Development

In our last article we made a bunch of promises about our speech synthesis.

After a lot of hard work we finally have delivered upon these promises:

• Model size reduced 2x;
• New models are 10x faster;
• We added flags to control stress;
• Now the models can make proper pauses;
• High quality voice added (and unlimited "random" voices);
• All speakers squeezed into the same model;
• Input length limitations lifted, now models can work with paragraphs of text;
• Pauses, speed and pitch can be controlled via SSML;
• Sampling rates of 8, 24 or 48 kHz are supported;
• Models are much more stable — they do not omit words anymore;

This is a truly break-through achievement for us and we are not planning to stop anytime soon. We will be adding as many languages as possible shortly (the CIS languages, English, European languages, Hindic languages). Also we are still planning to make our models additional 2-5x faster.

We are also planning to add phonemes and a new model for stress, as well as to reduce the minimum amount of audio required to train a high-quality voice to 5 — 15 minutes.

As usual you can try our model in our repo or in colab.

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.

Google Colorizing Transformer vs Deoldify

The Art of Designing Road Networks

Transport problems of a city through the eyes of a Computer Scientist

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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.

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.

As Senior Software Engineer at company building messaging platform for healthcare industry I am responsible, including other duties, for performance of our application. We develop pretty standard web-service using Ruby on Rails application for business logic and API, React + Redux for users' facing single page application, as database we use PostgreSQL. Common reasons for performance problems in similar stacks are heavy queries to database and I would like to tell the story how we applied non-standard but fairly simple optimisations to improve performance.

Our business operates in US, so we have to be HIPAA compliant and follow certain security policies, security audit is something that we are always prepared for. To reduce risks and costs we rely on a special cloud provider to run our applications and databases, very similar to what Heroku does. On one hand it allows us to focus on building our platform but on the other hand it adds an additional limitation to our infrastructure. Talking shortly — we cannot scale up infinitely. As a successful startup we double number of users every few month and one day our monitoring told us that we were exceeding disk IO quota on the database server. Underlying AWS started throttling which was resulting in a significant performance degradation. Ruby application was not capable to serve all incoming traffic because Unicorn workers were spending too much time awaiting for database's response, customers were unhappy.