Tag Archives: Core

Raspberry Jam Cameroon #PiParty

Post Syndicated from Ben Nuttall original https://www.raspberrypi.org/blog/raspberry-jam-cameroon-piparty/

Earlier this year on 3 and 4 March, communities around the world held Raspberry Jam events to celebrate Raspberry Pi’s sixth birthday. We sent out special birthday kits to participating Jams — it was amazing to know the kits would end up in the hands of people in parts of the world very far from Raspberry Pi HQ in Cambridge, UK.

The Raspberry Jam Camer team: Damien Doumer, Eyong Etta, Loïc Dessap and Lionel Sichom, aka Lionel Tellem

Preparing for the #PiParty

One birthday kit went to Yaoundé, the capital of Cameroon. There, a team of four students in their twenties — Lionel Sichom (aka Lionel Tellem), Eyong Etta, Loïc Dessap, and Damien Doumer — were organising Yaoundé’s first Jam, called Raspberry Jam Camer, as part of the Raspberry Jam Big Birthday Weekend. The team knew one another through their shared interests and skills in electronics, robotics, and programming. Damien explains in his blog post about the Jam that they planned ahead for several activities for the Jam based on their own projects, so they could be confident of having a few things that would definitely be successful for attendees to do and see.

Show-and-tell at Raspberry Jam Cameroon

Loïc presented a Raspberry Pi–based, Android app–controlled robot arm that he had built, and Lionel coded a small video game using Scratch on Raspberry Pi while the audience watched. Damien demonstrated the possibilities of Windows 10 IoT Core on Raspberry Pi, showing how to install it, how to use it remotely, and what you can do with it, including building a simple application.

Loïc Dessap, wearing a Raspberry Jam Big Birthday Weekend T-shirt, sits at a table with a robot arm, a laptop with a Pi sticker and other components. He is making an adjustment to his set-up.

Loïc showcases the prototype robot arm he built

There was lots more too, with others discussing their own Pi projects and talking about the possibilities Raspberry Pi offers, including a Pi-controlled drone and car. Cake was a prevailing theme of the Raspberry Jam Big Birthday Weekend around the world, and Raspberry Jam Camer made sure they didn’t miss out.

A round pink-iced cake decorated with the words "Happy Birthday RBP" and six candles, on a table beside Raspberry Pi stickers, Raspberry Jam stickers and Raspberry Jam fliers

Yay, birthday cake!!

A big success

Most visitors to the Jam were secondary school students, while others were university students and graduates. The majority were unfamiliar with Raspberry Pi, but all wanted to learn about Raspberry Pi and what they could do with it. Damien comments that the fact most people were new to Raspberry Pi made the event more interactive rather than creating any challenges, because the visitors were all interested in finding out about the little computer. The Jam was an all-round success, and the team was pleased with how it went:

What I liked the most was that we sensitized several people about the Raspberry Pi and what one can be capable of with such a small but powerful device. — Damien Doumer

The Jam team rounded off the event by announcing that this was the start of a Raspberry Pi community in Yaoundé. They hope that they and others will be able to organise more Jams and similar events in the area to spread the word about what people can do with Raspberry Pi, and to help them realise their ideas.

The Raspberry Jam Camer team, wearing Raspberry Jam Big Birthday Weekend T-shirts, pose with young Jam attendees outside their venue

Raspberry Jam Camer gets the thumbs-up

The Raspberry Pi community in Cameroon

In a French-language interview about their Jam, the team behind Raspberry Jam Camer said they’d like programming to become the third official language of Cameroon, after French and English; their aim is to to popularise programming and digital making across Cameroonian society. Neither of these fields is very familiar to most people in Cameroon, but both are very well aligned with the country’s ambitions for development. The team is conscious of the difficulties around the emergence of information and communication technologies in the Cameroonian context; in response, they are seizing the opportunities Raspberry Pi offers to give children and young people access to modern and constantly evolving technology at low cost.

Thanks to Lionel, Eyong, Damien, and Loïc, and to everyone who helped put on a Jam for the Big Birthday Weekend! Remember, anyone can start a Jam at any time — and we provide plenty of resources to get you started. Check out the Guidebook, the Jam branding pack, our specially-made Jam activities online (in multiple languages), printable worksheets, and more.

The post Raspberry Jam Cameroon #PiParty appeared first on Raspberry Pi.

Police Forces Around Europe Hit Pirate IPTV Operation

Post Syndicated from Andy original https://torrentfreak.com/police-forces-around-europe-hit-pirate-iptv-operation-180519/

Once upon a time, torrent and web streaming sites were regularly in the headlines while being targeted by the authorities. With the rise of set-top box streaming, actions against pirate IPTV operations are more regularly making the news.

In an operation coordinated by the public prosecutor’s office in Rome, 150 officers of the Provincial Command of the Guardia di Finanza (GdF) this week targeted what appears to be a fairly large unauthorized IPTV provider.

Under the banner Operation Spinoff, in Italy, more than 50 searches were carried out in 20 provinces of 11 regions. Five people were arrested. Elsewhere in Europe – in Switzerland, Germany and Spain – the Polizei Basel-Landschaft, the Kriminal Polizei and the Policia Nacional coordinated to execute warrants.

A small selection of the service on offer

“Through technical and ‘in-the-field’ investigations and the meticulous reconstruction of financial flows, carried out mainly through prepaid credit cards or payment web platforms, investigators have reconstructed the activity of a pyramid-like criminal structure dedicated to the illegal decryption and diffusion of pay-per-view television content through the Internet,” the GdF said in a statement.

Italian authorities report that the core of the IPTV operation were its sources of original content and channels. These were located in a range of diverse locations such as companies, commercial premises, garages and even private homes. Inside each location was equipment to receive, decrypt and capture signals from broadcasters including Sky TV.

Italian police examine hardware

These signals were collected together to form a package of channels which were then transmitted via the Internet and sold to the public in the form of an IPTV subscription. Packages were reportedly priced between 15 and 20 euros per month.

It’s estimated that between the 49 individuals said to be involved in the operation, around one million euros was generated. All are suspected of copyright infringement and money laundering offenses. Of the five Italian citizens reported to be at the core of the operations, four were taken into custody and one placed under house arrest.

Reports identify the suspects as: ‘AS’, born 1979 and residing in Lorrach, Germany. ‘RM’, born 1987 and living in Sarno, Italy. ‘LD’, born 1996 and also living in Sarno, Italy. ‘GP’, born 1990, living in Pordenone, Italy. And ‘SM’, born 1981 and living in Zagarolo, Italy.

More hardware

Players at all levels of the business are under investigation, from the sources who decrypted the signals to the sellers and re-sellers of the content to end users. Also under the microscope are people said to have laundered the operation’s money through credit cards and payment platforms.

The GdF describes the pirate IPTV operation in serious terms, noting that it aimed to set up a “parallel distribution company able to provide services that are entirely analogous to lawful companies, from checks on the feasibility of installing the service to maintaining adequate standards and technical assistance to customers.”

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

EC2 Instance Update – C5 Instances with Local NVMe Storage (C5d)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/ec2-instance-update-c5-instances-with-local-nvme-storage-c5d/

As you can see from my EC2 Instance History post, we add new instance types on a regular and frequent basis. Driven by increasingly powerful processors and designed to address an ever-widening set of use cases, the size and diversity of this list reflects the equally diverse group of EC2 customers!

Near the bottom of that list you will find the new compute-intensive C5 instances. With a 25% to 50% improvement in price-performance over the C4 instances, the C5 instances are designed for applications like batch and log processing, distributed and or real-time analytics, high-performance computing (HPC), ad serving, highly scalable multiplayer gaming, and video encoding. Some of these applications can benefit from access to high-speed, ultra-low latency local storage. For example, video encoding, image manipulation, and other forms of media processing often necessitates large amounts of I/O to temporary storage. While the input and output files are valuable assets and are typically stored as Amazon Simple Storage Service (S3) objects, the intermediate files are expendable. Similarly, batch and log processing runs in a race-to-idle model, flushing volatile data to disk as fast as possible in order to make full use of compute resources.

New C5d Instances with Local Storage
In order to meet this need, we are introducing C5 instances equipped with local NVMe storage. Available for immediate use in 5 regions, these instances are a great fit for the applications that I described above, as well as others that you will undoubtedly dream up! Here are the specs:

Instance Name vCPUs RAM Local Storage EBS Bandwidth Network Bandwidth
c5d.large 2 4 GiB 1 x 50 GB NVMe SSD Up to 2.25 Gbps Up to 10 Gbps
c5d.xlarge 4 8 GiB 1 x 100 GB NVMe SSD Up to 2.25 Gbps Up to 10 Gbps
c5d.2xlarge 8 16 GiB 1 x 225 GB NVMe SSD Up to 2.25 Gbps Up to 10 Gbps
c5d.4xlarge 16 32 GiB 1 x 450 GB NVMe SSD 2.25 Gbps Up to 10 Gbps
c5d.9xlarge 36 72 GiB 1 x 900 GB NVMe SSD 4.5 Gbps 10 Gbps
c5d.18xlarge 72 144 GiB 2 x 900 GB NVMe SSD 9 Gbps 25 Gbps

Other than the addition of local storage, the C5 and C5d share the same specs. Both are powered by 3.0 GHz Intel Xeon Platinum 8000-series processors, optimized for EC2 and with full control over C-states on the two largest sizes, giving you the ability to run two cores at up to 3.5 GHz using Intel Turbo Boost Technology.

You can use any AMI that includes drivers for the Elastic Network Adapter (ENA) and NVMe; this includes the latest Amazon Linux, Microsoft Windows (Server 2008 R2, Server 2012, Server 2012 R2 and Server 2016), Ubuntu, RHEL, SUSE, and CentOS AMIs.

Here are a couple of things to keep in mind about the local NVMe storage:

Naming – You don’t have to specify a block device mapping in your AMI or during the instance launch; the local storage will show up as one or more devices (/dev/nvme*1 on Linux) after the guest operating system has booted.

Encryption – Each local NVMe device is hardware encrypted using the XTS-AES-256 block cipher and a unique key. Each key is destroyed when the instance is stopped or terminated.

Lifetime – Local NVMe devices have the same lifetime as the instance they are attached to, and do not stick around after the instance has been stopped or terminated.

Available Now
C5d instances are available in On-Demand, Reserved Instance, and Spot form in the US East (N. Virginia), US West (Oregon), EU (Ireland), US East (Ohio), and Canada (Central) Regions. Prices vary by Region, and are just a bit higher than for the equivalent C5 instances.

Jeff;

PS – We will be adding local NVMe storage to other EC2 instance types in the months to come, so stay tuned!

Police Arrest Suspected Member of TheDarkOverlord Hacking Group

Post Syndicated from Andy original https://torrentfreak.com/police-arrest-suspected-member-of-the-dark-overlord-hacking-group-180517/

In April 2017, the first episode of the brand new season of Netflix’s Orange is the New Black was uploaded to The Pirate Bay, months ahead of its official release date.

The leak was the work of a hacking entity calling itself TheDarkOverlord (TDO). One of its members had contacted TorrentFreak months earlier claiming that the content was in its hands but until the public upload, nothing could be confirmed.

TDO told us it had obtained the episodes after hacking the systems of Hollywood-based Larson Studios, an ADR (additional dialogue recorded) studio, back in 2016. TDO had attempted to blackmail the company into paying a bitcoin ransom but when it wasn’t forthcoming, TDO pressed the nuclear button.

Netflix responded by issuing a wave of takedown notices but soon TDO moved onto a new target. In June 2017, TDO followed up on an earlier threat to leak content owned by ABC.

But while TDO was perhaps best known for its video-leaking exploits, the group’s core ‘business’ was hacking what many perceived to be softer targets. TDO ruthlessly slurped confidential data from weakly protected computer systems at medical facilities, private practices, and businesses large and small.

In each case, the group demanded ransoms in exchange for silence and leaked sensitive data to the public if none were paid. With dozens of known targets, TDO found itself at the center of an international investigation, led by the FBI. That now appears to have borne some fruit, with the arrest of an individual in Serbia.

Serbian police say that members of its Ministry of Internal Affairs, Criminal Police Directorate (UCC), in coordination with the Special Prosecution for High-Tech Crime, have taken action against a suspected member of TheDarkOverlord group.

Police say they tracked down a Belgrade resident, who was arrested and taken into custody. Identified only by the initials “S.S”, police say the individual was born in 1980 but have released no further personal details. A search of his apartment and other locations led to the seizure of items of digital equipment.

“According to the order of the Special Prosecutor’s Office for High-Tech Crime, criminal charges will be brought against him because of the suspicion that he committed the criminal offense of unauthorized access to a protected computer, computer networks and electronic processing, and the criminal offense of extortion,” a police statement reads.

In earlier correspondence with TF, the TDO member always gave the impression of working as part of a team but we only had a single contact point which appeared to be the same person. However, Serbian authorities say the larger investigation is aimed at uncovering “a large number of people” who operate under the banner of “TheDarkOverlord”.

Since June 2016, the group is said to have targeted at least 50 victims while demanding bitcoin ransoms to avoid disclosure of their content. Serbian authorities say that on the basis of available data, TDO received payments of more than $275,000.

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

[$] Subinterpreter support for Python

Post Syndicated from jake original https://lwn.net/Articles/754162/rss

Eric Snow kicked off the 2018 edition of
the Python Language Summit
with a look at getting a better story for
multicore Python by way of subinterpreters. Back in 2015, we looked at his efforts at that point; things
have been progressing since. There is more to do, of course, so he is
hoping to attract more developers to work on the project.

This is the start of the Python Language Summit coverage for this year; articles are being collected on a dedicated summit page as they are finished.

From Framework to Function: Deploying AWS Lambda Functions for Java 8 using Apache Maven Archetype

Post Syndicated from Ryosuke Iwanaga original https://aws.amazon.com/blogs/compute/from-framework-to-function-deploying-aws-lambda-functions-for-java-8-using-apache-maven-archetype/

As a serverless computing platform that supports Java 8 runtime, AWS Lambda makes it easy to run any type of Java function simply by uploading a JAR file. To help define not only a Lambda serverless application but also Amazon API Gateway, Amazon DynamoDB, and other related services, the AWS Serverless Application Model (SAM) allows developers to use a simple AWS CloudFormation template.

AWS provides the AWS Toolkit for Eclipse that supports both Lambda and SAM. AWS also gives customers an easy way to create Lambda functions and SAM applications in Java using the AWS Command Line Interface (AWS CLI). After you build a JAR file, all you have to do is type the following commands:

aws cloudformation package 
aws cloudformation deploy

To consolidate these steps, customers can use Archetype by Apache Maven. Archetype uses a predefined package template that makes getting started to develop a function exceptionally simple.

In this post, I introduce a Maven archetype that allows you to create a skeleton of AWS SAM for a Java function. Using this archetype, you can generate a sample Java code example and an accompanying SAM template to deploy it on AWS Lambda by a single Maven action.

Prerequisites

Make sure that the following software is installed on your workstation:

  • Java
  • Maven
  • AWS CLI
  • (Optional) AWS SAM CLI

Install Archetype

After you’ve set up those packages, install Archetype with the following commands:

git clone https://github.com/awslabs/aws-serverless-java-archetype
cd aws-serverless-java-archetype
mvn install

These are one-time operations, so you don’t run them for every new package. If you’d like, you can add Archetype to your company’s Maven repository so that other developers can use it later.

With those packages installed, you’re ready to develop your new Lambda Function.

Start a project

Now that you have the archetype, customize it and run the code:

cd /path/to/project_home
mvn archetype:generate \
  -DarchetypeGroupId=com.amazonaws.serverless.archetypes \
  -DarchetypeArtifactId=aws-serverless-java-archetype \
  -DarchetypeVersion=1.0.0 \
  -DarchetypeRepository=local \ # Forcing to use local maven repository
  -DinteractiveMode=false \ # For batch mode
  # You can also specify properties below interactively if you omit the line for batch mode
  -DgroupId=YOUR_GROUP_ID \
  -DartifactId=YOUR_ARTIFACT_ID \
  -Dversion=YOUR_VERSION \
  -DclassName=YOUR_CLASSNAME

You should have a directory called YOUR_ARTIFACT_ID that contains the files and folders shown below:

├── event.json
├── pom.xml
├── src
│   └── main
│       ├── java
│       │   └── Package
│       │       └── Example.java
│       └── resources
│           └── log4j2.xml
└── template.yaml

The sample code is a working example. If you install SAM CLI, you can invoke it just by the command below:

cd YOUR_ARTIFACT_ID
mvn -P invoke verify
[INFO] Scanning for projects...
[INFO]
[INFO] ---------------------------< com.riywo:foo >----------------------------
[INFO] Building foo 1.0
[INFO] --------------------------------[ jar ]---------------------------------
...
[INFO] --- maven-jar-plugin:3.0.2:jar (default-jar) @ foo ---
[INFO] Building jar: /private/tmp/foo/target/foo-1.0.jar
[INFO]
[INFO] --- maven-shade-plugin:3.1.0:shade (shade) @ foo ---
[INFO] Including com.amazonaws:aws-lambda-java-core:jar:1.2.0 in the shaded jar.
[INFO] Replacing /private/tmp/foo/target/lambda.jar with /private/tmp/foo/target/foo-1.0-shaded.jar
[INFO]
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-local-invoke) @ foo ---
2018/04/06 16:34:35 Successfully parsed template.yaml
2018/04/06 16:34:35 Connected to Docker 1.37
2018/04/06 16:34:35 Fetching lambci/lambda:java8 image for java8 runtime...
java8: Pulling from lambci/lambda
Digest: sha256:14df0a5914d000e15753d739612a506ddb8fa89eaa28dcceff5497d9df2cf7aa
Status: Image is up to date for lambci/lambda:java8
2018/04/06 16:34:37 Invoking Package.Example::handleRequest (java8)
2018/04/06 16:34:37 Decompressing /tmp/foo/target/lambda.jar
2018/04/06 16:34:37 Mounting /private/var/folders/x5/ldp7c38545v9x5dg_zmkr5kxmpdprx/T/aws-sam-local-1523000077594231063 as /var/task:ro inside runtime container
START RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74 Version: $LATEST
Log output: Greeting is 'Hello Tim Wagner.'
END RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74
REPORT RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74	Duration: 96.60 ms	Billed Duration: 100 ms	Memory Size: 128 MB	Max Memory Used: 7 MB

{"greetings":"Hello Tim Wagner."}


[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 10.452 s
[INFO] Finished at: 2018-04-06T16:34:40+09:00
[INFO] ------------------------------------------------------------------------

This maven goal invokes sam local invoke -e event.json, so you can see the sample output to greet Tim Wagner.

To deploy this application to AWS, you need an Amazon S3 bucket to upload your package. You can use the following command to create a bucket if you want:

aws s3 mb s3://YOUR_BUCKET --region YOUR_REGION

Now, you can deploy your application by just one command!

mvn deploy \
    -DawsRegion=YOUR_REGION \
    -Ds3Bucket=YOUR_BUCKET \
    -DstackName=YOUR_STACK
[INFO] Scanning for projects...
[INFO]
[INFO] ---------------------------< com.riywo:foo >----------------------------
[INFO] Building foo 1.0
[INFO] --------------------------------[ jar ]---------------------------------
...
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-package) @ foo ---
Uploading to aws-serverless-java/com.riywo:foo:1.0/924732f1f8e4705c87e26ef77b080b47  11657 / 11657.0  (100.00%)
Successfully packaged artifacts and wrote output template to file target/sam.yaml.
Execute the following command to deploy the packaged template
aws cloudformation deploy --template-file /private/tmp/foo/target/sam.yaml --stack-name <YOUR STACK NAME>
[INFO]
[INFO] --- maven-deploy-plugin:2.8.2:deploy (default-deploy) @ foo ---
[INFO] Skipping artifact deployment
[INFO]
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-deploy) @ foo ---

Waiting for changeset to be created..
Waiting for stack create/update to complete
Successfully created/updated stack - archetype
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 37.176 s
[INFO] Finished at: 2018-04-06T16:41:02+09:00
[INFO] ------------------------------------------------------------------------

Maven automatically creates a shaded JAR file, uploads it to your S3 bucket, replaces template.yaml, and creates and updates the CloudFormation stack.

To customize the process, modify the pom.xml file. For example, to avoid typing values for awsRegion, s3Bucket or stackName, write them inside pom.xml and check in your VCS. Afterward, you and the rest of your team can deploy the function by typing just the following command:

mvn deploy

Options

Lambda Java 8 runtime has some types of handlers: POJO, Simple type and Stream. The default option of this archetype is POJO style, which requires to create request and response classes, but they are baked by the archetype by default. If you want to use other type of handlers, you can use handlerType property like below:

## POJO type (default)
mvn archetype:generate \
 ...
 -DhandlerType=pojo

## Simple type - String
mvn archetype:generate \
 ...
 -DhandlerType=simple

### Stream type
mvn archetype:generate \
 ...
 -DhandlerType=stream

See documentation for more details about handlers.

Also, Lambda Java 8 runtime supports two types of Logging class: Log4j 2 and LambdaLogger. This archetype creates LambdaLogger implementation by default, but you can use Log4j 2 if you want:

## LambdaLogger (default)
mvn archetype:generate \
 ...
 -Dlogger=lambda

## Log4j 2
mvn archetype:generate \
 ...
 -Dlogger=log4j2

If you use LambdaLogger, you can delete ./src/main/resources/log4j2.xml. See documentation for more details.

Conclusion

So, what’s next? Develop your Lambda function locally and type the following command: mvn deploy !

With this Archetype code example, available on GitHub repo, you should be able to deploy Lambda functions for Java 8 in a snap. If you have any questions or comments, please submit them below or leave them on GitHub.

[$] Autoscaling for Kubernetes workloads

Post Syndicated from corbet original https://lwn.net/Articles/754153/rss

Technologies like containers, clusters, and Kubernetes offer the prospect
of rapidly scaling the available computing resources to match variable demands
placed on the system. Actually implementing that scaling can be a
challenge, though.
During KubeCon
+ CloudNativeCon Europe 2018
,
Frederic Branczyk from CoreOS (now
part of Red Hat) held a packed session
to introduce a standard and officially recommended way to scale workloads
automatically in Kubernetes
clusters.

Securing Your Cryptocurrency

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/backing-up-your-cryptocurrency/

Securing Your Cryptocurrency

In our blog post on Tuesday, Cryptocurrency Security Challenges, we wrote about the two primary challenges faced by anyone interested in safely and profitably participating in the cryptocurrency economy: 1) make sure you’re dealing with reputable and ethical companies and services, and, 2) keep your cryptocurrency holdings safe and secure.

In this post, we’re going to focus on how to make sure you don’t lose any of your cryptocurrency holdings through accident, theft, or carelessness. You do that by backing up the keys needed to sell or trade your currencies.

$34 Billion in Lost Value

Of the 16.4 million bitcoins said to be in circulation in the middle of 2017, close to 3.8 million may have been lost because their owners no longer are able to claim their holdings. Based on today’s valuation, that could total as much as $34 billion dollars in lost value. And that’s just bitcoins. There are now over 1,500 different cryptocurrencies, and we don’t know how many of those have been misplaced or lost.



Now that some cryptocurrencies have reached (at least for now) staggering heights in value, it’s likely that owners will be more careful in keeping track of the keys needed to use their cryptocurrencies. For the ones already lost, however, the owners have been separated from their currencies just as surely as if they had thrown Benjamin Franklins and Grover Clevelands over the railing of a ship.

The Basics of Securing Your Cryptocurrencies

In our previous post, we reviewed how cryptocurrency keys work, and the common ways owners can keep track of them. A cryptocurrency owner needs two keys to use their currencies: a public key that can be shared with others is used to receive currency, and a private key that must be kept secure is used to spend or trade currency.

Many wallets and applications allow the user to require extra security to access them, such as a password, or iris, face, or thumb print scan. If one of these options is available in your wallets, take advantage of it. Beyond that, it’s essential to back up your wallet, either using the backup feature built into some applications and wallets, or manually backing up the data used by the wallet. When backing up, it’s a good idea to back up the entire wallet, as some wallets require additional private data to operate that might not be apparent.

No matter which backup method you use, it is important to back up often and have multiple backups, preferable in different locations. As with any valuable data, a 3-2-1 backup strategy is good to follow, which ensures that you’ll have a good backup copy if anything goes wrong with one or more copies of your data.

One more caveat, don’t reuse passwords. This applies to all of your accounts, but is especially important for something as critical as your finances. Don’t ever use the same password for more than one account. If security is breached on one of your accounts, someone could connect your name or ID with other accounts, and will attempt to use the password there, as well. Consider using a password manager such as LastPass or 1Password, which make creating and using complex and unique passwords easy no matter where you’re trying to sign in.

Approaches to Backing Up Your Cryptocurrency Keys

There are numerous ways to be sure your keys are backed up. Let’s take them one by one.

1. Automatic backups using a backup program

If you’re using a wallet program on your computer, for example, Bitcoin Core, it will store your keys, along with other information, in a file. For Bitcoin Core, that file is wallet.dat. Other currencies will use the same or a different file name and some give you the option to select a name for the wallet file.

To back up the wallet.dat or other wallet file, you might need to tell your backup program to explicitly back up that file. Users of Backblaze Backup don’t have to worry about configuring this, since by default, Backblaze Backup will back up all data files. You should determine where your particular cryptocurrency, wallet, or application stores your keys, and make sure the necessary file(s) are backed up if your backup program requires you to select which files are included in the backup.

Backblaze B2 is an option for those interested in low-cost and high security cloud storage of their cryptocurrency keys. Backblaze B2 supports 2-factor verification for account access, works with a number of apps that support automatic backups with encryption, error-recovery, and versioning, and offers an API and command-line interface (CLI), as well. The first 10GB of storage is free, which could be all one needs to store encrypted cryptocurrency keys.

2. Backing up by exporting keys to a file

Apps and wallets will let you export your keys from your app or wallet to a file. Once exported, your keys can be stored on a local drive, USB thumb drive, DAS, NAS, or in the cloud with any cloud storage or sync service you wish. Encrypting the file is strongly encouraged — more on that later. If you use 1Password or LastPass, or other secure notes program, you also could store your keys there.

3. Backing up by saving a mnemonic recovery seed

A mnemonic phrase, mnemonic recovery phrase, or mnemonic seed is a list of words that stores all the information needed to recover a cryptocurrency wallet. Many wallets will have the option to generate a mnemonic backup phrase, which can be written down on paper. If the user’s computer no longer works or their hard drive becomes corrupted, they can download the same wallet software again and use the mnemonic recovery phrase to restore their keys.

The phrase can be used by anyone to recover the keys, so it must be kept safe. Mnemonic phrases are an excellent way of backing up and storing cryptocurrency and so they are used by almost all wallets.

A mnemonic recovery seed is represented by a group of easy to remember words. For example:

eye female unfair moon genius pipe nuclear width dizzy forum cricket know expire purse laptop scale identify cube pause crucial day cigar noise receive

The above words represent the following seed:

0a5b25e1dab6039d22cd57469744499863962daba9d2844243fec 9c0313c1448d1a0b2cd9e230a78775556f9b514a8be45802c2808e fd449a20234e9262dfa69

These words have certain properties:

  • The first four letters are enough to unambiguously identify the word.
  • Similar words are avoided (such as: build and built).

Bitcoin and most other cryptocurrencies such as Litecoin, Ethereum, and others use mnemonic seeds that are 12 to 24 words long. Other currencies might use different length seeds.

4. Physical backups — Paper, Metal

Some cryptocurrency holders believe that their backup, or even all their cryptocurrency account information, should be stored entirely separately from the internet to avoid any risk of their information being compromised through hacks, exploits, or leaks. This type of storage is called “cold storage.” One method of cold storage involves printing out the keys to a piece of paper and then erasing any record of the keys from all computer systems. The keys can be entered into a program from the paper when needed, or scanned from a QR code printed on the paper.

Printed public and private keys

Printed public and private keys

Some who go to extremes suggest separating the mnemonic needed to access an account into individual pieces of paper and storing those pieces in different locations in the home or office, or even different geographical locations. Some say this is a bad idea since it could be possible to reconstruct the mnemonic from one or more pieces. How diligent you wish to be in protecting these codes is up to you.

Mnemonic recovery phrase booklet

Mnemonic recovery phrase booklet

There’s another option that could make you the envy of your friends. That’s the CryptoSteel wallet, which is a stainless steel metal case that comes with more than 250 stainless steel letter tiles engraved on each side. Codes and passwords are assembled manually from the supplied part-randomized set of tiles. Users are able to store up to 96 characters worth of confidential information. Cryptosteel claims to be fireproof, waterproof, and shock-proof.

image of a Cryptosteel cold storage device

Cryptosteel cold wallet

Of course, if you leave your Cryptosteel wallet in the pocket of a pair of ripped jeans that gets thrown out by the housekeeper, as happened to the character Russ Hanneman on the TV show Silicon Valley in last Sunday’s episode, then you’re out of luck. That fictional billionaire investor lost a USB drive with $300 million in cryptocoins. Let’s hope that doesn’t happen to you.

Encryption & Security

Whether you store your keys on your computer, an external disk, a USB drive, DAS, NAS, or in the cloud, you want to make sure that no one else can use those keys. The best way to handle that is to encrypt the backup.

With Backblaze Backup for Windows and Macintosh, your backups are encrypted in transmission to the cloud and on the backup server. Users have the option to add an additional level of security by adding a Personal Encryption Key (PEK), which secures their private key. Your cryptocurrency backup files are secure in the cloud. Using our web or mobile interface, previous versions of files can be accessed, as well.

Our object storage cloud offering, Backblaze B2, can be used with a variety of applications for Windows, Macintosh, and Linux. With B2, cryptocurrency users can choose whichever method of encryption they wish to use on their local computers and then upload their encrypted currency keys to the cloud. Depending on the client used, versioning and life-cycle rules can be applied to the stored files.

Other backup programs and systems provide some or all of these capabilities, as well. If you are backing up to a local drive, it is a good idea to encrypt the local backup, which is an option in some backup programs.

Address Security

Some experts recommend using a different address for each cryptocurrency transaction. Since the address is not the same as your wallet, this means that you are not creating a new wallet, but simply using a new identifier for people sending you cryptocurrency. Creating a new address is usually as easy as clicking a button in the wallet.

One of the chief advantages of using a different address for each transaction is anonymity. Each time you use an address, you put more information into the public ledger (blockchain) about where the currency came from or where it went. That means that over time, using the same address repeatedly could mean that someone could map your relationships, transactions, and incoming funds. The more you use that address, the more information someone can learn about you. For more on this topic, refer to Address reuse.

Note that a downside of using a paper wallet with a single key pair (type-0 non-deterministic wallet) is that it has the vulnerabilities listed above. Each transaction using that paper wallet will add to the public record of transactions associated with that address. Newer wallets, i.e. “deterministic” or those using mnemonic code words support multiple addresses and are now recommended.

There are other approaches to keeping your cryptocurrency transaction secure. Here are a couple of them.

Multi-signature

Multi-signature refers to requiring more than one key to authorize a transaction, much like requiring more than one key to open a safe. It is generally used to divide up responsibility for possession of cryptocurrency. Standard transactions could be called “single-signature transactions” because transfers require only one signature — from the owner of the private key associated with the currency address (public key). Some wallets and apps can be configured to require more than one signature, which means that a group of people, businesses, or other entities all must agree to trade in the cryptocurrencies.

Deep Cold Storage

Deep cold storage ensures the entire transaction process happens in an offline environment. There are typically three elements to deep cold storage.

First, the wallet and private key are generated offline, and the signing of transactions happens on a system not connected to the internet in any manner. This ensures it’s never exposed to a potentially compromised system or connection.

Second, details are secured with encryption to ensure that even if the wallet file ends up in the wrong hands, the information is protected.

Third, storage of the encrypted wallet file or paper wallet is generally at a location or facility that has restricted access, such as a safety deposit box at a bank.

Deep cold storage is used to safeguard a large individual cryptocurrency portfolio held for the long term, or for trustees holding cryptocurrency on behalf of others, and is possibly the safest method to ensure a crypto investment remains secure.

Keep Your Software Up to Date

You should always make sure that you are using the latest version of your app or wallet software, which includes important stability and security fixes. Installing updates for all other software on your computer or mobile device is also important to keep your wallet environment safer.

One Last Thing: Think About Your Testament

Your cryptocurrency funds can be lost forever if you don’t have a backup plan for your peers and family. If the location of your wallets or your passwords is not known by anyone when you are gone, there is no hope that your funds will ever be recovered. Taking a bit of time on these matters can make a huge difference.

To the Moon*

Are you comfortable with how you’re managing and backing up your cryptocurrency wallets and keys? Do you have a suggestion for keeping your cryptocurrencies safe that we missed above? Please let us know in the comments.


*To the Moon — Crypto slang for a currency that reaches an optimistic price projection.

The post Securing Your Cryptocurrency appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PTData Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PTData Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.

Compute

May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PTAccelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.

Containers

May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.

Databases

May 21, 2018 | 01:00 PM – 01:45 PM PTHow to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.

DevOps

May 23, 2018 | 09:00 AM – 09:45 AM PT.NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PTHybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.

IoT

May 31, 2018 | 11:00 AM – 11:45 AM PTUsing AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PTUsing Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

May 24, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.

Management Tools

May 21, 2018 | 09:00 AM – 09:45 AM PTGaining Better Observability of Your VMs with Amazon CloudWatch – Learn how CloudWatch Agent makes it easy for customers like Rackspace to monitor their VMs.

Mobile

May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.

Networking

May 31, 2018 | 09:00 AM – 09:45 AM PTMaking Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

Security, Identity, & Compliance

May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

June 1, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.

Serverless

May 22, 2018 | 01:00 PM – 01:45 PM PTBuilding API-Driven Microservices with Amazon API Gateway – Learn how to build a secure, scalable API for your application in our tech talk about API-driven microservices.

Storage

May 30, 2018 | 11:00 AM – 11:45 AM PTAccelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.

June 1, 2018 | 11:00 AM – 11:45 AM PTLearn to Build a Cloud-Scale Website Powered by Amazon EFS – Technical deep dive where you’ll learn tips and tricks for integrating WordPress, Drupal and Magento with Amazon EFS.

 

 

 

 

The plan for merging CoreOS into Red Hat

Post Syndicated from corbet original https://lwn.net/Articles/754058/rss

The CoreOS blog is carrying an
article
describing the path forward now that CoreOS is owned by Red
Hat. “Since Red Hat’s acquisition of CoreOS was announced, we
received questions on the fate of Container Linux. CoreOS’s first project,
and initially its namesake, pioneered the lightweight, ‘over-the-air’
automatically updated container native operating system that fast rose in
popularity running the world’s containers. With the acquisition, Container
Linux will be reborn as Red Hat CoreOS, a new entry into the Red Hat
ecosystem. Red Hat CoreOS will be based on Fedora and Red Hat Enterprise
Linux sources and is expected to ultimately supersede Atomic Host as Red
Hat’s immutable, container-centric operating system.
” Some
information can also be found in this
Red Hat press release
.

UK Internet Filters Block Disney Sites, Internet Safety Tips, and More

Post Syndicated from Ernesto original https://torrentfreak.com/uk-internet-filters-block-disney-sites-internet-safety-tips-and-more-180505/

Over the past several years we have regularly written about court-ordered blockades of pirate sites in the UK.

Today, we take a closer look at another type of blocking, the Internet safety filters UK ISPs offer. These filters, which are sometimes enabled by default, help subscribers to block harmful content, especially for their children.

With help from the Open Rights Group’s Blocked initiative, which documents the scope of various ISP filters, we took a look at how these perform.

The first results are as expected. Many porn sites are blocked and so are sites that are clearly oriented at a mature audience. That’s more or less what these filters are meant for, so no issues there.

We also noticed that many proxies and VPNs are not accessible. While this may seem broad, as they’re not offensive, these tools could allow clever sorts to bypass parental controls, so there’s an argument to be made for their inclusion.

Oddly enough, the Tor browser, which can do the same, is freely accessible. But let’s not digress.

What really stood out to us is that some sites which are targeted at kids, or at least useful to them, are blocked too.

One prime example is the official UK Disney website, located at disney.co.uk, which is blocked by BT’s Strict filters. That seems a bit cruel. The same is true for disneymoviesanywhere.com, which is not very useful, but certainly doesn’t seem harmful to us either.

Apparently, BT doesn’t want children to visit these Disney sites.

No Disney

The parental control filters are supposed to make the web a safer place for kids. While this is a laudable aim, the execution is not always perfect. For example, several ISPs including BT, Plusnet and Virgin Media, are blocking the internetsafetyday.org website.

Admittedly, the site is targeted at parents, but since these will often be behind the same filters, they’re missing out on some good tips and tricks on how to educate their children.

No safety

Talking about education. It’s always good when kids start to experiment with coding at a young age. This is also one of the core messages of the non-profit organization Kidsandcode.org.

“Everyone should have the opportunity to learn how to code,” the site reads.

This makes sense, you’d think, but for kids who are trapped behind the BT Strict or BT Light filters, this is not an option.

What can kids do nowadays then? Play a few simple games? Ideally educational games such as the ones playkidsgames.com offers. As you may have guessed by now, that’s not an option either, at least not behind BT’s Strict filter.

Maybe kids should stick to more boring stuff. Perhaps finish that school project on Vikings, that should be doable, right? Well, it is, as long as you don’t look up vikingsword.com, a historical and educational side dedicated to Viking swords.

Both Three and Sky have blocked the site, with Sky explaining that it’s placed in the “Weapons, Violence, Gore and Hate” category.

Ancient weapons

Perhaps we’re too insensitive, but I think that most children can handle grainy drawings of swords. After all, the average cartoon is more violent nowadays.

And yes, it’s true that decades-old Viking swords are weapons, but Three and Sky are not very consistent as the website of today’s largest weapons manufacturer Lockheed Martin appears to pass through all parental filters just fine.

Luckily, the Open Rights Group allows us to check for these odd results, and report sites which are inaccurately blocked. Interested in checking if your favorite website is blocked? You can do so here. Feel free to report any unusual findings in the comments.

Open Rights Group is currently looking for donations and other means of support to keep its Blocked project up and running. More information is available here.

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

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

Post Syndicated from YongSeong Lee original https://aws.amazon.com/blogs/big-data/analyze-data-in-amazon-dynamodb-using-amazon-sagemaker-for-real-time-prediction/

Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.

Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.

DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.

To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.

The solution that I describe provides the following benefits:

  • Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
  • Automatically updates your model to get real-time predictions
  • Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
  • Makes it easier for developers of all skill levels to use Amazon SageMaker

All code and data set in this post are available in this .zip file.

Solution architecture

The following diagram shows the overall architecture of the solution.

The steps that data follows through the architecture are as follows:

  1. Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
  2. Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
  3. Amazon SageMaker renews the model artifact and update the endpoint.
  4. The converted CSV is available for ad hoc queries with Amazon Athena.
  5. Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.

Building the auto-updating model

This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.

Download sample scripts and data

Before you begin, take the following steps:

  1. Download sample scripts in this .zip file.
  2. Unzip the src.zip file.
  3. Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
  4. Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.

For this solution, the banking.csv  should be imported into a DynamoDB table.

Export a DynamoDB table

To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.

One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.

For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.

Add the script to an existing pipeline

After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:

  1. Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
  2. For Actions, choose Edit.
  3. In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.

Paste the following command into the new step after the data ­­upload step:

s3://#{myDDBRegion}.elasticmapreduce/libs/script-runner/script-runner.jar,s3://<your bucket name>/automation_script.sh,#{output.directoryPath},#{myDDBRegion}

The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.

The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.

Automation script: Convert JSON data to CSV with Hive

We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.

When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.

Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.

The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.

#!/bin/bash
hive -e "
ADD jar s3://<your bucket name>/json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar ; 
DROP TABLE IF EXISTS blog_backup_data ;
CREATE EXTERNAL TABLE blog_backup_data (
 customer_id map<string,string>,
 age map<string,string>, job map<string,string>, 
 marital map<string,string>,education map<string,string>, 
 default map<string,string>, housing map<string,string>,
 loan map<string,string>, contact map<string,string>, 
 month map<string,string>, day_of_week map<string,string>, 
 duration map<string,string>, campaign map<string,string>,
 pdays map<string,string>, previous map<string,string>, 
 poutcome map<string,string>, emp_var_rate map<string,string>, 
 cons_price_idx map<string,string>, cons_conf_idx map<string,string>,
 euribor3m map<string,string>, nr_employed map<string,string>, 
 y map<string,string> ) 
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' 
LOCATION '$1/';

INSERT OVERWRITE DIRECTORY 's3://<your bucket name>/<datasource path>/' 
SELECT concat( customer_id['s'],',', 
 age['n'],',', job['s'],',', 
 marital['s'],',', education['s'],',', default['s'],',', 
 housing['s'],',', loan['s'],',', contact['s'],',', 
 month['s'],',', day_of_week['s'],',', duration['n'],',', 
 campaign['n'],',',pdays['n'],',',previous['n'],',', 
 poutcome['s'],',', emp_var_rate['n'],',', cons_price_idx['n'],',',
 cons_conf_idx['n'],',', euribor3m['n'],',', nr_employed['n'],',', y['n'] ) 
FROM blog_backup_data
WHERE customer_id['s'] > 0 ; 

After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.

Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id  columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.

Automation script: Renew the Amazon SageMaker model

After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3.  For renewing model artifact, you must create a new training job.  Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.

In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.

#!/bin/bash
## Define variable 
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>" 


# Select containers image based on region.  
case "$REGION" in
"us-west-2" )
    IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
    ;;
"us-east-1" )
    IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest" 
    ;;
"us-east-2" )
    IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest" 
    ;;
"eu-west-1" )
    IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest" 
    ;;
 *)
    echo "Invalid Region Name"
    exit 1 ;  
esac

# Start training job and creating model artifact 
TRAINING_JOB_NAME=TRAIN-${DTTIME} 
S3OUTPUT="s3://<your bucket name>/model/" 
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5 
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE}  --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None"  }]'  --output-data-config S3OutputPath=${S3OUTPUT} --resource-config  InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier  

# Wait until job completed 
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME}  --region ${REGION}

# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME}  --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT}  --execution-role-arn ${ROLE}

# create a new endpoint configuration 
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker  create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME}  --production-variants  VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge

# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name  ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
    aws sagemaker  create-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}    
else
    aws sagemaker  update-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi

Grant permission

Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": [
 "sagemaker:CreateTrainingJob",
 "sagemaker:DescribeTrainingJob",
 "sagemaker:CreateModel",
 "sagemaker:CreateEndpointConfig",
 "sagemaker:DescribeEndpoint",
 "sagemaker:CreateEndpoint",
 "sagemaker:UpdateEndpoint",
 "iam:PassRole"
 ],
 "Resource": "*"
 }
 ]
}

Use real-time prediction

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.

Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.

=== Python sample for real-time prediction ===

#!/usr/bin/env python
import boto3
import json 

client = boto3.client('sagemaker-runtime', region_name ='<your region>' )
new_customer_info = '34,10,2,4,1,2,1,1,6,3,190,1,3,4,3,-1.7,94.055,-39.8,0.715,4991.6'
response = client.invoke_endpoint(
    EndpointName='ServiceEndpoint',
    Body=new_customer_info, 
    ContentType='text/csv'
)
result = json.loads(response['Body'].read().decode())
print(result)
--- output(response) ---
{u'predictions': [{u'score': 0.7528127431869507, u'predicted_label': 1.0}]}

Solution summary

The solution takes the following steps:

  1. Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
  2. Train the Amazon SageMaker model with the new data source.
  3. When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
  4. If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.

Running ad hoc queries using Amazon Athena

Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.

With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog. 

Creating an Amazon Athena table and running it

Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.

=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
 age int, 
 job string, 
 marital string , 
 education string, 
 default string, 
 housing string, 
 loan string, 
 contact string, 
 month string, 
 day_of_week string, 
 duration int, 
 campaign int, 
 pdays int , 
 previous int , 
 poutcome string, 
 emp_var_rate double, 
 cons_price_idx double,
 cons_conf_idx double, 
 euribor3m double, 
 nr_employed double, 
 y int 
)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n' 
LOCATION 's3://<your bucket name>/<datasource path>/';

The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.

=== Sample Query ===

SELECT corr(age,y) AS correlation_age_and_target, 
 corr(duration,y) AS correlation_duration_and_target, 
 corr(campaign,y) AS correlation_campaign_and_target,
 corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y , 
 CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact 
 FROM datasource 
 ) datasource ;

Conclusion

In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.

You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.

 


Additional Reading

If you found this post useful, be sure to check out Serving Real-Time Machine Learning Predictions on Amazon EMR and Analyzing Data in S3 using Amazon Athena.

 


About the Author

Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”

 

 

EC2 Fleet – Manage Thousands of On-Demand and Spot Instances with One Request

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/ec2-fleet-manage-thousands-of-on-demand-and-spot-instances-with-one-request/

EC2 Spot Fleets are really cool. You can launch a fleet of Spot Instances that spans EC2 instance types and Availability Zones without having to write custom code to discover capacity or monitor prices. You can set the target capacity (the size of the fleet) in units that are meaningful to your application and have Spot Fleet create and then maintain the fleet on your behalf. Our customers are creating Spot Fleets of all sizes. For example, one financial service customer runs Monte Carlo simulations across 10 different EC2 instance types. They routinely make requests for hundreds of thousands of vCPUs and count on Spot Fleet to give them access to massive amounts of capacity at the best possible price.

EC2 Fleet
Today we are extending and generalizing the set-it-and-forget-it model that we pioneered in Spot Fleet with EC2 Fleet, a new building block that gives you the ability to create fleets that are composed of a combination of EC2 On-Demand, Reserved, and Spot Instances with a single API call. You tell us what you need, capacity and instance-wise, and we’ll handle all the heavy lifting. We will launch, manage, monitor and scale instances as needed, without the need for scaffolding code.

You can specify the capacity of your fleet in terms of instances, vCPUs, or application-oriented units, and also indicate how much of the capacity should be fulfilled by Spot Instances. The application-oriented units allow you to specify the relative power of each EC2 instance type in a way that directly maps to the needs of your application. All three capacity specification options (instances, vCPUs, and application-oriented units) are known as weights.

I think you’ll find a number ways this feature makes managing a fleet of instances easier, and believe that you will also appreciate the team’s near-term feature roadmap of interest (more on that in a bit).

Using EC2 Fleet
There are a number of ways that you can use this feature, whether you’re running a stateless web service, a big data cluster or a continuous integration pipeline. Today I’m going to describe how you can use EC2 Fleet for genomic processing, but this is similar to workloads like risk analysis, log processing or image rendering. Modern DNA sequencers can produce multiple terabytes of raw data each day, to process that data into meaningful information in a timely fashion you need lots of processing power. I’ll be showing you how to deploy a “grid” of worker nodes that can quickly crunch through secondary analysis tasks in parallel.

Projects in genomics can use the elasticity EC2 provides to experiment and try out new pipelines on hundreds or even thousands of servers. With EC2 you can access as many cores as you need and only pay for what you use. Prior to today, you would need to use the RunInstances API or an Auto Scaling group for the On-Demand & Reserved Instance portion of your grid. To get the best price performance you’d also create and manage a Spot Fleet or multiple Spot Auto Scaling groups with different instance types if you wanted to add Spot Instances to turbo-boost your secondary analysis. Finally, to automate scaling decisions across multiple APIs and Auto Scaling groups you would need to write Lambda functions that periodically assess your grid’s progress & backlog, as well as current Spot prices – modifying your Auto Scaling Groups and Spot Fleets accordingly.

You can now replace all of this with a single EC2 Fleet, analyzing genomes at scale for as little as $1 per analysis. In my grid, each step in in the pipeline requires 1 vCPU and 4 GiB of memory, a perfect match for M4 and M5 instances with 4 GiB of memory per vCPU. I will create a fleet using M4 and M5 instances with weights that correspond to the number of vCPUs on each instance:

  • m4.16xlarge – 64 vCPUs, weight = 64
  • m5.24xlarge – 96 vCPUs, weight = 96

This is expressed in a template that looks like this:

"Overrides": [
{
  "InstanceType": "m4.16xlarge",
  "WeightedCapacity": 64,
},
{
  "InstanceType": "m5.24xlarge",
  "WeightedCapacity": 96,
},
]

By default, EC2 Fleet will select the most cost effective combination of instance types and Availability Zones (both specified in the template) using the current prices for the Spot Instances and public prices for the On-Demand Instances (if you specify instances for which you have matching RIs, your discounts will apply). The default mode takes weights into account to get the instances that have the lowest price per unit. So for my grid, fleet will find the instance that offers the lowest price per vCPU.

Now I can request capacity in terms of vCPUs, knowing EC2 Fleet will select the lowest cost option using only the instance types I’ve defined as acceptable. Also, I can specify how many vCPUs I want to launch using On-Demand or Reserved Instance capacity and how many vCPUs should be launched using Spot Instance capacity:

"TargetCapacitySpecification": {
	"TotalTargetCapacity": 2880,
	"OnDemandTargetCapacity": 960,
	"SpotTargetCapacity": 1920,
	"DefaultTargetCapacityType": "Spot"
}

The above means that I want a total of 2880 vCPUs, with 960 vCPUs fulfilled using On-Demand and 1920 using Spot. The On-Demand price per vCPU is lower for m5.24xlarge than the On-Demand price per vCPU for m4.16xlarge, so EC2 Fleet will launch 10 m5.24xlarge instances to fulfill 960 vCPUs. Based on current Spot pricing (again, on a per-vCPU basis), EC2 Fleet will choose to launch 30 m4.16xlarge instances or 20 m5.24xlarges, delivering 1920 vCPUs either way.

Putting it all together, I have a single file (fl1.json) that describes my fleet:

    "LaunchTemplateConfigs": [
        {
            "LaunchTemplateSpecification": {
                "LaunchTemplateId": "lt-0e8c754449b27161c",
                "Version": "1"
            }
        "Overrides": [
        {
          "InstanceType": "m4.16xlarge",
          "WeightedCapacity": 64,
        },
        {
          "InstanceType": "m5.24xlarge",
          "WeightedCapacity": 96,
        },
      ]
        }
    ],
    "TargetCapacitySpecification": {
        "TotalTargetCapacity": 2880,
        "OnDemandTargetCapacity": 960,
        "SpotTargetCapacity": 1920,
        "DefaultTargetCapacityType": "Spot"
    }
}

I can launch my fleet with a single command:

$ aws ec2 create-fleet --cli-input-json file://home/ec2-user/fl1.json
{
    "FleetId":"fleet-838cf4e5-fded-4f68-acb5-8c47ee1b248a"
}

My entire fleet is created within seconds and was built using 10 m5.24xlarge On-Demand Instances and 30 m4.16xlarge Spot Instances, since the current Spot price was 1.5¢ per vCPU for m4.16xlarge and 1.6¢ per vCPU for m5.24xlarge.

Now lets imagine my grid has crunched through its backlog and no longer needs the additional Spot Instances. I can then modify the size of my fleet by changing the target capacity in my fleet specification, like this:

{         
    "TotalTargetCapacity": 960,
}

Since 960 was equal to the amount of On-Demand vCPUs I had requested, when I describe my fleet I will see all of my capacity being delivered using On-Demand capacity:

"TargetCapacitySpecification": {
	"TotalTargetCapacity": 960,
	"OnDemandTargetCapacity": 960,
	"SpotTargetCapacity": 0,
	"DefaultTargetCapacityType": "Spot"
}

When I no longer need my fleet I can delete it and terminate the instances in it like this:

$ aws ec2 delete-fleets --fleet-id fleet-838cf4e5-fded-4f68-acb5-8c47ee1b248a \
  --terminate-instances   
{
    "UnsuccessfulFleetDletetions": [],
    "SuccessfulFleetDeletions": [
        {
            "CurrentFleetState": "deleted_terminating",
            "PreviousFleetState": "active",
            "FleetId": "fleet-838cf4e5-fded-4f68-acb5-8c47ee1b248a"
        }
    ]
}

Earlier I described how RI discounts apply when EC2 Fleet launches instances for which you have matching RIs, so you might be wondering how else RI customers benefit from EC2 Fleet. Let’s say that I own regional RIs for M4 instances. In my EC2 Fleet I would remove m5.24xlarge and specify m4.10xlarge and m4.16xlarge. Then when EC2 Fleet creates the grid, it will quickly find M4 capacity across the sizes and AZs I’ve specified, and my RI discounts apply automatically to this usage.

In the Works
We plan to connect EC2 Fleet and EC2 Auto Scaling groups. This will let you create a single fleet that mixed instance types and Spot, Reserved and On-Demand, while also taking advantage of EC2 Auto Scaling features such as health checks and lifecycle hooks. This integration will also bring EC2 Fleet functionality to services such as Amazon ECS, Amazon EKS, and AWS Batch that build on and make use of EC2 Auto Scaling for fleet management.

Available Now
You can create and make use of EC2 Fleets today in all public AWS Regions!

Jeff;

Google launches the gVisor container runtime

Post Syndicated from corbet original https://lwn.net/Articles/753324/rss

Google has announced
the open-sourcing of gVisor, a sandboxed container runtime.
gVisor is more lightweight than a VM while maintaining a similar
level of isolation. The core of gVisor is a kernel that runs as a normal,
unprivileged process that supports most Linux system calls. This kernel is
written in Go, which was chosen for its memory- and type-safety. Just like
within a VM, an application running in a gVisor sandbox gets its own kernel
and set of virtualized devices, distinct from the host and other
sandboxes.

[$] The slab and protected-memory allocators

Post Syndicated from corbet original https://lwn.net/Articles/753154/rss

One of the core jobs of the memory-management subsystem is to make memory
available to other parts of the kernel when the need arises. The
memory-management track of the 2018 Linux Storage, Filesystem, and
Memory-Management Summit hosted a pair of sessions on new or improved
allocation functions for the kernel covering the slab allocators and
protectable memory.

Security updates for Tuesday

Post Syndicated from ris original https://lwn.net/Articles/753257/rss

Security updates have been issued by Fedora (cups-filters, ghostscript, glusterfs, PackageKit, qpdf, and xen), Mageia (anki, libofx, ming, sox, webkit2, and xdg-user-dirs), Oracle (corosync, java-1.7.0-openjdk, and pcs), Red Hat (java-1.7.0-openjdk), Scientific Linux (corosync, firefox, gcc, glibc, golang, java-1.7.0-openjdk, java-1.8.0-openjdk, kernel, krb5, librelp, libvncserver, libvorbis, ntp, openssh, openssl, PackageKit, patch, pcs, policycoreutils, qemu-kvm, and xdg-user-dirs), Slackware (libwmf and mozilla), and Ubuntu (apache2, ghostscript, mysql-5.7, wavpack, and webkit2gtk).

Reddit Repeat Infringer Policy Shuts Down Megalinks Piracy Sub

Post Syndicated from Andy original https://torrentfreak.com/reddit-repeat-infringer-policy-shuts-down-megalinks-piracy-sub-180430/

Without doubt, Reddit is one of the most popular sites on the entire Internet. At the time of writing it’s the fourth most visited site in the US with 330 million users per month generating 14 billion screenviews.

The core of the site’s success is its communities. Known as ‘sub-Reddits’ or just ‘subs’, there are currently 138,000 of them dedicated to every single subject you can think of and tens of thousands you’d never considered.

Even though they’re technically forbidden, a small but significant number are dedicated to piracy, offering links to copyright-infringing content hosted elsewhere. One of the most popular is /r/megalinks, which is dedicated to listing infringing content (mainly movies and TV shows) uploaded to file-hosting site Mega.

Considering its activities, Megalinks has managed to stay online longer than most people imagined but following an intervention from Reddit, the content indexing sub has stopped accepting new submissions, which will effectively shut it down.

In an announcement Sunday, the sub’s moderators explained that following a direct warning from Reddit’s administrators, the decision had been taken to move on.

“As most of you know by now, we’ve had to deal with a lot of DMCA takedowns over the last 6 months. Everyone knew this day would come, eventually, and its finally here,” they wrote.

“We received a formal warning from Reddit’s administration 2 days ago, and have decided to restrict new submissions for the safety of the subreddit.”

The message from Reddit’s operators makes it absolutely clear that Reddit isn’t the platform to host what amounts to a piracy links forum.

“This is an official warning from Reddit that we are receiving too many copyright infringement notices about material posted to your community. We will be required to ban this community if you can’t adequately address the problem,” the warning reads.

Noting that Redditors aren’t allowed to post content that infringes copyrights, the administrators say they are required by law to handle DMCA notices and that in cases where infringement happens on multiple occasions, that needs to be handled in a more aggressive manner.

“The law also requires us to issue bans in cases of repeat infringement. Sometimes a repeat infringement problem is limited to just one user and we ban just that person. Other times the problem pervades a whole community and we ban the community,” the admins continue.

“This is our formal warning about repeat infringement in this community. Over the past three months we’ve had to remove material from the community in response to copyright notices 60 times. That’s an unusually high number taking into account the community’s size.

The warning suggests ways to keep infringing content down but in a sub dedicated to piracy, they’re all completely irrelevant. It also suggests removing old posts to ensure that Reddit doesn’t keep getting notices, but that would mean deleting pretty much everything. Backups exist but a simple file is a poor substitute for a community.

So, with Reddit warning that without change the sub will be banned, the moderators of /r/megalinks have decided to move on to a new home. Reportedly hosted ‘offshore’, their new forum already has more than 9,800 members and is likely to grow quickly as the word spreads.

A month ago, the /r/megaporn sub-Reddit suffered a similar fate following a warning from Reddit’s admins. It successfully launched a new external forum which is why the Megalinks crew decided on the same model.

“[A]fter seeing how /r/megaporn approached the same situation, we had started working on an offshore forum a week ago in anticipation of the ban. This allows us to work however we want, without having to deal with Reddit’s policies and administration,” the team explain.

Ever since the BMG v Cox case went bad ways for the ISP in 2015, repeat infringer policies have become a very hot topic in the US. That Reddit is now drawing a line in the sand over a relatively small number of complaints (at least compared to other similar platforms) is clear notice that Reddit and blatant piracy won’t be allowed to walk hand in hand.

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

#FixCopyright #SaveTheLink #CensorshipMachines

Post Syndicated from nellyo original https://nellyo.wordpress.com/2018/04/28/fixcopyright-savethelink-censorshipmachines/

На 26 април 2018 г. учени от 25 водещи изследователски института за интелектуална собственост в Европа  публикуваха отворено писмо, изразяващо сериозни опасения относно  предложената директива за авторско  право.

Според авторите след балансиран проектодоклад на докладчика на Европейския парламент Тереза ​​Комодини (Доклад Comodini,  10 март 2017 г.) неотдавнашни текстове от новия докладчик Axel Voss и българското председателство  на Съвета на Европейският съюз изглежда водят към компромис, който не отговаря на целите на директивата за подобряване на избора, достъпа и справедливостта в цифровата среда.

Има научен консенсус
– че предложеното изключение   по чл. 3 няма да постигне целта си да стимулира иновациите и научните изследвания, ако се ограничи до определени организации,
– че предложенията за право на  издателите по чл. 11 ще благоприятстват по-скоро интересите на издателските интереси в пресата, отколкото иновативната журналистика,
– и че предложенията за чл. 13 застрашават ползата от  Директивата за електронната търговия (2000/31 / ЕО), която споделя отговорността за прилагането между носителите на авторски права и доставчиците на услуги.

169 представители на академичните среди предупреждават за недобре замислените планове за въвеждането на ново право на интелектуална собственост (сродно право за издателите). Те напомнят, че широк спектър от заинтересовани групи вече представи становища, че Директивата за авторското право в настоящия си вид няма да бъде в обществен интерес.

Близо 150 неправителствени организации също се противопоставят на актуалния проект. Според тях предопределено е, че транспониране на  законодателство в този вид ще е  кошмар. Същото се отнася и до проверката на съответствието на директивата с  Хартата на основните права на ЕС и Бернската конвенция. Отворено писмо

По стечение на обстоятелствата името на България  ще остане свързано с тази директива. Ако това ни интересува.

*

На 27 април 2018 г. (вчера) по план в COREPER се обсъжда  предоставяне на мандат за преговори с Европейския парламент по Директивата за авторското право.  

 (текстът)

Kакво е решил COREPER? според Юлия Реда не е даден мандат: