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NASA AI Assisted-Air Quality Monitoring Project

The NASA Expert-System Ion Trap Mass Spectrometer (ES-ITMS) Project was a public-private partnership to develop an artificial intelligence assisted, air quality monitoring system and was qualified for use on the Space Shuttle. The partnership was also the first cost and intellectual property shared public-partnership implemented by NASA, which used the commercial Research and Development Limited Partnership (RDLP) model that had been adopted by the Reagan Administration for Department of Defense semiconductor development, and recommended for use by NASA for space commercialization. The project partners included NASA, the University of Florida and Finnigan MAT Corporation, was organized and administered by the NASA Joint Enterprise Institute (subsequently NASA Joint Sponsored Program) and ran from 1988 through 1990. The partnership concluded final testing in 1991, generating four patents, expert system software and application protocol reports. The system was space qualified for use on the Shuttle and elements of the ES-ITMS system were integrated into the product Improvements for Finnigan MAT corporation. The success of the partnership lead NASA to create a pilot program to develop partnership business models as an ongoing management practice. == Purpose and objectives == The need to monitor air quality in confined spaces represented an increasing challenge for NASA's planned space missions and private sector facility managers facing the increased scrutiny of possible air contaminants. Up to the early 1980's, air quality monitors generally required large spaces and human technicians to interpret readings. This created a need for miniaturized air quality monitors that could generate reliable and accurate analytic results without on-site technician presence. NASA initiated projects to develop..."mobile and/or portable mass spectrometers" that evaluated the "tradeoff between instrumentation capabilities and space, weight and power considerations." NASA selected a "commercial ITMS instrument capable of generating electron ionization, chemical ionization and mass spectrometry data", to develop a linked expert system to accomplish analysis without human intervention. The commercial instrumentation was from Finnigan MAT corporation while the scientific expertise to support expert system development was available at the University of Florida. The project managers at NASA Ames created a single, integrated project using the RDLP model with objectives to: Develop AI/expert system software for instrument control (NASA's role) Expand sensitivity, selectivity and speed of the spectrometer (Univ Florida role) Expand the spectrometer analytic capability and automate the screening (Finnigan role) == Membership == The partnership included seven specialists from five member organizations: Federal Government National Aeronautics and Space Administration (NASA) NASA Ames Research Center (ARC) NASA Kennedy Space Center (KSC) Commercial Finnigan MAT Corporation (Thermo-Fisher Scientific) TGS Technology, Inc. Research Management University of Florida == Organization, management and administration == The technical project was organized into two development teams, one located in at the NASA Ames Research Center covering expert systems and analytic capabilities and one in Florida covering improved sensitivity and testing. The partnership management and administration was provided by a non-profit, partnership support organization: the Joint Enterprise Institute operating through San Francisco State University Foundation (SFSUF) with a NASA employee liaison, Syed Shariq. == Public-private partnership == The partnership structure was as a prototype test of a pilot NASA program to develop public-private partnership business models. The pilot program was known as the NASA Joint Sponsored Research Program (JSRP), which operated as the NASA Joint Enterprise Institute between 1988 and 1991. The partnership was the first public-private, research and development partnership implemented by NASA in response to national policy shifts to increase technology transfer and space commercialization. The partnership structure included a two year technology development and testing plan that cost $610,000, of which NASA funded $310,000, Finnigan $175,000 and the University of Florida $95,000. == Results and commercialization == The project generated patents (4), software (2) and application protocol reports (8). NASA gained use of the patents and jointly development software while Finnigan received commercial utilization rights. The results were commercialized within eighteen months of project completion. == Recognition == NASA recognized the project as a space qualified instrument. Its achievements were reported to the NASA Administrator, directly leading to establishment of the agency-wide Joint Sponsored Research Program.
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KL-ONE

KL-ONE (pronounced "kay ell won") is a knowledge representation system in the tradition of semantic networks and frames; that is, it is a frame language. The system is an attempt to overcome semantic indistinctness in semantic network representations and to explicitly represent conceptual information as a structured inheritance network. == Overview == There is a whole family of KL-ONE-like systems. One of the innovations that KL-ONE initiated was the use of a deductive classifier, an automated reasoning engine that can validate a frame ontology and deduce new information about the ontology based on the initial information provided by a domain expert. Frames in KL-ONE are called concepts. These form hierarchies using subsume-relations; in the KL-ONE terminology a super class is said to subsume its subclasses. Multiple inheritance is allowed. Actually a concept is said to be well-formed only if it inherits from more than one other concept. All concepts, except the top concept (usually THING), must have at least one super class. In KL-ONE descriptions are separated into two basic classes of concepts: primitive and defined. Primitives are domain concepts that are not fully defined. This means that given all the properties of a concept, this is not sufficient to classify it. They may also be viewed as incomplete definitions. Using the same view, defined concepts are complete definitions. Given the properties of a concept, these are necessary and sufficient conditions to classify the concept. The slot-concept is called roles and the values of the roles are role-fillers. There are several different types of roles to be used in different situations. The most common and important role type is the generic RoleSet that captures the fact that the role may be filled with more than one filler.
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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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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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Containerization (computing)

In software engineering, containerization is operating-system-level virtualization or application-level virtualization over multiple resources so that software applications can run in isolated user spaces called containers in any cloud or non-cloud environment, regardless of type or vendor. The term "container" has different meanings in different contexts, and it is important to ensure that the intended definition aligns with the audience's understanding. == Usage == Each container is basically a fully functional and portable cloud or non-cloud computing environment surrounding the application and keeping it independent of other environments running in parallel. Individually, each container simulates a different software application and runs isolated processes by bundling related configuration files, libraries and dependencies. But, collectively, multiple containers share a common operating system kernel (OS). In recent times, containerization technology has been widely adopted by cloud computing platforms like Amazon Web Services, Microsoft Azure, Google Cloud Platform, and IBM Cloud. Containerization has also been pursued by the U.S. Department of Defense as a way of more rapidly developing and fielding software updates, with first application in its F-22 air superiority fighter. == History == The concept of containerization in computing originated from early operating system–level isolation mechanisms. One of the earliest implementations was the chroot system call introduced in Version 7 Unix in 1979, which changed the apparent root directory for a process and its children, providing a basic form of filesystem isolation. In the early 2000s, more advanced forms of operating system–level virtualization were developed. FreeBSD introduced "jails" in 2000, which extended isolation by restricting processes to a subset of system resources. Around the same time, Solaris introduced "zones" (also known as Solaris Containers), providing similar capabilities with resource management and isolation features. Linux later incorporated comparable functionality through kernel features such as namespaces and control groups (cgroups), which enabled isolation of process IDs, network stacks, filesystems, and resource allocation. These features formed the foundation for Linux Containers (LXC), which provided a userspace interface for managing containers. The widespread adoption of containerization accelerated with the release of Docker in 2013, which introduced a standardized format for packaging applications and their dependencies, along with tooling for image distribution and container management. == Types of containers == OS containers Application containers == Security issues == Because of the shared OS, security threats can affect the whole containerized system. In containerized environments, security scanners generally protect the OS, but not the application containers, which adds unwanted vulnerability. == Container management, orchestration, clustering == Container orchestration or container management is mostly used in the context of application containers. Implementations providing such orchestration include Kubernetes and Docker swarm. == Container cluster management == Container clusters need to be managed. This includes functionality to create a cluster, to upgrade the software or repair it, balance the load between existing instances, scale by starting or stopping instances to adapt to the number of users, to log activities and monitor produced logs or the application itself by querying sensors. Open-source implementations of such software include OKD and Rancher. Quite a number of companies provide container cluster management as a managed service, like Alibaba, Amazon, Google, and Microsoft.
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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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Kaggle

Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC. Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. Kaggle has also facilitated the use of unethical and unreliable data in medical research. == History == Kaggle was founded by Anthony Goldbloom in April 2010. Jeremy Howard, one of the first Kaggle users, joined in November 2010 and served as the President and Chief Scientist. Also on the team was Nicholas Gruen serving as the founding chair. In 2011, the company raised $12.5 million and Max Levchin became the chairman. On March 8, 2017, Fei-Fei Li, Chief Scientist at Google, announced that Google was acquiring Kaggle. In June 2017, Kaggle surpassed 1 million registered users, and as of October 2023, it has over 15 million users in 194 countries. In 2022, founders Goldbloom and Hamner stepped down from their positions and D. Sculley became the CEO. In February 2023, Kaggle introduced Models, allowing users to discover and use pre-trained models through deep integrations with the rest of Kaggle’s platform. In April 2025, Kaggle partnered with Wikimedia Foundation. == Site overview == === Competitions === Many machine-learning competitions have been run on Kaggle since the company was founded. Notable competitions include gesture recognition for Microsoft Kinect, making a association football AI for Manchester City, coding a trading algorithm for Two Sigma Investments, and improving the search for the Higgs boson at CERN. The competition host prepares the data and a description of the problem; the host may choose whether it's going to be rewarded with money or be unpaid. Participants experiment with different techniques and compete against each other to produce the best models. Work is shared publicly through Kaggle Kernels to achieve a better benchmark and to inspire new ideas. Submissions can be made through Kaggle Kernels, via manual upload or using the Kaggle API. For most competitions, submissions are scored immediately (based on their predictive accuracy relative to a hidden solution file) and summarized on a live leaderboard. After the deadline passes, the competition host pays the prize money in exchange for "a worldwide, perpetual, irrevocable and royalty-free license [...] to use the winning Entry", i.e. the algorithm, software and related intellectual property developed, which is "non-exclusive unless otherwise specified". Alongside its public competitions, Kaggle also offers private competitions, which are limited to Kaggle's top participants. Kaggle offers a free tool for data science teachers to run academic machine-learning competitions. Kaggle also hosts recruiting competitions in which data scientists compete for a chance to interview at leading data science companies like Facebook, Winton Capital, and Walmart. Kaggle's competitions have resulted in successful projects such as furthering HIV research, chess ratings and traffic forecasting. Geoffrey Hinton and George Dahl used deep neural networks to win a competition hosted by Merck. Vlad Mnih (one of Hinton's students) used deep neural networks to win a competition hosted by Adzuna. This resulted in the technique being taken up by others in the Kaggle community. Tianqi Chen from the University of Washington also used Kaggle to show the power of XGBoost, which has since replaced Random Forest as one of the main methods used to win Kaggle competitions. Several academic papers have been published based on findings from Kaggle competitions. A contributor to this is the live leaderboard, which encourages participants to continue innovating beyond existing best practices. The winning methods are frequently written on the Kaggle Winner's Blog. === Progression system === Kaggle has implemented a progression system to recognize and reward users based on their contributions and achievements within the platform. This system consists of five tiers: Novice, Contributor, Expert, Master, and Grandmaster. Each tier is achieved by meeting specific criteria in competitions, datasets, kernels (code-sharing), and discussions. The highest tier, Kaggle Grandmaster, is awarded to users who have ranked at the top of multiple competitions including high ranking in a solo team. As of April 2, 2025, out of 23.29 million Kaggle accounts, 2,973 have achieved Kaggle Master status and 612 have achieved Kaggle Grandmaster status. === Kaggle Notebooks === Kaggle includes a free, browser-based online integrated development environment, called Kaggle Notebooks, designed for data science and machine learning. Users can write and execute code in Python or R, import datasets, use popular libraries, and train models on CPUs, GPUs, or TPUs directly in the cloud. This environment is often used for competition submissions, tutorials, education, and exploratory data analysis. == Medical Research Problems == In December 2025, an article was published in The Transmitter titled "Exclusive: Springer Nature retracts, removes nearly 40 publications that trained neural networks on ‘bonkers’ dataset". The dataset in question was uploaded to Kaggle containing photographs of autistic and non-autistic children's faces. This dataset contained more than 2,900 images and it is unlikely that these children or their families gave consent for the photos for use in medical research or the images were ethically approved for research. The articles using the dataset in Springer Nature were retracted from the scientific literature. At least 90 other publications cite a version of the dataset. In April 2026, another two datasets were identified on Kaggle with no data provenance having been published in Nature titled: "Dozens of AI disease-prediction models were trained on dubious data". These datasets were used in 124 clinical prediction models, at least two of which have been used in hospitals in Indonesia and Spain, while one article using the dataset was referenced in a medical device patent. As of April 17, 2026, three of the articles using these datasets have been retracted from the scientific literature. In May 2026, an additional research publication using two image datasets from Kaggle is under investigation in Scientific Reports. An article in Retraction Watch "‘Comically bad’ datasets used to train clinical models for stroke and diabetes" highlighted the images included famous actors such as Sylvester Stallone as Rambo, George Clooney, Angelina Jolie and Daniel Craig as well as children. It would be unethical for the use of these child images in medical research without consent. Reverse searching images saw some of the images were not for stroke but for bell's palsy. One of the datasets is no longer available on Kaggle while the other one still remains and mentions the images may be subject to copyright. Kaggle relies on the community self-reporting metadata and provenance and mentions the stroke and diabetes dataset identified in "Evidence of unreliable data and poor data provenance in clinical prediction model research and clinical practice" does not violate their terms of service and they would have been removed if they had.
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Sentential decision diagram

In artificial intelligence, a sentential decision diagram (SDD) is a type of knowledge representation used in knowledge compilation to represent Boolean functions. SDDs can be viewed as a generalization of the influential ordered binary decision diagram (OBDD) representation, by allowing decisions on multiple variables at once. Like OBDDs, SDDs allow for tractable Boolean operations, while being exponentially more succinct. For this reason, they have become an important representation in knowledge compilation. == Properties == SDDs are defined with respect to a generalization of variable ordering known as a variable tree (vtree). Provided that they satisfy additional properties known as compression and trimming (which are analogous to ROBDDs), SDDs are a canonical representation of Boolean functions; that is, they are unique given a vtree. Like OBDDs, they allow for operations such as conjunction, disjunction and negation to be computed directly on the representation in polynomial time, while being potentially more compact. They also allow for polynomial-time model counting. SDDs are known to be exponentially more succinct than OBDDs. == Applications == SDDs are used as a compilation target for probabilistic logic programs by the ProbLog 2 system since they support tractable (weighted) model counting as well as tractable negation, conjunction and disjunction while being more succinct than BDDs. SDDs have also been extended to model probability distributions, in which context they are known as probabilistic sentential decision diagrams (PSDD).
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Easyrec

easyrec is an open-source program that provides personalized recommendations using RESTful Web services to be integrated into Web enabled applications. It is distributed under the GNU General Public License by the Studio Smart Agent Technologies and hosted at SourceForge. It is written in Java, uses a MySQL database and comes with an administration tool. == History == The development of easyrec, an implementation of the Adaptive Personalization approach, started in the course of several research and development projects conducted by the Studio Smart Agent Technologies in close cooperation with international companies. During the year of 2008 the core functionality of easyrec was developed forming the basis of research prototypes focusing on the music domain (e.g. MusicExplorer). In June 2009 a beta version of easyrec, containing basic administration features, was integrated into a movie streaming portal for evaluation purposes. Furthermore, in September 2009 easyrec was awarded a special recognition in the category “Award for Innovations – IT Innovations for an economic upswing” by the jury of the Austrian state prize for multimedia and e-business. After a comprehensive refactoring phase and the integration of the evaluation results easyrec was published on SourceForge on 18 February 2010. In course of the CeBIT tradeshow 2011 in Hanover easyrec has been awarded the German “INNOVATIONSPREIS-IT 2011”. == Principles == The following five primary goals guided the development of easyrec. It should be a ready-to-use application, not another algorithmic framework It should be easy to use, concerning installation, integration and administration It should be robust and scalable for serving real world applications It should be free of charge, so that anyone can profit from personalization features It should rely on a community-driven development == Uses == Although easyrec is a domain-agnostic, general purpose personalization system, the current Web service API is customized for providing online shops with item recommendations. Especially for small and medium enterprises, easyrec provides a low barrier entrance to personalization. == Features == A major feature of easyrec is a set of usage statistics and other business relevant information presented via an administration and management interface. Furthermore, the easyrec administrator is supported by a variety of administration and configuration functions including the manual import or adaptation of business rules. Integrators or developers benefit from the lightweight Web service APIs (REST and SOAP) as well as from the guided installation wizard. Concerning personalization functionality easyrec is providing the following services unpersonalized recommendations of the form "other users also bought/viewed/...", etc. personalized recommendation depending on individual preferences rankings such as "most bought items", "most viewed...", etc. Additionally, as an integration showcase, a MediaWiki extension was developed and is bundled with the application. Currently additional features like further recommender algorithms and a plugin-system are evaluated and prepared for integration into the easyrec system. == Architecture == The underlying architecture of easyrec is designed to be robust and scalable—separating time-consuming computations from the task of online assembling of recommendations. easyrec is designed as a multi-layer system consisting of a database layer as storage of user actions and pre-calculated business rules an application layer for hosting online and offline recommendation services and an API layer for various Web service interfaces. Moreover, the generator server contains different item association generators which create business rules that define a relation between two items.
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Imageability

Imageability is a measure of how easily a physical object, word or environment will evoke a clear mental image in the mind of any person observing it. It is used in architecture and city planning, in psycholinguistics, and in automated computer vision research. In automated image recognition, training models to connect images with concepts that have low imageability can lead to biased and harmful results. == History and components == Kevin A. Lynch first introduced the term, "imageability" in his 1960 book, The Image of the City. In the book, Lynch argues cities contain a key set of physical elements that people use to understand the environment, orient themselves inside of it, and assign it meaning. Lynch argues the five key elements that impact the imageability of a city are Paths, Edges, Districts, Nodes, and Landmarks. Paths: channels in which people travel. Examples: streets, sidewalks, trails, canals, railroads. Edges: objects that form boundaries around space. Examples: walls, buildings, shoreline, curbstone, streets, and overpasses. Districts: medium to large areas people can enter into and out of that have a common set of identifiable characteristics. Nodes: large areas people can enter, that serve as the foci of the city, neighborhood, district, etc. Landmarks: memorable points of reference people cannot enter into. Examples: signs, mountains and public art. In 1914, half a century before The Image of the City was published, Paul Stern discussed a concept similar to imageability in the context of art. Stern, in Susan Langer's Reflections on Art, names the attribute that describes how vividly and intensely an artistic object could be experienced apparency. == In computer vision == Automated image recognition was developed by using machine learning to find patterns in large, annotated datasets of photographs, like ImageNet. Images in ImageNet are labelled using concepts in WordNet. Concepts that are easily expressed verbally, like "early", are seen as less "imageable" than nouns referring to physical objects like "leaf". Training AI models to associate concepts with low imageability with specific images can lead to problematic bias in image recognition algorithms. This has particularly been critiqued as it relates to the "person" category of WordNet and therefore also ImageNet. Trevor Pagan and Kate Crawford demonstrated in their essay "Excavating AI" and their art project ImageNet Roulette how this leads to photos of ordinary people being labelled by AI systems as "terrorists" or "sex offenders". Images in datasets are often labelled as having a certain level of imageability. As described by Kaiyu Yang, Fei-Fei Li and co-authors, this is often done following criteria from Allan Paivio and collaborators' 1968 psycholinguistic study of nouns. Yang el.al. write that dataset annotators tasked with labelling imageability "see a list of words and rate each word on a 1-7 scale from 'low imagery' to 'high imagery'. To avoid biased or harmful image recognition and image generation, Yang et.al. recommend not training vision recognition models on concepts with low imageability, especially when the concepts are offensive (such as sexual or racial slurs) or sensitive (their examples for this category include "orphan", "separatist", "Anglo-Saxon" and "crossover voter"). Even "safe" concepts with low imageability, like "great-niece" or "vegetarian" can lead to misleading results and should be avoided.
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Linde–Buzo–Gray algorithm

The Linde–Buzo–Gray algorithm (named after its creators Yoseph Linde, Andrés Buzo and Robert M. Gray, who designed it in 1980) is an iterative vector quantization algorithm to improve a small set of vectors (codebook) to represent a larger set of vectors (training set), such that it will be locally optimal. It combines Lloyd's Algorithm with a splitting technique in which larger codebooks are built from smaller codebooks by splitting each code vector in two. The core idea of the algorithm is that by splitting the codebook such that all code vectors from the previous codebook are present, the new codebook must be as good as the previous one or better. == Description == The Linde–Buzo–Gray algorithm may be implemented as follows: algorithm linde-buzo-gray is input: set of training vectors training, codebook to improve old-codebook output: codebook that is twice the size and better or as good as old-codebook new-codebook ← {} for each old-codevector in old-codebook do insert old-codevector into new-codebook insert old-codevector + 𝜖 into new-codebook where 𝜖 is a small vector return lloyd(new-codebook, training) algorithm lloyd is input: codebook to improve, set of training vectors training output: improved codebook do previous-codebook ← codebook clusters ← divide training into |codebook| clusters, where each cluster contains all vectors in training who are best represented by the corresponding vector in codebook for each cluster cluster in clusters do the corresponding code vector in codebook ← the centroid of all training vectors in cluster while difference in error representing training between codebook and previous-codebook > 𝜖 return codebook
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AI-assisted virtualization software

AI-assisted virtualization software is a type of technology that combines the principles of virtualization with advanced artificial intelligence (AI) algorithms. This software is designed to improve efficiency and management of virtual environments and resources. This technology has been used in cloud computing and for various industries. == History == Virtualization originated in mainframe computers in the 1960s in order to divide system resources between different applications. The term has since broadened. The use of AI in virtualization significantly increased in the early 2020s. == Uses == AI-assisted virtualization software uses AI-related technology such as machine learning, deep learning, and neural networks to attempt to make more accurate predictions and decisions regarding the management of virtual environments. Features include intelligent automation, predictive analytics, and dynamic resource allocation. Intelligent Automation: Automating tasks such as resource provisioning and routine maintenance. The AI learns from ongoing operations and can predict and perform necessary tasks autonomously. Predictive Analytics: Utilizing AI to analyze data patterns and trends, predicting future issues or resource requirements. It aids in proactive management and mitigation of potential problems. Dynamic Resource Allocation: Through the analysis of real-time and historical data, the AI system dynamically assigns resources based on demand and need, optimizing overall system performance and reducing wastage. AI-assisted virtualization software has been used in cloud computing to optimize the use of resources and reduce costs. In healthcare, these technologies have been used to create virtual patient profiles. They are also used in data centers to improve performance and energy efficiency. It has also been used in network function virtualization (NFV) to improve virtual network infrastructure. Implementing this type of software requires a high degree of technological sophistication and can incur significant costs. There are also concerns about the risks associated with AI, such as algorithmic bias and security vulnerabilities. Additionally, there are issues related to governance, the ethics of artificial intelligence, and regulations of AI technologies.
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Content determination

Content determination is the subtask of natural language generation (NLG) that involves deciding on the information to be communicated in a generated text. It is closely related to the task of document structuring. == Example == Consider an NLG system which summarises information about sick babies. Suppose this system has four pieces of information it can communicate The baby is being given morphine via an IV drop The baby's heart rate shows bradycardia's (temporary drops) The baby's temperature is normal The baby is crying Which of these bits of information should be included in the generated texts? == Issues == There are three general issues which almost always impact the content determination task, and can be illustrated with the above example. Perhaps the most fundamental issue is the communicative goal of the text, i.e. its purpose and reader. In the above example, for instance, a doctor who wants to make a decision about medical treatment would probably be most interested in the heart rate bradycardias, while a parent who wanted to know how her child was doing would probably be more interested in the fact that the baby was being given morphine and was crying. The second issue is the size and level of detail of the generated text. For instance, a short summary which was sent to a doctor as a 160 character SMS text message might only mention the heart rate bradycardias, while a longer summary which was printed out as a multipage document might also mention the fact that the baby is on a morphine IV. The final issue is how unusual and unexpected the information is. For example, neither doctors nor parents would place a high priority on being told that the baby's temperature was normal, if they expected this to be the case. Regardless, content determination is very important to users, indeed in many cases the quality of content determination is the most important factor (from the user's perspective) in determining the overall quality of the generated text. == Techniques == There are three basic approaches to document structuring: schemas (content templates), statistical approaches, and explicit reasoning. Schemas are templates which explicitly specify the content of a generated text (as well as document structuring information). Typically, they are constructed by manually analysing a corpus of human-written texts in the target genre, and extracting a content template from these texts. Schemas work well in practice in domains where content is somewhat standardised, but work less well in domains where content is more fluid (such as the medical example above). Statistical techniques use statistical corpus analysis techniques to automatically determine the content of the generated texts. Such work is in its infancy, and has mostly been applied to contexts where the communicative goal, reader, size, and level of detail are fixed. For example, generation of newswire summaries of sporting events. Explicit reasoning approaches have probably attracted the most attention from researchers. The basic idea is to use AI reasoning techniques (such as knowledge-based rules, planning, pattern detection, case-based reasoning, etc.) to examine the information available to be communicated (including how unusual/unexpected it is), the communicative goal and reader, and the characteristics of the generated text (including target size), and decide on the optimal content for the generated text. A very wide range of techniques has been explored, but there is no consensus as to which is most effective.
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SERVQUAL

SERVQUAL is a research tool that measures customer perception of service quality by comparing what customers expect from a service to their assessment of the service actually delivered. The instrument was developed in the United States in the mid-1980s by researchers A. Parasuraman, Valarie Zeithaml, and Leonard L. Berry, and is designed for use in after-service evaluation processes. It assesses service quality across five dimensions: reliability, assurance, tangibles, empathy, and responsiveness. SERVQUAL has been applied in sectors including healthcare, banking, education, and libraries. == Overview == The SERVQUAL questionnaire consists of matched pairs of items, 22 expectation items and 22 perception items, organized into five dimensions that correspond to the consumer's mental framework for evaluating service quality. Each item is part of a pair: one question asks what excellent organizations in a given industry should offer (expectation), and the other asks how the specific organization being evaluated performs (perception). == The model of service quality == The model of service quality, referred to as the gaps model, was developed by Parasuraman, Zeithaml, and Berry during a systematic research program conducted in the 1980s. The model identifies five gaps that may cause customers to experience poor service quality. In this framework, gap 5 is the service quality gap, which represents the difference between customer expectations and their perceptions of the service. This is the only gap that can be directly measured, and the SERVQUAL instrument was designed specifically to capture it. Gaps 1 through 4 have diagnostic value and point to probable causes of service failures. == Development of the instrument == Development of the model of service quality began in 1983 and, after iterative refinements, led to the publication of the SERVQUAL instrument in 1988. The research team conducted in-depth interviews and focus groups in four service sectors: retail banking, credit card services, securities brokerage, and product repair and maintenance. The questionnaire was tested across multiple samples to verify its reliability, validity, and factor structure. == Adaptations and variants == SERVQUAL has been adapted for specific industries and contexts. Well‑known derivatives include: LibQUAL+ – a library service quality survey developed by the Association of Research Libraries. EDUQUAL – an instrument tailored for the evaluation of service quality in educational institutions. HEALTHQUAL – adapted for measuring patient perceptions of healthcare service quality. ARTSQUAL – used to evaluate visitor perceptions of quality in museums and performing arts venues. == Criticisms == Researchers have raised several concerns about SERVQUAL. Critics argue that the instrument's definition of expectations is ambiguous and that it does not adequately account for the dynamic nature of customer expectations over time. Other scholars question whether the five‑dimension structure is universally applicable across all service contexts, and whether a generic instrument can capture the unique attributes of specific industries without modification.
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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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