Best AI Writing Tools

Best AI Writing Tools — hands-on reviews, top picks, pricing, pros and cons and a practical how-to guide on Aizhi.

AI warfare

AI warfare refers to the use of artificial intelligence technologies to automate military operation and enhance or bypass human decision-making in armed conflicts. AI is used to rapidly analyze large volumes of military intelligence data, including making recommendations or decisions on who and what to target. Abdul-Rahman al-Rawi, a 20-year-old student, was the first acknowledged civilian killed by AI-assisted airstrike in a U.S. strike in Iraq in 2024. In 2026, the U.S. declared it would become an 'AI-first' warfighting force. Husain et al (2018) coined the term hyperwar to refer to warfare which is algorithmic or controlled by artificial intelligence, with little to no human decision-making. == 2026 Iran war == The 2026 Iran war has been described as the "first AI war", although the Untied States and Israel have previously used AI to identify targets during the Gaza war. The U.S. has used AI tools to attack Iran. These tools have been used for military intelligence, targeting, and damage assessment in the war in Iran. Using the Maven smart system, the U.S. attacked 1,000 targets in the first 24 hours of the war and 5,000 targets over the course of 10 days. While the U.S. had used Maven in 2022 to share targeting information with Ukraine and strike against Iraq, Syria, and against the Houthis in 2024, Iran's attacks are its biggest. Authorities are looking into whether artificial intelligence was involved in the airstrike on an Iranian girls' school that killed 170 civilians, the majority of whom were female students. The United States Central Command emphasized that humans were making final targeting decisions. Per a White House tally released on April 8, the U.S. military hit over 13,000 targets in Iran during the war's first 38 days, including more than 2,000 command-and-control sites, 1,500 air defense targets, and 1,450 industrial infrastructure targets. == Gaza war == As part of the Gaza war, the Israel Defense Forces (IDF) have used artificial intelligence to rapidly and automatically perform much of the process of determining what to bomb. IDF's Unit 8200 developed AI systems, dubbed the Gospel and Lavender, to find targets for the Israeli Air Force to bomb. The Gospel automatically provides targeting recommendations to human analysts, who decide whether to approve strikes. Lavender identified 37,000 Hamas-linked individuals early in the war, and was used alongside the Gospel, which chooses buildings or structures as targets. According to a report by +972 Magazine and Local Call, strikes assisted by Lavender were routinely permitted to kill 5–20 civilians for each suspected Hamas militant, who were often bombed at home with their families. The IDF denies these claims, maintaining that every strike is assessed to minimize collateral damage, and that there is no policy "to kill tens of thousands of people in their homes." Israel deployed AI technologies during the Gaza war for audio analysis, facial recognition, and airstrike targeting. One such system was used to help identify the location of Hamas commander Ibrahim Biari through phone call analysis, leading to strikes that killed him as well as more than 125 civilians. == 2022 Russian Ukraine war == Kyiv launched a project with Palantir called Brave1 Dataroom to build AI systems using the extensive combat data Ukraine has gathered since Russia’s full-scale invasion in 2022. The country has also created tools for in-depth airstrike analysis, introduced AI to process large volumes of intelligence, and incorporated these technologies into the planning of long-range strike operations. == Involved companies == Maven Smart System is developed by Palantir. It integrates Anthropic's Claude as its large language model, and uses Amazon's AWS servers as its cloud infrastructure. Since Anthropic's refusal to support autonomous weapons development and domestic surveillance efforts. In its place, other AI firms, including OpenAI, have been brought in to take over that role. == Involved state actors == In 2024, the United States Department of Defense had 800-plus active AI-related projects and requested $1.8 billion in AI funding, with Project Maven and Project Artemis (AI-resistant drones developed together with Ukraine) being the main ones. The technology has been used in Iran, Iraq, Syria and Yemen to identify targets. China is pursuing intelligentized warfare, integrating AI across all combat domains—land, sea, air, space, and cyber—with military AI spending exceeding $1.6 billion annually. == International regulation == Since 2014, states meeting within the framework of the Convention on Certain Conventional Weapons have discussed lethal autonomous weapon systems. In 2016, the treaty's states parties established an open-ended Group of Governmental Experts on Lethal Autonomous Weapons Systems to continue those discussions. The discussions have addressed international humanitarian law, accountability, possible prohibitions and regulations, and the extent of human control required over AI-enabled weapons.
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Web series

A web series, also known as a short-form series or web show, is a collection of short scripted or unscripted online videos released on the Internet (i.e., World Wide Web), generally in episodic form. A single installment of a web series can be called a webisode or an episode. The scale of a web series is small, and a typical episode can be anywhere from 3 to 15 minutes long (though some may run up to 20 minutes). Web series first emerged in the mid-1990s and became more prominent in the early 2000s. Web series are distributed online on video-sharing websites and apps, such as YouTube, Vimeo, and TikTok, and can be watched on devices such as smartphones, tablets, desktops, laptops, and Smart TVs (or television sets connected to the Internet with a media streaming device). They can also be released on social media platforms. Because of the nature of the Internet, a web series may be interactive and immersive. Web series are classified as new media. Web series are different from streaming television series, as the latter are designed to be watched on streaming platforms such as Netflix, Amazon Prime Video, or Hotstar, with the streaming services offering original productions made for and by them, as well as acquiring the rights to distribute licensed content. The length of a streaming television series episode is 30 to 60 minutes (runtimes can also be longer). Although the design of a web series can be similar to that of a television series, its development and production do not entail the same financial investment required for a television series. The popularity of some web series, however, has led to them being optioned for television. Web series differ from short-form content in that the latter are vertical videos specifically designed for smartphone viewing and intended for fast-paced consumption, with runtimes typically ranging from less than one minute to three minutes. There are film festivals for web series, like Webfest Berlin, NYC Web Fest, LA Web Fest, and Vancouver Web Fest. Awards organizations have also been established to celebrate excellence in web series, such as the Streamys, Webbys, IAWTV Awards, and Indie Series Awards. Most major award ceremonies have also created web series and digital media award categories, including the Emmy Awards and the Canadian Screen Awards. == History == === 1990s === In April 1995, "Global Village Idiots", an episode of the reality-based program Rox on public access cable television in Bloomington, Indiana, was uploaded to the Internet, making Rox the first show distributed via the web. The same year, Scott Zakarin created The Spot, an episodic online story that integrated photos, videos, and blogs into the storyline. Likened to Melrose Place-on-the-Web, The Spot featured a rotating cast of characters playing trendy twenty-somethings who rented rooms in a fabled Santa Monica, California beach house called "The Spot". The Spot earned Infoseek's "Cool Site of the Year," an award which later became the Webby. In January 1999, Showtime licensed the animated sci-fi web series WhirlGirl, making it the first independently produced web series licensed by a national television network. In February 1999, the show premiered simultaneously on Showtime and online. The character occasionally appeared on Showtime, for example, hosting a "Lethal Ladies" programming block, but spent most of her time online, appearing in 100 webisodes. === 2000s === As broadband bandwidth increased in speed and availability, delivering high-quality video over the Internet became a reality. In the early 2000s, the Japanese anime industry began broadcasting original net animation (ONA), a type of original video animation (OVA) series, on the Internet. Early examples of the ONA series include Infinite Ryvius: Illusion (2000), Ajimu (2001), and Mahou Yuugi (2001). In 2000, The Brothers Chaps launched the Adobe Flash-created web series Homestar Runner. After being put on hiatus in 2010, it returned in 2014. In 2002, Matt Jolly (better known as "Krinkels") released the first episode of Madness Combat to Newgrounds. The show is still ongoing, with the latest episode "Madness Combat 12: Contravention" released on Twitch in September 2024. In 2003, Microsoft launched MSN Video, offering NBC-related content. Its web series, Weird TV 2000, a spin-off of the syndicated television series Weird TV, featured dozens of shorts, comedy sketches, and mini-documentaries produced exclusively for MSN Video. The video-sharing site YouTube was launched in early 2005, allowing users to share television programs. YouTube co-founder Jawed Karim said the inspiration for YouTube first came from Janet Jackson's role in the 2004 Super Bowl incident, when her breast was exposed during her performance, and later from the 2004 Indian Ocean tsunami. Karim could not easily find video clips of either event online, which led to the idea of a video-sharing site. From 2003 to 2006, many independent web series gained significant popularity, most notably the science fiction series Red vs. Blue by Rooster Teeth. The series was distributed independently via online portals YouTube and Revver, as well as the Rooster Teeth website, acquiring over 100 million social media views during its run. (Rooster Teeth would eventually create the computer-animated web series RWBY in 2013.) In 2004, the adult-animated series Salad Fingers was created, which amassed a cult following. The comedy show The Burg, hailed as the internet's first sitcom and starring Kelli Giddish and Lindsey Broad, rapidly gained an audience and press attention before its creators signed a creation deal with Michael Eisner. The drama Sam Has 7 Friends, which ran in the summer and fall of 2006, was nominated for a Daytime Emmy Award and was temporarily removed from the Internet when it was also acquired by Eisner. In 2004–2005, Spanish producer Pedro Alonso Pablos recorded a series of video interviews featuring actors and directors such as Guillermo del Toro, Santiago Segura, Álex de la Iglesia, and Keanu Reeves, which were distributed through his own website. lonelygirl15, California Heaven, "The Burg", and SamHas7Friends also gained popularity during this time, acquiring audiences in the millions. (Science fiction thriller lonelygirl15 was so successful that it secured a sponsorship deal with Neutrogena in 2007.) In 2004, Stewart St. John, executive producer and head writer of 1990s webisodies The Spot, revived the brand for online audiences as The Spot (2.0), with a new cast, and as a separate soap opera on Sprint PCS Vision-enabled cell phones, creating the first American mobile phone series. St. John and partner Todd Fisher produced over 2,500 daily videos of the mobile soap, driving story lines across platforms to its web counterpart. In 2007, the creators of lonelygirl15 followed up on the show's success with KateModern, a comedy-drama series that debuted on social network Bebo, and took place in the same fictional universe as their previous show. Big Fantastic created and produced the soap opera Prom Queen, financed and distributed by Michael Eisner's production firm Vuguru, and debuted the series on MySpace. Vuguru partnered with Mark Cuban's channel HDNet to release All-for-nots, a mockumentary series by The Burg creators Kathleen Grace and Thom Woodley, which debuted at the SXSW Festival in 2008. These web series highlighted interactivity with the audience in addition to the narrative on relatively low budgets. In contrast, the eight-episode show Sanctuary, starring actor/producer Amanda Tapping, cost $4.3 million to produce. Both Sanctuary and Prom Queen were nominated for a Daytime Emmy Award. Award-winning producer/director Marshall Herskovitz created the drama Quarterlife, which debuted on MySpace and was later distributed on NBC. In 2008, major television studios began releasing web series, such as the ABC comedy show Squeegies, the NBC sci-fi show Gemini Division, and the Bravo reality series The Malan Show. Warner Bros. relaunched The WB as an online network beginning with original mystery web series, Sorority Forever, created and produced by Big Fantastic and executive produced by McG. Meanwhile, MTV announced a new original web series created by Craig Brewer, $5 Cover, that brought together the indie music world and new media expansion. Joss Whedon created, produced, and self-financed musical comedy-drama Dr. Horrible's Sing-Along Blog starring Neil Patrick Harris and Felicia Day. Big Fantastic wrote and produced Foreign Body, a mystery web series that served as a prequel to Robin Cook's novel of the same name. Beckett and Goodfried founded a new Internet studio, EQAL, and produced a spin-off of lonelygirl15 titled LG15: The Resistance. The mainstream press began to provide coverage. In the United Kingdom, KateModern ended its run on Bebo. Bebo also hosted a six-month-long reality travel show, The Gap Year, produced by Endemol UK, and produced an interactive sci-fi drama Kirill for
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SPACEMAP

SPACEMAP (Korean: 스페이스맵) is a South Korean satellite orbit optimization and satellite communications company headquartered in Seoul, South Korea. The company was founded in 2021 by CEO, Douglas Deok-Soo Kim, as an offshoot of Hanyang University. It was funded by the Leader Research grant from the National Research Foundation of Korea with the goal of capitalizing on the growing space industry. == History == Kim initially began research into Voronoi diagrams at the University of Michigan. He met with Dr. Misoon Ma, former director of the Asia Division of the U.S. Air Force Office of Scientific Research (AFOSR) and was recruited to work with the U.S. Air force, using Voronoi diagrams for a satellite collision prevention program. After his work with the U.S. Air Force, Kim founded SPACEMAP Inc in September 2021. In 2023, the company was selected by Korea's Tech Incubator Program for Startups (TIPS) to be funded up to 17 billion KRW (approx. US$13 million) in 3 years. == Technology == The services provided by SPACEMAP are based on using dynamic Voronoi diagrams to predict satellite orbits with the aim of enhancing space mission safety and efficiency. For complex problems involving many moving points, Voronoi diagrams maintain a near-constant computation time regardless of the number of points involved. By utilizing Voronoi diagrams and artificial intelligence, the software can easily determine the number of neighboring satellites surrounding a specific satellite and calculate the distances between them, thereby predicting the probability of a collision. SPACEMAP claims their method to be superior in computational time and memory efficiency, compared to the previously established three-filter method. == Products == SPACEMAP offers satellite products and services including the following: AstroOne, a conjunction assessment, and optimal collision avoidance service for all space vehicles in both orbital and non-orbital motions. AstroOrca, providing data transmission for satellites in multiple orbits, launch optimization, shuttle logistics for space gas stations, and Active Debris Removal (ADR) itinerary. AstroLibrary, a library of RESTful APIs to access the C++ implementation of SPACEMAP's Voronoi diagram algorithms wrapped in a Python interface. It also provides real-time tracking of the North Korean reconnaissance satellite, Malligyong-1.
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Digital goods

Digital goods or e-goods are intangible goods that exist in digital form. Examples are Wikipedia articles; digital media, such as e-books, downloadable music, internet radio, internet television and streaming media; fonts, logos, photos and graphics; digital subscriptions; online ads (as purchased by the advertiser); internet coupons; electronic tickets; electronically treated documentation in many different fields; downloadable software (Digital Distribution) and mobile apps; cloud-based applications and online games; virtual goods used within the virtual economies of online games and communities; community access; workbooks; worksheets; planners; e-learning (online courses); webinars, video tutorials, blog posts; cards; patterns; website themes and templates. == Legal concerns about digital goods == Special legal concerns regarding digital goods include copyright infringement and taxation. Also the question of the ownership (versus licensed use or service only) of purely digital goods is not finally resolved. For instance, the software installers of the digital software distributor gog.com are technically independent to the account but are still subject to the EULA, where a "licensed, not sold" formulation is used. Therefore, it is not clear if the software can be legally used after a hypothetical loss of the account; a question which was also raised before in practice for the similar service Steam. In July 2012, the European Court of Justice ruled in the case UsedSoft GMbH v. Oracle International Corp. that the sale of a software product, either through a physical support or download, constituted a transfer of ownership in EU law, thus the first sale doctrine applies; the ruling thereby breaks the "licensed, not sold" legal theory, but leaves open numerous questions. Therefore, it is also permissible to resell software licenses even if the digital good has been downloaded directly from the Internet, as the first-sale doctrine applied whenever software was originally sold to a customer for an unlimited amount of time, thus prohibiting any software maker from preventing the resale of their software by any of their legitimate owners. The court requires that the previous owner must no longer be able to use the licensed software after the resale, but finds that the practical difficulties in enforcing this clause should not be an obstacle to authorizing resale, as they are also present for software which can be installed from physical supports, where the first-sale doctrine is in force. In several cases, content providers have faced criticism for revoking access to digital goods due to expired licenses or the discontinuation of a product, such as ebooks (which resulted in a lawsuit against Amazon.com, Inc.), digital video (with Sony Interactive Entertainment revoking access to purchased StudioCanal content from its now-defunct PlayStation video store; a similar move involving Warner Bros. Discovery content was averted by an updated license agreement), and video games (such as Ubisoft discontinuing and revoking access to its game The Crew without providing refunds or the ability to redownload the game) In September 2024, the U.S. state of California implemented a consumer protection law that prohibits the use of terms such as "buy" or "purchase" during transactions involving digital goods if there is no way to obtain the purchases in a manner that cannot be revoked by the seller (such as allowing it to be downloaded for permanent, offline access), and requires a disclaimer to be displayed to the customer at the time of purchase.
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The Drivers Cooperative

The Drivers Cooperative or Co-Op Ride is an American ridesharing company and mobile app that is a workers cooperative, owned collectively by the drivers. The cooperative launched in May 2021 in New York City, with the first 2,500 drivers issued their ownership certificates in a media event. The cooperative was co-founded by Grenadan immigrant and for hire vehicle driver Ken Lewis, labor organizer Erik Forman, and former Uber executive Alissa Orlando. Mohammad Hossen is the first member of the drivers' advisory board, which they plan to expand democratically as more drivers are onboarded. Other staff include software and industry veterans and in addition to co-founder Lewis, there are other drivers in management roles such as ex-driver and organizer David Alexis. The Co-Op Ride app is on the iOS and Android platforms and is built on Google Maps, Stripe, and Waze. By July, the app had been downloaded by 30,000 users and the number of drivers increased to 3,400, and by August there were 40,000 users. The cooperative is owned by the drivers themselves, and takes 15% from each ride for business overhead costs, as opposed to the 25% to 40% ride hail apps like Uber or Lyft take per ride. While being ultimately owned by the driver members, not by investors, the cooperative began with seed money from the Minnesota-based Community Development Financial Institution Shared Capital Cooperative, the local Lower East Side People's Federal Credit Union, and welcomed individual donations via crowdfunding in the form of revenue sharing debt on Wefunder. Each driver is a member of the cooperative and owns one share of the company and one vote in business and leadership decisions. In addition to a larger percentage of the fees per ride driven, each driver as a part-owner will also receive a share of the company's profits after loans and other expenses are paid, in the form of weighted dividends. The drivers use their own cars. The cooperative vets its owner-members further than what is already performed by the New York City Taxi and Limousine Commission (TLC), and gives a fixed price when a car is ordered and does not engage in surge pricing. The TLC imposed a minimum payrate for mobile app ridesharing companies operating in New York city in 2018. In 2021 that is $1.26 per mile which Uber and Lyft do not pay above; the cooperative pays a minimum mileage of $1.64. The cooperative intends to be able to set aside 10% of profits to community foundations and other non-profits and community organizations. The cooperative has engaged in advocacy around a policy agenda voted on by its members. Legislation to achieve this policy goal was introduced by State Senator Julia Salazar and Assemblymember Jessica González-Rojas, with the support of a coalition led by The Drivers Cooperative, United Auto Workers Region 9 and 9A, Sunrise Movement, New York Lawyers for the Public Interest, and New York Communities for Change.
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Microformat

Microformats (μF) are predefined HTML markup (like HTML classes) created to serve as descriptive and consistent metadata about elements, designating them as representing a certain type of data (such as contact information, geographic coordinates, events, products, recipes, etc.). They allow software to process the information reliably by having set classes refer to a specific type of data rather than being arbitrary. Microformats emerged around 2005 and were predominantly designed for use by search engines, web syndication and aggregators such as RSS. Google confirmed in 2020 that it still parses microformats for use in content indexing. Microformats are referenced in several W3C social web specifications, including IndieAuth and Webmention. Although the content of web pages has been capable of some "automated processing" since the inception of the web, such processing is difficult because the markup elements used to display information on the web do not describe what the information means. Microformats can bridge this gap by attaching semantics, and thereby obviating other, more complicated, methods of automated processing, such as natural language processing or screen scraping. The use, adoption and processing of microformats enables data items to be indexed, searched for, saved or cross-referenced, so that information can be reused or combined. As of 2013, microformats allow the encoding and extraction of event details, contact information, social relationships and similar information. Microformats2, abbreviated as mf2, is the updated version of microformats. Mf2 provides an easier way of interpreting HTML structured syntax and vocabularies than the earlier ways that made use of RDFa and microdata. == Background == Microformats emerged around 2005 as part of a grassroots movement to make recognizable data items (such as events, contact details or geographical locations) capable of automated processing by software, as well as directly readable by end-users. Link-based microformats emerged first. These include vote links that express opinions of the linked page, which search engines can tally into instant polls. CommerceNet, a nonprofit organization that promotes e-commerce on the Internet, has helped sponsor and promote the technology and support the microformats community in various ways. CommerceNet also helped co-found the Microformats.org community site. Neither CommerceNet nor Microformats.org operates as a standards body. The microformats community functions through an open wiki, a mailing list, and an Internet relay chat (IRC) channel. Most of the existing microformats originated at the Microformats.org wiki and the associated mailing list by a process of gathering examples of web-publishing behaviour, then codifying it. Some other microformats (such as rel=nofollow and unAPI) have been proposed, or developed, elsewhere. == Technical overview == XHTML and HTML standards allow for the embedding and encoding of semantics within the attributes of markup elements. Microformats take advantage of these standards by indicating the presence of metadata using the following attributes: class Classname rel relationship, description of the target address in an anchor-element (...) rev reverse relationship, description of the referenced document (in one case, otherwise deprecated in microformats) For example, in the text "The birds roosted at 52.48, -1.89" is a pair of numbers which may be understood, from their context, to be a set of geographic coordinates. With wrapping in spans (or other HTML elements) with specific class names (in this case geo, latitude and longitude, all part of the geo microformat specification): Software agents can recognize exactly what each value represents and can then perform a variety of tasks such as indexing, locating it on a map and exporting it to a GPS device. === Examples === In this example, the contact information is presented as follows: With hCard microformat markup, that becomes: Here, the formatted name (fn), organisation (org), telephone number (tel) and web address (url) have been identified using specific class names and the whole thing is wrapped in class="vcard", which indicates that the other classes form an hCard (short for "HTML vCard") and are not merely coincidentally named. Other, optional, hCard classes also exist. Software, such as browser plug-ins, can now extract the information, and transfer it to other applications, such as an address book. == Specific microformats == Several microformats have been developed to enable semantic markup of particular types of information. However, only hCard and hCalendar have been ratified, the others remaining as drafts: hAtom (superseded by h-entry and h-feed) – for marking up Atom feeds from within standard HTML hCalendar – for events hCard – for contact information; includes: adr – for postal addresses geo – for geographical coordinates (latitude, longitude) hMedia – for audio/video content hAudio – for audio content hNews – for news content hProduct – for products hRecipe – for recipes and foodstuffs. hReview – for reviews rel-directory – for distributed directory creation and inclusion rel-enclosure – for multimedia attachments to web pages rel-license – specification of copyright license rel-nofollow, an attempt to discourage third-party content spam (e.g. spam in blogs) rel-tag – for decentralized tagging (Folksonomy) XHTML Friends Network (XFN) – for social relationships XOXO – for lists and outlines == Uses == Using microformats within HTML code provides additional formatting and semantic data that applications can use. For example, applications such as web crawlers can collect data about online resources, or desktop applications such as e-mail clients or scheduling software can compile details. The use of microformats can also facilitate "mash ups" such as exporting all of the geographical locations on a web page into (for example) Google Maps to visualize them spatially. Several browser extensions, such as Operator for Firefox and Oomph for Internet Explorer, provide the ability to detect microformats within an HTML document. When hCard or hCalendar are involved, such browser extensions allow microformats to be exported into formats compatible with contact management and calendar utilities, such as Microsoft Outlook. When dealing with geographical coordinates, they allow the location to be sent to applications such as Google Maps. Yahoo! Query Language can be used to extract microformats from web pages. On 12 May 2009 Google announced that they would be parsing the hCard, hReview and hProduct microformats, and using them to populate search result pages. They subsequently extended this in 2010 to use hCalendar for events and hRecipe for cookery recipes. Similarly, microformats are also processed by Bing and Yahoo!. As of late 2010, these are the world's top three search engines. Microsoft said in 2006 that they needed to incorporate microformats into upcoming projects, as did other software companies. Alex Faaborg summarizes the arguments for putting the responsibility for microformat user interfaces in the web browser rather than making more complicated HTML: Only the web browser knows what applications are accessible to the user and what the user's preferences are It lowers the barrier to entry for web site developers if they only need to do the markup and not handle "appearance" or "action" issues Retains backwards compatibility with web browsers that do not support microformats The web browser presents a single point of entry from the web to the user's computer, which simplifies security issues == Evaluation == Various commentators have offered review and discussion on the design principles and practical aspects of microformats. Microformats have been compared to other approaches that seek to serve the same or similar purpose. As of 2007, there had been some criticism of one, or all, microformats. The spread and use of microformats was being advocated as of 2007. Opera Software CTO and CSS creator Håkon Wium Lie said in 2005 "We will also see a bunch of microformats being developed, and that’s how the semantic web will be built, I believe." However, in August 2008 Toby Inkster, author of the "Swignition" (formerly "Cognition") microformat parsing service, pointed out that no new microformat specifications had been published since 2005. === Design principles === Computer scientist and entrepreneur, Rohit Khare stated that reduce, reuse, and recycle is "shorthand for several design principles" that motivated the development and practices behind microformats. These aspects can be summarized as follows: Reduce: favor the simplest solutions and focus attention on specific problems; Reuse: work from experience and favor examples of current practice; Recycle: encourage modularity and the ability to embed, valid XHTML can be reused in blog posts, RSS feeds, and anywhere else you can access the web. === Accessibi
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RR Media

RR Media was a NASDAQ listed provider of global digital media services to the broadcast industry and content owners. Its services can be divided into four main groups: global content distribution network (satellite, fiber and the internet); content management & playout; sports, news & live events; and online video services. The company was rebranded to RR Media from RRsat in September 2014. In February 2016, it was announced that, subject to regulatory approvals, RR Media was to be acquired by SES, based in Betzdorf, Luxembourg, and merged with SES subsidiary company, SES Platform Services a media services provider for television broadcasters, production companies and platform operators, based in Unterföhring near Munich, Germany. In July 2016, the merged company was named MX1. == Digital media services == Global content distribution services RR Media's global distribution network uses a combination of satellite, fiber and the internet. The network includes satellite downlink and uplink; fiber connectivity to digital media hubs; connectivity to TV service providers; and internet-based content delivery. RR Media's network delivers live television channels, streaming media and Video on demand (VOD) content in all formats including Standard-definition television (SD), High-definition television (HD), 4K resolution (4K) & 3D television (3D). End-to-end content management & playout services RR Media manages, prepares and plays out content from its media centers. Services include: content preparation (digitization, localization, conversion, ingest, multiple formatting, editing, restoration); content management (digital asset management, media ingest and library, streamlined workflows, metadata curation, Video on demand (VOD) delivery) and playout, channel creation, playlist management, advertising insertion/management, graphics, titles & overlay, live events operations). RR Media also creates branded or white label product television channels using live and archived materials. Sports, news & live events RR Media delivers live sports and event content for sports rights holders, broadcasters and news channels. Services include: live production (Outside broadcasting vans, Satellite news gathering (SNG), studios), global live distribution, sports content preparation and content management, playout and origination.RR Media provides downlink, uplink, simultaneous translation, turnaround and live production services for sports events like football, basketball, tennis and golf, news and entertainment channels. Online video services RR Media converts existing and archive content into programs, channels and other digital assets, and converges broadcast and internet delivery. Services include converged media (preparing content for broadcast or online use) Content Management Systems (CMS), VOD services, branded platforms, multi-screen delivery, web video portals and viewer measurement tools (using digital analytics). == Media centers == RR Media's media centers are based in Hawley, PA (USA), Emeq Ha’Ela (Israel) Bucharest (Romania), with another facility opened in London, (UK) in June 2015. An additional facility in Miami, FL United States was announced in April 2016. The centers provide RR Media's services, including content preparation, management, online video, live content and distribution, and 24/7 service and support. == Awards == In November 2014, RR Media won the award for Achievement in Legacy Content at the 2014 TVB Europe awards in London, in recognition for its work with British Pathe and the restoration for YouTube. In February 2014, the World Teleport Association named Avi Cohen, CEO of RR Media (formerly RRsat), as its 2014 Teleport Executive of the Year. In 2009, the World Teleport Association awarded RR Media (then RRsat) the Independent Teleport Operator of the Year award for excellence. == History == RR Media (as RRsat) was established in 1981 as a communications provider. The company was founded by David Rivel, an electronics, computers and communications engineer. Rivel is CEO of the company for 31 years and from 2012 a Member of RR Media's board of directors. Under management of Rivel RRsat Communications Network Ltd. went public on 2006-11-01 - NASDAQ:RRST In 2014, the Company rebranded from RRsat Global Communications Network to RR Media. The rebrand was launched at the International Broadcasting Convention (IBC) Show in Amsterdam. In 2015, RR Media announced its NASDAQ stock ticker symbol change to RRM. == Acquisitions == In April 2015, RR Media acquired Eastern Space Systems (ESS) in Romania, a privately held provider of content management and content distribution services and related consulting services. In June 2015, RR Media acquired Satlink Communications as part of strategy to increase scale and expand its global content distribution network and content management footprint, strengthening its customer mix and leverage media industry expertise.
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HtmlUnit

HtmlUnit is a headless web browser written in Java. It allows high-level manipulation of websites from other Java code, including filling and submitting forms and clicking hyperlinks. It also provides access to the structure and the details within received web pages. HtmlUnit emulates parts of browser behaviour including the lower-level aspects of TCP/IP and HTTP. A sequence such as getPage(url), getLinkWith("Click here"), click() allows a user to navigate through hypertext and obtain web pages that include HTML, JavaScript, Ajax and cookies. This headless browser can deal with HTTPS security, basic HTTP authentication, automatic page redirection and other HTTP headers. It allows Java test code to examine returned pages either as text, an XML DOM, or as collections of forms, tables, and links. The goal is to simulate real browsers; namely Chrome, Firefox and Edge. The most common use of HtmlUnit is test automation of web pages, but sometimes it can be used for web scraping, or downloading website content. == Benefits == Provides high-level API, taking away lower-level details away from the user. Compared to other WebDriver implementations, HtmlUnitDriver is the fastest to implement. It can be configured to simulate a specific browser. == Drawbacks == Element layout and rendering can not be tested. The JavaScript support is not complete, which is one of the areas of ongoing enhancements. == Used technologies == W3C DOM HTTP connection, using Apache HttpComponents JavaScript, using forked Rhino HTML Parsing, NekoHTML CSS: using CSS Parser XPath support, using Xalan == Libraries using HtmlUnit == Selenium WebDriver Spring MVC Test Framework Google Web Toolkit tests WebTest Wetator
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Rake (software)

Rake is a software task management and a build automation tool created by Jim Weirich. It allows the user to specify tasks and to describe dependencies as well as to group tasks into namespaces. It is similar to SCons and Make. Rake was written in Ruby and has been part of the standard library of Ruby since version 1.9. == Examples == The tasks that should be executed need to be defined in a configuration file called Rakefile. A Rakefile has no special syntax and contains executable Ruby code. === Tasks === The basic unit in Rake is the task. A task has a name and an action block, that defines its functionality. The following code defines a task called greet that will output the text "Hello, Rake!" to the console. When defining a task, you can optionally add dependencies, that is one task can depend on the successful completion of another task. Calling the "seed" task from the following example will first execute the "migrate" task and only then proceed with the execution of the "seed" task.Tasks can also be made more versatile by accepting arguments. For example, the "generate_report" task will take a date as argument. If no argument is supplied the current date is used.A special type of task is the file task, which can be used to specify file creation tasks. The following task, for example, is given two object files, i.e. "a.o" and "b.o", to create an executable program.Another useful tool is the directory convenience method, that can be used to create directories upon demand. === Rules === When a file is named as a prerequisite but it does not have a file task defined for it, Rake will attempt to synthesize a task by looking at a list of rules supplied in the Rakefile. For example, suppose we were trying to invoke task "mycode.o" with no tasks defined for it. If the Rakefile has a rule that looks like this: This rule will synthesize any task that ends in ".o". It has as a prerequisite that a source file with an extension of ".c" must exist. If Rake is able to find a file named "mycode.c", it will automatically create a task that builds "mycode.o" from "mycode.c". If the file "mycode.c" does not exist, Rake will attempt to recursively synthesize a rule for it. When a task is synthesized from a rule, the source attribute of the task is set to the matching source file. This allows users to write rules with actions that reference the source file. === Advanced rules === Any regular expression may be used as the rule pattern. Additionally, a proc may be used to calculate the name of the source file. This allows for complex patterns and sources. The following rule is equivalent to the example above: NOTE: Because of a quirk in Ruby syntax, parentheses are required around a rule when the first argument is a regular expression. The following rule might be used for Java files: === Namespaces === To better organize big Rakefiles, tasks can be grouped into namespaces. Below is an example of a simple Rake recipe:
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Web syndication

Web syndication is making content available from one website to other sites. Most commonly, websites are made available to provide either summaries or full renditions of a website's recently added content. The term may also describe other kinds of content licensing for reuse. Contemporary web syndicates include: MSN, Excite, and Yahoo! News. == Motivation == For the subscribing sites, syndication is an effective way of adding greater depth and immediacy of information to their pages, making them more attractive to users. For the provider site, syndication increases exposure. This generates new traffic for the provider site—making syndication an easy and relatively cheap, or even free, form of advertisement. Content syndication has become an effective strategy for link building, as search engine optimization has become an increasingly important topic among website owners and online marketers. Links embedded within the syndicated content are typically optimized around anchor terms that will point an optimized link back to the website that the content author is trying to promote. These links tell the algorithms of the search engines that the website being linked to is an authority for the keyword that is being used as the anchor text. However the rollout of Google Panda's algorithm may not reflect this authority in its SERP rankings based on quality scores generated by the sites linking to the authority. The prevalence of web syndication is also of note to online marketers, since web surfers are becoming increasingly wary of providing personal information for marketing materials (such as signing up for a newsletter) and expect the ability to subscribe to a feed instead. Although the format could be anything transported over HTTP, such as HTML or JavaScript, it is more commonly XML. Web syndication formats include RSS, Atom, and JSON Feed. == History == Syndication first arose in earlier media such as print, radio, and television, allowing content creators to reach a wider audience. In the case of radio, the United States Federal government proposed a syndicate in 1924 so that the country's executives could quickly and efficiently reach the entire population. In the case of television, it is often said that "Syndication is where the real money is." Additionally, syndication accounts for the bulk of TV programming. One predecessor of web syndication is the Meta Content Framework (MCF), developed in 1996 by Ramanathan V. Guha and others in Apple Computer's Advanced Technology Group. Today, millions of online publishers, including newspapers, commercial websites, and blogs, distribute their news headlines, product offers, and blog postings in the news feed. == As a commercial model == Conventional syndication businesses such as Reuters and Associated Press thrive on the internet by offering their content to media partners on a subscription basis, using business models established in earlier media forms. Commercial web syndication can be categorized in three ways: by business models by types of content by methods for selecting distribution partners Commercial web syndication involves partnerships between content producers and distribution outlets. There are different structures of partnership agreements. One such structure is licensing content, in which distribution partners pay a fee to the content creators for the right to publish the content. Another structure is ad-supported content, in which publishers share revenues derived from advertising on syndicated content with that content's producer. A third structure is free, or barter syndication, in which no currency changes hands between publishers and content producers. This requires the content producers to generate revenue from another source, such as embedded advertising or subscriptions. Alternatively, they could distribute content without remuneration. Typically, those who create and distribute content free are promotional entities, vanity publishers, or government entities. Types of content syndicated include RSS or Atom Feeds and full content. With RSS feeds, headlines, summaries, and sometimes a modified version of the original full content is displayed on users' feed readers. With full content, the entire content—which might be text, audio, video, applications/widgets, or user-generated content—appears unaltered on the publisher's site. There are two methods for selecting distribution partners. The content creator can hand-pick syndication partners based on specific criteria, such as the size or quality of their audiences. Alternatively, the content creator can allow publisher sites or users to opt into carrying the content through an automated system. Some of these automated "content marketplace" systems involve careful screening of potential publishers by the content creator to ensure that the material does not end up in an inappropriate environment. Just as syndication is a source of profit for TV producers and radio producers, it also functions to maximize profit for Internet content producers. As the Internet has increased in size it has become increasingly difficult for content producers to aggregate a sufficiently large audience to support the creation of high-quality content. Syndication enables content creators to amortize the cost of producing content by licensing it across multiple publishers or by maximizing the distribution of advertising-supported content. A potential drawback for content creators, however, is that they can lose control over the presentation of their content when they syndicate it to other parties. Distribution partners benefit by receiving content either at a discounted price, or free. One potential drawback for publishers, however, is that because the content is duplicated at other publisher sites, they cannot have an "exclusive" on the content. For users, the fact that syndication enables the production and maintenance of content allows them to find and consume content on the Internet. One potential drawback for them is that they may run into duplicate content, which could be an annoyance. == E-commerce == Web syndication has been used to distribute product content such as feature descriptions, images, and specifications. As manufacturers are regarded as authorities and most sales are not achieved on manufacturer websites, manufacturers allow retailers or dealers to publish the information on their sites. Through syndication, manufacturers may pass relevant information to channel partners. Such web syndication has been shown to increase sales. Web syndication has also been found effective as a search engine optimization technique.
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Blue check

A blue check is used on social media platforms, notably X (formerly known as Twitter), to indicate the authenticity of an account. Since November 2022, Twitter users whose accounts are at least 90 days old and have a verified phone number receive verification upon subscribing to X Premium or Verified Organizations; this status persists as long as the subscription remains active. When introduced in June 2009, the system provided the site's readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. Until November 2022, a blue checkmark displayed against an account name indicated that Twitter had taken steps to ensure that the account was actually owned by the person or organization whom it claimed to represent. The checkmark does not imply endorsement from Twitter, and does not mean that tweets from a verified account are necessarily accurate or truthful in any way. People with verified accounts on Twitter are often colloquially referred to as "blue checks" on social media and by reporters. In November 2022, the verification program was modified heavily by new owner Elon Musk, extending verification to any account with a verified phone number and an active subscription to an eligible X Premium (formerly Twitter Blue) plan. These changes faced criticism from users and the media, who believed that the changes would ease impersonation, and allow accounts spreading misleading information to feign credibility. In a related change, Twitter introduced additional gold and gray checkmarks, used by Verified Organizations and government-affiliated accounts, respectively. Twitter claims that the changes to verification are required to "reduce fraudulent accounts and bots". Twitter users who had been verified through the previous system were known as "legacy verified" accounts; legacy verification was deprecated in April 2023, and stripped from accounts who do not meet the new payment requirements. Musk later implied that he had been personally paying for the X Premium subscriptions of several notable celebrities. == Until November 2022 == In June 2009, after being criticized by Kanye West and sued by Tony La Russa over unauthorized accounts run by impersonators, the company launched their "Verified Accounts" program. Twitter stated that an account with a "blue tick" verification badge indicates "we've been in contact with the person or entity the account is representing and verified that it is approved". After the beta period, the company stated in their FAQ that it "proactively verifies accounts on an ongoing basis to make it easier for users to find who they're looking for" and that they "do not accept requests for verification from the general public". Originally, Twitter took on the responsibility of reaching out to celebrities and other notable people to confirm their identities in order to establish a verified account. In July 2016, Twitter announced a public application process to grant verified status to an account "if it is determined to be of public interest" and that verification "does not imply an endorsement". In 2016, the company began accepting requests for verification, but it was discontinued the same year. Twitter explained that the volume of requests for verified accounts had exceeded its ability to cope; rather, Twitter determines on its own whom to approach about verified accounts, limiting verification to accounts which are "authentic, notable, and active". In November 2020, Twitter announced a relaunch of its verification system in 2021. According to the new policy, Twitter verifies six different types of accounts; for three of them (companies, brands, and influential individuals like activists), the existence of a Wikipedia page will be one criterion for showing that the account has "Off Twitter Notability". === Controversy === On June 21, 2014, actor William Shatner raised an issue with several Engadget editorial staff and their verification status on Twitter. Besides the site's social media editor, John Colucci, Shatner also targeted several junior members of the staff for being "nobodies", unlike some of his actor colleagues who did not bear such distinction. Shatner claimed Colucci and the team were bullying him when giving a text interview to Mashable. Over a month later, Shatner continued to discuss the issue on his Tumblr page, to which Engadget replied by defending its team and discussing the controversy surrounding the social media verification. Twitter's practice and process for verifying accounts came under scrutiny again in 2017 after the company verified the account of white supremacist and far-right political activist, Jason Kessler. Many who criticized Twitter's decision to verify Kessler's account saw this as a political act on the company's behalf. In response, Twitter put its verification process on hold. The company tweeted, "Verification was meant to authenticate identity & voice but it is interpreted as an endorsement or an indicator of importance. We recognize that we have created this confusion and need to resolve it. We have paused all general verifications while we work and will report back soon." As of November 2017, Twitter continued to deny verification of Julian Assange's account following his requests. In November 2019, Dalit activists of India alleged that higher-caste people get Twitter verification easily and trended hashtags #CancelAllBlueTicksInIndia and #CasteistTwitter. Critics have said that the company's verification process is not transparent and causes digital marginalisation of already marginalised communities. Twitter India rejected the allegations, calling them "impartial" and working on a "case-by-case" policy. == Since November 2022 == On April 20, 2023, Twitter (known as X since July 2023) began removing verification status for users of public interest, causing a controversy among Twitter users. The website's system was altered, allowing any individual to receive verification for a monthly fee, an act which saw significant criticism. Following the acquisition of Twitter by Elon Musk on October 28, 2022, Musk told Twitter employees to introduce paid verification by November 7 through Twitter Blue. The Verge reported that the updated Blue subscription would cost $19.99 per month, and users would lose their verification status if they did not join within 90 days. Following backlash, Musk tweeted, in response to author Stephen King, a lowered $8 price on November 1, 2022. Twitter confirmed the new price of $7.99 per month on November 5, 2022. The new verification system began rollout on November 9, 2022, a day after the 2022 United States elections. The decision to delay its rollout was to address concerns about users potentially spreading misinformation about voting results by posing as news outlets and lawmakers. At the same time, Twitter introduced a secondary gray "Official" label on some high-profile accounts, but removed them hours after launch. Less than 48 hours later, Twitter reinstated the gray "Official" label, after multiple users were suspended for deliberately impersonating reporters and high-profile athletes like LeBron James. A viral tweet from an account purporting to be the pharmaceutical company Eli Lilly and Company caused the company's stock to fall after announcing "insulin is free now". As a result, Twitter disabled new Blue subscriptions on November 11, 2022. === Announcement === In October 2022, Casey Newton of Platformer reported that executives at Twitter began discussing the possibility of users being forced to pay for Twitter Blue in order to keep their verification status. Musk publicly announced that verification was "being revamped right now" after Newton's article; according to The Verge, Twitter planned to increase the price of Twitter Blue from US$4.99 per month to US$19.99 per month. Users would have had 90 days to subscribe or face losing their verification status, and employees were told to implement paid verification by November 9 or risk getting fired. Upon the news that Twitter Blue would cost US$19.99 per month, author Stephen King expressed displeasure towards Twitter and stated that he would leave. Musk, replying to King's tweet, proposed that the service should cost US$7.99 instead. In a separate tweet, Musk wrote that Twitter Blue subscribers would receive priority in replies, mentions, and search, fewer advertisements, and longer audio and video. Although paid verification was expected to be launched by November 7, the reintroduction of Twitter Blue was delayed until after the 2022 United States elections on November 9, according to a memo obtained by The New York Times. The announcement of paid verification resulted in several accounts facetiously impersonating Musk, such as those of comedians Kathy Griffin and Sarah Silverman, being suspended. In response, Musk announced that impersonators using Twitter Blue "will be permanently suspended". An "official
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Information element

An information element, sometimes informally referred to as a field, is an item in Q.931 and Q.2931 messages, IEEE 802.11 management frames, and cellular network messages sent between a base transceiver station and a mobile phone or similar piece of user equipment. An information element is often a type–length–value item, containing 1) a type (which corresponds to the label of a field), a length indicator, and a value, although any combination of one or more of those parts is possible. A single message may contain multiple information elements. The abbreviation IE is found in many technical specification documents from 3GPP. It is not uncommon for a single specification document to contain thousands of references to IEs.
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Algorithm selection

Algorithm selection (sometimes also called per-instance algorithm selection or offline algorithm selection) is a meta-algorithmic technique to choose an algorithm from a portfolio on an instance-by-instance basis. It is motivated by the observation that on many practical problems, different algorithms have different performance characteristics. That is, while one algorithm performs well in some scenarios, it performs poorly in others and vice versa for another algorithm. If we can identify when to use which algorithm, we can optimize for each scenario and improve overall performance. This is what algorithm selection aims to do. The only prerequisite for applying algorithm selection techniques is that there exists (or that there can be constructed) a set of complementary algorithms. == Definition == Given a portfolio P {\displaystyle {\mathcal {P}}} of algorithms A ∈ P {\displaystyle {\mathcal {A}}\in {\mathcal {P}}} , a set of instances i ∈ I {\displaystyle i\in {\mathcal {I}}} and a cost metric m : P × I → R {\displaystyle m:{\mathcal {P}}\times {\mathcal {I}}\to \mathbb {R} } , the algorithm selection problem consists of finding a mapping s : I → P {\displaystyle s:{\mathcal {I}}\to {\mathcal {P}}} from instances I {\displaystyle {\mathcal {I}}} to algorithms P {\displaystyle {\mathcal {P}}} such that the cost ∑ i ∈ I m ( s ( i ) , i ) {\displaystyle \sum _{i\in {\mathcal {I}}}m(s(i),i)} across all instances is optimized. == Examples == === Boolean satisfiability problem (and other hard combinatorial problems) === A well-known application of algorithm selection is the Boolean satisfiability problem. Here, the portfolio of algorithms is a set of (complementary) SAT solvers, the instances are Boolean formulas, the cost metric is for example average runtime or number of unsolved instances. So, the goal is to select a well-performing SAT solver for each individual instance. In the same way, algorithm selection can be applied to many other N P {\displaystyle {\mathcal {NP}}} -hard problems (such as mixed integer programming, CSP, AI planning, TSP, MAXSAT, QBF and answer set programming). Competition-winning systems in SAT are SATzilla, 3S and CSHC === Machine learning === In machine learning, algorithm selection is better known as meta-learning. The portfolio of algorithms consists of machine learning algorithms (e.g., Random Forest, SVM, DNN), the instances are data sets and the cost metric is for example the error rate. So, the goal is to predict which machine learning algorithm will have a small error on each data set. == Instance features == The algorithm selection problem is mainly solved with machine learning techniques. By representing the problem instances by numerical features f {\displaystyle f} , algorithm selection can be seen as a multi-class classification problem by learning a mapping f i ↦ A {\displaystyle f_{i}\mapsto {\mathcal {A}}} for a given instance i {\displaystyle i} . Instance features are numerical representations of instances. For example, we can count the number of variables, clauses, average clause length for Boolean formulas, or number of samples, features, class balance for ML data sets to get an impression about their characteristics. === Static vs. probing features === We distinguish between two kinds of features: Static features are in most cases some counts and statistics (e.g., clauses-to-variables ratio in SAT). These features ranges from very cheap features (e.g. number of variables) to very complex features (e.g., statistics about variable-clause graphs). Probing features (sometimes also called landmarking features) are computed by running some analysis of algorithm behavior on an instance (e.g., accuracy of a cheap decision tree algorithm on an ML data set, or running for a short time a stochastic local search solver on a Boolean formula). These feature often cost more than simple static features. === Feature costs === Depending on the used performance metric m {\displaystyle m} , feature computation can be associated with costs. For example, if we use running time as performance metric, we include the time to compute our instance features into the performance of an algorithm selection system. SAT solving is a concrete example, where such feature costs cannot be neglected, since instance features for CNF formulas can be either very cheap (e.g., to get the number of variables can be done in constant time for CNFs in the DIMACs format) or very expensive (e.g., graph features which can cost tens or hundreds of seconds). It is important to take the overhead of feature computation into account in practice in such scenarios; otherwise a misleading impression of the performance of the algorithm selection approach is created. For example, if the decision which algorithm to choose can be made with perfect accuracy, but the features are the running time of the portfolio algorithms, there is no benefit to the portfolio approach. This would not be obvious if feature costs were omitted. == Approaches == === Regression approach === One of the first successful algorithm selection approaches predicted the performance of each algorithm m ^ A : I → R {\displaystyle {\hat {m}}_{\mathcal {A}}:{\mathcal {I}}\to \mathbb {R} } and selected the algorithm with the best predicted performance a r g min A ∈ P m ^ A ( i ) {\displaystyle arg\min _{{\mathcal {A}}\in {\mathcal {P}}}{\hat {m}}_{\mathcal {A}}(i)} for an instance i {\displaystyle i} . === Clustering approach === A common assumption is that the given set of instances I {\displaystyle {\mathcal {I}}} can be clustered into homogeneous subsets and for each of these subsets, there is one well-performing algorithm for all instances in there. So, the training consists of identifying the homogeneous clusters via an unsupervised clustering approach and associating an algorithm with each cluster. A new instance is assigned to a cluster and the associated algorithm selected. A more modern approach is cost-sensitive hierarchical clustering using supervised learning to identify the homogeneous instance subsets. === Pairwise cost-sensitive classification approach === A common approach for multi-class classification is to learn pairwise models between every pair of classes (here algorithms) and choose the class that was predicted most often by the pairwise models. We can weight the instances of the pairwise prediction problem by the performance difference between the two algorithms. This is motivated by the fact that we care most about getting predictions with large differences correct, but the penalty for an incorrect prediction is small if there is almost no performance difference. Therefore, each instance i {\displaystyle i} for training a classification model A 1 {\displaystyle {\mathcal {A}}_{1}} vs A 2 {\displaystyle {\mathcal {A}}_{2}} is associated with a cost | m ( A 1 , i ) − m ( A 2 , i ) | {\displaystyle |m({\mathcal {A}}_{1},i)-m({\mathcal {A}}_{2},i)|} . == Requirements == The algorithm selection problem can be effectively applied under the following assumptions: The portfolio P {\displaystyle {\mathcal {P}}} of algorithms is complementary with respect to the instance set I {\displaystyle {\mathcal {I}}} , i.e., there is no single algorithm A ∈ P {\displaystyle {\mathcal {A}}\in {\mathcal {P}}} that dominates the performance of all other algorithms over I {\displaystyle {\mathcal {I}}} (see figures to the right for examples on complementary analysis). In some application, the computation of instance features is associated with a cost. For example, if the cost metric is running time, we have also to consider the time to compute the instance features. In such cases, the cost to compute features should not be larger than the performance gain through algorithm selection. == Application domains == Algorithm selection is not limited to single domains but can be applied to any kind of algorithm if the above requirements are satisfied. Application domains include: hard combinatorial problems: SAT, Mixed Integer Programming, CSP, AI Planning, TSP, MAXSAT, QBF and Answer Set Programming combinatorial auctions in machine learning, the problem is known as meta-learning software design black-box optimization multi-agent systems numerical optimization linear algebra, differential equations evolutionary algorithms vehicle routing problem power systems For an extensive list of literature about algorithm selection, we refer to a literature overview. == Variants of algorithm selection == === Online selection === Online algorithm selection refers to switching between different algorithms during the solving process. This is useful as a hyper-heuristic. In contrast, offline algorithm selection selects an algorithm for a given instance only once and before the solving process. === Computation of schedules === An extension of algorithm selection is the per-instance algorithm scheduling problem, in which we do not select only one solver, but we select a time budget for each algorithm
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Bare machine

In information technology, a bare machine (or bare-metal computer) is a computer which has no operating system. The software executed by a bare machine, commonly called a bare metal program or bare metal application, is designed to interact directly with hardware. Bare machines are widely used in embedded systems, particularly in cases where resources are limited or high performance is required. == Bare machine computing == Bare Machine Computing is a computing paradigm in which application software runs directly on a bare machine as a single, stand-alone executable, without an operating system or device drivers. The application software has direct access to hardware resources, and there is typically no distinction between user and kernel mode. It is self-managed software that boots, loads and runs without using any other software components. Bare metal programs are typically written in a close-to-hardware language such as C or assembly language. == Advantages == Typically, a bare-metal application will run faster, use less memory and be more power efficient than an equivalent program that relies on an operating system, due to the inherent overhead imposed by system calls. For example, hardware inputs and outputs are directly accessible to bare metal software, whereas they must usually be accessed through system calls when using an OS. It has no OS and therefore has no OS-related vulnerabilities. == Disadvantages == Bare metal applications typically require more effort to develop because operating system services such as memory management and task scheduling are not available. Debugging a bare-metal program may be complicated by factors such as: Lack of a standard output. The target machine may differ from the hardware used for program development (e.g., emulator, simulator). This forces setting up a way to load the bare-metal program onto the target (flashing), start the program execution and access the target resources. == Examples == === Early computers === Early computers, such as the PDP-11, allowed programmers to load a program, supplied in machine code, to RAM. The resulting operation of the program could be monitored by lights, and output derived from magnetic tape, print devices, or storage. Amdahl UTS's performance improves by 25% when run on bare metal without VM, the company said in 1986. === Embedded systems === Bare machine programming is a common practice in embedded systems, in which microcontrollers or microprocessors boot directly into monolithic, single-purpose software without loading an operating system. Such embedded software can vary in structure. For example, one such program paradigm, known as foreground-background or superloop architecture, consists of an infinite main loop in which each task is executed sequentially and must voluntarily return control back to the loop. The loop runs these cooperative background processes that are not time-critical, while interrupt service routines momentarily interrupt the loop to handle time-critical foreground tasks.
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ISO/IEC JTC 1/SC 6

ISO/IEC JTC 1/SC 6 Telecommunications and information exchange between systems is a standardization subcommittee of the Joint Technical Committee ISO/IEC JTC 1. It is part of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), which develops and facilitates standards within the field of telecommunications and information exchange between systems. ISO/IEC JTC 1/SC 6 was established in 1964, following the creation of a Special Working Group under ISO/TC 97 on Data Link Control Procedures and Modem Interfaces. The international secretariat of ISO/IEC JTC 1/SC 6 is the Korean Agency for Technology and Standards (KATS), located in South Korea. == Scope == The scope of ISO/IEC JTC 1/SC 6 is “Standardization in the field of telecommunications dealing with the exchange of information between open systems including system functions, procedures, parameters as well as the conditions for their use. The standardization encompasses protocols and services of lower layers, including physical, data link, network, and transport as well as those of upper layers including but not limited to Directory and ASN.1.” Future Network has recently been added as an important work scope. A considerable part of the work is done in effective cooperation with ITU-T and other standardization bodies including IEEE 802 and Ecma International. == Structure == ISO/IEC JTC 1/SC 6 has three active working groups (WGs), each of which carries out specific tasks in standards development within the field of telecommunications and information exchange between systems. The focus of each working group is described in the group’s terms of reference. Working groups can be established if new working areas arise, or disbanded if the group’s working area is no longer relevant to standardization needs. Active working groups of ISO/IEC JTC 1/SC 6 are: == Collaborations == ISO/IEC JTC 1/SC 6 works in close collaboration with a number of other organizations or subcommittees, both internal and external to ISO or IEC. Organizations internal to ISO or IEC that collaborate with or are in liaison with ISO/IEC JTC 1/SC 6 include: ISO/IEC JTC 1/WG 7, Sensor networks ISO/IEC JTC 1/SC 17, Cards and personal identification ISO/IEC JTC 1/SC 25, Interconnection of information technology equipment ISO/IEC JTC 1/SC 27, IT security techniques ISO/IEC JTC 1/SC 29, Coding of audio, picture, multimedia and hypermedia information ISO/IEC JTC 1/SC 31, Automatic identification and data capture techniques ISO/IEC JTC 1/SC 38, Distributed application platforms & services (DAPS) ISO/TC 68, Financial services ISO/TC 122, Packaging ISO/TC 184/SC 5, Interoperability, integration, and architectures for enterprise systems and automation applications ISO/TC 215, Health Informatics IEC/SC 46A, Coaxial cables IEC/SC 46C, Wires and symmetric cables IEC/TC 48, Electrical connectors and mechanical structures for electrical and electronic equipment IEC/SC 48B, Electrical connectors IEC/TC 65, Industrial-process measurement, control and automation IEC/SC 65C, Industrial networks IEC/TC 86, Fibre optics IEC/SC 86C, Fibre optic systems and active devices IEC/TC 93, Design automation Some organizations external to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 6 include: European Conference of Postal and Telecommunications Administrations (CEPT) European Organization for Nuclear Research (CERN) European Commission (EC) European Telecommunications Standards Institute (ETSI) Ecma International International Civil Aviation Organization (ICAO) IEEE 802 LMSC (LAN/MAN Standards Committee) Internet Society (ISOC) International Telecommunications Satellite Organization (ITSO) ITU-T Organization for the Advancement of Structured Information Standards (OASIS) NFC Forum MFA Forum United Nations Conference on Trade and Development (UNCTAD) United Nations Economic Commission for Europe (UNECE) Universal Postal Union (UPU) World Meteorological Organization (WMO) CEN/TC 247/WG 4 == Member countries == Countries pay a fee to ISO to be members of subcommittees. The 19 "P" (participating) members of ISO/IEC JTC 1/SC 6 are: Austria, Belgium, Canada, China, Czech Republic, Finland, Germany, Greece, Jamaica, Japan, Kazakhstan, Republic of Korea, Netherlands, Russian Federation, Spain, Switzerland, Tunisia, United Kingdom, and United States. The 31 "O" (observing) members of ISO/IEC JTC 1/SC 6 are: Argentina, Bosnia and Herzegovina, Colombia, Cuba, Cyprus, France, Ghana, Hong Kong, Hungary, Iceland, India, Indonesia, Islamic Republic of Iran, Ireland, Italy, Kenya, Luxembourg, Malaysia, Malta, New Zealand, Norway, Philippines, Poland, Romania, Saudi Arabia, Serbia, Singapore, Slovenia, Thailand, Turkey, and Ukraine. == Published standards == There are 365 published standards under the direct responsibility of ISO/IEC JTC 1/SC 6. Published standards by ISO/IEC JTC 1/SC 6 include:
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