AI Google Grammar Checker

AI Google Grammar Checker — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Multi-exposure HDR capture

In photography and videography, multi-exposure HDR capture is a technique that creates high dynamic range (HDR) images (or extended dynamic range images) by taking and combining multiple exposures of the same subject matter at different exposures. Combining multiple images in this way results in an image with a greater dynamic range than what would be possible by taking one single image. The technique can also be used to capture video by taking and combining multiple exposures for each frame of the video. The term "HDR" is used frequently to refer to the process of creating HDR images from multiple exposures. Many smartphones have an automated HDR feature that relies on computational imaging techniques to capture and combine multiple exposures. A single image captured by a camera provides a finite range of luminosity inherent to the medium, whether it is a digital sensor or film. Outside this range, tonal information is lost and no features are visible; tones that exceed the range are "burned out" and appear pure white in the brighter areas, while tones that fall below the range are "crushed" and appear pure black in the darker areas. The ratio between the maximum and the minimum tonal values that can be captured in a single image is known as the dynamic range. In photography, dynamic range is measured in exposure value (EV) differences, also known as stops. The human eye's response to light is non-linear: halving the light level does not halve the perceived brightness of a space, it makes it look only slightly dimmer. For most illumination levels, the response is approximately logarithmic. Human eyes adapt fairly rapidly to changes in light levels. HDR can thus produce images that look more like what a human sees when looking at the subject. This technique can be applied to produce images that preserve local contrast for a natural rendering, or exaggerate local contrast for artistic effect. HDR is useful for recording many real-world scenes containing a wider range of brightness than can be captured directly, typically both bright, direct sunlight and deep shadows. Due to the limitations of printing and display contrast, the extended dynamic range of HDR images must be compressed to the range that can be displayed. The method of rendering a high dynamic range image to a standard monitor or printing device is called tone mapping; it reduces the overall contrast of an HDR image to permit display on devices or prints with lower dynamic range. == Benefits == One aim of HDR is to present a similar range of luminance to that experienced through the human visual system. The human eye, through non-linear response, adaptation of the iris, and other methods, adjusts constantly to a broad range of luminance present in the environment. The brain continuously interprets this information so that a viewer can see in a wide range of light conditions. Most cameras are limited to a much narrower range of exposure values within a single image, due to the dynamic range of the capturing medium. With a limited dynamic range, tonal differences can be captured only within a certain range of brightness. Outside of this range, no details can be distinguished: when the tone being captured exceeds the range in bright areas, these tones appear as pure white, and when the tone being captured does not meet the minimum threshold, these tones appear as pure black. Images captured with non-HDR cameras that have a limited exposure range (low dynamic range, LDR), may lose detail in highlights or shadows. Modern CMOS image sensors have improved dynamic range and can often capture a wider range of tones in a single exposure reducing the need to perform multi-exposure HDR. Color film negatives and slides consist of multiple film layers that respond to light differently. Original film (especially negatives versus transparencies or slides) feature a very high dynamic range (in the order of 8 for negatives and 4 to 4.5 for positive transparencies). Multi-exposure HDR is used in photography and also in extreme dynamic range applications such as welding or automotive work. In security cameras the term "wide dynamic range" is used instead of HDR. === Limitations === A fast-moving subject, or camera movement between the multiple exposures, will generate a "ghost" effect or a staggered-blur strobe effect due to the merged images not being identical. Unless the subject is static and the camera mounted on a tripod there may be a tradeoff between extended dynamic range and sharpness. Sudden changes in the lighting conditions (strobed LED light) can also interfere with the desired results, by producing one or more HDR layers that do have the luminosity expected by an automated HDR system, though one might still be able to produce a reasonable HDR image manually in software by rearranging the image layers to merge in order of their actual luminosity. Because of the nonlinearity of some sensors image artifacts can be common. Camera characteristics such as gamma curves, sensor resolution, noise, photometric calibration and color calibration affect resulting high-dynamic-range images. == Process == High-dynamic-range photographs are generally composites of multiple standard dynamic range images, often captured using exposure bracketing. Afterwards, photo manipulation software merges the input files into a single HDR image, which is then also tone mapped in accordance with the limitations of the planned output or display. === Capturing multiple images (exposure bracketing) === Any camera that allows manual exposure control can perform multi-exposure HDR image capture, although one equipped with automatic exposure bracketing (AEB) facilitates the process. Some cameras have an AEB feature that spans a far greater dynamic range than others, from ±0.6 in simpler cameras to ±18 EV in top professional cameras, as of 2020. The exposure value (EV) refers to the amount of light applied to the light-sensitive detector, whether film or digital sensor such as a CCD. An increase or decrease of one stop is defined as a doubling or halving of the amount of light captured. Revealing detail in the darkest of shadows requires an increased EV, while preserving detail in very bright situations requires very low EVs. EV is controlled using one of two photographic controls: varying either the size of the aperture or the exposure time. A set of images with multiple EVs intended for HDR processing should be captured only by altering the exposure time; altering the aperture size also would affect the depth of field and so the resultant multiple images would be quite different, preventing their final combination into a single HDR image. Multi-exposure HDR photography generally is limited to still scenes because any movement between successive images will impede or prevent success in combining them afterward. Also, because the photographer must capture three or more images to obtain the desired luminance range, taking such a full set of images takes extra time. Photographers have developed calculation methods and techniques to partially overcome these problems, but the use of a sturdy tripod is advised to minimize framing differences between exposures. === Merging the images into an HDR image === Tonal information and details from shadow areas can be recovered from images that are deliberately overexposed (i.e., with positive EV compared to the correct scene exposure), while similar tonal information from highlight areas can be recovered from images that are deliberately underexposed (negative EV). The process of selecting and extracting shadow and highlight information from these over/underexposed images and then combining them with image(s) that are exposed correctly for the overall scene is known as exposure fusion. Exposure fusion can be performed manually, relying on the HDR operator's judgment, experience, and training, but usually, fusion is performed automatically by software. === Storing === Information stored in high-dynamic-range images typically corresponds to the physical values of luminance or radiance that can be observed in the real world. This is different from traditional digital images, which represent colors as they should appear on a monitor or a paper print. Therefore, HDR image formats are often called scene-referred, in contrast to traditional digital images, which are device-referred or output-referred. Furthermore, traditional images are usually encoded for the human visual system (maximizing the visual information stored in the fixed number of bits), which is usually called gamma encoding or gamma correction. The values stored for HDR images are often gamma compressed using mathematical functions such as power laws logarithms, or floating point linear values, since fixed-point linear encodings are increasingly inefficient over higher dynamic ranges. HDR images often do not use fixed ranges per color channel, other than traditional images, to represent many more colors over a much wi
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ZipBooks

ZipBooks is a free online accounting software company based in American Fork, Utah. The cloud-based software is an accounting and bookkeeping tool that helps business owners process credit cards, track finances, and send invoices, among other features. == History == ZipBooks was founded by Tim Chaves in June 2015, backed by venture capital firm Peak Ventures. The company secured an additional $2 million of funding in July 2016, and in 2017 it was awarded a $100,000 economic grant by the Utah Governor's Office of Economic Development Technology Commercialization and Innovation Program. == Products == ZipBooks' core modules are invoicing, transactions, bills, reporting, time tracking, contacts, and payroll. Accrual accounting was added in 2017. The application is available on G Suite, iOS, Slack, and as a web application. == Reception == Computerworld compared ZipBooks favorably with other accounting software. PC Magazine praised its user experience, but stated it lacked "a lot of features that competing sites offer".
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Google Messages

Google Messages (formerly known as Messenger, Android Messages, and Messages by Google) is a text messaging software application developed by Google for its Android and Wear OS mobile operating systems. It is also available as a web app. Google's official universal messaging platform for the Android ecosystem, Messages employs SMS, MMS, and Rich Communication Services (RCS). Starting in 2023, Google has RCS activated by default on participating Android devices, similar to the implementation of iMessage on Apple devices. Samsung Messages will be discontinued on July 6th 2026, with Samsung transitioning users to Google Messages as the default messaging application. == History == The original code for Android SMS messaging was released in 2009 integrated into the operating system. It was released as a standalone application independent of Android with the release of Android 5.0 Lollipop in 2014, replacing Google Hangouts as the default SMS app on Google's Nexus line of phones. In 2018, Messages adopted RCS messages and evolved to send larger data files, sync with other apps, and even create mass messages. This was in preparation for when Google launched Messages for web. In December 2019, Google began to introduce support for Rich Communication Services (RCS) messaging via an RCS service hosted by Google, referred to in the user interface as "chat features". This was followed by a wider global rollout throughout 2020. The app surpassed 1 billion installs in April 2020, doubling its number of installs in less than a year. Initially, RCS did not support end-to-end encryption. In June 2021, Google introduced end-to-end encryption in Messages by default using the Signal Protocol, for all one-to-one RCS-based conversations, for all RCS group chats in December 2022 for beta users, and for all RCS users by August 2023, as well as enabling RCS for all users by default to encourage encryption. In July 2023, Google announced it would build the Message Layer Security (MLS) end-to-end encryption protocol into Google Messages. Beginning with the Samsung Galaxy S21, Messages replaces Samsung's in-house Messages app as the default text messaging app for One UI for some regions and carriers. In April 2021, the app began to receive UI modifications on Samsung devices to follow aspects of One UI, including pushing the top of the message list towards the middle of the screen to improve ergonomics. In February 2023, Google began to replace references to "chat features" in the Messages user interface with "RCS". In August 2023, Google announced that Messages will use RCS by default for all users unless they opt out, to allow them to benefit from secure messaging. In December 2023, with the arrival of several new features, the app was renamed "Google Messages". In July 2024, Samsung announced it would no longer pre-install Samsung Messages on its Galaxy devices in some regions, starting with the Galaxy Z Fold 6 and Flip, favoring Google Messages instead. In April 2026, Samsung announced that Samsung Messages would be discontinued in July 2026. It encouraged users to switch to Google Messages. == Features == Some of the most important features in Google Messages are: Send instant text and voice messages in 1:1 or group chat conversations over mobile data and Wi-Fi, via Android, Wear OS or the web. End-to-end encryption for RCS chats. Typing, sent, delivered and read status Reply and react to specific messages Share files and high-resolution photos Voice message transcriptions Schedule messages In-app reminders for birthdays and messages you didn't respond to after some time with Nudges Tight integration with the Google ecosystem, e.g. Google Calendar, Meet, Maps, YouTube, Photos, Contacts, Assistant, Search, Safe Browsing etc. Web interface: Users can visit https://messages.google.com/web and either sign in with their Google account or scan the QR code that is shown with their smartphone to access a limited web version of the app that allows them to send and receive messages, provided the smartphone remains connected. Phone number recognition: The app shows the country and province of the caller. Additionally, it can show the company's name or a warning for spam calls if the number is registered in a data base. Access to the Gemini chatbot on select Pixel, Galaxy and Android devices.
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List of assembly software and tools

This is a list of assembly software and tools, including software used for assembly language programming, machine code generation, disassembly, debugging, binary analysis, reverse engineering, and instruction-set simulation. == Assemblers and machine-code generators == == Disassemblers and binary-analysis tools == == Debuggers with assembly-level features == == Educational IDEs, simulators and emulators == == Portable and intermediate assembly-like languages == == Assembly language families == Assembly language is not a single programming language, but a family of low-level languages associated with particular instruction set architectures and processor families. Examples include:
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Action model learning

Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with the creation and modification of a software agent's knowledge about the effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in a logic-based action description language and used as input for automated planners. Learning action models is important when goals change. When an agent acted for a while, it can use its accumulated knowledge about actions in the domain to make better decisions. Thus, learning action models differs from reinforcement learning. It enables reasoning about actions instead of expensive trials in the world. Action model learning is a form of inductive reasoning, where new knowledge is generated based on the agent's observations. The usual motivation for action model learning is the fact that manual specification of action models for planners is often a difficult, time-consuming, and error-prone task (especially in complex environments). == Action models == Given a training set E {\displaystyle E} consisting of examples e = ( s , a , s ′ ) {\displaystyle e=(s,a,s')} , where s , s ′ {\displaystyle s,s'} are observations of a world state from two consecutive time steps t , t ′ {\displaystyle t,t'} and a {\displaystyle a} is an action instance observed in time step t {\displaystyle t} , the goal of action model learning in general is to construct an action model ⟨ D , P ⟩ {\displaystyle \langle D,P\rangle } , where D {\displaystyle D} is a description of domain dynamics in action description formalism like STRIPS, ADL or PDDL and P {\displaystyle P} is a probability function defined over the elements of D {\displaystyle D} . However, many state of the art action learning methods assume determinism and do not induce P {\displaystyle P} . In addition to determinism, individual methods differ in how they deal with other attributes of domain (e.g. partial observability or sensoric noise). == Action learning methods == === State of the art === Recent action learning methods take various approaches and employ a wide variety of tools from different areas of artificial intelligence and computational logic. As an example of a method based on propositional logic, we can mention SLAF (Simultaneous Learning and Filtering) algorithm, which uses agent's observations to construct a long propositional formula over time and subsequently interprets it using a satisfiability (SAT) solver. Another technique, in which learning is converted into a satisfiability problem (weighted MAX-SAT in this case) and SAT solvers are used, is implemented in ARMS (Action-Relation Modeling System). Two mutually similar, fully declarative approaches to action learning were based on logic programming paradigm Answer Set Programming (ASP) and its extension, Reactive ASP. In another example, bottom-up inductive logic programming approach was employed. Several different solutions are not directly logic-based. For example, the action model learning using a perceptron algorithm or the multi level greedy search over the space of possible action models. In the older paper from 1992, the action model learning was studied as an extension of reinforcement learning. Nonetheless, further algorithms can be found that operate under different assumptions: FAMA can work even when some observations are missing, and it produces a general (lifted) planning model. It treats learning an action model like a planning problem, making sure the learned model matches the observations given. NOLAM can learn general action models even from noisy or imperfect data. LOCM focuses only on the order of actions in the data, ignoring any details about the states between those actions. The family of safe action model (SAM) learning methods create models that guarantee any plans made with them will actually work in the real world. There's also an extension called N-SAM that can learn action models with numeric conditions and effects. Additionally, numeric action models like N-SAM can be used to improve reinforcement learning (RL) performance through the RAMP algorithm. === Literature === Most action learning research papers are published in journals and conferences focused on artificial intelligence in general (e.g. Journal of Artificial Intelligence Research (JAIR), Artificial Intelligence, Applied Artificial Intelligence (AAI) or AAAI conferences). Despite mutual relevance of the topics, action model learning is usually not addressed in planning conferences like the International Conference on Automated Planning and Scheduling (ICAPS).
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Apache Pig

Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. The language for this platform is called Pig Latin. Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark. Pig Latin abstracts the programming from the Java MapReduce idiom into a notation which makes MapReduce programming high level, similar to that of SQL for relational database management systems. Pig Latin can be extended using user-defined functions (UDFs) which the user can write in Java, Python, JavaScript, Ruby or Groovy and then call directly from the language. == History == Apache Pig was originally developed at Yahoo Research around 2006 for researchers to have an ad hoc way of creating and executing MapReduce jobs on very large data sets. In 2007, it was moved into the Apache Software Foundation. === Naming === Regarding the naming of the Pig programming language, the name was chosen arbitrarily and stuck because it was memorable, easy to spell, and for novelty. The story goes that the researchers working on the project initially referred to it simply as 'the language'. Eventually they needed to call it something. Off the top of his head, one researcher suggested Pig, and the name stuck. It is quirky yet memorable and easy to spell. While some have hinted that the name sounds coy or silly, it has provided us with an entertaining nomenclature, such as Pig Latin for the language, Grunt for the shell, and PiggyBank for the CPAN-like shared repository. == Example == Below is an example of a "Word Count" program in Pig Latin: The above program will generate parallel executable tasks which can be distributed across multiple machines in a Hadoop cluster to count the number of words in a dataset such as all the webpages on the internet. == Pig vs SQL == In comparison to SQL, Pig has a nested relational model, uses lazy evaluation, uses extract, transform, load (ETL), is able to store data at any point during a pipeline, declares execution plans, supports pipeline splits, thus allowing workflows to proceed along DAGs instead of strictly sequential pipelines. On the other hand, it has been argued DBMSs are substantially faster than the MapReduce system once the data is loaded, but that loading the data takes considerably longer in the database systems. It has also been argued RDBMSs offer out of the box support for column-storage, working with compressed data, indexes for efficient random data access, and transaction-level fault tolerance. Pig Latin is procedural and fits very naturally in the pipeline paradigm while SQL is instead declarative. In SQL users can specify that data from two tables must be joined, but not what join implementation to use (You can specify the implementation of JOIN in SQL, thus "... for many SQL applications the query writer may not have enough knowledge of the data or enough expertise to specify an appropriate join algorithm."). Pig Latin allows users to specify an implementation or aspects of an implementation to be used in executing a script in several ways. In effect, Pig Latin programming is similar to specifying a query execution plan, making it easier for programmers to explicitly control the flow of their data processing task. SQL is oriented around queries that produce a single result. SQL handles trees naturally, but has no built in mechanism for splitting a data processing stream and applying different operators to each sub-stream. Pig Latin script describes a directed acyclic graph (DAG) rather than a pipeline. Pig Latin's ability to include user code at any point in the pipeline is useful for pipeline development. If SQL is used, data must first be imported into the database, and then the cleansing and transformation process can begin.
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Friendica

Friendica (formerly Friendika, originally Mistpark) is a free and open-source software distributed social network. It forms one part of the Fediverse, an interconnected and decentralized network of independently operated servers. == Features == Friendica users can connect with others via their own Friendica server, but may also fully integrate contacts from other platforms including Diaspora, Pump.io, GNU social, email, Discourse and more recently ActivityPub (including Mastodon, Pleroma and Pixelfed) and Bluesky into their 'newsfeed'. In addition to these two way connections, users can also use Friendica as a publishing platform to post content to WordPress, Tumblr, Insanejournal and Libertree. Posting to Google+ was also supported until that service was shut down. In addition, RSS feeds can be ingested. Because users are distributed across many servers, their "addresses" consist of a username, the "@" symbol, and the domain name of the Friendica instance in the same manner email addresses are formed. Twitter support was available but was deprecated due to API changes under Elon Musk's leadership rendering it unusable. Most of the functionality from major microblogging and social networking platforms are available in Friendica; for example, tagging users and groups via "@ mentions"; direct messages; hashtags; photo albums; "likes"; "dislikes"; comments; and re-shares of publicly visible posts. Published items can be edited and updated across the network. Comprehensive settings for privacy and the public visibility of posts allow users to regulate who can read which contributions, or see specific information about the user. Users can also create multiple profiles, allowing different groups of people (such as friends, or work mates) to see a different profile entirely when viewing the same page. User accounts can be downloaded or deleted, and can be imported to a different Friendica server if so required. Public forums can be created under different accounts, which can be switched between if the accounts are registered with the same email address. == Development == There is no corporation behind Friendica. The developers work on a voluntary basis and the project is run informally; the platform itself is used for the communication between the developers. There are different forums within Friendica, such as "Friendica Developers" and "Friendica Support". The source code of Friendica is hosted on GitHub. == Installation == The developers aim to make installation of the software as simple as possible for technical laymen. They argue that decentralization on small servers is a key condition for the freedom of users and their self-determination. The difficulty level is similar to an installation of WordPress. However, the installing on shared hosting is sometimes difficult because of missing PHP5 modules. Some volunteers also run public servers so that newcomers can also avoid the installation of their own software. == List of clients == Friendica implements multiple client-server API variants simultaneously. Along with endpoints needed to use enhanced Friendica features, it also implements the API used by GNU social, Twitter and since version 2021.06 also the one used by Mastodon. As a result, most GNU social and Mastodon clients can be used for Friendica. Examples of Friendica compatible clients include: Raccoon for Friendica, Friendiqa, Fedilab, AndStatus, Twidere and DiCa for Android, friendly for Sailfish OS, friclicli (CLI client), choqok and Friendiqa for Linux and Friendica Mobile for Windows 10. == Reception == Friendica was cited in January 2012 by Infoshop News as an "alternative to Google+ and Facebook" to be used on the Occupy Nigeria movement. In January 2012 Free Software Foundation Europe's blog cited Friendica as a reasonable alternative to centralized and controlled social networks such as Facebook or Google+. Biblical Notes writer J. Randal Matheny described Friendica in January 2012 as "One social networking option flying under the radar until recently deserves consideration as an already stable platform with a wide range of options, applications, plug-ins, and possibilities for opening up the Internet." In February 2012, the German computer magazine c't wrote: "Friendica demonstrates how decentralized social networks can become widely accepted." Another German publication, the professional magazine t3n listed Friendica as a Facebook rival in an online article in March 2012 about Facebook alternatives. It compared Friendica with similar social networks like Diaspora and identi.ca. MSN Tech & Gadgets contributor Emma Boyes wrote about Friendica in May 2012: "why you'll love it: you can use it to access all the other social networks and get recommendations of new friends and groups to join. Friendica is open source and decentralised. There's no corporation behind it and there are extensive privacy settings. You can choose from a variety of user interfaces and it boasts some cool features—for instance, being able to key in a list of your interests and use the 'profile match' feature to recommend other users who share them with you. A word of warning, though, the site is not as user-friendly as the others on this list, so it may be this one is one for the geeks." == Later reviews == Acquisition of Twitter by Elon Musk had revitalized public interest in Fediverse technologies in April 2022. Friendica received favorable reviews, with a PCMag article describing it as "mostly comparable to Facebook", drawing a parallel to Google+ and highlighting using it "for planning events, and its multiple profile feature means you can show a different face to your friends, coworkers, and family". The September 2022 issue of Linux Magazine contains a detailed comparison and walk-through of registering to and using basic functions of Diaspora, Friendica and Mastodon. They describe Friendica as "intuitive" and highlight the "huge choice of account settings" and that "Friendica does not require any specific hardware, so you can use an old computer system as a server." == Vulnerabilities == In September 2020, a hotfix was released to patch a security vulnerability that could leak sensitive information from the server environment since versions released in April 2019 (develop branch) and June 2019 (stable).
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CEITON

CEITON is a web-based software system for facilitating and automating business processes such as planning, scheduling, and payroll using workflow technologies. The system is used by several media companies such as MDR, Yle, RAI and Red Bull Media House. In December 2018, the first CEITON User Group Meeting took place in Leipzig, Germany. == Architecture == The software runs on a server (on premises) or in the cloud and is scalable on parallel servers. Data security is warranted by role-based access control (RBAC). The software is used via web-browsers and not dependent on particular system software. == Structure and Features == CEITON combines the two classical approaches of production planning and control and workflow management. === Project Management === The scheduling system plans, manages, bills, and analyzes projects or tasks. It manages human and technical resources, material, and locations on a single GUI. The system uses a gantt chart to assign tasks to be done to available and eligible resources (i.e. staff), automatically or by drag-and-drop. The scheduling module includes material management, resource management/ human resource management, integration of freelancers, clients and suppliers, long-term budget planning, time-tracking, shift scheduling, quality management, delivery and logistics, document management, archive, analysis and controlling, business reporting, as well as all accounting and documentation processes. === Workflow === The workflow management system module coordinates business processes. Processes are defined once as a workflow and then repeatedly executed. Human resources are automatically assigned to steps (tasks) and integrated in workflow forms. Systems are integrated with an EAI/SOAP module, allowing data exchange with arbitrary external systems which are also involved in the business process. It also features a 3-D workflow overview in which the status of each project step can be determined by its color in the overview. === Process Management === For project and order processing management, business processes are designed as workflows, and coordinate communication automatically. Different user interfaces for staff, customers or suppliers can be created so each gets only relevant information. Different workflow forms are associated with different log-ins. The main application for the system is knowledge-based business processes, in which many people are involved and virtual results are produced, e.g. in research, or development of media products, such as TV and movies. Broadcasters and media companies such as MDR and Yle use CEITON to control their production processes for products and services and coordinate complex workflows with all kinds of resources. === Integrations === An integrated EAI module allows CEITON to integrate every external system in any business process without programming, using SOAP and similar technologies. Aspera and FileCatalyst were integrated for faster data transfer, yet complex ERP systems and numerous SAP modules have also been integrated, for example, to extract working times to payroll. === Mobile Working === Since Version 7, released in 2015, CEITON includes a time-tracking module allowing employees to enter their times from mobile devices such as tablets running Android, iPhones etc. == History == Ceiton Technologies (SME tech firm), the company developing CEITON, was founded in Leipzig, Germany in 2000, staffing solutions for the Bureau of Internal Revenue in Manila, Philippines, were implemented in 2000 together with the Deutsche Gesellschaft für Technische Zusammenarbeit of the German government. The first version (1.0) of the software was released in July 2001. The product was originally developed for German broadcasting companies. CEITON is named after the Japanese concept Seiton, one of the principles of Japanese workplace design methodology known as 5S. Since version 7, released in 2015, CEITON includes a time-tracking module allowing employees to enter their times from mobile devices such as tablets running Android, iPhones etc. In May 2005 CEITON won the IQ innovation award, sponsored by Siemens, in the category Excellent innovation in the IT-sector. Since 2007, CEITON has been present at the broadcast trade fairs NAB in Las Vegas and IBC in Amsterdam. In 2020, the company celebrated its 20th anniversary.
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Ugly duckling theorem

The ugly duckling theorem is an argument showing that classification is not really possible without some sort of bias. More particularly, it assumes finitely many properties combinable by logical connectives, and finitely many objects; it asserts that any two different objects share the same number of (extensional) properties. The theorem is named after Hans Christian Andersen's 1843 story "The Ugly Duckling", because it shows that a duckling is just as similar to a swan as two swans are to each other. It was derived by Satosi Watanabe in 1969. == Mathematical formula == Suppose there are n things in the universe, and one wants to put them into classes or categories. One has no preconceived ideas or biases about what sorts of categories are "natural" or "normal" and what are not. So one has to consider all the possible classes that could be, all the possible ways of making a set out of the n objects. There are 2 n {\displaystyle 2^{n}} such ways, the size of the power set of n objects. One can use that to measure the similarity between two objects, and one would see how many sets they have in common. However, one cannot. Any two objects have exactly the same number of classes in common if we can form any possible class, namely 2 n − 1 {\displaystyle 2^{n-1}} (half the total number of classes there are). To see this is so, one may imagine each class is represented by an n-bit string (or binary encoded integer), with a zero for each element not in the class and a one for each element in the class. As one finds, there are 2 n {\displaystyle 2^{n}} such strings. As all possible choices of zeros and ones are there, any two bit-positions will agree exactly half the time. One may pick two elements and reorder the bits so they are the first two, and imagine the numbers sorted lexicographically. The first 2 n / 2 {\displaystyle 2^{n}/2} numbers will have bit #1 set to zero, and the second 2 n / 2 {\displaystyle 2^{n}/2} will have it set to one. Within each of those blocks, the top 2 n / 4 {\displaystyle 2^{n}/4} will have bit #2 set to zero and the other 2 n / 4 {\displaystyle 2^{n}/4} will have it as one, so they agree on two blocks of 2 n / 4 {\displaystyle 2^{n}/4} or on half of all the cases, no matter which two elements one picks. So if we have no preconceived bias about which categories are better, everything is then equally similar (or equally dissimilar). The number of predicates simultaneously satisfied by two non-identical elements is constant over all such pairs. Thus, some kind of inductive bias is needed to make judgements to prefer certain categories over others. === Boolean functions === Let x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\dots ,x_{n}} be a set of vectors of k {\displaystyle k} booleans each. The ugly duckling is the vector which is least like the others. Given the booleans, this can be computed using Hamming distance. However, the choice of boolean features to consider could have been somewhat arbitrary. Perhaps there were features derivable from the original features that were important for identifying the ugly duckling. The set of booleans in the vector can be extended with new features computed as boolean functions of the k {\displaystyle k} original features. The only canonical way to do this is to extend it with all possible Boolean functions. The resulting completed vectors have 2 k {\displaystyle 2^{k}} features. The ugly duckling theorem states that there is no ugly duckling because any two completed vectors will either be equal or differ in exactly half of the features. Proof. Let x and y be two vectors. If they are the same, then their completed vectors must also be the same because any Boolean function of x will agree with the same Boolean function of y. If x and y are different, then there exists a coordinate i {\displaystyle i} where the i {\displaystyle i} -th coordinate of x {\displaystyle x} differs from the i {\displaystyle i} -th coordinate of y {\displaystyle y} . Now the completed features contain every Boolean function on k {\displaystyle k} Boolean variables, with each one exactly once. Viewing these Boolean functions as polynomials in k {\displaystyle k} variables over GF(2), segregate the functions into pairs ( f , g ) {\displaystyle (f,g)} where f {\displaystyle f} contains the i {\displaystyle i} -th coordinate as a linear term and g {\displaystyle g} is f {\displaystyle f} without that linear term. Now, for every such pair ( f , g ) {\displaystyle (f,g)} , x {\displaystyle x} and y {\displaystyle y} will agree on exactly one of the two functions. If they agree on one, they must disagree on the other and vice versa. (This proof is believed to be due to Watanabe.) == Discussion == A possible way around the ugly duckling theorem would be to introduce a constraint on how similarity is measured by limiting the properties involved in classification, for instance, between A and B. However Medin et al. (1993) point out that this does not actually resolve the arbitrariness or bias problem since in what respects A is similar to B: "varies with the stimulus context and task, so that there is no unique answer, to the question of how similar is one object to another". For example, "a barberpole and a zebra would be more similar than a horse and a zebra if the feature striped had sufficient weight. Of course, if these feature weights were fixed, then these similarity relations would be constrained". Yet the property "striped" as a weight 'fix' or constraint is arbitrary itself, meaning: "unless one can specify such criteria, then the claim that categorization is based on attribute matching is almost entirely vacuous". Stamos (2003) remarked that some judgments of overall similarity are non-arbitrary in the sense they are useful: "Presumably, people's perceptual and conceptual processes have evolved that information that matters to human needs and goals can be roughly approximated by a similarity heuristic... If you are in the jungle and you see a tiger but you decide not to stereotype (perhaps because you believe that similarity is a false friend), then you will probably be eaten. In other words, in the biological world stereotyping based on veridical judgments of overall similarity statistically results in greater survival and reproductive success." Unless some properties are considered more salient, or 'weighted' more important than others, everything will appear equally similar, hence Watanabe (1986) wrote: "any objects, in so far as they are distinguishable, are equally similar". In a weaker setting that assumes infinitely many properties, Murphy and Medin (1985) give an example of two putative classified things, plums and lawnmowers: "Suppose that one is to list the attributes that plums and lawnmowers have in common in order to judge their similarity. It is easy to see that the list could be infinite: Both weigh less than 10,000 kg (and less than 10,001 kg), both did not exist 10,000,000 years ago (and 10,000,001 years ago), both cannot hear well, both can be dropped, both take up space, and so on. Likewise, the list of differences could be infinite… any two entities can be arbitrarily similar or dissimilar by changing the criterion of what counts as a relevant attribute." According to Woodward, the ugly duckling theorem is related to Schaffer's Conservation Law for Generalization Performance, which states that all algorithms for learning of boolean functions from input/output examples have the same overall generalization performance as random guessing. The latter result is generalized by Woodward to functions on countably infinite domains.
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IOS SDK

The iOS SDK (iOS Software Development Kit), formerly the iPhone SDK, is a software development kit (SDK) developed by Apple Inc. The kit allows for the development of mobile apps on Apple's iOS 17 and iPadOS operating systems. The iOS SDK is a free download for users of Macintosh (or Mac) personal computers. It is not available for Microsoft Windows PCs. The SDK contains sets giving developers access to various functions and services of iOS devices, such as hardware and software attributes. It also contains an iPhone simulator to mimic the look and feel of the device on the computer while developing. New versions of the SDK accompany new versions of iOS. In order to test applications, get technical support, and distribute apps through App Store, developers are required to subscribe to the Apple Developer Program. Combined with Xcode, the iOS SDK helps developers write iOS apps using officially supported programming languages, including Swift and Objective-C. Other companies have also created tools that allow for the development of native iOS apps using their respective programming languages. == History == While originally developing iPhone prior to its unveiling in 2007, Apple's then-CEO Steve Jobs did not intend to let third-party developers build native apps for the iOS operating system, instead directing them to make web applications for the Safari web browser. However, backlash from developers prompted the company to reconsider, with Jobs announcing on October 17, 2007, that Apple would have a software development kit (SDK) available for developers by February 2008. The SDK was released on March 6, 2008. == Features == The iOS SDK is a free download for Mac users. It is not available for Microsoft Windows. To test the application, get technical support, and distribute applications through App Store, developers are required to subscribe to the Apple Developer Program. The SDK contents are separated into the following sets: UIKit Multi-touch events and controls Accelerometer support View hierarchy Localization (i18n) Camera support Media OpenAL audio mixing and recording Video playback Image file formats Quartz Core Animation OpenGL ES Core Services Networking Embedded SQLite database Core Location Threads CoreMotion Mac OS X Kernel TCP/IP Sockets Power management File system Security The SDK also contains an iPhone simulator, a program used to simulate the look and feel of iPhone on the developer's computer. New SDK versions accompany new iOS versions. == Programming languages == The iOS SDK, combined with Xcode, helps developers write iOS applications using officially supported programming languages, including Swift and Objective-C. An .ipa (iOS App Store Package) file is an iOS application archive file which stores an iOS app. === Java === In 2008, Sun Microsystems announced plans to release a Java Virtual Machine (JVM) for iOS, based on the Java Platform, Micro Edition version of Java. This would enable Java applications to run on iPhone and iPod Touch. Soon after the announcement, developers familiar with the SDK's terms of agreement believed that by not allowing third-party applications to run in the background (answer a phone call and still run the application, for example), and not allowing an application to download code from another source, nor allowing an application to interact with a third-party application, Sun's development efforts could be hindered without Apple's cooperation. Sun also worked with a third-party company called Innaworks in attempts to get Java on iPhone. Despite the apparent lack of interest from Apple, a firmware leak of the 2007 iPhone release revealed an ARM chip with a processor with Jazelle support for embedded Java execution. === .NET === Novell announced in September 2009 that they had successfully developed MonoTouch, a software framework that let developers write native iPhone applications in the C# and .NET programming languages, while still maintaining compatibility with Apple's requirements. === Flash === iOS does not support Adobe Flash, and although Adobe has two versions of its software: Flash and Flash Lite, Apple views neither as suitable for the iPhone, claiming that full Flash is "too slow to be useful", and Flash Lite to be "not capable of being used with the Web". In October 2009, Adobe announced that an upcoming update to its Creative Suite would feature a component to let developers build native iPhone apps using the company's Flash development tools. The software was officially released as part of the company's Creative Suite 5 collection of professional applications. === 2010 policy on development tools === In April 2010, Apple made controversial changes to its iPhone Developer Agreement, requiring developers to use only "approved" programming languages in order to publish apps on App Store, and banning applications that used third-party development tools; the ban affected Adobe's Packager tool, which converted Flash apps into iOS apps. After developer backlash and news of a potential anti-trust investigation, Apple again revised its agreement in September, allowing the use of third-party development tools. === Mac Catalyst === Originally called "Project Marzipan", Mac Catalyst helps developers bring iPadOS app experiences to macOS, and make it easier to take apps developed for iPadOS devices to Macs by avoiding the need to write the underlying software code twice.
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Zhura

Zhura ( ZUR-ə) is a free, web-based screenwriting software application for writing and formatting screenplays to the film industry standard, as well as other formats. Zhura allows users to collaborate on scripts in public or private groups and uses Creative Commons Licensing for all work in the public workspace. On March 29, 2010, Zhura announced its merger with Scripped. Scripped's CEO, Sunil Rajaraman, remains the company's Chief Executive Officer (CEO) as of 2022. The Zhura CEO was Eric MacDonald, a former Cascade Communications engineer. Scripped later closed on April 1, 2015 after a catastrophic, irrecoverable data loss. == Script editor == Screenplay Template – The script editor provides a built-in screenplay template which formats the document to a standard for scripts as recommended by the AMPAS. The screenplay document is composed of seven elements: scene, action, character, dialogue, parenthetical, transition, and shot (see image). Each element has a specific style to which the script editor conforms as you type.Script Formats – Other major script formats for stage play, sitcom, audio drama and comic book are also supported as well as the ability to switch between them.Auto-Complete – Characters, scene headings and custom transitions are “remembered” as they are written and “recalled” with tab-completion when a writer starts a new character, scene heading or transition, respectively.Multiple Editors – With a collaborative editing model comparable to Google Docs, two or more users can edit the same script simultaneously, regardless of having a different operating system or web browser. Import/Export – A screenplay written in another program can be imported into the script editor and automatically conformed to the screenplay template. The closer the original script has adhered to the standard format, the better it will appear when imported. Supported import/export formats include Text (.txt) Word (.doc) Rich Text (.rtf) and OpenDocument (.odt). Scripts can also be exported as a PDF file with additional options.Tracking Changes – Similar to the “tracking” feature in Microsoft Word, a user can review all changes made to a script in the revision history as well as highlight the contributions of each writer. Offline Mode – The Google Gears-based offline functionality is in the process of being updated and is not available for new subscribers, according to the company founders. == Community == Scripped supports typical social networking features such as discussion boards, comments, user profiles, public and private writing groups, internal web mail and instant messaging within the script editor. There is also the option to share scripts with others outside of Scripped by making scripts externally viewable. Scripped is made up entirely of user-generated scripts that other users can share, critique and edit, offering creative support to a community of writers. == Licensing of user-created work == There are three types of work-spaces on Scripped (personal, group and public) with unique copyright and licensing management for the work created in each area. Any work a user originates may be moved from the personal area to a public or group area at any time. Once another user edits a script, however, it cannot be moved into the originator’s personal area. Personal Workspace – Any script created or video uploaded in the user’s personal workspace remains copyrighted to that user. Until the user moves that script or video from their personal area into a group or public area, no other user shares a copyright or license to that work. Private Group Workspace – The copyright to any script created or video uploaded in a private group workspace is allocated by the individual members of the group, however they see fit. Public Workspace – Any script created or video uploaded in the public workspace is assigned a Creative Commons license by the originator of that work. The originator of a script may select one of four Creative Commons licenses before introducing that script to the public. The selection of the license is determined by what the author wants to allow others to do with the work. Below is a list of Creative Commons licenses available for all scripts and videos in the public workspace. Share Alike (BY-SA) This license lets others remix, tweak, and build upon your work even for commercial reasons, as long as they credit the original user and license their new creations under the identical terms. This license is often compared to open source software licenses. All new works based on the original user's will carry the same license, so any derivatives will also allow commercial use. No Derivatives (BY-ND) This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the original user. Non-Commercial, No Derivatives (BY-NC-ND) This license is the most restrictive of the four licenses, allowing redistribution. This license is often called the "free advertising" license because it allows others to download the original user work and share them with others as long as they mention the original user and link back to them, but they can't change them in any way or use them commercially. Non-Commercial, Share Alike (BY-NC-SA) This license lets others remix, tweak, and build upon the original user's work non-commercially, as long as they credit the original user and license their new creations under the identical terms. Others can download and redistribute the original user's work just like the BY-NC-ND license, but they can also translate, make remixes, and produce new stories based on the original user's work. All new work based on the original user's work will carry the same license, so any derivatives will also be non-commercial in nature. == Events == In April 2008, Zhura partnered with Improv Asylum, a comedy troupe in Boston, Massachusetts to produce a live sketch comedy show called "You Wrote It, Live" entirely written by the public on Zhura. Another show was produced in June.
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Fuse Mediation Router

Fuse Mediation Router is an open source tool for integrating services using Enterprise Integration Patterns based on Apache Camel for use in enterprise IT organizations. It is certified, productized and fully supported by the people who wrote the code. Fuse Mediation Router uses a standard method of notation to go from diagram to implementation without coding. Fuse Mediation Router is a rule-based routing and process mediation engine that combines the ease of basic POJO development with the clarity of the standard Enterprise Integration Patterns. It can be deployed inside any container or be used stand-alone, and works directly with any kind of transport or messaging model to rapidly integrate existing services and applications. Fuse Mediation Router is now a part of Red Hat JBoss Fuse. == Tooling == FuseSource offers graphical, Eclipse-based tooling for Apache Camel for download.
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Bayesian programming

Bayesian programming is a formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available. Edwin T. Jaynes proposed that probability could be considered as an alternative and an extension of logic for rational reasoning with incomplete and uncertain information. In his founding book Probability Theory: The Logic of Science he developed this theory and proposed what he called "the robot," which was not a physical device, but an inference engine to automate probabilistic reasoning—a kind of Prolog for probability instead of logic. Bayesian programming is a formal and concrete implementation of this "robot". Bayesian programming may also be seen as an algebraic formalism to specify graphical models such as, for instance, Bayesian networks, dynamic Bayesian networks, Kalman filters or hidden Markov models. Indeed, Bayesian programming is more general than Bayesian networks and has a power of expression equivalent to probabilistic factor graphs. == Formalism == A Bayesian program is a means of specifying a family of probability distributions. The constituent elements of a Bayesian program are presented below: Program { Description { Specification ( π ) { Variables Decomposition Forms Identification (based on δ ) Question {\displaystyle {\text{Program}}{\begin{cases}{\text{Description}}{\begin{cases}{\text{Specification}}(\pi ){\begin{cases}{\text{Variables}}\\{\text{Decomposition}}\\{\text{Forms}}\\\end{cases}}\\{\text{Identification (based on }}\delta )\end{cases}}\\{\text{Question}}\end{cases}}} A program is constructed from a description and a question. A description is constructed using some specification ( π {\displaystyle \pi } ) as given by the programmer and an identification or learning process for the parameters not completely specified by the specification, using a data set ( δ {\displaystyle \delta } ). A specification is constructed from a set of pertinent variables, a decomposition and a set of forms. Forms are either parametric forms or questions to other Bayesian programs. A question specifies which probability distribution has to be computed. === Description === The purpose of a description is to specify an effective method of computing a joint probability distribution on a set of variables { X 1 , X 2 , ⋯ , X N } {\displaystyle \left\{X_{1},X_{2},\cdots ,X_{N}\right\}} given a set of experimental data δ {\displaystyle \delta } and some specification π {\displaystyle \pi } . This joint distribution is denoted as: P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) {\displaystyle P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)} . To specify preliminary knowledge π {\displaystyle \pi } , the programmer must undertake the following: Define the set of relevant variables { X 1 , X 2 , ⋯ , X N } {\displaystyle \left\{X_{1},X_{2},\cdots ,X_{N}\right\}} on which the joint distribution is defined. Decompose the joint distribution (break it into relevant independent or conditional probabilities). Define the forms of each of the distributions (e.g., for each variable, one of the list of probability distributions). ==== Decomposition ==== Given a partition of { X 1 , X 2 , … , X N } {\displaystyle \left\{X_{1},X_{2},\ldots ,X_{N}\right\}} containing K {\displaystyle K} subsets, K {\displaystyle K} variables are defined L 1 , ⋯ , L K {\displaystyle L_{1},\cdots ,L_{K}} , each corresponding to one of these subsets. Each variable L k {\displaystyle L_{k}} is obtained as the conjunction of the variables { X k 1 , X k 2 , ⋯ } {\displaystyle \left\{X_{k_{1}},X_{k_{2}},\cdots \right\}} belonging to the k t h {\displaystyle k^{th}} subset. Recursive application of Bayes' theorem leads to: P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) = P ( L 1 ∧ ⋯ ∧ L K ∣ δ ∧ π ) = P ( L 1 ∣ δ ∧ π ) × P ( L 2 ∣ L 1 ∧ δ ∧ π ) × ⋯ × P ( L K ∣ L K − 1 ∧ ⋯ ∧ L 1 ∧ δ ∧ π ) {\displaystyle {\begin{aligned}&P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)\\={}&P\left(L_{1}\wedge \cdots \wedge L_{K}\mid \delta \wedge \pi \right)\\={}&P\left(L_{1}\mid \delta \wedge \pi \right)\times P\left(L_{2}\mid L_{1}\wedge \delta \wedge \pi \right)\times \cdots \times P\left(L_{K}\mid L_{K-1}\wedge \cdots \wedge L_{1}\wedge \delta \wedge \pi \right)\end{aligned}}} Conditional independence hypotheses then allow further simplifications. A conditional independence hypothesis for variable L k {\displaystyle L_{k}} is defined by choosing some variable X n {\displaystyle X_{n}} among the variables appearing in the conjunction L k − 1 ∧ ⋯ ∧ L 2 ∧ L 1 {\displaystyle L_{k-1}\wedge \cdots \wedge L_{2}\wedge L_{1}} , labelling R k {\displaystyle R_{k}} as the conjunction of these chosen variables and setting: P ( L k ∣ L k − 1 ∧ ⋯ ∧ L 1 ∧ δ ∧ π ) = P ( L k ∣ R k ∧ δ ∧ π ) {\displaystyle P\left(L_{k}\mid L_{k-1}\wedge \cdots \wedge L_{1}\wedge \delta \wedge \pi \right)=P\left(L_{k}\mid R_{k}\wedge \delta \wedge \pi \right)} We then obtain: P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) = P ( L 1 ∣ δ ∧ π ) × P ( L 2 ∣ R 2 ∧ δ ∧ π ) × ⋯ × P ( L K ∣ R K ∧ δ ∧ π ) {\displaystyle {\begin{aligned}&P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)\\={}&P\left(L_{1}\mid \delta \wedge \pi \right)\times P\left(L_{2}\mid R_{2}\wedge \delta \wedge \pi \right)\times \cdots \times P\left(L_{K}\mid R_{K}\wedge \delta \wedge \pi \right)\end{aligned}}} Such a simplification of the joint distribution as a product of simpler distributions is called a decomposition, derived using the chain rule. This ensures that each variable appears at the most once on the left of a conditioning bar, which is the necessary and sufficient condition to write mathematically valid decompositions. ==== Forms ==== Each distribution P ( L k ∣ R k ∧ δ ∧ π ) {\displaystyle P\left(L_{k}\mid R_{k}\wedge \delta \wedge \pi \right)} appearing in the product is then associated with either a parametric form (i.e., a function f μ ( L k ) {\displaystyle f_{\mu }\left(L_{k}\right)} ) or a question to another Bayesian program P ( L k ∣ R k ∧ δ ∧ π ) = P ( L ∣ R ∧ δ ^ ∧ π ^ ) {\displaystyle P\left(L_{k}\mid R_{k}\wedge \delta \wedge \pi \right)=P\left(L\mid R\wedge {\widehat {\delta }}\wedge {\widehat {\pi }}\right)} . When it is a form f μ ( L k ) {\displaystyle f_{\mu }\left(L_{k}\right)} , in general, μ {\displaystyle \mu } is a vector of parameters that may depend on R k {\displaystyle R_{k}} or δ {\displaystyle \delta } or both. Learning takes place when some of these parameters are computed using the data set δ {\displaystyle \delta } . An important feature of Bayesian programming is this capacity to use questions to other Bayesian programs as components of the definition of a new Bayesian program. P ( L k ∣ R k ∧ δ ∧ π ) {\displaystyle P\left(L_{k}\mid R_{k}\wedge \delta \wedge \pi \right)} is obtained by some inferences done by another Bayesian program defined by the specifications π ^ {\displaystyle {\widehat {\pi }}} and the data δ ^ {\displaystyle {\widehat {\delta }}} . This is similar to calling a subroutine in classical programming and provides an easy way to build hierarchical models. === Question === Given a description (i.e., P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) {\displaystyle P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)} ), a question is obtained by partitioning { X 1 , X 2 , ⋯ , X N } {\displaystyle \left\{X_{1},X_{2},\cdots ,X_{N}\right\}} into three sets: the searched variables, the known variables and the free variables. The 3 variables S e a r c h e d {\displaystyle Searched} , K n o w n {\displaystyle Known} and F r e e {\displaystyle Free} are defined as the conjunction of the variables belonging to these sets. A question is defined as the set of distributions: P ( S e a r c h e d ∣ Known ∧ δ ∧ π ) {\displaystyle P\left(Searched\mid {\text{Known}}\wedge \delta \wedge \pi \right)} made of many "instantiated questions" as the cardinal of K n o w n {\displaystyle Known} , each instantiated question being the distribution: P ( Searched ∣ Known ∧ δ ∧ π ) {\displaystyle P\left({\text{Searched}}\mid {\text{Known}}\wedge \delta \wedge \pi \right)} === Inference === Given the joint distribution P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) {\displaystyle P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)} , it is always possible to compute any possible question using the following general inference: P ( Searched ∣ Known ∧ δ ∧ π ) = ∑ Free [ P ( Searched ∧ Free ∣ Known ∧ δ ∧ π ) ] = ∑ Free [ P ( Searched ∧ Free ∧ Known ∣ δ ∧ π ) ] P ( Known ∣ δ ∧ π ) = ∑ Free [ P ( Searched ∧ Free ∧ Known ∣ δ ∧ π ) ] ∑ Free ∧ Searched [ P ( Searched ∧ Free ∧ Known ∣ δ ∧ π ) ] = 1 Z × ∑ Free [ P ( Searched ∧ Free ∧ Known ∣ δ ∧ π ) ] {\displaystyle {\begin{aligned}&P\left({\text{Searched}}\mid {\text{Known}}\wedge \delta \wedge \pi \right)\\={}&\sum _{\text{Free}}\left[P\left({\text{Searched}}\wedge {\text{Free}}\mid {\text{Known}}\wedge \delta \wedge \
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Apache Kudu

Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data. The open source project to build Apache Kudu began as internal project at Cloudera. The first version Apache Kudu 1.0 was released 19 September 2016. == Comparison with other storage engines == Kudu was designed and optimized for OLAP workloads. Like HBase, it is a real-time store that supports key-indexed record lookup and mutation. Kudu differs from HBase since Kudu's datamodel is a more traditional relational model, while HBase is schemaless. Kudu's "on-disk representation is truly columnar and follows an entirely different storage design than HBase/Bigtable".
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Griffon (framework)

Griffon is an open source rich client platform framework which uses the Java, Apache Groovy, and/or Kotlin programming languages. Griffon is intended to be a high-productivity framework by rewarding use of the Model-View-Controller paradigm, providing a stand-alone development environment and hiding much of the configuration detail from the developer. The first release is the fruit of the effort by the Groovy Swing team and an attempt to take the best of rapid application development, as indicated by its Grails-like structure, the agility of Groovy, and the availability of components for Swing. The framework was redesign from scratch for version 2, allowing different JVM programming languages to be used either in isolation or in conjunction. Supported UI toolkits are Java Swing JavaFX Apache Pivot Lanterna == Overview == Griffon aims to reduce the typical confusion that occurs with traditional Java UI development. Due to the MVC structure of Griffon, developers never have to go searching for files or be confused on how to start a new project. Everything begins with: lazybones create The generated project follows this structure: %PROJECT_HOME% + griffon-app + conf ---> location of configuration artifacts like builder configuration + controllers ---> location of controller classes + i18n ---> location of message bundles for i18n + lifecycle ---> location of lifecycle scripts + models ---> location of model classes + resources ---> location of non code resources (images, etc) + views ---> location of view classes + src + main ---> optional; location for Groovy and Java source files (of types other than those in griffon-app/) The builder infrastructure enables seamless integration of different widget libraries such as Swing, JIDE, and SwingX. In the first release, three sample applications are included : Greet, a Groovy Twitter client featured in the JavaOne 2009 Script Bowl, FontPicker, an application to view the available fonts on one's machine, SwingPad, a lightweight designer application for Griffon user interfaces. == Plugins == Griffon can be extended with the use of plugins. Plugins provide run-time access to testing libraries such as Easyb and FEST, and all widget libraries besides core Swing are provided as plugins. The plugin system allows for a wide range of additions, for example Polyglot Programming with Java, Apache Groovy, Kotlin. SQL and NoSQL datastores like Berkleydb, CouchDB, Db4O, Neo4j, NeoDatis, Memcached and Riak. == Publications == === Books === Features that would eventually become integral parts of Griffon (UI builders) were featured in these books: Groovy In Action (published by Manning) Beginning Groovy and Grails Books that cover Griffon: Griffon In Action (published by Manning) Beginning Groovy, Grails and Griffon === Magazine === GroovyMag for Groovy and Grails developers
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