AI Excel Spreadsheet Maker

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  MY F.C.

  MY F.C. is a freemium app designed to organise and administer football teams. It is developed by MY F.C. Limited, a private company headquartered in Auckland, New Zealand. The app allows users to build a team by adding players and from there they can create trainings and matches, keep up with relevant news in the curated newsfeed, record statistics both individually and team based, follow the games live in the match-centre. The app also features integrated lineup builder with custom team kits. == History == Founders Sam Jenkins, Mike Simpson and Sam Jasper started MY F.C. in 2015 to help them "run their football lives". The app was launched on Android and iOS on 14 February 2017. == Accolades == MY F.C. won the first place prize at Bank of New Zealand Start-up Alley 2017 competition that aims to discover New Zealand start-ups who are doing innovative work and ready to establish themselves as long-term, sustainable businesses. The prize package included $15,000 and a trip to San Francisco.

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  Pointer jumping

  Pointer jumping or path doubling is a design technique for parallel algorithms that operate on pointer structures, such as linked lists and directed graphs. Pointer jumping allows an algorithm to follow paths with a time complexity that is logarithmic with respect to the length of the longest path. It does this by "jumping" to the end of the path computed by neighbors. The basic operation of pointer jumping is to replace each neighbor in a pointer structure with its neighbor's neighbor. In each step of the algorithm, this replacement is done for all nodes in the data structure, which can be done independently in parallel. In the next step when a neighbor's neighbor is followed, the neighbor's path already followed in the previous step is added to the node's followed path in a single step. Thus, each step effectively doubles the distance traversed by the explored paths. Pointer jumping is best understood by looking at simple examples such as list ranking and root finding. == List ranking == One of the simpler tasks that can be solved by a pointer jumping algorithm is the list ranking problem. This problem is defined as follows: given a linked list of N nodes, find the distance (measured in the number of nodes) of each node to the end of the list. The distance d(n) is defined as follows, for nodes n that point to their successor by a pointer called next: If n.next is nil, then d(n) = 0. For any other node, d(n) = d(n.next) + 1. This problem can easily be solved in linear time on a sequential machine, but a parallel algorithm can do better: given n processors, the problem can be solved in logarithmic time, O(log N), by the following pointer jumping algorithm: The pointer jumping occurs in the last line of the algorithm, where each node's next pointer is reset to skip the node's direct successor. It is assumed, as in common in the PRAM model of computation, that memory access are performed in lock-step, so that each n.next.next memory fetch is performed before each n.next memory store; otherwise, processors may clobber each other's data, producing inconsistencies. The following diagram follows how the parallel list ranking algorithm uses pointer jumping for a linked list with 11 elements. As the algorithm describes, the first iteration starts initialized with all ranks set to 1 except those with a null pointer for next. The first iteration looks at immediate neighbors. Each subsequent iteration jumps twice as far as the previous. Analyzing the algorithm yields a logarithmic running time. The initialization loop takes constant time, because each of the N processors performs a constant amount of work, all in parallel. The inner loop of the main loop also takes constant time, as does (by assumption) the termination check for the loop, so the running time is determined by how often this inner loop is executed. Since the pointer jumping in each iteration splits the list into two parts, one consisting of the "odd" elements and one of the "even" elements, the length of the list pointed to by each processor's n is halved in each iteration, which can be done at most O(log N) time before each list has a length of at most one. == Root finding == Following a path in a graph is an inherently serial operation, but pointer jumping reduces the total amount of work by following all paths simultaneously and sharing results among dependent operations. Pointer jumping iterates and finds a successor — a vertex closer to the tree root — each time. By following successors computed for other vertices, the traversal down each path can be doubled every iteration, which means that the tree roots can be found in logarithmic time. Pointer doubling operates on an array successor with an entry for every vertex in the graph. Each successor[i] is initialized with the parent index of vertex i if that vertex is not a root or to i itself if that vertex is a root. At each iteration, each successor is updated to its successor's successor. The root is found when the successor's successor points to itself. The following pseudocode demonstrates the algorithm. algorithm Input: An array parent representing a forest of trees. parent[i] is the parent of vertex i or itself for a root Output: An array containing the root ancestor for every vertex for i ← 1 to length(parent) do in parallel successor[i] ← parent[i] while true for i ← 1 to length(successor) do in parallel successor_next[i] ← successor[successor[i]] if successor_next = successor then break for i ← 1 to length(successor) do in parallel successor[i] ← successor_next[i] return successor The following image provides an example of using pointer jumping on a small forest. On each iteration the successor points to the vertex following one more successor. After two iterations, every vertex points to its root node. == History and examples == Although the name pointer jumping would come later, JáJá attributes the first uses of the technique in early parallel graph algorithms and list ranking. The technique has been described with other names such as shortcutting, but by the 1990s textbooks on parallel algorithms consistently used the term pointer jumping. Today, pointer jumping is considered a software design pattern for operating on recursive data types in parallel. As a technique for following linked paths, graph algorithms are a natural fit for pointer jumping. Consequently, several parallel graph algorithms utilizing pointer jumping have been designed. These include algorithms for finding the roots of a forest of rooted trees, connected components, minimum spanning trees, and biconnected components. However, pointer jumping has also shown to be useful in a variety of other problems including computer vision, image compression, and Bayesian inference.

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  Pointer algorithm

  In computer science, a pointer algorithm (sometimes called a pointer machine, or a reference machine; see the article Pointer machine for a close but non-identical concept) is a type of algorithm that manages a linked data structure. This concept is used as a model for lower-bound proofs and specific restrictions on the linked data structure and on the algorithm's access to the structure vary. This model has been used extensively with problems related to the disjoint-set data structure. Thus, Tarjan and La Poutré used this model to prove lower bounds on the amortized complexity of a disjoint-set data structure (La Poutré also addressed the interval split-find problem). Blum used this model to prove a lower bound on the single operation worst-case time of disjoint set data structure. Blum and Rochow proved a worst-case lower bound for the interval union-find problem. == Example == In Tarjan's lower bound for the disjoint set union problem, the assumptions on the algorithm are: The algorithm maintains a linked structure of nodes. Each element of the problem is associated with a node. Each set is represented by a node. The nodes of each set constitute a distinct connected component in the structure (this property is called separability). The find operation is performed by following links from the element node to the set node. Under these assumptions, the lower bound of Ω ( m α ( m , n ) ) {\displaystyle \Omega (m\alpha (m,n))} on the cost of a sequence of m operations is proven.

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  Known-item search

  Known-item search is a specialization of information exploration which represents the activities carried out by searchers who have a particular item in mind. In the context of library catalogs, known‐item search means a search for an item for which the author or title is known. Although the concept of known-item search originated in library science, it is now applied in the context of web search and other online search activities. Known-item search is distinguished from exploratory search, in which a searcher is unfamiliar with the domain of their search goal, unsure about the ways to achieve their goal, and/or unsure about what their goal is.

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  Super-resolution optical fluctuation imaging

  Super-resolution optical fluctuation imaging (SOFI) is a post-processing method for the calculation of super-resolved images from recorded image time series that is based on the temporal correlations of independently fluctuating fluorescent emitters. SOFI has been developed for super-resolution of biological specimen that are labelled with independently fluctuating fluorescent emitters (organic dyes, fluorescent proteins). In comparison to other super-resolution microscopy techniques such as STORM or PALM that rely on single-molecule localization and hence only allow one active molecule per diffraction-limited area (DLA) and timepoint, SOFI does not necessitate a controlled photoswitching and/ or photoactivation as well as long imaging times. Nevertheless, it still requires fluorophores that are cycling through two distinguishable states, either real on-/off-states or states with different fluorescence intensities. In mathematical terms SOFI-imaging relies on the calculation of cumulants, for what two distinguishable ways exist. For one thing an image can be calculated via auto-cumulants that by definition only rely on the information of each pixel itself, and for another thing an improved method utilizes the information of different pixels via the calculation of cross-cumulants. Both methods can increase the final image resolution significantly although the cumulant calculation has its limitations. Actually SOFI is able to increase the resolution in all three dimensions. == Principle == Likewise to other super-resolution methods SOFI is based on recording an image time series on a CCD- or CMOS camera. In contrary to other methods the recorded time series can be substantially shorter, since a precise localization of emitters is not required and therefore a larger quantity of activated fluorophores per diffraction-limited area is allowed. The pixel values of a SOFI-image of the n-th order are calculated from the values of the pixel time series in the form of a n-th order cumulant, whereas the final value assigned to a pixel can be imagined as the integral over a correlation function. The finally assigned pixel value intensities are a measure of the brightness and correlation of the fluorescence signal. Mathematically, the n-th order cumulant is related to the n-th order correlation function, but exhibits some advantages concerning the resulting resolution of the image. Since in SOFI several emitters per DLA are allowed, the photon count at each pixel results from the superposition of the signals of all activated nearby emitters. The cumulant calculation now filters the signal and leaves only highly correlated fluctuations. This provides a contrast enhancement and therefore a background reduction for good measure. As it is implied in the figure on the left the fluorescence source distribution: ∑ k = 1 N δ ( r → − r → k ) ⋅ ε k ⋅ s k ( t ) {\displaystyle \sum _{k=1}^{N}\delta ({\vec {r}}-{\vec {r}}_{k})\cdot \varepsilon _{k}\cdot s_{k}(t)} is convolved with the system's point spread function (PSF) U(r). Hence the fluorescence signal at time t and position r → {\displaystyle {\vec {r}}} is given by F ( r → , t ) = ∑ k = 1 N U ( r → − r → k ) ⋅ ε k ⋅ s k ( t ) . {\displaystyle F({\vec {r}},t)=\sum _{k=1}^{N}U({\vec {r}}-{\vec {r}}_{k})\cdot \varepsilon _{k}\cdot s_{k}(t).} Within the above equations N is the amount of emitters, located at the positions r → k {\displaystyle {\vec {r}}_{k}} with a time-dependent molecular brightness ε k ⋅ s k {\displaystyle \varepsilon _{k}\cdot s_{k}} where ε k {\displaystyle \varepsilon _{k}} is a variable for the constant molecular brightness and s k ( t ) {\displaystyle s_{k}(t)} is a time-dependent fluctuation function. The molecular brightness is just the average fluorescence count-rate divided by the number of molecules within a specific region. For simplification it has to be assumed that the sample is in a stationary equilibrium and therefore the fluorescence signal can be expressed as a zero-mean fluctuation: δ F ( r → , t ) = F ( r → , t ) − ⟨ F ( r → , t ) ⟩ t {\displaystyle \delta F({\vec {r}},t)=F({\vec {r}},t)-\langle F({\vec {r}},t)\rangle _{t}} where ⟨ ⋯ ⟩ t {\displaystyle \langle \cdots \rangle _{t}} denotes time-averaging. The auto-correlation here e.g. the second-order can then be described deductively as follows for a certain time-lag τ {\displaystyle \tau } : δ F ( r → , t ) = ⟨ δ F ( r → , t + τ ) ⋅ δ F ( r → , t ) ⟩ t {\displaystyle \delta F({\vec {r}},t)=\langle \delta F({\vec {r}},t+\tau )\cdot \delta F({\vec {r}},t)\rangle _{t}} From these equations it follows that the PSF of the optical system has to be taken to the power of the order of the correlation. Thus in a second-order correlation the PSF would be reduced along all dimensions by a factor of 2 {\displaystyle {\sqrt {2}}} . As a result, the resolution of the SOFI-images increases according to this factor. === Cumulants versus correlations === Using only the simple correlation function for a reassignment of pixel values, would ascribe to the independency of fluctuations of the emitters in time in a way that no cross-correlation terms would contribute to the new pixel value. Calculations of higher-order correlation functions would suffer from lower-order correlations for what reason it is superior to calculate cumulants, since all lower-order correlation terms vanish. == Cumulant-calculation == === Auto-cumulants === For computational reasons it is convenient to set all time-lags in higher-order cumulants to zero so that a general expression for the n-th order auto-cumulant can be found: A C n ( r → , τ 1 … n − 1 = 0 ) = ∑ k = 1 N U n ( r → − r → k ) ε k n w k ( 0 ) {\displaystyle AC_{n}({\vec {r}},\tau _{1\ldots n-1}=0)=\sum _{k=1}^{N}U^{n}({\vec {r}}-{\vec {r}}_{k})\varepsilon _{k}^{n}w_{k}(0)} w k {\displaystyle w_{k}} is a specific correlation based weighting function influenced by the order of the cumulant and mainly depending on the fluctuation properties of the emitters. Albeit there is no fundamental limitation in calculating very high orders of cumulants and thereby shrinking the FWHM of the PSF there are practical limitations according to the weighting of the values assigned to the final image. Emitters with a higher molecular brightness will show a strong increase in terms of the pixel cumulant value assigned at higher-orders as well as this performance can be expected from a diverse appearance of fluctuations of different emitters. A wide intensity range of the resulting image can therefore be expected and as a result dim emitters can get masked by bright emitters in higher-order images:. The calculation of auto-cumulants can be realized in a very attractive way in a mathematical sense. The n-th order cumulant can be calculated with a basic recursion from moments K n ( r → ) = μ n ( r → ) − ∑ i = 1 n − 1 ( n − 1 i ) K n − i ( r → ) μ i ( r → ) {\displaystyle K_{n}({\vec {r}})=\mu _{n}({\vec {r}})-\sum _{i=1}^{n-1}{\begin{pmatrix}n-1\\i\end{pmatrix}}K_{n-i}({\vec {r}})\mu _{i}({\vec {r}})} where K is a cumulant of the index's order, likewise μ {\displaystyle \mu } represents the moments. The term within the brackets indicates a binomial coefficient. This way of computation is straightforward in comparison with calculating cumulants with standard formulas. It allows for the calculation of cumulants with only little time of computing and is, as it is well implemented, even suitable for the calculation of high-order cumulants on large images. === Cross-cumulants === In a more advanced approach cross-cumulants are calculated by taking the information of several pixels into account. Cross-cumulants can be described as follows: C C n ( r → , τ 1 … n − 1 = 0 ) = ∏ j < l n U ( r → j − r → l n ) ⋅ ∑ i = 1 N U n ( r → i − ∑ k n r → k n ) ε i n w i ( 0 ) {\displaystyle CC_{n}({\vec {r}},\tau _{1\ldots n-1}=0)=\prod _{j Read more →

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  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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  Artificial intelligence in Indonesia

  Artificial intelligence in Indonesia refers to development, use and governance of artificial intelligence in Indonesia. Indonesia has treated AI as a national policy area through the Strategi Nasional Kecerdasan Artifisial or National Artificial Intelligence Strategy for 2020–2045. Public discussion has focused on the role of AI in sectors such as health, agriculture, education, mobile technology and e-commerce. Recent developments include AI ethics guidance issued by the communications ministry. Proposals for a national AI roadmap and sovereign AI fund, investment in cloud and AI infrastructure, and local-language AI initiatives for Bahasa Indonesia and regional Indonesian languages. == National strategy == Indonesia's National Artificial Intelligence Strategy is known in Indonesian as Strategi Nasional Kecerdasan Artifisial or Stranas KA. The strategy was published as a long-term framework for the development and use of AI between 2020 and 2045. It is intended to guide ministries, government agencies, regional governments and other stakeholders. The strategy identifies five priority sectors: health services, bureaucratic reform, education and research, food security, and mobility and smart cities. OECD lists the Ministry of Research and Technology and the National Research and Innovation Agency as organisations associated with the strategy. The strategy was developed through consultation with public and private stakeholders. == Institutions == The Indonesian Artificial Intelligence Industry Research and Innovation Collaboration, known as KORIKA is the nodal agency for the national AI strategy. KORIKA describes its vision as creating a collaborative ecosystem to accelerate implementation of the national AI strategy towards Vision Indonesia 2045. The Ministry of Communication and Digital Affairs has also been involved in AI governance, digital policy and public communication. In 2025, Reuters reported that the ministry was preparing a national AI roadmap to give investors and developers a clearer view of Indonesia's market, infrastructure and computing capacity. == AI Governance == Indonesia has introduced policy guidance on the ethical use of artificial intelligence. The policy sets out ethical values for the development and use of AI. These include humanity, security, transparency, credibility and accountability, personal data protection, sustainable development and intellectual property protection. A UNESCO country profile on Indonesia noted that Indonesia had adopted a national AI strategy and had policy frameworks. It also identified gaps in internet access, gender inclusion, language datasets, digital talent and cybersecurity. UNESCO recommended that Indonesia update its AI standards, invest in ethical AI, strengthen research coordination and consider establishing a national agency for artificial intelligence. In May 2026, Antara News reported comments by Deputy Minister of Communication and Digital Affairs Nezar Patria. Who said that AI safety requires partnerships, shared standards and continuing dialogue. == Sectors == AI policy discussions in Indonesia have identified health, agriculture, education, government services, mobility and smart cities as areas where AI could be applied. Mobile technology and e-commerce have been discussed as important areas of AI adoption in Indonesia. Research on AI adoption in Indonesia by Siddhartha Paul Tiwari and Adi Fahrudin has also examined mobile and e-commerce sectors. UNESCO has also noted that Indonesia's large digital economy and startup ecosystem have supported AI adoption, while also pointing to challenges in talent, research capacity and cybersecurity. Indonesia is one of the developing-country markets attracting AI infrastructure investment, including data centres. == Challenges == Indonesia faces several challenges in developing and governing AI. These include gaps in computing infrastructure, uneven connectivity outside major cities, shortages of skilled workers, limited research funding, cybersecurity risks, misinformation, data leaks and the underrepresentation of Indonesian and indigenous languages in AI datasets. UNESCO noted that Bahasa is spoken by around 200 million people but remains underrepresented in AI. It also noted that Indonesia has more than 700 indigenous languages, many of which face the risk of extinction. UNESCO recommended stronger coordination in AI research and a more unified strategy for using AI in language preservation.

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  Knowledge organization system

  Knowledge organization system (KOS), concept system, or concept scheme is the generic term used in knowledge organization (KO) for the selection of concepts with an indication of selected semantic relations. Despite their differences in type, coverage, and application, all KOS aim to support the organization of knowledge and information to facilitate their management and retrieval. KOS vary in complexity from simple sorted lists to complex relational networks. They represent both structural and functional features, and serve to eliminate ambiguity, control synonyms, establish relationships, and present properties. From their origins in library and information science (LIS), KOS have been applied to other domains and disciplines within science and industry, although scholarly research and debate remain primarily within the KO field. Challenges of KOS include ambiguity of terminology, repercussions of biased systems, and potential obsolescence. KOS can be expressed in RDF and RDFS as per the Simple Knowledge Organization System (SKOS) recommendation by W3C, which aims to enable the sharing and linking of KOS via the Web. One of the largest collections of KOS is the BARTOC registry. == Types == While different schema of KOS have been proposed, most are generally arranged in terms of the complexity of their construction and maintenance. Some scholars argue that organizing KOS on a spectrum oversimplifies the shared characteristics among them, and may even result in a non-ideal structure being chosen. The following types are not exhaustive, and are often not mutually-exclusive in practice. === Term lists === Term lists are the least structured form of KOS. They include lists, glossaries, dictionaries, and synonym rings. Authority files and gazetteers may also be considered term lists, however other scholars categorize them and directories as "metadata-like models". Examples include the Union List of Artist Names name authority file and the GeoNames gazetteer. === Categorization and classification === KOS that emphasize specific (and often hierarchical) structures include subject headings, taxonomies, categorization schema, and classification schema & systems. Despite inconsistent use of the terms "categorization" and "classification" in some literature, categorization is generally loosely-assembled grouping schema and may include attributes that are not mutually exclusive (or having fuzzy boundaries), while classification is related to the arrangement of non-overlapping and mutually-exclusive classes. Classification schema may be universal (such as Dewey Decimal Classification and Information Coding Classification) or domain-specific (such as the National Library of Medicine Classification). === Relationship models === The types of KOS with greatest complexity and which utilize connections between concepts include thesauri, semantic networks, and ontologies. One of the most prominent examples of a semantic network is WordNet. === Others === Certain structures proposed to be considered types of KOS—but are not consistently included in schema—include folksonomies, topic maps, web directory structures, publication organization systems, and bibliometric maps. Some KOS organize other KOS themselves—for instance, PeriodO is a gazetteer of periodization categories. == Applications == Some early KOS were developed as a support system for abstracting and indexing services to be used by specially-trained searchers. With the growth of information digitization, usability became increasingly accessible, and more complex structures were developed. Prominent examples of KOS outside of LIS include organism taxonomy in biology, the periodic table of elements in chemistry, SIC and NAICS classification systems for industry & business, and AGROVOC agricultural controlled vocabulary. == Challenges == The study and design of KOS is an ongoing topic of discussion among KO scholars. === Terminology === [There is] a serious lack of vocabulary control in the literature on controlled vocabulary. Inconsistency of terminology within the study of KOS is a common issue. For instance, "ontology" is used for both a specific type of KOS as well as a generic term for any KOS. The terms "taxonomy", "classification", and "categorization" are also sometimes used interchangeably. === Bias === As knowledge can be historically and culturally biased, scholars have also discussed how KOS themselves can perpetuate harmful practices or stereotypes. For example, a number of concerns and criticisms about the classification of mental disorders in the Diagnostic and Statistical Manual of Mental Disorders have been raised, contributing to ongoing revisions. Ethical and intentional design approaches have been proposed for multi-perspective KOS in efforts to mitigate bias and other harmful practices. === Obsolescence === The possible obsolescence of the thesaurus and other simpler KOS has been the topic of debate, especially in the face of increasingly complex ontologies, the growing usage of "Google-like retrieval systems", and the move of KO theory and research away from LIS and toward computer science. Supporters of thesauri argue its continued usefulness for metadata enrichment, vocabulary mapping, and web services, as well as its usage in specific domains such as corporate intranets and digital image libraries.

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  Neural computation

  Neural computation is the information processing performed by networks of neurons. Neural computation is affiliated with the philosophical tradition of computationalism, which advances the thesis that neural computation explains cognition. Warren McCulloch and Walter Pitts were the first to propose an account of neural activity as being computational in their seminal 1943 paper "A Logical Calculus of the Ideas Immanent in Nervous Activity." There are three general branches of computationalism, including classicism, connectionism, and computational neuroscience. All three branches agree that cognition is computation, however, they disagree on what sorts of computations constitute cognition. The classicism tradition believes that computation in the brain is digital, analogous to digital computing. Both connectionism and computational neuroscience do not require that the computations that realize cognition are necessarily digital computations. However, the two branches greatly disagree upon which sorts of experimental data should be used to construct explanatory models of cognitive phenomena. Connectionists rely upon behavioral evidence to construct models to explain cognitive phenomena, whereas computational neuroscience leverages neuroanatomical and neurophysiological information to construct mathematical models that explain cognition. When comparing the three main traditions of the computational theory of mind, as well as the different possible forms of computation in the brain, it is helpful to define what we mean by computation in a general sense. Computation is the processing of information, otherwise known as variables or entities, according to a set of rules. A rule in this sense is simply an instruction for executing a manipulation on the current state of the variable, in order to produce a specified output. In other words, a rule dictates which output to produce given a certain input to the computing system. A computing system is a mechanism whose components must be functionally organized to process the information in accordance with the established set of rules. The types of information processed by a computing system determine which type of computations it performs. Traditionally in cognitive science, there have been two proposed types of computation related to neural activity, digital and analog, with the vast majority of theoretical work incorporating a digital understanding of cognition. Computing systems that perform digital computation are functionally organized to execute operations on strings of digits with respect to the type and location of the digit on the string. It has been argued that neural spike train signaling implements some form of digital computation, since neural spikes may be considered as discrete units or digits, like 0 or 1—the neuron either fires an action potential or it does not. Accordingly, neural spike trains could be seen as strings of digits. Alternatively, analog computing systems perform manipulations on non-discrete, irreducibly continuous variables, that is, entities that vary continuously as a function of time. These sorts of operations are characterized by systems of differential equations. Neural computation can be studied by, for example, building models of neural computation. Work on artificial neural networks has been somewhat inspired by knowledge of neural computation.

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  Wiki survey

  Wiki surveys or wikisurveys are a software-based survey method that crowdsource discussions and help participants to find areas of agreement. Other names include bridging systems and collective response systems. The approach, inspired by Wikipedia, is to open up surveys where participants can shape the questions, instead of traditional 'closed' surveys where participants can only respond to the questions asked. Wiki surveys have been used for purposes including facilitating deliberative democracy, crowdsourcing opinions from experts and figuring out common beliefs on a given topic. A notable usage of wiki surveys is in Taiwan's government system, where citizens can participate in crowdsourced lawmaking through Pol.is wiki surveys. == Implementations == === All Our Ideas === All Our Ideas founders coined the term "wiki survey," explaining how they took inspiration from the organic evolution of Wikipedia and hoped to create something similar for surveys. They hosted 5000 surveys between 2010 and 2014. A 2020 survey using the tool found 3 of its top 10 findings were user-generated. === Decidim === Decidim has been used by governments throughout Spain and Europe to help with participatory budgeting and other public policy decisions. === Polis === Polis (also known as Pol.is) was developed in 2012. The focus of Polis is to project participants into an 'opinion space' where they can see how their voting behavior compares to other participants. The opinion space clusters participants into groups of similar opinion and is designed in a way to avoid tyranny of the majority by being able to include groups that have small numbers of participants. The questions participants are presented with are agree/disagree/pass on a single 'comment' submitted by a participant. The code for Polis is free and open-source software under the GNU AGPL. === Remesh === Remesh was founded in 2013 and has partnered with the United Nations and Alliance for Middle East Peace efforts to bring peaceful resolutions to conflicts. Participants are anonymous and the algorithm can be fine-tuned to better understand local dialects in specific regions. == Examples == PlaNYC used All Our Ideas to gather ideas on how to establish New York City's sustainability plan vTaiwan, a citizen-lead government process in Taiwan, uses Polis for enabling large amounts of citizens to deliberate and consequently provide input on Taiwan's legislative decisions OECD used All Our Ideas to gather ideas from the public prior to meeting for a forum and meeting on which skills are most important to invest in for the 21st century March On, an offshoot of the Women's March Movement, used Polis to understand the opinions of people wanting to support the movement Residents of Harrogate use Polis to debate issues in their community, with the results being released publicly to everyone == Characteristics == Wiki surveys often have these three characteristics: === Collaborativeness === Wiki surveys allow participants to contribute questions, as well as answer questions created by its participants. === Adaptivity === Wiki surveys adapt to elicit the most useful information from its participants. One example involves changing the ordering of questions based on the voting behavior of previous participants so as to maximize consensus. The heuristic determining the ordering of questions highly values showing the comments that have been voted on the least. === 'Greediness' === In the context of wiki surveys, 'greediness' simply means making full use of information that participants are willing to provide. Wiki surveys do not require participants to answer a fixed amount of questions, so participants can answer as little or as much as they want. This is intended to be more efficient in capturing participants' preferences by allowing more organic sharing of their perspectives. == Traditional survey methods vs. wiki surveys == Questions in traditional survey methods fall into two categories: Open and closed questions. Open questions ask the person taking the survey to write an open response while closed questions give a fixed set of responses to select from. Wiki surveys are like a hybrid of the two, enabling insightful consensus in certain situations where traditional survey methods may lack. Closed questions are easy to analyze quantitively, but the limited options to select from for a given question may cause bias. Open questions are not as subject to bias, but are difficult to analyze quantitatively at scale. Wiki surveys allow for open responses by the users' contribution of survey questions (also called 'items'), and uses machine learning techniques to (at least partially) automate the quantitative analysis of the responses to those questions.

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  Semantic integration

  Semantic integration is the process of interrelating information from diverse sources, for example calendars and to do lists, email archives, presence information (physical, psychological, and social), documents of all sorts, contacts (including social graphs), search results, and advertising and marketing relevance derived from them. In this regard, semantics focuses on the organization of and action upon information by acting as an intermediary between heterogeneous data sources, which may conflict not only by structure but also context or value. == Applications and methods == In enterprise application integration (EAI), semantic integration can facilitate or even automate the communication between computer systems using metadata publishing. Metadata publishing potentially offers the ability to automatically link ontologies. One approach to (semi-)automated ontology mapping requires the definition of a semantic distance or its inverse, semantic similarity and appropriate rules. Other approaches include so-called lexical methods, as well as methodologies that rely on exploiting the structures of the ontologies. For explicitly stating similarity/equality, there exist special properties or relationships in most ontology languages. OWL, for example has "owl:equivalentClass", "owl:equivalentProperty" and "owl:sameAs". Eventually system designs may see the advent of composable architectures where published semantic-based interfaces are joined together to enable new and meaningful capabilities. These could predominately be described by means of design-time declarative specifications, that could ultimately be rendered and executed at run-time. Semantic integration can also be used to facilitate design-time activities of interface design and mapping. In this model, semantics are only explicitly applied to design and the run-time systems work at the syntax level. This "early semantic binding" approach can improve overall system performance while retaining the benefits of semantic driven design. == Semantic integration situations == From the industry use case, it has been observed that the semantic mappings were performed only within the scope of the ontology class or the datatype property. These identified semantic integrations are (1) integration of ontology class instances into another ontology class without any constraint, (2) integration of selected instances in one ontology class into another ontology class by the range constraint of the property value and (3) integration of ontology class instances into another ontology class with the value transformation of the instance property. Each of them requires a particular mapping relationship, which is respectively: (1) equivalent or subsumption mapping relationship, (2) conditional mapping relationship that constraints the value of property (data range) and (3) transformation mapping relationship that transforms the value of property (unit transformation). Each identified mapping relationship can be defined as either (1) direct mapping type, (2) data range mapping type or (3) unit transformation mapping type. == KG vs. RDB approaches == In the case of integrating supplemental data source, KG(Knowledge graph) formally represents the meaning involved in information by describing concepts, relationships between things, and categories of things. These embedded semantics with the data offer significant advantages such as reasoning over data and dealing with heterogeneous data sources. The rules can be applied on KG more efficiently using graph query. For example, the graph query does the data inference through the connected relations, instead of repeated full search of the tables in relational database. KG facilitates the integration of new heterogeneous data by just adding new relationships between existing information and new entities. This facilitation is emphasized for the integration with existing popular linked open data source such as Wikidata.org. SQL query is tightly coupled and rigidly constrained by datatype within the specific database and can join tables and extract data from tables, and the result is generally a table, and a query can join tables by any columns which match by datatype. SPARQL query is the standard query language and protocol for Linked Open Data on the web and loosely coupled with the database so that it facilitates the reusability and can extract data through the relations free from the datatype, and not only extract but also generate additional knowledge graph with more sophisticated operations(logic: transitive/symmetric/inverseOf/functional). The inference based query (query on the existing asserted facts without the generation of new facts by logic) can be fast comparing to the reasoning based query (query on the existing plus the generated/discovered facts based on logic). The information integration of heterogeneous data sources in traditional database is intricate, which requires the redesign of the database table such as changing the structure and/or addition of new data. In the case of semantic query, SPARQL query reflects the relationships between entities in a way that aligned with human's understanding of the domain, so the semantic intention of the query can be seen on the query itself. Unlike SPARQL, SQL query, which reflects the specific structure of the database and derived from matching the relevant primary and foreign keys of tables, loses the semantics of the query by missing the relationships between entities. Below is the example that compares SPARQL and SQL queries for medications that treats "TB of vertebra". SELECT ?medication WHERE { ?diagnosis a example:Diagnosis . ?diagnosis example:name “TB of vertebra” . ?medication example:canTreat ?diagnosis . } SELECT DRUG.medID FROM DIAGNOSIS, DRUG, DRUG_DIAGNOSIS WHERE DIAGNOSIS.diagnosisID=DRUG_DIAGNOSIS.diagnosisID AND DRUG.medID=DRUG_DIAGNOSIS.medID AND DIAGNOSIS.name=”TB of vertebra” == Examples == The Pacific Symposium on Biocomputing has been a venue for the popularization of the ontology mapping task in the biomedical domain, and a number of papers on the subject can be found in its proceedings.

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  Information pollution

  Information pollution (also referred to as info pollution) is the contamination of an information supply with irrelevant, redundant, unsolicited, hampering, and low-value information. Examples include misinformation, disinformation, junk e-mail, and media violence. The spread of useless and undesirable information can have a detrimental effect on human activities. It is considered to be an adverse effect of the information revolution. == Overview == Information pollution generally applies to digital communication, such as e-mail, instant messaging (IM), and social media. The term acquired particular relevance in 2003 when web usability expert Jakob Nielsen published articles discussing the topic. As early as 1971 researchers were expressing doubts about the negative effects of having to recover "valuable nodules from a slurry of garbage in which it is a randomly dispersed minor component." People use information in order to make decisions and adapt to circumstances. Cognitive studies demonstrated human beings can process only limited information before the quality of their decisions begins to deteriorate. Information overload is a related concept that can also harm decision-making. It refers to an abundance of available information, without respect to its quality. Although technology is thought to have exacerbated the problem, it is not the only cause of information pollution. Anything that distracts attention from the essential facts required to perform a task or make a decision could be considered an information pollutant. Information pollution is seen as the digital equivalent of the environmental pollution generated by industrial processes. Some authors claim that information overload is a crisis of global proportions, on the same scale as threats faced by environmental destruction. Others have expressed the need for the development of an information management paradigm that parallels environmental management practices. == Manifestations == The manifestations of information pollution can be classified into two groups: those that provoke disruption, and those that damage information quality. Typical examples of disrupting information pollutants include unsolicited electronic messages (spam) and instant messages, particularly in the workplace. Mobile phones (ring tones and content) are disruptive in many contexts. Disrupting information pollution is not always technology based. A common example are newspapers, where subscribers read less than half or even none of the articles provided. Superfluous messages, such as unnecessary labels on a map, also distract. Alternatively, information may be polluted when its quality is reduced. This may be due to inaccurate or outdated information, but it also happens when information is badly presented. For example, when content is unfocused or unclear or when they appear in cluttered, wordy, or poorly organised documents it is difficult for the reader to understand. Laws and regulations undergo changes and revisions. Handbooks and other sources used for interpreting these laws can fall years behind the changes, which can cause the public to be misinformed. == Causes == === Cultural factors === Traditionally, information has been seen positively. People are accustomed to statements like "you cannot have too much information", "the more information the better", and "knowledge is power". The publishing and marketing industries have become used to printing many copies of books, magazines, and brochures regardless of customer demand, just in case they are needed. Democratised information sharing is an example of a new technology that has made it easier for information to reach everyone. Such technologies are perceived as a sign of progress and individual empowerment, as well as a positive step to bridge the digital divide. However, they also increase the volume of distracting information, making it more difficult to distinguish valuable information from noise. The continuous use of advertising in websites, technologies, newspapers, and everyday life is known as "cultural pollution". === Information technology === Technological advances of the 20th century and, in particular, the internet play a key role in the increase of information pollution. Blogs, social networks, personal websites, and mobile technology all contribute to increased "noise". The level of pollution may depend on the context. For example, e-mail is likely to cause more information pollution in a corporate setting, whereas mobile phones are likely to be particularly disruptive in a confined space shared by multiple people, such as a train carriage. == Effects == The effects of information pollution can be seen at multiple levels. === Individual === At a personal level, information pollution affects individuals' capacity to evaluate options and find adequate solutions. This can lead to information overload, anxiety, decision paralysis, and stress. It can disrupt the learning process. === Society === Some authors argue that information pollution and information overload can cause loss of perspective and moral values. This argument may explain the indifferent attitude that society shows toward topics such as scientific discoveries, health warnings, or politics. Pollution makes people less sensitive to headlines and more cynical toward new messages. === Business === Information pollution contributes to information overload and stress, which can disrupt the kinds information processing and decision-making needed to complete tasks at work. This leads to delayed or flawed decisions, which can translate into loss of productivity and revenue as well as an increased risk of critical errors. == Solutions == Proposed solutions include management techniques and refined technology. Technology-based alternatives include decision support systems and dashboards that enable prioritisation of information. Technologies that create frequent interruptions can be replaced with less-"polluting" options. Further, technology can improve the presentation quality, aiding understanding. E-mail usage policies and information integrity assurance strategies can help. Time management and stress management can be applied; these solutions would involve setting priorities and minimising interruptions. Improved writing and presentation practices can minimise information pollution effects on others. == Related terms == The term infollution or informatization pollution was coined by Dr. Paek-Jae Cho, former president & CEO of KTC (Korean Telecommunication Corp.), in a 2002 speech at the International Telecommunications Society (ITS) 14th biennial conference to describe any undesirable side effect brought about by information technology and its applications.

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  Sunrise Calendar

  Sunrise is a discontinued electronic calendar application for mobile and desktop. The service was launched in 2013 by designers Pierre Valade and Jeremy Le Van. In October 2015, Microsoft announced that they had merged the Sunrise Calendar team into the larger Microsoft Outlook team where they will work closely with the Microsoft Outlook Mobile service. == History == The first iteration of Sunrise launched in 2012 and was a daily email digest of appointments, events and birthdays. Sunrise was launched initially as an iPhone application on February 19, 2013. In June 2013, Sunrise raised $2.2 million (~$2.91 million in 2024) in venture funding from Resolute.vc, NextView Ventures, Lerer Hippeau Ventures, SV Angel, and other angel investment firms like Loïc Le Meur, Dave Morin, Fabrice Grinda. In May 2014, Sunrise launched on Android as well as on the web via a web application. In July 2014, Sunrise announced it had raised $6 million (~$7.81 million in 2024) Series A from Balderton Capital. Bernard Liautaud joined the board. On February 11, 2015, Sunrise Atelier, Inc. was acquired by Microsoft for US$100 million (~$129 million in 2024). On October 28, 2015, Microsoft announced that Sunrise would be discontinued, and its functionality merged into Outlook Mobile. Microsoft later stated that the app would permanently cease functioning on August 31, 2016, but the shutdown was delayed to September 13, 2016, to coincide with an update to Outlook Mobile that incorporates aspects of Sunrise into its calendar interface. == Features == Sunrise allowed users to connect with Google Calendar, iCloud calendar and with Exchange Server. The following third-party services featured integration with Sunrise: Foursquare, GitHub, TripIt, Asana, Evernote, Google Tasks, Trello, Songkick, and Wunderlist. As a web app, users could sign-in and use Sunrise in a web browser, with no downloads required. A native Sunrise app could also be downloaded for OS X 10.9 and later, iOS 8.0 and later (both iPhone and iPad) as well as Android phones and tablets. In May 2015, Sunrise launched Meet, a keyboard for Android and iOS that lets users select available time slots in their calendar to schedule one-to-ones.

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  AlphaTensor

  AlphaTensor is an artificial intelligence system developed by DeepMind for discovering efficient matrix multiplication algorithms using reinforcement learning. Introduced in 2022, the system was based on AlphaZero and formulated the search for matrix multiplication algorithms as a single-player game called TensorGame. AlphaTensor was designed to search for new ways to multiply matrices with fewer scalar multiplication operations. Matrix multiplication is a fundamental operation in linear algebra, numerical analysis, scientific computing, computer graphics, and machine learning. The system discovered thousands of matrix multiplication algorithms, including algorithms that rediscovered known human-designed methods and others that improved on previously known results for particular matrix sizes and mathematical settings. == Background == Matrix multiplication is one of the basic operations in numerical computing. The standard algorithm for multiplying two square matrices has cubic time complexity, while faster algorithms such as the Strassen algorithm reduce the number of multiplication operations by using more complex algebraic decompositions. Finding optimal matrix multiplication algorithms can be difficult because it involves searching through a large space of possible tensor decompositions. AlphaTensor approached this problem by representing algorithm discovery as TensorGame, in which each move corresponds to an operation that reduces a tensor representing matrix multiplication. The goal of the game is to find a low-rank decomposition of the matrix multiplication tensor, corresponding to an efficient multiplication algorithm. == Development == AlphaTensor was developed by DeepMind and described in a paper published in Nature in October 2022. The system built on the reinforcement-learning approach used in AlphaZero, which had previously been applied to games such as Go, chess, and shogi. Unlike those games, TensorGame involved a very large search space, requiring changes to the AlphaZero-style search method and neural network architecture. DeepMind released source code and discovered algorithms associated with the publication through a public GitHub repository. == Results == AlphaTensor discovered matrix multiplication algorithms over both standard arithmetic and finite fields. One widely reported result was a method for multiplying 4 × 4 matrices over the field with two elements using 47 multiplication operations, improving on the 49 operations required by applying Strassen's algorithm recursively in that setting. The system also found algorithms optimized for particular computer hardware, including algorithms designed for graphics processing units and Tensor Processing Units. DeepMind stated that some of the hardware-specific algorithms improved practical execution time compared with commonly used algorithms on the tested hardware. == Significance == AlphaTensor was described as an example of using machine learning not only to apply existing algorithms, but to assist in discovering new ones. The work was connected to broader research in algorithm discovery, automated machine learning, program synthesis, and computational complexity theory, especially the open problem of determining the optimal complexity of matrix multiplication. AlphaTensor later became part of a broader group of Google DeepMind systems for algorithm and mathematical discovery, alongside systems such as AlphaDev and AlphaEvolve.

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  QuickPar

  QuickPar is a computer program that creates parchives used as verification and recovery information for a file or group of files, and uses the recovery information, if available, to attempt to reconstruct the originals from the damaged files and the PAR volumes. Designed for the Microsoft Windows operating system, in the past it was often used to recover damaged or missing files that have been downloaded through Usenet. QuickPar may also be used under Linux via Wine. There are two main versions of PAR files: PAR and PAR2. The PAR2 file format lifts many of its previous restrictions. QuickPar is freeware but not open-source. It uses the Reed-Solomon error correction algorithm internally to create the error correcting information. == Replacement == Since QuickPar hasn't been updated in 21 years, it is considered abandonware. Currently, MultiPar is accepted as the software that replaces QuickPar. MultiPar is actively being developed by Yutaka Sawada. == 64-bit versions == At present the command line version of QuickPar for Linux command line is available as a 64-bit version. None of the GUI versions available presently offer a 64-bit version.

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