AI Chat Image Generator Free

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AUTINDEX

AUTINDEX is a commercial text mining software package based on sophisticated linguistics. AUTINDEX, resulting from research in information extraction, is a product of the Institute of Applied Information Sciences (IAI) which is a non-profit institute that has been researching and developing language technology since its foundation in 1985. IAI is an institute affiliated to Saarland University in Saarbrücken, Germany. AUTINDEX is the result of a number of research projects funded by the EU (Project BINDEX), by Deutsche Forschungsgemeinschaft and the German Ministry for Economy. Amongst the latter there are the projects LinSearch, and WISSMER, see also the reference to IAI-Website. The basic functionality of AUTINDEX is the extraction of key words from a document to represent the semantics of the document. Ideally the system is integrated with a thesaurus that defines the standardised terms to be used for key word assignment. AUTINDEX is used in library applications (e.g. integrated in dandelon.com) as well as in high quality (expert) information systems, and in document management and content management environments. Together with AUTINDEX a number of additional software comes along such as an integration with Apache Solr / Lucene to provide a complete information retrieval environment, a classification and categorisation system on the basis of a machine learning software that assigns domains to the document, and a system for searching with semantically similar terms that are collected in so called tag clouds.
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Historical Thesaurus of English

The Historical Thesaurus of English (HTE) is the largest thesaurus in the world. It is called a historical thesaurus as it arranges the whole vocabulary of English, from the earliest written records in Old English to the present, according to the first documented occurrence of a word in the entire history of the English language. The HTE was conceived and begun in 1965 by the English Language & Linguistics department of the University of Glasgow, who have ever since continued to compile the thesaurus. From the 1980s onwards the project was moved from paper-based records to a computer database. Today, the HTE is available to the public online, but a print version, the Historical Thesaurus of the Oxford English Dictionary (HTOED), was published in 2009. == Main project: The Historical Thesaurus of English (HTE) == The Historical Thesaurus of English (HTE) is a complete database of all the words in the Oxford English Dictionary and other dictionaries (including Old English), arranged by semantic field and date. In this way, the HTE arranges the whole vocabulary of English, from the earliest written records in Old English to the present, alongside dates of use. It is the first historical thesaurus to be compiled for any of the world's languages and contains 800,000 meanings for 600,000 words, within 230,000 categories. As the HTE website states, "in addition to providing hitherto unavailable information for linguistic and textual scholars, the Historical Thesaurus online is a rich resource for students of social and cultural history, showing how concepts developed through the words that refer to them." === Structure === The work is divided into three main sections: the External World, the Mind, and Society. These are broken down into successively narrower domains. The text eventually discriminates more than 236,000 categories. The second order categories are: === History === The ambitious project was announced at a 1965 meeting of the Philological Society by its originator, Michael Samuels. Work on the HTE started in the same year. In 2017, the University of Glasgow was awarded the Queen's Anniversary Prize for Higher Education for the HTE. A second edition of the online HTE is currently in progress and is expected to be launched in late 2020. Work is released on the freely-available HTE website when available. == Print edition: Historical Thesaurus of the Oxford English Dictionary (HTOED) == On 22 October 2009, after 44 years of work, version 1.0 of the HTE was published by Oxford University Press in a two-volume slipcased set as the Historical Thesaurus of the Oxford English Dictionary (HTOED). The two hardcover volumes together total nearly 4,500 pages.
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OpenAI Operator

OpenAI Operator was an AI agent developed by OpenAI, capable of autonomously performing tasks through web browser interactions, including filling forms, placing online orders, scheduling appointments, and other repetitive browser-based tasks. It uses OpenAI's advanced models to expand practical automation capabilities for users in daily activities. Operator was launched on January 23, 2025. It was released as a limited-access research preview to ChatGPT Pro-tier subscribers in the United States on February 1, 2025, with future plans to broaden availability. Operator was deprecated after the release of ChatGPT agent, and shut down on August 31, 2025. == Performance and limitations == In benchmark assessments, Operator achieved notable success, scoring 38.1% on OSWorld benchmarks (OS-level tasks) and 58.1% on WebArena benchmarks (web interactions). However, it did not reach human-level accuracy and faced limitations with intricate user interfaces and extended workflows. == Safety and privacy == OpenAI emphasized privacy and safety measures within Operator, including stringent data protection protocols and built-in safety checks designed to prevent unauthorized sensitive actions or information misuse. == Availability == Initially, Operator was only available to ChatGPT Pro subscribers in the U.S., with plans for broader availability to Plus, Team, and Enterprise users in the future.
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International Journal of Pattern Recognition and Artificial Intelligence

The International Journal of Pattern Recognition and Artificial Intelligence was founded in 1987 and is published by World Scientific. The journal covers developments in artificial intelligence, and its sub-field, pattern recognition. This includes articles on image and language processing, robotics and neural networks. == Abstracting and indexing == The journal is abstracted and indexed in: SciSearch ISI Alerting Services CompuMath Citation Index Current Contents/Engineering, Computing & Technology Inspec io-port.net Compendex Computer Abstracts
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Grammatik

Grammatik was the first grammar-checking program for home computers. Aspen Software of Albuquerque, NM, released the earliest version of this diction and style checker for personal computers. It was first released no later than 1981, and was inspired by the Writer's Workbench. Grammatik was first available for the TRS-80, and soon had versions for CP/M and the IBM PC. Reference Software International of San Francisco, California, acquired Grammatik in 1985. Development of Grammatik continued, and it became an actual grammar checker that could detect writing errors beyond simple style checking. Subsequent versions were released for MS-DOS, Windows, Macintosh, and Unix. Grammatik was ultimately acquired by WordPerfect Corporation and is integrated into the WordPerfect word processor.
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No Fakes Act

The NO FAKES Act or the Nurture Originals, Foster Art, and Keep Entertainment Safe Act, is proposed United States federal legislation concerning digital replicas. The bill was first introduced in 2023 as a discussion draft, formally introduced in 2024, and reintroduced in 2025. If enacted, the bill would establish a federal right of publicity, giving public figures and private individuals greater control over the creation and use of digital replicas of their likenesses, including artificial intelligence (AI)-generated content. If passed, the NO FAKES Act would create a legal framework for licensing digital replicas, including provisions for liability, safe harbors, and statutory exceptions. The proposal has received broad support from the entertainment and technology industries. However, digital rights organizations have raised concerns that the Act risks chilling protected speech. == Background == === Entertainment industry concerns === Actors’ concerns over studios' use of their digital likeness were one of the primary drivers of the Screen Actors Guild–American Federation of Television and Radio Artists (SAG-AFTRA) strike in 2023. Negotiators for SAG-AFTRA alleged that the Alliance of Motion Picture and Television Producers (AMPTP) sought to use the digital likenesses of actors in perpetuity and would try to replace union members, especially background actors. The AMPTP denied SAG-AFTRA's interpretation of its proposal. In November 2023, AMPTP and SAG-AFTRA reached an agreement on the use of actors’ digital replicas, which included requirements for consent and compensation. Recording labels have also expressed concerns over unauthorized digital replicas of their performers' likeness. In 2023, TikTok user Ghostwriter977 released "Heart on My Sleeve," an AI-produced song in the styles of Drake and the Weeknd. After the song received millions of streams, the Universal Music Group (UMG) initiated takedown requests to TikTok and YouTube, which removed the song from their platforms. The legal arguments attorneys made were not disclosed; however, commentators noted that they likely used the Digital Millennium Copyright Act (DMCA). This presented a novel scenario, since UMG did not have licensing rights to "Heart on My Sleeve." According to The Verge, UMG based its DMCA takedown request on an unauthorized sample used at the start of the song for the producer tag. While legal commentators noted that UMG could have asserted a violation of the artists’ rights of publicity, existing state right of publicity laws do not provide notice-and-takedown mechanisms comparable to those under the DMCA. === Legal landscape === Legal scholars have observed that AI-generated digital replicas raise questions under existing copyright and intellectual property law. U.S. copyright law generally requires that original authorship be attributable to a human; however, the extent of human intervention needed to satisfy this requirement is not clear. Copyright holders have filed lawsuits against AI companies alleging unauthorized usage of copyrighted material to train their models, though many of these cases remain pending. In terms of outputs, record labels often hold rights to artists’ musical works but do not necessarily control the artists’ voice, appearance, or likeness in the same way. As a result, AI-generated recordings such as "Heart on My Sleeve" may fall outside the scope of certain traditional copyright protections. Individuals' likenesses have historically been governed under the Lanham Act, the Federal Trade Commission Act, and right of publicity laws. The right of publicity, recognized in many state-level statutes and common law, allows individuals to bring legal claims against unauthorized commercial use of their identities. It has often, but not exclusively, been applied to celebrities or other recognizable individuals. There is no federal-level right to publicity, and state-level protections vary, especially on issues relating to digital replicas and posthumous rights, which makes it difficult for creators or other individuals to prevent unauthorized use of their likenesses. In July 2024, the U.S. Copyright Office released a report on digital replicas and recommended that Congress create a federal law to protect individuals from unauthorized uses of their digital replicas, noting the inadequacy, narrowness, and inconsistency of existing laws. == Provisions == Under the NO FAKES Act of 2025, a digital replica is defined as "a newly created, computer-generated, highly realistic electronic representation that is readily identifiable as the voice or visual likeness of an individual," living or dead. A digital replica can be embodied in sound recordings, images, or audiovisual works in which the individual did not perform or in which the individual did perform but the "fundamental character of the performance or appearance has been materially altered." The Act specifies that digital replicas do not include reproduced samples of works authorized by the copyright holder. The Act defines a "right holder" as either the individual who is the subject of a digital replica or an entity that has acquired the rights to that individual’s likeness. The Act grants right holders the exclusive right to authorize the use of an individual’s likeness in a digital replica. This right is not assignable during the individual’s lifetime; however, it can be licensed to a living individual for up to 10 years under certain conditions. Postmortem rights The Act provides that the right does not automatically expire upon an individual’s death. It may be transferred to executors, heirs, or other parties designated by the individual. The right is held by the right holder for 10 years following the individual’s death. If the right holder demonstrates active use of the digital replica within the 2 years preceding the end of the 10-year term, the right may be extended for an additional 5-year period. These five-year extensions may be renewed for up to 70 years after the individual’s death. Liability The Act establishes liability for individuals who knowingly distribute a digital replica without authorization from the right holder, as well as for entities that make available a service primarily designed to produce unlawful digital replicas. Safe harbor provisions Similar to the Communications Decency Act and the DMCA, the Act establishes safe harbor provisions for online service providers. Providers are shielded from liability if they adopt and inform users of a policy for terminating accounts that repeatedly violate the Act. The NO FAKES Act does not require online services to proactively monitor content. Instead, it creates a notice-and-takedown mechanism under which providers must promptly respond to notifications seeking the removal of unauthorized digital replicas. These safe harbor protections apply only if the online service provider designates an agent with the U.S. Copyright Office to receive notifications of alleged violations. Remedies The NO FAKES Act provides remedies that are similar to those available under U.S. copyright law. Under the Act, individuals may be held liable for either statutory damages of $5,000 or actual damages for creating or distributing an unauthorized digital replica. The legislation also establishes a tiered liability framework for online service providers. Those that make good faith efforts to comply with the Act may face statutory damages of up to $25,000 per work for violations or actual damages. Providers that do not undertake such compliance efforts may be liable for $5,000 per unauthorized display or transmission of a digital replica, with damages capped at $750,000 per work. Exclusions The Act includes several exceptions to liability that are modeled in part on fair use principles. Digital replicas are excluded from liability when "used in a bona fide news, public affairs, or sports broadcast or account;" in a documentary or historical context; or in a way that is "consistent with the public interest." These exclusions do not apply to de minimis uses or to digital replicas that are sexually explicit in nature. The Act further states that licensing requirements do not apply to licenses established through collective bargaining agreements that contain provisions governing the use of digital replicas. The Act does not impose secondary liability on providers of generative artificial intelligence tools or services whose primary purpose is not the creation of unauthorized digital replicas. Preemption The NO FAKES Act preempts laws that protect "an individual's voice and visual likeness rights in connection with a digital replica, as defined in this Act, in an expressive work." However, the Act preserves state laws governing digital replicas enacted before January 2, 2025, as well as state laws addressing digital replicas that portray sexually explicit conduct. == History == In 2023, Senators Marsha Blackburn, Chris Coons, Amy Klobuchar, and Th
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RevoScaleR

RevoScaleR is a machine learning package in R created by Microsoft. It is available as part of Machine Learning Server, Microsoft R Client, and Machine Learning Services in Microsoft SQL Server 2016. The package contains functions for creating linear model, logistic regression, random forest, decision tree and boosted decision tree, and K-means, in addition to some summary functions for inspecting and visualizing data. It has a Python package counterpart called revoscalepy. Another closely related package is MicrosoftML, which contains machine learning algorithms that RevoScaleR does not have, such as neural network and SVM. In June 2021, Microsoft announced to open source the RevoScaleR and revoscalepy packages, making them freely available under the MIT License. == Concepts == Many R packages are designed to analyze data that can fit in the memory of the machine and usually do not make use of parallel processing. RevoScaleR was designed to address these limitations. The functions in RevoScaleR orientate around three main abstraction concepts that users can specify to process large amount of data that might not fit in memory and exploit parallel resources to speed up the analysis. === Compute Contexts === A compute context refers to the location where the computation on the data happens. It could be "local" (on the client machine) or "remote" (on a data platform such as a SQL server, or Spark). Pushing the computation to a remote server allows people to take advantage of the greater compute resources that a remote machine may have. If the data being analyzed reside on the same machine, using a remote compute context also removes the need to pull data across the network onto the client machine. === Data source === Data source defines where the data comes from. There are various data sources available in RevoScaleR, such as text data, Xdf data, in-SQL data, and a spark dataframe. People can wrap their data in a data source object and use that as run analytics in different compute context. Different data sources are available in different compute context. For example, if the compute context is set to SQL server, then the only data source one can use would be an in-SQL data source. === Analytics === Analytic functions in RevoScaleR takes in data source object, a compute context, and the other parameters needed to build the specific model, such as formula for the logistic regression or the number of trees in a decision tree. In addition to those parameters, one can also specify the level of parallelism, such as the size of the data chunk for each process or number of processes to build the model. However, parallelism is only available in non-express edition. == Limitations == The package is mostly meant to be used with a SQL server or other remote machines. To fully leverage the abstractions it uses to process a large dataset, one needs a remote server and non-Express free edition of the package. It cannot be easily installed such as by running "install.packages("RevoScaleR")" like most open source R packages. It's available only through Microsoft R Client, a distribution of R for data science, or Microsoft Machine Learning Server (stand-alone with no SQL server attached), or Microsoft Machine Learning Services (a SQL server services). However, one can still use the analytics functions in an Express, free version of the package.
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Project Mariner

Project Mariner was a research prototype developed by Google DeepMind that explored human-agent interactions, particularly within web browsers. It automated tasks such as online shopping, information retrieval, and form-filling, aiming to enhance user productivity by delegating routine web-based tasks to an AI agent. Project Mariner operated as an experimental Chrome extension that understands the contents of your screen, including images, code, forms, and more. It could interpret complex goals, plan actionable steps, and navigate websites to carry out tasks, while keeping the user informed and allowing them to intervene at any time. As of May 2025, Project Mariner was available to Google AI Ultra subscribers in the US and was being integrated into the Gemini API and Vertex AI, allowing developers to build applications powered by the agent Google plans to bring Project Mariner’s capabilities to more countries and integrate it into Google Search's AI Mode, which was currently in the Search Labs testing phase. Project Mariner was discontinued on May 4, 2026.
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Teknomo–Fernandez algorithm

The Teknomo–Fernandez algorithm (TF algorithm), is an efficient algorithm for generating the background image of a given video sequence. By assuming that the background image is shown in the majority of the video, the algorithm is able to generate a good background image of a video in O ( R ) {\displaystyle O(R)} -time using only a small number of binary operations and Boolean bit operations, which require a small amount of memory and has built-in operators found in many programming languages such as C, C++, and Java. == History == People tracking from videos usually involves some form of background subtraction to segment foreground from background. Once foreground images are extracted, then desired algorithms (such as those for motion tracking, object tracking, and facial recognition) may be executed using these images. However, background subtraction requires that the background image is already available and unfortunately, this is not always the case. Traditionally, the background image is searched for manually or automatically from the video images when there are no objects. More recently, automatic background generation through object detection, medial filtering, medoid filtering, approximated median filtering, linear predictive filter, non-parametric model, Kalman filter, and adaptive smoothening have been suggested; however, most of these methods have high computational complexity and are resource-intensive. The Teknomo–Fernandez algorithm is also an automatic background generation algorithm. Its advantage, however, is its computational speed of only O ( R ) {\displaystyle O(R)} -time, depending on the resolution R {\displaystyle R} of an image and its accuracy gained within a manageable number of frames. Only at least three frames from a video is needed to produce the background image assuming that for every pixel position, the background occurs in the majority of the videos. Furthermore, it can be performed for both grayscale and colored videos. == Assumptions == The camera is stationary. The light of the environment changes only slowly relative to the motions of the people in the scene. The number of people does not occupy the scene for most of the time at the same place. Generally, however, the algorithm will certainly work whenever the following single important assumption holds: For each pixel position, the majority of the pixel values in the entire video contain the pixel value of the actual background image (at that position).As long as each part of the background is shown in the majority of the video, the entire background image needs not to appear in any of its frames. The algorithm is expected to work accurately. == Background image generation == === Equations === For three frames of image sequence x 1 {\displaystyle x_{1}} , x 2 {\displaystyle x_{2}} , and x 3 {\displaystyle x_{3}} , the background image B {\displaystyle B} is obtained using B = x 3 ( x 1 ⊕ x 2 ) + x 1 x 2 {\displaystyle B=x_{3}(x_{1}\oplus x_{2})+x_{1}x_{2}} where ⊕ {\displaystyle \oplus } denotes the exclusive disjunctive bit operator. The Boolean mode function S {\displaystyle S} of the table occurs when the number of 1 entries is larger than half of the number of images such that S = { 1 , if ∑ i = 1 n x i ≥ ⌈ n 2 + 1 ⌉ , and n ≥ 3 0 , otherwise {\displaystyle S={\begin{cases}1,&{\text{if }}\sum _{i=1}^{n}x_{i}\geq \left\lceil {\frac {n}{2}}+1\right\rceil ,{\text{ and }}n\geq 3\\0,&{\text{otherwise}}\end{cases}}} For three images, the background image B {\displaystyle B} can be taken as the value x ¯ 1 x 2 x 3 + x 1 x ¯ 2 x 3 + x 1 x 2 x ¯ 3 + x 1 x 2 x 3 {\displaystyle {\bar {x}}_{1}x_{2}x_{3}+x_{1}{\bar {x}}_{2}x_{3}+x_{1}x_{2}{\bar {x}}_{3}+x_{1}x_{2}x_{3}} === Background generation algorithm === At the first level, three frames are selected at random from the image sequence to produce a background image by combining them using the first equation. This yields a better background image at the second level. The procedure is repeated until desired level L {\displaystyle L} . == Theoretical accuracy == At level ℓ {\displaystyle \ell } , the probability p ℓ {\displaystyle p_{\ell }} that the modal bit predicted is the actual modal bit is represented by the equation p ℓ = ( p ℓ − 1 ) 3 + 3 ( p ℓ − 1 ) 2 ( 1 − p ℓ − 1 ) {\displaystyle p_{\ell }=(p_{\ell -1})^{3}+3(p_{\ell -1})^{2}(1-p_{\ell -1})} . The table below gives the computed probability values across several levels using some specific initial probabilities. It can be observed that even if the modal bit at the considered position is at a low 60% of the frames, the probability of accurate modal bit determination is already more than 99% at 6 levels. == Space complexity == The space requirement of the Teknomo–Fernandez algorithm is given by the function O ( R F + R 3 L ) {\displaystyle O(RF+R3^{L})} , depending on the resolution R {\displaystyle R} of the image, the number F {\displaystyle F} of frames in the video, and the desired number L {\displaystyle L} of levels. However, the fact that L {\displaystyle L} will probably not exceed 6 reduces the space complexity to O ( R F ) {\displaystyle O(RF)} . == Time complexity == The entire algorithm runs in O ( R ) {\displaystyle O(R)} -time, only depending on the resolution of the image. Computing the modal bit for each bit can be done in O ( 1 ) {\displaystyle O(1)} -time while the computation of the resulting image from the three given images can be done in O ( R ) {\displaystyle O(R)} -time. The number of the images to be processed in L {\displaystyle L} levels is O ( 3 L ) {\displaystyle O(3^{L})} . However, since L ≤ 6 {\displaystyle L\leq 6} , then this is actually O ( 1 ) {\displaystyle O(1)} , thus the algorithm runs in O ( R ) {\displaystyle O(R)} . == Variants == A variant of the Teknomo–Fernandez algorithm that incorporates the Monte-Carlo method named CRF has been developed. Two different configurations of CRF were implemented: CRF9,2 and CRF81,1. Experiments on some colored video sequences showed that the CRF configurations outperform the TF algorithm in terms of accuracy. However, the TF algorithm remains more efficient in terms of processing time. == Applications == Object detection Face detection Face recognition Pedestrian detection Video surveillance Motion capture Human-computer interaction Content-based video coding Traffic monitoring Real-time gesture recognition
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Predictive Model Markup Language

The Predictive Model Markup Language (PMML) is an XML-based predictive model interchange format conceived by Robert Lee Grossman, then the director of the National Center for Data Mining at the University of Illinois at Chicago. PMML provides a way for analytic applications to describe and exchange predictive models produced by data mining and machine learning algorithms. It supports common models such as logistic regression and other feedforward neural networks. Version 0.9 was published in 1998. Subsequent versions have been developed by the Data Mining Group. Since PMML is an XML-based standard, the specification comes in the form of an XML schema. PMML itself is a mature standard with over 30 organizations having announced products supporting PMML. == PMML components == A PMML file can be described by the following components: Header: contains general information about the PMML document, such as copyright information for the model, its description, and information about the application used to generate the model such as name and version. It also contains an attribute for a timestamp which can be used to specify the date of model creation. Data Dictionary: contains definitions for all the possible fields used by the model. It is here that a field is defined as continuous, categorical, or ordinal (attribute optype). Depending on this definition, the appropriate value ranges are then defined as well as the data type (such as, string or double). Data Transformations: transformations allow for the mapping of user data into a more desirable form to be used by the mining model. PMML defines several kinds of simple data transformations. Normalization: map values to numbers, the input can be continuous or discrete. Discretization: map continuous values to discrete values. Value mapping: map discrete values to discrete values. Functions (custom and built-in): derive a value by applying a function to one or more parameters. Aggregation: used to summarize or collect groups of values. Model: contains the definition of the data mining model. E.g., A multi-layered feedforward neural network is represented in PMML by a "NeuralNetwork" element which contains attributes such as: Model Name (attribute modelName) Function Name (attribute functionName) Algorithm Name (attribute algorithmName) Activation Function (attribute activationFunction) Number of Layers (attribute numberOfLayers) This information is then followed by three kinds of neural layers which specify the architecture of the neural network model being represented in the PMML document. These attributes are NeuralInputs, NeuralLayer, and NeuralOutputs. Besides neural networks, PMML allows for the representation of many other types of models including support vector machines, association rules, Naive Bayes classifier, clustering models, text models, decision trees, and different regression models. Mining Schema: a list of all fields used in the model. This can be a subset of the fields as defined in the data dictionary. It contains specific information about each field, such as: Name (attribute name): must refer to a field in the data dictionary Usage type (attribute usageType): defines the way a field is to be used in the model. Typical values are: active, predicted, and supplementary. Predicted fields are those whose values are predicted by the model. Outlier Treatment (attribute outliers): defines the outlier treatment to be use. In PMML, outliers can be treated as missing values, as extreme values (based on the definition of high and low values for a particular field), or as is. Missing Value Replacement Policy (attribute missingValueReplacement): if this attribute is specified then a missing value is automatically replaced by the given values. Missing Value Treatment (attribute missingValueTreatment): indicates how the missing value replacement was derived (e.g. as value, mean or median). Targets: allows for post-processing of the predicted value in the format of scaling if the output of the model is continuous. Targets can also be used for classification tasks. In this case, the attribute priorProbability specifies a default probability for the corresponding target category. It is used if the prediction logic itself did not produce a result. This can happen, e.g., if an input value is missing and there is no other method for treating missing values. Output: this element can be used to name all the desired output fields expected from the model. These are features of the predicted field and so are typically the predicted value itself, the probability, cluster affinity (for clustering models), standard error, etc. The latest release of PMML, PMML 4.1, extended Output to allow for generic post-processing of model outputs. In PMML 4.1, all the built-in and custom functions that were originally available only for pre-processing became available for post-processing too. == PMML 4.0, 4.1, 4.2 and 4.3 == PMML 4.0 was released on June 16, 2009. Examples of new features included: Improved Pre-Processing Capabilities: Additions to built-in functions include a range of Boolean operations and an If-Then-Else function. Time Series Models: New exponential Smoothing models; also place holders for ARIMA, Seasonal Trend Decomposition, and Spectral density estimation, which are to be supported in the near future. Model Explanation: Saving of evaluation and model performance measures to the PMML file itself. Multiple Models: Capabilities for model composition, ensembles, and segmentation (e.g., combining of regression and decision trees). Extensions of Existing Elements: Addition of multi-class classification for Support Vector Machines, improved representation for Association Rules, and the addition of Cox Regression Models. PMML 4.1 was released on December 31, 2011. New features included: New model elements for representing Scorecards, k-Nearest Neighbors (KNN) and Baseline Models. Simplification of multiple models. In PMML 4.1, the same element is used to represent model segmentation, ensemble, and chaining. Overall definition of field scope and field names. A new attribute that identifies for each model element if the model is ready or not for production deployment. Enhanced post-processing capabilities (via the Output element). PMML 4.2 was released on February 28, 2014. New features include: Transformations: New elements for implementing text mining New built-in functions for implementing regular expressions: matches, concat, and replace Simplified outputs for post-processing Enhancements to Scorecard and Naive Bayes model elements PMML 4.3 was released on August 23, 2016. New features include: New Model Types: Gaussian Process Bayesian Network New built-in functions Usage clarifications Documentation improvements Version 4.4 was released in November 2019. == Release history == == Data Mining Group == The Data Mining Group is a consortium managed by the Center for Computational Science Research, Inc., a nonprofit founded in 2008. The Data Mining Group also developed a standard called Portable Format for Analytics, or PFA, which is complementary to PMML.
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VoID

The Vocabulary of Interlinked Datasets (VoID) is a vocabulary for providing concise summaries (metadata) of Resource Description Framework (RDF) datasets—meaningful collections of semantic triples—using the syntax of RDF Schema. It can be used for general metadata (such as information about the license of the dataset), access metadata (information about how to access the dataset), structural metadata (information about how the dataset is structured), and linking metadata (information about links between datasets). A linked dataset is a collection of data, published and maintained by a single provider, available as RDF on the Web, where at least some of the resources in the dataset are identified by dereferencable Uniform Resource Identifiers (URIs). VoID is used to provide metadata on RDF datasets to facilitate query processing on a graph of interlinked datasets in the Semantic Web.
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Frame (artificial intelligence)

Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations". They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence frame languages; they are stored as ontologies of sets. Frames are also an extensive part of knowledge representation and reasoning schemes. They were originally derived from semantic networks and are therefore part of structure-based knowledge representations. According to Russell and Norvig's Artificial Intelligence: A Modern Approach, structural representations assemble "facts about particular object and event types and [arrange] the types into a large taxonomic hierarchy analogous to a biological taxonomy". == Frame structure == The frame contains information on how to use the frame, what to expect next, and what to do when these expectations are not met. Some information in the frame is generally unchanged while other information, stored in "terminals", usually change. Terminals can be considered as variables. Top-level frames carry information, that is always true about the problem in hand, however, terminals do not have to be true. Their value might change with the new information encountered. Different frames may share the same terminals. Each piece of information about a particular frame is held in a slot. The information can contain: Facts or Data Values (called facets) Procedures (also called procedural attachments) IF-NEEDED: deferred evaluation IF-ADDED: updates linked information Default Values For Data For Procedures Other Frames or Subframes == Features and advantages == A frame's terminals are already filled with default values, which is based on how the human mind works. For example, when a person is told "a boy kicks a ball", most people will visualize a particular ball (such as a familiar soccer ball) rather than imagining some abstract ball with no attributes. One particular strength of frame-based knowledge representations is that, unlike semantic networks, they allow for exceptions in particular instances. This gives frames a degree of flexibility that allows representations to reflect real-world phenomena more accurately. Like semantic networks, frames can be queried using spreading activation. Following the rules of inheritance, any value given to a slot that is inherited by subframes will be updated (IF-ADDED) to the corresponding slots in the subframes and any new instances of a particular frame will feature that new value as the default. Because frames are based on structures, it is possible to generate a semantic network given a set of frames even though it lacks explicit arcs. References to Noam Chomsky and his generative grammar of 1950 are generally missing from Minsky's work. The simplified structures of frames allow for easy analogical reasoning, a much prized feature in any intelligent agent. The procedural attachments provided by frames also allow a degree of flexibility that makes for a more realistic representation and gives a natural affordance for programming applications. == Example == Worth noticing here is the easy analogical reasoning (comparison) that can be done between a boy and a monkey just by having similarly named slots. Also notice that Alex, an instance of a boy, inherits default values like "Sex" from the more general parent object Boy, but the boy may also have different instance values in the form of exceptions such as the number of legs. == Frame language == A frame language is a technology used for knowledge representation in artificial intelligence. They are similar to class hierarchies in object-oriented languages although their fundamental design goals are different. Frames are focused on explicit and intuitive representation of knowledge whereas objects focus on encapsulation and information hiding. Frames originated in AI research and objects primarily in software engineering. However, in practice, the techniques and capabilities of frame and object-oriented languages overlap significantly. === Example === A simple example of concepts modeled in a frame language is the Friend of A Friend (FOAF) ontology defined as part of the Semantic Web as a foundation for social networking and calendar systems. The primary frame in this simple example is a Person. Example slots are the person's email, home page, phone, etc. The interests of each person can be represented by additional frames describing the space of business and entertainment domains. The slot knows links each person with other persons. Default values for a person's interests can be inferred by the web of people they are friends of. === Implementations === The earliest frame-based languages were custom developed for specific research projects and were not packaged as tools to be re-used by other researchers. Just as with expert system inference engines, researchers soon realized the benefits of extracting part of the core infrastructure and developing general-purpose frame languages that were not coupled to specific applications. One of the first general-purpose frame languages was KRL. One of the most influential early frame languages was KL-ONE. KL-ONE spawned several subsequent Frame languages. One of the most widely used successors to KL-ONE was the Loom language developed by Robert MacGregor at the Information Sciences Institute. In the 1980s, Artificial Intelligence generated a great deal of interest in the business world fueled by expert systems. This led to the development of many commercial products for the development of knowledge-based systems. These early products were usually developed in Lisp and integrated constructs such as IF-THEN rules for logical reasoning with Frame hierarchies for representing data. One of the most well known of these early Lisp knowledge-base tools was the Knowledge Engineering Environment (KEE) from Intellicorp. KEE provided a full Frame language with multiple inheritance, slots, triggers, default values, and a rule engine that supported backward and forward chaining. As with most early commercial versions of AI software KEE was originally deployed in Lisp on Lisp machine platforms but was eventually ported to PCs and Unix workstations. The research agenda of the Semantic Web spawned a renewed interest in automatic classification and frame languages. An example is the Web Ontology Language (OWL) standard for describing information on the Internet. OWL is a standard to provide a semantic layer on top of the Internet. The goal is that rather than searching the web using keywords as most search engines (e.g. Google) do today, the web can be organized by concepts organized in an ontology, like a directory structure. The name of the OWL language itself provides a good example of the value of a Semantic Web. If one were to search for "OWL" using the Internet today most of the pages retrieved would be on the bird Owl rather than the standard OWL. With a Semantic Web it would be possible to specify the concept "Web Ontology Language" and the user would not need to worry about the various possible acronyms or synonyms as part of the search. Likewise, the user would not need to worry about homonyms crowding the search results with irrelevant data such as information about birds of prey as in this simple example. In addition to OWL, various standards and technologies that are relevant to the Semantic Web and were influenced by Frame languages include OIL and DAML. The Protege Open Source software tool from Stanford University provides an ontology editing capability that is built on OWL and has the full capabilities of a classifier. However it ceased to explicitly support frames as of version 3.5 (which is maintained for those preferring frame orientation), with the current version being 5.6.8 as of 2025. The justification for moving from explicit frames being that OWL DL is more expressive and "industry standard". === Comparison of frames and objects === Frame languages have a significant overlap with object-oriented languages. The terminologies and goals of the two communities were different but as they moved from the academic world and labs to the commercial world developers tended to not care about philosophical issues and focused primarily on specific capabilities, taking the best from either camp regardless of where the idea began. What both paradigms have in common is a desire to reduce the distance between concepts in the real world and their implementation in software. As such both paradigms arrived at the idea of representing the primary software objects in taxonomies starting with very general types and progressing to more specific types. The following table illustrates the correlation between standard terminology from the object-oriented and frame language communities: The primary difference between the two paradigms was in the degree that encapsulation was considered a majo
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Interlacing (bitmaps)

In computing, interlacing (also known as interleaving) is a method of encoding a bitmap image such that a person who has partially received it sees a degraded copy of the entire image. When communicating over a slow communications link, this is often preferable to seeing a perfectly clear copy of one part of the image, as it helps the viewer decide more quickly whether to abort or continue the transmission. Interlacing is supported by the following formats, where it is optional: GIF interlacing stores the lines in the order 0 , 8 , 16 , … , ( 8 n ) , 4 , 12 , … , ( 8 n + 4 ) , 2 , 6 , 10 , 14 , … , ( 4 n + 2 ) , 1 , 3 , 5 , 7 , 9 , … , ( 2 n + 1 ) . {\displaystyle 0,8,16,\dots ,(8n),\ 4,12,\dots ,(8n+4),\ 2,6,10,14,\dots ,(4n+2),\ 1,3,5,7,9,\dots ,(2n+1).} PNG uses the Adam7 algorithm, which interlaces in both the vertical and horizontal direction. TGA uses two optional interlacing algorithms: Two-way: 0 , 2 , 4 , … , ( 2 n ) , 1 , 3 , … , ( 2 n + 1 ) , {\displaystyle 0,2,4,\dots ,(2n),\ 1,3,\dots ,(2n+1),} And four-way: 0 , 4 , 8 , … , ( 4 n ) , 1 , 5 , … , ( 4 n + 1 ) , 2 , 6 , … , ( 4 n + 2 ) , 3 , 7 , … , ( 4 n + 3 ) . {\displaystyle 0,4,8,\dots ,(4n),\ 1,5,\dots ,(4n+1),\ 2,6,\dots ,\ (4n+2),3,7,\dots ,(4n+3).} JPEG, JPEG 2000, and JPEG XR (actually using a frequency decomposition hierarchy rather than interlacing of pixel values) PGF (also using a frequency decomposition) Interlacing is a form of incremental decoding, because the image can be loaded incrementally. Another form of incremental decoding is progressive scan. In progressive scan the loaded image is decoded line for line, so instead of becoming incrementally clearer it becomes incrementally larger. The main difference between the interlace concept in bitmaps and in video is that even progressive bitmaps can be loaded over multiple frames. For example: Interlaced GIF is a GIF image that seems to arrive on your display like an image coming through a slowly opening Venetian blind. A fuzzy outline of an image is gradually replaced by seven successive waves of bit streams that fill in the missing lines until the image arrives at its full resolution. Interlaced graphics were once widely used in web design and before that in the distribution of graphics files over bulletin board systems and other low-speed communications methods. The practice is much less common today, as common broadband internet connections allow most images to be downloaded to the user's screen nearly instantaneously, and interlacing is usually an inefficient method of encoding images. Interlacing has been criticized because it may not be clear to viewers when the image has finished rendering, unlike non-interlaced rendering, where progress is apparent (remaining data appears as blank). Also, the benefits of interlacing to those on low-speed connections may be outweighed by having to download a larger file, as interlaced images typically do not compress as well.
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New Classification Scheme for Chinese Libraries

The New Classification Scheme for Chinese Libraries is a system of library classification developed by Lai Yung-hsiang since 1956. It is modified from "A System of Book Classification for Chinese Libraries" of Liu Guojun, which is based on the Dewey Decimal System. The scheme is developed for Chinese books and commonly used in Taiwan, Hong Kong and Macau. == Main classes == 000 Generalities 100 Philosophy 200 Religion 300 Sciences 400 Applied sciences 500 Social sciences 600 History of China and Geography of China 700 World history and Geography 800 Linguistics and Literature 900 Arts == Outline of the classification tables == 000 Generalities 000 Special collections 010 Bibliography; Literacy (Documentation) 020 Library and information science; Archive management 030 Sinology 040 General encyclopedia 050 Serial publications; Periodicals 060 General organization; Museology 070 General collected essays 080 General series 090 Collected Chinese classics 100 Philosophy 100 Philosophy: general 110 Thought; Learning 120 Chinese philosophy 130 Oriental philosophy 140 Western philosophy 150 Logic 160 Metaphysics 170 Psychology 180 Esthetics (Aesthetics) 190 Ethics 200 Religion 200 Religion: general 210 Science of religion 220 Buddhism 230 Taoism 240 Christianity 250 Islam (Mohammedanism) 260 Judaism 270 Other religions 280 Mythology 290 Astrology; Superstition 300 Sciences 300 Sciences: general 310 Mathematics 320 Astronomy 330 Physics 340 Chemistry 350 Earth science; Geology 360 Biological science 370 Botany 380 Zoology 390 Anthropology 400 Applied sciences 400 Applied sciences: general 410 Medical sciences 420 Home economics 430 Agriculture 440 Engineering 450 Mining and metallurgy 460 Chemical engineering 470 Manufacture 480 Commerce: various business 490 Commerce: administration and management 500 Social sciences 500 Social sciences: general 510 Statistics 520 Education 530 Rite and custom 540 Sociology 550 Economy 560 Finance 570 Political science 580 Law; Jurisprudence 590 Military science 600-700 History and geography 600 History and geography: General History and geography of China 610 General history of China 620 Chinese history by period 630 History of Chinese civilization 640 Diplomatic history of China 650 Historical sources 660 Geography of China 670 Local history 680 Topical topography 690 Chinese travels World history and geography 710 World: general history and geography 720 Oceans and seas 730 Asia: history and geography 740 Europe: history and geography 750 America: history and geography 760 Africa: history and geography 770 Oceania: history and geography 780 Biography 790 Antiquities and archaeology 800 Linguistics and literature 800 Linguistics: general 810 Literature: general 820 Chinese literature 830 Chinese literature: general collections 840 Chinese literature: individual works 850 Various Chinese literature 860 Oriental literature 870 Western literature 880 Other countries literatures 890 Journalism 900 Arts 900 Arts: general 910 Music 920 Architecture 930 Sculpture 940 Drawing and painting; Calligraphy 950 Photography; Computer art 960 Decorative arts 970 Arts and Crafts movement 980 Theatre 990 Recreation and leisure
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Parents & Kids Safe AI Coalition

The Parents & Kids Safe AI Coalition is a political action committee that advocates for regulation of artificial intelligence on child safety. As of April 2026, the group is funded solely by the artificial intelligence company OpenAI, which pledged $10 million to the effort. == History == In October 2025, California Gov. Gavin Newsom vetoed Assembly Bill 1064. Sponsored by Common Sense Media, the bill would have introduced stronger child safety protections for AI chatbots. The following month, Common Sense Media founder Jim Steyer filed a ballot initiative intended to restore the "guardrails" lost in the veto. In response, OpenAI introduced a competing initiative. In January 2026, Common Sense Media and OpenAI announced that they would be working together on a compromise ballot initiative, the Parents & Kids Safe AI Act. Reporting indicated that initial outreach emails to child safety organizations failed to disclose OpenAI's involvement. Several advocacy groups signed an open letter claiming the initiative would shield AI companies from liability and undermine age verification, among other concerns. After Common Sense Media met with opposing groups in February, the ballot initiative was put on hold and the organizations involved sought to negotiate with the Legislature instead. The Parents & Kids Safe AI Coalition was founded to support this effort. In March 2026, the group reached out to some of the same groups contacted earlier, asking them to endorse its list of policy priorities. Again, some organizations reported being unaware of OpenAI's level of involvement. At least two groups withdrew from the coalition after learning about the financial ties. The priorities themselves were described as "vague but fairly uncontroversial" by The San Francisco Standard.
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