DataViva is an information visualization engine created by the Strategic Priorities Office of the government of Minas Gerais. DataViva makes official data about exports, industries, locations and occupations available for the entirety of Brazil through eight apps and more than 100 million possible visualizations. The first set of datum – also available at ALICEWEB – is provided by MDIC (Ministry of Development, Industry and Foreign Trade) / SECEX (Secretariat of Foreign Trade), an official institution of the Government of Brazil and shows foreign trade statistics for all exporting municipalities in the country. The other database, provided by Ministério do Trabalho e Emprego (MTE – Ministry of Labor and Employment), shows information about all the industries and occupations in Brazil (RAIS – Annual Social Information Report). The platform consists of eight core applications, each of which allows different ways of visualizing the data available. Some applications are descriptive, that is, showing data aggregated at various levels in a simple and comparative way, such as Treemapping. Others are prescriptive, using calculations that allow an analytic visualization of the data, based on theories such as the Product Space. All the applications are generated using D3plus, an open source JavaScript library built on top of D3.js by Alexander Simoes and Dave Landry. Inspired by The Observatory of Economic Complexity, DataViva is an open data, open-source, and free to use tool. It was developed in a partnership with Datawheel, co-founded by MIT Media Lab Professor César Hidalgo, and is maintained by the Government of Minas Gerais.
Kernel embedding of distributions
In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which a probability distribution is represented as an element of a reproducing kernel Hilbert space (RKHS). A generalization of the individual data-point feature mapping done in classical kernel methods, the embedding of distributions into infinite-dimensional feature spaces can preserve all of the statistical features of arbitrary distributions, while allowing one to compare and manipulate distributions using Hilbert space operations such as inner products, distances, projections, linear transformations, and spectral analysis. This learning framework is very general and can be applied to distributions over any space Ω {\displaystyle \Omega } on which a sensible kernel function (measuring similarity between elements of Ω {\displaystyle \Omega } ) may be defined. For example, various kernels have been proposed for learning from data which are: vectors in R d {\displaystyle \mathbb {R} ^{d}} , discrete classes/categories, strings, graphs/networks, images, time series, manifolds, dynamical systems, and other structured objects. The theory behind kernel embeddings of distributions has been primarily developed by Alex Smola, Le Song, Arthur Gretton, and Bernhard Schölkopf. A review of recent works on kernel embedding of distributions can be found in. The analysis of distributions is fundamental in machine learning and statistics, and many algorithms in these fields rely on information theoretic approaches such as entropy, mutual information, or Kullback–Leibler divergence. However, to estimate these quantities, one must first either perform density estimation, or employ sophisticated space-partitioning/bias-correction strategies which are typically infeasible for high-dimensional data. Commonly, methods for modeling complex distributions rely on parametric assumptions that may be unfounded or computationally challenging (e.g. Gaussian mixture models), while nonparametric methods like kernel density estimation (Note: the smoothing kernels in this context have a different interpretation than the kernels discussed here) or characteristic function representation (via the Fourier transform of the distribution) break down in high-dimensional settings. Methods based on the kernel embedding of distributions sidestep these problems and also possess the following advantages: Data may be modeled without restrictive assumptions about the form of the distributions and relationships between variables Intermediate density estimation is not needed Practitioners may specify the properties of a distribution most relevant for their problem (incorporating prior knowledge via choice of the kernel) If a characteristic kernel is used, then the embedding can uniquely preserve all information about a distribution, while thanks to the kernel trick, computations on the potentially infinite-dimensional RKHS can be implemented in practice as simple Gram matrix operations Dimensionality-independent rates of convergence for the empirical kernel mean (estimated using samples from the distribution) to the kernel embedding of the true underlying distribution can be proven. Learning algorithms based on this framework exhibit good generalization ability and finite sample convergence, while often being simpler and more effective than information theoretic methods Thus, learning via the kernel embedding of distributions offers a principled drop-in replacement for information theoretic approaches and is a framework which not only subsumes many popular methods in machine learning and statistics as special cases, but also can lead to entirely new learning algorithms. == Definitions == Let X {\displaystyle X} denote a random variable with domain Ω {\displaystyle \Omega } and distribution P {\displaystyle P} . Given a symmetric, positive-definite kernel k : Ω × Ω → R {\displaystyle k:\Omega \times \Omega \rightarrow \mathbb {R} } the Moore–Aronszajn theorem asserts the existence of a unique RKHS H {\displaystyle {\mathcal {H}}} on Ω {\displaystyle \Omega } (a Hilbert space of functions f : Ω → R {\displaystyle f:\Omega \to \mathbb {R} } equipped with an inner product ⟨ ⋅ , ⋅ ⟩ H {\displaystyle \langle \cdot ,\cdot \rangle _{\mathcal {H}}} and a norm ‖ ⋅ ‖ H {\displaystyle \|\cdot \|_{\mathcal {H}}} ) for which k {\displaystyle k} is a reproducing kernel, i.e., in which the element k ( x , ⋅ ) {\displaystyle k(x,\cdot )} satisfies the reproducing property ⟨ f , k ( x , ⋅ ) ⟩ H = f ( x ) ∀ f ∈ H , ∀ x ∈ Ω . {\displaystyle \langle f,k(x,\cdot )\rangle _{\mathcal {H}}=f(x)\qquad \forall f\in {\mathcal {H}},\quad \forall x\in \Omega .} One may alternatively consider x ↦ k ( x , ⋅ ) {\displaystyle x\mapsto k(x,\cdot )} as an implicit feature mapping φ : Ω → H {\displaystyle \varphi :\Omega \rightarrow {\mathcal {H}}} (which is therefore also called the feature space), so that k ( x , x ′ ) = ⟨ φ ( x ) , φ ( x ′ ) ⟩ H {\displaystyle k(x,x')=\langle \varphi (x),\varphi (x')\rangle _{\mathcal {H}}} can be viewed as a measure of similarity between points x , x ′ ∈ Ω . {\displaystyle x,x'\in \Omega .} While the similarity measure is linear in the feature space, it may be highly nonlinear in the original space depending on the choice of kernel. === Kernel embedding === The kernel embedding of the distribution P {\displaystyle P} in H {\displaystyle {\mathcal {H}}} (also called the kernel mean or mean map) is given by: μ X := E [ k ( X , ⋅ ) ] = E [ φ ( X ) ] = ∫ Ω φ ( x ) d P ( x ) {\displaystyle \mu _{X}:=\mathbb {E} [k(X,\cdot )]=\mathbb {E} [\varphi (X)]=\int _{\Omega }\varphi (x)\ \mathrm {d} P(x)} If P {\displaystyle P} allows a square integrable density p {\displaystyle p} , then μ X = E k p {\displaystyle \mu _{X}={\mathcal {E}}_{k}p} , where E k {\displaystyle {\mathcal {E}}_{k}} is the Hilbert–Schmidt integral operator. A kernel is characteristic if the mean embedding μ : { family of distributions over Ω } → H {\displaystyle \mu :\{{\text{family of distributions over }}\Omega \}\to {\mathcal {H}}} is injective. Each distribution can thus be uniquely represented in the RKHS and all statistical features of distributions are preserved by the kernel embedding if a characteristic kernel is used. === Empirical kernel embedding === Given n {\displaystyle n} training examples { x 1 , … , x n } {\displaystyle \{x_{1},\ldots ,x_{n}\}} drawn independently and identically distributed (i.i.d.) from P , {\displaystyle P,} the kernel embedding of P {\displaystyle P} can be empirically estimated as μ ^ X = 1 n ∑ i = 1 n φ ( x i ) {\displaystyle {\widehat {\mu }}_{X}={\frac {1}{n}}\sum _{i=1}^{n}\varphi (x_{i})} === Joint distribution embedding === If Y {\displaystyle Y} denotes another random variable (for simplicity, assume the co-domain of Y {\displaystyle Y} is also Ω {\displaystyle \Omega } with the same kernel k {\displaystyle k} which satisfies ⟨ φ ( x ) ⊗ φ ( y ) , φ ( x ′ ) ⊗ φ ( y ′ ) ⟩ = k ( x , x ′ ) k ( y , y ′ ) {\displaystyle \langle \varphi (x)\otimes \varphi (y),\varphi (x')\otimes \varphi (y')\rangle =k(x,x')k(y,y')} ), then the joint distribution P ( x , y ) ) {\displaystyle P(x,y))} can be mapped into a tensor product feature space H ⊗ H {\displaystyle {\mathcal {H}}\otimes {\mathcal {H}}} via C X Y = E [ φ ( X ) ⊗ φ ( Y ) ] = ∫ Ω × Ω φ ( x ) ⊗ φ ( y ) d P ( x , y ) {\displaystyle {\mathcal {C}}_{XY}=\mathbb {E} [\varphi (X)\otimes \varphi (Y)]=\int _{\Omega \times \Omega }\varphi (x)\otimes \varphi (y)\ \mathrm {d} P(x,y)} By the equivalence between a tensor and a linear map, this joint embedding may be interpreted as an uncentered cross-covariance operator C X Y : H → H {\displaystyle {\mathcal {C}}_{XY}:{\mathcal {H}}\to {\mathcal {H}}} from which the cross-covariance of functions f , g ∈ H {\displaystyle f,g\in {\mathcal {H}}} can be computed as Cov ( f ( X ) , g ( Y ) ) := E [ f ( X ) g ( Y ) ] − E [ f ( X ) ] E [ g ( Y ) ] = ⟨ f , C X Y g ⟩ H = ⟨ f ⊗ g , C X Y ⟩ H ⊗ H {\displaystyle \operatorname {Cov} (f(X),g(Y)):=\mathbb {E} [f(X)g(Y)]-\mathbb {E} [f(X)]\mathbb {E} [g(Y)]=\langle f,{\mathcal {C}}_{XY}g\rangle _{\mathcal {H}}=\langle f\otimes g,{\mathcal {C}}_{XY}\rangle _{{\mathcal {H}}\otimes {\mathcal {H}}}} Given n {\displaystyle n} pairs of training examples { ( x 1 , y 1 ) , … , ( x n , y n ) } {\displaystyle \{(x_{1},y_{1}),\dots ,(x_{n},y_{n})\}} drawn i.i.d. from P {\displaystyle P} , we can also empirically estimate the joint distribution kernel embedding via C ^ X Y = 1 n ∑ i = 1 n φ ( x i ) ⊗ φ ( y i ) {\displaystyle {\widehat {\mathcal {C}}}_{XY}={\frac {1}{n}}\sum _{i=1}^{n}\varphi (x_{i})\otimes \varphi (y_{i})} === Conditional distribution embedding === Given a conditional distribution P ( y ∣ x ) , {\displaystyle P(y\mid x),} one can define the corresponding RKHS embedding as μ Y ∣ x = E [ φ ( Y ) ∣ X ] = ∫ Ω φ ( y ) d P ( y ∣ x ) {\displaystyle \mu _{Y\mid x}=\mathbb {E} [\varphi (Y)\mid X]=\int _{\Omega
Digital heritage
The Charter on the Preservation of Digital Heritage of UNESCO defines digital heritage as embracing "cultural, educational, scientific and administrative resources, as well as technical, legal, medical and other kinds of information created digitally, or converted into digital form from existing analogue resources". Digital heritage also includes the use of digital media in the service of understanding and preserving cultural or natural heritage. The digitization of both cultural heritage and Natural heritage serves to enable the permanent access of current and future generations to culturally important objects ranging from literature and paintings to flora, fauna, or habitats. It is also used in the preservation and access of objects with enduring or significant historical, scientific, or cultural value including buildings, archeological sites, and natural phenomena. The main idea is the transformation of a material object into a virtual copy. It should not be confused with digital humanities, which uses digitizing technology to specifically help with research. There have been several debates concerning the efficiency of the process of digitizing heritage. Some of the drawbacks refer to the deterioration and technological obsolescence due to the lack of funding for archival materials and underdeveloped policies that would regulate such a process. Another main social debate has taken place around the restricted accessibility due to the digital divide that exists around the world. Nevertheless, new technologies enable easy, instant and cross boarder access to the digitized work. Many of these technologies include spatial and surveying technology to gain aerial or 3D images. Digital heritage is also used to monitor cultural heritage sites over years to help with preservation, maintenance, and sustainable tourism. It aims to observe any changes, diseases, or deterioration that may occur on objects. == Cultural and natural heritage == Digital Heritage that is not born-digital can be divided into two separate groups—digital cultural heritage and digital natural heritage. Digital cultural heritage is the maintenance or preservation of cultural objects through digitization. These are objects, in some cases entire cities, that are considered of cultural importance. These objects are sometimes able to be digitized or physically represented in minute detail. Digital cultural heritage also includes intangible heritage. These are things such as "oral traditions, customs, value systems, skills, traditional dances, diets, performances" and other unique features of a culture. Intangible heritage is particularly vulnerable to destruction due to urbanization. There are several projects and programs which concentrate on digital cultural heritage. One such project is Mapping Gothic France, which aims to document and preserve cathedrals across France using images, VR tours, laser scans, and panoramas. This allows for scientific and historical study and preservation of the cathedrals and also provides detailed access to the sites for anyone in the world. The aim of projects like these is to help with the preservation and restoration of cultural objects. After the fire at Notre-Dame de Paris in 2019, digital scans are a major component in the ongoing restoration. Digital natural heritage pertains to objects of natural heritage that are considered of cultural, scientific, or aesthetic importance. Digital heritage in this instance is used not only to grant access to these objects, but to monitor any changes over time, such as with plant or animal habitats. Geographic information systems are a form of technology that is used primarily in the study of natural heritage. Western Australia has one such digital heritage project where they have created a digital repository of native plants important to both the region and the Aboriginal people. This is in order to protect and preserve the important biological heritage of Western Australia. == Educational impact == The digitization of these heritage objects has impacts around the world and across many disciplines. The increase of digital items means that people, especially the youth, are able to learn about new objects and cultures online through various media. They provide viewers with a more in-depth experience with an item or place, instead of just an image. The media is also able to be curated to age- or educational-level appropriateness, making learning easier. Some of the technology used in education, especially in museums, includes mobile apps, virtual reality, social media, and video games. Cultural heritage institutions are using this technology to try to expand access, increase appreciation for these items, and to gain new viewpoints on their collections. Digital heritage also helps scientists, archeologists, or other historians and specialists collect data on these objects, providing more information on the objects and the past. Digital Heritage is still currently being studied and improved by several sectors invested in cultural and intellectual preservation. It is particularly of interest to museums, governments, and academic institutions. Research by these groups are creating new concepts, methodologies, and techniques for the implementation of digital heritage to protect this type of cultural and natural heritage. As new technologies are created, museums and other heritage institutions are provided with more ways of disseminating their information and engaging with the public. A lack of resources within certain groups may still hinder everyone from accessing digital heritage. == Technologies used == The digitization of cultural heritage is attained through several means. Some of the main technology used is spatial and surveying technology. Space archaeological technology - Observations from space satellites are non-intrusive and can be integrated with other technologies on the ground. It is used to photograph vast areas of earth and help with research. Remnants of ancient civilizations or other human objects are also able to be spotted via satellite imaging. Unmanned aerial vehicles - UAV, such as drones, are commonly used in digitization of cultural heritage objects. The Great Wall of China is one such site that has been digitized and analyzed through unmanned aerial vehicle investigation. The resulting images, 3-D scans, maps, and other data are used to evaluate and maintain the Great Wall. Laser Scanning - Laser scanning is used to scan an area and recreate spatially accurate depictions, such as a 3D model. Virtual and Augmented Reality - VR is used primarily for education but does have uses for reconstruction and research. It is used to provide users with an immersive experience, as though they are actually at the site. Geographic Information systems - GIS are used primarily to study objects and sites over time. It is also important in studying the socioeconomic status of the past. 3D Modeling - 3D modeling has become more widely used due to an increase in technology that works specifically with heritage sites. It is often used in tandem with GIS to reconstruct objects for restoration, documentation, preservation, and educational purposes. Data is collected using satellite or other aerial imaging and ground-based imaging. There is some concern about the accuracy and authenticity of these types of digital reconstructions and their effects on the sites themselves. A major barrier to digital heritage is the amount of resources it takes to undertake such projects, such as money, time, and technology. Money and the lack of qualified personnel are two that are considered the most obstructive. This is especially an issue in less developed areas or within underfunded groups such as minorities. == Virtual heritage == A particular branch of digital heritage, known as "virtual heritage", is formed by the use of information technology with the aim of recreating the experience of existing cultural heritage, as in (approximations of) virtual reality. It is hard to differentiate this branch from the core contribution of digital heritage which is storing the heritage data digitally. Parsinejad et al. developed two techniques for Digital Twinning of the architectural assets and representation of the physical assets virtually in the museum context. Two techniques are hand recording and digital recording and both have challenges in adoption and implementation of Digital Twin as a revolutionary concept. == Digital heritage stewardship == Digital heritage stewardship is a form of digital curation which is modeled after collaborative curation. Digital heritage stewardship means stepping away from typical curatorial practices (e.g. discovering, arranging, and sharing information, material, and/or content) in favor of practices which allow its stakeholders the opportunity to contribute historical, political, and social context and culture. The collaborative practice encourages the creation, engagement, and maintena
List of UPnP AV media servers and clients
This is a list of UPnP AV media servers and client application or hard appliances. == UPnP AV media servers == === Software === === Cross-platform === Allonis myServer, a multi-faceted media player/organizer with a DLNA/UPnP server, controller, and renderer, including conversion. Runs on Microsoft Windows. Supports most all HTML5 devices as remote controls. Asset UPnP (DLNA compatible) from Illustrate. An audio specific UPnP/DLNA server for Windows, QNAP, macOS and Linux. Features audio WAVE/LPCM transcoding from a range of audio codecs, ReplayGain and playlists. FreeMi UPnP Media Server, very simple server, historically used to stream to the STB Freebox, based on .net/mono. Home Media Server, a free media server/player/controller for Windows, Linux, macOS, individual device settings, transcoding, external and internal subtitles, restricted device access to folders, uploading files, Internet-Radio, Internet-Television, Digital Video Broadcasting (DVB), DMR-control and "Play To", Music (Visualization), Photo (Slideshow), support for 3D-subtitles, support for BitTorrent files, Web-navigation with HTML5 player, Digital Media Renderer (DMR) emulation for AirPlay and Google Cast devices. Jellyfin, a free and open-source suite of multimedia applications designed to organize, manage, and share digital media files to networked devices. JRiver Media Center, a multi-faceted media player/organizer with a DLNA/UPnP server, controller, and renderer, including conversion. Supports Microsoft Windows, macOS and Linux. Kodi (previously XBMC), a cross platform open source software media-player/media center for Android, Apple TV, Linux, macOS and Windows. LimboMedia, a free cross platform home- and UPnP/DLNA mediaserver with android app and WebM transcoding for browser playback (build with java and FFmpeg). MinimServer, a Java-based highly configurable uPnP/DNLA music server with additional consideration given to Classical Music, supports transcoding with MinimStreamer, supports Microsoft Windows, macOS, Linux, and various NAS devices. Neutron Music Player, acts as a cross platform UPnP/DLNA Media Renderer server available for Android, iOS, BlackBerry 10 & PlayBook platforms. Supports gapless playback and has possibility to output rendered audio further to the high-resolution internal DAC or external USB DAC or another UPnP/DLNA Media Renderer with all supported DSP effects applied. Plex, a cross-platform and closed source software media player and entertainment hub for digital media, available for macOS, Microsoft Windows, Linux, as well as mobile clients for iOS (including Apple TV (2nd generation) onwards), Android, Windows Phone, and many devices such as Xbox. Supports on-the-fly transcoding of video and music. PonoMusic World. Based on the JRiver Media Center software, includes similar features along with a store for purchasing HD audio tracks. PS3 Media Server, a free cross platform Java based UPnP DLNA server especially good for AVC and other current HD media codecs with on-the-fly transcoding. Serviio, is available with a free and a pro license. It can stream media files (music, video or images) to renderer devices (e.g. a TV set, Blu-ray player, games console or mobile phone) on a local area network. TVMOBiLi, a cross platform, high performance UPnP/DLNA Media Server for Windows, macOS and Linux. TwonkyMedia server, a cross-platform multimedia server and entertainment hub for digital media, available for Android, Apple TV, iOS, Linux, macOS, Microsoft Windows, Windows Phone, and Xbox 360. Universal Media Server, a free (open source) DLNA-compliant UPnP Media Server for Windows, macOS and Linux (originally based on the PS3 Media Server). It is able to stream videos, audio and images to any DLNA-capable device. It contains more features than most paid UPnP/DLNA Media Servers. It streams to many devices including TVs (Samsung, Sony, Panasonic, LG, Philips and more.), PS3, Xbox(One/360), smartphones, Blu-ray players and more. vGet Cast, a simple, cross platform (Chrome App) DLNA server and controller for single, local video files. Vuze, an open-source Java-based BitTorrent client which contains MediaServer plugin. Wild Media Server, a media server/player/controller for Windows, Linux, macOS, individual device settings, transcoding, external and internal subtitles, restricted device access to folders, uploading files, Internet-Radio, Internet-Television, Digital Video Broadcasting (DVB), DMR-control and "Play To", Music (Visualization), Photo (Slideshow), support for 3D-subtitles, support for BitTorrent files, Web-navigation with HTML5 player, Digital Media Renderer (DMR) emulation for AirPlay and Google Cast devices. === Android === BubbleUPnP Android UPnP/DLNA server, player, controller and renderer CastLab Android UPnP/DLNA server. Pixel Media Server, Android UPnP/DLNA Media Server. Supports all popular Video and Audio files. It also support external subtitle file (SRT) Plato is an Android UPnP client app that can play videos and audio. Toaster Cast Android UPnP/DLNA server, controller and renderer vGet, Android App that can play videos embedded in websites on DLNA renderers. Media Cast UPnP, Android UPnP client app that can play videos/Audio. Media Server Pro is a DLNA server that allows individual file selections for sharing. Slick UPnP A minimal and intuitive open-source Android UPnP client app that can play video/audio. (It is not DMS) YAACC Open source UPnP controller, renderer and server app === Linux === === Microsoft Windows === Sundtek Streamingserver a native Windows TV Server providing DVB, ATSC and ISDB-T via UPnP/DLNA, it also supports streaming media files (it only supports TV devices from Sundtek). Stream What You Hear, a Windows application that streams the sound of your computer (i.e.: “what you hear”) to UPnP/DLNA device such as TVs, amps, network receivers, game consoles, etc... TVersity Media Server, a Windows application that streams multimedia content from a personal computer to UPnP, DLNA and mobile devices (Chromecast is also supported). It was the first media server to offer real-time transcoding (back in 2005). TVersity Screen Server, a Windows application that mirrors the screen of a personal computer to UPnP, DLNA and mobile devices. DVBViewer, a Windows application, mainly for TV/Radio recording/playback, but with the ability to stream live TV/radio as well as multimedia files via UPnP/DLNA. DivX, a Windows application, mainly for video encoding into DivX format, but has the ability to stream multimedia files via DLNA. foobar2000, a freeware audio player for Windows. Highly customizable, audio only. Download of dlna-extension from the developers' webpage necessary. Home Media Center, a free and open source media server compatible with DLNA. Includes web interface for streaming content to web browser (Android, iOS, ...), subtitles integration and Windows desktop streaming. This server is easy to use. KooRaRoo Media, a commercial DLNA media server and organizer for Windows. Includes on-the-fly transcoding, per-file and per-folder parental controls, powerful organizing features with dynamic playlists, Internet radio streaming, "Play To" functionality and remote device control, burned-in and external subtitles, extensive format support including RAW photo formats. Streams all files to all devices. Media Go, media player and tagger MediaMonkey, a free media player/tagger/editor with an UPnP/DLNA client and server for Microsoft Windows MusicBee, an audio player, supports UPnP via a plugin. Mezzmo, a commercial software package. Mezzmo streams music, movies, photos and subtitles to the UPnP and DLNA-enabled devices. It automatically finds and organizes music, movies and photos, imports multimedia files from iPad, iPhone, iPod, Audio CDs, iTunes, Windows Media Player and WinAmp. DLNA server supports all popular media file formats with real time transcoding to meet the device specifications. PlayOn, a commercial UPnP/DLNA media server for Windows, includes a transcoder for streaming web video. TVble, a cloud connected (Rotten tomatoes/TMDB etc.), Torrent streaming, DLNA enabled media server. Allows single file or playlist downloads. Windows Media Connect from Microsoft, a free UPnP AV MediaServer and control point (server and client) for Microsoft Windows WMC version 2.0 can be installed for usage with Windows Media Player 10 for Windows XP WMC version 3.0 can be installed for usage with Windows Media Player 11 for Windows XP WMC version 4.0 comes pre-installed on Windows Vista with its Windows Media Player 11 WMC can also refer to Windows Media Center. From the Windows Media Center entry in Wikipedia: In May 2015, Microsoft announced that Windows Media Center would be discontinued on Windows 10, and that it would be uninstalled when upgrading; but stated that those upgrading from a version of Windows that included the Media Center application would receive the paid Windows DVD Player app to maintain DVD playback functio
Social media use by the Islamic State
The Islamic State is widely known for its posting of disturbing content, such as beheading videos, on the internet. This propaganda is disseminated through websites and many social media platforms such as Twitter, Facebook, Telegram, and YouTube. By utilizing social media, the organization has garnered a strong following and successfully recruited tens of thousands of followers from around the world. In response to its successful use of social media, many websites and social media platforms have banned accounts and removed content promoting the Islamic State from their platforms. == Background == The Islamic State is a Jihadist militant group and a former unrecognised proto-state. The group sophisticatedly utilizes social media as a tool for spreading its message and for international recruitment. == Target audience == IS targets a variety of different groups both in the Middle East and Western Countries. There are a wide variety of motives for why fighters may be prompted to join IS. Researchers from Quantum cite nine attributes characteristic of a fighter looking to join IS: status seeking, identity seeking, revenge, redemption, thrill, ideology, justice, and death. The standard IS recruit, both from the Middle East and Western countries, is relatively young. The average age of IS fighters is around 26 years old, with 86% of recruits being male. Middle Eastern recruits come from economically disadvantaged backgrounds in Northern Iraq. Recent destruction in the Iraq War and Syrian Civil War has created hatred of Western Powers in the region. By 2025, researchers identified a significant shift toward targeting minors and adolescents, a phenomenon dubbed the "Alt-Jihad." This younger demographic is targeted not through theological arguments, but through a "victimhood-revenge" narrative that blends extremist ideology with pop-culture aesthetics in gaming environments like Roblox and Minecraft. In 2024 alone, 42 minors were arrested in Europe for involvement in IS-related plotting or propaganda. Western recruits are often second or third-generation immigrants. Computer scientists Zeeshan ul-hassan Usmani also found that the majority of the Western recruits do not feel "at home" in their home country. As a result, these fighters often have desires to go abroad and escape conditions in their home country. In addition to recruitment, IS's social media presence is also meant to intimidate and spread terror around the world. IS's posting of beheadings and other execution videos primarily target the Western world. == Content and messages == IS produces propaganda videos that range from video executions to full-length documentaries. The videos have a high production quality and incorporate montages, slow motion scenes, and are often accompanied by a short dialogue. IS has a dedicated team of over 100 media insurgents dedicated to recording these videos. While the group previously relied on glossy magazines like Dabiq, post-territorial strategies have shifted focus to the weekly newsletter Al-Naba. Unlike previous publications designed for recruitment, Al-Naba serves as a "central pillar" of the group's media strategy, focusing on bureaucratic reporting and military statistics to project a narrative of endurance and maintain internal cohesion among dispersed fighters. The IS executions typically consist of beheadings or mass shootings in retaliation to western intervention in IS territory. The particular videos that IS often post include executions of "enemies of the Caliphate," which often consist of westerners or Jordanian nationals. Most infamously, an executioner nicknamed Jihadi John was seen in many of these videos prior to his death in 2015. Jihadi John is notorious for executing many US, UK, and Japanese citizens such as Steven Sotloff, David Haines, and Alan Henning. In many of the videos and materials produced by IS, there is the theme of inclusion and brotherhood. Additionally, the videos also focus on three main messages: Convey narrative of global war and ultimate victory Radicalize populations globally Encourage international lone state actor and small cell attacks in support of IS These messages can be seen throughout all content produced by the Islamic State such as war documentaries, execution videos, and Rumiyah (magazine). == Social media usage == From 2013 to 2014, the organization primarily used mainstream platforms such as Twitter, Facebook, and YouTube. In 2014, these large social media platforms removed IS content. Since then, IS has chosen to utilize social media platforms that either protect their content or allow for content to quickly be reposted. These platforms of choice are Telegram, Justpaste.it, and Surespot, until the latter's shutdown in 2022. By 2025, the group had further diversified into decentralized platforms like Rocket.Chat and TamTam to evade moderation. IS also implements marketing initiatives like “Jihadist Follow Friday,” which encourages users to follow new IS-related accounts each Friday. This specific hashtag mirrors commonly used hashtags such as #motivation monday or #throwbackthursday. To augment their online presence and popularity, the organization encourages their followers to use a plethora of Arabic hashtags, which translate to #theFridayofSupportingISIS, and #CalamityWillBefalltheUS. This allows them to gain followers each week while promoting their community and message on a weekly basis. === Twitter === During 2014, there were an estimated 46,000 to 90,000 Twitter accounts that advocated for IS or were run by supporters of the group. In 2015, Twitter reported that it banned 125,000 IS sympathetic accounts. In 2016, it published an update of 325,000 deleted accounts. Though many accounts have been suspended, IS supporters often create new accounts. Twitter defines those who recreate accounts as “resurgents” and explains that these are often difficult accounts to remove completely, since they tend to pop back up in alternate forms. It is estimated that approximately 20% of all IS affiliated Twitter accounts can be traced back to fake accounts created by the same user. Many of these accounts are traced back to the “Baqiya family,” which is an online network of thousands of IS followers. Many of these accounts are active during important IS military victories. During the IS march on Mosul, there were about 42,000 tweets on Twitter supporting the invasion. === Telegram === During 2014, IS became very active on Telegram after many major social media platforms banned IS content and sympathetic accounts. Telegram is an encrypted messaging application. The platform by nature is created as an end-to-end user encryption platform. Further, it also has special features such as the self-destruct timer which erase all evidence and messages. The app has a user data protection policy because violating this policy could potentially damage the app’s brand of customer privacy. Government agencies have been unable to break Telegram's encryption technology. On Telegram, IS often uses the hashtag #KhilafahNews to attract their users. Telegram is used by IS to plan social media campaigns on alternate platforms. The organization also uses Telegram as an anchor platform to connect with their user base when their other accounts are banned on Twitter and Facebook. On 28 February 2016 a video was uploaded threatening to expose the najaasah and shoot the hesitates. Produced by Ibn-Altayb and distributed by Al-Hayat, the video shows footage of Bruxelles attacks and the victims. In July 2017, Telegram came under scrutiny from the media and news media outlets. It has been documented that IS gunmen have used this app to maintain contact with IS leaders in Raqqa days before terror attacks in Turkey, Berlin, and St. Petersburg. Despite concerns from Western media, there has been little to no action taken against IS accounts on Telegram. In April 2019 a video was uploaded in which they urged lone wolves to attempt to attack during the Holy Week in Sevilla and Málaga. In Sevilla, a jihadist who intended to perform a lone wolf attack was arrested. === TikTok === In October 2019, it was reported that IS recruitment content was discovered on TikTok. Approximately two dozen accounts were subsequently shut down in response. By 2025, TikTok had evolved into a "low-threshold" gateway for extremist recruitment, characterized by researchers as part of a "Virtual Caliphate Complex." Nearly 93 unofficial IS support groups, known as "feeder groups," were found to be repackaging official IS content into short-form videos with pink hearts, catchy music, and internet memes to evade detection and appeal to the "TikTok generation." This content often promotes a "victimhood-revenge" narrative rather than complex theology, specifically designed to radicalize minors. === Justpaste.it === Justpaste.it, an anonymous photo and text sharing website, has also been utilized heavily. With the option to lock images, the website allows anonymous
Graphics
Graphics (from Ancient Greek γραφικός (graphikós) 'pertaining to drawing, painting, writing, etc.') are visual images or designs on some surface, such as a wall, canvas, screen, paper, or stone, to inform, illustrate, or entertain. In contemporary usage, it includes a pictorial representation of data, as in design and manufacture, in typesetting and the graphic arts, and in educational and recreational software. Images that are generated by a computer are called computer graphics. Examples are photographs, drawings, line art, mathematical graphs, line graphs, charts, diagrams, typography, numbers, symbols, geometric designs, maps, engineering drawings, or other images. Graphics often combine text, illustration, and color. Graphic design may consist of the deliberate selection, creation, or arrangement of typography alone, as in a brochure, flyer, poster, web site, or book without any other element. The objective can be clarity or effective communication, association with other cultural elements, or merely the creation of a distinctive style. Graphics can be functional or artistic. The latter can be a recorded version, such as a photograph, or an interpretation by a scientist to highlight essential features, or an artist, in which case the distinction with imaginary graphics may become blurred. It can also be used for architecture. == History == The earliest graphics known to anthropologists studying prehistoric periods are cave paintings and markings on boulders, bone, ivory, and antlers, which were created during the Upper Palaeolithic period from 40,000 to 10,000 B.C. or earlier. Many of these were found to record astronomical, seasonal, and chronological details. Some of the earliest graphics and drawings are known to the modern world, from almost 6,000 years ago, are that of engraved stone tablets and ceramic cylinder seals, marking the beginning of the historical periods and the keeping of records for accounting and inventory purposes. Records from Egypt predate these and papyrus was used by the Egyptians as a material on which to plan the building of pyramids; they also used slabs of limestone and wood. From 600 to 250 BC, the Greeks played a major role in geometry. They used graphics to represent their mathematical theories such as the Circle Theorem and the Pythagorean theorem. In art, "graphics" is often used to distinguish work in a monotone and made up of lines, as opposed to painting. === Drawing === Drawing generally involves making marks on a surface by applying pressure from a tool or moving a tool across a surface. In which a tool is always used as if there were no tools it would be art. Graphical drawing is an instrumental guided drawing. === Printmaking === Woodblock printing, including images is first seen in China after paper was invented (about A.D. 105). In the West, the main techniques have been woodcut, engraving and etching, but there are many others. ==== Etching ==== Etching is an intaglio method of printmaking in which the image is incised into the surface of a metal plate using an acid. The acid eats the metal, leaving behind roughened areas, or, if the surface exposed to the acid is very thin, burning a line into the plate. The use of the process in printmaking is believed to have been invented by Daniel Hopfer (c. 1470–1536) of Augsburg, Germany, who decorated armour in this way. Etching is also used in the manufacturing of printed circuit boards and semiconductor devices. === Line art === Line art is a rather non-specific term sometimes used for any image that consists of distinct straight and curved lines placed against a (usually plain) background, without gradations in shade (darkness) or hue (color) to represent two-dimensional or three-dimensional objects. Line art is usually monochromatic, although lines may be of different colors. === Illustration === An illustration is a visual representation such as a drawing, painting, photograph or other work of art that stresses the subject more than form. The aim of an illustration is to elucidate or decorate a story, poem or piece of textual information (such as a newspaper article), traditionally by providing a visual representation of something described in the text. The editorial cartoon, also known as a political cartoon, is an illustration containing a political or social message. Illustrations can be used to display a wide range of subject matter and serve a variety of functions, such as: giving faces to characters in a story displaying a number of examples of an item described in an academic textbook (e.g. A Typology) visualizing step-wise sets of instructions in a technical manual communicating subtle thematic tone in a narrative linking brands to the ideas of human expression, individuality, and creativity making a reader laugh or smile for fun (to make laugh) funny === Graphs === A graph or chart is a graphic that represents tabular or numeric data. Charts are often used to make it easier to understand large quantities of data and the relationships between different parts of the data. === Diagrams === A diagram is a simplified and structured visual representation of concepts, ideas, constructions, relations, statistical data, etc., used to visualize and clarify the topic. === Symbols === A symbol, in its basic sense, is a representation of a concept or quantity; i.e., an idea, object, concept, quality, etc. In more psychological and philosophical terms, all concepts are symbolic in nature, and representations for these concepts are simply token artifacts that are allegorical to (but do not directly codify) a symbolic meaning, or symbolism. === Maps === A map is a simplified depiction of a space, a navigational aid which highlights relations between objects within that space. Usually, a map is a two-dimensional, geometrically accurate representation of a three-dimensional space. One of the first 'modern' maps was made by Waldseemüller. === Photography === One difference between photography and other forms of graphics is that a photographer, in principle, just records a single moment in reality, with seemingly no interpretation. However, a photographer can choose the field of view and angle, and may also use other techniques, such as various lenses to choose the view or filters to change the colors. In recent times, digital photography has opened the way to an infinite number of fast, but strong, manipulations. Even in the early days of photography, there was controversy over photographs of enacted scenes that were presented as 'real life' (especially in war photography, where it can be very difficult to record the original events). Shifting the viewer's eyes ever so slightly with simple pinpricks in the negative could have a dramatic effect. The choice of the field of view can have a strong effect, effectively 'censoring out' other parts of the scene, accomplished by cropping them out or simply not including them in the photograph. This even touches on the philosophical question of what reality is. The human brain processes information based on previous experience, making us see what we want to see or what we were taught to see. Photography does the same, although the photographer interprets the scene for their viewer. === Engineering drawings === An engineering drawing is a type of drawing and is technical in nature, used to fully and clearly define requirements for engineered items. It is usually created in accordance with standardized conventions for layout, nomenclature, interpretation, appearance (such as typefaces and line styles), size, etc. === Computer graphics === There are two types of computer graphics: raster graphics, where each pixel is separately defined (as in a digital photograph), and vector graphics, where mathematical formulas are used to draw lines and shapes, which are then interpreted at the viewer's end to produce the graphic. Using vectors results in infinitely sharp graphics and often smaller files, but, when complex, like vectors take time to render and may have larger file sizes than a raster equivalent. In 1950, the first computer-driven display was attached to MIT's Whirlwind I computer to generate simple pictures. This was followed by MIT's TX-0 and TX-2, interactive computing which increased interest in computer graphics during the late 1950s. In 1962, Ivan Sutherland invented Sketchpad, an innovative program that influenced alternative forms of interaction with computers. In the mid-1960s, large computer graphics research projects were begun at MIT, General Motors, Bell Labs, and Lockheed Corporation. Douglas T. Ross of MIT developed an advanced compiler language for graphics programming. S.A.Coons, also at MIT, and J. C. Ferguson at Boeing, began work in sculptured surfaces. GM developed their DAC-1 system, and other companies, such as Douglas, Lockheed, and McDonnell, also made significant developments. In 1968, ray tracing was first described by Arthur Appel of the IBM Research Center, Yorktown Heights, N
Scalable Video Coding
Scalable Video Coding (SVC) is a video compression standard developed jointly by the ITU-T and the ISO/IEC. The two organizations formed the Joint Video Team (JVT) to create the H.264/MPEG-4 AVC standard (ITU-T Rec. H.264 | ISO/IEC 14496-10 AVC). SVC aims to provide adaptable or scalable content, allowing a single encoded video stream to be decoded at various bitrates, resolutions, and quality levels, thus catering to diverse devices and network conditions. == History == In October 2003, the Moving Picture Experts Group (MPEG) issued a Call for Proposals on SVC Technology. Fourteen proposals were submitted, twelve of which utilized wavelet compression, while the remaining two were extensions of H.264/MPEG-4 AVC. The proposal from the Heinrich-Hertz-Institut (HHI) was selected by MPEG as the foundation for the SVC standardization project. In January 2005, MPEG and the Video Coding Experts Group (VCEG) agreed to finalize SVC as an amendment to the H.264/MPEG-4 AVC standard. In November 2008, Google launched Gmail Video Chat, which employed an H.264/SVC codec, marking the first consumer application of the standard. This service was succeeded by Google+ Hangouts in 2012. In 2011, Google Code highlighted SVC as the successor to the open-source RVC video chat engine, noting its prominence in 2010. == Principles of scalability == === Overview === Scalability refers to the ability to represent a video signal at multiple levels of detail within a single encoded bitstream. This enables decoding of a base layer for basic quality and additional enhancement layers for progressively higher quality. SVC defines three types of scalability: Spatial scalability: Supports multiple resolution levels. Temporal scalability: Enables varying frame rates. Quality scalability: Provides different image quality levels. === Spatial scalability === Spatial scalability allows the reconstruction of video at different resolutions, such as QCIF, CIF, or SD. This is achieved through a pyramidal decomposition into multiple spatial layers. === Temporal scalability === Temporal scalability adjusts the frame rate of the decoded video stream. Various frame rates are supported using a hierarchical structure of video frames. === Quality scalability === Quality scalability, or Signal-to-Noise Ratio (SNR) scalability, improves the signal-to-noise ratio of a layer, reducing quantization distortion between the original and reconstructed images. SVC supports two approaches: Fine Grain Scalability (FGS) and Coarse Grain Scalability (CGS). ==== Coarse Grain Scalability (CGS) ==== CGS incorporates quality scalability across spatial resolutions. Each spatial resolution is encoded as a separate layer, refining texture and motion data. For a given resolution, quality scalability is achieved by encoding multiple quality layers with progressively finer quantization steps, starting from a base layer with minimal quality. ==== Fine Grain Scalability (FGS) ==== FGS enables progressive refinement of transformed coefficients within a single spatial layer. The base quality layer is encoded using the AVC standard with an initial quantization parameter (QP) ensuring minimal acceptable quality. Subsequent refinement layers reduce the QP by six, halving the quantization step. The refinement data stream can be truncated at any point, allowing fine-grained quality scalability.