All streams

FL_PyTorch: Optimization Research Simulator for Federated Learning is publicly available on GitHub.

FL_PyTorch is a suite of open-source software written in python that builds on top of one of the most popular research Deep Learning (DL) frameworks PyTorch. We built FL_PyTorch as a research simulator for FL to enable fast development, prototyping, and experimenting with new and existing FL optimization algorithms. Our system supports abstractions that provide researchers with sufficient flexibility to experiment with existing and novel approaches to advance the state-of-the-art. The work is in proceedings of the 2nd International Workshop on Distributed Machine Learning DistributedML 2021. The paper, presentation, and appendix are available in DistributedML’21 Proceedings (https://dl.acm.org/doi/abs/10.1145/3488659.3493775).

The project is distributed in open source form under Apache License Version 2.0. Code Repository: https://github.com/burlachenkok/flpytorch.

To become familiar with that tool, I recommend the following sequence of steps:

Once the Teacher asked the Author:

Are there methods of redundancy introducing at an informational level, other than those that are studied by the theory of error-correcting codes? Emphasizing that he is talking about information redundancy, the Teacher thus made it clear that the question does not imply various ways of energy redundancy introducing, which are well studied in communication theory. After all, the noise immunity of information transmission is traditionally assessed by means of a threshold value that is calculated as the ratio of signal energy to noise energy. It is known that the methods of the theory of error-correcting codes offer an alternative solution, allowing energy saving.

After a cogitative pause, the Author answered in the affirmative, following intuition rather than rational knowledge. Upon hearing the answer, the Teacher noticed that this is a wrong conclusion and there are no such methods.

However, over time, the Author began to suspect that the immutability of the paradigm formulated above could be questioned.

Testing is one of the key processes in development. However, without analysis it is tough to say how effective testers really are. Innotech’s lead tester-engineer Pavel Petrov shared a number of metrics that are being used in project work.

Creating a product startup can be an exciting experience, but it can be a daunting one as well. On average, only 1 out of 10 startups is successful, according to the Global Startup Ecosystem Report. Therefore, to raise your prospects, there are quite a number of important considerations to make in advance. 

Bearing in mind everything you need when launching a startup is a challenging task, so it’d be a sound idea to rely on some well-established methodology. That's why we were inspired by the Systems Engineering methodology, presented in such industry standards as ISO 15288 and CFR21. In this article, we’ll make a brief overview of this methodology and highlight how it can help entrepreneurs to encompass and structure the process of creating and developing a startup.

The second quarter of the year has ended and, as usual, we take a look back at the mitigated DDoS attacks activity and BGP incidents that occurred between April and June 2022.

A tester is one of the most stressful roles in IT. You constantly need to be concentrated and report bugs to developers in your team. Lidiya Yegorova, Innotech’s “Scoring conveyor” team QA-Lead shared her practices on how to minimize the stress while testing.

Problems of multithreaded servers with blocking I/O

Author: Denis Zherdetskiy

I know that my article can help a designer who spends a lot of time working and not feeling his or her growth. This article has some tips on how to start building your soft skills.

Alexander Volchek, IT entrepreneur, CEO educational platform GeekBrains

Pretty much everyone in the IT community is talking metaverses, NFTs, blockchain and cryptocurrency. This time we will discuss metaverses, and come back to everything else in the letters to follow. Entrepreneurs and founders of tech giants are passionate about this idea, and investors are allocating millions of dollars for projects dealing with metaverses. Let's start with the basics.

Explaining through simple examples

For a long time, people have been thinking on how to create a computer that could think like a person. The advent of artificial neural networks is a significant step in this direction. Our brain consists of neurons that receive information from sensory organs and process it: we recognize people we know by their faces, and we feel hungry when we see delicious food. All of this is the result of brain neurons working and interacting with each other. This is also the principle that artificial neural networks are based on, simulating the processes occurring in the human brain.

What are neural networks

Artificial neural networks are a software code that imitates the work of a brain and is capable of self-learning. Like a biological network, an artificial network also consists of neurons, but they have a simpler structure.

If you connect neurons into a sufficiently large network with controlled interaction, they will be able to perform quite complex tasks. For example, determining what is shown in a picture, or independently creating a photorealistic image based on a text description.

1) OpenTracing (OT) != Logs but they are very similar.

2) Every application has 2 types of scopes: ApplicationScope (AScope) and RequestScope (RScope).

This is an article that describes my vision of building a system that actively uses Go as the main programming language and SOA/microservices as a design paradigm. 

Here I will try to cover 4 chapters that together allow us to build a solid and reliable system.

Conference participation is one of the most important practices for professional development. Hence, Innotech is actively sending out both its speakers and listeners for the biggest events. Senior Analyst Anastasia Kochetova shares her impressions from the Analyst Days/14 conference.

In this article, we shall provide some background on how multilingual multi-speaker models work and test an Indic TTS model that supports 9 languages and 17 speakers (Hindi, Malayalam, Manipuri, Bengali, Rajasthani, Tamil, Telugu, Gujarati, Kannada).

It seems a bit counter-intuitive at first that one model can support so many languages and speakers provided that each Indic language has its own alphabet, but we shall see how it was implemented.

Also, we shall list the specs of these models like supported sampling rates and try something cool – making speakers of different Indic languages speak Hindi. Please, if you are a native speaker of any of these languages, share your opinion on how these voices sound, both in their respective language and in Hindi.

In Part 1 of this article, I built and compared two classifiers to detect trolls on Twitter. You can check it out here.

Now, time has come to look more deeply into the datasets to find some patterns using exploratory data analysis and topic modelling.

EDA

To do just that, I first created a word cloud of the most common words, which you can see below.

During the last decades, the world’s population has been developing as an information society, which means that information started to play a substantial end-to-end role in all life aspects and processes. In view of the growing demand for a free flow of information, social networks have become a force to be reckoned with. The ways of war-waging have also changed: instead of conventional weapons, governments now use political warfare, including fake news, a type of propaganda aimed at deliberate disinformation or hoaxes. And the lack of content control mechanisms makes it easy to spread any information as long as people believe in it.  

Based on this premise, I’ve decided to experiment with different NLP approaches and build a classifier that could be used to detect either bots or fake content generated by trolls on Twitter in order to influence people. 

In this first part of the article, I will cover the data collection process, preprocessing, feature extraction, classification itself and the evaluation of the models’ performance. In Part 2, I will dive deeper into the troll problem, conduct exploratory analysis to find patterns in the trolls’ behaviour and define the topics that seemed of great interest to them back in 2016.

Features for analysis

From all possible data to use (like hashtags, account language, tweet text, URLs, external links or references, tweet date and time), I settled upon English tweet text, Russian tweet text and hashtags. Tweet text is the main feature for analysis because it contains almost all essential characteristics that are typical for trolling activities in general, such as abuse, rudeness, external resources references, provocations and bullying. Hashtags were chosen as another source of textual information as they represent the central message of a tweet in one or two words. 

The IDS Bypass contest was held at the Positive Hack Days conference for the third time (for retrospective, here's . This year we created six game hosts, each with a flag. To get the flag, participants had either to exploit a vulnerability on the server or to fulfill another condition, for example, to enumerate lists of domain users.

The tasks and vulnerabilities themselves were quite straightforward. The difficulty laid in bypassing the IDS: the system inspected network traffic from participants using special rules that look for attacks. If such a rule was triggered, the participant's network request was blocked, and the bot sent them the text of the triggered rule in Telegram.

And yes, this year we tried to move away from the usual CTFd and IDS logs towards a more convenient Telegram bot. All that was needed to take part was to message the bot and pick a username. The bot then sent an OVPN file to connect to the game network, after which all interaction (viewing tasks and the game dashboard, delivering flags) took place solely through the bot. This approach paid off 100%!

In this article, I would like to describe how we’ve tackled the named entity recognition (aka NER) issue at Sber with the help of advanced AI techniques. It is one of many natural language processing (NLP) tasks that allows you to automatically extract data from unstructured text. This includes monetary values, dates, or names, surnames and positions.

Just imagine countless textual documents even a medium-sized organisation deals with on a daily basis, let alone huge corporations. Take Sber, for example: it is the largest financial institution in Russia, Central and Eastern Europe that has about 16,500 offices with over 250,000 employees, 137 million retail and 1.1 million corporate clients in 22 countries. As you can imagine, with such an enormous scale, the company collaborates with hundreds of suppliers, contractors and other counterparties, which implies thousands of contracts. For instance, the estimated number of legal documents to be processed in 2022 has been over 65,000, each of them consisting of 30 pages on average. During the lifecycle of a contract, a contract usually updated with 3 to 5 additional agreements. On top of this, a contract is accompanied by various source documents describing transactions. And in the PDF format, too.

Previously, the processing duty befell our service centre’s employees who checked whether payment details in a bill match those in the contract and then sent the document to the Accounting Department where an accountant double-checked everything. This is quite a long journey to a payment, right?

Typically when a Node falls out of the OpenShift cluster, this is resolved by simply restarting the offending element. What should you do, however, if you’ve forgotten the SSH key or left it in the office? You can attempt to restore access by using your wit and knowledge of Linux commands. Renat Garaev, lead developer at Innotech, described how he found the solution for this riddle and what was the outcome.