Cloud computing is defined by the International Organization for Standardization (ISO) as "a paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on demand". It is commonly referred to as "the cloud". == Characteristics == In 2011, the National Institute of Standards and Technology (NIST) identified five "essential characteristics" for cloud systems. Below are the exact definitions according to NIST: On-demand self-service: "A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider." Broad network access: "Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations)." Resource pooling: " The provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand." Rapid elasticity: "Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear unlimited and can be appropriated in any quantity at any time." Measured service: "Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. By 2023, the International Organization for Standardization (ISO) had expanded and refined the list. == History == The history of cloud computing extends to the 1960s, with the initial concepts of time-sharing becoming popularized via remote job entry (RJE). The "data center" model, where users submitted jobs to operators to run on mainframes, was predominantly used during this era. This period saw broad experimentation with making large-scale computing power more accessible through time-sharing, while optimizing infrastructure, platforms, and applications to improve efficiency for end users. The "cloud" metaphor for virtualized services dates to 1994, when it was used by General Magic for the universe of "places" that mobile agents in the Telescript environment could "go". The metaphor is credited to David Hoffman, a General Magic communications specialist, based on its long-standing use in networking and telecom. The expression cloud computing became more widely known in 1996 when Compaq Computer Corporation drew up a business plan for future computing and the Internet. The company's ambition was to supercharge sales with "cloud computing-enabled applications". The business plan foresaw that online consumer file storage would likely be commercially successful. As a result, Compaq decided to sell server hardware to internet service providers. In the 2000s, the application of cloud computing began to take shape with the establishment of Amazon Web Services (AWS) in 2002, which allowed developers to build applications independently. In 2006 Amazon Simple Storage Service, known as Amazon S3, and the Amazon Elastic Compute Cloud (EC2) were released. In 2008 NASA's development of the first open-source software for deploying private and hybrid clouds. The following decade saw the launch of various cloud services. In 2010, Microsoft launched Microsoft Azure, and Rackspace Hosting and NASA initiated an open-source cloud-software project, OpenStack. IBM introduced the IBM SmartCloud framework in 2011, and Oracle announced the Oracle Cloud in 2012. In December 2019, Amazon launched AWS Outposts, a service that extends AWS infrastructure, services, APIs, and tools to customer data centers, co-location spaces, or on-premises facilities. == Value proposition == Cloud computing can shorten time to market by offering pre-configured tools, scalable resources, and managed services, allowing users to focus on core business value rather than maintaining infrastructure. Cloud platforms can enable organizations and individuals to reduce upfront capital expenditures on physical infrastructure by shifting to an operational expenditure model, where costs scale with usage. Cloud platforms also offer managed services and tools, such as artificial intelligence, data analytics, and machine learning, which might otherwise require significant in-house expertise and infrastructure investment. While cloud computing can offer cost advantages through effective resource optimization, organizations often face challenges such as unused resources, inefficient configurations, and hidden costs without proper oversight and governance. Many cloud platforms provide cost management tools, such as AWS Cost Explorer and Azure Cost Management, and frameworks like FinOps have emerged to standardize financial operations in the cloud. Cloud computing also facilitates collaboration, remote work, and global service delivery by enabling secure access to data and applications from any location with an internet connection. Cloud providers offer various redundancy options for core services, such as managed storage and managed databases, though redundancy configurations often vary by service tier. Advanced redundancy strategies, such as cross-region replication or failover systems, typically require explicit configuration and may incur additional costs or licensing fees. Cloud environments operate under a shared responsibility model, where providers are typically responsible for infrastructure security, physical hardware, and software updates, while customers are accountable for data encryption, identity and access management (IAM), and application-level security. These responsibilities vary depending on the cloud service model—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS)—with customers typically having more control and responsibility in IaaS environments and progressively less in PaaS and SaaS models, often trading control for convenience and managed services. == Adoption and suitability == The decision to adopt cloud computing or maintain on-premises infrastructure depends on factors such as scalability, cost structure, latency requirements, regulatory constraints, and infrastructure customization. Organizations with variable or unpredictable workloads, limited capital for upfront investments, or a focus on rapid scalability benefit from cloud adoption. Startups, SaaS companies, and e-commerce platforms often prefer the pay-as-you-go operational expenditure (OpEx) model of cloud infrastructure. Additionally, companies prioritizing global accessibility, remote workforce enablement, disaster recovery, and leveraging advanced services such as AI/ML and analytics are well-suited for the cloud. In recent years, some cloud providers have started offering specialized services for high-performance computing and low-latency applications, addressing some use cases previously exclusive to on-premises setups. On the other hand, organizations with strict regulatory requirements, highly predictable workloads, or reliance on deeply integrated legacy systems may find cloud infrastructure less suitable. Businesses in industries like defense, government, or those handling highly sensitive data often favor on-premises setups for greater control and data sovereignty. Additionally, companies with ultra-low latency requirements, such as high-frequency trading (HFT) firms, rely on custom hardware (e.g., FPGAs) and physical proximity to exchanges, which most cloud providers cannot fully replicate despite recent advancements. Similarly, tech giants like Google, Meta, and Amazon build their own data centers due to economies of scale, predictable workloads, and the ability to customize hardware and network infrastructure for optimal efficiency. However, these companies also use cloud services selectively for certain workloads and applications where it aligns with their operational needs. In practice, many organizations are increasingly adopting hybrid cloud architectures, combining on-premises infrastructure with cloud services. This approach allows businesses to balance scalability, cost-effectiveness, and control, offering the benefits of both deployment models while mitigating their respective limitations. == Challenges and limitations == One of the primary challenges of cloud computing, compared with traditional on-premises systems, is maintaining data security and privacy. Cloud users entrust their sensitive data to third-party providers, who may not have adequate measures to protect it from unau
Physicalization
Physicalization of computer hardware (the opposite of virtualization), is a way to place multiple physical machines in a rack unit. It can be a way to reduce hardware costs, since in some cases, server processors cost more per core than energy efficient laptop processors, which may make up for added cost of board level integration. While Moore's law makes increasing integration less expensive, some jobs require much I/O bandwidth, which may be less expensive to provide using many less-integrated processors. Applications and services that are I/O bound are likely to benefit from such physicalized environments. This ensures that each operating system instance is running on a processor that has its own network interface card, host bus and I/O sub-system unlike in the case of a multi-core servers where a single I/O sub-system is shared between all the cores / VMs.
Digital classics
Digital classics is the application of the tools of digital humanities to the field of classics, or more broadly to the study of the ancient world. == History == Classics was one of the first of the humanities disciplines to adopt computing approaches; the first references to the use of computing in the classical humanities date to the early 1960s, which might be surprising considering the reputation of the discipline as old-fashioned and stuffily traditionalist. Major projects such as the Thesaurus Linguae Graecae, founded in 1972, and the text collections of the Packard Humanities Institute set the trend, and there are still a significantly large number of ancient world projects among Humanities Computing projects today. Also, the success of traditional scholarly publications in digital guises, such as seen in the Bryn Mawr Classical Review, and the early adoption of hypertext in high profile projects like the Perseus Digital Library helped to legitimize computing in the study of classics in ways that has not always been the case in other areas of the humanities. This apparent paradox may be as a result of the many methodologies and different sources of evidence that classicists have always had to embrace, from literary sources and linguistics, to art history and archaeology, history, philosophy, religious theory, ancient documents such as inscriptions and papyri, and so forth. The fragmentary nature of many of the texts and languages of the ancient world, the scattered evidence from the material culture of ancient Greece and Rome, and the necessity to evaluate all these varieties of evidence in context are particularly likely to benefit from digital approaches such as databases, text markup, image manipulation and machine learning. == Digital classics projects == There are currently several major projects that aim to encourage and develop digital approaches to classical scholarship. The Stoa Consortium at the University of Kentucky distributes news of the discipline, and serves as a peer-reviewed electronic publication venue, and encourages open source approaches to digital classics. The Perseus Project is a digital library that also provides a collection of digital texts and analysis tools to the public; principally (but not exclusively) classical. Digital Classicist is another project and community which shares information and advice about the digital humanities applied to the field of classics. Epigraphy.info is an international open community pursuing a collaborative environment for digital epigraphy. The Liverpool Classics Mailing List is a project which can be subscribed to in which one receives email regarding Classics events around the world, as well as call for papers, studentships and public lectures.
Bookmarklet
A bookmarklet is a bookmark stored in a web browser that contains JavaScript commands that add new features to the browser. They are stored as the URL of a bookmark in a web browser or as a hyperlink on a web page. Bookmarklets are usually small snippets of JavaScript executed when a user clicks on them. When clicked, bookmarklets can perform a wide variety of operations, such as running a search query from selected text or extracting data from a table. Another name for bookmarklet is favelet or favlet, derived from favorites (synonym of bookmark). == History == Steve Kangas of bookmarklets.com coined the word bookmarklet when he started to create short scripts based on a suggestion in Netscape's JavaScript guide. Before that, Tantek Çelik called these scripts favelets and used that word as early as on 6 September 2001 (personal email). Brendan Eich, who developed JavaScript at Netscape, gave this account of the origin of bookmarklets: They were a deliberate feature in this sense: I invented the javascript: URL along with JavaScript in 1995, and intended that javascript: URLs could be used as any other kind of URL, including being bookmark-able. In particular, I made it possible to generate a new document by loading, e.g. javascript:'hello, world', but also (key for bookmarklets) to run arbitrary script against the DOM of the current document, e.g. javascript:alert(document.links[0].href). The difference is that the latter kind of URL uses an expression that evaluates to the undefined type in JS. I added the void operator to JS before Netscape 2 shipped to make it easy to discard any non-undefined value in a javascript: URL. The increased implementation of Content Security Policy (CSP) in websites has caused problems with bookmarklet execution and usage (2013–2015), with some suggesting that this hails the end or death of bookmarklets. William Donnelly created a work-around solution for this problem (in the specific instance of loading, referencing and using JavaScript library code) in early 2015 using a Greasemonkey userscript (Firefox / Pale Moon browser add-on extension) and a simple bookmarklet-userscript communication protocol. It allows (library-based) bookmarklets to be executed on any and all websites, including those using CSP and having an https:// URI scheme. However, if/when browsers support disabling/disallowing inline script execution using CSP, and if/when websites begin to implement that feature, it will "break" this "fix". == Concept == Web browsers use URIs for the href attribute of the tag and for bookmarks. The URI scheme, such as http or ftp, and which generally specifies the protocol, determines the format of the rest of the string. Browsers also implement javascript: URIs that to a parser is just like any other URI. The browser recognizes the specified javascript scheme and treats the rest of the string as a JavaScript program which is then executed. The expression result, if any, is treated as the HTML source code for a new page displayed in place of the original. The executing script has access to the current page, which it may inspect and change. If the script returns an undefined type (rather than, for example, a string), the browser will not load a new page, with the result that the script simply runs against the current page content. This permits changes such as in-place font size and color changes without a page reload. An immediately invoked function that returns no value or an expression preceded by the void operator will prevent the browser from attempting to parse the result of the evaluation as a snippet of HTML markup: == Usage == Bookmarklets are saved and used as normal bookmarks. As such, they are simple "one-click" tools which add functionality to the browser. For example, they can: Modify the appearance of a web page within the browser (e.g., change font size, background color, etc.) Extract data from a web page (e.g., hyperlinks, images, text, etc.) Remove redirects from (e.g. Google) search results, to show the actual target URL Submit the current page to a blogging service such as Posterous, link-shortening service such as bit.ly, or bookmarking service such as Delicious Query a search engine or online encyclopedia with highlighted text or by a dialog box Submit the current page to a link validation service or translation service Set commonly chosen configuration options when the page itself provides no way to do this Control HTML5 audio and video playback parameters such as speed, position, toggling looping, and showing/hiding playback controls, the first of which can be adjusted beyond HTML5 players' typical range setting. Installing a bookmarklet follows the same process as adding a normal bookmark; the only difference is that in place of the URL destination field is JavaScript code preceded by javascript:. Once created, bookmarklets can be run by clicking on them.
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
You Only Look Once
You Only Look Once (YOLO) is a series of real-time object detection systems based on convolutional neural networks. First introduced by Joseph Redmon et al. in 2015, YOLO has undergone several iterations and improvements, becoming one of the most popular object detection frameworks. The name "You Only Look Once" refers to the fact that the algorithm requires only one forward propagation pass through the neural network to make predictions, unlike previous region proposal-based techniques like R-CNN that require thousands for a single image. == Overview == Compared to previous methods like R-CNN and OverFeat, instead of applying the model to an image at multiple locations and scales, YOLO applies a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities. === OverFeat === OverFeat was an early influential model for simultaneous object classification and localization. Its architecture is as follows: Train a neural network for image classification only ("classification-trained network"). This could be one like the AlexNet. The last layer of the trained network is removed, and for every possible object class, initialize a network module at the last layer ("regression network"). The base network has its parameters frozen. The regression network is trained to predict the ( x , y ) {\displaystyle (x,y)} coordinates of two corners of the object's bounding box. During inference time, the classification-trained network is run over the same image over many different zoom levels and croppings. For each, it outputs a class label and a probability for that class label. Each output is then processed by the regression network of the corresponding class. This results in thousands of bounding boxes with class labels and probability. These boxes are merged until only one single box with a single class label remains. == Versions == There are two parts to the YOLO series. The original part contained YOLOv1, v2, and v3, all released on a website maintained by Joseph Redmon. === YOLOv1 === The original YOLO algorithm, introduced in 2015, divides the image into an S × S {\displaystyle S\times S} grid of cells. If the center of an object's bounding box falls into a grid cell, that cell is said to "contain" that object. Each grid cell predicts B bounding boxes and confidence scores for those boxes. These confidence scores reflect how confident the model is that the box contains an object and how accurate it thinks the box is that it predicts. In more detail, the network performs the same convolutional operation over each of the S 2 {\displaystyle S^{2}} patches. The output of the network on each patch is a tuple as follows: ( p 1 , … , p C , c 1 , x 1 , y 1 , w 1 , h 1 , … , c B , x B , y B , w B , h B ) {\displaystyle (p_{1},\dots ,p_{C},c_{1},x_{1},y_{1},w_{1},h_{1},\dots ,c_{B},x_{B},y_{B},w_{B},h_{B})} where p i {\displaystyle p_{i}} is the conditional probability that the cell contains an object of class i {\displaystyle i} , conditional on the cell containing at least one object. x j , y j , w j , h j {\displaystyle x_{j},y_{j},w_{j},h_{j}} are the center coordinates, width, and height of the j {\displaystyle j} -th predicted bounding box that is centered in the cell. Multiple bounding boxes are predicted to allow each prediction to specialize in one kind of bounding box. For example, slender objects might be predicted by j = 2 {\displaystyle j=2} while stout objects might be predicted by j = 1 {\displaystyle j=1} . c j {\displaystyle c_{j}} is the predicted intersection over union (IoU) of each bounding box with its corresponding ground truth. The network architecture has 24 convolutional layers followed by 2 fully connected layers. During training, for each cell, if it contains a ground truth bounding box, then only the predicted bounding boxes with the highest IoU with the ground truth bounding boxes is used for gradient descent. Concretely, let j {\displaystyle j} be that predicted bounding box, and let i {\displaystyle i} be the ground truth class label, then x j , y j , w j , h j {\displaystyle x_{j},y_{j},w_{j},h_{j}} are trained by gradient descent to approach the ground truth, p i {\displaystyle p_{i}} is trained towards 1 {\displaystyle 1} , other p i ′ {\displaystyle p_{i'}} are trained towards zero. If a cell contains no ground truth, then only c 1 , c 2 , … , c B {\displaystyle c_{1},c_{2},\dots ,c_{B}} are trained by gradient descent to approach zero. === YOLOv2 === Released in 2016, YOLOv2 (also known as YOLO9000) improved upon the original model by incorporating batch normalization, a higher resolution classifier, and using anchor boxes to predict bounding boxes. It could detect over 9000 object categories. It was also released on GitHub under the Apache 2.0 license. === YOLOv3 === YOLOv3, introduced in 2018, contained only "incremental" improvements, including the use of a more complex backbone network, multiple scales for detection, and a more sophisticated loss function. === YOLOv4 and beyond === Subsequent versions of YOLO (v4, v5, etc.) have been developed by different researchers, further improving performance and introducing new features. These versions are not officially associated with the original YOLO authors but build upon their work. As of 2026, versions up to YOLO26 have been released..
JQuery
jQuery is a JavaScript library designed to simplify HTML DOM tree traversal and manipulation, as well as event handling, CSS animations, and Ajax. It is free, open-source software using the permissive MIT License. As of August 2022, jQuery is used by 77% of the 10 million most popular websites. Web analysis indicates that it is the most widely deployed JavaScript library by a large margin, having at least three to four times more usage than any other JavaScript library. jQuery's syntax is designed to make it easier to navigate a document, select DOM elements, create animations, handle events, and develop Ajax applications. jQuery also provides capabilities for developers to create plug-ins on top of the JavaScript library. This enables developers to create abstractions for low-level interaction and animation, advanced effects and high-level, theme-able widgets. The modular approach to the jQuery library allows the creation of powerful dynamic web pages and Web applications. The set of jQuery core features—DOM element selections, traversal, and manipulation—enabled by its selector engine (named "Sizzle" from v1.3), created a new "programming style", fusing algorithms and DOM data structures. This style influenced the architecture of other JavaScript frameworks like YUI v3 and Dojo, later stimulating the creation of the standard Selectors API. Microsoft and Nokia bundle jQuery on their platforms. Microsoft includes it with Visual Studio for use within Microsoft's ASP.NET AJAX and ASP.NET MVC frameworks while Nokia has integrated it into the Web Run-Time widget development platform. == Overview == jQuery, at its core, is a Document Object Model (DOM) manipulation library. The DOM is a tree-structure representation of all the elements of a Web page. jQuery simplifies the syntax for finding, selecting, and manipulating these DOM elements. For example, jQuery can be used for finding an element in the document with a certain property (e.g. all elements with the h1 tag), changing one or more of its attributes (e.g. color, visibility), or making it respond to an event (e.g. a mouse click). jQuery also provides a paradigm for event handling that goes beyond basic DOM element selection and manipulation. The event assignment and the event callback function definition are done in a single step in a single location in the code. jQuery also aims to incorporate other highly used JavaScript functionality (e.g. fade ins and fade outs when hiding elements, animations by manipulating CSS properties). The principles of developing with jQuery are: Separation of JavaScript and HTML: The jQuery library provides simple syntax for adding event handlers to the DOM using JavaScript, rather than adding HTML event attributes to call JavaScript functions. Thus, it encourages developers to completely separate JavaScript code from HTML markup. Brevity and clarity: jQuery promotes brevity and clarity with features like "chainable" functions and shorthand function names. Elimination of cross-browser incompatibilities: The JavaScript engines of different browsers differ slightly so JavaScript code that works for one browser may not work for another. Like other JavaScript toolkits, jQuery handles all these cross-browser inconsistencies and provides a consistent interface that works across different browsers. Extensibility: New events, elements, and methods can be easily added and then reused as a plugin. == History == jQuery was originally created in January 2006 at BarCamp NYC by John Resig, influenced by Dean Edwards' earlier cssQuery library. It is currently maintained by a team of developers led by Timmy Willison (with the jQuery selector engine, Sizzle, being led by Richard Gibson). jQuery was originally licensed under the CC BY-SA 2.5, and relicensed to the MIT License in 2006. At the end of 2006, it was dual-licensed under GPL and MIT licenses. As this led to some confusion, in 2012 the GPL was dropped and is now only licensed under the MIT license. === Popularity === In 2015, jQuery was used on 62.7% of the top 1 million websites (according to BuiltWith), and 17% of all Internet websites. In 2017, jQuery was used on 69.2% of the top 1 million websites (according to Libscore). In 2018, jQuery was used on 78% of the top 1 million websites. In 2019, jQuery was used on 80% of the top 1 million websites (according to BuiltWith), and 74.1% of the top 10 million (per W3Techs). In 2021, jQuery was used on 77.8% of the top 10 million websites (according to W3Techs). == Features == jQuery includes the following features: DOM element selections using the multi-browser open source selector engine Sizzle, a spin-off of the jQuery project DOM manipulation based on CSS selectors that uses elements' names and attributes, such as id and class, as criteria to select nodes in the DOM Events Effects and animations Ajax Deferred and Promise objects to control asynchronous processing JSON parsing Extensibility through plug-ins Utilities, such as feature detection Compatibility methods that are natively available in modern browsers, but need fallbacks for old browsers, such as jQuery.inArray() and jQuery.each(). Cross-browser support === Browser support === jQuery 3.0 and newer supports "current−1 versions" (meaning the current stable version of the browser and the version that preceded it) of Firefox (and ESR), Chrome, Safari, and Edge as well as Internet Explorer 9 and newer. On mobile it supports iOS 7 and newer, and Android 4.0 and newer. == Distribution == The jQuery library is typically distributed as a single JavaScript file that defines all its interfaces, including DOM, Events, and Ajax functions. It can be included within a Web page by linking to a local copy or by linking to one of the many copies available from public servers. jQuery has a content delivery network (CDN) hosted by MaxCDN. Google in Google Hosted Libraries service and Microsoft host the library as well. Example of linking a copy of the library locally (from the same server that hosts the Web page): Example of linking a copy of the library from jQuery's public CDN: == Interface == === Functions === jQuery provides two kinds of functions, static utility functions and jQuery object methods. Each has its own usage style. Both are accessed through jQuery's main identifier: jQuery. This identifier has an alias named $. All functions can be accessed through either of these two names. ==== jQuery methods ==== The jQuery function is a factory for creating a jQuery object that represents one or more DOM nodes. jQuery objects have methods to manipulate these nodes. These methods (sometimes called commands), are chainable as each method also returns a jQuery object. Access to and manipulation of multiple DOM nodes in jQuery typically begins with calling the $ function with a CSS selector string. This returns a jQuery object referencing all the matching elements in the HTML page. $("div.test"), for example, returns a jQuery object with all the div elements that have the class test. This node set can be manipulated by calling methods on the returned jQuery object. ==== Static utilities ==== These are utility functions and do not directly act upon a jQuery object. They are accessed as static methods on the jQuery or $ identifier. For example, $.ajax() is a static method. === No-conflict mode === jQuery provides a $.noConflict() function, which relinquishes control of the $ name. This is useful if jQuery is used on a Web page also linking another library that demands the $ symbol as its identifier. In no-conflict mode, developers can use jQuery as a replacement for $ without losing functionality. === Typical start-point === Typically, jQuery is used by putting initialization code and event handling functions in $(handler). This is triggered by jQuery when the browser has finished constructing the DOM for the current Web page. or Historically, $(document).ready(callback) has been the de facto idiom for running code after the DOM is ready. However, since jQuery 3.0, developers are encouraged to use the much shorter $(handler) signature instead. === Chaining === jQuery object methods typically also return a jQuery object, which enables the use of method chains: This line finds all div elements with class attribute test , then registers an event handler on each element for the "click" event, then adds the class attribute foo to each element. Certain jQuery object methods retrieve specific values (instead of modifying a state). An example of this is the val() method, which returns the current value of a text input element. In these cases, a statement such as $('#user-email').val() cannot be used for chaining as the return value does not reference a jQuery object. === Creating new DOM elements === Besides accessing existing DOM nodes through jQuery, it is also possible to create new DOM nodes, if the string passed as the argument to $() factory looks like HTML. For example, the below code finds an HTML select element, and cr