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  Neighborhood operation

  In computer vision and image processing a neighborhood operation is a commonly used class of computations on image data which implies that it is processed according to the following pseudo code: Visit each point p in the image data and do { N = a neighborhood or region of the image data around the point p result(p) = f(N) } This general procedure can be applied to image data of arbitrary dimensionality. Also, the image data on which the operation is applied does not have to be defined in terms of intensity or color, it can be any type of information which is organized as a function of spatial (and possibly temporal) variables in p. The result of applying a neighborhood operation on an image is again something which can be interpreted as an image, it has the same dimension as the original data. The value at each image point, however, does not have to be directly related to intensity or color. Instead it is an element in the range of the function f, which can be of arbitrary type. Normally the neighborhood N is of fixed size and is a square (or a cube, depending on the dimensionality of the image data) centered on the point p. Also the function f is fixed, but may in some cases have parameters which can vary with p, see below. In the simplest case, the neighborhood N may be only a single point. This type of operation is often referred to as a point-wise operation. == Examples == The most common examples of a neighborhood operation use a fixed function f which in addition is linear, that is, the computation consists of a linear shift invariant operation. In this case, the neighborhood operation corresponds to the convolution operation. A typical example is convolution with a low-pass filter, where the result can be interpreted in terms of local averages of the image data around each image point. Other examples are computation of local derivatives of the image data. It is also rather common to use a fixed but non-linear function f. This includes median filtering, and computation of local variances. The Nagao-Matsuyama filter is an example of a complex local neighbourhood operation that uses variance as an indicator of the uniformity within a pixel group. The result is similar to a convolution with a low-pass filter with the added effect of preserving sharp edges. There is also a class of neighborhood operations in which the function f has additional parameters which can vary with p: Visit each point p in the image data and do { N = a neighborhood or region of the image data around the point p result(p) = f(N, parameters(p)) } This implies that the result is not shift invariant. Examples are adaptive Wiener filters. == Implementation aspects == The pseudo code given above suggests that a neighborhood operation is implemented in terms of an outer loop over all image points. However, since the results are independent, the image points can be visited in arbitrary order, or can even be processed in parallel. Furthermore, in the case of linear shift-invariant operations, the computation of f at each point implies a summation of products between the image data and the filter coefficients. The implementation of this neighborhood operation can then be made by having the summation loop outside the loop over all image points. An important issue related to neighborhood operation is how to deal with the fact that the neighborhood N becomes more or less undefined for points p close to the edge or border of the image data. Several strategies have been proposed: Compute result only for points p for which the corresponding neighborhood is well-defined. This implies that the output image will be somewhat smaller than the input image. Zero padding: Extend the input image sufficiently by adding extra points outside the original image which are set to zero. The loops over the image points described above visit only the original image points. Border extension: Extend the input image sufficiently by adding extra points outside the original image which are set to the image value at the closest image point. The loops over the image points described above visit only the original image points. Mirror extension: Extend the image sufficiently much by mirroring the image at the image boundaries. This method is less sensitive to local variations at the image boundary than border extension. Wrapping: The image is tiled, so that going off one edge wraps around to the opposite side of the image. This method assumes that the image is largely homogeneous, for example a stochastic image texture without large textons.

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  Library history

  Library history is a subdiscipline within library science and library and information science focusing on the history of libraries and their role in societies and cultures. Some see the field as a subset of information history. Library history is an academic discipline and should not be confused with its object of study (history of libraries): the discipline is much younger than the libraries it studies. Library history begins in ancient societies through contemporary issues facing libraries today. Topics include recording mediums, cataloguing systems, scholars, scribes, library supporters and librarians. == Earliest libraries == The earliest records of a library institution as it is presently understood can be dated back to around 5,000 years ago in the Southwest Asian regions of the world. One of the oldest libraries found is that of the ancient library at Ebla (circa 2500 BCE) in present-day Syria. In the 1970s, the excavation at Ebla's library unearthed over 20,000 clay tablets written in cuneiform script. === Library in Mesopotamia === The Assyrian King Assurbanipal created one of the greatest libraries in Nineveh in the seventh century BCE. The collection consisted of over 30,000 tablets written in a variety of languages. The collection was cataloged both by the shape of the tablet and by the subject of the content. The library had separate rooms for the different topics: government, history, law, astronomy, geography, and so on. The tablets also contained myths, hymns, and even jokes. Assurbanipal would send scribes to visit every corner of his kingdom to copy the content of other libraries. His library contained many of the most important literary works of the day, including the epic of Gilgamesh. Assurbanipal's Royal Library also had one of the first library catalogs. Unfortunately, Nineveh was eventually destroyed, and the library was lost in a fire. === Libraries in Ancient Greece === The Greek government was the first to sponsor public libraries. By 500 BCE both Athens and Samos had begun creating libraries for the public, though as most of the population was illiterate these spaces were serving a small, educated portion of the community. Athens developed a city archive at the Metroon in 405 BCE, where documents were stored in sealed jars. These would have saved the documents, but they would have been difficult to consult regularly. In Paros, around the same time, contracts were placed in the temple for safe keeping, and a book curse was placed for extra protection. === Library of Alexandria === The Library at Alexandria, Egypt, was renowned in the third century BCE while kings Ptolemy I Soter and Ptolemy II Philadelphus reigned. The library included a museum, garden, meeting areas and of course reading rooms. The Great Library, as it is known, was one of many in Alexandria. From its inception around the second century BCE, Alexandria was a well-known center for learning. It earned renown as the intellectual capital of the Western world up through the third century CE. The librarians at Alexandria collected, copied, and organized scrolls from across the known world. According to a primary source, every ship that came to Alexandria was required to hand over their books to be copied, and the copies would be returned to the owner, the library keeping the original. The Library of Alexandria was damaged by various disasters over time, including fire, invasion, and earthquake. Scholars believe the collection slowly diminished over time due to theft and efforts to remove it ahead of invading armies. While there are popular stories about how the library was ultimately destroyed, most of these are more myth than fact. === Libraries in Rome === Julius Caesar and his successor Augustus were the first to establish public libraries in ancient Rome, including the library of Apollo on the Palatine Hill. Several emperors followed suit over the next four centuries, including Hadrian, Tiberius, and Vespasian. Roman aristocrats also had personal libraries, which usually contained works in both Greek and Latin. A valuable example of this has been found at Herculaneum near Pompeii. Papyrus manuscripts in Herculaneum's Villa of the Papyri were encased in ash after the eruption of Vesuvius in 79 CE. Modern archaeology is now able to scan these artifacts and discern their contents, including many writings from Philodemus. The average Roman would not have been familiar with books beyond what they might hear read aloud in the forum. Public figures would pay for particular passages to be read aloud to the public from the steps of a public library. === Libraries in the Middle Ages === In the European Middle Ages, libraries began to become more prevalent, despite a widespread reduction in new writing beyond religious themes. Most libraries were initially connected to monasteries or religious institutions. Scriptoriums copied Christian religious texts to share with other religious centers or to be read aloud to their own parishioners. The Holy Roman Emperor Charlemagne (r. 786-814) had a large impact on the advancement of written culture in the Medieval Christian world, acquiring as many written works as he could, and employing many scribes to copy and recirculate vernacular versions of religious works. Most of the text held in small personal libraries was still religious in nature. == Early modern libraries == === Libraries of the Renaissance === During the Renaissance era the merchant middle class grew, and more people found benefits in education. They relied on libraries as a place to study and gain knowledge. Libraries provided a valuable resource, enriching the culture of those who were educated. Universities that had been started in the Middle Ages, founded their own libraries. Books in these libraries could not be borrowed from these libraries and were generally chained to the shelves to prevent theft. As more of the population became literate, new ideas like Humanism and Natural Law spawned an increase personal libraries, although they remained small. Gutenberg's invention of the printing press in 1456 opened the door to the modern era for libraries. == Oldest working libraries == According to the German librarian Michael Knoche, it is not possible to determine which library is the “oldest”: "Precise year dates are a construct, especially in the case of very old libraries. When a collection of books deserves to be called a library depends very much on the point of view of the observer." Various libraries are referred to as the “oldest”: The library founded in the 6th century of the Saint Catherine's Monastery in Sinai is "reputedly the oldest continuously run library in existence today", according to the Library of Congress. Its collection of religious and secular manuscripts is ranging from Bibles, liturgies and prayer books to legal documents such as deeds, court cases and fatwahs (legal opinions). The Al Qarawiyyin Library was founded in 859 by Fatima al-Fihri and is often regarded as the oldest working library in the world. It is in Fez, Morocco and is part of the oldest continually operating university in the world, the University of al-Qarawiyyin. The library houses approximately 4,000 ancient Islamic manuscripts. These manuscripts include 9th century Qurans and the oldest known accounts of the Islamic prophet Muhammed. The Malatestiana Library (Italian: Biblioteca Malatestiana) is a public library in the city of Cesena in northern Italy. Opened in 1454 it is significant for being the first civic library in Europe open to the general public. == Library history reports and writings of the early 19th and 20th century == In the early 19th and 20th century, representative titles were created reporting library history in the United States and the United Kingdom. American titles include Public Libraries in the United States of America, Their History, Condition, and Management (1876), Memorial History of Boston (1881) by Justin Winsor, Public Libraries in America (1894) by William I. Fletcher, and History of the New York Public Library (1923) by Henry M. Lydenberg. British titles include Old English Libraries (1911) by Earnest A. Savage and The Chained Library: A Survey of Four Centuries in the Evolution of the English Library by Burnett Hillman Streeter. In the beginning of the 20th century, library historians began applying scientific research methodologies to examine the library as a social agency. Two works that demonstrate this argument are Geschichte der Bibliotheken (1925) by Alfred Hessel and the Library Quarterly article from 1931, “The Sociological Beginnings of the Library Movement in America” by Arnold Borden. With the establishment of library schools, master's theses and doctoral dissertations represented the shift in serious research regarding libraries and library history. Two published doctoral dissertations that mark this trend are Foundations of the Public Library: The Origins of the American Public Library Movement in Ne

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  Data management plan

  A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this may lead to data being well-managed in the present, and prepared for preservation in the future. DMPs were originally used in 1966 to manage aeronautical and engineering projects' data collection and analysis, and expanded across engineering and scientific disciplines in the 1970s and 1980s. Up until the early 2000s, DMPs were used "for projects of great technical complexity, and for limited mid-study data collection and processing purposes". In the 2000s and later, E-research and economic policies drove the development and uptake of DMPs. == Importance == Preparing a data management plan before data are collected is claimed to ensure that data are in the correct format, organized well, and better annotated. This could arguably save time in the long term because there is no need to re-organize, re-format, or try to remember details about data. It is also claimed to increase research efficiency since both the data collector and other researchers might be able to understand and use well-annotated data in the future. One component of a data management plan is data archiving and preservation. By deciding on an archive ahead of time, the data collector can format data during collection to make its future submission to a database easier. If data are preserved, they are more relevant since they can be re-used by other researchers. It also allows the data collector to direct requests for data to the database, rather than address requests individually. A frequent argument in favor of preservation is that data that are preserved have the potential to lead to new, unanticipated discoveries, and they prevent duplication of scientific studies that have already been conducted. Data archiving also provides insurance against loss by the data collector. In the 2010s, funding agencies increasingly required data management plans as part of the proposal and evaluation process, despite little or no evidence of their efficacy. == Major components == "There is no general and definitive list of topics that should be covered in a DMP for a research project", and researchers are often left to their own devices as to how to fill out a DMP. === Information about data and data format === A description of data to be produced by the project. This might include (but is not limited to) data that are: Experimental Observational Raw or derived Physical collections Models Simulations Curriculum materials Software Images How will the data be acquired? When and where will they be acquired? After collection, how will the data be processed? Include information about Software used Algorithms Scientific workflows File formats that will be used, justify those formats, and describe the naming conventions used. Quality assurance & quality control measures that will be taken during sample collection, analysis, and processing. If existing data are used, what are their origins? How will the data collected be combined with existing data? What is the relationship between the data collected and existing data? How will the data be managed in the short-term? Consider the following: Version control for files Backing up data and data products Security & protection of data and data products Who will be responsible for management === Metadata content and format === Metadata are the contextual details, including any information important for using data. This may include descriptions of temporal and spatial details, instruments, parameters, units, files, etc. Metadata is commonly referred to as "data about data". Issues to be considered include: How detailed has the metadata to be in order to make the data meaningful? How will the metadata be created and/or captured? Examples include lab notebooks, GPS hand-held units, Auto-saved files on instruments, etc. What format will be used for the metadata? What are the metadata standards commonly used in the respective scientific discipline? There should be justification for the format chosen. === Policies for access, sharing, and re-use === Describe any obligations that exist for sharing data collected. These may include obligations from funding agencies, institutions, other professional organizations, and legal requirements. Include information about how data will be shared, including when the data will be accessible, how long the data will be available, how access can be gained, and any rights that the data collector reserves for using data. Address any ethical or privacy issues with data sharing Address intellectual property & copyright issues. Who owns the copyright? What are the institutional, publisher, and/or funding agency policies associated with intellectual property? Are there embargoes for political, commercial, or patent reasons? Describe the intended future uses/users for the data Indicate how the data should be cited by others. How will the issue of persistent citation be addressed? For example, if the data will be deposited in a public archive, will the dataset have a persistent identifier (e.g., ARK, DOI, Handle, PURL, URN) assigned to it? === Long-term storage and data management === Researchers should identify an appropriate archive for the long-term preservation of their data. By identifying the archive early in the project, the data can be formatted, transformed, and documented appropriately to meet the requirements of the archive. Researchers should consult colleagues and professional societies in their discipline to determine the most appropriate database, and include a backup archive in their data management plan in case their first choice goes out of existence. Early in the project, the primary researcher should identify what data will be preserved in an archive. Usually, preserving the data in its most raw form is desirable, although data derivatives and products can also be preserved. An individual should be identified as the primary contact person for archived data, and ensure contact information is always kept up-to-date in case there are requests for data or information about data. === Budget === Data management and preservation costs may be considerable, depending on the nature of the project. By anticipating costs ahead of time, researchers ensure that the data will be properly managed and archived. Potential expenses that should be considered are Human resources and staff as they handle data preparation, management, documentation, and preservation Hardware and/or software needed for data management, backing up, security, documentation, and preservation Costs associated with submitting the data to an archive The data management plan should include how these costs will be paid. == NSF Data Management Plan == All grant proposals submitted to National Science Foundation (NSF) must include a Data Management Plan that is no more than two pages. This is a supplement (not part of the 15-page proposal) and should describe how the proposal will conform to the Award and Administration Guide policy (see below). It may include the following: The types of data The standards to be used for data and metadata format and content Policies for access and sharing Policies and provisions for re-use Plans for archiving data Policy summarized from the NSF Award and Administration Guide, Section 4 (Dissemination and Sharing of Research Results): Promptly publish with appropriate authorship Share data, samples, physical collections, and supporting materials with others, within a reasonable time frame Share software and inventions Investigators can keep their legal rights over their intellectual property, but they still have to make their results, data, and collections available to others Policies will be implemented via Proposal review Award negotiations and conditions Support/incentives == ESRC Data Management Plan == Since 1995, the UK's Economic and Social Research Council (ESRC) have had a research data policy in place. The current ESRC Research Data Policy states that research data created as a result of ESRC-funded research should be openly available to the scientific community to the maximum extent possible, through long-term preservation and high-quality data management. ESRC requires a data management plan for all research award applications where new data are being created. Such plans are designed to promote a structured approach to data management throughout the data lifecycle, resulting in better quality data that is ready to archive for sharing and re-use. The UK Data Service, the ESRC's flagship data service, provides practical guidance on research data management planning suitable for social science researchers in the UK and around the world. ESRC has a longstanding arrangement with the UK Data A

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  Mobile content management system

  A mobile content management system (MCMs) is a type of content management system (CMS) capable of storing and delivering content and services to mobile devices, such as mobile phones, smart phones, and PDAs. Mobile content management systems may be discrete systems, or may exist as features, modules or add-ons of larger content management systems capable of multi-channel content delivery. Mobile content delivery has unique, specific constraints including widely variable device capacities, small screen size, limitations on wireless bandwidth, sometimes small storage capacity, and (for some devices) comparatively weak device processors. Demand for mobile content management increased as mobile devices became increasingly ubiquitous and sophisticated. MCMS technology initially focused on the business to consumer (B2C) mobile market place with ringtones, games, text-messaging, news, and other related content. Since, mobile content management systems have also taken root in business-to-business (B2B) and business-to-employee (B2E) situations, allowing companies to provide more timely information and functionality to business partners and mobile workforces in an increasingly efficient manner. A 2008 estimate put global revenue for mobile content management at US$8 billion. == Key features == === Multi-channel content delivery === Multi-channel content delivery capabilities allow users not to manage a central content repository while simultaneously delivering that content to mobile devices such as mobile phones, smartphones, tablets and other mobile devices. Content can be stored in a raw format (such as Microsoft Word, Excel, PowerPoint, PDF, Text, HTML etc.) to which device-specific presentation styles can be applied. === Content access control === Access control includes authorization, authentication, access approval to each content. In many cases the access control also includes download control, wipe-out for specific user, time specific access. For the authentication, MCM shall have basic authentication which has user ID and password. For higher security many MCM supports IP authentication and mobile device authentication. === Specialized templating system === While traditional web content management systems handle templates for only a handful of web browsers, mobile CMS templates must be adapted to the very wide range of target devices with different capacities and limitations. There are two approaches to adapting templates: multi-client and multi-site. The multi-client approach makes it possible to see all versions of a site at the same domain (e.g. sitename.com), and templates are presented based on the device client used for viewing. The multi-site approach displays the mobile site on a targeted sub-domain (e.g. mobile.sitename.com). === Location-based content delivery === Location-based content delivery provides targeted content, such as information, advertisements, maps, directions, and news, to mobile devices based on current physical location. Currently, GPS (global positioning system) navigation systems offer the most popular location-based services. Navigation systems are specialized systems, but incorporating mobile phone functionality makes greater exploitation of location-aware content delivery possible.

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  Underwater computer vision

  Underwater computer vision is a subfield of computer vision. In recent years, with the development of underwater vehicles ( ROV, AUV, gliders), the need to be able to record and process huge amounts of information has become increasingly important. Applications range from inspection of underwater structures for the offshore industry to the identification and counting of fishes for biological research. However, no matter how big the impact of this technology can be to industry and research, it still is in a very early stage of development compared to traditional computer vision. One reason for this is that, the moment the camera goes into the water, a whole new set of challenges appear. On one hand, cameras have to be made waterproof, marine corrosion deteriorates materials quickly and access and modifications to experimental setups are costly, both in time and resources. On the other hand, the physical properties of the water make light behave differently, changing the appearance of a same object with variations of depth, organic material, currents, temperature etc. == Applications == Seafloor survey Vehicle navigation and positioning Biological monitoring {possibly aquatic biomonitoring) Video mosaics as visual navigation maps Submarine pipeline inspection Wreckage visualization Maintenance of underwater structures Drowning detection systems == Medium differences == === Illumination === In air, light comes from the whole hemisphere on cloudy days, and is dominated by the sun. In water direct lighting comes from a cone about 96° wide above the scene. This phenomenon is called Snell's window. Artificial lighting can be used where natural light levels are insufficient and where the light path is too long to produce acceptable colour, as the loss of colour is a function of the total distance through water from the source to the camera lens port. === Light attenuation === Unlike air, water attenuates light exponentially. This results in hazy images with very low contrast. The main reasons for light attenuation are light absorption (where energy is removed from the light) and light scattering, by which the direction of light is changed. Light scattering can further be divided into forward scattering, which results in an increased blurriness and backward scattering that limits the contrast and is responsible for the characteristic veil of underwater images. Both scattering and attenuation are heavily influenced by the amount of organic matter dissolved or suspended in the water. Light attenuation in water is also a function of the wavelength. This means that different colours are attenuated at different rates, leading to colour degradation.with depth and distance. Red and orange light are attenuated faster, followed by yellows and greens. Blue is the least attenuated visible wavelength. === Artificial lighting === == Challenges == In high level computer vision, human structures are frequently used as image features for image matching in different applications. However, the sea bottom lacks such features, making it hard to find correspondences in two images. In order to be able to use a camera in the water, a watertight housing is required. However, refraction will happen at the water-glass and glass-air interface due to differences in density of the materials. This has the effect of introducing a non-linear image deformation. The motion of the vehicle presents another special challenge. Underwater vehicles are constantly moving due to currents and other phenomena. This introduces another uncertainty to algorithms, where small motions may appear in all directions. This can be specially important for video tracking. In order to reduce this problem image stabilization algorithms may be applied. == Relevant technology == === Image restoration === Image restoration< techniques are intended to model the degradation process and then invert it, obtaining the new image after solving. It is generally a complex approach that requires plenty of parameters that vary a lot between different water conditions. === Image enhancement === Image enhancement only tries to provide a visually more appealing image without taking the physical image formation process into account. These methods are usually simpler and less computational intensive. === Color correction === Various algorithms exist that perform automatic color correction. The UCM (Unsupervised Color Correction Method), for example, does this in the following steps: It firstly reduces the color cast by equalizing the color values. Then it enhances contrast by stretching the red histogram towards the maximum and finally saturation and intensity components are optimized. == Underwater stereo vision == It is usually assumed that stereo cameras have been calibrated previously, geometrically and radiometrically. This leads to the assumption that corresponding pixels should have the same color. However this can not be guaranteed in an underwater scene, because of dispersion and backscatter. However, it is possible to digitally model this phenomenon and create a virtual image with those effects removed == Other application fields == Imaging sonars have become more and more accessible and gained resolution, delivering better images. Sidescan sonars are used to produce complete maps of regions of the sea floor stitching together sequences of sonar images. However, sonar images often lack proper contrast and are degraded by artefacts and distortions due to noise, attitude changes of the AUV/ROV carrying the sonar or non uniform beam patterns. Another common problem with sonar computer vision is the comparatively low frame rate of sonar images.

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  Run-to-completion scheduling

  Run-to-completion scheduling or nonpreemptive scheduling is a scheduling model in which each task runs until it either finishes, or explicitly yields control back to the scheduler. Run-to-completion systems typically have an event queue which is serviced either in strict order of admission by an event loop, or by an admission scheduler which is capable of scheduling events out of order, based on other constraints such as deadlines. Some preemptive multitasking scheduling systems behave as run-to-completion schedulers in regard to scheduling tasks at one particular process priority level, at the same time as those processes still preempt other lower priority tasks and are themselves preempted by higher priority tasks.

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  NewSQL

  NewSQL is a class of relational database management systems that seek to provide the scalability of NoSQL systems for online transaction processing (OLTP) workloads while maintaining the ACID guarantees of a traditional database system. Many enterprise systems that handle high-profile data (e.g., financial and order processing systems) are too large for conventional relational databases, but have transactional and consistency requirements that are not practical for NoSQL systems. The only options previously available for these organizations were to either purchase more powerful computers or to develop custom middleware that distributes requests over conventional DBMS. Both approaches feature high infrastructure costs and/or development costs. NewSQL systems attempt to reconcile the conflicts. == History == The term was first used by 451 Group analyst Matthew Aslett in a 2011 research paper discussing the rise of a new generation of database management systems. One of the first NewSQL systems was the H-Store parallel database system. == Applications == Typical applications are characterized by heavy OLTP transaction volumes. OLTP transactions; are short-lived (i.e., no user stalls) touch small amounts of data per transaction use indexed lookups (no table scans) have a small number of forms (a small number of queries with different arguments). However, some support hybrid transactional/analytical processing (HTAP) applications. Such systems improve performance and scalability by omitting heavyweight recovery or concurrency control. == List of NewSQL-databases == Apache Trafodion Clustrix CockroachDB Couchbase CrateDB Google Spanner MySQL Cluster NuoDB OceanBase Pivotal GemFire XD SequoiaDB SingleStore was formerly known as MemSQL. TIBCO Active Spaces TiDB TokuDB TransLattice Elastic Database VoltDB YDB YugabyteDB == Features == The two common distinguishing features of NewSQL database solutions are that they support online scalability of NoSQL databases and the relational data model (including ACID consistency) using SQL as their primary interface. NewSQL systems can be loosely grouped into three categories: === New architectures === NewSQL systems adopt various internal architectures. Some systems employ a cluster of shared-nothing nodes, in which each node manages a subset of the data. They include components such as distributed concurrency control, flow control, and distributed query processing. === SQL engines === The second category are optimized storage engines for SQL. These systems provide the same programming interface as SQL, but scale better than built-in engines. === Transparent sharding === These systems automatically split databases across multiple nodes using Raft or Paxos consensus algorithm.

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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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  Iterative reconstruction

  Iterative reconstruction refers to iterative algorithms used to reconstruct 2D and 3D images in certain imaging techniques. For example, in computed tomography an image must be reconstructed from projections of an object. Here, iterative reconstruction techniques are usually a better, but computationally more expensive alternative to the common filtered back projection (FBP) method, which directly calculates the image in a single reconstruction step. In recent research works, scientists have shown that extremely fast computations and massive parallelism is possible for iterative reconstruction, which makes iterative reconstruction practical for commercialization. == Basic concepts == The reconstruction of an image from the acquired data is an inverse problem. Often, it is not possible to exactly solve the inverse problem directly. In this case, a direct algorithm has to approximate the solution, which might cause visible reconstruction artifacts in the image. Iterative algorithms approach the correct solution using multiple iteration steps, which allows to obtain a better reconstruction at the cost of a higher computation time. There are a large variety of algorithms, but each starts with an assumed image, computes projections from the image, compares the original projection data and updates the image based upon the difference between the calculated and the actual projections. === Algebraic reconstruction === The Algebraic Reconstruction Technique (ART) was the first iterative reconstruction technique used for computed tomography by Hounsfield. === Iterative Sparse Asymptotic Minimum Variance === The iterative sparse asymptotic minimum variance algorithm is an iterative, parameter-free superresolution tomographic reconstruction method inspired by compressed sensing, with applications in synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging (MRI). === Statistical reconstruction === There are typically five components to statistical iterative image reconstruction algorithms, e.g. An object model that expresses the unknown continuous-space function f ( r ) {\displaystyle f(r)} that is to be reconstructed in terms of a finite series with unknown coefficients that must be estimated from the data. A system model that relates the unknown object to the "ideal" measurements that would be recorded in the absence of measurement noise. Often this is a linear model of the form A x + ϵ {\displaystyle \mathbf {A} x+\epsilon } , where ϵ {\displaystyle \epsilon } represents the noise. A statistical model that describes how the noisy measurements vary around their ideal values. Often Gaussian noise or Poisson statistics are assumed. Because Poisson statistics are closer to reality, it is more widely used. A cost function that is to be minimized to estimate the image coefficient vector. Often this cost function includes some form of regularization. Sometimes the regularization is based on Markov random fields. An algorithm, usually iterative, for minimizing the cost function, including some initial estimate of the image and some stopping criterion for terminating the iterations. === Learned Iterative Reconstruction === In learned iterative reconstruction, the updating algorithm is learned from training data using techniques from machine learning such as convolutional neural networks, while still incorporating the image formation model. This typically gives faster and higher quality reconstructions and has been applied to CT and MRI reconstruction. == Advantages == The advantages of the iterative approach include improved insensitivity to noise and capability of reconstructing an optimal image in the case of incomplete data. The method has been applied in emission tomography modalities like SPECT and PET, where there is significant attenuation along ray paths and noise statistics are relatively poor. Statistical, likelihood-based approaches: Statistical, likelihood-based iterative expectation-maximization algorithms are now the preferred method of reconstruction. Such algorithms compute estimates of the likely distribution of annihilation events that led to the measured data, based on statistical principle, often providing better noise profiles and resistance to the streak artifacts common with FBP. Since the density of radioactive tracer is a function in a function space, therefore of extremely high-dimensions, methods which regularize the maximum-likelihood solution turning it towards penalized or maximum a-posteriori methods can have significant advantages for low counts. Examples such as Ulf Grenander's Sieve estimator or Bayes penalty methods, or via I.J. Good's roughness method may yield superior performance to expectation-maximization-based methods which involve a Poisson likelihood function only. As another example, it is considered superior when one does not have a large set of projections available, when the projections are not distributed uniformly in angle, or when the projections are sparse or missing at certain orientations. These scenarios may occur in intraoperative CT, in cardiac CT, or when metal artifacts require the exclusion of some portions of the projection data. In Magnetic Resonance Imaging it can be used to reconstruct images from data acquired with multiple receive coils and with sampling patterns different from the conventional Cartesian grid and allows the use of improved regularization techniques (e.g. total variation) or an extended modeling of physical processes to improve the reconstruction. For example, with iterative algorithms it is possible to reconstruct images from data acquired in a very short time as required for real-time MRI (rt-MRI). In Cryo Electron Tomography, where the limited number of projections are acquired due to the hardware limitations and to avoid the biological specimen damage, it can be used along with compressive sensing techniques or regularization functions (e.g. Huber function) to improve the reconstruction for better interpretation. Here is an example that illustrates the benefits of iterative image reconstruction for cardiac MRI.

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  Information flow

  In discourse-based grammatical theory, information flow is any tracking of referential information by speakers. Information may be new, i.e., just introduced into the conversation; given, i.e., already active in the speakers' consciousness; or old, i.e., no longer active. The various types of activation, and how these are defined, are model-dependent. Information flow affects grammatical structures such as: Word order (topic, focus, and afterthought constructions). Active, passive, or middle voice. Choice of deixis, such as articles; "medial" deictics such as Spanish ese and Japanese sore are generally determined by the familiarity of a referent rather than by physical distance. Overtness of information, such as whether an argument of a verb is indicated by a lexical noun phrase, a pronoun, or not mentioned at all. Clefting: Splitting a single clause into two clauses, each with its own verb, e.g. ‘The chicken turtles tasted like chicken.’ becomes ‘It was the chicken turtle | that tasted like chicken.’ In this case, clefting is used to shift the focus of the sentence to the subject, the chicken turtle. Front focus: Placing at the start (front) of a sentence information that would normally occur later in the sentence, to give it extra prominence. For example, in pop culture, Yoda's speech often utilizes such syntactic construction, such as when he says 'much to learn you still have' to Luke Skywalker. End focus (or end weight): Given or familiar information followed by new information. This gives prominence to the final part of the sentences and can enable suspense to build, e.g. ‘Through the door came a gigantic wolf’.(Umer Prince)

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  Vector-field consistency

  Vector-Field Consistency is a consistency model for replicated data (for example, objects), initially described in a paper which was awarded the best-paper prize in the ACM/IFIP/Usenix Middleware Conference 2007. It has since been enhanced for increased scalability and fault-tolerance in a recent paper. == Description == This consistency model was initially designed for replicated data management in ad hoc gaming in order to minimize bandwidth usage without sacrificing playability. Intuitively, it captures the notion that although players require, wish, and take advantage of information regarding the whole of the game world (as opposed to a restricted view to rooms, arenas, etc. of limited size employed in many multiplayer video games), they need to know information with greater freshness, frequency, and accuracy as other game entities are located closer and closer to the player's position. It prescribes a multidimensional divergence bounding scheme, based on a vector field that employs consistency vectors k=(θ,σ,ν), standing for maximum allowed time - or replica staleness, sequence - or missing updates, and value - or user-defined measured replica divergence, applied to all space coordinates in game scenario or world. The consistency vector-fields emanate from field-generators designated as pivots (for example, players) and field intensity attenuates as distance grows from these pivots in concentric or square-like regions. This consistency model unifies locality-awareness techniques employed in message routing and consistency enforcement for multiplayer games, with divergence bounding techniques traditionally employed in replicated database and web scenarios.

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  Principles for a Data Economy

  The Principles for a Data Economy – Data Rights and Transactions is a transatlantic legal project carried out jointly by the American Law Institute (ALI) and the European Law Institute (ELI). The Principles for a Data Economy deals with a range of different legal questions that arise in the data economy. Since data is different from other tradeable items, the Principles draw up legal rules for data transactions and data rights that take into account the interests of different stakeholders involved in the data economy. The Principles are designed to facilitate contractual relations as well as the drafting of model agreements and can guide courts and legislators worldwide. The project proposes a set of principles that can be implemented in any legal system and is designed to work in conjunction with any kind of data privacy/data protection law, intellectual property law or trade secret law. The Principles do not address or seek to change any of the substantive rules of these bodies of law. The Project Team consists of Neil B Cohen and Christiane Wendehorst (as Project Reporters) and Lord John Thomas as well as Steven O. Weise (as Project Chairs). == Characteristics of data == The law governing trades in commerce has historically focused on trade in items that are tangible like goods or on intangible assets, such as shares or licenses. However, data does not fit into any of these traditional categories, nor does it qualify as a service. It is often unclear how traditional legal rules and doctrines can apply to data, as data is different from other assets in many ways. For example, data can be multiplied at basically no cost and can be used in parallel for a variety of different purposes by many different people at the same time (data is a “non-rivalrous” resource). Uncertainty regarding the applicable rules to govern the data economy may inhibit innovation and growth and trouble stakeholders like data-driven industries, start-ups, and consumers. == Stakeholders in the data economy == The Principles have taken the basic types of players and relations which can be found in data ecosystems as a starting point to provide guidance in different situations. The central actors in the data economy are data controllers (also called “data holders”). They are in a position to access the data and decide for which purposes and means this data should be processed. A controller may exercise control all by itself or share it with co-controllers, such as under a data pooling arrangement. Data processors provide the processing of data on a controller’s behalf as a service. Another important group of stakeholders includes those that contribute to the generation of data (e.g. data subjects). Other players in the data economy include data assemblers or data intermediaries (e.g. data trusts). == History of the project and timeline == Before the official adoption of the project by ALI and ELI bodies in 2018, the project team carried out a Feasibility Study from October 2016 to February 2018. In the following years, the project team produced a number of drafts (e.g. “Preliminary Drafts” No. 1 to 4, “Tentative Draft No. 1”) and project progress were regularly discussed with advisory bodies and members of both the ALI and the ELI. The project reporters also included feedback and insights from industry stakeholders and experts that was gained after several meetings and workshops, hosted, inter alia by UNCITRAL, UNIDROIT and several national governmental institutions. Tentative Draft No. 2 was presented at the ALI Annual Meeting in May 2021 and approved by ALI membership. The latest draft ("Final Council Draft") was also approved by the ELI Council and ELI Membership. The Principles for a Data Economy were presented at an international conference with representatives from institutions such as the Uniform Law Commission (ULC), the European Commission, UNIDROIT, the OECD, the International Chamber of Commerce (ICC) and the World Economic Forum (WEF) in October 2021. == Project structure == The current draft (“Tentative Draft No. 2”) of the Principles consists of five Parts that each governs different aspects of the data economy: General Provisions, Data Contracts, Data Rights, Third Party Aspects of Data Activities, and Multi-State Issues. === General Provisions === Part I includes general provisions that apply to all other Parts of the Principles for a Data Economy. This Part sets out the purpose of the Principles: they aim to make existing law in the field of the data economy more coherent and support the development of the law in this field by courts and legislators worldwide. It is also clarified that the Principles have a wide scope of application and can be used in a variety of ways by stakeholders in the data economy. The Principles may, for example, serve private parties as a basis for contract formation, guide the deliberations of arbitral tribunals or inspire national legislation. Part I then defines several key terms, such as ‘digital data’ and ‘data right’. The scope of the Principles is limited to matters where information is recorded as an asset, resource or tradeable commodity and where large amounts of data, rather than single pieces of information, are concerned. This Part also clarifies that remedies with respect to data contracts and data rights are left to the applicable national law. === Data Contracts === Part II lists different types of contracts that often occur in the data economy and establishes two broad categories, namely contracts for the supply and sharing of data and contracts for services with regard to data. Contracts for the supply and sharing of data include, e.g. data transfer contracts or data pooling arrangements, while contracts for services with regard to data cover contracts for the processing of data or data intermediary contracts. The Principles provide default terms for each contract type, on issues such as the manner in which data should supply or which characteristics the data supplied should meet. These default terms 'automatically' become part of the contract unless the parties agree otherwise. === Data Rights === Part III governs legally protected interests of players in the data economy that stem from the characteristics of data as a resource (e.g. its non-rivalrous nature) or from public interest considerations. Such data rights may include the right to data access, the right to require the controller to desist from data activities or to correct incorrect/incomplete data, or even to receive an economic share in profits derived from the use of data. For example, the Principles deal with data rights of stakeholders that had a share in the co-generation of data and identify different factors to be considered in determining whether to afford a party a data right. The underlying idea that parties who have contributed to the generation of data should have some rights in the utilization of the data is also recognized by governmental institutions, such as by the Japanese Ministry of Economy, Trade and Industry (METI), and the term co-generated data, which was coined by the Principles for a Data Economy, has been adopted, inter alia by the European Commission, the German Data Ethics Commission and the Global Partnership on Artificial Intelligence (GPAI). This Part also deals with data rights for the public interest, such as data sharing rights in the field of innovation. === Third Party Aspects === Part IV governs different situations in which data transactions interfere with the rights of third parties. Such rights include intellectual property rights or rights derived from data privacy or data protection law. This Part sets out under which circumstances data activities should be considered wrongful vis à vis another party. For example, a data activity (like data processing or the onward supply of data) could be considered wrongful, if a controller interferes with the rights of data subjects that are protected by data-protection law. A data activity could also be wrongful if the controller is non-compliant with contractual limitations on data activities, enforceable by the protected party (e.g. a controller may only process data for a certain purpose). If someone obtained access to data by unauthorized means (i.e. data “theft”) this could also be considered wrongful. The Part on Third-Party Aspects also takes a detailed look at the effects of the onward supply of data can have on third parties, while balancing the protection of third parties on the one hand, with the interests of data recipients and the desire to encourage data sharing on the other. === Multi-State Issues === As transactions in the data economy are international by nature and hardly occur within one legal system alone, the Part V of the Principles also briefly touches upon the applicability of the rules and doctrines of private international law to such transactions. == Links == Website of the “Principles for a Data Economy – Data Rights and Transaction

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  Data augmentation

  Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. Data augmentation has important applications in Bayesian analysis, and the technique is widely used in machine learning to reduce overfitting when training machine learning models, achieved by training models on several slightly-modified copies of existing data. == Synthetic oversampling techniques for traditional machine learning == Synthetic Minority Over-sampling Technique (SMOTE) is a method used to address imbalanced datasets in machine learning. In such datasets, the number of samples in different classes varies significantly, leading to biased model performance. For example, in a medical diagnosis dataset with 90 samples representing healthy individuals and only 10 samples representing individuals with a particular disease, traditional algorithms may struggle to accurately classify the minority class. SMOTE rebalances the dataset by generating synthetic samples for the minority class. For instance, if there are 100 samples in the majority class and 10 in the minority class, SMOTE can create synthetic samples by randomly selecting a minority class sample and its nearest neighbors, then generating new samples along the line segments joining these neighbors. This process helps increase the representation of the minority class, improving model performance. == Data augmentation for image classification == When convolutional neural networks grew larger in mid-1990s, there was a lack of data to use, especially considering that some part of the overall dataset should be spared for later testing. It was proposed to perturb existing data with affine transformations to create new examples with the same labels, which were complemented by so-called elastic distortions in 2003, and the technique was widely used as of 2010s. Data augmentation can enhance CNN performance and acts as a countermeasure against CNN profiling attacks. Data augmentation has become fundamental in image classification, enriching training dataset diversity to improve model generalization and performance. The evolution of this practice has introduced a broad spectrum of techniques, including geometric transformations, color space adjustments, and noise injection. === Geometric Transformations === Geometric transformations alter the spatial properties of images to simulate different perspectives, orientations, and scales. Common techniques include: Affine Transformation Rotation: Rotating images by a specified degree to help models recognize objects at various angles. Reflection: Reflecting images horizontally or vertically to introduce variability in orientation. Translation: Shifting images in different directions to teach models positional invariance. Scaling Shear Mapping Cropping: Removing sections of the image to focus on particular features or simulate closer views. Elastic Distortion Morphing within the same class: Generating new samples by applying morphing techniques between two images belonging to the same class, thereby increasing intra-class diversity. === Color Space Transformations === Color space transformations modify the color properties of images, addressing variations in lighting, color saturation, and contrast. Techniques include: Brightness Adjustment: Varying the image's brightness to simulate different lighting conditions. Contrast Adjustment: Changing the contrast to help models recognize objects under various clarity levels. Saturation Adjustment: Altering saturation to prepare models for images with diverse color intensities. Color Jittering: Randomly adjusting brightness, contrast, saturation, and hue to introduce color variability. === Noise Injection === Injecting noise into images simulates real-world imperfections, teaching models to ignore irrelevant variations. Techniques involve: Gaussian Noise: Adding Gaussian noise mimics sensor noise or graininess. Salt and Pepper Noise: Introducing black or white pixels at random simulates sensor dust or dead pixels. == Data augmentation for signal processing == Residual or block bootstrap can be used for time series augmentation. === Biological signals === Synthetic data augmentation is of paramount importance for machine learning classification, particularly for biological data, which tend to be high dimensional and scarce. The applications of robotic control and augmentation in disabled and able-bodied subjects still rely mainly on subject-specific analyses. Data scarcity is notable in signal processing problems such as for Parkinson's Disease Electromyography signals, which are difficult to source - Zanini, et al. noted that it is possible to use a generative adversarial network (in particular, a DCGAN) to perform style transfer in order to generate synthetic electromyographic signals that corresponded to those exhibited by sufferers of Parkinson's Disease. The approaches are also important in electroencephalography (brainwaves). Wang, et al. explored the idea of using deep convolutional neural networks for EEG-Based Emotion Recognition, results show that emotion recognition was improved when data augmentation was used. A common approach is to generate synthetic signals by re-arranging components of real data. Lotte proposed a method of "Artificial Trial Generation Based on Analogy" where three data examples x 1 , x 2 , x 3 {\displaystyle x_{1},x_{2},x_{3}} provide examples and an artificial x s y n t h e t i c {\displaystyle x_{synthetic}} is formed which is to x 3 {\displaystyle x_{3}} what x 2 {\displaystyle x_{2}} is to x 1 {\displaystyle x_{1}} . A transformation is applied to x 1 {\displaystyle x_{1}} to make it more similar to x 2 {\displaystyle x_{2}} , the same transformation is then applied to x 3 {\displaystyle x_{3}} which generates x s y n t h e t i c {\displaystyle x_{synthetic}} . This approach was shown to improve performance of a Linear Discriminant Analysis classifier on three different datasets. Current research shows great impact can be derived from relatively simple techniques. For example, Freer observed that introducing noise into gathered data to form additional data points improved the learning ability of several models which otherwise performed relatively poorly. Tsinganos et al. studied the approaches of magnitude warping, wavelet decomposition, and synthetic surface EMG models (generative approaches) for hand gesture recognition, finding classification performance increases of up to +16% when augmented data was introduced during training. More recently, data augmentation studies have begun to focus on the field of deep learning, more specifically on the ability of generative models to create artificial data which is then introduced during the classification model training process. In 2018, Luo et al. observed that useful EEG signal data could be generated by Conditional Wasserstein Generative Adversarial Networks (GANs) which was then introduced to the training set in a classical train-test learning framework. The authors found classification performance was improved when such techniques were introduced. === Mechanical signals === The prediction of mechanical signals based on data augmentation brings a new generation of technological innovations, such as new energy dispatch, 5G communication field, and robotics control engineering. In 2022, Yang et al. integrate constraints, optimization and control into a deep network framework based on data augmentation and data pruning with spatio-temporal data correlation, and improve the interpretability, safety and controllability of deep learning in real industrial projects through explicit mathematical programming equations and analytical solutions.

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  Block swap algorithms

  In computer algorithms, block swap algorithms swap two regions of elements of an array. It is simple to swap two non-overlapping regions of an array of equal size. However, it is not as simple to swap two contiguous regions of an array of unequal sizes (algorithms that perform such swapping are called rotation algorithms). A few well-known algorithms can accomplish this: Bentley's juggling (also known as the dolphin algorithm), Gries-Mills rotation, triple reversal algorithm, conjoined triple reversal algorithm (also known as the trinity rotation) and Successive rotation. == Triple reversal algorithm == The triple reversal algorithm is the simplest to explain, using rotations. A rotation is an in-place reversal of array elements. This method swaps two elements of an array from outside in within a range. The rotation works for an even or odd number of array elements. The reversal algorithm uses three in-place rotations to accomplish an in-place block swap: Rotate region A Rotate region B Rotate region AB Where A and B are adjacent regions of an array that together form the region AB. Gries-Mills and reversal algorithms perform better than Bentley's juggling, because of their cache-friendly memory access pattern behavior. The triple reversal algorithm parallelizes well, because rotations can be split into sub-regions, which can be rotated independently of others.

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  Controlled vocabulary

  A controlled vocabulary provides a way to organize knowledge for subsequent retrieval. Controlled vocabularies are used in subject indexing schemes, subject headings, thesauri, taxonomies and other knowledge organization systems. Controlled vocabulary schemes mandate the use of predefined, preferred terms that have been preselected by the designers of the schemes, in contrast to natural language vocabularies, which have no such restriction. == In library and information science == In library and information science, controlled vocabulary is a carefully selected list of words and phrases, which are used to tag units of information (document or work) so that they may be more easily retrieved by a search. Controlled vocabularies solve the problems of homographs, synonyms and polysemes by a bijection between concepts and preferred terms. In short, controlled vocabularies reduce unwanted ambiguity inherent in normal human languages where the same concept can be given different names and ensure consistency. For example, in the Library of Congress Subject Headings (a subject heading system that uses a controlled vocabulary), preferred terms—subject headings in this case—have to be chosen to handle choices between variant spellings of the same word (American versus British), choice among scientific and popular terms (cockroach versus Periplaneta americana), and choices between synonyms (automobile versus car), among other difficult issues. Choices of preferred terms are based on the principles of user warrant (what terms users are likely to use), literary warrant (what terms are generally used in the literature and documents), and structural warrant (terms chosen by considering the structure, scope of the controlled vocabulary). Controlled vocabularies also typically handle the problem of homographs with qualifiers. For example, the term pool has to be qualified to refer to either swimming pool or the game pool to ensure that each preferred term or heading refers to only one concept. === Types used in libraries === There are two main kinds of controlled vocabulary tools used in libraries: subject headings and thesauri. While the differences between the two are diminishing, there are still some minor differences: Historically, subject headings were designed to describe books in library catalogs by catalogers while thesauri were used by indexers to apply index terms to documents and articles. Subject headings tend to be broader in scope describing whole books, while thesauri tend to be more specialized covering very specific disciplines. Because of the card catalog system, subject headings tend to have terms that are in indirect order (though with the rise of automated systems this is being removed), while thesaurus terms are always in direct order. Subject headings tend to use more pre-coordination of terms such that the designer of the controlled vocabulary will combine various concepts together to form one preferred subject heading. (e.g., children and terrorism) while thesauri tend to use singular direct terms. Thesauri list not only equivalent terms but also narrower, broader terms and related terms among various preferred and non-preferred (but potentially synonymous) terms, while historically most subject headings did not. For example, the Library of Congress Subject Heading itself did not have much syndetic structure until 1943, and it was not until 1985 when it began to adopt the thesauri type term "Broader term" and "Narrow term". The terms are chosen and organized by trained professionals (including librarians and information scientists) who possess expertise in the subject area. Controlled vocabulary terms can accurately describe what a given document is actually about, even if the terms themselves do not occur within the document's text. Well known subject heading systems include the Library of Congress system, Medical Subject Headings (MeSH) created by the United States National Library of Medicine, and Sears. Well known thesauri include the Art and Architecture Thesaurus and the ERIC Thesaurus. When selecting terms for a controlled vocabulary, the designer has to consider the specificity of the term chosen, whether to use direct entry, inter consistency and stability of the language. Lastly the amount of pre-coordination (in which case the degree of enumeration versus synthesis becomes an issue) and post-coordination in the system is another important issue. Controlled vocabulary elements (terms/phrases) employed as tags, to aid in the content identification process of documents, or other information system entities (e.g. DBMS, Web Services) qualifies as metadata. == Indexing languages == There are three main types of indexing languages. Controlled indexing language – only approved terms can be used by the indexer to describe the document Natural language indexing language – any term from the document in question can be used to describe the document Free indexing language – any term (not only from the document) can be used to describe the document When indexing a document, the indexer also has to choose the level of indexing exhaustivity, the level of detail in which the document is described. For example, using low indexing exhaustivity, minor aspects of the work will not be described with index terms. In general the higher the indexing exhaustivity, the more terms indexed for each document. In recent years free text search as a means of access to documents has become popular. This involves using natural language indexing with an indexing exhaustively set to maximum (every word in the text is indexed). These methods have been compared in some studies, such as the 2007 article, "A Comparative Evaluation of Full-text, Concept-based, and Context-sensitive Search". === Advantages === Controlled vocabularies are often claimed to improve the accuracy of free text searching, such as to reduce irrelevant items in the retrieval list. These irrelevant items (false positives) are often caused by the inherent ambiguity of natural language. Take the English word football for example. Football is the name given to a number of different team sports. Worldwide the most popular of these team sports is association football, which also happens to be called soccer in several countries. The word football is also applied to rugby football (rugby union and rugby league), American football, Australian rules football, Gaelic football, and Canadian football. A search for football therefore will retrieve documents that are about several completely different sports. Controlled vocabulary solves this problem by tagging the documents in such a way that the ambiguities are eliminated. Compared to free text searching, the use of a controlled vocabulary can dramatically increase the performance of an information retrieval system, if performance is measured by precision (the percentage of documents in the retrieval list that are actually relevant to the search topic). In some cases controlled vocabulary can enhance recall as well, because unlike natural language schemes, once the correct preferred term is searched, there is no need to search for other terms that might be synonyms of that term. === Disadvantages === A controlled vocabulary search may lead to unsatisfactory recall, in that it will fail to retrieve some documents that are actually relevant to the search question. This is particularly problematic when the search question involves terms that are sufficiently tangential to the subject area such that the indexer might have decided to tag it using a different term (but the searcher might consider the same). Essentially, this can be avoided only by an experienced user of controlled vocabulary whose understanding of the vocabulary coincides with that of the indexer. Another possibility is that the article is just not tagged by the indexer because indexing exhaustivity is low. For example, an article might mention football as a secondary focus, and the indexer might decide not to tag it with "football" because it is not important enough compared to the main focus. But it turns out that for the searcher that article is relevant and hence recall fails. A free text search would automatically pick up that article regardless. On the other hand, free text searches have high exhaustivity (every word is searched) so although it has much lower precision, it has potential for high recall as long as the searcher overcome the problem of synonyms by entering every combination. Controlled vocabularies may become outdated rapidly in fast developing fields of knowledge, unless the preferred terms are updated regularly. Even in an ideal scenario, a controlled vocabulary is often less specific than the words of the text itself. Indexers trying to choose the appropriate index terms might misinterpret the author, while this precise problem is not a factor in a free text, as it uses the author's own words. The use of controlled vocabularies can be costly compared to free

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