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You want to be using argon2id.
A KDF is a function that takes some input (in this case the user's password) and generates a key.
Good KDFs reduce this risk by being what's technically referred to as "expensive". Rather than performing one simple calculation to turn a password into a key, they perform a lot of calculations.
However, there's another axis of expense that can be considered - memory. If the KDF algorithm requires a significant amount of RAM, the degree to which it can be performed in parallel on a GPU is massively reduced.
Hello, I am currently searchin for a way to convert several Word documents into a single PDF file. The original Word documents are attachments to a One Order object in CRM 5.0, and I want to create an
The “vanilla” store implementation you get by calling createStore only supports plain object actions and hands them immediately to the reducer.
However, if you wrap createStore with applyMiddleware, the middleware can interpret actions differently, and provide support for dispatching async actions. Async actions are usually asynchronous primitives like Promises, Observables, or thunks.
When webpack bundles your javascript it wraps all of your individual files/modules in functions so they are no longer run in the global scope, therefore if you want to make a variable global you have to explicitly set it on the window object, i.e.
window.a = 1;
Definition 4: This value: in JavaScript this value is dynamically scoped, unless used in an arrow function.
That’s correct: as we know, the value of this is determined and provided exactly by the caller.
function produce() {
console.log(this.x);
}
const alpha = {produce, x: 1};
const beta = {produce, x: 2};
const gamma = {produce, x: 3};
console.log(
alpha.produce(), // 1
beta.produce(), // 2
gamma.produce(), // 3
);
instead of calling a function directly, the dyncall library provides a mechanism to push the function parameters manually and to issue the call afterwards.
Exponential sums are a specialized area of math that studies series with terms that are complex exponentials. Estimating such sums is delicate work. General
The Contrast Sensitivity Function
Prepared by Peter Wenderoth
Imagine that you are driving a car in a very thick fog. Objects which are normally easily seen, like black writing on a white billboard, will be hard to see because the black writing and the white background will both be greyish. That is the difference between whites and blacks - contrast - will be reduced
The Digital Library of Mathematical Functions (DLMF) Project was initiated to perform a complete revision of Abramowitz and Stegun’s Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, published in 1964 by the National Bureau of Standards. See R. F. Boisvert and D. W. Lozier (2001) for historical background about this important publication. The DLMF Project has updated and expanded the coverage for current needs. The results have been published in book form as the NIST Handbook of Mathematical Functions, by Cambridge University Press, and disseminated in the free electronic Digital Library of Mathematical Functions. For further details about the project see Preface.
CREATE FUNCTION pg_catalog.text(integer) RETURNS text STRICT IMMUTABLE LANGUAGE SQL AS 'SELECT textin(int4out($1));';
CREATE CAST (integer AS text) WITH FUNCTION pg_catalog.text(integer) AS IMPLICIT;
Die Levenshtein-Distanz ist eine effiziente Möglichkeit, die Ähnlichkeit von Strings zu bestimmen, Damit können beispielsweise Tippfehler bei der Verwendung von Suchfunktionen elegant korrigiert werden. Wenn man eine Anzeige im Stile von "Meinten Sie vielleicht ..." anzeigen möchte, braucht man hierzu eine Funktion die "das ähnlichste" Wort in einer Datenbanktabelle findet. Hierzu habe ich eine MySQL-GROUP-Funktion geschrieben.