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Yongge Wang
Yongge Wang (born 1967) is a computer science professor at the University of North Carolina at Charlotte specialized in algorithmic complexity and cryptography. He is the inventor of IEEE P1363 cryptographic standards SRP5 and WANG-KE and has contributed to the mathematical theory of algorithmic randomness. He co-authored a paper demonstrating that a recursively enumerable real number In mathematics, a real number is a number that can be used to measure a continuous one- dimensional quantity such as a duration or temperature. Here, ''continuous'' means that pairs of values can have arbitrarily small differences. Every re ... is an algorithmically random sequence if and only if it is a Chaitin's constant for some encoding of programs. He also showed the separation of Schnorr randomness from recursive randomness. He also invented a distance based statistical testing technique to improve NIST SP800-22 testing in randomness tests. In cryptographic research, he is known fo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Science
Computer science is the study of computation, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, applied disciplines (including the design and implementation of Computer architecture, hardware and Software engineering, software). Algorithms and data structures are central to computer science. The theory of computation concerns abstract models of computation and general classes of computational problem, problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and preventing security vulnerabilities. Computer graphics (computer science), Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
University Of North Carolina At Charlotte
The University of North Carolina at Charlotte (UNC Charlotte, or simply Charlotte) is a public research university in Charlotte, North Carolina, United States. UNC Charlotte offers 24 doctoral, 66 master's, and 79 bachelor's degree programs through nine colleges. It is classified among "R1: Very High Research Spending and Doctorate Production". The university experienced rapid enrollment growth in the late 2000s and early-mid 2010s when it was the fastest-growing institution in the UNC System. It has two campuses: the Main Campus, located in University City, and the Center City Campus in Uptown Charlotte. The main campus sits on 1,000 wooded acres with approximately 85 buildings about from Uptown Charlotte. History Prior to UNC Charlotte's founding, Charlotte had long sought a public university. In the late 1880s, the city bid for what would become North Carolina State University, but lost to Raleigh after a local farmer offered to donate land for the campus.Sanford, Ken. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cryptography
Cryptography, or cryptology (from "hidden, secret"; and ''graphein'', "to write", or ''-logy, -logia'', "study", respectively), is the practice and study of techniques for secure communication in the presence of Adversary (cryptography), adversarial behavior. More generally, cryptography is about constructing and analyzing Communication protocol, protocols that prevent third parties or the public from reading private messages. Modern cryptography exists at the intersection of the disciplines of mathematics, computer science, information security, electrical engineering, digital signal processing, physics, and others. Core concepts related to information security (confidentiality, data confidentiality, data integrity, authentication, and non-repudiation) are also central to cryptography. Practical applications of cryptography include electronic commerce, Smart card#EMV, chip-based payment cards, digital currencies, password, computer passwords, and military communications. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
IEEE P1363
IEEE P1363 is an Institute of Electrical and Electronics Engineers (IEEE) standardization project for public-key cryptography. It includes specifications for: * Traditional public-key cryptography (IEEE Std 1363-2000 and 1363a-2004) * Lattice-based public-key cryptography (IEEE Std 1363.1-2008) * Password-based public-key cryptography (IEEE Std 1363.2-2008) * Identity-based public-key cryptography using pairings (IEEE Std 1363.3-2013) The chair of the working group as of October 2008 is William Whyte of NTRU Cryptosystems, Inc., who has served since August 2001. Former chairs were Ari Singer, also of NTRU (1999–2001), and Burt Kaliski of RSA Security (1994–1999). The IEEE Standard Association withdrew all of the 1363 standards except 1363.3-2013 on 7 November 2019. Traditional public-key cryptography (IEEE Std 1363-2000 and 1363a-2004) This specification includes key agreement, signature, and encryption schemes using several mathematical approaches: integer factorizati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
IEEE
The Institute of Electrical and Electronics Engineers (IEEE) is an American 501(c)(3) organization, 501(c)(3) public charity professional organization for electrical engineering, electronics engineering, and other related disciplines. The IEEE has a corporate office in New York City and an operations center in Piscataway, New Jersey. The IEEE was formed in 1963 as an amalgamation of the American Institute of Electrical Engineers and the Institute of Radio Engineers. History The IEEE traces its founding to 1884 and the American Institute of Electrical Engineers. In 1912, the rival Institute of Radio Engineers was formed. Although the AIEE was initially larger, the IRE attracted more students and was larger by the mid-1950s. The AIEE and IRE merged in 1963. The IEEE is headquartered in New York City, but most business is done at the IEEE Operations Center in Piscataway, New Jersey, opened in 1975. The Australian Section of the IEEE existed between 1972 and 1985, after which it s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithmic Randomness
Intuitively, an algorithmically random sequence (or random sequence) is a sequence of binary digits that appears random to any algorithm running on a (prefix-free or not) universal Turing machine. The notion can be applied analogously to sequences on any finite alphabet (e.g. decimal digits). Random sequences are key objects of study in algorithmic information theory. In measure-theoretic probability theory, introduced by Andrey Kolmogorov in 1933, there is ''no such thing'' as a random sequence. For example, consider flipping a fair coin infinitely many times. Any particular sequence, be it 0000\dots or 011010\dots, has equal probability of exactly zero. There is no way to state that one sequence is "more random" than another sequence, using the language of measure-theoretic probability. However, it is intuitively obvious that 011010\dots looks more random than 0000\dots. Algorithmic randomness theory formalizes this intuition. As different types of algorithms are sometimes co ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Recursively Enumerable
In computability theory, a set ''S'' of natural numbers is called computably enumerable (c.e.), recursively enumerable (r.e.), semidecidable, partially decidable, listable, provable or Turing-recognizable if: *There is an algorithm such that the set of input numbers for which the algorithm halts is exactly ''S''. Or, equivalently, *There is an algorithm that enumerates the members of ''S''. That means that its output is a list of all the members of ''S'': ''s''1, ''s''2, ''s''3, ... . If ''S'' is infinite, this algorithm will run forever, but each element of S will be returned after a finite amount of time. Note that these elements do not have to be listed in a particular way, say from smallest to largest. The first condition suggests why the term ''semidecidable'' is sometimes used. More precisely, if a number is in the set, one can ''decide'' this by running the algorithm, but if the number is not in the set, the algorithm can run forever, and no information is returned. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Real Number
In mathematics, a real number is a number that can be used to measure a continuous one- dimensional quantity such as a duration or temperature. Here, ''continuous'' means that pairs of values can have arbitrarily small differences. Every real number can be almost uniquely represented by an infinite decimal expansion. The real numbers are fundamental in calculus (and in many other branches of mathematics), in particular by their role in the classical definitions of limits, continuity and derivatives. The set of real numbers, sometimes called "the reals", is traditionally denoted by a bold , often using blackboard bold, . The adjective ''real'', used in the 17th century by René Descartes, distinguishes real numbers from imaginary numbers such as the square roots of . The real numbers include the rational numbers, such as the integer and the fraction . The rest of the real numbers are called irrational numbers. Some irrational numbers (as well as all the rationals) a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithmically Random Sequence
Intuitively, an algorithmically random sequence (or random sequence) is a sequence of binary digits that appears random to any algorithm running on a (prefix-free or not) universal Turing machine. The notion can be applied analogously to sequences on any finite alphabet (e.g. decimal digits). Random sequences are key objects of study in algorithmic information theory. In measure-theoretic probability theory, introduced by Andrey Kolmogorov in 1933, there is ''no such thing'' as a random sequence. For example, consider flipping a fair coin infinitely many times. Any particular sequence, be it 0000\dots or 011010\dots, has equal probability of exactly zero. There is no way to state that one sequence is "more random" than another sequence, using the language of measure-theoretic probability. However, it is intuitively obvious that 011010\dots looks more random than 0000\dots. Algorithmic randomness theory formalizes this intuition. As different types of algorithms are sometimes ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Chaitin's Constant
In the computer science subfield of algorithmic information theory, a Chaitin constant (Chaitin omega number) or halting probability is a real number that, informally speaking, represents the probability that a randomly constructed program will halt. These numbers are formed from a construction due to Gregory Chaitin. Although there are infinitely many halting probabilities, one for each (universal, see below) method of encoding programs, it is common to use the letter to refer to them as if there were only one. Because depends on the program encoding used, it is sometimes called Chaitin's construction when not referring to any specific encoding. Each halting probability is a normal and transcendental real number that is not computable, which means that there is no algorithm to compute its digits. Each halting probability is Martin-Löf random, meaning there is not even any algorithm which can reliably guess its digits. Background The definition of a halting probability ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Randomness Tests
A randomness test (or test for randomness), in data evaluation, is a test used to analyze the distribution of a set of data to see whether it can be described as random (patternless). In stochastic modeling, as in some computer simulations, the hoped-for randomness of potential input data can be verified, by a formal test for randomness, to show that the data are valid for use in simulation runs. In some cases, data reveals an obvious non-random pattern, as with so-called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1..." and rarely going above 4). If a selected set of data fails the tests, then parameters can be changed or other randomized data can be used which does pass the tests for randomness. Background The issue of randomness is an important philosophical and theoretical question. Tests for randomness can be used to determine whether a data set has a recognisable pattern, which would indicate that the process that generated it is sig ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
