Algorithms

This is the third part of a series covering the implementation of algorithms in Go. In this article, we discuss the Merge Interval algorithm. Usually, when you start learning algorithms you have to deal with some problems like finding the least common denominator or finding the next Fibonacci number. While these are indeed important problems, it is not something that we solve every day. What I like about the Merge Interval algorithm is that we apply it in our everyday life, usually without even noticing that we are solving an algorithmic problem.

Let's say that we need to organize a meeting for our team. We have three colleagues Jay, May, and Ray and their time schedule look as follows (a colored line represents an occupied timeslot):

There’s a lot of talk about machine learning nowadays. A big topic – but, for a lot of people, covered by this terrible layer of mystery. Like black magic – the chosen ones’ art, above the mere mortal for sure. One keeps hearing the words “numpy”, “pandas”, “scikit-learn” - and looking each up produces an equivalent of a three-tome work in documentation.

I’d like to shatter some of this mystery today. Let’s do some machine learning, find some patterns in our data – perhaps even make some predictions. With good old Python only – no 2-gigabyte library, and no arcane knowledge needed beforehand.

Interested? Come join us.

This is the second part of the article covering the Sliding Window Pattern and its implementation in Go, the first part can be found here.

Let's have a look at the following problem: we have an array of words, and we want to check whether a concatenation of these words is present in the given string. The length of all words is the same, and the concatenation must include all the words without any overlapping. Would it be possible to solve the problem with linear time complexity?

Let's start with string catdogcat and target words cat and dog.

How can we handle this problem?

Let's consider the following problem: we have an array of integers and we need to find out the length of the smallest subarray the sum of which is no less than the target number. If we don't have such a subarray we shall return -1.

We can start with a naive approach and consider every possible subarray in the input:

I came to Mail.ru Group in 2013, and I required a queue for one task. First of all, I decided to check what the company had already got. They told me they had this Tarantool product, and I checked how it worked and decided that adding a queue broker to it could work perfectly well.

I contacted Kostja Osipov, the senior expert in Tarantool, and the next day he gave me a 250-string script that was capable of managing almost everything I needed. Since that moment, I have been in love with Tarantool. It turned out that a small amount of code written with a quite simple script language was capable of ensuring some totally new performance for this DBMS.

Today, I’m going to tell you how to instantiate your own queue in Tarantool 2.2.

A linked list is a linear data structure, which contains node structure and each node contains two elements. A data part that stores the value at that node and next part that stores the link to the next node as shown in the below image:

The first node also known as HEAD is usually used to traverse through the linked list. The last node (next part of the last node) points to NULL. The list can be visualized as a chain of nodes, where every node points to the next node.

Implementation of Singly Linked List

Representation:

In PHP, singly linked list can be represented as a class and a Node as a separate class. The LinkedList class contains a reference of Node class type.

//node structure
class Node {
  public $data;
  public $next;
}

class LinkedList {
  public $head;

  //constructor to create an empty LinkedList
  public function __construct(){
    $this->head = null;
  }
};

When it's necessary to evaluate the orientation angles of an object you may have the question — which MEMS sensor to choose. Sensors manufacturers provide a great amount of different parameters and it may be hard to understand if the sensor fit your needs.

Brief: this article is the description of the Octave/Matlab script which allows to estimate the orientation angles evaluation errors, derived from MEMS accelerometers and magnetometers measurements. The input data for the script are datasheet parameters for the sensors. Article can be useful for those who start using MEMS sensors in their devices. You can find the project on GitHub.

What do you imagine when you hear the word "Queue"? If you are not familiar with Programming you maybe think about the queue in shop or hospital. But if you are a programmer you associate it 99% with Data Structures and Algorithms. Whoever you are, today we will discuss how to implement Queue Data Structure in JavaScript and what are its differences with a simple Array. Let's get started!

Hello, Habr Readers! Usually, when we talk about Algorithms it is hard not to mention Linked List. It is one of the main Data Structures in programming. Today we will understand how we can implement Linked List in JavaScript.

For me, these two models are quite similar, but in fact they don’t have obvious characteristics to measure this similarity. These models have different numbers of vertices, edges and polygons. They are of different sizes, rotated differently and both have the same transforms (Location = [0,0,0], Rotation in radians = [0,0,0], Scale = [1,1,1]). So how to determine their similarity?

Introduction

Quick Sort is one of the most famous and effective Sorting Algorithms. The comprehension of how it works will undoubtedly help you in your JavaScript learning. Also, questions on algorithms are popular in job interviews, so there is a big chance you will be asked to describe how Quick Sort works.

I’m sure that I convinced you that Quick Sort is important. Let’s start!

Last year the number of private investors at Moscow Stock Exchange (MOEX) has doubled and reached 3.86 million: about 1.9 million people have opened accounts at MOEX in 2019. The Saint Petersburg Stock Exchange which specializes in trading of foreign company shares has seen its accounts increase three times from 910,000 to 3,06 million over the past year.



This means that almost 2 million newbies without any actual trading experience and lacking any specialized software for trading/position analysis have entered the market.

ClickHouse users already know that its biggest advantage is its high-speed processing of analytical queries. But claims like this need to be confirmed with reliable performance testing. That's what we want to talk about today.



We started running tests in 2013, long before the product was available as open source. Back then, just like now, our main concern was data processing speed in Yandex.Metrica. We had been storing that data in ClickHouse since January of 2009. Part of the data had been written to a database starting in 2012, and part was converted from OLAPServer and Metrage (data structures previously used by Yandex.Metrica). For testing, we took the first subset at random from data for 1 billion pageviews. Yandex.Metrica didn't have any queries at that point, so we came up with queries that interested us, using all the possible ways to filter, aggregate, and sort the data.

ClickHouse performance was compared with similar systems like Vertica and MonetDB. To avoid bias, testing was performed by an employee who hadn't participated in ClickHouse development, and special cases in the code were not optimized until all the results were obtained. We used the same approach to get a data set for functional testing.

After ClickHouse was released as open source in 2016, people began questioning these tests.

A palette makes it easy for painters to arrange and mix paints of different colors as they create art on the canvas before them. Having a similar tool that could allow AI to jointly learn from diverse data sources such as those for conversations, narratives, images, and knowledge could open doors for researchers and scientists to develop AI systems capable of more general intelligence.


A palette allows a painter to arrange and mix paints of different colors. SpaceFusion seeks to help AI scientists do similar things for different models trained on different datasets.

A couple of reader alerts:

In order to (somewhat) simplify the description process and tighten the volume of the article we are going to write, it is essential to make a significant remark and state the primary constraint right away — everything we are going to tell you today on the practical side of the problematics is viable only in terms of TLS 1.3. Meaning that while your ECDSA certificate would still work in TLS 1.2 if you wish it worked, providing backwards compatibility, the description of the actual handshake process, cipher suits and client-server benchmarks covers TLS 1.3 only. Of course, this does not relate to the mathematical description of algorithms behind modern encryption systems.

This article was written by neither a mathematician nor an engineer — although those helped to find a way around scary math and reviewed this article. Many thanks to Qrator Labs employees.

(Elliptic Curve) Diffie-Hellman (Ephemeral)

The Diffie–Hellman legacy in the 21 century

Of course, this has started with neither Diffie nor Hellman. But to provide a correct timeline, we need to point out main dates and events.

There were several major personas in the development of modern cryptography. Most notably, Alan Turing and Claud Shannon both laid an incredible amount of work over the field of theory of computation and information theory as well as general cryptanalysis, and both Diffie and Hellman, are officially credited for coming up with the idea of public-key (or so-called asymmetric) cryptography (although it is known that in the UK there were made serious advances in cryptography that stayed under secrecy for a very long time), making those two gentlemen pioneers.

In what exactly?

Chapter 2.
(the link to Chapter 1)

The Art of Designing Road Networks

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

If I were recommended an article with the title “The Art of Designing Road Networks,” I would immediately ask how many road networks were built with the participation of its author. I must admit, my professional activity was far from road construction and was recently associated with the design of microprocessors where I, among other responsibilities, was engaged in the resource consumption of data switching. At that time my table stood just opposite the panoramic window which opened up a beautiful view of the long section of the Volgograd Highway and part of the Third Transport Ring with their endless traffic jams from morning to evening, from horizon to horizon. One day, I had a sudden shock of recognition: “The complexities of the data switching process that I struggle with on a chip may be similar to the difficulties the cars face as they flow through the labyrinth of road network”.
Probably, this view from the outside and the application of methods that were not traditional for the area in question gave me a chance to understand the cause of traffic jams and make recommendations on how to overcome the problem in practice.

With the advent of mobile phones with high-quality cameras, we started making more and more pictures and videos of bright and memorable moments in our lives. Many of us have photo archives that extend back over decades and comprise thousands of pictures which makes them increasingly difficult to navigate through. Just remember how long it took to find a picture of interest just a few years ago.

One of Mail.ru Cloud’s objectives is to provide the handiest means for accessing and searching your own photo and video archives. For this purpose, we at Mail.ru Computer Vision Team have created and implemented systems for smart image processing: search by object, by scene, by face, etc. Another spectacular technology is landmark recognition. Today, I am going to tell you how we made this a reality using Deep Learning.

Manual lung segmentation takes about 10 minutes and it requires a certain skill to get the same high-quality result as with automatic segmentation. Automatic segmentation takes about 15 seconds.

I assumed that without a neural network it would be possible to get an accuracy of no more than 70%. I also assumed, that morphological operations are only the preparation of an image for more complex algorithms. But as a result of processing of those, although few, 40 samples of tomographic data on hand, the algorithm segmented the lungs without errors. Moreover, after testing in the first five cases, the algorithm didn’t change significantly and correctly worked on the other 35 studies without changing the settings.

Also, neural networks have a disadvantage — for their training we need hundreds of training samples of lungs, which need to be marked up manually.

Hi everybody! I’m a research engineer at the Mail.ru Group computer vision team. In this article, I’m going to tell a story of how we’ve created AI-based photo restoration project for old military photos. What is «photo restoration»? It consists of three steps:

• we find all the image defects: fractures, scuffs, holes;
  
• we inpaint the discovered defects, based on the pixel values around them;
  
• we colorize the image.
  

Further, I’ll describe every step of photo restoration and tell you how we got our data, what nets we trained, what we accomplished, and what mistakes we made.

We used to think of Telegram as a reliable and secure transmission medium for messages of any sort. But under the hood, it has a rather common combination of a- and symmetric encryptions. Where's fun in that? And anyway, why would anyone trust their messages to the third-party?

TL;DR — inventing a private covert channel over users blocking each other.