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Last week, Google notified its users that the ‘stable channel’ desktop Chrome browser is being updated to version 78.0.3904.87 for Windows, Mac, and Linux and will be rolled out in the coming weeks. This comes after some external researchers found two high severity vulnerabilities in the Chrome web browser. The first zero-day vulnerability, assigned CVE-2019-13720, was found by two malware researchers Anton Ivanov and Alexey Kulaev from Kaspersky, a private internet security solutions company. This vulnerability is present in Chrome’s PDFium library. Google has confirmed that this vulnerability still “exists in the wild.” The other vulnerability CVE-2019-13721 was found by banananapenguin and affects Chrome's audio component. No exploitation of this vulnerability has been reported so far. Google has not revealed the technical details of both vulnerabilities. “Access to bug details and links may be kept restricted until a majority of users are updated with a fix. We will also retain restrictions if the bug exists in a third party library that other projects similarly depend on, but haven’t yet fixed.” Both vulnerabilities are use-after-free vulnerabilities, which means that they have a type of memory flaw that can be leveraged by hackers to execute arbitrary code.  The Kaspersky researchers have named the CVE-2019-13720 vulnerability as Operation WizardOpium, as they have not been able to establish a definitive link of this vulnerability with any known threat actors.  According to Kaspersky, this vulnerability leverages a waterhole-style injection on a Korean-language news portal. This enabled a malicious JavaScript code to be inserted on the main page, which in turn, loads a profiling script from a remote site. The main index page then hosts a small JavaScript tag that loads the remote script. This JavaScript tag checks if the victim’s system can be infected by performing a comparison with the browser’s user agent.  The Kaspersky researchers say, “The exploit used a race condition bug between two threads due to missing proper synchronization between them. It gives an attacker a Use-After-Free (UaF) condition that is very dangerous because it can lead to code execution scenarios, which is exactly what happens in our case.” The attacker can use this vulnerability to perform numerous operations to allocate/free memory along with other techniques that eventually give the attackers an arbitrary read/write primitive. This technique is used by attackers to create a “special object that can be used with WebAssembly and FileReader together to perform code execution for the embedded shellcode payload.” You can read Kaspersky detailed report for more information on the zero-day vulnerability. Adobe confirms security vulnerability in one of their Elasticsearch servers that exposed 7.5 million Creative Cloud accounts Mobile-aware phishing campaign targets UNICEF, the UN, and many other humanitarian organizations NordVPN reveals it was affected by a data breach in 2018

Last week, Fastly Inc., a provider of an edge cloud platform announced that it has filed its proposed initial public offering (ipo) with the US Securities and Exchange Commission. Last year in July, in its last round of financing before a public offering,  the company raised $40 million investment. The book-running managers for the proposed offering are BofA Merrill Lynch, Citigroup, and Credit Suisse. William Blair, Raymond James, Baird, Oppenheimer & Co., Stifel, Craig-Hallum Capital Group and D.A. Davidson & Co. are co-managers for the proposed offering. Founded by Artur Bergman in 2011, Fastly is an American cloud computing services provider. Its edge cloud platform provides a content delivery network, Internet security services, load balancing, and video & streaming services. The edge cloud platform is designed from the ground up to be programmable and to support agile software development. This programmable edge cloud platform gives developers real-time visibility and control by stream logging data. So, developers are able to instantly see the impact of new code in production, troubleshoot issues as they occur, and rapidly identify suspicious traffic. Fastly boasts of catering to customers like The New York Times, Reddit, GitHub, Stripe, Ticketmaster and Pinterest. The company, in the unfinished prospectus shared how it has grown over the years, the risks of investing in the company, what are its plans for the future, and more. The company shows a steady growth in its revenue, while in December 2017 it was $104.9 million, it increased to $144.6 million, by the end of 2018. Its loss has also shown some decline from $32.5 million in December 2017 to $30.9 million in December 2018. Predicting its future market value, the prospectus says, “When incorporating these additional offerings, we estimate a total market opportunity of approximately $18.0 billion in 2019, based on expected growth from 2017, to $35.8 billion in 2022, growing with an expected CAGR of 25.6%.“ Fastly has not yet determined the number of shares to offered and the price range for the proposed offering. Currently, the company’s public filing has a placeholder amount of $100 million. However, looking at the amount of funding the company has received, TechCrunch predicts that it is more likely to get closer to $1 billion when it finally prices its shares. Fastly has two classes of authorized common stock: Class A and Class B. The rights of both the common stockholders are identical, except with respect to voting and conversion. Each Class A share is entitled to one vote per share and each Class B share is entitled to 10 votes per share. Class B shares are convertible into one shares of Class A common stock. The Class A common stock will be listed on The New York Stock Exchange under the symbol “FSLY.” To read more in detail, check out the ipo filing by Fastly. Fastly open sources Lucet, a native WebAssembly compiler and runtime Cloudflare raises $150M with Franklin Templeton leading the latest round of funding Dark Web Phishing Kits: Cheap, plentiful and ready to trick you  

The attempts of porting Swift to WebAssembly has been going on for very long, and finally, a team of developers has come up with SwiftWasm, which was released last week. With this tool, you will now be able to run your Swift code on the web by compiling it to WebAseembly. https://twitter.com/swiftwasm/status/1127324144121536512 The SwiftWasm tool is built on top of the WASI SDK, which is a WASI-enabled C/C++ toolchain. This makes the WebAssembly executables generated by SwiftWasm work on both browsers and standalone WebAssembly runtimes such as Wasmtime, Fastly’s Lucet, or any other WASI-compatible WebAssembly runtime. How you can work with SwiftWasm? While macOS does not need any dependencies to be installed, some dependencies need to be installed on Ubuntu and Windows: On Ubuntu install ‘libatomic1’: sudo apt-get install libatomic1 On Windows: First Install the Windows Subsystem for Linux, and then install the libatomic1 library. The next step is to compile SwiftWasm by running the following commands: ./swiftwasm example/hello.swift hello.wasm To run the resulting ‘hello.wasm’ file, go to the SwiftWasm polyfill and upload the file. You will see the output in the textbox. This polyfill supports Firefox 66, Chrome 74, and Safari 12.1. The news of having a tool for running Swift on the web has got many developers excited. https://twitter.com/pvieito/status/1127620197668487169 https://twitter.com/johannesweiss/status/1126913408455053312 https://twitter.com/jedisct1/status/1126909145926569986 The project is still work-in-progress and thus has some limitations. Currently, only the Swift ‘stdlib’ is compiled and other libraries such as Foundation or SwiftPM are not included. Few functions such as ‘Optional.Map’ does not work because of the calling convention differences between throwing and non-throwing closures. If you want to contribute to this project, check out its pull request on Swift’s GitHub repository to know more about its current status. You can try SwiftWasm on its official website. Swift is improving the UI of its generics model with the “reverse generics” system Swift 5 for Xcode 10.2 is here! Implementing Dependency Injection in Swift [Tutorial]

PyTorch has announced a preview of the blueprint for PyTorch 1.0, the next major release of the framework. This breakthrough version is expected to bring more stability, integration support and complete production backing allowing developers to move from core research to production in an amicable way without having to deal with any migration challenges. PyTorch is an open-source Python-based scientific computing package which provides powerful GPU acceleration. PyTorch is known for advanced indexing and functions, imperative style, integration support and API simplicity. This is one of the key reasons why developers prefer PyTorch for research and hackability. To know more about how Facebook-based PyTorch competes with Google’s TensorFlow read our take on this deep learning war. Some of the noteworthy changes in the roadmap for PyTorch 1.0 are: Production support One of the biggest challenges faced by developers in terms of using PyTorch is production support. There are n number of issues faced while trying to run the models efficiently in production environments. Even though PyTorch provides excellent simplicity and flexibility, due to its tight coupling to Python, the performance at production-scale is a challenge.   To counter these challenges, the PyTorch team has decided to bring PyTorch and Caffe2 together to provide production-scale readiness to the developers. However, adding production support brings complexity and configurable options for models in the API. The PyTorch team will stick to the goal of keeping the platform -- a favorable choice -- for researchers and developers. Hence, they are introducing a new just-in-time (JIT) compiler, named torch.jit. torch.jit compiler rewrites PyTorch models during runtime in order to achieve scalability and efficiency in production environments. It can also export PyTorch models to run in a C++ environment. (runtime based on Caffe2 bits) Note: In PyTorch version 1.0, your existing code will continue to work as-is. Let’s go through how JIT compiler can be used to export models to a Python-less environment in order to improve their working performance. torch.jit: The go-to compiler for your PyTorch models Building models using Python code, no doubt gives maximum productivity and makes PyTorch very simple and easy-to-use. However, this also means PyTorch finding it difficult to know which operation you will run next. This can be frustrating for the developers during model export and automatic performance optimizations because they need to be aware of how the computations will look like before it even gets implemented. To deal with these issues, PyTorch provides two ways of recovering information from the Python code. Both these methods will be useful based on different contexts, giving you the leverage to use/mix them with ease. Tracing the native Python code Compiling a subset of the Python language Tracing mode torch.jit.trace function allows you to record the native PyTorch operations performed along with the data dependencies between them. PyTorch version 0.3 already had a tracer function which is used to export models through ONNX. This new version uses a high-performance C++ runtime that allows PyTorch to re-execute programs for you. The key advantage of using this method is that it doesn’t have to deal with how your Python code is structured since we only trace through native PyTorch operations. Script mode PyTorch team has come up with a solution called scripting mode made specially for those models such as RNNs which make use of control flow. However, you will have to write out a regular Python function (avoiding complex language features) In order to get your function compiled, you can assign @script decorator. This will make sure it alters your Python function directly into high-performance C++ during runtime. Advantages in optimization and export techniques Irrespective of you using a trace or a script function, the technique allows you to optimize/export the model for use in production environments (i.e. Python-free portrayal of the model) Now you can derive bigger segments of the model into an intermediate representation to work with sophisticated models. You can use high-performance backends available in Caffe2 to run the models efficiently Usability If you don’t need to export or optimize your model, you do not need to use these set of new features. These modes will be included into the core of the PyTorch ecosystem, thus allowing you to mix and match them with the existing code seamlessly as per your needs. Additional changes and improvements In addition to the major update in the production support for 1.0, PyTorch team will continue working on optimizing, working on the stability of the interface, and fixing other modules in PyTorch ecosystem PyTorch 1.0 will see some changes in the backend side which might affect user-written C and C++ extensions. In order to incorporate new features and optimization techniques from Caffe2, PyTorch team is replacing (optimizing) the backend ATen library. PyTorch team is planning to release 1.0 during the summer. For the detailed preview of the roadmap, you can refer the official PyTorch blog. Top 10 deep learning frameworks The Deep Learning Framework Showdown: TensorFlow vs CNTK Why you should use Keras for deep learning

Last month, two security researchers, Noam Rotem and Ran Locar found an unprotected database managed by TrueDialog. The database exposed tens of millions of SMS text messages exchanged between businesses and their customers. TrueDialog is a US-based SMS text service provider for enterprise businesses and higher education. Its cloud-based texting platform enables users to send both one-to-one as well as bulk messages to customers. What data TrueDialog’s database exposed Along with millions of sent and received text messages, this database included phone numbers, marketing messages from businesses with discount codes, job alerts, and more. Some of the two-way messages had a unique conversation code using which anyone would be able to read the entire thread of conversations. What concerning is that there were also text messages with sensitive information. As per TechCrunch, the database included “two-factor codes and other security messages, which may have allowed anyone viewing the data to gain access to a person’s online accounts.” TechCrunch further shared that the database also included messages containing codes to access online medical services, password reset and login codes for sites including Facebook and Google, and usernames and passwords of TrueDialog’s customers. TrueDialog took the database offline shortly after being contacted by TechCrunch. However, the company’s chief executive John Wright did not acknowledge the breach or gave any clarity on whether TrueDialog will be informing this to its customers. This is another case of companies being negligent towards their customers’ data. In October this year, an Elasticsearch server, allegedly belonging to two data enrichment companies exposed the personal information of nearly 1.2 billion users. In another case, security researcher Oliver Hough discovered that printing company Vistaprint left an online database containing customer interactions unencrypted. Check out the report by Noam Rotem and Ran Locar to know more about TrueDialog data leak in detail. GDPR complaint in EU claim billions of personal data leaked via online advertising bids How to protect your VPN from Data Leaks DoorDash data breach leaks personal details of 4.9 million customers, workers, and merchants  

GNU project made version 5.0 of its popular POSIX shell Bash ( Bourne Again Shell) available yesterday. Bash 5.0 explores new improvements and features such as BASH_ARGV0, EPOCHSECONDS, and EPOCHREALTIME among others. Bash was first released in 1989 and was created for the GNU project as a replacement for their Bourne shell. It is capable of performing functions such as interactive command line editing, and job control on architectures that support it. It is a complete implementation of the IEEE POSIX shell and tools specification. Key Updates New features Bash 5.0 comes with a newly added EPOCHSECONDS variable, which is capable of expanding to the time in seconds. There is another newly added EPOCHREALTIME variable which is similar to EPOCHSECONDS in Bash 5.0. EPOCHREALTIME is capable of obtaining the number of seconds since the Unix Epoch, the only difference being that this variable is a floating point with microsecond granularity. BASH_ARGV0 is also a newly added variable in Bash 5.0 that expands to $0 and sets $0 on assignment. There is a newly defined config-top.h in Bash 5.0. This allows the shell to use a static value for $PATH. Bash 5.0 has a new shell option that can enable and disable sending history to syslog at runtime. Other Changes The `globasciiranges' option is now enabled by default in Bash 5.0 and can be set to off by default at configuration time. POSIX mode is now capable of enabling the `shift_verbose' option. The `history' builtin option in Bash 5.0 can now delete ranges of history entries using   `-d start-end'. A change that caused strings containing + backslashes to be flagged as glob patterns has been reverted in Bash 5.0. For complete information on bash 5.0, check out its official release notes. GNU ed 1.15 released! GNU Bison 3.2 got rolled out GNU Guile 2.9.1 beta released JIT native code generation to speed up all Guile programs

Java 5 brought generics to the Java language. In this article, I introduce you to generics and discuss generic types, generic methods, generics and type inference, generics controversy, and generics and heap pollution. download Get the code Download the source code for examples in this Java 101 tutorial. Created by Jeff Friesen for JavaWorld. What are generics? Generics are a collection of related language features that allow types or methods to operate on objects of various types while providing compile-time type safety. Generics features address the problem of java.lang.ClassCastExceptions being thrown at runtime, which are the result of code that is not type safe (i.e., casting objects from their current types to incompatible types). To read this article in full, please click here

Last week, the Neo4j community announced that the Neo4j Enterprise Edition will be available under a commercial license. The source code is available only for the Neo4j Community Edition. The Neo4j Community Edition will continue to be provided under an open source GPLv3 license. According to the Neo4j community, this new change won’t affect any Neo4j open source projects. Also, it won’t create an impact over customers, partners or OEM users operating under a Neo4j subscription license. The Neo4j Desktop users using Neo4j Enterprise Edition under free development license also won’t get affected. It doesn’t impact the members of Neo4j Startup program. The reason for choosing an open core licensing model The idea behind getting Neo4j Enterprise Edition under commercial license was to clarify and simplify the licensing model and remove ambiguity. Also, the community wanted to clear the confusion regarding what they sell and what they open source. Also, the community wanted to clarify about options they offer. The Enterprise Edition source and object code were initially available under multiple licenses. This led to multiple interpretations of these multiple licenses which ultimately created confusion in the open source community, in the buyers, and even in legal reviewers’ minds. According to the Neo4j blog, “ >99% of Neo4j Enterprise Edition code was written by individuals on Neo4j’s payroll – employed or contracted by Neo4j-the-company. As for the fractional <1%... that code is still available in older versions. We’re not removing it. And we have reached out to the few who make up the fractional <1% to affirm their contributions are given proper due.” Developers can use the Enterprise Edition for free by using the Neo4j Desktop for desktop-based development. Startups can benefit through the startup license offered by Neo4j, which is also available now to the startups with up to 20 employees. Data journalists, such as the ICIJ and NBC News can use the Enterprise Edition for free via the Data Journalism Accelerator Program. Neo4j also offers a free license to universities for teaching and learning. To know more about this news, check out Neo4j’s blog. Neo4j rewarded with $80M Series E, plans to expand company Why Neo4j is the most popular graph database Neo4j 3.4 aims to make connected data even more accessible

For a powerful, high quality mobile device experience, Google recently rolled out the Go edition of its AI packed Android 9.0 Pie version. The Go edition comes with additional storage of upto 500 MB, faster boot time, better security and more improvements for entry level phones. Source: Google Blog page Added Google Go features: A first time smartphone user will be able to experience a fully redesigned set of Google apps with the Go edition. Let us look at each in detail: Google Go will read aloud your webpages and highlight each word so you can follow along. YouTube Go will let you download videos and save it as in the Gallery mode using less data. Maps Go features navigation, making it possible for people with Go edition devices or unstable connections to use turn-by-turn directions whether you’re traveling by car, by bus, or on foot. Files Go, which has saved users ~90TB of space since launch, is now capable of transferring data peer-to-peer, without using mobile data, at speeds up to ~490Mbips. Assistant Go supports additional languages including Spanish, Brazilian Portuguese and Indonesian. It has expanded support for device actions like controlling Bluetooth, camera, flashlight and added reminders. Android Messages App for Android (Go edition) is now ~50 percent smaller in size and the Phone App includes caller ID and spam detection. "We welcomed our first wave of Android (Go edition) phones this April, and now there are more than 200 devices available in 120+ countries including India, South Africa, US, Nigeria and Brazil." says Sagar Kamdar, Director of Product Management, Google. Devices that participated in the P Beta programme include Sony Mobile, Xiaomi, HMD Global, Oppo, Vivo, OnePlus and all other qualifying Android One devices. To read the full coverage of the Go edition, visit the official Google blog page. Introducing Android 9 Pie, filled with machine learning and baked-in UI features Android 9 pie’s Smart Linkify: How Android’s new machine learning based feature works All new Android apps on Google Play must target API Level 26 (Android Oreo) or higher, to publish

Yesterday, June Yang, the director of product management at Google announced a new beta version of the E2 VMs for Google Compute Engine. It features a dynamic resource management that delivers a reliable performance with flexible configurations and the best total cost of ownership (TCO) than any other VMs in Google Cloud. According to Yang, “E2 VMs are a great fit for a broad range of workloads including web servers, business-critical applications, small-to-medium sized databases, and development environments.” He further adds, “For all but the most demanding workloads, we expect E2 to deliver similar performance to N1, at a significantly lower cost.” What are the key features offered by E2 VMs E2 VMs are built to offer 31% savings compared to N1, which is the lowest total cost of ownership of any VM in Google Cloud. Thus, the VMs acquire a sustainable performance at a consistently low price point. Unlike comparable options from other cloud providers, E2 VMs can support a high CPU load without complex pricing. The E2 VMs can be tailored up to 16 vCPUs and 128 GB of memory and will only distribute the resources that the user needs or with the ability to use custom machine types. Custom machine types are ideal for scenarios when workloads that require more processing power or more memory but don't need all of the upgrades that are provided by the next machine type level. How E2 VMs achieve optimal efficiency Large, efficient physical servers E2 VMs automatically take advantage of the continual improvements in machines by flexibly scheduling across the zone’s available CPU platforms. With new hardware upgrades, the E2 VMs are live migrated to newer and faster hardware which allows it to automatically take advantage of these new resources. Intelligent VM placement In E2 VMs, Borg, Google’s cluster management system predicts how a newly added VM will perform on a physical server by observing the CPU, RAM, memory bandwidth, and other resource demands of the VMs. Post this, Borg searches across thousands of servers to find the best location to add a VM. These observations by Borg ensures that a newly placed VM will be compatible with its neighbors and will not experience any interference from them. Performance-aware live migration After the VMs are placed on a host, its performance is continuously monitored so that if there is an increase in demand for VMs, a live migration can be used to transparently shift the E2 load to other hosts in the data center. A new hypervisor CPU scheduler In order to meet E2 VMs performance goals, Google has built a custom CPU scheduler with better latency and co-scheduling behavior than Linux’s default scheduler. The new scheduler yields sub-microsecond average wake-up latencies with fast context switching which helps in keeping the overhead of dynamic resource management negligible for nearly all workloads. https://twitter.com/uhoelzle/status/1204972503921131521 Read the official announcement to know the custom VM shapes and predefined configurations offered by E2 VMs. You can also read part- 2 of the announcement to know more about the dynamic resource management in E2 VMs. Why use JVM (Java Virtual Machine) for deep learning Brad Miro talks TensorFlow 2.0 features and how Google is using it internally EU antitrust regulators are investigating Google’s data collection practices, reports Reuters Google will not support Cloud Print, its cloud-based printing solution starting 2021 Google Chrome ‘secret’ experiment crashes browsers of thousands of IT admins worldwide

How the depth and breadth of the Microsoft Azure partner ecosystem enables thousands of organizations to bring their mission-critical applications to Azure. In the past few years, IT organizations have been realizing compelling benefits when they transitioned their business-critical applications to the cloud, enabling them to address the top challenges they face with running the same applications on-premises. As even more companies embark on their digital transformation journey, the range of mission and business-critical applications has continued to expand, even more so because technology drives innovation and growth. This has further accelerated in the past months, spurred in part by our rapidly changing global economy. As a result, the definition of mission-critical applications is evolving and goes well beyond systems of record for many businesses. It’s part of why we never stopped investing across the platform to enable you to increase the availability, security, scalability, and performance of your core applications running on Azure. The expansion of mission-critical apps will only accelerate as AI, IoT, analytics, and new capabilities become more pervasive. We’re seeing the broadening scope of mission-critical scenarios both within Microsoft and in many of our customers’ industry sectors. For example, Eric Boyd, in his blog, outlined how companies in healthcare, insurance, sustainable farming, and other fields have chosen Microsoft Azure AI to transform their businesses. Applications like Microsoft Teams have now become mission-critical, especially this year, as many organizations had to enable remote workforces. This is also reflected by the sheer number of meetings happening in Teams. Going beyond Azure services and capabilities Many organizations we work with are eager to realize myriad benefits for their own business-critical applications, but first need to address questions around their cloud journey, such as: Are the core applications I use on-premises certified and supported on Azure? As I move to Azure, can I retain the same level of application customization that I have built over the years on-premises? Will my users experience any impact in the performance of my applications? In essence, they want to make sure that they can continue to capitalize on the strategic collaboration they’ve forged with their partners and ISVs as they transition their core business processes to the cloud. They want to continue to use the very same applications that they spent years customizing and optimizing on-premises. Microsoft understands that running your business on Azure goes beyond the services and capabilities that any platform can provide. You need a comprehensive ecosystem. Azure has always been partner-oriented, and we continue to strengthen our collaboration with a large number of ISVs and technology partners, so you can run the applications that are critical to the success of your business operations on Azure. A deeper look at the growing spectrum of mission-critical applications Today, you can run thousands of third-party ISV applications on Azure. Many of these ISVs in turn depend on Azure to deliver their software solutions and services. Azure has become a mission-critical platform for our partner community as well as our customers. When most people think of mission-critical applications, enterprise resource planning systems (ERP), supply chain management (SCM), product lifecycle management (PLM), and customer relationship management (CRM) applications are often the first examples that come to mind. However, to illustrate the depth and breadth of our mission-critical ecosystem, consider these distinct and very different categories of applications that are critical for thousands of businesses around the world: Enterprise resource planning (ERP) systems. Data management and analytics applications. Backup, and business continuity solutions. High-performance computing (HPC) scenarios that exemplify the broadening of business-critical applications that rely on public cloud infrastructure. Azure’s deep ecosystem addresses the needs of customers in all of these categories and more. ERP systems When most people think of mission-critical applications ERP, SCM, PLM, and CRM applications are often the first examples that come to mind. Some examples on Azure include: SAP—We have been empowering our enterprise customers to run their most mission-critical SAP workloads on Azure, bringing the intelligence, security, and reliability of Azure to their SAP applications and data. Viewpoint, a Trimble company—Viewpoint has been helping the construction industry transform through integrated construction management software and solutions for more than 40 years. To meet the scalability and flexibility needs of both Viewpoint and their customers, a significant portion of their clients are now running their software suite on Azure and experiencing tangible benefits. Data management and analytics Data is the lifeblood of the enterprise. Our customers are experiencing an explosion of mission-critical data sources, from the cloud to the edge, and analytics are key to unlocking the value of data in the cloud. AI is a key ingredient, and yet another compelling reason to modernize your core apps on Azure. DataStax—DataStax Enterprise, a scale out, hybrid, cloud-native NoSQL database built on Apache Cassandra™, in conjunction with Azure, can provide a foundation for personalized, real-time scalable applications. Learn how this combination can enable enterprises to run mission critical workloads to increase business agility, without compromising compliance and data governance. Informatica—Informatica has been working with Microsoft to help businesses ensure that the data that is driving your customer and business decisions is trusted, authenticated, and secure. Specifically, Informatica is focused on the quality of the data that is powering your mission-critical applications and can help you derive the maximum value from your existing investments. SAS®—Microsoft and SAS are enabling customers to easily run their SAS workloads in the cloud, helping them unlock critical value from their digital transformation initiatives. As part of our collaboration, SAS is migrating its analytical products and industry solutions onto Azure as the preferred cloud provider for the SAS Cloud. Discover how mission-critical analytics is finding a home in the cloud. Backup and disaster recovery solutions Uptime and disaster recovery plans that minimize recovery time objective (RTO) and recovery point objective (RPO) are the top metrics senior IT decision-makers pay close attention to when it comes to mission-critical environments. Backing up critical data is a key element of putting in place robust business continuity plans. Azure provides built-in backup and disaster recovery features, and we also partner with industry leaders like Commvault, Rubrik, Veeam, Veritas, Zerto, and others so you can keep using your existing applications no matter where your data resides. Commvault—We continue to work with Commvault to deliver data management solutions that enable higher resiliency, visibility, and agility for business-critical workloads and data in our customers’ hybrid environments. Learn about Commvault’s latest offerings—including support for Azure VMware Solution and why their Metallic SaaS suite relies exclusively on Azure. Rubrik—Learn how Rubrik helps enterprises achieve low RTOs, self-service automation at scale, and accelerated cloud adoption. Veeam—Read how you can use Veeam’s solution portfolio to backup, recover, and migrate mission-critical workloads to Azure. Veritas—Find out how Veritas InfoScale has advanced integration with Azure that simplifies the deployment and management of your mission-critical applications in the cloud. Zerto—Discover how the extensive capabilities of Zerto’s platform help you protect mission critical applications on Azure. Teradici—Finally, Teradici underscores how the lines between mission-critical and business-critical are blurring. Read how business continuity plans are being adjusted to include longer term scenarios. HPC scenarios HPC applications are often the most intensive and highest-value workloads in a company, and are business-critical in many industries, including financial services, life sciences, energy, manufacturing and more. The biggest and most audacious innovations from supporting the fight against COVID-19, to 5G semiconductor design; from aerospace engineering design processes to the development of autonomous vehicles, and so much more are being driven by HPC. Ansys—Explore how Ansys Cloud on Azure has proven to be vital for business continuity during unprecedented times. Rescale—Read how Rescale can provide a turnkey platform for engineers and researchers to quickly access Azure HPC resources, easing the transition of business-critical applications to the cloud. You can rely on the expertise of our partner community Many organizations continue to accelerate the migration of their core applications to the cloud, realizing tangible and measurable value in collaboration with our broad partner community, which includes global system integrators like Accenture, Avanade, Capgemini, Wipro, and many others. For example, UnifyCloud recently helped a large organization in the financial sector modernize their data estate on Azure while achieving 69 percent reduction in IT costs. We are excited about the opportunities ahead of us, fueled by the power of our collective imagination. Learn more about how you can run business-critical applications on Azure and increase business resiliency. Watch our Microsoft Ignite session for a deeper diver and demo.   “The construction industry relies on Viewpoint to build and host the mission-critical technology used to run their businesses, so we have the highest possible standards when it comes to the solutions we provide. Working with Microsoft has allowed us to meet those standards in the Azure cloud by increasing scalability, flexibility and reliability – all of which enable our customers to accelerate their own digital transformations and run their businesses with greater confidence.” —Dan Farner, Senior Vice President of Product Development, Viewpoint (a Trimble Company) Read the Gaining Reliability, Scalability, and Customer Satisfaction with Viewpoint on Microsoft Azure blog.     “Business critical applications require a transformational data architecture built on scale-out data and microservices to enable dramatically improved operations, developer productivity, and time-to-market. With Azure and DataStax, enterprises can now run mission critical workloads with zero downtime at global scale to achieve business agility, compliance, data sovereignty, and data governance.”—Ed Anuff, Chief Product Officer, DataStax Read the Application Modernization for Data-Driven Transformation with DataStax Enterprise on Microsoft Azure blog.     “As Microsoft’s 2020 Data Analytics Partner of Year, Informatica works hand-in-hand with Azure to solve mission critical challenges for our joint customers around the world and across every sector.  The combination of Azure’s scale, resilience and flexibility, along with Informatica’s industry-leading Cloud-Native Data Management platform on Azure, provides customers with a platform they can trust with their most complex, sensitive and valuable business critical workloads.”—Rik Tamm-Daniels, Vice President of strategic ecosystems and technology, Informatica Read the Ensuring Business-Critical Data Is Trusted, Available, and Secure with Informatica on Microsoft Azure blog.       “SAS and Microsoft share a vision of helping organizations make better decisions as they strive to serve customers, manage risks and improve operations. Organizations are moving to the cloud at an accelerated pace. Digital transformation projects that were scheduled for the future now have a 2020 delivery date. Customers realize analytics and cloud are critical to drive their digital growth strategies. This partnership helps them quickly move to Microsoft Azure, so they can build, deploy, and manage analytic workloads in a reliable, high-performant and cost-effective manner.”—Oliver Schabenberger, Executive Vice President, Chief Operating Officer and Chief Technology Officer, SAS Read the Mission-critical analytics finds a home in the cloud blog.   “Microsoft is our Foundation partner and selecting Microsoft Azure as our platform to host and deliver Metallic was an easy decision. This decision sparks customer confidence due to Azure’s performance, scale, reliability, security and offers unique Best Practice guidance for customers and partners. Our customers rely on Microsoft and Azure-centric Commvault solutions every day to manage, migrate and protect critical applications and the data required to support their digital transformation strategies.”—Randy De Meno, Vice President/Chief Technology Officer, Microsoft Practice & Solutions Read the Commvault extends collaboration with Microsoft to enhance support for mission-critical workloads blog.     “Enterprises depend on Rubrik and Azure to protect mission-critical applications in SAP, Oracle, SQL and VMware environments. Rubrik helps enterprises move to Azure securely, faster, and with a low TCO using Rubrik’s automated tiering to Azure Archive Storage. Security minded customers appreciate that with Rubrik and Microsoft, business critical data is immutable, preventing ransomware threats from accessing backups, so businesses can quickly search and restore their information on-premises and in Azure.”—Arvind Nithrakashyap, Chief Technology Officer and Co-Founder, Rubrik Learn how enterprises use Rubrik on Azure.     “Veeam continues to see increased adoption of Microsoft Azure for business-critical applications and data across our 375,000 plus global customers. While migration of applications and data remains the primary barrier to the public cloud, we are committed to helping eliminate these challenges through a unified Cloud Data Management platform that delivers simplicity, flexibility and reliability at its core, while providing unrivaled data portability for greater cost controls and savings. Backed by the unique Veeam Universal License – a portable license that moves with workloads to ensure they're always protected – our customers are able to take control of their data by easily migrating workloads to Azure, and then continue protecting and managing them in the cloud.”—Danny Allan, Chief Technology Officer and Senior Vice President for Product Strategy, Veeam Read the Backup, recovery, and migration of mission-critical workloads on Azure blog.     “Thousands of customers rely on Veritas to protect their data both on-premises and in Azure. Our partnership with Microsoft helps us drive the data protection solutions that our enterprise customers rely on to keep their business-critical applications optimized and immediately available.”—Phil Brace, Chief Revenue Officer, Veritas Read the Migrate and optimize your mission-critical applications in Microsoft Azure with Veritas InfoScale blog.     “Microsoft has always leveraged the expertise of its partners to deliver the most innovative technology to customers. Because of Zerto’s long-standing collaboration with Microsoft, Zetro's IT Resilience platform is fully integrated with Azure and provides a robust, fully orchestrated solution that reduces data loss to seconds and downtime to minutes. Utilizing Zerto’s end-to-end, converged backup, DR, and cloud mobility platform, customers have proven time and time again they can protect mission-critical applications during planned or unplanned disruptions that include ransomware, hardware failure, and numerous other scenarios using the Azure cloud – the best cloud platform for IT resilience in the hybrid cloud environment.”—Gil Levonai, CMO and SVP of Product, Zerto Read the Protecting Critical Applications in the Cloud with the Zerto Platform blog.     “The longer business continues to be disrupted, the more the lines blur and business critical functions begin to shift to mission critical, making virtual desktops and workstations on Microsoft Azure an attractive option for IT managers supporting remote workforces in any function or industry. Teradici Cloud Access Software offers a flexible and secure solution that supports demanding business critical and mission critical workloads on Microsoft Azure and Azure Stack with exceptional performance and fidelity, helping businesses gain efficiency and resilience within their business continuity strategy.”—John McVay, Director of Strategic Alliances, Teradici Read the Longer IT timelines shift business critical priorities to mission critical blog.         "It is imperative for Ansys to support our customers' accelerating needs for on-demand high performance computing to drive their increasingly complex engineering requirements. Microsoft Azure, with its purpose-built HPC and robust go-to market capabilities, was a natural choice for us, and together we are enabling our joint customers to keep designing innovative products even as they work from home.”—Navin Budhiraja, Vice President and General Manager, Cloud and Platform, Ansys Read the Ansys Cloud on Microsoft Azure: A vital resource for business continuity during the pandemic blog.     “Robust and stable business critical systems are paramount for success. Rescale customers leveraging Azure HPC resources are taking advantage of the scalability, flexibility and intelligence to improve R&D, accelerate development and reduce costs not possible with a fixed infrastructure.”—Edward Hsu, Vice President of Product, Rescale Read the Business Critical Systems that Drive Innovation blog.     “Customers are transitioning business-critical workloads to Azure and realizing significant cost benefits while modernizing their applications. Our solutions help customers develop cloud strategy, modernize quickly, and optimize cloud environments while minimizing risk and downtime.”—Vivek Bhatnagar, Co-Founder and Chief Technology Officer, UnifyCloud Read the Moving mission-critical applications to the cloud: More important than ever blog.

On Tuesday, Racket, a general-purpose programming language announced Racket 7.5. Racket is based on the Scheme dialect of Lisp programming language and is designed to be a platform for programming language design and implementation. Racket is also used to refer to the family of Racket programming languages and the set of tools supporting development on and with Racket. Key features in Racket 7.5 This new release will be distributed under a new and less-restrictive license, either the Apache 2.0 or the MIT license Racket CS will remain in beta for the v7.5, but the compatibility and performance continue to improve. It is expected to be ready for production use by the next release In this release of Racket 7.5 the Web Server provides a standard JSON MIME type, including a response/jsexpr form for HTTP responses bearing JSON In this release GNU MPFR operations run about 3x faster Typed Racket supports definitions of new struct type properties and type checks uses existing struct type properties in struct definitions. Previously, these were ignored by the type checker, so type errors may have been hidden The performance bug in v7.4’s big bang has been repaired DrRacket supports Dark Mode for interface elements. With this release plot can display parametric 3d surfaces and redex supports modeless judgment forms Additionally with the above changes, in the Racket 7.5 MacOS Catalina 10.15 includes a new requirement that executables be “notarized”, to give Apple the ability to prevent certain kinds of malware. In this release, all of the disk images (.dmg’s) are notarized, along with the applications that they contain (.app’s). Many users may not notice any difference, but two groups of Catalina users will be affected; First those who use the “racket” binary directly, and second, those that download the .tgz bundles. In both cases, the operating system is likely to inform that the given executable is not trusted, or that the developer can’t be verified. Fortunately, both groups of users are probably also running commands in a shell, hence the solution for both groups will be the same that is to disable the quarantine flag using the xattr command, for example, xattr -d com.apple.quarantine /path/to/racket. To know more about this news, check out the official announcement on the Racket page. Matthew Flatt’s proposal to change Racket’s s-expressions based syntax to infix representation creates a stir in the community Racket 7.3 releases with improved Racket-on-Chez, refactored IO system, and more Racket 7.2, a descendent of Scheme and Lisp, is now out! Racket v7.0 is out with overhauled internals, updates to DrRacket, TypedRacket among others  

Thousands of Google employees have raised their concerns regarding the use of Artificial Intelligence for military purposes. The employees, which included many senior engineers as well, have signed a petition requesting Google CEO Sundar Pichai to pull Google out of Project Maven - a Pentagon-backed project harvesting AI to improve the military technology. Pichai was also urged by employees to establish and enforce strict policies which keep Google and its associated subsidiaries from indulging in ‘the business of war’. What does the petition say? The letter, signed by over 3000 Google employees, argues that collaborating with the government to work on military projects is strictly against Google’s core ideology that technology must be used for welfare and not for destruction of mankind. It argues that backing the military could backfire tremendously by creating a negative image of Google in the minds of customers, and also affect potential recruitment. The concerned employees are of the opinion that since Google is currently engaged in a serious competition with many other companies to hire the best possible talent, some candidates could be put off by Google’s military connections with the government. What is Project Maven? Project Maven is a Pentagon-backed initiative which was announced in May 2017. The main purpose of this project was to integrate Artificial Intelligence with various defense programs to make them smarter. Backed with Google’s technology, this program aims to improve the image and video processing capabilities of drones to accurately pick out human targets for strikes, while identifying innocent civilians to reduce or prevent their accidental killing. Google have declared their participation in this program in a ‘non-offensive capacity’, and have maintained that their products or technology would not be used to create autonomous weapons that operate without human intervention. Connections with the Pentagon It is also interesting to note that some of Google’s top executives are connected to Pentagon in some capacity. Eric Schmidt, the former executive chairman of Google who is still a member of the executive board of Google’s parent company Alphabet, serves in the Defense Innovation Board, a Pentagon advisory body. Milo Medin, Vice President of Access Services, Google Capital is also a part of this body. What about Amazon and Microsoft? When it comes to connections with the Pentagon, Google aren’t the only ones involved. Amazon has collaborated with the Department of Defense through the Amazon Rekognition API for image recognition. Also, Microsoft announced their collaboration with the US government by providing IaaS (Infrastructure as a Service) and PaaS (Platform as a Service) capabilities to meet the data storage and security needs of the government. The news related to the dispute and the subsequent petition was initially reported by Gizmodo earlier this March. Considering the project is expected to cost close to $70 million in its first year, the petitioners are aiming to discourage Google from getting into more lucrative contracts as the demand for AI in defense and military applications grows.

“Security, customizability, flexibility and cost are a few of the benefits of open-source software for developers. They’ll get all these and more from NVIDIA’s Material Definition Language software development kit.” —NVIDIA Blog. The material definition language (MDL) is a programming language used to define and render physical materials. This includes the creation of a wide range of physical materials such as woods, fabrics, translucent plastics and more. It is a set of tools integrating the precise look and feel of real-world materials into rendering applications. It gives users the freedom to share these materials between applications that support them. Now users can move a library of these materials between applications without worrying about them losing their appearance. This will allow developers to focus on building their applications. Allegorithmic had already built an entire MDL authoring tool, but with NVIDIA making the MDL SDK open source, developers get a deeper unrestricted access to the entire spec. The Blog states that Unreal Studio 4.20 now offers native support for MDL. “Being able to use a single material definition, like NVIDIA’s MDL, across multiple applications and render engines is a huge benefit to the end-user,” said Ken Pimentel, senior product manager of the Enterprise team at Epic Games. “Now that we’ve added MDL support to Unreal Studio, our enterprise customers can see their material representations converted to real time in Unreal Engine without baking every parameter. This means their creative intent can be carried to new forms of expression.” The MDL SDK API is C++ based and used for integration and customization tasks. It can be loaded dynamically and linked to visualization applications. The API also allows applications to load MDL modules, and analyze and understand the structure of a material. Therefore it can build a UI for editing materials then rendering the results. Some of the features in MDL SDK include: Can be used on GPU as well as CPU Database view on the imported MDL package space MDL editing C++ component-based API, and plugin architecture for extensibility MDL SDK supports Windows (only 64-bit), Linux, and macOS. To know more, visit the NVIDIA website and to get started here is the GitHub repository. Nvidia unveils a new Turing architecture: “The world’s first ray tracing GPU” Nvidia and AI researchers create AI agent Noise2Noise that can denoise images Nvidia GPUs offer Kubernetes for accelerated deployments of Artificial Intelligence workloads

[box type="note" align="" class="" width=""]The following is an excerpt from the book Mastering Machine Learning with Spark, Chapter 1, Introduction to Large-Scale Machine Learning and Spark written by Alex Tellez, Max Pumperla, and Michal Malohlava. This article introduces Sparkling water - H2O's integration of their platform within the Spark project, which combines the machine learning capabilities of H2O with all the functionality of Spark. [/box] H2O is an open source, machine learning platform that plays extremely well with Spark; in fact, it was one of the first third-party packages deemed "Certified on Spark". Sparkling Water (H2O + Spark) is H2O's integration of their platform within the Spark project, which combines the machine learning capabilities of H2O with all the functionality of Spark. This means that users can run H2O algorithms on Spark RDD/DataFrame for both exploration and deployment purposes. This is made possible because H2O and Spark share the same JVM, which allows for seamless transitions between the two platforms. H2O stores data in the H2O frame, which is a columnar-compressed representation of your dataset that can be created from Spark RDD and/or DataFrame. Throughout much of this book, we will be referencing algorithms from Spark's MLlib library and H2O's platform, showing how to use both the libraries to get the best results possible for a given task. The following is a summary of the features Sparkling Water comes equipped with: Use of H2O algorithms within a Spark workflow Transformations between Spark and H2O data structures Use of Spark RDD and/or DataFrame as inputs to H2O algorithms Use of H2O frames as inputs into MLlib algorithms (will come in handy when we do feature engineering later) Transparent execution of Sparkling Water applications on top of Spark (for example, we can run a Sparkling Water application within a Spark stream) The H2O user interface to explore Spark data Design of Sparkling Water Sparkling Water is designed to be executed as a regular Spark application. Consequently, it is launched inside a Spark executor created after submitting the application. At this point, H2O starts services, including a distributed key-value (K/V) store and memory manager, and orchestrates them into a cloud. The topology of the created cloud follows the topology of the underlying Spark cluster. As stated previously, Sparkling Water enables transformation between different types of RDDs/DataFrames and H2O's frame, and vice versa. When converting from a hex frame to an RDD, a wrapper is created around the hex frame to provide an RDD-like API. In this case, data is not duplicated but served directly from the underlying hex frame. Converting from an RDD/DataFrame to a H2O frame requires data duplication because it transforms data from Spark into H2O-specific storage. However, data stored in an H2O frame is heavily compressed and does not need to be preserved as an RDD anymore: If you enjoyed this excerpt, be sure to check out the book it appears in.