AI for Business

Explore the best AI for Business — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

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  Paint.NET

  Paint.NET (sometimes stylized as paint.net) is a freeware general-purpose raster graphics editor program for Microsoft Windows, developed with the .NET platform. Paint.NET was originally created by Rick Brewster as a Washington State University student project, and has evolved from a simple replacement for the Microsoft Paint program into a program for editing mainly graphics, with support for plugins. == History == Paint.NET originated as a computer science senior design project by Rick Brewster during spring 2004 at Washington State University. Version 1.0 consisted of 36,000 lines of code and was written in four months. In contrast, version 3.35 has approximately 162,000 lines of code. The Paint.NET project continued over the summer and into the autumn 2004 semester for both the version 1.1 and 2.0 releases. Development continued with one programmer who worked on previous versions of Paint.NET while he was a student at WSU. As of May 2006 the program had been downloaded at least 2 million times, at a rate of about 180,000 per month. Initially, Paint.NET was released under a modified version of the MIT License, with the exclusion of the installer, text, and graphics. However, citing issues with the open source code being plagiarized by others that had rebranded the software as their own and bundled user content without their permission, the availability of the source code was restricted, in December 2007 Brewster announced his intent to restrict access to components of the program (including its installer, resources, and user interface). In November 2009, the software was made proprietary, restricting the sale or creation of derivative works of the software. Starting with version 4.0.18, Paint.NET is published in two editions: A classic edition remains freeware, similar to all other versions since 3.5. Another edition, however, is published to Microsoft Store under a trialware license and is available to purchase for US$14.99. According to the developer, this was done to enable the users to contribute to the development with more convenience, even though the old avenue of donation was not closed. In May 2026, Brewster revealed that he obtained the paint.net domain after attempting to do so for 22 years. Historically, the editor was hosted on getpaint.net, and according to Brewster, the previous owners of paint.net would not sell the domain and asked for "lots and lots of money". In December of the previous year, paint.net began hosting content that impersonated Paint.NET, therefore becoming a clear case of trademark infringement and domain squatting. Brewster stated that he was able to obtain the domain afterwards with the help of a lawyer. == Overview == Paint.NET is primarily programmed in the C# programming language. Its native image format, .PDN, is a compressed representation of the application's internal object format, which preserves layering and other information. == Plugins == Paint.NET supports plugins, which add image adjustments, effects, and support for additional file types. They can be programmed using any .NET Framework programming language, though they are most commonly written in C#. These are created by volunteer coders on the program's discussion board, the Paint.NET Forum. Though most are simply published via the discussion board, some have been included with a later release of the program. For instance, a DirectDraw Surface file type plugin, (originally by Dean Ashton) and an Ink Sketch and Soften Portrait effect (originally by David Issel) were added to Paint.NET in version 3.10. Hundreds of plugins have been produced; such as Shape3D, which renders a 2D drawing into a 3D shape. Some plugins expand on the functionality that comes with Paint.NET, such as Curves+ and Sharpen+, which extend the included tools Curves and Sharpen, respectively. Examples of file type plugins include an Animated Cursor and Icon plugin and an Adobe Photoshop file format plugin. Several of these plugins are based on existing open source software, such as a raw image format plugin that uses dcraw and a PNG optimization plugin that uses OptiPNG. == Forks == === paint-mono === Paint.NET was created exclusively for Windows and has no native support for other operating systems. Due to its former open-source licensing, the development of alternative versions was possible. In May 2007, Miguel de Icaza officially started a porting project called paint-mono. This project had partially ported Paint.NET 3.0 to Mono, an open-source implementation of the Common Language Infrastructure on which the .NET Framework is based. This allowed Paint.NET to be run on Mono-supported platforms, such as Linux. This port is no longer maintained and has not been updated since March 2009. Newer Mono runtime 6 versions are able to run original Paint.NET releases up to 3.5.11 with only minor issues. === Pinta === In 2010, developer Jonathan Pobst started a project called Pinta, describing it as a clone of Paint.NET for Mono and Gtk#. Pinta reused the adjustments and effects code from Paint.NET but otherwise is original code.

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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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  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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  Europa-Lehrmittel

  The Verlag Europa-Lehrmittel Nourney, Vollmer GmbH & Co. KG (English: European educational media), based in Haan (district Gruiten) near Düsseldorf, Germany, develops educational media for vocational training and further education as used in vocational schools, universities, companies and further education institutions for industrial-technical, business, social, health as well as gastronomic professions. The contents are delivered in print as well as in digital formats, including offered as apps, simulations, software and through an exam preparation portal. The publisher has its own digital learning platform, the Europathek. General partner is Nourney, Vollmer & Co. GmbH, a school and specialist book publisher. == History == The publishing house was founded in Wuppertal in 1948, when the first books were published. In Eislingen/Fils a construction office was set up to create the technical drawings for the textbooks - today the drawing office of the publisher in Ostfildern. A little over 40 years later, in 1989, a new company building was built in Haan-Gruiten. In 1997, Europa-Lehrmittel acquired the Fachbuchverlag Pfanneberg with its program aimed at gastronomic professions. Further program expansions in vocational training were realized by adding titles of the publishing houses Lau, Gerber, Pluspunkt, parts of the Verlag Handwerk und Technik program and that of the Gildebuchverlag. In 2013, the scientific and technical university titles of Verlag Harri Deutsch were taken over, including the bestselling "Handbook of Mathematics" by Ilya Nikolaevich Bronstein and Konstantin Adolfovic Semendyayev. In addition, Europa-Lehrmittel's first exam apps appeared. In 2017, the publisher introduced its own digital learning platform named Europathek. The Prüfungsdoc (English: Examination Doc) platform, which enables targeted online preparation for exams, went online the same year. In 2018, the titles of the Düsseldorf publishing house SOL for self-organized learning were incorporated into Europa-Lehrmittel's program. == Program == The program meanwhile includes well over 2000 print and digital publications, including specialist titles on metal technology, automotive and electrical engineering (like "Tabellenbuch Metall" (English: Metal Book of Tables)), on business administration, as well as works for the gastronomic profession (e.g. "Der junge Koch" (English: The Young Cook)). Many titles also appear as licensed editions in over 20 different languages on all continents. All contents are presented in an annual catalog, on the website, in schools, in companies, and at trade fairs and congresses. The publisher is a member of the Deutscher Hauswirtschaftsrat. == Digital educational media == Europathek: In addition to digital books, Europathek also provides media packages, additional materials and e-learning contents. The online contents of the 'media shelf' can be used per web browser. There are also software versions and apps for offline use, with the help of which the obtained titles can be viewed after downloading. Prüfungsdoc (English: Examination Doc): Prüfungsdoc offers web-based learning, practice and repetition to prepare for the intermediate and final examinations. This includes simulations of the exams with direct evaluation of the results.

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  Acoustic model

  An acoustic model is used in automatic speech recognition to represent the relationship between an audio signal and the phonemes or other linguistic units that make up speech. The model is learned from a set of audio recordings and their corresponding transcripts. It is created by taking audio recordings of speech, and their text transcriptions, and using software to create statistical representations of the sounds that make up each word. == Background == Modern speech recognition systems use both an acoustic model and a language model to represent the statistical properties of speech. The acoustic model models the relationship between the audio signal and the phonetic units in the language. The language model is responsible for modeling the word sequences in the language. These two models are combined to get the top-ranked word sequences corresponding to a given audio segment. Most modern speech recognition systems operate on the audio in small chunks known as frames with an approximate duration of 10ms per frame. The raw audio signal from each frame can be transformed by applying the mel-frequency cepstrum. The coefficients from this transformation are commonly known as mel-frequency cepstral coefficients (MFCCs) and are used as an input to the acoustic model along with other features. Recently, the use of convolutional neural networks has led to major improvements in acoustic modeling. == Speech audio characteristics == Audio can be encoded at different sampling rates (i.e. samples per second – the most common being: 8, 16, 32, 44.1, 48, and 96 kHz), and different bits per sample (the most common being: 8-bits, 16-bits, 24-bits or 32-bits). Speech recognition engines work best if the acoustic model they use was trained with speech audio which was recorded at the same sampling rate/bits per sample as the speech being recognized. == Telephony-based speech recognition == The limiting factor for telephony based speech recognition is the bandwidth at which speech can be transmitted. For example, a standard land-line telephone only has a bandwidth of 64 kbit/s at a sampling rate of 8 kHz and 8-bits per sample (8000 samples per second 8-bits per sample = 64000 bit/s). Therefore, for telephony based speech recognition, acoustic models should be trained with 8 kHz/8-bit speech audio files. In the case of voice over IP, the codec determines the sampling rate/bits per sample of speech transmission. Codecs with a higher sampling rate/bits per sample for speech transmission (which improve the sound quality) necessitate acoustic models trained with audio data that matches that sampling rate/bits per sample. == Desktop-based speech recognition == For speech recognition on a standard desktop PC, the limiting factor is the sound card. Most sound cards today can record at sampling rates of between 16–48 kHz of audio, with bit rates of 8- to 16-bits per sample, and playback at up to 96 kHz. As a general rule, a speech recognition engine works better with acoustic models trained with speech audio data recorded at higher sampling rates/bits per sample. But using audio with too high a sampling rate/bits per sample can slow the recognition engine down. A compromise is needed. Thus for desktop speech recognition, the current standard is acoustic models trained with speech audio data recorded at sampling rates of 16 kHz/16 bits per sample.

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  Algorithmic radicalization

  Algorithmic radicalization is the concept that recommender algorithms on popular social media sites, such as YouTube and Facebook, drive users toward progressively more extreme content over time, leading to the development of radicalized extremist political views. Algorithms meticulously record user interactions, encompassing likes, dislikes and the duration of time watching content, with the objective of generating an endless stream of media designed to sustain user engagement. The phenomenon of echo chamber channels has been demonstrated to exacerbate the polarization of consumers, primarily through the reinforcement of media preferences and the validation of one's existing beliefs. Algorithmic radicalization remains a controversial phenomenon as it is often not in the best interest of social media companies to remove echo chamber channels. To what extent recommender algorithms are actually responsible for radicalization remains disputed. Studies have found contradictory results regarding the promotion of extremist content by algorithms. == Social media echo chambers and filter bubbles == Social media platforms learn the interests and likes of the user to modify their experiences in their feed to keep them engaged and scrolling, known as a filter bubble. An echo chamber is formed when users come across beliefs that magnify or reinforce their thoughts and form a group of like-minded users in a closed system. Echo chambers spread information without any opposing beliefs and can possibly lead to confirmation bias. According to group polarization theory, an echo chamber can potentially lead users and groups towards more extreme radicalized positions. According to the National Library of Medicine, "Users online tend to prefer information adhering to their worldviews, ignore dissenting information, and form polarized groups around shared narratives. Furthermore, when polarization is high, misinformation quickly proliferates." == By site == === Facebook === Facebook's algorithm focuses on recommending content that makes the user want to interact. They rank content by prioritizing popular posts by friends, viral content, and sometimes divisive content. Each feed is personalized to the user's specific interests which can sometimes lead users towards an echo chamber of troublesome content. Users can find their list of interests the algorithm uses by going to the "Your ad Preferences" page. According to a Pew Research study, 74% of Facebook users did not know that list existed until they were directed towards that page in the study. It is also relatively common for Facebook to assign political labels to their users. In recent years, Facebook has started using artificial intelligence to change the content users see in their feed and what is recommended to them. A document known as The Facebook Files has revealed that their AI system prioritizes user engagement over everything else. The Facebook Files has also demonstrated that controlling the AI systems has proven difficult to handle. In an August 2019 internal memo leaked in 2021, Facebook has admitted that "the mechanics of our platforms are not neutral", concluding that in order to reach maximum profits, optimization for engagement is necessary. In order to increase engagement, algorithms have found that hate, misinformation, and politics are instrumental for app activity. As referenced in the memo, "The more incendiary the material, the more it keeps users engaged, the more it is boosted by the algorithm." According to a 2018 study, "false rumors spread faster and wider than true information... They found falsehoods are 70% more likely to be retweeted on Twitter than the truth, and reach their first 1,500 people six times faster. This effect is more pronounced with political news than other categories." === YouTube === YouTube has been around since 2005 and has more than 2.5 billion monthly users. YouTube discovery content systems focus on the user's personal activity (watched, favorites, likes) to direct them to recommended content. YouTube's algorithm is accountable for roughly 70% of users' recommended videos and what drives people to watch certain content. According to a 2022 study by the Mozilla Foundation, users have little power to keep unsolicited videos out of their suggested recommended content. This includes videos about hate speech, livestreams, etc. YouTube has been identified as an influential platform for spreading radicalized content. Al-Qaeda and similar extremist groups have been linked to using YouTube for recruitment videos and engaging with international media outlets. In a research study published by the American Behavioral Scientist Journal, they researched "whether it is possible to identify a set of attributes that may help explain part of the YouTube algorithm's decision-making process". The results of the study showed that YouTube's algorithm recommendations for extremism content factor into the presence of radical keywords in a video's title. In February 2023, in the case of Gonzalez v. Google, the question at hand is whether or not Google, the parent company of YouTube, is protected from lawsuits claiming that the site's algorithms aided terrorists in recommending ISIS videos to users. Section 230 is known to generally protect online platforms from civil liability for the content posted by its users. Multiple studies have found little to no evidence to suggest that YouTube's algorithms direct attention towards far-right content to those not already engaged with it. === TikTok === TikTok is a platform that recommends videos to a user's 'For You Page' (FYP), making every users' page different. With the nature of the algorithm behind the app, TikTok's FYP has been linked to showing more explicit and radical videos over time based on users' previous interactions on the app. Since TikTok's inception, the app has been scrutinized for misinformation and hate speech as those forms of media usually generate more interactions to the algorithm. Various extremist groups, including jihadist organizations, have utilized TikTok to disseminate propaganda, recruit followers, and incite violence. The platform's algorithm, which recommends content based on user engagement, can expose users to extremist content that aligns with their interests or interactions. As of 2022, TikTok's head of US Security has put out a statement that "81,518,334 videos were removed globally between April – June for violating our Community Guidelines or Terms of Service" to cut back on hate speech, harassment, and misinformation. Studies have noted instances where individuals were radicalized through content encountered on TikTok. For example, in early 2023, Austrian authorities thwarted a plot against an LGBTQ+ pride parade that involved two teenagers and a 20-year-old who were inspired by jihadist content on TikTok. The youngest suspect, 14 years old, had been exposed to videos created by Islamist influencers glorifying jihad. These videos led him to further engagement with similar content, eventually resulting in his involvement in planning an attack. Another case involved the arrest of several teenagers in Vienna, Austria, in 2024, who were planning to carry out a terrorist attack at a Taylor Swift concert. The investigation revealed that some of the suspects had been radicalized online, with TikTok being one of the platforms used to disseminate extremist content that influenced their beliefs and actions. == Self-radicalization == The U.S. Department of Justice defines 'Lone-wolf' (self) terrorism as "someone who acts alone in a terrorist attack without the help or encouragement of a government or a terrorist organization". Through social media outlets on the internet, 'Lone-wolf' terrorism has been on the rise, being linked to algorithmic radicalization. Through echo-chambers on the internet, viewpoints typically seen as radical were accepted and quickly adopted by other extremists. These viewpoints are encouraged by forums, group chats, and social media to reinforce their beliefs. == References in media == === The Social Dilemma === The Social Dilemma is a 2020 docudrama about how algorithms behind social media enables addiction, while possessing abilities to manipulate people's views, emotions, and behavior to spread conspiracy theories and disinformation. The film repeatedly uses buzz words such as 'echo chambers' and 'fake news' to prove psychological manipulation on social media, therefore leading to political manipulation. In the film, Ben falls deeper into a social media addiction as the algorithm found that his social media page has a 62.3% chance of long-term engagement. This leads into more videos on the recommended feed for Ben and he eventually becomes more immersed into propaganda and conspiracy theories, becoming more polarized with each video. == Proposed solutions == === United States: Weakening Section 230 protections === In the Communications Decency Act, Section 230 states t

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  Virtual advertising

  Virtual advertising is the use of digital technology to insert virtual advertisements into a live or pre-recorded television show, often in sports events. This technique is often used to allow broadcasters to overlay existing physical advertising panels inside the sports venue with virtual content on the screen when broadcasting the same event in multiple regions; a Spanish football game can be broadcast in Mexico with Mexican advertisements. Similarly, virtual content can be inserted onto empty space within the sports venue such as the pitch, where physical advertising cannot be placed due to regulatory or safety reasons. Virtual advertising content is intended to be photorealistic, so that the viewer has the impression they are seeing the real in-stadium advertising. == History == Throughout the 1980s, 1990s, and 2000s, advertising on television and in newspapers was a popular method of spreading information. The marketer Jeremiah Lynwood stated that "Thirty years ago, [U.S.] consumers viewed an average of 560 ads per day", mostly from newspapers, television shows, gasoline pumps, and so on. Lynwood also stated that, at the time, "American consumers may be exposed to 3,000 commercial messages every day". Within that time frame, the exposure of daily ads have supported many local and big businesses. With the arrival of the 2000s and 2010s, technological advances have created new opportunities for many businesses to grow. In the 21st century, virtual advertising has been used to create virtual product placements in television shows hours, days, or years after they have been produced. Advertisements can be targeted to regional markets and updated over time to ensure maximum efficiency of advertising money. A good example of how virtual advertising is used in everyday life is in sports. Virtual advertising uses the latest technology to place an ad in position to the field of play, regardless of camera motion, and the players' movement over the logos. Recently, the NHL have virtually inserted sponsors on the glass above the physical boards in NHL stadiums. Big brands will not spend their time or money on hitting a certain region when their main goal is to build global brand awareness. Digital signage opportunities allow these larger brands to purchase signage in a stadium during games that are instead nationally televised. This gets even more expansive thanks to social media outlets like Twitter, Facebook, and Amazon. On the other hand, local businesses sign when there are smaller games going on. The signage is much more affordable and still reaches a vast number of people. Virtual advertising may even make live attendance more attractive to sport fans because the technology allows the playing field and surrounding areas to be cleared of advertisements while television viewers at home are exposed to commercials. For the most part, virtual advertising makes a live attendance more attractive to sports fans, because instead of being at home watching commercials, live fans are able to be clear of advertisements and enjoy the game without pop-up ads. == Technology == The technology used in virtual insertions often uses automated processes such as: automatic detection of playfield limits, automatic detection of cuts, recognition of playfield surface, recognition of existing logos for logo replacements, etc. An operator is usually dedicated to the visual control of the effect but new systems allow to use the instant replay operator. == Examples == === Live events === Virtual advertisements can be effectively integrated into live television in real-time. For example, Fox Sports Net places a virtual advertisement on the glass behind the goaltender that can only be seen on television. The advertising in the playfields is property of the club, except in some professional sports where the league or federation owns the advertising rights. However, the advertising rights broadcast on the screen are property of the broadcasters or the TV channel. This means that second right holders can benefit from selling this virtual advertising. The number of TV viewers is also higher than the people in the stadium, generating more visibility to the advertised marks and more income to the broadcasters. Virtual advertising was first introduced in football during the 2015 Audi Cup at the Allianz Arena in Munich. AIM Sport implemented the technology to digitally overlay advertisements on the stadium's perimeter boards, allowing different sponsors to be displayed to viewers in different broadcast regions. In Formula One, virtual ads are placed on the grass or as virtual billboards. In baseball, Major League Baseball places virtual advertisements on a back-board behind the batter which can be targeted differently in local markets or countries. During the World Series, MLB international broadcasts of the World Series feature different advertisements on a per market basis, showing a different ad in the US, Canadian, Latin American and Japanese markets. In tennis, e.g. during the 2019 ATP Finals in London's O2 Arena certain logos in the background were replaced for various country feeds. In table tennis e.g. during the ITTF World Tour Australian Open 2019 virtual advertising overlays were used by uniqFEED AG in Switzerland. Since the 2022–23 season, the National Hockey League (NHL) has used digitally enhanced dasherboards (DED) to erase and replace ads on each arena's boards with up to 120 thirty-second segments on all or part of the rink. Each broadcaster can use a different set of ads. DED were first used at the 2016 World Cup of Hockey, which was organized by the NHL. At UEFA Euro 2024, AIM Sport provided virtual advertising for all matches, marking one of the largest implementations of the technology in an international tournament. In addition to the tournament itself, virtual advertising was also used in the participating teams' domestic matches, extending region-specific advertising beyond the competition itself.

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  DBOS

  DBOS (Formerly Database-Oriented Operating System, now just DBOS) is an open source durable workflow execution software library written for the Python, TypeScript, Java, and Go programming languages. DBOS arose from a joint open source project from MIT and Stanford, after a discussion between Michael Stonebraker and Matei Zaharia on how to scale and improve scheduling and performance of millions of Apache Spark tasks. Today it is a commercial company that offers an open source system to add durable computing to any software, built on concepts derived from the joint research project. == History == === 2020: Academic R&D Project === DBOS originated in 2020 as a joint open source project between MIT, Stanford, and Carnegie Mellon. The project explored the idea of operating system services built atop a distributed database - a database-oriented operating system meant to simplify and improve the scalability, security and resilience of large-scale distributed applications. The basic concept was to run a multi-node multi-core, transactional, highly-available distributed database, such as VoltDB, as the only application for a microkernel, and then to implement scheduling, messaging, file systems and other operating system services on top of the database. The architectural philosophy is described by this quote from the abstract of their initial preprint: All operating system state should be represented uniformly as database tables, and operations on this state should be made via queries from otherwise stateless tasks. This design makes it easy to scale and evolve the OS without whole-system refactoring, inspect and debug system state, upgrade components without downtime, manage decisions using machine learning, and implement sophisticated security features. A prototype was built with competitive performance to existing systems. ==

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  ElabFTW

  eLabFTW is a web application written by Nicolas Carpi in PHP which can be used to create personal and common logbooks. It has been developed at the Curie Institute originally. Besides there, it is used on universities around the world eLabFTW is licensed under the GNU Affero General Public License as free software. It is translated into seven languages. == Description == eLabFTW is a free and open-source lab book. It is written in PHP and uses a MySQL database. Docker containers are also available. Among the various features are Secure. Entries and transmission are encrypted Timestamps. RFC 3161 compliant timestamping of experiments. Inventory management. Apart from experience logs, it also can manage the inventory Import and export. Entries can be imported and exported == Platforms == eLabFTW is a PHP package with Mysql database. Therefore, it can be executed on most servers. Furthermore, the docker containers allow to run it almost everywhere. == Usage == eLabFTW is used by various universities, like University of Alberta, Berkeley University, Hanover Medical School, Cardiff University and UMC Utrecht

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  Data communication

  Data communication is the transfer of data over a point-to-point or point-to-multipoint communication channel. Data communication comprises data transmission and data reception and can be classified as analog transmission and digital communications. Analog data communication conveys voice, data, image, signal or video information using a continuous signal, which varies in amplitude, phase, or some other property. In baseband analog transmission, messages are represented by a sequence of pulses by means of a line code; in passband analog transmission, they are communicated by a limited set of continuously varying waveforms, using a digital modulation method. Passband modulation and demodulation are carried out by modem equipment. Digital transmission and digital reception are the transfer of either a digitized analog signal or a born-digital bitstream. Baseband digital transmission is regarded as comprising part of a digital signal, whereas passband transmission of digital data may also or alternatively be considered a form of digital-to-analog conversion. Data communication channels include copper wires, optical fibers, wireless communication using radio spectrum, storage media and computer buses. The data are represented as an electromagnetic signal, such as an electrical voltage, radiowave, microwave, or infrared signal. == Distinction between related subjects == Digital transmission or data transmission traditionally belongs to telecommunications and electrical engineering. Basic principles of data transmission may also be covered within the computer science or computer engineering topic of data communications, which also includes computer networking applications and communication protocols, for example, routing, switching and inter-process communication. Although the Transmission Control Protocol (TCP) involves transmission, TCP and other transport layer protocols are covered in computer networking but not discussed in a textbook or course about data transmission. In most textbooks, the term analog transmission only refers to the transmission of an analog message signal (without digitization) by means of an analog signal, either as a non-modulated baseband signal or as a passband signal using an analog modulation method such as AM or FM. It may also include analog-over-analog pulse modulated baseband signals such as pulse-width modulation. In a few books within the computer networking tradition, analog transmission also refers to passband transmission of bit-streams using digital modulation methods such as FSK, PSK and ASK. The theoretical aspects of data transmission are covered by information theory and coding theory. == Protocol layers and sub-topics == Courses and textbooks in the field of data transmission typically deal with the following OSI model protocol layers and topics: Layer 1, the physical layer: Channel coding including Digital modulation schemes Line coding schemes Forward error correction (FEC) codes Bit synchronization Multiplexing Equalization Channel models Layer 2, the data link layer: Channel access schemes, media access control (MAC) Packet mode communication and Frame synchronization Error detection and automatic repeat request (ARQ) Flow control Layer 6, the presentation layer: Source coding (digitization and data compression), and information theory. Cryptography (may occur at any layer) It is also common to deal with the cross-layer design of those three layers. == Applications and history == Data (mainly but not exclusively informational) has been sent via non-electronic (e.g. optical, acoustic, mechanical) means since the advent of communication. Analog signal data has been sent electronically since the advent of the telephone. However, the first data electromagnetic transmission applications in modern time were electrical telegraphy (1809) and teletypewriters (1906), which are both digital signals. The fundamental theoretical work in data transmission and information theory by Harry Nyquist, Ralph Hartley, Claude Shannon and others during the early 20th century, was done with these applications in mind. In the early 1960s, Paul Baran invented distributed adaptive message block switching for digital communication of voice messages using switches that were low-cost electronics. Donald Davies invented and implemented modern data communication during 1965–7, including packet switching, high-speed routers, communication protocols, hierarchical computer networks and the essence of the end-to-end principle. Baran's work did not include routers with software switches and communication protocols, nor the idea that users, rather than the network itself, would provide the reliability. Both were seminal contributions that influenced the development of computer networks. Data transmission is utilized in computers in computer buses and for communication with peripheral equipment via parallel ports and serial ports such as RS-232 (1969), FireWire (1995) and USB (1996). The principles of data transmission are also utilized in storage media for error detection and correction since 1951. The first practical method to overcome the problem of receiving data accurately by the receiver using digital code was the Barker code invented by Ronald Hugh Barker in 1952 and published in 1953. Data transmission is utilized in computer networking equipment such as modems (1940), local area network (LAN) adapters (1964), repeaters, repeater hubs, microwave links, wireless network access points (1997), etc. In telephone networks, digital communication is utilized for transferring many phone calls over the same copper cable or fiber cable by means of pulse-code modulation (PCM) in combination with time-division multiplexing (TDM) (1962). Telephone exchanges have become digital and software controlled, facilitating many value-added services. For example, the first AXE telephone exchange was presented in 1976. Digital communication to the end user using Integrated Services Digital Network (ISDN) services became available in the late 1980s. Since the end of the 1990s, broadband access techniques such as ADSL, Cable modems, fiber-to-the-building (FTTB) and fiber-to-the-home (FTTH) have become widespread to small offices and homes. The current tendency is to replace traditional telecommunication services with packet mode communication such as IP telephony and IPTV. Transmitting analog signals digitally allows for greater signal processing capability. The ability to process a communications signal means that errors caused by random processes can be detected and corrected. Digital signals can also be sampled instead of continuously monitored. The multiplexing of multiple digital signals is much simpler compared to the multiplexing of analog signals. Because of all these advantages, because of the vast demand to transmit computer data and the ability of digital communications to do so and because recent advances in wideband communication channels and solid-state electronics have allowed engineers to realize these advantages fully, digital communications have grown quickly. The digital revolution has also resulted in many digital telecommunication applications where the principles of data transmission are applied. Examples include second-generation (1991) and later cellular telephony, video conferencing, digital TV (1998), digital radio (1999), and telemetry. Data transmission, digital transmission or digital communications is the transfer of data over a point-to-point or point-to-multipoint communication channel. Examples of such channels include copper wires, optical fibers, wireless communication channels, storage media and computer buses. The data are represented as an electromagnetic signal, such as an electrical voltage, radio wave, microwave, or infrared light. While analog transmission is the transfer of a continuously varying analog signal over an analog channel, digital communication is the transfer of discrete messages over a digital or an analog channel. The messages are either represented by a sequence of pulses by means of a line code (baseband transmission) or by a limited set of continuously varying waveforms (passband transmission), using a digital modulation method. The passband modulation and corresponding demodulation (also known as detection) are carried out by modem equipment. According to the most common definition of a digital signal, both baseband and passband signals representing bit-streams are considered as digital transmission, while an alternative definition only considers the baseband signal as digital, and passband transmission of digital data as a form of digital-to-analog conversion. Data transmitted may be digital messages originating from a data source, for example, a computer or a keyboard. It may also be an analog signal, such as a phone call or a video signal, digitized into a bit-stream, for example,e using pulse-code modulation (PCM) or more advanced source coding (analog-to-digital conversion and

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  Digital exhibition

  Digital Exhibition includes both the projection technologies, such as High Definition, and delivery technologies of a film to a movie theater. Delivery technologies include disk drives, satellite relay, and fiber optics. This can save money in distribution but is usually more expensive overall due to maintenance and standardization of technology. However, there are benefits to digital exhibition, for example it requires less assembly by the exhibitor and can contain the trailers that the distributor wishes.

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  Conversion path

  A conversion path is a description of the steps taken by a user of a website towards a desired end from the standpoint of the website operator or marketer. The typical conversion path begins with a user arriving at a landing page or a product page and proceeding through a series of page transitions until reaching a final state, either positive (e.g. purchase) or negative (e.g. abandoned session). In practice, the study of the dynamics of this process by the interested party has evolved into a sophisticated field, where various statistical methods are being applied to the optimization of outcomes. This includes real-time adjustment of presented content, in which a website operator tries to provide deliberate incentives to increase the odds of conversion based on various sources of information, including demographic traits, search history, and browsing events. In practice, this reflects in different content presented to users arriving from online advertising versus search engines, and similarly, different content is presented depending on their demographic segments. The fundamental metric describing this process in the aggregate is known as conversion rate.

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  Facial age estimation

  Facial age estimation is the use of artificial intelligence to estimate the age of a person based on their facial features. Computer vision techniques are used to analyse the facial features in the images of millions of people whose age is known and then deep learning is used to create an algorithm that tries to predict the age of an unknown person. The key use of the technology is to prevent access to age-restricted goods and services. Examples include restricting children from accessing internet pornography, checking that they meet a mandatory minimum age when registering for an account on social media, or preventing adults from accessing websites, online chat or games designed only for use by children. The technology is distinct from facial recognition systems as the software does not attempt to uniquely identify the individual. Researchers have applied neural networks for age estimation since at least 2010. == Evaluation == An ongoing study by the National Institute of Standards and Technology (NIST) entitled 'Face Analysis Technology Evaluation' seeks to establish the technical performance of prototype age estimation algorithms submitted by academic teams and software vendors including Brno University of Technology, Czech Technical University in Prague, Dermalog, IDEMIA, Incode Technologies Inc, Jumio, Nominder, Rank One Computing, Unissey and Yoti. == Public sector use == The UK government has explored using facial age estimation at the UK border as an alternative to bone X-rays and MRI scans when determining child status of asylum seekers. == Commercial use == Commercial users of facial age estimation include Instagram and OnlyFans. In January 2025, John Lewis & Partners announced that had started using the technology to check the age of people shopping for knives on its website, to comply with UK legislation to limit knife crime. In the UK, several supermarket chains have taken part in Home Office trials of the technology to automate the checking of a customer's age when buying age-restricted goods such as alcohol. UK legislation introduced in January 2025 mandates robust forms of age verification hosting adult content viewable in the UK by July 2025. Allowable methods include facial age estimation. == Criticism == Adam Schwartz, a lawyer for the Electronic Frontier Foundation, criticized the use of facial age estimation software, noting its inaccuracy especially in cases of minorities and women, as was found in NIST's 2024 report. Twenty organisations jointly under European Digital Rights called the practice a "systematic and invasive processing of young people's data" that risks discriminatory profiling.

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  Software-defined mobile network

  Software-defined mobile networking (SDMN) is an approach to the design of mobile networks where all protocol-specific features are implemented in software, maximizing the use of generic and commodity hardware and software in both the core network and radio access network (RAN). == History == Through the 20th century, telecommunications technology was driven by hardware development, with most functions implemented in special-purpose equipment. In the early 2000s, generally available CPUs became cheap enough to enable commercial software-defined radio (SDR) technology and softswitches. SDMN extends these trends into the design of mobile networks, moving nearly all network functions into software. The term "software-defined mobile network" first appeared in public literature in early 2014, used independently by Lime Microsystems and researchers from University of Oulu, Finland. == Limitations of hardware-based mobile networks == Mobile networks based on special-purpose hardware suffer from the following limitations: They have limited provisions for upgrades and usually must be replaced entirely when new standards are introduced. The individual components are not scalable in terms of performance and capacity, because the capacity of a component is fixed by the hardware implementation. Specialized equipment and its associated specialized software require vendor-specific training for the mobile operator's staff. Specialized hardware systems are usually supported and serviced by a single vendor, resulting in vendor lock-in. == Characteristics of SDMN designs == === Use of software-defined radio === SDR is an important element of SDMN, because it replaces protocol-specific radio hardware with protocol-agnostic digital transceivers. While many earlier digital radio systems used field-programmable gate arrays (FPGAs) or special-purposed digital signal processors (DSPs) for calculations on baseband radio waveforms, the SDMN approach moves all of the baseband processing into general-purpose CPUs. SDMN radio systems also use hardware with publicly-documented interfaces that is designed to be readily reproducible by multiple manufacturers. === Commodity components === SDMN designs avoid the use of components that are specialized as to their functions or that are available from only a single vendor. This is true of both the hardware and software elements of the network. === Software switching and transcoding === The telephony switches of SDMN networks are software-based, including software transcoding for speech codecs. === Centralized, distributed, or hybrid? === A new SDN architecture for wireless distribution systems (WDSs) is explored that eliminates the need for multi-hop flooding of route information and therefore enables WDNs to easily expand. The key idea is to split network control and data forwarding by using two separate frequency bands. The forwarding nodes and the SDN controller exchange link-state information and other network control signaling in one of the bands, while actual data forwarding takes place in the other band. == Advantages of SDMN == The SDMN approach has many advantages over hardware-based mobile network designs. Because SDMN hardware is protocol-agnostic, upgrades are software-only, even across technology generations. In the radio network, these changes can even be made on a site-by-site basis. Because SDMN hardware is designed to be easily sourced and reproduced: SDMN equipment can be serviced by a wider range of vendors, lowering maintenance costs. SDMN equipment can be manufactured anywhere in the world, lowering production costs. Because SDMN software is based on commodity operating systems and development tools: Support staff can be trained more quickly because they are already familiar with the underlying software systems. Many aspects of the SDMN can be monitored and managed with pre-existing tools, because they are already available in the commodity operating systems. Because SDMN network components run on general purpose computers, the network components can be scaled up in capacity by adding more computing power.

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  False answer supervision

  False answer supervision (FAS) refers to VoIP fraud where the billed duration for the caller is more than the duration of the actual connection duration. The FAS is usually performed by VoIP wholesalers in their softswitches for randomly selected calls. Adding a small amount of extra billed seconds for many calls results in significant revenue for the VoIP wholesaler. == Implementation of FAS == The FAS fraud can be implemented in a softswitch in many different ways. These include: False billing of party A without calling a party B. Usually a fake ringback tone, loopback audio or voicemail message is played Start of billing before actual answer of party B Extra billing after disconnection of party B == Detection of FAS == The FAS can be detected and blocked in a softswitch. Common methods are: Manual verification of call detail records: listening to voice recordings Identification of FAS types and using algorithms to automatically detect the FAS RTP audio signal processing: detection of voice RTP audio signal processing: detection of silence RTP audio signal processing: detection of ringback tone

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