AI Face Judge

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

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  Computational humor

  Computational humor is a branch of computational linguistics and artificial intelligence which uses computers in humor research. It is a relatively new area, with the first dedicated conference organized in 1996. The first "computer model of a sense of humor" was suggested by Suslov as early as 1992. Investigation of the general scheme of the information processing show a possibility of a specific malfunction, conditioned by the necessity of a quick deletion from consciousness of a false version. This specific malfunction can be identified with a humorous effect on the psychological grounds; however, an essentially new ingredient, a role of timing, is added to a well known role of ambiguity. In biological systems, a sense of humour inevitably develops in the course of evolution, because its biological function consists in quickening the transmission of processed information into consciousness and in a more effective use of brain resources. A realization of this algorithm in neural networks explains naturally the mechanism of laughter: deletion of a false version corresponds to zeroing of some part of the neural network and excessive energy of neurons is thrown out to the motor cortex, arousing muscular contractions. Unfortunately, a practical realization of this algorithm needs extensive databases, whose creation in the automatic regime was suggested only recently . As a result, this magistral direction was not developed properly and subsequent investigations (see below) accepted somewhat specialized colouring. == Joke generators == === Pun generation === An approach to analysis of humor is classification of jokes. A further step is an attempt to generate jokes basing on the rules that underlie classification. Simple prototypes for computer pun generation were reported in the early 1990s, based on a natural language generator program, VINCI. Graeme Ritchie and Kim Binsted in their 1994 research paper described a computer program, JAPE, designed to generate question-answer-type puns from a general, i.e., non-humorous, lexicon. (The program name is an acronym for "Joke Analysis and Production Engine".) Some examples produced by JAPE are: Q: What is the difference between leaves and a car? A: One you brush and rake, the other you rush and brake. Q: What do you call a strange market? A: A bizarre bazaar. Since then the approach has been improved, and the latest report, dated 2007, describes the STANDUP joke generator, implemented in the Java programming language. The STANDUP generator was tested on children within the framework of analyzing its usability for language skills development for children with communication disabilities, e.g., because of cerebral palsy. (The project name is an acronym for "System To Augment Non-speakers' Dialog Using Puns" and an allusion to standup comedy.) Children responded to this "language playground" with enthusiasm, and showed marked improvement on certain types of language tests. The two young people, who used the system over a ten-week period, regaled their peers, staff, family and neighbors with jokes such as: "What do you call a spicy missile? A hot shot!" Their joy and enthusiasm at entertaining others was inspirational. === Other === Stock and Strapparava described a program to generate funny acronyms. == Joke recognition == A statistical machine learning algorithm to detect whether a sentence contained a "That's what she said" double entendre was developed by Kiddon and Brun (2011). There is an open-source Python implementation of Kiddon & Brun's TWSS system. A program to recognize knock-knock jokes was reported by Taylor and Mazlack. This kind of research is important in analysis of human–computer interaction. An application of machine learning techniques for the distinguishing of joke texts from non-jokes was described by Mihalcea and Strapparava (2006). Takizawa et al. (1996) reported on a heuristic program for detecting puns in the Japanese language. == Applications == A possible application for assistance in language acquisition is described in the section "Pun generation". Another envisioned use of joke generators is in cases of a steady supply of jokes where quantity is more important than quality. Another obvious, yet remote, direction is automated joke appreciation. It is known that humans interact with computers in ways similar to interacting with other humans that may be described in terms of personality, politeness, flattery, and in-group favoritism. Therefore, the role of humor in human–computer interaction is being investigated. In particular, humor generation in user interface to ease communications with computers was suggested. Craig McDonough implemented the Mnemonic Sentence Generator, which converts passwords into humorous sentences. Based on the incongruity theory of humor, it is suggested that the resulting meaningless but funny sentences are easier to remember. For example, the password AjQA3Jtv is converted into "Arafat joined Quayle's Ant, while TARAR Jeopardized thurmond's vase," an example chosen by combining politicians names with verbs and common nouns. == Related research == John Allen Paulos is known for his interest in mathematical foundations of humor. His book Mathematics and Humor: A Study of the Logic of Humor demonstrates structures common to humor and formal sciences (mathematics, linguistics) and develops a mathematical model of jokes based on catastrophe theory. Conversational systems which have been designed to take part in Turing test competitions generally have the ability to learn humorous anecdotes and jokes. Because many people regard humor as something particular to humans, its appearance in conversation can be quite useful in convincing a human interrogator that a hidden entity, which could be a machine or a human, is in fact a human.

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  Ultra (cryptography)

  Ultra was the designation adopted by British military intelligence in June 1941 for wartime signals intelligence obtained by breaking high-level encrypted enemy radio and teleprinter communications at the Government Code and Cypher School (GC&CS) at Bletchley Park. Ultra eventually became the standard designation among the western Allies for all such intelligence. The name arose because the intelligence obtained was considered more important than that designated by the highest British security classification then used (Most Secret) and so was regarded as being Ultra Secret. Several other cryptonyms had been used for such intelligence. The code name "Boniface" was used as a cover name for Ultra. In order to ensure that the successful code-breaking did not become apparent to the Germans, British intelligence created a fictional MI6 master spy, Boniface, who controlled a fictional series of agents throughout Germany. Information obtained through code-breaking was often attributed to the human intelligence from the Boniface network. The U.S. used the codename Magic for its decrypts from Japanese sources, including the "Purple" cipher. Much of the German cipher traffic was encrypted on the Enigma machine. Used properly, the German military Enigma would have been virtually unbreakable; in practice, shortcomings in operation allowed it to be broken. The term "Ultra" has often been used almost synonymously with "Enigma decrypts". However, Ultra also encompassed decrypts of the German Lorenz SZ 40/42 machines that were used by the German High Command, and the Hagelin machine. Many observers, at the time and later, regarded Ultra as immensely valuable to the Allies. Winston Churchill was reported to have told King George VI, when presenting to him Stewart Menzies (head of the Secret Intelligence Service and the person who controlled distribution of Ultra decrypts to the government): "It is thanks to the secret weapon of General Menzies, put into use on all the fronts, that we won the war!" F. W. Winterbotham quoted the western Supreme Allied Commander, Dwight D. Eisenhower, at war's end describing Ultra as having been "decisive" to Allied victory. Sir Harry Hinsley, Bletchley Park veteran and official historian of British Intelligence in World War II, made a similar assessment of Ultra, saying that while the Allies would have won the war without it, "the war would have been something like two years longer, perhaps three years longer, possibly four years longer than it was." However, Hinsley and others have emphasized the difficulties of counterfactual history in attempting such conclusions, and some historians, such as John Keegan, have said the shortening might have been as little as the three months it took the United States to deploy the atomic bomb. == Sources of intelligence == Most Ultra intelligence was derived from reading radio messages that had been encrypted with cipher machines, complemented by material from radio communications using traffic analysis and direction finding. In the early phases of the war, particularly during the eight-month Phoney War, the Germans could transmit most of their messages using land lines and so had no need to use radio. This meant that those at Bletchley Park had some time to build up experience of collecting and starting to decrypt messages on the various radio networks. German Enigma messages were the main source, with those of the German air force (the Luftwaffe) predominating, as they used radio more and their operators were particularly ill-disciplined. === German === ==== Enigma ==== "Enigma" refers to a family of electro-mechanical rotor cipher machines. These produced a polyalphabetic substitution cipher and were widely thought to be unbreakable in the 1920s, when a variant of the commercial Model D was first used by the Reichswehr. The German Army (Heer), Navy, Air Force, Nazi party, Gestapo and German diplomats used Enigma machines in several variants. Abwehr (German military intelligence) used a four-rotor machine without a plugboard and Naval Enigma used different key management from that of the army or air force, making its traffic far more difficult to cryptanalyse; each variant required different cryptanalytic treatment. The commercial versions were not as secure and Dilly Knox of GC&CS is said to have broken one before the war. German military Enigma was first broken in December 1932 by Marian Rejewski and the Polish Cipher Bureau, using a combination of brilliant mathematics, the services of a spy in the German office responsible for administering encrypted communications, and good luck. The Poles read Enigma to the outbreak of World War II and beyond, in France. At the turn of 1939, the Germans made the systems ten times more complex, which required a tenfold increase in Polish decryption equipment, which they could not meet. On 25 July 1939, the Polish Cipher Bureau handed reconstructed Enigma machines and their techniques for decrypting ciphers to the French and British. Gordon Welchman wrote, Ultra would never have got off the ground if we had not learned from the Poles, in the nick of time, the details both of the German military Enigma machine, and of the operating procedures that were in use. At Bletchley Park, some of the key people responsible for success against Enigma included mathematicians Alan Turing and Hugh Alexander and, at the British Tabulating Machine Company, chief engineer Harold Keen. After the war, interrogation of German cryptographic personnel led to the conclusion that German cryptanalysts understood that cryptanalytic attacks against Enigma were possible but were thought to require impracticable amounts of effort and investment. The Poles' early start at breaking Enigma and the continuity of their success gave the Allies an advantage when World War II began. ==== Lorenz cipher ==== In June 1941, the Germans started to introduce on-line stream cipher teleprinter systems for strategic point-to-point radio links, to which the British gave the code-name Fish. Several systems were used, principally the Lorenz SZ 40/42 (codenamed "Tunny" by the British) and Geheimfernschreiber ("Sturgeon"). These cipher systems were cryptanalysed, particularly Tunny, which the British thoroughly penetrated. It was eventually attacked using Colossus machines, which were the first digital programme-controlled electronic computers. In many respects the Tunny work was more difficult than for the Enigma, since the British codebreakers had no knowledge of the machine producing it and no head-start such as that the Poles had given them against Enigma. Although the volume of intelligence derived from this system was much smaller than that from Enigma, its importance was often far higher because it produced primarily high-level, strategic intelligence that was sent between Wehrmacht high command (Oberkommando der Wehrmacht, OKW). The eventual bulk decryption of Lorenz-enciphered messages contributed significantly, and perhaps decisively, to the defeat of Nazi Germany. Nevertheless, the Tunny story has become much less well known among the public than the Enigma one. At Bletchley Park, some of the key people responsible for success in the Tunny effort included mathematicians W. T. "Bill" Tutte and Max Newman and electrical engineer Tommy Flowers. === Italian === In June 1940, the Italians were using book codes for most of their military messages, except for the Italian Navy, which in early 1941 had started using a version of the Hagelin rotor-based cipher machine C-38. This was broken from June 1941 onwards by the Italian subsection of GC&CS at Bletchley Park. === Japanese === In the Pacific theatre, a Japanese cipher machine, called "Purple" by the Americans, was used for highest-level Japanese diplomatic traffic. It produced a polyalphabetic substitution cipher, but unlike Enigma, was not a rotor machine, being built around electrical stepping switches. It was broken by the US Army Signal Intelligence Service and disseminated as Magic. Detailed reports by the Japanese ambassador to Germany were encrypted on the Purple machine. His reports included reviews of German assessments of the military situation, reviews of strategy and intentions, reports on direct inspections by the ambassador (in one case, of Normandy beach defences), and reports of long interviews with Hitler. The Japanese are said to have obtained an Enigma machine in 1937, although it is debated whether they were given it by the Germans or bought a commercial version, which, apart from the plugboard and internal wiring, was the German Heer/Luftwaffe machine. Having developed a similar machine, the Japanese did not use the Enigma machine for their most secret communications. The chief fleet communications code system used by the Imperial Japanese Navy was called JN-25 by the Americans, and by early 1942 the US Navy had made considerable progress in decrypting Japanese naval messages. The US Army also made progress on the

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  Cryptochannel

  In telecommunications, a cryptochannel is a complete system of crypto-communications between two or more holders or parties. It includes: (a) the cryptographic aids prescribed; (b) the holders thereof; (c) the indicators or other means of identification; (d) the area or areas in which effective; (e) the special purpose, if any, for which provided; and (f) pertinent notes as to distribution, usage, etc. A cryptochannel is analogous to a radio circuit.

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  Embedded analytics

  Embedded analytics enables organisations to integrate analytics capabilities into their own, often software as a service, applications, portals, or websites. This differs from embedded software and web analytics (also commonly known as product analytics). This integration typically provides contextual insights, quickly, easily and conveniently accessible since these insights should be present on the web page right next to the other, operational, parts of the host application. Insights are provided through interactive data visualisations, such as charts, diagrams, filters, gauges, maps and tables often in combination as dashboards embedded within the system. This setup enables easier, in-depth data analysis without the need to switch and log in between multiple applications. Embedded analytics is also known as customer facing analytics. Embedded analytics is the integration of analytic capabilities into a host, typically browser-based, business-to-business, software as a service, application. These analytic capabilities would typically be relevant and contextual to the use-case of the host application. == History == The term "embedded analytics" was first used by Howard Dresner: consultant, author, former Gartner analyst and inventor of the term "business intelligence" said Howard Dresner while he was working for Hyperion Solutions, a company that Oracle bought in 2007. Oracle started then to use the term "embedded analytics" at their press release for Oracle Rapid Planning on 2009 . == Considerations with embedded analytics == When evaluating embedding analytics, consideration would normally be given to integration at various levels, these would likely include: security integration, data integration, application logic integration, business rules integration, and user experience integration. This is in contrast to traditional BI, which expects users to leave their workflow applications to look at data insights in a separate set of tools. This immediacy makes embedded analytics much more intuitive and likely to be valued by users. A December 2016 report from Nucleus Research found that using BI tools, which require toggling between applications, can take up as much as 1–2 hours of an employee's time each week, whereas embedded analytics eliminate the need to toggle between apps.

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  Ave!Comics

  Ave!Comics Production is a privately owned French company editing comics on smartphones, tablets and computers. It was founded in 2008 and it is a subsidiary of Aquafadas, a software development company in digital publishing owned by Kobo Inc. AveComics is a comic book store for digital comic books that can be used on computers, tablets, and smartphones.(iOS, Android) Readers can buy and read comic books, manga and graphic novels in French, English and Spanish. AveComics uses a technology created by Aquafadas for comics transformation, distribution and reading, based around its AVE format. The AveComics application was also a finalist in the BlackBerry Innovation Awards 2009, in the "Entertainment" category. == Company history == Aquafadas, a company working on creative software for Flash, HTML5, photo, and video editing, created the application MyComics to allow the reading of comics on mobile in 2006. This application was made available in 2008, to enable the reading and storing of comics on iPhone and iPod Touch. A reading system adapted to low resolution screens was also available. In October of the same year, the company launched a comics library on both devices, in partnership with the Angoulême International Comics Festival, Fnac and SNCF. This library included the official selection of the festival, and was downloaded over 150 000 times. In December 2008 "The Adventures of Lucky Luke n°3", at Lucky Comics was published on both devices. The comic made a 50 000 € turnover. In April 2009, "Les Blondes" 10th volume was the top-selling comic for 10 months on the AppStore. After, in August 2009, the AveComics application was launched on iPhone, iPod Touch and BlackBerry. The company's website was launched in September when more than 100 titles were available on smartphones and computers. == Catalogue == AveComics works with over 80 international publishers including Glénat, Marsu Productions, Delcourt, Casterman, Soleil, Ubisoft, Les Humanoïdes Associés and Mad Fabrik. Comics such as "Assassin's Creed", "Talisman", "Titeuf", and "Seoul District" are sold by the company. == Award == Grand Prix Software Venture Capital - Senate 2008.

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  SCinet

  SCinet is the high-performance network built annually by volunteers in support of SC (formerly Supercomputing, the International Conference for High Performance Computing, Networking, Storage and Analysis). SCinet is the primary network for the yearly conference and is used by attendees and exhibitors to demonstrate and test high-performance computing and networking applications. == International Community == SCinet is also a hub for the international networking community. It provides a platform to share the latest research, technologies, and demonstrations for networks, network technology providers, and even software developers who are in charge of supporting HPC communities at their own institutions or organizations. == Volunteers == Nearly 200 volunteers from educational institutions, high performance computing sites, equipment vendors, research and education networks, government agencies and telecommunications carriers collaborate via technology and in-person to design, build and operate SCinet. While many of these credentialed individuals have volunteered at SCinet for years, first timers join the team each year. They include international students and participants in the National Science Foundation-funded Women in IT Networking at SC (WINS) program. The 2017 SCinet team included women and men from high performance computing institutions in the U.S. and throughout the world. == History == Originated in 1991 as an initiative within the SC conference to provide networking to attendees, SCinet has grown to become the "World's Fastest Network" during the duration of the conference. For 29 years, SCinet has provided SC attendees and the high performance computing (HPC) community with the innovative network platform necessary to internationally interconnect, transport, and display HPC research during SC. Historically, SCinet has been used as a platform to test networking technology and applications which have found their way into common use. == Research and development == In the past years, SCinet deployed conference wide networking technologies such as ATM, FDDI, HiPPi before they were deployed commercially.

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  Format-preserving encryption

  In cryptography, format-preserving encryption (FPE), refers to encrypting in such a way that the output (the ciphertext) is in the same format as the input (the plaintext). The meaning of "format" varies. Typically only finite sets of characters are used; numeric, alphabetic or alphanumeric. For example: Encrypting a 16-digit credit card number so that the ciphertext is another 16-digit number. Encrypting an English word so that the ciphertext is another English word. Encrypting an n-bit number so that the ciphertext is another n-bit number (this is the definition of an n-bit block cipher). For such finite domains, and for the purposes of the discussion below, the cipher is equivalent to a permutation of N integers {0, ... , N−1} where N is the size of the domain. == Motivation == === Restricted field lengths or formats === One motivation for using FPE comes from the problems associated with integrating encryption into existing applications, with well-defined data models. A typical example would be a credit card number, such as 1234567812345670 (16 bytes long, digits only). Adding encryption to such applications might be challenging if data models are to be changed, as it usually involves changing field length limits or data types. For example, output from a typical block cipher would turn credit card number into a hexadecimal (e.g.0x96a45cbcf9c2a9425cde9e274948cb67, 34 bytes, hexadecimal digits) or Base64 value (e.g. lqRcvPnCqUJc3p4nSUjLZw==, 24 bytes, alphanumeric and special characters), which will break any existing applications expecting the credit card number to be a 16-digit number. Apart from simple formatting problems, using AES-128-CBC, this credit card number might get encrypted to the hexadecimal value 0xde015724b081ea7003de4593d792fd8b695b39e095c98f3a220ff43522a2df02. In addition to the problems caused by creating invalid characters and increasing the size of the data, data encrypted using the CBC mode of an encryption algorithm also changes its value when it is decrypted and encrypted again. This happens because the random seed value that is used to initialize the encryption algorithm and is included as part of the encrypted value is different for each encryption operation. Because of this, it is impossible to use data that has been encrypted with the CBC mode as a unique key to identify a row in a database. FPE attempts to simplify the transition process by preserving the formatting and length of the original data, allowing a drop-in replacement of plaintext values with their ciphertexts in legacy applications. == Comparison to truly random permutations == Although a truly random permutation is the ideal FPE cipher, for large domains it is infeasible to pre-generate and remember a truly random permutation. So the problem of FPE is to generate a pseudorandom permutation from a secret key, in such a way that the computation time for a single value is small (ideally constant, but most importantly smaller than O(N)). == Comparison to block ciphers == An n-bit block cipher technically is a FPE on the set {0, ..., 2n-1}. If an FPE is needed on one of these standard sized sets (for example, n = 64 for DES and n = 128 for AES) a block cipher of the right size can be used. However, in typical usage, a block cipher is used in a mode of operation that allows it to encrypt arbitrarily long messages, and with an initialization vector as discussed above. In this mode, a block cipher is not an FPE. == Definition of security == In cryptographic literature (see most of the references below), the measure of a "good" FPE is whether an attacker can distinguish the FPE from a truly random permutation. Various types of attackers are postulated, depending on whether they have access to oracles or known ciphertext/plaintext pairs. == Algorithms == In most of the approaches listed here, a well-understood block cipher (such as AES) is used as a primitive to take the place of an ideal random function. This has the advantage that incorporation of a secret key into the algorithm is easy. Where AES is mentioned in the following discussion, any other good block cipher would work as well. === The FPE constructions of Black and Rogaway === Implementing FPE with security provably related to that of the underlying block cipher was first undertaken in a paper by cryptographers John Black and Phillip Rogaway, which described three ways to do this. They proved that each of these techniques is as secure as the block cipher that is used to construct it. This means that if the AES algorithm is used to create an FPE algorithm, then the resulting FPE algorithm is as secure as AES because an adversary capable of defeating the FPE algorithm can also defeat the AES algorithm. Therefore, if AES is secure, then the FPE algorithms constructed from it are also secure. In all of the following, E denotes the AES encryption operation that is used to construct an FPE algorithm and F denotes the FPE encryption operation. ==== FPE from a prefix cipher ==== One simple way to create an FPE algorithm on {0, ..., N-1} is to assign a pseudorandom weight to each integer, then sort by weight. The weights are defined by applying an existing block cipher to each integer. Black and Rogaway call this technique a "prefix cipher" and showed it was provably as good as the block cipher used. Thus, to create an FPE on the domain {0,1,2,3}, given a key K apply AES(K) to each integer, giving, for example, weight(0) = 0x56c644080098fc5570f2b329323dbf62 weight(1) = 0x08ee98c0d05e3dad3eb3d6236f23e7b7 weight(2) = 0x47d2e1bf72264fa01fb274465e56ba20 weight(3) = 0x077de40941c93774857961a8a772650d Sorting [0,1,2,3] by weight gives [3,1,2,0], so the cipher is F(0) = 3 F(1) = 1 F(2) = 2 F(3) = 0 This method is only useful for small values of N. For larger values, the size of the lookup table and the required number of encryptions to initialize the table gets too big to be practical. ==== FPE from cycle walking ==== If there is a set M of allowed values within the domain of a pseudorandom permutation P (for example P can be a block cipher like AES), an FPE algorithm can be created from the block cipher by repeatedly applying the block cipher until the result is one of the allowed values (within M). CycleWalkingFPE(x) { if P(x) is an element of M then return P(x) else return CycleWalkingFPE(P(x)) } The recursion is guaranteed to terminate. (Because P is one-to-one and the domain is finite, repeated application of P forms a cycle, so starting with a point in M the cycle will eventually terminate in M.) This has the advantage that the elements of M do not have to be mapped to a consecutive sequence {0,...,N-1} of integers. It has the disadvantage, when M is much smaller than P's domain, that too many iterations might be required for each operation. If P is a block cipher of a fixed size, such as AES, this is a severe restriction on the sizes of M for which this method is efficient. For example, an application may want to encrypt 100-bit values with AES in a way that creates another 100-bit value. With this technique, AES-128-ECB encryption can be applied until it reaches a value which has all of its 28 highest bits set to 0, which will take an average of 228 iterations to happen. ==== FPE from a Feistel network ==== It is also possible to make a FPE algorithm using a Feistel network. A Feistel network needs a source of pseudo-random values for the sub-keys for each round, and the output of the AES algorithm can be used as these pseudo-random values. When this is done, the resulting Feistel construction is good if enough rounds are used. One way to implement an FPE algorithm using AES and a Feistel network is to use as many bits of AES output as are needed to equal the length of the left or right halves of the Feistel network. If a 24-bit value is needed as a sub-key, for example, it is possible to use the lowest 24 bits of the output of AES for this value. This may not result in the output of the Feistel network preserving the format of the input, but it is possible to iterate the Feistel network in the same way that the cycle-walking technique does to ensure that format can be preserved. Because it is possible to adjust the size of the inputs to a Feistel network, it is possible to make it very likely that this iteration ends very quickly on average. In the case of credit card numbers, for example, there are 1015 possible 16-digit credit card numbers (accounting for the redundant check digit), and because the 1015 ≈ 249.8, using a 50-bit wide Feistel network along with cycle walking will create an FPE algorithm that encrypts fairly quickly on average. === The Thorp shuffle === A Thorp shuffle is like an idealized card-shuffle, or equivalently a maximally-unbalanced Feistel cipher where one side is a single bit. It is easier to prove security for unbalanced Feistel ciphers than for balanced ones. === VIL mode === For domain sizes that are a power of two, and an existing block cipher with a smaller bl

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  Social network game

  A social network game (sometimes simply referred to as a social media game, social gaming, or online social game) is a type of online game that is played through social networks or social media. They typically feature gamification systems with multiplayer gameplay mechanics. Social network games were originally implemented as browser games. As mobile gaming took off, the games moved to mobile as well. While they share many aspects of traditional video games, social network games often employ additional ones that make them distinct. Traditionally they are oriented to be social games and casual games. The first cross-platform "Facebook-to-Mobile" social network game was developed in 2011 by a Finnish company Star Arcade. Social network games are amongst the most popular games played in the world, with several products with tens of millions of players. (Lil) Green Patch, Happy Farm, and Mob Wars were some of the first successful games of this genre. FarmVille, Mafia Wars, Kantai Collection, and The Sims Social are more recent examples of popular social network game. Major companies that made or published social network games include Zynga, Wooga and Bigpoint Games. == Demographics == As of 2010, it was reported that 55 percent of the social network gaming demographic in the United States consisted of women while in the United Kingdom, women made up nearly 60 percent of the demographic. In addition, most social gamers were around the 30 to 59 age range, with the average social gamer being 43 years old. Social gaming may appeal more to the older demographic because it is free, easier to advance through in a short period, does not involve as much violence as traditional video games, and is easier to grasp. Other games target certain demographics that use social media, such as Pot Farm creating a community by involving elements of cannabis subculture in its gameplay. == Technology and platforms == A social network video game is a client-server application. The client in the web era was implemented with a mix of web technologies like Flash, HTML5, PHP and JavaScript. When mobile games moved to mobile, social game front ends were developed using mobile platform technologies like Java, Objective-C, Swift and C++. The back end was a mix of programming languages and systems, including PHP, Ruby, C++ and go. Where social network video games diverged from traditional game development was the combination of real-time analytics to continuously optimize game mechanics to drive growth, revenue, and engagement. == Distinct features == The following table outlines common characteristics of social games, mentioned by Björk at the 2010 GCO Games Convention Online: A social network game may employ any of the following features: asynchronous gameplay, which allows rules to be resolved without needing players to play at the same time. gamification, which video game mechanics such as achievements and points are applied to those experienced when playing games in order to motivate and engage users. community, as one of the most distinct features of social video games is in leveraging the player's social network. Quests or game goals may only be possible if a player "shares" with friends connected by the social network hosting the game or gets them to play, as well as "neighbors" or "allies". a lack of victory conditions: there are generally no victory conditions since most developers count on users playing their games often. The game never ends and no one is ever declared winner. Instead, many casual games have "quests" or "missions" for players to complete. This is not true for board game-like social games, such as Scrabble. a virtual currency which players usually must purchase with real-world money. With the in-game currency, players can buy upgrades that would otherwise take much longer to earn through in-game achievements. In many cases, some upgrades are only available with the virtual currency. == Engagement strategies == Since social network games are often less challenging than console games and they have relatively shorter game play, they use different techniques to stretch game play and tools to retain users. Continuous goals: The games assign specific goals for users to achieve. As they advance in the game, the goals become more challenging and time-consuming. They also provide frequent feedback with their performance. Every action will translate towards a certain goal that will be used to attain higher gaming capitals. Gaming capitals: Players are encouraged to earn different badges, trophies, and accolades that indicate their progress and accomplishments. Some achievements are unlocked just by advancing in the game while others may significantly alter the rationale behind the game and require extensive investment from players. The ways of gaining gaming capital are not limited to playing games but the games-related productive activities that are appreciated in the player's social circle too. By accumulating gaming capitals, they provide an intrinsic benefit to gamers as there is an avenue to boost their accomplishment and showcase their expertise of the game. The achievements are visible to their network of friends. Gaming capitals are a way for developers to increase replay value provides extended play time, and players get more value from the game. Motivation for collecting gaming capitals: 1. Legitimization: refers to society's willingness to approve or condone certain behavior. Collecting is about channeling one's materialistic desires into more meaningful pursuits. Game achievements serve a similar purpose, allowing players to justify the hours spent playing the game. 2. Self-extension: Gathering and controlling meaningful objects or experiences can work to gain one an improved sense of self. The collector's goal to complete a collection is symbolically about completing the self too. Events timed to real world: Popular games such as Dragon City and Wild Ones require users to wait a certain time period before their "energy bars" replenish. Without energy, they are unable to conduct any form of action. Gamers are forced to wait and return after their energy replenishes to continue playing. == Monetization == Social network games frequently monetize based on virtual good transactions, but other games are emerging that utilize newer economic models. === Virtual goods === Gamers will be able to purchase in game items like power-ups, avatar accessories, or decorative items users purchase within the game itself. This is realized by monetize products that do not technically exist. Virtual goods account for over 90% of all revenue generated by the world's top social game developers. Designers optimize user experience through additional gameplay, missions, and quests, without having to worry about overhead or unused stock. == Advertising == The following are common ways of advertising in social network games: === Banner advertisements === As banner ads within social networks tend to be where ad response is low, they tend to be priced at bottom-of-the-barrel CPMs of around $2. However, because social games generate so many page views, they are the biggest part of advertising revenue for the social gaming industry. === Video ads === Videos are the ad format with the most revenue per view. They tend to be higher-priced, either by CPMs ($35+ CPM in social games) or cost-per-completed-view. According to studies, video ads result in highest brand recall thus a good return on investment for advertisers. Video ads are shown either in in-game interstitials (e.g. when the game is loading a new screen) or through incentive-based advertising, i.e. you will get either an in-game reward or Facebook credits for watching an advertisement. === Product placement === A brand or product will be injected in a game in some way. Due to the variety of ways in which product placement can be accomplished in any media, and because the category is nascent, this category is not standardized at all, but some examples include branded in-game goods or even in-game quests. For example, in a game where you run a restaurant, you might be asked to collect ingredients to make a Starbucks Frappuccino, and receive in-game rewards for doing so. As these product placement deals are non-standard, they are largely charged with a production fee, which can be $350,000 to $750,000 depending on the type of placement and the popularity of the game. === Lead generation offers === Another form of advertising that is prevalent in many social games are lead generation offers. In this form of advertising, companies, usually from different industries, aim to convince players to sign up for their goods or services and in exchange, players will receive virtual gifts or advance in the game as a reward. === Sponsorship === ==== White label games ==== Applications that are built once, then individualized and licensed again and again. Developer can create a quality app focused on fun while leaving the edge

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  Mean shift

  Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. Application domains include cluster analysis in computer vision and image processing. == History == The mean shift procedure is usually credited to work by Fukunaga and Hostetler in 1975. It is, however, reminiscent of earlier work by Schnell in 1964. == Overview == Mean shift is a procedure for locating the maxima—the modes—of a density function given discrete data sampled from that function. This is an iterative method, and we start with an initial estimate x {\displaystyle x} . Let a kernel function K ( x i − x ) {\displaystyle K(x_{i}-x)} be given. This function determines the weight of nearby points for re-estimation of the mean. Typically a Gaussian kernel on the distance to the current estimate is used, K ( x i − x ) = e − c | | x i − x | | 2 {\displaystyle K(x_{i}-x)=e^{-c||x_{i}-x||^{2}}} . The weighted mean of the density in the window determined by K {\displaystyle K} is m ( x ) = ∑ x i ∈ N ( x ) K ( x i − x ) x i ∑ x i ∈ N ( x ) K ( x i − x ) {\displaystyle m(x)={\frac {\sum _{x_{i}\in N(x)}K(x_{i}-x)x_{i}}{\sum _{x_{i}\in N(x)}K(x_{i}-x)}}} where N ( x ) {\displaystyle N(x)} is the neighborhood of x {\displaystyle x} , a set of points for which K ( x i − x ) ≠ 0 {\displaystyle K(x_{i}-x)\neq 0} . The difference m ( x ) − x {\displaystyle m(x)-x} is called mean shift in Fukunaga and Hostetler. The mean-shift algorithm now sets x ← m ( x ) {\displaystyle x\leftarrow m(x)} , and repeats the estimation until m ( x ) {\displaystyle m(x)} converges. Although the mean shift algorithm has been widely used in many applications, a rigid proof for the convergence of the algorithm using a general kernel in a high dimensional space is still not known. Aliyari Ghassabeh showed the convergence of the mean shift algorithm in one dimension with a differentiable, convex, and strictly decreasing profile function. However, the one-dimensional case has limited real world applications. Also, the convergence of the algorithm in higher dimensions with a finite number of the stationary (or isolated) points has been proved. However, sufficient conditions for a general kernel function to have finite stationary (or isolated) points have not been provided. Gaussian Mean-Shift is an Expectation–maximization algorithm. == Details == Let data be a finite set S {\displaystyle S} embedded in the n {\displaystyle n} -dimensional Euclidean space, X {\displaystyle X} . Let K {\displaystyle K} be a flat kernel that is the characteristic function of the λ {\displaystyle \lambda } -ball in X {\displaystyle X} , In each iteration of the algorithm, s ← m ( s ) {\displaystyle s\leftarrow m(s)} is performed for all s ∈ S {\displaystyle s\in S} simultaneously. The first question, then, is how to estimate the density function given a sparse set of samples. One of the simplest approaches is to just smooth the data, e.g., by convolving it with a fixed kernel of width h {\displaystyle h} , where x i {\displaystyle x_{i}} are the input samples and k ( r ) {\displaystyle k(r)} is the kernel function (or Parzen window). h {\displaystyle h} is the only parameter in the algorithm and is called the bandwidth. This approach is known as kernel density estimation or the Parzen window technique. Once we have computed f ( x ) {\displaystyle f(x)} from the equation above, we can find its local maxima using gradient ascent or some other optimization technique. The problem with this "brute force" approach is that, for higher dimensions, it becomes computationally prohibitive to evaluate f ( x ) {\displaystyle f(x)} over the complete search space. Instead, mean shift uses a variant of what is known in the optimization literature as multiple restart gradient descent. Starting at some guess for a local maximum, y k {\displaystyle y_{k}} , which can be a random input data point x 1 {\displaystyle x_{1}} , mean shift computes the gradient of the density estimate f ( x ) {\displaystyle f(x)} at y k {\displaystyle y_{k}} and takes an uphill step in that direction. == Types of kernels == Kernel definition: Let X {\displaystyle X} be the n {\displaystyle n} -dimensional Euclidean space, R n {\displaystyle \mathbb {R} ^{n}} . The norm of x {\displaystyle x} is a non-negative number, ‖ x ‖ 2 = x ⊤ x ≥ 0 {\displaystyle \|x\|^{2}=x^{\top }x\geq 0} . A function K : X → R {\displaystyle K:X\rightarrow \mathbb {R} } is said to be a kernel if there exists a profile, k : [ 0 , ∞ ] → R {\displaystyle k:[0,\infty ]\rightarrow \mathbb {R} } , such that K ( x ) = k ( ‖ x ‖ 2 ) {\displaystyle K(x)=k(\|x\|^{2})} and k is non-negative. k is non-increasing: k ( a ) ≥ k ( b ) {\displaystyle k(a)\geq k(b)} if a < b {\displaystyle a Read more →

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  Localhost

  In computer networking, localhost is a hostname that refers to the current computer used to access it. The name localhost is reserved for loopback purposes. It is used to access the network services that are running on the host via the loopback network interface. Using the loopback interface bypasses any local network interface hardware. == Loopback == The local loopback mechanism may be used to run a network service on a host without requiring a physical network interface, or without making the service accessible from the networks the computer may be connected to. For example, a locally installed website may be accessed from a Web browser by the URL http://localhost to display its home page. IPv4 network standards reserve the entire address block 127.0.0.0/8 (more than 16 million addresses) for loopback purposes. That means any packet sent to any of those addresses is looped back. The address 127.0.0.1 is the standard address for IPv4 loopback traffic; the rest are not supported by all operating systems. However, they can be used to set up multiple server applications on the host, all listening on the same port number. In the IPv6 addressing architecture there is only a single address assigned for loopback: ::1. The standard precludes the assignment of that address to any physical interface, as well as its use as the source or destination address in any packet sent to remote hosts. == Name resolution == The name localhost normally resolves to the IPv4 loopback address 127.0.0.1, and to the IPv6 loopback address ::1. This resolution is normally configured by the following lines in the operating system's hosts file: 127.0.0.1 localhost ::1 localhost The name may also be resolved by Domain Name System (DNS) servers, but there are special considerations governing the use of this name: An IPv4 or IPv6 address query for the name localhost must always resolve to the respective loopback address. Applications may resolve the name to a loopback address themselves, or pass it to the local name resolver mechanisms. When a name resolver receives an address (A or AAAA) query for localhost, it should return the appropriate loopback addresses, and negative responses for any other requested record types. Queries for localhost should not be sent to caching name servers. To avoid burdening the Domain Name System root servers with traffic, caching name servers should never request name server records for localhost, or forward resolution to authoritative name servers. When authoritative name servers receive queries for 'localhost' in spite of the provisions mentioned above, they should resolve them appropriately. In addition to the mapping of localhost to the loopback addresses (127.0.0.1 and ::1), localhost may also be mapped to other IPv4 (loopback) addresses and it is also possible to assign other, or additional, names to any loopback address. The mapping of localhost to addresses other than the designated loopback address range in the hosts file or in DNS is not guaranteed to have the desired effect, as applications may map the name internally. In the Domain Name System, the name .localhost is reserved as a top-level domain name, originally set aside to avoid confusion with the hostname localhost. Domain name registrars are precluded from delegating domain names in the top-level .localhost domain. == Historical notes == In 1981, the block 127.0.0.0/8 got a 'reserved' status, as not to assign it as a general purpose class A IP network. This block was officially assigned for loopback purposes in 1986. Its purpose as a Special Use IPv4 Address block was confirmed in 1994,, 2002, 2010,, and last in 2013. From the outset, in 1995, the single IPv6 loopback address ::1 was defined. Its purpose and definition was unchanged in 1998,, 2003,, and up to the current definition, in 2006. == Packet processing == The processing of any packet sent to a loopback address, is implemented in the link layer of the TCP/IP stack. Such packets are never passed to any network interface controller (NIC) or hardware device driver and must not appear outside of a computing system, or be routed by any router. This permits software testing and local services, even in the absence of any hardware network interfaces. Looped-back packets are distinguished from any other packets traversing the TCP/IP stack only by the special IP address they were addressed to. Thus, the services that ultimately receive them respond according to the specified destination. For example, an HTTP service could route packets addressed to 127.0.0.99:80 and 127.0.0.100:80 to different Web servers, or to a single server that returns different web pages. To simplify such testing, the hosts file may be configured to provide appropriate names for each address. Packets received on a non-loopback interface with a loopback source or destination address must be dropped. Such packets are sometimes referred to as Martian packets. As with any other bogus packets, they may be malicious and any problems they might cause can be avoided by applying bogon filtering. == Special cases == The releases of the MySQL database differentiate between the use of the hostname localhost and the use of the addresses 127.0.0.1 and ::1. When using localhost as the destination in a client connector interface of an application, the MySQL application programming interface connects to the database using a Unix domain socket, while a TCP connection via the loopback interface requires the direct use of the explicit address. One notable exception to the use of the 127.0.0.0/8 addresses is their use in Multiprotocol Label Switching (MPLS) traceroute error detection, in which their property of not being routable provides a convenient means to avoid delivery of faulty packets to end users.

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  BREACH

  BREACH (a backronym: Browser Reconnaissance and Exfiltration via Adaptive Compression of Hypertext) is a security vulnerability against HTTPS when using HTTP compression. BREACH is built based on the CRIME security exploit. BREACH was announced at the August 2013 Black Hat USA conference by security researchers Angelo Prado, Neal Harris and Yoel Gluck. == Details == While the CRIME attack was presented as a general attack that could work effectively against a large number of protocols, only exploits against SPDY request compression and TLS compression were demonstrated and largely mitigated in browsers and servers. The CRIME exploits against HTTP compression has not been mitigated at all, even though the authors of CRIME have warned that this vulnerability might be even more widespread than SPDY and TLS compression combined. BREACH is an instance of the CRIME attack against HTTP compression—the use of gzip or DEFLATE data compression algorithms via the content-encoding option within HTTP by many web browsers and servers. Given this compression oracle, the rest of the BREACH attack follows the same general lines as the CRIME exploit, by performing an initial blind brute-force search to guess a few bytes, followed by divide-and-conquer search to expand a correct guess to an arbitrarily large amount of content. == Mitigation == BREACH exploits the compression in the underlying HTTP protocol. Therefore, turning off TLS compression makes no difference to BREACH, which can still perform a chosen-plaintext attack against the HTTP payload. As a result, clients and servers are either forced to disable HTTP compression completely (thus reducing performance), or to adopt workarounds to try to foil BREACH in individual attack scenarios, such as using cross-site request forgery (CSRF) protection. Another suggested approach is to disable HTTP compression whenever the referrer header indicates a cross-site request, or when the header is not present. This approach allows effective mitigation of the attack without losing functionality, only incurring a performance penalty on affected requests. Another approach is to add padding at the TLS, HTTP header, or payload level. Around 2013–2014, there was an IETF draft proposal for a TLS extension for length-hiding padding that, in theory, could be used as a mitigation against this attack. It allows the actual length of the TLS payload to be disguised by the insertion of padding to round it up to a fixed set of lengths, or to randomize the external length, thereby decreasing the likelihood of detecting small changes in compression ratio that is the basis for the BREACH attack. However, this draft has since expired without further action. A very effective mitigation is HTB (Heal-the-BREACH) that adds random-sized padding to compressed data, providing some variance in the size of the output contents. This randomness delays BREACH from guessing the correct characters in the secret token by a factor of 500 (10-byte max) to 500,000 (100-byte max). HTB protects all websites and pages in the server with minimal CPU usage and minimal bandwidth increase.

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  Data lineage

  Data lineage refers to the process of tracking how data is generated, transformed, transmitted and used across systems over time. It documents data's origins, transformations and movements, providing detailed visibility into its life cycle. This process simplifies the identification of errors in data analytics workflows, by enabling users to trace issues back to their root causes. Data lineage facilitates the ability to replay specific segments or inputs of the dataflow. This can be used in debugging or regenerating lost outputs. In database systems, this concept is closely related to data provenance, which involves maintaining records of inputs, entities, systems and processes that influence data. Data provenance provides a historical record of data origins and transformations. It supports forensic activities such as data-dependency analysis, error/compromise detection, recovery, auditing and compliance analysis: "Lineage is a simple type of why provenance." Data governance plays a critical role in managing metadata by establishing guidelines, strategies and policies. Enhancing data lineage with data quality measures and master data management adds business value. Although data lineage is typically represented through a graphical user interface (GUI), the methods for gathering and exposing metadata to this interface can vary. Based on the metadata collection approach, data lineage can be categorized into three types: Those involving software packages for structured data, programming languages and Big data systems. Data lineage information includes technical metadata about data transformations. Enriched data lineage may include additional elements such as data quality test results, reference data, data models, business terminology, data stewardship information, program management details and enterprise systems associated with data points and transformations. Data lineage visualization tools often include masking features that allow users to focus on information relevant to specific use cases. To unify representations across disparate systems, metadata normalization or standardization may be required. == Representation of data lineage == Representation broadly depends on the scope of the metadata management and reference point of interest. Data lineage provides sources of the data and intermediate data flow hops from the reference point with backward data lineage, leading to the final destination's data points and its intermediate data flows with forward data lineage. These views can be combined with end-to-end lineage for a reference point that provides a complete audit trail of that data point of interest from sources to their final destinations. As the data points or hops increase, the complexity of such representation becomes incomprehensible. Thus, the best feature of the data lineage view is the ability to simplify the view by temporarily masking unwanted peripheral data points. Tools with the masking feature enable scalability of the view and enhance analysis with the best user experience for both technical and business users. Data lineage also enables companies to trace sources of specific business data to track errors, implement changes in processes and implementing system migrations to save significant amounts of time and resources. Data lineage can improve efficiency in business intelligence BI processes. Data lineage can be represented visually to discover the data flow and movement from its source to destination via various changes and hops on its way in the enterprise environment. This includes how the data is transformed along the way, how the representation and parameters change and how the data splits or converges after each hop. A simple representation of the Data Lineage can be shown with dots and lines, where dots represent data containers for data points, and lines connecting them represent transformations the data undergoes between the data containers. Data lineage can be visualized at various levels based on the granularity of the view. At a very high-level, data lineage is visualized as systems that the data interacts with before it reaches its destination. At its most granular, visualizations at the data point level can provide the details of the data point and its historical behavior, attribute properties and trends and data quality of the data passed through that specific data point in the data lineage. The scope of the data lineage determines the volume of metadata required to represent its data lineage. Usually, data governance and data management of an organization determine the scope of the data lineage based on their regulations, enterprise data management strategy, data impact, reporting attributes and critical data elements of the organization. == Rationale == Distributed systems like Google Map Reduce, Microsoft Dryad, Apache Hadoop (an open-source project) and Google Pregel provide such platforms for businesses and users. However, even with these systems, Big Data analytics can take several hours, days or weeks to run, simply due to the data volumes involved. For example, a ratings prediction algorithm for the Netflix Prize challenge took nearly 20 hours to execute on 50 cores, and a large-scale image processing task to estimate geographic information took 3 days to complete using 400 cores. "The Large Synoptic Survey Telescope is expected to generate terabytes of data every night and eventually store more than 50 petabytes, while in the bioinformatics sector, the 12 largest genome sequencing houses in the world now store petabytes of data apiece. It is very difficult for a data scientist to trace an unknown or an unanticipated result. === Big data debugging === Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Machine learning, among other algorithms, is used to transform and analyze the data. Due to the large size of the data, there could be unknown features in the data. The massive scale and unstructured nature of data, the complexity of these analytics pipelines, and long runtimes pose significant manageability and debugging challenges. Even a single error in these analytics can be extremely difficult to identify and remove. While one may debug them by re-running the entire analytics through a debugger for stepwise debugging, this can be expensive due to the amount of time and resources needed. Auditing and data validation are other major problems due to the growing ease of access to relevant data sources for use in experiments, the sharing of data between scientific communities and use of third-party data in business enterprises. As such, more cost-efficient ways of analyzing data intensive scale-able computing (DISC) are crucial to their continued effective use. === Challenges in Big Data debugging === ==== Massive scale ==== According to an EMC/IDC study, 2.8 ZB of data were created and replicated in 2012. Furthermore, the same study states that the digital universe will double every two years between now and 2020, and that there will be approximately 5.2 TB of data for every person in 2020. Based on current technology, the storage of this much data will mean greater energy usage by data centers. ==== Unstructured data ==== Unstructured data usually refers to information that doesn't reside in a traditional row-column database. Unstructured data files often include text and multimedia content, such as e-mail messages, word processing documents, videos, photos, audio files, presentations, web pages and many other kinds of business documents. While these types of files may have an internal structure, they are still considered "unstructured" because the data they contain doesn't fit neatly into a database. The amount of unstructured data in enterprises is growing many times faster than structured databases are growing. Big data can include both structured and unstructured data, but IDC estimates that 90 percent of Big Data is unstructured data. The fundamental challenge of unstructured data sources is that they are difficult for non-technical business users and data analysts alike to unbox, understand and prepare for analytic use. Beyond issues of structure, the sheer volume of this type of data contributes to such difficulty. Because of this, current data mining techniques often leave out valuable information and make analyzing unstructured data laborious and expensive. In today's competitive business environment, companies have to find and analyze the relevant data they need quickly. The challenge is going through the volumes of data and accessing the level of detail needed, all at a high speed. The challenge only grows as the degree of granularity increases. One possible solution is hardware. Some vendors are using increased memory and parallel processing to crunch large volumes of data quickly. Another method is putting data in-memory but using a grid

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  Boris FX

  Boris FX is a visual effects, video editing, photography, and audio software plug-in developer based in Miami, Florida, USA. The developer is known for its flagship products, Continuum (formerly Boris Continuum Complete/BCC), Sapphire, Mocha, and Silhouette. Boris FX creates plug-in tools for feature film, broadcast television, and multimedia post-production workflows. The plug-ins are compatible with various NLEs, including Adobe After Effects and Premiere Pro, Avid Media Composer, Apple Final Cut Pro, and OFX hosts such as Autodesk Flame, Foundry Nuke, Blackmagic Design DaVinci Resolve and Fusion, and VEGAS Pro. Boris FX has incorporated artificial intelligence into its software, introducing features for noise reduction, rotoscoping, upscaling, and masking. The company has acquired technologies via mergers and acquisitions from Imagineer Systems, GenArts, Silhouette FX, Digital Film Tools, CrumplePop and Andersson Technologies to expand its visual effects, editing, photography, and audio tools. == History == Boris FX was founded in 1995 by Boris Yamnitsky. The former Media 100 engineer (a member of the original Media 100 launch team in 1993) released “Boris FX,” the first plug-in-based digital video effects (DVE) for Adobe Premiere and Media 100, in 1995. The plug-in won Best of Show at Apple Macworld in Boston, MA that same year. The Boris FX Suite includes a range of visual effects and post-production tools, such as Sapphire, Continuum, Mocha Pro, Silhouette, SynthEyes, CrumplePop, Optics, and Particle Illusion. == Media 100 == In October 2005, Yamnitsky acquired Media 100 the company that launched his plug-in career. Boris FX had a long relationship with Media 100 which bundled Boris RED software as its main titling and compositing solution. Media 100's video editing software is available as freeware for macOS. == Continuum == Continuum is a visual effect and compositing plugin suite that includes a library of over 300 effects and more than 40 transitions, including tools for image restoration, compositing, titling, particle generation, and stylized effects, along with features such as lens flares, lighting effects, and cinematic color grading presets. A key component of Continuum is its integration with the Mocha planar tracking and masking system, enabling advanced tracking and rotoscoping within the effects. The suite also includes Particle Illusion, a real-time particle generator used for creating visual effects such as explosions, smoke, and abstract motion graphics, as well as Primatte Studio, a chroma keying and compositing toolset for green screen and blue screen workflows. Continuum supports GPU acceleration and offers compatibility with HDR and 360/VR content. Regular updates introduce new effects, presets, and performance enhancements to expand its capabilities. In October 2018, Continuum relaunched Particle Illusion, a Mocha Essentials workflow with magnetic edge-snapping, and updates to Title Studio. In October 2019, Continuum introduced Corner Pin Studio with built-in Mocha tracking for quick screen replacement and inserts, 6 stylized transitions, and 4 creative effects. In October 2020, Continuum released an update that included over 80 GPU-accelerated effects such as film stocks, color grades, optical filter simulations, and a digital gobo library. The update also introduced a custom FX Editor interface, real-time particles, and more than 1,000 drag-and-drop presets. In November 2021, it added multi-frame rendering for After Effects, native Apple M1 support, fluid dynamics in Particle Illusion, and 60 color-grade presets. In October 2022, the software introduced 10 additional transitions, a revised Particle Illusion workflow, an atmospheric glow effect, and more than 250 curated presets. Continuum plugins have been used in television, streaming, and film projects, including A Black Lady Sketch Show (HBO/HBO Max), Star Trek: Discovery (CBS), Andor (Disney+), The Curse of Oak Island (History Channel), Keeping up with the Kardashians (E!), This Old House (PBS), Ms. Marvel (Disney+), MasterChef (Fox), WipeOut (TBS), The Boys (Prime Video), and The Today Show (NBC). == Mocha Pro == In December 2014, Boris FX merged with Imagineer Systems, the UK-based developer of the Academy Award-winning planar motion tracking software, Mocha Pro. Mocha Pro's features include planar tracking (motion tracking), rotoscoping, image stabilization, 3D camera tracking, and object removal. In June 2016, Mocha released (v5) which introduced Mocha Pro's tools as plug-ins for Adobe After Effects and Premiere Pro, Avid Media Composer, and OFX hosts Foundry's NUKE, Blackmagic Design Fusion, VEGAS Pro, and HitFilm. A simplified version, Mocha AE, is included with Adobe After Effects Creative Cloud and has been bundled with the software since CS4. A similar version is also available with HitFilm Pro from FXhome and VEGAS Pro. Mocha's tracking SDK is integrated into other visual effects tools, including SAM Quantel Pablo Rio, Silhouette FX, CoreMelt, and Motion VFX. Mocha Pro has been used in various film and television productions, including Birdman, Black Swan, the Harry Potter series, The Hobbit, Star Wars, The Mandalorian, Star Trek: Discovery, and The Umbrella Academy. It has also been employed in projects such as Gone Girl, The Hunger Games: Mockingjay – Part 1, Game of Thrones, and House of Cards. == Sapphire == GenArts, founded by Karl Sims in 1996, developed visual effects plug-ins that were used by studios and post-production facilities. In September 2016, Boris FX merged with former competitor, GenArts, Inc., developer of Sapphire high-end visual effects plug-ins, to expand its suite of motion graphics and VFX tools. The merger brought Sapphire alongside Boris Continuum Complete (BCC) and Mocha Pro, integrating these tools for film and television post-production. The Sapphire suite includes a library of over 270 effects and transitions, organized into categories such as lighting, stylization, distortions, textures, and transitions. Commonly used effects include glows, lens flares, film looks, and blurs. The plug-ins are designed to be GPU-accelerated, allowing for improved rendering performance and real-time previews in supported host applications. A central feature of Sapphire is the Builder tool, a node-based workspace that allows users to create custom effects and transitions by combining multiple Sapphire plug-ins. This enables a high level of creative flexibility and reusability, making it a popular tool for both editors and VFX artists. Sapphire also integrates with Mocha, Boris FX's planar tracking and masking system, allowing for advanced control of visual elements within an effect. In October 2017, Boris FX released its first new version of Sapphire since the GenArts acquisition. Sapphire (v11) now includes integrated Mocha tracking and masking tools. Sapphire is available for Adobe, Avid, the Autodesk Flame family, and OFX hosts including Blackmagic DaVinci Resolve and Fusion, and Foundry's NUKE. As part of the merger, Boris FX acquired the rights to Particle Illusion. In 2018, Boris FX reintroduced the product to the larger NLE/Compositing market. Sapphire's plug-ins transitioned from C to C++ to improve performance and support higher-resolution visual effects. This update enhanced floating-point calculations, compatibility with film editing APIs, and integration with NVIDIA's CUDA for faster rendering. The plug-ins have been used in various films, including Avatar, the Harry Potter and the Prisoner of Azkaban, Iron Man, The Lord of the Rings, The Matrix trilogy, Titanic, and X-Men. == Particle Illusion == As part of the merger with GenArts in 2016, Boris FX acquired the rights to the Particle Illusion (formerly particleIllusion) product, a storied particle system from the original developer Alan Lorence, the founder of Wondertouch. In 2018, Boris FX released a redesigned version of the product to a larger NLE/compositing market as part of Continuum (2019). The new Particle Illusion plug-in supports Adobe, Avid, and many OFX hosts. == Silhouette == In September 2019, Boris FX merged with SilhouetteFX, Academy Award-winning developer of Silhouette, a high-end digital paint, advanced rotoscoping, motion tracking, and node-based compositing application for visual effects in film post-production. The acquisition integrated Silhouette's advanced rotoscoping and paint technology, recognized by the Academy of Motion Pictures, into Boris FX's suite of products, alongside Sapphire, Continuum, and Mocha Pro. In May 2021, Boris FX released Silhouette 2021, the first version of Silhouette released by Boris FX to function both as a standalone application and as a plug-in for Adobe, Autodesk, Nuke, and other OFX hosts. Silhouette has been used in the visual effects of films such as Avatar, Avengers: Infinity War, Blade Runner 2049, Ex Machina, and Interstellar. == Optics == In June 2020, Boris FX launched Optics, its first plugin deve

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  Data definition specification

  In computing, a data definition specification (DDS) is a guideline to ensure comprehensive and consistent data definition. It represents the attributes required to quantify data definition. A comprehensive data definition specification encompasses enterprise data, the hierarchy of data management, prescribed guidance enforcement and criteria to determine compliance. == Overview == A data definition specification may be developed for any organization or specialized field, improving the quality of its products through consistency and transparency. It eliminates redundancy (since all contributing areas are referencing the same specification) and provides standardization and degrees of compliance, making it easier and more efficient to create, modify, verify, analyze and share information across the enterprise. To understand how a data definition specification works in an enterprise, we must look at the elements of a DDS. Writing data definitions, defining business terms (or rules) in the context of a particular environment, provides structure for an organization's data architecture. In developing these definitions, the words used must be traceable to clearly defined data. A data definition specification may be used in the following activities: Business intelligence Business process modeling Business rules management Data analysis and modeling Information architecture Metadata modeling Data mastering Report generation == Criteria == A data definition specification requires data definitions to be: Atomic – singular, describing only one concept. Commonly used and ambiguous terms should be defined. While a term refers to one concept, several words may be used in a term: File – A concept identifiable with one word File extension – A concept identifiable with more than one word Traceable – Mapped to a specific data element. In business, a term may be traced to an entity (for example, a customer) or an attribute (such as a customer's name). A term may be a value in a data set (such as gender), or designate the data set itself. Traceability indicates relationships in the data hierarchy. Consistent - Used in a standard syntax; if used in a specific context, the context is noted Accurate - Precise, correct and unambiguous, stating what the term is and is not Clear - Readily understood by the reader Complete - With the term, its description and contextual references Concise - To avoid circular references == Applications == === Enterprise data === A data definition specification was produced by the Open Mobile Alliance to document charging data. The document, the centralized catalog of data elements defined for interfaces, specifies the mapping of these data elements to protocol fields in the interfaces. Created for the exchange of financial data, Market Data Definition Language (MDDL) is an XML specification designed to enable the interchange of information necessary to account, to analyze, and to trade financial instruments of the world's markets. It defines an XML-based interchange format and common data dictionary on the fields needed to describe: (1) financial instruments, (2) corporate events affecting value and tradability, and (3) market-related, economic and industrial indicators. The principal function of MDDL is to allow entities to exchange market data by standardizing formats and definitions. MDDL provides a common format for market data so that it can be efficiently passed from one processing system to another and provides a common understanding of market data content by standardizing terminology and by normalizing the relationships of various data elements to one another ... From the user perspective, the goal of MDDL is to enable users to integrate data from multiple sources by standardizing both the input feeds used for data warehousing (i.e., define what's being provided by vendors) and the output methods by which client applications request the data (i.e., ensure compatibility on how to get data in and out of applications)." === Clinical submissions === The Clinical Data Interchange Standards Consortium, a global, multidisciplinary, non-profit organization, has established standards to support the acquisition, exchange, submission and archiving of clinical research data and metadata. CDISC standards are vendor-neutral, platform-independent and freely available from the CDISC website. The Case Report Tabulation Data Definition Specification (define.xml) draft version 2.0, the oldest data definition specification, is part of the evolution from the 1999 FDA electronic submission (eSub) guidance and electronic Common Technical Document (eCTD) documents specifying that a document describing the content and structure of included data be included in a submission. Define.xml was developed to automate the review process by generating a machine-readable data-definition document. Define.xml has standardized submissions to the Food and Drug Administration, reducing review times from over two years to several months. === Archival data === A data definition specification is the foundation of metadata for scientific data archiving. The Metadata Encoding and Transmission Standard (METS) uses one principle of a DDS: consistent use of key terms to catalog digital objects for global use. The METS schema is a flexible mechanism for encoding descriptive, administrative and structural metadata for a digital library object and expressing complex links between metadata, and can provide a useful standard for the exchange of digital-library objects between repositories. A similar effort is underway to preserve complex data associated with video-game archiving. Preserving Virtual Worlds attempted to address archival-format deficiencies, citing the lack of suitable documentation for interactive fiction and games at the bit level: specifically, the absence of "representation information" needed to map raw bits into higher-level data constructs. Preserving Virtual Worlds 2 is a research project expanding on initial efforts in this field.

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  Kruskal count

  The Kruskal count (also known as Kruskal's principle, Dynkin–Kruskal count, Dynkin's counting trick, Dynkin's card trick, coupling card trick or shift coupling) is a probabilistic concept originally demonstrated by the Russian mathematician Evgenii Borisovich Dynkin in the 1950s or 1960s discussing coupling effects and rediscovered as a card trick by the American mathematician Martin David Kruskal in the early 1970s as a side-product while working on another problem. It was published by Kruskal's friend Martin Gardner and magician Karl Fulves in 1975. This is related to a similar trick published by magician Alexander F. Kraus in 1957 as Sum total and later called Kraus principle. Besides uses as a card trick, the underlying phenomenon has applications in cryptography, code breaking, software tamper protection, code self-synchronization, control-flow resynchronization, design of variable-length codes and variable-length instruction sets, web navigation, object alignment, and others. == Card trick == The trick is performed with cards, but is more a magical-looking effect than a conventional magic trick. The magician has no access to the cards, which are manipulated by members of the audience. Thus sleight of hand is not possible. Rather the effect is based on the mathematical fact that the output of a Markov chain, under certain conditions, is typically independent of the input. A simplified version using the hands of a clock performed by David Copperfield is as follows. A volunteer picks a number from one to twelve and does not reveal it to the magician. The volunteer is instructed to start from 12 on the clock and move clockwise by a number of spaces equal to the number of letters that the chosen number has when spelled out. This is then repeated, moving by the number of letters in the new number. The output after three or more moves does not depend on the initially chosen number and therefore the magician can predict it.

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