AI Analytics Certification

AI Analytics Certification — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Color science

  Color science is the scientific study of color including lighting and optics; measurement of light and color; the physiology, psychophysics, and modeling of color vision; and color reproduction. It is the modern extension of traditional color theory. == Organizations == International Commission on Illumination (CIE) Illuminating Engineering Society (IES) Inter-Society Color Council (ISCC) Society for Imaging Science and Technology (IS&T) International Colour Association (AIC) Optica, formerly the Optical Society of America (OSA) The Colour Group Society of Dyers and Colourists (SDC) American Association of Textile Chemists and Colorists (AATCC) Association for Research in Vision and Ophthalmology (ARVO) ACM SIGGRAPH Vision Sciences Society (VSS) Council for Optical Radiation Measurements (CORM) == Journals == The preeminent scholarly journal publishing research papers in color science is Color Research and Application, started in 1975 by founding editor-in-chief Fred Billmeyer, along with Gunter Wyszecki, Michael Pointer and Rolf Kuehni, as a successor to the Journal of Colour (1964–1974). Previously most color science work had been split between journals with broader or partially overlapping focus such as the Journal of the Optical Society of America (JOSA), Photographic Science and Engineering (1957–1984), and the Journal of the Society of Dyers and Colourists (renamed Coloration Technology in 2001). Other journals where color science papers are published include the Journal of Imaging Science & Technology, the Journal of Perceptual Imaging, the Journal of the International Colour Association (JAIC), the Journal of the Color Science Association of Japan, Applied Optics, and the Journal of Vision. == Conferences == Congress of the International Color Association IS&T Color and Imaging Conference (CIC) SIGGRAPH International Symposium for Color Science and Art == Selected books == Berns, Roy S. (2019). Billmeyer and Saltzman's Principles of Color Technology (4th ed.). Wiley. doi:10.1002/9781119367314. 3rd ed. (2000). Daw, Nigel (2012). How Vision Works: The Physiological Mechanisms Behind What We See. Oxford. doi:10.1093/acprof:oso/9780199751617.001.0001. Elliot, Andrew J.; Fairchild, Mark D.; Franklin, Anna, eds. (2015). Handbook of Color Psychology. Cambridge. doi:10.1017/CBO9781107337930. Fairchild, Mark D. (2013). Color Appearance Models (3rd ed.). Wiley. doi:10.1002/9781118653128. Author's website. 2nd ed. (2005). Hunt, Robert W. G. (2004). The Reproduction of Colour (6th ed.). Wiley. doi:10.1002/0470024275. Kuehni, Rolf G. (2012). Color: An Introduction to Practice and Principles (3rd ed.). Wiley. doi:10.1002/9781118533567. 1st ed. (1997). Luo, Ming R., ed. (2016). Encyclopedia of Color Science and Technology. Springer. doi:10.1007/978-1-4419-8071-7. MacAdam, David L., ed. (1970). Sources of Color Science. MIT Press. Reinhard, Erik; Khan, Erum Arif; Akyuz, Ahmet Oguz; Johnson, Garrett (2008). Color Imaging: Fundamentals and Applications. CRC Press. doi:10.1201/b10637. Schanda, János, ed. (2007). Colorimetry: Understanding the CIE System. Wiley. doi:10.1002/9780470175637. Shamey, Renzo; Kuehni, Rolf G. (2020). Pioneers of Color Science. Springer. doi:10.1007/978-3-319-30811-1. Wyszecki, Günter; Stiles, Walter S. (1982). Color Science: Concepts and Methods, Quantitative Data and Formulae (2nd ed.). Wiley.

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  Hashtag

  A hashtag is a metadata tag operator that is prefaced by the hash symbol, #. On social media, hashtags are used on microblogging and photo-sharing services–especially Twitter and Tumblr–as a form of user-generated tagging that enables cross-referencing of content by topic or theme. For example, a search within Instagram for the hashtag #flowers returns all posts that have been tagged with that term. After the initial hash symbol, a hashtag may include letters, numerals or other punctuation. The use of hashtags was first proposed by American blogger and product consultant Chris Messina in a 2007 tweet. Messina made no attempt to patent the use because he felt that "they were born of the internet, and owned by no one". Hashtags became entrenched in the culture of Twitter and soon emerged across Instagram, Facebook, and YouTube. In June 2014, hashtag was added to the Oxford English Dictionary as "a word or phrase with the symbol # in front of it, used on social media websites and apps so that you can search for all messages with the same subject". == Origin and acceptance == The number sign or hash symbol, #, has long been used in information technology to highlight specific pieces of text. In 1970, the number sign was used to denote immediate address mode in the assembly language of the PDP-11 when placed next to a symbol or a number, and around 1973, '#' was introduced in the C programming language to indicate special keywords that the C preprocessor had to process first. The pound sign was adopted for use within IRC (Internet Relay Chat) networks around 1988 to label groups and topics. Channels or topics that are available across an entire IRC network are prefixed with a hash symbol # (as opposed to those local to a server, which uses an ampersand '&'). The use of the pound sign in IRC inspired Chris Messina to propose a similar system on Twitter to tag topics of interest on the microblogging network. He proposed the usage of hashtags on Twitter: How do you feel about using # (pound) for groups. As in #barcamp [msg]? According to Messina, he suggested use of the hashtag to make it easy for lay users without specialized knowledge of search protocols to find specific relevant content. Therefore, the hashtag "was created organically by Twitter users as a way to categorize messages". The first published use of the term "hash tag" was in a blog post "Hash Tags = Twitter Groupings" by Stowe Boyd, on August 26, 2007, according to lexicographer Ben Zimmer, chair of the American Dialect Society's New Words Committee. Messina's suggestion to use the hashtag was not immediately adopted by Twitter, but the convention gained popular acceptance when hashtags were used in tweets relating to the 2007 San Diego forest fires in Southern California. The hashtag gained international acceptance during the 2009–2010 Iranian election protests; Twitter users used both English- and Persian-language hashtags in communications during the events. Hashtags have since played critical roles in recent social movements such as #jesuischarlie, #BLM, and #MeToo. Beginning July 2, 2009, Twitter began to hyperlink all hashtags in tweets to Twitter search results for the hashtagged word (and for the standard spelling of commonly misspelled words). In 2010, Twitter introduced "Trending Topics" on the Twitter front page, displaying hashtags that are rapidly becoming popular, and the significance of trending hashtags has become so great that the company makes significant efforts to foil attempts to spam the trending list. During the 2010 World Cup, Twitter explicitly encouraged the use of hashtags with the temporary deployment of "hashflags", which replaced hashtags of three-letter country codes with their respective national flags. Other platforms such as YouTube and Gawker Media followed in officially supporting hashtags, and real-time search aggregators such as Google Real-Time Search began supporting hashtags. == Format == A hashtag must begin with a hash (#) character followed by other characters, and is terminated by a space or the end of the line. Some platforms may require the # to be preceded with a space. Most or all platforms that support hashtags permit the inclusion of letters (without diacritics), numerals, and underscores. Other characters may be supported on a platform-by-platform basis. Some characters, such as "&", are generally not supported as they may already serve other search functions. Hashtags are not case sensitive (a search for "#hashtag" will match "#HashTag" as well), but the use of embedded capitals (i.e., CamelCase) increases legibility and improves accessibility. Languages that do not use word dividers handle hashtags differently. In China, microblogs Sina Weibo and Tencent Weibo use a double-hashtag-delimited #HashName# format, since the lack of spacing between Chinese characters necessitates a closing tag. Twitter uses a different syntax for Chinese characters and orthographies with similar spacing conventions: the hashtag contains unspaced characters, separated from preceding and following text by spaces (e.g., '我 #爱 你' instead of '我#爱你') or by zero-width non-joiner characters before and after the hashtagged element, to retain a linguistically natural appearance (displaying as unspaced '我‌#爱‌你', but with invisible non-joiners delimiting the hashtag). === Etiquette and regulation === Some communities may limit, officially or unofficially, the number of hashtags permitted on a single post. Misuse of hashtags can lead to account suspensions. Twitter warns that adding hashtags to unrelated tweets, or repeated use of the same hashtag without adding to a conversation can filter an account from search results, or suspend the account. Individual platforms may deactivate certain hashtags either for being too generic to be useful, such as #photography on Instagram, or due to their use to facilitate illegal activities. === Alternate formats === In 2009, StockTwits began using ticker symbols preceded by the dollar sign (e.g., $XRX). In July 2012, Twitter began supporting the tag convention and dubbed it the "cashtag". The convention has extended to national currencies, and Cash App has implemented the cashtag to mark usernames. == Function == Hashtags are particularly useful in unmoderated forums that lack a formal ontological organization. Hashtags help users find content similar interest. Hashtags are neither registered nor controlled by any one user or group of users. They do not contain any set definitions, meaning that a single hashtag can be used for any number of purposes, and that the accepted meaning of a hashtag can change with time. Hashtags intended for discussion of a particular event tend to use an obscure wording to avoid being caught up with generic conversations on similar subjects, such as a cake festival using #cakefestival rather than simply #cake. However, this can also make it difficult for topics to become "trending topics" because people often use different spelling or words to refer to the same topic. For topics to trend, there must be a consensus, whether silent or stated, that the hashtag refers to that specific topic. Hashtags may be used informally to express context around a given message, with no intent to categorize the message for later searching, sharing, or other reasons. Hashtags may thus serve as a reflexive meta-commentary. This can help express contextual cues or offer more depth to the information or message that appears with the hashtag. "My arms are getting darker by the minute. #toomuchfaketan". AnoHashtags can also be used to express personal feelings and emotions. ther function of the hashtag can be used to express personal feelings and emotions. For example, with "It's Monday!! #excited #sarcasm" in which the adjectives are directly indicating the emotions of the speaker. Verbal use of the word hashtag is sometimes used in informal conversations. Use may be humorous, such as "I'm hashtag confused!" By August 2012, use of a hand gesture, sometimes called the "finger hashtag", in which the index and middle finger both hands are extended and arranged perpendicularly to form the hash, was documented. === Co-optation by other industries === Companies, businesses, and advocacy organizations have taken advantage of hashtag-based discussions for promotion of their products, services or campaigns. In the early 2010s, some television broadcasters began to employ hashtags related to programs in digital on-screen graphics, to encourage viewers to participate in a backchannel of discussion via social media prior to, during, or after the program. Television commercials have sometimes contained hashtags for similar purposes. The increased usage of hashtags as brand promotion devices has been compared to the promotion of branded "keywords" by AOL in the late 1990s and early 2000s, as such keywords were also promoted at the end of television commercials and series episodes. Organized real-world events have used hashta

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  Hardware-based encryption

  Hardware-based encryption is the use of computer hardware to assist software, or sometimes replace software, in the process of data encryption. Typically, this is implemented as part of the processor's instruction set. For example, the AES encryption algorithm (a modern cipher) can be implemented using the AES instruction set on the ubiquitous x86 architecture. Such instructions also exist on the ARM architecture. However, more unusual systems exist where the cryptography module is separate from the central processor, instead being implemented as a coprocessor, in particular a secure cryptoprocessor or cryptographic accelerator, of which an example is the IBM 4758, or its successor, the IBM 4764. Hardware implementations can be faster and less prone to exploitation than traditional software implementations, and furthermore can be protected against tampering. == History == Prior to the use of computer hardware, cryptography could be performed through various mechanical or electro-mechanical means. An early example is the Scytale used by the Spartans. The Enigma machine was an electro-mechanical system cipher machine notably used by the Germans in World War II. After World War II, purely electronic systems were developed. In 1987 the ABYSS (A Basic Yorktown Security System) project was initiated. The aim of this project was to protect against software piracy. However, the application of computers to cryptography in general dates back to the 1940s and Bletchley Park, where the Colossus computer was used to break the encryption used by German High Command during World War II. The use of computers to encrypt, however, came later. In particular, until the development of the integrated circuit, of which the first was produced in 1960, computers were impractical for encryption, since, in comparison to the portable form factor of the Enigma machine, computers of the era took the space of an entire building. It was only with the development of the microcomputer that computer encryption became feasible, outside of niche applications. The development of the World Wide Web lead to the need for consumers to have access to encryption, as online shopping became prevalent. The key concerns for consumers were security and speed. This led to the eventual inclusion of the key algorithms into processors as a way of both increasing speed and security. == Implementations == === In the instruction set === ==== x86 ==== The X86 architecture, as a CISC (Complex Instruction Set Computer) Architecture, typically implements complex algorithms in hardware. Cryptographic algorithms are no exception. The x86 architecture implements significant components of the AES (Advanced Encryption Standard) algorithm, which can be used by the NSA for Top Secret information. The architecture also includes support for the SHA Hashing Algorithms through the Intel SHA extensions. Whereas AES is a cipher, which is useful for encrypting documents, hashing is used for verification, such as of passwords (see PBKDF2). ==== ARM ==== ARM processors can optionally support Security Extensions. Although ARM is a RISC (Reduced Instruction Set Computer) architecture, there are several optional extensions specified by ARM Holdings. === As a coprocessor === IBM 4758 – The predecessor to the IBM 4764. This includes its own specialised processor, memory and a Random Number Generator. IBM 4764 and IBM 4765, identical except for the connection used. The former uses PCI-X, while the latter uses PCI-e. Both are peripheral devices that plug into the motherboard. === Proliferation === Advanced Micro Devices (AMD) processors are also x86 devices, and have supported the AES instructions since the 2011 Bulldozer processor iteration. Due to the existence of encryption instructions on modern processors provided by both Intel and AMD, the instructions are present on most modern computers. They also exist on many tablets and smartphones due to their implementation in ARM processors. == Advantages == Implementing cryptography in hardware means that part of the processor is dedicated to the task. This can lead to a large increase in speed. In particular, modern processor architectures that support pipelining can often perform other instructions concurrently with the execution of the encryption instruction. Furthermore, hardware can have methods of protecting data from software. Consequently, even if the operating system is compromised, the data may still be secure (see Software Guard Extensions). == Disadvantages == If, however, the hardware implementation is compromised, major issues arise. Malicious software can retrieve the data from the (supposedly) secure hardware – a large class of method used is the timing attack. This is far more problematic to solve than a software bug, even within the operating system. Microsoft regularly deals with security issues through Windows Update. Similarly, regular security updates are released for Mac OS X and Linux, as well as mobile operating systems like iOS, Android, and Windows Phone. However, hardware is a different issue. Sometimes, the issue will be fixable through updates to the processor's microcode (a low level type of software). However, other issues may only be resolvable through replacing the hardware, or a workaround in the operating system which mitigates the performance benefit of the hardware implementation, such as in the Spectre exploit.

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  Svelte

  Svelte is a free and open-source component-based front-end software framework and language created by Rich Harris and maintained by the Svelte core team. Svelte is not a monolithic JavaScript library imported by applications: instead, Svelte compiles HTML templates to specialized code that manipulates the DOM directly, which may reduce the size of transferred files and give better client performance. Application code is also processed by the compiler, inserting calls to automatically recompute data and re-render UI elements when the data they depend on is modified. This also avoids the overhead associated with runtime intermediate representations, such as virtual DOM, unlike traditional frameworks (such as React and Vue) which carry out the bulk of their work at runtime, i.e. in the browser. The compiler itself is written in JavaScript. Its source code is licensed under MIT License and hosted on GitHub. Among comparable frontend libraries, Svelte has one of the smallest bundle footprints at merely 2KB. == History == The predecessor of Svelte is Ractive.js, which Rich Harris created in 2013. Version 1 of Svelte was written in JavaScript and was released on 29 November 2016. The name Svelte was chosen by Rich Harris and his coworkers at The Guardian. Version 2 of Svelte was released on 19 April 2018. It set out to correct what the maintainers viewed as mistakes in the earlier version such as replacing double curly braces with single curly braces. Version 3 of Svelte was written in TypeScript and was released on 21 April 2019. It rethought reactivity by using the compiler to instrument assignments behind the scenes. The SvelteKit web framework was announced in October 2020 and entered beta in March 2021. SvelteKit 1.0 was released in December 2022 after two years in development. Version 4 of Svelte was released on 22 June 2023. It was a maintenance release, smaller and faster than version 3. A part of this release was an internal rewrite from TypeScript back to JavaScript, with JSDoc type annotations. This was met with confusion from the developer community, which was addressed by the creator of Svelte, Rich Harris. Version 5 of Svelte was released on October 19, 2024 at Svelte Summit Fall 2024 with Rich Harris cutting the release live while joined by other Svelte maintainers. Svelte 5 was a ground-up rewrite of Svelte, changing core concepts such as reactivity and reusability. Its primary feature, runes, reworked how reactive state is declared and used. Runes are function-like macros that are used to declare a reactive state, or code that uses reactive states. These runes are used by the compiler to indicate values that may change and are depended on by other states or the DOM. Svelte 5 also introduces Snippets, which are reusable "snippets" of code that are defined once and can be reused anywhere else in the component. Svelte 5 was initially met with controversy due to its many changes, and thus deprecations caused primarily by runes. However, most of this has subsided since the initial announcement of runes, and the further refining of Svelte 5. Also at Svelte Summit Fall 2024, Ben McCann announced the Svelte CLI under the sv package name on npm. In early 2025, the Svelte team announced Asynchronous Svelte, an experimental feature set centered around asynchronous reactivity in Svelte using await expressions. As of August 2025, the feature is available via an experimental compiler option. This coincided with the experimental release of remote functions, an RPC feature in SvelteKit, Svelte's metaframework. Key early contributors to Svelte became involved with Conduitry joining with the release of Svelte 1, Tan Li Hau joining in 2019, and Ben McCann joining in 2020. Rich Harris and Simon Holthausen joined Vercel to work on Svelte fulltime in 2022. Dominic Gannaway joined Vercel from the React core team to work on Svelte fulltime in 2023. == Syntax == Svelte applications and components are defined in .svelte files, which are HTML files extended with templating syntax that is based on JavaScript and is similar to JSX. Svelte's core features are accessed through runes, which syntactically look like functions, but are used as macros by the compiler. These runes include: The $state rune, used for declaring a reactive state value The $derived rune, used for declaring reactive state derived from one or more states The $effect rune, used for declaring code that reruns whenever its dependencies change Starting with Svelte 5, the framework introduced a significant reactivity overhaul that replaces the previous `$:` reactive declarations with new runes such as $state, $derived, and $effect. The $effect rune is now used for post-render operations without modifying state, while $derived is used for computations that depend on other reactive values. This change aims to simplify the mental model of reactivity and make component logic more explicit. Additionally, the { JavaScript code } syntax can be used for templating in HTML elements and components, similar to template literals in JavaScript. This syntax can also be used in element attributes for uses such as two-way data binding, event listeners, and CSS styling. A Todo List example made in Svelte is below: == Associated projects == The Svelte maintainers created SvelteKit as the official way to build projects with Svelte. It is a Next.js/Nuxt-style full-stack framework that dramatically reduces the amount of code that gets sent to the browser. The maintainers had previously created Sapper, which was the predecessor of SvelteKit. The Svelte maintainers also maintain a number of integrations for popular software projects under the Svelte organization including integrations for Vite, Rollup, Webpack, TypeScript, VS Code, Chrome Developer Tools, ESLint, and Prettier. A number of external projects such as Storybook have also created integrations with Svelte and SvelteKit. == Influence == Vue.js modeled its API and single-file components after Ractive.js, the predecessor of Svelte. == Adoption == Svelte is widely praised by developers. Taking the top ranking in multiple large scale developer surveys, it was chosen as the Stack Overflow 2021 most loved web framework and 2020 State of JS frontend framework with the most satisfied developers. Recent surveys continue to show Svelte's strong developer satisfaction, with the 2024 State of JS survey maintaining its position among the most praised frontend frameworks. The 2024 Stack Overflow Developer Survey reported that 73% of developers who used Svelte want to continue working with it, and noted that Stack Overflow's own team used Svelte for building their 2024 Developer Survey results site. Svelte has been adopted by a number of high-profile web companies including The New York Times, Google, Apple, Spotify, Radio France, Square, Yahoo, ByteDance, Rakuten, Bloomberg, Reuters, Ikea, Facebook, Logitech, and Brave. A community group of primarily non-maintainers, known as the Svelte Society, run the Svelte Summit conference, write a Svelte newsletter, host a Svelte podcast, and host a directory of Svelte tooling, components, and templates.

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  Domain adaptation

  Domain adaptation is a field associated with machine learning and transfer learning. It addresses the challenge of training a model on one data distribution (the source domain) and applying it to a related but different data distribution (the target domain). A common example is spam filtering, where a model trained on emails from one user (source domain) is adapted to handle emails for another user with significantly different patterns (target domain). Domain adaptation techniques can also leverage unrelated data sources to improve learning. When multiple source distributions are involved, the problem extends to multi-source domain adaptation. Domain adaptation is a specific type of transfer learning. According to the taxonomy laid out by Pan and Yang (2010), it falls into the category of transductive transfer learning. In this setting, the source and target tasks are the same (e.g., both are object recognition), but the domains differ (different marginal distributions). This distinguishes it from inductive transfer learning (where labeled data is available for the target task) and unsupervised transfer learning (where labels are unavailable in both domains). == Classification of domain adaptation problems == Domain adaptation setups are classified in two different ways: according to the distribution shift between the domains, and according to the available data from the target domain. === Distribution shifts === Common distribution shifts are classified as follows: Covariate Shift occurs when the input distributions of the source and destination change, but the relationship between inputs and labels remains unchanged. The above-mentioned spam filtering example typically falls in this category. Namely, the distributions (patterns) of emails may differ between the domains, but emails labeled as spam in the one domain should similarly be labeled in another. Prior Shift (Label Shift) occurs when the label distribution differs between the source and target datasets, while the conditional distribution of features given labels remains the same. An example is a classifier of hair color in images from Italy (source domain) and Norway (target domain). The proportions of hair colors (labels) differ, but images within classes like blond and black-haired populations remain consistent across domains. A classifier for the Norway population can exploit this prior knowledge of class proportions to improve its estimates. Concept Shift (Conditional Shift) refers to changes in the relationship between features and labels, even if the input distribution remains the same. For instance, in medical diagnosis, the same symptoms (inputs) may indicate entirely different diseases (labels) in different populations (domains). === Data available during training === Domain adaptation problems typically assume that some data from the target domain is available during training. Problems can be classified according to the type of this available data: Unsupervised: Unlabeled data from the target domain is available, but no labeled data. In the above-mentioned example of spam filtering, this corresponds to the case where emails from the target domain (user) are available, but they are not labeled as spam. Domain adaptation methods can benefit from such unlabeled data, by comparing its distribution (patterns) with the labeled source domain data. Semi-supervised: Most data that is available from the target domain is unlabelled, but some labeled data is also available. In the above-mentioned case of spam filter design, this corresponds to the case that the target user has labeled some emails as being spam or not. Supervised: All data that is available from the target domain is labeled. In this case, domain adaptation reduces to refinement of the source domain predictor. In the above-mentioned example classification of hair-color from images, this could correspond to the refinement of a network already trained on a large dataset of labeled images from Italy, using newly available labeled images from Norway. == Formalization == Let X {\displaystyle X} be the input space (or description space) and let Y {\displaystyle Y} be the output space (or label space). The objective of a machine learning algorithm is to learn a mathematical model (a hypothesis) h : X → Y {\displaystyle h:X\to Y} able to attach a label from Y {\displaystyle Y} to an example from X {\displaystyle X} . This model is learned from a learning sample S = { ( x i , y i ) ∈ ( X × Y ) } i = 1 m {\displaystyle S=\{(x_{i},y_{i})\in (X\times Y)\}_{i=1}^{m}} . Usually in supervised learning (without domain adaptation), we suppose that the examples ( x i , y i ) ∈ S {\displaystyle (x_{i},y_{i})\in S} are drawn i.i.d. from a distribution D S {\displaystyle D_{S}} of support X × Y {\displaystyle X\times Y} (unknown and fixed). The objective is then to learn h {\displaystyle h} (from S {\displaystyle S} ) such that it commits the least error possible for labelling new examples coming from the distribution D S {\displaystyle D_{S}} . The main difference between supervised learning and domain adaptation is that in the latter situation we study two different (but related) distributions D S {\displaystyle D_{S}} and D T {\displaystyle D_{T}} on X × Y {\displaystyle X\times Y} . The domain adaptation task then consists of the transfer of knowledge from the source domain D S {\displaystyle D_{S}} to the target one D T {\displaystyle D_{T}} . The goal is then to learn h {\displaystyle h} (from labeled or unlabelled samples coming from the two domains) such that it commits as little error as possible on the target domain D T {\displaystyle D_{T}} . The major issue is the following: if a model is learned from a source domain, what is its capacity to correctly label data coming from the target domain? == Four algorithmic principles == === Reweighting algorithms === The objective is to reweight the source labeled sample such that it "looks like" the target sample (in terms of the error measure considered). === Iterative algorithms === A method for adapting consists in iteratively "auto-labeling" the target examples. The principle is simple: a model h {\displaystyle h} is learned from the labeled examples; h {\displaystyle h} automatically labels some target examples; a new model is learned from the new labeled examples. Note that there exist other iterative approaches, but they usually need target labeled examples. === Search of a common representation space === The goal is to find or construct a common representation space for the two domains. The objective is to obtain a space in which the domains are close to each other while keeping good performances on the source labeling task. This can be achieved through the use of Adversarial machine learning techniques where feature representations from samples in different domains are encouraged to be indistinguishable. === Hierarchical Bayesian Model === The goal is to construct a Bayesian hierarchical model p ( n ) {\displaystyle p(n)} , which is essentially a factorization model for counts n {\displaystyle n} , to derive domain-dependent latent representations allowing both domain-specific and globally shared latent factors. == Software packages == Several compilations of domain adaptation and transfer learning algorithms have been implemented over the past decades: SKADA (Python) ADAPT (Python) TLlib (Python) Domain-Adaptation-Toolbox (MATLAB)

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  Digital asset

  A digital asset is anything that exists only in digital form and comes with a distinct usage right or distinct permission for use. Data that do not possess those rights are not considered assets. Digital assets include, but are not limited to: digital documents, audio content, motion pictures, and other relevant digital data currently in circulation or stored on digital appliances, such as personal computers, laptops, portable media players, tablets, data storage devices, and telecommunication devices. This encompasses any apparatus that currently exists or will exist as technology progresses to accommodate the conception of new modalities capable of carrying digital assets. This holds true regardless of the ownership of the physical device on which the digital asset is located. == Types == Types of digital assets include, but are not limited to: software, photography, logos, illustrations, animations, audiovisual media, presentations, spreadsheets, digital paintings, word documents, electronic mails, websites, and various other digital formats with their respective metadata. The number of different types of digital assets is exponentially increasing due to the rising number of devices that leverage these assets, such as smartphones, serving as conduits for digital media. In Intel's presentation at the 'Intel Developer Forum 2013,' they introduced several new types of digital assets related to medicine, education, voting, friendships, conversations, and reputation, among others. == Digital asset management system == A digital asset management (DAM) is an integrated structure that combines software, hardware, and/or other services to manage, store, ingest, organize, and retrieve digital assets. These systems enable users to find and use content when needed. == Digital asset metadata == Metadata is data about other data. Any structured information that defines a specification of any form of data is referred to as metadata. Metadata is also a claimed relationship between two entities, often used to establish connections or associations. Librarian Lorcan Dempsey says "Think of metadata as data which removes from a user (human or machine) the need to have full advance knowledge of the existence or characteristics of things of potential interest in the environment". At first, the term metadata was used for digital data exclusively, but nowadays metadata can apply to both physical and digital data. Catalogs, inventories, registers, and other similar standardized forms of organizing, managing, and retrieving resources contain metadata. Metadata can be stored and contained directly within the file it refers to or independently from it with the help of other forms of data management such as a DAM system. The more metadata is assigned to an asset the easier it gets to categorize it, especially as the amount of information grows. The asset's value rises the more metadata it has for it becomes more accessible, easier to manage, and more complex. Structured metadata can be shared with open protocols like OAI-PMH to allow further aggregation and processing. Open data sources like institutional repositories have thus been aggregated to form large datasets and academic search engines comprising tens of millions of open access works, like BASE, CORE, and Unpaywall. == Issues == Due to a lack of either legislation or legal precedent, there is limited existing governmental control and regulation surrounding digital assets in the United States and other large economies globally. Many of the control issues relating to access and transferability are maintained by individual companies. Some consequences of this include 'What is to become of the assets once their owner is deceased?' as well as can, and, if so, how, may they be inherited. This subject was broached in a bogus story about Bruce Willis allegedly looking to sue Apple as the end user agreement prevented him from bequeathing his iTunes collection to his children. Another case of this was when a soldier died on duty and the family requested access to the Yahoo! account. When Yahoo! refused to grant access, the probate judge ordered them to give the emails to the family but Yahoo! still was not required to give access. The Music Modernization Act was passed in September 2018 by the U.S. Congress to create a new music licensing system, with the aim to help songwriters get paid more.

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  DiscoVision

  DiscoVision is the name of several things related to the video LaserDisc format. It was the original name of the "Reflective Optical Videodisc System" format later known as "LaserVision" or LaserDisc. == Description == MCA DiscoVision, Inc. was a division of entertainment giant MCA (Music Corporation of America), established in 1969 to develop and sell an optical videodisc system. MCA released discs pressed in Carson and Costa Mesa, California on the DiscoVision label from the format's Atlanta, Georgia launch in 1978 to 1982 and the release of the film The Four Seasons. DiscoVision titles included films from Universal Pictures, Paramount Pictures, Warner Bros. Pictures, and Disney content. Agreements were made with Columbia Pictures and United Artists, though no discs were released on the DiscoVision label from either studio. Most of these companies later established their own labels for the format, the first being Paramount with a dozen movies released on the Paramount Home Video label in the summer of 1981. The successor to MCA DiscoVision, DiscoVision Associates (DVA), was the result of a partnership between IBM and MCA. It was hoped that the merger would provide the basis for improvement of the quality of DiscoVision pressings, but no appreciable improvement ever took hold. In 1981, responsibility for the laser videodisc was sold to Pioneer Electronic Corporation, after MCA Discovision had previously started a partnership in 1977 with Pioneer, Universal Pioneer, to produce the Pioneer PR-7820 player (the first industrial model of DiscoVision player from 1978), as well as establishing disc pressing plants in Japan. As part of the partnership, Pioneer, in association with MCA, had a disc replication facility in Kofu, Japan that produced discs. Some of the last DiscoVision label discs were manufactured by Pioneer in Japan. In the same year, MCA discontinued their DiscoVision branding, due to the sale of the technology to Pioneer (who then rebranded the format as LaserDisc) and in turn rebranded their laserdisc releases, now fabricated by Pioneer, under the MCA Videodisc banner; this was changed to the "MCA Home Video" name for both its VHS and videodisc releases. Some of DiscoVision's technical staff went on to form MCA Video Games, in an effort to produce video game cartridges. DiscoVision Associates later evolved into a patent holding company which manages and licenses intellectual property related to LaserDisc, Compact Disc, and optical disc technologies, as well as other non-disc related fields. In 1989, Pioneer acquired DiscoVision Associates where it continues to license its technologies independently. As the portfolio of patent expired, the presence of DiscoVision became less visible. However, it established the success of a patent holding company, which other companies are stimulated to generate royalty income from their own patent portfolio.

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  Digital inclusion

  Digital inclusion involves the activities necessary to ensure equitable access to and use of information and communications technologies for participation in social and economic life including for education, social services, health, social, and community participation. Digital inclusion includes access to affordable broadband Internet services, Internet-enabled devices, access to digital literacy training, quality technical support, and applications and online content designed to enable and encourage self-sufficiency, participation, and collaboration. Related concepts include digital divide, digital exclusion and digital inequality, however digital inclusion focuses more on the strategies, policies, and programs required to address the digital divide. As many services have moved online and with the increasing use of telehealth to deliver primary care, particularly during the COVID-19 pandemic in 2020, digital inclusion, including digital literacy and internet access is increasingly regarded as a social determinant of health. Accessibility, relevance, and impact have been identified as essential elements of digital inclusion as it pertains to health information systems. "Digital inclusion is broadly defined as different strategies designed to ensure that all people have equal access, opportunities and skills to benefit from digital technologies and systems" (ITU, 2019, as cited in Carmi and Yates, 2020). Since 2020, there have been many technology companies that have begun implementing different features or roles within their companies to support breaking down the digital divide. For example, HP has announced the digital divide accelerator. This accelerator will support nonprofits in Greece, Indonesia, Nigeria, and Spain. The goal for this role is to help equip children and other people within the community to understand the skills needed to become a part of the digital community. == Background == With the increasing use of computers and the Internet in the 1990s and early 2000s concerns rose around digital equality, however this primarily focused on the physical access to technology. This gave rise to the concept of the digital divide which was originally developed to describe the growing disparity in Internet access between rural and urban areas of the United States of America. This gradually expanded to considerations of digital access between countries in what is termed the global digital divide, which mirrors many of the disparities seen within countries but on an international scale. However, with the adoption of digital technologies across most sectors of society, and the increasing diversity of technologies and programs, access and use of ICT became more complex and essential for many aspects of daily life. This led to new terminology and a second wave of research on digital inequality which has been identified as the (1) usage gap, (2) second level digital divide, (3) emerging digital differentiation, and (4) digital inclusion. == Strategies for digital inclusion == A review of the literature in 2019 found that while physical access to digital technologies and the internet continues to be a barrier to digital inclusion, digital ability and attitude were also potential barriers. Key strategies identified for improving digital inclusion are social support, direct user experience and collaborative learning/design. Education is a key aspect of digital inclusion as digital technologies have become a key means of engaging with all levels of the education system, requiring levels of digital competence for successful engagement with the curriculum. In addition lifelong learning is required as technologies, services and systems are changing constantly. Public libraries and community service providers play a key role in supporting digital inclusion through access to computers, internet connection and expertise and training. Designing for digital inclusion may also help with poor written literacy, which remains a barrier for 10% of the world's population. UNESCO has developed Guidelines for designing digital technologies in ways that could assist those who are illiterate. == Indigenous digital inclusion == Digital inclusion is a critical issue for many Indigenous communities across the globe, many of whom lack access to adequate resources. The Australian Government has set a National Closing the Gap target for Aboriginal and Torres Strait Islander people to have equal levels of digital inclusion by 2026. Many people on tribal land and in Native Hawaiian land struggle with the technology gap. The Native Entities Capacity and Planning Grant Program has $45.3 million available to help address these challenges and empower Indian Tribes, Alaska Native entities, and Native Hawaiian organizations. Some of the impacts so far are in the education and workforce development and healthcare access through telehealth.[13] == Measuring digital inclusion == The Australian Digital Inclusion Index (ADII) is a research project which has been tracking digital inclusion throughout Australia since 2016. It uses survey data to measure digital inclusion across three dimensions of access, affordability and digital ability. == The Future of Digital Inclusion == On February 16, 2021, a global dialogue within the United Nations (UN) took a look at the future of digital inclusion. Through the adoption of the 2030 UN Agenda for Sustainable Development, Member States made a commitment. They pledged to "leave no one behind." By 2030, the UN's goal is to close the digital divide by providing access to the Internet and mobile technologies for all nations and peoples and for all segments of society. The UN sees the crisis of too many people in our global society still living unconnected and how the digital divide remains a challenge that must be addressed. == Gaming == The Xbox Adaptive Controller is a groundbreaking example of digital inclusion, designed to make gaming more accessible to people with limited mobility. Developed by Microsoft, it features large programmable buttons and ports that connect to a wide range of external devices like switches, joysticks, and mounts, allowing users to customize their gaming experience based on their unique needs. By removing traditional physical barriers to gameplay, the Xbox Adaptive Controller empowers more people to participate in digital entertainment, promotes equal access to technology, and fosters a more inclusive gaming community. == Digital inclusion advocacy groups == Australian Digital Inclusion Alliance National Digital Inclusion Alliance (US)

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  Learnable function class

  In statistical learning theory, a learnable function class is a set of functions for which an algorithm can be devised to asymptotically minimize the expected risk, uniformly over all probability distributions. The concept of learnable classes are closely related to regularization in machine learning, and provides large sample justifications for certain learning algorithms. == Definition == === Background === Let Ω = X × Y = { ( x , y ) } {\displaystyle \Omega ={\mathcal {X}}\times {\mathcal {Y}}=\{(x,y)\}} be the sample space, where y {\displaystyle y} are the labels and x {\displaystyle x} are the covariates (predictors). F = { f : X ↦ Y } {\displaystyle {\mathcal {F}}=\{f:{\mathcal {X}}\mapsto {\mathcal {Y}}\}} is a collection of mappings (functions) under consideration to link x {\displaystyle x} to y {\displaystyle y} . L : Y × Y ↦ R {\displaystyle L:{\mathcal {Y}}\times {\mathcal {Y}}\mapsto \mathbb {R} } is a pre-given loss function (usually non-negative). Given a probability distribution P ( x , y ) {\displaystyle P(x,y)} on Ω {\displaystyle \Omega } , define the expected risk I P ( f ) {\displaystyle I_{P}(f)} to be: I P ( f ) = ∫ L ( f ( x ) , y ) d P ( x , y ) {\displaystyle I_{P}(f)=\int L(f(x),y)dP(x,y)} The general goal in statistical learning is to find the function in F {\displaystyle {\mathcal {F}}} that minimizes the expected risk. That is, to find solutions to the following problem: f ^ = arg ⁡ min f ∈ F I P ( f ) {\displaystyle {\hat {f}}=\arg \min _{f\in {\mathcal {F}}}I_{P}(f)} But in practice the distribution P {\displaystyle P} is unknown, and any learning task can only be based on finite samples. Thus we seek instead to find an algorithm that asymptotically minimizes the empirical risk, i.e., to find a sequence of functions { f ^ n } n = 1 ∞ {\displaystyle \{{\hat {f}}_{n}\}_{n=1}^{\infty }} that satisfies lim n → ∞ P ( I P ( f ^ n ) − inf f ∈ F I P ( f ) > ϵ ) = 0 {\displaystyle \lim _{n\rightarrow \infty }\mathbb {P} (I_{P}({\hat {f}}_{n})-\inf _{f\in {\mathcal {F}}}I_{P}(f)>\epsilon )=0} One usual algorithm to find such a sequence is through empirical risk minimization. === Learnable function class === We can make the condition given in the above equation stronger by requiring that the convergence is uniform for all probability distributions. That is: The intuition behind the more strict requirement is as such: the rate at which sequence { f ^ n } {\displaystyle \{{\hat {f}}_{n}\}} converges to the minimizer of the expected risk can be very different for different P ( x , y ) {\displaystyle P(x,y)} . Because in real world the true distribution P {\displaystyle P} is always unknown, we would want to select a sequence that performs well under all cases. However, by the no free lunch theorem, such a sequence that satisfies (1) does not exist if F {\displaystyle {\mathcal {F}}} is too complex. This means we need to be careful and not allow too "many" functions in F {\displaystyle {\mathcal {F}}} if we want (1) to be a meaningful requirement. Specifically, function classes that ensure the existence of a sequence { f ^ n } {\displaystyle \{{\hat {f}}_{n}\}} that satisfies (1) are known as learnable classes. It is worth noting that at least for supervised classification and regression problems, if a function class is learnable, then the empirical risk minimization automatically satisfies (1). Thus in these settings not only do we know that the problem posed by (1) is solvable, we also immediately have an algorithm that gives the solution. == Interpretations == If the true relationship between y {\displaystyle y} and x {\displaystyle x} is y ∼ f ∗ ( x ) {\displaystyle y\sim f^{}(x)} , then by selecting the appropriate loss function, f ∗ {\displaystyle f^{}} can always be expressed as the minimizer of the expected loss across all possible functions. That is, f ∗ = arg ⁡ min f ∈ F ∗ I P ( f ) {\displaystyle f^{}=\arg \min _{f\in {\mathcal {F}}^{}}I_{P}(f)} Here we let F ∗ {\displaystyle {\mathcal {F}}^{}} be the collection of all possible functions mapping X {\displaystyle {\mathcal {X}}} onto Y {\displaystyle {\mathcal {Y}}} . f ∗ {\displaystyle f^{}} can be interpreted as the actual data generating mechanism. However, the no free lunch theorem tells us that in practice, with finite samples we cannot hope to search for the expected risk minimizer over F ∗ {\displaystyle {\mathcal {F}}^{}} . Thus we often consider a subset of F ∗ {\displaystyle {\mathcal {F}}^{}} , F {\displaystyle {\mathcal {F}}} , to carry out searches on. By doing so, we risk that f ∗ {\displaystyle f^{}} might not be an element of F {\displaystyle {\mathcal {F}}} . This tradeoff can be mathematically expressed as In the above decomposition, part ( b ) {\displaystyle (b)} does not depend on the data and is non-stochastic. It describes how far away our assumptions ( F {\displaystyle {\mathcal {F}}} ) are from the truth ( F ∗ {\displaystyle {\mathcal {F}}^{}} ). ( b ) {\displaystyle (b)} will be strictly greater than 0 if we make assumptions that are too strong ( F {\displaystyle {\mathcal {F}}} too small). On the other hand, failing to put enough restrictions on F {\displaystyle {\mathcal {F}}} will cause it to be not learnable, and part ( a ) {\displaystyle (a)} will not stochastically converge to 0. This is the well-known overfitting problem in statistics and machine learning literature. == Example: Tikhonov regularization == A good example where learnable classes are used is the so-called Tikhonov regularization in reproducing kernel Hilbert space (RKHS). Specifically, let F ∗ {\displaystyle {\mathcal {F^{}}}} be an RKHS, and | | ⋅ | | 2 {\displaystyle ||\cdot ||_{2}} be the norm on F ∗ {\displaystyle {\mathcal {F^{}}}} given by its inner product. It is shown in that F = { f : | | f | | 2 ≤ γ } {\displaystyle {\mathcal {F}}=\{f:||f||_{2}\leq \gamma \}} is a learnable class for any finite, positive γ {\displaystyle \gamma } . The empirical minimization algorithm to the dual form of this problem is arg ⁡ min f ∈ F ∗ { ∑ i = 1 n L ( f ( x i ) , y i ) + λ | | f | | 2 } {\displaystyle \arg \min _{f\in {\mathcal {F}}^{}}\left\{\sum _{i=1}^{n}L(f(x_{i}),y_{i})+\lambda ||f||_{2}\right\}} This was first introduced by Tikhonov to solve ill-posed problems. Many statistical learning algorithms can be expressed in such a form (for example, the well-known ridge regression). The tradeoff between ( a ) {\displaystyle (a)} and ( b ) {\displaystyle (b)} in (2) is geometrically more intuitive with Tikhonov regularization in RKHS. We can consider a sequence of { F γ } {\displaystyle \{{\mathcal {F}}_{\gamma }\}} , which are essentially balls in F ∗ {\displaystyle {\mathcal {F^{}}}} with centers at 0. As γ {\displaystyle \gamma } gets larger, F γ {\displaystyle {\mathcal {F}}_{\gamma }} gets closer to the entire space, and ( b ) {\displaystyle (b)} is likely to become smaller. However we will also suffer smaller convergence rates in ( a ) {\displaystyle (a)} . The way to choose an optimal γ {\displaystyle \gamma } in finite sample settings is usually through cross-validation. == Relationship to empirical process theory == Part ( a ) {\displaystyle (a)} in (2) is closely linked to empirical process theory in statistics, where the empirical risk { ∑ i = 1 n L ( y i , f ( x i ) ) , f ∈ F } {\displaystyle \{\sum _{i=1}^{n}L(y_{i},f(x_{i})),f\in {\mathcal {F}}\}} are known as empirical processes. In this field, the function class F {\displaystyle {\mathcal {F}}} that satisfies the stochastic convergence are known as uniform Glivenko–Cantelli classes. It has been shown that under certain regularity conditions, learnable classes and uniformly Glivenko-Cantelli classes are equivalent. Interplay between ( a ) {\displaystyle (a)} and ( b ) {\displaystyle (b)} in statistics literature is often known as the bias-variance tradeoff. However, note that in the authors gave an example of stochastic convex optimization for General Setting of Learning where learnability is not equivalent with uniform convergence.

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  Account verification

  Account verification is the process of verifying that a new or existing account is owned and operated by a specified real individual or organization. A number of websites, for example social media websites, offer account verification services. Verified accounts are often visually distinguished by check mark icons or badges next to the names of individuals or organizations. Account verification can enhance the quality of online services, mitigating sockpuppetry, bots, trolling, spam, vandalism, fake news, disinformation and election interference. == History == Account verification was introduced by Twitter in June 2009, initially as a feature for public figures and accounts of interest, individuals in "music, acting, fashion, government, politics, religion, journalism, media, sports, business and other key interest areas". A similar verification system was adopted by Google+ in 2011, Facebook page in October 2015 (Available in United States, Canada, United Kingdom, Australia and New Zealand) Facebook profile and Facebook page in 2018 (Available in Worldwide) Instagram in 2014, and Pinterest in 2015. On YouTube, users are able to submit a request for a verification badge once they obtain 100,000 or more subscribers. It also has an "official artist" badge for musicians and bands. In July 2016, Twitter announced that, beyond public figures, any individual would be able to apply for account verification. This was temporarily suspended in February 2018, following a backlash over the verification of one of the organisers of the far-right Unite the Right rally due to a perception that verification conveys "credibility" or "importance". In March 2018, during a live-stream on Periscope, Jack Dorsey, co-founder and CEO of Twitter, discussed the idea of allowing any individual to get a verified account. Twitter reopened account verification applications in May 2021 after revamping their account verification criteria. This time offering notability criteria for the account categories of government, companies, brands, and organizations, news organizations and journalists, entertainment, sports and activists, organizers, and other influential individuals. Instagram began allowing users to request verification in August 2018. In April 2018, Mark Zuckerberg, co-founder and CEO of Facebook, announced that purchasers of political or issue-based advertisements would be required to verify their identities and locations. He also indicated that Facebook would require individuals who manage large pages to be verified. In May 2018, Kent Walker, senior vice president of Google, announced that, in the United States, purchasers of political-leaning advertisements would need to verify their identities. In November 2022, Elon Musk included a blue verification check mark with a paid Twitter Blue monthly membership. Prior to Musk's acquisition of Twitter, Twitter offered this check mark at no charge to confirmed high profile users. On December 19, 2022, Twitter introduced two new check mark colors: gold for accounts from official businesses and organizations, and grey for accounts from governments or multilateral organizations. The type of check mark can be confirmed by visiting the profile page, then clicking or tapping on the check mark. == Techniques == === Identity verification services === Identity verification services are third-party solutions which can be used to ensure that a person provides information which is associated with the identity of a real person. Such services may verify the authenticity of identity documents such as drivers licenses or passports, called documentary verification, or may verify identity information against authoritative sources such as credit bureaus or government data, called nondocumentary verification. === Identity documents verification === The uploading of scanned or photographed identity documents is a practice in use, for example, at Facebook. According to Facebook, there are two reasons that a person would be asked to send a scan of or photograph of an ID to Facebook: to show account ownership and to confirm their name. In January 2018, Facebook purchased Confirm.io, a startup that was advancing technologies to verify the authenticity of identification documentation. === Biometric verification === === Behavioral verification === Behavioral verification is the computer-aided and automated detection and analysis of behaviors and patterns of behavior to verify accounts. Behaviors to detect include those of sockpuppets, bots, cyborgs, trolls, spammers, vandals, and sources and spreaders of fake news, disinformation and election interference. Behavioral verification processes can flag accounts as suspicious, exclude accounts from suspicion, or offer corroborating evidence for processes of account verification. === Bank account verification === Identity verification is required to establish bank accounts and other financial accounts in many jurisdictions. Verifying identity in the financial sector is often required by regulation such as Know Your Customer or Customer Identification Program. Accordingly, bank accounts can be of use as corroborating evidence when performing account verification. Bank account information can be provided when creating or verifying an account or when making a purchase. === Postal address verification === Postal address information can be provided when creating or verifying an account or when making and subsequently shipping a purchase. A hyperlink or code can be sent to a user by mail, recipients entering it on a website verifying their postal address. === Telephone number verification === A telephone number can be provided when creating or verifying an account or added to an account to obtain a set of features. During the process of verifying a telephone number, a confirmation code is sent to a phone number specified by a user, for example in an SMS message sent to a mobile phone. As the user receives the code sent, they can enter it on the website to confirm their receipt. === Email verification === An email account is often required to create an account. During this process, a confirmation hyperlink is sent in an email message to an email address specified by a person. The email recipient is instructed in the email message to navigate to the provided confirmation hyperlink if and only if they are the person creating an account. The act of navigating to the hyperlink confirms receipt of the email by the person. The added value of an email account for purposes of account verification depends upon the process of account verification performed by the specific email service provider. === Multi-factor verification === Multi-factor account verification is account verification which simultaneously utilizes a number of techniques. === Multi-party verification === The processes of account verification utilized by multiple service providers can corroborate one another. OpenID Connect includes a user information protocol which can be used to link multiple accounts, corroborating user information. == Account verification and good standing == On some services, account verification is synonymous with good standing. Twitter reserves the right to remove account verification from users' accounts at any time without notice. Reasons for removal may reflect behaviors on and off Twitter and include: promoting hate and/or violence against, or directly attacking or threatening other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or disease; supporting organizations or individuals that promote the above; inciting or engaging in the harassment of others; violence and dangerous behavior; directly or indirectly threatening or encouraging any form of physical violence against an individual or any group of people, including threatening or promoting terrorism; violent, gruesome, shocking, or disturbing imagery; self-harm, suicide; and engaging in other activity on Twitter that violates the Twitter Rules. In April 2023, Blue ticks were removed from all Twitter accounts that had not subscribed to Twitter Blue.

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  Microformat

  Microformats (μF) are predefined HTML markup (like HTML classes) created to serve as descriptive and consistent metadata about elements, designating them as representing a certain type of data (such as contact information, geographic coordinates, events, products, recipes, etc.). They allow software to process the information reliably by having set classes refer to a specific type of data rather than being arbitrary. Microformats emerged around 2005 and were predominantly designed for use by search engines, web syndication and aggregators such as RSS. Google confirmed in 2020 that it still parses microformats for use in content indexing. Microformats are referenced in several W3C social web specifications, including IndieAuth and Webmention. Although the content of web pages has been capable of some "automated processing" since the inception of the web, such processing is difficult because the markup elements used to display information on the web do not describe what the information means. Microformats can bridge this gap by attaching semantics, and thereby obviating other, more complicated, methods of automated processing, such as natural language processing or screen scraping. The use, adoption and processing of microformats enables data items to be indexed, searched for, saved or cross-referenced, so that information can be reused or combined. As of 2013, microformats allow the encoding and extraction of event details, contact information, social relationships and similar information. Microformats2, abbreviated as mf2, is the updated version of microformats. Mf2 provides an easier way of interpreting HTML structured syntax and vocabularies than the earlier ways that made use of RDFa and microdata. == Background == Microformats emerged around 2005 as part of a grassroots movement to make recognizable data items (such as events, contact details or geographical locations) capable of automated processing by software, as well as directly readable by end-users. Link-based microformats emerged first. These include vote links that express opinions of the linked page, which search engines can tally into instant polls. CommerceNet, a nonprofit organization that promotes e-commerce on the Internet, has helped sponsor and promote the technology and support the microformats community in various ways. CommerceNet also helped co-found the Microformats.org community site. Neither CommerceNet nor Microformats.org operates as a standards body. The microformats community functions through an open wiki, a mailing list, and an Internet relay chat (IRC) channel. Most of the existing microformats originated at the Microformats.org wiki and the associated mailing list by a process of gathering examples of web-publishing behaviour, then codifying it. Some other microformats (such as rel=nofollow and unAPI) have been proposed, or developed, elsewhere. == Technical overview == XHTML and HTML standards allow for the embedding and encoding of semantics within the attributes of markup elements. Microformats take advantage of these standards by indicating the presence of metadata using the following attributes: class Classname rel relationship, description of the target address in an anchor-element (...) rev reverse relationship, description of the referenced document (in one case, otherwise deprecated in microformats) For example, in the text "The birds roosted at 52.48, -1.89" is a pair of numbers which may be understood, from their context, to be a set of geographic coordinates. With wrapping in spans (or other HTML elements) with specific class names (in this case geo, latitude and longitude, all part of the geo microformat specification): Software agents can recognize exactly what each value represents and can then perform a variety of tasks such as indexing, locating it on a map and exporting it to a GPS device. === Examples === In this example, the contact information is presented as follows: With hCard microformat markup, that becomes: Here, the formatted name (fn), organisation (org), telephone number (tel) and web address (url) have been identified using specific class names and the whole thing is wrapped in class="vcard", which indicates that the other classes form an hCard (short for "HTML vCard") and are not merely coincidentally named. Other, optional, hCard classes also exist. Software, such as browser plug-ins, can now extract the information, and transfer it to other applications, such as an address book. == Specific microformats == Several microformats have been developed to enable semantic markup of particular types of information. However, only hCard and hCalendar have been ratified, the others remaining as drafts: hAtom (superseded by h-entry and h-feed) – for marking up Atom feeds from within standard HTML hCalendar – for events hCard – for contact information; includes: adr – for postal addresses geo – for geographical coordinates (latitude, longitude) hMedia – for audio/video content hAudio – for audio content hNews – for news content hProduct – for products hRecipe – for recipes and foodstuffs. hReview – for reviews rel-directory – for distributed directory creation and inclusion rel-enclosure – for multimedia attachments to web pages rel-license – specification of copyright license rel-nofollow, an attempt to discourage third-party content spam (e.g. spam in blogs) rel-tag – for decentralized tagging (Folksonomy) XHTML Friends Network (XFN) – for social relationships XOXO – for lists and outlines == Uses == Using microformats within HTML code provides additional formatting and semantic data that applications can use. For example, applications such as web crawlers can collect data about online resources, or desktop applications such as e-mail clients or scheduling software can compile details. The use of microformats can also facilitate "mash ups" such as exporting all of the geographical locations on a web page into (for example) Google Maps to visualize them spatially. Several browser extensions, such as Operator for Firefox and Oomph for Internet Explorer, provide the ability to detect microformats within an HTML document. When hCard or hCalendar are involved, such browser extensions allow microformats to be exported into formats compatible with contact management and calendar utilities, such as Microsoft Outlook. When dealing with geographical coordinates, they allow the location to be sent to applications such as Google Maps. Yahoo! Query Language can be used to extract microformats from web pages. On 12 May 2009 Google announced that they would be parsing the hCard, hReview and hProduct microformats, and using them to populate search result pages. They subsequently extended this in 2010 to use hCalendar for events and hRecipe for cookery recipes. Similarly, microformats are also processed by Bing and Yahoo!. As of late 2010, these are the world's top three search engines. Microsoft said in 2006 that they needed to incorporate microformats into upcoming projects, as did other software companies. Alex Faaborg summarizes the arguments for putting the responsibility for microformat user interfaces in the web browser rather than making more complicated HTML: Only the web browser knows what applications are accessible to the user and what the user's preferences are It lowers the barrier to entry for web site developers if they only need to do the markup and not handle "appearance" or "action" issues Retains backwards compatibility with web browsers that do not support microformats The web browser presents a single point of entry from the web to the user's computer, which simplifies security issues == Evaluation == Various commentators have offered review and discussion on the design principles and practical aspects of microformats. Microformats have been compared to other approaches that seek to serve the same or similar purpose. As of 2007, there had been some criticism of one, or all, microformats. The spread and use of microformats was being advocated as of 2007. Opera Software CTO and CSS creator Håkon Wium Lie said in 2005 "We will also see a bunch of microformats being developed, and that’s how the semantic web will be built, I believe." However, in August 2008 Toby Inkster, author of the "Swignition" (formerly "Cognition") microformat parsing service, pointed out that no new microformat specifications had been published since 2005. === Design principles === Computer scientist and entrepreneur, Rohit Khare stated that reduce, reuse, and recycle is "shorthand for several design principles" that motivated the development and practices behind microformats. These aspects can be summarized as follows: Reduce: favor the simplest solutions and focus attention on specific problems; Reuse: work from experience and favor examples of current practice; Recycle: encourage modularity and the ability to embed, valid XHTML can be reused in blog posts, RSS feeds, and anywhere else you can access the web. === Accessibi

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  Access-independent services

  Access-independent service (AIS) is a service concept in which a service does not depend on guaranteed access network cooperation for service delivery. Telecommunications industry analyst Dean Bubley first used the term in a report on Telco-OTT in February 2012. Traditionally, most telecom company or internet service provider services are access-dependent, because they rely heavily on guaranteed access cooperation on the network the service is delivered over. For instance, traditional IP-based TV service (IPTV) delivered by a telecom company is generally a managed service. This means that IPTV service assumes the IPTV service provider has control over the access network that the IPTV service is delivered over, and network quality of service (QoS) guarantees are available for IPTV service delivery. As a result, the reach of a telecom company's IPTV service is generally restricted by the reach of the telecom company's access network. In contrast, services offered by non-traditional video content delivery service providers such as Netflix, Hulu, and Amazon Video are considered access-independent services. Netflix's video content streaming service, for example, dynamically adapts to network conditions in real-time to strive for the best overall quality of experience (QoE) and does not assume guaranteed cooperation from the underlying IP network, such as QoS. As a result, without considering content rights and different countries' government restrictions, the reach of Netflix's video content streaming service is, in theory, the reach of the Internet. Skype is another example of AIS, because Skype offers an IP-based telephony service over the Internet without depending on IP network cooperation guarantees other than basic IP network connectivity. In the context of telecom service delivery, the concept of access independent services is also commonly described by the term "over-the-top" (OTT) services. OTT service providers such as but not limited to Facebook, WeChat, and Netflix generally do not own or directly manage any wide-area access network to begin with, so they design their services for overall quality of experience, with no assumptions on guaranteed access network cooperation.

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  Prequel (mobile application)

  Prequel, Inc. is an American technology company and mobile app developer known for developing the Prequel mobile application, which enables editing photos and videos with filters and effects generated using artificial intelligence. Prequel was founded in 2018 by Serge Aliseenko and Timur Khabirov, who currently serves as the company's CEO. It is headquartered in New York City. As of August 2022, it had been downloaded more than 100 million times. == History == In 2016, entrepreneur Timur Khabirov and investor Serge Aliseenko registered a US corporation named AIAR Labs Inc, which was developing AR solutions as an outsourced contractor. Of several proprietary products, Prequel was selected for beta-testing as a product focused on editing photos and videos. In 2018, Prequel was released on the Apple App Store. The launch cost $3 million USD, financed with the founders’ personal funds. The first release included approximately 10 filters for photos and the same amount of effects that augmented images with rose petals, rain and snow, VHS and film reel simulations, glitch, grain, sun puddles, and lomography. By June 2020, the app had also been released for Android. In 2021, Prequel founders Timur Khabirov and Serge Aliseenko launched a venture studio for startups working with artificial, computer vision, and AR-based visual art. In December 2022, Prequel reached the number 14 slot on the global rankings for Apple App Store’s Top Charts and the number 5 slot on the App Store’s U.S. charts. In March 2023, Prequel launched a new app called Artique, which is an AI-powered image editing app for businesses. Artique provides advertising and marketing graphic design using ready-made templates that users can customize, while giving suggestions and visual cues through artificial intelligence. Prequel was also one of the companies participating in discussions about artificial intelligence at SXSW 2023. == Features == Prequel describes its app as an "Aesthetic Pic Editor. The app uses artificial intelligence to create and edit content. Prequel can be used to touch up faces on images and videos and can also tie various decorative elements to certain points on the human body and face. Prequel filters include the "Cartoon" filter, which converts selfies into cartoon-style pictures. Other filters include Kidcore, Dust, Grain, Fisheye, Retro Style, Miami, Disco, and VHS-style filters, as well as the ability to create Renaissance-style pictures. Prequel also gives users the ability to apply color correction tools and to make moving images with 3D effects out of 2D images. Prequel allows users to take photos and videos directly through the app and apply filters and effects in real time. The app also comes with manual editing options for photos, such as adjusting the brightness and/or exposure and cropping photos, as well as an option to automatically apply adjustments. The Prequel app uses the Core ML, MNN, and TFLight frameworks to work with its neural networks. Some AI solutions are launched server-side, and some on the user's mobile device. A resulting photo or video edited with the app is called "a prequel." The app daily generates over 2 million such prequels, which are published by users in Instagram, TikTok, and other social media. As of 2022, the app has more than 800 filters and effects, along with video templates and support for GIFs and stickers. Prequel is free-to-use, but has a premium version that gives users access to more effects, filters, and beauty tools. Since its launch in 2018, Prequel has been downloaded more than 100 million times.

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  Directed-energy weapon wildfire conspiracy theories

  The directed-energy weapon wildfire conspiracy theories are claims circulating on social media and in fringe commentary that 2020s wildfires in places such as California, Hawaii and Texas were started or steered by directed-energy weapons or other lasers or directed-energy systems rather than by the documented ignition sources identified by investigators. Fact-checking organisations and newsrooms have repeatedly shown that widely shared images and clips said to depict “beams from the sky” are unrelated, miscaptioned or fabricated, and that official inquiries point to causes such as damaged or re-energised power lines, vegetation and extreme wind conditions. Coverage of the January 2025 Los Angeles fires described a resurgence of familiar hoaxes while local and federal agencies coordinated public rebuttals. == Background == Rumours linking directed-energy weapons to wildfire outbreaks appeared during earlier disaster seasons, then re-emerged at scale during the 2018 Camp Fire and again with the 2023 Maui wildfires and the 2025 Los Angeles fires. Journalists documented how large disasters reliably attract miscaptioned imagery and speculative narratives that portray official explanations as cover stories, while researchers and emergency managers noted that such claims tend to flourish during the information vacuum that accompanies fast-moving events. == Narratives and debunks == Recurring claims include assertions that videos show lasers igniting neighbourhoods, that “green” or “blue” items or roofs were spared because lasers cannot burn those colours, that trees remaining upright indicate precision targeting of houses, and that beams recorded over Hawaii or Texas came from secret platforms. Investigations show that a purported laser-strike video was actually an explosion at a Russian gas station recorded years earlier, that a photograph said to capture an “attack” was an Ohio gas flare from 2018, and that a separate video of green lights over Hawaii was captured months before the Maui fires by an astronomical camera and is unrelated. Fact-checks addressing colour myths have further explained that images of intact blue roofs were either misinterpreted or in at least one widely shared instance artificially generated, and that laser interaction with materials is not governed by such simplistic rules. == Investigations and identified causes == Authorities who examined specific incidents have published findings that contradict DEW narratives. A multi-agency investigation into the Maui disaster concluded that downed and later re-energised lines ignited an initial morning fire that re-kindled under extreme winds in the afternoon, with reports detailing the timeline and infrastructure context; summaries by national outlets echoed those conclusions. Investigators of the February 2024 Smokehouse Creek Fire in the Texas Panhandle reported that power lines ignited both the state’s largest wildfire and another major blaze, and the regional utility acknowledged its facilities appeared to have been involved; subsequent media coverage outlined the findings and regulatory follow-up. For the 2018 Camp Fire in Northern California, public reports from Butte County and subsequent proceedings identified PG&E transmission equipment as the source of ignition, with documentation of maintenance issues on the Caribou–Palermo line preceding the event. == Platform and agency responses == As major fires burned in and around Los Angeles in January 2025, officials from city agencies and national partners pursued a coordinated strategy to counter falsehoods by issuing timely updates, flagging fake imagery and directing residents to verified resources. Reporters described how federal emergency managers and local departments used social channels and briefings to rebut specific rumours, including claims about lasers and targeted ignition, and to clarify that early imagery often misleads during fast-moving disasters.

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  Hardware backdoor

  A hardware backdoor is a backdoor implemented within the physical components of a computer system, also known as its hardware. They can be created by introducing malicious code to a component's firmware, or even during the manufacturing process of an integrated circuit. Often, they are used to undermine security in smartcards and cryptoprocessors, unless investment is made in anti-backdoor design methods. They have also been considered for car hacking. Backdoors differ from hardware Trojans as backdoors are introduced intentionally by the original designer or during the design process, whereas hardware Trojans are inserted later by an external party. == Background == The existence of hardware backdoors poses significant security risks for several reasons. They are difficult to detect and are impossible to remove using conventional methods like antivirus software. They can also bypass other security measures, such as disk encryption. Hardware trojans can be introduced during manufacturing where the end-user lacks control over the production chain. == History == In 2008, the FBI reported the discovery of approximately 3,500 counterfeit Cisco network components in the United States, some of which were introduced in military and government infrastructure. In the same year, the possibility of a backdoor SPARC CPU was demonstrated with an FPGA running Linux that supported various hidden malicious services. A few years later, in 2011, Jonathan Brossard presented "Rakshasa", a proof-of-concept hardware backdoor. This backdoor could be installed by an individual with physical access to the hardware. It utilized coreboot to re-flash the BIOS with a SeaBIOS and iPXE-based bootkit composed of legitimate, open-source tools, allowing malware to be fetched from the internet during the boot process. The following year, in 2012, Sergei Skorobogatov and Christopher Woods from the University of Cambridge Computer Laboratory reported the discovery of a backdoor in a military-grade FPGA device, which could be exploited to access and modify sensitive information. It has been said that this was proven to be a software problem and not a deliberate attempt at sabotage. This still brought to attention that equipment manufacturers should ensure that microchips operate as intended. Later that year, two mobile phones developed by the Chinese company ZTE were found to carry a root access backdoor. According to security researcher Dmitri Alperovitch, the exploit used a hard-coded password in its software. Starting in 2012, the United States stated that Huawei might have backdoors present in their products. In 2013, researchers at the University of Massachusetts devised a method of breaking a CPU's internal cryptographic mechanisms by introducing specific impurities into the crystalline structure of transistors to change Intel's random-number generator. Documents revealed from 2013 onwards during the surveillance disclosures initiated by Edward Snowden showed that the Tailored Access Operations (TAO) unit and other NSA employees intercepted servers, routers, and other network gear being shipped to organizations targeted for surveillance to install covert implant firmware onto them before delivery. These tools include custom BIOS exploits that survive the reinstallation of operating systems and USB cables with spy hardware and radio transceiver packed inside. In June 2016 it was reported that University of Michigan Department of Electrical Engineering and Computer Science had built a hardware backdoor that leveraged "analog circuits to create a hardware attack" so that after the capacitors store up enough electricity to be fully charged, it would be switched on, to give an attacker complete access to whatever system or device − such as a PC − that contains the backdoored chip. In the study that won the "best paper" award at the IEEE Symposium on Privacy and Security they also note that microscopic hardware backdoor wouldn't be caught by practically any modern method of hardware security analysis, and could be planted by a single employee of a chip factory. In October 2018 Bloomberg reported that an attack by Chinese spies reached almost 30 U.S. companies, including Amazon and Apple, by compromising America's technology supply chain. == Countermeasures == Skorobogatov has developed a technique capable of detecting malicious insertions into chips. New York University Tandon School of Engineering researchers have developed a way to corroborate a chip's operation using verifiable computing whereby "manufactured for sale" chips contain an embedded verification module that proves the chip's calculations are correct and an associated external module validates the embedded verification module. Another technique developed by researchers at University College London (UCL) relies on distributing trust between multiple identical chips from disjoint supply chains. Assuming that at least one of those chips remains honest the security of the device is preserved. Researchers at the University of Southern California Ming Hsieh Department of Electrical and Computer Engineering and the Photonic Science Division at the Paul Scherrer Institute have developed a new technique called Ptychographic X-ray laminography. This technique is the only current method that allows for verification of the chips blueprint and design without destroying or cutting the chip. It also does so in significantly less time than other current methods. Anthony F. J. Levi Professor of electrical and computer engineering at University of Southern California explains “It’s the only approach to non-destructive reverse engineering of electronic chips—[and] not just reverse engineering but assurance that chips are manufactured according to design. You can identify the foundry, aspects of the design, who did the design. It’s like a fingerprint.” This method currently is able to scan chips in 3D and zoom in on sections and can accommodate chips up to 12 millimeters by 12 millimeters easily accommodating an Apple A12 chip but not yet able to scan a full Nvidia Volta GPU. "Future versions of the laminography technique could reach a resolution of just 2 nanometers or reduce the time for a low-resolution inspection of that 300-by-300-micrometer segment to less than an hour, the researchers say."

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