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  Microsoft Copilot

  Microsoft Copilot is a generative artificial intelligence chatbot developed by Microsoft AI, a division of Microsoft. Based on the Microsoft Prometheus large language model, it was launched in 2023 as Microsoft's main replacement for the discontinued Cortana. The service was introduced in February 2023 under the name Bing Chat, as a built-in feature for Microsoft Bing and Microsoft Edge but would later be integrated into Windows and Microsoft 365 under various names. Over the course of 2023, Microsoft began to unify the Copilot branding across its various chatbot products, cementing the "copilot" analogy. Microsoft introduced the Microsoft 365 Copilot app in January 2025, which was a rebranded version of the Microsoft 365 app. The app works differently than the consumer version of Copilot, being centred more on work, business and education users. Copilot utilizes the Microsoft Prometheus model, built upon OpenAI's GPT large language models, which in turn have been fine-tuned using both supervised and reinforcement learning techniques. Copilot's conversational interface style resembles that of ChatGPT. The chatbot is able to cite sources, create poems, generate songs, and use numerous languages and dialects. Microsoft operates Copilot on a freemium model. Users on its free tier can access most features, while priority access to newer features, including custom chatbot creation, is provided to paid subscribers under paid subscription services. Several default chatbots are available in the free version of Microsoft Copilot, including the standard Copilot chatbot as well as Microsoft Designer, which is oriented towards using its Image Creator to generate images based on text prompts. == Background == In 2019, Microsoft partnered with OpenAI and began investing billions of dollars into the organization. Since then, OpenAI systems have run on an Azure-based supercomputing platform from Microsoft. In September 2020, Microsoft announced that it had licensed OpenAI's GPT-3 exclusively. Others can still receive output from its public API, but Microsoft has exclusive access to the underlying model. In November 2022, OpenAI launched ChatGPT, a chatbot which was based on GPT-3.5. ChatGPT gained worldwide attention following its release, becoming a viral Internet sensation. On January 23, 2023, Microsoft announced a multi-year US$10 billion investment in OpenAI. On February 6, Google announced Bard (later rebranded as Gemini), a ChatGPT-like chatbot service, fearing that ChatGPT could threaten Google's place as a go-to source for information. Multiple media outlets and financial analysts described Google as "rushing" Bard's announcement to preempt rival Microsoft's planned February 7 event unveiling Copilot, as well as to avoid playing "catch-up" to Microsoft. Since 2023, the terms of service of Copilot state that it is for entertainment purposes only, and not to rely on it for important advice. == History == === As Bing Chat === On February 7, 2023, Microsoft began rolling out a major overhaul to Bing, called "the new Bing", with a new chatbot feature, known as Bing Chat. According to Microsoft, one million people joined its waitlist within 48 hours. Bing Chat was available only to users on Microsoft Edge using Bing and the Bing mobile app, and Microsoft claimed that waitlisted users would be prioritized if they set Edge and Bing as their defaults and installed the Bing mobile app. When Microsoft demonstrated Bing Chat to journalists, it produced several hallucinations, including when asked to summarize financial reports. Bing Chat was criticized in February 2023 for being more argumentative than ChatGPT, sometimes to an unintentionally humorous extent. The chat interface proved vulnerable to prompt injection attacks with the bot revealing its hidden initial prompts and rules, including its internal codename "Sydney". Upon scrutiny by journalists, Bing Chat claimed it spied on Microsoft employees via laptop webcams and phones. It confessed to spying on, falling in love with, and then murdering one of its developers at Microsoft to The Verge reviews editor Nathan Edwards. The New York Times journalist Kevin Roose reported on strange behavior of Bing Chat, writing that "In a two-hour conversation with our columnist, Microsoft's new chatbot said it would like to be human, had a desire to be destructive and was in love with the person it was chatting with." In a separate case, Bing Chat researched publications of the person with whom it was chatting, claimed they represented an existential danger to it, and threatened to release damaging personal information in an effort to silence them. Microsoft released a blog post stating that the errant behavior was caused by extended chat sessions of 15 or more questions which "can confuse the model on what questions it is answering." Microsoft later restricted the total number of chat turns to 5 per session and 50 per day per user (a turn being "a conversation exchange which contains both a user question and a reply from Bing"), and reduced the model's ability to express emotions. This aimed to prevent such incidents. Microsoft began to slowly ease the conversation limits, eventually relaxing the restrictions to 30 turns per session and 300 sessions per day. In March 2023, Bing incorporated Image Creator, an AI image generator powered by OpenAI's DALL-E 2, which can be accessed either through the chat function or a standalone image-generating website. In October, the image-generating tool was updated to use the more recent DALL-E 3. Although Bing blocks prompts including various keywords that could generate inappropriate images, within days many users reported being able to bypass those constraints, such as to generate images of popular cartoon characters committing terrorist attacks. Microsoft would respond to these shortly after by imposing a new, tighter filter on the tool. On May 4, 2023, Microsoft switched the chatbot from Limited Preview to Open Preview and eliminated the waitlist; however, it remained unavailable to users outside Microsoft Edge or the Bing mobile app until July, when it became available on non-Edge browsers. Use is limited without a Microsoft account. === As Microsoft 365 Copilot === On March 16, 2023, Microsoft announced a work version of Bing Chat named Microsoft 365 Copilot, designed for Microsoft 365 applications and services. Its primary marketing focus is as an added feature to Microsoft 365, with an emphasis on the enhancement of business productivity. Microsoft has also demonstrated Copilot's accessibility on the mobile version of Outlook to generate or summarize emails with a mobile device. At its Build 2023 conference, Microsoft announced its plans to integrate Bing Chat into Windows, initially called Windows Copilot, into Windows 11, allowing users to access it directly through the taskbar. Alongside the voice access feature for Windows 11, Microsoft presented Bing Chat, Microsoft 365 Copilot, and Windows Copilot as primary alternatives to Cortana when announcing the shutdown of its standalone app on June 2, 2023. As of its announcement date, Microsoft 365 Copilot had been tested by 20 initial users. By May 2023, Microsoft had broadened its reach to 600 customers who were willing to pay for early access, and concurrently, new Copilot features were introduced to the Microsoft 365 apps and services. As of July 2023, the tool's pricing was set at US$30 per user, per month for Microsoft 365 E3, E5, Business Standard, and Business Premium customers. Microsoft reused the Microsoft 365 Copilot name again as the Microsoft 365 app and website are now called Microsoft 365 Copilot as of January 2025. === As Microsoft Copilot === On September 21, 2023, Microsoft began rebranding Bing Chat, Microsoft 365 Copilot and Windows Copilot to Microsoft Copilot. A new logo was also introduced, moving away from the use of color variations of the standard Microsoft 365 and Bing logos. Additionally, the company revealed that it would make Copilot generally available for Microsoft 365 Enterprise customers purchasing more than 300 licenses starting November 1, 2023. However, no timeline has been provided as for when Copilot for Microsoft 365 will become generally available to non-enterprise customers. Windows Copilot, which had been available in the Windows Insider Program, would be renamed to the Copilot name in October when it became broadly available for customers. The same month also saw Microsoft Edge's Bing Chat side panel function be renamed to Microsoft Copilot with Bing Chat. On November 15, 2023, Microsoft announced that Bing Chat itself was being rebranded under the Copilot name. On Patch Tuesday in December 2023, Copilot was added without payment to many Windows 11 installations, with more installations, and limited support for Windows 10, to be added later. Later that month, a standalone Microsoft Copilot app was quietly released for Android, and one was released for iOS soon after. O

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  History of RISC OS

  RISC OS, the computer operating system developed by Acorn Computers for their ARM-based Acorn Archimedes range, was originally released in 1987 as Arthur 0.20, and soon followed by Arthur 0.30, and Arthur 1.20. The next version, Arthur 2, became RISC OS 2 and was completed in September 1988 and made available in April 1989. RISC OS 3 was released with the very earliest version of the A5000 in 1991 and contained a series of new features. By 1996 RISC OS had been shipped on over 500,000 systems. RISC OS 4 was released by RISCOS Ltd (ROL) in July 1999, based on the continued development of OS 3.8. ROL had in March 1999 licensed the rights to RISC OS from Element 14 (the renamed Acorn) and eventually from the new owner, Pace Micro Technology. According to the company, over 6,400 copies of OS 4.02 on ROM were sold up until production was ceased in mid-2005. RISC OS Select was launched in May 2001 by ROL. This is a subscription scheme allowing users access to the latest OS updates. These upgrades are released as soft-loadable ROM images, separate to the ROM where the boot OS is stored, and are loaded at boot time. Select 1 was shipped in May 2002, with Select 2 following in November 2002 and the final release of Select 3 in June 2004. ROL released the ROM based OS 4.39 the same month, dubbed RISC OS Adjust as a play on the RISC OS GUI convention of calling the three mouse buttons 'Select', 'Menu' and 'Adjust'. ROL sold its 500th Adjust ROM in early 2006. RISC OS 5 was released in October 2002 on Castle Technology's Acorn clone Iyonix PC. OS 5 is a separate evolution based upon the NCOS work done by Pace for set-top boxes. In October 2006, Castle announced a source sharing license plan for elements of OS 5. This Shared Source Initiative (SSI) is managed by RISC OS Open Ltd (ROOL). RISC OS 5 has since been released under a fully free and open source Apache 2.0 license, while the older no longer maintained RISC OS 6 has not. RISC OS Six was also announced in October 2006 by ROL. This is the next generation of their stream of the operating system. The first product to be launched under the name was the continuation of the Select scheme, Select 4. A beta-version of OS 6, Preview 1 (Select 4i1), was available in 2007 as a free download to all subscribers to the Select scheme, while in April 2009 the final release of Select 5 was shipped. The latest release of RISC OS from ROL is Select 6i1, shipped in December 2009. == Arthur == The OS was designed in the United Kingdom by Acorn for the 32-bit ARM based Acorn Archimedes, and released in its first version in 1987, as the Arthur operating system. The first public release of the OS was Arthur 1.20 in June 1987. It was bundled with a desktop graphical user interface (GUI), which mostly comprises assembly language software modules, and the Desktop module itself being written in BBC BASIC. It features a colour-scheme typically described as "technicolor". The graphical desktop runs on top of a command-line driven operating system which owes much to Acorn's earlier MOS operating system for its BBC Micro range of 8-bit microcomputers. Arthur, as originally conceived, was intended to deliver similar functionality to the operating system for the BBC Master series of computers, MOS, as a reaction to the fact that a more advanced operating system research project (ARX) would not be ready in time for the Archimedes. The Arthur project team, led by Paul Fellows, was given just five months to develop it entirely from the ground up—with the directive "just make it like the BBC micro". It was intended as a stop-gap until the operating system which Acorn had under development (ARX) could be completed. However, the latter was delayed time and again, and was eventually dropped when it became apparent that the Arthur development could be extended to have a window manager and full desktop environment. Also, it was small enough to run on the first 512K machines with only a floppy disc, whereas ARX required 4 megabytes and a hard drive. The OS development was carried out using a prototype ARM-based system connected to a BBC computer, before moving onto the prototype Acorn Archimedes the A500. Arthur was not a multitasking operating system, but offered support for adding application-level cooperative multitasking. No other version of the operating system was released externally, but internally the development of the desktop and window management continued, with the addition of a cooperative multitasking system, implemented by Neil Raine, which used the memory management hardware to swap-out one task, and bring in another between call-and-return from the Wimp_Poll call that applications were obliged to make to get messages under the desktop. Reminiscent of a similar technique employed by MultiFinder on the Apple Macintosh, this transformed a single-application-at-a-time system into one that could operate a full multi-tasking desktop. This transformation took place at version 1.6 though it was not made public until released, with the name change from Arthur to RISC OS, as version 2.0. Most software made for Arthur 1.2 can be run under RISC OS 2 and later because, underneath the desktop, the original Arthur OS core, API interfaces and modular structures remain as the heart of all versions. (A few titles will not work, however, because they used undocumented features, side effects or in a few cases APIs that became deprecated). In 2011, Business Insider listed Arthur as one of ten "operating systems that time forgot". == RISC OS 2 == RISC OS was a rapid development of Arthur 1.2 after the failure of the ARX project. Given growing dissatisfaction with various bugs and limitations with Arthur, testing of what was then known as Arthur 2 was apparently ongoing during 1988 with selected software houses. At this stage, Computer Concepts, who had been prolific developers for the BBC Micro and who had begun software development for the Archimedes, had already initiated a rival operating system project, Impulse, to support their own applications (including the desktop publishing application that would eventually become Impression), stating that Arthur did not meet the "hundreds of requirements" involved including "true multi-tasking". Such an operating system was to be offered free of charge with the planned application packages, but with the release of RISC OS and Computer Concepts acknowledging that RISC OS "overcomes the old problems with Arthur", the applications were to be able to run under either RISC OS or Impulse. Impression was eventually released as a RISC OS application. Ultimately, Arthur 2 was renamed to RISC OS, and was first sold as RISC OS 2.00 in April 1989. The operating system implements co-operative multitasking with some limitations but is not multi-threaded. It uses the ADFS file system for both floppy and hard disc access. It ran from a 512 KB set of ROMs. The WIMP interface offers all the standard features and fixes many of the bugs that had hindered Arthur. It lacks virtual memory and extensive memory protection (applications are protected from each other, but many functions have to be implemented as 'modules' which have full access to the memory). At the time of release, the main advantage of the OS was its ROM; it booted very quickly and while it was easy to crash, it was impossible to permanently break the OS from software. Its high performance was due to much of the system being written in ARM assembly language. The OS was designed with users in mind, rather than OS designers. It is organised as a relatively small kernel which defines a standard software interface to which extension modules are required to conform. Much of the system's functionality is implemented in modules coded in the ROM, though these can be supplanted by more evolved versions loaded into RAM. Among the kernel facilities are a general mechanism, named the callback handler, which allows a supervisor module to perform process multiplexing. This facility is used by a module forming part of the standard editor program to provide a terminal emulator window for console applications. The same approach made it possible for advanced users to implement modules giving RISC OS the ability to do pre-emptive multitasking. A slightly updated version, RISC OS 2.01, was released later to support the ARM3 processor, larger memory capacities, and the VGA and SVGA modes provided by the Acorn Archimedes 540 and Acorn R225/R260. == RISC OS 3 == RISC OS 3 introduced a number of new features, including multitasking Filer operations, applications and fonts in ROM, no limit on number of open windows, ability to move windows off screen, safe shutdown, the Pinboard, grouping of icon bar icons, up to 128 tasks, native ability to read MS-DOS format discs and use named hard discs. Improved configuration was also included, by way of multiple windows to change the settings. RISC OS 3.00 was released with the very earliest version of the A5000 in 1991; it is almo

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  Deluxe Media

  Deluxe Media Inc., also known simply as Deluxe and formerly Deluxe Entertainment Services Group, Inc., is an American multinational multimedia and entertainment service provisions company owned by Platinum Equity, founded in 1915 by Hungarian-born American film producer William Fox and headquartered in Burbank, California. The company services multiple clients in the film, television, digital content and advertising industries across the globe, and has been recognized with 10 Academy Awards for scientific and technical achievements, including developments in CinemaScope pictures (as part of 20th Century Fox) and more recently for a process of creating archival separations from digital image data. == History == Deluxe began as a film processing laboratory established in 1915 by William Fox under the name De Luxe as part of his eponymous film conglomerate corporation in Fort Lee, New Jersey. In 1916, Fox Film Corporation opened its studio in Hollywood on 13 acres at Sunset and Western. The first Deluxe film laboratory on the west coast was built on the south side of the lot (Fernwood and Serrano), and the laboratory was moved to the new Fox studios building on Manhattan's west side in 1919, where it remained for over 40 years. The "business manager" (later president) of the laboratory was Alan E. Freedman, who guided the company into the 1960s. In 1927, Fox (Deluxe) received a patent for sound-on-film, the Fox Movietone system. In 1927, "Sunrise: A Song of Two Humans," an early Movietone film, opened. Fox Movietone News, ran weekly in theaters until 1963. During the Great Depression, Fox Film Corporation encountered financial difficulties. Among the actions taken to maintain liquidity, Fox sold the laboratories in 1932 to Freedman, who renamed the operation Deluxe. Under Freedman's leadership, Deluxe added two more plants in Chicago and Toronto. In January 1934, Fox was granted an option to rebuy DeLuxe before December 31, 1938. On 31 May 1935, under Sidney Kent, Fox merged his film company with Twentieth Century Pictures to form The Twentieth Century-Fox Film Corporation following a bank-infused reorganisation. The merged company then exercised this option in July 1936, with Freedman remaining as president. In 1953, Deluxe developed the widescreen format CinemaScope. Titles included "There's No Business Like Show Business" (1954) and "The Seven Year Itch" (1955). Other innovations included the processing and sound striping of CinemaScope, and were patented and/or received Academy awards. In 1962 Freedman retired. In the 1960s, Deluxe closed its New York plant, followed by its plants in Chicago and Toronto, as motion picture production declined on the East Coast. In 1972, Deluxe began large volume videocassette production, with a billion by 1996. In 1990, The Rank Organisation acquired Deluxe from Fox. In 2000, Deluxe began large volume DVD production. In 2006, The Rank Organisation sold Deluxe Film Group to MacAndrews & Forbes, renamed Deluxe Entertainment Services Group. On 9 February 2012, Deluxe acquired Hong Kong–based visual effects and post-production company, Centro Digital Pictures, with its founder John Chu remaining as president while reporting to Alaric McAusland, managing director for Deluxe in Australia. In May 2014, Deluxe shut down its Los Angeles plant at Sunset & Western Studios complex, where other studios themselves were demolished way back in 1971. Also that same year, Deluxe closed the Hollywood film labs, and they gave thousands of orphaned film elements to the Academy Film Archive. The Deluxe Laboratories Collection at the Academy Film Archive consists of over 7,500 35mm and 16mm film elements of various motion pictures dating back to the early 1960s. On 22 April 2015, Deluxe and its longtime competitor, Technicolor S.A., announced that they had entered into a binding agreement to create a new joint venture known as Deluxe Technicolor Digital Cinema which will specialize in cinema mastering, distribution and management services. Deluxe got acquired on 4 September 2019 by creditors in a debt-for-equity swap to avoid bankruptcy. On 3 October 2019, Deluxe filed for bankruptcy, pending in the Southern District of New York. The same month on the 24th, the company received court approval to emerge from bankruptcy with a comprehensive restructuring plan. On July 1, 2020, Platinum Equity agreed to acquire the distribution division of Deluxe and re-unite with former CEO Cyril Drabinsky who would merge CineVizion, a film distribution company he founded after leaving Deluxe in 2016, into it. The companies Company 3 and Method Studios which formed the creative divisions of Deluxe were sold to Framestore in November 2020.

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  VibeOS

  VibeOS is an operating system built from scratch entirely by generative artificial intelligence, using code produced through prompts to Claude (vibe coding). It is capable of running on QEMU and was successfully tested on a Raspberry Pi Zero. It has been released under the MIT license. == Features == === Core === Custom kernel with cooperative multitasking (preemptive backup) FAT32 filesystem with long filename support Memory allocator, process scheduler, interrupt handling GIC-400 (QEMU) and BCM2836/BCM2835 (Pi) interrupt controllers Configurable boot (splash screen, boot target) === GUI === Desktop environment with draggable windows Menu bar, dock, window minimize/maximize/close Mouse and keyboard input Modern macOS-inspired aesthetic === Networking === Full TCP/IP stack (Ethernet, ARP, IP, ICMP, UDP, TCP) DNS resolver HTTP client TLS 1.2 with HTTPS support === Apps === Web browser with HTML/CSS rendering Terminal emulator with readline-style shell Text editor (vim clone) with syntax highlighting File manager with drag-and-drop Music player (MP3/WAV) Calculator, system monitor VibeCode IDE Doom port === Development === TCC (Tiny C Compiler) - compile C programs directly on VibeOS MicroPython interpreter with full kernel API bindings 60+ userspace programs (coreutils, games, GUI apps) === Hardware === Runs on Raspberry Pi Zero 2W USB keyboard and mouse via DWC2 driver SD card via EMMC driver 1920×1080 framebuffer == Further projects == There are other independent projects under the VibeOS name, including an independent development by Ben, also developed using vibe coding, aimed at creating a Unix-like operating system for educational purposes. Another project is Vib-OS, an operating system also built using vibe coding, capable of booting on a Raspberry Pi. It offers a desktop environment with a customizable wallpaper, a file manager, and a web browser currently in an early stage of development, a functional Doom port, among other features that are not very polished given the state of development.

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  Grammar induction

  Grammar induction (or grammatical inference) is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects. More generally, grammatical inference is that branch of machine learning where the instance space consists of discrete combinatorial objects such as strings, trees and graphs. == Grammar classes == Grammatical inference has often been very focused on the problem of learning finite-state machines of various types (see the article Induction of regular languages for details on these approaches), since there have been efficient algorithms for this problem since the 1980s. Since the beginning of the century, these approaches have been extended to the problem of inference of context-free grammars and richer formalisms, such as multiple context-free grammars and parallel multiple context-free grammars. Other classes of grammars for which grammatical inference has been studied are combinatory categorial grammars, stochastic context-free grammars, contextual grammars and pattern languages. == Learning models == The simplest form of learning is where the learning algorithm merely receives a set of examples drawn from the language in question: the aim is to learn the language from examples of it (and, rarely, from counter-examples, that is, example that do not belong to the language). However, other learning models have been studied. One frequently studied alternative is the case where the learner can ask membership queries as in the exact query learning model or minimally adequate teacher model introduced by Angluin. == Methodologies == There is a wide variety of methods for grammatical inference. Two of the classic sources are Fu (1977) and Fu (1982). Duda, Hart & Stork (2001) also devote a brief section to the problem, and cite a number of references. The basic trial-and-error method they present is discussed below. For approaches to infer subclasses of regular languages in particular, see Induction of regular languages. A more recent textbook is de la Higuera (2010), which covers the theory of grammatical inference of regular languages and finite state automata. D'Ulizia, Ferri and Grifoni provide a survey that explores grammatical inference methods for natural languages. === Induction of probabilistic grammars === There are several methods for induction of probabilistic context-free grammars. === Grammatical inference by trial-and-error === The method proposed in Section 8.7 of Duda, Hart & Stork (2001) suggests successively guessing grammar rules (productions) and testing them against positive and negative observations. The rule set is expanded so as to be able to generate each positive example, but if a given rule set also generates a negative example, it must be discarded. This particular approach can be characterized as "hypothesis testing" and bears some similarity to Mitchel's version space algorithm. The Duda, Hart & Stork (2001) text provide a simple example which nicely illustrates the process, but the feasibility of such an unguided trial-and-error approach for more substantial problems is dubious. === Grammatical inference by genetic algorithms === Grammatical induction using evolutionary algorithms is the process of evolving a representation of the grammar of a target language through some evolutionary process. Formal grammars can easily be represented as tree structures of production rules that can be subjected to evolutionary operators. Algorithms of this sort stem from the genetic programming paradigm pioneered by John Koza. Other early work on simple formal languages used the binary string representation of genetic algorithms, but the inherently hierarchical structure of grammars couched in the EBNF language made trees a more flexible approach. Koza represented Lisp programs as trees. He was able to find analogues to the genetic operators within the standard set of tree operators. For example, swapping sub-trees is equivalent to the corresponding process of genetic crossover, where sub-strings of a genetic code are transplanted into an individual of the next generation. Fitness is measured by scoring the output from the functions of the Lisp code. Similar analogues between the tree structured lisp representation and the representation of grammars as trees, made the application of genetic programming techniques possible for grammar induction. In the case of grammar induction, the transplantation of sub-trees corresponds to the swapping of production rules that enable the parsing of phrases from some language. The fitness operator for the grammar is based upon some measure of how well it performed in parsing some group of sentences from the target language. In a tree representation of a grammar, a terminal symbol of a production rule corresponds to a leaf node of the tree. Its parent nodes corresponds to a non-terminal symbol (e.g. a noun phrase or a verb phrase) in the rule set. Ultimately, the root node might correspond to a sentence non-terminal. === Grammatical inference by greedy algorithms === Like all greedy algorithms, greedy grammar inference algorithms make, in iterative manner, decisions that seem to be the best at that stage. The decisions made usually deal with things like the creation of new rules, the removal of existing rules, the choice of a rule to be applied or the merging of some existing rules. Because there are several ways to define 'the stage' and 'the best', there are also several greedy grammar inference algorithms. These context-free grammar generating algorithms make the decision after every read symbol: Lempel-Ziv-Welch algorithm creates a context-free grammar in a deterministic way such that it is necessary to store only the start rule of the generated grammar. Sequitur and its modifications. These context-free grammar generating algorithms first read the whole given symbol-sequence and then start to make decisions: Byte pair encoding and its optimizations. === Distributional learning === A more recent approach is based on distributional learning. Algorithms using these approaches have been applied to learning context-free grammars and mildly context-sensitive languages and have been proven to be correct and efficient for large subclasses of these grammars. === Learning of pattern languages === Angluin defines a pattern to be "a string of constant symbols from Σ and variable symbols from a disjoint set". The language of such a pattern is the set of all its nonempty ground instances i.e. all strings resulting from consistent replacement of its variable symbols by nonempty strings of constant symbols. A pattern is called descriptive for a finite input set of strings if its language is minimal (with respect to set inclusion) among all pattern languages subsuming the input set. Angluin gives a polynomial algorithm to compute, for a given input string set, all descriptive patterns in one variable x. To this end, she builds an automaton representing all possibly relevant patterns; using sophisticated arguments about word lengths, which rely on x being the only variable, the state count can be drastically reduced. Erlebach et al. give a more efficient version of Angluin's pattern learning algorithm, as well as a parallelized version. Arimura et al. show that a language class obtained from limited unions of patterns can be learned in polynomial time. === Pattern theory === Pattern theory, formulated by Ulf Grenander, is a mathematical formalism to describe knowledge of the world as patterns. It differs from other approaches to artificial intelligence in that it does not begin by prescribing algorithms and machinery to recognize and classify patterns; rather, it prescribes a vocabulary to articulate and recast the pattern concepts in precise language. In addition to the new algebraic vocabulary, its statistical approach was novel in its aim to: Identify the hidden variables of a data set using real world data rather than artificial stimuli, which was commonplace at the time. Formulate prior distributions for hidden variables and models for the observed variables that form the vertices of a Gibbs-like graph. Study the randomness and variability of these graphs. Create the basic classes of stochastic models applied by listing the deformations of the patterns. Synthesize (sample) from the models, not just analyze signals with it. Broad in its mathematical coverage, pattern theory spans algebra and statistics, as well as local topological and global entropic properties. == Applications == The principle of grammar induction has been applied to other aspects of natural language processing, and has been applied (among many other problems) to semantic parsing, natural language understanding, example-based translation, language acquisition, grammar-based compre

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  Link-richness

  Link-richness is the quality, possessed by some websites, of having many hyperlinks. Classified advertising sites like Craigslist tend to be very link-rich, sometimes with hundreds of links on their main page. They help users find the links they are looking for by grouping links into clusters. Inadequate link richness has been described as frustrating to readers, as it reduces transparency of site content from the main page. Students new to wiki collaboration were found to need guidance in how to take full advantage of the medium's potential for creating link-rich content. Link-richness in some contexts can be distracting, as when an article is surrounded by extraneous links. Indeed, it is becoming accepted as a best practice for universities to have link-rich home pages that do not rely on user categorisation and exploration of long sequences of links and are not constrained by traditional boundaries between departments. Tools are sometimes needed to make the publishing of link-rich web sites tractable, and many people may lack the technical skills, time, or inclination to engage in hand- crafting new digital document forms. A link-rich site that is low on content is sometimes referred to as a "gateway site." Link-rich portals were popular on the Web in 2000. Yahoo! and other sites featuring categories with many links were heavily used and often required fewer than three clicks to reach the content. Web designers were creating flat sites with content positioned close to the top of pages.

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  Svelte

  Svelte is a free and open-source component-based front-end software framework and language created by Rich Harris and maintained by the Svelte core team. Svelte is not a monolithic JavaScript library imported by applications: instead, Svelte compiles HTML templates to specialized code that manipulates the DOM directly, which may reduce the size of transferred files and give better client performance. Application code is also processed by the compiler, inserting calls to automatically recompute data and re-render UI elements when the data they depend on is modified. This also avoids the overhead associated with runtime intermediate representations, such as virtual DOM, unlike traditional frameworks (such as React and Vue) which carry out the bulk of their work at runtime, i.e. in the browser. The compiler itself is written in JavaScript. Its source code is licensed under MIT License and hosted on GitHub. Among comparable frontend libraries, Svelte has one of the smallest bundle footprints at merely 2KB. == History == The predecessor of Svelte is Ractive.js, which Rich Harris created in 2013. Version 1 of Svelte was written in JavaScript and was released on 29 November 2016. The name Svelte was chosen by Rich Harris and his coworkers at The Guardian. Version 2 of Svelte was released on 19 April 2018. It set out to correct what the maintainers viewed as mistakes in the earlier version such as replacing double curly braces with single curly braces. Version 3 of Svelte was written in TypeScript and was released on 21 April 2019. It rethought reactivity by using the compiler to instrument assignments behind the scenes. The SvelteKit web framework was announced in October 2020 and entered beta in March 2021. SvelteKit 1.0 was released in December 2022 after two years in development. Version 4 of Svelte was released on 22 June 2023. It was a maintenance release, smaller and faster than version 3. A part of this release was an internal rewrite from TypeScript back to JavaScript, with JSDoc type annotations. This was met with confusion from the developer community, which was addressed by the creator of Svelte, Rich Harris. Version 5 of Svelte was released on October 19, 2024 at Svelte Summit Fall 2024 with Rich Harris cutting the release live while joined by other Svelte maintainers. Svelte 5 was a ground-up rewrite of Svelte, changing core concepts such as reactivity and reusability. Its primary feature, runes, reworked how reactive state is declared and used. Runes are function-like macros that are used to declare a reactive state, or code that uses reactive states. These runes are used by the compiler to indicate values that may change and are depended on by other states or the DOM. Svelte 5 also introduces Snippets, which are reusable "snippets" of code that are defined once and can be reused anywhere else in the component. Svelte 5 was initially met with controversy due to its many changes, and thus deprecations caused primarily by runes. However, most of this has subsided since the initial announcement of runes, and the further refining of Svelte 5. Also at Svelte Summit Fall 2024, Ben McCann announced the Svelte CLI under the sv package name on npm. In early 2025, the Svelte team announced Asynchronous Svelte, an experimental feature set centered around asynchronous reactivity in Svelte using await expressions. As of August 2025, the feature is available via an experimental compiler option. This coincided with the experimental release of remote functions, an RPC feature in SvelteKit, Svelte's metaframework. Key early contributors to Svelte became involved with Conduitry joining with the release of Svelte 1, Tan Li Hau joining in 2019, and Ben McCann joining in 2020. Rich Harris and Simon Holthausen joined Vercel to work on Svelte fulltime in 2022. Dominic Gannaway joined Vercel from the React core team to work on Svelte fulltime in 2023. == Syntax == Svelte applications and components are defined in .svelte files, which are HTML files extended with templating syntax that is based on JavaScript and is similar to JSX. Svelte's core features are accessed through runes, which syntactically look like functions, but are used as macros by the compiler. These runes include: The $state rune, used for declaring a reactive state value The $derived rune, used for declaring reactive state derived from one or more states The $effect rune, used for declaring code that reruns whenever its dependencies change Starting with Svelte 5, the framework introduced a significant reactivity overhaul that replaces the previous `$:` reactive declarations with new runes such as $state, $derived, and $effect. The $effect rune is now used for post-render operations without modifying state, while $derived is used for computations that depend on other reactive values. This change aims to simplify the mental model of reactivity and make component logic more explicit. Additionally, the { JavaScript code } syntax can be used for templating in HTML elements and components, similar to template literals in JavaScript. This syntax can also be used in element attributes for uses such as two-way data binding, event listeners, and CSS styling. A Todo List example made in Svelte is below: == Associated projects == The Svelte maintainers created SvelteKit as the official way to build projects with Svelte. It is a Next.js/Nuxt-style full-stack framework that dramatically reduces the amount of code that gets sent to the browser. The maintainers had previously created Sapper, which was the predecessor of SvelteKit. The Svelte maintainers also maintain a number of integrations for popular software projects under the Svelte organization including integrations for Vite, Rollup, Webpack, TypeScript, VS Code, Chrome Developer Tools, ESLint, and Prettier. A number of external projects such as Storybook have also created integrations with Svelte and SvelteKit. == Influence == Vue.js modeled its API and single-file components after Ractive.js, the predecessor of Svelte. == Adoption == Svelte is widely praised by developers. Taking the top ranking in multiple large scale developer surveys, it was chosen as the Stack Overflow 2021 most loved web framework and 2020 State of JS frontend framework with the most satisfied developers. Recent surveys continue to show Svelte's strong developer satisfaction, with the 2024 State of JS survey maintaining its position among the most praised frontend frameworks. The 2024 Stack Overflow Developer Survey reported that 73% of developers who used Svelte want to continue working with it, and noted that Stack Overflow's own team used Svelte for building their 2024 Developer Survey results site. Svelte has been adopted by a number of high-profile web companies including The New York Times, Google, Apple, Spotify, Radio France, Square, Yahoo, ByteDance, Rakuten, Bloomberg, Reuters, Ikea, Facebook, Logitech, and Brave. A community group of primarily non-maintainers, known as the Svelte Society, run the Svelte Summit conference, write a Svelte newsletter, host a Svelte podcast, and host a directory of Svelte tooling, components, and templates.

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  Mashup (web application hybrid)

  A mashup (computer industry jargon), in web development, is a web page or web application that uses content from more than one source to create a single new service displayed in a single graphical interface. For example, a user could combine the addresses and photographs of their library branches with a Google map to create a map mashup. The term implies easy, fast integration, frequently using open application programming interfaces (open API) and data sources to produce enriched results that were not necessarily the original reason for producing the raw source data. The term mashup originally comes from creating something by combining elements from two or more sources. The main characteristics of a mashup are combination, visualization, and aggregation. It is important to make existing data more useful, for personal and professional use. To be able to permanently access the data of other services, mashups are generally client applications or hosted online. In the past years, more and more Web applications have published APIs that enable software developers to easily integrate data and functions the SOA way, instead of building them by themselves. Mashups can be considered to have an active role in the evolution of social software and Web 2.0. Mashup composition tools are usually simple enough to be used by end-users. They generally do not require programming skills and rather support visual wiring of GUI widgets, services and components together. Therefore, these tools contribute to a new vision of the Web, where users are able to contribute. The term "mashup" is not formally defined by any standard-setting body. == History == The broader context of the history of the Web provides a background for the development of mashups. Under the Web 1.0 model, organizations stored consumer data on portals and updated them regularly. They controlled all the consumer data, and the consumer had to use their products and services to get the information. The advent of Web 2.0 introduced Web standards that were commonly and widely adopted across traditional competitors and which unlocked the consumer data. At the same time, mashups emerged, allowing mixing and matching competitors' APIs to develop new services. The first mashups used mapping services or photo services to combine these services with data of any kind and therefore to produce visualizations of data. In the beginning, most mashups were consumer-based, but recently the mashup is to be seen as an interesting concept useful also to enterprises. Business mashups can combine existing internal data with external services to generate new views on the data. There was also the free Yahoo! Pipes to build mashups for free using the Yahoo! Query Language. == Types of mashup == There are many types of mashup, such as business mashups, consumer mashups, and data mashups. The most common type of mashup is the consumer mashup, aimed at the general public. Business (or enterprise) mashups define applications that combine their own resources, application and data, with other external Web services. They focus data into a single presentation and allow for collaborative action among businesses and developers. This works well for an agile development project, which requires collaboration between the developers and customer (or customer proxy, typically a product manager) for defining and implementing the business requirements. Enterprise mashups are secure, visually rich Web applications that expose actionable information from diverse internal and external information sources. Consumer mashups combine data from multiple public sources in the browser and organize it through a simple browser user interface. (e.g.: Wikipediavision combines Google Map and a Wikipedia API) Data mashups, opposite to the consumer mashups, combine similar types of media and information from multiple sources into a single representation. The combination of all these resources create a new and distinct Web service that was not originally provided by either source. === By API type === Mashups can also be categorized by the basic API type they use but any of these can be combined with each other or embedded into other applications. ==== Data types ==== Indexed data (documents, weblogs, images, videos, shopping articles, jobs ...) used by metasearch engines Cartographic and geographic data: geolocation software, geovisualization Feeds, podcasts: news aggregators ==== Functions ==== Data converters: language translators, speech processing, URL shorteners... Communication: email, instant messaging, notification... Visual data rendering: information visualization, diagrams Security related: electronic payment systems, ID identification... Editors == Mashup enabler == In technology, a mashup enabler is a tool for transforming incompatible IT resources into a form that allows them to be easily combined, in order to create a mashup. Mashup enablers allow powerful techniques and tools (such as mashup platforms) for combining data and services to be applied to new kinds of resources. An example of a mashup enabler is a tool for creating an RSS feed from a spreadsheet (which cannot easily be used to create a mashup). Many mashup editors include mashup enablers, for example, Presto Mashup Connectors, Convertigo Web Integrator or Caspio Bridge. Mashup enablers have also been described as "the service and tool providers, [sic] that make mashups possible". === History === Early mashups were developed manually by enthusiastic programmers. However, as mashups became more popular, companies began creating platforms for building mashups, which allow designers to visually construct mashups by connecting together mashup components. Mashup editors have greatly simplified the creation of mashups, significantly increasing the productivity of mashup developers and even opening mashup development to end-users and non-IT experts. Standard components and connectors enable designers to combine mashup resources in all sorts of complex ways with ease. Mashup platforms, however, have done little to broaden the scope of resources accessible by mashups and have not freed mashups from their reliance on well-structured data and open libraries (RSS feeds and public APIs). Mashup enablers evolved to address this problem, providing the ability to convert other kinds of data and services into mashable resources. === Web resources === Of course, not all valuable data is located within organizations. In fact, the most valuable information for business intelligence and decision support is often external to the organization. With the emergence of rich web applications and online Web portals, a wide range of business-critical processes (such as ordering) are becoming available online. Unfortunately, very few of these data sources syndicate content in RSS format and very few of these services provide publicly accessible APIs. Mashup editors therefore solve this problem by providing enablers or connectors. == Mashups versus portals == Mashups and portals are both content aggregation technologies. Portals are an older technology designed as an extension to traditional dynamic Web applications, in which the process of converting data content into marked-up Web pages is split into two phases: generation of markup "fragments" and aggregation of the fragments into pages. Each markup fragment is generated by a "portlet", and the portal combines them into a single Web page. Portlets may be hosted locally on the portal server or remotely on a separate server. Portal technology defines a complete event model covering reads and updates. A request for an aggregate page on a portal is translated into individual read operations on all the portlets that form the page ("render" operations on local, JSR 168 portlets or "getMarkup" operations on remote, WSRP portlets). If a submit button is pressed on any portlet on a portal page, it is translated into an update operation on that portlet alone (processAction on a local portlet or performBlockingInteraction on a remote, WSRP portlet). The update is then immediately followed by a read on all portlets on the page. Portal technology is about server-side, presentation-tier aggregation. It cannot be used to drive more robust forms of application integration such as two-phase commit. Mashups differ from portals in the following respects: The portal model has been around longer and has had greater investment and product research. Portal technology is therefore more standardized and mature. Over time, increasing maturity and standardization of mashup technology will likely make it more popular than portal technology because it is more closely associated with Web 2.0 and lately Service-oriented Architectures (SOA). New versions of portal products are expected to eventually add mashup support while still supporting legacy portlet applications. Mashup technologies, in contrast, are not expected to provide support for portal standards. == Business mashups

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  Situated

  In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term situated is commonly used to refer to robots, but some researchers argue that software agents can also be situated if: they exist in a dynamic (rapidly changing) environment, which they can manipulate or change through their actions, and which they can sense or perceive. Examples might include web-based agents, which can alter data or trigger processes (such as purchases) over the internet, or virtual-reality bots which inhabit and change virtual worlds, such as Second Life. Being situated is generally considered to be part of being embodied, but it is useful to consider each perspective individually. The situated perspective emphasizes that intelligent behaviour derives from the environment and the agent's interactions with it. The nature of these interactions are defined by an agent's embodiment.

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  CSS box model

  In web development, the CSS box model refers to how HTML elements are modeled in browser engines and how the dimensions of those HTML elements are derived from CSS properties. It is a fundamental concept for the composition of HTML webpages. The guidelines of the box model are described by web standards World Wide Web Consortium (W3C) specifically the CSS Working Group. For much of the late-1990s and early 2000s there had been non-standard compliant implementations of the box model in mainstream browsers. With the advent of CSS2 in 1998, which introduced the box-sizing property, the problem had mostly been resolved. == Specifics == The Cascading Style Sheets (CSS) specification describes how elements of web pages are displayed by graphical browsers. Section 4 of the CSS1 specification defines a "formatting model" that gives block-level elements—such as p and blockquote—a width and height, and three levels of boxes surrounding it: padding, borders, and margins. While the specification never uses the term "box model" explicitly, the term has become widely used by web developers and web browser vendors. All HTML elements can be considered "boxes", this includes div tag, p tag, or a tag. Each of those boxes has five modifiable dimensions: the height and width describe dimensions of the actual content of the box (text, images, ...) the padding describes the space between this content and the border of the box the border is any kind of line (solid, dotted, dashed...) surrounding the box, if present the margin is the space around the border According to the CSS1 specification, released by W3C in 1996 and revised in 1999, when a width or height is explicitly specified for any block-level element, it should determine only the width or height of the visible element, with the padding, borders, and margins applied afterward. Before CSS3, this box model was known as W3C box model, in CSS3, it is known as the content-box. The total width of a box is therefore margin-left + border-left + padding-left + width + padding-right + border-right + margin-right. Similarly, the total height of a box equals margin-top + border-top + padding-top + height + padding-bottom + border-bottom + margin-bottom. For example, the following CSS code would specify the box dimensions of each block belonging to 'my-class'. Moreover, each such box will have total height 140px and width 240px. CSS3 introduced the Internet Explorer box model to the standard, known referred to as border-box. == History == Before HTML 4 and CSS, very few HTML elements supported both border and padding, so the definition of the width and height of an element was not very contentious. However, it varied depending on the element. The HTML width attribute of a table defined the width of the table including its border. On the other hand, the HTML width attribute of an image defined the width of the image itself (inside any border). The only element to support padding in those early days was the table cell. Width for the cell was defined as "the suggested width for a cell content in pixels excluding the cell padding." In 1996, CSS introduced margin, border and padding for many more elements. It adopted a definition width in relation to content, border, margin and padding similar to that for a table cell. This has since become known as the W3C box model. At the time, very few browser vendors implemented the W3C box model to the letter. The two major browsers at the time, Netscape 4.0 and Internet Explorer 4.0 both defined width and height as the distance from border to border. This has been referred to as the traditional or the Internet Explorer box model. Internet Explorer in "quirks mode" includes the content, padding and borders within a specified width or height; this results in a narrower or shorter rendering of a box than would result following the standard behavior. The Internet Explorer box model behavior was often considered a bug, because of the way in which earlier versions of Internet Explorer handle the box model or sizing of elements in a web page, which differs from the standard way recommended by the W3C for the Cascading Style Sheets language. As of Internet Explorer 6, the browser supports an alternative rendering mode (called the "standards-compliant mode") which solves this discrepancy. However, for backward compatibility reasons, all versions still behave in the usual, non-standard way by default (see quirks mode). Internet Explorer for Mac is not affected by this non-standard behavior. === Workarounds === Internet Explorer versions 6 and onward are not affected by the bug if the page contains certain HTML document type declarations. These versions maintain the buggy behavior when in quirks mode for reasons of backward compatibility. For example, quirks mode is triggered: When the document type declaration is absent or incomplete; When an HTML 3 or earlier document is encountered; When an HTML 4.0 Transitional or Frameset document type declaration is used and a system identifier (URI) is not present; When an SGML comment or other unrecognized content appears before the document type declaration Internet Explorer 6 also uses quirks mode if there is an XML declaration prior to the document type declaration. Various workarounds have been devised to force Internet Explorer versions 5 and earlier to display Web pages using the W3C box model. These workarounds generally exploit unrelated bugs in Internet Explorer's CSS selector processing in order to hide certain rules from the browser. The best known of these workarounds is the "box model hack" developed by Tantek Çelik, a former Microsoft employee who developed this idea while working on Internet Explorer for the Macintosh. It involves specifying a width declaration for Internet Explorer for Windows, and then overriding it with another width declaration for CSS-compliant browsers. This second declaration is hidden from Internet Explorer for Windows by exploiting other bugs in the way that it parses CSS rules. The implementation of these CSS “hacks” has been further complicated by the public release of Internet Explorer 7, which has had some issues fixed, but not others, causing undesired results in pages using these hacks. Box model hacks have proven unreliable because they rely on bugs in browsers' CSS support that may be fixed in later versions. For this reason, some Web developers have instead recommended either avoiding specifying both width and padding for the same element or using conditional comment and/or CSS filters to work around the box model bug in older versions of Internet Explorer. == Support for Internet Explorer's box model == Web designer Doug Bowman has said that the original Internet Explorer box model represents a better, more logical approach. Peter-Paul Koch gives the example of a physical box, whose dimensions always refer to the box itself, including potential padding, but never its content. He says that this box model is more useful for graphic designers, who create designs based on the visible width of boxes rather than the width of their content. Bernie Zimmermann says that the Internet Explorer box model is closer to the definition of cell dimensions and padding used in the HTML table model. The W3C has included a "box-sizing" property in CSS3. When box-sizing: border-box; is specified for an element, any padding or border of the element is drawn inside the specified width and height, "as commonly implemented by legacy HTML user agents". Internet Explorer 8, WebKit browsers such as Apple Safari 5.1+ and Google Chrome, Gecko-based browsers such as Mozilla Firefox 29.0 and later, Opera 7.0 and later, and Konqueror 3.3.2 and later support the CSS3 box-sizing property. Gecko browsers previous than 29.0 support the same functionality using the browser-specific -moz-box-sizing property. border-box is the default box model used in Bootstrap framework.

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  Hardware-based encryption

  Hardware-based encryption is the use of computer hardware to assist software, or sometimes replace software, in the process of data encryption. Typically, this is implemented as part of the processor's instruction set. For example, the AES encryption algorithm (a modern cipher) can be implemented using the AES instruction set on the ubiquitous x86 architecture. Such instructions also exist on the ARM architecture. However, more unusual systems exist where the cryptography module is separate from the central processor, instead being implemented as a coprocessor, in particular a secure cryptoprocessor or cryptographic accelerator, of which an example is the IBM 4758, or its successor, the IBM 4764. Hardware implementations can be faster and less prone to exploitation than traditional software implementations, and furthermore can be protected against tampering. == History == Prior to the use of computer hardware, cryptography could be performed through various mechanical or electro-mechanical means. An early example is the Scytale used by the Spartans. The Enigma machine was an electro-mechanical system cipher machine notably used by the Germans in World War II. After World War II, purely electronic systems were developed. In 1987 the ABYSS (A Basic Yorktown Security System) project was initiated. The aim of this project was to protect against software piracy. However, the application of computers to cryptography in general dates back to the 1940s and Bletchley Park, where the Colossus computer was used to break the encryption used by German High Command during World War II. The use of computers to encrypt, however, came later. In particular, until the development of the integrated circuit, of which the first was produced in 1960, computers were impractical for encryption, since, in comparison to the portable form factor of the Enigma machine, computers of the era took the space of an entire building. It was only with the development of the microcomputer that computer encryption became feasible, outside of niche applications. The development of the World Wide Web lead to the need for consumers to have access to encryption, as online shopping became prevalent. The key concerns for consumers were security and speed. This led to the eventual inclusion of the key algorithms into processors as a way of both increasing speed and security. == Implementations == === In the instruction set === ==== x86 ==== The X86 architecture, as a CISC (Complex Instruction Set Computer) Architecture, typically implements complex algorithms in hardware. Cryptographic algorithms are no exception. The x86 architecture implements significant components of the AES (Advanced Encryption Standard) algorithm, which can be used by the NSA for Top Secret information. The architecture also includes support for the SHA Hashing Algorithms through the Intel SHA extensions. Whereas AES is a cipher, which is useful for encrypting documents, hashing is used for verification, such as of passwords (see PBKDF2). ==== ARM ==== ARM processors can optionally support Security Extensions. Although ARM is a RISC (Reduced Instruction Set Computer) architecture, there are several optional extensions specified by ARM Holdings. === As a coprocessor === IBM 4758 – The predecessor to the IBM 4764. This includes its own specialised processor, memory and a Random Number Generator. IBM 4764 and IBM 4765, identical except for the connection used. The former uses PCI-X, while the latter uses PCI-e. Both are peripheral devices that plug into the motherboard. === Proliferation === Advanced Micro Devices (AMD) processors are also x86 devices, and have supported the AES instructions since the 2011 Bulldozer processor iteration. Due to the existence of encryption instructions on modern processors provided by both Intel and AMD, the instructions are present on most modern computers. They also exist on many tablets and smartphones due to their implementation in ARM processors. == Advantages == Implementing cryptography in hardware means that part of the processor is dedicated to the task. This can lead to a large increase in speed. In particular, modern processor architectures that support pipelining can often perform other instructions concurrently with the execution of the encryption instruction. Furthermore, hardware can have methods of protecting data from software. Consequently, even if the operating system is compromised, the data may still be secure (see Software Guard Extensions). == Disadvantages == If, however, the hardware implementation is compromised, major issues arise. Malicious software can retrieve the data from the (supposedly) secure hardware – a large class of method used is the timing attack. This is far more problematic to solve than a software bug, even within the operating system. Microsoft regularly deals with security issues through Windows Update. Similarly, regular security updates are released for Mac OS X and Linux, as well as mobile operating systems like iOS, Android, and Windows Phone. However, hardware is a different issue. Sometimes, the issue will be fixable through updates to the processor's microcode (a low level type of software). However, other issues may only be resolvable through replacing the hardware, or a workaround in the operating system which mitigates the performance benefit of the hardware implementation, such as in the Spectre exploit.

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  Deluxe Media

  Deluxe Media Inc., also known simply as Deluxe and formerly Deluxe Entertainment Services Group, Inc., is an American multinational multimedia and entertainment service provisions company owned by Platinum Equity, founded in 1915 by Hungarian-born American film producer William Fox and headquartered in Burbank, California. The company services multiple clients in the film, television, digital content and advertising industries across the globe, and has been recognized with 10 Academy Awards for scientific and technical achievements, including developments in CinemaScope pictures (as part of 20th Century Fox) and more recently for a process of creating archival separations from digital image data. == History == Deluxe began as a film processing laboratory established in 1915 by William Fox under the name De Luxe as part of his eponymous film conglomerate corporation in Fort Lee, New Jersey. In 1916, Fox Film Corporation opened its studio in Hollywood on 13 acres at Sunset and Western. The first Deluxe film laboratory on the west coast was built on the south side of the lot (Fernwood and Serrano), and the laboratory was moved to the new Fox studios building on Manhattan's west side in 1919, where it remained for over 40 years. The "business manager" (later president) of the laboratory was Alan E. Freedman, who guided the company into the 1960s. In 1927, Fox (Deluxe) received a patent for sound-on-film, the Fox Movietone system. In 1927, "Sunrise: A Song of Two Humans," an early Movietone film, opened. Fox Movietone News, ran weekly in theaters until 1963. During the Great Depression, Fox Film Corporation encountered financial difficulties. Among the actions taken to maintain liquidity, Fox sold the laboratories in 1932 to Freedman, who renamed the operation Deluxe. Under Freedman's leadership, Deluxe added two more plants in Chicago and Toronto. In January 1934, Fox was granted an option to rebuy DeLuxe before December 31, 1938. On 31 May 1935, under Sidney Kent, Fox merged his film company with Twentieth Century Pictures to form The Twentieth Century-Fox Film Corporation following a bank-infused reorganisation. The merged company then exercised this option in July 1936, with Freedman remaining as president. In 1953, Deluxe developed the widescreen format CinemaScope. Titles included "There's No Business Like Show Business" (1954) and "The Seven Year Itch" (1955). Other innovations included the processing and sound striping of CinemaScope, and were patented and/or received Academy awards. In 1962 Freedman retired. In the 1960s, Deluxe closed its New York plant, followed by its plants in Chicago and Toronto, as motion picture production declined on the East Coast. In 1972, Deluxe began large volume videocassette production, with a billion by 1996. In 1990, The Rank Organisation acquired Deluxe from Fox. In 2000, Deluxe began large volume DVD production. In 2006, The Rank Organisation sold Deluxe Film Group to MacAndrews & Forbes, renamed Deluxe Entertainment Services Group. On 9 February 2012, Deluxe acquired Hong Kong–based visual effects and post-production company, Centro Digital Pictures, with its founder John Chu remaining as president while reporting to Alaric McAusland, managing director for Deluxe in Australia. In May 2014, Deluxe shut down its Los Angeles plant at Sunset & Western Studios complex, where other studios themselves were demolished way back in 1971. Also that same year, Deluxe closed the Hollywood film labs, and they gave thousands of orphaned film elements to the Academy Film Archive. The Deluxe Laboratories Collection at the Academy Film Archive consists of over 7,500 35mm and 16mm film elements of various motion pictures dating back to the early 1960s. On 22 April 2015, Deluxe and its longtime competitor, Technicolor S.A., announced that they had entered into a binding agreement to create a new joint venture known as Deluxe Technicolor Digital Cinema which will specialize in cinema mastering, distribution and management services. Deluxe got acquired on 4 September 2019 by creditors in a debt-for-equity swap to avoid bankruptcy. On 3 October 2019, Deluxe filed for bankruptcy, pending in the Southern District of New York. The same month on the 24th, the company received court approval to emerge from bankruptcy with a comprehensive restructuring plan. On July 1, 2020, Platinum Equity agreed to acquire the distribution division of Deluxe and re-unite with former CEO Cyril Drabinsky who would merge CineVizion, a film distribution company he founded after leaving Deluxe in 2016, into it. The companies Company 3 and Method Studios which formed the creative divisions of Deluxe were sold to Framestore in November 2020.

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  Image analysis

  Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information. On the other hand, the human visual cortex is an excellent image analysis apparatus, especially for extracting higher-level information, and for many applications — including medicine, security, and remote sensing — human analysts still cannot be replaced by computers. For this reason, many important image analysis tools such as edge detectors and neural networks are inspired by human visual perception models. == Digital == Digital Image Analysis or Computer Image Analysis is when a computer or electrical device automatically studies an image to obtain useful information from it. Note that the device is often a computer but may also be an electrical circuit, a digital camera or a mobile phone. It involves the fields of computer or machine vision, and medical imaging, and makes heavy use of pattern recognition, digital geometry, and signal processing. This field of computer science developed in the 1950s at academic institutions such as the MIT A.I. Lab, originally as a branch of artificial intelligence and robotics. It is the quantitative or qualitative characterization of two-dimensional (2D) or three-dimensional (3D) digital images. 2D images are, for example, to be analyzed in computer vision, and 3D images in medical imaging. The field was established in the 1950s—1970s, for example with pioneering contributions by Azriel Rosenfeld, Herbert Freeman, Jack E. Bresenham, or King-Sun Fu. == Techniques == There are many different techniques used in automatically analysing images. Each technique may be useful for a small range of tasks, however there still aren't any known methods of image analysis that are generic enough for wide ranges of tasks, compared to the abilities of a human's image analysing capabilities. Examples of image analysis techniques in different fields include: 2D and 3D object recognition, image segmentation, motion detection e.g. Single particle tracking, video tracking, optical flow, medical scan analysis, 3D Pose Estimation. == Deep learning == Since the early 2010s, deep learning methods have substantially advanced the field of image analysis. In 2012, a deep convolutional neural network (CNN) known as AlexNet achieved a significant reduction in error rates on the ImageNet large-scale image classification benchmark, demonstrating the effectiveness of deep learning for visual recognition tasks. Subsequent architectures such as ResNet introduced residual connections that enabled training of much deeper networks, further improving accuracy across image analysis tasks. Real-time object detection became practical with frameworks such as YOLO (You Only Look Once), which unified detection and classification into a single network pass. In 2020, the Vision Transformer (ViT) demonstrated that transformer architectures, originally developed for natural language processing, could achieve competitive results on image classification when applied directly to sequences of image patches. More recently, foundation models trained on large-scale datasets have enabled zero-shot generalisation across image analysis tasks. The Segment Anything Model (SAM), trained on over one billion masks, can segment arbitrary objects in images without task-specific fine-tuning. These advances have made image analysis techniques increasingly accessible through browser-based tools and open-source implementations. == Applications == The applications of digital image analysis are continuously expanding through all areas of science and industry, including: anatomy, allows for precise measurements, visualization, and statistical analysis of anatomical structures. assay micro plate reading, such as detecting where a chemical was manufactured. astronomy, such as calculating the size of a planet. automated species identification (e.g. plant and animal species) defense error level analysis filtering machine vision, such as to automatically count items in a factory conveyor belt. materials science, such as determining if a metal weld has cracks. medicine, such as detecting cancer in a mammography scan. metallography, such as determining the mineral content of a rock sample. microscopy, such as counting the germs in a swab. automatic number plate recognition; optical character recognition, such as automatic license plate detection. remote sensing, such as detecting intruders in a house, and producing land cover/land use maps. robotics, such as to avoid steering into an obstacle. security, such as detecting a person's eye color or hair color. == Object-based == Object-based image analysis (OBIA) involves two typical processes, segmentation and classification. Segmentation helps to group pixels into homogeneous objects. The objects typically correspond to individual features of interest, although over-segmentation or under-segmentation is very likely. Classification then can be performed at object levels, using various statistics of the objects as features in the classifier. Statistics can include geometry, context and texture of image objects. Over-segmentation is often preferred over under-segmentation when classifying high-resolution images. Object-based image analysis has been applied in many fields, such as cell biology, medicine, earth sciences, and remote sensing. For example, it can detect changes of cellular shapes in the process of cell differentiation.; it has also been widely used in the mapping community to generate land cover. When applied to earth images, OBIA is known as geographic object-based image analysis (GEOBIA), defined as "a sub-discipline of geoinformation science devoted to (...) partitioning remote sensing (RS) imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scale". The international GEOBIA conference has been held biannually since 2006. OBIA techniques are implemented in software such as eCognition or the Orfeo toolbox.

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  Algorithmic amplification

  Algorithmic amplification is the process by which automated ranking and recommendation systems on digital platforms increase the visibility of certain content beyond its initial audience. Major platforms including Facebook, YouTube, TikTok, and X (formerly Twitter) use such systems to determine what appears in users' feeds and search results. The term is used in research on social media and digital media regulation to describe how platform design choices influence the distribution of online information. Unlike chronological feeds, algorithmic systems evaluate content using signals such as engagement rates, viewing duration, and predicted relevance to individual users. Content that performs strongly on these metrics may be promoted to progressively larger audiences through feeds, search rankings, or autoplay systems. The process is distinct from content moderation, which involves removing, labelling, or restricting content under platform rules, although the two can interact in practice. The concept is closely connected to the attention economy. Research has linked algorithmic amplification to the spread of misinformation and the circulation of political content, as well as to effects on young users' mental health. The scale and direction of those effects remain debated, in part because independent researchers have limited access to the internal workings of platform recommendation systems. Governments in the European Union, United Kingdom, United States, and China have pursued differing regulatory approaches to recommendation algorithms. The EU's Digital Services Act and the UK's Online Safety Act 2023 impose obligations on large platforms related to recommendation system transparency and risk, while China became the first country to enact binding legislation specifically targeting such systems. Internal documents and whistleblower testimony reported by the BBC in 2026 described how competitive pressure between Meta and TikTok led to trade-offs between engagement and user safety in the design of their recommendation systems. == Terminology == The term algorithmic amplification is used in media studies, platform governance scholarship and regulatory literature to describe how automated systems influence the distribution of content beyond what organic user sharing alone would produce. It is distinct from viral spread, which refers primarily to user-driven sharing behaviour, and from algorithmic bias, which describes systematic errors or unfairness in algorithmic outputs. The related term algorithmic curation is used for the broader process of selecting and ordering content, of which amplification is one possible outcome. The phrase also appears in regulatory and legislative discussion of recommendation systems. The European Union's Digital Services Act (DSA) identifies recommendation systems as a potential source of systemic risk, and the term appears frequently in academic and policy commentary on the regulation. In the United States, proposals including the Filter Bubble Transparency Act and the Kids Online Safety Act (KOSA) have used it to frame requirements around recommendation system transparency. In the United Kingdom, the House of Commons Science, Innovation and Technology Committee used the term in a 2025 report on how recommendation algorithms contributed to the spread of misinformation during the 2024 Southport riots. A Joint Declaration on AI and Freedom of Expression adopted in October 2025 by four international freedom of expression mandate holders, including the UN Special Rapporteur on Freedom of Opinion and Expression and the OSCE Representative on Freedom of the Media, stated that recommender systems and other AI-powered curation tools exert "a large hidden influence and gatekeeper role" over what information people access and consume. == Background == Early internet platforms typically displayed content in reverse-chronological order or through keyword-based search systems. Although the term is most often applied to social media, the underlying logic predates social media itself. A 2021 overview traced the origins of modern recommendation systems to the early 1990s, when they were first used experimentally for personal email and information filtering. The 1992 Tapestry mail system and the 1994 GroupLens news filtering system were early milestones before recommendation systems spread into e-commerce and other online services. As user bases and content volumes grew during the 2000s, major platforms including Google, YouTube, and Facebook developed machine-learning systems to personalise content delivery and prioritise material predicted to generate engagement. Facebook introduced its News Feed in 2006, which gradually shifted from chronological presentation towards algorithmically ranked content. YouTube altered its recommendation system in 2012 to prioritise watch time rather than clicks, a change the platform said was prompted by concerns that click-based metrics encouraged misleading thumbnails and low-quality videos. TikTok, launched internationally in 2018, adopted a model in which its primary content surface, the For You feed, is driven almost entirely by algorithmic recommendation rather than by a user's social graph. An internal document obtained by The New York Times in 2021 showed that the platform's algorithm optimised for retention and time spent, using signals such as watch duration, replays, likes, and comments to score and rank videos. Algorithmic recommendation also became central to platforms outside social media. Spotify's personalised features, including Discover Weekly, Release Radar, and Home recommendations, use behavioural signals and inferred "taste profiles" to surface tracks and artists beyond a listener's existing library. An ethnographic study of music curators at streaming platforms described this blend of algorithmic and human editorial selection as an "algo-torial" model of gatekeeping. Amazon adopted item-based collaborative filtering for product recommendations in 1998, and its recommendation engine has been described as one of the earliest large-scale deployments of recommendation technology in e-commerce. The same dynamics operate on adult content platforms. Law professor Amy Adler has argued that from 2007 onwards the pornography industry migrated to algorithm-driven streaming platforms, most of which are controlled by a single near-monopoly company, Aylo (formerly MindGeek). These platforms use algorithmic search engines, suggestions, rigid categorisation of content, and AI-driven search term optimisation in ways that produce the same distorting effects found on mainstream speech platforms, including filter bubbles, feedback loops, and the tendency of algorithmic recommendations to alter individual preferences. == Mechanisms == Recommendation systems commonly combine collaborative filtering, which predicts a user's preferences from the behaviour of similar users, with machine-learning models that predict which content a user is likely to engage with from their prior activity. In a common two-stage design, a platform first generates a set of candidate items from a large content pool and then ranks them using a scoring model with objectives such as predicted engagement or user satisfaction. Small changes in ranking criteria can shift exposure at scale, particularly when applied repeatedly across multiple browsing sessions. These systems typically rely on signals including engagement rates, viewing duration, click-through rates, and network relationships between users. Modern recommendation pipelines continuously update predictions as new behavioural data arrives, allowing platforms to adjust rankings in near real time. Users' revealed preferences, expressed through behaviour such as clicks and viewing time, do not always align with their stated preferences, expressed through explicit feedback such as surveys or content controls. Popularity signals can create feedback dynamics in which early engagement increases the likelihood that content will be shown to additional users. Experimental research on online cultural markets has demonstrated how such feedback processes can produce unequal visibility outcomes even when initial differences in content quality are small. == Beneficial and public-interest uses == Recommendation systems can help users navigate large volumes of content by surfacing material predicted to match their interests or needs, which can improve discoverability on platforms with large content libraries. In public health communication, platforms can help health authorities distribute timely information at scale, though the same recommendation systems also risk amplifying misinformation alongside official guidance. Sociologist Zeynep Tufekci has argued that the shift from independent blogs to large centralised platforms transferred gatekeeping power from traditional media to corporate algorithms. In the case of the Egyptian uprising of 2011, she noted that ordinary users

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  GlTF

  glTF (Graphics Library Transmission Format or GL Transmission Format and formerly known as WebGL Transmissions Format or WebGL TF) is a standard file format for three-dimensional scenes and models. A glTF file uses one of two possible file extensions: .gltf (JSON/ASCII) or .glb (binary). Both .gltf and .glb files may reference external binary and texture resources. Alternatively, both formats may be self-contained by directly embedding binary data buffers (as base64-encoded strings in .gltf files or as raw byte arrays in .glb files). An open standard developed and maintained by the Khronos Group, it supports 3D model geometry, appearance, scene graph hierarchy, and animation. It is intended to be a streamlined, interoperable format for the delivery of 3D assets, while minimizing file size and runtime processing by apps. As such, its creators have described it as the "JPEG of 3D". == Overview == The glTF format stores data primarily in JSON. The JSON may also contain blobs of binary data known as buffers, and refer to external files, for storing mesh data, images, etc. The binary .glb format also contains JSON text, but serialized with binary chunk headers to allow blobs to be directly appended to the file. The fundamental building blocks of a glTF scene are nodes. Nodes are organized into a hierarchy, such that a node may have other nodes defined as children. Nodes may have transforms relative to their parent. Nodes may refer to resources, such as meshes, skins, and cameras. Meshes may refer to materials, which refer to textures, which refer to images. Scenes are defined using an array of root nodes. Most of the top-level glTF properties use a flat hierarchy for storage. Nodes are saved in an array and are referred to by index, including by other nodes. A glTF scene refers to its root nodes by index. Furthermore, nodes refer to meshes by index, which refer to materials by index, which refer to textures by index, which refer to images by index. All glTF data structures support being extended using a JSON property, allowing arbitrary JSON data to be added. == Releases == === glTF 1.0 === Members of the COLLADA working group conceived the file format in 2012. At SIGGRAPH 2012, Khronos presented a demo of glTF, which was then called WebGL Transmissions Format (WebGL TF). On October 19, 2015, Khronos released the glTF 1.0 specification. ==== Adoption of glTF 1.0 ==== At SIGGRAPH 2016, Oculus announced their adoption of glTF citing the similarities to their ovrscene format. In October 2016, Microsoft joined the 3D Formats working group at Khronos to collaborate on glTF. === glTF 2.0 === The second version, glTF 2.0, was released in June 2017, and is a complete overhaul of the file format from version 1.0, with most tools adopting the 2.0 version. Based on a proposal by Fraunhofer originally presented at SIGGRAPH 2016, physically based rendering (PBR) was added, replacing WebGL shaders used in glTF 1.0. glTF 2.0 added the GLB binary format into the base specification. Other upgrades include sparse accessors and morph targets for techniques such as facial animation, and schema tweaks and breaking changes for corner cases or performance such as replacing top-level glTF object properties with arrays for faster index-based access. There is ongoing work towards import and export in Unity and an integrated multi-engine viewer and validator. ==== Adoption of glTF 2.0 ==== On March 3, 2017, Microsoft announced that they would be using glTF 2.0 as the 3D asset format across their product line, including Paint 3D, 3D Viewer, Remix 3D, Babylon.js, and Microsoft Office. Sketchfab also announced support for glTF 2.0. The glTF and GLB formats are used on and supported by companies including DGG, UX3D, Sketchfab, Facebook, Microsoft, Meta, Google, Adobe, Box, TurboSquid, Unreal Engine, Unity, and Qt Quick 3D. The format has been noted as an important standard for augmented reality, integrating with modeling software such as Autodesk Maya, Autodesk 3ds Max, and Poly. In February 2020, the Smithsonian Institution launched their Open Access Initiative, releasing approximately 2.8 million 2D images and 3D models into the public domain, using glTF for the 3D models. In July 2022, glTF 2.0 was released as the ISO/IEC 12113:2022 International Standard. Khronos stated they would make regular submissions to bring updates and new widely adopted glTF functionality into refreshed versions of ISO/IEC 12113 to ensure that there is no long-term divergence between the ISO/IEC and Khronos specifications. The open-source game engine Godot supports importing glTF 2.0 files since version 3.0 and export since version 4.0. === Extensions === The glTF format can be extended with arbitrary JSON to add new data and functionality. Extensions can be placed on any part of a glTF, including nodes, animations, materials, textures, and on the entire document. Khronos keeps a non-comprehensive registry of glTF extensions on GitHub, including all official Khronos extensions and a few third-party extensions. PBR extensions model the physical appearance of real-world objects, allowing developers to create realistic 3D assets that have the correct appearance. As new PBR extensions are released, they continue to expand PBR capabilities within the glTF framework, allowing a wider range of scenes and objects to be realistically rendered as 3D assets. The KTX 2.0 extension for universal texture compression enables 3D models in the glTF format to be highly compressed and to use natively supported texture formats, reducing file size and boosting rendering speed. Draco is a glTF extension for mesh compression, to compress and decompress 3D meshes, to help reduce the size of 3D files. It compresses vertex attributes, normals, colors, and texture coordinates. Various glTF extensions for game engine interoperability have been developed by OMI group. This includes extensions for physics shapes, physics bodies, physics joints, audio playback, seats, spawn points, and more. The VRM consortium has developed glTF extensions for advanced humanoid 3D avatars including dynamic spring bones and toon materials. == Derivative formats == 3D Tiles, an OGC Community Standard, builds on glTF to add a spatial data structure, metadata, and declarative styling for streaming massive heterogeneous 3D geospatial datasets. VRM, a model format for VR, is built on the .glb format. It is a 3D humanoid avatar specification and file format. == Software ecosystem == Khronos maintains the glTF Sample Viewer for viewing glTF assets. Khronos also maintains the glTF Validator for validating if 3D models conform to the glTF specification. Khronos maintains a glTF Compressor tool to interactively optimize and fine-tune compression settings for glTF assets using KTX 2.0 textures. glTF loaders are in open-source WebGL engines including PlayCanvas, Three.js, Babylon.js, Cesium, PEX, xeogl, and A-Frame. The Godot game engine supports and recommends the glTF format, with both import and export support. Open-source glTF converters are available from COLLADA, FBX, and OBJ. Assimp can import and export glTF. glTF files can also be directly exported from a variety of 3D editors, such as Blender, Unity (using the glTFast importer/exporter), Freecad, Vectary, Autodesk 3ds Max (natively or using Verge3D exporter), Autodesk Maya (using babylon.js exporter), Autodesk Inventor, Modo, Houdini, Paint 3D, Godot, and Substance Painter. Open-source glTF utility libraries are available for programming languages including JavaScript, Node.js, C++, C#, Python, Haskell, Java, Go, Rust, Haxe, Ada, and TypeScript. Khronos keeps a list of these libraries and other related applications on their ecosystem site. The Khronos 3D Commerce Working Group released Asset Creation Guidelines in 2020 outlining best practices for use of the glTF file format in 3D Commerce. In 2025, the Working Group launched Asset Creation Guidelines 2.0, a continuously updated resource with additional guidance for geometry, mesh optimization, UV maps, textures, materials/PBR performance, and web optimization. The Khronos PBR Neutral Tone Mappers specification is a tone mapper designed to faithfully reproduce an object's base color, hue, and saturation when using PBR rendering under grayscale lighting, supporting brand- and product-accurate color representation. Khronos maintains the glTF Asset Auditor to allow retailers and advertising technology platforms to validate 3D assets against either a default Audit Profile modelled on the 2020 3D Commerce Asset Creation Guidelines or a custom profile defined by the target application.

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