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  Connection string

  In computing, a connection string is a string that specifies information about a data source and the means of connecting to it. It is passed in code to an underlying driver or provider in order to initiate the connection. Whilst commonly used for a database connection, the data source could also be a spreadsheet or text file. The connection string may include attributes such as the name of the driver, server and database, as well as security information such as user name and password. == Examples == This example shows a PostgreSQL connection string for connecting to wikipedia.com with SSL and a connection timeout of 180 seconds: DRIVER={PostgreSQL Unicode};SERVER=www.wikipedia.com;SSL=true;SSLMode=require;DATABASE=wiki;UID=wikiuser;Connect Timeout=180;PWD=ashiknoor Users of Oracle databases can specify connection strings: on the command line (as in: sqlplus scott/tiger@connection_string ) via environment variables ($TWO_TASK in Unix-like environments; %TWO_TASK% in Microsoft Windows environments) in local configuration files (such as the default $ORACLE_HOME/network/admin.tnsnames.ora) in LDAP-capable directory services

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  Super column

  A super column is a tuple (a pair) with a binary super column name and a value that maps it to many columns. They consist of a key–value pairs, where the values are columns. Theoretically speaking, super columns are (sorted) associative array of columns. Similar to a regular column family where a row is a sorted map of column names and column values, a row in a super column family is a sorted map of super column names that maps to column names and column values. A super column is part of a keyspace together with other super columns and column families, and columns. == Code example == Written in the JSON-like syntax, a super column definition can be like this: Where: "databases" are keyspace; "Cassandra" and "HBase" are rowKeys; "name" and "address" are super column names; "firstName", "city", "age", etc. are column names.

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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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  Operational historian

  In manufacturing, an operational historian is a time-series database application that is developed for operational process data. Historian software is often embedded or used in conjunction with standard DCS and PLC control systems to provide enhanced data capture, validation, compression, and aggregation capabilities. Historians have been deployed in almost every industry and contribute to functions such as supervisory control, performance monitoring, quality assurance, and, more recently, machine learning applications which can learn from vast quantities of historical data. These systems were originally developed to capture instrumentation and control data, which led many to use the term "tag" for a stream of process data, referring to the physical "tags" which had been placed on instrumentation for manually capturing data. Raw data may be accessed via OPC HDA, SQL, or REST API interfaces. == Operational Support == Operational historians are typically used within the manufacturing facility by engineers and operators for supervisory functions and analysis. An operational historian will typically capture all instrumentation and control data, whereas an enterprise historian that is deployed to support business functions will capture only a subset of the plant data. Typically, these applications offer data access through dedicated APIs (Application Programming Interfaces) and SDKs (Software Development Kits) which offer high-performance read and write operations. These operate through vendor-specific or custom applications. Front-end tools for trending process data over time are the most common interfaces to these databases. Because these applications are typically deployed next to or near the source of their process data, they are often marketed and sold as 'real-time database systems.' This distinction varies among vendors, who often have to make tradeoffs in performance between data capture and presentation, and application and analysis functionality. The following is a list of typical challenges for operational historians: data collection from instrumentation and controls storage and archiving of very large volumes of data organization of data in the form of "tags" or "points" limiting of monitoring (alarms) and validation aggregation and interpolation manual data entry (MDE) == Data access == As opposed to enterprise historians, the data access layer in the operational historian is designed to offer sophisticated data fetching modes without complex information analysis facilities. The following settings are typically available for data access operations: Data scope (single point or tag, history based on time range, history based on sample count) Request modes (raw data, last-known value, aggregation, interpolation) Sampling (single point, all points without sampling, all points with interval sampling) Data omission (based on the sample quality, based on the sample value, based on the count) Even though the operational historians are rarely relational database management systems, they often offer SQL-based interfaces to query the database. In most of such implementations, the dialect does not follow the SQL standard in order to provide syntax for specifying data access operations parameters.

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  Shape factor (image analysis and microscopy)

  Shape factors are dimensionless quantities used in image analysis and microscopy that numerically describe the shape of a particle, independent of its size. Shape factors are calculated from measured dimensions, such as diameter, chord lengths, area, perimeter, centroid, moments, etc. The dimensions of the particles are usually measured from two-dimensional cross-sections or projections, as in a microscope field, but shape factors also apply to three-dimensional objects. The particles could be the grains in a metallurgical or ceramic microstructure, or the microorganisms in a culture, for example. The dimensionless quantities often represent the degree of deviation from an ideal shape, such as a circle, sphere or equilateral polyhedron. Shape factors are often normalized, that is, the value ranges from zero to one. A shape factor equal to one usually represents an ideal case or maximum symmetry, such as a circle, sphere, square or cube. == Aspect ratio == The most common shape factor is the aspect ratio, a function of the largest diameter and the smallest diameter orthogonal to it: A R = d min d max {\displaystyle A_{R}={\frac {d_{\min }}{d_{\max }}}} The normalized aspect ratio varies from approaching zero for a very elongated particle, such as a grain in a cold-worked metal, to near unity for an equiaxed grain. The reciprocal of the right side of the above equation is also used, such that the AR varies from one to approaching infinity. == Circularity == Another very common shape factor is the circularity (or isoperimetric quotient), a function of the perimeter P and the area A: f circ = 4 π A P 2 {\displaystyle f_{\text{circ}}={\frac {4\pi A}{P^{2}}}} The circularity of a circle is 1, and much less than one for a starfish footprint. The reciprocal of the circularity equation is also used, such that fcirc varies from one for a circle to infinity. == Elongation shape factor == The less-common elongation shape factor is defined as the square root of the ratio of the two second moments in of the particle around its principal axes. f elong = i 2 i 1 {\displaystyle f_{\text{elong}}={\sqrt {\frac {i_{2}}{i_{1}}}}} == Compactness shape factor == The compactness shape factor is a function of the polar second moment in of a particle and a circle of equal area A. f comp = A 2 2 π i 1 2 + i 2 2 {\displaystyle f_{\text{comp}}={\frac {A^{2}}{2\pi {\sqrt {{i_{1}}^{2}+{i_{2}}^{2}}}}}} The fcomp of a circle is one, and much less than one for the cross-section of an I-beam. == Waviness shape factor == The waviness shape factor of the perimeter is a function of the convex portion Pcvx of the perimeter to the total. f wav = P cvx P {\displaystyle f_{\text{wav}}={\frac {P_{\text{cvx}}}{P}}} Some properties of metals and ceramics, such as fracture toughness, have been linked to grain shapes. == An application of shape factors == Greenland, the largest island in the world, has an area of 2,166,086 km2; a coastline (perimeter) of 39,330 km; a north–south length of 2670 km; and an east–west length of 1290 km. The aspect ratio of Greenland is A R = 1290 2670 = 0.483 {\displaystyle A_{R}={\frac {1290}{2670}}=0.483} The circularity of Greenland is f circ = 4 π ( 2166086 ) 39330 2 = 0.0176. {\displaystyle f_{\text{circ}}={\frac {4\pi (2166086)}{39330^{2}}}=0.0176.} The aspect ratio is agreeable with an eyeball-estimate on a globe. Such an estimate on a typical flat map, using the Mercator projection, would be less accurate due to the distorted scale at high latitudes. The circularity is deceptively low, due to the fjords that give Greenland a very jagged coastline (see the coastline paradox). A low value of circularity does not necessarily indicate a lack of symmetry, and shape factors are not limited to microscopic objects.

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  NCSA Brown Dog

  NCSA Brown Dog is a research project to develop a method for easily accessing historic research data stored in order to maintain the long-term viability of large bodies of scientific research. It is supported by the National Center for Supercomputing Applications (NCSA) that is funded by the National Science Foundation (NSF). == History == Brown Dog is part of the DataNet partners program funded by NSF in 2008. DataNet was conceived to address the increasingly digital and data-intensive nature of science, engineering and education. Brown Dog is part of a follow-on effort called Data Infrastructure Building Blocks (DIBBs), focused on building software to support DataNet. The project was proposed by researchers at NCSA and the University of Illinois Urbana-Champaign as well as researchers from Boston University and the University of North Carolina at Chapel Hill. == Unstructured, uncurated, long tail data == Much scientific data is smaller, unstructured and uncurated and thus not easily shared. Such data is sometimes referred to as "long tail" data. This borrows a term from statistics and refers to the tail of the distribution of project sizes. The majority of smaller projects lack the resources to properly steward the data they produce. This so-called "long tail" data, both past and present, has the potential to inform future research in many study areas. Much of this data has become inaccessible due to obsolete software and file formats. The resulting impossibility of reviewing data from older research disrupts the overall scientific research project. == Approach == Brown Dog describes itself as the "super mutt" of software (thus the name "Brown Dog"), serving as a low-level data infrastructure to interface digital data content across the internet. Its approach is to use every possible source of automated help (i.e., software) in existence in a robust and provenance-preserving manner to create a service that can deal with as much of this data as possible. The project sees the broader impact of its work in its potential to serve the general public as a sort of "DNS for data", with the goal of making all data and all file formats as accessible as webpages are today. == Technology == Brown Dog seeks to address problems involving the use of uncurated and unstructured data collections through the development of two services: the Data Access Proxy (DAP) to aid in the conversion of file formats and the Data Tilling Services (DTS) for the automatic extraction of metadata from file contents. Once developed, researchers and general public users will be able to download browser plugins and other tools from the Brown Dog tool catalog. === Data Tilling Service === Data Tilling Service (DTS) will allow users to search data collections using an existing file to discover other similar files in a collection. A DTS search field will be appended to configured browsers where example files can be dropped. This tells DTS to search all the files under a given URL for files similar to the dropped file. For example, while browsing an online image collection, a user could drop an image of three people into the search field, and the DTS would return all images in the collection that also contain three people. If DTS encounters a foreign file format, it will utilize DAP to make the file accessible. DTS also indexes the data and extract and appends metadata to files and collections enabling users to gain some sense of the type of data they are encountering. This service runs on port 9443. === Data Access Proxy === Data Access Proxy (DAP) allows users to access data files that would otherwise be unreadable. Similar to an internet gateway or Domain Name Service, the DAP configuration would be entered into a user's machine and browser settings. Data requests over HTTP would first be examined by DAP to determine if the native file format is readable on the client device. If not, DAP converts the file into the best available format readable by the client machine. Alternatively, the user could specify the desired format themselves. This service runs on port 8184. == Use cases == Brown Dog targets three use cases proposed by groups within the EarthCube research communities. Developers and researchers from these communities will work together on use cases that span geoscience, engineering, biology and social science. === Long tail vegetation data in ecology and global change biology === This use case is led by Michael Dietze, Boston University Data on the abundance, species composition, and size structure of vegetation is critically important for a wide array of sub-disciplines in ecology, conservation, natural resource management, and global change biology. However, addressing many of the pressing questions in these disciplines will require that terrestrial biosphere and hydrologic models are able to assimilate the large amount of long-tail data that exists but is largely inaccessible. The Brown Dog team in cooperation with researches from Dietze's lab will facilitate the capture of a huge body of smaller research-oriented vegetation data sets collected over many decades and historical vegetation data embedded in Public Land Survey data dating back to 1785. This data will be used as initial conditions for models, to make sense of other large data sets and for model calibration and validation. === Designing green infrastructure considering storm water and human requirements === This use case is led by Barbara Minsker], University of Illinois at Urbana-Champaign]; William Sullivan, University of Illinois at Urbana-Champaign; Arthur Schmidt, University of Illinois at Urbana-Champaign. This case study involves developing novel green infrastructure design criteria and models that integrate requirements for storm water management and ecosystem and human health and well being. To address the scientific and social problems associated with the design of green spaces, data accessibility and availability is a major challenge. This study will focus on identified areas of the Green Healthy Neighborhood Planning region within the City of Chicago where existing local sewer performance is most deficient and where changes in impervious area through green infrastructure would be beneficial to under served neighborhoods. Brown Dog will be used to extract long-tail experimental data on human landscape preferences and health impacts. This data will be used to develop a human health impacts model that will then be linked together with a terrestrial biosphere model and a storm water model using Brown Dog technology. === Development and application for critical zone studies === This use case is led by Praveen Kumar, University of Illinois at Urbana-Champaign Critical Zone (CZ) is the "skin" of the earth that extends from the treetops to the bedrock that is created by life processes working at scales from microbes to biomes. The Critical Zone supports all terrestrial living systems. Its upper part is the bio-mantle. This is where terrestrial biota live, reproduce, use and expend energy, and where their wastes and remains accumulate and decompose. It encompasses the soil, which acts as a geomembrane through which water and solutes, energy, gases, solids, and organisms interact with the atmosphere, biosphere, hydrosphere, and lithosphere. A variety of drivers affect this bio-dynamic zone, ranging from climate and deforestation to agriculture, grazing and human development. Understanding and predicting these effects is central to managing and sustaining vital ecosystem services such as soil fertility, water purification, and production of food resources, and, at larger scales, global carbon cycling and carbon sequestration. The CZ provides a unifying framework for integrating terrestrial surface and near-surface environments, and reflects an intricate web of biological and chemical processes and human impacts occurring at vastly different temporal and spatial scales. The nature of these data create significant challenges for inter-disciplinary studies of the CZ because integration of the variety and number of data products and models has been a barrier. On the other hand, CZ data provides an excellent opportunity for defining, testing and implementing Brown Dog technologies. In this context "unstructured" data is viewed broadly as consisting of a collection of heterogeneous data with formats that reflect temporal and disciplinary legacies, data from emerging low cost open hardware based sensors and embedded sensor networks that lack well defined metadata and sensor characteristics, as well as data that are available as maps, images and text. == NSF Award == CIF21 DIBBs: Brown Dog was awarded in the winter of 2013 with a start date of October 1, 2013. Estimated expiration date is September 30, 2018. The award amount was $10,519,716.00, the largest DIBB award. The principal investigator is Kenton McHenry of NCSA at the University of Illinois at Urbana-Champaign. Coleaders are Jong Lee NCSA/UIU

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  System of record

  A system of record (SOR) or source system of record (SSoR) is a data management term for an information storage system (commonly implemented on a computer system running a database management system) that is the authoritative data source for a given data element or piece of information, like for example a row (or record) in a table. In data vault it is referred to as the record source. == Background == The need to identify systems of record can become acute in organizations where management information systems have been built by taking output data from multiple source systems, re-processing this data, and then re-presenting the result for a new business use. In these cases, multiple information systems may disagree about the same piece of information. These disagreements may stem from semantic differences, differences in opinion, use of different sources, differences in the timing of the extract, transform, load processes that create the data they report against, or may simply be the result of bugs. == Use == The integrity and validity of any data set is open to question when there is no traceable connection to a good source, and listing a source system of record is a solution to this. Where the integrity of the data is vital, if there is an agreed system of record, the data element must either be linked to, or extracted directly from it. In other cases, the provenance and estimated data quality should be documented. The "system of record" approach is a good fit for environments where both: there is a single authority over all data consumers, and all consumers have similar needs == Trade-offs == In diverse environments, one instead needs to support the presence of multiple opinions. Consumers may accept different authorities or may differ on what constitutes an authoritative source—researchers may prefer carefully vetted data, while tactical military systems may require the most recent credible report.

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  Conceptualization (information science)

  In information science, a conceptualization is an abstract simplified view of some selected parts of the world, containing the objects, concepts, and other entities that are presumed of interest for some particular purpose and the relationships between them. An explicit specification of a conceptualization is an ontology, and it may occur that a conceptualization can be realized by several distinct ontologies. An ontological commitment in describing ontological comparisons is taken to refer to that subset of elements of an ontology shared with all the others. "An ontology is language-dependent", its objects and interrelations described within the language it uses, while a conceptualization is always the same, more general, its concepts existing "independently of the language used to describe it". The relation between these terms is shown in the figure to the right. Not all workers in knowledge engineering use the term "conceptualization", but instead refer to the conceptualization itself, or to the ontological commitment of all its realizations, as an overarching ontology. == Purpose and implementation == As a higher level abstraction, a conceptualization facilitates the discussion and comparison of its various ontologies, facilitating knowledge sharing and reuse. Each ontology based upon the same overarching conceptualization maps the conceptualization into specific elements and their relationships. The question then arises as to how to describe the "conceptualization" in terms that can encompass multiple ontologies. This issue has been called the Tower of Babel problem, that is, how can persons used to one ontology talk with others using a different ontology? This problem is easily grasped, but a general resolution is not at hand. It can be a "bottom-up" or a "top-down" approach, or something in between. However, in more artificial situations, such as information systems, the idea of a "conceptualization" and the "ontological commitment" of various ontologies that realize the "conceptualization" is possible. The formation of a conceptualization and its ontologies involves these steps: specification of the conceptualization ontology concepts: every definition involves the definitions of other terms relationships between the concepts: this step maps conceptual relationships onto the ontology structure groups of concepts: this step may lead to the creation of sub-ontologies formal description of ontology commitments, for example, to make them computer readable An example of moving conception into a language leading to a variety of ontologies is the expression of a process in pseudocode (a strictly structured form of ordinary language) leading to implementation in several different formal computer languages like Lisp or Fortran. The pseudocode makes it easier to understand the instructions and compare implementations, but the formal languages make possible the compilation of the ideas as computer instructions. Another example is mathematics, where a very general formulation (the analog of a conceptualization) is illustrated with "applications" that are more specialized examples. For instance, aspects of a function space can be illustrated using a vector space or a topological space that introduce interpretations of the "elements" of the conceptualization and additional relationships between them but preserve the connections required in the function space.

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  Inductive programming

  Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints. Depending on the programming language used, there are several kinds of inductive programming. Inductive functional programming, which uses functional programming languages such as Lisp or Haskell, and most especially inductive logic programming, which uses logic programming languages such as Prolog and other logical representations such as description logics, have been more prominent, but other (programming) language paradigms have also been used, such as constraint programming or probabilistic programming. == Definition == Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired behavior of the intended program, traces or action sequences which describe the process of calculating specific outputs, constraints for the program to be induced concerning its time efficiency or its complexity, various kinds of background knowledge such as standard data types, predefined functions to be used, program schemes or templates describing the data flow of the intended program, heuristics for guiding the search for a solution or other biases. Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of Turing-complete representation language. In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration of inductive programming as a special area inside automatic programming or program synthesis, usually opposed to 'deductive' program synthesis, where the specification is usually complete. In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general machine learning, the more specific area of structure mining or the area of symbolic artificial intelligence. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples. The diversity of inductive programming usually comes from the applications and the languages that are used: apart from logic programming and functional programming, other programming paradigms and representation languages have been used or suggested in inductive programming, such as functional logic programming, constraint programming, probabilistic programming, abductive logic programming, modal logic, action languages, agent languages and many types of imperative languages. == History == The early works of Plotkin, and his "relative least general generalization (rlgg)", had an enormous impact in inductive logic programming. There were some encouraging results on learning recursive Prolog programs such as quicksort from examples together with suitable background knowledge, for example with GOLEM. However, after initial success, the community got disappointed by limited progress about the induction of recursive programs with ILP less and less focusing on recursive programs and leaning more and more towards a machine learning setting with applications in relational data mining and knowledge discovery. In parallel to work in ILP, Koza proposed genetic programming in the early 1990s as a generate-and-test based approach to learning programs. The idea of genetic programming was further developed into the inductive programming system ADATE and the systematic-search-based system MagicHaskeller. Here again, functional programs are learned from sets of positive examples together with an output evaluation (fitness) function which specifies the desired input/output behavior of the program to be learned. The early work in grammar induction (also known as grammatical inference) is related to inductive programming, as rewriting systems or logic programs can be used to represent production rules. In fact, early works in inductive inference considered grammar induction and Lisp program inference as basically the same problem. The results in terms of learnability were related to classical concepts, such as identification-in-the-limit, as introduced in the seminal work of Gold. More recently, the language learning problem was addressed by the inductive programming community. In the recent years, the classical approaches have been resumed and advanced with great success. Therefore, the synthesis problem has been reformulated on the background of constructor-based term rewriting systems taking into account modern techniques of functional programming, as well as moderate use of search-based strategies and usage of background knowledge as well as automatic invention of subprograms. Many new and successful applications have recently appeared beyond program synthesis, most especially in the area of data manipulation, programming by example and cognitive modelling (see below). Other ideas have also been explored with the common characteristic of using declarative languages for the representation of hypotheses. For instance, the use of higher-order features, schemes or structured distances have been advocated for a better handling of recursive data types and structures; abstraction has also been explored as a more powerful approach to cumulative learning and function invention. One powerful paradigm that has been recently used for the representation of hypotheses in inductive programming (generally in the form of generative models) is probabilistic programming (and related paradigms, such as stochastic logic programs and Bayesian logic programming). == Application areas == The first workshop on Approaches and Applications of Inductive Programming (AAIP) Archived 2016-03-03 at the Wayback Machine held in conjunction with ICML 2005 identified all applications where "learning of programs or recursive rules are called for, [...] first in the domain of software engineering where structural learning, software assistants and software agents can help to relieve programmers from routine tasks, give programming support for end users, or support of novice programmers and programming tutor systems. Further areas of application are language learning, learning recursive control rules for AI-planning, learning recursive concepts in web-mining or for data-format transformations". Since then, these and many other areas have shown to be successful application niches for inductive programming, such as end-user programming, the related areas of programming by example and programming by demonstration, and intelligent tutoring systems. Other areas where inductive inference has been recently applied are knowledge acquisition, artificial general intelligence, reinforcement learning and theory evaluation, and cognitive science in general. There may also be prospective applications in intelligent agents, games, robotics, personalisation, ambient intelligence and human interfaces.

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  Lancichinetti–Fortunato–Radicchi benchmark

  Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods. The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes. == The algorithm == The node degrees and the community sizes are distributed according to a power law, with different exponents. The benchmark assumes that both the degree and the community size have power law distributions with different exponents, γ {\displaystyle \gamma } and β {\displaystyle \beta } , respectively. N {\displaystyle N} is the number of nodes and the average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . There is a mixing parameter μ {\displaystyle \mu } , which is the average fraction of neighboring nodes of a node that do not belong to any community that the benchmark node belongs to. This parameter controls the fraction of edges that are between communities. Thus, it reflects the amount of noise in the network. At the extremes, when μ = 0 {\displaystyle \mu =0} all links are within community links, if μ = 1 {\displaystyle \mu =1} all links are between nodes belonging to different communities. One can generate the benchmark network using the following steps. Step 1: Generate a network with nodes following a power law distribution with exponent γ {\displaystyle \gamma } and choose extremes of the distribution k min {\displaystyle k_{\min }} and k max {\displaystyle k_{\max }} to get desired average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . Step 2: ( 1 − μ ) {\displaystyle (1-\mu )} fraction of links of every node is with nodes of the same community, while fraction μ {\displaystyle \mu } is with the other nodes. Step 3: Generate community sizes from a power law distribution with exponent β {\displaystyle \beta } . The sum of all sizes must be equal to N {\displaystyle N} . The minimal and maximal community sizes s min {\displaystyle s_{\min }} and s max {\displaystyle s_{\max }} must satisfy the definition of community so that every non-isolated node is in at least in one community: s min > k min {\displaystyle s_{\min }>k_{\min }} s max > k max {\displaystyle s_{\max }>k_{\max }} Step 4: Initially, no nodes are assigned to communities. Then, each node is randomly assigned to a community. As long as the number of neighboring nodes within the community does not exceed the community size a new node is added to the community, otherwise stays out. In the following iterations the “homeless” node is randomly assigned to some community. If that community is complete, i.e. the size is exhausted, a randomly selected node of that community must be unlinked. Stop the iteration when all the communities are complete and all the nodes belong to at least one community. Step 5: Implement rewiring of nodes keeping the same node degrees but only affecting the fraction of internal and external links such that the number of links outside the community for each node is approximately equal to the mixing parameter μ {\displaystyle \mu } . == Testing == Consider a partition into communities that do not overlap. The communities of randomly chosen nodes in each iteration follow a p ( C ) {\displaystyle p(C)} distribution that represents the probability that a randomly picked node is from the community C {\displaystyle C} . Consider a partition of the same network that was predicted by some community finding algorithm and has p ( C 2 ) {\displaystyle p(C_{2})} distribution. The benchmark partition has p ( C 1 ) {\displaystyle p(C_{1})} distribution. The joint distribution is p ( C 1 , C 2 ) {\displaystyle p(C_{1},C_{2})} . The similarity of these two partitions is captured by the normalized mutual information. I n = ∑ C 1 , C 2 p ( C 1 , C 2 ) log 2 ⁡ p ( C 1 , C 2 ) p ( C 1 ) p ( C 2 ) 1 2 H ( { p ( C 1 ) } ) + 1 2 H ( { p ( C 2 ) } ) {\displaystyle I_{n}={\frac {\sum _{C_{1},C_{2}}p(C_{1},C_{2})\log _{2}{\frac {p(C_{1},C_{2})}{p(C_{1})p(C_{2})}}}{{\frac {1}{2}}H(\{p(C_{1})\})+{\frac {1}{2}}H(\{p(C_{2})\})}}} If I n = 1 {\displaystyle I_{n}=1} the benchmark and the detected partitions are identical, and if I n = 0 {\displaystyle I_{n}=0} then they are independent of each other.

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  Reference data

  Reference data is data used to classify or categorize other data. Typically, they are static or slowly changing over time. Examples of reference data include: Units of measurement Country codes Corporate codes Fixed conversion rates e.g., weight, temperature, and length Calendar structure and constraints Reference data sets are sometimes alternatively referred to as a "controlled vocabulary" or "lookup" data. Reference data differs from master data. While both provide context for business transactions, reference data is concerned with classification and categorisation, while master data is concerned with business entities. A further difference between reference data and master data is that a change to the reference data values may require an associated change in business process to support the change, while a change in master data will always be managed as part of existing business processes. For example, adding a new customer or sales product is part of the standard business process. However, adding a new product classification (e.g. "restricted sales item") or a new customer type (e.g. "gold level customer") will result in a modification to the business processes to manage those items. == Externally-defined reference data == For most organisations, most or all reference data is defined and managed within that organisation. Some reference data, however, may be externally defined and managed, for example by standards organizations. An example of externally defined reference data is the set of country codes as defined in ISO 3166-1. == Reference data management == Curating and managing reference data is key to ensuring its quality and thus fitness for purpose. All aspects of an organisation, operational and analytical, are greatly dependent on the quality of an organization's reference data. Without consistency across business process or applications, for example, similar things may be described in quite different ways. Reference data gain in value when they are widely re-used and widely referenced. Examples of good practice in reference data management include: Formalize the reference data management Use external reference data as much as possible Govern the reference data specific to your enterprise Manage reference data at enterprise level Version control your reference data

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  Document capture software

  Document capture software refers to applications that provide the ability and feature set to automate the process of scanning paper documents or importing electronic documents, often for the purposes of feeding advanced document classification and data collection processes. Most scanning hardware, both scanners and copiers, provides the basic ability to scan to any number of image file formats, including: PDF, TIFF, JPG, BMP, etc. This basic functionality is augmented by document capture software, which can add efficiency and standardization to the process. == Typical features == Typical features of Document Capture Software include: Barcode recognition Patch Code recognition Separation Optical Character Recognition (OCR) Optical Mark Recognition (OMR) Quality Assurance Indexing Migration === Goal for implementation of a document capture solution === The goal for implementing a document capture solution is to reduce the amount of time spent scanning, separating, enhancing, organizing, classifying, normalizing, and collecting information from document collections, and to produce metadata along with an image/PDF file, and/or OCR text. This information is then migrated to a file share, FTP site, database, Document Management or Enterprise Content Management system. These systems often provide a search function, allowing search of the assets based on the produced metadata, and then viewed using document imaging software. == General document capture system solutions == === Integration with document management system === ECM (Enterprise Content management) and their DMS component (Document Management System) are being adopted by many organizations as a corporate document management system for all types of electronic files, e.g. MS word, PDF ... However, much of the information held by organisations is on paper and this needs to be integrated within the same document repository. By converting paper documents into digital format through scanning, organizations convert paper into image formats such as TIF, JPG, and PDF, and also extract valuable index information or business data from the document using OCR technology. Digital documents and associated metadata can easily be stored in the ECM in a variety of formats. The most popular of these formats is PDF which not only provides an accurate representation of the document but also allows all the OCR text in the document to be stored behind the PDF image. This format is known as PDF with hidden text or text-searchable PDF. This allows users to search for documents by using keywords in the metadata fields or by searching the content of PDF files across the repository. ==== Advantages of scanning documents into a ECM/DMS ==== Information held on paper is usually just as valuable to organisations as the electronic documents that are generated internally. Often this information represents a large proportion of the day to day correspondence with suppliers and customers. Having the ability to manage and share this information internally through a document management system such as SharePoint or a CMIS-compatible repository improves collaboration between departments or employees and also eliminates the risk of losing this information through disasters such as floods or fire. Organisations adopting an ECM/DMS often implement electronic workflow which allows the information held on paper to be included as part of an electronic business process and incorporated into a customer record file along with other associated office documents and emails. For business critical documents, such as purchase orders and supplier invoices, digitising documents helps speed up business transactions as well as reduce manual effort involved in keying data into business systems, such as CRM, ERP and Accounting. Scanned invoices can also be routed to managers for payment approval via email or an electronic workflow. == Electronic document capture == In the earlier implementations of Document Capture Software, the technology focused solely on the digitization and capture of information from paper documents. Document images were acquired from document scanners via TWAIN/ISIS drivers. Only image-based file formats like TIF, JPG, and BMP were typically compatible with these solutions. But in recent years, as the volume of electronically-created documents and the number of proprietary file formats continues to increase at exponential rates, the need for handling documents existing in electronic formats has grown. The relevant document capture products have adapted to function with non-image file formats with the end-goal of creating a unified processing workflow capable of handling all incoming documents The ability to import files from a variety of sources is one example of such adaptation. Importing documents from ECM/DMS software solutions, email servers, FTP, and EDI is now as much of a requirement of document capture software as is paper capture. The normalization of output files to text-based PDF format is now another critical factor in long-term archival of proprietary electronic file formats. Normalization expands access and usage of files to users throughout the enterprise, rather than only those that created the original electronic file.

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  Rejoyn

  Rejoyn is a prescription-only digital therapeutic smartphone app approved by the US FDA for the treatment of major depressive disorder (MDD) in adults ages 22 and up. It is prescribed in conjunction with standard antidepressant medication and professional guidance and support. Rejoyn was developed by Click Therapeutics and Otsuka America Pharmaceutical Inc., and gained FDA clearance as a "medical device" on March 30th, 2024. The smartphone app helps patients with depression using exercises based on cognitive behavioral therapy (CBT) along with timed notifications to keep the patient engaged and in treatment. Randomized controlled trials showed that the Rejoyn app was more effective at relieving depression symptoms compared to a "sham app", a placebo app that required similar effort but was not intended to be helpful. Dr. John Torous, MD, MBI,[a] a psychiatrist at the Beth Israel Deaconess Medical Center in Boston, said that the app seems to pose minimal risks, and is an important step forward in unlocking the power of smartphones in treating psychiatric disorders. Some experts have signaled that the claims should be taken with caution, since the app was "tested only in a narrow subset of patients." and its benefits are "not statistically significant," according to the study’s primary outcome."

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  Ubiquitous robot

  Ubiquitous robot is a term used in an analogous way to ubiquitous computing. Software useful for "integrating robotic technologies with technologies from the fields of ubiquitous and pervasive computing, sensor networks, and ambient intelligence". The emergence of mobile phone, wearable computers and ubiquitous computing makes it likely that human beings will live in a ubiquitous world in which all devices are fully networked. The existence of ubiquitous space resulting from developments in computer and network technology will provide motivations to offer desired services by any IT device at any place and time through user interactions and seamless applications. This shift has hastened the ubiquitous revolution, which has further manifested itself in the new multidisciplinary research area, ubiquitous robotics. It initiates the third generation of robotics following the first generation of the industrial robot and the second generation of the personal robot. Ubiquitous robot (Ubibot) is a robot incorporating three components including virtual software robot or avatar, real-world mobile robot and embedded sensor system in surroundings. Software robot within a virtual world can control a real-world robot as a brain and interact with human beings. Researchers of KAIST, Korea describe these three components as a Sobot (Software robot), Mobot (Mobile robot), and Embot (Embedded robot).

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  Algorism

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

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