AI Analytics Masters

AI Analytics Masters — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Lossless join decomposition

  In database design, a lossless join decomposition is a decomposition of a relation r {\displaystyle r} into relations r 1 , r 2 {\displaystyle r_{1},r_{2}} such that a natural join of the two smaller relations yields back the original relation. This is central in removing redundancy safely from databases while preserving the original data. Lossless join can also be called non-additive. == Definition == A relation r {\displaystyle r} on schema R {\displaystyle R} decomposes losslessly onto schemas R 1 {\displaystyle R_{1}} and R 2 {\displaystyle R_{2}} if π R 1 ( r ) ⋈ π R 2 ( r ) = r {\displaystyle \pi _{R_{1}}(r)\bowtie \pi _{R_{2}}(r)=r} , that is r {\displaystyle r} is the natural join of its projections onto the smaller schemas. A pair ( R 1 , R 2 ) {\displaystyle (R_{1},R_{2})} is a lossless-join decomposition of R {\displaystyle R} or said to have a lossless join with respect to a set of functional dependencies F {\displaystyle F} if any relation r ( R ) {\displaystyle r(R)} that satisfies F {\displaystyle F} decomposes losslessly onto R 1 {\displaystyle R_{1}} and R 2 {\displaystyle R_{2}} . Decompositions into more than two schemas can be defined in the same way. == Criteria == A decomposition R = R 1 ∪ R 2 {\displaystyle R=R_{1}\cup R_{2}} has a lossless join with respect to F {\displaystyle F} if and only if the closure of R 1 ∩ R 2 {\displaystyle R_{1}\cap R_{2}} includes R 1 ∖ R 2 {\displaystyle R_{1}\setminus R_{2}} or R 2 ∖ R 1 {\displaystyle R_{2}\setminus R_{1}} . In other words, one of the following must hold: ( R 1 ∩ R 2 ) → ( R 1 ∖ R 2 ) ∈ F + {\displaystyle (R_{1}\cap R_{2})\to (R_{1}\setminus R_{2})\in F^{+}} ( R 1 ∩ R 2 ) → ( R 2 ∖ R 1 ) ∈ F + {\displaystyle (R_{1}\cap R_{2})\to (R_{2}\setminus R_{1})\in F^{+}} === Criteria for multiple sub-schemas === Multiple sub-schemas R 1 , R 2 , . . . , R n {\displaystyle R_{1},R_{2},...,R_{n}} have a lossless join if there is some way in which we can repeatedly perform lossless joins until all the schemas have been joined into a single schema. Once we have a new sub-schema made from a lossless join, we are not allowed to use any of its isolated sub-schema to join with any of the other schemas. For example, if we can do a lossless join on a pair of schemas R i , R j {\displaystyle R_{i},R_{j}} to form a new schema R i , j {\displaystyle R_{i,j}} , we use this new schema (rather than R i {\displaystyle R_{i}} or R j {\displaystyle R_{j}} ) to form a lossless join with another schema R k {\displaystyle R_{k}} (which may already be joined (e.g., R k , l {\displaystyle R_{k,l}} )). == Example == Let R = { A , B , C , D } {\displaystyle R=\{A,B,C,D\}} be the relation schema, with attributes A, B, C and D. Let F = { A → B C } {\displaystyle F=\{A\rightarrow BC\}} be the set of functional dependencies. Decomposition into R 1 = { A , B , C } {\displaystyle R_{1}=\{A,B,C\}} and R 2 = { A , D } {\displaystyle R_{2}=\{A,D\}} is lossless under F because R 1 ∩ R 2 = A {\displaystyle R_{1}\cap R_{2}=A} and we have a functional dependency A → B C {\displaystyle A\rightarrow BC} . In other words, we have proven that ( R 1 ∩ R 2 → R 1 ∖ R 2 ) ∈ F + {\displaystyle (R_{1}\cap R_{2}\rightarrow R_{1}\setminus R_{2})\in F^{+}} .

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  Color balance

  In photography and image processing, color balance is the global adjustment of the intensities of the colors (typically red, green, and blue primary colors). An important goal of this adjustment is to render specific colors – particularly neutral colors like white or grey – correctly. Hence, the general method is sometimes called gray balance, neutral balance, or white balance. Color balance changes the overall mixture of colors in an image and is used for color correction. Generalized versions of color balance are used to correct colors other than neutrals or to deliberately change them for effect. White balance is one of the most common kinds of balancing, and is when colors are adjusted to make a white object (such as a piece of paper or a wall) appear white and not a shade of any other colour. Image data acquired by sensors – either film or electronic image sensors – must be transformed from the acquired values to new values that are appropriate for color reproduction or display. Several aspects of the acquisition and display process make such color correction essential – including that the acquisition sensors do not match the sensors in the human eye, that the properties of the display medium must be accounted for, and that the ambient viewing conditions of the acquisition differ from the display viewing conditions. The color balance operations in popular image editing applications usually operate directly on the red, green, and blue channel pixel values, without respect to any color sensing or reproduction model. In film photography, color balance is typically achieved by using color correction filters over the lights or on the camera lens. == Generalized color balance == Sometimes the adjustment to keep neutrals neutral is called white balance, and the phrase color balance refers to the adjustment that in addition makes other colors in a displayed image appear to have the same general appearance as the colors in an original scene. It is particularly important that neutral (gray, neutral, white) colors in a scene appear neutral in the reproduction. === Psychological color balance === Humans relate to flesh tones more critically than other colors. Trees, grass and sky can all be off without concern, but if human flesh tones are 'off' then the human subject can look sick or dead. To address this critical color balance issue, the tri-color primaries themselves are formulated to not balance as a true neutral color. The purpose of this color primary imbalance is to more faithfully reproduce the flesh tones through the entire brightness range. == Illuminant estimation and adaptation == Most digital cameras have means to select color correction based on the type of scene lighting, using either manual lighting selection, automatic white balance, or custom white balance. The algorithms for these processes perform generalized chromatic adaptation. Many methods exist for color balancing. Setting a button on a camera is a way for the user to indicate to the processor the nature of the scene lighting. Another option on some cameras is a button which one may press when the camera is pointed at a gray card or other neutral colored object. This captures an image of the ambient light, which enables a digital camera to set the correct color balance for that light. There is a large literature on how one might estimate the ambient lighting from the camera data and then use this information to transform the image data. A variety of algorithms have been proposed, and the quality of these has been debated. A few examples and examination of the references therein will lead the reader to many others. Examples are Retinex, an artificial neural network or a Bayesian method. == Chromatic colors == Color balancing an image affects not only the neutrals, but other colors as well. An image that is not color balanced is said to have a color cast, as everything in the image appears to have been shifted towards one color. Color balancing may be thought in terms of removing this color cast. Color balance is also related to color constancy. Algorithms and techniques used to attain color constancy are frequently used for color balancing, as well. Color constancy is, in turn, related to chromatic adaptation. Conceptually, color balancing consists of two steps: first, determining the illuminant under which an image was captured; and second, scaling the components (e.g., R, G, and B) of the image or otherwise transforming the components so they conform to the viewing illuminant. Viggiano found that white balancing in the camera's native RGB color model tended to produce less color inconstancy (i.e., less distortion of the colors) than in monitor RGB for over 4000 hypothetical sets of camera sensitivities. This difference typically amounted to a factor of more than two in favor of camera RGB. This means that it is advantageous to get color balance right at the time an image is captured, rather than edit later on a monitor. If one must color balance later, balancing the raw image data will tend to produce less distortion of chromatic colors than balancing in monitor RGB. == Mathematics of color balance == Color balancing is sometimes performed on a three-component image (e.g., RGB) using a 3x3 matrix. This type of transformation is appropriate if the image was captured using the wrong white balance setting on a digital camera, or through a color filter. Changing the color balance of an image can improve classifier results on a trained ML model. === Scaling monitor R, G, and B === In principle, one wants to scale all relative luminances in an image so that objects which are believed to be neutral appear so. If, say, a surface with R = 240 {\displaystyle R=240} was believed to be a white object, and if 255 is the count which corresponds to white, one could multiply all red values by 255/240. Doing analogously for green and blue would result, at least in theory, in a color balanced image. In this type of transformation the 3x3 matrix is a diagonal matrix. [ R G B ] = [ 255 / R w ′ 0 0 0 255 / G w ′ 0 0 0 255 / B w ′ ] [ R ′ G ′ B ′ ] {\displaystyle \left[{\begin{array}{c}R\\G\\B\end{array}}\right]=\left[{\begin{array}{ccc}255/R'_{w}&0&0\\0&255/G'_{w}&0\\0&0&255/B'_{w}\end{array}}\right]\left[{\begin{array}{c}R'\\G'\\B'\end{array}}\right]} where R {\displaystyle R} , G {\displaystyle G} , and B {\displaystyle B} are the color balanced red, green, and blue components of a pixel in the image; R ′ {\displaystyle R'} , G ′ {\displaystyle G'} , and B ′ {\displaystyle B'} are the red, green, and blue components of the image before color balancing, and R w ′ {\displaystyle R'_{w}} , G w ′ {\displaystyle G'_{w}} , and B w ′ {\displaystyle B'_{w}} are the red, green, and blue components of a pixel which is believed to be a white surface in the image before color balancing. This is a simple scaling of the red, green, and blue channels, and is why color balance tools in Photoshop have a white eyedropper tool. It has been demonstrated that performing the white balancing in the phosphor set assumed by sRGB tends to produce large errors in chromatic colors, even though it can render the neutral surfaces perfectly neutral. === Scaling X, Y, Z === If the image may be transformed into CIE XYZ tristimulus values, the color balancing may be performed there. This has been termed a "wrong von Kries" transformation. Although it has been demonstrated to offer usually poorer results than balancing in monitor RGB, it is mentioned here as a bridge to other things. Mathematically, one computes: [ X Y Z ] = [ X w / X w ′ 0 0 0 Y w / Y w ′ 0 0 0 Z w / Z w ′ ] [ X ′ Y ′ Z ′ ] {\displaystyle \left[{\begin{array}{c}X\\Y\\Z\end{array}}\right]=\left[{\begin{array}{ccc}X_{w}/X'_{w}&0&0\\0&Y_{w}/Y'_{w}&0\\0&0&Z_{w}/Z'_{w}\end{array}}\right]\left[{\begin{array}{c}X'\\Y'\\Z'\end{array}}\right]} where X {\displaystyle X} , Y {\displaystyle Y} , and Z {\displaystyle Z} are the color-balanced tristimulus values; X w {\displaystyle X_{w}} , Y w {\displaystyle Y_{w}} , and Z w {\displaystyle Z_{w}} are the tristimulus values of the viewing illuminant (the white point to which the image is being transformed to conform to); X w ′ {\displaystyle X'_{w}} , Y w ′ {\displaystyle Y'_{w}} , and Z w ′ {\displaystyle Z'_{w}} are the tristimulus values of an object believed to be white in the un-color-balanced image, and X ′ {\displaystyle X'} , Y ′ {\displaystyle Y'} , and Z ′ {\displaystyle Z'} are the tristimulus values of a pixel in the un-color-balanced image. If the tristimulus values of the monitor primaries are in a matrix P {\displaystyle \mathbf {P} } so that: [ X Y Z ] = P [ L R L G L B ] {\displaystyle \left[{\begin{array}{c}X\\Y\\Z\end{array}}\right]=\mathbf {P} \left[{\begin{array}{c}L_{R}\\L_{G}\\L_{B}\end{array}}\right]} where L R {\displaystyle L_{R}} , L G {\displaystyle L_{G}} , and L B {\displaystyle L_{B}} are the un-gamma corrected monitor RGB, one may use: [ L R L G L B ] = P − 1 [ X w / X w ′ 0 0

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  Bring your own encryption

  Bring your own encryption (BYOE), also known as bring your own key (BYOK), is a cloud computing security model that allows cloud service customers to use their own encryption software and manage their own encryption keys. == Overview == BYOE enables cloud service customers to utilize a virtual instance of their encryption software alongside their cloud-hosted business applications to encrypt their data. In this model, hosted business applications are configured to process all data through the encryption software. This software then writes the ciphertext version of the data to the cloud service provider's physical data store and decrypts ciphertext data upon retrieval requests. This approach provides enterprises with control over their keys and the ability to generate their own master key using internal hardware security modules (HSM), which are then transmitted to the cloud provider's HSM. When the data is no longer needed, such as when users discontinue the cloud service, the keys can be deleted, rendering the encrypted data permanently inaccessible. This practice is known as crypto-shredding. == Potential Advantages == Organizations can store data with unique encryption that only they can access. Multiple organizations can share the same hardware infrastructure via cloud services like Amazon Web Services (AWS) or Google Cloud while maintaining encryption to comply with regulations such as HIPAA. == Potential Challenges == Resource utilization may be higher compared to traditional encryption practices when multiple users share the same hardware and use their own encryption. Efforts to minimize resource utilization issues may potentially impact security benefits.

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  Mastodon (social network)

  Mastodon is a free and open-source software platform for decentralized social networking with microblogging features similar to Twitter. It operates as a federated network of independently managed servers that communicate using the ActivityPub protocol, allowing users to connect across different instances within the Fediverse. Each Mastodon instance establishes its own moderation policies and content guidelines, distinguishing it from centrally controlled social media platforms. First released in 2016 by Eugen Rochko, Mastodon has positioned itself as an alternative to mainstream social media, particularly for users seeking decentralized, community-driven spaces. The platform has experienced multiple surges in adoption, most notably following the Twitter acquisition by Elon Musk in 2022, as users sought alternatives to Twitter. It is part of a broader shift toward decentralized social networks, including Bluesky and Lemmy. Mastodon emphasizes user privacy and moderation flexibility, offering features such as granular post visibility controls, content warning options, and local community-driven moderation. The software is written in Ruby on Rails and Node.js, with a web interface built using React and Redux. It is interoperable with other ActivityPub-based platforms, such as Threads, and supports various third-party applications on desktop and mobile devices. == Functionality == Users post short-form status messages, historically known as "toots", for others to see and interact with. On a standard Mastodon instance, these messages can include up to 500 text-based characters, greater than Twitter's 280-character limit. Some instances support even longer messages. Images, audio files, videos or polls can also be added to a message. Users join a specific Mastodon server, rather than a single centralized website or application. The servers are connected as nodes in a network, and each server can administer its own rules, account privileges, and whether to share messages to and from other servers. Users can communicate and follow each other across connected Mastodon servers with usernames similar in format to full email addresses. Since version 2.9.0, Mastodon's web user interface has offered a single-column mode for new users by default. In advanced mode, the interface approximates the microblogging interface of TweetDeck. === Privacy === Mastodon includes a number of specific privacy features. Each message has a variety of privacy options available, and users can choose whether the message is public or private. Messages can display public on a global feed, known as a timeline, or can be shared only to the user's followers. Messages can also be marked as unlisted from timelines or direct between users. Users can also mark their accounts as completely private. In the timeline, messages can display with an optional content warning feature, which requires readers to click on the hidden main body of the message to reveal it. Mastodon servers have used this feature to hide spoilers, trigger warnings, and not safe for work (NSFW) content, though some accounts use the feature to hide links and thoughts others might not want to read. Mastodon aggregates messages in local and federated timelines in real time. The local timeline shows messages from users on a singular server, while the federated timeline shows messages across all participating Mastodon servers. === Content moderation === In early 2017, journalists like Sarah Jeong distinguished Mastodon from Twitter for its approach to combating harassment. Mastodon uses community-based moderation, in which each server can limit or filter out undesirable types of content, while Twitter uses a single, global policy on content moderation. Servers can choose to limit or filter out messages with disparaging content. The founder of Mastodon, Eugen Rochko, believes that small, closely related communities deal with unwanted behavior more effectively than a large company's small safety team. In Move Slowly and Build Bridges, Robert W. Gehl argues that predominantly white participation has shaped Mastodon in ways that affect how reports of racism are received and limit its ability to replicate Black Twitter on Twitter. Users can also block and report others to administrators, much like on Twitter. Instance administrators can block other instances from interacting with their own, an action called defederation. By posting toots hashtagged with #fediblock, some instance administrators and users alert others of issues requiring moderation. === Searching === Mastodon by default allows searching for hashtags and mentioned accounts in the Fediverse. Server administrators can optionally enable Elasticsearch to search the full-text of public posts that have opted in to being indexed. == Versions == In September 2018, with the release of version 2.5 with redesigned public profile pages, Mastodon marked its 100th release. Mastodon 2.6 was released in October 2018, introducing the possibilities of verified profiles and live, in-stream link previews for images and videos. Version 2.7, in January 2019, made it possible to search for multiple hashtags at once, instead of searching for just a single hashtag, with more robust moderation capabilities for server administrators and moderators, while accessibility, such as contrast for users with sight issues, was improved. The ability for users to create and vote in polls, as well as a new invitation system to manage registrations was integrated in April 2019. Mastodon 2.8.1, released in May 2019, made images with content warnings blurred instead of completely hidden. In version 2.9 in June 2019, an optional single-column view was added. This view became the default displayed to new users, with a user "preferences" option to switch to a multiple-column-based view. In August 2020, Mastodon 3.2 was released. It included a redesigned audio player with custom thumbnails and the ability to add personal notes to one's profile. In July 2021, an official client for iOS devices was released. According to the project's then CEO, Eugen Rochko, the release was part of an effort to attract new users. Mastodon 4.0 was released in November 2022, including language support for translating posts, editing posts and following hashtags. Mastodon 4.5 was released in November 2025. Among other features it introduced quote posts, which were previously rejected from being implemented due to concerns about toxicity and harassment. To mitigate these issues Mastodon's quote post feature has been designed in a way that lets users decide if and by whom their posts can be quoted. == Software == Mastodon is published as free and open-source software under the Affero GPL license, allowing anyone to use the software or modify it as they wish. Servers can be run by any individual or organization, and users can join these servers as they wish. The server software itself is powered by Ruby on Rails and Node.js, with its web client being written in React.js and Redux. The only database software supported is PostgreSQL, with Redis being used for job processing and various actions that Mastodon needs to process. The service is interoperable with the fediverse, a collection of social networking services which use the ActivityPub protocol for communication between each other, with previous versions containing support for OStatus. Client apps for interacting with the Mastodon API are available for desktop computer operating systems, including Windows, macOS and the Linux family of operating systems, as well as mobile phones running iOS and Android. The API is open for anyone to utilize, allowing clients to be built for any operating system that can connect to the internet. === Integration with Fediverse === Mastodon uses the ActivityPub protocol for federation; this allows users to communicate between independent Mastodon instances and other ActivityPub compatible services. Thus, Mastodon is generally considered to be a part of the Fediverse. Services utilizing the ActivityPub protocol exist which allow for searching all posts on all instances as long as users opt-in. For similar reasons, only hashtags can appear in a Mastodon instance's trending topics, not arbitrary popular words. Trending topics vary between instances, since individual instances are aware of different subsets of posts from the whole fediverse. === Security concerns === While Mastodon's decentralized structure is one of its most distinctive features, it also poses additional security challenges. Since many Mastodon instances are run by volunteers, some security experts are concerned about data security and responsiveness to new threats and vulnerabilities across the network, considering the difficulty of configuring and maintaining an instance as well as uneven skill levels among administrators. Administrators of an instance also have access to the private information of any users that are either registered with that instance or have federated

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  DigitaltMuseum

  DigitaltMuseum (lit. 'The Digital Museum') is a website database in Norwegian and Swedish for art, images and cultural history museums. The service was established in 2009 after a trial period. The database is developed and operated by KulturIT. KulturIT ANS was established by the Norwegian Museum of Cultural History and Maihaugen in consultation with the Norwegian Archive, Library and Museum Authority (ABM) in 2007. In 2015, the company underwent a corporate transformation and KulturIT AS was established on 12 February. The website has per 2025 around 2,548,022 images. Many of the images are in the public domain or under Creative Commons licenses and are being imported into Wikimedia Commons. The website's API was developed in 2012. == Institutions == As of 2025, there are 223 collaborating museums. == Mission == DigitaltMuseum aims to make the museums' collections accessible to all interested parties, regardless of time and place. The website aims to facilitate easy use of the collections through various methods including image searches, research, teaching and joint knowledge development. DigitaltMuseum contains collections from several hundred Norwegian and Swedish museums, totalling around five million objects. The website contains both historical images from the areas and themes covered by the museums, as well as images of artefacts from the collections. Parts of the collection have previously only been shown in the museums' exhibitions and books and have therefore rarely or never been shown to the public.

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  WikiTok

  WikiTok is a web application that shows random Wikipedia articles in an infinite scrolling format similar to TikTok. Created by New York-based civil and software engineer Isaac Gemal in February 2025, the application aims to provide an "anti-algorithmic" alternative to traditional social media platforms while combating doomscrolling. == Development == WikiTok was developed on February 3, 2025, after Gemal saw a viral tweet from developer Tyler Angert proposing "all of wikipedia on a single, scrollable page". Gemal created the initial prototype in approximately two hours, using the Claude LLM and Cursor coding editor, completing it by 2 a.m. that same night. The application was built with React 18, TypeScript, Tailwind CSS, and Vite, consisting of only a few hundred lines of code with no backend infrastructure. Gemal has made the project open-source, with the code publicly available on GitHub. == Functionality == WikiTok serves users completely random Wikipedia article snippets from Wikipedia's nearly 9.5 million entries. Each article preview includes a full-screen image from Wikipedia and a short text excerpt. Users can click "Read More" to access the full Wikipedia article, or continue scrolling to see the next random entry. The application functions as a progressive web app that can be downloaded as a hybrid between an app and a website. It works on both mobile and desktop browsers, and is designed to be responsive whether users scroll with their thumb on mobile devices or use a cursor on desktop computers. As of 2025, WikiTok supports 14 different languages for article translation. == Philosophy == Gemal has explicitly positioned WikiTok as "anti-algorithmic," resisting numerous requests to implement personalized content algorithms. In an interview with Business Insider, Gemal stated: "I have had plenty of people message me and even make issues on my GitHub asking for some insane crazy WikiTok algorithm... we're already ruled by ruthless, opaque algorithms in our everyday life; why can't we just have one little corner in the world without them?" The application aims to combat "doomscrolling" – the practice of mindlessly consuming negative news online that can cause anxiety, helplessness, and anger. Unlike traditional social media platforms, WikiTok deliberately excludes algorithms, advertisements, and user tracking.

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  Transportation Economic Development Impact System

  Transportation Economic Development Impact System (TREDIS) is an economic analysis system sold by consulting firm Economic Development Research Group that is used in planning major transportation investments in the US and Canada. The role of economic impact analysis and TREDIS in the transportation planning process is explained in guidebooks of the US Department of Transportation and the American Association of State Highway and Transportation Officials. TREDIS has been most commonly used for assessing the expected economic impacts of statewide highway programs, regional multi-modal plans and public transport investment. Its history and theoretical foundation are explained in peer reviewed journal articles. == How It Works == TREDIS has a series of modules that calculate different forms of impacts and benefits. One module is an accounting framework that calculates user benefits, including impacts on cargo transportation and commuting costs, based on transportation forecasting results. A second module calculates wider economic development benefits, including impacts on business productivity, economic development and multiplier effects from the input-output analysis. It applies an economic model to estimate impacts on jobs, income, gross regional product and business output, by sector of the economy. A third module applies cost-benefit analysis from alternative perspectives.

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  Easy8

  Easy8 is a project management platform. It is an extension to Redmine. == History == Easy8 Group, the company behind Easy8, was established in 2006 by Filip Morávek who serves as the company's CEO and is also a founder of the Mindfulness Foundation. In 2007, the company released an open-source project management software based on Redmine that included modules for project financing. The Easy8 Group has also developed an identical product distributed in Czechia and Hungary. In 2021 Easy8 11 was released with mobile application, Rails 6, Ruby 3.0, Sidekiq B2B CRM features. In 2022 Easy8 was available in 70 countries. In 2023 Easy8 13 was released in collaboration with Scrum certified expert. In March 2026, Easy Redmine and Easy Project rebranded to Easy8. == Overview == Easy8 covers Waterfall and Agile project management individually or simultaneously. It is available in public and private cloud hosting or on-premises server. It's based on open-source technologies such as Redmine. It covers the complete process from planning through implementation to helpdesk support. Easy8 also implements techniques such as risk and resource management, mind maps and Gantt charts. The application includes a CRM module focused on the B2B segment with partner access control and partner network management. Easy8 13 also has integration MediaWiki, the software that runs Wikipedia and GitLab, an AI-powered DevSecOps Platform. Easy8 is used by the Kazakh state administration, Bosch, Zentiva, Innogy, Ministry of Foreign Affairs of the Czech Republic, Axa, RTL Radio Berlin, Continental and Ogilvy among others. It features separately installable extensions. In 2017, it was reviewed by iX Special in comparison to GitKraken (previously known as Axosoft) and Agilo for Trac. PCmag while analyzing Redmine highlights that Easy8 enhances the core features of Redmine with a more polished interface and offers proprietary plug-ins for additional functionalities, such as tools for resource management, financial management, and support for agile methodologies. == Easy AI == Easy AI is an artificial intelligence extension integrated into the Easy8 project management suite, offering both cloud-based and on-premises deployment options. Easy AI uses the Llama 3.1 AI model and supports organizational data controls. The system includes assistants for personal, project, and service workflows, supporting tasks such as text summarization, project planning, and helpdesk ticket management. == License == The Easy8 website claims that "Easy8 is an Open Source software", but its source is neither freely downloadable nor is it licensed under an open-source license according to The Open Source Definition, since the Easy8 Group Commercial License does not allow free redistribution (among other restrictions).

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  Learning automaton

  A learning automaton is one type of machine learning algorithm studied since 1970s. Learning automata select their current action based on past experiences from the environment. It will fall into the range of reinforcement learning if the environment is stochastic and a Markov decision process (MDP) is used. == History == Research in learning automata can be traced back to the work of Michael Lvovitch Tsetlin in the early 1960s in the Soviet Union. Together with some colleagues, he published a collection of papers on how to use matrices to describe automata functions. Additionally, Tsetlin worked on reasonable and collective automata behaviour, and on automata games. Learning automata were also investigated by researches in the United States in the 1960s. However, the term learning automaton was not used until Narendra and Thathachar introduced it in a survey paper in 1974. == Definition == A learning automaton is an adaptive decision-making unit situated in a random environment that learns the optimal action through repeated interactions with its environment. The actions are chosen according to a specific probability distribution which is updated based on the environment response the automaton obtains by performing a particular action. With respect to the field of reinforcement learning, learning automata are characterized as policy iterators. In contrast to other reinforcement learners, policy iterators directly manipulate the policy π. Another example for policy iterators are evolutionary algorithms. Formally, Narendra and Thathachar define a stochastic automaton to consist of: a set X of possible inputs, a set Φ = { Φ1, ..., Φs } of possible internal states, a set α = { α1, ..., αr } of possible outputs, or actions, with r ≤ s, an initial state probability vector p(0) = ≪ p1(0), ..., ps(0) ≫, a computable function A which after each time step t generates p(t+1) from p(t), the current input, and the current state, and a function G: Φ → α which generates the output at each time step. In their paper, they investigate only stochastic automata with r = s and G being bijective, allowing them to confuse actions and states. The states of such an automaton correspond to the states of a "discrete-state discrete-parameter Markov process". At each time step t=0,1,2,3,..., the automaton reads an input from its environment, updates p(t) to p(t+1) by A, randomly chooses a successor state according to the probabilities p(t+1) and outputs the corresponding action. The automaton's environment, in turn, reads the action and sends the next input to the automaton. Frequently, the input set X = { 0,1 } is used, with 0 and 1 corresponding to a nonpenalty and a penalty response of the environment, respectively; in this case, the automaton should learn to minimize the number of penalty responses, and the feedback loop of automaton and environment is called a "P-model". More generally, a "Q-model" allows an arbitrary finite input set X, and an "S-model" uses the interval [0,1] of real numbers as X. A visualised demo/ Art Work of a single Learning Automaton had been developed by μSystems (microSystems) Research Group at Newcastle University. == Finite action-set learning automata == Finite action-set learning automata (FALA) are a class of learning automata for which the number of possible actions is finite or, in more mathematical terms, for which the size of the action-set is finite.

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  Teaspiller

  Teaspiller was a US-based web application for customers to find accountants and hire them to do their taxes and accounting online. In 2013 the company was acquired by Intuit, Inc and added to its TurboTax product line. The Teaspiller employees and code were all acquired and the product was renamed as "TurboTax CPA select". It enabled accountants to work remotely with clients (share files, send secure messages, schedule appointments), as well as find new clients looking for their specific skills through a complex search algorithm. This was done through extended profiles containing licensing information, professional histories, user ratings, peer endorsements, association memberships, and practice areas. The service had been called an H&R Block killer by Business Insider as it helped customers find accountants to prepare tax returns online. As of 2011 it had 20,000 US accountants listed on the site. The application was built using the Django framework. == History == Teaspiller was built by Vemdara, LLC, a web company based in New York and founded in 2009 by Amit Vemuri (a former VP at Travelocity). The web application was launched in 2010. In 2013 the company was acquired by Intuit as part of their TurboTax product line and renamed as "TurboTax CPA select".

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  Web container

  A web container (also known as a servlet container; and compare "webcontainer") is the component of a web server that interacts with Jakarta Servlets. A web container is responsible for managing the lifecycle of servlets, mapping a URL to a particular servlet and ensuring that the URL requester has the correct access-rights. A web container handles requests to servlets, Jakarta Server Pages (JSP) files, and other types of files that include server-side code. The Web container creates servlet instances, loads and unloads servlets, creates and manages request and response objects, and performs other servlet-management tasks. A web container implements the web component contract of the Jakarta EE architecture. This architecture specifies a runtime environment for additional web components, including security, concurrency, lifecycle management, transaction, deployment, and other services. == List of Servlet containers == The following is a list of notable applications which implement the Jakarta Servlet specification from Eclipse Foundation, divided depending on whether they are directly sold or not. === Open source Web containers === Apache Tomcat (formerly Jakarta Tomcat) is an open source web container available under the Apache Software License. Apache Tomcat 6 and above are operable as general application container (prior versions were web containers only) Apache Geronimo is a full Java EE 6 implementation by Apache Software Foundation. Enhydra, from Lutris Technologies. GlassFish from Eclipse Foundation (an application server, but includes a web container). Jetty, from the Eclipse Foundation. Also supports SPDY and WebSocket protocols. Open Liberty, from IBM, is a fully compliant Jakarta EE server Virgo from Eclipse Foundation provides modular, OSGi based web containers implemented using embedded Tomcat and Jetty. Virgo is available under the Eclipse Public License. WildFly (formerly JBoss Application Server) is a full Java EE implementation by Red Hat, division JBoss. === Commercial Web containers === iPlanet Web Server, from Oracle. JBoss Enterprise Application Platform from Red Hat, division JBoss is subscription-based/open-source Jakarta EE-based application server. WebLogic Application Server, from Oracle Corporation (formerly developed by BEA Systems). Orion Application Server, from IronFlare. Resin Pro, from Caucho Technology. IBM WebSphere Application Server. SAP NetWeaver.

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  Topincs

  Topincs is a software for rapid development of web databases and web applications. It is based on LAMP and the semantic technology Topic Maps. A Topincs web database makes information accessible through browsing very much like a Wiki. Editing a page on a subject is done through forms rather than markup editing. A web database can be tailored into a web application to provide specific user groups a contextualized approach to the data. All modeling and development tasks are performed in the web browser. No other development tools are necessary. The server requires Apache, MySQL and PHP. The client works on any standards-compliant web browser on desktops, laptops, tablets, and mobile phones. The layout is automatically adjusted to smaller screens. The programmatic access to data is done via a virtual object-oriented programming interface which is set up over the schema in a few minutes. It is interpreted rather than generated. Portions of the database can be pulled into memory to accelerate bulk access. == Features == Browseable data High-quality web forms Little to no programming Development done in the browser, no other tools required Client runs in any standard-compliant web browser Virtual object-oriented programming interface User interface adjusts to screen size Supports desktops, laptops, tablets, and mobile phones Flexible data modeling == Challenges == Requires rethinking the development process and dropping many hard learned habits Requires a familiarity with two ISO standards ISO 13259 and 19756 Forms cannot be easily adjusted in layout and behavior Server installation difficult and prone to error == License == Topincs can be used in a private network for any purpose for free. The use in a public network is restricted to non-commercial applications.

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  Separable filter

  A separable filter in image processing can be written as product of two more simple filters. Typically a 2-dimensional convolution operation is separated into two 1-dimensional filters. This reduces the computational costs on an N × M {\displaystyle N\times M} image with a m × n {\displaystyle m\times n} filter from O ( M ⋅ N ⋅ m ⋅ n ) {\displaystyle {\mathcal {O}}(M\cdot N\cdot m\cdot n)} down to O ( M ⋅ N ⋅ ( m + n ) ) {\displaystyle {\mathcal {O}}(M\cdot N\cdot (m+n))} . == Examples == 1. A two-dimensional smoothing filter: 1 3 [ 1 1 1 ] ∗ 1 3 [ 1 1 1 ] = 1 9 [ 1 1 1 1 1 1 1 1 1 ] {\displaystyle {\frac {1}{3}}{\begin{bmatrix}1\\1\\1\end{bmatrix}}{\frac {1}{3}}{\begin{bmatrix}1&1&1\end{bmatrix}}={\frac {1}{9}}{\begin{bmatrix}1&1&1\\1&1&1\\1&1&1\end{bmatrix}}} 2. Another two-dimensional smoothing filter with stronger weight in the middle: 1 4 [ 1 2 1 ] ∗ 1 4 [ 1 2 1 ] = 1 16 [ 1 2 1 2 4 2 1 2 1 ] {\displaystyle {\frac {1}{4}}{\begin{bmatrix}1\\2\\1\end{bmatrix}}{\frac {1}{4}}{\begin{bmatrix}1&2&1\end{bmatrix}}={\frac {1}{16}}{\begin{bmatrix}1&2&1\\2&4&2\\1&2&1\end{bmatrix}}} 3. The Sobel operator, used commonly for edge detection: [ 1 2 1 ] ∗ [ 1 0 − 1 ] = [ 1 0 − 1 2 0 − 2 1 0 − 1 ] {\displaystyle {\begin{bmatrix}1\\2\\1\end{bmatrix}}{\begin{bmatrix}1&0&-1\end{bmatrix}}={\begin{bmatrix}1&0&-1\\2&0&-2\\1&0&-1\end{bmatrix}}} This works also for the Prewitt operator. In the examples, there is a cost of 3 multiply–accumulate operations for each vector which gives six total (horizontal and vertical). This is compared to the nine operations for the full 3x3 matrix. Another notable example of a separable filter is the Gaussian blur whose performance can be greatly improved the bigger the convolution window becomes.

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  Inbox by Gmail

  Inbox by Gmail was an email service developed by Google. Announced on a limited invitation-only basis on October 22, 2014, it was officially released to the public on May 28, 2015. Inbox was shut down by Google on April 2, 2019. Available on the web, and through mobile apps for Android and iOS, Inbox by Gmail aimed to improve email productivity and organization through several key features. Bundles gathered emails on the same topic together; highlighted surface key details from messages, reminders and assists; and a "snooze" functionality enabled users to control when specific information would appear. Updates to the service enabled an "undo send" feature; a "Smart Reply" feature that automatically generated short reply examples for certain emails; integration with Google Calendar for event organization, previews of newsletters; and a "Save to Inbox" feature that let users save links for later use. Inbox by Gmail received generally positive reviews. At its launch, it was called "minimalist and lovely, full of layers and easy to navigate", with features deemed helpful in finding the right messages—one reviewer noted that the service felt "a lot like the future of email". However, it also received criticism, particularly for a low density of information, algorithms that needed tweaking, and because the service required users to "give up the control" of organizing their own email, meaning that "Anyone who already has a system for organizing their emails will likely find themselves fighting Google's system". Google noted in March 2016 that 10% of all replies on mobile originated from Inbox's Smart Reply feature. Google announced it would discontinue Inbox by Gmail in March 2019, with many of its features integrated into Gmail proper. == Features == Inbox by Gmail scanned the user's incoming Gmail messages for information. It gathered email messages related to the same overall topic into an organized bundle, with a title describing the bundle's content. For example, flight tickets, car rentals, and hotel reservations were grouped under "Travel", giving the user an easier overview of emails. Users could also group emails together manually, to "teach" the Inbox how the user worked. The service highlighted key details and important information in messages, such as flight itineraries, event information, photos and documents. Inbox could retrieve updated information from the Internet, including the real-time status of flights and package deliveries. Users could set reminders to bring up important messages later. When a user needed particular information, Inbox could assist the user by displaying the necessary details. Where Inbox highlights information was not needed immediately, users could "snooze" a message or reminder, with options to make the information reappear at a later time or specific location. In June 2015, Google added an "Undo Send" feature to Inbox, giving the user 10 seconds to undo sending a message. In November 2015, Google added "Smart Reply" functionality to the mobile apps. With Smart Reply, Inbox determined which emails could be answered with a short reply, generating three example responses from which the user could select one with a single tap. Smart Reply (initially available only on the Android and iOS mobile apps) was added to the Inbox website in March 2016, Google announcing that "10% of all your replies on mobile already use Smart Reply". By May 2017, Google said Smart Reply was driving about 12% of replies in inbox on mobile. In April 2016, Google updated Inbox with three new features; Google Calendar event organization, newsletter previews, and a "Save to Inbox" functionality that let the user save links for later use, rather than having to email links to themselves. In December 2017, Google introduced an "Unsubscribe" card that let users easily unsubscribe from mailing lists. The card appeared for email messages (from specific senders) that the user had not opened for a month. A few popular Inbox by Gmail features were subsequently added to Gmail: "Snoozing" of emails Nudges: Gmail could move old messages back to the top of the inbox when it thought a follow up or reply might be required. Hover actions: Placing the mouse cursor over a certain part of the message could quickly effect an action, such as archiving, without its being opened. Smart reply: This feature employed boilerplate text to suggest appropriate replies. Google reportedly wished, at a time then to be decided, to add the "bundles" feature to Gmail, which at the time was available only in Inbox for Gmail. By March 2020, many Inbox features were still missing from Gmail. == Platforms == Inbox by Gmail was announced on a limited invitation-only basis on October 22, 2014, available on the web, and through the Android and iOS mobile operating systems. It was officially released to the public on May 28, 2015. == Reception == David Pierce of The Verge praised the service, writing that it was "minimalist and lovely, full of layers and easy to navigate. It's remarkably fast and smooth on all platforms, and far better on iOS than the Gmail app". However, he criticized the app's low density of information, with only a few emails visible on the screen at a time, making it "a bit of a challenge" for users who need to go through "hundreds of emails" every day. Although positive that "Inbox feels a lot like the future of email", Pierce wrote that there was "plenty of algorithm tweaking and design condensing to do", with particular attention needed on a "compact view" for denser view of information on the screen. Sarah Mitroff of CNET also praised Inbox, writing, "Not only is it visually appealing, it's also full of features that help you find every message you need, when you need it". She added that users must "give up the control" to organize their email, and that it "won't vibe with everyone", but admitted that "if you're willing ... the app will reward you with a smarter and cleaner inbox." Mitroff noted that, initially, users had to coach the app about which bundle was appropriate for certain emails, writing, "It's a tedious process at first, by [sic] in just a few days Inbox starts to get it right." Regarding any downsides of the service, Mitroff wrote that "Inbox has a built-in strategy for managing your emails that works best on its own. Anyone who already has a system for organizing their emails will likely find themselves fighting Google's system". == Discontinuation and legacy == Google ended the service in March 2019. Google called Inbox "a great place to experiment with new ideas" and noted that many of those ideas had been migrated to Gmail. The company wanted, going forward, to focus its resources on a single email system. Several services, like Shortwave, attempted to resurrect some of the features of Inbox by Gmail to attract its old users. Similarly, Inbox Reborn, an actively maintained browser extension developed by a team of volunteer developers from around the world since 2018, aims to recreate the core features and visual style of Inbox by Gmail within the standard Gmail interface. The project continues to focus on preserving functionalities such as email bundling and streamlined workflows to provide users with a familiar productivity experience. Afterwards, most people moved to Spark, Spike, or Newton. According to a product manager at Google, a "more focused approach" regarding email was the companies goal. This is likely the reason they moved away from Inbox.

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  Directional cubic convolution interpolation

  Directional cubic convolution interpolation (DCCI) is an edge-directed image scaling algorithm created by Dengwen Zhou and Xiaoliu Shen. By taking into account the edges in an image, this scaling algorithm reduces artifacts common to other image scaling algorithms. For example, staircase artifacts on diagonal lines and curves are eliminated. The algorithm resizes an image to 2x its original dimensions, minus 1.

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