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Tech News

3711 Articles
article-image-introducing-intels-openvino-computer-vision-toolkit-for-edge-computing
Pravin Dhandre
17 May 2018
2 min read
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Introducing Intel's OpenVINO computer vision toolkit for edge computing

Pravin Dhandre
17 May 2018
2 min read
Almost after a week of Microsoft’s announcement about its plan to develop a computer vision develop kit for edge computing, Intel smartly introduced its latest offering, called OpenVINO in the domain of Internet of Things (IoT) and Artificial Intelligence (AI). This toolkit is a comprehensive computer vision solution, that brings computer vision and deep learning capabilities to the edge devices smoothly. OpenVINO (Open Visual Inference and Neural Network Optimization) toolkit supports popular open source frameworks like OpenCV, Caffe and TensorFlow. It supports and works with Intel’s traditional CPUs, AI chips, field programmable gate array (FPGA) chips and Movidius vision processing unit (VPU). The toolkit presumes the potential to address a wide number of challenges faced by developers in delivering distributed and end-to-end intelligence. With OpenVINO, developers can simply streamline their deep learning inferences and deploy high-performance computer vision solutions across a wide range of use-cases. Computer vision limitations related to bandwidth, latency and storage are expected to be resolved to an extent. This toolkit would also help developers in optimizing AI-integrated computer vision applications and scaling distributed vision applications which generally needs a complete redesign of solution. Until now, edge computing has been more of a prospect for an IoT market. With OpenVINO, Intel stands as the the only industry leader in delivering IoT solutions from the edges, providing an unparalleled solution to meet AI needs of businesses. OpenVINO is already being used by companies like GE Healthcare, Dahua, Amazon Web Services and Honeywell across their Digital Imaging and IoT Solutions. To explore more information on its capabilities and performance, visit Intel’s official OpenVINO product documentation. A gentle note to readers: OpenVINO  is not to be confused with Openvino, an open-source winery and wine-backed cryptoasset, Openvino. Should you go with Arduino Uno or Raspberry Pi 3 for your next IoT project? AWS Greengrass brings machine learning to the edge Cognitive IoT: How Artificial Intelligence is remoulding Industrial and Consumer IoT
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Richard Gall
17 May 2018
3 min read
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Twitter's disdain for third-party clients gets real

Richard Gall
17 May 2018
3 min read
There are a huge range of Twitter clients out there offering alternative ways to access Twitter. For many users, these provide a better experience. They provide a level of functionality that Twitter's own suite of applications don't. However, Twitter has revealed that this August it will be bringing in new restrictions and limitations on how these applications are built. Of course, for Twitter these restrictions aren't restrictions as such; it's actually a new developer API called 'Account Activity API'. The reason for the change is that it will give Twitter more power and control over what developers build - it also allows them to monetize the developer API in a different way too. [caption id="attachment_19298" align="aligncenter" width="300"] From blog.twitter.com[/caption] This is undoubtedly going to make applications like Twitterific significantly worse.  For Twitter, that might make some sense. It goes without saying that the platform would prefer users to use its own applications to access the service. But for developers and users of these applications, it might make life a little more difficult. What restrictions will third party Twitter clients face? Twitter will be changing the way developers of third party Twitter clients access Twitter. Back in April, Twitter explained the changes it planned to make to it's developer API in a Twitter thread: https://twitter.com/TwitterDev/status/982346370882461696 Because of the delay, the date that this change will come into effect is now August 16 2018. Essentially, on that date Twitter will turn off a number of legacy services including Site Streams and User Streams. Developers will then have to migrate to the new Account Activity API. Learn more about migrating to Account Activity API here. What impact will Twitter's change have? As already mentioned, this is going to have a big impact on the way developers build third party Twitter clients. The knock-on effect on users will be substantial. Essentially the 'real-time' experience of Twitter that you get in Twitter's own applications will be missing. Users will have to refresh their Twitter feed; push notifications are unlikely to work, and Direct Messages may be hampered too, especially on mobile. A lot of people are very unhappy about the changes Twitter is making. On Twitter the response was incredibly negative: https://twitter.com/objectivechad/status/982353715708362752 https://twitter.com/merrickluo/status/983001459078742021 https://twitter.com/DanDuivel/status/982806945772912641 However, there was some support, or awareness for the change when it was announced... https://twitter.com/trisweb/status/984005164372779010 Of course, this is one step in Twitter trying to give itself a boost - the company has been struggling for some time. However, you do wonder how successful this change will be for Twitter. Although the premium Account Activity API costs around $11.60 a month, this is only open to applications with less than 250 users. Clearly, this isn't going to be feasible for many of the leading Twitter clients with thousands of users. Read next: Facebook’s F8 Conference – 5 key announcements The Cambridge Analytica scandal and ethics in data science Sentiment Analysis of the 2017 US elections on Twitter
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article-image-amazon-open-sources-amazon-sumerian-its-popular-ar-vr-app-toolkit
Sugandha Lahoti
17 May 2018
2 min read
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Amazon open sources Amazon Sumerian, its popular AR/VR app toolkit

Sugandha Lahoti
17 May 2018
2 min read
Last year at re:invent 2017, Amazon unveiled Amazon Sumerian, a toolkit for easily creating AR, VR, and 3D apps. Now Amazon has open-sourced it to allow all developers to create compelling virtual environments and scenes for their AR, VR, and 3D apps without having to acquire or master specialized tools. The open sourcing of Amazon Sumerian comes as a part of Amazon’s strategy to expand its reach and revenues by offering its cloud services to the largest number of developers, startups, and organizations as possible. As Kyle Roche, the GM of Amazon Sumerian, puts it “We are targeting enterprises who don’t have the talent in-house. Tackling new tech can sometimes be too overwhelming, and this is one way of getting inspiration or prototypes going. Sumerian is a stable way to bootstrap ideas and start conversations. There is a huge business opportunity here.” Most importantly, with Amazon Sumerian, you don’t necessarily need 3D graphics or programming experience to build rich, interactive VR and AR scenes. And hence, open sourcing Sumerian will only give it more traction from both non-developers, and trained professionals alike. Amazon Sumerian is equipped with multiple user-friendly features. Editor: A web-based editor for constructing 3D scenes, importing assets, scripting interactions, and special effects, with cross-platform publishing. Object Library: a library of pre-built objects and templates. Asset Import: Upload 3D assets to use in your scene. Sumerian supports importing FBX, OBJ, and Unity projects. Scripting Library: provides a JavaScript scripting library via its 3D engine for advanced scripting capabilities. Hosts: animated, lifelike 3D characters that can be customized for gender, voice, and language. Amazon Sumerian also has baked in integration with Amazon Polly and Amazon Lex to add speech and natural language into Sumerian hosts. Additionally, the scripting library can be used with AWS Lambda allowing the use of the full range of AWS services. The VR and AR apps created using Sumerian ca run in browsers that support WebGL or WebVR and on popular devices such as the Oculus Rift, HTC Vive, and those powered by iOS or Android. You can learn more details by visiting the Amazon Sumerian homepage and browsing through Sumerian Tutorials. Google open sources Seurat to bring high precision graphics to Mobile VR [news] Verizon launches AR Designer, a new tool for developers [news] Getting started with building an ARCore application for Android [tutorial]
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article-image-what-google-redhat-oracle-and-others-announced-at-kubercon-cloudnativecon-2018
Savia Lobo
17 May 2018
6 min read
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What Google, RedHat, Oracle, and others announced at KubeCon + CloudNativeCon 2018

Savia Lobo
17 May 2018
6 min read
Earlier this month, 4000+ developers attended the Cloud Native Computing Foundation’s flagship event, KubeCon + CloudNativeCon 2018 conference, held at Copenhagen, Europe from May 2nd to 4th. This conference focussed on a series of announcements on microservices, containers, and other open source tools for building applications for the web. Top vendors including Google, RedHat, Oracle, and many more announced a myriad of releases and improvements with respect to Kubernetes. Read our article on Big vendor announcements at KubeCon + CloudNativeCon Europe. Let’s brush through the top 7 vendors and their release highlights in this conference. Google released Stackdriver Kubernetes Monitoring and open sourced gVisor Released in beta, the Stackdriver Kubernetes Monitoring enables both developers and operators to use Kubernetes in a comprehensive fashion and also simplifies operations for them. Features of Stackdriver Kubernetes Monitoring include: Scalable Comprehensive Observability: Stackdriver Kubernetes Monitoring sums up logs, events and metrics from the Kubernetes environment to understand the behaviour of one’s application. These are rich, unified set of signals which are used by developers to build higher quality applications faster. It also helps operators speed root cause analysis and reduce mean time to resolution (MTTR). Seamless integration with Prometheus: The Stackdriver Kubernetes Monitoring integrates seamlessly with Prometheus--a leading Kubernetes open source monitoring approach--without any change. Unified view: Stackdriver Kubernetes Monitoring provides a unified view into signals from infrastructure, applications and services across multiple Kubernetes clusters. With this, developers, operators and security analysts, can effectively manage Kubernetes workloads. This allows them to easily observe system information from various sources, in flexible ways. Some instances include, inspecting a single container, or scaling up to explore massive, multi-cluster deployments. Get started on-cloud or on-premise easily: Stackdriver Kubernetes Monitoring is pre-integrated with Google Kubernetes Engine. Thus, one can immediately use it within their Kubernetes Engine workloads. It is easily integrated with Kubernetes deployments on other clouds or on-premise infrastructure. Hence, one can access a unified collection of logs, events, and metrics for their application, regardless of where the containers are deployed. Also, Google has open-sourced gVisor, a sandboxed container runtime. gVisor, which is lighter than a Virtual machine, enables secure isolation for containers. It also integrates with Docker and Kubernetes and thus makes it simple to run sandboxed containers in production environments. gVisor is written in Go to avoid security pitfalls that can plague kernels. RedHat shared an open source toolkit called Operator Framework RedHat in collaboration with Kubernetes open source community has shared the Operator Framework to make it easy to build Kubernetes applications. The Operator Framework is an open source toolkit designed in order to manage Kubernetes native applications named as Operators in an effective, automated and scalable manner. The Operator Framework comprises of an: Operator SDK that helps developers in building Operators based on their expertise. This does not require any knowledge of the complexities of Kubernetes API. Operator Lifecycle Manager which supervises the lifecycle of all the operators running across a kubernetes cluster. It also keep a check on the services associated with the operators. Operator Metering, which is soon to be added, allows creating a usage report for Operators providing specialized services. Oracle added new open serverless support and key Kubernetes features to Oracle Container Engine According to a report, security, storage and networking are the major challenges that companies face while working with containers. In order to address these challenges, the Oracle Container Engine have proposed some solutions, which include getting new governance, compliance and auditing features such as Identity and Access Management, role-based access control, support for the Payment Card Industry Data Security Standard, and cluster management auditing capabilities. Scalability features: Oracle is adding support for small and virtualized environments, predictable IOPS, and the ability to run Kubernetes on NVIDIA Tesla GPUs. New networking features: These include load balancing and virtual cloud network. Storage features: The company has added the OCI volume provisioner and flexvolume driver. Additionally, Oracle Container Engine features support for Helm and Tiller, and the ability to run existing apps with Kubernetes. Kublr announced that its version 1.9 provides easy configuration of Kubernetes clusters for enterprise users Kublr unleashed an advanced configuration capability in its version 1.9. This feature is designed to provide customers with flexibility that enables Kubernetes clusters to meet specific use cases. The use cases include: GPU-enabled nodes for Data Science applications Hybrid clusters spanning data centers and clouds, Custom Kubernetes tuning parameters, and Meeting other advanced requirements. New features in the Kublr 1.9 include: Kubernetes 1.9.6 and new Dashboard Improved backups in AWS with full cluster restoration An introduction to Centralized monitoring, IAM, Custom cluster specification Read more about Kublr 1.9 on Kublr blog. Kubernetes announced the availability of Kubeflow 0.1 Kubernetes brought forward a power-packed package for tooling, known as Kubeflow 0.1. Kubeflow 0.1 provides a basic set of packages for developing, training, and deploying machine learning models. This package: Supports Argo, for managing ML workflows Offers Jupyter Hub to create interactive Jupyter notebooks for collaborative and interactive model training. Provides a number of TensorFlow tools, which includes Training Controller for native distributed training. The Training Controller can be configured to CPUs or GPUs and can also be adjusted to fit the size of a cluster by a single click. Additional features such as a simplified setup via bootstrap container, improved accelerator integration, and support for more ML frameworks like Spark ML, XKGBoost, and sklearn will be released soon in the 0.2 version of KubeFlow. CNCF(Cloud Native Computing Foundation) announced a new Certified Kubernetes Application Developer program The Cloud Native Computing Foundation has successfully launched the Certified Kubernetes Application Developer (CKAD) exam and corresponding Kubernetes for Developers course. The CKAD exam certifies that users are fit to design, build, configure, and expose cloud native applications on top of Kubernetes. A Certified Kubernetes Application Developer can define application resources and use core primitives to build, monitor, and troubleshoot scalable applications and tools in Kubernetes. Read more about this program on the Cloud Native Computing Foundation blog. DigitalOcean launched managed Kubernetes service DigitalOcean cloud computing platform launched DigitalOcean Kubernetes, which is a simple and cost-effective solution for deploying, orchestrating, and managing container workloads on cloud. With the DigitalOcean Kubernetes service, developers can save time and deploy their container workloads without the need to configure things from scratch. The organization has also provided an early access to this Kubernetes service. Read more on the DigitalOcean blog. Apart, from these 7 vendors, many others such as DataDog, Humio, Weaveworks and so on have also announced features, frameworks, and services based on Kubernetes, serverless, and cloud computing. This is not the end to the announcements, read the KubeCon + CloudNativeCon 2018 website to know about other announcements rolled out in this event. Top 7 DevOps tools in 2018 Apache Spark 2.3 now has native Kubernetes support! Polycloud: a better alternative to cloud agnosticism
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Sugandha Lahoti
16 May 2018
2 min read
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SteamVR introduces new controllers for game developers, the SteamVR Input system

Sugandha Lahoti
16 May 2018
2 min read
SteamVR announced new controllers adding accessibility features to the Virtual reality ecosystem. The SteamVR input system, lets you build controller bindings for any game, “even for controllers that didn’t exist when the game was written”, says Valve’s Joe Ludwig in a Steam forum post. What this essentially means is that any past, present or future game can hypothetically add support for any SteamVR compatible controller. Source: Steam community Supported controllers include the XBox One gamepad, Vive Tracker, Oculus Touch, and motion controllers for HTC Vive and Windows Mixed Reality VR headsets. The key-binding system of the SteamVR input system allows users to build binding configurations. Users can adapt the controls of games to take into account user behavior such as left-handedness, a disability, or personal preference. These configurations can also be shared easily with other users of the same game via the Steam Workshop. For developers, the new SteamVR input system means easier adaptation of games to diverse controllers. Developers entirely control the default bindings for each controller type. They can also offer alternate control schemes directly without the need to change the games themselves. SteamVR Input works with every SteamVR application; it doesn’t require developers to update their app to support it. Hardware designers are also free to try more types of input, apart from Vive Tracker, Oculus Touch etc. They can expose whatever input controls exist on their device and then describe that device to the system. Most importantly, the entire mechanism is captured in an easy to use UI that is available in-headset under the Settings menu. Source: Steam community For now, SteamVR Input is in beta. Details for developers are available on the OpenVR SDK 1.0.15 page. You can also see the documentation to enable native support in your applications. Hardware developers can read the driver API documentation to see how they can enable this new system for their devices. Google open sources Seurat to bring high precision graphics to Mobile VR Oculus Go, the first stand-alone VR headset arrives! Google Daydream powered Lenovo Mirage solo hits the market
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article-image-shiny-1-1-0-releasing-soon
Amey Varangaonkar
16 May 2018
2 min read
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Shiny 1.1.0 releasing soon

Amey Varangaonkar
16 May 2018
2 min read
Developers will now find it easier to build interactive web applications using R, with RStudio formally announcing that the release of Shiny 1.1.0 is on the horizon. This is expected to be a major release, with support for asynchronous operations and quite a few other important feature updates. What’s new in Shiny 1.1.0 Shiny 1.1.0 brings asynchronous programming capabilities to R, with the integration of the promises package. The main aim of this is to move away from R’s single-threaded nature and increase the scalability and overall responsiveness of the web application. This is quite an important enhancement, considering a web application traditionally designed in R was quite slow and one-dimensional. Users running a long calculation or task on a web app using Shiny would bring the process to a halt for other users. This will not be the case anymore, with the introduction of asynchronous programming features. Some of the other significant features introduced in this release include: The functions extractStackTrace and formatStackTrace are deprecated and will be removed in the future versions of Shiny Improved support for JavaScript, with a new function for comparing version strings called Shiny.compareVersion() Improved functionality of stack traces and support for deep stack traces for efficient memory allocation File drag and drop feature breaking in the presence of jQuery 3.0 has been fixed Improved error handling Bug fixes for significant performance improvement, and a lot more. You can check the full changelog for Shiny 1.1.0 on Shiny’s official Github page. Shiny has been R’s premier package for designing interactive graphics for web applications, and has been rivalling the likes of Tableau and other Business Intelligence tools. It will be interesting to see how users receive the new features introduced in 1.1.0, especially the asynchronous programming features allowing the web apps to perform faster and more efficiently. Introducing R, RStudio, and Shiny When do we use R over Python? Top 5 programming languages for crunching Big Data effectively
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article-image-google-employees-quit-over-companys-continued-ai-ties-with-the-pentagon
Amey Varangaonkar
16 May 2018
2 min read
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Google employees quit over company’s continued Artificial Intelligence ties with the Pentagon

Amey Varangaonkar
16 May 2018
2 min read
Raising ethical concerns over Google’s continued involvement in developing Artificial Intelligence for military and warfare purposes, about a dozen Google employees have reportedly resigned. Since inception, many Googlers have been against Project Maven - Google’s project with the Pentagon, regarding the supply of machine learning technologies for image recognition and object detection purposes in the military drones. Earlier in April, Google employees had signed a petition, urging Google CEO Sundar Pichai to dissociate themselves from the Department of Defence by pulling out of Project Maven. They were of the opinion that humans, not AI algorithms, should be responsible for the sensitive and potentially life-threatening military work, and Google should invest in the betterment of human lives, not in war. Google had reassured their employees that the technology would be used in a non-offensive manner, and that policies were in effect regarding the use of AI in military projects. However, the resigning employees are of the view that these policies were not being strictly followed. The employees also felt that Google were less transparent about communicating controversial business decisions and were not receptive of the employee feedback like before. One of the employees who has resigned said, “Over the last couple of months, I’ve been less and less impressed with Google’s response and the way our concerns are being listened to.” The resignation of the employees sheds a bad light on Google’s employee retention strategy, and their reputation as a whole. These resignations might encourage more employees to evaluate their position within the company, given the lack of grievance redressal from Google’s end. Surrounded by fierce competition, losing talent to their rivals should be the last thing on Google’s agenda right now, and it will be interesting to see what Google’s plan of action will be in this regard. On the other hand, rivals Microsoft and Amazon have also signed partnerships with the US government, offering the required infrastructure and services to improve the defence functionalities. While there has been no reports of protests by their employees, Google seem to have found themselves in a soup, on ethical and moral grounds. Google Employees Protest against the use of Artificial Intelligence in Military Google News’ AI revolution strikes balance between personalization and the bigger picture Google announce the largest overhaul of their Cloud Speech-to-Text
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article-image-google-compute-engine-plugin-makes-it-easy-to-use-jenkins-on-google-cloud-platform
Savia Lobo
15 May 2018
2 min read
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Google Compute Engine Plugin makes it easy to use Jenkins on Google Cloud Platform

Savia Lobo
15 May 2018
2 min read
Google recently announced the Google Compute Engine Plugin for Jenkins, which helps to provision, configure and scale Jenkins build environments on Google Cloud Platform (GCP). Jenkins is one of the most popular tools for Continuous Integration(CI), a standard practice carried out by many software organizations. CI assists in automatically detecting changes that were committed to one’s software repositories, running them through unit tests, integration tests and functional tests, to finally create an artifact (JAR, Docker image, or binary). Jenkins helps one to define, build and test a process, then run it continuously against the latest software changes. However, as one scales up their continuous integration practice, one may need to run builds across fleets of machines rather than on a single server. With the Google Compute Engine Plugin, The DevOps teams can intuitively manage instance templates and launch build instances that automatically register themselves with Jenkins. The plugin automatically deletes one’s unused instances, once work in the build system has slowed down,so that one only pays for the instances needed. One can also configure the Google Compute Engine Plugin to create build instances as Preemptible VMs, which can save up to 80% on per-second pricing of builds. One can attach accelerators like GPUs and Local SSDs to instances to run builds faster. One can configure build instances as per their choice, including the networking. For instance: Disable external IPs so that worker VMs are not publicly accessible Use Shared VPC networks for greater isolation in one’s GCP projects Apply custom network tags for improved placement in firewall rules One can improve security risks present in CI using the Compute Engine Plugin as it uses the latest and most secure version of the Jenkins Java Network Launch Protocol (JNLP) remoting protocol. One can create an ephemeral build farm in Compute Engine while keeping Jenkins master and other necessary build dependencies behind firewall while using Jenkins on-premises. Read more about the Compute Engine Plugin in detail, on the Google Research blog. How machine learning as a service is transforming cloud Polaris GPS: Rubrik’s new SaaS platform for data management applications Google announce the largest overhaul of their Cloud Speech-to-Text
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Sugandha Lahoti
15 May 2018
2 min read
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Firefox 60 arrives with exciting updates for web developers: Quantum CSS engine, new Web APIs and more

Sugandha Lahoti
15 May 2018
2 min read
Today, web developers are greeted with a new update of the popular Firefox web browser. Firefox 60 hosts a variety of feature additions and updates targeted specifically to the web developer community. Quantum CSS for Android available now Firefox has brought their new CSS engine called Quantum CSS (previously known as Stylo) in Firefox for Android. This engine takes advantage of modern hardware, parallelizing the work across all of the cores in your machine running upto almost 18 times faster. New Web APIs Two new Web APIs have been added. The Web Authentication API has been enabled which allows USB tokens for website authentication. The WebVR API has also been enabled by default on macOS. It provides support for exposing virtual reality devices to web apps. Firefox 60 also brings a new policy engine and Group Policy support for customized enterprise deployments, using Windows Group Policy or a cross-platform JSON file. Changes in Javascript ECMAScript 2015 modules have been enabled by default. The Array.prototype.values() method has been added again. It was disabled due to compatibility issues in earlier versions. Changes in CSS The align-content, align-items, align-self, justify-content, and place-content property values have been updated as per the latest CSS Box Alignment Module Level 3 spec. The paint-order property has been implemented. Changes in Developer Tools In the CSS Pane rules view, the keyboard shortcuts for precise value increments (increase/decrease by 0.1) have changed from Alt + Up/Down to Ctrl + Up/Down and the CSS variable names will now auto-complete. In Responsive Design Mode, a Reload when... dropdown has been added to allow users to enable/disable automatic page reloads when touch simulation is toggled, or simulated user agent is changed. Changes in DOM The dom.workers.enabled pref has been removed, meaning workers can no longer be disabled. PerformanceResourceTiming is now available in workers. The PerformanceObserver.takeRecords() method has been implemented. The Animation.updatePlaybackRate() method has been implemented. The Gecko-only options object storage option of the IDBFactory.open() method has been deprecated. Promises can now be used within IndexedDB code. The entire list of developer centric changes are available on the Mozilla Developer page. You can also file a bug in Bugzilla or see the system requirements of this release. Get ready for Bootstrap v4.1; Web developers to strap up their boots npm v6 is out! What’s new in ECMAScript 2018 (ES9)?
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article-image-android-p-new-features-artificial-intelligence-digital-wellbeing-and-simplicity
Kunal Chaudhari
14 May 2018
9 min read
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Android P new features: artificial intelligence, digital wellbeing, and simplicity

Kunal Chaudhari
14 May 2018
9 min read
Google announced the beta version of Android P at the I/O 2018 conference last week. This is one of the major updates to the mobile operating system since the release of Android 5.0 Lollipop, with a myriad of features like design changes, new animations, better notification system, and plenty of helpful shortcuts that improve the overall user experience. A decade has gone by since Google showcased the first version of Android in 2008. So it was obvious that this 10th version of the OS called for an update that would grab the attention of users, and developers alike. The previous version, Android Oreo, failed to delight users by going beyond their expectations. It holds the least amount of market share when compared to its previous three predecessors. So the stakes are higher than usual this time around for the world’s favorite mobile OS by Google. In his opening keynote, Sundar Pichai, CEO at Google, came all guns blazing with the focus, as usual, on the new developments in AI, a somewhat controversial demo from the Google’s voice assistant, and Google’s very own AI-specific processing units (TPUs). But amidst all these cool AI related stuff, he gave the world a peek into the new features of the much awaited Android P. He spoke of how Google have introduced some key capabilities in Android to help people find the right balance between digital and real life. After some more keynotes and sessions, it became clear that these new Android features have a theme which can be classified into three broad areas: Intelligence, Digital Wellbeing, and Simplicity. Machine intelligence on mobile Machine learning has been a key area of development for Google since the last few years. With each Android release, more and more features have started using these machine learning capabilities. And Android P is a step in this direction to bring AI at the core of the operating system, making smartphones smarter. Here’s a quick rundown of enhancements in this category: Adaptive battery Pretty much found in every user survey, battery life has been a top priority. With Android P, Google has partnered with its AI subsidiary DeepMind, to provide a more consistent battery experience to the users. It uses a ‘deep convolutional neural network’ or in simple words ‘on-device machine learning’, to figure out which apps the users are likely to use in the next few hours and which apps are not going to be used at all throughout the day. This usage pattern is taken into consideration by the Android P operating system to spend battery power on the apps which you are actually going to use. This results in a considerable improvement in the battery performance by the OS, which is mostly required to update the apps in the background. Image source: Google Blogs Adaptive Brightness Another AI-powered feature learns how users set their brightness according to the surrounding ambient lightning. Based on these user preferences Android P automatically sets the brightness, in a power efficient way. Although most smartphones already have ‘auto-brightness’ as an inbuilt feature, the main difference here is, they do not take the user preference and the environmental conditions into a picture. Google claims that more than 50% of the users testing on Android P have stopped adjusting the brightness manually. App Actions Last year in Android Oreo, Google launched a new feature called ‘predicted apps’, which predicts the next app that the user is most likely to launch. If this wasn’t spooky enough, Google released App Actions this year, which predicts the next action or the task that the user is going to perform and pins it on top of the Google Launcher. Image source: Google Blogs Slices This is one interesting feature where Google tries to bring a slice of the app UI to the users while they are searching for the app on the phone. Suppose you were searching for the ride-sharing app Lyft on Google, it would provide a slice of the UI from Lyft in the search drop down with your preferred options. In this case, it might show your predetermined rides to home or work which you can select right then and there from the search menu in Google. This feature totally depends on the developers, if they decide to provide a snapshot of the UI from the app on Google as it risks the users from not visiting the actual app. While all these AI features sound cool and claim to provide a rich user experience, it also factors in the ‘big question’ about the user data. From the looks of it, these features leverage a lot of user data and utilize app usage patterns, which to some or most of the users is quite alarming. Take the case of the recent breach of user data on Facebook. Google claims that these features are a result of ‘on-device machine learning’ where the data is kept private or restricted to just the users’ phone. Image source: Google Blogs Digital wellbeing takes center stage The next set of features and tools is what Google is calling ‘Digital Wellbeing’. The goal here is to enable users to understand their habits, control the demands technology places on their attention, and focus on what actually matters. Digital wellbeing was started by Tristan Harris, a former Product Manager at Google and co-founder of the Center for Humane Technology. While working on the Inbox app, Tristan found himself becoming increasingly disillusioned by the overwhelming demands of the tech industry and wrote a memo on Digital wellbeing that went viral in the company. Sameer Samat, vice president of Product Management at Google, gave an interesting talk at I/O this year, which extended Tristan’s philosophy and talked about the digital wellbeing of the users and how Android P claims to help users achieve it with its brand new set of tools. Image source: Google Blogs Dashboard Just as a Fitbit tracker gauges for activity and informs to motivate you, Google's Android P update includes a dashboard to monitor how long you've been using your phone and specific apps. It's supposed to aid you in understanding what you're spending too much time on so that you can adjust your behavior. App timer While Dashboard gives a summary of the time spent on the phone, it also allows users to tap into the apps they are using and set a time limit on it for daily usage. Once the app crosses the time limit the app icon will soon fade to gray on the home screen and it won’t launch, suggesting that the user has crossed the time limit. Do Not Disturb Do Not Disturb is already available as a feature on Android devices to prevent users from hearing any kind of notification from text or emails. This feature comes in handy particularly when you are in a meeting or away or not paying attention to your phone. The new Do Not Disturb in Android P one step further and takes out all the visual indicators or notifications even if you have the device in your hand allowing you to do better things with your phone, like reading. Google is also adding a feature where you can turn the phone on its face to activate do not disturb automatically. No more dinner interruptions. Wind down Generally, people tend to spend a lot of time on their phones while they are in bed just before sleeping. Previously smartphones used to block the notification light at bedtime but Google is going one step further with the ‘Wind Down’ feature. As your bedtime approaches this feature would turn your screen to grayscale making the apps less tempting. Google hopes this will let users "remember to get to sleep at the time [they] want”. Overall, these features sound like a real step forward taken by Google in making the phones less addictive, but there is no proven research. Much of what we know about these features is based not on peer-reviewed research but on anecdotal data. And if users don’t enable any of these Digital Wellbeing features then the new version of Android isn’t going to do anything better. UI Simplicity once again in vogue One of the key takeaways from the previous versions of Android releases has been the simplicity in the UI. Google has been trying to make the UI more accessible and approachable to the current as well as the new users. Android P is not only banking on the suggestions and patterns from the machine learning capabilities but also making the user experience more simplistic. Gesture-based navigation controls Navigation gestures aren’t new. Mobile operating systems such as webOS, MeeGo, and Blackberry 10 all had previously supported navigation gestures. But iPhone X popularized it with the removal of the home button. this meant that gestures are the only way to navigate the device. This change has been generally appreciated by users as it is simple and easy to learn. Google has introduced Gestures in the Android P to substitute the buttons for various actions such as swipe up to open the recent apps menu called Overview while double tapping it opens the app drawer. Swipe down to return to the home screen, and swipe left and right to switch between the recently-opened apps. Image source: Google Blogs Other features in this segment include manual rotation, smart text selection, quick setting, among others. You can read about these features on the official web page of Android. Beyond Intelligence, simplicity and digital wellbeing, there are hundreds of additional improvements coming in Android P, including security and privacy improvements such as DNS over TLS, encrypted backups, Protected Confirmations and more. The initial reaction to all these features was decidedly mixed; while some praised the evolution of Google’s operating system, others slammed it for looking or adopting similar features from Apple’s iOS. Overall the features look great, but we would like to see some rigorous investigation that suggests that people actually feel empowered while using the latest version of Android. Google still haven’t told us what dessert-themed name the Android update will take, saving the naming announcement for later in the summer, closer to the actual release date. Pancake, Peanut butter, Pumpkin Pie, and Popsicle are some of our top predictions. The Android P Beta is available now on Google Pixel, Essential Phone, OnePlus, Mi, Sony, Essential and Oppo handsets. Top 5 Google I/O 2018 conference Day 1 Highlights Google News’ AI revolution strikes balance between personalization and the bigger picture Google’s Android Things, developer preview 8: First look  
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Richa Tripathi
14 May 2018
3 min read
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What can you expect from the upcoming Java 11 JDK?

Richa Tripathi
14 May 2018
3 min read
After the release of Java Development Kit 10 in March this year, Oracle is all set to release JDK 11. Expected in September 2018 as part of Oracle’s new six-month release cadence for the standard edition of Java, Version 11 has just a handful of features announced so far. JDK 11 is set to be a long-term support unlike JDK 10, hence it will be a reference implementation of Java Platform, Standard Edition (Java SE) 11. JDK 11 is set to receive premier-level support from Oracle until September 2023 and extended support, featuring patches and security alerts, until 2026. Java 11 is also set to lose some capabilities as the support for CORBA, Java EE, and JavaFX will be removed. For now, JDK 11 is set to have four new features, although more are expected later. This post summarizes some details about each of the four JEPs (JDK Enhancement Proposal) currently targeted for JDK 11. Epsilon: An Arbitrarily Low-Overhead Garbage Collector The Epsilon garbage collector, billed as a “no-op” collector, will handle memory allocation without implementing any actual memory reclamation mechanisms. Epsilon’s use cases include testing for performance, memory pressure, and the virtual machine interface. It also could be used for short-lived jobs. Local-Variable Syntax for Lambda Parameters A local-variable syntax for lambda parameters aims to achieve one goal which is to allow var reserved word to be used to declare the formal parameters of an implicitly typed lambda expression. Dynamic Class-File Constants The Java class-file format will be extended to support a new constant pool form, CONSTANT_Dynamic. The goal is to reduce the cost and disruption of developing new forms of materializable class-file constraints. Remove the Java EE and CORBA Modules The Java EE and CORBA modules were deprecated in Java SE 9, with the intent to remove them in a later release—that is now set to be JDK 11. Changes under consideration for Java JDK 11 The builders of Java 11 are also looking at several proposed changes or additions to JDK 11: Adding raw string literals to Java This would make it easier to express character sequences in a readable form, with no Java indicators. It also would make it simpler to supply strings targeted for grammar syntaxes other than Java, as well as supply strings spanning several lines of source code without supplying special indicators. Extending the switch statement This functionality can be used as either a statement or an expression. This also would improve how switch handles nulls. These changes would simplify coding and prepare for pattern matching in switch. Nest-based access control This is a context that aligns with the current notion of nested types in Java. Nests allow classes that are logically part of the same code entity but compiled to distinct class files to access each other’s private members without needing compilers to insert accessibility-broadening bridge methods. To know more about Java 11 JDK read Oracle’s official blog. Read Next 26 new Java 9 enhancements you will love How to recognize Patterns with Neural Networks in Java How to create a standard Java HTTP Client in ElasticSearch
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Savia Lobo
11 May 2018
2 min read
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Red Hat Enterprise Linux 7.5 (RHEL 7.5) now generally available

Savia Lobo
11 May 2018
2 min read
Red Hat recently announced that its latest enterprise distribution, Red Hat Enterprise linux version 7.5 (RHEL 7.5) is now generally available. This release aims at simplifying hybrid computing. The RHEL 7.5 is packed with multiple features for the server administrators and developers. New features in the RHEL 7.5 RHEL 7.5 provides support for Network Bound Disk Encrypted (NBDE) devices, new Red Hat cluster management capabilities, and compliance management features. Enhancements to the cockpit administrator console. Cockpit provides a simplified web interface to help eliminate complexities around Linux system administration. This makes it easier for new administrators, or administrators moving over from non-Linux systems, to better understand the health and status of their operations. Helps cut down storage costs by providing improved compliance controls and security, enhanced usability, and tools to cut down storage costs. Better Integration with Microsoft Windows infrastructure both in Microsoft Azure and on-premise. This includes improved management and communication with Windows Server, more secure data transfers with Azure, and performance improvements when used within Active Directory architectures. If one wishes to use both RHEL and Windows for their network, RHEL 7.5 serves this purpose. Delivers improved software security controls to alleviate risk while also augmenting IT operations. A significant component of these controls is security automation via the integration of OpenSCAP with Red Hat Ansible Automation. This is aimed at facilitating the development of Ansible Playbooks straight from OpenSCAP scans which, in turn, can be leveraged to execute remediations more consistently and quickly across a hybrid IT environment. Provides high availability support for enterprise applications running on Amazon Web Services or Microsoft Azure with Pacemaker support in public clouds via the Red Hat High Availability Add-On and Red Hat Enterprise Linux for SAP® Solutions. To know more about this release in detail read Red Hat official blog. Linux Foundation launches the Acumos Al Project to make AI accessible How to implement In-Memory OLTP on SQL Server in Linux Kali Linux2 released    
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Vijin Boricha
11 May 2018
3 min read
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What to expect from vSphere 6.7

Vijin Boricha
11 May 2018
3 min read
VMware has announced the latest release of the industry-leading virtualization platform vSphere 6.7. With vSphere 6.7, IT organizations can address key infrastructure demands like: Extensive growth in quantity and diversity of applications delivered Increased adoption of hybrid cloud environments Global expansion of data centers Robust infrastructure and application security Let’s take a look at some of the key capabilities of vSphere 6.7: Effortless and Efficient management: vSphere 6.7 is built on the industrial innovations delivered by vSphere 6.5, which advances customer experience to a another level. With vSphere 6.7 you can leverage management simplicity, operational efficiency, and faster time to market, all at scale. It comes with an enhanced vCenter Server Appliance (vCSA), new APIs that improve multiple vCenters deployments, which results in easier management of vCenter Server Appliance, as well as backup and restore. Customers can now link multiple vCenters and have seamless visibility across their environment without external platform services or load balancers dependencies. Extensive Security capabilities: vSphere 6.7 has enhanced its security capabilities from vSphere 6.5. It has added support for Trusted Platform Module (TPM) 2.0 hardware devices and has also introduced Virtual TPM 2.0, where you will notice significant enhancements in both the hypervisor and the guest operating system security. With this capability VMs and hosts cannot be tampered, preventing loading of unauthorized components and this enables desired guest operating system security features. With vSphere 6.7, VM Encryption is further enhanced and more operationally simple to manage, enabling encrypted vMotion across different vCenter instances. vSphere 6.7 has also extended its security features keeping in mind the collaboration between VMware and Microsoft ensuring secured Windows VMs on vSphere. Universal Application Platform: vSphere is now a universal application platform that supports existing mission critical applications along with new workloads such as 3D Graphics, Big Data, Machine Learning, Cloud-Native and more. It has also extended its support to some of the latest hardware innovations in the industry, delivering exceptional performance for a variety of workloads. With collaboration of VMware and Nvidia, vSphere 6.7 has further extended its support for GPUs by virtualizing Nvidia GPUs for non-VDI and non-general-purpose-computing use cases such as artificial intelligence, machine learning, big data and more. With these enhancements, customers are now able to better lifecycle management of hosts, reducing disruption for end-users. VMware plans to invest more in this area in order to bring full vSphere support to GPUs in future releases. Hybrid Cloud Experience is now flawless: Since customers have started looking for hybrid cloud options vSphere 6.7 introduces vCenter Server Hybrid Linked Mode. It makes customers have a unified manageability and visibility across an on-premises vSphere environments running on similar versions and a VMware Cloud on AWS environment, running on a different version of vSphere. To ensure seamless hybrid cloud experience, vSphere 6.7 delivers a new capability, called Per-VM EVC which allows for seamless migration across different CPUs. This is only an overview of the key capabilities of vSphere 6.7. You can know more about this release from VMware vSphere Blog and VMware release. Microsoft’s Azure Container Service (ACS) is now Azure Kubernetes Services (AKS) VMware vSphere storage, datastores, snapshots The key differences between Kubernetes and Docker Swarm
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Savia Lobo
10 May 2018
2 min read
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What's new in Wireshark 2.6 ?

Savia Lobo
10 May 2018
2 min read
In less than ten months of Wireshark’s last release, the Wireshark community has now released Wireshark 2.6. Wireshark is one of the popular tools to analyze traffic over a network interface or a network stream. It is used for troubleshooting, analysis, development and education. Wireshark is based on the Gerald Combs-initiated "Ethereal" project, released under the terms of the GNU General Public License (GNU GPL). Wireshark 2.6 is released with numerous innovations, improvements and bug fixes. The highlight of Wireshark 2.6 is that, it is the last release that will support the legacy (GTK+) user interface. It will not be supported or available in Wireshark 3.0. Major improvements since 2.5, the last version, include: This version now supports HTTP Request sequences. Support for MaxMind DB files, GeoIP and GeoLite Legacy databases has been removed. Windows packages are now built using Microsoft Visual Studio 2017. The IP map feature (the “Map” button in the “Endpoints” dialog) has been removed. Some other improvements since the version 2.4 Display filter buttons can now be edited, disabled, and removed via a context menu directly from the toolbar Support for hardware-timestamping of packets has been added Application startup time has been reduced. Some keyboard shortcut mix-ups have been resolved by assigning new shortcuts to Edit → Copy methods New Protocol Support: Many protocols have been added including the following. ActiveMQ Artemis Core Protocol: This supports interceptors to intercept packets entering and exiting the server. Bluetooth Mesh Protocol : This allows (Bluetooth Low Energy) BLE devices to network together to carry data back to a gateway device, where it can be further routed to the internet. Steam In-Home Streaming discovery protocol: This allows one to use input and output on a single computer, and lets another computer actually handle the rendering, calculations, networking etc. Bug Fix: Dumpcap, a network traffic dump tool which lets one capture packet data from a live network and write the packets to a file, might not quit if Wireshark or TShark crashes. (Bug 1419) To know more about the updates in detail, read Wireshark 2.6.0 Release Notes What is Digital Forensics? Microsoft Cloud Services get GDPR Enhancements IoT Forensics: Security in an always connected world where things talk
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Pravin Dhandre
10 May 2018
2 min read
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Microsoft Open Sources ML.NET, a cross-platform machine learning framework

Pravin Dhandre
10 May 2018
2 min read
Microsoft Corporation at its three day build conference held in Seattle, Washington announced the preview release of a machine learning framework called ML.NET. Developed by the research subsidiary, Microsoft Research, the framework will assist .NET developers in developing their own models for their web apps across Windows, Linux and macOS platform. Developers can infuse the custom machine learning models into applications without much prior experience in building machine learning models. The current release 0.1 is the debut preview compatible with any of the platforms that support .NET Core 2.0 or .NET Framework. Developers can access the framework directly from Github. Apart from the machine learning capabilities, this debut preview of ML.NET also uncovers draft of .NET APIs schemed for developing models for prediction, and training of machine learning models, different machine learning algorithms and core ML data structures. Although it is the first release, Microsoft and its team have been using this framework in their various product groups like Azure, Bing and Windows. Microsoft has also mentioned clearly that soon, ML.NET will include more advanced machine learning scenarios such as recommendation systems and anomaly detection. Popular concepts like deep learning, and support for libraries like TensorFlow, CNTK, and Caffe2 would be added. Support for general machine learning libraries like Accord.NET framework would also be included in the near soon release. The framework would also add miscellaneous support to ONNX, scaling out on Azure, Better GUI for ML tasks simplification and integration support with VS Tools. To follow the progress on this framework, visit .NET Blog on Microsoft’s official site. Azure meets Artificial Intelligence Microsoft’s Azure Container Service (ACS) is now Azure Kubernetes Services (AKS) Google I/O 2018 conference Day 1 Highlights: Android P, Android Things, ARCore, ML kit and Lighthouse  
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