AI Detector And Fixer

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Luxafor

Luxafor () is a brand of office productivity tools designed to improve efficiency and communication in workplaces. The brands main product is LED status indicators for use in office settings. Luxafor is a product line under the company SIA Greynut, based in Riga, Latvia. == History == Luxafor was developed by the technology company SIA Greynut. The brand first gained attention through a Kickstarter campaign in 2015, which aimed to fund its initial product, the Luxafor Flag. Although the campaign was unsuccessful in reaching its funding goal, the product was still brought to market. In 2017, Luxafor launched another Kickstarter campaign for the Luxafor Bluetooth, a wireless version of its LED status indicator. This campaign also did not meet its funding goal, but like its predecessor, the product was still developed and released. Despite initial setbacks, Luxafor Bluetooth has become one of the brand's leading products. == Products == Luxafors main product range is LED status indicators, including: === Luxafor Flag === A USB-powered LED indicator that shows different colors to signal the user's availability. === Luxafor Bluetooth === A wireless LED indicator controlled via Bluetooth, integrating with productivity tools like Slack and Microsoft Teams. === Luxafor Switch === An advanced status indicator designed to manage room and workspace availability. === Other === Other Luxafor products include CO2 Dongle, Smart Button, Mute Button, Pomodoro Timer and others. == Features == Luxafor products are known for their customizable indicators, integration capabilities with IFTTT, Zapier, and remote control features. They are compatible with various operating systems, including Windows and macOS, and can be integrated with numerous communication and productivity platforms, like Microsoft Teams and Cisco Jabber.
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Outline of telecommunication

The following outline is provided as an overview of and topical guide to telecommunication: Telecommunication – the transmission of signals over a distance for the purpose of communication. In modern times, this process almost always involves the use of electromagnetic waves by transmitters and receivers, but in earlier years it also involved the use of drums and visual signals such as smoke, fire, beacons, semaphore lines and other optical communications. == Modes of telecommunication == E-mail Fax Instant messaging Radio Satellite SMS Telegraphy Telephony Television Television broadcasting mobile telephony Videoconferencing VoIP Voicemail == Types of telecommunication networks == Telecommunications network Computer networks ARPANET Ethernet Internet Wireless networks Public switched telephone networks (PSTN) Packet switched networks Radio network Broadband Wireless Broadband == Aspects of telecommunication transmission == Telecommunication Analog Digital Functional profile Optics === Telecommunication technology === Modulation Amplitude modulation Frequency modulation Quadrature amplitude modulation Nyquist rate Nyquist ISI criterion Pulse shaping Intersymbol interference === Communications media types === Physical media for Telecommunication Twisted pair Coaxial cable Optical fiber Telecommunication through Free Space Broadcast radio frequency including television and radio Line-of-sight Communications satellite Terrestrial Microwave Wireless LAN === Relationship between media and transmitters === Physical access to media Simplex Duplex (telecommunications) Logical relationships Return channel Two-way alternating Two-way simultaneous === Multiple access to media === Multiplexing Analog Frequency division multiplexing Space division multiplexing Digital Time-division multiplexing Statistical multiplexing and Packet switching Media Access Control Contention Token-based Centralized token control Distributed token control == History of telecommunication == History of telecommunication History of telegraphy History of the telephone Invention of the telephone Timeline of the telephone History of radio History of television History of videophones History of mobile phones History of computing hardware History of the Internet == Major telecommunications equipment manufacturers == Alcatel-Lucent – French global telecommunications equipment company Aricent – Former company AT&T – American telecommunications company Avaya – American technology company Ciena – American telecommunications company Cisco Systems – American multinational technology companyPages displaying short descriptions of redirect targets Ericsson – Swedish multinational networking and telecommunications company Fujitsu – Japanese multinational technology company HCL Technologies – Indian multinational technology companyPages displaying short descriptions of redirect targets Huawei – Chinese multinational technology company NEC – Japanese technology corporation Nokia – Multinational data networking and telecommunications equipment company ShoreTel – US telecommunications company Verizon – American telecommunications company ZTE – Chinese telecommunications company == Major telecommunications service providers == List of mobile network operators List of telephone operating companies == Telecommunication organizations == Alliance for Telecommunications Industry Solutions Telecommunications Industry Association == Telecommunication publications == Magazines Billing and OSS World Cabling Installation & Maintenance Call Center Communications News Communications System Design Lightwave Mobile Radio Technology (MRT) New Telephony Phone+ RCR Wireless News Telecom Asia Telecommunications Magazine Telephony WhatSatphone Magazine Wireless Systems Design Wireless Week Xchange == Persons influential in telecommunication == Edwin Howard Armstrong – American radio-frequency engineer and inventor (1890–1954) John Logie Baird – Scottish inventor (1888–1946) Paul Baran – American-Jewish engineer (1926–2011) Alexander Graham Bell – Inventor of the telephone (1847–1922) Tim Berners-Lee – English computer scientist (born 1955) Jagadish Chandra Bose – Physicist, biologist and botanist (1857–1937) Vint Cerf – American computer scientist and Internet pioneer (born 1943) Claude Chappe – Late 18th-century French inventor Donald Davies – British computer scientist (1924–2000) Louis Pouzin – French computer scientist and Internet pioneer (born 1931) Lee de Forest – American inventor (1873–1961) Philo Farnsworth – American inventor (1906–1971) Reginald Fessenden – Canadian-American electrical engineer and inventor (1866–1932) Elisha Gray – American electrical engineer (1835–1901) Innocenzo Manzetti – Italian inventor (1826–1877) Guglielmo Marconi – Italian radio-frequency engineer and inventor (1874–1937) Antonio Meucci – Italian inventor (1808–1889) Alexander Stepanovich Popov – Russian physicist (1859–1906)Pages displaying short descriptions of redirect targets Johann Philipp Reis – German scientist and inventor Almon Brown Strowger – American inventor of the telephone exchange (1839–1902) Nikola Tesla – Serbian-American engineer and inventor (1856–1943) Camille Tissot – French physicist (1868–1917) Alfred Vail – 19th-century American machinist and inventor Charles Wheatstone – English physicist and inventor (1802–1875) Vladimir K. Zworykin – Russian-American engineer (1888–1982)
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GlTF

glTF (Graphics Library Transmission Format or GL Transmission Format and formerly known as WebGL Transmissions Format or WebGL TF) is a standard file format for three-dimensional scenes and models. A glTF file uses one of two possible file extensions: .gltf (JSON/ASCII) or .glb (binary). Both .gltf and .glb files may reference external binary and texture resources. Alternatively, both formats may be self-contained by directly embedding binary data buffers (as base64-encoded strings in .gltf files or as raw byte arrays in .glb files). An open standard developed and maintained by the Khronos Group, it supports 3D model geometry, appearance, scene graph hierarchy, and animation. It is intended to be a streamlined, interoperable format for the delivery of 3D assets, while minimizing file size and runtime processing by apps. As such, its creators have described it as the "JPEG of 3D". == Overview == The glTF format stores data primarily in JSON. The JSON may also contain blobs of binary data known as buffers, and refer to external files, for storing mesh data, images, etc. The binary .glb format also contains JSON text, but serialized with binary chunk headers to allow blobs to be directly appended to the file. The fundamental building blocks of a glTF scene are nodes. Nodes are organized into a hierarchy, such that a node may have other nodes defined as children. Nodes may have transforms relative to their parent. Nodes may refer to resources, such as meshes, skins, and cameras. Meshes may refer to materials, which refer to textures, which refer to images. Scenes are defined using an array of root nodes. Most of the top-level glTF properties use a flat hierarchy for storage. Nodes are saved in an array and are referred to by index, including by other nodes. A glTF scene refers to its root nodes by index. Furthermore, nodes refer to meshes by index, which refer to materials by index, which refer to textures by index, which refer to images by index. All glTF data structures support being extended using a JSON property, allowing arbitrary JSON data to be added. == Releases == === glTF 1.0 === Members of the COLLADA working group conceived the file format in 2012. At SIGGRAPH 2012, Khronos presented a demo of glTF, which was then called WebGL Transmissions Format (WebGL TF). On October 19, 2015, Khronos released the glTF 1.0 specification. ==== Adoption of glTF 1.0 ==== At SIGGRAPH 2016, Oculus announced their adoption of glTF citing the similarities to their ovrscene format. In October 2016, Microsoft joined the 3D Formats working group at Khronos to collaborate on glTF. === glTF 2.0 === The second version, glTF 2.0, was released in June 2017, and is a complete overhaul of the file format from version 1.0, with most tools adopting the 2.0 version. Based on a proposal by Fraunhofer originally presented at SIGGRAPH 2016, physically based rendering (PBR) was added, replacing WebGL shaders used in glTF 1.0. glTF 2.0 added the GLB binary format into the base specification. Other upgrades include sparse accessors and morph targets for techniques such as facial animation, and schema tweaks and breaking changes for corner cases or performance such as replacing top-level glTF object properties with arrays for faster index-based access. There is ongoing work towards import and export in Unity and an integrated multi-engine viewer and validator. ==== Adoption of glTF 2.0 ==== On March 3, 2017, Microsoft announced that they would be using glTF 2.0 as the 3D asset format across their product line, including Paint 3D, 3D Viewer, Remix 3D, Babylon.js, and Microsoft Office. Sketchfab also announced support for glTF 2.0. The glTF and GLB formats are used on and supported by companies including DGG, UX3D, Sketchfab, Facebook, Microsoft, Meta, Google, Adobe, Box, TurboSquid, Unreal Engine, Unity, and Qt Quick 3D. The format has been noted as an important standard for augmented reality, integrating with modeling software such as Autodesk Maya, Autodesk 3ds Max, and Poly. In February 2020, the Smithsonian Institution launched their Open Access Initiative, releasing approximately 2.8 million 2D images and 3D models into the public domain, using glTF for the 3D models. In July 2022, glTF 2.0 was released as the ISO/IEC 12113:2022 International Standard. Khronos stated they would make regular submissions to bring updates and new widely adopted glTF functionality into refreshed versions of ISO/IEC 12113 to ensure that there is no long-term divergence between the ISO/IEC and Khronos specifications. The open-source game engine Godot supports importing glTF 2.0 files since version 3.0 and export since version 4.0. === Extensions === The glTF format can be extended with arbitrary JSON to add new data and functionality. Extensions can be placed on any part of a glTF, including nodes, animations, materials, textures, and on the entire document. Khronos keeps a non-comprehensive registry of glTF extensions on GitHub, including all official Khronos extensions and a few third-party extensions. PBR extensions model the physical appearance of real-world objects, allowing developers to create realistic 3D assets that have the correct appearance. As new PBR extensions are released, they continue to expand PBR capabilities within the glTF framework, allowing a wider range of scenes and objects to be realistically rendered as 3D assets. The KTX 2.0 extension for universal texture compression enables 3D models in the glTF format to be highly compressed and to use natively supported texture formats, reducing file size and boosting rendering speed. Draco is a glTF extension for mesh compression, to compress and decompress 3D meshes, to help reduce the size of 3D files. It compresses vertex attributes, normals, colors, and texture coordinates. Various glTF extensions for game engine interoperability have been developed by OMI group. This includes extensions for physics shapes, physics bodies, physics joints, audio playback, seats, spawn points, and more. The VRM consortium has developed glTF extensions for advanced humanoid 3D avatars including dynamic spring bones and toon materials. == Derivative formats == 3D Tiles, an OGC Community Standard, builds on glTF to add a spatial data structure, metadata, and declarative styling for streaming massive heterogeneous 3D geospatial datasets. VRM, a model format for VR, is built on the .glb format. It is a 3D humanoid avatar specification and file format. == Software ecosystem == Khronos maintains the glTF Sample Viewer for viewing glTF assets. Khronos also maintains the glTF Validator for validating if 3D models conform to the glTF specification. Khronos maintains a glTF Compressor tool to interactively optimize and fine-tune compression settings for glTF assets using KTX 2.0 textures. glTF loaders are in open-source WebGL engines including PlayCanvas, Three.js, Babylon.js, Cesium, PEX, xeogl, and A-Frame. The Godot game engine supports and recommends the glTF format, with both import and export support. Open-source glTF converters are available from COLLADA, FBX, and OBJ. Assimp can import and export glTF. glTF files can also be directly exported from a variety of 3D editors, such as Blender, Unity (using the glTFast importer/exporter), Freecad, Vectary, Autodesk 3ds Max (natively or using Verge3D exporter), Autodesk Maya (using babylon.js exporter), Autodesk Inventor, Modo, Houdini, Paint 3D, Godot, and Substance Painter. Open-source glTF utility libraries are available for programming languages including JavaScript, Node.js, C++, C#, Python, Haskell, Java, Go, Rust, Haxe, Ada, and TypeScript. Khronos keeps a list of these libraries and other related applications on their ecosystem site. The Khronos 3D Commerce Working Group released Asset Creation Guidelines in 2020 outlining best practices for use of the glTF file format in 3D Commerce. In 2025, the Working Group launched Asset Creation Guidelines 2.0, a continuously updated resource with additional guidance for geometry, mesh optimization, UV maps, textures, materials/PBR performance, and web optimization. The Khronos PBR Neutral Tone Mappers specification is a tone mapper designed to faithfully reproduce an object's base color, hue, and saturation when using PBR rendering under grayscale lighting, supporting brand- and product-accurate color representation. Khronos maintains the glTF Asset Auditor to allow retailers and advertising technology platforms to validate 3D assets against either a default Audit Profile modelled on the 2020 3D Commerce Asset Creation Guidelines or a custom profile defined by the target application.
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The Culture of Connectivity

The Culture of Connectivity: A Critical History of Social Media is a book by José van Dijck published by Oxford University Press in 2013 on social media platforms and their history. The author considers the histories of five social media platforms: Facebook, Twitter, Flickr, YouTube, and Wikipedia. She focuses on how their technological, social and cultural dimensions contribute to their current status.
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Case-based reasoning

Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. In everyday life, an auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry) is treating nature as a database of solutions to problems. Case-based reasoning is a prominent type of analogy solution making. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving; or, more radically, that all reasoning is based on past cases personally experienced. This view is related to prototype theory, which is most deeply explored in cognitive science. == Process == Case-based reasoning has been formalized for purposes of computer reasoning as a four-step process: Retrieve: Given a target problem, retrieve cases relevant to solving it from memory. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case. Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries. Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue – an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his new-found procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands. == Comparison to other methods == At first glance, CBR may seem similar to the rule induction algorithms of machine learning. Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem. If for instance a procedure for plain pancakes is mapped to blueberry pancakes, a decision is made to use the same basic batter and frying method, thus implicitly generalizing the set of situations under which the batter and frying method can be used. The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization. For instance, if a rule-induction algorithm were given recipes for plain pancakes, Dutch apple pancakes, and banana pancakes as its training examples, it would have to derive, at training time, a set of general rules for making all types of pancakes. It would not be until testing time that it would be given, say, the task of cooking blueberry pancakes. The difficulty for the rule-induction algorithm is in anticipating the different directions in which it should attempt to generalize its training examples. This is in contrast to CBR, which delays (implicit) generalization of its cases until testing time – a strategy of lazy generalization. In the pancake example, CBR has already been given the target problem of cooking blueberry pancakes; thus it can generalize its cases exactly as needed to cover this situation. CBR therefore tends to be a good approach for rich, complex domains in which there are myriad ways to generalize a case. In law, there is often explicit delegation of CBR to courts, recognizing the limits of rule based reasons: limiting delay, limited knowledge of future context, limit of negotiated agreement, etc. While CBR in law and cognitively inspired CBR have long been associated, the former is more clearly an interpolation of rule based reasoning, and judgment, while the latter is more closely tied to recall and process adaptation. The difference is clear in their attitude toward error and appellate review. Another name for case-based reasoning in problem solving is symptomatic strategies. It does require à priori domain knowledge that is gleaned from past experience which established connections between symptoms and causes. This knowledge is referred to as shallow, compiled, evidential, history-based as well as case-based knowledge. This is the strategy most associated with diagnosis by experts. Diagnosis of a problem transpires as a rapid recognition process in which symptoms evoke appropriate situation categories. An expert knows the cause by virtue of having previously encountered similar cases. Case-based reasoning is the most powerful strategy, and that used most commonly. However, the strategy won't work independently with truly novel problems, or where deeper understanding of whatever is taking place is sought. An alternative approach to problem solving is the topographic strategy which falls into the category of deep reasoning. With deep reasoning, in-depth knowledge of a system is used. Topography in this context means a description or an analysis of a structured entity, showing the relations among its elements. Also known as reasoning from first principles, deep reasoning is applied to novel faults when experience-based approaches aren't viable. The topographic strategy is therefore linked to à priori domain knowledge that is developed from a more a fundamental understanding of a system, possibly using first-principles knowledge. Such knowledge is referred to as deep, causal or model-based knowledge. Hoc and Carlier noted that symptomatic approaches may need to be supported by topographic approaches because symptoms can be defined in diverse terms. The converse is also true – shallow reasoning can be used abductively to generate causal hypotheses, and deductively to evaluate those hypotheses, in a topographical search. == Criticism == Critics of CBR argue that it is an approach that accepts anecdotal evidence as its main operating principle. Without statistically relevant data for backing and implicit generalization, there is no guarantee that the generalization is correct. However, all inductive reasoning where data is too scarce for statistical relevance is inherently based on anecdotal evidence. == History == CBR traces its roots to the work of Roger Schank and his students at Yale University in the early 1980s. Schank's model of dynamic memory was the basis for the earliest CBR systems: Janet Kolodner's CYRUS and Michael Lebowitz's IPP. Other schools of CBR and closely allied fields emerged in the 1980s, which directed at topics such as legal reasoning, memory-based reasoning (a way of reasoning from examples on massively parallel machines), and combinations of CBR with other reasoning methods. In the 1990s, interest in CBR grew internationally, as evidenced by the establishment of an International Conference on Case-Based Reasoning in 1995, as well as European, German, British, Italian, and other CBR workshops. CBR technology has resulted in the deployment of a number of successful systems, the earliest being Lockheed's CLAVIER, a system for laying out composite parts to be baked in an industrial convection oven. CBR has been used extensively in applications such as the Compaq SMART system and has found a major application area in the health sciences, as well as in structural safety management. There is recent work that develops CBR within a statistical framework and formalizes case-based inference as a specific type of probabilistic inference. Thus, it becomes possible to produce case-based predictions equipped with a certain level of confidence. One description of the difference between CBR and induction from instances is that statistical inference aims to find what tends to make cases similar while CBR aims to encode what suffices to claim similarly.
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The Culture of Connectivity

The Culture of Connectivity: A Critical History of Social Media is a book by José van Dijck published by Oxford University Press in 2013 on social media platforms and their history. The author considers the histories of five social media platforms: Facebook, Twitter, Flickr, YouTube, and Wikipedia. She focuses on how their technological, social and cultural dimensions contribute to their current status.
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Friending and following

Friending is the act of adding someone to a list of "friends" on a social networking service. The notion does not necessarily involve the concept of friendship. It is also distinct from the idea of a "fan"—as employed on the WWW sites of businesses, bands, artists, and others—since it is more than a one-way relationship. A "fan" only receives things. A "friend" can communicate back to the person friending. The act of "friending" someone usually grants that person special privileges (on the service) with respect to oneself. On Facebook, for example, one's "friends" have the privilege of viewing and posting to one's "timeline". Following is a similar concept on other social network services, such as Twitter and Instagram, where a person (follower) chooses to add content from a person or page to their newsfeed. Unlike friending, following is not necessarily mutual, and a person can unfollow (stop following) or block another user at any time without affecting that user's following status. The first scholarly definition and examination of friending and defriending (the act of removing someone from one's friend list, also called unfriending) was David Fono and Kate Raynes-Goldie's "Hyperfriendship and beyond: Friends and Social Norms on LiveJournal" from 2005, which identified the use of the term as both a noun and a verb by users of early social network site and blogging platform LiveJournal, which was originally launched in 1999. == Friend/follower count, friend collecting, and multiple accounts == The addition of people to a friend list without regard to whether one actually is their friend is sometimes known as friend whoring. Matt Jones of Dopplr went so far as to coin the expression "friending considered harmful" to describe the problem of focusing upon the friending of more and more people at the expense of actually making any use of a social network. Friend collecting is the adding of hundreds or thousands of friends/followers, a not uncommon order of magnitude on some social sites. As a result, many teen users feel pressured to heavily curate their posts, posting only carefully posed and edited photographs with well-thought-out captions. Some Instagram users will create a second account, known as a Finsta (short for "Fake Instagram"). A Finsta is typically private, and the owner only allows close friends to follow it. Since the follower count is kept down, the posts can be more candid and silly in nature. Users may also create multiple accounts based on their interests. Someone with a personal social media account might be a photographer and maintain a separate account for that. There is risk associated with following large numbers of people: scholars say that social anxiety could be an effect of managing a large social media network, as users can feel jealous and have a "fear of missing out". == Unfriending and unfollowing == Unfriending is the act of removing someone from a friends list. On Facebook, this means the action is unilateral, meaning, the friendship is terminated on both sides. The act of unfriending is often used when one user was flirting and made the other uncomfortable. Unfollowing is a little different. When a user unfollows someone on Instagram or Twitter, it continues a one-sided relationship. Often, the unfollowed user doesn't realize they were unfollowed, so they continue the following. == Social network friending and friendship == There are distinct groups of "friends" that one can friend on a social networking service. The notion of a social network friend does not necessarily embody the concept of friendship. Although terminology has not yet evolved to distinguish the different types of social networking friends, they can be broken into the following three categories. friends who are actually known These are people that may be one's friends or family in real life, with whom one has regular interaction either on-line or off-line. organizational friends These are companies and other organizations who maintain a "friending" relationship as a contacts list. complete strangers These are social networking "friends" with whom one has no relationship at all. Within these categories "friends" can be made up of strong ties, weak existing ties, weak latent ties, and parasocial ties. Strong ties can be made up of close family members and friends where self-disclosure, intimacy and frequent content occur. Weak existing ties can be made up of acquaintances, co-workers and distance relatives with whom the user has inconsistent contact. Weak latent ties can be made up of people within a similar geographical location or profession that can be used as a potential future bridge to other connections. Parasocial ties can be made up of celebrities, public figures and media personas. Human nature is to reciprocate a friending, marking someone as a friend who has marked oneself as a friend. This is a social norm for social networking services. However, this leads to mixing up who is an actual friend, and who is a contact. Tagging someone as a "contact" who has marked one as a "friend" can be perceived as impolite. Other concerns about this issue are treated in Sherry Turkle's Alone Together which analyses many behavioral dynamics in social media friendships. Turkle defines herself as "cautiously optimistic", but expresses concern that distance communications may undermine genuine face-to-face spoken discourses, lessening people's expectations of one another. One social networking service, FriendFeed, allows one to friend someone as a "fake" friend. The person "fake" friended receives the usual notifications for friending, but that person's updates are not received. Gavin Bell, author of Building Social Web Applications, describes this mechanism as "ludicrous". Results from a 2007 survey the Center for the Digital Future stated that only 23% of internet users have at least one virtual friend whom they have only met online. Ideally the number of virtual friends is directly proportional to the use of the Internet, but the same survey showed 20% of heavy-users (more than 3 hours/day) who claimed an average of 8.7% online friends, reported at least one relationship that started virtually and migrated to in-person contact. This results and other concerning issues are included in the book Networked: The New Social Operating System co-written by Lee Rainie and Barry Wellman in 2012. == Ethical considerations == The act of "friending" someone on a social networking service has particular ethical implications for judges in the United States. Judicial codes of conducts in the various states generally incorporate some form of provision that judges should avoid even the appearance of impropriety. Whether this regulates and even prohibits judges "friending" attorneys that appear before them, and law enforcement personnel, has been the subject of some analysis by the judicial ethics panels of the various states. They haven't all agreed on the guidance that they have given to judges: The New York state Judicial Ethics committee in 2009 simply advised judges to employ caution, noting that the issue of "friending" someone on a social networking service is a publicly observable act that has little difference from other public behavior concerns judges already face. The Florida Judicial Ethics Advisory committee in 2009 noted that, judges being normal human beings, it was unavoidable for judges to form friendships without the responsibilities of their job. It prohibited judges from friending any attorneys that appeared before them, whilst allowing friending of those who do not, on the grounds that it may give the appearance to the general public (even if the substance is otherwise) that those attorneys who are friended hold special sway with the judge. A minority opinion of the committee asserted that there is a substantive difference between "friending" on a social networking service and actual friendship, and that the general public, being aware of the norms of social networking services, was capable of drawing this distinction and would not reasonably conclude either a special degree of influence or a violation of the code of judicial conduct. This minority opinion was outnumbered twice in 2009, both in the Judicial Ethics Advisory and in the Florida Supreme Court Judicial Ethics Advisory committee. The South Carolina judicial conduct committee in 2009 permitted judges to friend attorneys and law enforcement personnel, with the proviso that no judicial business should be conducted upon nor discussed via the social networking service. "... a judge should not become isolated from the community in which the judge lives.", the committee stated. The Kentucky Judicial Ethics committee in 2010 took the same position as the minority opinion in Florida. It urged judges to exercise caution, but recognized that the act of friending "does not, in and of itself, indicate the degree or intensity of a judge's relationship with the person who is the 'friend'
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Military communications

Military communications or military signals involve all aspects of communications, or conveyance of information, by armed forces. Examples from Jane's Military Communications include text, audio, facsimile, tactical ground-based communications, naval signalling, terrestrial microwave, tropospheric scatter, satellite communications systems and equipment, surveillance and signal analysis, security, direction finding and jamming. The most urgent purposes are to communicate information to commanders and orders from them. Military communications span from pre-history to the present. The earliest military communications were delivered by runners. Later, communications progressed to visual signals. For example, Naval ships would use flag signaling to communicate from ship to ship. These flags are a uniform set of easily identifiable nautical codes that would convey visual messages and codes between ships and from ship to shore. Then militaries discovered methods to use audible signaling to communicate with each other. This way of communicating was possible because of telegraphs. They are an electronic device that is used by a sender and when the sender presses on the telegraph key, they interrupt the current creating an audible pulse that is heard at the receiving station. The receiver then decodes the pulses to decode the messages. Since then, military communication has evolved and advanced much further. Today, there are many perspectives used to examine how troops around the world communicate. Anthony King states how Military sociologists have attempted to explain how military institutions develop and maintain high levels of social cohesion. == History == In past centuries communicating a message usually required someone to go to the destination, bringing the message. Thus, the term communication often implied the ability to transport people and supplies. A place under siege was one that lost communication in both senses. The association between transport and messaging declined in recent centuries. The first military communications involved the use of runners or the sending and receiving of simple signals (sometimes encoded to be unrecognizable). The first distinctive uses of military communications were called semaphore. Modern units specializing in these tactics are usually designated as signal corps. The Roman system of military communication (cursus publicus or cursus vehicularis) is an early example of this. Later, the terms signals and signaller became words referring to a highly-distinct military occupation dealing with general communications methods (similar to those in civil use) rather than with weapons. Present-day military forces of an informational society conduct intense and complicated communicating activities on a daily basis, using modern telecommunications and computing methods. Only a small portion of these activities are directly related to combat actions. Modern concepts of network-centric warfare (NCW) rely on network-oriented methods of communications and control to make existing forces more effective. == Military communications equipment == Drums, horns, flags, and riders on horseback were some of the early methods the military used to send messages over distances. The advent of distinctive signals led to the formation of the signal corps, a group specialized in the tactics of military communications. The signal corps evolved into a distinctive occupation where the signaller became a highly technical job dealing with all available communications methods including civil ones. In the middle 20th century radio equipment came to dominate the field. Many modern pieces of military communications equipment are built to both encrypt and decode transmissions and survive rough treatment in hostile climates. They use different frequencies to send signals to other radio stations to communicate. Radios have played a major role in military communication. Since they are capable of sending radio waves to transmit voice signals over long distances. This can be helpful for communication on the battlefield since it is a good way to send messages undetected over long distances. Radios are also very reliable because even in harsh weather conditions they are still able to help communicate among the soldiers. Militaries still use radios and continue to improve the technology because of their durability and reliability for military communication. Spelling alphabets such as the NATO phonetic alphabet are used to aid radio communications by reducing ambiguity between letters. Military communications – or "comms" – are activities, equipment, techniques, and tactics used by the military in some of the most hostile areas of the earth and in challenging environments such as battlefields, on land (compare radio in a box), underwater and also in air. Military comms include command, control and communications and intelligence and were known as the C3I model before computers were fully integrated. The U.S. Army expanded the model to C4I when it recognized the vital role played by automated computer equipment to send and receive large, bulky amounts of data. In the modern world, most nations attempt to minimize the risk of war caused by miscommunication or inadequate communication. As a result, military communication is intense and complicated and often motivates the development of advanced technology for remote systems such as satellites. Satellites have been improving and are being used more and more for communication. They are being made to have higher transmission capacity to help with their communication abilities. The military is upgrading satellites to be immune to interference during combat operations. This advancement will establish stable, high-quality information highways for long distance communication. Aircraft are also beneficial for communication, both crewed and uncrewed, as well as computers. Computers and their varied applications have revolutionized military comms. Although military communication is designed for warfare, it also supports intelligence-gathering and communication between adversaries, and thus sometimes prevents war. The six categories of military comms are: alert measurement systems cryptography military radio systems command and control signal corps network-centric warfare The alert measurement systems are various states of alertness or readiness for the armed forces used around the world during a state of war, act of terrorism or a military attack against a state. They are known by different acronyms, such as DEFCON, or defense readiness condition, used by the U.S. Armed Forces. Cryptography is the study of methods of converting messages to a form unreadable except to one who knows how to decrypt them. This ancient military comms art gained new importance with the rise of radio systems whose signals traveled far and were easily intercepted. Cryptographic software is also widely used in civilian commerce. == Commercial refile == In United States military communications systems, commercial refile refers to sending a military message via a commercial communications network. The message may come from a military network, such as a tape relay network, a point-to-point telegraph network, a radio-telegraph network, or the Defense Switched Network. Commercial refiling of a message will usually require a reformatting of the message, particularly the heading.
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AI-complete

In the field of artificial intelligence (AI), tasks that are hypothesized to require artificial general intelligence to solve are informally known as AI-complete or AI-hard. Calling a problem AI-complete reflects the belief that it cannot be solved by a simple specific algorithm. Prior to 2013, problems supposed to be AI-complete included computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem. AI-complete tasks were notably considered useful for distinguishing humans from automated agents, as CAPTCHAs aim to do. == History == The term was coined by Fanya Montalvo by analogy with NP-complete and NP-hard in complexity theory, which formally describes the most famous class of difficult problems. Early uses of the term are in Erik Mueller's 1987 PhD dissertation and in Eric Raymond's 1991 Jargon File. Expert systems, that were popular in the 1980s, were able to solve very simple and/or restricted versions of AI-complete problems, but never in their full generality. When AI researchers attempted to "scale up" their systems to handle more complicated, real-world situations, the programs tended to become excessively brittle without commonsense knowledge or a rudimentary understanding of the situation: they would fail as unexpected circumstances outside of its original problem context would begin to appear. When human beings are dealing with new situations in the world, they are helped by their awareness of the general context: they know what the things around them are, why they are there, what they are likely to do and so on. They can recognize unusual situations and adjust accordingly. Expert systems lacked this adaptability and were brittle when facing new situations. DeepMind published a work in May 2022 in which they trained a single model to do several things at the same time. The model, named Gato, can "play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens." Similarly, some tasks once considered to be AI-complete, like machine translation, are among the capabilities of large language models. == AI-complete problems == AI-complete problems have been hypothesized to include: AI peer review (composite natural language understanding, automated reasoning, automated theorem proving, formalized logic expert system) Bongard problems Computer vision (and subproblems such as object recognition) Natural language understanding (and subproblems such as text mining, machine translation, and word-sense disambiguation) Autonomous driving Dealing with unexpected circumstances while solving any real world problem, whether navigation, planning, or even the kind of reasoning done by expert systems. == Formalization == Computational complexity theory deals with the relative computational difficulty of computable functions. By definition, it does not cover problems whose solution is unknown or has not been characterized formally. Since many AI problems have no formalization yet, conventional complexity theory does not enable a formal definition of AI-completeness. == Research == Roman Yampolskiy suggests that a problem C {\displaystyle C} is AI-Complete if it has two properties: It is in the set of AI problems (Human Oracle-solvable). Any AI problem can be converted into C {\displaystyle C} by some polynomial time algorithm. On the other hand, a problem H {\displaystyle H} is AI-Hard if and only if there is an AI-Complete problem C {\displaystyle C} that is polynomial time Turing-reducible to H {\displaystyle H} . This also gives as a consequence the existence of AI-Easy problems, that are solvable in polynomial time by a deterministic Turing machine with an oracle for some problem. Yampolskiy has also hypothesized that the Turing Test is a defining feature of AI-completeness. Groppe and Jain classify problems which require artificial general intelligence to reach human-level machine performance as AI-complete, while only restricted versions of AI-complete problems can be solved by the current AI systems. For Šekrst, getting a polynomial solution to AI-complete problems would not necessarily be equal to solving the issue of artificial general intelligence, while emphasizing the lack of computational complexity research being the limiting factor towards achieving artificial general intelligence. For Kwee-Bintoro and Velez, solving AI-complete problems would have strong repercussions on society.
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DiscoVision

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

Active networking is a communication pattern that allows packets flowing through a telecommunications network to dynamically modify the operation of the network. Active network architecture is composed of execution environments (similar to a unix shell that can execute active packets), a node operating system capable of supporting one or more execution environments. It also consists of active hardware, capable of routing or switching as well as executing code within active packets. This differs from the traditional network architecture which seeks robustness and stability by attempting to remove complexity and the ability to change its fundamental operation from underlying network components. Network processors are one means of implementing active networking concepts. Active networks have also been implemented as overlay networks. == What does it offer? == Active networking allows the possibility of highly tailored and rapid "real-time" changes to the underlying network operation. This enables such ideas as sending code along with packets of information allowing the data to change its form (code) to match the channel characteristics. The smallest program that can generate a sequence of data can be found in the definition of Kolmogorov complexity. The use of real-time genetic algorithms within the network to compose network services is also enabled by active networking. == How it relates to other networking paradigms == Active networking relates to other networking paradigms primarily based upon how computing and communication are partitioned in the architecture. === Active networking and software-defined networking === Active networking is an approach to network architecture with in-network programmability. The name derives from a comparison with network approaches advocating minimization of in-network processing, based on design advice such as the "end-to-end argument". Two major approaches were conceived: programmable network elements ("switches") and capsules, a programmability approach that places computation within packets traveling through the network. Treating packets as programs later became known as "active packets". Software-defined networking decouples the system that makes decisions about where traffic is sent (the control plane) from the underlying systems that forward traffic to the selected destination (the data plane). The concept of a programmable control plane originated at the University of Cambridge in the Systems Research Group, where (using virtual circuit identifiers available in Asynchronous Transfer Mode switches) multiple virtual control planes were made available on a single physical switch. Control Plane Technologies (CPT) was founded to commercialize this concept. == Fundamental challenges == Active network research addresses the nature of how best to incorporate extremely dynamic capability within networks. In order to do this, active network research must address the problem of optimally allocating computation versus communication within communication networks. A similar problem related to the compression of code as a measure of complexity is addressed via algorithmic information theory. One of the challenges of active networking has been the inability of information theory to mathematically model the active network paradigm and enable active network engineering. This is due to the active nature of the network in which communication packets contain code that dynamically change the operation of the network. Fundamental advances in information theory are required in order to understand such networks. == Nanoscale active networks == As the limit in reduction of transistor size is reached with current technology, active networking concepts are being explored as a more efficient means accomplishing computation and communication. More on this can be found in nanoscale networking.
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Link-richness

Link-richness is the quality, possessed by some websites, of having many hyperlinks. Classified advertising sites like Craigslist tend to be very link-rich, sometimes with hundreds of links on their main page. They help users find the links they are looking for by grouping links into clusters. Inadequate link richness has been described as frustrating to readers, as it reduces transparency of site content from the main page. Students new to wiki collaboration were found to need guidance in how to take full advantage of the medium's potential for creating link-rich content. Link-richness in some contexts can be distracting, as when an article is surrounded by extraneous links. Indeed, it is becoming accepted as a best practice for universities to have link-rich home pages that do not rely on user categorisation and exploration of long sequences of links and are not constrained by traditional boundaries between departments. Tools are sometimes needed to make the publishing of link-rich web sites tractable, and many people may lack the technical skills, time, or inclination to engage in hand- crafting new digital document forms. A link-rich site that is low on content is sometimes referred to as a "gateway site." Link-rich portals were popular on the Web in 2000. Yahoo! and other sites featuring categories with many links were heavily used and often required fewer than three clicks to reach the content. Web designers were creating flat sites with content positioned close to the top of pages.
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Journal of Machine Learning Research

The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the first editor-in-chief was Leslie Kaelbling. The current editors-in-chief are Francis Bach (Inria) and David Blei (Columbia University). == History == The journal was established as an open-access alternative to the journal Machine Learning. In 2001, forty editorial board members of Machine Learning resigned, saying that in the era of the Internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. The open access model employed by the Journal of Machine Learning Research allows authors to publish articles for free and retain copyright, while archives are freely available online. Print editions of the journal were published by MIT Press until 2004 and by Microtome Publishing thereafter. From its inception, the journal received no revenue from the print edition and paid no subvention to MIT Press or Microtome Publishing. In response to the prohibitive costs of arranging workshop and conference proceedings publication with traditional academic publishing companies, the journal launched a proceedings publication arm in 2007 and now publishes proceedings for several leading machine learning conferences, including the International Conference on Machine Learning, COLT, AISTATS, and workshops held at the Conference on Neural Information Processing Systems.
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The Culture of Connectivity

The Culture of Connectivity: A Critical History of Social Media is a book by José van Dijck published by Oxford University Press in 2013 on social media platforms and their history. The author considers the histories of five social media platforms: Facebook, Twitter, Flickr, YouTube, and Wikipedia. She focuses on how their technological, social and cultural dimensions contribute to their current status.
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Creepy treehouse

Creepy treehouse is a social media term, or internet slang, referring to websites or technologies that are used for educational purposes but regarded by students as an invasion of privacy. == History == The term was first described in 2008 by Utah Valley University instructional-design services director Jared Stein as "institutionally controlled technology/tool that emulates or mimics pre-existing [sic] technologies or tools that may already be in use by the learners, or by learners' peer groups." This was when social media such as Facebook was starting to become mainstream and professors would try and get students to interact with them on the site for educational purposes. Some professors would require their students to use Facebook or Twitter as part of class assignments. == Usage == The term was first described as "technological innovations by faculty members that make students’ skin crawl." The term also refers to online accounts and websites that users tend to avoid, especially young people who avoid visiting the pages of educators and other adults. Author Martin Weller defines creepy treehouse as a digital space where authority figures are viewed as invading younger people's privacy. One such example is a professor giving his students an option to use a popular video game to learn about history instead of writing an essay. Students in that class chose to write the essay instead as the method was previously unmentioned and it was not an unnatural method of interaction. Another example given was Blackboard Sync, a feature that was used to connect the school website Blackboard with students' Facebook accounts. == Solutions == University of Regina professor Alec Couros suggests that instead of "forcing" student participation with their own digital platforms, professors should use methods like online forums. Jason Jones of chronicle.com suggested letting students create social media groups for the class themselves and explaining why using technologies is required and important.
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