This article continues the series of publications on usage of PVS-Studio in cloud systems. This time we'll look at the way the analyzer works along with GitLab CI, which is a product made by GitLab Inc. Static analyzer integration in a CI system allows detecting bugs right after the project build and is a highly effective way to reduce the cost of finding bugs.

Hi! Though the 2019 conference season is not over yet, we'd like to talk about the bug-finding challenges we offered to visitors at our booth during the past conferences. Starting with the fall of 2019, we've been bringing a new set of challenges, so we can now reveal the solutions to the previous tasks of 2018 and the first half of 2019 – after all, many of them came from previously posted articles, and we had a link or QR code with information about the respective articles printed on our challenge leaflets.

AHURATUS Smart Home Voice Assistant

Developed by Ehsan Shaghaei
Innopolis University
AHURATUS Scientific Club.

Introduction

AHURATUS Smart Home Voice Assistant is an IOT device developed in order to control other home devices by voice detection. Note: This device is made ONLY for academic purposes.

Approach

Description

"AHURATUS Smart Home Voice Assistant" uses an ARM Cortex-M3 process for running the instructions as well as several peripheral devices in order to decrease the complexity of data bus and RF-Circuit calculations.

Bill of Materials

Though it seems we are just a few months away from reaching 2020, these months are also important in the field of software development. Here in this article, we will see how the coming year 2020 will change the lives of software developers!

Future Software Development Is Here!

Traditional software development is about developing software by writing code and following some fixed rules. But the present-day software development witnessed a paradigm shift with advances in Artificial Intelligence, Machine Learning, and Deep Learning. With the integration of these three technologies, developers will be able to build software solutions that learn the instructions and add extra features and patterns in data that are needed for the desired outcome.

Let’s Try Out With Some Code

Over time, the neural network software development systems have become more complex in terms of integrations as well as layers of functionality and interfaces. Developers can build a very simple neural network with Python 3.6. Here’s an example of a program that does binary classification with 1 or 0.

Of course, we can start by creating a neural network class:


import numpy as np

X=np.array([[0,1,1,0],[0,1,1,1],[1,0,0,1]])
y=np.array([[0],[1],[1]])

Applying the Sigmoid function:

def sigmoid ():
   return 1/(1 + np.exp(-x))
def derivatives_sigmoid ():
   return x * (1-x)

Training the Model With Initial Weights and Biases:

epoch=10000
lr=0.1
inputlayer_neurons = X.shape[1]
hiddenlayer_neurons = 3
output_neurons = 1

wh=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons))
bh=np.random.uniform(size=(1,hiddenlayer_neurons))
wout=np.random.uniform(size=(hiddenlayer_neurons,output_neurons))
bout=np.random.uniform(size=(1,output_neurons))

For beginners, if you need help regarding neural networks, you can get in touch with top software development company.Or, you can hire AI/ML developers to work on your project.

When you think about Jupyter Notebooks, you probably think about writing your code in Python, R, Julia, or Scala and not .NET. Today we are excited to announce you can write .NET code in Jupyter Notebooks.

Try .NET has grown to support more interactive experiences across the web with runnable code snippets, interactive documentation generator for .NET core with dotnet try global tool, and now .NET in Jupyter Notebooks.

ML.NET is a cross-platform, machine learning framework for .NET developers, and Model Builder is the UI tooling in Visual Studio that uses Automated Machine Learning (AutoML) to easily allow you to train and consume custom ML.NET models. With ML.NET and Model Builder, you can create custom machine learning models for scenarios like sentiment analysis, price prediction, and more without any machine learning experience!

ML.NET Model Builder

This release of Model Builder comes with bug fixes and two exciting new features:

• Image classification scenario – locally train image classification models with your own images
• Try your model – make predictions on sample input data right in the UI

We’re announcing .NET Core 3.1 Preview 2. .NET Core 3.1 will be a small and short release focused on key improvements in Blazor and Windows desktop, the two big additions in .NET Core 3.0.. It will be a long term support (LTS) release with an expected final ship date of December 2019.

You can download .NET Core 3.1 Preview 2 on Windows, macOS, and Linux.

• .NET Core 3.1 Preview 2 SDK and Runtime
• Docker images

ASP.NET Core and EF Core are also releasing updates today.

Visual Studio 16.4 Preview 3 and Visual Studio for Mac 8.4 Preview 3 are also releasing today. They are required updates to use .NET Core 3.1 Preview 2. Visual Studio 16.4 includes .NET Core 3.1, so just updating Visual Studio will give you both releases.

Details:

• .NET Core 3.1 release notes
• GitHub release
• GitHub issue for Preview 2

The cryptocurrency space is incredibly popular among investors and traders alike. Many of the most popular crypto assets have grown a lot in recent years. Let’s take Bitcoin.

As everyone accepts the flutter technology is the best open-source mobile app development framework introduced by Google. using this technology mobile app developer can able to create a cross-platform mobile application with a beautiful user interface. The way of designing views is similar to the web application and you can find many similarities to CSS and HTML. Without knowing anything about this technology, you may have already used mobile apps which are made by using flutter technology.

Flutter technology is the mobile app SDK to build great performance, extraordinary accuracy, apps for iOS and android both platforms from the single codebase. The important goal of this app is to enable the developers to deliver the expected results apps which feel natural on different platforms. This difference is in scrolling behaviors, typography, icons and many more. It's not like other major solutions, this technology is not the framework as it completes the SDK software development kit which already holds everything that the users need to build cross-platform applications.

Now, let’s discuss the major use of this technology in mobile application development and how it can help us in building mobile apps as:

Yesterday my friend said something like "I’m writing simple offline app, I don’t need these streams and all that jazz". I was confused, but I thought, that there may be other coders in this delusion.

Below, literally in 50 lines I will show, on known example that reactivity is:

a) not about offline/online
b) very easy
c) very good for simplifying almost any code

In June, we heard about SwiftUI for the first time — a totally new way of creating and working with UI elements in iOS and macOS (also iPadOS) apps. It felt like Christmas in the summer. It's new, it's declarative, it's sexy! And now, just a few weeks after iOS 13 has been released, we can start to use SwiftUI in all our projects. Let's learn how to use this amazing tool that Apple gave us, to create the classic Tinder-esque Swipe Cards.

In this article, I would like to show you how to achieve a Tinder-like card view and behavior (swipe to action), with just a few lines of code.

To achieve this, we need to do the following things, in order:

• Create UserView
• Create NavigationView
• Create BottomBarView
• Create SwipeView
• Put all this together inside ContentView

So let's get started.

After hitting the keyboard buttons for the past 20 minutes, as if he was typing for his life, ghostinushanka turns to me with a half-mad look in his eyes and a sly smile, “Dude, I think I got it.

Look at this” — as he points to one of the characters on screen — “I bet my red hat that if we add what I’ve just sent you here” — as he points to another place in the code — “there will be no error anymore.”
Slightly puzzled and tired I modify the sed expression we’ve been figuring out for some time now, save the file and run systemctl varnish reload. Error message gone…

“Those emails I’ve exchanged with the candidate,” my colleague continues, as his smile changes to a wide and genuine grin, “It suddenly struck me that this is the very same exact problem!”