Natural language processing is computer activity in which computers are entailed to analyze, understand, alter, or generate natural language. This includes the automation of any or all linguistic forms, activities, or methods of communication, such as conversation, correspondence, reading, written composition, dictation, publishing, translation, lip reading, and so on. Natural-language processing is also the name of the branch of computer science, artificial intelligence, and linguistics concerned with enabling computers to engage in communication using natural language(s) in all forms, including but not limited to speech, print, writing, and signing. The following outline is provided as an overview of and topical guide to natural-language processing: == Natural-language processing == Natural-language processing can be described as all of the following: A field of science – systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe. An applied science – field that applies human knowledge to build or design useful things. A field of computer science – scientific and practical approach to computation and its applications. A branch of artificial intelligence – intelligence of machines and robots and the branch of computer science that aims to create it. A subfield of computational linguistics – interdisciplinary field dealing with the statistical or rule-based modeling of natural language from a computational perspective. An application of engineering – science, skill, and profession of acquiring and applying scientific, economic, social, and practical knowledge, in order to design and also build structures, machines, devices, systems, materials and processes. An application of software engineering – application of a systematic, disciplined, quantifiable approach to the design, development, operation, and maintenance of software, and the study of these approaches; that is, the application of engineering to software. A subfield of computer programming – process of designing, writing, testing, debugging, and maintaining the source code of computer programs. This source code is written in one or more programming languages (such as Java, C++, C#, Python, etc.). The purpose of programming is to create a set of instructions that computers use to perform specific operations or to exhibit desired behaviors. A subfield of artificial intelligence programming – A type of system – set of interacting or interdependent components forming an integrated whole or a set of elements (often called 'components' ) and relationships which are different from relationships of the set or its elements to other elements or sets. A system that includes software – software is a collection of computer programs and related data that provides the instructions for telling a computer what to do and how to do it. Software refers to one or more computer programs and data held in the storage of the computer. In other words, software is a set of programs, procedures, algorithms and its documentation concerned with the operation of a data processing system. A type of technology – making, modification, usage, and knowledge of tools, machines, techniques, crafts, systems, methods of organization, in order to solve a problem, improve a preexisting solution to a problem, achieve a goal, handle an applied input/output relation or perform a specific function. It can also refer to the collection of such tools, machinery, modifications, arrangements and procedures. Technologies significantly affect human as well as other animal species' ability to control and adapt to their natural environments. A form of computer technology – computers and their application. NLP makes use of computers, image scanners, microphones, and many types of software programs. Language technology – consists of natural-language processing (NLP) and computational linguistics (CL) on the one hand, and speech technology on the other. It also includes many application oriented aspects of these. It is often called human language technology (HLT). == Prerequisite technologies == The following technologies make natural-language processing possible: Communication – the activity of a source sending a message to a receiver Language – Speech – Writing – Computing – Computers – Computer programming – Information extraction – User interface – Software – Text editing – program used to edit plain text files Word processing – piece of software used for composing, editing, formatting, printing documents Input devices – pieces of hardware for sending data to a computer to be processed Computer keyboard – typewriter style input device whose input is converted into various data depending on the circumstances Image scanners – == Subfields of natural-language processing == Information extraction (IE) – field concerned in general with the extraction of semantic information from text. This covers tasks such as named-entity recognition, coreference resolution, relationship extraction, etc. Ontology engineering – field that studies the methods and methodologies for building ontologies, which are formal representations of a set of concepts within a domain and the relationships between those concepts. Speech processing – field that covers speech recognition, text-to-speech and related tasks. Statistical natural-language processing – Statistical semantics – a subfield of computational semantics that establishes semantic relations between words to examine their contexts. Distributional semantics – a subfield of statistical semantics that examines the semantic relationship of words across a corpora or in large samples of data. == Related fields == Natural-language processing contributes to, and makes use of (the theories, tools, and methodologies from), the following fields: Automated reasoning – area of computer science and mathematical logic dedicated to understanding various aspects of reasoning, and producing software which allows computers to reason completely, or nearly completely, automatically. A sub-field of artificial intelligence, automatic reasoning is also grounded in theoretical computer science and philosophy of mind. Linguistics – scientific study of human language. Natural-language processing requires understanding of the structure and application of language, and therefore it draws heavily from linguistics. Applied linguistics – interdisciplinary field of study that identifies, investigates, and offers solutions to language-related real-life problems. Some of the academic fields related to applied linguistics are education, linguistics, psychology, computer science, anthropology, and sociology. Some of the subfields of applied linguistics relevant to natural-language processing are: Bilingualism / Multilingualism – Computer-mediated communication (CMC) – any communicative transaction that occurs through the use of two or more networked computers. Research on CMC focuses largely on the social effects of different computer-supported communication technologies. Many recent studies involve Internet-based social networking supported by social software. Contrastive linguistics – practice-oriented linguistic approach that seeks to describe the differences and similarities between a pair of languages. Conversation analysis (CA) – approach to the study of social interaction, embracing both verbal and non-verbal conduct, in situations of everyday life. Turn-taking is one aspect of language use that is studied by CA. Discourse analysis – various approaches to analyzing written, vocal, or sign language use or any significant semiotic event. Forensic linguistics – application of linguistic knowledge, methods and insights to the forensic context of law, language, crime investigation, trial, and judicial procedure. Interlinguistics – study of improving communications between people of different first languages with the use of ethnic and auxiliary languages (lingua franca). For instance by use of intentional international auxiliary languages, such as Esperanto or Interlingua, or spontaneous interlanguages known as pidgin languages. Language assessment – assessment of first, second or other language in the school, college, or university context; assessment of language use in the workplace; and assessment of language in the immigration, citizenship, and asylum contexts. The assessment may include analyses of listening, speaking, reading, writing or cultural understanding, with respect to understanding how the language works theoretically and the ability to use the language practically. Language pedagogy – science and art of language education, including approaches and methods of language teaching and study. Natural-language processing is used in programs designed to teach language, including first- and second-language training. Language planning – Language policy – Lexicography – Literacies – Pragmatics – Second-language acquisition – Stylistics – Translation – Comp
List of .NET libraries and frameworks
This article contains a list of libraries that can be used in .NET languages. These languages require .NET Framework, Mono, or .NET, which provide a basis for software development, platform independence, language interoperability and extensive framework libraries. Standard Libraries (including the Base Class Library) are not included in this article. == Introduction == Apps created with .NET Framework or .NET run in a software environment known as the Common Language Runtime (CLR), an application virtual machine that provides services such as security, memory management, and exception handling. The framework includes a large class library called Framework Class Library (FCL). Thanks to the hosting virtual machine, different languages that are compliant with the .NET Common Language Infrastructure (CLI) can operate on the same kind of data structures. These languages can therefore use the FCL and other .NET libraries that are also written in one of the CLI compliant languages. When the source code of such languages are compiled, the compiler generates platform-independent code in the Common Intermediate Language (CIL, also referred to as bytecode), which is stored in CLI assemblies. When a .NET app runs, the just-in-time compiler (JIT) turns the CIL code into platform-specific machine code. To improve performance, .NET Framework also comes with the Native Image Generator (NGEN), which performs ahead-of-time compilation to machine code. This architecture provides language interoperability. Each language can use code written in other languages. Calls from one language to another are exactly the same as would be within a single programming language. If a library is written in one CLI language, it can be used in other CLI languages. Moreover, apps that consist only of pure .NET assemblies, can be transferred to any platform that contains an implementation of CLI and run on that platform. For example, apps written using .NET can run on Windows, macOS, and various versions of Linux. .NET apps or their libraries, however, may depend on native platform features, e.g. COM. As such, platform independence of .NET apps depends on the ability to transfer necessary native libraries to target platforms. In 2019, the Windows Forms and Windows Presentation Foundation portions of .NET Framework were made open source. === .NET implementations === There are four primary .NET implementations that are actively developed and maintained: .NET Framework: The original .NET implementation that has existed since 2002. While not yet discontinued, Microsoft does not plan on releasing its next major version, 5.0. Mono: A cross-platform implementation of .NET Framework by Ximian, introduced in 2004. It is free and open-source. It is now developed by Xamarin, a subsidiary of Microsoft. Universal Windows Platform (UWP): An implementation of .NET used for building UWP apps. It's designed to unify development for different targeted types of devices, including PCs, tablets, phablets, phones, and the Xbox. .NET: A cross-platform re-implementation of .NET Framework, introduced in 2016 and initially called .NET Core. It is free and open-source. .NET superseded .NET Framework with the release of .NET 5. Each implementation of .NET includes the following components: One or more runtime environments, e.g. Common Language Runtime (CLR) for .NET Framework and CoreCLR for .NET A class library The .NET Standard is a set of common APIs that are implemented in the Base Class Library of any .NET implementation. The class library of each implementation must implement the .NET Standard, but may also implement additional APIs. Traditionally, .NET apps targeted a certain version of a .NET implementation, e.g. .NET Framework 4.6. Starting with the .NET Standard, an app can target a version of the .NET Standard and then it could be used (without recompiling) by any implementation that supports that level of the standard. This enables portability across different .NET implementations. The following table lists the .NET implementations that adhere to the .NET Standard and the version number at which each implementation became compliant with a given version of .NET Standard. For example, according to this table, .NET Core 3.0 was the first version of .NET Core that adhered to .NET Standard 2.1. This means that any version of .NET Core bigger than 3.0 (e.g. .NET Core 3.1) also adheres to .NET Standard 2.1. == Web frameworks == === ASP.NET === First released in 2002, ASP.NET is an open-source server-side web application framework designed for web development to produce dynamic web pages. It is the successor to Microsoft's Active Server Pages (ASP) technology, built on the Common Language Runtime (CLR). === ASP.NET Core === ASP.NET was completely rewritten in 2016 as a modular web framework, together with other frameworks like Entity Framework. The re-written framework uses the new open-source .NET Compiler Platform (also known by its codename "Roslyn") and is cross platform. The programming models ASP.NET MVC, ASP.NET Web API, and ASP.NET Web Pages (a model using only Razor pages) were merged into a unified MVC 6. === Blazor === Blazor is a free and open-source web framework that enables developers to create Single-page Web apps using C# and HTML in ASP.NET Razor pages ("components"). Blazor is part of the ASP.NET Core framework. Blazor Server apps are hosted on a web server, while Blazor WebAssembly apps are downloaded to the client's web browser before running. In addition, a Blazor Hybrid framework is available with server-based and client-based application components. == Numerical libraries == === Open-source numerical libraries === ==== AForge.NET ==== This is a computer vision and artificial intelligence library. It implements a number of genetic, fuzzy logic and machine learning algorithms with several architectures of artificial neural networks with corresponding training algorithms. ==== ALGLIB ==== This is a cross-platform open source numerical analysis and data processing library. It consists of algorithm collections written in different programming languages (C++, C#, FreePascal, Delphi, VBA) and has dual licensing – commercial and GPL. ==== Math.NET Numerics ==== This library aims to provide methods and algorithms for numerical computations in science, engineering and everyday use. Covered topics include special functions, linear algebra, probability models, random numbers, interpolation, integral transforms and more. MIT/X11 license. ==== Meta.Numerics ==== This is a library for advanced scientific computation in the .NET Framework. ==== ML.NET ==== This is a free software machine learning library. The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions. === Proprietary numerical libraries === ==== ILNumerics.Net ==== This is a high performance, typesafe numerical array set of classes and functions for general math, FFT and linear algebra. The library, developed for .NET/Mono, aims to provide 32- and 64-bit script-like syntax in C#, 2D & 3D plot controls, and efficient memory management. It is released under GPLv3 or commercial license. ==== Measurement Studio ==== This is an integrated suite of UI controls and class libraries for use in developing test and measurement applications. The analysis class libraries provide various digital signal processing, signal filtering, signal generation, peak detection, and other general mathematical functionality. ==== NMath ==== This is a numerical component library for the .NET platform developed by CenterSpace Software. It includes signal processing (FFT) classes, a linear algebra (LAPACK & BLAS) framework, and a statistics package. == 3D graphics == === Open-source 3D graphics === ==== Open Toolkit (OpenTK) ==== This is a low-level C# binding for OpenGL, OpenGL ES and OpenAL. It runs on Windows, Linux, Mac OS X, BSD, Android and iOS. It can be used standalone or integrated into a GUI. ==== Windows Presentation Foundation (WPF) ==== This is a graphical subsystem for rendering user interfaces, developed by Microsoft. It also contains a 3D rendering engine. In addition, interactive 2D content can be overlaid on 3D surfaces natively. It only runs on Windows operating systems. === Proprietary 3D graphics === ==== Unity ==== This is a cross-platform game engine developed by Unity Technologies and used to develop video games for PC, consoles, mobile devices and websites. == Image processing == === AForge.NET === This is a computer vision and artificial intelligence library. It implements a number of image processing algorithms and filters. It is released under the LGPLv3 and partly GPLv3 license. Majority of the library is written in C# and th
Pinoy baiting
Pinoy baiting is a phrase that has been used to refer to acts by non-Filipino individuals, usually celebrities or YouTubers, of posting content online purportedly with the intention of getting the attention of Filipinos, by being surprised about the Philippines or its people. Pinoy baiters are defined as giving superficial and allegedly insincere praises and similar reactions that give recognition to the Philippines or its people. Subsequent responses by Filipinos to what have been referred to as acts of Pinoy baiting have been criticized as a form of cultural cringe. This criticism would subsequently give the advice that Filipinos should not constantly require validation from non-Filipinos about themselves or their country. == Pinoy baiting mediums == === Reaction videos === On social media such as YouTube, channels with specific focus on showing their reaction towards and opinions about certain videos or topics are called reaction channels. Reaction videos are very popular and require minimal effort to create, and thus made it easy for alleged Pinoy baiting to thrive within this video-making genre. === Travel vlogs === Vlogging, short for video blogging, grew in popularity in the 2020s. Most of the popular alleged Pinoy-baiting channels tend to be vlog channels, normally following the same script under such titles as "The Philippines changed us/me", "First impression of the Philippines", "Is this really Manila?" and "Filipinos are such Kind/Good People!", and made while travelling to touristy areas such as Boracay or Bonifacio Global City and taste-testing the fast food chain Jollibee, among others. == Criticism of the phrase == Philippines-based Korean vlogger Jessica Lee had been accused by some YouTube viewers of engaging in Pinoy baiting. In a response vlog, Lee acknowledged that there may be individuals engaging in this "business strategy" of gaining views and subscribers from one of the largest communities online. However, she questioned the objectivity of some use of the phrase, citing any vlogging subject as fair game for a negative impression of being a "baiting" tool for the vlogger treating of that subject. She also invoked vloggers' freedom to choose whatever subject they want to talk about in a deep or shallow manner, while enjoining citizens to exercise their free-market right to unfollow vloggers they hate and follow those vloggers that "make them happy". She also gave her critics an explanation why she ended up vlogging about Philippine and Filipino subjects.
Verifiable secret sharing
In cryptography, a secret sharing scheme is verifiable if auxiliary information is included that allows players to verify their shares as consistent. More formally, verifiable secret sharing ensures that even if the dealer is malicious there is a well-defined secret that the players can later reconstruct. (In standard secret sharing, the dealer is assumed to be honest.) The concept of verifiable secret sharing (VSS) was first introduced in 1985 by Benny Chor, Shafi Goldwasser, Silvio Micali and Baruch Awerbuch. In a VSS protocol a distinguished player who wants to share the secret is referred to as the dealer. The protocol consists of two phases: a sharing phase and a reconstruction phase. Sharing: Initially the dealer holds secret as input and each player holds an independent random input. The sharing phase may consist of several rounds. At each round each player can privately send messages to other players and can also broadcast a message. Each message sent or broadcast by a player is determined by its input, its random input and messages received from other players in previous rounds. Reconstruction: In this phase each player provides its entire view from the sharing phase and a reconstruction function is applied and is taken as the protocol's output. An alternative definition given by Oded Goldreich defines VSS as a secure multi-party protocol for computing the randomized functionality corresponding to some (non-verifiable) secret sharing scheme. This definition is stronger than that of the other definitions and is very convenient to use in the context of general secure multi-party computation. Verifiable secret sharing is important for secure multiparty computation. Multiparty computation is typically accomplished by making secret shares of the inputs, and manipulating the shares to compute some function. To handle "active" adversaries (that is, adversaries that corrupt nodes and then make them deviate from the protocol), the secret sharing scheme needs to be verifiable to prevent the deviating nodes from throwing off the protocol. == Feldman's scheme == A commonly used example of a simple VSS scheme is the protocol by Paul Feldman, which is based on Shamir's secret sharing scheme combined with any encryption scheme which satisfies a specific homomorphic property (that is not necessarily satisfied by all homomorphic encryption schemes). The following description gives the general idea, but is not secure as written. (Note, in particular, that the published value gs leaks information about the dealer's secret s.) First, a cyclic group G of prime order q, along with a generator g of G, is chosen publicly as a system parameter. The group G must be chosen such that computing discrete logarithms is hard in this group. (Typically, one takes an order-q subgroup of (Z/pZ)×, where q is a prime dividing p − 1.) The dealer then computes (and keeps secret) a random polynomial P of degree t with coefficients in Zq, such that P(0) = s, where s is the secret. Each of the n share holders will receive a value P(1), ..., P(n) modulo q. Any t + 1 share holders can recover the secret s by using polynomial interpolation modulo q, but any set of at most t share holders cannot. (In fact, at this point any set of at most t share holders has no information about s.) So far, this is exactly Shamir's scheme. To make these shares verifiable, the dealer distributes commitments to the coefficients of P modulo q. If P(x) = s + a1x + ... + atxt, then the commitments that must be given are: c0 = gs, c1 = ga1, ... ct = gat. Once these are given, any party can verify their share. For instance, to verify that v = P(i) modulo q, party i can check that g v = c 0 c 1 i c 2 i 2 ⋯ c t i t = ∏ j = 0 t c j i j = ∏ j = 0 t g a j i j = g ∑ j = 0 t a j i j = g P ( i ) {\displaystyle g^{v}=c_{0}c_{1}^{i}c_{2}^{i^{2}}\cdots c_{t}^{i^{t}}=\prod _{j=0}^{t}c_{j}^{i^{j}}=\prod _{j=0}^{t}g^{a_{j}i^{j}}=g^{\sum _{j=0}^{t}a_{j}i^{j}}=g^{P(i)}} . This scheme is, at best, secure against computationally bounded adversaries, namely the intractability of computing discrete logarithms. Pedersen proposed later a scheme where no information about the secret is revealed even with a dealer with unlimited computing power. == Baghery's hash-based scheme == A recent line of research has proposed a unified framework, for building practical VSS schemes that do not necessarily require homomorphic commitments —a key requirement in traditional constructions such as Feldman's and Pedersen's schemes. The framework allows instantiations with different commitment schemes, including post-quantum secure options such as hash-based commitments. This offers a flexible and efficient approach to build VSS schemes, in which the verifiability of shares is decoupled from the need for homomorphic commitments, which are often tied to assumptions like the Discrete Logarithm (DL) problem, known to be insecure against quantum adversaries. One instantiation of the new framework uses hash-based commitments and a random oracle to construct a hash-based VSS scheme based on Shamir's secret sharing. === Protocol Overview === Sharing Phase: Given a secure hash-based commitment scheme C {\displaystyle {\mathcal {C}}} and a hash function H {\displaystyle {\mathcal {H}}} (modeled as a random oracle), to share a secret value s {\displaystyle s} among n {\displaystyle n} parties with threshold t {\displaystyle t} , the dealer acts as follows: Following Shamir sharing, the dealer samples a random degree- t {\displaystyle t} polynomial P ( X ) {\displaystyle P(X)} over a filed or ring, with P ( 0 ) = s {\displaystyle P(0)=s} . Each of the n {\displaystyle n} parties will receive a value v i = P ( i ) {\displaystyle v_{i}=P(i)} modulo q {\displaystyle q} as a share. To prove the validity of the shares, the dealer acts as follows: Samples another random degree- t {\displaystyle t} polynomial R ( X ) {\displaystyle R(X)} and n {\displaystyle n} random values γ 1 , … , γ n {\displaystyle \gamma _{1},\dots ,\gamma _{n}} from the same filed or ring. Computes a set of commitments c i = C ( P ( i ) , R ( i ) , γ i ) {\displaystyle c_{i}={\mathcal {C}}(P(i),R(i),\gamma _{i})} for i = 1 , 2 , … , n {\displaystyle i=1,2,\dots ,n} . Note that, the additional randomness γ i {\displaystyle \gamma _{i}} is used when the secret s {\displaystyle s} does not have sufficient entropy, but it can be omitted when sharing a uniformly random secret. Each of the n {\displaystyle n} parties will also receive a value γ i {\displaystyle \gamma _{i}} modulo q {\displaystyle q} as a share. Calculates a challenge value d {\displaystyle d} via a hash function d = H ( c 1 , … , c n ) {\displaystyle d={\mathcal {H}}(c_{1},\dots ,c_{n})} and then computes a polynomial Z ( X ) = R ( X ) + d ⋅ P ( X ) {\displaystyle Z(X)=R(X)+d\cdot P(X)} . Broadcasts the commitments c 1 , … , c n {\displaystyle c_{1},\dots ,c_{n}} along with Z ( X ) {\displaystyle Z(X)} as the proof and privately sends ( v i , γ i ) {\displaystyle (v_{i},\gamma _{i})} as the individual share to party i {\displaystyle i} . Verification Phase: Given an individual share ( v i , γ i ) {\displaystyle (v_{i},\gamma _{i})} and a proof ( c 1 , … , c n , Z ( X ) ) {\displaystyle (c_{1},\dots ,c_{n},Z(X))} , party i {\displaystyle i} verifies the correctness of it as below: Checks that Z ( X ) {\displaystyle Z(X)} is a valid (up to) degree- t {\displaystyle t} polynomial. Recomputes the challenge value d = H ( c 1 , … , c n ) {\displaystyle d={\mathcal {H}}(c_{1},\dots ,c_{n})} , and verifies the commitment equation c i = C ( v i , Z ( i ) − d v i , γ i ) {\displaystyle c_{i}={\mathcal {C}}(v_{i},Z(i)-dv_{i},\gamma _{i})} . If the verification fails, similar to Feldman’s and Pedersen’s schemes, the party raises a complaint. If too many complaints (more than t {\displaystyle t} ) are raised, the dealer is disqualified. In case of a complaint, the dealer can publicly reveal the disputed share to allow global verification. Honest parties can then collectively agree to either continue or disqualify the dealer. This scheme supports the sharing of both low-entropy and high-entropy secrets. Moreover, since it relies solely on secure hash functions for commitments and on a (quantum) random oracle, it plausibly achieves security even against quantum adversaries. Additionally, by using only lightweight cryptographic primitives, the scheme is considerably more efficient in practice compared to traditional VSS constructions based on number-theoretic assumptions. == Benaloh's scheme == Once n shares are distributed to their holders, each holder should be able to verify that all shares are collectively t-consistent (i.e., any subset t of n shares will yield the same, correct, polynomial without exposing the secret). In Shamir's secret sharing scheme the shares s 1 , s 2 , . . . , s n {\displaystyle s_{1},s_{2},...,s_{n}} are t-consistent if and only if the interpolation of the points ( 1 , s 1 ) , ( 2 , s 2 ) , . . . , (
Service Assurance Agent
IP SLA (Internet Protocol Service Level Agreement) is an active computer network measurement technology that was initially developed by Cisco Systems. IP SLA was previously known as Service Assurance Agent (SAA) or Response Time Reporter (RTR). IP SLA is used to track network performance like latency, ping response, and jitter, it also helps to provide service quality. == Functions == Routers and switches enabled with IP SLA perform periodic network tests or measurements such as Hypertext Transfer Protocol (HTTP) GET File Transfer Protocol (FTP) downloads Domain Name System (DNS) lookups User Datagram Protocol (UDP) echo, for VoIP jitter and mean opinion score (MOS) Data-Link Switching (DLSw) (Systems Network Architecture (SNA) tunneling protocol) Dynamic Host Configuration Protocol (DHCP) lease requests Transmission Control Protocol (TCP) connect Internet Control Message Protocol (ICMP) echo (remote ping) The exact number and types of available measurements depends on the IOS version. IP SLA is very widely used in service provider networks to generate time-based performance data. It is also used together with Simple Network Management Protocol (SNMP) and NetFlow, which generate volume-based data. == Usage considerations == For IP SLA tests, devices with IP SLA support are required. IP SLA is supported on Cisco routers and switches since IOS version 12.1. Other vendors like Juniper Networks or Enterasys Networks support IP SLA on some of their devices. IP SLA tests and data collection can be configured either via a console (command-line interface) or via SNMP. When using SNMP, both read and write community strings are needed. The IP SLA voice quality feature was added starting with IOS version 12.3(4)T. All versions after this, including 12.4 mainline, contain the MOS and ICPIF voice quality calculation for the UDP jitter measurement.
Keith Youngin George II
Keith "Youngin" George II is a former mixtape DJ, music executive, manager, producer, and technology app director. He has collaborated with Maino, T-Pain, Nas and Soulja Boy, among others. He was instrumental in the launch of social media app and website, Kandiid in 2021 and served as Fliiks App Director of Regional Development. == Career == Keith Anthony George II was born in Upper Heyford, Oxfordshire, England. His father was in the Air Force which exposed him to different cultures and music. He graduated from Allen High School and attended San Antonio College. George's music career began in 2006 as a mixtape DJ working as DJ Youngin Beatz. He performed at various shows and worked with a variety of artists, managers, and music executives. In 2007, George released the mixtape, Untapped market Vol. 1 (Da Underdogz), which featured tracks from artists including Kanye West, Lil Wayne, 50 Cent, Yung Berg, and Nelly. In 2008, he began working with Def Jam executive Sarah Alminawi who was managing Maino at the time. George played a key role in the marketing and promotional success of Maino's single, Hi Hater, which peaked at #8 on Billboard's US Bubbling Under Hot 100 chart. In 2021, George was an advisor and infrastructure head at Kandiid, a social media app which won a W3 Award in 2022. In 2023, he became involved with Fliiks App as Director of Regional Development which earned a Telly Award, two Muse Awards, and a W3 Award in 2025. In 2025, George was a composer and producer on two singles on Sekou Andrews's album, Koumami; The Chosen One: ACT 1 (featuring Lion Babe) and Love Don't Care (featuring Jordin Sparks and Omari Hardwick). In 2025, he was awarded an Atlanta City Proclamation for Philanthropy and Community Leadership for his partnership with Women's International Grail, a nonprofit organization that assists women, single mothers, and low-income families. He also collaborates with local youth programs, creative networks, and minority-owned startups, providing access to mentorship and industry knowledge. == Awards ==
Data storage
Data storage is the recording (storing) of information (data) in a storage medium. Handwriting, phonographic recording, magnetic tape, and optical discs are all examples of storage media. Biological molecules such as RNA and DNA are considered by some as data storage. Recording may be accomplished with virtually any form of energy. Electronic data storage requires electrical power to store and retrieve data. Data stored in a digital, machine-readable medium is called digital data. Computer data storage is one of the core functions of a general-purpose computer. Electronic documents can be stored in much less space than paper documents. Barcodes and magnetic ink character recognition (MICR) are two ways of recording machine-readable data on paper. == Recording media == A recording medium is physical material that holds information. Newly created information is distributed and can be stored in four storage media–print, film, magnetic, and optical–and seen or heard in four information flows–telephone, radio, TV, and the Internet as well as being observed directly. Digital information is stored on electronic media in many different recording formats. With electronic media, the data and the recording media are sometimes referred to as "software" despite the more common use of the word to describe computer software. With (traditional art) static media, art materials such as crayons may be considered both equipment and medium as the wax, charcoal or chalk material from the equipment becomes part of the surface of the medium. Some recording media may be temporary, either by design or by nature. Volatile organic compounds may be used to purposely make data expire over time or to reduce environmental impact. Data such as smoke signals or skywriting are temporary by nature. Depending on the volatility, a gas (e.g., atmosphere, smoke) or a liquid surface such as a lake would be considered a temporary recording medium, if it could be considered a recording medium at all. == Global capacity, digitization, and trends == A 2003 UC Berkeley report estimated that about five exabytes of new information were produced in 2002 and that 92% of this data was stored on magnetic media (primarily hard disk drives). This was about twice the data produced in 1999. The amount of data transmitted over telecommunications systems in 2002 was nearly 18 exabytes—three and a half times more than was recorded on non-volatile storage. Telephone calls constituted 98% of the telecommunicated information in 2002. The researchers' highest estimate for the growth rate of newly stored information (uncompressed) was more than 30% per year. In a more limited study, the International Data Corporation estimated that the total amount of digital data in 2007 was 281 exabytes and that the total amount of digital data produced exceeded the global storage capacity for the first time. A 2011 article in Science estimated that the year 2002 was the beginning of the digital age for information storage: an age in which more information is stored on digital storage devices than on analog storage devices. In 1986, approximately 1% of the world's capacity to store information was in digital format; this grew to 3% by 1993, to 25% by 2000, and to 94% by 2007. These figures correspond to less than three compressed exabytes in 1986, and 295 compressed exabytes in 2007. The quantity of digital storage doubled roughly every three to four years. It is estimated that around 120 zettabytes of data will be generated in 2023, an increase of 60x from 2010, and that it will increase to 181 zettabytes generated in 2025. == Mass storage ==