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One of the main tenets of the Unix philosophy is that a good tool for a good cause. Suppose you have a task to develop a large application that should have multiple threads of execution, possibly be distributed and, of course, have a graphical interface. I would like to make such a program quickly and without unnecessary mistakes. 

I think the first question to ask in a situation like this is, "Which programming language is right?" C is not a bad choice, but not for such a project. It does not scale very well, and does not have the means of working with processes at all. Then C++? But C++ is a complex language, and past experience has shown that it will take a fair amount of time to debug memory allocation problems. What else? 

There is a well-designed tool for just such a job. It is a Modula-3 language developed and implemented by the Digital Equipment Corporation Systems Research Center (SRC). Modula-3 is a modern, modular, object-oriented language. Other features include automatic memory management (built-in garbage collector), exception handling, support for dynamic types, and multi-threaded programming. 

The SRC implementation includes a compiler, a minimal recompilation system (m3build), and a wide range of libraries and sample applications. It must be said that SRC Modula-3 is a free system supplied with source code, including a compiler and a run-time kernel. In addition, SRC Modula-3 has been implemented for a dozen platforms, including Windows 95/NT.

  The goal of the developers of the language, in their own words, was not innovation, but the careful selection and consolidation of ideas, time-tested and proven to be useful in practice. Modula-3 is a simple but full-featured language for building large and reliable software packages with a long life cycle.

Project repository.
Year old article about general concepts of the project.

So you want to build a multitasking system using python? But you actually hesitate because you know you'll have to either use multitasking module, which is slow and/or somewhat inconvenient, or a more powerfull external tool like Redis or RabbitMQ or even large DBMS like MongoDB or PostgreSQL, which require some glue (i.e. very far from native python code) and apply their own restrictions on what you can do with your data. If you think «why do I need so much hassle if I just want to run few worker threads in python using the data structures I already have in my python program and using functions I've already written? I just want to run this code in threads! Oh, I wish there was no GIL in Python» — then welcome to the club.

Of course many of us can build from scratch a decent tool that would make use of multiple cores. However, having already existing working software (Pandas, Tensorflow, SciPy, etc) is always cheaper than any development of new software. But the status quo in CPython tells us one thing: you cannot remove GIL because everything is based on GIL. Although making shit into gold could require much work, the ability to alleviate the transition from slow single-threaded shit to a slow not-so-single-threaded gold-looking shit might be worth it, so you won't have to rewrite your whole system from scratch.

On the stackoverflow.com website, we frequently see questions about how to look for bugs of a certain type. We know that PVS-Studio can solve the problem. Unfortunately, we have to keep silent. Otherwise, StackOverflow moderators may consider it as an obvious attempt to promote our product. This article describes a particular case of such a situation that makes us suffer deeply.

The OWASP Top Ten 2017 category A9 (which became A6 in OWASP Top Ten 2021) is dedicated to using components with known vulnerabilities. To cover this category in PVS-Studio, developers have to turn the analyzer into a full SCA solution. How will the analyzer look for vulnerabilities in the components used? What is SCA? Let's try to find the answers in this article!

I created a new console for Microsoft System Center Orchestrator. It doesn't require Silverlight.

The third quarter of 2021 brought a massive upheaval in the scale and intensity of DDoS attacks worldwide.

It all led to September when together with Yandex, we uncovered one of the most devastating botnets since the Mirai and named it Meris, as it was held accountable for a series of attacks with a very high RPS rate. And as those attacks were aimed all over the world, our quarterly statistics also changed.

This quarter, we've also prepared for your consideration a slice of statistics on the application layer (L7) DDoS attacks. Without further ado, let us elaborate on the details of DDoS attacks statistics and BGP incidents for Q3, 2021.

Digital communication APIs and SDKs! the most powerful tool in the era of digitalization. Unlike other tools, these real time communication APIs have spread their impact over all industries and have successfully grabbed the attention of proficient developers too.

Based on the demand and need of developers to know more about these digital communication APIs and SDKs concerning their market availability with pricing, features and functionalities, I have posted this article to get you some clarity with research on the top most real time chat API and SDK providers. So, let’s start over.

When we move towards the digital world, we shouldn’t forget that cybersecurity has been playing a major role in our life. Talks about digital security have been stiff. The main challenge we would face is abnormality.

During an online transaction, most of the product-lovers prefer credit cards. The credit limit available in credit cards would allow us to purchase even when our bank balance is insufficient. But this is great news for cyber attackers eyeing your money.

For tackling this problem, we should depend upon a system to make hardpressed transactions effortless.

This is where we need a system to track the transaction patterns. With AI, we can abort any abnormal transaction, precisely for credit card fraud detection AI.

As of now, we will come across a number of machine learning algorithms to classify unusual transactions where Artificial Intelligence detect fraud. We only need past data and the right algorithm to fit the data in the right form in case of credit card fraud detection ai.

How do we make this happen? Let’s look into the process of credit card fraud detection AI:

Import the needed libraries

The best step to detect credit card fraud detection with AI is to import the libraries. The best practice would be to import the necessary libraries in a single section for the purpose of quick modification. To use the credit card data, we can use the PCA’s transformed version or RFECV, RFE, VIF and SelectKBest to get the best model features.

Import Dataset

Machine learning helps with fraud detection. It’s quite simple to import the dataset when you use the pandas module in python. You can run the run command for importing your data. 

This article covers the new version of the C# language - C# 10. Compared to C# 9, C# 10 includes a short list of enhancements. Below we described the enhancements and added explanatory code fragments. Let's look at them.

Let’s talk about app architecture and the approach I apply as an iOS software engineer in a few companies. My team and I were trying to build something solid without slipping into a dense swamp where following the rules distracts you from actual business domain code. As a result, we got something that works for us and good enough to be told from my point of view.

I am not an economist, but in light of current events with cryptocurrencies and the economy in general, I would like to share my thoughts on some kind of ideal economy, around which everything is happening now.

My name is Serhii Pimenov. I’m a web developer from Kyiv, Ukraine (maybe you know me by the nickname olton).

Today I'm going to speak about one of my tools for the Mina blockchain - “Mina Monitor”. It’s the first article in the series about Mina and Mina Tools. In this article, I will introduce you to my tool for monitoring the Mina nodes.

UIKit first appeared in iOS 2, and it is still here. Eventually we got to know it well and learned how to work with it. We have found many architectural approaches. MVVM, the most popular architecture in my opinion, has strengthened its position with the release of SwiftUI, while other architectures seemed to have some kind of problematic relationships with SwiftUI.

But what if I told you that Clean Swift, VIPER and other approaches can be adapted to SwiftUI. What if I told you that there are some modern architectures which might be as good as MVVM or even better.

We will talk about MVI.

Modern applications have lots of code. And the C++ language doesn't get easier. Nowadays, code reviews are not enough to fully analyze program code. Here's where static code analysis comes in.

In this video, we are making a cable for connecting a quadrature encoder to a Servosila brushless motor controller, and and then running a servo motor in Direct Drive mode. To make the cable we are using a cable assembly kit that can be purchased from the internet store. Alternatively, the components for the cable can be bought in other places. The part numbers are given in the controller's datasheet.

The cable assembly kit consists of a connector and a set of wires with pre-crimped socket blades. If you have a crimper tool, you can also attach the socket blades to wires by yourself.

Lets open a datasheet document that comes with the brushless motor controller. Note that each connector has its first pin clearly marked with a "1" sign. Conventionally, the numbering of pins is done in such a way that there are rows of odd-numbered and even-numbered pins.

The quadrature encoder's electrical interface has 5 wires in total. Positions of the pins of each of the wires are given in the table. The socket blades need to be pushed into the connector until you feel a "click". The blades lock into the connector's sockets. Optionally, primarily for cosmetic reasons, you may want to add a heat-shrink tubing to your cable.

The brushless motor controllers come in two distinct forms, a circular and a rectangular one. Both models are identical in terms of capabilities, features, firmware, and external electrical connectors.

The connector has a locking mechanism that keeps it in place. I soldered a mating connector to the other side of the cable - a connector that my brushless motor needs. Note that your motor will likely require a different connector, or no connector at all. It is always a good idea to test an end-to-end integrity of the cable and its connectors. Lets buzz the wires using a multimeter. The cable is ready.

We are actively developing the PVS-Studio static analysis tool towards detecting Safety and Security-related errors. To be more precise, we've expanded the coverage of the MISRA C:2012 and OWASP ASVS standards. We have supported the MISRA Compliance 2020 standard. One of the more extraordinary innovations is the Best Warnings display mode. This mode displays the most significant (meaningful) warnings and can be useful when you are just starting out with the analyzer.

SEPIC-Ćuk split-rail converter can be used to make positive and negative supplies from a single input voltage for relatively well-matched loads like operational amplifiers.

Transient models are time consuming. Average models reduce modeling time drastically.

The PWM switch average models for current- and voltage-mode are described in details in Christophe Basso’s book “Switch-Mode Power Supplies, Second Edition: SPICE Simulations and Practical Designs”. Using of these models for SEPIC and Ćuk converters is also shown.

This text shows how to use the PWM switch average model to design a split-rail SEPIC-Ćuk converter.

Commercial static analyzers perform deeper and fuller code analysis compared to compilers. Let's see what PVS-Studio found in the source code of the LLVM 13.0.0 project.

Working with speech recognition models we often encounter misconceptions among potential customers and users (mostly related to the fact that people have a hard time distinguishing substance over form). People also tend to believe that punctuation marks and spaces are somehow obviously present in spoken speech, when in fact real spoken speech and written speech are entirely different beasts.

Of course you can just start each sentence with a capital letter and put a full stop at the end. But it is preferable to have some relatively simple and universal solution for "restoring" punctuation marks and capital letters in sentences that our speech recognition system generates. And it would be really nice if such a system worked with any texts in general.

For this reason, we would like to share a system that:

• Inserts capital letters and basic punctuation marks (dot, comma, hyphen, question mark, exclamation mark, dash for Russian);
• Works for 4 languages (Russian, English, German, Spanish) and can be extended;
• By design is domain agnostic and is not based on any hard-coded rules;
• Has non-trivial metrics and succeeds in the task of improving text readability;

To reiterate — the purpose of such a system is only to improve the readability of the text. It does not add information to the text that did not originally exist.

Test automation, with product built in microservice architecture could be very situational in context of testing goals and ways to achieve them. You got an easy life if you testing a service, that is an isolated entity, which is receiving some data and providing a result of it's work in a response, by callback or through additional endpoint. In this case all you need to do is cover all the endpoints of the service, and probably learn to catch it's callbacks. However, it's not the only case. Sometimes you need to test service which isn't totally isolated, but a part of a chain of interactions. This service could send some data to other services within your infrastructure or even to third parties. This time you got plenty of additional things to bother of: