90 – 1 = excellent (A)

0.8 – 0.9 = good (B)

0.7 – 0.8 = fair (C)

.6 – 0.7 = poor (D)

0.5 – 0.5 = fail (F)

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

Metric Model 3 GBM Model Deep Learning Model

Accuracy

(max)

0.882038

(t=0.435479)

0.876676

(t=0.442757)

0.865952

(t=0.999999)

Precision

(max)

1.0

(t=0.821606)

1.0

(t=0802184)

1.0

(t=1.0)

Recall

(max)

1.0 1.0

1.0

(t=0)

Specificity

(max)

1.0 1.0

1.0

(t=1)

Sensitivity

 

0.2033898 0.1355932

0.3898305

(t=0.5)

AUC 0.8492389 0.8630573 0.756882

Note: ‘t’ denotes threshold.

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

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

Conclusion

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

If you have questions or suggestions, please comment below.


Additional Reading

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


About the Authors

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

 

 

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

 

 

Popcorn Time Creator Readies BitTorrent & Blockchain-Powered Video Platform

Post Syndicated from Andy original https://torrentfreak.com/popcorn-time-creator-readies-bittorrent-blockchain-powered-youtube-competitor-171012/

Without a doubt, YouTube is one of the most important websites available on the Internet today.

Its massive archive of videos brings pleasure to millions on a daily basis but its centralized nature means that owner Google always exercises control.

Over the years, people have looked to decentralize the YouTube concept and the latest project hoping to shake up the market has a particularly interesting player onboard.

Until 2015, only insiders knew that Argentinian designer Federico Abad was actually ‘Sebastian’, the shadowy figure behind notorious content sharing platform Popcorn Time.

Now he’s part of the team behind Flixxo, a BitTorrent and blockchain-powered startup hoping to wrestle a share of the video market from YouTube. Here’s how the team, which features blockchain startup RSK Labs, hope things will play out.

The Flixxo network will have no centralized storage of data, eliminating the need for expensive hosting along with associated costs. Instead, transfers will take place between peers using BitTorrent, meaning video content will be stored on the machines of Flixxo users. In practice, the content will be downloaded and uploaded in much the same way as users do on The Pirate Bay or indeed Abad’s baby, Popcorn Time.

However, there’s a twist to the system that envisions content creators, content consumers, and network participants (seeders) making revenue from their efforts.

At the heart of the Flixxo system are digital tokens (think virtual currency), called Flixx. These Flixx ‘coins’, which will go on sale in 12 days, can be used to buy access to content. Creators can also opt to pay consumers when those people help to distribute their content to others.

“Free from structural costs, producers can share the earnings from their content with the network that supports them,” the team explains.

“This way you get paid for helping us improve Flixxo, and you earn credits (in the form of digital tokens called Flixx) for watching higher quality content. Having no intermediaries means that the price you pay for watching the content that you actually want to watch is lower and fairer.”

The Flixxo team

In addition to earning tokens from helping to distribute content, people in the Flixxo ecosystem can also earn currency by watching sponsored content, i.e advertisements. While in a traditional system adverts are often considered a nuisance, Flixx tokens have real value, with a promise that users will be able to trade their Flixx not only for videos, but also for tangible and semi-tangible goods.

“Use your Flixx to reward the producers you follow, encouraging them to create more awesome content. Or keep your Flixx in your wallet and use them to buy a movie ticket, a pair of shoes from an online retailer, a chest of coins in your favourite game or even convert them to old-fashioned cash or up-and-coming digital assets, like Bitcoin,” the team explains.

The Flixxo team have big plans. After foundation in early 2016, the second quarter of 2017 saw the completion of a functional alpha release. In a little under two weeks, the project will begin its token generation event, with new offices in Los Angeles planned for the first half of 2018 alongside a premiere of the Flixxo platform.

“A total of 1,000,000,000 (one billion) Flixx tokens will be issued. A maximum of 300,000,000 (three hundred million) tokens will be sold. Some of these tokens (not more than 33% or 100,000,000 Flixx) may be sold with anticipation of the token allocation event to strategic investors,” Flixxo states.

Like all content platforms, Flixxo will live or die by the quality of the content it provides and whether, at least in the first instance, it can persuade people to part with their hard-earned cash. Only time will tell whether its content will be worth a premium over readily accessible YouTube content but with much-reduced costs, it may tempt creators seeking a bigger piece of the pie.

“Flixxo will also educate its community, teaching its users that in this new internet era value can be held and transferred online without intermediaries, a value that can be earned back by participating in a community, by contributing, being rewarded for every single social interaction,” the team explains.

Of course, the elephant in the room is what will happen when people begin sharing copyrighted content via Flixxo. Certainly, the fact that Popcorn Time’s founder is a key player and rival streaming platform Stremio is listed as a partner means that things could get a bit spicy later on.

Nevertheless, the team suggests that piracy and spam content distribution will be limited by mechanisms already built into the system.

“[A]uthors have to time-block tokens in a smart contract (set as a warranty) in order to upload content. This contract will also handle and block their earnings for a certain period of time, so that in the case of a dispute the unfair-uploader may lose those tokens,” they explain.

That being said, Flixxo also says that “there is no way” for third parties to censor content “which means that anyone has the chance of making any piece of media available on the network.” However, Flixxo says it will develop tools for filtering what it describes as “inappropriate content.”

At this point, things start to become a little unclear. On the one hand Flixxo says it could become a “revolutionary tool for uncensorable and untraceable media” yet on the other it says that it’s necessary to ensure that adult content, for example, isn’t seen by kids.

“We know there is a thin line between filtering or curating content and censorship, and it is a fact that we have an open network for everyone to upload any content. However, Flixxo as a platform will apply certain filtering based on clear rules – there should be a behavior-code for uploaders in order to offer the right content to the right user,” Flixxo explains.

To this end, Flixxo says it will deploy a centralized curation function, carried out by 101 delegates elected by the community, which will become progressively decentralized over time.

“This curation will have a cost, paid in Flixx, and will be collected from the warranty blocked by the content uploaders,” they add.

There can be little doubt that if Flixxo begins ‘curating’ unsuitable content, copyright holders will call on it to do the same for their content too. And, if the platform really takes off, 101 curators probably won’t scratch the surface. There’s also the not inconsiderable issue of what might happen to curators’ judgment when they’re incentivized to block curate content.

Finally, for those sick of “not available in your region” messages, there’s good and bad news. Flixxo insists there will be no geo-blocking of content on its part but individual creators will still have that feature available to them, should they choose.

The Flixx whitepaper can be downloaded here (pdf)

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

Taringa Hack – 27 Million User Records Leaked

Post Syndicated from Darknet original https://www.darknet.org.uk/2017/10/taringa-hack-27-million-user-records-leaked/?utm_source=rss&utm_medium=social&utm_campaign=darknetfeed

Taringa Hack – 27 Million User Records Leaked

The Taringa hack is actually one of the biggest leaks of the year with 27 million weakly hashed passwords breached, but it’s not often covered in the Western media with it being a Latin American site (something like Reddit).

The leak happened in August and it seems like the hackers were able to brute force around 95% of the account passwords fairly quickly with Taringa using an outdated and flawing hashing algorithm – md5.

Read the rest of Taringa Hack – 27 Million User Records Leaked now! Only available at Darknet.

Kim Dotcom Plots Hollywood Execs’ Downfall in Wake of Weinstein Scandal

Post Syndicated from Andy original https://torrentfreak.com/kim-dotcom-plots-hollywood-execs-downfall-in-wake-of-weinstein-scandal-171011/

It has been nothing short of a disastrous week for movie mogul Harvey Weinstein.

Accused of sexual abuse and harassment by a string of actresses, the latest including Angelina Jolie and Gwyneth Paltrow, the 65-year-old is having his life taken apart.

This week, the influential producer was fired by his own The Weinstein Company, which is now seeking to change its name. And yesterday, following allegations of rape made in The New Yorker magazine, his wife, designer Georgina Chapman, announced she was leaving the Miramax co-founder.

“My heart breaks for all the women who have suffered tremendous pain because of these unforgivable actions,” the 41-year-old told People magazine.

As the scandal continues and more victims come forward, there are signs of a general emboldening of women in Hollywood, some of whom are publicly speaking out about their own experiences. If that continues to gain momentum – and the opportunity is certainly there – one man with his own experiences of Hollywood’s wrath wants to play a prominent role.

“Just the beginning. Sexual abuse and slavery by the Hollywood elites is as common as dirt. Tsunami,” Kim Dotcom wrote on Twitter.

Dotcom initially suggested that via a website, victims of Hollywood abuse could share their stories anonymously, shining light on a topic that is often shrouded in fear and secrecy. But soon the idea was growing legs.

“Looking for a Los Angeles law firm willing to represent hundreds of sexual abuse victims of Hollywood elites, pro-bono. I’ll find funding,” he said.

Within hours, Dotcom announced that he’d found lawyers in the US who are willing to help victims, for free.

“I had talks with Hollywood lawyers. Found a big law firm willing to represent sexual abuse victims, for free. Next, the website,” he teased.

It’s not hard to see why Dotcom is making this battle his own. Aside from any empathy he feels towards victims on a personal level, he sees his family as kindred spirits, people who have also felt the wrath of Hollywood executives.

That being said, the Megaupload founder is extremely clear that framing this as revenge or a personal vendetta would be not only wrong, but also disrespectful to the victims of abuse.

“I want to help victims because I’m a victim,” he told TorrentFreak.

“I’m an abuse victim of Hollywood, not sexual abuse, but certainly abuse of power. It’s time to shine some light on those Hollywood elites who think they are above the law and untouchable.”

Dotcom told NZ Herald that people like Harvey Weinstein rub shoulders with the great and the good, hoping to influence decision-makers for their own personal gain. It’s something Dotcom, his family, and his colleagues have felt the effects of.

“They dine with presidents, donate millions to powerful politicians and buy favors like tax breaks and new copyright legislation, even the Megaupload raid. They think they can destroy lives and businesses with impunity. They think they can get away with anything. But they can’t. We’ll teach them,” he warned.

The Megaupload founder says he has both “the motive and the resources” to help victims and he’s promising to do that with proven skills. Ironically, many of these have been honed as a direct result of Hollywood’s attack on Megaupload and Dotcom’s relentless drive to bounce back with new sites like Mega and his latest K.im / Bitcache project.

“I’m an experienced fundraiser. A high traffic crowdfunding campaign for this cause can raise millions. The costs won’t be an issue,” Dotcom informs TF. “There seems to be an appetite for these cases because defendants usually settle quickly. I have calls with LA firms today and tomorrow.

“Just the beginning. Watch me,” he concludes.

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

Purism Meets Its $1.5 Million Goal for Security Focused Librem 5 Smartphone

Post Syndicated from ris original https://lwn.net/Articles/735954/rss

Purism has reached
its crowdfunding goal
to create the Librem 5, an encrypted, open
smartphone ecosystem that gives users complete device control. “Reaching the $1.5 million milestone weeks ahead of schedule enables Purism to accelerate the production of the physical product. The company plans to move into hardware production as soon as possible to assemble a developer kit as well as initiate building the base software platform, which will be publicly available and open to the developer community.

Roku Shows FBI Warning to Pirate Channel Users

Post Syndicated from Ernesto original https://torrentfreak.com/roku-shows-fbi-warning-to-pirate-channel-users-171009/

In recent years it has become much easier to stream movies and TV-shows over the Internet.

Legal services such as Netflix and HBO are flourishing, but at the same time millions of people are streaming from unauthorized sources, often paired with perfectly legal streaming platforms and devices.

Hollywood insiders have dubbed this trend “Piracy 3.0” and are actively working with stakeholders to address the threat. One of the companies rightsholders are working with is Roku, known for its easy-to-use media players.

Earlier this year a Mexican court ordered retailers to take the Roku media player off the shelves. This legal battle is still ongoing, but it was a clear signal to the company, which now has its own anti-piracy team.

Several third-party “private” channels have been removed from the player in recent weeks as they violate Roku’s terms and conditions. These include the hugely popular streaming channel XTV, which offered access to infringing content.

After its removal, XTV briefly returned as XTV 2, but that didn’t last for long. The infringing channel was soon removed again, this time showing the FBI’s anti-piracy seal followed by a rather ominous message.

“FBI Anti-Piracy Warning: Unauthorized copying is punishable under federal law,” it reads. “Roku has removed this unauthorized service due to repeated claims of copyright infringement.”

FBI Warning (via Cordcuttersnews)

The unusual warning was picked up by Cordcuttersnews and states that Roku itself removed the channel.

To some it may seem that the FBI is cracking down on Roku channels, but this is not the case. The anti-piracy seal and associated warning are often used in cases where the organization is not actively involved, to add extra weight. The FBI supports this, as long as certain standards are met.

A Roku spokesperson confirmed to TorrentFreak that they’re using it on their own accord here.

“We want to send a clear message to Roku customers and to publishers that any publication of pirated content on our platform is a violation of law and our platform rules,” the company says.

“We have recently expanded the messaging that we display to customers that install non-certified channels to alert them to the associated risks, and we display the FBI’s publicly available warning when we remove channels for copyright violations.”

The strong language shows that Roku is taking its efforts to crack down on infringing channels very seriously. A few weeks ago the company started to warn users that pirate channels may be removed without prior notice.

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

Students Overwhelmingly Vote Pirate Party in Simulated “General Election”

Post Syndicated from Andy original https://torrentfreak.com/students-overwhelmingly-vote-pirate-party-in-simulated-general-election-171007/

While millions of people have died throughout history fighting for the right to vote, there is a significant wave of apathy among large swathes of the population in democracies where the ballot box is taken for granted.

With this in mind, an educational project in the Czech Republic aims to familiarize high school students with basic democratic principles, acquaint them with the local electoral system, while promoting dialog among students, teachers, and parents. The main goal is to increase participation of young people in elections.

In line with this project, young students across the country are invited to take part in a simulated general election, to get a taste of what things will be like when they reach voting age. This year, these Student Elections took place over two days starting October 3 in secondary schools across the Czech Republic.

Under the One World Education Program at People in Need, a nonprofit that implements educational and human rights programs in crisis zones, 40,068 students from 281 schools cast their votes for political parties, movements and coalition candidates standing for the Chamber of Deputies of the Czech Parliament in the upcoming real elections.

Students 15-years-old and above were eligible to vote and when they were all counted, the results were quite something for any follower of the worldwide Pirate Party movement.

Of all groups, the Czech Pirate Party won a decisive victory, netting 24.5% of the overall vote, double that achieved by the ANO movement (11.9%) and the right-wing TOP 09 (11.8%). The fourth and fifth-placed candidates topped out at 7.76% and 6.33% respectively.

“The results of the Student Elections will be compared to the results of the election in a couple of weeks. It is certain they will vary greatly,” says Karel Strachota, director of the One World at School Education Program and the person who launched the Student Election project seven years ago.

“At the same time, however, the choice of students seems to indicate a certain trend in the development of voter preferences. From our teachers and school visits, we know that, as in the past, most of the pupils have been able to choose responsibly.”

According to Prague Monitor, opinion polls for the upcoming election (October 20-21) place the ANO movement as the clear favorites, with the Pirates having “a big chance to succeed” with up to 7% of the vote. Given the results of the simulation, elections in coming years could be something really special for the Pirates.

The full results of the Student Elections 2017 can be found on the One World website here. Meanwhile, Czech Pirate Party President Ivan Bartos sings to voters in the pre-election video below, explaining why Pirates are needed in Parliament in 2017.

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