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The Master Algorithm

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos released in 2015. Domingos wrote the book in order to generate interest from people outside the field. == Overview == The book outlines five approaches of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes' theorem and analogical modelling. The author explains these tribes to the reader by referring to more understandable processes of logic, connections made in the brain, natural selection, probability and similarity judgments. Throughout the book, it is suggested that each different tribe has the potential to contribute to a unifying "master algorithm". Towards the end of the book the author pictures a "master algorithm" in the near future, where machine learning algorithms asymptotically grow to a perfect understanding of how the world and people in it work. Although the algorithm doesn't yet exist, he briefly reviews his own invention of the Markov logic network. == In the media == In 2016 Bill Gates recommended the book, alongside Nick Bostrom's Superintelligence, as one of two books everyone should read to understand AI. In 2018 the book was noted to be on Chinese Communist Party general secretary Xi Jinping's bookshelf. === Reception === A computer science educator stated in Times Higher Education that the examples are clear and accessible. In contrast, The Economist agreed Domingos "does a good job" but complained that he "constantly invents metaphors that grate or confuse". Kirkus Reviews praised the book, stating that "Readers unfamiliar with logic and computer theory will have a difficult time, but those who persist will discover fascinating insights." A New Scientist review called it "compelling but rather unquestioning".
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MADI

Multichannel Audio Digital Interface (MADI) standardized as AES10 by the Audio Engineering Society (AES) defines the data format and electrical characteristics of an interface that carries multiple channels of digital audio. The AES first documented the MADI standard in AES10-1991 and updated it in AES10-2003 and AES10-2008. The MADI standard includes a bit-level description and has features in common with the two-channel AES3 interface. MADI supports serial digital transmission over coaxial cable or fibre-optic lines of 28, 56, 32, or 64 channels; and sampling rates to 96 kHz and beyond with an audio bit depth of up to 24 bits per channel. Like AES3 and ADAT Lightpipe, it is a unidirectional interface from one sender to one receiver. == Development and applications == MADI was developed by AMS Neve, Solid State Logic, Sony and Mitsubishi and is widely used in the audio industry, especially in the professional audio sector. It provides advantages over other audio digital interface protocols and standards such as AES3, ADAT Lightpipe, TDIF (Tascam Digital Interface), and S/PDIF (Sony/Philips Digital Interface). These advantages include: Support for a greater number of channels per line Use of coaxial and optical fiber media that support transmission of audio signals over 100 meters, up to 3000 meters over multi-mode and 40,000 meters over single-mode optical fiber The original specification (AES10-1991) defined the MADI link as a 56-channel transport for linking large-format mixing consoles to digital multitrack recording devices. Large broadcast studios also adopted it for routing multi-channel audio throughout their facilities. The 2003 revision (AES10-2003) adds a 64-channel capability by removing varispeed operation and supports 96 kHz sampling frequency with reduced channel capacity. The latest AES10-2008 standard includes minor clarifications and updates to correspond to the current AES3 standard. Audio over Ethernet of various types is the primary alternative to MADI for transport of many channels of professional digital audio. == Transmission format == MADI links use a transmission format similar to Fiber Distributed Data Interface (FDDI) networking. Since MADI is most often transmitted on copper links via 75-ohm coaxial cables, it more closely compares to the FDDI specification for copper-based links, called CDDI. AES10-2003 recommends using BNC connectors with coaxial cables and SC connectors with optic fibers. MADI over fibre can support a range of up to 2 km. The basic data rate is 100 Mbit/s of data using 4B5B encoding to produce a 125 MHz physical baud rate. Unlike AES3, this clock is not synchronized to the audio sample rate, and the audio data payload is padded using JK sync symbols. Sync symbols may be inserted at any subframe boundary, and must occur at least once per frame. Though the standard disassociates the transmission clock from the audio sample rate, and thus requires a separate word clock connection to maintain synchronization, some vendors do give the option of locking to parts of the transmission timing information for purposes of deriving a word clock. The audio data is almost identical to the AES3 payload, though with more channels. Rather than letters, MADI assigns channel numbers from 0–63. Frame synchronization is provided by sync symbols outside the data itself, rather than an embedded preamble sequence, and the first four time slots of each sub-channel are encoded as normal data, used for sub-channel identification: Bit 0: Set to 1 to mark channel 0, the first channel in each frame Bit 1: Set to 1 to indicate that this channel is active (contains interesting data) Bit 2: notA/B channel marker, used to mark left (0) and right (1) channels. Generally, even channels are A and odd channels are B. Bit 3: Set to 1 to mark the beginning of a 192-sample data block == Sampling frequency == The original AES10-1991 specification allowed 56 channels at sample rates from 32 to 48 kHz with an additional vari-speed range of ± 12.5%. This leads to a total range of 28 to 54 kHz. At the highest frequency, this produces a total of 56 × 32 × 54 = 96768 kbit/s, leaving 3.232% of the channel for synchronization marks and transmit clock error. The 2003 revision specifies different relations between sampling frequency and number of channels. 32 kHz to 48 kHz ± 12.5%, 56 channels; 32 kHz to 48 kHz nominal, 64 channels; 64 kHz to 96 kHz ± 12.5%, 28 channels. With a 48 kHz sampling frequency, 64 channels take 64 × 32 × 48000 = 98.304 Mbit/s. Adding the minimum 8 × 58 kbit/s of framing produces 98688 bit/s, leaving 1.312% free for timing variation and other overhead. Both versions of the standard accommodate higher sampling frequencies (for example, 96 kHz or 192 kHz) by using two or more channels per audio sample on the link.
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Ajax (programming)

The Asynchronous JavaScript and XML, usually referred to as Ajax (or AJAX, ) is a set of web development techniques that uses various web technologies on the client-side to create asynchronous web applications. With Ajax, web applications can send and retrieve data from a server asynchronously (in the background) without interfering with the display and behaviour of the existing page. By decoupling the data interchange layer from the presentation layer, Ajax allows web pages and, by extension, web applications, to change content dynamically without the need to reload the entire page. In practice, modern implementations commonly utilize JSON instead of XML. Ajax is not a technology, but rather a programming pattern. HTML and CSS can be used in combination to mark up and style information. The webpage can be modified by JavaScript to dynamically display (and allow the user to interact with) the new information. The built-in XMLHttpRequest object is used to execute Ajax on webpages, allowing websites to load content onto the screen without refreshing the page. == History == In the early-to-mid 1990s, most Websites were based on complete HTML pages. Each user action required a complete new page to be loaded from the server. This process was inefficient, as reflected by the user experience: all page content disappeared, then the new page appeared. Each time the browser reloaded a page because of a partial change, all the content had to be re-sent, even though only some of the information had changed. This placed additional load on the server and made bandwidth a limiting factor in performance. The foundations of AJAX originate back in 1996 with the introduction of JavaScript 1. Developers quickly discovered that any HTML element which accepted a "src" attribute could be used to fetch remote data. By changing the src of a hidden frame, a developer could fetch remote data, process or display it without a page refresh. The remote data could be a string, JavaScript code, XML or a partial HTML page generated on the server. The same could be done with and tags, but many developers were alarmed at the concept of an executable GIF and preferred to use the hidden . This technique was used for both Netscape Navigator and Internet Explorer, the two main browsers at the time, until developers discovered the XMLHTTP server object (MSXML 1.0) was included in Internet Explorer 4. Browser detection was then used to either implement the hidden <iframe> in Netscape or use the XMLHTTP (MSXML 1.0) object in IE 4. In 1996, the iframe tag was introduced by Internet Explorer; like the object element, it can load a part of the web page asynchronously. In 1998, the Microsoft Outlook Web Access team developed the concept behind the XMLHttpRequest scripting object. It appeared as XMLHTTP in the second version of the MSXML library, which shipped with Internet Explorer 5.0 in March 1999. The functionality of the Windows XMLHTTP ActiveX control in IE 5 was later implemented by Mozilla Firefox, Safari, Opera, Google Chrome, and other browsers as the XMLHttpRequest JavaScript object. Microsoft adopted the native XMLHttpRequest model as of Internet Explorer 7. Support for the ActiveX version remained in Internet Explorer and on "Internet Explorer mode" in Microsoft Edge. The utility of these background HTTP requests and asynchronous Web technologies remained fairly obscure until it started appearing in large scale online applications such as Outlook Web Access (2000) and Oddpost (2002). Google made a wide deployment of standards-compliant, cross browser Ajax with Gmail (2004) and Google Maps (2005). In October 2004 Kayak.com's public beta release was among the first large-scale e-commerce uses of what their developers at that time called "the xml http thing". This increased interest in Ajax among web program developers. The term AJAX was publicly used on 18 February 2005 by Jesse James Garrett in an article titled Ajax: A New Approach to Web Applications, based on techniques used on Google pages. On 5 April 2006, the World Wide Web Consortium (W3C) released the first draft specification for the XMLHttpRequest object in an attempt to create an official Web standard. The latest draft of the XMLHttpRequest object was published on 6 October 2016, and the XMLHttpRequest specification is now a living standard. == Technologies == The term Ajax has come to represent a broad group of Web technologies that can be used to implement a Web application that communicates with a server in the background, without interfering with the current state of the page. In the article that coined the term Ajax, Jesse James Garrett explained that the following technologies are incorporated: HTML (or XHTML) and CSS for presentation The Document Object Model (DOM) for dynamic display of and interaction with data JSON or XML for the interchange of data, and XSLT for XML manipulation The XMLHttpRequest object for asynchronous communication JavaScript to bring these technologies together Since then, however, there have been a number of developments in the technologies used in an Ajax application, and in the definition of the term Ajax itself. XML is no longer required for data interchange and, therefore, XSLT is no longer required for the manipulation of data. JavaScript Object Notation (JSON) is often used as an alternative format for data interchange, although other formats such as preformatted HTML or plain text can also be used. A variety of popular JavaScript libraries, including jQuery, include abstractions to assist in executing Ajax requests. == Examples == === JavaScript example === An example of a simple Ajax request using the GET method, written in JavaScript. get-ajax-data.js: send-ajax-data.php: === Fetch example === Fetch is a native JavaScript API. According to Google Developers Documentation, "Fetch makes it easier to make web requests and handle responses than with the older XMLHttpRequest." ==== ES7 async/await example ==== Fetch relies on JavaScript promises. The fetch specification differs from Ajax in the following significant ways: The Promise returned from fetch() won't reject on HTTP error status even if the response is an HTTP 404 or 500. Instead, as soon as the server responds with headers, the Promise will resolve normally (with the ok property of the response set to false if the response isn't in the range 200–299), and it will only reject on network failure or if anything prevented the request from completing. fetch() won't send cross-origin cookies unless you set the credentials init option. (Since April 2018. The spec changed the default credentials policy to same-origin. Firefox changed since 61.0b13.) == Benefits == Ajax offers several benefits that can significantly enhance web application performance and user experience. By reducing server traffic and improving speed, Ajax plays a crucial role in modern web development. One key advantage of Ajax is its capacity to render web applications without requiring data retrieval, resulting in reduced server traffic. This optimization minimizes response times on both the server and client sides, eliminating the need for users to endure loading screens. Furthermore, Ajax facilitates asynchronous processing by simplifying the utilization of XmlHttpRequest, which enables efficient handling of requests for asynchronous data retrieval. Additionally, the dynamic loading of content enhances the application's performance significantly. Besides, Ajax enjoys broad support across all major web browsers, including Microsoft Internet Explorer versions 5 and above, Mozilla Firefox versions 1.0 and beyond, Opera versions 7.6 and above, and Apple Safari versions 1.2 and higher.</p> <a href="https://bbs.aizhi.co/news/323e999667.html" class="read-more" title="Ajax (programming)">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/455e999535.html" class="card-thumb-link" title="Open Rights Group"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/d/d7/Hierarchy_of_Evidence.png/960px-Hierarchy_of_Evidence.png" alt="Open Rights Group" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/455e999535.html" title="Open Rights Group">Open Rights Group</a></h2> <p class="article-excerpt">The Open Rights Group (ORG) is a UK-based organisation that works to preserve digital rights and freedoms by campaigning on digital rights issues and by fostering a community of grassroots activists. It campaigns on numerous issues including mass surveillance, internet filtering and censorship, and intellectual property rights. == History == The organisation was started by Danny O'Brien, Cory Doctorow, Ian Brown, Rufus Pollock, James Cronin, Stefan Magdalinski, Louise Ferguson and Suw Charman after a panel discussion at Open Tech 2005. O'Brien created a pledge on PledgeBank, placed on 23 July 2005, with a deadline of 25 December 2005: "I will create a standing order of 5 pounds per month to support an organisation that will campaign for digital rights in the UK but only if 1,000 other people will too." The pledge reached 1000 people on 29 November 2005. The Open Rights Group was launched at a "sell-out" meeting in Soho, London. == Work == The group has made submissions to the All Party Internet Group (APIG) inquiry into digital rights management and the Gowers Review of Intellectual Property. The group was honoured in the 2008 Privacy International Big Brother Awards alongside No2ID, Liberty, Genewatch UK and others, as a recognition of their efforts to keep state and corporate mass surveillance at bay. In 2010 the group worked with 38 Degrees to oppose the introduction of the Digital Economy Act, which was passed in April 2010. The group opposes measures in the draft Online Safety Bill introduced in 2021, that it sees as infringing free speech rights and online anonymity. The group campaigns against the Department for Digital, Culture, Media and Sport's plan to switch to an opt-out model for cookies. The group spokesperson stated that "[t]he UK government propose to make online spying the default option" in response to the proposed switch. == Areas of interest == The organisation, though focused on the impact of digital technology on the liberty of UK citizens, operates with an apparently wide range of interests within that category. Its interests include: === Access to knowledge === Copyright Creative Commons Free and open source software The public domain Crown copyright Digital Restrictions Management Software patents === Free speech and censorship === Internet filtering Right to parody s. 127 Communications Act 2003 === Government and democracy === Electronic voting Freedom of information legislation === Privacy, surveillance and censorship === Automatic Vehicle Tracking Communications data retention Identity management Net Neutrality NHS patients' medical database Police DNA Records RFID == Structure == ORG has a paid staff, whose members include: Jim Killock (executive director) Former staff include Suw Charman-Anderson and Becky Hogge, both executive directors, e-voting coordinator Jason Kitcat, campaigner Peter Bradwell, grassroots campaigner Katie Sutton and administrator Katerina Maniadaki. Neil Gaiman was previously the group's patron. As of October 2022, the group had over 43,000 supporters. == ORGCON == ORGCON was the first ever conference dedicated to digital rights in the UK, marketed as "a crash course in digital rights". It was held for the first time in 2010 at City University in London and included keynote talks from Cory Doctorow, politicians and similar pressure groups including Liberty, NO2ID and Big Brother Watch. ORGCON has since been held in 2012, 2013, 2014, 2017, and 2019 where the keynote was given by Edward Snowden.</p> <a href="https://bbs.aizhi.co/news/455e999535.html" class="read-more" title="Open Rights Group">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/243e499752.html" class="card-thumb-link" title="Meta-Labeling"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/c/c1/Luciano_Floridi_a_Milano.jpg/960px-Luciano_Floridi_a_Milano.jpg" alt="Meta-Labeling" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/243e499752.html" title="Meta-Labeling">Meta-Labeling</a></h2> <p class="article-excerpt">Meta-labeling, also known as corrective AI, is a machine learning (ML) technique utilized in quantitative finance to enhance the performance of investment and trading strategies, developed in 2017 by Marcos López de Prado at Guggenheim Partners and Cornell University. The core idea is to separate the decision of trade direction (side) from the decision of trade sizing, addressing the inefficiencies of simultaneously learning both side and size predictions. The side decision involves forecasting market movements (long, short, neutral), while the size decision focuses on risk management and profitability. It serves as a secondary decision-making layer that evaluates the signals generated by a primary predictive model. By assessing the confidence and likely profitability of those signals, meta-labeling allows investors and algorithms to dynamically size positions and suppress false positives. == Motivation == Meta-labeling is designed to improve precision without sacrificing recall. As noted by López de Prado, attempting to model both the direction and the magnitude of a trade using a single algorithm can result in poor generalization. By separating these tasks, meta-labeling enables greater flexibility and robustness: Enhances control over capital allocation. Reduces overfitting by limiting model complexity. Allows the use of interpretability tools and tailored thresholds to manage risk. Enables dynamic trade suppression in unfavorable regimes. == Applications == Meta-labeling has been applied in a variety of financial ML contexts, including: Algorithmic trading: Filtering and sizing trades to reduce false positives. Portfolio optimization: Scaling exposure across multiple signals with differing confidence levels. Risk management: Dynamically disabling strategies in adverse market conditions. Model validation: Interpreting when and why a model may be underperforming due to regime shifts. == General architecture == Meta-labeling decouples two core components of systematic trading strategies: directional prediction and position sizing. The process involves training a primary model to generate trade signals (e.g., buy, sell, or hold) and then training a secondary model to determine whether each signal is likely to lead to a profitable trade. The second model outputs a probability that is interpreted as the confidence in the forecast, which can be used to adjust the position size or to filter out unreliable trades. Meta-labeling is typically implemented as a three-stage process: Primary model (M1): Predicts the direction or label of a financial outcome using features such as market prices, returns, or volatility indicators. A typical output is directional, e.g., Y ∈ {−1,0,1}, representing short, neutral, or long positions. Secondary model (M2): A binary classifier trained to predict whether the primary model's prediction will be profitable. The target variable is a binary meta-label F ∈ { 0 , 1 } {\displaystyle F\in \{0,1\}} . Inputs can include features used in the primary model, performance diagnostics, or market regime data. Position sizing algorithm (M3): Translates the output probability of the secondary model into a position size. Higher confidence scores result in larger allocations, while lower confidence leads to reduced or zero exposure. === Stage 1: Forecasting side === Primary model architecture Figure 1 Figure 1 presents the architecture of a primary model. It focuses on forecasting the side of the trade. Following the example, this model (M1) takes in input data – such as open-high-low-close data and determines the side of the position to take: a negative number is a short position, and positive number is a long position, the range is set between −1 and 1 (the closer it is to −1 or 1, the stronger the models conviction is). When training the model, the labels are −1 and 1, based on the direction of forward returns for some predefined investment horizon. The researcher may decide to apply a recall check (τ: "Tau") by setting a minimum threshold that the initial output needs to be to qualify of a short or long position (if the threshold is not met, no side forecast is predicted, leading to closing of any open positions), this leads to the primary model output which is one of three possible side forecasts: −1, 0, or 1. The primary model also generates evaluation data which can be used by the secondary model, to improve performance of size forecasts. Some examples of evaluation data include rolling accuracy, F1, recall, precision, and AUC scores. === Stage 2: Filtering out false positives === General meta-labeling architecture Figure 2 Next comes the phase of filtering out false positives, by applying a secondary machine learning model (M2), which is a binary classifier trained to determine if the trade will be profitable or not. The model takes as input four general groupings of data: General input data which is predictive of a false positive. For example the last 30 days rolling volatility of the underlying asset. Evaluation data. Market state and regime data, one may find that macro economic data or clustering the market into regimes may help as specific trading strategies are known to perform better in particular regimes. Example: momentum based strategies perform best in periods with low volatility and strong directional moves. Primary models initial input which is a value between −1 and 1. This highlights the strength of the primary models conviction. The output of the model is a value between −1 and 1 (if using a Tanh function) which will indicate the strength of the conviction that a short or long position is profitable, or it could simply be between 0 and 1 (using a sigmoid function) if one only wanted to know if it made money or not. This output allows filtering out trades that are likely to lead to losses. One could stop at this point or use the outputs of the secondary model as inputs to a position sizing algorithm (M3) which could further enhance strategy performance metrics by translating the output probability of the secondary model into a position size. Higher confidence scores result in larger allocations, while lower confidence leads to reduced or zero exposure. === Stage 3: Optimizing position sizes === ==== Position sizing methods (M3) ==== Various algorithms have been proposed for transforming predicted probabilities into trade sizes: All-or-nothing: Allocate 100% of capital if the probability exceeds a predefined threshold (e.g., 0.5); otherwise, do not trade. Model confidence: Use the probability score directly as the fraction of capital allocated. Linear scaling: Rescale the model's probabilities using min-max normalization based on the training data. Normal CDF (NCDF): Use a normal cumulative distribution function applied to a z-statistic derived from the predicted probability. Empirical CDF (ECDF): Rank probabilities based on their percentile in the training data to ensure relative allocation. Sigmoid Optimal Position Sizing (SOPS): Applies a smooth non-linear sigmoid transformation optimized to maximize risk-adjusted returns (Sharpe ratio). ==== Model calibration ==== Each machine learning algorithm used in meta-labeling tends to produce outputs with different characteristic distributions; for example, some are approximately normally distributed, whereas others exhibit a pronounced U-shape, concentrating probabilities near the extremes. Due to these varying distributions, simply summing the outputs of different models can inadvertently lead to uneven weighting of signals, biasing trade decisions. To address this, model calibration techniques are essential to adjust the predicted probabilities towards frequentist probabilities, ensuring that model outputs reflect true likelihoods more accurately. Two common calibration techniques are: Platt scaling (Sigmoid scaling): Suitable for correcting S-shaped calibration plots typically produced by models such as support vector machines (SVMs). Isotonic regression: Fits a non-decreasing step function to probabilities and is effective particularly with larger datasets, though it can sometimes lead to overfitting. Transforming predictions to frequentist probabilities is crucial as it provides probabilistic outputs that are directly interpretable as the actual likelihood of an event occurring. Such calibration significantly enhances the effectiveness of fixed position sizing methods, reducing maximum drawdowns and increasing risk-adjusted returns. However, calibration has less impact on position sizing methods that directly estimate parameters from the training data, such as ECDF and SOPS, suggesting that calibration is a critical step mainly for fixed methods that rely heavily on raw model outputs. =</p> <a href="https://bbs.aizhi.co/news/243e499752.html" class="read-more" title="Meta-Labeling">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/306d999684.html" class="card-thumb-link" title="JQuery"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/9/99/Kounta_Standard_Logo.png" alt="JQuery" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/306d999684.html" title="JQuery">JQuery</a></h2> <p class="article-excerpt">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</p> <a href="https://bbs.aizhi.co/news/306d999684.html" class="read-more" title="JQuery">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/99b999891.html" class="card-thumb-link" title="Electric Literature"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/5/55/Turing_test_diagram.png" alt="Electric Literature" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/99b999891.html" title="Electric Literature">Electric Literature</a></h2> <p class="article-excerpt">Electric Literature is an American literary magazine. == History == Founded by Andy Hunter and Scott Lindenbaum in 2009 as a print quarterly journal, Electric Literature transitioned to a daily website in 2012 under the helm of Halimah Marcus and Benjamin Samuel. Electric Literature publishes essays, reading lists, interviews, fiction, poetry, graphic narratives, humor, and book news, all available to read online for free without a paywall. It launched the first fiction magazine on the iPhone and iPad. Work published has been recognized by Best American Short Stories, Essays, Poetry, and Comics, the Pushcart Prize, Best Canadian Short Stories, The Best of the Small Presses, and the O. Henry Prize. in 2014, Electric Literature became a registered non-profit. In 2016, Halimah Marcus was appointed the first executive director of Electric Literature. She has been with the magazine since 2010. In 2021, Denne Michele Norris became editor-in-chief of Electric Literature, the first Black and openly trans editor-in-chief of a major U.S. literary publication. In 2022, Electric Literature was the Digital Prize Winner of the Whiting Literary Magazine Prizes. In 2023, Electric Literature partnered with Banned Books USA to offer free banned and challenged books to residents of Florida. In 2025, Electric Literature published their first book, edited by Norris and published by HarperOne: Both/And: Essays by Trans and Gender-Nonconforming Writers of Color. It builds on a prior essay series that Electric Literature sponsored for trans writers of color. Both/And became a finalist for the 2026 Lambda Literary Award for Transgender Nonfiction. == Recommended reading == In May 2012, Electric Literature launched Recommended Reading, a weekly fiction magazine. Each issue is curated by a well known editor or writer. == The Commuter == The Commuter, a weekly magazine for poetry, flash, graphic, or experimental narrative, debuted in January 2018, helmed by writer Kelly Luce.</p> <a href="https://bbs.aizhi.co/news/99b999891.html" class="read-more" title="Electric Literature">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/295d999695.html" class="card-thumb-link" title="Fediverse"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/b/b6/Mehryar_Mohri%2C_Scholar_Photo.jpg" alt="Fediverse" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/295d999695.html" title="Fediverse">Fediverse</a></h2> <p class="article-excerpt">The Fediverse (commonly shortened to fedi) is a collection of social networking services that can communicate with each other (formally known as federation) using a common protocol. Users of different websites can send and receive status updates, multimedia files and other data across the network. The term Fediverse is a portmanteau of federation and universe. The majority of Fediverse platforms are based on free and open-source software, and create connections between servers using the ActivityPub protocol. Some software still supports older federation protocols as well, such as OStatus, the Diaspora protocol and Zot, while newer protocols such as AT Protocol connect via network bridges. Diaspora is the only actively developed software project classified under the original definition of Fediverse that does not support ActivityPub. == Design == While a traditional social networking service will host all its content on servers managed by the owner of the website, the decentralized structure of the Fediverse allows any individual or organization to host a social platform using their own servers (referred to as an "instance"). Every instance is independent, and can set its own rules and expectations. Even so, much like how users of one email service such as Gmail can still send emails to users of another service such as Outlook, users may still view content and interact with users on any other instance in the Fediverse. A user on one Mastodon instance, for example, may view and interact with posts made by a user on a different instance even if it is not running Mastodon. Instances hosted by different social networking services may also communicate with one another. A user on the microblogging platform Misskey, for example, may view and interact with posts made by users on Mastodon. Some Fediverse networks even allow users to interact with different social networking formats from the same platform. For example, a user on a social news instance running Lemmy can interact with another post from an mbin instance, a similar service, as well as microblog statuses from Mastodon. === Content moderation and user safety === Decentralized social networking platforms introduce new challenges and difficulties for user trust and safety. By nature of the Fediverse, operators of an instance are solely responsible for moderation of its content. As there is no form of centralized governance or moderation across the Fediverse, it is impossible for an instance to be "removed" from the Fediverse; it can only be defederated per an instance operator's choice, which makes that instance's content inaccessible from the operator's instance. Individual instances are responsible for defining their own content policies, which may then be enforced by its staff. Moderation of a Fediverse instance differs significantly from that of traditional social media platforms, as moderators are responsible not only for content posted by users of that instance ("local users"), but also for content posted by users of other instances ("remote users"). == History == === Historical protocols === The concept and the functionality of the Fediverse existed before the ActivityPub protocol and the term itself. One of the first projects that included support for a decentralized social networking service was Laconica, a microblogging platform which implemented the OpenMicroBlogging protocol for communicating between different installations of the software. The software was later renamed to StatusNet in 2009, before being merged into the GNU social project in 2013 along with Free Social, with the two latter servers being a fork of StatusNet. Over time, the limitations of the OpenMicroBlogging protocol became more apparent, being designed as a one-way text messaging system. To replace the ageing protocol, OStatus was devised as an open standard for microblogging, combining various other technologies like Salmon, Atom, WebSub and ActivityStreams into a single protocol used for communicating between instances. StatusNet first implemented the OStatus protocol on March 3, 2010, with version 0.9.0, and OStatus quickly became the most popular federated protocol in usage. Around the same time as OStatus was gaining popularity, the Diaspora social network was formed, using its own federated protocol. To illustrate the differences between the two protocols, the terms of the Fediverse and the federation began to enter common usage, mainly after 2017. The term "the Fediverse" was used to describe the network formed by software using the OStatus protocol, such as GNU Social, Mastodon, and Friendica, in contrast to the competing diaspora protocol under "the federation". === ActivityPub === In December 2012, the flagship StatusNet instance at the time, identi.ca, transitioned away to a new software named pump.io, with a new federation protocol to replace OStatus. The new protocol was designed to be useful for general activity streams and not just status updates, and replaced many of OStatus' external dependencies with JSON-LD and a REST API for its messaging and inbox systems, as well as making more use of ActivityStreams. While not as utilized as its OStatus predecessor, it would later become influential in the development of the ActivityPub standard. In January 2018, the W3C presented the ActivityPub protocol as a recommended standard. The standard aimed to improve the interoperability between different software packages running on a wide network of servers and to supersede both the OStatus protocol and Pump.io. By 2019, almost all software that was using OStatus had added support for ActivityPub. While Mastodon began to remove OStatus support, other projects maintained it in their code, such as Friendica (which also maintained diaspora support along with ActivityPub). === AT Protocol === A major protocol often contrasted with ActivityPub is the AT Protocol, which powers the Bluesky social network. While both protocols aim to create decentralized social networks, they employ different technical philosophies regarding user identity. Developers of the AT Protocol, including Bluesky CEO Jay Graber, have stated they chose not to use ActivityPub because it did not natively support easy "account portability", the ability for a user to move their account, data, and social graph to a new provider without relying on the original server to authorize the move. In the ActivityPub model (used by Mastodon), a user's identity is typically tied to a specific server, similar to an email address; if that server goes offline, the identity can be lost. The AT Protocol aims to solve this by separating identity from hosting, allowing users to switch providers without losing their identity. Although the two protocols are technically incompatible by default, third-party "bridges" such as Bridgy Fed have been developed to allow users on ActivityPub networks to follow and interact with users on the AT Protocol network, and vice versa. === Other Fediverse protocols === While the Fediverse has traditionally been the network most commonly referred to and used as an example regarding the subject of decentralized social networks, alternatives to it and the accompanying ActivityPub have been developed and deployed. Smaller competitors such as Nostr and Farcaster have become popular within the cryptocurrency community. These protocols have used ActivityPub as a frame of reference for which to design their own architecture, as these newer protocols use a different federation model based on publishing content to relays for distribution rather than ActivityPub's server-centric model. Despite their differences, software exists that permit the bridging of user content between these protocols, including "double-bridges" that span multiple protocols for the purpose of distributing the same content. == Adoption == Users have been slow to embrace the Fediverse due to poor user experience and excessive complexity. Following the acquisition of Twitter by Elon Musk in November 2022, certain major social networks, including Threads, Tumblr and Flipboard, expressed interest in supporting the ActivityPub protocol, as a large number of users began to migrate to Mastodon, a server that supports the Fediverse and was also the most popular alternative to Twitter at the time. Flickr also expressed support in supporting ActivityPub. As of November 2022, no information had been released by Flickr after the initial tweets by the CEO, with support for ActivityPub suspected to be on hold or cancelled. In 2024, the local government of the Stary Sącz municipality in Poland launched their own PeerTube instance in order to de facto abolish its presence on YouTube. According to the government, they stopped using YouTube for official communications "in order to adhere to the appropriate regulations". In the same year, VIVERSE, HTC Vive's metaverse platform, implemented support for ActivityPub in their chat feature, allowing users to send direct messages to other</p> <a href="https://bbs.aizhi.co/news/295d999695.html" class="read-more" title="Fediverse">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/413e499582.html" class="card-thumb-link" title="POP-11"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/2/22/Supertorus_table.png" alt="POP-11" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/413e499582.html" title="POP-11">POP-11</a></h2> <p class="article-excerpt">POP-11 is a reflective, incrementally compiled programming language with many of the features of an interpreted language. It is the core language of the Poplog programming environment developed originally by the University of Sussex, and recently in the School of Computer Science at the University of Birmingham, which hosts the main Poplog website. POP-11 is an evolution of the language POP-2, developed in Edinburgh University, and features an open stack model (like Forth, among others). It is mainly procedural, but supports declarative language constructs, including a pattern matcher, and is mostly used for research and teaching in artificial intelligence, although it has features sufficient for many other classes of problems. It is often used to introduce symbolic programming techniques to programmers of more conventional languages like Pascal, who find POP syntax more familiar than that of Lisp. One of POP-11's features is that it supports first-class functions. POP-11 is the core language of the Poplog system. The availability of the compiler and compiler subroutines at run-time (a requirement for incremental compiling) gives it the ability to support a far wider range of extensions (including run-time extensions, such as adding new data-types) than would be possible using only a macro facility. This made it possible for (optional) incremental compilers to be added for Prolog, Common Lisp and Standard ML, which could be added as required to support either mixed language development or development in the second language without using any POP-11 constructs. This made it possible for Poplog to be used by teachers, researchers, and developers who were interested in only one of the languages. The most successful product developed in POP-11 was the Clementine data mining system, developed by ISL. After SPSS bought ISL, they renamed Clementine to SPSS Modeler and decided to port it to C++ and Java, and eventually succeeded with great effort, and perhaps some loss of the flexibility provided by the use of an AI language. POP-11 was for a time available only as part of an expensive commercial package (Poplog), but since about 1999 it has been freely available as part of the open-source software version of Poplog, including various added packages and teaching libraries. An online version of ELIZA using POP-11 is available at Birmingham. At the University of Sussex, David Young used POP-11 in combination with C and Fortran to develop a suite of teaching and interactive development tools for image processing and vision, and has made them available in the Popvision extension to Poplog. == Simple code examples == Here is an example of a simple POP-11 program: define Double(Source) -> Result; Source2 -> Result; enddefine; Double(123) => That prints out: 246 This one includes some list processing: define RemoveElementsMatching(Element, Source) -> Result; lvars Index; [[% for Index in Source do unless Index = Element or Index matches Element then Index; endunless; endfor; %]] -> Result; enddefine; RemoveElementsMatching("the", [[the cat sat on the mat]]) => ;;; outputs [[cat sat on mat]] RemoveElementsMatching("the", [[the cat] [sat on] the mat]) => ;;; outputs [[the cat] [sat on] mat] RemoveElementsMatching([[= cat]], [[the cat]] is a [[big cat]]) => ;;; outputs [[is a]] Examples using the POP-11 pattern matcher, which makes it relatively easy for students to learn to develop sophisticated list-processing programs without having to treat patterns as tree structures accessed by 'head' and 'tail' functions (CAR and CDR in Lisp), can be found in the online introductory tutorial. The matcher is at the heart of the SimAgent (sim_agent) toolkit. Some of the powerful features of the toolkit, such as linking pattern variables to inline code variables, would have been very difficult to implement without the incremental compiler facilities.</p> <a href="https://bbs.aizhi.co/news/413e499582.html" class="read-more" title="POP-11">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/64f999926.html" class="card-thumb-link" title="Mediated intercultural communication"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/b/b7/Gantry2.jpg" alt="Mediated intercultural communication" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/64f999926.html" title="Mediated intercultural communication">Mediated intercultural communication</a></h2> <p class="article-excerpt">Mediated intercultural communication is digital communication between people of different cultural backgrounds. Media include social networks, blogs and conferencing services. Digital communication is distinct from traditional media, creating new avenues for intercultural communication. User take online classes; post, consume and comment on others content; and play multi-player video games. This creates spaces to form virtual communities that can ease communication across boundaries of space, time and culture. New media technologies can change culture in positive ways or become a tool of repression. == History == Intercultural communication is as ancient as human movement in search of food sources. The systematic study of intercultural communication began with Edward Hall's labor at the Foreign Service Institute, and the publication of his The Silent Language (1959). Later research, primarily focused on face-to-face communication in various areas such as interpersonal, group, and organizational and cultural identity. International and development media have been studied under the umbrella of international communication. Media imperialism, cultural imperialism and dependency theories inform this research. Mediated intercultural communication examines the bidirectional relationships between media and intercultural communication.</p> <a href="https://bbs.aizhi.co/news/64f999926.html" class="read-more" title="Mediated intercultural communication">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/412a999578.html" class="card-thumb-link" title="Nuclear electronics"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/1/16/Umbrello_1.png" alt="Nuclear electronics" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/412a999578.html" title="Nuclear electronics">Nuclear electronics</a></h2> <p class="article-excerpt">Nuclear electronics is a subfield of electronics concerned with the design and use of high-speed electronic systems for nuclear physics and elementary particle physics research, and for industrial and medical use. Essential elements of such systems include fast detectors for charged particles, discriminators for separating them by energy, counters for counting the pulses produced by individual particles, fast logic circuits (including coincidence and veto gates), for identification of particular types of complex particle events, and pulse height analyzers (PHAs) for sorting and counting gamma rays or particle interactions by energy, for spectral analysis. == Elementary components == Some of the essential components that make up the elements of a nuclear electronic analysis system include: Detectors Bias voltage supplies Preamplifiers Discriminators Coincidence and veto logic gates Counters Pulse height analyzers These elements were originally developed and built in the laboratories of the scientists doing the pioneering work in the field, but are nowadays designed, developed, and manufactured by a variety of specialized vendors: EG&G Ortec Oxford Instruments Stanford Research Systems Tennelec CAEN</p> <a href="https://bbs.aizhi.co/news/412a999578.html" class="read-more" title="Nuclear electronics">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/82d999908.html" class="card-thumb-link" title="FutureMedia"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/42/Apache_Drill_logo.svg/960px-Apache_Drill_logo.svg.png" alt="FutureMedia" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/82d999908.html" title="FutureMedia">FutureMedia</a></h2> <p class="article-excerpt">FutureMedia is a program that analyzes the state and future of digital, social, and mobile media. It functions as a collaborative initiative at Georgia Tech and the Georgia Tech Research Institute. FutureMedia consults approximately 500 faculty members working in those fields. == History == In 2019, Future Media expanded into the Direct-To-Consumer market by acquiring Australian watchmaker Oak & Jackal. == Programs == === FutureMedia Fest === The organization most recently hosted FutureMedia Fest 2010, a four-day conference (Oct 4–7, 2010) with a keynote addresses from Michael Jones, the chief technology advocate at Google. The event featured panels, workshops, and technology demonstrations. === FutureMedia Outlook === Contemporaneous with FutureMedia Fest 2010, the organization released the FutureMedia Outlook, an analysis of the future of media, concentrating on six major trends in those fields, including information overload, personalization, data integrity, an expectation of multimedia, augmented reality, and collaborative software.</p> <a href="https://bbs.aizhi.co/news/82d999908.html" class="read-more" title="FutureMedia">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/62b399934.html" class="card-thumb-link" title="Logic form"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/5/59/GLM-Logo.png" alt="Logic form" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/62b399934.html" title="Logic form">Logic form</a></h2> <p class="article-excerpt">Logic forms are simple, first-order logic knowledge representations of natural language sentences formed by the conjunction of concept predicates related through shared arguments. Each noun, verb, adjective, adverb, pronoun, preposition and conjunction generates a predicate. Logic forms can be decorated with word senses to disambiguate the semantics of the word. There are two types of predicates: events are marked with e, and entities are marked with x. The shared arguments connect the subjects and objects of verbs and prepositions together. Example input/output might look like this: Input: The Earth provides the food we eat every day. Output: Earth:n_#1(x1) provide:v_#2(e1, x1, x2) food:n_#1(x2) we(x3) eat:v_#1(e2, x3, x2; x4) day:n_#1(x4) Logic forms are used in some natural language processing techniques, such as question answering, as well as in inference both for database systems and QA systems.</p> <a href="https://bbs.aizhi.co/news/62b399934.html" class="read-more" title="Logic form">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/307b999683.html" class="card-thumb-link" title="HTTP cookie"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/en/f/ff/AIVA_logo.jpg" alt="HTTP cookie" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/307b999683.html" title="HTTP cookie">HTTP cookie</a></h2> <p class="article-excerpt">An HTTP cookie (also called web cookie, Internet cookie, browser cookie, or simply cookie) is a small block of data created by a web server while a user is browsing a website and placed on the user's computer or other device by the user's web browser. Cookies are placed on the device used to access a website, and more than one cookie may be placed on a user's device during a session. Cookies serve useful and sometimes essential functions on the web. They enable web servers to store stateful information (such as items added in the shopping cart in an online store) on the user's device or to track the user's browsing activity (including clicking particular buttons, logging in, or recording which pages were visited in the past). They can also be used to save information that the user previously entered into form fields, such as names, addresses, passwords, and payment card numbers for subsequent use. Authentication cookies are commonly used by web servers to authenticate that a user is logged in, and with which account they are logged in. Without the cookie, users would need to authenticate themselves by logging in on each page containing sensitive information that they wish to access. The security of an authentication cookie generally depends on the security of the issuing website and the user's web browser, and on whether the cookie data is encrypted. Security vulnerabilities may allow a cookie's data to be read by an attacker, used to gain access to user data, or used to gain access (with the user's credentials) to the website to which the cookie belongs (see cross-site scripting and cross-site request forgery for examples). Tracking cookies, and especially third-party tracking cookies, are commonly used as ways to compile long-term records of individuals' browsing histories — a potential privacy concern that prompted European and U.S. lawmakers to take action in 2011. European law requires that all websites targeting European Union member states gain "informed consent" from users before storing non-essential cookies on their device. == Background == === Origin of the name === The term cookie was coined by web-browser programmer Lou Montulli. It was derived from the term magic cookie, which is a packet of data a program receives and sends back unchanged, used by Unix programmers. === History === Magic cookies were already used in computing when computer programmer Lou Montulli had the idea of using them in web communications in June 1994. At the time, he was an employee of Netscape Communications, which was developing an e-commerce application for MCI. Vint Cerf and John Klensin represented MCI in technical discussions with Netscape Communications. MCI did not want its servers to have to retain partial transaction states, which led them to ask Netscape to find a way to store that state in each user's computer instead. Cookies provided a solution to the problem of reliably implementing a virtual shopping cart. Together with John Giannandrea, Montulli wrote the initial Netscape cookie specification the same year. Version 0.9beta of Mosaic Netscape, released on 13 October 1994, supported cookies. The first use of cookies (out of the labs) was checking whether visitors to the Netscape website had already visited the site. Montulli applied for a patent for the cookie technology in 1995, which was granted in 1998. Support for cookies was integrated with Internet Explorer in version 2, released in October 1995. The introduction of cookies was not widely known to the public at the time. In particular, cookies were accepted by default, and users were not notified of their presence. The public learned about cookies after the Financial Times published an article about them on 12 February 1996. In the same year, cookies received a lot of media attention, especially because of potential privacy implications. Cookies were discussed in two U.S. Federal Trade Commission hearings in 1996 and 1997. The development of the formal cookie specifications was already ongoing. In particular, the first discussions about a formal specification started in April 1995 on the www-talk mailing list. A special working group within the Internet Engineering Task Force (IETF) was formed. Two alternative proposals for introducing state in HTTP transactions had been proposed by Brian Behlendorf and David Kristol respectively. But the group, headed by Kristol himself and Lou Montulli, soon decided to use the Netscape specification as a starting point. In February 1996, the working group identified third-party cookies as a considerable privacy threat. The specification produced by the group was eventually published as RFC 2109 in February 1997. It specifies that third-party cookies were either not allowed at all, or at least not enabled by default. At this time, advertising companies were already using third-party cookies. The recommendation about third-party cookies of RFC 2109 was not followed by Netscape and Internet Explorer. RFC 2109 was superseded by RFC 2965 in October 2000. RFC 2965 added a Set-Cookie2 header field, which informally came to be called "RFC 2965-style cookies" as opposed to the original Set-Cookie header field which was called "Netscape-style cookies". Set-Cookie2 was seldom used, however, and was deprecated in RFC 6265 in April 2011 which was written as a definitive specification for cookies as used in the real world. No modern browser recognizes the Set-Cookie2 header field. == Terminology == === Session cookie === A session cookie (also known as an in-memory cookie, transient cookie or non-persistent cookie) exists only in temporary memory while the user navigates a website. Session cookies expire or are deleted when the user closes the web browser. Session cookies are identified by the browser by the absence of an expiration date assigned to them. === Persistent cookie === A persistent cookie expires at a specific date or after a specific length of time. For the persistent cookie's lifespan set by its creator, its information will be transmitted to the server every time the user visits the website that it belongs to, or every time the user views a resource belonging to that website from another website (such as an advertisement). For this reason, persistent cookies are sometimes referred to as tracking cookies because they can be used by advertisers to record information about a user's web browsing habits over an extended period of time. Persistent cookies are also used for reasons such as keeping users logged into their accounts on websites, to avoid re-entering login credentials at every visit. (See § Uses, below.) === Secure cookie === A secure cookie can only be transmitted over an encrypted connection (i.e. HTTPS). They cannot be transmitted over unencrypted connections (i.e. HTTP). This makes the cookie less likely to be exposed to cookie theft via eavesdropping. A cookie is made secure by adding the Secure flag to the cookie. === Http-only cookie === An http-only cookie cannot be accessed by client-side APIs, such as JavaScript. This restriction eliminates the threat of cookie theft via cross-site scripting (XSS). However, the cookie remains vulnerable to cross-site tracing (XST) and cross-site request forgery (CSRF) attacks. A cookie is given this characteristic by adding the HttpOnly flag to the cookie. === Same-site cookie === In 2016 Google Chrome version 51 introduced a new kind of cookie with attribute SameSite with possible values of Strict, Lax or None. With attribute SameSite=Strict, the browsers would only send cookies to a target domain that is the same as the origin domain. This would effectively mitigate cross-site request forgery (CSRF) attacks. With SameSite=Lax, browsers would send cookies with requests to a target domain even it is different from the origin domain, but only for safe requests such as GET (POST is unsafe) and not third-party cookies (inside iframe). Attribute SameSite=None would allow third-party (cross-site) cookies, however, most browsers require secure attribute on SameSite=None cookies. The Same-site cookie is incorporated into a new RFC draft for "Cookies: HTTP State Management Mechanism" to update RFC 6265 (if approved). Chrome, Firefox, and Edge started to support Same-site cookies. The key of rollout is the treatment of existing cookies without the SameSite attribute defined, Chrome has been treating those existing cookies as if SameSite=None, this would let all website/applications run as before. Google intended to change that default to SameSite=Lax in Chrome 80 planned to be released in February 2020, but due to potential for breakage of those applications/websites that rely on third-party/cross-site cookies and COVID-19 circumstances, Google postponed this change to Chrome 84. === Supercookie === A supercookie is a cookie with an origin of a top-level domain (such as .com) or a public suffix (such as .co.uk). Ordinary cookies, by contrast, have an origin of a specific domain name, such as ex</p> <a href="https://bbs.aizhi.co/news/307b999683.html" class="read-more" title="HTTP cookie">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/25a999965.html" class="card-thumb-link" title="Digital anthropology"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/0/0e/Terragen_render.jpg/960px-Terragen_render.jpg" alt="Digital anthropology" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/25a999965.html" title="Digital anthropology">Digital anthropology</a></h2> <p class="article-excerpt">Digital anthropology is the anthropological study of the relationship between humans and digital-era technology. The field is new, and thus has a variety of names with a variety of emphases. These include techno-anthropology, digital ethnography, cyberanthropology, and virtual anthropology. == Definition and scope == Most anthropologists who use the phrase "digital anthropology" are specifically referring to online and Internet technology. The study of humans' relationship to a broader range of technology may fall under other subfields of anthropological study, such as cyborg anthropology. The Digital Anthropology Group (DANG) is classified as an interest group in the American Anthropological Association. DANG's mission includes promoting the use of digital technology as a tool of anthropological research, encouraging anthropologists to share research using digital platforms, and outlining ways for anthropologists to study digital communities. Cyberspace or the "virtual world" itself can serve as a "field" site for anthropologists, allowing the observation, analysis, and interpretation of the sociocultural phenomena springing up and taking place in any interactive space. National and transnational communities, enabled by digital technology, establish a set of social norms, practices, traditions, storied history and associated collective memory, migration periods, internal and external conflicts, potentially subconscious language features and memetic dialects comparable to those of traditional, geographically confined communities. This includes the various communities built around free and open-source software, online platforms such as Facebook, Twitter/X, Instagram, 4chan and Reddit and their respective sub-sites, and politically motivated groups like Anonymous, WikiLeaks, or the Occupy movement. A number of academic anthropologists have conducted traditional ethnographies of virtual worlds, such as Bonnie Nardi's study of World of Warcraft or Tom Boellstorff's study of Second Life. Academic Gabriella Coleman has done ethnographic work on the Debian software community and the Anonymous hacktivist network. Theorist Nancy Mauro-Flude conducts ethnographic field work on computing arts and computer subcultures such as systerserver.net a part of the communities of feminist web servers and the Feminist Internet network. Eitan Y. Wilf examines the intersection of artists' creativity and digital technology and artificial intelligence. Yongming Zhou studied how in China the internet is used to participate in politics. Eve M. Zucker and colleagues study the shift to digital memorialization of mass atrocities and the emergent role of artificial intelligence in these processes. Victoria Bernal conducted ethnographic research on the themes of nationalism and citizenship among Eritreans participating in online political engagement with their homeland. Anthropological research can help designers adapt and improve technology. Australian anthropologist Genevieve Bell did extensive user experience research at Intel that informed the company's approach to its technology, users, and market. == Methodology == === Digital fieldwork === Many digital anthropologists who study online communities use traditional methods of anthropological research. They participate in online communities in order to learn about their customs and worldviews, and back their observations with private interviews, historical research, and quantitative data. Their product is an ethnography, a qualitative description of their experience and analyses. Other anthropologists and social scientists have conducted research that emphasizes data gathered by websites and servers. However, academics often have trouble accessing user data on the same scale as social media corporations like Facebook and data mining companies like Acxiom. In terms of method, there is a disagreement in whether it is possible to conduct research exclusively online or if research will only be complete when the subjects are studied holistically, both online and offline. Tom Boellstorff, who conducted a three-year research as an avatar in the virtual world Second Life, defends the first approach, stating that it is not just possible, but necessary to engage with subjects “in their own terms”. Others, such as Daniel Miller, have argued that an ethnographic research should not exclude learning about the subject's life outside the internet. === Digital technology as a tool of anthropology === The American Anthropological Association offers an online guide for students using digital technology to store and share data. Data can be uploaded to digital databases to be stored, shared, and interpreted. Text and numerical analysis software can help produce metadata, while a codebook may help organize data. == Ethics == Online fieldwork offers new ethical challenges. According to the American Anthropological Association's ethics guidelines, anthropologists researching a community must make sure that all members of that community know they are being studied and have access to data the anthropologist produces. However, many online communities' interactions are publicly available for anyone to read, and may be preserved online for years. Digital anthropologists debate the extent to which lurking in online communities and sifting through public archives is ethical. The Association also asserts that anthropologists' ability to collect and store data at all is "a privilege", and researchers have an ethical duty to store digital data responsibly. This means protecting the identity of participants, sharing data with other anthropologists, and making backup copies of all data. == Prominent figures == Genevieve Bell is an Australian cultural anthropologist credited for pioneering the User Experience field. During her time working for Intel Corporation, Bell studied how various cultures from around the world interacted with and experienced technology. Researching and improving user experience allows companies and designers to gather data regarding how users utilize their digital products and what requires improvement or expansion. Tom Boellstorff is an anthropologist known for Coming of Age in Second Life: An Anthropologist Explores the Virtually Human where he conducted research on how engaging in virtual worlds affects the player’s sense of self. Gabriella Coleman is an American anthropologist concerned with the politics, ethics, and culture of hacking and online activism. Coleman’s most notable ethnography features the hacktivist collective Anonymous, where she argues that various genres of hacking exist according to the social conditions at play. Coleman is dedicated to making her ethnography accessible to a diverse audience, including academics and non-academics. Diana E. Forsythe was an American anthropologist of science and technology and the author of the essays featured in Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence. She asked relevant questions such as how should humans interact with computers and how gender roles are maintained in technology-oriented occupations. Heather Horst is a sociocultural anthropologist interested in the relationship between digital social relations and material culture. Nancy Mauro-Flude is a design anthropologist whose work explores the tacit relations between embodied cognition, computational materiality, maker culture, self-hosted webserver cooperatives, creative practice, and artistic research in digital infrastructure and Internet publishing. Mizuko Ito is a Japanese cultural anthropologist specializing in technology use and the intersection between computers and the social sciences. Her primary interest is in how young people utilize media technology and how it can be used to engage students in education. Daniel Miller is an anthropologist with a concentration in digital anthropology. His research includes the smartphone and perpetual opportunism, the intent and consequences of posting on social media in various geographical locations, and how hospice patients use media to socialize in the last stage of their lives. Mike Wesch is a cultural anthropologist interested in how people share their lives, cultures, and beliefs through digital media.</p> <a href="https://bbs.aizhi.co/news/25a999965.html" class="read-more" title="Digital anthropology">Read more →</a> </div> </article> </li> </ul> <nav class="pagination" aria-label="Pagination"> <a href="https://bbs.aizhi.co/aiemailtranslator/1/" class="page-num">1</a><a href="https://bbs.aizhi.co/aiemailtranslator/2/" class="page-num">2</a><a href="https://bbs.aizhi.co/aiemailtranslator/3/" class="page-num">3</a><a href="https://bbs.aizhi.co/aiemailtranslator/4/" class="page-num">4</a><a href="https://bbs.aizhi.co/aiemailtranslator/5/" class="page-num">5</a><a href="https://bbs.aizhi.co/aiemailtranslator/6/" class="page-num">6</a><a href="https://bbs.aizhi.co/aiemailtranslator/7/" class="page-num">7</a><a href="https://bbs.aizhi.co/aiemailtranslator/8/" class="page-num">8</a><a href="https://bbs.aizhi.co/aiemailtranslator/9/" class="page-num">9</a><a href="https://bbs.aizhi.co/aiemailtranslator/10/" class="page-num">10</a> </nav> </main> <aside class="sidebar"> <section class="sidebar-section"> <h2>All Categories</h2> <ul> <li><a href="https://bbs.aizhi.co/aiwritingtools/">AI Writing Tools</a></li><li><a href="https://bbs.aizhi.co/aiimagegenerators/">AI Image Generators</a></li><li><a href="https://bbs.aizhi.co/aiforbusiness/">AI for Business</a></li><li><a href="https://bbs.aizhi.co/aichatbotsandassistants/">AI Chatbots and Assistants</a></li><li><a href="https://bbs.aizhi.co/aivideotools/">AI Video Tools</a></li><li><a href="https://bbs.aizhi.co/ainewsandguides/">AI News and Guides</a></li><li><a href="https://bbs.aizhi.co/aicodingtools/">AI Coding Tools</a></li> </ul> </section> <section class="sidebar-section"> <h2>Trending Guides</h2> <ul> <li><a href="https://bbs.aizhi.co/news/331a399665.html" title="Scene text">Scene text</a></li><li><a href="https://bbs.aizhi.co/news/468f999522.html" title="Scalable Video Coding">Scalable Video Coding</a></li><li><a href="https://bbs.aizhi.co/news/95c999895.html" title="DiscoVision">DiscoVision</a></li><li><a href="https://bbs.aizhi.co/news/386c999604.html" title="Svelte">Svelte</a></li><li><a href="https://bbs.aizhi.co/news/383a399613.html" title="WaveNet">WaveNet</a></li><li><a href="https://bbs.aizhi.co/news/354d999636.html" title="SPACEMAP">SPACEMAP</a></li><li><a href="https://bbs.aizhi.co/news/18b999972.html" title="Digital goods">Digital goods</a></li><li><a href="https://bbs.aizhi.co/news/96c999894.html" title="Europa-Lehrmittel">Europa-Lehrmittel</a></li><li><a href="https://bbs.aizhi.co/news/62d099937.html" title="UpScrolled">UpScrolled</a></li><li><a href="https://bbs.aizhi.co/news/228d999762.html" title="Bluelight (web forum)">Bluelight (web forum)</a></li> </ul> </section> </aside> </div> </div> </div> <footer class="site-footer"> <div class="container"> <div class="footer-cols"> <div class="footer-col footer-about"> <a class="brand" href="https://bbs.aizhi.co/" aria-label="Aizhi"> <span class="brand-mark" aria-hidden="true">✦</span> <span class="brand-text">Aizhi</span> </a> <p class="footer-tagline">Hand-picked AI tools, generators and practical how-to guides — independent reviews, updated for 2026.</p> </div> <nav class="footer-col" aria-label="Categories"> <h2 class="footer-h">Categories</h2> <ul> <li><a href="https://bbs.aizhi.co/aichatbotsandassistants/">AI Chatbots and Assistants</a></li><li><a href="https://bbs.aizhi.co/aivideotools/">AI Video Tools</a></li><li><a href="https://bbs.aizhi.co/aicodingtools/">AI Coding Tools</a></li><li><a href="https://bbs.aizhi.co/aiimagegenerators/">AI Image Generators</a></li><li><a href="https://bbs.aizhi.co/aiwritingtools/">AI Writing Tools</a></li><li><a href="https://bbs.aizhi.co/aiforbusiness/">AI for Business</a></li><li><a href="https://bbs.aizhi.co/ainewsandguides/">AI News and Guides</a></li> </ul> </nav> <nav class="footer-col" aria-label="Site"> <h2 class="footer-h">Site</h2> <ul> <li><a href="https://bbs.aizhi.co/">Home</a></li> <li><a href="/sitemap.xml">XML Sitemap</a></li> </ul> </nav> </div> <div class="partner-links" aria-label="Network"> </div> <p class="footer-copy"> © Aizhi. 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