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 . 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.
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From Bing to Google
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?
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…
The PVS-Studio team has been keeping the blog about the checks of open-source projects by the same-name 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 PVS-Studio, 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 full-text 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 PVS-Studio for Java manage to detect? Further talk will be right about the results of the check.
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...