AI Chatbot Q

AI Chatbot Q — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Luminoso

  Luminoso is a Cambridge, MA-based text analytics and artificial intelligence company. It spun out of the MIT Media Lab and its crowd-sourced Open Mind Common Sense (OMCS) project. The company has raised $20.6 million in financing, and its clients include Sony, Autodesk, Scotts Miracle-Gro, and GlaxoSmithKline. == History == Luminoso was co-founded in 2010 by Dennis Clark, Jason Alonso, Robyn Speer, and Catherine Havasi, a research scientist at MIT in artificial intelligence and computational linguistics. The company builds on the knowledge base of MIT’s Open Mind Common Sense (OMCS) project, co-founded in 1999 by Havasi, who continues to serve as its director. The OCMS knowledge base has since been combined with knowledge from other crowdsourced resources to become ConceptNet. ConceptNet consists of approximately 28 million statements in 304 languages, with full support for 10 languages and moderate support for 77 languages. ConceptNet is a resource for making an AI that understands the meanings of the words people use. During the World Cup in June 2014, the company provided a widely reported real-time sentiment analysis of the U.S. vs. Germany match, analyzing 900,000 posts on Twitter, Facebook and Google+. == Applications == The company uses artificial intelligence, natural language processing, and machine learning to derive insights from unstructured data such as contact center interactions, chatbot and live chat transcripts, product reviews, open-ended survey responses, and email. Luminoso's software identifies and quantifies patterns and relationships in text-based data, including domain-specific or creative language. Rather than human-powered keyword searches of data, the software automates taxonomy creation around concepts, allowing related words and phrases to be dynamically generated and tracked. Commercial applications include analyzing, prioritizing, and routing contact center interactions; identifying consumer complaints before they begin to trend; and tracking sentiment during product launches. The software natively analyzes text in fourteen languages, as well as emoji. == Products == Luminoso's technology can be accessed via two products: Luminoso Daylight and Luminoso Compass. Luminoso Daylight enables a deep-dive analysis into batch or real-time data, whereas Luminoso Compass automates the categorization of real-time data. Both products offer a user interface as well as an API. Luminoso's products can be implemented through either a cloud-based or an on-premise solution. == Research == Luminoso continues to actively conduct research in natural language processing and word embeddings and regularly participates in evaluations such as SemEval. At SemEval 2017, Luminoso participated in Task 2, measuring the semantic similarity of word pairs within and across five languages. Its solution outperformed all competing systems in every language pair tested, with the exception of Persian. == Recognition == Luminoso has been listed as a "Cool Vendor in AI for Marketing" by Gartner, and has also been named a "Boston Artificial Intelligence Startup to Watch" by BostInno. In May 2017, Luminoso was recognized as having the Best Application for AI in the Enterprise by AI Business, and was also shortlisted as the Best AI Breakthrough and Best Innovation in NLP. == Competitors == Major competitors include Clarabridge and Lexalytics. == Investors == The company raised $1.5 million from angel investors led by Basis Technology in 2012. Its first institutional funding round of $6.5 was completed in July 2014, led by Acadia Woods with participation from Japan’s Digital Garage. The company followed that with a $10M series B funding round in December 2018, led by DVI Equity Partners, with participation from Liberty Global Ventures, DF Enterprises, Raptor Holdco, Acadia Woods Partners, and Accord Ventures, among others.

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  Pamphlet war

  A pamphlet war is a protracted argument or discussion through printed media, especially between the time the printing press became common, and when state intervention like copyright laws made such public discourse more difficult. The purpose was to defend or attack a certain perspective or idea. Pamphlet wars have occurred multiple times throughout history, as both social and political platforms. Pamphlet wars became viable platforms for this protracted discussion with the advent and spread of the printing press. Cheap printing presses, and increased literacy made the late 17th century a key stepping stone for the development of pamphlet wars, a period of prolific use of this type of debate. Over 2200 pamphlets were published between 1600–1715 alone. Pamphlet wars are generally credited for powering many key social changes of the era, including the Reformation and the Revolution Controversy, the English philosophical debate set off by the French Revolution. == History of the pamphlet in England == Throughout Europe in the 16th century, printed tracts were used to argue religious doctrine and foment support for religious causes. In England, Henry VIII used print literature to justify his break from the Catholic Church. During the subsequent reigns of Edward and Mary, print polemics escalated into propaganda warfare, as print media gained enormous potential to sway common opinion. By the 1560s, print was widely used to convey news. In 1562, the first pamphlets appeared, which discussed the English forces sent to aid the Protestant French Huguenots. In 1569, pamphlets reported the revolt of the Northern Earls and the subsequent Rebellion of the same year. In the 1580s, pamphlets began to replace broadsheet ballads as the means to convey information to the general public. Over the next century, the pamphlet became the principal means of garnering support for a cause or an idea, and was particularly influential during the English Civil Wars (1642-1651) and the Glorious Revolution of 1688. Through the ensuing decades, the pamphlet lost some popularity due to the emergence of newspapers and journals, but continued to be an important medium of public debate, as illustrated by the Revolution Controversy a full century later in the 1790s. == Pamphlet printing == Coming from a Latin word, "pamphlet" literally means "small book." In the early days of printing, the format of the book or pamphlet depended on the size of the paper used and the number of times it was folded. If a page was only folded once, it was called a folio. If it was folded twice, it was known as a quarto. An octave was a paper folded three times. A pamphlet was usually 1-12 sheets of paper folded in quarto, or 8-96 pages. It was sold for one or two pennies apiece. The printing of a pamphlet involved many people: the author, the printer, suppliers, print-makers, compositor, correctors, pressmen, binders, and distributors. Once the pamphleteer had written the pamphlet, it was sent to the printing house to be corrected, set into type, and printed. The papers were then given to the printer's warehouse-keeper, who bundled the copies and sent them to the bookseller, who was probably the one financing the printing. He was responsible to bind the pamphlets, usually by sewing them, and then sold them wholesale to individual bookselling vendors. The booksellers then sold them from a stall in the marketplace. == Pamphlet subjects == Pamphlets began as the means of conveyance for religious debates, and therefore religious topics were one of the main subjects they dealt with. The definition of a pamphlet came to mean a short work dealing with social, political, or religious issues. Typical topics included the Civil war, Church of England doctrines, Acts of Parliament, the Popish Plot (see below), the Stuart Era, and Cromwell propaganda. In addition, pamphlets were also used for romantic fiction, autobiography, scurrilous personal abuse, and social criticism. They contained much of the propaganda of the 17th century in the midst of the religious and political turmoil. They were also used for debates between the Puritans and the Anglican. During the Glorious Revolution, pamphlets were political weapons. == Authors == There were many authors of pamphlets. However some of the more popular authors include Daniel Defoe, Thomas Hobbes, Jonathan Swift, John Milton, and Samuel Pepys. Also included in the midst are Thomas Nashe, Joseph Addison, Richard Steele, and Matthew Prior. In 1591–1592, Robert Greene released a series of pamphlets which later inspired many other authors including Thomas Middleton and Thomas Dekker. == Critics == Pamphlets, along with their vast popularity, received criticism. There were many in the time period who believed that pamphlets were full of foolishness. They thought the pamphlets were not good enough literature and that they would turn people from "good" writing. They believed that pamphlets would be the end of the great volumes of literature and that great writing would be forgotten. == News reporting == Pamphlets made a great difference in the way news was reported to the general public. With the publication of pamphlets, it was no longer difficult for people to hear of events taking place far away. The closer the occurrence was to London, the easier and faster people heard of it. For example, the Battle of Edgehill took place on 23 October 1642. The first pamphlet reporting the incident was printed on 25 October 24 hours after some of the orders reported had been given. While not entirely accurate, and hurriedly made, the pamphlet nonetheless was able to tell the general public what had happened in the battle. A more accurate, specific, and readable account was available in a pamphlet printed on 26 October, and the "authorized" version was available only five days after the battle took place. == Marprelate pamphlets == In 1588, a series of pamphlets marked a turning point for the Puritans, dividing them from other Protestants in the country. The authors wrote under the pseudonym of Martin Marprelate and his two sons of the same name. The true identities of the authors were never discovered. The pamphlets aimed to provoke authorities to take action against censorship. The series was among the first to ask questions directly of its readers. == Early pamphlet wars == === Elizabethan pamphlet wars === As a means of forming or swaying public opinion, pamphlets like these had a part in influencing society, even as the content was itself influenced by society. During the 16th century and continuing for a short while in the early 17th century in England there was rise in the use of pamphlet wars to discuss a myriad of issues spanning from the civil war, to religious freedoms and the roles of women in society. The Queen herself participated in these discussions, making sure that she was widely read and understood by her people in order to gain favour and establish herself as the monarch despite being a woman. Examples of her use of this medium appear in To the Troops at Tilbury written in 1588, On Mary's Execution written in 1586, and many more. Another famous writer of this period to take advantage of the pamphlet was Emilia Lanier, famous for her arguments about the role of women. A common idea promoted by many literary works and the general attitude towards women, Lanier's work "Eve's Apology in Defence of Women" refuted the belief that Eve is responsible for the fall of man. A very uncommon and unpopular stance to take, Lanier accomplishes her defence through structuring it as an apology, one of the earliest subversive feminist texts. Similarly, Francis Bacon wrote his Essays to promote his idea of morality and other complicated social issues. For example, his work, "Of Love" examines the various understandings of the concept of love, particularly as it was perceived during the Elizabethan era. === Eikon Series === From 1649 until 1651, some five pamphlets were published in a debate about the execution of King Charles I of England (1600-1649). Prior to his execution, King Charles wrote the first pamphlet in the discussion, Eikon Basilike’’ (from the Greek “eikon” for image and “basileus” for king). The subtitle of this work - Portraiture of His Sacred Majesty in His Solitudes and Sufferings - indicates that Charles sought to portray himself as a martyr to the cause of regal prerogative. In the following months, several response pamphlets were published (collectively known as the "Eikon" series), including: Eikon Alethine, Eikon e Pistes, Eikonoklastes, and Eikon Aklastos,” alternately attacking or defending the king, his regicide, and his self-portrait in “Eikon Basilike.” == Popish Plot and Elizabeth Cellier == In the 1680s, after being acquitted of the "Meal-Tub Plot" for which she was accused, Elizabeth Cellier wrote Malice Defeated, which, along with The Matchless Picaro, sparked a pamphlet war surrounding debate of the ascension of a Catholic king to the thro

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  Data deduplication

  In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs. It can also be applied to network data transfers to reduce the number of bytes that must be sent. The deduplication process requires comparison of data 'chunks' (also known as 'byte patterns') which are unique, contiguous blocks of data. These chunks are identified and stored during a process of analysis, and compared to other chunks within existing data. Whenever a match occurs, the redundant chunk is replaced with a small reference that points to the stored chunk. Given that the same byte pattern may occur dozens, hundreds, or even thousands of times (the match frequency is dependent on the chunk size), the amount of data that must be stored or transferred can be greatly reduced. A related technique is single-instance (data) storage, which replaces multiple copies of content at the whole-file level with a single shared copy. While possible to combine this with other forms of data compression and deduplication, it is distinct from newer approaches to data deduplication (which can operate at the segment or sub-block level). Deduplication is different from data compression algorithms, such as LZ77 and LZ78. Whereas compression algorithms identify redundant data inside individual files and encodes this redundant data more efficiently, the intent of deduplication is to inspect large volumes of data and identify large sections – such as entire files or large sections of files – that are identical, and replace them with a shared copy. == Functioning principle == For example, a typical email system might contain 100 instances of the same 1 MB (megabyte) file attachment. Each time the email platform is backed up, all 100 instances of the attachment are saved, requiring 100 MB storage space. With data deduplication, only one instance of the attachment is actually stored; the subsequent instances are referenced back to the saved copy for deduplication ratio of roughly 100 to 1. Deduplication is often paired with data compression for additional storage saving: Deduplication is first used to eliminate large chunks of repetitive data, and compression is then used to efficiently encode each of the stored chunks. In computer code, deduplication is done by, for example, storing information in variables so that they don't have to be written out individually but can be changed all at once at a central referenced location. Examples are CSS classes and named references in MediaWiki. == Benefits == Storage-based data deduplication reduces the amount of storage needed for a given set of files. It is most effective in applications where many copies of very similar or even identical data are stored on a single disk. In the case of data backups, which routinely are performed to protect against data loss, most data in a given backup remain unchanged from the previous backup. Common backup systems try to exploit this by omitting (or hard linking) files that haven't changed or storing differences between files. Neither approach captures all redundancies, however. Hard-linking does not help with large files that have only changed in small ways, such as an email database; differences only find redundancies in adjacent versions of a single file (consider a section that was deleted and later added in again, or a logo image included in many documents). In-line network data deduplication is used to reduce the number of bytes that must be transferred between endpoints, which can reduce the amount of bandwidth required. See WAN optimization for more information. Virtual servers and virtual desktops benefit from deduplication because it allows nominally separate system files for each virtual machine to be coalesced into a single storage space. At the same time, if a given virtual machine customizes a file, deduplication will not change the files on the other virtual machines—something that alternatives like hard links or shared disks do not offer. Backing up or making duplicate copies of virtual environments is similarly improved. == Classification == === Post-process versus in-line deduplication === Deduplication may occur "in-line", as data is flowing, or "post-process" after it has been written. With post-process deduplication, new data is first stored on the storage device and then a process at a later time will analyze the data looking for duplication. The benefit is that there is no need to wait for the hash calculations and lookup to be completed before storing the data, thereby ensuring that store performance is not degraded. Implementations offering policy-based operation can give users the ability to defer optimization on "active" files, or to process files based on type and location. One potential drawback is that duplicate data may be unnecessarily stored for a short time, which can be problematic if the system is nearing full capacity. Alternatively, deduplication hash calculations can be done in-line: synchronized as data enters the target device. If the storage system identifies a block which it has already stored, only a reference to the existing block is stored, rather than the whole new block. The advantage of in-line deduplication over post-process deduplication is that it requires less storage and network traffic, since duplicate data is never stored or transferred. On the negative side, hash calculations may be computationally expensive, thereby reducing the storage throughput. However, certain vendors with in-line deduplication have demonstrated equipment which performs in-line deduplication at high rates. Post-process and in-line deduplication methods are often heavily debated. === Data formats === The SNIA Dictionary identifies two methods: Content-agnostic data deduplication – a data deduplication method that does not require awareness of specific application data formats. Content-aware data deduplication – a data deduplication method that leverages knowledge of specific application data formats. === Source versus target deduplication === Another way to classify data deduplication methods is according to where they occur. Deduplication occurring close to where data is created, is referred to as "source deduplication". When it occurs near where the data is stored, it is called "target deduplication". Source deduplication ensures that data on the data source is deduplicated. This generally takes place directly within a file system. The file system will periodically scan new files creating hashes and compare them to hashes of existing files. When files with same hashes are found then the file copy is removed and the new file points to the old file. Unlike hard links however, duplicated files are considered to be separate entities and if one of the duplicated files is later modified, then using a system called copy-on-write a copy of that changed file or block is created. The deduplication process is transparent to the users and backup applications. Backing up a deduplicated file system will often cause duplication to occur resulting in the backups being bigger than the source data. Source deduplication can be declared explicitly for copying operations, as no calculation is needed to know that the copied data is in need of deduplication. This leads to a new form of link on file systems, called a reference-counted link, or reflink, in some systems (e.g. Linux), or a cloned file on macOS, where one or more inodes (file information entries) are made to share some or all of their data. It is named analogously to hard links, which work at the inode level, and symbolic links, which work at the filename level.The individual entries have a copy-on-write behavior that is non-aliasing, i.e. changing one copy afterwards will not affect other copies. Microsoft's ReFS also supports this operation. Target deduplication is the process of removing duplicates when the data was not generated at that location. Example of this would be a server connected to a SAN/NAS, The SAN/NAS would be a target for the server (target deduplication). The server is not aware of any deduplication, the server is also the point of data generation. A second example would be backup. Generally this will be a backup store such as a data repository or a virtual tape library. === Deduplication methods === One of the most common forms of data deduplication implementations works by comparing chunks of data to detect duplicates. For that to happen, each chunk of data is assigned an identification, calculated by the software, typically using cryptographic hash functions. In many implementations, the assumption is made that if the identification is identical, the data is identical, even though this cannot be true in all cases due to the pigeonhole principle; other implementations do not as

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  Myrinet

  Myrinet, ANSI/VITA 26-1998, is a high-speed local area networking system designed by the company Myricom to be used as an interconnect between multiple machines to form computer clusters. == Description == Myrinet was promoted as having lower protocol overhead than standards such as Ethernet, and therefore better throughput, less interference, and lower latency while using the host CPU. Although it can be used as a traditional networking system, Myrinet is often used directly by programs that "know" about it, thereby bypassing a call into the operating system. Earlier versions of Myrinet used a variety of media and connectors: Generation 2 used copper media with DC-37 (Myrinet-LAN, M2L- controllers and switches) or microribbon (Myrinet-SAN, M2M-) connectors. Generation 3 used copper media with HSSDC (Myrinet-Serial, M3S-) or microribbon (Myrinet-SAN, M3M-) connectors, or fiber with LC-connectors (Myrinet-Fiber, M3F-). The later versions of Myrinet physically consist of two fibre optic cables, upstream and downstream, connected to the host computers with a single connector. Machines are connected via low-overhead routers and switches, as opposed to connecting one machine directly to another. Myrinet includes a number of fault-tolerance features, mostly backed by the switches. These include flow control, error control, and "heartbeat" monitoring on every link. The "fourth-generation" Myrinet, called Myri-10G, supported a 10 Gbit/s data rate and can use 10 Gigabit Ethernet on PHY, the physical layer (cables, connectors, distances, signaling). Myri-10G started shipping at the end of 2005. Myrinet was approved in 1998 by the American National Standards Institute for use on the VMEbus as ANSI/VITA 26-1998. One of the earliest publications on Myrinet is a 1995 IEEE article. === Performance === Myrinet is a lightweight protocol with little overhead that allows it to operate with throughput close to the basic signaling speed of the physical layer. For supercomputing, the low latency of Myrinet is even more important than its throughput performance, since, according to Amdahl's law, a high-performance parallel system tends to be bottlenecked by its slowest sequential process, which in all but the most embarrassingly parallel supercomputer workloads is often the latency of message transmission across the network. === Deployment === According to Myricom, 141 (28.2%) of the June 2005 TOP500 supercomputers used Myrinet technology. In the November 2005 TOP500, the number of supercomputers using Myrinet was down to 101 computers, or 20.2%, in November 2006, 79 (15.8%), and by November 2007, 18 (3.6%), a long way behind gigabit Ethernet at 54% and InfiniBand at 24.2%. In the June 2014 TOP500 list, the number of supercomputers using Myrinet interconnect was 1 (0.2%). In November 2013, the assets of Myricom (including the Myrinet technology) were acquired by CSP Inc. In 2016, it was reported that Google had also offered to buy the company.

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  WS-SecurityPolicy

  WS-Security Policy is a web services specification, created by IBM and 12 co-authors, that has become an OASIS standard as of version 1.2. It extends the fundamental security protocols specified by the WS-Security, WS-Trust and WS-Secure Conversation by offering mechanisms to represent the capabilities and requirements of web services as policies. Security policy assertions are based on the WS-Policy framework. Policy assertions can be used to require more generic security attributes like transport layer security , message level security or timestamps, and specific attributes like token types. Most policy assertion can be found in following categories: Protection assertions identify the elements of a message that are required to be signed, encrypted or existent. Token assertions specify allowed token formats (SAML, X509, Username etc.). Security binding assertions control basic security safeguards like transport and message level security, cryptographic algorithm suite and required timestamps. Supporting token assertions add functions like user sign-on using a username token. Policies can be used to drive development tools to generate code with certain capabilities, or may be used at runtime to negotiate the security aspects of web service communication. Policies may be attached to WSDL elements such as service, port, operation and message, as defined in WS Policy Attachment. == Sample Policies == Namespaces used by the following XML-snippets: ... Include a timestamp: Use either transport layer security (https) or message level security (XML Dsig/XML Enc): ... ... To define a SAML assertion as security token: ...#SAMLV2.0 Issued token assertion of providers with reference to the STS and required token format: http://sampleorg.com/sts http://docs.oasis-open.org/wss/oasis-wss-saml-token-profile-1.0#SAMLAssertionID ... ... Specify that message header and body need to be signed, and attachments are left unsigned: ? ... specify that message open source license need to be signed, and hydra security are left unsigned: ? ... == Other WS policy languages == The term Web Services Security Policy Language is used for two different XML-based languages: As described above, based on the WS-Policy framework, as defined in, published as version 1.3 in Feb. 2009 WSPL, based on XACML profile for Web-services, but that was not finalized.

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  Virtual influencer

  A virtual influencer, sometimes described as a virtual persona or virtual model, is a computer-generated fictional character that can be used for a variety of marketing-related purposes, but most frequently for social media marketing, in lieu of online human "influencers". Most virtual influencers are designed using computer graphics and motion capture technology to resemble real people in realistic situations. Common derivatives of virtual influencers include VTubers, which broadly refer to online entertainers and YouTubers who represent themselves using virtual avatars instead of their physical selves. == History == Virtual influencers are fundamentally synonymous with virtual idols, which originate from Japan's anime and Japanese idol culture that dates back to the 1980s. The first virtual idol created was Lynn Minmay, a fictional singer and main character of the anime television series Super Dimension Fortress Macross (1982) and the animated film adaptation Macross: Do You Remember Love? (1984). Minmay's success led to the production of more Japanese virtual idols, such as EVE from the Japanese cyberpunk anime Megazone 23 (1985), and Sharon Apple in Macross Plus (1994). Virtual idols were not always well received – in 1995, Japanese talent agency Horipro created Kyoko Date, which was inspired by the Macross franchise and dating sim games such as Tokimeki Memorial (1994). Date failed to gain commercial success despite drawing headlines for her debut as a CGI idol, largely due to technical limitations leading to issues such as unnatural movements, an issue also known as the uncanny valley. Since their inception, many virtual idols created have achieved continual success, with notable names including the Vocaloid singer Hatsune Miku, and the VTuber Kizuna AI. Technological advancements have also enabled production teams to use artificial intelligence and advanced techniques to customize the personalities and behavior of virtual idols. Due to modern-day advancements in technology, many virtual idols have held real-life tours and events. Notable ones include Hatsune Miku's titular tour Miku Expo and Hololive's concerts with many of their idols from their English, Japanese and Indonesian branches. Some notable events including virtual singers and influencers have included: Hatsune Miku opening for Lady Gaga in 2014 and Hoshimachi Suisei's concerts at the famous Budokan venue in Japan and her addition to the Forbes Japan list of '30 Under 30' individuals who are changing the world in their respective fields. == Benefits and criticism == From a branding perspective, virtual influencers are perceived to be much less likely to be mired in scandals. In China, celebrities caught in bad publicity such as singer Wang Leehom and entertainer Kris Wu have heightened the appeal of virtual influencers, since their existence relies entirely on computer-generated imagery and they are therefore unlikely to cause any damage to a brand's image by association. Some studies have also suggested that Generation Z consumers have a unique appetite for virtual idols and influencers, since they grew up in the age of the internet. Studies also show that human-like appearance of virtual influencers show higher message credibility than anime-like virtual influencers. Scholars and commentators have also questioned the ethics and cultural impact of virtual influencers, arguing that computer-generated personas can entrench unrealistic beauty standards while diffusing accountability for labor, identity, and consent. Business and marketing analysts have also warned that disclosure and governance remain inconsistent, recommending clearer guardrails and transparency when brands deploy synthetic spokespeople. In 2025, reporting highlighted concerns that AI-driven "virtual humans" could displace human creators and sales workers, intensifying debates over the future of creative labor and authenticity online. == Notable examples == === Virtual bands === Eternity - A South Korean virtual idol group formed by Pulse9. Gorillaz - A virtual band formed in 1998. K/DA - A virtual K-pop girl group created as part of the League of Legends video game franchise. MAVE: - A South Korean virtual girl group formed in 2023 by Metaverse Entertainment. Pentakill - A virtual heavy metal band created as part of the League of Legends video game franchise. Plave (band) - A South Korean virtual boy band formed by VLast. Squid Sisters and Off the Hook - Two virtual pop idol duos as part of the Splatoon series. Studio Killers - A Finnish-Danish-British virtual band formed in 2011. === Vocaloids === Hatsune Miku (modeled after Saki Fujita) Kagamine Rin/Len (modeled after Asami Shimoda) Megurine Luka (modeled after Yū Asakawa) Meiko (modeled after Meiko Haigō) Kaito (modeled after Naoto Fūga) === VTubers === Kano Kizuna AI Neuro-sama VShojo Ironmouse Projekt Melody Nijisanji Hololive Akai Haato Gawr Gura Hoshimachi Suisei Natsuiro Matsuri === Other examples === Ami Yamato Crazy Frog FN Meka IA Kuki AI Kyoko Date Kyra Miquela Naevis Shudu Gram

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  IBM 37xx

  IBM 37xx (or 37x5) is a family of IBM Systems Network Architecture (SNA) programmable front-end processors used mainly in mainframe environments. All members of the family ran one of three IBM-supplied programs. Emulation Program (EP) mimicked the operation of the older IBM 270x non-programmable controllers. Network Control Program (NCP) supported Systems Network Architecture devices. Partitioned Emulation Program (PEP) combined the functions of the two. == Models == === 370x series === 3705 — the oldest of the family, introduced in 1972 to replace the non-programmable IBM 270x family. The 3705 could control up to 352 communications lines. 3704 was a smaller version, introduced in 1973. It supported up to 32 lines. === 371x === The 3710 communications controller was introduced in 1984. === 372x series === The 3725 and the 3720 systems were announced in 1983. The 3725 replaced the hardware line scanners used on previous 370x machines with multiple microcoded processors. The 3725 was a large-scale node and front end processor. The 3720 was a smaller version of the 3725, which was sometimes used as a remote concentrator. The 3726 was an expansion unit for the 3725. With the expansion unit, the 3725 could support up to 256 lines at data rates up to 256 kbit/s, and connect to up to eight mainframe channels. Marketing of the 372x machines was discontinued in 1989. IBM discontinued support for the 3705, 3720, 3725 in 1999. === 374x series === The 3745, announced in 1988, provides up to eight T1 circuits. At the time of the announcement, IBM was estimated to have nearly 85% of the over US$825 million market for communications controllers over rivals such as NCR Comten and Amdahl Corporation. The 3745 is no longer marketed, but still supported and used. The 3746 "Nways Controller" model 900, unveiled in 1992, was an expansion unit for the 3745 supporting additional Token Ring and ESCON connections. A stand-alone model 950 appeared in 1995. == Successors == IBM no longer manufactures 37xx processors. The last models, the 3745/46, were withdrawn from marketing in 2002. Replacement software products are Communications Controller for Linux on System z and Enterprise Extender. == Clones == Several companies produced clones of 37xx controllers, including NCR COMTEN and Amdahl Corporation.

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  MIME Object Security Services

  MIME Object Security Services (MOSS) is a protocol that uses the multipart/signed and multipart/encrypted framework to apply digital signature and encryption services to MIME objects. == Details == The services are offered through the use of end-to-end cryptography between an originator and a recipient at the application layer. Asymmetric (public key) cryptography is used in support of the digital signature service and encryption key management. Symmetric (secret key) cryptography is used in support of the encryption service. The procedures are intended to be compatible with a wide range of public key management approaches, including both ad hoc and certificate-based schemes. Mechanisms are provided to support many public key management approaches. == Spreading == MOSS was never widely deployed and is now abandoned, largely due to the popularity of PGP.

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  AI Overviews

  AI Overviews is an artificial intelligence (AI) feature integrated into Google Search that produces AI-generated summaries of search results. The feature has been criticized for its inaccuracy and for reducing website traffic. == History and development == AI Overviews were first introduced as part of Google's Search Generative Experience (SGE), which was unveiled at the Google I/O conference in May 2023. In May 2024 at Google I/O 2024, the feature was rebranded as AI Overviews and launched in the United States. The introduction of AI Overviews was seen as a strategic move to compete with other generative AI advancements, including OpenAI's ChatGPT. By August 2024, AI Overviews was rolled out to several other countries, including the United Kingdom, India, Japan, Brazil, Mexico, and Indonesia, with support for multiple languages. In October 2024, Google expanded the feature globally, making it available in over 100 countries. In December 2024, Botify x Demandsphere released findings stating that when AI Overviews and featured snippets appear together on the search engine results page, they take up approximately 67.1% of the screen on desktop and 75.7% on mobile. Even if content is ranking in the #1 position, it may not be visible to consumers if other visual elements on the results page are more prominent. In March 2025, Google started testing an "AI Mode", where the search results page is AI-generated. The company was also considering adding advertisements to the AI Mode, as they already exist in AI Overviews. As of May 2025, AI Overviews are available in over 200 countries and territories and in more than 40 languages. As of March 2026, Google AI Overviews appear on more than 48% of total Google Search queries, compared to just 6.49% in the previous year (58% year-over-year growth). == Functionality == The AI Overviews feature uses large language models to generate summaries from web content. The overviews are designed to be concise, providing a snapshot of relevant information about the queried topic. Google allows users to adjust the language complexity in summaries, offering both simplified and detailed options. The overviews also include links to sources. According to a June 2025 study by Semrush, the most cited source is Quora, followed by Reddit. == Reception == The feature has faced criticism for inaccuracies, including instances where erroneous or nonsensical content was generated. Depending on what is searched for, the overview may also consist of hallucinated content, such as when searching for idioms that do not exist. In May 2024, Google temporarily restricted the AI tool after it provided suggestions that were seen as nonsensical and harmful, such as telling users to eat rocks or apply glue on pizza. Concerns were also raised by content publishers, who feared a decline in web traffic as users relied on the summaries instead of visiting source websites. A Google patent from 2026 raised the concern of webmasters that Google could entirely replace the landing page of websites by an AI optimized copy of the website in its results. There is also apprehension about the ethical implications of AI-driven content aggregation, including its impact on intellectual property rights and the visibility of smaller content providers. The European Commission announced in December 2025 that they were investigating whether AI Overviews breached European competition law. In response, Google has stated its commitment to improve content validation and refine the algorithms used to filter unreliable information. Google implemented measures to prioritize link placement within AI Overviews, aiming to balance user convenience with the needs of content creators. In January 2026, Google restricted AI Overviews on certain health-related searches following an investigation by The Guardian. == Lawsuits == On February 24, 2025, Chegg sued Alphabet over the AI Overviews feature, claiming that it was leading to students preferring "low-quality, unverified AI summaries", thus violating antitrust law. Chegg also said it was considering either a sale or a take-private transaction. In September 2025, Penske Media Corporation, the publisher of Rolling Stone and The Hollywood Reporter, sued Google, claiming that AI Overviews illegally regurgitate content from their websites and drive off potential site visitors by always appearing on top of the search results while leaving little incentive to see the linked sources. The company stated that "the future of digital media and [...] its integrity [...] is threatened by Google's current actions", alleging that 20% of searches that link to Penske-owned websites show AI Overviews and that the figure is expected to rise. Google spokesperson José Castañeda called the claims "meritless" and stated that "AI Overviews send traffic to a greater diversity of sites." In 2026, Canadian musician Ashley MacIsaac filed a lawsuit against Google claiming that the AI Overview feature had wrongly stated that MacIsaac had been convicted of numerous criminal offences and was on the sex offender registry. He claims this incorrect information led to the cancellation of a December 2025 gig organized by the Sipekne'katik First Nation.

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  Kurzsignale

  The Short Signal Code, also known as the Short Signal Book (German: Kurzsignalbuch), was a short code system used by the Kriegsmarine (German Navy) during World War II to minimize the transmission duration of messages. == Description == The transmission of radio messages had the potential risks of revealing the submarine's presence and direction; if decoded the content was also revealed. Submarines need to provide information, mostly in standard form (position of convoy to attack and of submarine, weather information), to their bases. Initially Morse code transmissions could be used. To inhibit detection, the duration of messages needed to be minimised; for this, Kurzsignale short-coding was used. To prevent interception, messages needed to be encrypted by the Enigma machine. To shorten transmission even further, the message could be sent by a fast machine instead of a human radio operator. For example, the Kurier system – not implemented in time – decreased the time to send a Morse dot from around 50 milliseconds for a human to 1 millisecond. == Short Signal book == The Kurzsignale code was intended to shorten transmission time to below the time required to get a directional fix. It was not primarily intended to hide signal contents; protection was intended to be achieved by encoding with the Enigma machine. A copy of the Kurzsignale code book was captured from German submarine U-110 on 9 May 1941. In August 1941, Dönitz began addressing U-boats by the names of their commanders, instead of boat numbers. The method of defining U-boat meeting points in the Short Signal Book was regarded as compromised, so a method was defined by B-Dienst cryptanalysts to disguise their positions on the Kriegsmarine German Naval Grid System (German:Gradnetzmeldeverfahren) was introduced and used until the end of the war == Radio direction finding == Aware of the danger presented by radio direction finding (RDF), the Kriegsmarine developed various systems to speed up broadcast. The Kurzsignale code system condensed messages into short codes consisting of short sequences for common terms such as "convoy location" so that additional descriptions would not be needed in the message. The resulting Kurzsignal was then encoded with the Enigma machine and subsequently transmitted as rapidly as possible, typically taking about 20 seconds. Typical length of an information or weather signal was about 25 characters. Conventional RDF needed about a minute to fix the bearing of a radio signal, and the Kurzsignale protected against this. However, the huff-duff system which was in use by the Allies could cope with these short transmissions. The fully automated burst transmission Kurier system, in testing from August 1944, could send a Kurzsignal in not more than 460 milliseconds; this was short enough to prevent location even by huff-duff and, if deployed, would have been a serious setback for Allied anti-submarine and code-breaking activities. By late 1944 the Kurier program was a top priority, but the war ended before the system was operational. == Short Weather cipher == A similar coding system was used for weather reports from U-boats, the Wetterkurzschlüssel (Short Weather Cipher). Code books were captured from U-559 on 30 October 1942.

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  Pepper (cryptography)

  In cryptography, a pepper is a secret added to an input such as a password during hashing with a cryptographic hash function. This value differs from a salt in that it is not stored alongside a password hash, but rather the pepper is kept separate using another meachanism, such as a Hardware Security Module. Note that the National Institute of Standards and Technology refers to this value as a secret key rather than a pepper. A pepper is similar in concept to a salt or an encryption key. It is like a salt in that it is a randomized value that is added to a password hash, and it is similar to an encryption key in that it should be kept secret. A pepper performs a comparable role to a salt or an encryption key, but while a salt is not secret (merely unique) and can be stored alongside the hashed output, a pepper is secret and must not be stored with the output. The hash and salt are usually stored in a database, but, if stored, a pepper must be stored separately to prevent it from being obtained by the attacker in case of a database breach. == History == The idea of a site- or service-specific salt (in addition to a per-user salt) has a long history, with Steven M. Bellovin proposing a local parameter in a Bugtraq post in 1995. In 1996 Udi Manber also described the advantages of such a scheme, terming it a secret salt. However, he suggested not storing the value of the secret salt, but instead rediscovering it by trial and error at password verification time. The term pepper has been used, by analogy to salt, but with a variety of meanings. For example, when discussing a challenge-response scheme, pepper has been used for a salt-like quantity, though not used for password storage; it has been used for a data transmission technique where a pepper must be guessed; and even as a part of jokes. The term pepper was proposed for a secret or local parameter stored separately from the password in a discussion of protecting passwords from rainbow table attacks. This usage did not immediately catch on: for example, Fred Wenzel added support to Django password hashing for storage based on a combination of bcrypt and HMAC with separately stored nonces, without using the term. Usage has since become more common. == Types == There are multiple different types of pepper: A shared secret that is common to all users. A randomly-selected number that must be re-discovered on every password input. These mechanisms could be combined with password salting, iterated hashing or even one another. == Shared-secret pepper == Bellovin and Webster suggest prepend a shared secret to the password before hashing, which allows easy use of existing hash functions. For example, consider two users to be added to a database. This table contains two combinations of username and password. The password is not saved, and the 8-byte (64-bit) 44534C70C6883DE2 pepper is saved in a safe place separate from the output values of the hash, in this case SHA256. Unlike the salt, the pepper does not provide protection to users who use the same password, but protects against dictionary attacks, unless the attacker has the pepper value available. Since the same pepper is not shared between different applications, an attacker is unable to reuse the hashes of one compromised database to another. A complete scheme for saving passwords may include both salt and pepper use. For example, it has been suggested to combine the pepper by encrypting salted password hashes, which allows rotation of the pepper. In the case of a shared-secret pepper, a single compromised password (via password reuse or other attack) along with a user's salt can lead to an attack to discover the pepper, rendering it ineffective. If an attacker knows a plaintext password and a user's salt, as well as the algorithm used to hash the password, then discovering the pepper can be a matter of brute forcing the values of the pepper. This is why NIST recommends the secret value be at least 112 bits, so that discovering it by exhaustive search is prohibitively expensive. The pepper must be generated anew for every application it is deployed in, otherwise a breach of one application would result in lowered security of another application. Without knowledge of the pepper, other passwords in the database will be far more difficult to extract from their hashed values, as the attacker would need to guess the password as well as the pepper. A pepper adds security to a database of salts and hashes because unless the attacker is able to obtain the pepper, cracking even a single hash is intractable, no matter how weak the original password. Even with a list of (salt, hash) pairs, an attacker must also guess the secret pepper in order to find the password which produces the hash. The NIST specification for a secret salt suggests using a Password-Based Key Derivation Function (PBKDF) with an approved Pseudorandom Function such as HMAC with SHA-3 as the hash function of the HMAC. The NIST recommendation is also to perform at least 1000 iterations of the PBKDF, and a further minimum 1000 iterations using the secret salt in place of the non-secret salt. == Randomly-selected pepper that must be re-discovered == The aim of this mechanism is to slow down password the password verification step, thus slowing attacks. The aim is similar increasing the iteration count on bcrypt or Argon2, but the mechanism is different. The secret salt or pepper must be rediscovered by the verifier or attacker each time by guessing. In this situation, the password hashing function is calculated using both the password and the pepper. At password storage time, the pepper is chosen randomly from a range between 1 and R, the hash output is calculated using the password and the pepper. The hash output is stored with the username. The pepper is then discarded. At password verification time, the verifier is provided with a username and password to verify. The originally calculated hash is retrieved for the given username, and then the hash of the password and each value between 1 and R is calculated. If any of these hash values match the stored password hash, the password is considered valid. Note, the possible values of the pepper should not be tested in a fixed order known to an attacker, otherwise a timing attack may reveal the pepper. If the password is correct, the correct pepper will be found in R/2 hash evaluations on average. If the password is incorrect, all R values must be tested before the password can be rejected.

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  Majal (organization)

  Majal is a regional not-for-profit organization focused on "amplifying voices of dissent" throughout the Middle East and North Africa via digital media. Founded in Bahrain, the organization "creates platforms and web applications that promote freedom of expression and social justice." Majal, which relies on open source platforms, like WordPress and Ruby on Rails, was launched in 2006 by Esra'a Al Shafei as a simple group-blogging idea. However, it has changed course to focus on the development of unique applications and tools. == Objectives and means == Majal's content, in addition to its projects and applications, is free open source content to ensure right to access information for everyone. The organization uses a broad spectrum of social media tools, ranging from written blogs, podcasts, vlogs, comics, video animation and pictures to live broadcasting through radio. == Projects and applications == Majal runs various active projects that include Alliance for Kurdish Rights, The Muslim Network for Baháʼí Rights, a discussion tool for Arab LGBT youth and various Mobile apps. == Funding == Majal is funded through private donations and grants from non-governmental organizations, as well as any potential revenues earned through freelance development. Its primary funders are the Shuttleworth Foundation and the Omidyar Network. In 2008, Majal won the Berkman Award from the Berkman Klein Center for Internet & Society at Harvard University in the Human Rights/Global Advocacy category. This $10,000 award was Majal’s first source of funding. This award is presented to “people or institutions that have made a significant contribution to the Internet and its impact on society over the past decade.” In 2009, the March 18 Movement, a project of Majal, received the Think Social Award, which demonstrates how social media can be used to solve the world’s problems. Esra'a Al-Shafei was named a 2009 Echoing Green Fellow for Civil and Human Rights, a seed funding award for young entrepreneurs engaged in social change. Financially, the fellowship consists of a $60,000 stipend paid over two years. Most recently, MEY has received a grant from the Arab Fund for Arts and Culture for its Mideast Tunes website. == Awards == Winner of Human Rights Tulip 2014 Human Rights Tulip - Human rights - Government.nl Ashoka Changemakers Citizen Media competition in 2011 for their CrowdVoice project. Monaco Media Prize 2011 for Majal founder and director Esra'a Al Shafei in 2011. The BOBS Special Topic Human Rights award in 2011 for the Majal website Migrant Rights. ThinkSocial Award in 2009, as powerful model for how social media can be used to address global problems. Echoing Green, 2009 Fellowship. TEDGlobal 2009 Fellowship. Berkman Award for Internet Innovation from Berkman Klein Center for Internet & Society at Harvard Law School in 2008 for the outstanding contributions to the internet and its impact on society. The Global Journal selected Majal as one of the Top 100 NGOs in 2013. 2013-2014 Shuttleworth Foundation Fellowship. == Leadership == Majal team is led primarily by women. The organization was founded by Esra'a Al Shafei, a blogger from Bahrain in 2006. Ahmed Zidan of Egypt has served for over three years as the Editor-in-Chief of Majal Arabic, and is the co-founder of Ahwaa, and is also a podcaster. Other team members include Mona Kareem, Rima Kalush, Abir Ghattas, Namita Malhotra, and Vani Saraswathi. == 2011 Middle East and North Africa protests == Blogs and video played a role in the documentation of protests throughout the Middle East and North Africa during 2010-2011, also known as the Arab Spring. During this period, MEY's project, CrowdVoice (launched in 2010) helped curate and archive the large amounts of videos, images, and eye-witness reports being aggregated and crowdsourced from across the region. As a result, it had been censored temporarily in Yemen and is still censored in Bahrain. == Media coverage == Majal claims to have received various coverage from news agencies, TV satellite channels, radio stations, newspapers, magazines. For instance, Sky News, CNN, New York Times, BBC, The Guardian, NPR, Time, MTV political blog "Act", VH1, Daily Telegraph, Die Zeit, Frankfurter Rundschau FR-online, Toronto Star, TechCrunch, Rolling Stone Middle East, Abu Dhabi TV, Gulf News, Al-Hasnaa' magazine, ReadWriteWeb, Mashable, The Next Web, Radio Sawt Beirut International, Radio Farda among many others.

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  Structural similarity index measure

  The structural similarity index measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. It is also used for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference. SSIM is a perception-based model that considers image degradation as perceived change in structural information, while also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms. This distinguishes from other techniques such as mean squared error (MSE) or peak signal-to-noise ratio (PSNR) that instead estimate absolute errors. Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially close. These dependencies carry important information about the structure of the objects in the visual scene. Luminance masking is a phenomenon whereby image distortions (in this context) tend to be less visible in bright regions, while contrast masking is a phenomenon whereby distortions become less visible where there is significant activity or "texture" in the image. == History == The predecessor of SSIM was called Universal Quality Index (UQI), or Wang–Bovik index, which was developed by Zhou Wang and Alan Bovik in 2001. This evolved, through their collaboration with Hamid Sheikh and Eero Simoncelli, into the current version of SSIM, which was published in April 2004 in the IEEE Transactions on Image Processing. In addition to defining the SSIM quality index, the paper provides a general context for developing and evaluating perceptual quality measures, including connections to human visual neurobiology and perception, and direct validation of the index against human subject ratings. The basic model was developed in the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin and further developed jointly with the Laboratory for Computational Vision (LCV) at New York University. Further variants of the model have been developed in the Image and Visual Computing Laboratory at University of Waterloo and have been commercially marketed. SSIM subsequently found strong adoption in the image processing community and in the television and social media industries. The 2004 SSIM paper has been cited over 50,000 times according to Google Scholar, making it one of the highest cited papers in the image processing and video engineering fields. It was recognized with the IEEE Signal Processing Society Best Paper Award for 2009. It also received the IEEE Signal Processing Society Sustained Impact Award for 2016, indicative of a paper having an unusually high impact for at least 10 years following its publication. Because of its high adoption by the television industry, the authors of the original SSIM paper were each accorded a Primetime Engineering Emmy Award in 2015 by the Television Academy. == Algorithm == The SSIM index is calculated between two windows of pixel values x {\displaystyle x} and y {\displaystyle y} of common size, from corresponding locations in two images to be compared. These SSIM values can be aggregated across the full images by averaging or other variations. === Special-case formula === In one simple special case, further explained in the next section, the SSIM measure between x {\displaystyle x} and y {\displaystyle y} is: SSIM ( x , y ) = ( 2 μ x μ y + c 1 ) ( 2 σ x y + c 2 ) ( μ x 2 + μ y 2 + c 1 ) ( σ x 2 + σ y 2 + c 2 ) {\displaystyle {\hbox{SSIM}}(x,y)={\frac {(2\mu _{x}\mu _{y}+c_{1})(2\sigma _{xy}+c_{2})}{(\mu _{x}^{2}+\mu _{y}^{2}+c_{1})(\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2})}}} with: μ x {\displaystyle \mu _{x}} the pixel sample mean of x {\displaystyle x} ; μ y {\displaystyle \mu _{y}} the pixel sample mean of y {\displaystyle y} ; σ x 2 {\displaystyle \sigma _{x}^{2}} the sample variance of x {\displaystyle x} ; σ y 2 {\displaystyle \sigma _{y}^{2}} the sample variance of y {\displaystyle y} ; σ x y {\displaystyle \sigma _{xy}} the sample covariance of x {\displaystyle x} and y {\displaystyle y} ; c 1 = ( k 1 L ) 2 {\displaystyle c_{1}=(k_{1}L)^{2}} , c 2 = ( k 2 L ) 2 {\displaystyle c_{2}=(k_{2}L)^{2}} two variables to stabilize the division with weak denominator; L {\displaystyle L} the dynamic range of the pixel-values (typically this is 2 # b i t s p e r p i x e l − 1 {\displaystyle 2^{\#bits\ per\ pixel}-1} ); k 1 = 0.01 {\displaystyle k_{1}=0.01} and k 2 = 0.03 {\displaystyle k_{2}=0.03} by default. === General formula and components === The SSIM formula is based on three comparison measurements between the samples of x {\displaystyle x} and y {\displaystyle y} : luminance ( l {\displaystyle l} ), contrast ( c {\displaystyle c} ), and structure ( s {\displaystyle s} ). The individual comparison functions are: l ( x , y ) = 2 μ x μ y + c 1 μ x 2 + μ y 2 + c 1 {\displaystyle l(x,y)={\frac {2\mu _{x}\mu _{y}+c_{1}}{\mu _{x}^{2}+\mu _{y}^{2}+c_{1}}}} c ( x , y ) = 2 σ x σ y + c 2 σ x 2 + σ y 2 + c 2 {\displaystyle c(x,y)={\frac {2\sigma _{x}\sigma _{y}+c_{2}}{\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2}}}} s ( x , y ) = σ x y + c 3 σ x σ y + c 3 {\displaystyle s(x,y)={\frac {\sigma _{xy}+c_{3}}{\sigma _{x}\sigma _{y}+c_{3}}}} The SSIM for each block is then a weighted combination of those comparative measures: SSIM ( x , y ) = l ( x , y ) α ⋅ c ( x , y ) β ⋅ s ( x , y ) γ {\displaystyle {\text{SSIM}}(x,y)=l(x,y)^{\alpha }\cdot c(x,y)^{\beta }\cdot s(x,y)^{\gamma }} Choosing the third denominator stabilizing constant as: c 3 = c 2 / 2 {\displaystyle c_{3}=c_{2}/2} leads to a simplification when combining the c and s components with equal exponents ( β = γ {\displaystyle \beta =\gamma } ), as the numerator of c is then twice the denominator of s, leading to a cancellation leaving just a 2. Setting the weights (exponents) α , β , γ {\displaystyle \alpha ,\beta ,\gamma } to 1, the formula can then be reduced to the special case shown above. === Mathematical properties === SSIM satisfies the identity of indiscernibles, and symmetry properties, but not the triangle inequality or non-negativity, and thus is not a distance function. However, under certain conditions, SSIM may be converted to a normalized root MSE measure, which is a distance function. The square of such a function is not convex, but is locally convex and quasiconvex, making SSIM a feasible target for optimization. === Application of the formula === In order to evaluate the image quality, this formula is usually applied only on luma, although it may also be applied on color (e.g., RGB) values or chromatic (e.g. YCbCr) values. The resultant SSIM index is a decimal value between -1 and 1, where 1 indicates perfect similarity, 0 indicates no similarity, and -1 indicates perfect anti-correlation. For an image, it is typically calculated using a sliding Gaussian window of size 11×11 or a block window of size 8×8. The window can be displaced pixel-by-pixel on the image to create an SSIM quality map of the image. In the case of video quality assessment, the authors propose to use only a subgroup of the possible windows to reduce the complexity of the calculation. === Variants === ==== Multi-scale SSIM ==== A more advanced form of SSIM, called Multiscale SSIM (MS-SSIM) is conducted over multiple scales through a process of multiple stages of sub-sampling, reminiscent of multiscale processing in the early vision system. It has been shown to perform equally well or better than SSIM on different subjective image and video databases. ==== Multi-component SSIM ==== Three-component SSIM (3-SSIM) is a form of SSIM that takes into account the fact that the human eye can see differences more precisely on textured or edge regions than on smooth regions. The resulting metric is calculated as a weighted average of SSIM for three categories of regions: edges, textures, and smooth regions. The proposed weighting is 0.5 for edges, 0.25 for the textured and smooth regions. The authors mention that a 1/0/0 weighting (ignoring anything but edge distortions) leads to results that are closer to subjective ratings. This suggests that edge regions play a dominant role in image quality perception. The authors of 3-SSIM have also extended the model into four-component SSIM (4-SSIM). The edge types are further subdivided into preserved and changed edges by their distortion status. The proposed weighting is 0.25 for all four components. ==== Structural dissimilarity ==== Structural dissimilarity (DSSIM) may be derived from SSIM, though it does not constitute a distance function as the triangle inequality is not necessarily satisfied. DSSIM ( x , y ) = 1 − SSIM ( x , y ) 2 {\displaystyle {\hbox{DSSIM}}(x,y)={\frac {1-{\hbox{SSIM}}(x,y)}{2}}} ==== Video quality metrics and temporal variants ==== It is worth noting that the original vers

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  Social media mining

  Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services. Social media mining uses concepts from computer science, data mining, machine learning, and statistics. Mining is based on social network analysis, network science, sociology, ethnography, optimization and mathematics. It attempts to formally represent, measure and model patterns from social media data. In the 2010s, major corporations, governments and not-for-profit organizations began mining to learn about customers, clients and others. Platforms such as Google, Facebook (partnered with Datalogix and BlueKai) conduct mining to target users with advertising. Scientists and machine learning researchers extract insights and design product features. Users may not understand how platforms use their data. Users tend to click through Terms of Use agreements without reading them, leading to ethical questions about whether platforms adequately protect users' privacy. During the 2016 United States presidential election, Facebook allowed Cambridge Analytica, a political consulting firm linked to the Trump campaign, to analyze the data of an estimated 87 million Facebook users to profile voters, creating controversy when this was revealed. == Background == As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Instagram, Photobucket, or Picasa), news aggregation (Google Reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegrees.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media. == Uses == Social media mining is used across several industries including business development, social science research, health services, and educational purposes. Once the data received goes through social media analytics, it can then be applied to these various fields. Often, companies use the patterns of connectivity that pervade social networks, such as assortativity—the social similarity between users that are induced by influence, homophily, and reciprocity and transitivity. These forces are then measured via statistical analysis of the nodes and connections between these nodes. Social analytics also uses sentiment analysis, because social media users often relay positive or negative sentiment in their posts. This provides important social information about users' emotions on specific topics. These three patterns have several uses beyond pure analysis. For example, influence can be used to determine the most influential user in a particular network. Companies would be interested in this information in order to decide who they may hire for influencer marketing. These influencers are determined by recognition, activity generation, and novelty—three requirements that can be measured through the data mined from these sites. Analysts also value measures of homophily: the tendency of two similar individuals to become friends. Users have begun to rely on information of other users' opinions in order to understand diverse subject matter. These analyses can also help create recommendations for individuals in a tailored capacity. By measuring influence and homophily, online and offline companies are able to suggest specific products for individuals consumers, and groups of consumers. Social media networks can use this information themselves to suggest to their users possible friends to add, pages to follow, and accounts to interact with. == Perception == Modern social media mining is a controversial practice that has led to exponential gains in user growth for tech giants such as Facebook, Inc., Twitter, and Google. Companies such as these, considered "Big Tech" are companies that build algorithms that take advantage of user input to understand their preferences, and keep them on the platform as much as possible. These inputs, that can be as simple as time spent on a given screen, provide the data being mined, and lead to companies profiting heavily from using that data to capitalize on extremely accurate predictions about user behavior. The growth of platforms accelerated rapidly once these strategies were put in place; Most of the largest platforms now average over 1 billion active users per month as of 2021. It has been claimed by a multitude of anti-algorithm personalities, like Tristan Harris or Chamath Palihapitiya, that certain companies (specifically Facebook) valued growth above all else, and ignored potential negative impacts from these growth engineering tactics. At the same time, users have now created their own data arbitrages with the help of their own data, through content monetization and becoming influencers. Users typically have access to a varied set of analytics specific to people that interact with them on social media, and can use these as building blocks for their own targeting and growth strategies through ads and posts that cater to their audiences. Influencers also commonly promote products and services for established brands, creating one of the largest digital industries: Influencer marketing. Instagram, Facebook, Twitter, YouTube, Google, and others have long given access to platform analytics, and allowed third parties to access that information as well, at times unbeknownst to even the user whose data is being viewed/bought. == Research == === Research areas === Social media event detection – Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, they can be seen as a potentially viable source of information to understand the current emerging topics/events. Public health monitoring and surveillance - Using large-scale analysis of social media to study large cohorts of patients and the general public, e.g. to obtain early warning signals of drug-drug interactions and adverse drug reactions, or understand human reproduction and sexual interest. Community structure (Community Detection/Evolution/Evaluation) – Identifying communities on social networks, how t

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  Data

  Data ( DAY-tə, US also DAT-ə, India: DEE-tə) is a collection of discrete or continuous values that conveys information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A data point or datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and may itself be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements. Data is commonly used in scientific research, economics, and virtually every other form of human organizational activity. Examples of data sets include price indices (such as the consumer price index), unemployment rates, literacy rates, and census data. In this context, data represents the raw facts and figures from which useful information can be extracted. Data is collected using techniques such as measurement, observation, query, or analysis, and is typically represented as numbers or characters that may be further processed. Field data is data that is collected in an uncontrolled, in-situ environment. Experimental data is data that is generated in the course of a controlled scientific experiment. Data is analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed, and obvious instrument or data entry errors are corrected. Data can be seen as the smallest unit of factual information that can be used as a basis for calculation, reasoning, or discussion. Data can range from abstract ideas to concrete measurements, including, but not limited to, statistics. Thematically connected data presented in some relevant context can be viewed as information. Contextually connected pieces of information can then be described as data insights or intelligence. The stock of insights and intelligence that accumulate over time, resulting from the synthesis of data into information, can then be described as knowledge. Data has been described as "the new oil of the digital economy". Data, as a general concept, refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Advances in computing technologies have led to the advent of big data, which generally refers to very large quantities of data, typically at the petabyte scale. If restricted to traditional data analysis methods and computing, working with such large (and growing) datasets is difficult, even impossible. In response, the relatively new field of data science uses machine learning (and other artificial intelligence) methods that allow for efficient applications of analytic methods to big data. == Etymology and terminology == The Latin word data is the plural of datum, "(thing) given," and the neuter past participle of dare, "to give". The first English use of the word "data" is from the 1640s. The word "data" was first used to mean "transmissible and storable computer information" in 1946. The expression "data processing" was first used in 1954. When "data" is used more generally as a synonym for "information", it is treated as a mass noun in singular form. This usage is common in everyday language and in technical and scientific fields such as software development and computer science. One example of this usage is the term "big data". When used more specifically to refer to the processing and analysis of sets of data, the term retains its plural form. This usage is common in the natural sciences, life sciences, social sciences, software development and computer science, and grew in popularity in the 20th and 21st centuries. Some style guides do not recognize the different meanings of the term and simply recommend the form that best suits the target audience of the guide. For example, APA style as of the 7th edition requires "data" to be treated as a plural form. == Meaning == Data, information, knowledge, and wisdom are closely related concepts, but each has its role concerning the other, and each term has its meaning. According to a common view, data is collected and analyzed; data only becomes information suitable for making decisions once it has been analyzed in some fashion. One can say that the extent to which a set of data is informative to someone depends on the extent to which it is unexpected by that person. The amount of information contained in a data stream may be characterized by its Shannon entropy. Knowledge is the awareness of its environment that some entity possesses, whereas data merely communicates that knowledge. For example, the entry in a database specifying the height of Mount Everest is a datum that communicates a precisely measured value. This measurement may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to decide on the best method to climb it. Awareness of the characteristics represented by this data is knowledge. Data are often assumed to be the least abstract concept, information the next least, and knowledge the most abstract. In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guidebook containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge". "Information" bears a diversity of meanings that range from everyday usage to technical use. This view, however, has also been argued to reverse how data emerges from information, and information from knowledge. Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation. Beynon-Davies uses the concept of a sign to differentiate between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something. Before the development of computing devices and machines, people had to manually collect data and impose patterns on it. With the development of computing devices and machines, these devices can also collect data. In the 2010s, computers were widely used in many fields to collect data and sort or process it, in disciplines ranging from marketing, analysis of social service usage by citizens to scientific research. These patterns in the data are seen as information that can be used to enhance knowledge. These patterns may be interpreted as "truth" (though "truth" can be a subjective concept) and may be authorized as aesthetic and ethical criteria in some disciplines or cultures. Events that leave behind perceivable physical or virtual remains can be traced back through data. Marks are no longer considered data once the link between the mark and observation is broken. Mechanical computing devices are classified according to how they represent data. An analog computer represents a datum as a voltage, distance, position, or other physical quantity. A digital computer represents a piece of data as a sequence of symbols drawn from a fixed alphabet. The most common digital computers use a binary alphabet, that is, an alphabet of two characters typically denoted "0" and "1". More familiar representations, such as numbers or letters, are then constructed from the binary alphabet. Some special forms of data are distinguished. A computer program is a collection of data, that can be interpreted as instructions. Most computer languages make a distinction between programs and the other data on which programs operate, but in some languages, notably Lisp and similar languages, programs are essentially indistinguishable from other data. It is also useful to distinguish metadata, that is, a description of other data. A similar yet earlier term for metadata is "ancillary data." The prototypical example of metadata is the library catalog, which is a description of the contents of books. == Data sources == With respect to ownership of data collected in the course of marketing or other corporate collection, data has been characterized according to party depending on how close the data is to the source or if it has been generated through additional processing. "Zero-party data" refers to data that customers "intentionally and proactively shares". This kind of data can come from a variety of sources, including: subscriptions, preference centers, quizzes, surveys, pop-up forms, and interactive digital experiences. "First-party data" may be collected by a company directly from its customers. The secure exchange of first-party data among companies can be done using data clean rooms. "S

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