AI Face Fusion

AI Face Fusion — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Leakage (machine learning)

  In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that would not be available at prediction time. This results in overly optimistic performance estimates, as the model appears to perform better during evaluation than it actually would in a production environment. Leakage is often subtle and indirect, making it difficult to detect and eliminate. It can lead a statistician or modeler to select a suboptimal model, which may be outperformed by a leakage-free alternative. == Leakage modes == Leakage can occur at multiple stages of the machine learning workflow. Broadly, its sources can be divided into two categories: those arising from features and those arising from training examples. === Feature leakage === Feature or column-wise leakage is caused by the inclusion of columns which are one of the following: a duplicate label, a proxy for the label, or the label itself. These features, known as anachronisms, will not be available when the model is used for predictions, and result in leakage if included when the model is trained. For example, including a "MonthlySalary" column when predicting "YearlySalary"; or "MinutesLate" when predicting "IsLate". === Training example leakage === Row-wise leakage is caused by improper sharing of information between rows of data. Types of row-wise leakage include: Premature featurization; leaking from premature featurization before Cross-validation/Train/Test split (must fit MinMax/ngrams/etc on only the train split, then transform the test set) Duplicate rows between train/validation/test (for example, oversampling a dataset to pad its size before splitting; or, different rotations/augmentations of a single image; bootstrap sampling before splitting; or duplicating rows to up sample the minority class) Non-independent and identically distributed random (non-IID) data Time leakage (for example, splitting a time-series dataset randomly instead of newer data in test set using a train/test split or rolling-origin cross-validation) Group leakage—not including a grouping split column (for example, Andrew Ng's group had 100k x-rays of 30k patients, meaning ~3 images per patient. The paper used random splitting instead of ensuring that all images of a patient were in the same split. Hence the model partially memorized the patients instead of learning to recognize pneumonia in chest x-rays.) A 2023 review found data leakage to be "a widespread failure mode in machine-learning (ML)-based science", having affected at least 294 academic publications across 17 disciplines, and causing a potential reproducibility crisis. == Detection == Data leakage in machine learning can be detected through various methods, focusing on performance analysis, feature examination, data auditing, and model behavior analysis. Performance-wise, unusually high accuracy or significant discrepancies between training and test results often indicate leakage. Inconsistent cross-validation outcomes may also signal issues. Feature examination involves scrutinizing feature importance rankings and ensuring temporal integrity in time series data. A thorough audit of the data pipeline is crucial, reviewing pre-processing steps, feature engineering, and data splitting processes. Detecting duplicate entries across dataset splits is also important. For language models, the Min-K% method can detect the presence of data in a pretraining dataset. It presents a sentence suspected to be present in the pretraining dataset, and computes the log-likelihood of each token, then compute the average of the lowest K of these. If this exceeds a threshold, then the sentence is likely present. This method is improved by comparing against a baseline of the mean and variance. Analyzing model behavior can reveal leakage. Models relying heavily on counter-intuitive features or showing unexpected prediction patterns warrant investigation. Performance degradation over time when tested on new data may suggest earlier inflated metrics due to leakage. Advanced techniques include backward feature elimination, where suspicious features are temporarily removed to observe performance changes. Using a separate hold-out dataset for final validation before deployment is advisable.

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  Horus Music

  Horus Music Limited is a global digital distribution and label services company. Established in 2006, Horus Music allows artists, labels and right-holders to send their music to over 200 download, streaming, and interactive platforms including iTunes, Google Play, Amazon, VEVO, 7digital, Spotify, Beatport, Deezer, Tidal, as well as offering digital marketing and playlisting opportunities. == History == The company were named Best Business Partner of 2014 by Huawei Technology of China, and were also a finalist in the International Trade category as part of the Leicester Mercury Business Awards during that same year. Their client base consists of unsigned and independent musicians and record labels, as well as well known recording artists. In November 2015, Horus Music sponsored the UK’s first Independent Label Week, in order to highlight the music that is released by the UK’s indie labels. In 2016, Horus Music celebrated their 10th anniversary Horus Music's sister companies Help for Bands and Help For Writers, provide advice and opportunities for musicians and E-book distribution for writers, respectively. Anara Publishing opened in 2017 which allows the company to work closely with a handpicked roster of musicians to provide royalty administration and sync licensing services. On 21 April 2017, Her Majesty Queen Elizabeth II’s 91st birthday, Horus Music was awarded with the Queen’s Award for Enterprise in International Trade. In 2021, Horus Music, UnitedMasters, and Symphonic Distribution partnered with pioneering music fintech company, beatBread, to offer clients access to more capital. beatBread's chordCashAI technology provides an automated advance experience for independent musicians while enable clients to choose their own terms and retain ownership of their music. == Clients == Horus Music has partnered with a number of charities including Save the Children, for the recording "Look into Your Heart", featuring Beverley Knight with Rolling Stones' Mick Jagger and Ronnie Wood, 100% of proceeds from the single were donated to the charity. The Pixel Project, who produced songs about violence against women and the blood cancer charity Bloodwise. The company have spoken openly about the state of the music industry and artists' rights and were one of the first distributors to remove their catalogue from Rdio after the streaming service was acquired by Pandora. Their relationships with artists and labels, as well as leading industry contacts, means they have the ability to work with musicians in a myriad of ways, including offering performance opportunities and even local auditions for TV shows such as The Voice UK. == Horus Music India == Horus Music India opened in 2016 and is based in Mumbai. By opening Horus Music India, the company are able to expand on their local connections as well as to provide a much more personalised service to musicians based in this area. The appointment of two Business Development Managers in India cemented their move.

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  ProjectExplorer

  ProjectExplorer is a documentary short film series. The films, directed and produced by ProjectExplorer's Founder, Jenny M Buccos, focus on histories and cultures of foreign places and people using interviews with subject experts, artists, and public figures including Archbishop Desmond Tutu, Dr. John Kani, Greg Marinovich, and Sipho “Hotstix” Mabuse. Produced for a child and young adult audience, segments in each series depict everyday life and the challenges and concerns of those living in the locations and regions featured. Each film is 2–4 minutes in length, with each series containing approximately 40 films. The ProjectExplorer series is distributed internationally without charge via the web by ProjectExplorer, LTD. an American not-for-profit organization. Three series have been produced and distributed. In fall 2009, ProjectExplorer's third series, Jordan, received a GOLD level Parents' Choice Award for excellence in web programming. == Film series == === Shakespeare's England (2006) === The first series was filmed in London, Stratford-upon-Avon, and New York City. The series includes more than 30 film segments. United Kingdom locations and individuals include: The London Eye The Tower of London The Whitechapel Bell Foundry, which demonstrates the process of making a bell Simon Hughes, Member of Parliament and President of the Liberal Democrats The Old Vic The Royal Shakespeare Company The National Archives (UK) Segments filmed in New York City include: Michael Cumpsty discusses and performs monologues from Hamlet (while starring in the Classic Stage Company production) Michael Stuhlbarg discusses and performs a monologue from Macbeth === South Africa (2007) === Filmed in Johannesburg, Cape Town, and KwaZulu Natal, the series contains over 40 film segments including: Ntate Thabong Phosa, a lesiba player from Lesotho. Due to the rarity of lesiba players globally, this is one of the only publicly available examples of the lesiba played on film. A Robben Island piece, filmed at the cell in which Nelson Mandela was held for 18 of his 27-year imprisonment. JSE Securities Exchange with Leigh Roberts, correspondent for CNBC Africa. A 3-part series on HIV/AIDS with amfAR Director of Research, Dr. Rowena Johnson. Dr. Johnson discusses high cost of anti-retroviral drugs and testing in South Africa. The June 16, 1976 Soweto Uprising, with archival film footage and photography from SABC and The Sowetan newspaper. Prominent South Africans featured in the series: Dr. John Kani, Chairperson of the Apartheid Museum and TONY Award Winning Actor Musician Sipho “Hotstix” Mabuse Former U.N. Ambassador Dave A. Steward, Executive Director of the FW de Klerk Foundation Director and producer, Duma Ndlovu Malcolm Purkey, Artistic Director of the Market Theatre === South Africa, Part II (2008) === Filmed in Johannesburg, Cape Town, and New York City, the series contains over 10 film segments. Prominent South Africans featured in the series: Archbishop Desmond Tutu, Nobel Peace Prize laureate Photojournalist Greg Marinovich, Pulitzer Prize winner and co-author of The Bang-Bang Club Vusi Mahlasela, musician Author, Max du Preez === Jordan (2008) === Filmed in Amman, Petra, Umm Qais, Jerash, Madaba, Bethany, the Dead Sea, and New York City, the series contains more than 45 film segments. Jordan series segments include: A tour of the throne room of King Abdullah II, at Raghadan Palace Sharing mansaf with a Bedouin family in the Wadi Rum desert The UNRWA Jabal Hussein refugee camp The Siq, Treasury, and Monastery at Petra The ruins of Gadara at Umm Qais Jerash, the capital and largest city of Jordan's Jerash Governorate Madaba, home of the Madaba Map and the mosaic capital of Jordan The archaeological site at Bethany Traditional clothing from Salt and Ma'an The reintroduction into the wild of the endangered Arabian Oryx The Desert Castles The science of the Dead Sea Her Royal Highness Princess Basma bint Ali and her Royal Botanic Garden

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  Information element

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

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  Ray tracing (graphics)

  In 3D computer graphics, ray tracing is a technique for modeling light transport for use in a wide variety of rendering algorithms for generating digital images. On a spectrum of computational cost and visual fidelity, ray tracing-based rendering techniques, such as ray casting, recursive ray tracing, distribution ray tracing, photon mapping and path tracing, are generally slower and higher fidelity than scanline rendering methods. Thus, ray tracing was first deployed in applications where taking a relatively long time to render could be tolerated, such as still CGI images, and film and television visual effects (VFX), but was less suited to real-time applications such as video games, where speed is critical in rendering each frame. Since 2018, however, hardware acceleration for real-time ray tracing has become standard on new commercial graphics cards, and graphics APIs have followed suit, allowing developers to use hybrid ray tracing and rasterization-based rendering in games and other real-time applications with a lesser hit to frame render times. Ray tracing is capable of simulating a variety of optical effects, such as reflection, refraction, soft shadows, scattering, depth of field, motion blur, caustics, ambient occlusion and dispersion phenomena (such as chromatic aberration). It can also be used to trace the path of sound waves in a similar fashion to light waves, making it a viable option for more immersive sound design in video games by rendering realistic reverberation and echoes. In fact, any physical wave or particle phenomenon with approximately linear motion can be simulated with ray tracing. Ray tracing–based rendering techniques that sample light over a domain typically generate multiple rays and often rely on denoising to reduce the resulting noise. == History == The idea of ray tracing comes from as early as the 16th century, when it was described by Albrecht Dürer, who is credited for its invention. Dürer described multiple techniques for projecting 3-D scenes onto an image plane. Some of these project chosen geometry onto the image plane, as is done with rasterization today. Others determine what geometry is visible along a given ray, as is done with ray tracing. Using a computer for ray tracing to generate shaded pictures was first accomplished by Arthur Appel in 1968. Appel used ray tracing for primary visibility (determining the closest surface to the camera at each image point) by tracing a ray through each point to be shaded into the scene to identify the visible surface. The closest surface intersected by the ray was the visible one. This non-recursive ray tracing-based rendering algorithm is today called "ray casting". His algorithm then traced secondary rays to the light source from each point being shaded to determine whether the point was in shadow or not. Later, in 1971, Goldstein and Nagel of MAGI (Mathematical Applications Group, Inc.) published "3-D Visual Simulation", wherein ray tracing was used to make shaded pictures of solids. At the ray-surface intersection point found, they computed the surface normal and, knowing the position of the light source, computed the brightness of the pixel on the screen. Their publication describes a short (30-second) film "made using the University of Maryland's display hardware outfitted with a 16mm camera. The film showed the helicopter and a simple ground-level gun emplacement. The helicopter was programmed to undergo a series of maneuvers including turns, take-offs, and landings, etc., until it eventually is shot down and crashed." A CDC 6600 computer was used. MAGI produced an animation video called MAGI/SynthaVision Sampler in 1974. Another early instance of ray casting came in 1976, when Scott Roth created a flip book animation in Bob Sproull's computer graphics course at Caltech. The scanned pages are shown as a video in the accompanying image. Roth's computer program noted an edge point at a pixel location if the ray intersected a bounded plane different from that of its neighbors. Of course, a ray could intersect multiple planes in space, but only the surface point closest to the camera was noted as visible. The platform was a DEC PDP-10, a Tektronix storage-tube display, and a printer which would create an image of the display on rolling thermal paper. Roth extended the framework, introduced the term ray casting in the context of computer graphics and solid modeling, and in 1982 published his work while at GM Research Labs. Turner Whitted was the first to show recursive ray tracing for mirror reflection and for refraction through translucent objects, with an angle determined by the solid's index of refraction, and to use ray tracing for anti-aliasing. Whitted also showed ray traced shadows. He produced a recursive ray traced film called The Compleat Angler in 1979 while an engineer at Bell Labs. Whitted's deeply recursive ray tracing algorithm reframed rendering from being primarily a matter of surface visibility determination to being a matter of light transport. His paper inspired a series of subsequent work by others that included distribution ray tracing and finally unbiased path tracing, which provides the rendering equation framework that has allowed computer-generated imagery to be faithful to reality. For decades, global illumination in major films using computer-generated imagery was approximated with additional lights. Ray tracing-based rendering eventually changed that by enabling physically based light transport. Early feature films rendered entirely using path tracing include Monster House (2006), Cloudy with a Chance of Meatballs (2009), and Monsters University (2013). == Algorithm overview == Optical ray tracing describes a method for producing visual images constructed in 3D computer graphics environments, with more photorealism than either ray casting or scanline rendering techniques. It works by tracing a path from an imaginary eye through each pixel in a virtual screen, and calculating the color of the object visible through it. Scenes in ray tracing are described mathematically by a programmer or by a visual artist (normally using intermediary tools). Scenes may also incorporate data from images and models captured by means such as digital photography. Typically, each ray must be tested for intersection with some subset of all the objects in the scene. Once the nearest object has been identified, the algorithm will estimate the incoming light at the point of intersection, examine the material properties of the object, and combine this information to calculate the final color of the pixel. Certain illumination algorithms and reflective or translucent materials may require more rays to be re-cast into the scene. It may at first seem counterintuitive or "backward" to send rays away from the camera, rather than into it (as actual light does in reality), but doing so is many orders of magnitude more efficient. Since the overwhelming majority of light rays from a given light source do not make it directly into the viewer's eye, a "forward" simulation could potentially waste a tremendous amount of computation on light paths that are never recorded. Therefore, the shortcut taken in ray tracing is to presuppose that a given ray intersects the view frame. After either a maximum number of reflections or a ray traveling a certain distance without intersection, the ray ceases to travel and the pixel's value is updated. === Calculate rays for rectangular viewport === On input we have (in calculation we use vector normalization and cross product): E ∈ R 3 {\displaystyle E\in \mathbb {R^{3}} } eye position T ∈ R 3 {\displaystyle T\in \mathbb {R^{3}} } target position θ ∈ [ 0 , π ] {\displaystyle \theta \in [0,\pi ]} field of view - for humans, we can assume ≈ π / 2 rad = 90 ∘ {\displaystyle \approx \pi /2{\text{ rad}}=90^{\circ }} m , k ∈ N {\displaystyle m,k\in \mathbb {N} } numbers of square pixels on viewport vertical and horizontal direction i , j ∈ N , 1 ≤ i ≤ k ∧ 1 ≤ j ≤ m {\displaystyle i,j\in \mathbb {N} ,1\leq i\leq k\land 1\leq j\leq m} numbers of actual pixel v → ∈ R 3 {\displaystyle {\vec {v}}\in \mathbb {R^{3}} } vertical vector which indicates where is up and down, usually v → = [ 0 , 1 , 0 ] {\displaystyle {\vec {v}}=[0,1,0]} - roll component which determine viewport rotation around point C (where the axis of rotation is the ET section) The idea is to find the position of each viewport pixel center P i j {\displaystyle P_{ij}} which allows us to find the line going from eye E {\displaystyle E} through that pixel and finally get the ray described by point E {\displaystyle E} and vector R → i j = P i j − E {\displaystyle {\vec {R}}_{ij}=P_{ij}-E} (or its normalization r → i j {\displaystyle {\vec {r}}_{ij}} ). First we need to find the coordinates of the bottom left viewport pixel P 1 m {\displaystyle P_{1m}} and find the next pixel by making a shift along directions parallel to viewport (vectors b → n {\displaystyle {\vec {b}}_{n

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  Cyber-Duck

  Cyber-Duck is a digital transformation agency founded in 2005 and based in Elstree, United Kingdom. The company specialises in user experience (UX), software development and digital optimisation. The company employs over 90 staff in the UK and Europe. It works with clients from the financial, pharmaceutical, sport, motoring and security sectors, among others. These include the Bank of England, Cancer Research UK, GOV.UK Verify partner CitizenSafe, The Commonwealth of Nations and Sport England. == History == Cyber-Duck was founded in 2005 by Danny Bluestone in his flat in Mill Hill, United Kingdom. After a few months, the firm moved into its first office in Borehamwood. Projects with Ogilvy, London Creative and Wisteria followed before Cyber-Duck moved to offices in Devonshire House, Borehamwood. In 2010, the firm was commissioned to develop a website for the European Commission in the UK. In 2011, the company moved to a self-contained premises in Elstree, Hertfordshire. Shortly afterward, Cyber-Duck was listed on the Deloitte Technology Fast 500 EMEA in recognition of its substantial revenue growth over the previous five years. As the company grew, its expertise also broadened. This resulted in guest spots on several television shows. Cyber-Duck was featured in an episode of the Gadget Show in 2011, and Chief Production Officer Matt Gibson appeared on BBC Watchdog in 2013 to assist in researching websites and their checkout processes. The firm continued to attract business from companies in London, so the decision was made to open a new office in central London. The Farringdon office opened in 2015, and was followed by a rebrand. In 2016, Cyber-Duck went on to work with the Bank of England. Ahead of the launch of the new polymer £5 note, featuring Winston Churchill, the company was tasked with creating a user-friendly website to showcase the new banknote and promote public awareness. The success of the campaign led to further commissions, including 2017's website the New Ten and a redesign of the Bank of England's main website. The firm underwent significant growth in 2020, beginning working partnerships with Sport England and the College of Policing. During this time they also launched DevOps as a new service. In 2022, the Farringdon office closed and was relocated to a new office space in Holborn. The Laravel, Drupal and DevOps teams expanded, and Cyber-Duck became the lead Digital Agency for Worcester, Bosch Group. Several members of the team appeared on The Digital Society on Sky UK. == Awards and accreditations == Cyber-Duck is known for its focus on process accreditation as a driver of creativity. In 2011, the company obtained its first ISO 9241 accreditation in Human Centred Design for interactive systems. Two years later, Cyber-Duck obtained a further certification, the ISO 9001 for Quality Management Systems. It acquired another certification in 2016 with the ISO 27001 – the focus of this accreditation was Information Security Management. In 2022, Cyber-Duck gained the ISO 14001 certification in Environmental Management. Cyber-Duck's digital products have won numerous Wirehive 100, BIMA and Webby awards. Notably, the company's UX Companion, a free iOS and Android app that is a glossary of UX theories, featured in Usability Geek and Smashing Magazine. In 2021 they were awarded as one of the UK's 100 Best Small Companies to work for, and BIMA10 shortlisted for their work with Sport England and This Girl Can.

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  FactorDaily

  FactorDaily is an Indian digital media publication founded in 2016 by Pankaj Mishra and Jayadevan PK. Mishra was formerly an Editor at TechCrunch and the Economic Times. The digital publication was launched with an intent to produce stories on the impact of technology on life in India. == History == FactorDaily began publishing in May 2016, with daily reported stories on technology, culture and life in India. Prior to its launch, the company had raised $1 million in seed funding from Accel India, Blume Ventures, Girish Mathrubootham of Freshdesk, Vijay Shekhar Sharma of PayTm, and Jay Vijayan of Tekion. Josey Puliyenthuruthel John, formerly Managing Editor at Business Today and National Corporate Editor at Mint, later joined the company as a Consulting Editor. In January 2017, FactorDaily launched its first Podcast called The Outliers. The inaugural episode featured a conversation with Manish Sharma of Printo on his journey starting up. == Awards == The FactorDaily team won the Bengaluru Editors Lab 2017, a journalism hackathon organised by the Global Editors Network (GEN). The story titled "India has 3,800 psychiatrists for 1.2bn people. Can tech step in to manage mental health?" won the first prize in the online category of the fifth Schizophrenia Research Foundation’s (SCARF) ‘Media for Mental Health’ awards. The story titled 'The dark hand of tech that stokes sex trafficking in India', won the Stop Slavery media Awards by the Thomson Reuters Foundation for the year 2020.

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  Cultural technology

  Cultural technology (Korean: 문화기술; Hanja: 文化技術; RR: munhwagisul) is a system used by South Korean talent agencies to promote K-pop culture throughout the world as part of the Korean Wave. The system was developed by Lee Soo-man, founder of talent agency and record company SM Entertainment. == History == === Coinage === During a speech at the Stanford Graduate School of Business in 2011, Lee said he coined the term "cultural technology" as a system about fourteen years prior, when S.M. Entertainment decided to promote its K-pop artists to all of Asia. In the late 1990s, Lee and his colleagues created a manual on cultural technology, which specified the steps needed to popularize K-pop artists outside South Korea. "The manual, which all S.M. employees are instructed to learn, explains when to bring in foreign composers, producers, and choreographers; what chord progressions to use in what country; the precise color of eyeshadow a performer should wear in a particular country; the exact hand gestures he or she should make; and the camera angles to be used in the videos (a three-hundred-and-sixty-degree group shot to open the video, followed by a montage of individual closeups)," according to The New Yorker. The term "cultural technology," apart from Lee's systemized definition, can be traced back to the lectures of Michael White, an Australian social worker, educator, and therapeutic theorist and his works Narrative Means to Therapeutic Ends (1990) and Maps of Narrative Practice (2007). Its usage may also date further back to French philosopher Michel Foucault (1977). South Korean computer scientist Kwangyun Wohn said he coined the term "culture technology" in 1994. Cultural technology has also been one of six technology initiatives of the South Korean government since 2001. In regards to cultural technology, the Korean Wave is considered one of the most successful outcomes of government support of exporting Korean entertainment products. === The Four Core Stages === The cultural technology system originally employed by SM Entertainment since the 1990s existed in four stages: Casting, Training, Producing, and Marketing/Managing. Each of these four stages were curated to help spread the Hallyu wave through the development of its artists, and are present in the strategies of many other South Korean talent agencies when creating, debuting, and marketing groups. ==== Casting ==== While the majority of K-pop idols are from South Korea, some are from Japan, China, or Thailand. Many of Korea's entertainment companies, such as SM's Global Auditions, Bighit's Hit It auditions, and YG's Next Generation, host worldwide auditions. Scouting and streetcasting are also common, with members like BTS's Jin recruited for their looks or other surface reasons. Sometimes, casting agents go to dance schools to recruit the top dancers to be trained further at the entertainment company. ==== Training ==== Idols train extensively before debut. They receive training in dance, vocal activities, presentation, and other areas that will benefit them in the industry. Oftentimes, this training will last for years at a time, and trainees are in the proverbial dungeon. Before debut, idols and groups attempt to gain fans through pre-debut activities. SM Entertainment has a system in place called SM Rookies, which is a pre-debut team that hosts concerts and releases videos that strengthen the fanbase of the group even before their first single is released. Other forms of pre-debut activities include featuring in other, more seasoned idols' videos—like Nu'est in Orange Caramel or Exo in Girls' Generation-TTS Twinkle or BTS in Jo Kwon. One particular method of pre-debut training is coupled with casting in production shows, like Sixteen and Produce 101, in which members for a final group are selected and trained. ==== Producing ==== The production of music is integral in culture technology. For cultural technology, production of music helps create differentiated content to set trends in the K-pop world—trends that vary from music to also costume, choreography, and music videos. SM in particular focuses heavily on the expansion globally. Some companies also outsource production to more internationally famed parties, like Cube Entertainment's partnership with Skrillex for 4minute's Act. 7. ==== Marketing/Managing ==== In the marketing and management stage, talent agencies seek to broaden their reach. Often, idols have potential for being actors and actresses in dramas, or perhaps hosts/permanent members of variety shows like Kim Hee-chul in Knowing Bros. This so-called omnidirectional marketing lineup ranges over lifestyle and seeks to reach many aspects of living, like music, TV, drama, entertainment, sports, and fashion. This is also where older groups find new life, like Super Junior. Companies are not complacent but experiment constantly to develop the best marketing for the best management system. Marketing also aspires to branch out to international audiences, sometimes via the implementation of variety shows. Despite being primarily in Korean, these variety shows are accessible to all due to the simplistic, easily understood nature of shows—game-oriented shows like Run BTS! or consistently subbed shows like Weekly Idol are popular in showing the fun-loving side of idols. == Evolution into New Culture Technology == In February 2016, SM hosted a press conference discussing the future of SM and its cultural technology. Lee Soo-man announced the implementation of New Culture Technology, an SM-specific system. While SM's cultural technology in the past relied on local, Korean artists like Rain and BoA, the updated model tries to embed more and more foreign singers from strategic markets into larger girl or boy bands. These imported singers are then used to promote their acts back in their respective home countries. New Culture Technology is five projects—SM Station, EDM, Digital Platforms, Rookies Entertainment, and MCN—and one experimental group, NCT. It is a convergence and expansion of SM's four core culture technologies developed and deals heavily with interaction and the desire to innovate through communication. === SM Station === SM announced their intention of creating a new song every week for 52 weeks. Through this constant output of music, they intend to stray away from conventional forms of music and show active movement in digital music market and physical album market through freely and continuously releasing music. Additionally, this SM Station will feature collaborations between artists, producers, composers, and company brands outside the SM label. The name of SM Station is both derived from the radio station and the metaphorical train station. === NCT === Neo Culture Technology (NCT) introduced the idea of "Interactive". SM company tried to connect the targeting market, customers and artist, in order to lead the K-pop culture. NCT (Neo Culture Technology) is the new artist group formed by SM that embodies the concepts of cultural technology. With the seemingly limitless combinations and groups, SM aspires to make the whole world a stage for NCT. Since 2023, there are six NCT groups, who debuted on the digital song sales: NCT U, NCT 127, NCT Dream, WayV, NCT DoJaeJung, and NCT Wish. As of October 2023, the group consists of 25 members: Johnny, Taeyong, Yuta, Kun, Doyoung, Ten, Jaehyun, Winwin, Jungwoo, Mark, Xiaojun, Hendery, Renjun, Jeno, Haechan, Jaemin, Yangyang, Chenle, Jisung, Sion, Riku, Yushi, Daeyoung, Ryo, and Sakuya. ScreaM Records ScreaM Records has been released by SM Entertainment as an EDM label since 2016 for "SM TOWN: New Culture Technology". ScreaM Records is made for "performances made to be enjoyed". It collaborates with inside and outside Korean well-known EDM DJs. ScreaM Records has first launched collaborated song "Wave" E-Mart's home electronics store, Electro Mart. "Our goal is to provide opportunities to producers who have yet to be discovered and produce world famous DJs from the Asian scene." a ScreaM Records representative said. == Three stages of globalization == According to Lee, there are three stages necessary to popularize Korean culture outside South Korea: exporting the product, collaborating with international companies to expand the product's presence abroad, and finally creating a joint venture with international companies. As part of their joint ventures with international companies, South Korean talent agencies may hire foreign composers, producers, and choreographers to ensure K-pop songs feel "local" to foreign countries.

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  Automated machine learning

  Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search. == Comparison to the standard approach == In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model. If deep learning is used, the architecture of the neural network must also be chosen manually by the machine learning expert. Each of these steps may be challenging, resulting in significant hurdles to using machine learning. AutoML aims to simplify these steps for non-experts, and to make it easier for them to use machine learning techniques correctly and effectively. AutoML plays an important role within the broader approach of automating data science, which also includes challenging tasks such as data engineering, data exploration and model interpretation and prediction. == Targets of automation == Automated machine learning can target various stages of the machine learning process. Steps to automate are: Data preparation and ingestion (from raw data and miscellaneous formats) Column type detection; e.g., Boolean, discrete numerical, continuous numerical, or text Column intent detection; e.g., target/label, stratification field, numerical feature, categorical text feature, or free text feature Task detection; e.g., binary classification, regression, clustering, or ranking Feature engineering Feature selection Feature extraction Meta-learning and transfer learning Detection and handling of skewed data and/or missing values Model selection - choosing which machine learning algorithm to use, often including multiple competing software implementations Ensembling - a form of consensus where using multiple models often gives better results than any single model Hyperparameter optimization of the learning algorithm and featurization Neural architecture search Pipeline selection under time, memory, and complexity constraints Selection of evaluation metrics and validation procedures Problem checking Leakage detection Misconfiguration detection Analysis of obtained results Creating user interfaces and visualizations == Challenges and Limitations == There are a number of key challenges being tackled around automated machine learning. A big issue surrounding the field is referred to as "development as a cottage industry". This phrase refers to the issue in machine learning where development relies on manual decisions and biases of experts. This is contrasted to the goal of machine learning which is to create systems that can learn and improve from their own usage and analysis of the data. Basically, it's the struggle between how much experts should get involved in the learning of the systems versus how much freedom they should be giving the machines. However, experts and developers must help create and guide these machines to prepare them for their own learning. To create this system, it requires labor intensive work with knowledge of machine learning algorithms and system design. Additionally, other challenges include meta-learning and computational resource allocation.

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  Contact center telephony

  In marketing, contact center telephony is the communication and collaboration system used by businesses to either manage high volumes of inbound queries or outbound telephone calls keeping their workforce or agents productive and in control to serve or acquire customers. This business communication system is an extension of computer telephony integration (CTI). == Overview == The interactions between callers and customer service representatives are supported by the collective system of computers, telephones and the Internet. The shift from CTI to contact center telephony is marked by the sheer change in the customer’s behavior when it comes to communication. Means customers are no longer confined only to voice-based communication i.e. phone to connect with their customer service departments. In addition, they are making use of email, SMS, chat, social media, and other virtual contact channels. This is also the reason for the shift in nomenclature from "call centers" to "contact centers", "contact" being a wider term than "call". Respecting the trend, contact center owners need to adopt unified communication or multi-channel approach to let customers get in touch with them via their preferred communication mediums, either voice or non-voice (data). Cloud-based phone system is a further advancement in the direction as it allows operators to access all the features and benefits of call center telephony over the Web against an affordable & flexible pay-as-you-go subscription model. Thus, in-house infrastructure deployment to manage public switched telephone networks, storage, communication applications, and collaboration servers is no more an obligation. Neither is the need to invest resources for their upgrade, repair, maintenance and security as cloud vendor would be responsible for the same. == India == India, a popular call center business process outsourcing destination, often uses a cloud-based phone system in order to cut operational expenses and downtime, and increase connectivity. == Promotion == Businesses can rely on contact center telephony services to respond to their customers’ queries over phone, email, chat, fax, etc. Integrating it with their customer relationship management tools, entire contact details of customers and their interaction sessions with different customer service representatives can be found at one place. The combination can manage not just sales and marketing but also deliver excellent post-sales customer service or technical support to allow customers derive the most from their products or services. Hence, it’s becoming instrumental in increasing customer satisfaction and loyalty and most of the call center services in India are taking refuge from it. The entire contact center telephony service can be availed by professionals over a browser. Hence, businesses can leverage the concept of BYOD (bring your own device) and mobility and serve their customers well using mobile applications. According to market analysts, BYOD increases satisfaction among workforce, and hence their individual and collective productivity as well. BYOD programme significantly reduces the TCO (total cost of ownership) as professionals prefer to work with their own devices rather than using company-provisioned devices. Next, they tend to be more caring towards such devices and can even shell out money to update and upgrade those when required. Integration of IM, along with audio and video conferencing services helps call center or contact center representatives to get real time assistance from their peers or seniors to resolve any complex issues. They can internally exchange information and knowledge articles as and when required. Real-time call monitoring/barging system can be used by quality assessment team to provide important guidelines to agents to maintain the standard of the service as per industry norms. Integrated recording feature is helpful for internal training and quality purposes to improve productivity and customer satisfaction in equal measures. It also helps in getting business insights and improving products or services to gain deeper penetration into the market.

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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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  Digital signal

  A digital signal is a signal that represents data as a sequence of discrete values; at any given time it can only take on, at most, one of a finite number of values. This contrasts with an analog signal, which represents continuous values; at any given time it represents a real number within an infinite set of values. Simple digital signals represent information in discrete bands of levels. All levels within a band of values represent the same information state. In most digital circuits, the signal can have two possible valid values; this is called a binary signal or logic signal. They are represented by two voltage bands: one near a reference value (typically termed as ground or zero volts), and the other a value near the supply voltage. These correspond to the two values zero and one (or false and true) of the Boolean domain, so at any given time a binary signal represents one binary digit (bit). Because of this discretization, relatively small changes to the signal levels do not leave the discrete envelope, and as a result are ignored by signal state sensing circuitry. As a result, digital signals have noise immunity; electronic noise, provided it is not too great, will not affect digital circuits, whereas noise always degrades the operation of analog signals to some degree. Digital signals having more than two states are occasionally used; circuitry using such signals is called multivalued logic. For example, signals that can assume three possible states are called three-valued logic. In a digital signal, the physical quantity representing the information may be a variable electric current or voltage, the intensity, phase or polarization of an optical or other electromagnetic field, acoustic pressure, the magnetization of a magnetic storage media, etcetera. Digital signals are used in all digital electronics, notably computing equipment and data transmission. == Definitions == The term digital signal has related definitions in different contexts. === In digital electronics === In digital electronics, a digital signal is a pulse amplitude modulated signal, i.e., a sequence of fixed-width electrical pulses or light pulses, each occupying one of a discrete number of levels of amplitude. A special case is a logic signal or a binary signal, which varies between a low and a high signal level. The pulse trains in digital circuits are typically generated by metal–oxide–semiconductor field-effect transistor (MOSFET) devices, due to their rapid on–off electronic switching speed and large-scale integration (LSI) capability. In contrast, bipolar junction transistors more slowly generate signals resembling sine waves. === In signal processing === In digital signal processing, a digital signal is a representation of a physical signal that is sampled and quantized. A digital signal is an abstraction that is discrete in time and amplitude. The signal's value only exists at regular time intervals, since only the values of the corresponding physical signal at those sampled moments are significant for further digital processing. The digital signal is a sequence of codes drawn from a finite set of values. The digital signal may be stored, processed or transmitted physically as a pulse-code modulation (PCM) signal. === In communications === In digital communications, a digital signal is a continuous-time physical signal, alternating between a discrete number of waveforms, representing a bitstream. The shape of the waveform depends on the transmission scheme, which may be either a line coding scheme allowing baseband transmission; or a digital modulation scheme, allowing passband transmission over long wires or over a limited radio frequency band. Such a carrier-modulated sine wave is considered a digital signal in literature on digital communications and data transmission, but considered as a bit stream converted to an analog signal in specific cases where the signal will be carried over a system meant for analog communication, such as an analog telephone line. In communications, sources of interference are usually present, and noise is frequently a significant problem. The effects of interference are typically minimized by filtering off interfering signals as much as possible and by using data redundancy. The main advantages of digital signals for communications are often considered to be noise immunity, and the ability, in many cases such as with audio and video data, to use data compression to greatly decrease the bandwidth that is required on the communication media. == Logic voltage levels == A waveform that switches representing the two states of a Boolean value (0 and 1, or low and high, or false and true) is referred to as a digital signal or logic signal or binary signal when it is interpreted in terms of only two possible digits. The two states are usually represented by some measurement of an electrical property: Voltage is the most common, but current is used in some logic families. Two ranges of voltages are typically defined for each logic family, which are frequently not directly adjacent. The signal is low when in the low range and high when in the high range, and in between the two ranges the behavior can vary between different types of gates. The clock signal is a special digital signal that is used to synchronize many digital circuits. The image shown can be considered the waveform of a clock signal. Logic changes are triggered either by the rising edge or the falling edge. The rising edge is the transition from a low voltage (level 1 in the diagram) to a high voltage (level 2). The falling edge is the transition from a high voltage to a low one. Although in a highly simplified and idealized model of a digital circuit, we may wish for these transitions to occur instantaneously, no real-world circuit is purely resistive, and therefore no circuit can instantly change voltage levels. This means that during a short, finite transition time, the output may not properly reflect the input, and will not correspond to either a logically high or low voltage. == Modulation == To create a digital signal, a signal must be modulated with a control signal to produce it. The simplest modulation, a type of unipolar encoding, is simply to switch on and off a DC signal so that high voltages represent a '1' and low voltages are '0'. In digital radio schemes, one or more carrier waves are amplitude, frequency or phase modulated by the control signal to produce a digital signal suitable for transmission. Asymmetric Digital Subscriber Line (ADSL) over telephone wires, does not primarily use binary logic; the digital signals for individual carriers are modulated with different-valued logics, depending on the Shannon capacity of the individual channel. == Clocking == Digital signals may be sampled by a clock signal at regular intervals by passing the signal through a flip-flop. When this is done, the input is measured at the clock edge and the signal from that time. The signal is then held steady until the next clock. This process is the basis of synchronous logic. Asynchronous logic also exists, which uses no single clock, and generally operates more quickly, and may use less power, but is significantly harder to design.

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  Yap (company)

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

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  Industry Dive

  Industry Dive is a United States-based business-to-business news organization with an estimated 18 million readers in more than 25 industries, such as banking and waste management. Since 2022, it has been owned by Informa plc. Industry Dive aims to serve business executives who read news on their mobile phones. The company had an estimated revenue of more than of more than $110 million in 2023. As of 2020, it has more than 300 employees, including 80 journalists and 12 engineers. Its headquarters is in Washington, D.C. == History == Industry Dive was formed in 2012 by Sean Griffey (president), Eli Dickinson (chief technology officer), and Ryan Willumson (chief revenue officer). It was funded with $900,000 from private investors in 2012 and 2013. The company covered five industries: construction, education, marketing, utility, and waste. In 2016, it began its Dive Awards. Industry Dive's revenues quadrupled from 2015 to 2018, putting it in the top half of the Deloitte Technology Fast 500 and the top 20 percent of the Inc. Top 5000 list. In 2019, Falfurrias Capital Partners acquired a majority stake in the company. ID's content marketing clients included IBM, Siemens, and UPS. In 2020, DCA Live named Industry Dive to its "Red Hot Companies" list, which recognizes the D.C. area's 'fastest-growing' companies. In the same year, Industry Dive acquired CFO. In 2021, Industry Dive acquired PharmaVOICE. In 2022, it was purchased by Informa plc, which bought its majority stake from Falfurrias Capital Partners for about $530 million. == Publications == Industry Dive provides news coverage of a variety of industries including agriculture, banking, construction, education, fashion, healthcare, and manufacturing, each using a different website: == Awards == Industry Dive publications have received several national and regional Awards of Excellence from the American Society of Business Publication Editors, including for a series of 2020 articles about Big Pharma and the race for the coronavirus vaccine. The Washington Post recognized Industry Dive as a top place to work for four consecutive years, from 2016 to 2020.

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  Timeline of operating systems

  This article presents a timeline of events in the history of computer operating systems from 1951 to the current day. For a narrative explaining the overall developments, see the History of operating systems. == 20th Century == == 1940s == 1949 EDSAC was considered the first operating system developed by Maurice Wilkes and manufactured by the University of Cambridge == 1950s == 1951 LEO I 'Lyons Electronic Office' was the commercial development of EDSAC computing platform, supported by British firm J. Lyons and Co. 1953 DYSEAC - an early machine capable of distributing computing 1955 General Motors Operating System made for IBM 701 MIT's Tape Director operating system made for UNIVAC 1103 1956 GM-NAA I/O for IBM 704, based on General Motors Operating System 1957 Atlas Supervisor (Manchester University) (Atlas computer project start) BESYS (Bell Labs), for IBM 704, later IBM 7090 and IBM 7094 1958 University of Michigan Executive System (UMES), for IBM 704, 709, and 7090 1959 SHARE Operating System (SOS), based on GM-NAA I/O == 1960s == 1960 IBSYS (IBM for its 7090 and 7094) 1961 CTSS demonstration (MIT's Compatible Time-Sharing System for the IBM 7094) MCP (Burroughs Master Control Program) for B5000 1962 Atlas Supervisor (Manchester University) (Atlas computer commissioned) BBN Time-Sharing System GCOS (GE's General Comprehensive Operating System, originally GECOS, General Electric Comprehensive Operating Supervisor) 1963 ADMIRAL AN/FSQ-32, another early time-sharing system begun CTSS becomes operational (MIT's Compatible Time-Sharing System for the IBM 7094) JOSS, an interactive time-shared system that did not distinguish between operating system and language Titan Supervisor, early time-sharing system begun 1964 Berkeley Timesharing System (for Scientific Data Systems' SDS 940) Chippewa Operating System (for CDC 6600 supercomputer) Dartmouth Time-Sharing System (Dartmouth College's DTSS for GE computers) EXEC 8 (UNIVAC) KDF9 Timesharing Director (English Electric) – an early, fully hardware secured, fully pre-emptive process switching, multi-programming operating system for KDF9 (originally announced in 1960) OS/360 (IBM's primary OS for its S/360 series) (announced) PDP-6 Monitor (DEC) descendant renamed TOPS-10 in 1970 SCOPE (CDC 3000 series) 1965 BOS/360 (IBM's Basic Operating System) DECsys TOS/360 (IBM's Tape Operating System) Livermore Time Sharing System (LTSS) Multics (MIT, GE, Bell Labs for the GE-645) (announced) Pick operating system SIPROS 66 (Simultaneous Processing Operating System) THE multiprogramming system (Technische Hogeschool Eindhoven) development TSOS (later VMOS) (RCA) 1966 DOS/360 (IBM's Disk Operating System) GEORGE 1 & 2 for ICT 1900 series Mod 1 Mod 2 Mod 8 MS/8 (Richard F. Lary's DEC PDP-8 system) MSOS (Mass Storage Operating System) OS/360 (IBM's primary OS for its S/360 series) PCP and MFT (shipped) RAX Remote Users of Shared Hardware (RUSH), a time-sharing system developed by Allen-Babcock for the IBM 360/50 SODA for Elwro's Odra 1204 Universal Time-Sharing System (XDS Sigma series) 1967 CP-40, predecessor to CP-67 on modified IBM System/360 Model 40 CP-67 (IBM, also known as CP/CMS) Conversational Programming System (CPS), an IBM time-sharing system under OS/360 Michigan Terminal System (MTS) (time-sharing system for the IBM S/360-67 and successors) ITS (MIT's Incompatible Timesharing System for the DEC PDP-6 and PDP-10) OS/360 MVT ORVYL (Stanford University's time-sharing system for the IBM S/360-67) TSS/360 (IBM's Time-sharing System for the S/360-67, never officially released, canceled in 1969 and again in 1971) WAITS (SAIL, Stanford Artificial Intelligence Laboratory, time-sharing system for DEC PDP-6 and PDP-10, later TOPS-10) 1968 Airline Control Program (ACP) (IBM) B1 (NCR Century series) CALL/360, an IBM time-sharing system for System/360 HP Real-Time Executive (HP RTE) – Hewlett-Packard HP Time-Shared BASIC (HP TSB) – Hewlett-Packard (time-sharing system for the HP 2000) THE multiprogramming system (Eindhoven University of Technology) publication TSS/8 (DEC for the PDP-8) VP/CSS 1969 B2 (NCR Century series) B3 (NCR Century series) GEORGE 3 For ICL 1900 series MINIMOP Multics (MIT, GE, Bell Labs for the GE-645 and later the Honeywell 6180) (opened for paying customers in October) RC 4000 Multiprogramming System (RC) TENEX (Bolt, Beranek and Newman for DEC systems, later TOPS-20) Unics (later Unix) (AT&T, initially on DEC computers) Xerox Operating System == 1970s == 1970 DOS-11 (PDP-11) 1971 EMAS Kronos RSTS-11 2A-19 (First released version; PDP-11) RSX-15 OS/8 1972 B4 (NCR Century series) COS-300 Data General RDOS Edos MUSIC/SP OS/4 OS 1100 OS/2000 (Honeywell 2000-series) Operating System/Virtual Storage 1 (OS/VS1) Operating System/Virtual Storage 2 R1 (OS/VS2 SVS) PRIMOS (written in FORTRAN IV, that didn't have pointers, while later versions, around version 18, written in a version of PL/I, called PL/P) Virtual Machine/Basic System Extensions Program Product (BSEPP or VM/SE) Virtual Machine/System Extensions Program Product (SEPP or VM/BSE) Virtual Machine Facility/370 (VM/370), sometimes known as VM/CMS 1973 Эльбрус-1 (Elbrus-1) – Soviet computer – created using high-level language uЭль-76 (AL-76/ALGOL 68) Alto OS CP-V (Control Program V) RSX-11D RT-11 VME – implementation language S3 (ALGOL 68) 1974 ACOS-2 (NEC) ACOS-4 ACOS-6 CP/M DOS-11 V09-20C (Last stable release, June 1974) Hydra – capability-based, multiprocessing OS kernel MONECS Multi-Programming Executive (MPE) – Hewlett-Packard Operating System/Virtual Storage 2 R2 (MVS) OS/7 OS/16 OS/32 Sintran III 1975 BS2000 V2.0 (First released version) COS-350 ISIS NOS (Control Data Corporation) OS/3 (Univac) VS/9 (formerly RCA's TSOS, later named VMOS) Version 6 Unix XVM/DOS XVM/RSX 1976 Cambridge CAP computer – all operating system procedures written in ALGOL 68C, with some closely associated protected procedures in BCPL Cray Operating System DX10 FLEX TOPS-20 TX990/TXDS Tandem Nonstop OS v1 Thoth 1977 1BSD AMOS KERNAL OASIS operating system OS68 OS4000 RMX-80 System 88 (Exec) System Support Program (IBM System/34 and System/36) TRSDOS Virtual Memory System (VMS) V1.0 (Initial commercial release, October 25) VRX (Virtual Resource eXecutive) VS Virtual Memory Operating System 1978 2BSD Apple DOS Control Program Facility (IBM System/38) Cray Time Sharing System (CTSS) DPCX (IBM) DPPX (IBM) HDOS KSOS – secure OS design from Ford Aerospace KVM/370 – security retro-fit of IBM VM/370 Lisp machine (CADR) MVS/System Extensions (MVS/SE) OS4 (Naked Mini 4) PTDOS TRIPOS UCSD p-System (First released version) Z80-RIO 1979 Atari DOS 3BSD CP-6 Idris MP/M MVS/System Extensions R2 (MVS/SE2) NLTSS POS Sinclair BASIC Transaction Processing Facility (TPF) (IBM) UCLA Secure UNIX – an early secure UNIX OS based on security kernel UNIX/32V DOS/VSE Version 7 Unix == 1980s == 1980 86-DOS AOS/VS (Data General) Business Operating System CTOS DOSPLUS (TRS-80) MVS/System Product (MVS/SP) V1 NewDos/80 OS-9 RMX-86 RS-DOS SOS Virtual Machine/System Product (VM/SP) Xenix 1981 Acorn MOS Aegis SR1 (First Apollo/DOMAIN systems shipped on March 27) CP/M-86 DRX (Distributed Resource Executive) iMAX – OS for Intel's iAPX 432 capability machine MCS (Multi-user Control System) MS-DOS PC DOS Pilot (Xerox Star operating system) UNOS UTS V VERSAdos VRTX VSOS (Virtual Storage Operating System) Xinu first release 1982 Commodore DOS LDOS (By Logical Systems, Inc. – for the Radio Shack TRS-80 Models I, II & III) PCOS (Olivetti M20) pSOS QNX Stratus VOS Sun UNIX (later SunOS) 0.7 Ultrix Unix System III VAXELN 1983 Coherent DNIX EOS GNU (project start) Lisa Office System 7/7 LOCUS – UNIX compatible, high reliability, distributed OS MVS/System Product V2 (MVS/Extended Architecture, MVS/XA) Novell NetWare (S-Net) PERPOS ProDOS RTU (Real-Time Unix) STOP – TCSEC A1-class, secure OS for SCOMP hardware SunOS 1.0 VSE/System Package (VSE/SP) Version 1 1984 AMSDOS CTIX (Unix variant) DYNIX Mac OS (System 1.0) MSX-DOS NOS/VE PANOS PC/IX ROS Sinclair QDOS SINIX UNICOS Venix 2.0 Virtual Machine/Extended Architecture Migration Assistance (VM/XA MA) 1985 AmigaOS Atari TOS DG/UX DOS Plus Graphics Environment Manager Harmony MacOS 2 MIPS RISC/os Oberon – written in Oberon SunOS 2.0 Version 8 Unix Virtual Machine/Extended Architecture System Facility (VM/XA SF) Windows 1.0 Windows 1.01 Xenix 2.0 1986 AIX 1.0 Cronus distributed OS FlexOS GEMSOS – TCSEC A1-class, secure kernel for BLACKER VPN & GTNP GEOS Genera 7.0 HP-UX MacOS 3 SunOS 3.0 TR-DOS TRIX Version 9 Unix 1987 Arthur (much improved version came in 1989 under the name RISC OS) BS2000 V9.0 IRIX (3.0 is first SGI version) MacOS 4 MacOS 5 MDOS MINIX 1.0 OS/2 (1.0) PC-MOS/386 Topaz – semi-distributed OS for DEC Firefly workstation written in Modula-2+ and garbage collected VxWorks Windows 2.0 1988 A/UX (Apple Computer) AOS/VS II (Data General) CP/M rebranded as DR-DOS Flex machine – tagged, capability machine with OS and other software written

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