The article is an abstract of my book [1] based on previously presented publications [2], [3], [4], [5]
Search engines *
From Bing to Google
Collective meaning recognition
The published material is in the Appendix of my book [1]
Modern civilization finds itself at a crossroads in which to choose the meaning of life. Because of the development of technology, the majority of the world's population may be "superfluous"  not in demand in the production of values. There is another option, where each person is a supreme value, an absolute individual and can be indispensably useful in the technology of the collective mind.
In the eighties of the last century, the task of creating a scientific field of "collective intelligence" was set. Collective intelligence is defined as the ability of the collective to find solutions to problems more effectively than each participant individually. The right collective mind must be...
Concordance of sense
In [1,2,3] texts (sign sequences with repetitions) were transformed (coordinated) into algebraic systems using matrix units as word images. Coordinatization is a necessary condition of algebraization of any subject area. Function (arrow) (7) in [1]) is a matrix coordinatization of text. One can perform algebraic operations with words and fragments of matrix texts as with integers, but taking into account the noncommutativity of multiplication of words as matrices. Structurization of texts is reduced to the calculation of ideals and categories of texts in matrix form.
Context category
The mathematical model of signed sequences with repetitions (texts) is a multiset. The multiset was defined by D. Knuth in 1969 and later studied in detail by A. B. Petrovsky [1]. The universal property of a multiset is the existence of identical elements. The limiting case of a multiset with unit multiplicities of elements is a set. A set with unit multiplicities corresponding to a multiset is called its generating set or domain. A set with zero multiplicity is an empty set.
Algebra of text. Examples
The previous work from ref [1] describes the method of transforming a sign sequence into algebra through an example of a linguistic text. Two other examples of algebraic structuring of texts of a different nature are given to illustrate the method.
Converting text into algebra
Algebra and language (writing) are two different learning tools. When they are combined, we can expect new methods of machine understanding to emerge. To determine the meaning (to understand) is to calculate how the part relates to the whole. Modern search algorithms already perform the task of meaning recognition, and Google’s tensor processors perform matrix multiplications (convolutions) necessary in an algebraic approach. At the same time, semantic analysis mainly uses statistical methods. Using statistics in algebra, for instance, when looking for signs of numbers divisibility, would simply be strange. Algebraic apparatus is also useful for interpreting the calculations results when recognizing the meaning of a text.
Ray Cast Visual Search (RCVS). Fast and simple algorithm for searching 3D objects with similar shapes
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?
10 SEO Myths to Leave Behind in 2020
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At a certain point, any website owner wonders what's better: SEO or PPC? Which promotion strategy will be the most rational to use in this particular situation? Or maybe it's best to combine both?
Before you decide between SEO and PPC, you need to consider the differences between them…
International SEO  International SEO ranking factors
PVSStudio for Java hits the road. Next stop is Elasticsearch
The PVSStudio team has been keeping the blog about the checks of opensource projects by the samename static code analyzer for many years. To date, more than 300 projects have been checked, the base of errors contains more than 12000 cases. Initially the analyzer was implemented for checking C and C++ code, support of C# was added later. Therefore, from all checked projects the majority (> 80%) accounts for C and C++. Quite recently Java was added to the list of supported languages, which means that there is now a whole new open world for PVSStudio, so it's time to complement the base with errors from Java projects.
The Java world is vast and varied, so one doesn't even know where to look first when choosing a project to test the new analyzer. Ultimately, the choice fell on the fulltext search and analytical engine Elasticsearch. It is quite a successful project, and it's even especially pleasant to find errors in significant projects. So, what defects did PVSStudio for Java manage to detect? Further talk will be right about the results of the check.
How to Discover MongoDB and Elasticsearch Open Databases
Some time ago among security researchers, it was very “fashionable” to find improperly configured AWS cloud storages with various kinds of confidential information. At that time, I even published a small note about how Amazon S3 open cloud storage is discovered.
However, time passes and the focus in research has shifted to the search for unsecured and exposed public domain databases. More than half of the known cases of large data leaks over the past year are leaks from open databases.
Today we will try to figure out how such databases are discovered by security researchers...
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alizar 2588.6 
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