AI Content Remover

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  Outline of machine learning

  The following outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". ML involves the study and construction of algorithms that can learn from and make predictions on data. These algorithms operate by building a model from a training set of example observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions. == How can machine learning be categorized? == An academic discipline A branch of science An applied science A subfield of computer science A branch of artificial intelligence A subfield of soft computing Application of statistics === Paradigms of machine learning === Supervised learning, where the model is trained on labeled data Unsupervised learning, where the model tries to identify patterns in unlabeled data Reinforcement learning, where the model learns to make decisions by receiving rewards or penalties. == Applications of machine learning == Applications of machine learning Bioinformatics Biomedical informatics Computer vision Customer relationship management Data mining Earth sciences Email filtering Inverted pendulum (balance and equilibrium system) Natural language processing Named Entity Recognition Automatic summarization Automatic taxonomy construction Dialog system Grammar checker Language recognition Handwriting recognition Optical character recognition Speech recognition Text to Speech Synthesis Speech Emotion Recognition Machine translation Question answering Speech synthesis Text mining Term frequency–inverse document frequency Text simplification Pattern recognition Facial recognition system Handwriting recognition Image recognition Optical character recognition Speech recognition Recommendation system Collaborative filtering Content-based filtering Hybrid recommender systems Search engine Search engine optimization Social engineering == Machine learning hardware == Graphics processing unit Tensor processing unit Vision processing unit == Machine learning tools == Comparison of machine learning software Comparison of deep learning software === Machine learning frameworks === ==== Proprietary machine learning frameworks ==== Amazon Machine Learning Microsoft Azure Machine Learning Studio DistBelief (replaced by TensorFlow) ==== Open source machine learning frameworks ==== Apache Singa Apache MXNet Caffe PyTorch mlpack TensorFlow Torch CNTK Accord.Net Jax MLJ.jl – A machine learning framework for Julia === Machine learning libraries === Deeplearning4j Theano scikit-learn Keras === Machine learning algorithms === == Machine learning methods == === Instance-based algorithm === K-nearest neighbors algorithm (KNN) Learning vector quantization (LVQ) Self-organizing map (SOM) === Regression analysis === Logistic regression Ordinary least squares regression (OLSR) Linear regression Stepwise regression Multivariate adaptive regression splines (MARS) Regularization algorithm Ridge regression Least Absolute Shrinkage and Selection Operator (LASSO) Elastic net Least-angle regression (LARS) Classifiers Probabilistic classifier Naive Bayes classifier Binary classifier Linear classifier Hierarchical classifier === Dimensionality reduction === Dimensionality reduction Canonical correlation analysis (CCA) Factor analysis Feature extraction Feature selection Independent component analysis (ICA) Linear discriminant analysis (LDA) Multidimensional scaling (MDS) Non-negative matrix factorization (NMF) Partial least squares regression (PLSR) Principal component analysis (PCA) Principal component regression (PCR) Projection pursuit Sammon mapping t-distributed stochastic neighbor embedding (t-SNE) === Ensemble learning === Ensemble learning AdaBoost Boosting Bootstrap aggregating (also "bagging" or "bootstrapping") Ensemble averaging Gradient boosted decision tree (GBDT) Gradient boosting Random Forest Stacked Generalization === Meta-learning === Meta-learning Inductive bias Metadata === Reinforcement learning === Reinforcement learning Q-learning State–action–reward–state–action (SARSA) Temporal difference learning (TD) Learning Automata === Supervised learning === Supervised learning Averaged one-dependence estimators (AODE) Artificial neural network Case-based reasoning Gaussian process regression Gene expression programming Group method of data handling (GMDH) Inductive logic programming Instance-based learning Lazy learning Learning Automata Learning Vector Quantization Logistic Model Tree Minimum message length (decision trees, decision graphs, etc.) Nearest Neighbor Algorithm Analogical modeling Probably approximately correct learning (PAC) learning Ripple down rules, a knowledge acquisition methodology Symbolic machine learning algorithms Support vector machines Random Forests Ensembles of classifiers Bootstrap aggregating (bagging) Boosting (meta-algorithm) Ordinal classification Conditional Random Field ANOVA Quadratic classifiers k-nearest neighbor Boosting SPRINT Bayesian networks Naive Bayes Hidden Markov models Hierarchical hidden Markov model ==== Bayesian ==== Bayesian statistics Bayesian knowledge base Naive Bayes Gaussian Naive Bayes Multinomial Naive Bayes Averaged One-Dependence Estimators (AODE) Bayesian Belief Network (BBN) Bayesian Network (BN) ==== Decision tree algorithms ==== Decision tree algorithm Decision tree Classification and regression tree (CART) Iterative Dichotomiser 3 (ID3) C4.5 algorithm C5.0 algorithm Chi-squared Automatic Interaction Detection (CHAID) Decision stump Conditional decision tree ID3 algorithm Random forest SLIQ ==== Linear classifier ==== Linear classifier Fisher's linear discriminant Linear regression Logistic regression Multinomial logistic regression Naive Bayes classifier Perceptron Support vector machine === Unsupervised learning === Unsupervised learning Expectation-maximization algorithm Vector Quantization Generative topographic map Information bottleneck method Association rule learning algorithms Apriori algorithm Eclat algorithm ==== Artificial neural networks ==== Artificial neural network Feedforward neural network Extreme learning machine Convolutional neural network Recurrent neural network Long short-term memory (LSTM) Logic learning machine Self-organizing map ==== Association rule learning ==== Association rule learning Apriori algorithm Eclat algorithm FP-growth algorithm ==== Hierarchical clustering ==== Hierarchical clustering Single-linkage clustering Conceptual clustering ==== Cluster analysis ==== Cluster analysis BIRCH DBSCAN Expectation–maximization (EM) Fuzzy clustering Hierarchical clustering k-means clustering k-medians Mean-shift OPTICS algorithm ==== Anomaly detection ==== Anomaly detection k-nearest neighbors algorithm (k-NN) Local outlier factor === Semi-supervised learning === Semi-supervised learning Active learning Generative models Low-density separation Graph-based methods Co-training Transduction === Deep learning === Deep learning Deep belief networks Deep Boltzmann machines Deep Convolutional neural networks Deep Recurrent neural networks Hierarchical temporal memory Generative Adversarial Network Style transfer Transformer Stacked Auto-Encoders === Other machine learning methods and problems === Anomaly detection Association rules Bias-variance dilemma Classification Multi-label classification Clustering Data Pre-processing Empirical risk minimization Feature engineering Feature learning Learning to rank Occam learning Online machine learning PAC learning Regression Reinforcement Learning Semi-supervised learning Statistical learning Structured prediction Graphical models Bayesian network Conditional random field (CRF) Hidden Markov model (HMM) Unsupervised learning VC theory == Machine learning research == List of artificial intelligence projects List of datasets for machine learning research == History of machine learning == History of machine learning Timeline of machine learning == Machine learning projects == Machine learning projects: DeepMind Google Brain OpenAI Meta AI Hugging Face == Machine learning organizations == === Machine learning conferences and workshops === Artificial Intelligence and Security (AISec) (co-located workshop with CCS) Conference on Neural Information Processing Systems (NIPS) ECML PKDD International Conference on Machine Learning (ICML) ML4ALL (Machine Learning For All) == Machine learning publications == === Books on machine learning === Mathematics for Machine Learning Hands-On Machine Learning Scikit-Learn, Keras, and TensorFlow The Hundred-Page Machine Learning Book === Machine learning journals === Machine Learning Journal of Machine Learning Research (JMLR) Neural Computation == Pe

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  OpenWebRTC

  OpenWebRTC (OWR) is a free software stack that implements the WebRTC standard, a set of protocols and application programming interfaces defined by the World Wide Web Consortium (W3C) and the Internet Engineering Task Force (IETF). It is an alternative to the reference implementation that is based on software from Global IP Solutions (GIPS). It is published under the terms of the Simplified (2-clause) BSD license and officially supports iOS, Linux, OS X, and Android operating systems. It is meant to also work outside web browsers, e.g. to power native mobile apps. It is mostly written in C and based largely on the multimedia framework GStreamer and a number of other, smaller external libraries. It officially supports both VP8 and H.264 as video formats. For H.264 it uses OpenH264 to which Cisco pays the patent licensing bills. Development of OpenWebRTC started at Ericsson Research under the lead of Stefan Ålund. They released it as free software in September 2014, together with the proof-of-concept web browser "Bowser" that is based on the stack. Among other things, this initial version didn't support data channels yet and was said to still be less mature than Google's reference implementation.

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  Push technology

  Push technology, also known as server push, is a communication method where the communication is initiated by a server rather than a client. This approach is different from the "pull" method where the communication is initiated by a client. In push technology, clients can express their preferences for certain types of information or data, typically through a process known as the publish–subscribe model. In this model, a client "subscribes" to specific information channels hosted by a server. When new content becomes available on these channels, the server automatically sends, or "pushes," this information to the subscribed client. Under certain conditions, such as restrictive security policies that block incoming HTTP requests, push technology is sometimes simulated using a technique called polling. In these cases, the client periodically checks with the server to see if new information is available, rather than receiving automatic updates. == General use == Synchronous conferencing and instant messaging are examples of push services. Chat messages and sometimes files are pushed to the user as soon as they are received by the messaging service. Both decentralized peer-to-peer programs (such as WASTE) and centralized programs (such as IRC or XMPP) allow pushing files, which means the sender initiates the data transfer rather than the recipient. Email may also be a push system: SMTP is a push protocol (see Push e-mail). However, the last step—from mail server to desktop computer—typically uses a pull protocol like POP3 or IMAP. Modern e-mail clients make this step seem instantaneous by repeatedly polling the mail server, frequently checking it for new mail. The IMAP protocol includes the IDLE command, which allows the server to tell the client when new messages arrive. The original BlackBerry was the first popular example of push-email in a wireless context. Another example is the PointCast Network, which was widely covered in the 1990s. It delivered news and stock market data as a screensaver. Both Netscape and Microsoft integrated push technology through the Channel Definition Format (CDF) into their software at the height of the browser wars, but it was never very popular. CDF faded away and was removed from the browsers of the time, replaced in the 2000s with RSS (a pull system.) Other uses of push-enabled web applications include software updates distribution ("push updates"), market data distribution (stock tickers), online chat/messaging systems (webchat), auctions, online betting and gaming, sport results, monitoring consoles, and sensor network monitoring. == Examples == === Web push === The Web push proposal of the Internet Engineering Task Force is a simple protocol using HTTP version 2 to deliver real-time events, such as incoming calls or messages, which can be delivered (or "pushed") in a timely fashion. The protocol consolidates all real-time events into a single session which ensures more efficient use of network and radio resources. A single service consolidates all events, distributing those events to applications as they arrive. This requires just one session, avoiding duplicated overhead costs. Web Notifications are part of the W3C standard and define an API for end-user notifications. A notification allows alerting the user of an event, such as the delivery of an email, outside the context of a web page. As part of this standard, Push API is fully implemented in Chrome, Firefox, and Edge, and partially implemented in Safari as of February 2023. === HTTP server push === HTTP server push (also known as HTTP streaming) is a mechanism for sending unsolicited (asynchronous) data from a web server to a web browser. HTTP server push can be achieved through any of several mechanisms. As a part of HTML5 the Web Socket API allows a web server and client to communicate over a full-duplex TCP connection. Generally, the web server does not terminate a connection after response data has been served to a client. The web server leaves the connection open so that if an event occurs (for example, a change in internal data which needs to be reported to one or multiple clients), it can be sent out immediately; otherwise, the event would have to be queued until the client's next request is received. Most web servers offer this functionality via CGI (e.g., Non-Parsed Headers scripts on Apache HTTP Server). The underlying mechanism for this approach is chunked transfer encoding. Another mechanism is related to a special MIME type called multipart/x-mixed-replace, which was introduced by Netscape in 1995. Web browsers interpret this as a document that changes whenever the server pushes a new version to the client. It is still supported by Firefox, Opera, and Safari today, but it is ignored by Internet Explorer and is only partially supported by Chrome. It can be applied to HTML documents, and also for streaming images in webcam applications. The WHATWG Web Applications 1.0 proposal includes a mechanism to push content to the client. On September 1, 2006, the Opera web browser implemented this new experimental system in a feature called "Server-Sent Events". It is now part of the HTML5 standard. === Pushlet === In this technique, the server takes advantage of persistent HTTP connections, leaving the response perpetually "open" (i.e., the server never terminates the response), effectively fooling the browser to remain in "loading" mode after the initial page load could be considered complete. The server then periodically sends snippets of JavaScript to update the content of the page, thereby achieving push capability. By using this technique, the client doesn't need Java applets or other plug-ins in order to keep an open connection to the server; the client is automatically notified about new events, pushed by the server. One serious drawback to this method, however, is the lack of control the server has over the browser timing out; a page refresh is always necessary if a timeout occurs on the browser end. === Long polling === Long polling is itself not a true push; long polling is a variation of the traditional polling technique, but it allows emulating a push mechanism under circumstances where a real push is not possible, such as sites with security policies that require rejection of incoming HTTP requests. With long polling, the client requests to get more information from the server exactly as in normal polling, but with the expectation that the server may not respond immediately. If the server has no new information for the client when the poll is received, then instead of sending an empty response, the server holds the request open and waits for response information to become available. Once it does have new information, the server immediately sends an HTTP response to the client, completing the open HTTP request. Upon receipt of the server response, the client often immediately issues another server request. In this way the usual response latency (the time between when the information first becomes available and the next client request) otherwise associated with polling clients is eliminated. For example, BOSH is a popular, long-lived HTTP technique used as a long-polling alternative to a continuous TCP connection when such a connection is difficult or impossible to employ directly (e.g., in a web browser); it is also an underlying technology in the XMPP, which Apple uses for its iCloud push support. === Flash XML Socket relays === This technique, used by chat applications, makes use of the XML Socket object in a single-pixel Adobe Flash movie. Under the control of JavaScript, the client establishes a TCP connection to a unidirectional relay on the server. The relay server does not read anything from this socket; instead, it immediately sends the client a unique identifier. Next, the client makes an HTTP request to the web server, including this identifier with it. The web application can then push messages addressed to the client to a local interface of the relay server, which relays them over the Flash socket. The advantage of this approach is that it appreciates the natural read-write asymmetry that is typical of many web applications, including chat, and as a consequence it offers high efficiency. Since it does not accept data on outgoing sockets, the relay server does not need to poll outgoing TCP connections at all, making it possible to hold open tens of thousands of concurrent connections. In this model, the limit to scale is the TCP stack of the underlying server operating system. === Reliable Group Data Delivery (RGDD) === In services such as cloud computing, to increase reliability and availability of data, it is usually pushed (replicated) to several machines. For example, the Hadoop Distributed File System (HDFS) makes 2 extra copies of any object stored. RGDD focuses on efficiently casting an object from one location to many while saving bandwidth by sending minimal number of copies (only one in the best case) of

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  Digital cinematography

  Digital cinematography is the process of capturing (recording) a motion picture using digital image sensors rather than through film stock. As digital technology has improved in recent years, this practice has become dominant. Since the 2000s, most movies across the world have been captured as well as distributed digitally. Many vendors have brought products to market, including traditional film camera vendors like Arri and Panavision, as well as new vendors like Red, Blackmagic, Silicon Imaging, Vision Research and companies which have traditionally focused on consumer and broadcast video equipment, like Sony, GoPro, and Panasonic. As of 2023, professional 4K digital cameras were approximately equal to 35mm film in their resolution and dynamic range capacity. Some filmmakers still prefer to use film picture formats to achieve the desired results. == History == The basis for digital cameras are metal–oxide–semiconductor (MOS) image sensors. The first practical semiconductor image sensor was the charge-coupled device (CCD), based on MOS capacitor technology. Following the commercialization of CCD sensors during the late 1970s to early 1980s, the entertainment industry slowly began transitioning to digital imaging and digital video over the next two decades. The CCD was followed by the CMOS active-pixel sensor (CMOS sensor), developed in the 1990s. Beginning in the late 1980s, Sony began marketing the concept of "electronic cinematography," utilizing its analog Sony HDVS professional video cameras. The effort met with very little success. However, this led to one of the earliest high definition video shot feature movies, Julia and Julia (1987). Rainbow (1996) was the world's first film to utilize extensive digital post production techniques. Shot entirely with Sony's first Solid State Electronic Cinematography cameras and featuring over 35 minutes of digital image processing and visual effects, all post production, sound effects, editing and scoring were completed digitally. The Digital High Definition image was transferred to a 35mm negative via an electron beam recorder for theatrical release. The first digitally videoed and post produced feature was Windhorse, shot in Tibet and Nepal in 1996 on the Sony DVW-700WS Digital Betacam and the prosumer Sony DCR-VX1000. The offline editing (Avid) and the online post and color work (Roland House / da Vinci) were also all digital. The film, transferred to 35mm negative for theatrical release, won Best U.S. Feature at the Santa Barbara Film Festival in 1998. In 1997, with the introduction of HDCAM recorders and 1920 × 1080 pixel digital professional video cameras based on CCD technology, the idea, now re-branded as "digital cinematography," began to gain traction in the market. Shot and released in 1998, The Last Broadcast is believed by some to be the first feature-length video shot and edited entirely on consumer-level digital equipment. In May 1999, George Lucas challenged the supremacy of the movie-making medium of film for the first time by including footage filmed with high-definition digital cameras in Star Wars: Episode I – The Phantom Menace. The digital footage blended seamlessly with the footage shot on film and he announced later that year he would film its sequels entirely on hi-def digital video. Also in 1999, digital projectors were installed in four theaters for the showing of The Phantom Menace. In May 2000, Vidocq, which was directed by Pitof, began principal photography shot entirely using a Sony HDW-F900 camera, with the video being released in September the next year. According to the Guinness World Records, Vidocq is the first full length feature filmed in digital high resolution. In June 2000, Star Wars: Episode II – Attack of the Clones began principal photography shot entirely using a Sony HDW-F900 camera as Lucas had previously stated. The film was released in May 2002. In May 2001 Once Upon a Time in Mexico was also shot in 24 frame-per-second high-definition digital video, partially developed by George Lucas using a Sony HDW-F900 camera, following Robert Rodriguez's introduction to the camera at Lucas' Skywalker Ranch facility whilst editing the sound for Spy Kids. A lesser-known movie, Russian Ark (2002), was also shot with the same camera and was the first tapeless digital movie, recorded on HDD instead of tape. In 2009, Slumdog Millionaire became the first movie shot mainly in digital to be awarded the Academy Award for Best Cinematography. The highest-grossing movie in the history of cinema, Avatar (2009), not only was shot on digital cameras as well, but also made the main revenues at the box office no longer by film, but digital projection. Major movies shot on digital video overtook those shot on film in 2013. Since 2016 over 90% of major films were shot on digital video. As of 2017, 92% of films are shot on digital. Only 24 major films released in 2018 were shot on 35mm. Since the 2000s, most movies across the world have been captured as well as distributed digitally. Today, cameras from companies like Sony, Panasonic, JVC and Canon offer a variety of choices for shooting high-definition video. At the high-end of the market, there has been an emergence of cameras aimed specifically at the digital cinema market. These cameras from Sony, Vision Research, Arri, Blackmagic Design, Panavision, Grass Valley and Red offer resolution and dynamic range that exceeds that of traditional video cameras, which are designed for the limited needs of broadcast television. == Technology == Digital cinematography captures motion pictures digitally in a process analogous to digital photography. While there is a clear technical distinction that separates the images captured in digital cinematography from video, the term "digital cinematography" is usually applied only in cases where digital acquisition is substituted for film acquisition, such as when shooting a feature film. The term is seldom applied when digital acquisition is substituted for video acquisition, as with live broadcast television programs. === Recording === ==== Cameras ==== Professional cameras include the Sony CineAlta (F) Series, Blackmagic Cinema Camera, Red One, Arri D-20, D-21 and Alexa, Panavision Genesis, Silicon Imaging SI-2K, Thomson Viper, Vision Research Phantom, IMAX 3D camera based on two Vision Research Phantom cores, Weisscam HS-1 and HS-2, GS Vitec noX, and the Fusion Camera System. Independent micro-budget filmmakers have also pressed low-cost consumer and prosumer cameras into service for digital filmmaking. Flagship smartphones like the Apple iPhone have been used to shoot movies like Unsane (shot on the iPhone 7 Plus) and Tangerine (shot on three iPhone 5S phones) and in January 2018, Unsane's director and Oscar winner Steven Soderbergh expressed an interest in filming other productions solely with iPhones going forward. ==== Sensors ==== Digital cinematography cameras capture digital images using image sensors, either charge-coupled device (CCD) sensors or CMOS active-pixel sensors, usually in one of two arrangements. Single chip cameras designed specifically for the digital cinematography market often use a single sensor (much like digital photo cameras), with dimensions similar in size to a 16 or 35 mm film frame or even (as with the Vision 65) a 65 mm film frame. An image can be projected onto a single large sensor exactly the same way it can be projected onto a film frame, so cameras with this design can be made with PL, PV and similar mounts, in order to use the wide range of existing high-end cinematography lenses available. Their large sensors also let these cameras achieve the same shallow depth of field as 35 or 65 mm motion picture film cameras, which many cinematographers consider an essential visual tool. Codecs Professional raw video recording codecs include Blackmagic Raw, Red Raw, Arri Raw and Canon Raw. ==== Video formats ==== Unlike other video formats, which are specified in terms of vertical resolution (for example, 1080p, which is 1920×1080 pixels), digital cinema formats are usually specified in terms of horizontal resolution. As a shorthand, these resolutions are often given in "nK" notation, where n is the multiplier of 1024 such that the horizontal resolution of a corresponding full-aperture, digitized film frame is exactly 1024 n {\displaystyle 1024n} pixels. Here the "K" has a customary meaning corresponding to the binary prefix "kibi" (ki). For instance, a 2K image is 2048 pixels wide, and a 4K image is 4096 pixels wide. Vertical resolutions vary with aspect ratios though; so a 2K image with an HDTV (16:9) aspect ratio is 2048×1152 pixels, while a 2K image with a SDTV or Academy ratio (4:3) is 2048×1536 pixels, and one with a Panavision ratio (2.39:1) would be 2048×856 pixels, and so on. Due to the "nK" notation not corresponding to specific horizontal resolutions per format a 2K image lacking, for example, the typical 35mm film soundtrack space, is only 182

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  FreshBooks

  FreshBooks is accounting software operated by 2ndSite Inc. primarily for small and medium-sized businesses. It is a web-based software as a service (SaaS) model, that can be accessed through a desktop or mobile device. The company was founded in 2003 and is based in Toronto, Canada. == History == FreshBooks was founded in 2004 by Mike McDerment, Levi Cooperman, and Joe Sawada in Toronto, Ontario. McDerment incorporated a second company, BillSpring in January 2015 to work on new product development. It was rolled back into FreshBooks as an updated interface in 2016. Initially FreshBooks functioned like an electronic invoicing program targeting IT professionals. After the release of the new interface, the initial release of FreshBooks was referred to as "FreshBooks Classic." FreshBooks Classic was discontinued in 2022 after migrating users to the new platform. FreshBooks Classic's front-end application was built in PHP, and the backend services were built in Python while the new FreshBooks uses the same backend services with a JavaScript single-page application. == Product == FreshBooks is a subscription-based accounting software platform that provides features such as invoicing, accounts payable, expense and time tracking, retainers, fixed asset depreciation, purchase orders, payroll integrations, mileage tracking, double-entry accounting, and standard business reporting. Financial data is stored in the cloud on a unified ledger, enabling access from desktop and mobile devices. The platform includes a free API for integration with external applications and supports multiple tax rates and currencies. It also offers project management and payroll functionalities. Pricing is based on a recurring monthly fee. FreshBooks supports country-specific tax calculations, including GST and HST in Canada, sales taxes in the United States, and MTD compliance in the UK. == Operations == FreshBooks has its headquarters in Toronto, Canada with operations in North America, Europe and Australia. Founder Mike McDerment was the chief executive officer of the company from 2003 until 2021, when he stepped down and was replaced by Don Epperson, but stayed as the executive chair. Don Epperson had previously joined FreshBooks as executive director in 2019. == Funding == FreshBooks was initially self-funded. In 2014, the company raised a Series A venture investment of $30 million led by the venture capital firm Oak Investment Partners, with participation by Georgian Partners and Atlas Venture. In 2017, FreshBooks announced that it raised another $43 million in funding from Accomplice, Georgian Partners and Oak Investment Partners. On August 10, 2021, FreshBooks announced that it had secured $80.75 million in Series E funding and $50 million in debt financing. FreshBooks also reached a valuation of more than $1 billion.

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  Control communications

  In telecommunications, control communications is the branch of technology devoted to the design, development, and application of communications facilities used specifically for control purposes, such as for controlling (a) industrial processes, (b) movement of resources, (c) electric power generation, distribution, and utilization, (d) communications networks, and (e) transportation systems.

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  Anonymous social media

  Anonymous social media is a subcategory of social media wherein the main social function is to share and interact around content and information anonymously on mobile and web-based platforms. Another key aspect of anonymous social media is that content or information posted is not connected with particular online identities or profiles. == Background == Appearing very early on the web as mostly anonymous-confession websites, this genre of social media has evolved into various types and formats of anonymous self-expression. One of the earliest anonymous social media forums was 2channel, which was first introduced online on May 30, 1999, as a Japanese text board forum. With the way digital content is consumed and created continuously changing, the trending shift from web to mobile applications is also affecting anonymous social media. This can be seen as anonymous blogging, or various other format based content platforms such as nameless question and answer online platforms like Ask.fm introduced mobile versions of their services. The number of new networks joining the anonymous social sharing scene continues to grow rapidly. == Degrees of anonymity == Across different forms of anonymous social media there are varying degrees of anonymity. Some applications, such as Librex, require users to sign up for an account, even though their profile is not linked to their posts. While these applications remain anonymous, some of these sites can sync up with the user's contact list or location to develop a context within the social community and help personalize the user's experience, such as Yik Yak or Secret. Other sites, such as 4chan and 2channel, allow for a purer form of anonymity as users are not required to create an account, and posts default to the username of "Anonymous". While users can still be traced through their IP address, there are anonymizing services like I2P or various proxy server services that encrypt a user's identity online by running it through different routers. Secret users must provide a phone number or email when signing up for the service, and their information is encrypted into their posts. Stylometry poses a risk to the anonymity or pseudonymity of social media users, who may be identifiable by writing style; in turn, they may use adversarial stylometry to resist such identification. == Controversy == Apps such as Formspring, Ask, Sarahah, Whisper, and Secret have elicited discussion around the rising popularity of anonymity apps, including debate and anticipation about this social sharing class. As more and more platforms join the league of anonymous social media, there is growing concern about the ethics and morals of anonymous social networking as cases of cyber-bullying, and personal defamation occurs. Formspring, also known as spring.me, and Ask.fm have both been associated with teen suicides as a result of cyberbullying on the sites. Formspring has been associated with at least three teen suicides and Ask.fm with at least five. For instance, the app Secret got shut down due to its escalated use of cyberbullying. The app Yik Yak has also helped to contribute to more cyberbullying situations and, in turn, was blocked on some school networks. Their privacy policy meant that users could not be identified without a subpoena, search warrant, or court order. Another app called After School also sparked controversy for its app design that lets students post any anonymous content. Due to these multiple controversies, the app has been removed from both Apple and Google app stores. As the number of people using these platforms multiplies, unintended uses of the apps have increased, urging popular networks to enact in-app warnings and prohibit the use for middle and high school students. 70% of teens admit to making an effort to conceal their online behavior from their parents. Even Snapchat has some relation to the health of children after using social media. This is an app that is meant to be quick and simple but in many ways it can be overwhelming. A person can post something, and it will be gone in seconds. Oftentimes, the post that was made was inappropriate and harmful to another person. It's a never-ending cycle. Some of these apps have also been criticized for causing chaos in American schools, such as lockdowns and evacuations. In order to limit the havoc caused, anonymous apps are currently removing all abusive and harmful posts. Apps such as Yik Yak, Secret, and Whisper are removing these posts by outsourcing the job of content supervision to oversea surveillance companies. These companies hire a team of individuals to inspect and remove any harmful or abusive posts. Furthermore, algorithms are also used to detect and remove any abusive posts the individuals may have missed. Another method used by the anonymous app named Cloaq to reduce the number of harmful and abusive posts is to limit the number of users that can register during a certain period. Under this system, all contents are still available to the public, but only registered users can post. Other websites such as YouTube have gone on to create new policies regarding anonymity. YouTube now does not allow anonymous comments on videos. Users must have a Google account to like, dislike, comment or reply to comments on videos. Once a sign-in user "likes" a video, it will be added to that user's 'Liked video playlist'. YouTube changed their "Liked video playlist" policy in December 2019, allowing a signed-in user to keep their "Liked video playlist" private. Historically, these controversies and the rise of cyberbullying have been blamed on the anonymous aspect of many social media platforms, but about half of US adult online harassment cases do not involve anonymity, and researchers have found that if targeted harassment exists offline it will also be found online, because online harassment is a reflection of existing prejudices. == As platforms for anonymous discussion == Anonymous social media can be used for political discussion in countries where political opinions opposed to the government are normally suppressed, and allow persons of different genders to communicate freely in cultures where such communication is not generally accepted. In the United States, the 2016 presidential election led to an increase in the use of anonymous social media websites to express political stances. Moreover, anonymous social media can also provide authentic connection to complete anonymous communication. There have been cases where these anonymous platforms have saved individuals from life-threatening situation or spread news about a social cause. Additionally, anonymous social websites also allow internet users to communicate while also safeguarding personal information from criminal actors and corporations that sell users' data. A study in 2017 on the content posted to 4chan's /pol/ board found that the majority of the content was unique, including 70% of the 1 million images included in the studied data set. == Revenue generated by anonymous social media == === Anonymous apps === Generating revenue from anonymous apps has been a discussion for investors. Since little information is collected about the users, it is difficult for anonymous apps to advertise to users. However some apps, such as Whisper, have found a method to overcome this obstacle. They have developed a "keyword-based" approach, where advertisements are shown to users depending on certain words they type. The app Yik Yak has been able to capitalize on the features they provide. Anonymous apps such a Chrends take the approach of using anonymity to provide freedom of speech. Telephony app Burner has regularly been a top grossing utilities app in the iOS and Android app stores using its phone number generation technology. Despite the success of some anonymous apps, there are also apps, such as Secret, which have yet to find a way to generate revenue. The idea of an anonymous app has also caused mixed opinions within investors. Some investors have invested a large sum of money because they see the potential revenue generated within these apps. Other investors have stayed away from investing these apps because they feel these apps bring more harm than good. === Anonymous sites === There are several sources to generate revenue for anonymous social media sites. One source of revenue is by implementing programs such as a premium membership or a gift-exchanging program. Another source of revenue is by merchandising goods and specific usernames to users. In addition, sites such as FMyLife, have implemented a policy where the anonymous site will receive 50% of profit from apps that makes money off it. In terms of advertisements, some anonymous sites have had troubles implementing or attracting them. There are several reasons for this problem. Anonymous sites, such as 4chan, have received few advertisement offers due to some of the contents it generates. Other anonymous sites, such as Reddit, have been ca

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  Electronic game

  An electronic game is a game that uses electronics to create an interactive system with which a player can play. Video games are the most common form today, and for this reason the two terms are often used interchangeably. There are other common forms of electronic games, including handheld electronic games, standalone arcade game systems (e.g. pinball, slot machines), and exclusively non-visual products (e.g. audio games). == Arcade games == === Arcade video games === Electronic video arcade games make extensive use of solid state electronics and integrated circuits. In the past coin-operated arcade video games generally used custom per-game hardware often with multiple CPUs, highly specialized sound and graphics chips and/or boards, and the latest in computer graphics display technology. Recent arcade game hardware is often based on modified video game console hardware or high end pc components. Arcade games may feature specialized ambiance or control accessories, including fully enclosed dynamic cabinets with force feedback controls, dedicated lightguns, rear-projection displays, reproductions of car or plane cockpits and even motorcycle or horse-shaped controllers, or even highly dedicated controllers such as dancing mats and fishing rods. These accessories are usually what set modern arcade games apart from PC or console games, and they provide an experience that some gamers consider more immersive and realistic. Examples of arcade video games include: Galaxy Game (1971) Pong (1972) Space Invaders (1978) Galaxian (1979) Pac-Man (1980) Battlezone (1980) Donkey Kong (1981) Street Fighter II (1991) Mortal Kombat (1992) Fatal Fury (1992) Killer Instinct (1994) King of Fighters (1994–2005) Time Crisis (1995) Dance Dance Revolution (1998) DrumMania (1999) House of the Dead (1998) === Pinball and pachinko machines === Since the introduction of electromechanics to the pinball machine in 1933's Contact, pinball has become increasingly dependent on electronics as a means to keep score on the backglass and to provide quick impulses on the playfield (as with bumpers and flippers) for exciting gameplay. Unlike games with electronic visual displays, pinball has retained a physical display that is viewed on a table through glass. Similar games such as pachinko have also become increasingly dependent on electronics in modern times. Examples of pinball games include: The Addams Family (1991) Indiana Jones: The Pinball Adventure (1993) Star Trek: The Next Generation (1993) List of pinball machines === Redemption games and merchandisers === Redemption games such as Skee-Ball have been around since the days of the carnival game - well earlier than the development of the electronic game, however with modern advances many of these games have been re-worked to employ electronic scoring and other game mechanics. The use of electronic scoring mechanisms has allowed carnival or arcade attendants to take a more passive role, simply exchanging prizes for electronically dispensed coupons and occasionally emptying out the coin boxes or banknote acceptors of the more popular games. Merchandisers such as the Claw Crane are more recent electronic games in which the player must accomplish a seemingly simple task (e.g. remotely controlling a mechanical arm) with sufficient ability to earn a reward. Examples of redemption games include: Whac-A-Mole (1976) Skee-Ball - modern electric versions Examples of merchandisers include: Claw crane (1980) === Slot machines === The slot machine is a casino gambling machine with three or more reels which spin when a button is pushed. Though slot machines were originally operated mechanically by a lever on the side of the machine (the one arm) instead of an electronic button on the front panel as used on today's models, many modern machines still have a "legacy lever" in addition to the button on the front. Slot machines include a currency detector that validates the coin or money inserted to play. The machine pays off based on patterns of symbols visible on the front of the machine when it stops. Modern computer technology has resulted in many variations on the slot machine concept. == Audio games == An audio game is a game played on an electronic device such as—but not limited to—a personal computer. It is similar to a video game save that the only feedback device is audible rather than visual. Audio games originally started out as 'blind accessible'-games, but recent interest in audio games has come from sound artists, game accessibility researchers, mobile game developers, and mainstream video gamers. Most audio games run on a computer platform, although there are a few audio games for handhelds and video game consoles. Audio games feature the same variety of genres as video games, such as adventure games, racing games, etc. Examples of audio games include: Real Sound: Kaze no Regret (1997) Chillingham (2004) BBBeat (2005) === Tabletop games === A tabletop audio game is an audio game that is designed to be played on a table rather than a handheld game. Examples of tabletop audio games include: Brain Shift (1998) Who Wants to be a Millionaire? (2000) Electronic Battleship (1977) (Milton Bradley) Electronic battleship is a portable game with the objective of marking all enemy ships. When an enemy ship is marked, an electronic battleship makes an explosion sound. Milton Bradley created the Electronic battleship game in 1977 and was later acquired by Hasbro in 1984. Modern day electronic battleship features an interactive missile launching platform and advanced mode that features custom special attack pegs. Tabletop non-audio games include: Electronic Chess Boards (DGT) DGT is a line of electronic chess boards that are commonly used in FIDE chess tournaments and national tournaments such as USCF. Electronic Chess boards can be used to broadcast games live. == Electronic handhelds == The earliest form of dedicated console, handheld electronic games are characterized by their size and portability. Used to play interactive games, handheld electronic games are often miniaturized versions of video games. The controls, display and speakers are all part of a single unit, and rather than a general-purpose screen made up of a grid of small pixels, they usually have custom displays designed to play one game. This simplicity means they can be made as small as a digital watch, which they sometimes are. The visual output of these games can range from a few small light bulbs or LED lights to calculator-like alphanumerical screens; later these were mostly displaced by liquid crystal and Vacuum fluorescent display screens with detailed images and in the case of VFD games, color. Handhelds were at their most popular from the late 1970s into the early 1990s. They are both the predecessors to and inexpensive alternatives to the handheld game console. Examples of handheld electronic games include: Mattel Auto Race (1976) Simon (1978) Merlin (1978) Game & Watch (1980) MB Omni (1980) Bandai LCD Solarpower (1982) Entex Adventure Vision (1982) Lights Out (1995) == Home video games == A video game is a game that involves interaction with a user interface to generate visual feedback on a video device. The word video in video game traditionally referred to a raster display device. However, with the popular use of the term "video game", it now implies any type of display device. Term "digital game" has been offered by some in academia as an alternative term. === Computer games === A personal computer video game (also known as a computer game or simply PC game) is a video game played on a personal computer. This is opposed to video game consoles or arcade machines, which are not considered personal computers. Computer games became a form of video games, and since the earliest days of the medium, visual displays such as the cathode-ray tube have been used to relay game information. === Console games === A console game is a form of interactive multimedia used for entertainment. The game consists of manipulable images (and usually sounds) generated by a video game console, and displayed on a television or similar audio-video system. The game itself is usually controlled and manipulated using a handheld device connected to the console called a controller. The controller generally contains a number of buttons and directional controls (such as analog joysticks) each of which has been assigned a purpose for interacting with and controlling the images on the screen. The display, speakers, console, and controls of a console can also be incorporated into one small object known as a handheld game console. Console games are most frequently differentiated between by their compatibility with consoles belonging in the following categories: Traditional console, also called "home console" - A multi-game system that uses the screen of a television to produce graphics. Handheld game console - A multi-game system the screen and controls of which are compacted into a singl

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  Algorithmic inference

  Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field are computational learning theory, granular computing, bioinformatics, and, long ago, structural probability (Fraser 1966). The main focus is on the algorithms which compute statistics rooting the study of a random phenomenon, along with the amount of data they must feed on to produce reliable results. This shifts the interest of mathematicians from the study of the distribution laws to the functional properties of the statistics, and the interest of computer scientists from the algorithms for processing data to the information they process. == The Fisher parametric inference problem == Concerning the identification of the parameters of a distribution law, the mature reader may recall lengthy disputes in the mid 20th century about the interpretation of their variability in terms of fiducial distribution (Fisher 1956), structural probabilities (Fraser 1966), priors/posteriors (Ramsey 1925), and so on. From an epistemology viewpoint, this entailed a companion dispute as to the nature of probability: is it a physical feature of phenomena to be described through random variables or a way of synthesizing data about a phenomenon? Opting for the latter, Fisher defines a fiducial distribution law of parameters of a given random variable that he deduces from a sample of its specifications. With this law he computes, for instance "the probability that μ (mean of a Gaussian variable – omeur note) is less than any assigned value, or the probability that it lies between any assigned values, or, in short, its probability distribution, in the light of the sample observed". == The classic solution == Fisher fought hard to defend the difference and superiority of his notion of parameter distribution in comparison to analogous notions, such as Bayes' posterior distribution, Fraser's constructive probability and Neyman's confidence intervals. For half a century, Neyman's confidence intervals won out for all practical purposes, crediting the phenomenological nature of probability. With this perspective, when you deal with a Gaussian variable, its mean μ is fixed by the physical features of the phenomenon you are observing, where the observations are random operators, hence the observed values are specifications of a random sample. Because of their randomness, you may compute from the sample specific intervals containing the fixed μ with a given probability that you denote confidence. === Example === Let X be a Gaussian variable with parameters μ {\displaystyle \mu } and σ 2 {\displaystyle \sigma ^{2}} and { X 1 , … , X m } {\displaystyle \{X_{1},\ldots ,X_{m}\}} a sample drawn from it. Working with statistics S μ = ∑ i = 1 m X i {\displaystyle S_{\mu }=\sum _{i=1}^{m}X_{i}} and S σ 2 = ∑ i = 1 m ( X i − X ¯ ) 2 , where X ¯ = S μ m {\displaystyle S_{\sigma ^{2}}=\sum _{i=1}^{m}(X_{i}-{\overline {X}})^{2},{\text{ where }}{\overline {X}}={\frac {S_{\mu }}{m}}} is the sample mean, we recognize that T = S μ − m μ S σ 2 m − 1 m = X ¯ − μ S σ 2 / ( m ( m − 1 ) ) {\displaystyle T={\frac {S_{\mu }-m\mu }{\sqrt {S_{\sigma ^{2}}}}}{\sqrt {\frac {m-1}{m}}}={\frac {{\overline {X}}-\mu }{\sqrt {S_{\sigma ^{2}}/(m(m-1))}}}} follows a Student's t distribution (Wilks 1962) with parameter (degrees of freedom) m − 1, so that f T ( t ) = Γ ( m / 2 ) Γ ( ( m − 1 ) / 2 ) 1 π ( m − 1 ) ( 1 + t 2 m − 1 ) m / 2 . {\displaystyle f_{T}(t)={\frac {\Gamma (m/2)}{\Gamma ((m-1)/2)}}{\frac {1}{\sqrt {\pi (m-1)}}}\left(1+{\frac {t^{2}}{m-1}}\right)^{m/2}.} Gauging T between two quantiles and inverting its expression as a function of μ {\displaystyle \mu } you obtain confidence intervals for μ {\displaystyle \mu } . With the sample specification: x = { 7.14 , 6.3 , 3.9 , 6.46 , 0.2 , 2.94 , 4.14 , 4.69 , 6.02 , 1.58 } {\displaystyle \mathbf {x} =\{7.14,6.3,3.9,6.46,0.2,2.94,4.14,4.69,6.02,1.58\}} having size m = 10, you compute the statistics s μ = 43.37 {\displaystyle s_{\mu }=43.37} and s σ 2 = 46.07 {\displaystyle s_{\sigma ^{2}}=46.07} , and obtain a 0.90 confidence interval for μ {\displaystyle \mu } with extremes (3.03, 5.65). == Inferring functions with the help of a computer == From a modeling perspective the entire dispute looks like a chicken-egg dilemma: either fixed data by first and probability distribution of their properties as a consequence, or fixed properties by first and probability distribution of the observed data as a corollary. The classic solution has one benefit and one drawback. The former was appreciated particularly back when people still did computations with sheet and pencil. Per se, the task of computing a Neyman confidence interval for the fixed parameter θ is hard: you do not know θ, but you look for disposing around it an interval with a possibly very low probability of failing. The analytical solution is allowed for a very limited number of theoretical cases. Vice versa a large variety of instances may be quickly solved in an approximate way via the central limit theorem in terms of confidence interval around a Gaussian distribution – that's the benefit. The drawback is that the central limit theorem is applicable when the sample size is sufficiently large. Therefore, it is less and less applicable with the sample involved in modern inference instances. The fault is not in the sample size on its own part. Rather, this size is not sufficiently large because of the complexity of the inference problem. With the availability of large computing facilities, scientists refocused from isolated parameters inference to complex functions inference, i.e. re sets of highly nested parameters identifying functions. In these cases we speak about learning of functions (in terms for instance of regression, neuro-fuzzy system or computational learning) on the basis of highly informative samples. A first effect of having a complex structure linking data is the reduction of the number of sample degrees of freedom, i.e. the burning of a part of sample points, so that the effective sample size to be considered in the central limit theorem is too small. Focusing on the sample size ensuring a limited learning error with a given confidence level, the consequence is that the lower bound on this size grows with complexity indices such as VC dimension or detail of a class to which the function we want to learn belongs. === Example === A sample of 1,000 independent bits is enough to ensure an absolute error of at most 0.081 on the estimation of the parameter p of the underlying Bernoulli variable with a confidence of at least 0.99. The same size cannot guarantee a threshold less than 0.088 with the same confidence 0.99 when the error is identified with the probability that a 20-year-old man living in New York does not fit the ranges of height, weight and waistline observed on 1,000 Big Apple inhabitants. The accuracy shortage occurs because both the VC dimension and the detail of the class of parallelepipeds, among which the one observed from the 1,000 inhabitants' ranges falls, are equal to 6. == The general inversion problem solving the Fisher question == With insufficiently large samples, the approach: fixed sample – random properties suggests inference procedures in three steps: === Definition === For a random variable and a sample drawn from it a compatible distribution is a distribution having the same sampling mechanism M X = ( Z , g θ ) {\displaystyle {\mathcal {M}}_{X}=(Z,g_{\boldsymbol {\theta }})} of X with a value θ {\displaystyle {\boldsymbol {\theta }}} of the random parameter Θ {\displaystyle \mathbf {\Theta } } derived from a master equation rooted on a well-behaved statistic s. === Example === You may find the distribution law of the Pareto parameters A and K as an implementation example of the population bootstrap method as in the figure on the left. Implementing the twisting argument method, you get the distribution law F M ( μ ) {\displaystyle F_{M}(\mu )} of the mean M of a Gaussian variable X on the basis of the statistic s M = ∑ i = 1 m x i {\textstyle s_{M}=\sum _{i=1}^{m}x_{i}} when Σ 2 {\displaystyle \Sigma ^{2}} is known to be equal to σ 2 {\displaystyle \sigma ^{2}} (Apolloni, Malchiodi & Gaito 2006). Its expression is: F M ( μ ) = Φ ( m μ − s M σ m ) , {\displaystyle F_{M}(\mu )=\Phi {\left({\frac {m\mu -s_{M}}{\sigma {\sqrt {m}}}}\right)},} shown in the figure on the right, where Φ {\displaystyle \Phi } is the cumulative distribution function of a standard normal distribution. Computing a confidence interval for M given its distribution function is straightforward: we need only find two quantiles (for instance δ / 2 {\displaystyle \delta /2} and 1 − δ / 2 {\displaystyle 1-\delta /2} quantiles in case we are interested in a confidence interval of level δ symmetric in the tail's probabilities) as indicated on the left in the diagram showing the behavior of

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  Bridgefy

  Bridgefy is a Mexican software company with offices in Mexico and California, the United States, dedicated to developing mesh-networking technology for mobile apps. It was founded circa 2014 by Jorge Rios, Roberto Betancourt and Diego Garcia who conceived the idea while participating in a tech competition called StartupBus. Bridgefy's smartphone ad hoc network technology, apparently using Bluetooth Mesh, is licensed to other apps. The app gained popularity during protests in different countries since it can operate without Internet, using Bluetooth instead. Aware of the security issues of not using cryptography and the criticism surrounding it, Bridgefy announced in late October 2020 that they adopted the Signal protocol, in both their app and SDK, to keep information private, though security researchers have demonstrated that Bridgefy's usage of the Signal Protocol is insecure. == Usage == The app gained popularity as a communication tactic during the 2019–2020 Hong Kong protests and Citizenship Amendment Act protests in India, because it requires people who want to intercept the message to be physically close because of Bluetooth's limited range, and the ability to daisy-chain devices to send messages further than Bluetooth's range. == Security == In August 2020, researchers published a paper describing numerous attacks against the application, which allow de-anonymizing users, building social graphs of users’ interactions (both in real time and after the fact), decrypting and reading direct messages, impersonating users to anyone else on the network, completely shutting down the network, performing active man-in-the-middle attacks to read messages and even modify them. In response to the disclosures, developers acknowledged that "no part of the Bridgefy app is encrypted now" and gave a vague promise to release a new version "encrypted with top security protocols". Later developers said they plan to switch to Signal Protocol, which is widely recognized by cryptographers and used by Signal and WhatsApp. The Signal Protocol was integrated into the Bridgefy app and SDK by late October 2020, with the developers claiming to have included improvements such as the impossibility of a third person impersonating any other user, man-in-the-middle attacks done by modifying stored keys, and historical proximity tracking, among others. However, in 2022, the same security researchers, now including Kenny Paterson, published a paper describing how Bridgefy's usage of the Signal Protocol was incorrect, failing to remedy the previously discovered issues. The researchers performed a demonstration, showing that it was possible for users to intercept messages intended for others without the sender noticing. The researchers disclosed the vulnerabilities to the developers of Bridgefy in August 2021, but, according to the researchers, the developers had yet to resolve the issues as of June 2022. On July 31, 2023, the security firm 7asecurity released a blog post and pentest report of a white box penetration test and overall security review of the Bridgefy app in collaboration with the platform's developers. Their review, which began in November 2022 and concluded in May 2023, identified multiple critical vulnerabilities throughout the application. Many of the issues were fixed, or partially fixed, before the end of the audit, including user impersonation and biometric bypass. Bridgefy also published a blog post on August 8, 2023, announcing the audit results.

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  RadioVIS

  RadioVIS is a protocol for sideband signalling of images and text messages for a broadcast audio service to provide a richer visual experience. It is an application and sub-project of RadioDNS, which allows radio consumption devices to look up an IP-based service based on the parameters of the currently tuned broadcast station. In January 2015, the functionality of RadioVIS was integrated to Visual Slideshow (ETSI TS 101 499 v3.1.1). The original RVIS01 document is now deprecated. == Details == The protocol enables either Streaming Text Oriented Messaging Protocol (STOMP) or Comet to deliver text and image URLs to a client, with the images being acquired over a HTTP connection. The technology is currently implemented by a number of broadcasters across the world, including Global Radio, Bauer Radio in the UK, RTÉ in the Republic Of Ireland, Südwestrundfunk in Germany and a number of Australian media groups amongst others. A number of software clients exist to show the protocol, as well as hardware devices such as the Pure Sensia from Pure Digital, and the Colourstream from Roberts Radio.

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  CSS box model

  In web development, the CSS box model refers to how HTML elements are modeled in browser engines and how the dimensions of those HTML elements are derived from CSS properties. It is a fundamental concept for the composition of HTML webpages. The guidelines of the box model are described by web standards World Wide Web Consortium (W3C) specifically the CSS Working Group. For much of the late-1990s and early 2000s there had been non-standard compliant implementations of the box model in mainstream browsers. With the advent of CSS2 in 1998, which introduced the box-sizing property, the problem had mostly been resolved. == Specifics == The Cascading Style Sheets (CSS) specification describes how elements of web pages are displayed by graphical browsers. Section 4 of the CSS1 specification defines a "formatting model" that gives block-level elements—such as p and blockquote—a width and height, and three levels of boxes surrounding it: padding, borders, and margins. While the specification never uses the term "box model" explicitly, the term has become widely used by web developers and web browser vendors. All HTML elements can be considered "boxes", this includes div tag, p tag, or a tag. Each of those boxes has five modifiable dimensions: the height and width describe dimensions of the actual content of the box (text, images, ...) the padding describes the space between this content and the border of the box the border is any kind of line (solid, dotted, dashed...) surrounding the box, if present the margin is the space around the border According to the CSS1 specification, released by W3C in 1996 and revised in 1999, when a width or height is explicitly specified for any block-level element, it should determine only the width or height of the visible element, with the padding, borders, and margins applied afterward. Before CSS3, this box model was known as W3C box model, in CSS3, it is known as the content-box. The total width of a box is therefore margin-left + border-left + padding-left + width + padding-right + border-right + margin-right. Similarly, the total height of a box equals margin-top + border-top + padding-top + height + padding-bottom + border-bottom + margin-bottom. For example, the following CSS code would specify the box dimensions of each block belonging to 'my-class'. Moreover, each such box will have total height 140px and width 240px. CSS3 introduced the Internet Explorer box model to the standard, known referred to as border-box. == History == Before HTML 4 and CSS, very few HTML elements supported both border and padding, so the definition of the width and height of an element was not very contentious. However, it varied depending on the element. The HTML width attribute of a table defined the width of the table including its border. On the other hand, the HTML width attribute of an image defined the width of the image itself (inside any border). The only element to support padding in those early days was the table cell. Width for the cell was defined as "the suggested width for a cell content in pixels excluding the cell padding." In 1996, CSS introduced margin, border and padding for many more elements. It adopted a definition width in relation to content, border, margin and padding similar to that for a table cell. This has since become known as the W3C box model. At the time, very few browser vendors implemented the W3C box model to the letter. The two major browsers at the time, Netscape 4.0 and Internet Explorer 4.0 both defined width and height as the distance from border to border. This has been referred to as the traditional or the Internet Explorer box model. Internet Explorer in "quirks mode" includes the content, padding and borders within a specified width or height; this results in a narrower or shorter rendering of a box than would result following the standard behavior. The Internet Explorer box model behavior was often considered a bug, because of the way in which earlier versions of Internet Explorer handle the box model or sizing of elements in a web page, which differs from the standard way recommended by the W3C for the Cascading Style Sheets language. As of Internet Explorer 6, the browser supports an alternative rendering mode (called the "standards-compliant mode") which solves this discrepancy. However, for backward compatibility reasons, all versions still behave in the usual, non-standard way by default (see quirks mode). Internet Explorer for Mac is not affected by this non-standard behavior. === Workarounds === Internet Explorer versions 6 and onward are not affected by the bug if the page contains certain HTML document type declarations. These versions maintain the buggy behavior when in quirks mode for reasons of backward compatibility. For example, quirks mode is triggered: When the document type declaration is absent or incomplete; When an HTML 3 or earlier document is encountered; When an HTML 4.0 Transitional or Frameset document type declaration is used and a system identifier (URI) is not present; When an SGML comment or other unrecognized content appears before the document type declaration Internet Explorer 6 also uses quirks mode if there is an XML declaration prior to the document type declaration. Various workarounds have been devised to force Internet Explorer versions 5 and earlier to display Web pages using the W3C box model. These workarounds generally exploit unrelated bugs in Internet Explorer's CSS selector processing in order to hide certain rules from the browser. The best known of these workarounds is the "box model hack" developed by Tantek Çelik, a former Microsoft employee who developed this idea while working on Internet Explorer for the Macintosh. It involves specifying a width declaration for Internet Explorer for Windows, and then overriding it with another width declaration for CSS-compliant browsers. This second declaration is hidden from Internet Explorer for Windows by exploiting other bugs in the way that it parses CSS rules. The implementation of these CSS “hacks” has been further complicated by the public release of Internet Explorer 7, which has had some issues fixed, but not others, causing undesired results in pages using these hacks. Box model hacks have proven unreliable because they rely on bugs in browsers' CSS support that may be fixed in later versions. For this reason, some Web developers have instead recommended either avoiding specifying both width and padding for the same element or using conditional comment and/or CSS filters to work around the box model bug in older versions of Internet Explorer. == Support for Internet Explorer's box model == Web designer Doug Bowman has said that the original Internet Explorer box model represents a better, more logical approach. Peter-Paul Koch gives the example of a physical box, whose dimensions always refer to the box itself, including potential padding, but never its content. He says that this box model is more useful for graphic designers, who create designs based on the visible width of boxes rather than the width of their content. Bernie Zimmermann says that the Internet Explorer box model is closer to the definition of cell dimensions and padding used in the HTML table model. The W3C has included a "box-sizing" property in CSS3. When box-sizing: border-box; is specified for an element, any padding or border of the element is drawn inside the specified width and height, "as commonly implemented by legacy HTML user agents". Internet Explorer 8, WebKit browsers such as Apple Safari 5.1+ and Google Chrome, Gecko-based browsers such as Mozilla Firefox 29.0 and later, Opera 7.0 and later, and Konqueror 3.3.2 and later support the CSS3 box-sizing property. Gecko browsers previous than 29.0 support the same functionality using the browser-specific -moz-box-sizing property. border-box is the default box model used in Bootstrap framework.

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  Hardware for artificial intelligence

  Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. Since 2017, several consumer grade CPUs and SoCs have on-die NPUs. As of 2023, the market for AI hardware is dominated by GPUs. As of the 2020s, AI computation is dominated by graphics processing units (GPUs) and newer domain-specific accelerators such as Google's Tensor Processing Units (TPUs), AMD's Instinct MI300 series, and various on-device neural-processing units (NPUs) found in consumer hardware. == Scope == For the purposes of this article, AI hardware refers to computing components and systems specifically designed or optimized to accelerate artificial-intelligence workloads such as machine-learning training or inference. This includes general-purpose accelerators used for AI (for example, GPUs) and domain-specific accelerators (for example, TPUs, NPUs, and other AI ASICs). Event-based cameras are sometimes discussed in the context of neuromorphic computing, but they are input sensors rather than AI compute devices. Conversely, components such as memristors are basic circuit elements rather than specialized AI hardware when considered alone. == Lisp machines == Lisp machines were developed in the late 1970s and early 1980s to make artificial intelligence programs written in the programming language Lisp run faster. == Dataflow architecture == Dataflow architecture processors used for AI serve various purposes with varied implementations like the polymorphic dataflow Convolution Engine by Kinara (formerly Deep Vision), structure-driven dataflow by Hailo, and dataflow scheduling by Cerebras. == Component hardware == === AI accelerators === Since the 2010s, advances in computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. By 2019, graphics processing units (GPUs), often with AI-specific enhancements, had displaced central processing units (CPUs) as the dominant means to train large-scale commercial cloud AI. OpenAI estimated the hardware compute used in the largest deep learning projects from Alex Net (2012) to Alpha Zero (2017), and found a 300,000-fold increase in the amount of compute needed, with a doubling-time trend of 3.4 months. === General-purpose GPUs for AI === Since the 2010s, graphics processing units (GPUs) have been widely used to train and deploy deep learning models because of their highly parallel architecture and high memory bandwidth. Modern data-center GPUs include dedicated tensor or matrix-math units that accelerate neural-network operations. In 2022, NVIDIA introduced the Hopper-generation H100 GPU, adding FP8 precision support and faster interconnects for large-scale model training. AMD and other vendors have also developed GPUs and accelerators aimed at AI and high-performance computing workloads. === Domain-specific accelerators (ASICs / NPUs) === Beyond general-purpose GPUs, several companies have developed application-specific integrated circuits (ASICs) and neural processing units (NPUs) tailored for AI workloads. Google introduced the Tensor Processing Unit (TPU) in 2016 for deep-learning inference, with later generations supporting large-scale training through dense systolic-array designs and optical interconnects. Other vendors have released similar devices—such as Apple's Neural Engine and various on-device NPUs—that emphasize energy-efficient inference in mobile or edge computing environments. === Memory and interconnects === AI accelerators rely on fast memory and inter-chip links to manage the large data volumes of training and inference. High-bandwidth memory (HBM) stacks, standardized as HBM3 in 2022, provide terabytes-per-second throughput on modern GPUs and ASICs. These accelerators are often connected through dedicated fabrics such as NVIDIA's NVLink and NVSwitch or optical interconnects used in TPU systems to scale performance across thousands of chips.

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  W3C Device Description Working Group

  The W3C Device Description Working Group (DDWG), operating as part of the World Wide Web Consortium (W3C) Mobile Web Initiative (MWI), was chartered to "foster the provision and access to device descriptions that can be used in support of Web-enabled applications that provide an appropriate user experience on mobile devices." Mobile devices exhibit the greatest diversity of capabilities, and therefore present the greatest challenge to content adaptation technologies. The group published several documents, including a list of requirements for an interface to a Device Description Repository (DDR) and a standard interface meeting those requirements. The group was rechartered in 2006 to work in public towards the development of the Application Programming Interface (API) for a DDR. Early in 2007, the group launched a wiki and a blog to add to the public mailing list. The group subsequently published a formal vocabulary of core device properties, and an API called the DDR Simple API, which became a W3C Recommendation in December 2008. The group closed at the end of 2008, but with the intention of maintaining the Web pages, blog and wiki through W3C volunteer effort. == Publications == The DDWG published several W3C Working Group Notes and one W3C Recommendation. A W3C WG Note that articulates what the W3C and other organizations are doing or have already done with regard to device information. This document suggests an environment in which these technologies work together to meet the goals of content adaptation. The completed document was published on 31 October 2007. A W3C WG Note describing the ecosystem surrounding creation, maintenance and use of device descriptions. The completed document was published on 31 October 2007. A W3C WG Note describing a set of requirements for a reference repository of device descriptions. The completed document was published on 17 December 2007. A W3C WG Note describing a process to manage contributions to an initial core vocabulary, identification of key device properties, a formal initial core vocabulary and the identification of a maintainer for the core vocabulary. The details were contained in the Working Group Note describing the DDWG Core Vocabulary published on 14 April 2008. A W3C WG Note defining useful grouping and structure patterns in device descriptions. The Device Description Structures document was published as a Working Draft on 5 December 2008. The intention is that this document will be future input to other W3C groups. A W3C Recommendation defining a language-neutral programming interface to a Device Description Repository. The DDR Simple API was published on 5 December 2008. There is the possibility of future publications on the DDWG wiki describing implementations of the API in various languages, including Java, IDL, WSDL, C# etc. Much of the DDWG's material was developed in public via the DDWG Wiki and through their public mailing lists.

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  International Teletraffic Congress

  The International Teletraffic Congress (ITC) is the first international conference in networking science and practice. It was created in 1955 by Arne Jensen to initially cater to the emerging need to understand and model traffic in telephone networks using stochastic methodologies, and to bring together researchers with these considerations as a common theme. Up through World War II, teletraffic research was done mainly by engineers and mathematicians working in telephone companies. Most of their work was published in local or company journals. In 1955, however, the field acquired a formal, international, institutional structure, with the organization of the first International Teletraffic Congress (ITC). Over the years, it has broaden its scope to address a wide spectrum ranging from the mathematical theory of traffic processes, stochastic system modelling and analysis, traffic and performance measurements, network management, traffic engineering to network capacity planning and cost optimization, including network economics and reliability for various types of networks. ITC served as a forum for all theoretical fundamentals and engineering practices for large-scale deployment and operation of telecommunications networks. Since its inception, ITC witnessed the evolution of communications and networking: the influence of computer science on telecommunication, the advent of the Internet and the massive deployment of mobile communications and optics, the appearance of peer-to-peer networking and social networks, the ever increasing speed and flexibility of new communication technologies, networks, user devices, and applications, and the ever changing operation challenges arising from this development. ITC documented this evolution with contemporary measurement studies, performance analyses of new technologies, recommendations for provisioning and configuration, and greatly contributed to the methodological toolbox of network scientists. Today, with its conferences, specialist seminars, regional seminars, training courses and publications, the ITC aims at a worldwide forum for all questions related to network and service performance, management, and assessment, both present and futuristic. The notion of traffic is broadly used to encompass data traffic from the MAC layer all the way to application traffic in the application layer. The scope of ITC is thus ranging all issues embedding operations, design, planning, economics and performance analysis of current and emerging communication networks and services, to be addressed by applying a variety of tools from different fields, such as Stochastic Processes, Information theory, Control theory, Signal and Processing, Game theory and optimization techniques, Statistical methodologies and Artificial Intelligence techniques. The target audience of such issues is experts from research organizations, universities, equipment vendors and suppliers, network operators, service providers, system integrators and international technical organizations, guaranteeing a well-balanced contribution from theory, application, and practice. The general goal remains to bring researchers and practitioners together toward operational understanding of all types of current and future networks. The ITC is ruled by the International Advisory Council (IAC) which gathers a number of technical experts, from universities and the research arms of key corporations in the industry, from countries having a strong tradition in teletraffic development. The IAC responsibilities are to disseminate information on teletraffic which is of interest for the whole community and: to select the locations of Plenary Congresses and to ensure their high-level technical programme to support Specialist Seminars on specific topics of current interest to promote Regional Seminars for the dissemination of teletraffic concepts in developing countries to facilitate the liaison activity with the ITU through participation in the standardization process and in the Development Programme The technical program and the organization of each ITC event remains within the responsibilities of the hosting country, but with significant IAC support to guarantee that the event is consistent with the quality standards established during the previous congresses. The ITC Plenary Congresses were scheduled tri-annually from 1955 until 1995 when the interval became bi-annual to account for the ever-accelerating development of network technologies, products and services and the associated dramatic increases in network demands. Similarly, to better cover the impact of dramatic changes undergoing in the field of computer and communication systems, networks and usage, it has been decided to hold the Plenary Congress on an annual basis from 2009. == Content == Teletraffic science is the traditional term for all theoretical fundamentals and engineering practices to describe data flows in telecommunication networks, the performance of the usage of network resources, procedures for sizing of resources and engineering the networks for given traffic load and quality of service requirements. For more than 50 years of the 20th century, traffic or teletraffic has been identified primarily with telephone networks. With the huge development of computers, stored program control of network nodes and computer communication, the traditional teletraffic science field naturally extended to computer networks, mobile and wireless/optical networks, and for a wide spectrum of new applications. The convergence between the voice network, the Internet, the television and mobility raised new questions that request new models and tools to be developed. In addition, the development of community networks, home networking, multiple access networking technologies, and the advent of pervasive and ambient communications dictates new challenges to be addressed. Today, ITC addresses the emerging paradigms such as an increasing diversity of distributed applications and services over various media like mobile/optical networks, enabling new markets and economy. ITC has steered the evolutions in communications since its creation in 1955 and remains at the forefront of innovation regarding modeling and performance. The scientific roots of communications traffic are based on the theory of probability and stochastic processes, modelling and performance evaluation. Modelling is the key for the mathematical description and quantitative performance analysis. Traffic flows are described by stochastic processes with complex dependencies which have to be validated by traffic measurements. Modelling also includes operational properties of resource control reflected by service strategies such as queueing disciplines, admission control, and routing. The results of such performance analyses are used for resource dimensioning (sizing), resource management, and network optimization while providing targeted Quality of Service. Teletraffic science is closely related to methods of operation research (queueing theory, optimization, forecasting) and computational sciences (simulation technology distributed systems). In this context, ITC represents a wide community of researchers and practitioners and is regularly organizing events like Congresses, Specialist Seminars and Workshops in order to discuss the latest changes in the modelling, design and performance of communication systems, networks and services. === The evolution of technologies of the 20th century === ITC has been witnessing the change of communication and networking technologies which are reflected in the proceedings and programs of the congresses. The specialist seminars and the motto of the congresses thereby reflect the hot topics of that time and the evolution. Selected topics of the 70's, 80's and 90's were 1998: Traffic Issues related to Multimedia and Nomadic Communications 1995: Traffic Modeling and Measurement in Broadband and Mobile Communications 1990: Broadband Technologies: Architectures, Applications, Control and Performance 1986: ISDN Traffic Issues 1984: Fundamentals of Teletraffic Theory 1977: Modeling of SPC Exchanges and Data Networks === Recent topics in the 21st century === With the rise of the Internet, new networking paradigms and technologies but also new challenges emerged: 2020: Teletraffic in the era of beyond-5G and AI 2019: Networked Systems and Services 2018: Teletraffic in the Smart World 2017: Ubiquitous, software-based, and sustainable networks and services 2016: Digital Connected World 2015: Traffic, Performance and Big Data 2014: Towards a Sustainable World 2013: Energy Efficient and Green Networking 2010: Multimedia Applications - Traffic, Performance and QoE 2009: Network Virtualization - Concepts and Performance 2008: Future Internet Design and Experimental Facilities 2008: Quality of Experience 2002: Internet Traffic Engineering and Traffic Management == Arne Jensen Lifetime Achievement Awards == The Arne Jensen Lifetime A

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