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  • CamScanner

    CamScanner

    CamScanner is a Chinese mobile app first released in 2010 that allows iOS and Android devices to be used as image scanners. It allows users to 'scan' documents (by taking a photo with the device's camera) and share the photo as either a JPEG or PDF. This app is available free of charge on the Google Play Store and the Apple App Store. The app is based on freemium model, with ad-supported free version and a premium version with additional functions. == History == On August 27, 2019, Russian cyber security company Kaspersky Lab discovered that recent versions of the Android app distributed an advertising library containing a Trojan Dropper, which was also included in some apps preinstalled on several Chinese mobiles. The advertising library decrypts a Zip archive which subsequently downloads additional files from servers controlled by hackers, allowing the hackers to control the device, including by showing intrusive advertising or charging paid subscriptions. Google took the app down after Kaspersky reported its findings. An updated version of the app with the advertising library removed was made available on the Google Play Store as of September 5, 2019. Kaspersky later acknowledged "We appreciate the willingness to cooperate that we've seen from CamScanner representatives, as well as the responsible attitude to user safety they demonstrated while eliminating the threat…The malicious modules were removed from the app immediately upon Kaspersky's warning, and Google Play has restored the app." In June 2020, as tensions along the Line of Actual Control between China and India continued, the Government of India decided to ban 118 Chinese apps, including TikTok and CamScanner citing data and privacy issues. On January 5, 2021, US President Donald Trump signed Executive Order 13971 banning Alipay, Tencent's QQ, QQ Wallet, WeChat Pay, CamScanner, Shareit, VMate and WPS Office to conduct US transactions. The Trump administration explained this act by saying that this move helps prevent personal information such as text, phone calls and photos collected from rivals. However, the Biden administration did not meet the February 2021 deadline for implementing the executive order, allowing these apps to operate in the US and revoked the previous executive order Executive Order 14034 of June 9, 2021.

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  • Kleene's algorithm

    Kleene's algorithm

    In theoretical computer science, in particular in formal language theory, Kleene's algorithm transforms a given nondeterministic finite automaton (NFA) into a regular expression. Together with other conversion algorithms, it establishes the equivalence of several description formats for regular languages. Alternative presentations of the same method include the "elimination method" attributed to Brzozowski and McCluskey, the algorithm of McNaughton and Yamada, and the use of Arden's lemma. == Algorithm description == According to Gross and Yellen (2004), the algorithm can be traced back to Kleene (1956). A presentation of the algorithm in the case of deterministic finite automata (DFAs) is given in Hopcroft and Ullman (1979). The presentation of the algorithm for NFAs below follows Gross and Yellen (2004). Given a nondeterministic finite automaton M = (Q, Σ, δ, q0, F), with Q = { q0,...,qn } its set of states, the algorithm computes the sets Rkij of all strings that take M from state qi to qj without going through any state numbered higher than k. Here, "going through a state" means entering and leaving it, so both i and j may be higher than k, but no intermediate state may. Each set Rkij is represented by a regular expression; the algorithm computes them step by step for k = -1, 0, ..., n. Since there is no state numbered higher than n, the regular expression Rn0j represents the set of all strings that take M from its start state q0 to qj. If F = { q1,...,qf } is the set of accept states, the regular expression Rn01 | ... | Rn0f represents the language accepted by M. The initial regular expressions, for k = -1, are computed as follows for i≠j: R−1ij = a1 | ... | am where qj ∈ δ(qi,a1), ..., qj ∈ δ(qi,am) and as follows for i=j: R−1ii = a1 | ... | am | ε where qi ∈ δ(qi,a1), ..., qi ∈ δ(qi,am) In other words, R−1ij mentions all letters that label a transition from i to j, and we also include ε in the case where i=j. After that, in each step the expressions Rkij are computed from the previous ones by Rkij = Rk-1ik (Rk-1kk) Rk-1kj | Rk-1ij Another way to understand the operation of the algorithm is as an "elimination method", where the states from 0 to n are successively removed: when state k is removed, the regular expression Rk-1ij, which describes the words that label a path from state i>k to state j>k, is rewritten into Rkij so as to take into account the possibility of going via the "eliminated" state k. By induction on k, it can be shown that the length of each expression Rkij is at most ⁠1/3⁠(4k+1(6s+7) - 4) symbols, where s denotes the number of characters in Σ. Therefore, the length of the regular expression representing the language accepted by M is at most ⁠1/3⁠(4n+1(6s+7)f - f - 3) symbols, where f denotes the number of final states. This exponential blowup is inevitable, because there exist families of DFAs for which any equivalent regular expression must be of exponential size. In practice, the size of the regular expression obtained by running the algorithm can be very different depending on the order in which the states are considered by the procedure, i.e., the order in which they are numbered from 0 to n. == Example == The automaton shown in the picture can be described as M = (Q, Σ, δ, q0, F) with the set of states Q = { q0, q1, q2 }, the input alphabet Σ = { a, b }, the transition function δ with δ(q0,a)=q0, δ(q0,b)=q1, δ(q1,a)=q2, δ(q1,b)=q1, δ(q2,a)=q1, and δ(q2,b)=q1, the start state q0, and set of accept states F = { q1 }. Kleene's algorithm computes the initial regular expressions as After that, the Rkij are computed from the Rk-1ij step by step for k = 0, 1, 2. Kleene algebra equalities are used to simplify the regular expressions as much as possible. Step 0 Step 1 Step 2 Since q0 is the start state and q1 is the only accept state, the regular expression R201 denotes the set of all strings accepted by the automaton.

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  • Subject (documents)

    Subject (documents)

    In library and information science documents (such as books, articles and pictures) are classified and searched by subject – as well as by other attributes such as author, genre and document type. This makes "subject" a fundamental term in this field. Library and information specialists assign subject labels to documents to make them findable. There are many ways to do this and in general there is not always consensus about which subject should be assigned to a given document. To optimize subject indexing and searching, we need to have a deeper understanding of what a subject is. The question: "what is to be understood by the statement 'document A belongs to subject category X'?" has been debated in the field for more than 100 years (see below) == Theoretical view == === Charles Ammi Cutter (1837–1903) === For Cutter the stability of subjects depends on a social process in which their meaning is stabilized in a name or a designation. A subject "referred [...] to those intellections [...] that had received a name that itself represented a distinct consensus in usage" (Miksa, 1983a, p. 60) and: the "systematic structure of established subjects" is "resident in the public realm" (Miksa, 1983a, p. 69); "[s]ubjects are by their very nature locations in a classificatory structure of publicly accumulated knowledge (Miksa, 1983a, p. 61). Bernd Frohmann adds: "The stability of the public realm in turn relies upon natural and objective mental structures which, with proper education, govern a natural progression from particular to general concepts. Since for Cutter, mind, society, and SKO [Systems of Knowledge Organization] stand one behind the other, each supporting each, all manifesting the same structure, his discursive construction of subjects invites connections with discourses of mind, education, and society. The Dewey Decimal Classification (DDC), by contrast, severs those connections. Melvil Dewey emphasized more than once that his system maps no structure beyond its own; there is neither a "transcendental deduction" of its categories nor any reference to Cutter's objective structure of social consensus. It is content-free: Dewey disdained any philosophical excogitation of the meaning of his class symbols, leaving the job of finding verbal equivalents to others. His innovation and the essence of the system lay in the notation. The DDC is a poorly semiotic system of expanding nests of ten digits, lacking any referent beyond itself. In it, a subject is wholly constituted in terms of its position in the system. The essential characteristic of a subject is a class symbol which refers only to other symbols. Its verbal equivalent is accidental, a merely pragmatic characteristic... .... The conflict of interpretations over "subjects" became explicit in the battles between "bibliography" (an approach to subjects having much in common with Cutter's) and Dewey's "close classification". William Fletcher spoke for the scholarly bibliographer.... Fletcher's "subjects", like Cutter's, referred to the categories of a fantasized, stable social order, whereas Dewey's subjects were elements of a semiological system of standardized, techno-bureaucratic administrative software for the library in its corporate, rather than high culture, incarnation". (Frohmann, 1994, 112–113). Cutter's early view on what a subject is, is probably wiser than most understandings that dominated the 20th century – and also the understanding reflected in the ISO-standard quoted below. The early statements quoted by Frohmann indicate that subjects are somehow shaped in social processes. When that is said, it should be added that they are not particularly detailed or clear. We only get a vague idea of the social nature of subjects. === S. R. Ranganathan (1892–1972) === A classification system with an explicit theoretical foundation is Ranganathan's Colon Classification. Ranganathan provided an explicit definition of the concept of "subject": Subject – an organized body of ideas, whose extension and intension are likely to fall coherently within the field of interests and comfortably within the intellectual competence and the field of inevitable specialization of a normal person. A related definition is given by one of Ranganathan's students: A subject is an organized and systematized body of ideas. It may consist of one idea or a combination of several... Ranganathan's definition of "subject" is strongly influenced by his Colon Classification system. The colon system is based on the combination of single elements from facets to subject designation. This is the reason why the combined nature of subjects are emphasized so strongly. It leads, however, to absurdities such as the claim that gold cannot be a subject (but is alternatively termed "an isolate"). This aspect of the theory has been criticized by Metcalfe (1973, p. 318). Metcalfe's skepticism regarding Ranganathan's theory is formulated in hard words (op. cit., p. 317): "This pseudo-science imposed itself on British disciples from about 1950 on...". It seems unacceptable that Ranganathan defines the word subject in a way that favors his own system. A scientific concept like "subject" should make it possible to compare different ways of establishing access to information. Whether or not subjects are combined or not should be examined once their definition has been given, it should not determined a priori, in the definition. Besides the emphasis on the combined, organizing and systematizing nature of subjects contains Ranganathan's definition of subject the pragmatic demand, that a subject should be determined in a way that suits a normal person's competency or specialization. Again we see a strange kind of wishful thinking mixing a general understanding of a concept with demands put by his own specific system. One thing is what the word subject means, quite another issue is how to provide subject descriptions that fulfill demands such as the specificity of a given information retrieval language which fulfill demands put on the system, such as precision and recall. If researchers too often define terms in ways that favor specific kinds of systems, that are such definitions not useful to provide more general theories about subjects, subject analysis and IR. Among other things are comparative studies of different kinds of systems made difficult. Based on these arguments, as well as additional arguments which have been used in the literature, we may conclude that Ranganathan's definition of the concept "subject" is not suited for scientific use. Like the definition of "subject" given by the ISO-standard for topic maps, may Ranganathan's definition be useful within his own closed system. The purpose of a scientific and scholarly field is, however, to examine the relative fruitfulness of systems such as topic maps and Colon Classification. For such purpose is another understanding of "subject" necessary. === Patrick Wilson (1927–2003) === In his book Wilson (1968) examined – in particular by thought experiments – the suitability of different methods of examining the subject of a document. The methods were: identifying the author's purpose for writing the document, weighing the relative dominance and subordination of different elements in the picture, which the reading imposes on the reader, grouping or count the document's use of concepts and references, construing a set of rules for selecting elements deemed necessary (as opposed to unnecessary) for the work as a whole. Patrick Wilson shows convincingly that each of these methods are insufficient to determine the subject of a document and is led to conclude ( p. 89): "The notion of the subject of a writing is indeterminate..." or, on p. 92 (about what users may expect to find using a particular position in a library classification system): "For nothing definite can be expected of the things found at any given position". In connection to the last quote has Wilson an interesting footnote in which he writes that authors of documents often use terms in ambiguous ways ("hostility" is used as an example). Even if the librarian could personally develop a very precise understanding of a concept, he would be unable to use it in his classification, because none of the documents use the term in the same precise way. Based on this argumentation is Wilson led to conclude: "If people write on what are for them ill-defined phenomena, a correct description of their subjects must reflect the ill-definedness". Wilson's concept of subject was discussed by Hjørland (1992) who found that it is problematic to give up the precise understanding of such a basic term in LIS. Wilson's arguments led him to an agnostic position which Hjørland found unacceptable and unnecessary. Concerning the authors' use of ambiguous terms, the role of the subject analysis is to determine which documents would be fruitful for users to identify whether or not the documents use one or another term or whether a given term i

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  • Information professional

    Information professional

    The term information professional or information specialist refers to professionals responsible for the collection, documentation, organization, storage, preservation, retrieval, and dissemination of printed and digital information. The service delivered to the client is known as an information service. The term "information professional" is a versatile one, used to describe similar and sometimes overlapping professions, such as librarians, archivists, information managers, information systems specialists, information scientists, records managers, and information consultants. However, terminology differs among sources and organisations. Information professionals are employed in a variety of private, public, and academic institutions, as well as independently. == Skills == Since the term information professional is broad, the skills required for this profession are also varied. A Gartner report in 2011 pointed out that "Professional roles focused on information management will be different to that of established IT roles. An 'information professional' will not be one type of role or skill set, but will in fact have a number of specializations". Thus, an information professional can possess a variety of different skills, depending on the sector in which the person is employed. Some essential cross-sector skills are: IT skills, such as word-processing and spreadsheets, digitisation skills, and conducting Internet searches, together with skills loan systems, databases, content management systems, and specially designed programmes and packages. Customer service. An information professional should have the ability to address the information needs of customers. Language proficiency. This is essential in order to manage the information at hand and deal with customer needs. Soft skills. These include skills such as negotiating, conflict resolution, and time management. Management training. An information professional should be familiar with notions such as strategic planning and project management. Moreover, an information professional should be skilled in planning and using relevant systems, in capturing and securing information, and in accessing it to deliver service whenever the information is required. == Associations == Most countries have a professional association who oversee the professional and academic standards of librarians and other information professionals. There are also international associations related to LIS (library and information science), the most prominent of which is the International Federation of Library Associations and Institutions (IFLA). In many countries, LIS courses are accredited by the relevant professional association, as the American Library Association (ALA) in the USA, the Chartered Institute of Library and Information Professionals (CILIP) in the UK, and the Australian Library and Information Association (ALIA) in Australia. == Qualifications == Educational institutions around the world offer academic degrees, or degrees on related subjects such as Archival Studies, Information Systems, Information Management, and Records Management. Some of the institutions offering information science education refer to themselves as an iSchool, such as the CiSAP (Consortium of iSchools Asia Pacific, founded 2006) in Asia and the iSchool Caucus in the USA. There are also online e-learning resources, some of which offer certification for information professionals. === Africa === Information development in Africa started later than in other continents, mainly due to a lack of internet access, expertise and resources to manage digital infrastructure, and "opportunities for capacity development and knowledge-sharing". Nowadays, academic degrees in information studies are available at many universities of African countries, such as the University of Pretoria (South Africa), University of Nairobi (Kenya), Makerere University (Uganda), University of Botswana (Botswana), and University of Nigeria (Nigeria). === Asia === LIS-related studies are available in more than 30 Asian countries. Some examples listed by iSchools Inc. are the University of Hong Kong, University of Tsukuba, Japan, Yonsei University, South Korea, National Taiwan University and Wuhan University, China. Centre of Library and Information Management Science (CLIMS) at Tata Institute of Social Science in Mumbai, India. In Southeast Asia, the Congress of Southeast Asian Librarians (CONSAL) connects librarians and libraries in more than 10 countries with resources, networking opportunities, and support for growing library systems. === Australasia === The Australian Library and Information Association (ALIA) as of 2021 lists six schools offering undergraduate and postgraduate accredited university courses for "Librarian and Information Specialists" on their website. In New Zealand, the Open Polytechnic of New Zealand and the Victoria University of Wellington offer undergraduate and postgraduate degree courses for information professionals. === Europe === The majority of European countries have universities, colleges, or schools which offer bachelor's degrees in LIS studies. Over 40 universities offer master's degrees in LIS-related fields, and many institutions, such as the Swedish School of Library and Information Science at the University of Borås (Sweden), the University of Barcelona (Spain), Loughborough University (UK), and Aberystwyth University (Wales, UK) also offer PhD degrees. === North America === Information studies and degrees are available at numerous academic institutions throughout the U.S. and Canada. U.S. professional associations, together with their European counterparts, have undertaken many educational initiatives and pioneered many advances in the field of Information studies, such as increased interdisciplinarity and more effective delivery of distance learning. The Association for Intelligent Information Management, based in Silver Spring, Maryland, offers a qualification called Certified Information Professional (CIP), earned upon passing an examination, with certification remaining valid for three years. === South America === There are many schools and colleges in Latin America, which offer courses in Library Science, Archival Studies, and Information Studies, however these subjects are taught completely separately.

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  • GasBuddy

    GasBuddy

    GasBuddy is a technology company headquartered in Dallas, United States, that offers mobile applications and websites for tracking crowd-sourced locations and prices of gas stations and convenience stores in the United States and Canada. Their platforms offer information sourced from users, gas station operators, and partner companies. They also provide business-to-business services to gas stations and convenience store owners. == History == GasBuddy was founded in Minneapolis in 2000 by Dustin Coupal, Jason Toews as a community website for sharing gas prices. In 2004, they filed as a for-profit corporation in Minnesota under the name GasBuddy Organization Inc. In 2009, GasBuddy launched OpenStore, a platform that allows convenience stores to build and manage their own mobile apps. In 2010, the company launched its own mobile apps that allowed users to input gas prices from their smartphones. In 2013, Oil Price Information Service (OPIS), a subsidiary of UCG, acquired GasBuddy. OPIS is a provider of petroleum pricing and news for businesses. In 2016, IHS acquired OPIS, separating from GasBuddy, which remained with UCG as a subsidiary company. Initially only available in the United States and Canada, GasBuddy launched in Australia in March 2016. Also in that year, GasBuddy released a completely redesigned app, its first major redesign since its release in 2010. GasBuddy also unveiled a new logo and launched GasBuddy Business Pages. GasBuddy shut down the Australian version of their app in 2022. In 2017, GasBuddy launched a gas savings program titled "Pay with GasBuddy" intended to let consumers save at gas stations in the United States. In the same year, GasBuddy was involved in a lawsuit with Reveal Mobile, a location-based marketing company, over the sale of user location data. It was revealed that GasBuddy sold information on more than 4.5 million users to Reveal each month for $9.50 per 1000 users. According to CNET, that information included "users' latitude, longitude, IP address, and time stamps on the data collected," which sparked concern in the media and between its users. In 2021, the GasBuddy app rose to the most popular app on both Android and iPhone platforms in the wake of the Colonial Pipeline ransomware attack PDI acquired GasBuddy in 2021.

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  • Least-squares spectral analysis

    Least-squares spectral analysis

    Least-squares spectral analysis (LSSA) is a class of methods for estimating a frequency spectrum by fitting sinusoids to data using a least-squares fit. Unlike Fourier analysis, the most widely used spectral method in science, data need not be equally spaced to use LSSA. Furthermore, while Fourier analysis generally amplifies long-period noise in long or gapped records, LSSA mitigates such problems. The first strictly least-squares LSSA method was developed in 1969 and 1971, and is known as the Vaníček method or the Gauss–Vaniček method, after its inventor Petr Vaníček and Carl Friedrich Gauss, the inventor of the least-squares method for error minimization. A widely known LSSA variant is the Lomb method or the Lomb–Scargle periodogram, based on dated computational simplifications of the Vaníček method introduced in the 1970s and 1980s, first by Nicholas R. Lomb and later by Jeffrey D. Scargle. Other LSSA variants have been subsequently developed. == Historical background == The close connections between Fourier analysis, the periodogram, and the least-squares fitting of sinusoids have been known for a long time. However, most developments are restricted to complete data sets of equally spaced samples. In 1963, Freek J. M. Barning of Mathematisch Centrum, Amsterdam, handled unequally spaced data by similar techniques, including both a periodogram analysis equivalent to what nowadays is called the Lomb method and least-squares fitting of selected frequencies of sinusoids determined from such periodograms — and connected by a procedure known today as the matching pursuit with post-back fitting or the orthogonal matching pursuit. Petr Vaníček, a Canadian geophysicist and geodesist of the University of New Brunswick, proposed in 1969 also the matching-pursuit approach for equally and unequally spaced data, which he called "successive spectral analysis" and the result a "least-squares periodogram". He generalized this method to account for any systematic components beyond a simple mean, such as a "predicted linear (quadratic, exponential, ...) secular trend of unknown magnitude", and applied it to a variety of samples, in 1971. Vaníček's strictly least-squares method was then simplified in 1976 by Nicholas R. Lomb of the University of Sydney, who pointed out its close connection to periodogram analysis. Subsequently, the definition of a periodogram of unequally spaced data was modified and analyzed by Jeffrey D. Scargle of NASA Ames Research Center, who showed that, with minor changes, it becomes identical to Lomb's least-squares formula for fitting individual sinusoid frequencies. Scargle states that his paper "does not introduce a new detection technique, but instead studies the reliability and efficiency of detection with the most commonly used technique, the periodogram, in the case where the observation times are unevenly spaced," and further points out regarding least-squares fitting of sinusoids compared to periodogram analysis, that his paper "establishes, apparently for the first time, that (with the proposed modifications) these two methods are exactly equivalent." Press summarizes the development this way: A completely different method of spectral analysis for unevenly sampled data, one that mitigates these difficulties and has some other very desirable properties, was developed by Lomb, based in part on earlier work by Barning and Vanicek, and additionally elaborated by Scargle. In 1989, Michael J. Korenberg of Queen's University in Kingston, Ontario, developed the "fast orthogonal search" method of more quickly finding a near-optimal decomposition of spectra or other problems, similar to the technique that later became known as the orthogonal matching pursuit. == Development of LSSA and variants == === The Vaníček method === In the Vaníček method, a discrete data set is approximated by a weighted sum of sinusoids of progressively determined frequencies using a standard linear regression or least-squares fit. The frequencies are chosen using a method similar to Barning's, but going further in optimizing the choice of each successive new frequency by picking the frequency that minimizes the residual after least-squares fitting (equivalent to the fitting technique now known as matching pursuit with pre-backfitting). The number of sinusoids must be less than or equal to the number of data samples (counting sines and cosines of the same frequency as separate sinusoids). The relationship between the DFT and the approximation of trigonometric functions using the least-squares method is well explained in (Strutz, 2017). A data vector Φ is represented as a weighted sum of sinusoidal basis functions, tabulated in a matrix A by evaluating each function at the sample times, with weight vector x: ϕ ≈ A x , {\displaystyle \phi \approx {\textbf {A}}x,} where the weights vector x is chosen to minimize the sum of squared errors in approximating Φ. The solution for x is closed-form, using standard linear regression: x = ( A T A ) − 1 A T ϕ . {\displaystyle x=({\textbf {A}}^{\mathrm {T} }{\textbf {A}})^{-1}{\textbf {A}}^{\mathrm {T} }\phi .} Here the matrix A can be based on any set of functions mutually independent (not necessarily orthogonal) when evaluated at the sample times; functions used for spectral analysis are typically sines and cosines evenly distributed over the frequency range of interest. If we choose too many frequencies in a too-narrow frequency range, the functions will be insufficiently independent, the matrix ill-conditioned, and the resulting spectrum meaningless. When the basis functions in A are orthogonal (that is, not correlated, meaning the columns have zero pair-wise dot products), the matrix ATA is diagonal; when the columns all have the same power (sum of squares of elements), then that matrix is an identity matrix times a constant, so the inversion is trivial. The latter is the case when the sample times are equally spaced and sinusoids chosen as sines and cosines equally spaced in pairs on the frequency interval 0 to a half cycle per sample (spaced by 1/N cycles per sample, omitting the sine phases at 0 and maximum frequency where they are identically zero). This case is known as the discrete Fourier transform, slightly rewritten in terms of measurements and coefficients. x = A T ϕ {\displaystyle x={\textbf {A}}^{\mathrm {T} }\phi } — DFT case for N equally spaced samples and frequencies, within a scalar factor. === The Lomb method === Trying to lower the computational burden of the Vaníček method in 1976 (no longer an issue), Lomb proposed using the above simplification in general, except for pair-wise correlations between sine and cosine bases of the same frequency, since the correlations between pairs of sinusoids are often small, at least when they are not tightly spaced. This formulation is essentially that of the traditional periodogram but adapted for use with unevenly spaced samples. The vector x is a reasonably good estimate of an underlying spectrum, but since we ignore any correlations, Ax is no longer a good approximation to the signal, and the method is no longer a least-squares method — yet in the literature continues to be referred to as such. Rather than just taking dot products of the data with sine and cosine waveforms directly, Scargle modified the standard periodogram formula so to find a time delay τ {\displaystyle \tau } first, such that this pair of sinusoids would be mutually orthogonal at sample times t j {\displaystyle t_{j}} and also adjusted for the potentially unequal powers of these two basis functions, to obtain a better estimate of the power at a frequency. This procedure made his modified periodogram method exactly equivalent to Lomb's method. Time delay τ {\displaystyle \tau } by definition equals to tan ⁡ 2 ω τ = ∑ j sin ⁡ 2 ω t j ∑ j cos ⁡ 2 ω t j . {\displaystyle \tan {2\omega \tau }={\frac {\sum _{j}\sin 2\omega t_{j}}{\sum _{j}\cos 2\omega t_{j}}}.} Then the periodogram at frequency ω {\displaystyle \omega } is estimated as: P x ( ω ) = 1 2 [ [ ∑ j X j cos ⁡ ω ( t j − τ ) ] 2 ∑ j cos 2 ⁡ ω ( t j − τ ) + [ ∑ j X j sin ⁡ ω ( t j − τ ) ] 2 ∑ j sin 2 ⁡ ω ( t j − τ ) ] , {\displaystyle P_{x}(\omega )={\frac {1}{2}}\left[{\frac {\left[\sum _{j}X_{j}\cos \omega (t_{j}-\tau )\right]^{2}}{\sum _{j}\cos ^{2}\omega (t_{j}-\tau )}}+{\frac {\left[\sum _{j}X_{j}\sin \omega (t_{j}-\tau )\right]^{2}}{\sum _{j}\sin ^{2}\omega (t_{j}-\tau )}}\right],} which, as Scargle reports, has the same statistical distribution as the periodogram in the evenly sampled case. At any individual frequency ω {\displaystyle \omega } , this method gives the same power as does a least-squares fit to sinusoids of that frequency and of the form: ϕ ( t ) = A sin ⁡ ω t + B cos ⁡ ω t . {\displaystyle \phi (t)=A\sin \omega t+B\cos \omega t.} In practice, it is always difficult to judge if a given Lomb peak is significant or not, especially when the nature of the noise is unknown, so for example a false-alarm spectr

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  • Media aggregation platform

    Media aggregation platform

    A Media Aggregation Platform or Media Aggregation Portal (MAP) is an over the top service for distributing web-based streaming media content from multiple sources to a large audience. MAPs consist of networks of sources who host their own content which viewers can choose and access directly from a larger variety of content to choose from than a single source can offer. The service is used by content providers, looking to extend the reach of their content. Unlike multichannel video programming distributor (MVPD) or multiple-system operators (MSO), MAPs rely on the Internet rather than cables or satellite. As more network television channels have moved online in the early 21st century, joining web-native channels like Netflix, MAPs aggregate content the way that MSOs and MVPDs have used cable, and to a lesser extent satellite and IPTV infrastructure. There are companies that offer a similar service for free, including Yidio and StreamingMoviesRight, while others charge a subscription fee like as FreeCast Inc's Rabbit TV Plus. When compared with MSOs and MVPDs, MAP networks have much lower costs due to lack of physical infrastructure. The majority of revenue from MAP services are retained by the content creators, and revenue is instead collected from advertisements, pay-per-view, and subscription-based content offerings instead of licensing and reselling content. MAP service consumers interact and purchase content directly from its source, without the markup added by a middleman.

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  • Artificial intelligence industry in Canada

    Artificial intelligence industry in Canada

    The artificial intelligence industry in Canada is a rapidly expanding sector. Although Canada held a pioneering role in the early development of artificial intelligence, transforming research excellence into broad commercial adoption has proven challenging. Despite globally recognized scientific achievements and a deep pool of skilled experts, by June 2024, Canada recorded the lowest rate of AI integration among OECD countries, with only 12% of firms implementing AI in their products or services. However, AI adoption has shown significant momentum—doubling from mid-2024 to mid-2025, rising from 6.1% to 12.2%. As of September 2025, Statistics Canada indicated that while about one-third of Canadian businesses had no plans to adopt artificial intelligence in the next year, 14.5% reported intentions to begin using AI for producing goods or delivering services. The primary reasons for not moving forward with AI were lack of relevance, insufficient knowledge, and privacy concerns. According to Public Works Canada (PwC), the pace of AI adoption in Canada is roughly three-quarters of the United States rate, highlighting a notable gap between the two countries in business integration of this technology. British-Canadian computer scientist Geoffrey Hinton stated in 2025 that Canadian companies are adopting artificial intelligence at a slower pace, which may result in the loss of the country's early advantages in the field. At the "All In AI" conference held in Montreal in September 2025, the Minister of Artificial Intelligence and Digital Innovation Evan Solomon, described "Building digital sovereignty" as the most pressing democratic issue of the time. He introduced a 26-person task force focused on updating Canada's AI strategy. In their 2024 report " "Learning Together for Responsible Artificial Intelligence" report, the Innovation, Science, and Economic Development Canada stressed that public awareness, trust, and AI literacy are essential for the responsible adoption and governance of AI in Canada. Montreal workshops in 2021 expanded the OECD's 2019 definition of AI as "the set of computer techniques that enable a machine (e.g., a computer or telephone) to perform tasks that typically require intelligence, such as reasoning or learning. It is also referred to as the automation of intelligent tasks. Scientific developments in AI, such as deep-learning techniques, have made it possible to design access to huge amounts of data and ever-increasing computing power. These new techniques have been rapidly deployed on a large scale in all areas of social life, in transport, education, culture and health." == Federal investments and policy == The 2025 federal budget allocates over $1 billion over the next five years to bolster Canada's artificial intelligence and quantum computing ecosystem. == Industry landscape or research hubs == AlexNet, an influential deep convolutional neural network developed at the University of Toronto by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, marked a pivotal turning point in modern artificial intelligence. In 2012, it achieved a dramatic reduction in error rates for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), showcasing the practical power of deep learning and GPU acceleration. The success of AlexNet helped cement Canada’s reputation for AI leadership and inspired rapid adoption of deep learning across the technology sector, with ongoing impact in both academic and commercial domains. In healthcare, AlexNet has been adapted for medical imaging to assist with analyzing radiographs, mammograms, and other scans, including identifying abnormalities and supporting clinical diagnosis. In 2015, the Ottawa-based start-up Advanced Symbolics Inc. (ASI) began developing Polly, an artificial intelligence system designed to analyze and anticipate how target audiences behave—enabling more effective communication strategies and advertising campaigns. Polly was named after its first assignment analyzing the politics of Brexit. The AI gained widespread attention in 2016 for accurately forecasting both the Brexit referendum and the 2016 U.S. presidential election won by Donald Trump. The company states that Polly is used by organizations in diverse sectors—including healthcare, politics, entertainment, and mental health research—to support decision-making based on predictive analytics. Chartwatch, an AI tool developed in Canada, has been shown to reduce unexpected hospital deaths by 26% according to a 2024 study. The system analyzes patient data to detect subtle signs of deterioration, supporting healthcare teams in providing timely interventions. === Notable figures in AI in Canada === Geoffrey Hinton's decades-long work eventually formed the foundation of artificial intelligence, which earned him the Nobel Prize for physics in 2024. Yoshua Bengio, who won the Turing Award in 2018 for his pioneering work in deep learning, founded what would become Mila in 1993. Mila, is currently a collaboration between four Montreal-based academic partners.—the Pan-Canadian Artificial Intelligence Strategy includes Alberta's Amii, Toronto's Vector Institute, and Mila. Fakhreddine Karray's work on operational AI has had tangible impact across several Canadian-relevant sectors, notably intelligent transportation systems, virtual healthcare, and driver safety. === AI in the oil and gas industry === According to a 2020 Ernst & Young report the oil and gas industry in Canada is using AI in automating routine, repetitive, and dangerous tasks with technologies like robotic process automation and machine learning; optimizing production and processing; enhancing transportation logistics; improving equipment operation and monitoring; and enabling preventative maintenance. AI is also deployed for data analysis to improve prediction and decision-making, and is expected to automate up to 50% of job competencies in upstream oil and gas by 2040. Oilsands giant Suncor Energy operates a large fleet of autonomous trucks and has started using AI in its dispatch system at the Mildred Lake mine. As of 2024, AI manages routine tasks such as allocating trucks to dump stations and sending them to refuelling locations. === Indigenous and Inuit Innovation in AI === Indigenous organizations have been working on the creation of new technologies for language revitalization in partnership with National Research Council of Canada since the mid-2010s. In 2025, Inuit researchers and technology partners launched an AI-powered initiative to support the revitalization and preservation of Inuktitut, demonstrating how artificial intelligence can be adapted for Indigenous language and cultural priorities. A 2025 CBC article notes that, while AI can help revitalize Inuktitut, Inuit leaders emphasize concerns about data sovereignty, information ownership, and the need for Indigenous leadership to ensure transparency, privacy, and accountability in AI development. == Regulation == Canada's Artificial Intelligence and Data Act (AIDA) was proposed in November 2022, as part of the Digital Charter Implementation Act (Bill C-27). As well voluntary codes, such as the September 2023 Code of Conduct for Generative AI, and landmark investments in advanced computing infrastructure and the Canadian Artificial Intelligence Safety Institute (CAISI) reflect Canada's commitment to both safety and global competitiveness. == AI infrastructure == Canada has undertaken efforts to expand its AI computing infrastructure at both provincial and federal levels. The federal government's Canadian Sovereign AI Compute Strategy, allocated up to C$2 billion in Budget 2024, aims to enhance computing capacity to support domestic AI industry growth and AI adoption across the economy, with up to C$700 million designated to mobilize private sector investment in new or expanded data centres. Alberta has introduced an AI Data Centres Strategy to position itself as a leading North American destination for data centre investment, targeting C$100 billion worth of AI data centres under development by 2030. One major project under Alberta's strategy is the Wonder Valley AI Data Centre Park near Grande Prairie, which was exempted from provincial environmental impact assessment in April 2026 but still requires permits demonstrating safe construction and operation. According to Statista, as of April 2026, Canada has 287 data centres.

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  • Gorn address

    Gorn address

    A Gorn address (Gorn, 1967) is a method of identifying and addressing any node within a tree data structure. This notation is often used for identifying nodes in a parse tree defined by phrase structure rules. The Gorn address is a sequence of zero or more integers conventionally separated by dots, e.g., 0 or 1.0.1. The root which Gorn calls can be regarded as the empty sequence. And the j {\displaystyle j} -th child of the i {\displaystyle i} -th child has an address i . j {\displaystyle i.j} , counting from 0. It is named after American computer scientist Saul Gorn.

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  • Holographic algorithm

    Holographic algorithm

    In computer science, a holographic algorithm is an algorithm that uses a holographic reduction. A holographic reduction is a constant-time reduction that maps solution fragments many-to-many such that the sum of the solution fragments remains unchanged. These concepts were introduced by Leslie Valiant, who called them holographic because "their effect can be viewed as that of producing interference patterns among the solution fragments". The algorithms are unrelated to laser holography, except metaphorically. Their power comes from the mutual cancellation of many contributions to a sum, analogous to the interference patterns in a hologram. Holographic algorithms have been used to find polynomial-time solutions to problems without such previously known solutions for special cases of satisfiability, vertex cover, and other graph problems. They have received notable coverage due to speculation that they are relevant to the P versus NP problem and their impact on computational complexity theory. Although some of the general problems are #P-hard problems, the special cases solved are not themselves #P-hard, and thus do not prove FP = #P. Holographic algorithms have some similarities with quantum computation, but are completely classical. == Holant problems == Holographic algorithms exist in the context of Holant problems, which generalize counting constraint satisfaction problems (#CSP). A #CSP instance is a hypergraph G=(V,E) called the constraint graph. Each hyperedge represents a variable and each vertex v {\displaystyle v} is assigned a constraint f v . {\displaystyle f_{v}.} A vertex is connected to an hyperedge if the constraint on the vertex involves the variable on the hyperedge. The counting problem is to compute ∑ σ : E → { 0 , 1 } ∏ v ∈ V f v ( σ | E ( v ) ) , ( 1 ) {\displaystyle \sum _{\sigma :E\to \{0,1\}}\prod _{v\in V}f_{v}(\sigma |_{E(v)}),~~~~~~~~~~(1)} which is a sum over all variable assignments, the product of every constraint, where the inputs to the constraint f v {\displaystyle f_{v}} are the variables on the incident hyperedges of v {\displaystyle v} . A Holant problem is like a #CSP except the input must be a graph, not a hypergraph. Restricting the class of input graphs in this way is indeed a generalization. Given a #CSP instance, replace each hyperedge e of size s with a vertex v of degree s with edges incident to the vertices contained in e. The constraint on v is the equality function of arity s. This identifies all of the variables on the edges incident to v, which is the same effect as the single variable on the hyperedge e. In the context of Holant problems, the expression in (1) is called the Holant after a related exponential sum introduced by Valiant. == Holographic reduction == A standard technique in complexity theory is a many-one reduction, where an instance of one problem is reduced to an instance of another (hopefully simpler) problem. However, holographic reductions between two computational problems preserve the sum of solutions without necessarily preserving correspondences between solutions. For instance, the total number of solutions in both sets can be preserved, even though individual problems do not have matching solutions. The sum can also be weighted, rather than simply counting the number of solutions, using linear basis vectors. === General example === It is convenient to consider holographic reductions on bipartite graphs. A general graph can always be transformed it into a bipartite graph while preserving the Holant value. This is done by replacing each edge in the graph by a path of length 2, which is also known as the 2-stretch of the graph. To keep the same Holant value, each new vertex is assigned the binary equality constraint. Consider a bipartite graph G=(U,V,E) where the constraint assigned to every vertex u ∈ U {\displaystyle u\in U} is f u {\displaystyle f_{u}} and the constraint assigned to every vertex v ∈ V {\displaystyle v\in V} is f v {\displaystyle f_{v}} . Denote this counting problem by Holant ( G , f u , f v ) . {\displaystyle {\text{Holant}}(G,f_{u},f_{v}).} If the vertices in U are viewed as one large vertex of degree |E|, then the constraint of this vertex is the tensor product of f u {\displaystyle f_{u}} with itself |U| times, which is denoted by f u ⊗ | U | . {\displaystyle f_{u}^{\otimes |U|}.} Likewise, if the vertices in V are viewed as one large vertex of degree |E|, then the constraint of this vertex is f v ⊗ | V | . {\displaystyle f_{v}^{\otimes |V|}.} Let the constraint f u {\displaystyle f_{u}} be represented by its weighted truth table as a row vector and the constraint f v {\displaystyle f_{v}} be represented by its weighted truth table as a column vector. Then the Holant of this constraint graph is simply f u ⊗ | U | f v ⊗ | V | . {\displaystyle f_{u}^{\otimes |U|}f_{v}^{\otimes |V|}.} Now for any complex 2-by-2 invertible matrix T (the columns of which are the linear basis vectors mentioned above), there is a holographic reduction between Holant ( G , f u , f v ) {\displaystyle {\text{Holant}}(G,f_{u},f_{v})} and Holant ( G , f u T ⊗ ( deg ⁡ u ) , ( T − 1 ) ⊗ ( deg ⁡ v ) f v ) . {\displaystyle {\text{Holant}}(G,f_{u}T^{\otimes (\deg u)},(T^{-1})^{\otimes (\deg v)}f_{v}).} To see this, insert the identity matrix T ⊗ | E | ( T − 1 ) ⊗ | E | {\displaystyle T^{\otimes |E|}(T^{-1})^{\otimes |E|}} in between f u ⊗ | U | f v ⊗ | V | {\displaystyle f_{u}^{\otimes |U|}f_{v}^{\otimes |V|}} to get f u ⊗ | U | f v ⊗ | V | {\displaystyle f_{u}^{\otimes |U|}f_{v}^{\otimes |V|}} = f u ⊗ | U | T ⊗ | E | ( T − 1 ) ⊗ | E | f v ⊗ | V | {\displaystyle =f_{u}^{\otimes |U|}T^{\otimes |E|}(T^{-1})^{\otimes |E|}f_{v}^{\otimes |V|}} = ( f u T ⊗ ( deg ⁡ u ) ) ⊗ | U | ( f v ( T − 1 ) ⊗ ( deg ⁡ v ) ) ⊗ | V | . {\displaystyle =\left(f_{u}T^{\otimes (\deg u)}\right)^{\otimes |U|}\left(f_{v}(T^{-1})^{\otimes (\deg v)}\right)^{\otimes |V|}.} Thus, Holant ( G , f u , f v ) {\displaystyle {\text{Holant}}(G,f_{u},f_{v})} and Holant ( G , f u T ⊗ ( deg ⁡ u ) , ( T − 1 ) ⊗ ( deg ⁡ v ) f v ) {\displaystyle {\text{Holant}}(G,f_{u}T^{\otimes (\deg u)},(T^{-1})^{\otimes (\deg v)}f_{v})} have exactly the same Holant value for every constraint graph. They essentially define the same counting problem. === Specific examples === ==== Vertex covers and independent sets ==== Let G be a graph. There is a 1-to-1 correspondence between the vertex covers of G and the independent sets of G. For any set S of vertices of G, S is a vertex cover in G if and only if the complement of S is an independent set in G. Thus, the number of vertex covers in G is exactly the same as the number of independent sets in G. The equivalence of these two counting problems can also be proved using a holographic reduction. For simplicity, let G be a 3-regular graph. The 2-stretch of G gives a bipartite graph H=(U,V,E), where U corresponds to the edges in G and V corresponds to the vertices in G. The Holant problem that naturally corresponds to counting the number of vertex covers in G is Holant ( H , OR 2 , EQUAL 3 ) . {\displaystyle {\text{Holant}}(H,{\text{OR}}_{2},{\text{EQUAL}}_{3}).} The truth table of OR2 as a row vector is (0,1,1,1). The truth table of EQUAL3 as a column vector is ( 1 , 0 , 0 , 0 , 0 , 0 , 0 , 1 ) T = [ 1 0 ] ⊗ 3 + [ 0 1 ] ⊗ 3 {\displaystyle (1,0,0,0,0,0,0,1)^{T}={\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}} . Then under a holographic transformation by [ 0 1 1 0 ] , {\displaystyle {\begin{bmatrix}0&1\\1&0\end{bmatrix}},} OR 2 ⊗ | U | EQUAL 3 ⊗ | V | {\displaystyle {\text{OR}}_{2}^{\otimes |U|}{\text{EQUAL}}_{3}^{\otimes |V|}} = ( 0 , 1 , 1 , 1 ) ⊗ | U | ( [ 1 0 ] ⊗ 3 + [ 0 1 ] ⊗ 3 ) ⊗ | V | {\displaystyle =(0,1,1,1)^{\otimes |U|}\left({\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}\right)^{\otimes |V|}} = ( 0 , 1 , 1 , 1 ) ⊗ | U | [ 0 1 1 0 ] ⊗ | E | [ 0 1 1 0 ] ⊗ | E | ( [ 1 0 ] ⊗ 3 + [ 0 1 ] ⊗ 3 ) ⊗ | V | {\displaystyle =(0,1,1,1)^{\otimes |U|}{\begin{bmatrix}0&1\\1&0\end{bmatrix}}^{\otimes |E|}{\begin{bmatrix}0&1\\1&0\end{bmatrix}}^{\otimes |E|}\left({\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}\right)^{\otimes |V|}} = ( ( 0 , 1 , 1 , 1 ) [ 0 1 1 0 ] ⊗ 2 ) ⊗ | U | ( ( [ 0 1 1 0 ] [ 1 0 ] ) ⊗ 3 + ( [ 0 1 1 0 ] [ 0 1 ] ) ⊗ 3 ) ⊗ | V | {\displaystyle =\left((0,1,1,1){\begin{bmatrix}0&1\\1&0\end{bmatrix}}^{\otimes 2}\right)^{\otimes |U|}\left(\left({\begin{bmatrix}0&1\\1&0\end{bmatrix}}{\begin{bmatrix}1\\0\end{bmatrix}}\right)^{\otimes 3}+\left({\begin{bmatrix}0&1\\1&0\end{bmatrix}}{\begin{bmatrix}0\\1\end{bmatrix}}\right)^{\otimes 3}\right)^{\otimes |V|}} = ( 1 , 1 , 1 , 0 ) ⊗ | U | ( [ 0 1 ] ⊗ 3 + [ 1 0 ] ⊗ 3 ) ⊗ | V | {\displaystyle =(1,1,1,0)^{\otimes |U|}\left({\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}\right)^{\otimes |V|}} = NAND 2 ⊗ | U | EQUAL 3 ⊗ | V | , {\displaystyle ={\text{NAND}}_{2}^{\otim

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  • Manhattan address algorithm

    Manhattan address algorithm

    The Manhattan address algorithm is a series of formulas used to estimate the closest east–west cross street for building numbers on north–south avenues in the New York City borough of Manhattan. == Algorithm == To find the approximate number of the closest cross street, divide the building number by a divisor (generally 20) and add (or subtract) the "tricky number" from the table below: For the north–south avenues, there are typically 20 address numbers between consecutive east–west streets (10 on either side of the avenue). A standard land lot on each avenue was originally 20 feet (6.1 m) wide, and there is about 200 feet (61 m) between each pair of east–west streets, for ten land lots between each pair of streets. The exceptions are Riverside Drive, as well as Fifth Avenue and Central Park West between 59th and 110th streets, which use a divisor of 10. These avenues all have buildings only on one side of the street, with a park on the other side. The "tricky number" often corresponds to a street near the southern end of the avenue. There are some notable exceptions: York Avenue address numbers are continuations of Avenue A address numbers, since the avenue was originally called Avenue A. East End Avenue address numbers are continuations of Avenue B address numbers, since the avenue was originally called Avenue B. Sixth Avenue and Broadway start south of Houston Street, the southern boundary of the Manhattan street numbering system. Although Park Avenue's southern terminus is at 32nd Street, a homeowner at 34th Street wanted the address "1 Park Avenue" (this was later changed). === Examples === For example, if you are at 62 Avenue B, 62 ÷ 20 ≈ 3 {\displaystyle 62\div 20\approx 3} , then add the "tricky number" 3 {\displaystyle 3} to give 6 {\displaystyle 6} . The nearest cross street to 62 Avenue B is East 6th Street. If you are at 78 Riverside Drive, 78 ÷ 10 ≈ 8 {\displaystyle 78\div 10\approx 8} , then add the "tricky number" 72 {\displaystyle 72} to give 80 {\displaystyle 80} . The nearest cross street to 78 Riverside Drive is West 80th Street. If you are at 501 5th Avenue, 501 ÷ 20 ≈ 25 {\displaystyle 501\div 20\approx 25} , then add the "tricky number" 18 {\displaystyle 18} to give 43 {\displaystyle 43} . The nearest cross street to 501 5th Avenue is actually 42nd Street, not 43rd Street, as the Manhattan address algorithm only gives approximate answers.

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  • Algorism

    Algorism

    Algorism is the technique of performing basic arithmetic by writing numbers in place value form and applying a set of memorized rules and facts to the digits. One who practices algorism is known as an algorist. This positional notation system has largely superseded earlier calculation systems that used a different set of symbols for each numerical magnitude, such as Roman numerals, and in some cases required a device such as an abacus. == Etymology == The word algorism comes from the name Al-Khwārizmī (c. 780–850), a Persian mathematician, astronomer, geographer and scholar in the House of Wisdom in Baghdad, whose name means "the native of Khwarezm", which is now in modern-day Uzbekistan. He wrote a treatise in Arabic language in the 9th century, which was translated into Latin in the 12th century under the title Algoritmi de numero Indorum. This title means "Algoritmi on the numbers of the Indians", where "Algoritmi" was the translator's Latinization of Al-Khwarizmi's name. Al-Khwarizmi was the most widely read mathematician in Europe in the late Middle Ages, primarily through his other book, the Algebra. In late medieval Latin, algorismus, the corruption of his name, simply meant the "decimal number system" that is still the meaning of modern English algorism. During the 17th century, the French form for the word – but not its meaning – was changed to algorithm, following the model of the word logarithm, this form alluding to the ancient Greek arithmos = number. English adopted the French very soon afterwards, but it wasn't until the late 19th century that "algorithm" took on the meaning that it has in modern English. In English, it was first used about 1230 and then by Chaucer in 1391. Another early use of the word is from 1240, in a manual titled Carmen de Algorismo composed by Alexandre de Villedieu. It begins thus: Haec algorismus ars praesens dicitur, in qua / Talibus Indorum fruimur bis quinque figuris. which translates as: This present art, in which we use those twice five Indian figures, is called algorismus. The word algorithm also derives from algorism, a generalization of the meaning to any set of rules specifying a computational procedure. Occasionally algorism is also used in this generalized meaning, especially in older texts. == History == Starting with the integer arithmetic developed in India using base 10 notation, Al-Khwārizmī along with other mathematicians in medieval Islam, documented new arithmetic methods and made many other contributions to decimal arithmetic (see the articles linked below). These included the concept of the decimal fractions as an extension of the notation, which in turn led to the notion of the decimal point. This system was popularized in Europe by Leonardo of Pisa, now known as Fibonacci.

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  • Argumentation framework

    Argumentation framework

    In artificial intelligence and related fields, an argumentation framework is a way to deal with contentious information and draw conclusions from it using formalized arguments. In an abstract argumentation framework, entry-level information is a set of abstract arguments that, for instance, represent data or a proposition. Conflicts between arguments are represented by a binary relation on the set of arguments. In concrete terms, an argumentation framework is represented with a directed graph such that the nodes are the arguments, and the arrows represent the attack relation. There exist some extensions of the Dung's framework, like the logic-based argumentation frameworks or the value-based argumentation frameworks. == Abstract argumentation frameworks == === Formal framework === Abstract argumentation frameworks, also called argumentation frameworks à la Dung, are defined formally as a pair: A set of abstract elements called arguments, denoted A {\displaystyle A} A binary relation on A {\displaystyle A} , called attack relation, denoted R {\displaystyle R} For instance, the argumentation system S = ⟨ A , R ⟩ {\displaystyle S=\langle A,R\rangle } with A = { a , b , c , d } {\displaystyle A=\{a,b,c,d\}} and R = { ( a , b ) , ( b , c ) , ( d , c ) } {\displaystyle R=\{(a,b),(b,c),(d,c)\}} contains four arguments ( a , b , c {\displaystyle a,b,c} and d {\displaystyle d} ) and three attacks ( a {\displaystyle a} attacks b {\displaystyle b} , b {\displaystyle b} attacks c {\displaystyle c} and d {\displaystyle d} attacks c {\displaystyle c} ). Dung defines some notions : an argument a ∈ A {\displaystyle a\in A} is acceptable with respect to E ⊆ A {\displaystyle E\subseteq A} if and only if E {\displaystyle E} defends a {\displaystyle a} , that is ∀ b ∈ A {\displaystyle \forall b\in A} such that ( b , a ) ∈ R , ∃ c ∈ E {\displaystyle (b,a)\in R,\exists c\in E} such that ( c , b ) ∈ R {\displaystyle (c,b)\in R} , a set of arguments E {\displaystyle E} is conflict-free if there is no attack between its arguments, formally : ∀ a , b ∈ E , ( a , b ) ∉ R {\displaystyle \forall a,b\in E,(a,b)\not \in R} , a set of arguments E {\displaystyle E} is admissible if and only if it is conflict-free and all its arguments are acceptable with respect to E {\displaystyle E} . === Different semantics of acceptance === ==== Extensions ==== To decide if an argument can be accepted or not, or if several arguments can be accepted together, Dung defines several semantics of acceptance that allows, given an argumentation system, sets of arguments (called extensions) to be computed. For instance, given S = ⟨ A , R ⟩ {\displaystyle S=\langle A,R\rangle } , E {\displaystyle E} is a complete extension of S {\displaystyle S} only if it is an admissible set and every acceptable argument with respect to E {\displaystyle E} belongs to E {\displaystyle E} , E {\displaystyle E} is a preferred extension of S {\displaystyle S} only if it is a maximal element (with respect to the set-theoretical inclusion) among the admissible sets with respect to S {\displaystyle S} , E {\displaystyle E} is a stable extension of S {\displaystyle S} only if it is a conflict-free set that attacks every argument that does not belong in E {\displaystyle E} (formally, ∀ a ∈ A ∖ E , ∃ b ∈ E {\displaystyle \forall a\in A\backslash E,\exists b\in E} such that ( b , a ) ∈ R {\displaystyle (b,a)\in R} , E {\displaystyle E} is the (unique) grounded extension of S {\displaystyle S} only if it is the smallest element (with respect to set inclusion) among the complete extensions of S {\displaystyle S} . There exists some inclusions between the sets of extensions built with these semantics : Every stable extension is preferred, Every preferred extension is complete, The grounded extension is complete, If the system is well-founded (there exists no infinite sequence a 0 , a 1 , … , a n , … {\displaystyle a_{0},a_{1},\dots ,a_{n},\dots } such that ∀ i > 0 , ( a i + 1 , a i ) ∈ R {\displaystyle \forall i>0,(a_{i+1},a_{i})\in R} ), all these semantics coincide—only one extension is grounded, stable, preferred, and complete. Some other semantics have been defined. One introduce the notation E x t σ ( S ) {\displaystyle Ext_{\sigma }(S)} to note the set of σ {\displaystyle \sigma } -extensions of the system S {\displaystyle S} . In the case of the system S {\displaystyle S} in the figure above, E x t σ ( S ) = { { a , d } } {\displaystyle Ext_{\sigma }(S)=\{\{a,d\}\}} for every Dung's semantic—the system is well-founded. That explains why the semantics coincide, and the accepted arguments are: a {\displaystyle a} and d {\displaystyle d} . ==== Labellings ==== Labellings are a more expressive way than extensions to express the acceptance of the arguments. Concretely, a labelling is a mapping that associates every argument with a label in (the argument is accepted), out (the argument is rejected), or undec (the argument is undefined—not accepted or refused). One can also note a labelling as a set of pairs ( a r g u m e n t , l a b e l ) {\displaystyle ({\mathit {argument}},{\mathit {label}})} . Such a mapping does not make sense without additional constraint. The notion of reinstatement labelling guarantees the sense of the mapping. L {\displaystyle L} is a reinstatement labelling on the system S = ⟨ A , R ⟩ {\displaystyle S=\langle A,R\rangle } if and only if : ∀ a ∈ A , L ( a ) = i n {\displaystyle \forall a\in A,L(a)={\mathit {in}}} if and only if ∀ b ∈ A {\displaystyle \forall b\in A} such that ( b , a ) ∈ R , L ( b ) = o u t {\displaystyle (b,a)\in R,L(b)={\mathit {out}}} ∀ a ∈ A , L ( a ) = o u t {\displaystyle \forall a\in A,L(a)={\mathit {out}}} if and only if ∃ b ∈ A {\displaystyle \exists b\in A} such that ( b , a ) ∈ R {\displaystyle (b,a)\in R} and L ( b ) = i n {\displaystyle L(b)={\mathit {in}}} ∀ a ∈ A , L ( a ) = u n d e c {\displaystyle \forall a\in A,L(a)={\mathit {undec}}} if and only if L ( a ) ≠ i n {\displaystyle L(a)\neq {\mathit {in}}} and L ( a ) ≠ o u t {\displaystyle L(a)\neq {\mathit {out}}} One can convert every extension into a reinstatement labelling: the arguments of the extension are in, those attacked by an argument of the extension are out, and the others are undec. Conversely, one can build an extension from a reinstatement labelling just by keeping the arguments in. Indeed, Caminada proved that the reinstatement labellings and the complete extensions can be mapped in a bijective way. Moreover, the other Datung's semantics can be associated to some particular sets of reinstatement labellings. Reinstatement labellings distinguish arguments not accepted because they are attacked by accepted arguments from undefined arguments—that is, those that are not defended cannot defend themselves. An argument is undec if it is attacked by at least another undec. If it is attacked only by arguments out, it must be in, and if it is attacked some argument in, then it is out. The unique reinstatement labelling that corresponds to the system S {\displaystyle S} above is L = { ( a , i n ) , ( b , o u t ) , ( c , o u t ) , ( d , i n ) } {\displaystyle L=\{(a,{\mathit {in}}),(b,{\mathit {out}}),(c,{\mathit {out}}),(d,{\mathit {in}})\}} . === Inference from an argumentation system === In the general case when several extensions are computed for a given semantic σ {\displaystyle \sigma } , the agent that reasons from the system can use several mechanisms to infer information: Credulous inference: the agent accepts an argument if it belongs to at least one of the σ {\displaystyle \sigma } -extensions—in which case, the agent risks accepting some arguments that are not acceptable together ( a {\displaystyle a} attacks b {\displaystyle b} , and a {\displaystyle a} and b {\displaystyle b} each belongs to an extension) Skeptical inference: the agent accepts an argument only if it belongs to every σ {\displaystyle \sigma } -extension. In this case, the agent risks deducing too little information (if the intersection of the extensions is empty or has a very small cardinal). For these two methods to infer information, one can identify the set of accepted arguments, respectively C r σ ( S ) {\displaystyle Cr_{\sigma }(S)} the set of the arguments credulously accepted under the semantic σ {\displaystyle \sigma } , and S c σ ( S ) {\displaystyle Sc_{\sigma }(S)} the set of arguments accepted skeptically under the semantic σ {\displaystyle \sigma } (the σ {\displaystyle \sigma } can be missed if there is no possible ambiguity about the semantic). Of course, when there is only one extension (for instance, when the system is well-founded), this problem is very simple: the agent accepts arguments of the unique extension and rejects others. The same reasoning can be done with labellings that correspond to the chosen semantic : an argument can be accepted if it is in for each labelling and refused if it is out for each labelling, the others being in an undecided state (the status of the arguments can remind the

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  • Kleene's algorithm

    Kleene's algorithm

    In theoretical computer science, in particular in formal language theory, Kleene's algorithm transforms a given nondeterministic finite automaton (NFA) into a regular expression. Together with other conversion algorithms, it establishes the equivalence of several description formats for regular languages. Alternative presentations of the same method include the "elimination method" attributed to Brzozowski and McCluskey, the algorithm of McNaughton and Yamada, and the use of Arden's lemma. == Algorithm description == According to Gross and Yellen (2004), the algorithm can be traced back to Kleene (1956). A presentation of the algorithm in the case of deterministic finite automata (DFAs) is given in Hopcroft and Ullman (1979). The presentation of the algorithm for NFAs below follows Gross and Yellen (2004). Given a nondeterministic finite automaton M = (Q, Σ, δ, q0, F), with Q = { q0,...,qn } its set of states, the algorithm computes the sets Rkij of all strings that take M from state qi to qj without going through any state numbered higher than k. Here, "going through a state" means entering and leaving it, so both i and j may be higher than k, but no intermediate state may. Each set Rkij is represented by a regular expression; the algorithm computes them step by step for k = -1, 0, ..., n. Since there is no state numbered higher than n, the regular expression Rn0j represents the set of all strings that take M from its start state q0 to qj. If F = { q1,...,qf } is the set of accept states, the regular expression Rn01 | ... | Rn0f represents the language accepted by M. The initial regular expressions, for k = -1, are computed as follows for i≠j: R−1ij = a1 | ... | am where qj ∈ δ(qi,a1), ..., qj ∈ δ(qi,am) and as follows for i=j: R−1ii = a1 | ... | am | ε where qi ∈ δ(qi,a1), ..., qi ∈ δ(qi,am) In other words, R−1ij mentions all letters that label a transition from i to j, and we also include ε in the case where i=j. After that, in each step the expressions Rkij are computed from the previous ones by Rkij = Rk-1ik (Rk-1kk) Rk-1kj | Rk-1ij Another way to understand the operation of the algorithm is as an "elimination method", where the states from 0 to n are successively removed: when state k is removed, the regular expression Rk-1ij, which describes the words that label a path from state i>k to state j>k, is rewritten into Rkij so as to take into account the possibility of going via the "eliminated" state k. By induction on k, it can be shown that the length of each expression Rkij is at most ⁠1/3⁠(4k+1(6s+7) - 4) symbols, where s denotes the number of characters in Σ. Therefore, the length of the regular expression representing the language accepted by M is at most ⁠1/3⁠(4n+1(6s+7)f - f - 3) symbols, where f denotes the number of final states. This exponential blowup is inevitable, because there exist families of DFAs for which any equivalent regular expression must be of exponential size. In practice, the size of the regular expression obtained by running the algorithm can be very different depending on the order in which the states are considered by the procedure, i.e., the order in which they are numbered from 0 to n. == Example == The automaton shown in the picture can be described as M = (Q, Σ, δ, q0, F) with the set of states Q = { q0, q1, q2 }, the input alphabet Σ = { a, b }, the transition function δ with δ(q0,a)=q0, δ(q0,b)=q1, δ(q1,a)=q2, δ(q1,b)=q1, δ(q2,a)=q1, and δ(q2,b)=q1, the start state q0, and set of accept states F = { q1 }. Kleene's algorithm computes the initial regular expressions as After that, the Rkij are computed from the Rk-1ij step by step for k = 0, 1, 2. Kleene algebra equalities are used to simplify the regular expressions as much as possible. Step 0 Step 1 Step 2 Since q0 is the start state and q1 is the only accept state, the regular expression R201 denotes the set of all strings accepted by the automaton.

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  • Five safes

    Five safes

    The Five Safes is a framework for helping make decisions about making effective use of data which is confidential or sensitive. It is mainly used to describe or design research access to statistical data held by government and health agencies, and by data archives such as the UK Data Service. It is not an internationally accepted standard. Two of the Five Safes refer to statistical disclosure control, and so the Five Safes is usually used to contrast statistical and non-statistical controls when comparing data management options. == Concept == The Five Safes proposes that data management decisions be considered as solving problems in five 'dimensions': projects, people, settings, data and outputs. The combination of the controls leads to 'safe use'. These are most commonly expressed as questions, for example: These dimensions are scales, not limits. That is, solutions can have a mix of more or fewer controls in each dimension, but the overall aim of 'safe use' independent of the particular mix. For example, a public use file available for open download cannot control who uses it, where or for what purpose, and so all the control (protection) must be in the data itself. In contrast, a file which is only accessed through a secure environment with certified users can contain very sensitive information: the non-statistical controls allow the data to be 'unsafe'. One academic likened the process to a graphic equalizer, where bass and treble can be combined independently to produce a sound the listener likes, which has proven to be a very useful metaphor. This 2023 Data Foundation webinar is an expert discussion of how the elements interact, including an excellent introductory representation. There is no 'order' to the Five Safes, in that one is necessarily more important than the others. However, Ritchie argued that the 'managerial' controls (projects, people, setting) should be addressed before the 'statistical' controls (data, output). The Five Safes concept is associated with other topics which developed from the same programme at ONS, although these are not necessarily implemented. Safe people is associated with 'active researcher management', while safe outputs is linked with principles-based output statistical disclosure control. The Five Safes is a positive framework, describing what is and is not. The EDRU ('evidence-based, default-open, risk-managed, user-centred') attitudinal model is sometimes used to give a normative context == The 'data access spectrum' == From 2003 the Five Safes was also represented in a simpler form as a 'Data Access Spectrum'. The non-data controls (project, people, setting, outputs) tend to work together, in that organisations often see these as a complementary set of restrictions on access. These can then be contrasted with choices about data anonymisation to present a linear representation of data access options. This presentation is consistent with the idea of 'data as a residual', as well as data protection laws of the time which often characterised data simply as anonymous or not anonymous. A similar idea had already been developed independently in 2001 by Chuck Humphrey of the Canadian RDC network, the 'continuum of access'. More recently, The Open Data Institute has developed a 'Data Spectrum toolkit' which includes industry-specific examples. == History and terminology == The Five Safes was devised in the winter of 2002/2003 by Felix Ritchie at the UK Office for National Statistics (ONS) to describe its secure remote-access Virtual Microdata Laboratory (VML). It was described at this time as the 'VML Security Model'. This was adopted by the NORC data enclave, and more widely in the US, as the 'portfolio model' (although this is now also used to refer to a slightly different legal/statistical/educational breakdown). In 2012 the framework as was still being referred to as the 'VML security model', but its increasing use among non-UK organisations led to the adoption of the more general and informative phrase 'Five Safes'. The original framework only had four safes (projects, people, settings and outputs): the framework was used to describe highly detailed data access through a secure environment, and so the 'data' dimension was irrelevant. From 2007 onwards, 'safe data' was included as the framework was used to a describe a wider range of ONS activities. As the US version was based upon the 2005 specification, some US iterations uses have the original four dimensions (eg). Some discussions, such as the OECD, use the term 'secure' instead 'safe'. However, the use of both these terms can cause presentational problems: less control in a particular dimension could be seen to imply 'unsafe users' or 'insecure settings', for example, which distracts from the main message. Hence, the Australian government uses the term "five data sharing principles". The 'Anonymisation Decision-Making Framework' uses a framework based on the Five Safes but relabelling "projects", "people", and "settings" as "governance", "agency" and "infrastructure", respectively; "Output" is omitted, and "safe use" becomes "functional anonymisation". There is no reference to the Five Safes or any associated literature. The Australian version was required to include references to the Five Safes, and presented it as an alternative without comment. == Application == The framework has had three uses: pedagogical, descriptive, and design. Since 2016, it has also been used, directly and indirectly in legislation. See for more detailed examples. === Pedagogy === The first significant use of the framework, other than internal administrative use, was to structure researcher training courses at the UK Office for National Statistics from 2003. UK Data Archive, Administrative Data Research Network, Eurostat, Statistics New Zealand, the Mexican National Institute of Statistics and Geography, NORC, Statistics Canada and the Australian Bureau of Statistics, amongst others, have also used this framework. Most of these courses are for researchers using restricted-access facilities; the Eurostat courses are unusual in that they are designed for all users of sensitive data. === Description === The framework is often used to describe existing data access solutions (e.g. UK HMRC Data Lab, UK Data Service, Statistics New Zealand) or planned/conceptualised ones (e.g. Eurostat in 2011). An early use was to help identify areas where ONS' still had 'irreducible risks' in its provision of secure remote access. The framework is mostly used for confidential social science data. To date it appears to have made little impact on medical research planning, although it is now included in the revised guidelines on implementing HIPAA regulations in the US, and by Cancer Research UK and the Health Foundation in the UK. It has also been used to describe a security model for the Scottish Health Informatics Programme. === Design === In general the Five Safes has been used to describe solutions post-factum, and to explain/justify choices made, but an increasing number of organisations have used the framework to design data access solutions. For example, the Hellenic Statistical Agency developed a data strategy built around the Five Safes in 2016; the UK Health Foundation used the Five Safes to design its data management and training programmes. Use in the private sector is less common but some organisations have incorporated the Five Safes into consulting services. In 2015 the UK Data Service organized a workshop to encourage data users from the academic and private sectors to think about how to manage confidential research data, using the Five Safes to demonstrate alternative options and best practice. Early adopters for strategic design use were in Australia: both the Australian Bureau of Statistics and the Australian Department of Social Service used the Five Safes as an ex ante design tool. In 2017 the Australian Productivity Commission recommended adopting a version of the framework to support cross-government data sharing and re-use. This underwent extensive consultation and culminated in the DAT Act 2022. Since 2020 the Five Safes has been the overriding framework for the design of new secure facilities and data sharing arrangements in the UK for public health and social sciences. This has been promoted by the Office for Statistics Regulation, the UK Statistics Authority, NHS DIgital, and the research funding bodies Administrative Data Research UK and DARE UK. === Regulation and legislation === Three laws have incorporated the Fives Safes. They are explicit in the South Australian Public Sector (Data Sharing) Act 2016, and implicit in the research provisions of the UK Digital Economy Act 2017. The Australian Data Availability and Transparency Act 2022 renames the Five Safes as the Five Data Sharing Principles.A 2025 statutory review of the DAT Act 2022 found "that the DAT Act has not been effective in achieving its objectives.". The review includes specific referen

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