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2021 was an action-packed year for Qrator Labs.

It started with the official celebration of our tenth year anniversary, continued with massive routing incidents, and ended with the infamous Meris botnet we reported back in September.

Now it is time to look at the events of the last quarter of 2021. There are interesting details in the BGP section, like the new records in route leaks and hijacking ASes, but first things first, as we start with the DDoS attacks statistics.

Comparing PHP-FPM, NGINX Unit, and Laravel Octane - what to choose for developing microservices.

Protocol AXI4 was developed for High-bandwidth and low latency applications. It is designed to allow communication between master and slave devices. Master is typically a DMA or CPU and slaves are DRAM controllers, or other specific protocol controllers: UART, SPI, and others. Sometimes one component can implement multiple instances of this protocol. Usually, a prefix is used to differentiate between multiple AXI4 interfaces.

For example, Ethernet MAC can integrate DMA and slave interface used to command MAC. MAC can accept commands on the slave interface that contain data about the location of the next ethernet packet and MAC can start fetching this packet using the separate master interface instance.

This article was motivated by common design mistakes AXI4 designers make when they are designing their Digital IP. (Looking at you Xilinx)

As Intel Threading Building Blocks (TBB) is being refreshed using new C++ standard, deprecating tbb::task interface, the need for high-level tasking interface becomes more obvious. In this article, I’m proposing yet another way of defining what a high-level parallel task programming model can look like in modern C++. I created it in 2014 and it was my last contribution to TBB project as its core developer after 9 wonderful years of working there. However, this proposal has not been used in production yet, so a new discussion might help it to be adopted.

UPD: When we posted this article, Uncle Bob had our conferences in his schedule. Unfortunately, the situation has changed. We updated this post on March 12, to avoid any misunderstanding.

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We are beyond excited to announce Windows Terminal! Windows Terminal is a new, modern, fast, efficient, powerful, and productive terminal application for users of command-line tools and shells like Command Prompt, PowerShell, and WSL.

Windows Terminal will be delivered via the Microsoft Store in Windows 10 and will be updated regularly, ensuring you are always up to date and able to enjoy the newest features and latest improvements with minimum effort.

Indibiome labs

Project Executive Summary indicating current developments and basic goals of the Project

In this article, I decided to describe how the functionality of selecting and displaying photos on a specific place on the map was implemented in our photo service gfranq.com. The photo service does not work now.

Since we had a lot of photos in our service and sending requests to database every time the viewport changes was too resource-intensive, it was logical to divide the map into several areas that contain information about the retrieved data. For obvious reasons, these areas have rectangular shape (although hexagonal grid was considered too). As the areas become more spherical at large scales, elements of spherical geometry and tools for it were also considered.

In this article, the following issues were raised:

• Storing and retrieving photos from the database and caching them on the server (SQL, C#, ASP.NET).
• Downloading necessary photos on the client side and saving them to the client cache (JavaScript).
• Recalculation of photos that must be hidden or shown when the viewport changes.
• Elements of spherical geometry.

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more!.

Today we’re announcing the ML.NET 1.0 RC (Release Candidate) (version 1.0.0-preview) which is the last preview release before releasing the final ML.NET 1.0 RTM in 2019 Q2 calendar year.

Soon we will be ending the first main milestone of a great journey in the open that started on May 2018 when releasing ML.NET 0.1 as open source. Since then we’ve been releasing monthly, 12 preview releases so far, as shown in the roadmap below:

In this release (ML.NET 1.0 RC) we have initially concluded our main API changes. For the next sprint we are focusing on improving documentation and samples and addressing major critical issues if needed.

The goal is to avoid any new breaking changes moving forward.

The last decades the world economy regularly falls into this vortex of financial crises that have affected each country. It almost led to the collapse of the existing financial system, due to this fact, experts in mathematical and economic modelling have become to use methods for controlling the losses of the asset and portfolio in the financial world (Lechner, L. A., and Ovaert, T. C. (2010). There is an increasing trend towards mathematical modelling of an economic process to predict the market behaviour and an assessment of its sustainability (ibid). Having without necessary attention to control and assess properly threats, everybody understands that it is able to trigger tremendous cost in the development of the organisation or even go bankrupt.

Value at Risk (VaR) has eventually been a regular approach to catch the risk among institutions in the finance sector and its regulator (Engle, R., and Manganelli S., 2004). The model is originally applied to estimate the loss value in the investment portfolio within a given period of time as well as at a given probability of occurrence. Besides the fact of using VaR in the financial sector, there are a lot of examples of estimation of value at risk in different area such as anticipating the medical staff to develop the healthcare resource management Zinouri, N. (2016). Despite its applied primitiveness in a real experiment, the model consists of drawbacks in evaluation, (ibid).

The goal of the report is a description of the existing VaR model including one of its upgrade versions, namely, Conditional Value at Risk (CVaR). In the next section and section 3, the evaluation algorithm and testing of the model are explained. For a vivid illustration, the expected loss is estimated on the asset of one of the Kazakhstani company trading in the financial stock exchange market in a long time period. The final sections 4 and 5 discuss and demonstrate the findings of the research work.