• A tale of how PVS-Studio reimagined the bug

    You all know our mascot — a unicorn — many people grew fond of him! However, PVS-Studio has a supporting character who is also the antagonist of our product — a bug! Well, a bug is not omnipresent, indestructible evil. It's more like an everyday or a work-related trouble. In this article, you'll learn how we created a new character, and why he looks like a ladybug. Oh, and if you wonder why the hell he has a belly button — keep reading!

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  • Backup & Recovery Solutions from China

    There are new challenges that force IT companies to look for non-trivial approaches to solve the problems of their customers every year.  And as you know LANIT-Integration is not an exception. Our team has already managed to work with many products, but we never stop discovering new ones.

    In this article I would like to provide an overview of backup and recovery software from Chinese vendors and to compare these solutions with domestic ones.

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  • FL_PyTorch is publicly available on GitHub

    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:

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  • Systematic coding and digital signature

    • Tutorial

    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.

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  • How abortion in the age of surveillance capitalism turns Internet into a dystopia

    The reversal of Roe v. Wade, which launched a furious debate about abortion rights, has a side — and a very itchy side. In June 2022, the Supreme Court struck down federal protections for abortion rights in the United States, turning the decision on the legality of abortion over to the state level, many of whom had long been waiting for it: they had «trigger» laws banning abortion, and state prosecutors were preparing to prosecute for violating or trying to circumvent them.

    Not even a week later, news emerged that the blow to women's rights might come from an unexpected (for naive Americans who are not familiar with the «Yarovaya Package» and other niceties of Russian legislation) side, when the willingness to «leak» personal data even without a decision was confirmed by the developers of major applications for women. Thus, suddenly, own gadgets and all the IT infrastructure that surrounds the modern man for his convenience, suddenly showed its downside: the possibility of total control over human life and actions.

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  • The Systems Engineering Methodology for Startups

    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.

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  • Ads
    AdBlock has stolen the banner, but banners are not teeth — they will be back

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  • Metaverses: hype or the future to come?

    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.

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  • What are neural networks and what do we need them for?

    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.

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  • Blood, sweat and pixels: releasing a mobile game with no experience

    In January 2022, we, at Kaspersky, released our first mobile game – Disconnected. The game was designed for companies that want to strengthen their employees’ knowledge of cybersecurity basics. Even though game development is not something you would expect from a cybersecurity company, our motivation was quite clear – we wanted to create an appealing, interactive method of teaching cybersecurity.



    Over our many years of experience in security awareness and experimentation with learning approaches (e.g. online adaptive platforms, interactive workshops and even VR simulations), we’ve noticed that even if the material is presented in a highly engaging way, people still lack the opportunity to apply the knowledge in practice. This means that although they are taking in the information, it won’t necessarily be applied.
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  • ArGOtecture

    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.

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  • Multilingual Text-to-Speech Models for Indic Languages

    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.

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  • Detecting attempts of mass influencing via social networks using NLP. Part 2

    • Tutorial

    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.

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  • Detecting attempts of mass influencing via social networks using NLP. Part 1

    • Tutorial

    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. 

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