AI Art Checker

AI Art Checker — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Concordancer

  A concordancer is a computer program that automatically constructs a concordance—an alphabetised index of every occurrence of a word or phrase in a body of text, each entry displayed with its surrounding context. Concordancers are primary tools in corpus linguistics, lexicography, computer-assisted translation, and language teaching. The most common display format is the key word in context (KWIC) layout, in which each hit appears centred on a line with a fixed span of words to its left and right, enabling rapid scanning of usage patterns across many occurrences. == History == === Pre-computational concordances === The compilation of concordances predates computers by many centuries. Around 1230, the French Dominican cardinal Hugh of Saint-Cher directed a team of friars in assembling a concordance of the Latin Vulgate Bible, generally regarded as the first systematic concordance of any text. To help readers locate passages, Hugh divided each biblical chapter into lettered sections. Later milestones include a Hebrew Old Testament concordance compiled by Rabbi Mordecai Nathan (1448), Alexander Cruden's Complete Concordance to the Holy Scriptures (1737), and the manuscript Asaf ha-Mazkir, an unfinished concordance to the Babylonian Talmud compiled by Moses Rigotz around the turn of the 19th century. === First computer concordance === The first concordance produced with computing assistance was the Index Thomisticus, a comprehensive lexical index of the writings of and around Thomas Aquinas, totalling approximately 10.6 million Latin words. The Italian Jesuit priest Roberto Busa conceived the project in 1946 and secured the sponsorship of IBM in 1949 after a meeting with chairman Thomas J. Watson. Keypunch operators in Gallarate, Italy, encoded the texts onto punched cards from around 1950. IBM executive Paul Tasman developed the processing methods. The full 56-volume printed edition was completed around 1980, followed by a CD-ROM edition in 1989 and a web-accessible version in 2005. === The KWIC format === The key word in context (KWIC) display was formalised as a computational technique by Hans Peter Luhn, a researcher at IBM, in a 1960 paper in American Documentation. In KWIC output, each instance of the search term (the node word) is centred on a line with a fixed window of words to each side; sorting the resulting lines alphabetically by the immediately adjacent word reveals collocational and phraseological patterns at a glance. === COCOA === One of the first dedicated concordancing programs was COCOA (COunt and COncordance Generation on Atlas), created in 1965 by D. B. Russell at University College London and the Atlas Computer Laboratory in Harwell, Oxfordshire. Written in approximately 4,000 cards of FORTRAN, it processed text annotated with flat, non-hierarchical markup tags and could produce word counts and concordances in multiple languages. Within its first six months COCOA had been applied to texts in at least six languages. A second version designed for multiple mainframe platforms was distributed to British computing centres in the mid-1970s. Growing dissatisfaction with its interface and the eventual withdrawal of Atlas Laboratory support prompted British funding bodies to commission a successor program. === Oxford Concordance Program === The Oxford Concordance Program (OCP) was designed and written in FORTRAN by Susan Hockey and Ian Marriott at Oxford University Computing Services (OUCS) between 1979 and 1980 and first released in 1981. Hockey and Marriott acknowledged that OCP owed much to COCOA and the CLOC system at the University of Birmingham. OCP accepted COCOA-format markup to encode metadata such as author, act, scene, and line number, and was described by its authors as "a machine-independent text analysis program for producing word lists, indices and concordances in a variety of languages and alphabets." By the mid-1980s it had been licensed to approximately 240 institutions in 23 countries. A personal computer version, Micro-OCP, was developed for the IBM PC and sold by Oxford University Press from the late 1980s. Version 2 was rewritten in 1985–86 and documented in the same 1987 article by Hockey and co-author John Martin. === Personal computer era === The availability of affordable personal computers in the 1980s and 1990s enabled standalone concordancing applications that analysts could run locally without specialist computing facilities. MicroConcord, developed by Mike Scott and Tim Johns and published by Oxford University Press in 1993 for MS-DOS, was among the first concordancers designed specifically for classroom language teaching. WordSmith Tools, also developed by Mike Scott, was first released in 1996 and became one of the most widely used corpus analysis suites in academic linguistics research. Other tools from this era include TACT (University of Toronto, 1989), a suite of MS-DOS freeware programs for literary text analysis, and MonoConc, a Windows concordancer created by Michael Barlow. === Web-based concordancers === From the late 1990s onwards, web-based concordancers hosted on remote servers gave researchers browser access to large preloaded corpora without requiring local storage or processing. The Sketch Engine, developed by Adam Kilgarriff and Pavel Rychlý (Masaryk University), was launched commercially in July 2003 by Lexical Computing Limited and introduced word sketches—automatically generated one-page profiles of a word's typical grammatical relations and collocations. AntConc, created by Laurence Anthony at Waseda University, Tokyo, was first released in 2002 as freeware for Windows, macOS, and Linux. == Features == Modern concordancers typically offer a range of analytical functions beyond basic KWIC display. These commonly include: KWIC display with the node word centred and context words in aligned columns, sortable by the word one, two, or three positions to the left or right of the node (L1–L3 and R1–R3) Concordance plots, visualising the distribution of hits as marks along a scaled bar representing each text in the corpus Frequency and word lists, both alphabetical and ranked by frequency Collocation statistics, identifying words that co-occur with the search term more often than chance, quantified by measures such as mutual information, the t-score, or log-likelihood Keyword analysis, comparing word frequencies between a study corpus and a reference corpus to identify statistically distinctive items N-gram analysis, finding frequently recurring word sequences of a specified length Part-of-speech tagging integration, allowing searches filtered to particular grammatical categories Unicode support for multilingual text Bilingual and parallel concordancers additionally display aligned text in two or more languages side by side, enabling comparison of translation equivalents across language pairs. == Notable concordancers == === WordSmith Tools === Created by Mike Scott and first released in 1996, WordSmith Tools is a Windows corpus analysis suite that evolved from MicroConcord. Its three core modules are Concord (KWIC concordances), WordList (frequency and alphabetical word lists), and Keywords (statistical keyword identification relative to a reference corpus). Oxford University Press used WordSmith Tools for dictionary preparation work. Version 4.0 is freely available; later versions are sold by Lexical Analysis Software Limited. === AntConc === AntConc is a freeware, multiplatform concordancing toolkit created by Laurence Anthony, Professor of Applied Linguistics at Waseda University, Tokyo. First released in 2002 and formally described in a 2005 academic paper, it runs on Windows, macOS, and Linux. Its tools include a KWIC concordancer, a concordance plot for visualising distribution across texts, a collocates tool, a keyword list, and an n-gram analysis module. Because it is free and requires only plain text files, AntConc is widely used in linguistics courses and independent research worldwide. === Sketch Engine === The Sketch Engine is a corpus management and query system co-created by Adam Kilgarriff and Pavel Rychlý and launched in 2003 by Lexical Computing Limited. It provides browser-based access to over 800 corpora in more than 100 languages. Beyond concordance searching, it offers word sketches, collocation analysis, distributional thesaurus construction, keyword and terminology extraction, and diachronic analysis. It is used by major publishers including Macmillan and Oxford University Press for lexicographic research. A subset tool, SKELL (Sketch Engine for Language Learning), is freely accessible to individual learners. === Wmatrix === Wmatrix is a web-based corpus processing environment developed by Paul Rayson at the University Centre for Computer Corpus Research on Language (UCREL), Lancaster University. Alongside concordances and frequency lists, Wmatrix integrates CLAWS part-of-speech tagging and the USAS semantic tagger, enabling keyword analysis simultane

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  Honeywell JetWave

  Honeywell's JetWave is a piece of satellite communications hardware produced by Honeywell that enables global in-flight internet connectivity. Its connectivity is provided using Inmarsat’s GX Aviation network. The JetWave platform is used in business and general aviation, as well as defense and commercial airline users. == History == In 2012, Honeywell announced it would provide Inmarsat with the hardware for its GX Ka-band in-flight connectivity network. The Ka-band (pronounced either "kay-ay band" or "ka band") is a portion of the microwave part of the electromagnetic spectrum defined as frequencies in the range 27.5 to 31 gigahertz (GHz). In satellite communications, the Ka-band allows higher bandwidth communication. In 2017, after five years and more than 180 flight hours and testing, JetWave was launched as part of GX Aviation with Lufthansa Group. Honeywell’s JetWave was the exclusive terminal hardware option for the Inmarsat GX Aviation network; however, the exclusivity clause in that contract has expired. In July 2019, the United States Air Force selected Honeywell’s JetWave satcom system for 70 of its C-17 Globemaster III cargo planes. In December 2019, it was reported that six AirAsia aircraft had been fitted with Inmarsat’s GX Aviation Ka-band connectivity system and is slated to be implemented fleetwide across AirAsia’s Airbus A320 and A330 models in 2020, requiring installation of JetWave atop AirAsia’s fuselages. Today, Honeywell’s JetWave hardware is installed on over 1,000 aircraft worldwide. In August 2021, the Civil Aviation Administration of China approved a validation of Honeywell’s MCS-8420 JetWave satellite connectivity system for Airbus 320 aircraft. In December 2021, Honeywell, SES, and Hughes Network Systems demonstrated multi-orbit high-speed airborne connectivity for military customers using Honeywell’s JetWave MCX terminal with a Hughes HM-series modem, and SES satellites in both medium Earth orbit (MEO) and geostationary orbit (GEO). The tests achieved full duplex data rates of more than 40 megabits per second via a number of SES' (GEO) satellites including GovSat-1, and the high-throughput, low-latency O3b MEO satellite constellation, with connections moving between GEO/MEO links in under 30 sec. == Uses == === Commercial aviation === Honeywell’s JetWave enables air transport and regional aircraft to connect to Inmarsat’s GX Aviation network. The multichannel satellite (MSC) JetWave terminals share the same antenna controller, modem and router hardware with the business market, but have an MCS-8200 fuselage-mounted antenna. === Business aviation === Honeywell’s JetWave hardware allows users to connect to Inmarsat’s Jet ConneX, a business aviation broadband connectivity offering to provide Wi-Fi for connected devices. JetWave offers a tail-mount antenna for business jets. === Defense === Honeywell’s JetWave satellite communications system for defense allows users to connect to the Inmarsat GX network, offering global coverage for military airborne operators, including over water, over nontraditional flight paths and in remote areas. JetWave and the Inmarsat GX network enable mission-critical applications like real-time weather; videoconferencing; large file transfers; encryption capabilities; in-flight briefings; intelligence, surveillance, and reconnaissance video; and secure communications. JetWave is configurable for a variety of military platforms and offers antennas for large and small airframes.

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  Single address space operating system

  In computer science, a single address space operating system (or SASOS) is an operating system that provides only one globally shared address space for all processes. In a single address space operating system, numerically identical (virtual memory) logical addresses in different processes all refer to exactly the same byte of data. In a traditional OS with private per-process address space, memory protection is based on address space boundaries ("address space isolation"). Single address-space operating systems make translation and protection orthogonal, which in no way weakens protection. The core advantage is that pointers (i.e. memory references) have global validity, meaning their meaning is independent of the process using it. This allows sharing pointer-connected data structures across processes, and making them persistent, i.e. storing them on backup store. Some processor architectures have direct support for protection independent of translation. On such architectures, a SASOS may be able to perform context switches faster than a traditional OS. Such architectures include Itanium, and Version 5 of the Arm architecture, as well as capability architectures such as CHERI. A SASOS should not be confused with a flat memory model, which provides no address translation and generally no memory protection. In contrast, a SASOS makes protection orthogonal to translation: it may be possible to name a data item (i.e. know its virtual address) while not being able to access it. SASOS projects using hardware-based protection include the following: Angel IBM i (formerly called OS/400) Iguana at NICTA, Australia Mungi at NICTA, Australia Nemesis Opal Scout Sombrero Related are OSes that provide protection through language-level type safety: Br1X Genera JX a research Java OS Phantom OS Singularity Theseus OS Torsion

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  Server-sent events

  Server-Sent Events (SSE) is a server push technology enabling a client to receive automatic updates from a server via an HTTP connection, and describes how servers can initiate data transmission towards clients once an initial client connection has been established. They are commonly used to send message updates or continuous data streams to a browser client and designed to enhance native, cross-browser streaming through a JavaScript API called EventSource, through which a client requests a particular URL in order to receive an event stream. The EventSource API is standardized as part of HTML Living Standard by the WHATWG. The media type for SSE is text/event-stream. All modern browsers support server-sent events: Firefox 6+, Google Chrome 6+, Opera 11.5+, Safari 5+, Microsoft Edge 79+, Brave. Since SSE does not use either persistent connections nor chunked transfer encoding, HTTP/1.1 is not a technical requirement. == History == The SSE mechanism was first specified by Ian Hickson as part of the "WHATWG Web Applications 1.0" proposal starting in 2004. In September 2006, the Opera web browser implemented the experimental technology in a feature called "Server-Sent Events". The W3C published Server-Sent Events as a Recommendation on February 3, 2015, after years of development through Working Drafts and Candidate Recommendations. == Example == == Technology == When sending high-frequency data , the server must manage backpressure to prevent saturating clients. This is mitigated in the following ways: Client-side buffering: Browsers have limited buffer space for incoming server-sent events Adaptive rate limiting: Servers can adjust event frequency and monitor connection health Event batching: Combining multiple events into larger and less frequent transmissions

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  Hierarchical Risk Parity

  Hierarchical Risk Parity (HRP) is an advanced investment portfolio optimization framework developed in 2016 by Marcos López de Prado at Guggenheim Partners and Cornell University. HRP is a probabilistic graph-based alternative to the prevailing mean-variance optimization (MVO) framework developed by Harry Markowitz in 1952, and for which he received the Nobel Prize in economic sciences. HRP algorithms apply discrete mathematics and machine learning techniques to create diversified and robust investment portfolios that outperform MVO methods out-of-sample. HRP aims to address the limitations of traditional portfolio construction methods, particularly when dealing with highly correlated assets. Following its publication, HRP has been implemented in numerous open-source libraries, and received multiple extensions. == Key features == HRP portfolios have been proposed as a robust alternative to traditional quadratic optimization methods, including the Critical Line Algorithm (CLA) of Markowitz. HRP addresses three central issues commonly associated with quadratic optimizers: numerical instability, excessive concentration in a small number of assets, and poor out-of-sample performance. HRP leverages techniques from graph theory and machine learning to construct diversified portfolios using only the information embedded in the covariance matrix. Unlike quadratic programming methods, HRP does not require the covariance matrix to be invertible. Consequently, HRP remains applicable even in cases where the covariance matrix is ill-conditioned or singular—conditions under which standard optimizers fail. Monte Carlo simulations indicate that HRP achieves lower out-of-sample variance than CLA, despite the fact that minimizing variance is the explicit optimization objective of CLA. Furthermore, HRP portfolios exhibit lower realized risk compared to those generated by traditional risk parity methodologies. Empirical backtests have demonstrated that HRP would have historically outperformed conventional portfolio construction techniques. Algorithms within the HRP framework are characterized by the following features: Machine Learning Approach: HRP employs hierarchical clustering, a machine learning technique, to group similar assets based on their correlations. This allows the algorithm to identify the underlying hierarchical structure of the portfolio, and avoid that errors spread through the entire network. Risk-Based Allocation: The algorithm allocates capital based on risk, ensuring that assets only compete with similar assets for representation in the portfolio. This approach leads to better diversification across different risk sources, while avoiding the instability associated with noisy returns estimates. Covariance Matrix Handling: Unlike traditional methods like Mean-Variance Optimization, HRP does not require inverting the covariance matrix. This makes it more stable and applicable to portfolios with a large number of assets, particularly when the covariance matrix's condition number is high. == The problem: Markowitz's Curse == Portfolio construction is perhaps the most recurrent financial problem. On a daily basis, investment managers must build portfolios that incorporate their views and forecasts on risks and returns. Despite the theoretical elegance of Markowitz's mean-variance framework, its practical implementation is hindered by several limitations that undermine the reliability of solutions derived from the Critical Line Algorithm (CLA). A principal concern is the high sensitivity of optimal portfolios to small perturbations in expected returns: even minor forecasting errors can result in significantly different allocations (Michaud, 1998). Given the inherent difficulty of producing accurate return forecasts, numerous researchers have advocated for approaches that forgo expected returns entirely and instead rely solely on the covariance structure of asset returns. This has given rise to risk-based allocation methods, among which risk parity is a widely cited example (Jurczenko, 2015). While eliminating return forecasts mitigates some instability, it does not eliminate it. Quadratic programming techniques employed in portfolio optimization require the inversion of a positive-definite covariance matrix, meaning all eigenvalues must be strictly positive. When the matrix is numerically ill-conditioned—that is, when the ratio of its largest to smallest eigenvalue (its condition number) is large—matrix inversion becomes unreliable and prone to significant numerical errors (Bailey and López de Prado, 2012). The condition number of a covariance, correlation, or any symmetric (and thus diagonalizable) matrix is defined as the absolute value of the ratio between its largest and smallest eigenvalues in modulus. The figure on the right presents the sorted eigenvalues of several correlation matrices; the condition number is represented by the ratio of the first to last eigenvalues in each sequence. A diagonal correlation matrix, which is equal to its own inverse, exhibits the minimum possible condition number. As the number of correlated (or multicollinear) assets in a portfolio increases, the condition number rises. At high levels, this leads to severe numerical instability, whereby slight modifications in any matrix entry may result in drastically different inverses. This phenomenon, often referred to as Markowitz’s curse, encapsulates the paradox wherein increased correlation among assets heightens the theoretical need for diversification, yet simultaneously increases the likelihood of unstable optimization outcomes. Consequently, the potential benefits of diversification are frequently overshadowed by estimation errors. These problems are exacerbated as the dimensionality of the covariance matrix increases. The estimation of each covariance term consumes degrees of freedom, and in general, a minimum of 1 2 N ( N + 1 ) {\displaystyle {\frac {1}{2}}N(N+1)} independent and identically distributed (IID) observations is required to estimate a non-singular covariance matrix of dimension N {\displaystyle N} . For example, constructing an invertible covariance matrix of dimension 50 necessitates at least five years of daily IID observations. However, empirical evidence suggests that the correlation structure of financial assets is highly unstable over such extended periods. These difficulties are highlighted by the observation that even naïve allocation strategies—such as equally weighted portfolios—have frequently outperformed both mean-variance and risk-based optimizations in out-of-sample tests (De Miguel et al., 2009). == The solution: Hierarchical Risk Parity == The HRP algorithm addresses Markowitz's curse in three steps: Hierarchical Clustering: Assets are grouped into clusters based on their correlations, forming a hierarchical tree structure. Quasi-Diagonalization: The correlation matrix is reordered based on the clustering results, revealing a block diagonal structure. Recursive Bisection: Weights are assigned to assets through a top-down approach, splitting the portfolio into smaller sub-portfolios and allocating capital based on inverse variance. === Step 1: Hierarchical clustering === Given a T × N {\displaystyle T\times N} matrix of asset returns X {\displaystyle X} , where each column represents a time series of returns for one of N {\displaystyle N} assets over T {\displaystyle T} time periods, a hierarchical clustering process can be used to construct a tree-based representation of asset relationships. First, we compute the N × N {\displaystyle N\times N} correlation matrix ρ = ρ i , j i , j = 1 . . . N {\displaystyle \rho ={\rho _{i,j}}\;{i,j=1\;...\;N}} , where ρ i , j = c o r r ( X i , X j ) {\displaystyle \rho _{i,j}=\mathrm {corr} (X_{i},X_{j})} . From this, a pairwise distance matrix D = d i , j {\displaystyle D={d_{i,j}}} is defined using the transformation: d i , j = 1 2 ( 1 − ρ i , j ) {\displaystyle d_{i,j}={\sqrt {{\frac {1}{2}}(1-\rho _{i,j})}}} This distance function defines a proper metric space, satisfying non-negativity, identity of indiscernibles, symmetry, and the triangle inequality. Next, a secondary distance matrix D ~ = d ~ i , j {\displaystyle {\tilde {D}}={{\tilde {d}}_{i,j}}} is computed, where each entry measures the Euclidean distance between the distance profiles of two assets: d ~ i , j = ∑ n = 1 N ( d n , i − d n , j ) 2 {\displaystyle {\tilde {d}}_{i,j}={\sqrt {\sum _{n=1}^{N}(d_{n,i}-d_{n,j})^{2}}}} While d i , j {\displaystyle d_{i,j}} reflects correlation-based proximity between two assets, d ~ i , j {\displaystyle {\tilde {d}}_{i,j}} quantifies dissimilarity across the entire system, as it depends on all pairwise distances. Hierarchical clustering proceeds by identifying the pair ( i , j ) {\displaystyle (i,j)} with the smallest value of d ~ i , j {\displaystyle {\tilde {d}}_{i,j}} (for i ≠ j {\displaystyle i\neq j} ), and forming a new cluster u [ 1 ] = ( i , j ) {\displaystyle u[1]=(i,j)} .

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  Nitro Zeus

  Nitro Zeus is the project name for a well funded comprehensive cyber attack plan created as a mitigation strategy after the Stuxnet malware campaign and its aftermath. Unlike Stuxnet, that was loaded onto a system after the design phase to affect its proper operation, Nitro Zeus's objectives are built into a system during the design phase unbeknownst to the system users. This built-in feature allows a more assured and effective cyber attack against the system's users. The information about its existence was raised during research and interviews carried out by Alex Gibney for his Zero Days documentary film. The proposed long term widespread infiltration of major Iranian systems would disrupt and degrade communications, power grid, and other vital systems as desired by the cyber attackers. This was to be achieved by electronic implants in Iranian computer networks. The project was seen as one pathway in alternatives to full-scale war.

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  Digital media in education

  Digital media in education refers to the use of digital technologies to support and enhance teaching and learning processes. This includes the application of multiple digital software applications, devices, and online platforms as tools for learning. Learners interact with these technologies to access, analyze, evaluate, and create media content and communication in various forms. The integration of digital media in education has dramatically increased over time, significantly transforming traditional educational practices. When viewed through a global and inclusive lens, digital education should be guided by principles of equity, inclusion, and public infrastructure to ensure meaningful participation of all learners. == History == === 20th century === Technological advances in the 20th century, particularly the invention of the Internet, laid the foundation for incorporating technology into education. In the early 1900s, the overhead projector and instructional radio broadcasts were among the first technologies used for educational purposes. The introduction of computers in classrooms occurred in 1950, when a flight simulation program was developed to train pilots at the Massachusetts Institute of Technology. However, access to computers remained extremely limited for several decades. In 1964, John Kemeny and Thomas Kurtz developed the BASIC programming language, which simplified computer interaction and introduced time-sharing, enabling multiple users to work on the same system simultaneously. This innovation made computing increasingly accessible for educational settings. By the 1980s, schools began to show more interest in computers as companies released mass-market devices to the public. Networking further enabled the interconnection of computers into unified communication systems, which proved more efficient and cost-effective than previous stand-alone machines. This development prompted wider adoption of computing in educational institutions. The invention of the World Wide Web in 1992 further simplified internet navigation and sparked further interest in educational settings. Initially, computers were integrated into school curricula for tasks such as word processing, spreadsheet creation, and data organization. By the late 1990s, the Internet became a research tool, functioning as a vast library. By 1999, 99% of public school teachers in the United States reported having access to at least one computer in their schools, and 84% had a computer available in their classrooms. The emergence of World Wide Web also contributed to the development of learning management systems (LMS), which allowed educators to create online teaching environments for content storage, student activities, discussions, and assignments. Advances in digital compression and high-speed Internet made video creation and distribution more affordable, fostering the use of the systems designed for recording lectures. These tools were often incorporated into learning management platforms, supporting the expansion of fully online courses. === 21st century === By 2002, the Massachusetts Institute of Technology began offering recorded lectures to the public, marking a significant milestone in the movement toward accessible online education. The launch of YouTube in 2005 further transformed educational content distribution. Educators increasingly uploaded lectures and instructional videos on platforms with initiatives like Khan Academy, which was active in 2006, contributing to You Tube's role as a prominent educational resource. In 2007, Apple launched iTunesU, another platform for sharing educational resources and videos. Meanwhile, learning management systems gained popularity, with Blackboard and Canvas becoming two of the most widely used platforms with Canvas's release in 2008. That same year also marked the introduction of the first Massive Open Online Course (MOOC), which provided open access to webinars and expert-led instructions for global learners. As technology evolved, traditional projectors were gradually replaced by interactive whiteboards, which enabled educators to integrate digital tools more effectively in their classrooms. By 2009, 97% of classrooms in the United States had at least one computer, and 93% had Internet access. The COVID-19 pandemic, which forced schools across the world to close, significantly impacted education with schools shifting to distance education. Students attended classes remotely using devices such as laptops, phones, and tablets, supported by digital platforms that facilitated at-home learning environments. However, adapting assessment methods to the new learning environment posed certain challenges. A study conducted by Eddie M. Mulenga and José M. Marbán on Zambian students during the pandemic revealed difficulties in adapting to digital learning, particularly in subjects like mathematics. Similar issues were reported among students in Romania, where the transition to virtual learning presented significant obstacles in engagement and adaptability. === Post-pandemic developments === In the period following the onset of COVID-19, education systems worldwide rapidly adopted digital solutions to maintain continuity of learning and teaching. By the end of March 2020, all 46 OECD and partners countries closed some or all of their schools nationwide. By June 2020, the length of school closures in these countries ranged from 7 to over 18 weeks. These disruptions in formal education prompted governments and educators to quickly adopt digital learning. This global shift to online education highlighted considerable inequalities in digital access, although many systems struggled with inequitable access, especially in regions lacking devices, stable internet connections, or conducive home learning environments. Stimultaneously, commercial educational technology (ed-tech) companies introduced rapid digital solutions to the disruption caused by the pandemic. This led to what has been described as a "seller's market," where the urgency of implementation may cause the prioritization of availability and scale over pedagogical and equity considerations. In the post-pandemic era, digital media in education continues to evolve. It increasingly intersects with artificial intelligence (AI) technologies such as adaptive learning platforms, AI-enabled content generation, and personalized learning environments. These tools enhance global engagement and access but also raise concerns about infrastructure, inclusivity, ethical implementation as well as critical pedagogies. Scholars recommend that educators and policymakers adopt inclusive practices, prioritize equitable infrastructure, and develop critical digital literacy. Facer and Selwyn also emphasize the need for public digital infrastructure and sustainable and justice-oriented policies that empower all learners. Overall, these perspectives reflect a growing consensus that digital media in education should be implemented critically to promote inclusive, multimodal, and future-oriented learning environments.

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  Comparison of user features of operating systems

  Comparison of user features of operating systems refers to a comparison of the general user features of major operating systems in a narrative format. It does not encompass a full exhaustive comparison or description of all technical details of all operating systems. It is a comparison of basic roles and the most prominent features. It also includes the most important features of the operating system's origins, historical development, and role. == Overview == An operating system (OS) is system software that manages computer hardware, software resources, and provides common services for computer programs. Time-sharing operating systems schedule tasks for efficient use of the system and may also include accounting software for cost allocation of processor time, mass storage, printing, and other resources. For hardware functions such as input and output and memory allocation, the operating system acts as an intermediary between programs and the computer hardware, although the application code is usually executed directly by the hardware and frequently makes system calls to an OS function or is interrupted by it. Operating systems are found on many devices that contain a computer – from cellular phones and video game consoles to web servers and supercomputers. As of June 2024, the dominant general-purpose desktop operating system is Microsoft Windows with a market share of around 72.91%. macOS by Apple Inc. is in second place (14.93%), and the varieties of Linux are collectively in third place (4.04%). In the mobile sector, including both smartphones and tablets, Android is dominant with a market share of 71%, followed by Apple's iOS with 28%; for smartphones alone, Android has 72% and iOS has 28%. Linux distributions are dominant in the server and supercomputing sectors. Other specialized classes of operating systems (special-purpose operating systems)), such as embedded and real-time systems, exist for many applications. Security-focused operating systems also exist. Some operating systems have low system requirements (i.e. light-weight Linux distribution). Others may have higher system requirements. Some operating systems require installation or may come pre-installed with purchased computers (OEM-installation), whereas others may run directly from media (i.e. live cd) or flash memory (i.e. USB stick). == MS-DOS == === Overview === MS-DOS (acronym for Microsoft Disk Operating System) is an operating system for x86-based personal computers mostly developed by Microsoft. Collectively, MS-DOS, its rebranding as IBM PC DOS, and some operating systems attempting to be compatible with MS-DOS, are sometimes referred to as "DOS" (which is also the generic acronym for disk operating system). MS-DOS was the main operating system for IBM PC compatible personal computers during the 1980s, from which point it was gradually superseded by operating systems offering a graphical user interface (GUI), in various generations of the graphical Microsoft Windows operating system. IBM licensed and re-released it in 1981 as PC DOS 1.0 for use in its PCs. Although MS-DOS and PC DOS were initially developed in parallel by Microsoft and IBM, the two products diverged after twelve years, in 1993, with recognizable differences in compatibility, syntax, and capabilities. During its lifetime, several competing products were released for the x86 platform, and MS-DOS went through eight versions, until development ceased in 2000. Initially, MS-DOS was targeted at Intel 8086 processors running on computer hardware using floppy disks to store and access not only the operating system, but application software and user data as well. Progressive version releases delivered support for other mass storage media in ever greater sizes and formats, along with added feature support for newer processors and rapidly evolving computer architectures. Ultimately, it was the key product in Microsoft's development from a programming language company to a diverse software development firm, providing the company with essential revenue and marketing resources. It was also the underlying basic operating system on which early versions of Windows ran as a GUI. == Microsoft Windows == === Overview === Microsoft Windows, commonly referred to as Windows, is a group of several proprietary graphical operating system families, all of which are developed and marketed by Microsoft. Each family caters to a certain sector of the computing industry. Active Microsoft Windows families include Windows NT and Windows IoT; these may encompass subfamilies, (e.g. Windows Server or Windows Embedded Compact) (Windows CE). Defunct Microsoft Windows families include Windows 9x, Windows Mobile, and Windows Phone. Microsoft announced an operating environment named Windows on 10 November 1983, as a graphical operating system shell for MS-DOS in response to the growing interest in graphical user interfaces (GUIs); Windows 1.0 first shipped on 20 November 1985. Microsoft Windows came to dominate the world's personal computer (PC) market with over 90% market share, overtaking Mac OS, which had been introduced in 1984, while Microsoft has in 2020 lost its dominance of the consumer operating system market, with Windows down to 30%, lower than Apple's 31% mobile-only share (65% for desktop operating systems only, i.e. "PCs" vs. Apple's 28% desktop share) in its home market, the US, and 32% globally (77% for desktops), where Google's Android leads. Apple came to see Windows as an unfair encroachment on their innovation in GUI development as implemented on products such as the Lisa and Macintosh (eventually settled in court in Microsoft's favor in 1993). As of January 2023, on PCs, Windows is still the most popular operating system in all countries. However, in 2014, Microsoft admitted losing the majority of the overall operating system market to Android, because of the massive growth in sales of Android smartphones. In 2014, the number of Windows devices sold was less than 25% that of Android devices sold. This comparison, however, may not be fully relevant, as the two operating systems traditionally target different platforms. Still, numbers for server use of Windows (that are comparable to competitors) show one third market share, similar to that for end user use. As of October 2020, the most recent version of Windows for PCs, tablets and embedded devices is Windows 10, version 20H2. The most recent version for server computers is Windows Server, version 20H2. A specialized version of Windows also runs on the Xbox One video game console. === Windows 95 === Windows 95 introduced a redesigned shell based around a desktop metaphor; File shortcuts (also known as shell links) were introduced and the desktop was re-purposed to hold shortcuts to applications, files and folders, reminiscent of Mac OS. In Windows 3.1 the desktop was used to display icons of running applications. In Windows 95, the currently running applications were displayed as buttons on a taskbar across the bottom of the screen. The taskbar also contained a notification area used to display icons for background applications, a volume control and the current time. The Start menu, invoked by clicking the "Start" button on the taskbar or by pressing the Windows key, was introduced as an additional means of launching applications or opening documents. While maintaining the program groups used by its predecessor Program Manager, it also displayed applications within cascading sub-menus. The previous File Manager program was replaced by Windows Explorer and the Explorer-based Control Panel and several other special folders were added such as My Computer, Dial Up Networking, Recycle Bin, Network Neighborhood, My Documents, Recent documents, Fonts, Printers, and My Briefcase among others. AutoRun was introduced for CD drives. The user interface looked dramatically different from prior versions of Windows, but its design language did not have a special name like Metro, Aqua or Material Design. Internally it was called "the new shell" and later simply "the shell". The subproject within Microsoft to develop the new shell was internally known as "Stimpy". In 1994, Microsoft designers Mark Malamud and Erik Gavriluk approached Brian Eno to compose music for the Windows 95 project. The result was the six-second start-up music-sound of the Windows 95 operating system, The Microsoft Sound and it was first released as a startup sound in May 1995 on Windows 95 May Test Release build 468. When released for Windows 95 and Windows NT 4.0, Internet Explorer 4 came with an optional Windows Desktop Update, which modified the shell to provide several additional updates to Windows Explorer, including a Quick Launch toolbar, and new features integrated with Internet Explorer, such as Active Desktop (which allowed Internet content to be displayed directly on the desktop). Some of the user interface elements introduced in Windows 95, such as the desktop, taskbar, Start menu and Windows

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  Image destriping

  Image destriping is the process of removing stripes or streaks from images and videos without disrupting the original image/video. These artifacts plague a range of fields in scientific imaging including atomic force microscopy, light sheet fluorescence microscopy, and planetary satellite imaging. The most common image processing techniques to reduce stripe artifacts is with Fourier filtering. Unfortunately, filtering methods risk altering or suppressing useful image data. Methods developed for multiple-sensor imaging systems in planetary satellites use statistical-based methods to match signal distribution across multiple sensors. More recently, a new class of approaches leverage compressed sensing, to regularize an optimization problem, and recover stripe free images. In many cases, these destriped images have little to no artifacts, even at low signal to noise ratios.

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  Anti-social Media Bill (Nigeria)

  Anti-social Media Bill was introduced by the Senate of the Federal Republic of Nigeria on 5 November 2019 to criminalise the use of the social media in peddling false or malicious information. The original title of the bill is Protection from Internet Falsehood and Manipulations Bill 2019. It was sponsored by Senator Mohammed Sani Musa from the largely conservative northern Nigeria. After the bill passed second reading on the floor of the Nigeria Senate and its details were made public, information emerged on the social media accusing the sponsor of the bill of plagiarising a similar law in Singapore which is at the bottom of global ranking in the freedom of speech and of the press. But the senator denied that he plagiarised Singaporean law. == Opposition to the bill == Angry reactions trailed the introduction of the bill, and a number of civil society organisations, human rights activists, and Nigerian citizens unanimously opposed the bill. International rights group, Amnesty International and Human Rights Watch condemned the proposed legislation saying it is aimed at gagging freedom of speech which is a universal right in a country of over two hundred million people. Opposition political parties are very critical of the bill and accused the government of attempting to strip bare, Nigerian citizens of their rights to free speech and destroying same social media on whose power and influence the ruling All Progressives Congress, APC came to power in 2015. Nigeria Information Minister, Lai Mohammed has been at the center of public criticism because he is suspected to be the brain behind the proposed act. Lai was a former spokesman of then opposition All Progressives Congress. A "Stop the Social Media Bill! You can no longer take our rights from us" online petition campaign to force the Nigeria parliament to drop the bill received over 90,000 signatures within 24 hours. In November 2019, after the bill passed second reading in the senate, Akon Eyakenyi, a senator from Akwa Ibom State publicly said he would resist the bill. === Support for the bill === Those who support the proposed act especially Senators have often argued that the law would help curtail hate speech. President Muhammad Buhari who is seen as a beneficiary of the influence and power of the social media and free speech has been mute about it. But the president's senior aides and family members have publicly spoken in support of the bill. In November 2019, the wife of the president, Aisha Buhari, told a gathering at the Nigeria's National Mosque in the capital, Abuja that if China with over one billion people could regulate the social media, Nigeria should do same. But Nigerians reacted saying Nigeria is not a one-party communist state like China. Days later, a daughter to the president, Zahra Indimi told a gathering of young people in Abuja that social media had become a potent weapon for bullying those they thought were doing better than them in terms of social class and called for a critical regulation. == Key provisions of the bill == === Title === Protection from Internet Falsehoods, Manipulations and Other Related Matters Bill 2019. === Explanatory memorandum === This Act is to prevent Falsehoods and Manipulations in Internet transmission and correspondences in Nigeria. To suppress falsehoods and manipulations and counter the effects of such communications and transmissions and to sanction offenders with a view to encouraging and enhancing transparency by Social Media Platforms using the internet correspondences. === Objectives === One objective of the bill is to prevent the transmission of false statements or declaration of facts in Nigeria. Another objective of the bill is to end the financing of online mediums that transmit false statements. Measures will be taken to detect and control inauthentic behaviour and misuse of online accounts (parody accounts). When paid content is posted towards a political end, there will be measures to ensure the poster discloses such information. There will be sanction for offenders. === Transmission of false statement === According to the bill, a person must not: Transmit a statement that is false or, Transmit a statement that might: i. Affect the security or any part of Nigeria. ii. Affect public health, public safety or public finance. iii. Affect Nigeria's relationship with other countries. iv. influence the outcome of an election to any office in a general election. v. Cause enmity or hatred towards a person or group of persons. Anyone guilty of the above is liable to a fine of N300,000 or three years' imprisonment or both (for individual); and a fine not exceeding ten million naira (for corporate organisations). Same punishment applies for fake online accounts that transmit statements listed above. === Parody accounts === The bill says a person shall not open an account to transmit false statement. Anyone found guilty will be fined N200,000 or three years' imprisonment or both (for an individual) or five million naira (for corporate organisations). If such accounts transmit a statement that will affect security or influence the outcome of an election, such a person will be fined N300,000 or three years' imprisonment or both. If a person receives payment or reward to help another to transmit false statements knowingly, he/she is liable to a fine of N150,000 or three years' imprisonment or both. If a person receives payment or reward to help another to transmit a statement affects security or influence the outcome of an election, the fine is N300,000 or three years' imprisonment or both (for individual) and ten million naira for organisations. === Declaration === According to the bill, a law enforcement department can issue a "declaration" to offenders. And this declaration will be issued even if the "false statement" has been corrected or pulled down. The offender will be required to publish a "correction notice" in a specified newspaper, online location or other printed publication of Nigeria. Failure to comply, a person is liable to N200,000 or 12 months' imprisonment or both (for individual) and five million naira for organisations. === Access blocking order === The bill says the law enforcement department will also issue an access blocking order to offenders. The law enforcement department may direct the NCC to order the internet access service provider to disable access by users in Nigeria to the online location and the NCC must give the internet access service provider an access blocking order. An internet access service provider that does not comply with any access blocking order is liable on conviction to a fine not exceeding ten million naira for each day during any part of which that order is not fully complied with, up to a total of five million naira.

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  List of video games using NFC

  This is a list of video games that use near field communication (NFC) technology. Currently, games have leveraged NFC in unlocking additional features through payment. This takes the form of a direct transaction over NFC or by purchasing a physical item, which signals to the platform that a certain set of features has been purchased (e.g. Skylanders). This list catalogues gaming NFC platforms by device. == Mobile == === Android === Gun Bros. Near Field Ninja NFC Cards Skylanders, with an NFC base. The Haunted House: Soul Fighters, with an NFC base. === iOS === ==== As item-triggered game enhancement ==== Skylanders, with an NFC base. ==== As payment ==== In-App Purchases Here, games that leverage Apple's In-App Purchase framework use information stored in the NFC Secure Element to process the purchase through Apple Pay. While an NFC radio is not used here, the NFC protocol is used nonetheless. == Console == === Nintendo Wii, Wii U, Switch, Switch 2, 3DS and 2DS === ==== As item-triggered game enhancement ==== Pokémon Rumble U NFC Figure Amiibo, built into Nintendo consoles since 2014. Works with Wii U, New Nintendo 3DS/3DS XL, New Nintendo 2DS XL, Nintendo Switch, Nintendo Switch 2 and older Nintendo 3DS/Nintendo 2DS systems via a peripheral device. Disney Infinity, with an NFC base. Works with Wii, Nintendo 3DS, Nintendo 2DS and Wii U. Lego Dimensions, with an NFC base. Works with Wii U. Skylanders, with an NFC base. Works with Wii, Nintendo 3DS, Nintendo 2DS and Wii U. The Nintendo Switch version of Skylanders: Imaginators uses the NFC built into the game controller, it is also has full backward compatibility with Nintendo Switch 2. Some functionalities are missing compared to the other versions. ==== As payment ==== The Wii U GamePad controller, Joy-Con R, Joy-Con 2 R, Nintendo Switch Pro Controller and Nintendo Switch 2 Pro Controller can read information from an NFC data source. === PlayStation === Disney Infinity, with an NFC base. Works with PlayStation 3, PlayStation Vita, PlayStation 4 and PlayStation 5. Lego Dimensions, with an NFC base. Works with PlayStation 3, PlayStation 4 and PlayStation 5. Skylanders, with an NFC base. Works with PlayStation 3, PlayStation 4 and PlayStation 5. === Xbox === While NFC bases are normally interoperable between all platforms, the Xbox 360, Xbox One and Xbox Series X require specific bases that are compatible only with the respective platform. Disney Infinity, with an NFC base. Lego Dimensions, with an NFC base. Skylanders, with an NFC base.

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  Haul video

  A haul video is a video recording posted to the Internet in which a person discusses items that they recently purchased, sometimes going into detail about their experiences during the purchase and the cost of the items they bought. The posting of haul videos (or hauls) was a growing trend between 2008 and 2016. Often the items bought are books, clothing, groceries, household goods, makeup, or jewellery. == Details == The posting of haul videos grew as a trend between 2008 and 2016. By late 2010, nearly a quarter of a million haul videos had been shared on the website YouTube alone. Certain videos have each received tens of millions of views. Many young adults (mostly women) have displayed their shopping hauls, while including their beauty and design commentary in the narration. The videos are often grouped by store name or by the type of product (cosmetics, accessories, shoes, postage stamps, etc.). Before haul videos became an online trend, millions of people spent time watching other people, in technical product videos unbox their latest new gadgets and technology. The trend of "unboxing videos" had emerged during 2006. Haul videos have led to celebrity status for some people. Other haul video bloggers have entered sponsorship deals and advertising programs from major brands. The videos are rarely negative about the products being reviewed. This aspect of the genre of haul videos makes sponsorship by brand advertisers particularly appealing. Brands including J.C. Penney contacted haulers as part of their marketing efforts for Back to School 2010. Haul videos also convinced three San Francisco Bay Area area natives to launch HaulBlog–a parody site that creates fake haul videos which poke fun at the phenomenon. The site is also home to the original monthly web series "The Haul Monitor" a humorous commentary show that features haul videos from around the community. == Fashion media == Sarah Sykes and John Zimmerman of Carnegie Mellon University, HCII and School of Design wrote an article "Making Sense of Haul Videos: Self-created Celebrities Fill a Fashion Media Gap". They discuss their analysis and research project examining what makes video bloggers so popular on YouTube, as well as how it affects fashion media through the production of haul videos. == Federal Trade Commission == The United States Federal Trade Commission recently enacted laws to regulate many types of online publishers and content creators. The posted information includes blogging and podcasting in text, images, audio, and video. While any publishers (including the haul-video creators) are allowed to accept free merchandise and advertising, the gifts or payments must be fully (and clearly) disclosed to reveal being paid by a brand name, as a sponsor, to review a product. The Canadian Radio-television and Telecommunications Commission is also closely monitoring such Internet activities.

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  Data cube

  In computer programming, a data cube (or datacube) is a multi-dimensional array of values. Typically, the term "data cube" is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data. Even though it is called a cube, a data cube generally is a multi-dimensional concept which can be 1-dimensional, 2-dimensional, 3-dimensional, or higher-dimensional. The data cube is used to represent data (sometimes called facts) along some dimensions of interest. In satellite image timeseries, dimensions would be latitude and longitude coordinates and time; a fact (sometimes called measure) would be a pixel at a given space and time as taken by the satellite. For example, in online analytical processing, an OLAP cube about a company would have dimensions that could be the company subsidiaries, the company products, and time; in this setup, a fact would be a sales event where a particular product has been sold in a particular subsidiary at a particular time. In any case, every dimension divides data into groups of cells whereas each cell in the cube represents a single measure of interest. Sometimes cubes hold only a few values with the rest being empty, i.e. undefined, while sometimes most or all cube coordinates hold a cell value. In the first case such data are called sparse, and in the second case they are called dense, although there is no hard delineation between the two. Data cubes may be stored in database management systems (DBMS) as part of array DBMS. Spatio-temporal databases and geospatial databases may also be represented as coverage data. == History == Multi-dimensional arrays have long been familiar in programming languages. Fortran offers arbitrarily-indexed 1-D arrays and arrays of arrays, which allows the construction of higher-dimensional arrays, up to 15 dimensions. APL supports n-D arrays with a rich set of operations. All these have in common that arrays must fit into the main memory and are available only while the particular program maintaining them (such as image processing software) is running. A series of data exchange formats support storage and transmission of data cube-like data, often tailored towards particular application domains. Examples include MDX for statistical (in particular, business) data, Zarr and Hierarchical Data Format for general scientific data, and TIFF for imagery. In 1992, Peter Baumann introduced management of massive data cubes with high-level user functionality combined with an efficient software architecture. Datacube operations include subset extraction, processing, fusion, and in general queries in the spirit of data manipulation languages like SQL. Some years after, the data cube concept was applied to describe time-varying business data as data cubes by Jim Gray, et al., and by Venky Harinarayan, Anand Rajaraman and Jeff Ullman. Around that time, a working group on Multi-Dimensional Databases ("Arbeitskreis Multi-Dimensionale Datenbanken") was established at German Gesellschaft für Informatik. Datacube Inc. was an image processing company selling hardware and software applications for the PC market in 1996, however without addressing data cubes as such. The EarthServer initiative has established geo data cube service requirements. == Standardization == In 2018, the ISO SQL database language was extended with data cube functionality as "SQL – Part 15: Multi-dimensional arrays (SQL/MDA)". Web Coverage Processing Service is a geo data cube analytics language issued by the Open Geospatial Consortium in 2008. In addition to the common data cube operations, the language knows about the semantics of space and time and supports both regular and irregular grid data cubes, based on the concept of coverage data. An industry standard for querying business data cubes, originally developed by Microsoft, is MultiDimensional eXpressions. == Implementation == Many high-level computer languages treat data cubes and other large arrays as single entities distinct from their contents. These languages, of which Fortran, APL, IDL, NumPy, PDL, and S-Lang are examples, allow the programmer to manipulate complete film clips and other data en masse with simple expressions derived from linear algebra and vector mathematics. Some languages (such as PDL) distinguish between a list of images and a data cube, while many (such as IDL) do not. Array DBMSs (Database Management Systems) offer a data model which generically supports definition, management, retrieval, and manipulation of n-dimensional data cubes. This database category has been pioneered by the rasdaman system since 1994. == Applications == Multi-dimensional arrays can meaningfully represent spatio-temporal sensor, image, and simulation data, but also statistics data where the semantics of dimensions is not necessarily of spatial or temporal nature. Generally, any kind of axis can be combined with any other into a data cube. === Mathematics === In mathematics, a one-dimensional array corresponds to a vector, a two-dimensional array resembles a matrix; more generally, a tensor may be represented as an n-dimensional data cube. === Science and engineering === For a time sequence of color images, the array is generally four-dimensional, with the dimensions representing image X and Y coordinates, time, and RGB (or other color space) color plane. For example, the EarthServer initiative unites data centers from different continents offering 3-D x/y/t satellite image timeseries and 4-D x/y/z/t weather data for retrieval and server-side processing through the Open Geospatial Consortium WCPS geo data cube query language standard. A data cube is also used in the field of imaging spectroscopy, since a spectrally-resolved image is represented as a three-dimensional volume. Earth observation data cubes combine satellite imagery such as Landsat 8 and Sentinel-2 with Geographic information system analytics. === Business intelligence === In online analytical processing (OLAP), data cubes are a common arrangement of business data suitable for analysis from different perspectives through operations like slicing, dicing, pivoting, and aggregation.

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  Virtual Print Fee

  Virtual Print Fee (VPF) is a subsidy paid by a film distributor towards the purchase of digital cinema projection equipment for use by a film exhibitor in the presentation of first release motion pictures. The subsidy is paid in the form of a fee per booking of a movie, intended to match the savings that occurs by not shipping a film print. The model is designed to help redistribute the savings realized by studios when using digital distribution instead of film print distribution and is intended to vanish when the transition phase is over when the vast majority of cinemas screens are equipped. == History == The first public demonstration of digital projection for cinema took place at ShoWest in 1999, and it was readily apparent that the technology was further ahead than the business model. Early technology presentations attempted to claim that the technology would pay for itself through new revenues generated by new forms of content. But exhibitors knew their audience, and could see that digital projection was only a replacement technology, creating new financial liabilities, and not new revenue. It wasn’t until the rollout of digital 3-D years later in 2005 that digital projection demonstrated that it could be used to generate additional revenue. The economics were challenging. Film projectors and platters cost in the neighborhood of US$30,000, while early digital projectors cost up to US$150,000. Further, film projectors had a lifetime of 30 years with relatively small annual expenditures in maintenance and replacement parts. On the other hand, exhibitors felt they would be lucky to get 10 years of service from a digital projector, after which there would have to be a refresh in capital expenditure. Meanwhile, distributors would realize significant savings by eliminating the high cost of film prints with corresponding shipping costs, and instead distributing digital files either by satellite or hard drive. The Virtual Print Fee was designed to better balance savings and expenditures for both exhibitors and distributors. It is intended to primarily assist in the replacement of film projectors, and not assist in the purchase of new projection equipment for new construction. To give confidence to financial institutions that digital cinema technology was stable and worthy of investment, Digital Cinema Initiatives was created in 2002, resulting in the release of the first version of the DCI Digital Cinema System Specification in 2005. The DCI Specification continues to be the core specification for digital cinema, establishing the baseline technology and system requirements for which studios will release digital movies. The first set of VPF agreements executed with four major studios were announced by Christie/AIX in November 2005. Christie/AIX at that time was a subsidiary of Access Integrated Technology, now renamed Cinedigm Digital Cinema Corp. The agreements were for the rollout of digital cinema technology to 4000 screens. Since that time, numerous other Digital Cinema Deployment Agreements have been executed around the world, allowing exhibitors in nearly every territory to benefit from VPF subsidies in the conversion from film projection to digital projection.

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  Content reference identifier

  A content reference identifier or CRID is a concept from the standardization work done by the TV-Anytime forum. It is or closely matches the concept of the Uniform Resource Locator, or URL, as used on the World-Wide Web: A unit of content, in a broadcast stream, can be referred to by its globally unique CRID in the same way that a webpage can be referred to by its globally unique URL on the web. The concept of CRID permits referencing contents unambiguously, regardless of their location, i.e., without knowing specific broadcast information (time, date and channel) or how to obtain them through a network, for instance, by means of a streaming service or by downloading a file from an Internet server. The receiver must be capable of resolving these unambiguous references, i.e. of translating them into specific data that will allow it to obtain the location of that content in order to acquire it. This makes it possible for recording processes to take place without knowing that information, and even without knowing beforehand the duration of the content to be recorded: a complete series by a simple click, a program that has not been scheduled yet, a set of programs grouped by a specific criterion... This framework allows for the separation between the reference to a given content (the CRID) and the necessary information to acquire it, which is called a “locator”. Each CRID may lead to one or more locators which will represent different copies of the same content. They may be identical copies broadcast in different channels or dates, or cost different prices. They may also be distinct copies with different technical parameters such as format or quality. It may also be the case that the resolution process of a CRID provides another CRID as a result (for example, its reference in a different network, where it has an alternative identifier assigned by a different operator) or a set of CRIDs (for instance, if the original CRID represents a TV series, in which case the resolution process would result in the list of CRIDs representing each episode). From the above it can be concluded that provided that a given content can belong to many groups (each possibly defined by distinctive qualities), it is possible that many CRIDs carry the same content. That is, several CRIDs may be resolved into the same locator. A CRID is not exactly a universal, unique and exclusive identifier for a given content. It is closely related to the authority that creates it, to the resolution service provider, and to the content provider in such a way that the same content may have different CRIDs depending on the field in which they are used (for example, a different one for each television operator that has the rights to broadcast the content). == Format == A CRID is specified much like URLs. In fact, a CRID is a so-called URI. Typically, the content creator, the broadcaster or a third party will use their DNS-names in a combination with a product-specific name to create globally unique CRIDs. That is, the syntax of a CRID is: crid://authority/data The authority field represents the entity that created the CRID and its format is that of a DNS name. The data field represents a string of characters that will unambiguously identify the content within the authority scope (it is a string of characters assigned by the authority itself). As an example, let's assume that BBC wanted to make a CRID for (all the programs of) the Olympics in China. It may have looked something like this crid://bbc.co.uk/olympics/2008/ This would be a group CRID, that is, a CRID representing a group of contents. Then, to refer to a specific event – such as the women's shot-put final – they could have used the following inside their metadata. crid://bbc.co.uk/olympics/2008/final/shotput/women Currently, four types of CRIDs are playing a major role in some unidirectional television networks: programme CRID, series CRID, group CRID, and recommendation CRID. One of the most important applications of CRIDs is the so-called series link recording function (SL) of modern digital video recorders (DVR, PVR). In turn, a locator is a string of characters that contains all the necessary information for a receiver to find and acquire a given content, whether it is received through a transport stream, located in local storage, downloaded as a file from an Internet server, or through a streaming service. For example, a DVB locator will include all the necessary parameters to identify a specific content within a transport stream: network, transport stream, service, table and/or event identifiers. The locators' format, as established in TV-Anytime, is quite generic and simple, and corresponds to: [transport-mechanism]:[specific-data] The first part of the locator's format (the transport mechanism) must be a string of characters that is unique for each mechanism (transport stream, local file, HTTP Internet access...). The second part must be unambiguous only within the scope of a given transport mechanism and will be standardized by the organism in charge of the regulation of the mechanism itself. For instance, a DVB locator to identify a content within the transport stream of networks that follow this standard would be: dvb://112.4a2.5ec;2d22~20121212T220000Z—PT01H30M which would indicate a content (identified by the string “2d22”) that airs on a channel available on a DVB network identified by the address “112.4a2.5ec” (network “112”, transport stream “4a2” and service “5ec”), on 12 December 2012 at 10 p.m. and with a duration of 90 minutes. == The location resolution process == The location resolution process is the procedure by which, starting from the CRID of a given content, one or several locators of that content are obtained. Resolving a CRID can be a direct process, which leads immediately to one or many locators, or it may also happen that in the first place one or many intermediate CRIDs are returned, which must undergo the same procedure to finally obtain one or several locators. This procedure involves some information elements, among which we find two structures named resolving authority record (RAR) and ContentReferencingTable, respectively. Consulting them repeatedly will take the receiver from a CRID to one or many locators that will allow it to acquire the content. The RAR table The RAR table is one or many data structures that provide the receiver, for each authority that submits CRIDs, information on the corresponding resolution service provider. Among other things, it informs about which mechanism is used to provide information to resolve the CRIDs from each authority. That is, one or many RAR records must exist for each authority that indicate the receiver where it has to go to resolve the CRIDs of that particular authority. For example, in the record of the figure (expressed by means of a XML structure, according to the XML Schema defined in the TV-Anytime) there is an authority called “tve.es”, whose resolution service provider is the entity “rtve.es”, available on the URL "http://tva.rtve.es/locres/tve", which means there is resolution information in that URL. These RAR records will have reached the receiver in an indefinite form, unimportant for the TV-Anytime specification, which will depend on the specific transport mechanism of the network to which the receiver is connected. Each family of standards that regulates distribution networks (DVB, ATSC, ISDB, IPTV...) will have previously defined such procedure, which will be used by devices certified according to those standards. The ContentReferencingTable table The second structure involved in the location resolution process is a proper resolution table which, given a content's CRID, returns one or several locators that enable the receiver to access an instance of that content, or one or many CRIDs that allow it to move forward in the resolution process. The figure shows an example of this second structure, an XML document according to the specifications of the XML Schema defined in TV-Anytime. In it, several sections are included ( elements) that structure the information that describes each resolution case. The first one declares how a CRID (crid://tv.com/Friends/all), which corresponds to a group content that encompasses several episodes (two) of the “Friends” series is resolved. The result of the resolution process provides two new CRIDs each of them corresponding to one of the two episodes. The second element resolves the CRID of the first episode of the first season. The result of the resolution process is two DVB locators. The “acquire” attribute with “any” value indicates that any of them are good (the second one is a repetition broadcast a week later). The third element gives information about the second episode. It indicates that it cannot be resolved yet (“status” attribute with the “cannot yet resolve” value), indicating a date on which the request for resolution information must be repeated. The pro

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