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  • ReRites

    ReRites

    ReRites (also known as RERITES, ReadingRites, Big Data Poetry) is a literary work of "Human + A.I. poetry" by David Jhave Johnston that used neural network models trained to generate poetry which the author then edited. ReRites won the Robert Coover Award for a Work of Electronic Literature in 2022. == About the project == The ReRites project began as a daily rite of writing with a neural network, expanded into a series of performances from which video documentation has been published online, and concluded with a set of 12 books and an accompanying book of essays published by Anteism Books in 2019. In Electronic Literature, Scott Rettberg describes the early phases of the project in 2016, when it bore the preliminary name Big Data Poetry. Jhave (the artist name that David Jhave Johnston goes by) describes the process of writing ReRites as a rite: "Every morning for 2 hours (normally 6:30–8:30am) I get up and edit the poetic output of a neural net. Deleting, weaving, conjugating, lineating, cohering. Re-writing. Re-wiring authorship: hybrid augmented enhanced evolutionary". There is video documentation of the writing process. The human editing of the neural network's output is fundamental to this project, and Jhave gives examples of both unedited text extracts and his edited versions in publications about the project. Kyle Booten describes ReRites as "simultaneously dusty and outrageously verdant, monotonously sublime and speckled with beautiful and rare specimens". === Performances === ReRites was first shared with an audience through a series of performances where audience members and poets would participate in reading the automatically generated texts, which appeared on screen so fast that human readers could barely keep up. This has been described as allowing participants to "re-discover[..] the peculiar pleasures of being embodied", or, in Jhave's own words, as a space where human participants were "playing their wits and voices against an evocative infinite deep-learning muse". The first performance was at Brown University's Interrupt Festival in 2019. It has been performed many times since, including at the Barbican Centre in London and Anteism Books. === Print publications === For a single year Jhave published one book of poetry from the ReRites project each month. These twelve volumes are accompanied by a book of essays, all published by Anteism Books. The accompanying essays provide critical responses to the project from poets and scholars including Allison Parrish, Johanna Drucker, Kyle Booten, Stephanie Strickland, John Cayley, Lai-Tze Fan, Nick Montfort, Mairéad Byrne, and Chris Funkhouser. Allison Parrish notes elsewhere that these paratexts to ReRites serve a legitimising function for a genre of poetry that is not yet institutionally acknowledged. === Technical details === Starting in 2016 under the name Big Data Poetry, Jhave generated poems using, in his own words, "neural network code (..) adapted from three corporate github-hosted machine-learning libraries: TensorFlow (Google), PyTorch (Facebook), and AWD-LSTM (SalesForce)". He explains that the "models were trained on a customised corpus of 600,000 lines of poetry ranging from the romantic epoch to the 20th century avant garde". Jhave maintains a GitHub repository with some of the code supporting ReRites. == Reception == ReRites is described by John Cayley as "one of the most thorough and beautiful" poetic responses to machine learning. The work's influence on the field of electronic literature was acknowledged in 2022, when the work won the Electronic Literature Organization's Robert Coover Award for a Work of Electronic Literature. The jury described ReRites as particularly poignant in the time of the pandemic, as it was "a documentation of the performance of the private ritual of writing and the obsessive-compulsive need for writers to communicate — even when no one else is reading". The question of authorship and voice in ReRites has been raised by several critics. Although generated poetry is an established genre in electronic literature, Cayley notes that unlike the combinatory poems created by authors like Nick Montfort, where the author explicitly defines which words and phrases will be recombined, ReRites has "not been directed by literary preconceptions inscribed in the program itself, but only by patterns and rhythms pre-existing in the corpora". In an essay for the Australian journal TEXT, David Thomas Henry Wright asks how to understand authorship and authority in ReRites: "Who or what is the authority of the work? The original data fed into the machine, that is not currently retrievable or discernible from the final works? The code that was taken and adapted for his purposes? Or Jhave, the human editor?" Wright concludes that Jhave is the only actor with any intentionality and therefore the authority of the work. The centrality of the human editor is also emphasised by other scholars. In a chapter analysing ReRites Malthe Stavning Erslev argues that the machine learning misrepresents the dataset it is trained on. While ReRites uses 21st century neural networks, it has been compared to earlier literary traditions. Poet Victoria Stanton, who read at one of the ReRites performances, has compared ReRites to found poetry, while David Thomas Henry Wright compares it to the Oulipo movement and Mark Amerika to the cut-up technique. Scholars also position ReRites firmly within the long tradition of generative poetry both in electronic literature and print, stretching from the I Ching, Queneau's Cent Mille Milliards de Poemes and Nabokov's Pale Fire to computer-generated poems like Christopher Strachey's Love Letter Generator (1952) and more contemporary examples. Jhave describes the process of working with the output from the neural network as "carving". In his book My Life as an Artificial Creative Intelligence, Mark Amerika writes that the "method of carving the digital outputs provided by the language model as part of a collaborative remix jam session with GPT-2, where the language artist and the language model play off each other’s unexpected outputs as if caught in a live postproduction set, is one I share with electronic literature composer David Jhave Johnston, whose AI poetry experiments precede my own investigations."

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  • Data communication

    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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  • Active networking

    Active networking

    Active networking is a communication pattern that allows packets flowing through a telecommunications network to dynamically modify the operation of the network. Active network architecture is composed of execution environments (similar to a unix shell that can execute active packets), a node operating system capable of supporting one or more execution environments. It also consists of active hardware, capable of routing or switching as well as executing code within active packets. This differs from the traditional network architecture which seeks robustness and stability by attempting to remove complexity and the ability to change its fundamental operation from underlying network components. Network processors are one means of implementing active networking concepts. Active networks have also been implemented as overlay networks. == What does it offer? == Active networking allows the possibility of highly tailored and rapid "real-time" changes to the underlying network operation. This enables such ideas as sending code along with packets of information allowing the data to change its form (code) to match the channel characteristics. The smallest program that can generate a sequence of data can be found in the definition of Kolmogorov complexity. The use of real-time genetic algorithms within the network to compose network services is also enabled by active networking. == How it relates to other networking paradigms == Active networking relates to other networking paradigms primarily based upon how computing and communication are partitioned in the architecture. === Active networking and software-defined networking === Active networking is an approach to network architecture with in-network programmability. The name derives from a comparison with network approaches advocating minimization of in-network processing, based on design advice such as the "end-to-end argument". Two major approaches were conceived: programmable network elements ("switches") and capsules, a programmability approach that places computation within packets traveling through the network. Treating packets as programs later became known as "active packets". Software-defined networking decouples the system that makes decisions about where traffic is sent (the control plane) from the underlying systems that forward traffic to the selected destination (the data plane). The concept of a programmable control plane originated at the University of Cambridge in the Systems Research Group, where (using virtual circuit identifiers available in Asynchronous Transfer Mode switches) multiple virtual control planes were made available on a single physical switch. Control Plane Technologies (CPT) was founded to commercialize this concept. == Fundamental challenges == Active network research addresses the nature of how best to incorporate extremely dynamic capability within networks. In order to do this, active network research must address the problem of optimally allocating computation versus communication within communication networks. A similar problem related to the compression of code as a measure of complexity is addressed via algorithmic information theory. One of the challenges of active networking has been the inability of information theory to mathematically model the active network paradigm and enable active network engineering. This is due to the active nature of the network in which communication packets contain code that dynamically change the operation of the network. Fundamental advances in information theory are required in order to understand such networks. == Nanoscale active networks == As the limit in reduction of transistor size is reached with current technology, active networking concepts are being explored as a more efficient means accomplishing computation and communication. More on this can be found in nanoscale networking.

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  • VHS

    VHS

    VHS (Video Home System) is a discontinued standard for consumer-level analog video recording on tape cassettes, introduced in 1976 by JVC. It was the dominant home video format throughout the tape media period of the 1980s and 1990s. Magnetic tape video recording was adopted by the television industry in the 1950s in the form of the first commercialized video tape recorders (VTRs), but the devices were expensive and used only in professional environments. In the 1970s, videotape technology became affordable for home use, and widespread adoption of videocassette recorders (VCRs) began; the VHS became the most popular media format for VCRs as it would win the "format war" against Betamax (backed by Sony) and a number of other competing tape standards. The cassettes themselves use a 0.5-inch (12.7 mm) magnetic tape between two spools and typically offer a capacity of at least two hours. The popularity of VHS was intertwined with the rise of the video rental market, when films were released on pre-recorded videotapes for home viewing. Newer improved tape formats such as S-VHS were later developed, as well as the earliest optical disc format, LaserDisc; the lack of global adoption of these formats increased VHS's lifetime, which eventually peaked and started to decline in the late 1990s after the introduction of DVD, a digital optical disc format. VHS rentals were surpassed by DVD in the United States in 2003, which eventually became the preferred low-end method of movie distribution. For home recording purposes, VHS and VCRs were surpassed by (typically hard disk–based) digital video recorders (DVR) in the 2000s. Production of all VHS equipment ceased by 2016, although the format has since gained some popularity amongst collectors. A niche revival of VHS has taken place with This Is How The World Ends becoming the first straight-to-VHS release in 20 years. == History == === Before VHS === In 1956, after several attempts by other companies, the first commercially successful VTR, the Ampex VRX-1000, was introduced by Ampex Corporation. At a price of US$50,000 in 1956 (equivalent to $592,000 in 2025) and US$300 (equivalent to $3,600 in 2025) for a 90-minute reel of tape, it was intended only for the professional market. Kenjiro Takayanagi, a television broadcasting pioneer then working for JVC as its vice president, saw the need for his company to produce VTRs for the Japanese market at a more affordable price. In 1959, JVC developed a two-head video tape recorder and, by 1960, a color version for professional broadcasting. In 1964, JVC released the DV220, which would be the company's standard VTR until the mid-1970s. In 1969, JVC collaborated with Sony and Matsushita Electric (Matsushita was the majority stockholder of JVC until 2011) to build a video recording standard for the Japanese consumer. The effort produced the U-matic format in 1971, which was the first cassette format to become a unified standard for different companies. It was preceded by the reel-to-reel 1⁄2-inch EIAJ format. The U-matic format was successful in businesses and some broadcast television applications, such as electronic news-gathering, and was produced by all three companies until the late 1980s, but because of cost and limited recording time, very few of the machines were sold for home use. Therefore, soon after the U-Matic release, all three companies started working on new consumer-grade video recording formats of their own. Sony started working on Betamax, Matsushita started working on VX, and JVC released the CR-6060 in 1975, based on the U-matic format. === VHS development === In 1971, JVC engineers Yuma Shiraishi and Shizuo Takano put together a team to develop a VTR for consumers. By the end of 1971, they created an internal diagram, "VHS Development Matrix", which established twelve objectives for JVC's new VTR; among them: The system must be compatible with any ordinary television set. Picture quality must be similar to a normal air broadcast. The tape must have at least a two-hour recording capacity. Tapes must be interchangeable between machines. The overall system should be versatile, meaning it can be scaled and expanded, such as connecting a video camera, or dubbing between two recorders. Recorders should be affordable, easy to operate, and have low maintenance costs. Recorders must be capable of being produced in high volume, their parts must be interchangeable, and they must be easy to service. In early 1972, the commercial video recording industry in Japan took a financial hit. JVC cut its budgets and restructured its video division, shelving the VHS project. However, despite the lack of funding, Takano and Shiraishi continued to work on the project in secret. By 1973, the two engineers had produced a functional prototype. === Competition with Betamax === In 1974, the Japanese Ministry of International Trade and Industry (MITI), desiring to avoid consumer confusion, attempted to force the Japanese video industry to standardize on just one home video recording format. Later, Sony had a functional prototype of the Betamax format, and was very close to releasing a finished product. With this prototype, Sony persuaded the MITI to adopt Betamax as the standard, and allow it to license the technology to other companies. JVC believed that an open standard, with the format shared among competitors without licensing the technology, was better for the consumer. To prevent the MITI from adopting Betamax, JVC worked to convince other companies, in particular Matsushita (Japan's largest electronics manufacturer at the time, marketing its products under the National brand in most territories and the Panasonic brand in North America, and JVC's majority stockholder), to accept VHS, and thereby work against Sony and the MITI. Matsushita agreed, fearing Sony would dominate the market with a Betamax monopoly. Matsushita also regarded Betamax's one-hour recording time limit as a disadvantage. Matsushita's backing of JVC persuaded Hitachi, Mitsubishi, and Sharp to back the VHS standard as well. Sony's release of its Betamax unit to the Japanese market in 1975 placed further pressure on the MITI to side with the company. However, the collaboration of JVC and its partners was much stronger, which eventually led the MITI to drop its push for an industry standard. JVC released the first VHS machines in Japan in late 1976, and in the United States in mid-1977. Sony's Betamax competed with VHS throughout the late 1970s and into the 1980s (see Videotape format war). Betamax's major advantages were its smaller cassette size, theoretical higher video quality, and earlier availability, but its shorter recording time proved to be a major shortcoming. Originally, Beta I machines using the NTSC television standard were able to record one hour of programming at their standard tape speed of 1.5 inches per second (ips). The first VHS machines could record for two hours, due to both a slightly slower tape speed (1.31 ips) and significantly longer tape. Betamax's smaller cassette limited the size of the reel of tape, and could not compete with VHS's two-hour capability by extending the tape length. Instead, Sony had to slow the tape down to 0.787 ips (Beta II) in order to achieve two hours of recording in the same cassette size. Sony eventually created a Beta III speed of 0.524 ips, which allowed NTSC Betamax to break the two-hour limit, but by then VHS had already won the format battle. Additionally, VHS had a "far less complex tape transport mechanism" than Betamax, and VHS machines were faster at rewinding and fast-forwarding than their Sony counterparts. VHS eventually won the war, gaining 60% of the North American market by 1980. == Initial releases of VHS-based devices == The first VCR to use VHS was the Victor HR-3300, and was introduced by the president of JVC in Japan on September 9, 1976. JVC started selling the HR-3300 in Akihabara, Tokyo, Japan, on October 31, 1976. Region-specific versions of the JVC HR-3300 were also distributed later on, such as the HR-3300U in the United States, and the HR-3300EK in the United Kingdom. The United States received its first VHS-based VCR, the RCA VBT200, on August 23, 1977. The RCA unit was designed by Matsushita and was the first VHS-based VCR manufactured by a company other than JVC. It was also capable of recording four hours in LP (long play) mode. The UK received its first VHS-based VCR, the Victor HR-3300EK, in 1978. Quasar and General Electric followed-up with VHS-based VCRs – all designed by Matsushita. By 1999, Matsushita alone produced just over half of all Japanese VCRs. TV/VCR combos, combining a TV set with a VHS mechanism, were also once available for purchase. Combo units containing both a VHS mechanism and a DVD player were introduced in the late 1990s, and at least one combo unit, the Panasonic DMP-BD70V, included a Blu-ray player. == Technical details == VHS has been standardized in IEC 60774–1. === Cassette and

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  • AltStore

    AltStore

    AltStore is an alternative app store for the iOS and iPadOS[1] mobile operating systems, which allows users to download applications that are not available on the App Store, most commonly tweaked apps, jailbreak apps, and apps including paid apps on the app store. It was publicly announced on September 25, 2019, and launched on September 28. == History == Riley Testut is an American developer who began to work on AltStore after Apple declined to allow his Nintendo emulator Delta on the App Store. Since Xcode allowed him to temporarily install his Delta app to his iOS device for 7 days of testing, he created AltStore in 2019 to replicate this functionality, which could be extended to other .ipa files. As of 2022, AltStore had been downloaded 1.5 million times. In the following years, AltStore expanded beyond its initial sideloading functionality. The platform was founded by Testut, with Shane Gill later joining as co-founder. AltStore was initially supported through Patreon contributions from its user community, and later saw increased adoption following regulatory developments in the European Union that enabled broader third-party app distribution. The project has also been involved in notable industry collaborations, including a partnership with Epic Games. == Features == AltStore exploits a loophole in the Xcode developer platform, which allows developers to sideload their own apps which they are working on without needing to jailbreak. Sideloaded apps are signed like a developer project for testing and will expire after 7 days with a free account or one year with a paid developer account, by which they will need to be refreshed or reinstalled.

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  • Technology company

    Technology company

    A technology company, or tech company, is a company that focuses primarily on the manufacturing, support, research and development of—most commonly computing, telecommunication and consumer electronics–based—technology-intensive products and services, which include businesses relating to digital electronics, software, optics, new energy, and Internet-related services such as cloud storage and e-commerce services. Big Tech refers to the 6 largest companies, both in the United States and globally, symbolized by the metonym 'Silicon Valley', where 4 of them are based. == Details == According to Fortune, as of 2020, the ten largest technology companies by revenue are: Apple Inc., Samsung, Foxconn, Alphabet Inc., Microsoft, Huawei, Dell Technologies, Hitachi, IBM, and Sony. Amazon has higher revenue than Apple, but is classified by Fortune in the retail sector. The most profitable listed in 2020 are Apple Inc., Microsoft, Alphabet Inc., Intel, Meta Platforms, Samsung, and Tencent. Apple Inc., Alphabet Inc. (owner of Google), Meta Platforms (owner of Facebook), Microsoft, and Amazon.com, Inc. are often referred to as the Big Five multinational technology companies based in the United States. These five technology companies dominate major functions, e-commerce channels, and information of the entire Internet ecosystem. As of 2017, the Big Five had a combined valuation of over $3.3 trillion and make up more than 40 percent of the value of the Nasdaq-100 index. Many large tech companies have a reputation for innovation, spending large sums of money annually on research and development. According to PwC's 2017 Global Innovation 1000 ranking, tech companies made up nine of the 20 most innovative companies in the world, with the top R&D spender (as measured by expenditure) being Amazon, followed by Alphabet Inc., and then Intel. As a result of numerous influential tech companies and tech startups opening offices in proximity to one another, a number of technology districts have developed in various areas across the globe. These include: Silicon Valley in the San Francisco Bay Area, Silicon Wadi in Israel, Silicon Docks in Dublin, Silicon Hills in Austin, Tech City in London; Digital Media City in Seoul, Zhongguancun in Beijing, Cyberjaya in Malaysia and Cyberabad in Hyderabad, India. As of 2026, Europe has six of the world's 100 most valuable tech companies, compared with 56 in the United States and 16 in China.

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  • Algorithmic amplification

    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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  • Grid network

    Grid network

    A grid network is a computer network consisting of a number of computer systems connected in a grid topology. In a regular grid topology, each node in the network is connected with two neighbors along one or more dimensions. If the network is one-dimensional, and the chain of nodes is connected to form a circular loop, the resulting topology is known as a ring. Network systems such as FDDI use two counter-rotating token-passing rings to achieve high reliability and performance. In general, when an n-dimensional grid network is connected circularly in more than one dimension, the resulting network topology is a torus, and the network is called "toroidal". When the number of nodes along each dimension of a toroidal network is 2, the resulting network is called a hypercube. A parallel computing cluster or multi-core processor is often connected in regular interconnection network such as a de Bruijn graph, a hypercube graph, a hypertree network, a fat tree network, a torus, or cube-connected cycles. A grid network is not the same as a grid computer or a computational grid, although the nodes in a grid network are usually computers, and grid computing requires some kind of computer network or "universal coding" to interconnect the computers.

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  • Headway (app)

    Headway (app)

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

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  • Web developer

    Web developer

    A web developer is a programmer who develops World Wide Web applications using a client–server model. The applications typically use HTML, CSS, and JavaScript in the client, and any general-purpose programming language in the server. HTTP is used for communications between client and server. A web developer may specialize in client-side applications (Front-end web development), server-side applications (back-end development), or both (full-stack development). == Prerequisite == There are no formal educational or license requirements to become a web developer. However, many colleges and trade schools offer coursework in web development. There are also many tutorials and articles which teach web development, often freely available on the web - for example, on JavaScript. Even though there are no formal requirements, web development projects require web developers to have knowledge and skills such as: Using HTML, CSS, and JavaScript Programming/coding/scripting in one of the many server-side languages or frameworks Understanding server-side/client-side architecture and communication of the kind mentioned above Ability to utilize a database

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  • Deconfliction line

    Deconfliction line

    A deconfliction line is an official line of communications established between militaries who are or could be hostile, to avoid dangerous misunderstandings and miscalculations based on ignorance. The ultimate aim is to avoid accidents and conflict escalation. In the 2010s and 2020s, the US and Russia set up deconfliction lines during the Syrian civil war and Russo-Ukrainian War. They were regularly tested by military staff, and used by air traffic controllers and senior military officers. They were used to avoid midair collisions between aircraft in the same or adjacent airspace, and sometimes to give warning of airstrikes. In April 2017, Russia severed the Syrian line in retaliation for a called strike.

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  • Groundswell (book)

    Groundswell (book)

    Groundswell is a book by Forrester Research executives Charlene Li and Josh Bernoff that focuses on how companies can take advantage of emerging social technologies. It was published in 2008 by Harvard Business Press. A revised edition was published in 2011. The book attempts to explain a shift in the relationship between customers and companies, in which companies are no longer able to control customers' attitudes through market research, customer service, and advertising. Instead, customers are controlling the conversation by using new media to communicate about products and companies. == Synopsis == The groundswell is characterized by several tactics that guide companies into using social technologies strategically and effectively. Listening: Businesses should listen to their customers to understand what the market is looking for in their products. In order to do this, a company needs to find out if their customers are using social technologies and how they are using them. Talking: Instead of advertising to customers, marketing departments should find creative ways to connect with users about their experience with a product and their feelings about the brand. One common method is participation in social networks. Energizing: Enthusiastic customers are part of the groundswell, and companies can recognize and appreciate these customers by creating online communities and social platforms where they can connect with the brand and provide reviews. Supporting: Businesses can harness the support of their own employees by creating internal social applications for them to connect with the brand, also known as enterprise social software. == Groundswell in action == === Examples === Some companies distinguish their product through the use of social technologies. Tom Dickson successfully marketed his Blendtec line of blenders through the viral marketing campaign Will It Blend? The groundswell spread marketing messages through Digg and YouTube with a small budget and little marketing experience. Other companies have been able to listen to and talk with the groundswell by building their own online communities. Procter & Gamble created beinggirl.com Archived 2016-04-10 at the Wayback Machine to introduce girls to P&G feminine care products. The community approach worked because the company could reach girls with information that might seem embarrassing or sensitive in a traditional marketing campaign. === Risks === Features of particular industries or companies can make direct customer engagement more difficult. For instance, some companies must work within industry regulations, national or multinational corporations must balance corporate and local engagement, and other companies must find ways to engage with customers on time-sensitive issues. == Reception == Kevin Allison of the Financial Times praised the book for its focus on Web analytics: "[Groundswell] is not so much a manifesto or a dissection of online culture as it is a how-to manual for executives and mid-level managers trying to navigate this fast-changing and often confusing environment." The book won the American Marketing Association Foundation’s Berry-AMA Book Prize for best marketing book of 2009. It was also listed by: Amazon, as one of the Top 10 Business & Investing Books of 2008 CIO Insight, as one of the Top 10 Business-Tech Books of 2008 and one of 10 Insightful Web 2.0 Books Fortune as Magazine as one of the 3 best Web books of 2008 Advertising Age as number 3 of 10 Books You Should Have Read BusinessWeek as one of the Best Innovation & Design Books of 2008 "strategy+business" as one of the Best Business Books 2008 and “Top Shelf” in Marketing

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  • Foreign key

    Foreign key

    A foreign key is a set of attributes in a table that refers to the primary key of another table, linking these two tables. In the context of relational databases, a foreign key is subject to an inclusion dependency constraint that the tuples consisting of the foreign key attributes in one relation, R, must also exist in some other (not necessarily distinct) relation, S; furthermore that those attributes must also be a candidate key in S. In other words, a foreign key is a set of attributes that references a candidate key. For example, a table called TEAM may have an attribute, MEMBER_NAME, which is a foreign key referencing a candidate key, PERSON_NAME, in the PERSON table. Since MEMBER_NAME is a foreign key, any value existing as the name of a member in TEAM must also exist as a person's name in the PERSON table; in other words, every member of a TEAM is also a PERSON. == Summary == The table containing the foreign key is called the child table, and the table containing the candidate key is called the referenced or parent table. In database relational modeling and implementation, a candidate key is a set of zero or more attributes, the values of which are guaranteed to be unique for each tuple (row) in a relation. The value or combination of values of candidate key attributes for any tuple cannot be duplicated for any other tuple in that relation. Since the purpose of the foreign key is to identify a particular row of referenced table, it is generally required that the foreign key is equal to the candidate key in some row of the primary table, or else have no value (the NULL value.). This rule is called a referential integrity constraint between the two tables. Because violations of these constraints can be the source of many database problems, most database management systems provide mechanisms to ensure that every non-null foreign key corresponds to a row of the referenced table. For example, consider a database with two tables: a CUSTOMER table that includes all customer data and an ORDER table that includes all customer orders. Suppose the business requires that each order must refer to a single customer. To reflect this in the database, a foreign key column is added to the ORDER table (e.g., CUSTOMERID), which references the primary key of CUSTOMER (e.g. ID). Because the primary key of a table must be unique, and because CUSTOMERID only contains values from that primary key field, we may assume that, when it has a value, CUSTOMERID will identify the particular customer which placed the order. However, this can no longer be assumed if the ORDER table is not kept up to date when rows of the CUSTOMER table are deleted or the ID column altered, and working with these tables may become more difficult. Many real world databases work around this problem by 'inactivating' rather than physically deleting master table foreign keys, or by complex update programs that modify all references to a foreign key when a change is needed. Foreign keys play an essential role in database design. One important part of database design is making sure that relationships between real-world entities are reflected in the database by references, using foreign keys to refer from one table to another. Another important part of database design is database normalization, in which tables are broken apart and foreign keys make it possible for them to be reconstructed. Multiple rows in the referencing (or child) table may refer to the same row in the referenced (or parent) table. In this case, the relationship between the two tables is called a one to many relationship between the referencing table and the referenced table. In addition, the child and parent table may, in fact, be the same table, i.e. the foreign key refers back to the same table. Such a foreign key is known in SQL:2003 as a self-referencing or recursive foreign key. In database management systems, this is often accomplished by linking a first and second reference to the same table. A table may have multiple foreign keys, and each foreign key can have a different parent table. Each foreign key is enforced independently by the database system. Therefore, cascading relationships between tables can be established using foreign keys. A foreign key is defined as an attribute or set of attributes in a relation whose values match a primary key in another relation. The syntax to add such a constraint to an existing table is defined in SQL:2003 as shown below. Omitting the column list in the REFERENCES clause implies that the foreign key shall reference the primary key of the referenced table. Likewise, foreign keys can be defined as part of the CREATE TABLE SQL statement. If the foreign key is a single column only, the column can be marked as such using the following syntax: Foreign keys can be defined with a stored procedure statement. child_table: the name of the table or view that contains the foreign key to be defined. parent_table: the name of the table or view that has the primary key to which the foreign key applies. The primary key must already be defined. col3 and col4: the name of the columns that make up the foreign key. The foreign key must have at least one column and at most eight columns. == Referential actions == Because the database management system enforces referential constraints, it must ensure data integrity if rows in a referenced table are to be deleted (or updated). If dependent rows in referencing tables still exist, those references have to be considered. SQL:2003 specifies 5 different referential actions that shall take place in such occurrences: CASCADE RESTRICT NO ACTION SET NULL SET DEFAULT === CASCADE === Whenever rows in the parent (referenced) table are deleted (or updated), the respective rows of the child (referencing) table with a matching foreign key column will be deleted (or updated) as well. This is called a cascade delete (or update). === RESTRICT === A value cannot be updated or deleted when a row exists in a referencing or child table that references the value in the referenced table. Similarly, a row cannot be deleted as long as there is a reference to it from a referencing or child table. To understand RESTRICT (and CASCADE) better, it may be helpful to notice the following difference, which might not be immediately clear. The referential action CASCADE modifies the "behavior" of the (child) table itself where the word CASCADE is used. For example, ON DELETE CASCADE effectively says "When the referenced row is deleted from the other table (master table), then delete also from me". However, the referential action RESTRICT modifies the "behavior" of the master table, not the child table, although the word RESTRICT appears in the child table and not in the master table! So, ON DELETE RESTRICT effectively says: "When someone tries to delete the row from the other table (master table), prevent deletion from that other table (and of course, also don't delete from me, but that's not the main point here)." RESTRICT is not supported by Microsoft SQL 2012 and earlier. === NO ACTION === NO ACTION and RESTRICT are very much alike. The main difference between NO ACTION and RESTRICT is that with NO ACTION the referential integrity check is done after trying to alter the table. RESTRICT does the check before trying to execute the UPDATE or DELETE statement. Both referential actions act the same if the referential integrity check fails: the UPDATE or DELETE statement will result in an error. In other words, when an UPDATE or DELETE statement is executed on the referenced table using the referential action NO ACTION, the DBMS verifies at the end of the statement execution that none of the referential relationships are violated. This is different from RESTRICT, which assumes at the outset that the operation will violate the constraint. Using NO ACTION, the triggers or the semantics of the statement itself may yield an end state in which no foreign key relationships are violated by the time the constraint is finally checked, thus allowing the statement to complete successfully. === SET NULL, SET DEFAULT === In general, the action taken by the DBMS for SET NULL or SET DEFAULT is the same for both ON DELETE or ON UPDATE: the value of the affected referencing attributes is changed to NULL for SET NULL, and to the specified default value for SET DEFAULT. === Triggers === Referential actions are generally implemented as implied triggers (i.e. triggers with system-generated names, often hidden.) As such, they are subject to the same limitations as user-defined triggers, and their order of execution relative to other triggers may need to be considered; in some cases it may become necessary to replace the referential action with its equivalent user-defined trigger to ensure proper execution order, or to work around mutating-table limitations. Another important limitation appears with transaction isolation: your changes to a row may not be able to fully cascade because the row is ref

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  • RadioVIS

    RadioVIS

    RadioVIS is a protocol for sideband signalling of images and text messages for a broadcast audio service to provide a richer visual experience. It is an application and sub-project of RadioDNS, which allows radio consumption devices to look up an IP-based service based on the parameters of the currently tuned broadcast station. In January 2015, the functionality of RadioVIS was integrated to Visual Slideshow (ETSI TS 101 499 v3.1.1). The original RVIS01 document is now deprecated. == Details == The protocol enables either Streaming Text Oriented Messaging Protocol (STOMP) or Comet to deliver text and image URLs to a client, with the images being acquired over a HTTP connection. The technology is currently implemented by a number of broadcasters across the world, including Global Radio, Bauer Radio in the UK, RTÉ in the Republic Of Ireland, Südwestrundfunk in Germany and a number of Australian media groups amongst others. A number of software clients exist to show the protocol, as well as hardware devices such as the Pure Sensia from Pure Digital, and the Colourstream from Roberts Radio.

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  • The Culture of Connectivity

    The Culture of Connectivity

    The Culture of Connectivity: A Critical History of Social Media is a book by José van Dijck published by Oxford University Press in 2013 on social media platforms and their history. The author considers the histories of five social media platforms: Facebook, Twitter, Flickr, YouTube, and Wikipedia. She focuses on how their technological, social and cultural dimensions contribute to their current status.

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