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The Vulkan Portability implementation, gfx-portability allows non-Rust applications that use Vulkan to run with ease. After improving the functionality of gfx-portability’s Metal backend through benchmarking Dota2, and verifying certain functionalities through the Vulkan Conformance Test Suite (CTS), developers are now planning to expand their testing to other projects that are open source, already using Vulcan for rendering and finally lacking strong macOS/Metal support. The projects which matched their criteria were  RPCS3 and Dolphin. However, the team discovered various issues with both RPCS3 and Dolphin projects. RPCS3 Blockers RPCS3 satisfies all the above mentioned criteria. It is an open-source Sony PlayStation 3 emulator and debugger written in C++ for Windows and Linux. RPCS3 has a Vulkan backend, and some attempts were made to support macOS previously. The gfx-rs team added a surface and swapchain support to start of with the macOS integration. This process identified a number of blockers in both gfx-rs and RPCS3. The RPCS3 developers and the gfx-rs teams collaborated to quickly address the blockers. Once the blockers were addressed, gameplay was rendered within RPCS3. Dolphin support for macOS Dolphin, the emulator for two recent Nintendo video game consoles, was actively working on adding support for macOS. While being tested with gfx-portability the teams noticed some further minor bugs in gfx. The issues were addressed and the teams were able to render real gameplay. Continuous Releases for the masses The team has already started automatically releasing gfx-portability binaries under GitHub latest release -> the portability repository. Currently the team provides MacOS (Metal) and Linux (Vulkan) binaries, and will add Windows (Direct3D 12/11 and Vulkan) binaries soon. These releases ensure that users don’t have to build gfx-portability themselves in order to test it with an existing project. The binaries are compatible with both the Vulkan loader on macOS and by linking the binaries directly from an application.   The team was successfully able to run RPCS3 and Dolphin on top of gfx-portability’s Metal backend and only had to address some minor issues in the process. Stability and performance will improve as more real world use cases are tested. You can read more about this on gfx-rs.github.io.   OpenAI Five loses against humans in Dota 2 at The International 2018 How to use artificial intelligence to create games with rich and interactive environments [Tutorial] Best game engines for AI game development  

Amazon hits $1 trillion market value on September 4 joining Apple Inc in the $1 trillion club, and becoming the second member of the group after its stock price doubled in 15 months. Apple took almost 38 years as a public company to achieve the trillion dollar milestone, while Amazon got there in almost half that time, in 21 years. While Apple's iPhone and other devices remain popular and its revenue is growing, it can’t keep up with Amazon's blistering sales growth. Investors are impressed by Amazon’s success which has diversified its business virtually into every corner of the retail industry. It has altered how consumers buy products online putting major pressure on many brick-and-mortar stores. It also provides video streaming services and bought upscale supermarket Whole Foods. Its cloud computing services for companies have also become a major driver of earnings and revenue. "Amazon's a little bit more dynamic than Apple because the iPhone has become more mature. Amazon's cloud business is an extra growth driver that Apple doesn't have," said Daniel Morgan, Portfolio Manager at Synovus Trust in Atlanta, he describes Amazon's cloud services as its "crown jewel". On August 30, Amazon shares hit $2,000 for the first time, just $50 per share away from the company’s projection of reaching $1 trillion market value. Amazon shares were last up 1.1 percent at $2,035.69, pulling back slightly from the milestone level of $2050.2677. Amazon stocks surge past $2000, expect Amazon to join Apple in the $1 trillion market cap club anytime now Amazon is supporting research into conversational AI with Alexa fellowships Getting started with Amazon Machine Learning workflow [Tutorial]

Yesterday, Atlassian made two major announcements, the acquisition of OpsGenie and the release of Jira Ops. Both these products aim to help IT operations teams resolve downtime quickly and reduce the occurrence of these incidents over time. Atlassian is an Australian enterprise software company that develops collaboration software for teams with products including JIRA, Confluence, HipChat, Bitbucket, and Stash. OpsGenie: Alert the right people at the right time Source: Atlassian OpsGenie is an IT alert and notification management tool that helps notify critical alerts to all the right people (operations and software development teams). It uses a sophisticated combination of scheduling, escalation paths, and notifications that take things like time zone and holidays into account. OpsGenie is a prompt and reliable alerting system, which comes with the following features: It is integrated with monitoring, ticketing, and chat tools, to notify the team using multiple channels, providing the necessary information for your team to immediately begin resolution. It provides various notification methods such as, email, SMS, push, phone call, and group chat to ensure alerts are seen by the users. You can build and modify schedules and define escalation rules within one interface. It tracks everything related to alerts and incidents, which helps you to gain insight into areas of success and opportunities for improvement. You can define escalation policies and on-call schedules with rotations to notify the right people and escalate when necessary. Jira Ops: Resolve incidents faster Source: Atlassian Jira Ops is an unified incident command center that provides the response team with a single place for response coordination. It is integrated with OpsGenie, Slack, Statuspage, PagerDuty, and xMatters. It guides the response team through the response workflow and automates common steps such as creating a new Slack room for each incident. Jira Ops is available through Atlassian’s early access program. Jira Ops enables you to resolve a downtime quickly by providing the following functionalities: It quickly alerts you about what is affected and what the associated impacts are. You can check the status, severity level, and duration of the incident. You can see real-time response activities. You can also find the associated Slack channel, current incident manager, and technical lead. You can find more details on OpsGenie and Jira Ops on Atlassian’s official website. Atlassian sells Hipchat IP to Slack Atlassian open sources Escalator, a Kubernetes autoscaler project Docker isn’t going anywhere

Relayr, an IoT middleware startup based in Berlin, has been purchased by German insurance group Munich Re. The deal, which values relayr at $300 million, gives Munich Re’s subsidiary company HSB 100% equity in the startup. The move is significant, marking an important milestone in relayr’s life as it has moved from a crowdfunded chocolate-shaped IoT kit to an industrial IoT middleware platform used by 130 businesses. Essentially, relayr provides businesses with the software needed to connect industrial infrastructure to the internet in order for information and data about the performance and safety of that machinery to be managed and analyzed from a centralized place. But, perhaps even more importantly, the acquisition is evidence of just how attractive IoT is to an insurance industry that sees data as a potential goldmine in gaining a detailed understanding of behavior and risk in a huge range of contexts and across demographics. It’s worth noting that HSB has invested in relayr before - back in 2016 the company put money into the startup’s series B round of funding. What relayr and Munich Re had to say relayr CEO Josef Brunner had this to say about the acquisition: “We are delighted to strengthen our relationship with Munich Re/HSB to push digitalization in commercial and industrial markets and strive for our mission to help commercial and industrial businesses stay relevant… The unique combination of the companies demonstrates the importance to deliver business outcomes to customers and the need to combine first-class technology and its delivery with powerful financial and insurance offerings. This transaction is a great opportunity to build a global category leader.” Meanwhile, Torsten Jeworrek from Munich Re’s Board of Management said that the acquisition “supports our strategy to combine our knowledge of risk, data analysis skills and financial strength with the technological expertise of relayr. This is our basis to develop new ideas for tomorrow’s commercial and industrial worlds.” You can hear in the enthusiasm of both statements that this is a deal that works incredibly well for both parties. Munich Re now has its hands on an Industrial IoT startup that is already making headway in the market, while relayr now has the stability and support it needs to grow its business further. It will be interesting to see how the acquisition influences relayr’s product development and how involved its parent company will be. Read next Why the Industrial Internet of Things (IIoT) needs Architects Infosys and Siemens collaborate to build IoT solutions on MindSphere IoT Forensics: Security in an always connected world where things talk

Tech, Culture, and Society Google is using AI to help organizations detect and report child sexual abuse material online (Google blog) In Tight Labor Market, Inmates Learn to Code. Indiana program teaches women in prison to write computer code (Wall Street Journal) [divider style="normal" top="20" bottom="20"] Tech, Community and Open Source Facebook Reality Labs launch SUMO Challenge to improve 3D scene understanding and modeling algorithms (Packt Hub) Ubuntu free Linux Mint Project, LMDE 3 ‘Cindy’ Cinnamon, released (Packt Hub) Huawei may be bricking modded devices to kill community development (Notebook Check) Linus Torvalds thinks Intel has gotten better about keeping the Linux open-source community in the loop with CPU security problems, but it started out really badly. And it's still not fair that Linux has to fix hardware problems. (ZDNet) [divider style="normal" top="20" bottom="20"] Tech, Governance, and Politics UN meetings ended with US & Russia avoiding formal talks to ban AI enabled killer robots (Packt Hub) Japanese city becomes the first in the country to deploy blockchain-based voting (Chepicap) Japanese police to test AI use for better investigations of criminal activity (Japan Times) [divider style="normal" top="20" bottom="20"] Tech, Business, and Startups How Netflix uses AVA, an Image Discovery tool to find the perfect title image for each of its shows (Packt Hub) Google's Doors Hacked Wide Open By Own Employee (Forbes) After Patent Office Rejection, It is Time For Google To Abandon Its Attempt to Patent Use of Public Domain Algorithm (Electronic Frontier Foundation) Skype U-turns on Snapchat-like features after complaints (BBC) Swiss startup, Avrios has quietly raised $14M for an AI-fueled fleet management platform (Techcrunch) [divider style="normal" top="20" bottom="20"] Tools, Announcements, and Releases PyTorch-based HyperLearn Statsmodels aims to implement a faster and leaner GPU Sklearn (Packt Hub) The future of Jenkins is cloud native and a faster development pace with increased stability (Packt Hub) Deep Angel: AI that erases objects from images (MIT) Wasabi: A framework for dynamic analysis of WebAssembly programs (Wasabi software Labs)        

Last week, James Carter and Stephen Smalley presented the architecture and security mechanisms of two operating systems, Zephyr and Fuchsia at the Linux Security Summit 2018. James and Stephen are computer security researchers in the Information Assurance Research organization of the US National Security Agency (NSA). They discussed the current concerns in the operating systems and their contribution and others to further advance security of these emerging open source operating systems. They also compared the security features of Zephyr and Fucshia to Linux and Linux-based systems such as Android. Zephyr Zephyr is a scalable real-time operating system (RTOS) for IoT devices, supporting cross-architecture with security as the main focus. It targets devices that are resource constrained seeking to be a new "Linux" for little devices. Protection mechanisms in Zephyr Zephyr introduced basic hardware-enforced memory protections in the v1.8 release and these were officially supported in the v1.9 releases. The microcontrollers should either have a memory protection unit (MPU) or a memory management unit (MMU) to support these protection mechanisms. These mechanisms provide protection by the following ways: They enforce Read Only/No Execute (RO/NX) restrictions to protect the read-only data from tampering. Provides runtime support for stack depth overflow protections. The researchers’ contribution was to review the basic memory protections and also develop a set of kernel memory protection tests that were modeled after subset of lkdtm tests in Linux from KSPP. These tests were able to detect bugs and regression in Zephyr MPU drivers and are now a part of the standard regression testing that Zephyr performs on all future changes. Userspace support in Zephyr In previous versions, everything ran in a supervisor mode, so Zephyr introduced a userspace support in v1.10 and v1.11. This requires the basic memory protection support and MPU/MMU. It provides basic support for user mode threads with isolated memory. The researchers contribution, here, was to develop userspace tests to verify some of the security-relevant properties for user mode threads, confirm the correctness of x86 implementation, and validate initial ARM and ARC userspace implementations. App shared memory: A new feature contributed by the researchers Originally, Zephyr provided an access to all the user threads to the global variables of all applications. This imposed high burden on application developers to, Manually organize application global variable memory layout to meet (MPU-specific) size/alignment restrictions. Manually define and assign memory partitions and domains. To solve this problem, the researchers developed a new feature which will come out in v1.13 release, known as App Shared Memory, having features: It is a more developer-friendly way of grouping application globals based on desired protections. It automatically generates linker script, section markings, memory partition/domain structures. Provides helpers to ease application coding. Fucshia Fucshia is an open source microkernel-based operating system, primarily developed by Google. It is based on a new microkernel called Zircon and targets modern hardware such as phones and laptops. Security mechanisms in Fucshia Microkernel security primitives Regular handles: Through handles, userspace can access kernel objects. They can identify both the object and a set of access rights to the object. With proper rights, one can duplicate objects, pass them across IPC, and obtain handles to child objects. Some of the concerns pointed out in regular handles are: If you have a handle to a job, you can get handle to anything in the job using object_get_child() Leak of root job handle Refining default rights down to least privilege Not all operations check access rights Some rights are unimplemented, currently Resource handles: These are a variant of handles for platform resources such as, memory mapped I/O, I/O port, IRQ, and hypervisor guests. Some of the concerns pointed out in resource handles are: Coarse granularity of root resource checks Leak of root resource handle Refining root resource down to least privilege Job policy: In Fucshia, every process is a part of a job and these jobs can further have child jobs. Job policy is applied to all processes within the job. These policies include error handling behavior, object creation, and mapping of WX memory. Some of the concerns pointed out in job policies are: Write execute (WX) is not yet implemented Inflexible mechanism Refining job policies down to least privilege vDSO (virtual dynamic shared object) enforcement: This is the only way to invoke system calls and is fully read-only. Some of the concerns pointed out in vDSO enforcement are: Potential for tampering with or bypassing the vDSO, for example, processs_writes_memory() allows you to overwrite the vDSO Limited flexibility, for example,  as compared to seccomp Userspace mechanisms Namespaces: It is a collection of objects that you can enumerate and access. Sandboxing: Sandbox is the configuration of a process’s namespace created based on its manifest. Some of the concerns pointed out in namespaces and sandboxing are: Sandbox only for application packages (and not system services) Namespace and sandbox granularity No independent validation of sandbox configuration Currently uses global /data and /tmp To address the aforementioned concerns the researchers suggested a MAC framework. It could help in the following ways: Support finer-grained resource checks Validate namespace/sandbox It could help control propagation, support revocation, apply least privilege Just like in Android, it could provide a unified framework for defining, enforcing, and validating security goals for Fuchsia. This was a sneak peek from the talk. To know more about the architecture, hardware limitations, security features of Zephyr and Fucshia in detail, watch the presentation on YouTube: Security in Zephyr and Fucshia - Stephen Smalley & James Carter, National Security Agency. Cryptojacking is a growing cybersecurity threat, report warns Red Hat Enterprise Linux 7.6 Beta released with focus on security, cloud, and automation Red Hat Enterprise Linux 7.6 Beta released with focus on security, cloud, and automation

data Artisans, the stream processing team behind Apache Flink, today unveiled data Artisans Streaming Ledger at the Flink Forward Conference in Berlin. Streaming Ledger, according to data Artisans "extends the scope of stream processing with fast, serializable ACID transactions directly on streaming data." This is significant because previously performing serializable transactions across streaming data - without losing data consistency - was impossible. If data Artisans are right about Streaming Ledger that's not only good news for them, it's good news for developers and system architects struggling to manage streaming data within their applications. Read next: Say hello to streaming analytics How Streaming Ledger fits into a data streaming architecture Streaming Ledger is essentially a new component within data Artisans existing data streaming architecture, which includes Apache Flink. [caption id="attachment_22285" align="aligncenter" width="607"] The architecture of data Artisans Platform (via data-artisans.com)[/caption] Stephan Ewen, co-founder and CTO at data Artisans said that "guaranteeing serializable ACID transactions is the crown discipline of data management." He also claimed that Streaming Ledger does "something that even some large established databases fail to provide. We are very proud to have come up with a way to solve this problem for real time data streams, and make it fast and easy to use." Read next: Apache Flink version 1.6.0 released! How Streaming Ledger works It's not easy for streaming technologies to process event streams across shared states and tables. That's why streaming is so tough (okay, just about impossible) when used with relational databases. However, Streaming Ledger works by isolating tables from concurrent changes as they are modified in transactions. This helps to ensure consistency is maintained across your data, as you might expect in a really robust relational database. [caption id="attachment_22287" align="aligncenter" width="1263"] data Artisans Streaming Ledger functionality (via data-artisans.com)[/caption] data Artisans have also produced a white paper that details how Streaming Ledger works as well as further information about why you want to use it. You need to provide details to gain access, but you can find it here.

Jenkins has been a success for more than a decade now mainly due to its extensibility, community and it being general purpose. But there are some challenges and problems in it which have become more pronounced now. Kohsuke Kawaguchi, the creator of Jenkins, is now planning to take steps to solve these problems and make the platform better. Challenges in Jenkins With growing competition in the continuous integration (CI), The following limitations in Jenkins come in the way of teams. Some of them discourage admins from using and installing plugins. Service instability: CI is a critical service nowadays. People are running bigger workloads, needing more plugins, and high availability. Services like instant messaging platforms need to be online all the time. Jenkins is unable to keep up with this expectation and a large instance requires a lot of overhead to keep it running. It is common for someone to restart Jenkins every day and that delays processes. Errors need to be contained to a specific area without impacting the whole service. Brittle Configuration: Installing/upgrading plugins and tweaking job settings have caused side effects. This makes admins lose confidence to make these changes safely. There is a fear that the next upgrade might break something and cause problems for other teams and affect delivery. Assembly required: Jenkins requires an assembly of service blocks to make it work as a whole. As CI has become mainstream, the users want something that can be deployed in a few clicks. Having too many choices is confusing and leads to uncertainty when assembling. This is not something that can be solved by creating more plugins. Reduced Development Velocity: It is difficult for a contributor to make a change that spans across multiple plugins. The tests do not give enough confidence to shop code; many of them do not run automatically and the coverage is not deep. Changes and steps to make Jenkins better There are two key efforts here, Cloud Native Jenkins and Jolt. Cloud native is a CI engine that runs on Kubernetes and has a different architecture, Jolt will continue in Jenkins 2 and add faster development pace with increased stability. Cloud Native Jenkins It is a sub-project in the context of Cloud Native SIG. It will use Kubernetes as runtime. It will have a new extensibility mechanism to retain what works and to continue the development of the the automation platform's ecosystem. Data on Cloud Managed Data Services to achieve high availability and horizontal scalability, alleviating admins from additional responsibilities. Configuration as Code and Jenkins Evergreen help with the brittleness. There are also plans to make Jenkins secure by default design and to continue with Jenkins X which has been received very well. The aim is to get things going in 5 clicks through easy integration with key services. Jolt in Jenkins Cloud Native Jenkins is not usable for everyone and targets only a particular set of functionalities. It also requires a platform which has a limited adoption today, so Jenkins 2 will be continued at a faster pace. For this Jolt in Jenkins is introduced. This is inspired by what happened to the development of Java SE; change in the release model by shedding off parts to move faster. There will a major version number change every couple of months. The platform needs to be largely compatible and the pace needs to justify any inconvenience put on the users. For more, visit the official Jenkins Blog. How to build and enable the Jenkins Mesos plugin Google Compute Engine Plugin makes it easy to use Jenkins on Google Cloud Platform Everything you need to know about Jenkins X, the new cloud native CI/CD solution on Kubernetes

The United States and Russia were among a small number of countries that blocked the U.N. from moving toward talks on whether to ban so-called killer robots. As per Politico, a week of UN meetings in Geneva, concluded in the early hours of Saturday. During the meetings a group at the United Nations' Convention on Conventional Weapons (CCW) discussed whether to take negotiations on fully autonomous weapons powered by artificial intelligence to a formal level that could lead to a treaty banning them. However, a list of non-binding recommendations that participating countries agreed on, dodged the question of whether to move on to formal negotiations. Mary Wareham, coordinator of the Campaign to Stop Killer Robots, said that Russia, the U.S., South Korea, Israel and Australia were the main countries to oppose this call. "It's a disappointment, of course, that a small minority of large military powers can hold back the will of the majority," she said. Mary Wareham’s group represents 75 non-governmental organizations in 32 countries fighting for a ban on weapons that use AI technology to choose their targets. It says 26 countries endorse a full ban on the weapons. Throughout the meeting, many of those countries reiterated their call for strong regulation, pushing for the U.N. to start formal negotiations at least by next year. Doing so will be the next step toward binding international rules but opponents of the ban stood firm. The document issued at the end of the meeting recommends that non-binding talks should continue. Russian censorship board threatens to block search giant Yandex due to pirated content The New AI Cold War Between China and the USA Microsoft claims it halted Russian spearphishing cyberattacks

Netflix, the video-on-demand streaming company, has seen a surge in its growing number of users every day as well as in the viewership of its TV shows. It is constantly striving to provide an enriching experience to its viewers. To keep pace with the ever-increasing demands of user experience, Netflix is introducing a collection of tools and algorithms to make its content more audience relevant. AVA( Aesthetic Visual Analysis)- analyses large volumes of images obtained from video frames of a particular TV show to set as the title image for that show. Netflix understands that a more visually appealing title image plays an incredibly important role assisting a viewer find new shows and movies to watch. How title images are selected normally Usually, content editors had to go through tens of thousands of video frames for a show, to select a good title image. To give you a gist of the effort required-  a single one-hour episode of ‘Stranger Things’, consists of nearly 86,000 static video frames. Imagine sieving through each one of these frames painstakingly to find the perfect title image that will not only connect with the viewers, but also give them a gist of the storyline. To top it all up, the number of frames can go up to a million depending on the number of episodes in a show. This task of manually screening the frames is almost impossible and labor intensive, if not ineffective. Additionally, the editors choosing the image stills require an in-depth expertise of the source content that they’re intended to represent. Considering Netflix has an exponentially increasing catalog of shows, this presents a very challenging expectation for the editors to surface meaningful images from videos. Enter AVA, using its image classification algorithms for sorting the right image at the right time. What is AVA? The ever-growing number of images on the internet space has led to challenges in its processing and classification. To address this concern, a research team from University of Barcelona, Spain in collaboration with Xerox corporation has developed a method called Aesthetic Visual Analysis (AVA) as a research project. The project contains a vast database of over 2.5 lakh images combined with metadata such as aesthetic scores for images semantic labels for more than 60 classifications of images and many other characteristics. Using statistical concepts like standard deviation, mean score and variance, AVA rates images. Based on the distributions computed from these statistics, they assess the semantic challenges and choose the right images for the database. AVA primarily alleviates the issues of extensive benchmarking and trains more images. They also enable images to get a better aesthetic appeal. Computing performance can be significantly optimised to have lesser impact on the hardware. You can get more insights by reading the Research paper. The ‘AVA’ approach used at Netflix The process takes place in 3 steps: AVA starts by analysing images obtained through the process of frame annotation. This includes processing and annotating many different variables on every individual frame of video to best derive what the frame contains, and to understand its importance to the story. To keep up pace with the growing catalog of content, Netflix uses the Archer framework to process videos more efficiently. Archer splits the video into very tiny bits to aid parallel video processing. After the frames are obtained, they are subjected to a series of image recognition algorithms to build metadata. Metadata is further classified as visual, contextual and composition metadata.  To give you a brief overview- Visual Metadata: For brightness, sharpness and color Contextual Metadata: This is a combination of elements that are combined to derive meaning from the actions or movement of the actors, objects and camera in the frame. Eg: face detection, Motion estimation, Object Detection and camera shot identification Composition Metadata: For intricate image details based on core principles in photography, cinematography and visual aesthetic design such as depth of field and symmetry. Choosing the right Picture! The ‘best’ image is chosen considering three important aspects– the lead actors, visual range and sensitivity filters. Emphasis is given first to lead actors of the show since they make a visual impact. In order to identify the key character for a given episode, AVA utilizes a combination of face clustering and actor recognition to filter main characters from secondary characters or extras. The next thing, is the diversity of the images present in the video frames which includes camera positions, image details such as brightness, color, contrast to name a few. Keeping these in mind, image frames are easy to group based on similarities. This helps in developing image support vectors. The vectors primarily assist in designing an image diversity index where all the relevant images collected for an episode or even a movie can be scored based on visual appeal. Sensitive factors such as violence, nudity and advertisements are filtered and are allotted low priority in the image vectors. This way they are screened out completely in the process. Source: Netflix Blog What's in this for Netflix and its users? Netflix’s decision to use AVA will not only save manual labour, but also reduce the cost involved in having manpower source through millions of images in order to get that one perfect shot. This unique approach will help in obtaining meaningful images from video and thus enable creative teams to invest time in designing stunning artwork . As for its users, a good title image means establishing a deeper connection to the show’s characters and storyline, thus improving their overall experience. To understand the intricate  workings of AVA, you can read Netflix engineering team’s original post on this topic on Medium. How everyone at Netflix uses Jupyter notebooks from data scientists, machine learning engineers, to data analysts Netflix releases FlameScope Netflix bring in Verna Myers as new VP of Inclusion strategy to boost cultural diversity

The Linux Mint Project community announced the release of LMDE 3 Cinnamon, codenamed as ‘Cindy’. LMDE(Linux Mint Debian Edition) is a Linux Mint project where the main goal of Linux Mint team is to see how viable their distribution would be and how much work would be necessary if Ubuntu was ever to disappear. LMDE aims to be similar to Linux Mint, but without the use of Ubuntu. Instead, LMDE package base is provided by Debian. LMDE 3 Cindy includes some bug and security fixes. However, the Debian base package stands unchanged. Mint and desktop components are updated continuously. Once ready, the newly developed features get directly into LMDE. These changes are staged for inclusion in the next upcoming Linux Mint point release, which is not yet disclosed. System requirements for LMDE 3 ‘Cindy’ Cinnamon 1GB RAM (2GB recommended for a comfortable usage) 15GB of disk space (20GB recommended) 1024×768 resolution (on lower resolutions, press ALT to drag windows with the mouse if they don’t fit in the screen) Some known issues resolved Locked root account The root account is now locked by default. To use the recovery console (from the Grub menu) or log in as root, one has to first give a new password to root: sudo passwd root Secure Boot If the computer is using Secure Boot one needs to disable it. Debian Stretch and LMDE 3 does not support it. Virtualbox Guest Additions To add support for shared folders, drag and drop, proper acceleration and display resolution in Virtualbox, click on the "Devices" menu of Virtualbox and choose "Insert Guest Additions CD Image". Choose "download" when asked and follow the instructions. Read Installing the VirtualBox Guest Additions for more details. Sound and microphone issues If there’s any issue with the microphone or the sound output, install ‘pavucontrol’. This will add "PulseAudio Volume Control" to the menu. The ‘pavucontrol’ application has more configuration options than the default volume control. Issues with KDE apps If one’s experiencing issues with KDE apps (Okular, Gwenview, KStars..etc), they can run the following command: apt install kdelibs-bin kdelibs5-data kdelibs5-plugins Read more about this release in detail in LMDE 3 Documentation. Facebook and Arm join Yocto Project as platinum members for embedded Linux development Bodhi Linux 5.0.0 released with updated Ubuntu core 18.04 and a modern look Google becomes a new platinum member of the Linux Foundation  

Facebook Reality Labs have launched the Scene Understanding and Modeling SUMO Challenge. The challenge is designed by a group of computer vision researchers at Facebook with collaborators from Stanford, Princeton and Virginia Tech. The goal of the challenge is to aid the development of comprehensive 3D scene understanding and modeling algorithms. For the SUMO challenge, participants are required to generate an instance-based 3D representation of an indoor scene given only a 360-degree RGB-D image taken from a single viewpoint. The generated scene is modeled by a collection of elements, each of which represents one object, such as a wall, the floor, or a chair. Source: Facebook Blog What are the three types of tasks? The SUMO Challenge is organized into three performance tracks based on the output representation of the scene. Participants can join in any of the three increasingly detailed and difficult performance tracks. Bounding Boxes Track: The scene is represented by a collection of oriented bounding boxes. This is similar to the SUN RGB-D Object Detection Challenge. The bounding box is the coordinates of the rectangular border that fully encloses a digital image when it is placed over a page, a canvas, or a screen. Voxels Track: The scene is represented by a collection of oriented voxel grids. A voxel represents a value on a regular grid in three-dimensional space. Meshes Track: The scene is represented by a collection of textured surface meshes.  A mesh is a collection of vertices, edges and faces that defines the shape of a polyhedral object in 3D computer graphics and solid modeling. How are the tasks evaluated? The SUMO evaluation metrics focus on four aspects of the representation: geometry, appearance, semantics, and perception (GASP). Participants will be evaluated on their ability to consistently infer the correct geometry, pose, appearance and semantics of the elements in each scene. The challenge runs from August 29th until November 16th, 2018. The top winners in each track will receive prizes, including cash rewards and NVIDIA Titan X GPUs. 1st prize - winner of mesh track: $2,500 in cash + Titan X GPU 2nd prize - winner of voxel track: $2,000 in cash + Titan X GPU 3rd prize - winner of bounding box track: $1,500 in cash + Titan X GPU Winners will be announced at the SUMO Challenge Workshop on December 2nd at ACCV 2018, where they will present their results. How to Participate Familiarize yourself with the input and output formats. Download the SUMO software and the data set. See the data set page for details. Develop your algorithm. Submit your results using EvalAI. For more information, visit the SUMO Challenge website. Facebook Watch is now available world-wide challenging video streaming rivals, YouTube, Twitch, and more. Facebook launched new multiplayer AR games in Messenger. Facebook to launch AR ads on its news feed to let you try on products virtually.

HyperLearn is a Statsmodel, a result of the collaboration of languages such as PyTorch, NoGil Numba, Numpy, Pandas, Scipy & LAPACK, and has similarities to Scikit Learn. This project started last month by Daniel Hanchen and still has some unstable packages. He aims to make Linear Regression, Ridge, PCA, LDA/QDA faster, which then flows onto other algorithms being faster. This Statsmodels combo incorporates novel algorithms to make it 50% more faster and enables it to use 50% lesser RAM along with a leaner GPU Sklearn. HyperLearn also has an embedded statistical inference measures, and can be called similar to a Scikit Learn's syntax (model.confidence_interval_) HyperLearn’s Speed/ Memory comparison There is a  50%+ improvement on Quadratic Discriminant Analysis (similar improvements for other models) as can be seen below: Source: GitHub Time(s) is Fit + Predict. RAM(mb) = max( RAM(Fit), RAM(Predict) ) Key Methodologies and Aims of the HyperLearn project #1 Parallel For Loops Hyperlearn for loops will include Memory Sharing and Memory Management CUDA Parallelism will be made possible through PyTorch & Numba #2 50%+ faster and leaner Matrix operations that have been improved include  Matrix Multiplication Ordering, Element Wise Matrix Multiplication reducing complexity to O(n^2) from O(n^3), reducing Matrix Operations to Einstein Notation and Evaluating one-time Matrix Operations in succession to reduce RAM overhead. Applying QR Decomposition and then SVD(Singular Value decomposition) might be faster in some cases. Utilise the structure of the matrix to compute faster inverse Computing SVD(X) and then getting pinv(X) is sometimes faster than pure pinv(X) #3 Statsmodels is sometimes slow Confidence, Prediction Intervals, Hypothesis Tests & Goodness of Fit tests for linear models are optimized. Using Einstein Notation & Hadamard Products where possible. Computing only what is necessary to compute (Diagonal of matrix only) Fixing the flaws of Statsmodels on notation, speed, memory issues and storage of variables. #4 Deep Learning Drop In Modules with PyTorch Using PyTorch to create Scikit-Learn like drop in replacements. #5 20%+ Less Code along with Cleaner Clearer Code Using Decorators & Functions wherever possible. Intuitive Middle Level Function names like (isTensor, isIterable). Handles Parallelism easily through hyperlearn.multiprocessing #6 Accessing Old and Exciting New Algorithms Matrix Completion algorithms - Non Negative Least Squares, NNMF Batch Similarity Latent Dirichelt Allocation (BS-LDA) Correlation Regression and many more!         Daniel further went on to publish some prelim algorithm timing results on a range of algos from MKL Scipy, PyTorch, MKL Numpy, HyperLearn's methods + Numba JIT compiled algorithms Here are his key findings on the HyperLearn statsmodel: HyperLearn's Pseudoinverse has no speed improvement HyperLearn's PCA will have over 200% improvement in speed boost. HyperLearn's Linear Solvers will be over 1 times faster i.e  it will show a 100% improvement in speed You can find all the details of the test on reddit.com For more insights on HyperLearn, check out the release notes on Github. A new geometric deep learning extension library for PyTorch releases! NVIDIA leads the AI hardware race. But which of its GPUs should you use for deep learning? Introduction to Sklearn

Chinese internet giant Baidu released ‘EZDL’ on September 1. EZDL allows businesses to create and deploy AI and machine learning models without any prior coding skills. With a simple drag-and-drop interface, it takes only four steps to train a deep learning model that’s built specifically for a business’ needs. This is particularly good news for small and medium sized businesses for whom leveraging artificial intelligence might ordinarily prove challenging. Youping Yu, general manager of Baidu’s AI ecosystem division, claims that EZDL will allow everyone to access AI “in the most convenient and equitable way”. How does EZDL work? EZDL focuses on three important aspects of machine learning: image classification, sound classification, and object detection. One of the most notable features about EZDL is the small size of the training data sets required to create artificial intelligence models. For image classification and object recognition, it requires just 20 to 100 images per label. For sound classification, it needs only 50 audio files at the most. The training can be completed in just 15 minutes in some cases, or a maximum of one hour for more complex models. After a model has been trained, the algorithm can be downloaded as a SDK or uploaded into a public or private cloud platform. The algorithms created support a range of operating systems, including Android and iOS. Baidu also claims an accuracy of more than 90 percent in two-thirds of the models it creates. How EZDL is already being used by businesses Baidu has demonstrated many use cases for EZDL. For example: A home decorating website called ‘Idcool’ uses EZDL to train systems that automatically identify the design and style of a room with 90 percent accuracy. An unnamed medical institution is using EZDL to develop a detection model for blood testing. A security monitoring firm used it to make a sound-detecting algorithm that can recognize “abnormal” audio patterns that might signal a break-in. Baidu is clearly making its mark in the AI race. This latest release follows the launch of its Baidu Brain platform for enterprises two years ago. Baidu Brain is already used by more than 600,000 developers. Another AI service launched by the company is its conversational DuerOS digital assistant, which is installed on more than 100 million devices. As if all that weren't enough, Baidu has also been developing hardware for artificial intelligence systems in the form of its Kunlun chip, designed for edge computing and data center processing - it’s slated for launch later this year. Baidu will demo EZDL at TechCrunch Disrupt SF, September 5th to 7th at Moscone West, 800 Howard St., San Francisco. For more on EZDL visit the Baidu's website for the project. Read next Baidu Apollo autonomous driving vehicles gets machine learning based auto-calibration system Baidu announces ClariNet, a neural network for text-to-speech synthesis

Last week, the Yocto Project announced that Arm and Facebook will be joining the project as new platinum members. The Yocto Project is an open source collaboration project (originally an Intel Project) that was launched back in 2011. It aims to allow developers to create customized Linux-based systems for embedded products. The Yocto Project comes with a flexible set of tools and offers a space where embedded developers across the globe share technologies, software, and best practices. This helps them build tailored Linux images for embedded and Internet of Things (IOT) devices. According to Rhonda Dirvin, Senior Director, Marketing, Embedded & Automotive Line of Business, Arm, “The Yocto Project provides an excellent framework to facilitate embedded Linux development, and through our membership we will collaborate with the community to further advance Yocto Project’s custom open-source distribution.” Earlier, Linaro, which consolidates and optimizes open source software and tools for the Arm architecture, was considered a competitor of Yocto Project. However, that’s not entirely the case as both the groups have become complementary and Linaro’s Arm toolchain can be used within Yocto Project. Facebook's role in the Yocto Project and embedded Linux Facebook's role has been minor when it comes to embedded Linux. Facebook is said to join the Yocto Project either because of a new project or may be Facebook just wanted to expand its open source presence. “The Yocto Project is the basis for important open source and embedded firmware initiatives. We are happy to lend our support to the Yocto Project community, and look forward to joining with other members in this important work”, said Aaron Sullivan, Director of Hardware Engineering at Facebook The Yocto Project currently has more than 22 active members. “We are delighted to welcome Arm and Facebook to the Yocto Project at the Platinum level. With their continued support, we are furthering the embedded systems ecosystem and the Yocto Project as a whole.” mentioned Lieu Ta, Senior Director of Governance and Business Operations at Wind River and Chair of the Yocto Project Advisory Board. Yocto Project seems to be continually growing with Facebook and Arms joining in. Yocto will benefit from Facebook and Arm’s technical and financial support to consolidate it as a “secure, stable and adaptable industry standard”. For more information be sure to check out the official Yocto Project blog post. Read next Arm unveils its Client CPU roadmap designed for always-on, always-connected devices Facebook’s AI algorithm finds 20 Myanmar Military Officials guilty of spreading hate and misinformation, leads to their ban A new conservative employee group within Facebook to protest Facebook’s “intolerant” liberal policies