ARMA International (formerly the Association of Records Managers and Administrators) is an American not-for-profit professional association for information professionals – primarily information management (including records management) and information governance, and related industry practitioners and vendors. The association provides educational opportunities and publications covering aspects of information management broadly. == History == The Association was founded in 1955. In 1975, the Association of Records Executives and Administrators (AREA) and the American Records Management Association merged to form ARMA International. The headquarters for ARMA International is located in Overland Park, Kansas. == Operations == ARMA International services professionals in the United States, Canada, Japan, and the United Kingdom. Its members include records managers, attorneys, information technology professionals, consultants, and archivists involved in various aspects of managing records and information assets. ARMA hosts an annual conference with the goal of bringing together record and information management professionals from around the world – In 2023, ARMA hosted conferences in both the United States and Canada. Topics addressed in the 120+ educational sessions include advanced technology, creating information structure, ediscovery and information law, information management fundamentals, information project management, and reducing organizational information risk. The expo features exhibitors displaying records and information technologies, products, and services.
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
Cloud9 (service provider)
Cloud9 is a mobile network operator focussed on providing mobile subscriptions over the air to programmable SIM cards, SoftSIMs and eSIMs. Their service is used in both smartphones and IoT devices. The company is privately held with headquarters in the United Kingdom. == History == Cloud9, originally owned by Wire9 Telecom Plc, funded and established by investor and telecom specialist, Lee Jones, before being sold for an undisclosed sum by Jones to billionaire Romain Zaleski. It established in the UK, Gibraltar, and Isle of Man as a domestic Mobile Network Operator. Cloud9 obtained spectrum licenses in the Isle of Man in 2007 and Gibraltar in 2010. Around 2011, Cloud9 decided to focus on supplying global SIM cards to save roaming charges. The Gibraltar spectrum licence was sold to another company. The business relocated its core network to Telehouse in London and became a subsidiary of BlueMango Technologies Ltd. Later the company was acquired by Wireless Logic Ltd. In 2013, Cloud9 acquired the IPR of Zynetix Ltd. Through this acquisition, the company achieved sales as an MVNE. In 2014, the company was voted as a Red Herring Top 100 Europe finalist. == Features == Cloud9 has shipped several million 'Travel SIMs'; all SIM cards have been branded with the logo of these resellers. Additionally, the company provides the digital signatures ('profiles' or 'IMSIs') that provide a SIM card with the ability to register with a network and function. These can be provisioned over the air to dynamic SIM cards such as programmable removable UICCs, SoftSIMs and eSIMs. They are members of the GSM Association and are involved in the GSMA remote SIM provisioning standard for eSIMs that will be released soon. The Cloud9 core network also supports 4G (HSS/PDG). Its Mobile Country Code is 234 and its Mobile Network Code is 18. TADIG code is GBRC9. The company has been allocated the following UK number ranges by Ofcom: 4478722, 4477000, 4474409, 4479782, 4479783 and 4475588 The core network is hosted on Cloud9 servers at Telehouse near Canary Wharf in London. Additional components are hosted in Amazon Web Services facilities around the world in order to minimise latency and provide scalability.
Digital journalism
Digital journalism, also known as netizen journalism or online journalism, is a contemporary form of journalism where editorial content is distributed via the Internet, as opposed to publishing via print or broadcast. What constitutes digital journalism is debated amongst scholars. However, the primary product of journalism, which is news and features on current affairs, is presented solely or in combination as text, audio, video, or some interactive forms like storytelling stories or newsgames and disseminated through digital media technology. Fewer barriers to entry, lowered distribution costs and diverse computer networking technologies have led to the widespread practice of digital journalism. It has democratized the flow of information that was previously controlled by traditional media including newspapers, magazines, radio and television. Most readers expect online journalists to be reliable and competent, but these journalists often fail to meet this standard because they have very short deadlines and do not have enough resources to produce decent work. Some have asserted that a greater degree of creativity can be exercised with digital journalism when compared to traditional journalism and traditional media. The digital aspect may be central to the journalistic message and remains, to some extent, within the creative control of the writer, editor and/or publisher. It has been acknowledged that reports of its growth have tended to be exaggerated. In fact, a 2019 Pew survey showed a 16% decline in the time spent on online news sites since 2016. In the United States, reports issued by the Federal Communications Commission (FCC) in 2011 and by the Government Accountability Office (GAO) and the Congressional Research Service (CRS) in 2023 found that increases in newsroom staffing at digital-native news websites from 2008 to 2020 were not offsetting cuts in newsroom staffing among newspapers (which numbered in the tens of thousands of jobs), and that newspapers and television (which had been seeing declining newsroom staffing alongside newspapers) still employed the majority of payrolled newsroom staff in the United States in 2022 while online-only news websites employed less than 10%. The GAO and CRS reports noted further that the reduction in subscription and advertising revenue for the U.S. newspaper industry from 2000 to 2020 that constituted the overwhelming majority of its inflation-adjusted total revenue was not being offset by digital circulation or online advertising despite almost two-thirds of U.S. advertising spending in total by 2020 being online. Also, while the FCC report noted that local television stations in the United States had become some of the largest providers of local news online, the FCC found in a 2021 working paper that inflation-adjusted advertising revenue for television stations fell nationally from 2010 to 2018. == Overview == Digital journalism flows as journalism flows and is difficult to pinpoint where it is and where it is going. In partnership with digital media, digital journalism uses facets of digital media to perform journalist tasks, for example, using the internet as a tool rather than a singular form of digital media. There is no absolute agreement as to what constitutes digital journalism. Mu Lin argues that, "Web and mobile platforms demand us to adopt a platform-free mindset for an all-inclusive production approach – create the [digital] contents first, then distribute via appropriate platforms." The repurposing of print content for an online audience is sufficient for some, while others require content created with the digital medium's unique features like hypertextuality. Fondevila Gascón adds multimedia and interactivity to complete the digital journalism essence. For Deuze, online journalism can be functionally differentiated from other kinds of journalism by its technological component which journalists have to consider when creating or displaying content. Digital journalistic work may range from purely editorial content like CNN (produced by professional journalists) online to public-connectivity websites like Slashdot (communication lacking formal barriers of entry). The difference of digital journalism from traditional journalism may be in its re-conceptualised role of the reporter in relation to audiences and news organizations. The expectations of society for instant information was important for the evolution of digital journalism. However, it is likely that the exact nature and roles of digital journalism will not be fully known for some time. Some researchers even argue that the free distribution of online content, online advertisement and the new way recipients use news could undermine the traditional business model of mass media distributors that is based on single-copy sales, subscriptions and the selling of advertisement space. == History == The first type of digital journalism, called teletext, was invented in the UK in 1970. Teletext is a system allowing viewers to choose which stories they wish to read and see it immediately. The information provided through teletext is brief and instant, similar to the information seen in digital journalism today. The information was broadcast between the frames of a television signal in what was called the vertical blanking interval or VBI. American journalist Hunter S. Thompson relied on early digital communication technology beginning by using a fax machine to report from the 1971 US presidential campaign trail as documented in his book Fear and Loathing on the Campaign Trail. After the invention of teletext was the invention of videotex, of which Prestel was the world's first system, launching commercially in 1979 with various British newspapers, such as the Financial Times lining up to deliver newspaper stories online through it. Videotex closed down in 1986 due to failing to meet end-user demand. American newspaper companies took notice of the new technology and created their own videotex systems, the largest and most ambitious being Viewtron, a service of Knight-Ridder launched in 1981. Others were Keycom in Chicago and Gateway in Los Angeles. All of them had closed by 1986. Next came computer Bulletin Board Systems. In the late 1980s and early 1990s, several smaller newspapers started online news services using BBS software and telephone modems. The first of these was the Albuquerque Tribune in 1989. Computer Gaming World in September 1992 broke the news of Electronic Arts' acquisition of Origin Systems on Prodigy, before its next issue went to press. Online news websites began to proliferate in the 1990s. An early adopter was The News & Observer in Raleigh, North Carolina which offered online news as Nando. Steve Yelvington wrote on the Poynter Institute website about Nando, owned by The N&O, by saying "Nando evolved into the first serious, professional news site on the World Wide Web". It originated in the early 1990s as "NandO Land". It is believed that a major increase in digital online journalism occurred around this time when the first commercial web browsers, Netscape Navigator (1994) and Internet Explorer (1995). By 1996, most news outlets had an online presence. Although journalistic content was repurposed from original text/video/audio sources without change in substance, it could be consumed in different ways because of its online form through toolbars, topically grouped content, and intertextual links. A twenty-four-hour news cycle and new ways of user-journalist interaction web boards were among the features unique to the digital format. Later, portals such as AOL and Yahoo! and their news aggregators (sites that collect and categorize links from news sources) led to news agencies such as The Associated Press to supplying digitally suited content for aggregation beyond the limit of what client news providers could use in the past. Also, Salon, was founded in 1995. In 2001, the American Journalism Review called Salon the Internet's "preeminent independent venue for journalism." In 2008, for the first time, more Americans reported getting their national and international news from the internet, rather than newspapers. Young people aged 18 to 29 now primarily get their news via the Internet, according to a Pew Research Center report. Audiences to news sites continued to grow due to the launch of new news sites, continued investment in news online by conventional news organizations, and the continued growth in internet audiences overall. Sixty-five percent of youth now primarily access the news online. Mainstream news sites are the most widespread form of online news media production. As of 2000, the vast majority of journalists in the Western world now use the internet regularly in their daily work. In addition to mainstream news sites, digital journalism is found in index and category sites (sites without much original content but multiple links to existing news sites), meta- and comment sites (sites about
Digital Cinema Package
A Digital Cinema Package (DCP) is a collection of digital files used to store and convey digital cinema (DC) audio, image, and data streams. The term was popularized by Digital Cinema Initiatives, LLC in its original recommendation for packaging DC contents. However, the industry tends to apply the term to the structure more formally known as the composition. A DCP is a container format for compositions, a hierarchical file structure that represents a title version. The DCP may carry a partial composition (e.g. not a complete set of files), a single complete composition, or multiple and complete compositions. The composition consists of a Composition Playlist (in XML format) that defines the playback sequence of a set of Track Files. Track Files carry the essence (audio, image, subtitles), which is wrapped using Material eXchange Format (MXF). Track Files must contain only one essence type. Two track files at a minimum must be present in every composition (see SMPTE ST429-2 D-Cinema Packaging – DCP Constraints, or Cinepedia): a track file carrying picture essence, and a track file carrying audio essence. The composition, consisting of a Composition Playlist (CPL) and associated track files, are distributed as a Digital Cinema Package (DCP). A composition is a complete representation of a title version, while the DCP need not carry a full composition. However, as already noted, it is commonplace in the industry to discuss the title in terms of a DCP, as that is the deliverable to the cinema. The Picture Track File essence is compressed using JPEG 2000 and the Audio Track File carries a 24-bit linear PCM uncompressed multichannel WAV file. Encryption may optionally be applied to the essence of a track file to protect it from unauthorized use. The encryption used is AES 128-bit in CBC mode. In practice, there are two versions of composition in use. The original version is called Interop DCP. In 2009, a specification was published by SMPTE (SMPTE ST 429-2 Digital Cinema Packaging – DCP Constraints) for what is commonly referred to as SMPTE DCP. SMPTE DCP is similar but not backwards compatible with Interop DCP, resulting in an uphill effort to transition the industry from Interop DCP to SMPTE DCP. SMPTE DCP requires significant constraints to ensure success in the field, as shown by ISDCF. While legacy support for Interop DCP is necessary for commercial products, new productions are encouraged to be distributed in SMPTE DCP. == Technical specifications == The DCP root folder (in the storage medium) contains a number of files, some used to store the image and audio contents, and some other used to organize and manage the whole playlist. === Picture MXF files === Picture contents may be stored in one or more reels corresponding to one or more MXF files. Each reel contains pictures as MPEG-2 or JPEG 2000 essence, depending on the adopted codec. MPEG-2 is no longer compliant with the DCI specification. JPEG 2000 is the only accepted compression format. Supported frame rates are: SMPTE (JPEG 2000) 24, 25, 30, 48, 50, and 60 fps @ 2K 24, 25, and 30 fps @ 4K 24 and 48 fps @ 2K stereoscopic MXF Interop (JPEG 2000) – Deprecated 24 and 48 fps @ 2K (MXF Interop can be encoded at 25 frame/s but support is not guaranteed) 24 fps @ 4K 24 fps @ 2K stereoscopic MXF Interop (MPEG-2) – Deprecated 23.976 and 24 fps @ 1920 × 1080 Maximum frame sizes are 2048 × 1080 for 2K DC, and 4096 × 2160 for 4K DC. Common formats are: SMPTE (JPEG 2000) Flat (1998 × 1080 or 3996 × 2160), = 1.85:1 aspect ratio Scope (2048 × 858 or 4096 × 1716), ~2.39:1 aspect ratio HDTV (1920 × 1080 or 3840 × 2160), 16:9 aspect ratio (~1.78:1) (although not specifically defined in the DCI specification, this resolution is DCI compliant per section 8.4.3.2). Full (2048 × 1080 or 4096 × 2160) (~1.9:1 aspect ratio, official name by DCI is Full Container. Not widely accepted in cinemas.) MXF Interop (MPEG-2) – Deprecated Full Frame (1920 × 1080) 12 bits per component precision (36 bits total per pixel) XYZ' colorspace; the prime mark indicates gamma encoding (gamma=2.6) Maximum bit rate is 250 Mbit/s (1.3 MBytes per frame at 24 frame per second) === Sound MXF files === Sound contents are also stored in reels corresponding to picture reels in number and duration. In case of multilingual features, separate reels are required to convey different languages. Each file contains linear PCM essence. Sampling rate is 48,000 or 96,000 samples per second Sample precision of 24 bits Linear mapping (no companding) Up to 16 independent channels === Asset map file === List of all files included in the DCP, in XML format. === Composition playlist file === Defines the playback order during presentation. The order is saved in XML format in this file; each picture and sound reel is identified by its UUID. In the following example, a reel is composed by picture and sound: === Packing list file or package key list (PKL) === All files in the composition are hashed and their hash is stored here, in XML format. This file is generally used during ingestion in a digital cinema server to verify if data have been corrupted or tampered with in some way. For example, an MXF picture reel is identified by the following
Example-based machine translation
Example-based machine translation (EBMT) is a method of machine translation often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base at run-time. It is essentially a translation by analogy and can be viewed as an implementation of a case-based reasoning approach to machine learning. == Translation by analogy == At the foundation of example-based machine translation is the idea of translation by analogy. When applied to the process of human translation, the idea that translation takes place by analogy is a rejection of the idea that people translate sentences by doing deep linguistic analysis. Instead, it is founded on the belief that people translate by first decomposing a sentence into certain phrases, then by translating these phrases, and finally by properly composing these fragments into one long sentence. Phrasal translations are translated by analogy to previous translations. The principle of translation by analogy is encoded to example-based machine translation through the example translations that are used to train such a system. Other approaches to machine translation, including statistical machine translation, also use bilingual corpora to learn the process of translation. == History == Example-based machine translation was first suggested by Makoto Nagao in 1984. He pointed out that it is especially adapted to translation between two totally different languages, such as English and Japanese. In this case, one sentence can be translated into several well-structured sentences in another language, therefore, it is no use to do the deep linguistic analysis characteristic of rule-based machine translation. == Example == Example-based machine translation systems are trained from bilingual parallel corpora containing sentence pairs like the example shown in the table above. Sentence pairs contain sentences in one language with their translations into another. The particular example shows an example of a minimal pair, meaning that the sentences vary by just one element. These sentences make it simple to learn translations of portions of a sentence. For example, an example-based machine translation system would learn three units of translation from the above example: How much is that X ? corresponds to Ano X wa ikura desu ka. red umbrella corresponds to akai kasa small camera corresponds to chiisai kamera Composing these units can be used to produce novel translations in the future. For example, if we have been trained using some text containing the sentences: President Kennedy was shot dead during the parade. and The convict escaped on July 15th., then we could translate the sentence The convict was shot dead during the parade. by substituting the appropriate parts of the sentences. == Phrasal verbs == Example-based machine translation is best suited for sub-language phenomena like phrasal verbs. Phrasal verbs have highly context-dependent meanings. They are common in English, where they comprise a verb followed by an adverb and/or a preposition, which are called the particle to the verb. Phrasal verbs produce specialized context-specific meanings that may not be derived from the meaning of the constituents. There is almost always an ambiguity during word-to-word translation from source to the target language. As an example, consider the phrasal verb "put on" and its Hindustani translation. It may be used in any of the following ways: Ram put on the lights. (Switched on) (Hindustani translation: Jalana) Ram put on a cap. (Wear) (Hindustani translation: Pahenna)
MIDI Show Control
MIDI Show Control (MSC), is a real-time System Exclusive extension of the international Musical Instrument Digital Interface (MIDI) standard. MSC enables all types of entertainment equipment to communicate with each other through the process of show control. The MIDI Show Control protocol is a technical standard ratified by the MIDI Manufacturers Association in 1991, which allows entertainment control devices to talk with each other and with computers to perform show control functions in live and prerecorded entertainment applications. Just like musical MIDI, MSC does not transmit the actual show media - it simply transmits digital information about a multimedia performance. == How MSC works == When any cue is called by a user (typically a stage manager) and/or preprogrammed timeline in a show control software application, the show controller transmits one or more MSC messages from its 'MIDI Out' port. A typical MSC message sequence is: the user has just called a cue the cue is for lighting device 3 the cue is number 45.8 the cue is in cue list 7 MSC messages are serially transmitted in the same way as musical messages and are fully compatible with all conventional MIDI hardware; however, many modern MSC devices now use Ethernet communications for higher bandwidth and the flexibility afforded by networks. Other performance parameters are also transmitted, such as lighting desk submaster settings using MSC SET messages. All cues that a media control device is capable of playing are assigned MSC messages within the Show Controller's cue list and they are transmitted from its MIDI Out port at the appropriate show time, depending on the actions of the user and the show controller's internally timed sequences. All MSC-compatible instruments follow the MSC specification and thus transmit identical MSC messages for identical MSC events, such as the playing of a certain cue on the media controller. Since they follow a published standard, all MSC devices can communicate with and understand each other, as well as with computers that have been programmed to understand MSC messages using the MSC Command Set. All MSC compatible instruments have a built-in MIDI interface and many now follow one of the various MIDI-over-Ethernet protocols. == History == To create the MSC spec, Charlie Richmond headed the USITT MIDI Forum on their Callboard Network in 1990, which included developers and designers from the theatre sound and lighting industry from around the world. It is believed that this was the first international standard to be developed without a single physical meeting of the participants. This Forum created the MSC standard between January and September 1990. This was ratified by the MIDI Manufacturers Association (MMA) in January 1991, and the Japan MIDI Standards Committee (JMSC) later that year, becoming a part of the standard MIDI specification in August 1991. The first show to fully use the MSC specification was the Magic Kingdom Parade at Walt Disney World's Magic Kingdom in September 1991. == MIDI Show Control software ==