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  List of speech recognition software

  Speech recognition software is available for many computing platforms, operating systems, use models, and software licenses. Here is a listing of such, grouped in various useful ways. == Acoustic models and speech corpus (compilation) == The following list presents notable speech recognition software engines with a brief synopsis of characteristics. == Macintosh == == Cross-platform web apps based on Chrome == The following list presents notable speech recognition software that operate in a Chrome browser as web apps. They make use of HTML5 Web-Speech-API. == Mobile devices and smartphones == Many mobile phone handsets, including feature phones and smartphones such as iPhones and BlackBerrys, have basic dial-by-voice features built in. Many third-party apps have implemented natural-language speech recognition support, including: == Windows == === Windows built-in speech recognition === The Windows Speech Recognition version 8.0 by Microsoft comes built into Windows Vista, Windows 7, Windows 8 and Windows 10. Speech Recognition is available only in English, French, Spanish, German, Japanese, Simplified Chinese, and Traditional Chinese and only in the corresponding version of Windows; meaning you cannot use the speech recognition engine in one language if you use a version of Windows in another language. Windows 7 Ultimate and Windows 8 Pro allow you to change the system language, and therefore change which speech engine is available. Windows Speech Recognition evolved into Cortana (software), a personal assistant included in Windows 10. === Windows 7, 8, 10, 11 third-party speech recognition === Braina – Dictate into third party software and websites, fill web forms and execute vocal commands. Dragon NaturallySpeaking from Nuance Communications – Successor to the older DragonDictate product. Focus on dictation. 64-bit Windows support since version 10.1. Tazti – Create speech command profiles to play PC games and control applications – programs. Create speech commands to open files, folders, webpages, applications. Windows 7, Windows 8 and Windows 8.1 versions. Voice Finger – software that improves the Windows speech recognition system by adding several extensions to it. The software enables controlling the mouse and the keyboard by only using the voice. It is especially useful for aiding users to overcome disabilities or to heal from computer injuries. === Microsoft Speech API === The first version of the Microsoft Speech API was released for Windows NT 3.51 and Windows 95 in 1995, it was then part of Windows up to Windows Vista. This initial version already contained Direct Speech Recognition and Direct Text To Speech APIs which applications could use to directly control engines, as well as simplified 'higher-level' Voice Command and Voice Talk APIs. Speech recognition functionality included as part of Microsoft Office and on Tablet PCs running Microsoft Windows XP Tablet PC Edition. It can also be downloaded as part of the Speech SDK 5.1 for Windows applications, but since that is aimed at developers building speech applications, the pure SDK form lacks any user interface (numerous applications were available), and thus is unsuitable for end users. == Built-in software == Microsoft Kinect includes built-in software which allows speech recognition of commands. Older generations of Nokia phones like Nokia N Series (before using Windows 7 mobile technology) used speech-recognition with family names from contact list and a few commands. Siri, originally implemented in the iPhone 4S, Apple's personal assistant for iOS, which uses technology from Nuance Communications. Cortana (software), Microsoft's personal assistant built into Windows Phone and Windows 10. == Interactive voice response == The following are interactive voice response (IVR) systems: CSLU Toolkit Genesys HTK – copyrighted by Microsoft, but allows altering software for licensee's internal use LumenVox ASR Tellme Networks; acquired by Microsoft == Unix-like x86 and x86-64 speech transcription software == Janus Recognition Toolkit (JRTk) Mozilla DeepSpeech was developing an open-source Speech-To-Text engine based on Baidu's deep speech research paper. Weesper Neon Flow – professional voice-dictation software that provides offline speech-to-text processing on macOS and Windows using local AI models. It is not open source and offers a paid subscription after a 15‑day free trial. Vocalinux – open-source speech transcription software for Linux. == Discontinued software == IBM VoiceType (formerly IBM Personal Dictation System) IBM ViaVoice – Embedded version still maintained by IBM. No longer supported for versions above Windows Vista. Untested above macOS 10.4 or on Macintoshes with an Intel chipset. Quack.com; acquired by AOL; the name has now been reused for an iPad search app. SpeechWorks from Nuance Communications. Yap Speech Cloud – Speech-to-text platform acquired by Amazon.com.

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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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  Variable-message sign

  A variable- (also changeable-, electronic-, or dynamic-) message sign or message board, often abbreviated VMS, VMB, CMS, or DMS, and in the UK known as a matrix sign, is an electronic traffic sign often used on roadways to give travelers information about special events. Such signs warn of traffic congestion, accidents, incidents such as terrorist attacks, Amber/Silver/Blue Alerts, roadwork zones, or speed limits on a specific highway segment. In urban areas, VMS are used within parking guidance and information systems to guide drivers to available car parking spaces. They may also ask vehicles to take alternative routes, limit travel speed, warn of duration and location of the incidents, inform of the traffic conditions, or display general public safety messages. == History == VMS systems were deployed at least as early as the 1950s on the New Jersey Turnpike. The road's signs of that period, and up to around 2012, were capable of displaying a few messages in neon, all oriented around warning drivers to slow down: "REDUCE SPEED", followed by a warning of either construction, accident, congestion, ice, snow, or fog at a certain distance ahead. The New Jersey Turnpike Authority replaced those signs (along with 1990s-vintage dot-matrix VMS systems along the Garden State Parkway) with more flexible electronic signs between 2010 and 2016. The current VMS systems are largely deployed on freeways, trunk highways, or in work zones. On the interchange of I-5 and SR 120 in San Joaquin County, California, an automated visibility and speed warning system was installed in 1996 to warn traffic of reduced visibility due to fog (where tule fog is a common problem in the winter), and of slow or stopped traffic. Message Signs were deployed in Ontario during the 1990s and are now being upgraded on 400-series highways as well as two pilot secondary highways in northeastern Ontario. == Technologies and types == Early variable message signs included static signs with words that would illuminate (often using neon tubing) indicating the type of incident that occurred, or signs that used rotating prisms (trilons) to change the message being displayed. These were later replaced by dot matrix displays typically using eggcrate, fiber optic, or flip-disc technology, which were capable of displaying a much wider range of messages than earlier static variable message signs. Since the late 1990s, the most common technology used in new installations for variable message signs are LED displays. In recent years, some newer LED variable message signs have the ability to display colored text and graphics. Dot-matrix variable message signs are divided into three subgroups: character matrix, row matrix, and full matrix. In a character matrix VMS, each character is given its own matrix with equal horizontal spacing between them, typically with two or three rows of characters. In a full matrix VMS, the entire sign is a single large dot matrix display, allowing the display of different fonts and graphics. A row matrix VMS is a hybrid of the two types, divided into two or three rows like a character matrix display, except each row is a single long dot matrix display instead of being split per character horizontally. Overhead variable message signs are today available in three form factors: front access, rear access, and walk-in. In a front access variable message sign, maintenance is performed by lifting the sign open from the front. Most smaller VMS are of the front access form factor, and are typically installed today on major arterials. The rear access form factor is similar to the front access form factor, except that maintenance is performed from the rear of the sign, and are commonly used for medium-sized dynamic message signs installed along the roadside of freeways (instead of overhead). The walk-in form factor is a more recent introduction, where maintenance on the sign is performed from the inside of the sign. A key advantage of the walk-in form factor is that lane closures are generally not required to perform maintenance on the sign. Most of the largest VMS units installed today are walk-in units, and are typically installed overhead on freeways. The NJ Turnpike Authority counts five unique types of variable message signs under its jurisdiction, at least one of which has been replaced by newer signs. They are: "REDUCE SPEED" neon signs (1950s-2010, obsolete, have now been replaced). "Changeable message signs" (trilon/ rotating-drum signs that can be used for closing roads or moving traffic to other roadways). Electronic VMS: signs with remotely controlled messages displayed on them; the messages are sent from the State Traffic Management Center, updating the signs automatically. Variable speed limit signs - used for varying the posted speed limits within work zones and in emergencies. Portable VMS: movable "electronic VMS". A portable VMS has much the same characteristics as a fixed electronic VMS, but can be moved from location to location as the need dictates. == Usage == Early models required an operator to be physically present when programming a message, whereas newer models may be reprogrammed remotely via a wired or wireless network or cellphone connection. A complete message on a panel generally includes a problem statement indicating incident, roadwork, stalled vehicle etc.; a location statement indicating where the incident is located; an effect statement indicating lane closure, delay, etc. and an action statement giving suggestion what to do traffic conditions ahead. These signs are also used for Amber alert messages, and in some states, Silver and Blue Alert messages. In some places, VMSes are set up with permanent, semi-static displays indicating predicted travel times to important traffic destinations such as major cities or interchanges along the route of a highway. Typical messages provide the following information: Promotional messages about services provided by a road authority during non-critical hours, such as carpooling efforts, travelers' information stations and 5-1-1 lines Crashes, including vehicle spin-out or rollover Road Works Incidents affecting normal traffic flow in a lane or on shoulders Non-recurring congestion, often a residual effect of cleared crash Closures of an entire road, e.g. over a mountain pass in winter. Exit ramp closures Debris on roadway Vehicle fires Wildfires Short-term maintenance or construction lasting less than three days Pavement failure alerts AMBER, Silver, and Blue Alerts, as well as weather warnings via the warning infrastructure of NOAA Weather Radio's SAME system Travel times Variable speed limits Car park occupancy levels speed sign, for recommending a speed to approach the next traffic light in its green phase. The information comes from a variety of traffic monitoring and surveillance systems. It is expected that by providing real-time information on special events on the oncoming road, VMS can improve motorists' route selection, reduce travel time, mitigate the severity and duration of incidents and improve the performance of the transportation network. === United Kingdom === Do not enter the motorway when the red lamps are flashing in pairs from side to side. On 27 March 1972, the first motorway computer-controlled warning lights in the UK, with 59 miles on the M6 from Broughton, Lancashire to Barthomley, on the Cheshire boundary, and 26 miles on the M62 east of Whitefield, was switched on by Michael Heseltine and Charles Legh Shuldham Cornwall-Legh, 5th Baron Grey of Codnor at the headquarters of Cheshire Constabulary on Nuns Road. It was centred at a police computer centre at Westhoughton, that connected to police stations in Preston and Chester. The Chester site was soon be connected to the M53 and M57. Four other regional computer centres would be opened at Perry Barr near the M6, Scratchwood near the M1, at Hook near the M3, and at Almondsbury near the M4. Most British motorways would be covered by 1975. The system was designed by GEC and had taken five years to design. == Safety messages for drivers == Increasingly, signs have been used to remind drivers to buckle seat belts ("Click It or Ticket"), obey the speed limit, and stay off the road if impaired ("Drive sober or get pulled over"). In a federal study, a slight majority of drivers reported that public safety messages on dynamic message signs impacted their driving behaviors. The Ohio Department of Transportation began using humorous dynamic message signs in 2015, perplexing some drivers. Examples of humorous signs seen in New Jersey, Arizona, Texas, Pennsylvania, Delaware, Iowa, New York, Minnesota and Ohio include: "Hold on to your butts. Help prevent forest fires." "We'll be blunt. Don't drive high." "Visiting in-laws? Slow down, get there late." "Only sparklers should be lit." and “Don’t drive Star Spangled hammered." (for Fourth of July) "Hocus pocus – drive with focus." and "Slow down in work zones - my mummy works here." (f

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  Web testing

  Web testing is software testing that focuses on web applications. Complete testing of a web-based system before going live can help address issues before the system is revealed to the public. Issues may include the security of the web application, the basic functionality of the site, its accessibility to disabled and fully able users, its ability to adapt to the multitude of desktops, devices, and operating systems, as well as readiness for expected traffic and number of users and the ability to survive a massive spike in user traffic, both of which are related to load testing. == Web application performance tool == A web application performance tool (WAPT) is used to test web applications and web related interfaces. These tools are used for performance, load and stress testing of web applications, web sites, web API, web servers and other web interfaces. WAPT tends to simulate virtual users which will repeat either recorded URLs or specified URL and allows the users to specify number of times or iterations that the virtual users will have to repeat the recorded URLs. By doing so, the tool is useful to check for bottleneck and performance leakage in the website or web application being tested. A WAPT faces various challenges during testing and should be able to conduct tests for: Browser compatibility Operating System compatibility Windows application compatibility where required WAPT allows a user to specify how virtual users are involved in the testing environment.ie either increasing users or constant users or periodic users load. Increasing user load, step by step is called RAMP where virtual users are increased from 0 to hundreds. Constant user load maintains specified user load at all time. Periodic user load tends to increase and decrease the user load from time to time. == Web security testing == Web security testing tells us whether Web-based applications requirements are met when they are subjected to malicious input data. There is a web application security testing plug-in collection for Fire Fox == Web API testing == An application programming interface API exposes services to other software components, which can query the API. The API implementation is in charge of computing the service and returning the result to the component that send the query. A part of web testing focuses on testing these web API implementations. GraphQL is a specific query and API language. It is the focus of tailored testing techniques. Search-based test generation yields good results to generate test cases for GraphQL APIs.

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  Yap (company)

  Yap Speech Cloud was a multimodal speech recognition system developed by American technology company Yap Inc. It offered a fully cloud-based speech-to-text transcription platform that was used by customers such as Microsoft. The Company was a contestant at the inaugural TechCrunch conference and was subsequently acquired by Amazon in September 2011 to help develop products such as Alexa Voice Service, Echo, and Fire TV.

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  Honeywell JetWave

  Honeywell's JetWave is a piece of satellite communications hardware produced by Honeywell that enables global in-flight internet connectivity. Its connectivity is provided using Inmarsat’s GX Aviation network. The JetWave platform is used in business and general aviation, as well as defense and commercial airline users. == History == In 2012, Honeywell announced it would provide Inmarsat with the hardware for its GX Ka-band in-flight connectivity network. The Ka-band (pronounced either "kay-ay band" or "ka band") is a portion of the microwave part of the electromagnetic spectrum defined as frequencies in the range 27.5 to 31 gigahertz (GHz). In satellite communications, the Ka-band allows higher bandwidth communication. In 2017, after five years and more than 180 flight hours and testing, JetWave was launched as part of GX Aviation with Lufthansa Group. Honeywell’s JetWave was the exclusive terminal hardware option for the Inmarsat GX Aviation network; however, the exclusivity clause in that contract has expired. In July 2019, the United States Air Force selected Honeywell’s JetWave satcom system for 70 of its C-17 Globemaster III cargo planes. In December 2019, it was reported that six AirAsia aircraft had been fitted with Inmarsat’s GX Aviation Ka-band connectivity system and is slated to be implemented fleetwide across AirAsia’s Airbus A320 and A330 models in 2020, requiring installation of JetWave atop AirAsia’s fuselages. Today, Honeywell’s JetWave hardware is installed on over 1,000 aircraft worldwide. In August 2021, the Civil Aviation Administration of China approved a validation of Honeywell’s MCS-8420 JetWave satellite connectivity system for Airbus 320 aircraft. In December 2021, Honeywell, SES, and Hughes Network Systems demonstrated multi-orbit high-speed airborne connectivity for military customers using Honeywell’s JetWave MCX terminal with a Hughes HM-series modem, and SES satellites in both medium Earth orbit (MEO) and geostationary orbit (GEO). The tests achieved full duplex data rates of more than 40 megabits per second via a number of SES' (GEO) satellites including GovSat-1, and the high-throughput, low-latency O3b MEO satellite constellation, with connections moving between GEO/MEO links in under 30 sec. == Uses == === Commercial aviation === Honeywell’s JetWave enables air transport and regional aircraft to connect to Inmarsat’s GX Aviation network. The multichannel satellite (MSC) JetWave terminals share the same antenna controller, modem and router hardware with the business market, but have an MCS-8200 fuselage-mounted antenna. === Business aviation === Honeywell’s JetWave hardware allows users to connect to Inmarsat’s Jet ConneX, a business aviation broadband connectivity offering to provide Wi-Fi for connected devices. JetWave offers a tail-mount antenna for business jets. === Defense === Honeywell’s JetWave satellite communications system for defense allows users to connect to the Inmarsat GX network, offering global coverage for military airborne operators, including over water, over nontraditional flight paths and in remote areas. JetWave and the Inmarsat GX network enable mission-critical applications like real-time weather; videoconferencing; large file transfers; encryption capabilities; in-flight briefings; intelligence, surveillance, and reconnaissance video; and secure communications. JetWave is configurable for a variety of military platforms and offers antennas for large and small airframes.

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  Access-independent services

  Access-independent service (AIS) is a service concept in which a service does not depend on guaranteed access network cooperation for service delivery. Telecommunications industry analyst Dean Bubley first used the term in a report on Telco-OTT in February 2012. Traditionally, most telecom company or internet service provider services are access-dependent, because they rely heavily on guaranteed access cooperation on the network the service is delivered over. For instance, traditional IP-based TV service (IPTV) delivered by a telecom company is generally a managed service. This means that IPTV service assumes the IPTV service provider has control over the access network that the IPTV service is delivered over, and network quality of service (QoS) guarantees are available for IPTV service delivery. As a result, the reach of a telecom company's IPTV service is generally restricted by the reach of the telecom company's access network. In contrast, services offered by non-traditional video content delivery service providers such as Netflix, Hulu, and Amazon Video are considered access-independent services. Netflix's video content streaming service, for example, dynamically adapts to network conditions in real-time to strive for the best overall quality of experience (QoE) and does not assume guaranteed cooperation from the underlying IP network, such as QoS. As a result, without considering content rights and different countries' government restrictions, the reach of Netflix's video content streaming service is, in theory, the reach of the Internet. Skype is another example of AIS, because Skype offers an IP-based telephony service over the Internet without depending on IP network cooperation guarantees other than basic IP network connectivity. In the context of telecom service delivery, the concept of access independent services is also commonly described by the term "over-the-top" (OTT) services. OTT service providers such as but not limited to Facebook, WeChat, and Netflix generally do not own or directly manage any wide-area access network to begin with, so they design their services for overall quality of experience, with no assumptions on guaranteed access network cooperation.

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  Account verification

  Account verification is the process of verifying that a new or existing account is owned and operated by a specified real individual or organization. A number of websites, for example social media websites, offer account verification services. Verified accounts are often visually distinguished by check mark icons or badges next to the names of individuals or organizations. Account verification can enhance the quality of online services, mitigating sockpuppetry, bots, trolling, spam, vandalism, fake news, disinformation and election interference. == History == Account verification was introduced by Twitter in June 2009, initially as a feature for public figures and accounts of interest, individuals in "music, acting, fashion, government, politics, religion, journalism, media, sports, business and other key interest areas". A similar verification system was adopted by Google+ in 2011, Facebook page in October 2015 (Available in United States, Canada, United Kingdom, Australia and New Zealand) Facebook profile and Facebook page in 2018 (Available in Worldwide) Instagram in 2014, and Pinterest in 2015. On YouTube, users are able to submit a request for a verification badge once they obtain 100,000 or more subscribers. It also has an "official artist" badge for musicians and bands. In July 2016, Twitter announced that, beyond public figures, any individual would be able to apply for account verification. This was temporarily suspended in February 2018, following a backlash over the verification of one of the organisers of the far-right Unite the Right rally due to a perception that verification conveys "credibility" or "importance". In March 2018, during a live-stream on Periscope, Jack Dorsey, co-founder and CEO of Twitter, discussed the idea of allowing any individual to get a verified account. Twitter reopened account verification applications in May 2021 after revamping their account verification criteria. This time offering notability criteria for the account categories of government, companies, brands, and organizations, news organizations and journalists, entertainment, sports and activists, organizers, and other influential individuals. Instagram began allowing users to request verification in August 2018. In April 2018, Mark Zuckerberg, co-founder and CEO of Facebook, announced that purchasers of political or issue-based advertisements would be required to verify their identities and locations. He also indicated that Facebook would require individuals who manage large pages to be verified. In May 2018, Kent Walker, senior vice president of Google, announced that, in the United States, purchasers of political-leaning advertisements would need to verify their identities. In November 2022, Elon Musk included a blue verification check mark with a paid Twitter Blue monthly membership. Prior to Musk's acquisition of Twitter, Twitter offered this check mark at no charge to confirmed high profile users. On December 19, 2022, Twitter introduced two new check mark colors: gold for accounts from official businesses and organizations, and grey for accounts from governments or multilateral organizations. The type of check mark can be confirmed by visiting the profile page, then clicking or tapping on the check mark. == Techniques == === Identity verification services === Identity verification services are third-party solutions which can be used to ensure that a person provides information which is associated with the identity of a real person. Such services may verify the authenticity of identity documents such as drivers licenses or passports, called documentary verification, or may verify identity information against authoritative sources such as credit bureaus or government data, called nondocumentary verification. === Identity documents verification === The uploading of scanned or photographed identity documents is a practice in use, for example, at Facebook. According to Facebook, there are two reasons that a person would be asked to send a scan of or photograph of an ID to Facebook: to show account ownership and to confirm their name. In January 2018, Facebook purchased Confirm.io, a startup that was advancing technologies to verify the authenticity of identification documentation. === Biometric verification === === Behavioral verification === Behavioral verification is the computer-aided and automated detection and analysis of behaviors and patterns of behavior to verify accounts. Behaviors to detect include those of sockpuppets, bots, cyborgs, trolls, spammers, vandals, and sources and spreaders of fake news, disinformation and election interference. Behavioral verification processes can flag accounts as suspicious, exclude accounts from suspicion, or offer corroborating evidence for processes of account verification. === Bank account verification === Identity verification is required to establish bank accounts and other financial accounts in many jurisdictions. Verifying identity in the financial sector is often required by regulation such as Know Your Customer or Customer Identification Program. Accordingly, bank accounts can be of use as corroborating evidence when performing account verification. Bank account information can be provided when creating or verifying an account or when making a purchase. === Postal address verification === Postal address information can be provided when creating or verifying an account or when making and subsequently shipping a purchase. A hyperlink or code can be sent to a user by mail, recipients entering it on a website verifying their postal address. === Telephone number verification === A telephone number can be provided when creating or verifying an account or added to an account to obtain a set of features. During the process of verifying a telephone number, a confirmation code is sent to a phone number specified by a user, for example in an SMS message sent to a mobile phone. As the user receives the code sent, they can enter it on the website to confirm their receipt. === Email verification === An email account is often required to create an account. During this process, a confirmation hyperlink is sent in an email message to an email address specified by a person. The email recipient is instructed in the email message to navigate to the provided confirmation hyperlink if and only if they are the person creating an account. The act of navigating to the hyperlink confirms receipt of the email by the person. The added value of an email account for purposes of account verification depends upon the process of account verification performed by the specific email service provider. === Multi-factor verification === Multi-factor account verification is account verification which simultaneously utilizes a number of techniques. === Multi-party verification === The processes of account verification utilized by multiple service providers can corroborate one another. OpenID Connect includes a user information protocol which can be used to link multiple accounts, corroborating user information. == Account verification and good standing == On some services, account verification is synonymous with good standing. Twitter reserves the right to remove account verification from users' accounts at any time without notice. Reasons for removal may reflect behaviors on and off Twitter and include: promoting hate and/or violence against, or directly attacking or threatening other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or disease; supporting organizations or individuals that promote the above; inciting or engaging in the harassment of others; violence and dangerous behavior; directly or indirectly threatening or encouraging any form of physical violence against an individual or any group of people, including threatening or promoting terrorism; violent, gruesome, shocking, or disturbing imagery; self-harm, suicide; and engaging in other activity on Twitter that violates the Twitter Rules. In April 2023, Blue ticks were removed from all Twitter accounts that had not subscribed to Twitter Blue.

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  Action model learning

  Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with the creation and modification of a software agent's knowledge about the effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in a logic-based action description language and used as input for automated planners. Learning action models is important when goals change. When an agent acted for a while, it can use its accumulated knowledge about actions in the domain to make better decisions. Thus, learning action models differs from reinforcement learning. It enables reasoning about actions instead of expensive trials in the world. Action model learning is a form of inductive reasoning, where new knowledge is generated based on the agent's observations. The usual motivation for action model learning is the fact that manual specification of action models for planners is often a difficult, time-consuming, and error-prone task (especially in complex environments). == Action models == Given a training set E {\displaystyle E} consisting of examples e = ( s , a , s ′ ) {\displaystyle e=(s,a,s')} , where s , s ′ {\displaystyle s,s'} are observations of a world state from two consecutive time steps t , t ′ {\displaystyle t,t'} and a {\displaystyle a} is an action instance observed in time step t {\displaystyle t} , the goal of action model learning in general is to construct an action model ⟨ D , P ⟩ {\displaystyle \langle D,P\rangle } , where D {\displaystyle D} is a description of domain dynamics in action description formalism like STRIPS, ADL or PDDL and P {\displaystyle P} is a probability function defined over the elements of D {\displaystyle D} . However, many state of the art action learning methods assume determinism and do not induce P {\displaystyle P} . In addition to determinism, individual methods differ in how they deal with other attributes of domain (e.g. partial observability or sensoric noise). == Action learning methods == === State of the art === Recent action learning methods take various approaches and employ a wide variety of tools from different areas of artificial intelligence and computational logic. As an example of a method based on propositional logic, we can mention SLAF (Simultaneous Learning and Filtering) algorithm, which uses agent's observations to construct a long propositional formula over time and subsequently interprets it using a satisfiability (SAT) solver. Another technique, in which learning is converted into a satisfiability problem (weighted MAX-SAT in this case) and SAT solvers are used, is implemented in ARMS (Action-Relation Modeling System). Two mutually similar, fully declarative approaches to action learning were based on logic programming paradigm Answer Set Programming (ASP) and its extension, Reactive ASP. In another example, bottom-up inductive logic programming approach was employed. Several different solutions are not directly logic-based. For example, the action model learning using a perceptron algorithm or the multi level greedy search over the space of possible action models. In the older paper from 1992, the action model learning was studied as an extension of reinforcement learning. Nonetheless, further algorithms can be found that operate under different assumptions: FAMA can work even when some observations are missing, and it produces a general (lifted) planning model. It treats learning an action model like a planning problem, making sure the learned model matches the observations given. NOLAM can learn general action models even from noisy or imperfect data. LOCM focuses only on the order of actions in the data, ignoring any details about the states between those actions. The family of safe action model (SAM) learning methods create models that guarantee any plans made with them will actually work in the real world. There's also an extension called N-SAM that can learn action models with numeric conditions and effects. Additionally, numeric action models like N-SAM can be used to improve reinforcement learning (RL) performance through the RAMP algorithm. === Literature === Most action learning research papers are published in journals and conferences focused on artificial intelligence in general (e.g. Journal of Artificial Intelligence Research (JAIR), Artificial Intelligence, Applied Artificial Intelligence (AAI) or AAAI conferences). Despite mutual relevance of the topics, action model learning is usually not addressed in planning conferences like the International Conference on Automated Planning and Scheduling (ICAPS).

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  MicroTCA

  MicroTCA (short for Micro Telecommunications Computing Architecture, also: μTCA) is a modular, open standard, created and maintained by the PCI Industrial Computer Manufacturers Group (PICMG). It provides the electrical, mechanical, thermal and management specifications to create a switched fabric computer system, using Advanced Mezzanine Cards (AMC), connected directly to a backplane. MicroTCA is a descendant of the AdvancedTCA standard. == History == The rapid expansion of mobile telecommunications and their associated services (such as text messages) at the beginning of the millennium increased the demand of processing power in telecommunication systems. The existing "carrier grade" (see RAS) computing architectures were not fit to house the high performance processors of the time. In order to answer those demands, about 100 companies worked together in PICMG, resulting in the Advanced Telecommunications Architecture (AdvancedTCA, ATCA), published in 2002. After the introduction of AdvancedTCA, a standard was developed, to cater towards smaller telecommunications systems at the edge of the network. This standard was geared towards a more compact, less expensive systems, without cutting back on reliability or data throughput. This standard, called MicroTCA, was ratified 2006. MicroTCA systems migrated after its release into non-telecommunication sectors, like defence, avionics and science. This resulted in extensions to the base-standard, called modules. == Modules == === MicroTCA.0 === The base-specification for properties common to all other modules, ratified July 6, 2006. This includes: Mechanical specifications, like possible dimensions of card cages, backplanes and supported AMC-modules Electrical specifications, like power distribution and interface layout Thermal specifications, like possible cooling layouts or available cooling power Management specifications A second revision of the base-specifications was ratified January 16, 2020, containing some corrections, as well as alterations, necessary to implement higher speed Ethernet fabrics, like 10GBASE-KR and 40GBASE-KR4. === MicroTCA.1 === This module adds specifications for ruggedized systems, using forced air for cooling. Possible scenarios for MicroTCA.1-based systems include outside plant telecom, industrial and aerospace environments === MicroTCA.2 === This module adds specifications for more stringent requirements with regards to temperature, shock, vibration and other environmental conditions. These specifications are geared towards use in outside plant telecom, machine and transport industry, as well as military airborne, shipboard and ground mobile equipment. MicroTCA.2 allows the use of air- and conduction-cooled AMC-modules. === MicroTCA.3 === This module adds specifications for even more stringent requirements with regards to temperature, shock, vibration and other environmental conditions. These specifications are geared towards use in outside plant telecom, machine and transport industry, as well as military airborne, shipboard and ground mobile equipment. MicroTCA.3 requires the use of conduction-cooled AMC-modules. === MicroTCA.4 === This module extends the AMC with a Rear Transition Module (RTM), increasing PCB-space and modularity. AMC and RTM are connected with a connector, located in zone 3, defined in MicroTCA.0. These specifications are geared towards use in large-scale scientific devices, like particle accelerators or telescopes. == Components of MicroTCA == === Card Cage === The card cage (also: shelf, crate) houses all the other components and as such has two primary functions: Provide mechanical stability to the other components Ensure sufficient cooling There exist a wide array of card cages. They usually differ in: the type of modules they support (MTCA.0, MTCA.1, ...) the number of slots they provide (typically between 2 and 12) the architecture of the installed backplane (see below) the cooling scheme they use (i.e. airflow front-to-back, bottom-to-top, side-to-side, conductive,...) === Backplane === The backplane is a printed circuit board, mounted directly into the card cage. It connects all other components of a MicroTCA system to each other and provides power, data access and management access to them. Two types of power are distributed over the backplane, Management Power (+3.3 V) and Payload Power (+12 V). Unlike typical backplanes, where power is distributed to all components via a common "powerplane" in the PCB, on a MicroTCA backplane, Management and Payload Power are distributed to each component individually. While Management Power is provided to each module connected to a powered backplane, Payload Power has to be granted by the MicroTCA Carrier Hub (MCH), after ensuring that the module is MicroTCA-compatible. The standard defines various communication buses, which the backplane can/should provide: Gigabit Ethernet IPMI SATA Fat pipe (can be used for PCIe, SRIO or 10G/40G Ethernet) Point to Point Links Clocks JTAG === Cooling Unit === The Cooling Unit (CU) provides controlled air flow in air-flow-cooled card cages. It usually consists of an array of fans and a controller, which is connected to the backplane. The MicroTCA Carrier Hub (MCH) can read-out temperature sensors (if present) and fan speed, as well as change fan speed via IPMI. The Cooling Unit is usually fitted to a specific card cage. Some CUs are easily detachable (i.e. for cleaning or replacement), while other card cages come with integrated, non-detachable CUs. === Power Module === The Power Module (PM, also: Power Supply) converts the AC power from the power line to the +3.3 V Management Power (MP) and +12 V Payload Power (PP), both of which are DC. There exist a variety of power modules, which differ in: form factor (i.e. double width, single width) input voltage (110 V, 220 V, both) output power (i.e. 600 W, 1000 W) The power module senses the presence of a module in a slot via a specified pin in the module connector, and immediately provides that module with management power. Payload power is managed by the MicroTCA Carrier Hub (MCH), which communicates with the power module via IPMI. The power module uses its own type of connector, and can thus only be installed into designated slots, which in turn can't carry any other type of module. Some card cages provide an additional power module slot for redundancy. In such a case, one slot is the primary, which will provide power by default, and the other one is secondary, providing power only, if the primary does not. === MicroTCA Carrier Hub === The MicroTCA Carrier Hub (MCH) is the central managing device of a MicroTCA card cage. It manages power distribution and cooling. It usually also provides Gigabit Ethernet and/or PCIe/Serial RapidIO switching. Some MCHs additionally provide clocking. As the name indicates, they are the hub of various star topologies (i.e. for Ethernet, PCIe) on the backplane and thus require dedicated slot(s). Some backplanes support two MCHs for redundancy. In this case there are two MCH slots, with one being designated primary, and one secondary. === Advanced Mezzanine Card === Advanced Mezzanine Card (AMC) is a standard for hot-pluggable PCBs. It was originally developed to be used in AdvancedTCA systems. The standard specifies: the dimensions of the PCB with two width variants (single, double) and three height variants (Compact, Mid-size, Full) type, location and orientation of connectors (i.e. Zone 1, 2, 3) There is a huge variation of functionalities, an AMC can fulfill: Computing (i.e. a module with CPU, RAM, SSD and on-board graphics) Storage (i.e. SSD carrier) Graphics card FPGA card (i.e. for signal processing) FMC carrier Digitizer card (Analog-Digital and Digital-Analog Conversion) Clocking and Triggering and others === Rear Transition Module (MTCA.4 only) === The Rear Transition Module (RTM) was added in the MicroTCA.4 standard. It is connected directly to an AMC via a connector, located in zone 3, requiring a double width AMC and RTM. An RTM has about the same dimensions, as an AMC, basically doubling the available PCB-space per slot in an MTCA.4 card cage. Its power is provided by the AMC. Thus an RTM can not operate on its own, but requires a paired AMC. The zone 3 connector is electrically free configurable, making it possible, that a mechanically fitting AMC-RTM pair is electrically incompatible. To avoid damage due to that incompatibility, a mechanical code-pin was added to MTCA.4-compatible AMCs and RTMs, mechanically preventing the installation of an electrically incompatible RTM to an AMC. The functionality of RTMs includes, but is not limited to: RF-signal pre-/post-processing (i.e. filtering, Up-/Down-conversion, Vector De-/Modulation) Digital signal pre-/post-processing Clock-generation/-distribution Device interfaces Date storage CPU (only MCH-RTM)

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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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  Amplified conference

  An amplified conference is a conference or similar event in which the talks and discussions at the conference are 'amplified' through use of networked technologies in order to extend the reach of the conference deliberations. The term was originally coined by Lorcan Dempsey in a blog post. The term is now widely used within the academic and research community with Wankel proposing the following definition: The extension of a physical event (or a series of events) through the use of social media tools for expanding access to (aspects of) the event beyond physical and temporal bounds. Such amplification takes place in the context of intent to make the most of the intellectual content, discussion, networking, and discovery initiated by the event through the process of sharing with co-attendees, colleagues, friends and wider informed publics. A paper by Haider and others illustrates how amplified conferences are becoming mainstream in a discussion on "how social media have been employed as part of the project, particularly around event amplification". As described by Guy in the Ariadne ejournal the term is not a prescriptive one, but rather describes a pattern of behaviors which initially took place at IT and Web-oriented conferences once WiFi networks started to become available at conference venues and delegates started to bring with them networked devices such as laptops and, more recently, PDAs and mobile phones. == Different Approaches to 'Amplification' of Conferences == There are a number of ways in which conferences can be amplified through use of networked technologies: Amplification of the audiences' voice: Prior to the availability of real time chat technologies at events (whether use of IRC, Twitter, instant messaging clients, etc.) it was only feasible to discuss talks with immediate neighbours, and even then this may be considered rude. Amplification of the speaker's talk: The availability of video and audio-conferencing technologies make it possible for a speaker to be heard by an audience which isn't physically present at the conference. Although use of video technologies has been available to support conferences for some time, this has normally been expensive and require use of dedicated video-conferencing technologies. However the availability of lightweight desktop tools make it much easier to deploy such technologies, without even, requiring the involvement of conference organisers. Amplification across time: Video and audio technologies can also be used to allow a speaker's talk to be made available after the event, with use of podcasting or videocasting technologies allowing the talks to be easily syndicated to mobile devices as well as accessed on desktop computers. Amplification of the speaker's slides: The popularity of global repository services for slides, such as SlideShare, enable the slides used by a speaker to be more easily found, embedded on other Web sites and commented upon, in ways that were not possible when the slides, if made available at all, were only available on a conference Web site. Amplification of feedback to the speaker: Micro-blogging technologies, such as Twitter, are being used not only as a discussion channel for conference participants but also as a way of providing real-time feedback to a speaker during a talk. We are also now seeing dedicated microblogging technologies, such as Coveritlive and Scribblelive, being developed which aim to provide more sophisticated 'back channels' for use at conferences. Amplification of a conference's collective memory: The popularity of digital cameras and the photographic capabilities of many mobile phones is leading to many photographs being taken at conferences. With such photographs often being uploaded to popular photographic sharing services, such as Flickr, and such collections being made more easy to discover through agreed use of tags, we are seeing amplification of the memories of an event though the sharing of such resources. The ability of such photographic resources to be 'mashed up' with, say, accompanying music, can similarly help to enrich such collective experiences. Amplification of the learning: The ability to be able to follow links to resources and discuss the points made by a speaker during a talk can enrich the learning which takes place at an event, as described by Shabajee's article on "'Hot' or Not? Welcome to real-time peer review" published in the Times Higher Education Supplement in May 2003. Long term amplification of conference outputs: The availability in a digital format of conference resources, including 'official' resources such as slides, video and audio recordings, etc. which have been made by the conference organisers with the approval of speakers, together with more nebulous resources such as archives of conference back channels, and photographs and unofficial recordings taken at the event may help to provide a more authentic record of an event, which could potentially provide a valuable historical record. The amplification of conferences can be viewed as an example of how new technologies are altering standard practice. By using these techniques a different type of interaction is created at the conference itself, but also the boundaries around the conference can be seen as permeable, with remote participants engaging in discussion. An amplified conference also provides a considerably altered archive compared with a 'traditional' one. For the latter, the printed proceedings will be the main record, but for an amplified event this record is distributed across many media and takes in a wider range of content types, including the papers, videos of the presentations (for example on YouTube), the slides (e.g. on Slideshare), photos of the event (Flickr), interaction between participants (Twitter), reflections and comments (blogs), etc. The amplified conference represents an example of changing practice in digital scholarship.

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  Anomaly detection

  In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data. Anomaly detection finds application in many domains including cybersecurity, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud to name only a few. Anomalies were initially searched for clear rejection or omission from the data to aid statistical analysis, for example to compute the mean or standard deviation. They were also removed to better predictions from models such as linear regression, and more recently their removal aids the performance of machine learning algorithms. However, in many applications anomalies themselves are of interest and are the observations most desirous in the entire data set, which need to be identified and separated from noise or irrelevant outliers. Three broad categories of anomaly detection techniques exist. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier. However, this approach is rarely used in anomaly detection due to the general unavailability of labelled data and the inherent unbalanced nature of the classes. Semi-supervised anomaly detection techniques assume that some portion of the data is labelled. This may be any combination of the normal or anomalous data, but more often than not, the techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the model. Unsupervised anomaly detection techniques assume the data is unlabelled and are by far the most commonly used due to their wider and relevant application. == Definition == Many attempts have been made in the statistical and computer science communities to define an anomaly. The most prevalent ones include the following, and can be categorised into three groups: those that are ambiguous, those that are specific to a method with pre-defined thresholds usually chosen empirically, and those that are formally defined: === Ill defined === An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Anomalies are instances or collections of data that occur very rarely in the data set and whose features differ significantly from most of the data. An outlier is an observation (or subset of observations) which appears to be inconsistent with the remainder of that set of data. An anomaly is a point or collection of points that is relatively distant from other points in multi-dimensional space of features. Anomalies are patterns in data that do not conform to a well-defined notion of normal behaviour. === Specific === Let T be observations from a univariate Gaussian distribution and O a point from T. Then the z-score for O is greater than a pre-selected threshold if and only if O is an outlier. == History == === Intrusion detection === The concept of intrusion detection, a critical component of anomaly detection, has evolved significantly over time. Initially, it was a manual process where system administrators would monitor for unusual activities, such as a vacationing user's account being accessed or unexpected printer activity. This approach was not scalable and was soon superseded by the analysis of audit logs and system logs for signs of malicious behavior. By the late 1970s and early 1980s, the analysis of these logs was primarily used retrospectively to investigate incidents, as the volume of data made it impractical for real-time monitoring. The affordability of digital storage eventually led to audit logs being analyzed online, with specialized programs being developed to sift through the data. These programs, however, were typically run during off-peak hours due to their computational intensity. The 1990s brought the advent of real-time intrusion detection systems capable of analyzing audit data as it was generated, allowing for immediate detection of and response to attacks. This marked a significant shift towards proactive intrusion detection. As the field has continued to develop, the focus has shifted to creating solutions that can be efficiently implemented across large and complex network environments, adapting to the ever-growing variety of security threats and the dynamic nature of modern computing infrastructures. == Applications == Anomaly detection is applicable in a very large number and variety of domains, and is an important subarea of unsupervised machine learning. As such it has applications in cyber-security, intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, detecting ecosystem disturbances, defect detection in images using machine vision, medical diagnosis and law enforcement. === Intrusion detection === Anomaly detection was proposed for intrusion detection systems (IDS) by Dorothy Denning in 1986. Anomaly detection for IDS is normally accomplished with thresholds and statistics, but can also be done with soft computing, and inductive learning. Types of features proposed by 1999 included profiles of users, workstations, networks, remote hosts, groups of users, and programs based on frequencies, means, variances, covariances, and standard deviations. The counterpart of anomaly detection in intrusion detection is misuse detection. === Fintech fraud detection === Anomaly detection is vital in fintech for fraud prevention. === Preprocessing === Preprocessing data to remove anomalies can be an important step in data analysis, and is done for a number of reasons. Statistics such as the mean and standard deviation are more accurate after the removal of anomalies, and the visualisation of data can also be improved. In supervised learning, removing the anomalous data from the dataset often results in a statistically significant increase in accuracy. === Video surveillance === Anomaly detection has become increasingly vital in video surveillance to enhance security and safety. With the advent of deep learning technologies, methods using Convolutional Neural Networks (CNNs) and Simple Recurrent Units (SRUs) have shown significant promise in identifying unusual activities or behaviors in video data. These models can process and analyze extensive video feeds in real-time, recognizing patterns that deviate from the norm, which may indicate potential security threats or safety violations. An important aspect for video surveillance is the development of scalable real-time frameworks. Such pipelines are required for processing multiple video streams with low computational resources. === IT infrastructure === In IT infrastructure management, anomaly detection is crucial for ensuring the smooth operation and reliability of services. These are complex systems, composed of many interactive elements and large data quantities, requiring methods to process and reduce this data into a human and machine interpretable format. Techniques like the IT Infrastructure Library (ITIL) and monitoring frameworks are employed to track and manage system performance and user experience. Detected anomalies can help identify and pre-empt potential performance degradations or system failures, thus maintaining productivity and business process effectiveness. === IoT systems === Anomaly detection is critical for the security and efficiency of Internet of Things (IoT) systems. It helps in identifying system failures and security breaches in complex networks of IoT devices. The methods must manage real-time data, diverse device types, and scale effectively. Garg et al. have introduced a multi-stage anomaly detection framework that improves upon traditional methods by incorporating spatial clustering, density-based clustering, and locality-sensitive hashing. This tailored approach is designed to better handle the vast and varied nature of IoT data, thereby enhancing security and operational reliability in smart infrastructure and industrial IoT systems. === Petroleum industry === Anomaly detection is crucial in the petroleum industry for monitoring critical machinery. A 2015 paper proposed a novel segmentation algorithm using support vector machines to analyze sensor data for real-time anomaly detection. === Oil and gas pipeline monitoring === In the oil and gas sector, anomaly detection is not just crucial for maintenance and safety, but also for environmental protection. Aljameel et al. propose an advanced machine learning-based model for detecting minor leaks in oil and gas pipelines, a task traditional methods may miss.

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  Outline of electronics

  The following outline is provided as an overview of and topical guide to electronics: Electronics – branch of physics, engineering and technology dealing with electrical circuits that involve active semiconductor components and associated passive interconnection technologies. == Branches == === Classical electronics === Analog electronics Digital electronics Electronic instrumentation Electronic engineering Microelectronics Optoelectronics Power electronics Printed electronics Semiconductor technology Schematic capture Thermal management Automation Electronics === Advanced topics === Atomtronics Bioelectronics Failure modes of electronics Flexible electronics Low-power electronics Microelectromechanical systems (MEMS) Molecular electronics Nanoelectronics Organic electronics Photonics Piezotronics Quantum electronics Spintronics === History of electronics === History of electronic engineering History of radar History of radio History of television == General concepts == === Data converters === Analog-to-digital converters (ADC) Aliasing Successive approximation ADC Dual-slope ADC Quantization Sensor resolution Sampling Delta-sigma ADC Digital-to-analog converters (DAC) Digital potentiometer Binary weighted resistor converter Charge distribution DAC Pulse width modulator Reconstruction filter The R2R ladder === Digital electronics === Binary decision diagrams Boolean algebra Combinational logic Counters (digital) De Morgan's laws Digital circuit Formal verification Karnaugh maps Logic families Logic gate Logic minimization Logic simulation Logic synthesis Registers Sequential logic State machines Truth tables Transparent latch === Electrical element/discretes === Passive elements: Capacitor Inductor Memristor Resistor Transformer Active elements: Diode Zener diode Light-emitting diode PIN diode Schottky diode Avalanche diode Laser diode Microcontroller Operational amplifier Thyristor DIAC TRIAC IGBT Transistor Bipolar transistor (BJT) Field effect transistor (FET) Darlington transistor Other components Aural devices Battery (electricity) Crystal oscillator Electromechanical devices Sensors Surface acoustic wave (SAW) === Electronics analysis === Electronic packaging Electronic circuit simulation Electronic design automation Electronic noise Mathematical methods in electronics Thermal management of electronic devices and systems === Electronic circuits === Amplifiers Differential amplifiers Feedback amplifiers Power amplifiers Comparators Converters Filters Active filters Passive filters Digital filters Oscillators Phase-locked loops Timers === Electronic equipment === Air conditioner Breathalyzer Central heating Clothes dryer Computer/Notebook Dishwasher Freezer Home robot Home entertainment system Information technologies Cooker Microwave oven Refrigerator Robotic vacuum cleaner Tablet Telephone Water heater Washing machine === Television === Analog television History of television Television show Television broadcaster Timeline of the introduction of television in countries Mechanical television Color television Digital television Digital television transition Smart television Streaming television Internet Protocol television 3D television Terrestrial television ==== Television broadcasting ==== === Electronic instrumentation === Ammeter Capacitance meter Distortionmeter Electric energy meter LCR meter Microwave power meter Multimeter Network analyzer Ohmmeter Oscilloscope Psophometer Q meter Signal analyzer Signal generator Spectrum analyzer Transistor tester Tube tester Wattmeter Vectorscope Video signal generator Voltmeter VU meter === Memory technology === Flash memory Hard drive systems Optical storage Probe Storage Programmable read-only memory Read-only memory Solid-state drive (SSD) Volatile memory === Microcontrollers === Features Analog-to-digital converter Central processing unit (CPU) Clock generator (Quartz timing crystal, resonator or RC circuit) Debugging support Digital-to-analog converters Discrete input and output bits In-circuit programming Non-volatile memory (ROM, EPROM, EEPROM or Flash) Peripherals (Timers, event counters, PWM generators, and watchdog) Serial interface (Input/output such as serial ports (UARTs)) Serial communications (I²C, Serial Peripheral Interface and Controller Area Network) Volatile memory (RAM) 8-bit microcontroller families: AVR - PIC - COP8 - MCS-48 - MCS-51 - Z8 - eZ80 - HC08 - HC11 - H8 - PSoC Some notable suppliers: ARM Atmel Cypress Semiconductor Freescale Intel MIPS Microchip Technology NXP Semiconductors Parallax Propeller PowerPC Rabbit 2000 Renesas RX, V850 Silicon Laboratories STMicroelectronics Texas Instruments Toshiba TLCS === Optoelectronics === Optical fiber Optical properties Optical receivers Optical system design Optical transmitters === Physical laws === Ampère's law Coulomb's law Faraday's law of induction/Faraday-Lenz law Gauss's law Kirchhoff's circuit laws Current law Voltage law Maxwell's equations Gauss's law Faraday's law of induction Ampère's law Ohm's law === Power electronics === Power Devices Gate turn-off thyristor MOS-controlled thyristor (MCT) Power BJT/MOSFET Static induction devices Electric power conversion DC to DC DC to DC converter Voltage stabiliser Linear regulator AC to DC Rectifier Mains power supply unit (PSU) Switched-mode power supply DC to AC Inverter AC to AC Cycloconverter Transformer Variable frequency transformer Voltage converter Voltage regulator Power applications Automotive applications Capacitor charging applications Electronic ballasts Energy harvesting technologies Flexible AC transmission systems (FACTS) High frequency inverters HVDC transmission Motor controller Photovoltaic system Conversion Power factor correction circuits Power supply Renewable energy sources Switching power converters Uninterruptible power supply Wind power === Programmable devices === Application-specific integrated circuit (ASIC) Complex programmable logic device (CPLD) Erasable programmable logic device (EPLD) Simple programmable logic device (SPLD) Macrocell array Programmable array logic (PAL) Programmable logic array (PLA) Programmable logic device (PLD) Field-programmable gate array (FPGA) VHSIC Hardware Description Language (VHDL) Verilog Hardware Description Language Some notable suppliers: Altera - Atmel - Cypress Semiconductor - Lattice Semiconductor - Xilinx === Semiconductors theory === Properties Bipolar junction transistors Capacitance voltage profiling Charge carrier Charge-transfer complex Deep-level transient spectroscopy Depletion region Density of states Diode modelling Direct band gap Electronic band structure Energy level Exciton Field-effect transistors Metal–semiconductor junction MOSFETs N-type semiconductor Organic semiconductors P–n junction P-type semiconductor Photoelectric effect Quantum tunneling Semiconductor chip Semiconductor detector Solar cell Transistor model Thin film Tight-binding model Device Fabrication Semiconductor device fabrication Semiconductor industry Semiconductor consolidation == Applications == Audio electronics Automotive electronics Avionics Control Systems Consumer electronics Data acquisition E-health Electronic book Electronics industry Electronic warfare Embedded systems Home automation Integrated circuits Marine electronics Microwave technology Military electronics Multimedia Nuclear electronics Open hardware Radar and Radionavigation Radio electronics Terahertz technology Video hardware Wired and Wireless Communications

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  Digital divide

  Digital divide is inequitable access to and use of digital technology, encompassing four interrelated dimensions: motivational, material, skills, and usage access. The digital divide worsens inequality in access to information and resources. According to 2026 data from the U.S. Census Bureau, a significant 'digital divide' persists, with over 15.7 million Americans lacking access to high-speed broadband. Students from low-income households often face limited access to reliable internet and digital devices, which negatively affects their educational opportunities. In the Information Age, people without access to the Internet and other technology are at a disadvantage, for they are less able to connect with others, find and apply for jobs, shop, and learn. People living in poverty, in insecure housing or who are homeless, elderly people, and those living in rural communities may have limited access to the Internet; in contrast, urban middle class people have easy access to the Internet. Another divide is between producers and consumers of Internet content, which could be a result of educational disparities. While social media use varies across age groups, a US 2010 study reported no racial divide. == History == The historical roots of the digital divide in the United States refer to the increasing gap that occurred during the early modern period between those who could and could not access the real time forms of calculation, decision-making, and visualization offered via written and printed media. "Over time, focus has shifted from binary access to differentiated use, where quality and purpose of engagement vary across socio-economic groups." Within this context, ethical discussions regarding the relationship between education and the free distribution of information were raised by thinkers such as Immanuel Kant, Jean Jacques Rousseau, and Mary Wollstonecraft (1712–1778). The latter advocated that governments should intervene to ensure that any society's economic benefits should be fairly and meaningfully distributed. Amid the Industrial Revolution in Great Britain, Rousseau's idea helped to justify poor laws that created a safety net for those who were harmed by new forms of production. Later, when telegraph and postal systems evolved, many used Rousseau's ideas to argue for full access to those services, even if it meant subsidizing hard-to-serve citizens. Thus, "universal services" referred to innovations in regulation and taxation that would allow phone services such as AT&T in the United States to serve hard-to-serve rural users. In 1996, as telecommunications companies merged with Internet companies, the Federal Communications Commission adopted Telecommunications Act of 1996 to consider regulatory strategies and taxation policies to close the digital divide. Though the term "digital divide" was coined among consumer groups that sought to tax and regulate information and communications technology (ICeT) companies to close the digital divide, the topic soon moved onto a global stage. The focus was the World Trade Organization which passed the Telecommunications Services Act, which resisted regulation of ICT companies so that they would be required to serve hard-to-serve individuals and communities. In 1999, to assuage anti-globalization forces, the WTO hosted the "Financial Solutions to Digital Divide" in Seattle, US, co-organized by Craig Warren Smith of Digital Divide Institute and Bill Gates Sr. the chairman of the Bill and Melinda Gates Foundation. It catalyzed a full-scale global movement to close the digital divide, which quickly spread to all sectors of the global economy. In 2000, US president Bill Clinton mentioned the term in the State of the Union Address. Since the early 2000s, the international community has transitioned from a focus on domestic infrastructure to a global, multi-dimensional framework for digital equity. This shift was formalized through the World Summit on the Information Society (WSIS) in Geneva (2003) and Tunis (2005), where the International Telecommunication Union (ITU) established a roadmap for bridging the Global North-South disparity as part of the Sustainable Development Goals. Academic and policy discourse has since evolved to distinguish between the first-level divide (physical access), the second-level divide (digital literacy), and the third-level divide (the ability to translate technology use into socio-economic capital). By the 2020s, critical reflections on national development emphasized that the divide is fundamentally a socio-institutional gap. Research by Tiwari, Kostenko, and Yekhanurov (2025) identifies four pillars for achieving national digital maturity which are digital governance capacity, institutional design to prevent adverse digital incorporation, infrastructure resilience, and citizen capability. This modern era is characterized by the pursuit of meaningful connectivity, a standard that requires internet access to be not only available but affordable, high-speed, and supportive of active content creation. === During the COVID-19 pandemic === At the outset of the COVID-19 pandemic, governments worldwide issued stay-at-home orders that imposed lockdowns, quarantines, restrictions, and closures. The resulting interruptions to schooling, public services, and business operations drove nearly half of the world's population into seeking alternative methods to live while in isolation. These methods included telemedicine, virtual classrooms, online shopping, technology-based social interactions and working remotely, all of which require access to high-speed or broadband internet access and digital technologies. A Pew Research Centre study reports that 90% of Americans describe the use of the Internet as "essential" during the pandemic. The accelerated use of digital technologies created a landscape where the ability, or lack thereof, to access digital spaces became a crucial factor in everyday life. According to the Pew Research Center, 59% of children from lower-income families were likely to face digital obstacles in completing school assignments. These obstacles included the use of a cellphone to complete homework, having to use public Wi-Fi because of unreliable internet service in the home and lack of access to a computer in the home. This difficulty, titled the homework gap, affects more than 30% of K-12 students living below the poverty threshold, and disproportionally affects American Indian/Alaska Native, Black, and Hispanic students. These types of interruptions or privilege gaps in education exemplify problems in the systemic marginalization of historically oppressed individuals in primary education. The pandemic exposed inequity causing discrepancies in learning. "Large-scale events such as COVID-19 intensify both access and skills gaps, underlining the need for resilient digital inclusion policies. Studies during COVID-19 reveal first-level (access) and second-level (skills) divides, with underserved students struggling with reliable internet, devices, and platform navigation ” A lack of "tech readiness", that is, confident and independent use of devices, was reported among the US elderly population; with more than 50% reporting an inadequate knowledge of devices and more than one-third reporting a lack of confidence. "Older adults often face skills and confidence barriers, illustrating later-stage divides in van Dijk’s model." Moreover, according to a UN research paper, similar results can be found across various Asian countries, with those aged over 74, reporting less confident or inconsistent use of digital devices. This aspect of the digital divide and the elderly occurred during the pandemic as healthcare providers increasingly relied upon telemedicine to manage chronic and acute health conditions. == Aspects == There are various definitions of the digital divide, all with slightly different emphasis, which is evidenced by related concepts like digital inclusion, digital participation, digital skills, media literacy, and digital accessibility.“Van Dijk’s model identifies sequential barriers—motivational, material, skills, and usage—that must be addressed to bridge the divide.” === Infrastructure === The infrastructure by which individuals, households, businesses, and communities connect to the Internet addresses the physical mediums that people use to connect to the Internet such as desktop computers, laptops, basic mobile phones or smartphones, MP3 players, gaming consoles, electronic book readers, and tablets. Traditionally, the nature of the divide has been measured in terms of the existing numbers of subscriptions and digital devices. Given the increasing number of such devices, some have concluded that the digital divide among individuals has increasingly been closing as the result of a natural and almost automatic process. Others point to persistent lower levels of connectivity among women, racial and ethnic minorities, people with lower incomes, rura

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