OCR-A

OCR-A is a font issued in 1966 and first implemented in 1968. A special font was needed in the early days of computer optical character recognition, when there was a need for a font that could be recognized not only by the computers of that day, but also by humans. OCR-A uses simple, thick strokes to form recognizable characters. The font is monospaced (fixed-width), with the printer required to place glyphs 0.254 cm (0.10 inch) apart, and the reader required to accept any spacing between 0.2286 cm (0.09 inch) and 0.4572 cm (0.18 inch). == Standardization == The OCR-A font was standardized by the American National Standards Institute (ANSI) as ANSI X3.17-1981. X3.4 has since become the INCITS and the OCR-A standard is now called ISO 1073-1:1976. == Implementations == In 1968, American Type Founders produced OCR-A, one of the first optical character recognition typefaces to meet the criteria set by the U.S. Bureau of Standards. The design is simple so that it can be easily read by a machine, but it is more difficult for the human eye to read. As metal type gave way to computer-based typesetting, Tor Lillqvist used Metafont to describe the OCR-A font. That definition was subsequently improved by Richard B. Wales. Their work is available from CTAN. To make the free version of the font more accessible to users of Microsoft Windows, John Sauter converted the Metafont definitions to TrueType using potrace and FontForge in 2004. In 2007, Gürkan Sengün created a Debian package from this implementation. In 2008. Luc Devroye corrected the vertical positioning in John Sauter's implementation, and fixed the name of lower case z. Independently, Matthew Skala used mftrace to convert the Metafont definitions to TrueType format in 2006. In 2011 he released a new version created by rewriting the Metafont definitions to work with METATYPE1, generating outlines directly without an intermediate tracing step. On September 27, 2012, he updated his implementation to version 0.2. In addition to these free implementations of OCR-A, there are also implementations sold by several vendors. As a joke, Tobias Frere-Jones in 1995 created Estupido-Espezial, a redesign with swashes and a long s. It was used in a "technology"-themed section of Rolling Stone. Maxitype designed the OCR-X typeface—based on the OCR-A typeface with OpenType features, alien/technology-themed dingbats and available in six weights (Thin, Light, Regular, Medium, Bold, Black). Japanese typeface foundry Visual Design Laboratory (VDL) designed two typefaces based on the OCR-A typeface: one for Simplified Chinese characters named Jieyouti and one for Japanese characters named Yota G (ヨタG) , both available in five weights (Light, Regular, Medium, Semi Bold, Bold). == Use == Although optical character recognition technology has advanced to the point where such simple fonts are no longer necessary, the OCR-A font has remained in use. Its usage remains widespread in the encoding of checks around the world. Some lock box companies still insist that the account number and amount owed on a bill return form be printed in OCR-A. Also, because of its unusual look, it is sometimes used in advertising and display graphics. Notably, it is used for the subtitles in films and television series such as Blacklist and for the main titles in The Pretender. Additionally, OCR-A is used in the titles and subtitles for the films 13 Hours: The Secret Soldiers of Benghazi and Hoppers (film). It was also used for the logo, branding, and marketing material of the children's toy line Hexbug. == Code points == A font is a set of character shapes, or glyphs. For a computer to use a font, each glyph must be assigned a code point in a character set. When OCR-A was being standardized the usual character coding was the American Standard Code for Information Interchange or ASCII. Not all of the glyphs of OCR-A fit into ASCII, and for five of the characters there were alternate glyphs, which might have suggested the need for a second font. However, for convenience and efficiency all of the glyphs were expected to be accessible in a single font using ASCII coding, with the additional characters placed at coding points that would otherwise have been unused. The modern descendant of ASCII is Unicode, also known as ISO 10646. Unicode contains ASCII and has special provisions for OCR characters, so some implementations of OCR-A have looked to Unicode for guidance on character code assignments. === Pre-Unicode standard representation === The ISO standard ISO 2033:1983, and the corresponding Japanese Industrial Standard JIS X 9010:1984 (originally JIS C 6229–1984), define character encodings for OCR-A, OCR-B and E-13B. For OCR-A, they define a modified 7-bit ASCII set (also known by its ISO-IR number ISO-IR-91) including only uppercase letters, digits, a subset of the punctuation and symbols, and some additional symbols. Codes which are redefined relative to ASCII, as opposed to simply omitted, are listed below: Additionally, the long vertical mark () is encoded at 0x7C, corresponding to the ASCII vertical bar (|). === Dedicated OCR-A characters in Unicode === The following characters have been defined for control purposes and are now in the "Optical Character Recognition" Unicode range 2440–245F: === Space, digits, and unaccented letters === All implementations of OCR-A use U+0020 for space, U+0030 through U+0039 for the decimal digits, U+0041 through U+005A for the unaccented upper case letters, and U+0061 through U+007A for the unaccented lower case letters. === Regular characters === In addition to the digits and unaccented letters, many of the characters of OCR-A have obvious code points in ASCII. Of those that do not, most, including all of OCR-A's accented letters, have obvious code points in Unicode. === Remaining characters === Linotype coded the remaining characters of OCR-A as follows: === Additional characters === The fonts that descend from the work of Tor Lillqvist and Richard B. Wales define four characters not in OCR-A to fill out the ASCII character set. These shapes use the same style as the OCR-A character shapes. They are: Linotype also defines additional characters. === Exceptions === Some implementations do not use the above code point assignments for some characters. ==== PrecisionID ==== The PrecisionID implementation of OCR-A has the following non-standard code points: OCR Hook at U+007E OCR Chair at U+00C1 OCR Fork at U+00C2 Euro Sign at U+0080 ==== Barcodesoft ==== The Barcodesoft implementation of OCR-A has the following non-standard code points: OCR Hook at U+0060 OCR Chair at U+007E OCR Fork at U+005F Long Vertical Mark at U+007C (agrees with Linotype) Character Erase at U+0008 ==== Morovia ==== The Morovia implementation of OCR-A has the following non-standard code points: OCR Hook at U+007E (agrees with PrecisionID) OCR Chair at U+00F0 OCR Fork at U+005F (agrees with Barcodesoft) Long Vertical Mark at U+007C (agrees with Linotype) ==== IDAutomation ==== The IDAutomation implementation of OCR-A has the following non-standard code points: OCR Hook at U+007E (agrees with PrecisionID) OCR Chair at U+00C1 (agrees with PrecisionID) OCR Fork at U+00C2 (agrees with PrecisionID) OCR Belt Buckle at U+00C3 == Sellers of font standards == Hardcopy of ISO 1073-1:1976, distributed through ANSI, from Amazon.com ISO 1073-1 is also available from Techstreet, who distributes standards for ANSI and ISO

Cross-entropy method

The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorial and continuous problems, with either a static or noisy objective. The method approximates the optimal importance sampling estimator by repeating two phases: Draw a sample from a probability distribution. Minimize the cross-entropy between this distribution and a target distribution to produce a better sample in the next iteration. Reuven Rubinstein developed the method in the context of rare-event simulation, where tiny probabilities must be estimated, for example in network reliability analysis, queueing models, or performance analysis of telecommunication systems. The method has also been applied to the traveling salesman, quadratic assignment, DNA sequence alignment, max-cut and buffer allocation problems. == Estimation via importance sampling == Consider the general problem of estimating the quantity ℓ = E u [ H ( X ) ] = ∫ H ( x ) f ( x ; u ) d x {\displaystyle \ell =\mathbb {E} _{\mathbf {u} }[H(\mathbf {X} )]=\int H(\mathbf {x} )\,f(\mathbf {x} ;\mathbf {u} )\,{\textrm {d}}\mathbf {x} } , where H {\displaystyle H} is some performance function and f ( x ; u ) {\displaystyle f(\mathbf {x} ;\mathbf {u} )} is a member of some parametric family of distributions. Using importance sampling this quantity can be estimated as ℓ ^ = 1 N ∑ i = 1 N H ( X i ) f ( X i ; u ) g ( X i ) {\displaystyle {\hat {\ell }}={\frac {1}{N}}\sum _{i=1}^{N}H(\mathbf {X} _{i}){\frac {f(\mathbf {X} _{i};\mathbf {u} )}{g(\mathbf {X} _{i})}}} , where X 1 , … , X N {\displaystyle \mathbf {X} _{1},\dots ,\mathbf {X} _{N}} is a random sample from g {\displaystyle g\,} . For positive H {\displaystyle H} , the theoretically optimal importance sampling density (PDF) is given by g ∗ ( x ) = H ( x ) f ( x ; u ) / ℓ {\displaystyle g^{}(\mathbf {x} )=H(\mathbf {x} )f(\mathbf {x} ;\mathbf {u} )/\ell } . This, however, depends on the unknown ℓ {\displaystyle \ell } . The CE method aims to approximate the optimal PDF by adaptively selecting members of the parametric family that are closest (in the Kullback–Leibler sense) to the optimal PDF g ∗ {\displaystyle g^{}} . == Generic CE algorithm == Choose initial parameter vector v ( 0 ) {\displaystyle \mathbf {v} ^{(0)}} ; set t = 1. Generate a random sample X 1 , … , X N {\displaystyle \mathbf {X} _{1},\dots ,\mathbf {X} _{N}} from f ( ⋅ ; v ( t − 1 ) ) {\displaystyle f(\cdot ;\mathbf {v} ^{(t-1)})} Solve for v ( t ) {\displaystyle \mathbf {v} ^{(t)}} , where v ( t ) = argmax v ⁡ 1 N ∑ i = 1 N H ( X i ) f ( X i ; u ) f ( X i ; v ( t − 1 ) ) log ⁡ f ( X i ; v ) {\displaystyle \mathbf {v} ^{(t)}=\mathop {\textrm {argmax}} _{\mathbf {v} }{\frac {1}{N}}\sum _{i=1}^{N}H(\mathbf {X} _{i}){\frac {f(\mathbf {X} _{i};\mathbf {u} )}{f(\mathbf {X} _{i};\mathbf {v} ^{(t-1)})}}\log f(\mathbf {X} _{i};\mathbf {v} )} If convergence is reached then stop; otherwise, increase t by 1 and reiterate from step 2. In several cases, the solution to step 3 can be found analytically. Situations in which this occurs are When f {\displaystyle f\,} belongs to the natural exponential family When f {\displaystyle f\,} is discrete with finite support When H ( X ) = I { x ∈ A } {\displaystyle H(\mathbf {X} )=\mathrm {I} _{\{\mathbf {x} \in A\}}} and f ( X i ; u ) = f ( X i ; v ( t − 1 ) ) {\displaystyle f(\mathbf {X} _{i};\mathbf {u} )=f(\mathbf {X} _{i};\mathbf {v} ^{(t-1)})} , then v ( t ) {\displaystyle \mathbf {v} ^{(t)}} corresponds to the maximum likelihood estimator based on those X k ∈ A {\displaystyle \mathbf {X} _{k}\in A} . == Continuous optimization—example == The same CE algorithm can be used for optimization, rather than estimation. Suppose the problem is to maximize some function S {\displaystyle S} , for example, S ( x ) = e − ( x − 2 ) 2 + 0.8 e − ( x + 2 ) 2 {\displaystyle S(x)={\textrm {e}}^{-(x-2)^{2}}+0.8\,{\textrm {e}}^{-(x+2)^{2}}} . To apply CE, one considers first the associated stochastic problem of estimating P θ ( S ( X ) ≥ γ ) {\displaystyle \mathbb {P} _{\boldsymbol {\theta }}(S(X)\geq \gamma )} for a given level γ {\displaystyle \gamma \,} , and parametric family { f ( ⋅ ; θ ) } {\displaystyle \left\{f(\cdot ;{\boldsymbol {\theta }})\right\}} , for example the 1-dimensional Gaussian distribution, parameterized by its mean μ t {\displaystyle \mu _{t}\,} and variance σ t 2 {\displaystyle \sigma _{t}^{2}} (so θ = ( μ , σ 2 ) {\displaystyle {\boldsymbol {\theta }}=(\mu ,\sigma ^{2})} here). Hence, for a given γ {\displaystyle \gamma \,} , the goal is to find θ {\displaystyle {\boldsymbol {\theta }}} so that D K L ( I { S ( x ) ≥ γ } ‖ f θ ) {\displaystyle D_{\mathrm {KL} }({\textrm {I}}_{\{S(x)\geq \gamma \}}\|f_{\boldsymbol {\theta }})} is minimized. This is done by solving the sample version (stochastic counterpart) of the KL divergence minimization problem, as in step 3 above. It turns out that parameters that minimize the stochastic counterpart for this choice of target distribution and parametric family are the sample mean and sample variance corresponding to the elite samples, which are those samples that have objective function value ≥ γ {\displaystyle \geq \gamma } . The worst of the elite samples is then used as the level parameter for the next iteration. This yields the following randomized algorithm that happens to coincide with the so-called Estimation of Multivariate Normal Algorithm (EMNA), an estimation of distribution algorithm. === Pseudocode === // Initialize parameters μ := −6 σ2 := 100 t := 0 maxits := 100 N := 100 Ne := 10 // While maxits not exceeded and not converged while t < maxits and σ2 > ε do // Obtain N samples from current sampling distribution X := SampleGaussian(μ, σ2, N) // Evaluate objective function at sampled points S := exp(−(X − 2) ^ 2) + 0.8 exp(−(X + 2) ^ 2) // Sort X by objective function values in descending order X := sort(X, S) // Update parameters of sampling distribution via elite samples μ := mean(X(1:Ne)) σ2 := var(X(1:Ne)) t := t + 1 // Return mean of final sampling distribution as solution return μ == Related methods == Simulated annealing Genetic algorithms Harmony search Estimation of distribution algorithm Tabu search Natural Evolution Strategy Ant colony optimization algorithms

Digital supply chain security

Digital supply chain security refers to efforts to enhance cyber security within the supply chain. It is a subset of supply chain security and is focused on the management of cyber security requirements for information technology systems, software and networks, which are driven by threats such as cyber-terrorism, malware, data theft and the advanced persistent threat (APT). Typical supply chain cyber security activities for minimizing risks include buying only from trusted vendors, disconnecting critical machines from outside networks, and educating users on the threats and protective measures they can take. The acting deputy undersecretary for the National Protection and Programs Directorate for the United States Department of Homeland Security, Greg Schaffer, stated at a hearing that he is aware that there are instances where malware has been found on imported electronic and computer devices sold within the United States. == Examples of supply chain cyber security threats == Network or computer hardware that is delivered with malware installed on it already. Malware that is inserted into software or hardware (by various means) Vulnerabilities in software applications and networks within the supply chain that are discovered by malicious hackers Counterfeit computer hardware == Related U.S. government efforts == Comprehensive National Cyber Initiative Defense Procurement Regulations: Noted in section 806 of the National Defense Authorization Act International Strategy for Cyberspace: White House lays out for the first time the U.S.’s vision for a secure and open Internet. The strategy outlines three main themes: diplomacy, development and defense. Diplomacy: The strategy sets out to “promote an open, interoperable, secure and reliable information and communication infrastructure” by establishing norms of acceptable state behavior built through consensus among nations. Development: Through this strategy the government seeks to “facilitate cybersecurity capacity-building abroad, bilaterally and through multilateral organizations.” The objective is to protect the global IT infrastructure and to build closer international partnerships to sustain open and secure networks. Defense: The strategy calls out that the government “will ensure that the risks associated with attacking or exploiting our networks vastly outweigh the potential benefits” and calls for all nations to investigate, apprehend and prosecute criminals and non-state actors who intrude and disrupt network systems. == Related government efforts around the world == Common Criteria offers with Evaluation Assurance Level(EAL) 4 an opportunity to evaluate all relevant aspects of the digital supply chain security like the product, the development environment, IT systems security, the processes in human resource, physical security and with the module ALC_FLR.3 (Systematic Flaw Remediation) also security update processes and methods even by physical site visits. EAL 4 is mutually recognized in countries that signed the SOGIS-MRA and up to ELA 2 in countries the signed the CCRA but including ALC_FRL.3. Russia: Russia has had non-disclosed functionality certification requirements for several years and has recently initiated the National Software Platform effort based on open-source software. This reflects the apparent desire for national autonomy, reducing dependence on foreign suppliers. India: Recognition of supply chain risk in its draft National Cybersecurity Strategy. Rather than targeting specific products for exclusion, it is considering Indigenous Innovation policies, giving preferences to domestic ITC suppliers in order to create a robust, globally competitive national presence in the sector. China: Deriving from goals in the 11th Five Year Plan (2006–2010), China introduced and pursued a mix of security-focused and aggressive Indigenous Innovation policies. China is requiring an indigenous innovation product catalog be used for its government procurement and implementing a Multi-level Protection Scheme (MLPS) which requires (among other things) product developers and manufacturers to be Chinese citizens or legal persons, and product core technology and key components must have independent Chinese or indigenous intellectual property rights. == Private sector efforts == SLSA (Supply-chain Levels for Software Artifacts) is an end-to-end framework for ensuring the integrity of software artifacts throughout the software supply chain. The requirements are inspired by Google’s internal "Binary Authorization for Borg" that has been in use for the past 8+ years and that is mandatory for all of Google's production workloads. The goal of SLSA is to improve the state of the industry, particularly open source, to defend against the most pressing integrity threats. With SLSA, consumers can make informed choices about the security posture of the software they consume. == Other references == Financial Sector Information Sharing and Analysis Center International Strategy for Cyberspace (from the White House) NSTIC SafeCode Whitepaper Archived 2013-10-21 at the Wayback Machine Trusted Technology Forum and the Open Trusted Technology Provider Standard (O-TTPS) Archived 2012-01-03 at the Wayback Machine Cyber Supply Chain Security Solution Malware Implants in Firmware Supply Chain in the Software Era INFORMATION AND COMMUNICATIONS TECHNOLOGY SUPPLY CHAIN RISK MANAGEMENT TASK FORCE: INTERIM REPORT

Headway (app)

Headway, also known as the Headway App, is an educational technology (EdTech) product that provides short text and audio summaries of nonfiction books. The product was launched in 2019 by Anton Pavlovsky and is developed by Headway Inc, a global consumer tech company that operates in the lifelong learning space. == History == The Headway app was launched in January 2019, with the first version of the application released the same year. In 2021, Headway ranked first globally in downloads within the book summary application niche. In 2022, the application received the Golden Novum Design Award for product design. In 2023 and 2024, Headway appeared in several App Store editorial selections, including App of the Day in multiple countries, and received an Editors’ Choice label in the United States. In April 2025, the application was listed as a Webby Honoree in the Learning & Education category. The company has also launched the Headway Scholarship for Book Lovers. As of 2025, publicly available reporting notes that the Headway app has surpassed 50 million downloads and is among the Top 10 iOS applications by revenue in the Education category worldwide. == Products and features == The Headway app provides short-form summaries of nonfiction books in both text and audio formats. Content is produced by an in-house team of writers, editors, and voice actors. Features include highlighting and saving key insights, spaced repetition for knowledge retention, and offline access to downloaded summaries. The app is available on iOS, iPadOS, watchOS, Android, CarPlay, and Android Auto, and supports multiple languages. == Pricing == Headway operates on a subscription business model, with optional paid plans alongside free access. The company publicly provides its terms of use, privacy policy, subscription details, and AI usage policy on its official website. == Technology and integrations == Headway reports that its book summaries are written and edited manually, while artificial intelligence tools are used in limited supporting functions, such as experimental conversational features and selected marketing processes. == Adoption == According to figures released by the company, the app has exceeded 50 million downloads worldwide. Sensor Tower data indicates that Headway has been the most downloaded application in its niche since October 2020. In January 2025, the app claimed the #1 position in the Education category in both the United States and United Kingdom App Stores and remained among the Top 10 iOS applications globally by revenue within the Education category. == Awards == The Headway app has received several product-level distinctions. In 2023 and 2024, it appeared in multiple App Store editorial selections, including App of the Day features and an Editors’ Choice label in the United States. In 2025, the app was recognized as a Webby Honoree in the Learning & Education category. The product has also been featured in independent media roundups of notable educational applications.

ISO/IEC JTC 1/SC 24

ISO/IEC JTC 1/SC 24 Computer graphics, image processing and environmental data representation is a standardization subcommittee of the joint subcommittee ISO/IEC JTC 1 of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), which develops and facilitates standards within the field of computer graphics, image processing, and environmental data representation. The international secretariat of ISO/IEC JTC 1/SC 24 is the British Standards Institute (BSI) located in the United Kingdom. == History == ISO/IEC JTC 1/SC 24 was formed in 1987 from ISO/TC 97 as a result of Resolution 21 at the ISO/IEC JTC 1 plenary. The group's origins began in computer graphics, the standardization of which was originally under ISO/IEC JTC 1/SC 21/WG 2. However, when ISO/IEC JTC 1/SC 24 was created, the standardization activity of ISO/IEC JTC 1/SC 21/WG 2 was carried over to the new subcommittee. The initial five working groups of ISO/IEC JTC 1/SC 24 were titled, “Architecture,” “Application programming interfaces,” “Metafiles and interfaces,” “Language bindings,” and “Validation, testing and registration.” The work of ISO/IEC JTC 1/SC 24 began with the Graphical Kernel System (GKS), which was adopted from ISO/IEC JTC 1/SC 21/WG 2. However, since GKS only addressed 2D functionality, attention turned to the standardization of 3D functionality. This resulted in two standards being published: GKS-3D in 1988 and PHIGS in 1989, both of which addressed 3D functionality. Since 1991, ISO/IEC JTC 1/SC 24 has held plenaries in a number of countries, including the Netherlands, Germany, United States, France, Canada, Japan, Sweden, Korea, United Kingdom, Australia, and Czech Republic. == Scope == The scope of ISO/IEC JTC 1/SC 24 is the “Standardization of interfaces for information technology based applications relating to”: Computer graphics Image processing Environmental data representation Support for the Mixed and Augmented Reality (MAR) Interaction with, and visual representation of, information Included are the following related areas: Modeling and simulation and related reference models Virtual reality with accompanying augmented reality/augmented virtuality aspects and related reference models Application program interfaces Functional specifications Representation models Interchange formats, encodings and their specifications, including metafiles Device interfaces Testing methods Registration procedures Presentation and support for creation of multimedia, hypermedia, and mixed reality documents Excluded are the following areas: Character and image coding Coding of multimedia, hypermedia, and mixed reality document interchange formats JTC 1 work in user system interfaces and document presentation ISO/TC 207 work on ISO 14000 environment management, ISO/TC 211 work on geographic information and geomatics Software environments as described by ISO/IEC JTC 1/SC 22 == Structure == ISO/IEC JTC 1/SC 24 is made up of four active working groups, each of which carries out specific tasks in standards development within the field of computer graphics, image processing and environmental data representation, together with ITU-T Study Group 16. As a response to changing standardization needs, working groups of ISO/IEC JTC 1/SC 24 can be disbanded if their area of work is no longer applicable, or established if new working areas arise. The focus of each working group is described in the group's terms of reference. Active working groups of ISO/IEC JTC 1/SC 24 are: == Collaborations == ISO/IEC JTC 1/SC 24 works in close collaboration with a number of other organizations or subcommittees, both internal and external to ISO or IEC, in order to avoid conflicting or duplicative work. Organizations internal to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: ISO/IEC JTC 1/WG 7, Sensor Networks ISO/IEC JTC 1/SC 29, Coding of audio, picture, multimedia and hypermedia information ISO/IEC JTC 1/SC 32, Data management and interchange ISO/TAG 14, Imagery and technology ISO/TC 130, Graphic Technology ISO/TC 184/SC 4, Industrial data ISO/TC 211, Geographic information/Geomatics ISO/TC 215, Health informatics IEC TC 100, Audio, video and multimedia system and equipment Some organizations external to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: Defence Geospatial Information Working Group (DGIWG) Digital Imaging and Communications in Medicine (DICOM) International Hydrographic Organization (IHO) The Khronos Group NATO - Joint Intelligence Surveillance and Reconnaissance Capability Group (JISRCG) OMG Robotics DTF Open CGM Open Geospatial Consortium (OGC) SEDRIS Organization Simulation Interoperability Standards Organization (SISO) US National Imagery Transmission Format Standard (NITFS) Technical Board (US NTB) Web3D Consortium World Intellectual Property Organization (WIPO) World Wide Web Consortium (W3C) == Member countries == Countries pay a fee to ISO to be members of subcommittees. The 11 "P" (participating) members of ISO/IEC JTC 1/SC 24 are: Australia, China, Egypt, France, India, Japan, Republic of Korea, Portugal, Russian Federation, United Kingdom, and United States. The 22 "O" (observer) members of ISO/IEC JTC 1/SC 24 are: Argentina, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Cuba, Czech Republic, Finland, Ghana, Hungary, Iceland, Indonesia, Islamic Republic of Iran, Italy, Kazakhstan, Malaysia, Poland, Romania, Serbia, Slovakia, Switzerland, and Thailand. == Published standards == ISO/IEC JTC 1/SC 24 currently has 80 published standards under their direct responsibility within the field of computer graphics, image processing, and environmental data representation, including:

FoundationDB

FoundationDB is a free and open-source multi-model distributed NoSQL database owned by Apple Inc. with a shared-nothing architecture. The product was designed around a "core" database, with additional features supplied in "layers." The core database exposes an ordered key–value store with transactions. The transactions are able to read or write multiple keys stored on any machine in the cluster while fully supporting ACID properties. Transactions are used to implement a variety of data models via layers. The FoundationDB Alpha program began in January 2012 and concluded on March 4, 2013, with their public Beta release. Their 1.0 version was released for general availability on August 20, 2013. On March 24, 2015, it was reported that Apple has acquired the company. A notice on the FoundationDB web site indicated that the company has "evolved" its mission and would no longer offer downloads of the software. On April 19, 2018, Apple open sourced the software, releasing it under the Apache 2.0 license. == Main features == The main features of FoundationDB include the following: Ordered key–value store In addition to supporting standard key-based reads and writes, the ordering property enables range reads that can efficiently scan large swaths of data. Transactions Transaction processing employs multiversion concurrency control for reads and optimistic concurrency for writes. Transactions can span multiple keys stored on multiple machines. ACID properties FoundationDB guarantees serializable isolation and strong durability via redundant storage on disk before transactions are considered committed. Layers Layers map new data models, APIs, and query languages to the FoundationDB core. They employ FoundationDB's ability to update multiple data elements in a single transaction, ensuring consistency. An example is their SQL layer. Commodity clusters FoundationDB is designed for deployment on distributed clusters of commodity hardware running Linux. Replication FoundationDB stores each piece of data on multiple machines according to a configurable replication factor. Triple replication is the recommended mode for clusters of 5 or more machines. Scalability FoundationDB is designed to support horizontal scaling through the addition of machines to a cluster while automatically handling data replication and partitioning. Systems supported FoundationDB supports packages for Linux, Windows, and macOS. The Linux version supports production clusters, while the Windows and macOS versions support local operation for development purposes. Configurations on Amazon EC2 are also supported. Programming language bindings FoundationDB supports language bindings for Python, Go, Ruby, Node.js, Java, PHP, and C, all of which are made available with the product. == Design limitations == The design of FoundationDB results in several limitations: Long transactions FoundationDB does not support transactions running over five seconds. Large transactions Transaction size cannot exceed 10 MB of total written keys and values. Large keys and values Keys cannot exceed 10 kB in size. Values cannot exceed 100 kB in size. == History == FoundationDB, headquartered in Vienna, Virginia, was started in 2009 by Nick Lavezzo, Dave Rosenthal, and Dave Scherer, drawing on their experience in executive and technology roles at their previous company, Visual Sciences. In March 2015 the FoundationDB Community site was updated to state that the company had changed directions and would no longer be offering downloads of its product. The company was acquired by Apple Inc., which was confirmed March 25, 2015. On April 19, 2018, Apple open sourced the software, releasing it under the Apache 2.0 license.

DUAL table

The DUAL table is a special one-row, one-column table present by default in Oracle and other database installations. In Oracle, the table has a single VARCHAR2(1) column called DUMMY that has a value of 'X'. It is suitable for use in selecting a pseudo column such as SYSDATE or USER. == Example use == Oracle's SQL syntax requires the FROM clause but some queries don't require any tables - DUAL can be used in these cases. == History == Charles Weiss explains why he created DUAL: I created the DUAL table as an underlying object in the Oracle Data Dictionary. It was never meant to be seen itself, but instead used inside a view that was expected to be queried. The idea was that you could do a JOIN to the DUAL table and create two rows in the result for every one row in your table. Then, by using GROUP BY, the resulting join could be summarized to show the amount of storage for the DATA extent and for the INDEX extent(s). The name, DUAL, seemed apt for the process of creating a pair of rows from just one. == Optimization == Beginning with 10g Release 1, Oracle no longer performs physical or logical I/O on the DUAL table, though the table still exists. DUAL is readily available for all authorized users in a SQL database. == In other database systems == Several other databases (including Microsoft SQL Server, MySQL, PostgreSQL, SQLite, and Teradata) enable one to omit the FROM clause entirely if no table is needed. This avoids the need for any dummy table. ClickHouse has a one-row system table system.one with a single column named "dummy" of type UInt8 and value 0. This table is implicitly used when no table is specified in the SELECT query. Firebird has a one-row system table RDB$DATABASE that is used in the same way as Oracle's DUAL, although it also has a meaning of its own. IBM Db2 has a view that resolves DUAL when using Oracle Compatibility. It also has a table called sysibm.sysdummy1 that has similar properties to the Oracle DUAL one. Informix: Informix version 11.50 and later has a table named sysmaster:"informix".sysdual with the same functionality but a more verbose name. You can use CREATE PUBLIC SYNONYM dual FOR sysmaster:"informix".sysdual to create a name dual in the current database with the same functionality. Microsoft Access: A table named DUAL may be created and the single-row constraint enforced via ADO (Table-less UNION query in MS Access) Microsoft SQL Server: SQL Server does not require a dummy table. Queries like 'select 1 + 1' can be run without a "from" clause/table name. MySQL allows DUAL to be specified as a table in queries that do not need data from any tables. It is suitable for use in selecting a result function such as SYSDATE() or USER(), although it is not essential. PostgreSQL: A DUAL-view can be added to ease porting from Oracle. Snowflake: DUAL is supported, but not explicitly documented. It appears in sample SQL for other operations in the documentation. SQLite: A VIEW named "dual" that works the same as the Oracle "dual" table can be created as follows: CREATE VIEW dual AS SELECT 'x' AS dummy; SAP HANA has a table called DUMMY that works the same as the Oracle "dual" table. Teradata database does not require a dummy table. Queries like 'select 1 + 1' can be run without a "from" clause/table name. Vertica has support for a DUAL table in their official documentation.