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This $70 computer stick is designed for Ubuntu

Ubuntu is starting to show up in lot more places lately: tablets, phones, and this neat little computer-on-a-stick created by MeLE called the PGC02U. It’s $70, with an Intel BayTrail processor, 2GB of RAM, and 32GB of storage. It also comes in Ubuntu orange and has a wee little antenna to help with wireless reception. Liliputing points out that you might want to go ahead and install this build of Ubuntu created by Ian Morrison, as it’s designed specifically for stick computers.
We’re seeing more and more of these stick computers, by the way, including one for ChromeOS and of course Intel’s Compute Stick. None of them are very fast yet, but all of them are clever ways to get more out of that spare HDMI port on your TV. Then again, if you’re the sort of person who would want to own an Ubuntu computer on a stick, maybe you’re the sort of person who doesn’t have a spare HDMI port on your TV.

Source: http://www.theverge.com/circuitbreaker/2016/4/27/11518642/mele-pgc02u-computer-stick-ubuntu
Submitted by: Arnfried Walbrecht

Ubuntu Budgie 16.04 Officially Released Based on Ubuntu 16.04 and Budgie Desktop

The Budgie-Remix distro has been in development for the past couple of months, and it now finally sees an official release, based on the recently launched Ubuntu 16.04 LTS (Xenial Xerus) operating system and built around the awesome Budgie desktop environment from the Solus Project.
As you may very well be aware, Budgie-Remix aims to become an official Ubuntu flavor, under the name Ubuntu Budgie, as part of the upcoming Ubuntu 16.10 (Yakkety Yak) release, which entered its development cycle a few days ago with daily build ISO images and October 20 as a release date.
The budgie-remix team is currently formed of David Mohammed, HEXcube, SpotTech, Ploctaux, Foggalong, and our udara-u3, and on this occasion they would like to send their thanks and appreciation to Horst3180, Ikey Doherty, Pinguy, and everyone else who helped them make a dream become reality in only two months.

Source: http://news.softpedia.com/news/ubuntu-budgie-16-04-officially-released-based-on-ubuntu-16-04-and-budgie-desktop-503414.shtml
Submitted by: Arnfried Walbrecht

Samsung Developer Conference 2016!

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It’s arrived! Samsung’s big yearly developer event kicks off from April 27th – 28th in SF. A place where developers, creators and builders alike come together to discuss the latest technologies and future innovations.

This year, IOT will be one of the big focuses, especially around smart home and ARTIK – their new chipset for IOT. We’ll be there presenting via our developer evangelist – Didier – who’ll be showcasing a demo, plus a mention by ARTIK’s head of division within his talk.

Our demo will be an app enabled home gateway on which we can deploy several applications. We’ll show you how your smart home could interact with a home robot to serve you. The robot will use the camera you have in the house to recognise that you’ve arrived, then use a microphone in the house to take orders from you (!)

There are also other apps that can be deployed on the home gateway too, including an access point (home wifi), video service, local communication server (imagine a Skype or google hangout where data stays in your home), home automation etc. to name a few.

In the build-up to the event we’ve written a few pre-event blogs on their website, including this one here!

The most powerful Ubuntu phone is available to buy!

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We’re excited to announce that the most powerful Ubuntu phone, Meizu PRO 5 Ubuntu edition, is available to order here from en.jd.com, retailing at USD$369.99. And for our friends based in Russia, the phone can be ordered here.

The Meizu PRO 5 Ubuntu Edition features superior high spec hardware and ships with the latest Ubuntu OTA 10 OS, making content centric Scope experience on this Ubuntu device smoother than ever.

Here are a few key specs:

  • 5.7inch 1080p screen
  • 21.16 megapixel rear-facing camera
  • 32GB of internal memory


For those of you who have pre-ordered the Meizu PRO 5 Ubuntu Edition, here’s your chance to buy!

Buy Here

Flying mobile base stations are coming to the UK

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EE, the largest mobile operator in the UK and now part of BT, just announced a collaboration with Lime Micro, a leader in the next big phase of open source mobile network technology, and Canonical (Ubuntu) to ensure the UK gets better mobile coverage.

EE is heavily investing in getting to 95% geographical 4G coverage by 2020. But building a big new macro tower isn’t always possible or right. They would like to use existing infrastructure like lighthouses, high-buildings, mountains, and so on. Another major problem is the coverage for remote areas which are not economically or technically viable with the current approach. This is why EE is partnering with innovators for cheaper, smaller, more resilient and better solutions.

Lime Micro is about to crowdfund the first app-enabled open source software defined radio, the LimeSDR. Via a 4G app, the LimeSDR will form the basis of a fully fledged base station. Attach this base station to a balloon or a drone and you will be able to cover regions that are difficult to reach otherwise. Embed base stations inside equipment that are installed for other reasons like vending machines, cash points, smart light poles, digital signage, to name a few, and the cost of rolling out connectivity will also go down.

EE expects remote communities to participate as well. These communities can have a say in the features they require and even  participate in maintaining the network. This could bring communities to work with the operators in new ways and even reduce the need for trained technicians to travel long distances when you have the support of the local people – if a base station just needs rebooting, it’s not economical (or sensible) to send an engineer 300 miles from Edinburgh out to the islands.

EE will be challenging UK universities to come up with even more innovative and open source ideas on how to connect the unconnected regions and drive down operating costs. Anybody with good ideas can participate. Snappy Ubuntu Core is open source, app-enabled and production ready. Just get yourself a LimeSDR, download Ubuntu Core and show the world how your app or device can lower the cost of running a network. Covering unconnected regions will bring economic progress so your work will benefit society. We love to see how you will improve the future of wireless networks…

LimeSDR Crowd Supply video campaign.

Ubuntu 16.04 Brings More Privacy and Big Changes to the Desktop

The release of Ubuntu 16.04 last week is good news for computer users who are upset over the recent development of Microsoft turning Windows into an operating system that is essentially spyware. As an open-source Linux distribution, Ubuntu is a great operating system for users concerned about privacy.
This marks the 24th release of the Ubuntu operating system, which has become perhaps the most popular Linux distribution in the world. Ubuntu 16.04 — codenamed Xenial Xerus — is also the sixth Long Term Support (LTS) release, meaning it will receive free security updates and support for five years. Canonical — the UK software company which sponsors Ubuntu — has continued to show its commitment to providing a solid, smooth, reliable, open-source operating system for the desktop even while working toward convergence of the desktop, phone, and tablet into one seamless operating system.

For Windows users looking for a privacy-minded operating system, this means that 16.04 stands on a solid foundation and should prove to be a good daily-driver.

Source: http://www.thenewamerican.com/tech/computers/item/23037-ubuntu16-04-brings-more-privacy-and-big-changes-to-the-desktop
Submitted by: Arnfried Walbrecht

Making Deep Learning accessible on Openstack

This week at the Openstack Developers Summit we are excited to showcase how Canonical with IBM, Mesosphere, Skymind and Data Fellas are working together to make the opportunities of deep learning easier for everyone to access.

Deep learning is a completely new way of building applications. These applications, built around neuronet models rapidly become self learning and self evolving. Make no mistake this is, once again, a paradigm shift for our industry. It opens up a new world of very exciting possibilities.

Deep learning allows complex, big software to skip many of the traditional design and development processes by having the software itself evolve. This is important since we quickly encounter significant constraints when building and deploying big software and big data projects. These constraints not only include the complexity of the operations involved but also include the very stark realisation that there are not enough people, with the required skills and domain expertise, to meet industry demand. Unless we find a smarter way of addressing these constraints then we will severely limit the speed at which our industry can liberate the opportunities of big software and deep learning in particular.

At the heart of deep learning is the concept of neural networks that monitor a specific environment, searching for significant events and patterns and learning how best to adapt to these events and patterns. Of course a key part of this process is the period in which the artificial intelligence is in training. Once initiated the model continue to be self-improving as more data is analyzed over time.

Across all industries we see meaningful applications of deep learning emerging. In healthcare a recent challenge was launched to improve the process and outcomes around cardiac diagnosis. In personal concierge services and in retail neural networks are being married to image recognition to drive recommendation engines. In natural language processing deep learning is being used not only to automate to a higher level of interaction with customers but to also understand, through sentiment analysis, when the experience is degrading and when a warm body needs to intervene. There are of course many projects and many stories that are emerging in deep learning. These only scratch the surface of what is possible. This begs the question – “Why are we not seeing an explosion of new, real world experiences constructed around deep learning?”

The answer is that, as well as the constraints that were previously mentioned, there are also additional things to consider for anyone involved in this space. For instance, if you have a small set of data it is easy to set up a small project cheaply in a few days. When you start to tackle big data sets and to operate at scale your ability to do so quickly becomes significantly more challenging and your options become more limited.

Canonical and Ubuntu underpin the world of scale-out architectures and automation around big software projects. We wake up every day thinking about how we can help simplify, codify, automate and unleash the potential of technology such as deep learning. That is why we have been working with partners such as IBM Power Systems, Mesosphere, Skymind and Data Fellas.

  • IBM Power Systems accelerates the processing of deep learning applications, using Coherent Accelerator Processor Interface (CAPI) or GPU attach to increase throughput. This performance will improve even further when NVLink becomes available.
  • Mesosphere supplies the mechanism to run distributed systems and containers as simple as using a single computer. It makes it easy to deploy and operate complex datacenter services like Spark.
  • Skymind is the commercial support arm of the open-source framework DeepLearning4j (the java/scala deep learning framework), bringing the power of deep learning to enterprise on Spark and Hadoop.
  • Data Fellas brings an Agile Data Science Toolkit bringing the full power of distributed machine learning in the hands of the new generation of data scientists.

The first thing that we created is a model with Juju, Canonical’s Application Modelling Framework, that automates the process for building a complete, classic, deep learning stack. This includes everything from bare metal to essential interfaces. The model includes:

  • A data pipeline to push data into Hadoop HDFS.
  • An evolutive data computation stack made of Spark and Hadoop.
  • A Computing framework for the scheduling of Spark jobs based on Mesos.
  • An interactive notebook to create training pipelines and build Neural Networks.

The system is modelled by Juju is deployed on IBM Power GPU enabled machines for performance and operated by Juju on LXD containers. The Juju model for this looks like this:

We can provide guidance on how you can deploy your own machine/deep learning stack at scale and do your own data analysis. We believe that this early work significantly increases the ability for everyone to get their hands on classic big data infrastructure in just minutes.

There are many use cases for deep learning and it’s hard to pick only one! However, Canonical is engaged heavily in major OpenStack projects in all sectors including telco, retail, media, finance and others. Our initial projects have therefore gravitated towards how we make operations around Openstack more performant.

OpenStack Logs

Canonical runs the OpenStack Interoperability Lab (OIL). With over 35 vendors participating and over 170 configuration combinations Canonical typically builds over 3500 Openstack every month to complete interoperability testing.

This generates over 10GB of logs on OpenStack interoperability and performance every week. We use these log results to train the deep learning model to predict when an OpenStack cloud is going to fail. This produced two outcomes.

First, even at an early stage, this showed an improvement over traditional monitoring systems that only assess the status based on how OpenStack engineers and operators have configured the monitoring of the solution. Intelligent agents were able to trigger alarms based on “feeling” the network, rather than on straight values and probabilities. This is a bit like a spam robot reducing the amount of work of support teams by notifying them of the threat level.

Secondly, over time, as the cloud grows, losing a node becomes less and less manageable. These agents are able to make completely automated decisions such as “migrate all containers off this node” or “restart these services asap”

The beauty of this is that it doesn’t depend on OpenStack itself. The same network will be trainable on any form of applications, creating a new breed of monitoring and metrology systems, combining the power of logs with metrics. Ultimately this makes OpenStack more reliable and performant.

Network Intrusion

We also applied our reference architecture to anomaly detection using NIDS (network intrusion detection) data. This is a classic problem for NeuroNets. Models are trained to monitor and identify unauthorized, illicit and anomalous network behavior, notify network administrators and take autonomous actions to save the network integrity.

Several datasets were used for this initial proof of concept and the models used included:

  • MLP | Feedforward (currently used for streaming)
  • RNN
  • AutoEncoder
  • MLP simulated AutoEncoder

If you are at the Openstack Developer Summit we will also be demonstrating this all week at the Ubuntu/ Canonical Booth A20. If you are attending the Summit please drop by if you would like to discuss and see our work in this area.

If you are not attending the Openstack Summit and would like to start a conversation with Canonical to help us identify the applications and workloads that are most meaningful to you please get in touch with Samuel Cozannet. Or if you are keen to partner with us in this work please get in touch.

Sucuri becomes Linux Mint’s 3rd biggest sponsor

 

I’m happy to announce that Sucuri is now Linux Mint’s 3rd biggest sponsor.

Sucuri is a security company, specialized in incident response, monitoring and protection for web sites. With thousands of clients, their cloud-based firewall handles more than 16 billion page views every single month, while their incident response team can remediate hundreds of sites on a single day.

Working with Sucuri has been a great experience for us. Our project uses many servers spread across the world. Thanks to Sucuri’s expertise, their help and their products we were able to quickly recover from the attacks led on our distribution and set up malware monitoring and improved automated backups. Their firewall protects access to our servers and uses cache and compression techniques to speed up traffic on our web sites. Sucuri also helped us with the adoption of HTTPS and the hardening of our servers. We’re able to quickly get in touch and chat with them when needed. That proximity and relationship with our partners is very important to our project and their expertise in security is really appreciated.

We’re proud to welcome Sucuri as our new sponsor and very grateful for the help they’re giving us.

Ubuntu 16.10 named Yakkety Yak

Ubuntu founder has always provided colorful codenames in alphabetical order and the 16.10 release, due out in in October 2016 will be no exception. Last week, Ubuntu 16.04 the Xenial Xerus, debuted so its now time to pick the ‘Y’ name.
Unlike so many of the past African animal chosen as Ubuntu release mascots, Ubuntu 16.10 will actually be named for one i know – a Yak.
“Y is for …Yakkety yakkety yakkety yakkety yakkety yakkety yakkety yakkety yak. Naturally,” Mark Shuttleworth announced.
No word yet on if Shuttleworth and crew will also double down on WebRTC and/or communications bits as a key release goal for Ubunut 16.10.

Source: http://www.internetnews.com/blog/skerner/ubuntu-16.10-named-yakkety-yak.html
Submitted by: Arnfried Walbrecht

Convergence Features Of The BQ M10 Tablet

A quick look at the convergence features of the BQ Aquaris M10 Ubuntu Edition tablet.