AI Detector Reviews

AI Detector Reviews — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Deep tomographic reconstruction

    Deep tomographic reconstruction

    Deep Tomographic Reconstruction is a set of methods for using deep learning methods to perform tomographic reconstruction of medical and industrial images. It uses artificial intelligence and machine learning, especially deep artificial neural networks or deep learning, to overcome challenges such as measurement noise, data sparsity, image artifacts, and computational inefficiency. This approach has been applied across various imaging modalities, including CT, MRI, PET, SPECT, ultrasound, and optical imaging == Historical background == Traditional tomographic reconstruction relies on analytic methods such as filtered back-projection, or iterative methods which incrementally compute inverse transformations from measurement data (e.g., Radon or Fourier transform data). However, these approaches are not sufficient for certain imaging techniques such as low-dose CT and fast MRI, or scenarios involving metal artifacts and patient motion. == Use in imaging modalities == === Computed tomography (CT) === In CT, deep learning models can be particularly effective in reducing radiation exposure while maintaining image quality. Deep neural networks can also be able to reconstruct images of fair quality from sparsely sampled data without sacrificing diagnostic performance. Deep learning-based generative AI models can reduce CT metal artifacts. === Magnetic resonance imaging (MRI) === In magnetic resonance imaging (MRI), deep learning can lead to reduced MRI motion artifacts, and increased acquisition speed, referred to as fast MRI. Despite suffering from disadvantages such as lower signal-to-noise ratio (SNR), deep learning can enhance image quality in low field MRI, making these systems clinically viable. === Positron emission tomography (PET) and single-photon emission CT (SPECT) === For PET imaging, deep learning models can provide substantial improvements in low-dose imaging and motion artifact correction. Also, deep learning can help SPECT for generation of attenuation background. A notable technique for PET denoising involves integrating MR data through multimodal networks, which use anatomical information from MRI to enhance PET image quality. === Ultrasound imaging === Deep learning can enhance ultrasound imaging by reducing speckle noise and motion blur. For ultrasound beamforming, deep neural networks can allow superior image quality with limited data at high speed. === Optical imaging and microscopy === Diffuse optical tomography, optical coherence tomography and microscopy can be improved by deep neural networks beyond traditional methods. Furthermore, deep learning can also enhance Photoacoustic imaging (see Deep learning in photoacoustic imaging), addressing challenges like high noise, low contrast, and limited resolution. Deep learning has also been applied to label-free live-cell imaging, where convolutional neural networks predict fluorescence labels from transmitted light images, a technique known as in silico labeling. This method can enable high-throughput, non-invasive cell analysis and phenotyping without the need for traditional fluorescent dyes.

    Read more →
  • Color vision

    Color vision

    Color vision (CV), a feature of visual perception, is an ability to perceive differences between light composed of different frequencies independently of light intensity. Color perception is a part of the larger visual system and is mediated by a complex process between neurons that begins with differential stimulation of different types of photoreceptors by light entering the eye. Those photoreceptors then emit outputs that are propagated through many layers of neurons ultimately leading to higher cognitive functions in the brain. Color vision is found in many animals and is mediated by similar underlying mechanisms with common types of biological molecules and a complex history of the evolution of color vision within different animal taxa. In primates, color vision may have evolved under selective pressure for a variety of visual tasks including the foraging for nutritious young leaves, ripe fruit, and flowers, as well as detecting predator camouflage and emotional states in other primates. == Wavelength == Isaac Newton discovered that white light after being split into its component colors when passed through a dispersive prism could be recombined to make white light by passing them through a different prism. The visible light spectrum ranges from about 380 to 740 nanometers. Spectral colors (colors that are produced by a narrow band of wavelengths) such as red, orange, yellow, green, cyan, blue, and violet can be found in this range. These spectral colors do not refer to a single wavelength, but rather to a set of wavelengths: red, 625–740 nm; orange, 590–625 nm; yellow, 565–590 nm; green, 500–565 nm; cyan, 485–500 nm; blue, 450–485 nm; violet, 380–450 nm. Wavelengths longer or shorter than this range are called infrared or ultraviolet, respectively. Humans cannot generally see these wavelengths, but other animals may. === Hue detection === Sufficient differences in wavelength cause a difference in the perceived hue; the just-noticeable difference in wavelength varies from about 1 nm in the blue-green and yellow wavelengths to 10 nm and more in the longer red and shorter blue wavelengths. Although the human eye can distinguish up to a few hundred hues, when those pure spectral colors are mixed together or diluted with white light, the number of distinguishable chromaticities can be much higher. In very low light levels, vision is scotopic: light is detected by rod cells of the retina. Rods are maximally sensitive to wavelengths near 500 nm and play little, if any, role in color vision. In brighter light, such as daylight, vision is photopic: light is detected by cone cells which are responsible for color vision. Cones are sensitive to a range of wavelengths, but are most sensitive to wavelengths near 555 nm. Between these regions, mesopic vision comes into play and both rods and cones provide signals to the retinal ganglion cells. The shift in color perception from dim light to daylight gives rise to differences known as the Purkinje effect. The perception of "white" is formed by the entire spectrum of visible light, or by mixing colors of just a few wavelengths in animals with few types of color receptors. In humans, white light can be perceived by combining wavelengths such as red, green, and blue, or just a pair of complementary colors such as blue and yellow. === Non-spectral colors === There are a variety of colors in addition to spectral colors and their hues. These include grayscale colors, shades of colors obtained by mixing grayscale colors with spectral colors, violet-red colors, impossible colors, and metallic colors. Grayscale colors include white, gray, and black. Rods contain rhodopsin, which reacts to light intensity, providing grayscale coloring. Shades include colors such as pink or brown. Pink is obtained from mixing red and white. Brown may be obtained from mixing orange with gray or black. Navy is obtained from mixing blue and black. Violet-red colors include hues and shades of magenta. The light spectrum is a line on which violet is one end and the other is red, and yet we see hues of purple that connect those two colors. Impossible colors are a combination of cone responses that cannot be naturally produced. For example, medium cones cannot be activated completely on their own; if they were, we would see a 'hyper-green' color. == Dimensionality == Color vision is categorized foremost according to the dimensionality of the color gamut, which is defined by the number of primaries required to represent the color vision. This is generally equal to the number of photopsins expressed: a correlation that holds for vertebrates but not invertebrates. The common vertebrate ancestor possessed four photopsins (expressed in cones) plus rhodopsin (expressed in rods), so was tetrachromatic. However, many vertebrate lineages have lost one or many photopsin genes, leading to lower-dimension color vision. The dimensions of color vision range from 1-dimensional and up: == Physiology of color perception == Perception of color begins with specialized retinal cells known as cone cells. Cone cells contain different forms of opsin – a pigment protein – that have different spectral sensitivities. Humans contain three types, resulting in trichromatic color vision. Each individual cone contains pigments composed of opsin apoprotein covalently linked to a light-absorbing prosthetic group: either 11-cis-hydroretinal or, more rarely, 11-cis-dehydroretinal. The cones are conventionally labeled according to the ordering of the wavelengths of the peaks of their spectral sensitivities: short (S), medium (M), and long (L) cone types. These three types do not correspond well to particular colors as we know them. Rather, the perception of color is achieved by a complex process that starts with the differential output of these cells in the retina and which is finalized in the visual cortex and associative areas of the brain. For example, while the L cones have been referred to simply as red receptors, microspectrophotometry has shown that their peak sensitivity is in the greenish-yellow region of the spectrum. Similarly, the S cones and M cones do not directly correspond to blue and green, although they are often described as such. The RGB color model, therefore, is a convenient means for representing color but is not directly based on the types of cones in the human eye. The peak response of human cone cells varies, even among individuals with typical color vision; in some non-human species this polymorphic variation is even greater, and it may well be adaptive. === Theories === Two complementary theories of color vision are the trichromatic theory and the opponent process theory. The trichromatic theory, or Young–Helmholtz theory, proposed in the 19th century by Thomas Young and Hermann von Helmholtz, posits three types of cones preferentially sensitive to blue, green, and red, respectively. Others have suggested that the trichromatic theory is not specifically a theory of color vision but a theory of receptors for all vision, including color but not specific or limited to it. Equally, it has been suggested that the relationship between the phenomenal opponency described by Ewald Hering and the physiological opponent processes are not straightforward (see below), making of physiological opponency a mechanism that is relevant to the whole of vision, and not just to color vision alone. Hering proposed the opponent process theory in 1872. It states that the visual system interprets color in an antagonistic way: red vs. green, blue vs. yellow, black vs. white. Both theories are generally accepted as valid, describing different stages in visual physiology, visualized in the adjacent diagram. Green–magenta and blue–yellow are scales with mutually exclusive boundaries. In the same way that there cannot exist a "slightly negative" positive number, a single eye cannot perceive a bluish-yellow or a reddish-green. Although these two theories are both currently widely accepted theories, past and more recent work has led to criticism of the opponent process theory, stemming from a number of what are presented as discrepancies in the standard opponent process theory. For example, the phenomenon of an after-image of complementary color can be induced by fatiguing the cells responsible for color perception, by staring at a vibrant color for a length of time, and then looking at a white surface. This phenomenon of complementary colors shows that cyan, rather than green, is the complement of red, and that magenta, rather than red, is the complement of green. It therefore also shows that the reddish-green color supposed to be impossible by opponent process theory is actually the color yellow. Although this phenomenon is more readily explained by the trichromatic theory, explanations for the discrepancy may include alterations to the opponent process theory, such as redefining the opponent colors as red vs. cyan, to reflect this effect. Despite such criticis

    Read more →
  • Client-side persistent data

    Client-side persistent data

    Client-side persistent data or CSPD is a term used in computing for storing data required by web applications to complete internet tasks on the client-side as needed rather than exclusively on the server. As a framework it is one solution to the needs of Occasionally connected computing or OCC. A major challenge for HTTP as a stateless protocol has been asynchronous tasks. The AJAX pattern using XMLHttpRequest was first introduced by Microsoft in the context of the Outlook e-mail product. The first CSPD were the 'cookies' introduced by the Netscape Navigator. ActiveX components which have entries in the Windows registry can also be viewed as a form of client-side persistence.

    Read more →
  • Linux Trace Toolkit

    Linux Trace Toolkit

    The Linux Trace Toolkit (LTT) is a set of tools that is designed to log program execution details from a patched Linux kernel and then perform various analyses on them, using console-based and graphical tools. LTT has been mostly superseded by its successor LTTng (Linux Trace Toolkit Next Generation). LTT allows the user to see in-depth information about the processes that were running during the trace period, including when context switches occurred, how long the processes were blocked for, and how much time the processes spent executing vs. how much time the processes were blocked. The data is logged to a text file and various console-based and graphical (GTK+) tools are provided for interpreting that data. In order to do data collection, LTT requires a patched Linux kernel. The authors of LTT claim that the performance hit for a patched kernel compared to a regular kernel is minimal; Their testing has reportedly shown that this is less than 2.5% on a "normal use" system (measured using batches of kernel makes) and less than 5% on a file I/O intensive system (measured using batches of tar). == Usage == === Collecting trace data === Data collection is Started by: trace 15 foo This command will cause the LTT tracedaemon to do a trace that lasts for 15 seconds, writing trace data to foo.trace and process information from the /proc filesystem to foo.proc. The trace command is actually a script which runs the program tracedaemon with some common options. It is possible to run tracedaemon directly and in that case, the user can use a number of command-line options to control the data which is collected. For the complete list of options supported by tracedaemon, see the online manual page for tracedaemon. === Viewing the results === Viewing the results of a trace can be accomplished with: traceview foo This command will launch a graphical (GTK+) traceview tool that will read from foo.trace and foo.proc. This tool can show information in various interesting ways, including Event Graph, Process Analysis, and Raw Trace. The Event Graph is perhaps the most interesting view, showing the exact timing of events like page faults, interrupts, and context switches, in a simple graphical way. The traceview command is a wrapper for a program called tracevisualizer. For the complete list of options supported by tracevisualizer, see the online manual page for tracevisualizer.

    Read more →
  • Ameca (robot)

    Ameca (robot)

    Ameca is a robotic humanoid created in 2021 by Engineered Arts, headquarters in Falmouth, Cornwall, United Kingdom. The project commenced in February 2021, and the first public demonstration was at the CES 2022 show in Las Vegas. Ameca's appearance features grey rubber skin on the face and hands, and is specifically designed to appear genderless. In 2024, an Ameca unit was installed in Edinburgh in the UK to reside at the National Robotarium. Ameca generation 3 has been released and showcased at ICRA 2025 along with Ami. == History == The first generation of Ameca was developed at Engineered Arts headquarters in Falmouth, Cornwall, United Kingdom. The project started in February 2021, with the first video revealed publicly on 1 December 2021. Ameca gained widespread attention on Twitter and TikTok ahead of its first public demonstration at the Consumer Electronics Show 2022, where it was covered by CNET and other news outlets. In 2022, Ameca presented an Alternative Christmas message by British TV Channel 4 for Christmas Day. Ameca was associated with the Museum of the Future's robotic family, where it could interact with visitors. In 2024, an Ameca unit was installed in Edinburgh in the UK to reside at the National Robotarium. In January 2026, Ameca served as an ambassador for the European Space Agency (ESA) at the 18th European Space Conference. == Features == It is designed as a platform for further developing robotics technologies involving human-robot interaction. utilizes embedded microphones, binocular eye mounted cameras, a chest camera and facial recognition software to interact with the public. Interactions can be governed by either OpenAI's GPT-3 or human telepresence. It also features articulated motorized arms, fingers, neck and facial features. Ameca's appearance features grey rubber skin on the face and hands, and is specifically designed to appear genderless. == Public appearances == Computer History Museum, California Heinz Nixdorf MuseumsForum, Paderborn, Germany Copernicus Science Center, Warsaw, Poland Museum of the Future, Dubai Consumer Electronics Show 2022 Deutsches Museum Nuremberg OMR Festival 2022 Hosted by Vodafone GITEX 2022 International Conference on Robotics and Automation 2023 International Telecommunication Union AI for Good Global Summit 2023 Sphere (Not Ameca, Custom humanoid named Aura built on Ameca technology)

    Read more →
  • WriterDuet

    WriterDuet

    WriterDuet is a screenwriting software for writing and editing screenplays and other forms of mass media. == History == WriterDuet was founded in 2013 by Guy Goldstein. In April 2015, WriterDuet acquired the domain for Scripped.com after they closed, citing a serious technical failure. In August 2016, WriterDuet released a localized version of its software in China. In May 2018, WriterDuet included Bechdel test analysis functions to address issues of gender diversity in the screenwriting industry. In 2018, WriterDuet published WriterSolo, an offline version of their app that runs on the browser and opens/saves files on the computer, Dropbox, Google Drive, and iCloud. In July 2019, WriterDuet made the WriterSolo browser app and desktop app available as pay-what-you-want under the web address FreeScreenwriting.com. == Features == WriterDuet is primarily used to outline, write, and format screenplays to the standards recommended by the AMPAS. It also supports formats for theater, novels, and video games. The software is powered by Firebase allowing users to write together in real-time from multiple devices. WriterDuet's main competitors in the screenwriting industry are Final Draft, Celtx, and Movie Magic Screenwriter.

    Read more →
  • Rapid PHP Editor

    Rapid PHP Editor

    rapid PHP Editor is a PHP Editor that incorporates many functions such as AutoComplete, Syntax checker, debugger and many other tools for fast PHP development. Rapid PHP Editor also contain other development tools for helping on HTML, CSS, JavaScript and many other languages. Is part of a family of products covering most aspects of modern web development integrating as well many other capabilities used by developers. Some features: (X)HTML to HTML5 CSS to CSS3 Code intelligence Powerful search and replace Support for several frameworks Code beautifier FTP Explorer (FTP/SFTP/FTPS) File explorer Database explorer Code snippets Validators and Debuggers FAST, real fast Many other tools available (many more to describe all here) == History == Rapid PHP Editor was built using the Delphi programming language.

    Read more →
  • Dropbox Paper

    Dropbox Paper

    Dropbox Paper, or simply Paper, is a collaborative document-editing service developed by Dropbox. Originating from the company's acquisition of document collaboration company Hackpad in April 2014, Dropbox Paper was officially announced in October 2015, and launched in January 2017. It offers a web application, as well as mobile apps for Android and iOS. Dropbox Paper was described in the official announcement post as "a flexible workspace that brings people and ideas together. With Paper, teams can create, review, revise, manage, and organize — all in shared documents". Reception of Dropbox Paper has been mixed. Critics praised collaboration functionality, including content available immediately, the ability to mention specific collaborators, assign tasks, write comments, as well as editing attribution, and revision history. It received particular praise for its support for rich media from a variety of sources, with one reviewer noting that the Paper's support for rich media exceeds the capabilities of most of its competitors. However, it was criticized for a lack of formatting options and editing features. While the user interface was liked for being minimal, reviewers cited the lack of a fixed formatting bar and missing features present in competitors' products as making Dropbox Paper seem like a "light" tool. == History == Dropbox acquired document collaboration company Hackpad in April 2014. A year later, Dropbox launched a Dropbox Notes note-taking product in beta testing phase. Dropbox Paper was officially announced on October 15, 2015, followed by an open beta and release of mobile Android and iOS apps in August 2016. Dropbox Paper was officially released on January 30, 2017. == Reception == In a comparison between Dropbox Paper and Evernote, PC World's Michael Ansaldo wrote that "With its emphasis on document creation, you might expect formatting to be front and center in Dropbox Paper. That's not the case." Ansaldo noted the lack of a "fixed formatting toolbar as you'd find in Evernote or a word processor like Google Docs or Microsoft Word. Instead, the text editor appears as a floating ribbon only when you highlight selected text." The only formatting options available for emphasis were bolding, strikethrough, bulleted and numbered lists, and H1 and H2 tags. Users can also add links, convert text to checklists, and add comments. Ansaldo wrote that "Both Evernote and Dropbox Paper make it easy to add images to a document", but also noted that "Dropbox Paper doesn't support any image editing". Paper supports rich media, and users can "add rich content to your document just by pasting a link to the file. In addition to Dropbox, Paper supports media from a variety of popular services including YouTube, Spotify, Vimeo, SoundCloud, Facebook, and Google's productivity suite. Once the file appears, you can delete the link for a cleaner display." To start working with other people, Paper "allows you to invite people via email from within a document", with sharing options for who can view the link (anyone with the link or just the invited person), and action permissions (edit or only comment). Regarding collaboration, Ansaldo wrote that "Creative collaboration is Paper’s marquee feature, and it provides a variety of ways to work effectively with others in real time". Users can "make any content immediately visible and accessible to a specific collaborator with "@mentions"", and "You can also use @mentions to create and assign task lists within a document." Paper also "boasts essential collaboration tools including comments, editing attribution, and revision history." Writing for TechRadar, John Brandon wrote that Dropbox Paper "might be a 'light' tool for now without the extensive templates of Microsoft Office or the integration with other apps in the Zoho suite, but it does work well with the Dropbox storage service that's so popular with office workers these days." Kyle Wiggers of Digital Trends wrote that Paper is "all about minimizing distractions. Its interface is quite literally a big, blank canvas on which you tap out your agenda. You can organize notes by title and create to-do lists, but even basic formatting tools are obscured from view", noting Paper's "floating box above words and phrases highlighted by your cursor". Wiggers stated that "Paper is not a to-do organizer", but that it's "well suited to the purpose thanks to a bevy of labor-saving conveniences", highlighting that Paper "supports more media than most of its to-do and note-taking counterparts". He praised the collaboration tools, writing that they "are as extensive as you'd hope, and then some", citing its invitation system with permission controls, lists of changes and revision history, comment and chat support, and "perhaps best of all", the ability to assign tasks with a "@" mention. Business Insider's Alex Heath praised that "Paper's interface is spotless and friendly to write in. You don't feel overwhelmed with formatting options", but criticized the available features, writing that "Google Docs is much more full-featured in the formatting department, so Paper has some catching up to do if it wants to be on par with the competition". Writing for The Verge, Casey Newton praised Paper's handling of rich media, complimenting it for being "great", and added that "I imagine that creative types who work on teams will appreciate having rich media embedded in the documents they're working on rather than in a series of infinite tabs".

    Read more →
  • EffectsLab Pro

    EffectsLab Pro

    EffectsLab Pro is a discontinued visual effects software product developed by FXhome. It has since been superseded by the FXhome HitFilm range. The company also produced a limited functionality version, EffectsLab Lite, containing just the Particle engine. A more extensive product, VisionLab Studio, combined the functionality of EffectsLab Pro and the company's CompositeLab Pro product with enhancements to both. == Effects Engines == The effects are generated by the program's effect engines: The Neon Light engine allows light beams to be drawn onto the video, allowing the generation of lightsaber-like weapons, neon lighting, fantasy glow effects and laser blasts. The Particle engine is used for particle effects, such as smoke, fire, explosions, and weather effects. The Muzzle Flash engine is designed for creating and animating muzzle flashes such as machine gun firing, tank blasts, etc. It's possible to rotate the created muzzle flash in 3D, making it the only engine with 3D use. The Optics engine is designed for creating artificial lens flares and light sources. It is useful for enhancing other light-based effects, and mimicking the distinctive flashes of light that accompany Star Wars' lightsaber battles. The Laser engine (introduced in EffectsLab Pro in late 2007) is designed as a simplified method of creating laser weapon effects, including the ability to add simulated perspective to the effect. == Presets == EffectsLab Pro allows the user to save the effects using presets. Since all effects are generated from settings in the different engines, it is fairly easy to generate an XML style description of the effect. It is also possible to share presets on FXhome's website.

    Read more →
  • Yahoo Groups

    Yahoo Groups

    Yahoo! Groups was a free-to-use system of electronic mailing lists offered by Yahoo!. Prior to February 2020, Yahoo! Groups was one of the world's largest collections of online discussion boards. It allowed members to subscribe to various groups, read subscribed discussions online, view and share photos, files and bookmarks within a group, access a group calendar, create polls for group members, and receive email notifications of new discussion topics. Some groups were simply announcement boards, to which only the group moderators could post, while others were discussion forums. Depending on each group's settings, membership could be open to everyone or only to invited or approved people. On February 1, 2020, Yahoo! removed online access to discussions and all other features except simple membership management, essentially turning all groups into mailing lists, and on October 13, 2020, it announced that Yahoo Groups would shut down completely on December 15, 2020. == History == In 1998 Yahoo! Clubs was launched as an extension of services developed by Yahoo! Messenger. In August 2000 Yahoo acquired eGroups.com. Yahoo! Groups was launched in early 2001 as an integration of technology from eGroups.com and community groups from both eGroups.com and Yahoo! Clubs. In 2001 Yahoo! deleted adult groups from its search directory, making it very difficult to locate Yahoo! groups with adult content. The Groups Updates Email feature was introduced in 2010. It summarized, in a single email, all the updates that occurred every twenty-four hours in all groups. In September 2010, a major facelift was rolled out, making Yahoo! Groups look very similar to Facebook. In December, Yahoo! Groups Japan emailed its users and posted a notice on its homepage, to announce that its service, which commenced in February 2004, would be closing on May 28, 2014. In October 2019, Yahoo! announced that all content that had been posted to Yahoo! Groups will be deleted on December 14, 2019; that date was later amended to January 31, 2020. Yahoo! announced that adding new content would be blocked on October 28, 2019. Once the content was deleted, users of Yahoo! Groups were only able to browse the group directory, request invitations and, if members of a group, send messages to that group. On October 13, 2020, Yahoo! announced they would be shutting down Yahoo! Groups on December 15, 2020. The site was closed down a few days after the advertised date, displaying a message that the service was officially shut down. This message stopped appearing at the end of January 2021 and the Yahoo! Groups web address began redirecting to the main Yahoo! page. === Criticism and controversy === On August 31, 2010, Yahoo! Groups started rolling out a major software change, which was denounced by a large number of users. The re-model was completely abandoned on January 12, 2011. == Site statistics == In August 2008, Yahoo! Group staff reported that there were 113 million users, and nine million Groups using 22 languages. In July 2010, the web analytics website Quantcast reported around 915 thousand unique visitors daily to the Yahoo! Groups website (US). In January 2011, that number had increased to 933 thousand unique visitors daily. The number did not include Yahoo! Group members who accessed the Groups site via email. In September 2010, at its "Product Runway" event, Yahoo! told reporters that Yahoo! Groups had 115 million group members and that there were 10 million Yahoo! groups. == Archives ==

    Read more →
  • Software bot

    Software bot

    A software bot is a type of software agent in the service of software project management and software engineering. A software bot has an identity and potentially personified aspects in order to serve their stakeholders. Software bots often compose software services and provide an alternative user interface, which is sometimes, but not necessarily conversational. Software bots are typically used to execute tasks, suggest actions, engage in dialogue, and promote social and cultural aspects of a software project. The term bot is derived from robot. However, robots act in the physical world and software bots act only in digital spaces. Some software bots are designed and behave as chatbots, but not all chatbots are software bots. Discussions about the past and future of software bots show that software bots have been adopted for many years. == Usage == Software bots are used to support development activities, such as communication among software developers and automation of repetitive tasks. Software bots have been adopted by several communities related to software development, such as open-source communities on GitHub and Stack Overflow. GitHub bots have user accounts and can open, close, or comment on pull requests and issues. GitHub bots have been used to assign reviewers, ask contributors to sign the Contributor License Agreement, report continuous integration failures, review code and pull requests, welcome newcomers, run automated tests, merge pull requests, fix bugs and vulnerabilities, etc. The Slack tool includes an API for developing software bots. There are slack bots for keeping track of todo lists, coordinating standup meetings, and managing support tickets. The ChatBot company products further simplify the process of creating a custom Slack bot. On Wikipedia, Wikipedia bots automate a variety of tasks, such as creating stub articles, consistently updating the format of multiple articles, and so on. Bots like ClueBot NG are capable of recognizing vandalism and automatically remove disruptive content. == Taxonomies and Classification Frameworks == Lebeuf et al. provide a faceted taxonomy to characterize bots based on a literature review. It is composed of 3 main facets: (i) properties of the environment that the bot was created in; (ii) intrinsic properties of the bot itself; and (iii) the bot's interactions within its environment. They further detail the facets into sets of sub-facets under each of the main facets. Paikari and van der Hoek defined a set of dimensions to enable comparison of software bots, applied specifically to chatbots. It resulted in six dimensions: Type: the main purpose of the bot (information, collaboration, or automation) Direction of the "conversation" (input, output, or bi-directional) Guidance (human-mediated, or autonomous) Predictability (deterministic, or evolving) Interaction style (dull, alternate vocabulary, relationship-builder, human-like) Communication channel (text, voice, or both) Erlenhov et al. raised the question of the difference between a bot and simple automation, since much research done in the name of software bots uses the term bot to describe various different tools and sometimes things are "just" plain old development tools. After interviewing and surveying over 100 developers the authors found that not one, but three definitions dominated the community. They created three personas based on these definitions and the difference between what the three personas see as being a bot is mainly the association with a different set of human-like traits. The chat bot persona (Charlie) primarily thinks of bots as tools that communicates with the developer through a natural language interface (typically voice or chat), and caring little about what tasks the bot is used for or how it actually implements these tasks. The autonomous bot persona (Alex) thinks of bots as tools that work on their own (without requiring much input from a developer) on a task that would normally be done by a human. The smart bot persona (Sam) separates bots and plain old development tools through how smart (technically sophisticated) a tool is. Sam cares less about how the tool communicates, but more about if it is unusually good or adaptive at executing a task. The authors recommends that people doing research or writing about bots try to put their work in the context of one of the personas since the personas have different expectations and problems with the tools. == Example of notable bots == Dependabot and Renovatebot update software dependencies and detect vulnerabilities. (https://dependabot.com/) Probot is an organization that create and maintain bots for GitHub. The example bots using Probot are the following. Auto Assign (https://probot.github.io/apps/auto-assign/) license bot (https://probot.github.io/) Sentiment bot (https://probot.github.io/apps/sentiment-bot/) Untrivializer bot (https://probot.github.io/apps/untrivializer/) Refactoring-Bot (Refactoring-Bot): provides refactoring based on static code analysis Looks good to me bot (LGTM) is a Semmle product that inspects pull requests on GitHub for code style and unsafe code practices. == Issues and threats == Software bots may not be well accepted by humans. A study from the University of Antwerp has compared how developers active on Stack Overflow perceive answers generated by software bots. They find that developers perceive the quality of software bot-generated answers to be significantly worse if the identity of the software bot is made apparent. By contrast, answers from software bots with human-like identity were better received. In practice, when software bots are used on platforms like GitHub or Wikipedia, their username makes it clear that they are bots, e.g., DependaBot, RenovateBot, DatBot, SineBot. Bots may be subject to special rules. For instance, the GitHub terms of service does not allow 'bots' but accepts 'machine account', where a 'machine account' has two properties: 1) a human takes full responsibility of the bot's actions 2) it cannot create other accounts.

    Read more →
  • RemObjects Software

    RemObjects Software

    RemObjects Software is an American software company founded in 2002 by Alessandro Federici and Marc Hoffman. It develops and offers tools and libraries for software developers on a variety of development platforms, including Embarcadero Delphi, Microsoft .NET, Mono, and Apple's Xcode. == History == RemObjects Software was founded in the summer of 2002. Its first product was RemObjects SDK 1.0 for Delphi, the company's remoting solution which is now in its 6th version. In late 2003 RemObjects expanded its product portfolio to add Data Abstract for Delphi, a multi-tier database framework built on top of the SDK. In 2004, Carlo Kok, who would eventually become Chief Compiler Architect for Oxygene, joined the company, adding the open source Pascal Script library for Delphi to the company's portfolio. Initial development began on Oxygene (which was then named Chrome) based on Carlo's experience from writing the widely used Pascal Script scripting engine. Towards the end of 2004, RemObjects SDK for .NET was released, expanding the remoting framework to its second platform. Chrome 1.0 was released in mid-2005, providing support for .NET 1.1 and .NET 2.0, which was still in beta at the time - making Chrome the first shipping language for .NET that supported features such as generics. It was followed by Chrome 1.5 when .NET 2.0 shipped in November of the same year. 2005 also saw the expansion of Data Abstract to .NET as a second platform. Data Abstract for .NET was the first RemObjects product (besides Oxygene itself) to be written in Oxygene. Hydra 3.0, was released for .NET in December 2006, bringing a paradigm shift to the product, away from a regular plugin framework, and focusing on interoperability between plugins and host applications written in either .NET or Delphi/Win32, essentially enabling the use of both managed and unmanaged code in the same project. In Summer 2007, RemObjects released Chrome 'Joyride' which added official support for .NET 3.0 and 3.5. Chrome once again was the first language to ship release level support for new .NET framework features supported by that runtime - most importantly Sequences and Queries (aka LINQ). Development continued and in May 2008 Oxygene 3.0 was released, dropping the "Chrome" moniker. Oxygene once again brought major language enhancements, including extensive support for concurrency and parallel programming as part of the language syntax. In October 2008, RemObjects Software and Embarcadero Technologies announced plans to collaborate and ship future versions of Oxygene under the Delphi Prism moniker, later changed to Embarcadero Prism. The first of these releases of Prism became available in December 2008. Over the course of 2009, RemObjects software completed the expansion of its Data Abstract and RemObjects SDK product combo to a third development platform - Xcode and Cocoa, for both Mac OS X and iPhone SDK client development. RemObjects SDK for OS X shipped in the spring of 2009, followed by Data Abstract for OS X in the fall. In 2011, Oxygene was expanded to add support for the Java platform, in addition to NET. In 2014, RemObjects introduced a C# compiler which runs as a Visual Studio 2013 plugin, that can output code for iOS, MacOS (Cocoa) and Android, in addition to .NET compatible code. In addition, an IDE called Fire was introduced for macOS which works with their C# and Oxygene compilers. Together, the compiler supporting both Oxygene and C# was rebranded as the Elements Compiler, with CE# having the Code name "Hydrogene". In February 2015, RemObjects introduced a beta version of a Swift compiler called Silver as part of its Elements effort. Silver, too, could create code that will execute on Android, the JVM, .NET platform and also create native Cocoa code. Silver added new features to the Swift language, such as exceptions and has a few differences and limitations compared to Apple's Swift. In February 2020, support for the Go programming language was introduced with RemObjects Gold, including the ability to compile Go language code for all Elements platforms, and a port of the extensive Go Base Library available to all Elements languages. In 2021, Mercury was added to the Elements compiler as the sixth language, providing a future for the Visual Basic .NET language recently deprecated by Microsoft. Mercury supports building and maintaining existing VB.NET projects, as well as using the language for new projects both on .NET and the other platforms. == Commercial products == Elements is a development toolchain that targets .NET runtime, Java/Android virtual machines, the Apple ecosystem (macOS, iOS, tvOS), WebAssembly and native and Windows/Linux/Android NDK processor-native machine code in conjunction with a runtime library that does automatic garbage collection on non-ARC environments and ARC on ARC-based environments, such as iOS and MacOS. Because Java, C#, Swift, and Oxygene all can import each other's APIs, Elements effectively functions as Java bonded together with C# bonded together with Swift bonded together with Oxygene as a confederation of languages cooperating together quite intimately. Oxygene, a unique programming language based on Object Pascal, which can import Java, C#, and Swift APIs from the runtime of the target operating system; RemObjects C#, an implementation of C# programming language, which can import Java, Swift, and Oxygene APIs from the runtime of the target operating system and which is intended as a competitor of Xamarin, but Hydrogene's C# targets JVM bytecode instead of Xamarin's C# compiling to only Common Language Infrastructure byte code and needing the accompanying Mono Common Language Runtime to be present in such JVM-centric environments as Android; Silver, a free implementation of the Swift programming language, which can import Java, C#, and Oxygene APIs from the runtime of the target operating system; Iodine, an implementation of the Java programming language. Gold, an implementation of the Go programming language. Mercury, an implementation of the Visual Basic .NET programming language. Fire an integrated development environment for macOS. Water an integrated development environment for Windows. Data Abstract Remoting SDK, a.k.a. RemObjects SDK Hydra Oxfuscator Oxidizer, an automatic translator from Java, C#, Objective-C, and Delphi to Oxygene, from Java, Objective-C, and C# to Swift, and from Java and Objective-C to C#. == Open source projects == Train is an open-source JavaScript-based tool for building and running build scripts and automation. Internet Pack for .NET is a free, open source library for building network clients and servers using TCP and higher level protocols such as HTTP or FTP, using the .NET or Mono platforms. It includes a range of ready to use protocol implementations, as well as base classes that allow the creation of custom implementations. RemObjects Script for .NET is a fully managed ECMAScript implementation for .NET and Mono. Pascal Script for Delphi is a widely used implementation of Pascal as scripting language. == Involvement of other projects == The Oxygene Compiler Oxygene is a language based on Object Pascal and designed to efficiently target the Microsoft .NET and Mono managed runtimes; it expands Object Pascal with a range of additional language features, such as Aspect Oriented Programming, Class Contracts and support for Parallelism. It integrates with the Microsoft Visual Studio and MonoDevelop IDEs.

    Read more →
  • Stop Motion Studio

    Stop Motion Studio

    Stop Motion Studio is a stop motion animation software developed by Cateater LLC. It is available as both an app for iOS and Android and as a software for Windows and Mac. Two versions of the software exist, the standard Stop Motion Studio for free, and the paid Stop Motion Studio Pro, which contains extra, more advanced features. The software is commonly used in brickfilming.

    Read more →
  • SWILE

    SWILE

    SWILE (formerly: Lunchr) is a French app-based company that focuses on improving the employee experience. Among others, the platform offers meal vouchers, gift vouchers, mobility vouchers, and business travel solutions. In March 2020, it was renamed SWILE and entered the lunch break and meal voucher market. == History == The company was founded as Lunchr by Loïc Soubeyrand in 2016. Originally, Lunchr was an app for pre-ordering lunch on the spot or to go. In January 2017, the company raised €2.5 million in seed funding from Daphni. In 2018, the company raised €11 million (series A) from Idinvest, followed by another €30 million in February 2019 (series B), notably from Index Ventures and Kima Ventures. In January 2020, Lunchr became one of the first startups to join the French Tech 120. A few months later, in March, Lunchr diversified its services, adding team life management tools and changing its brand name to Swile. In June 2020, the company raised €70 million more in a new round of financing (Series C) from the same investors and the BPI. In November 2020, Swile acquired Briq, a startup specializing in employee engagement. In January 2021, Swile won a tender with Carrefour and distributed 62,000 Swile cards to its employees. In early October 2021, a new $200 million (€175 million) fundraising round, in which Japanese Softbank joined other investors, allowed Swile to capitalize on $1 billion. President Emmanuel Macron cited the company as "a further proof that FrenchTech is at the forefront internationally." In May 2022, the company acquired the travel management start-up Okarito for €6 million. == Overview == Swile operates in two countries (France and Brazil) and has a total of 1000 employees, 5.5 million users and 85,000 corporate customers, including Carrefour, Le Monde, JCDECAUX, PSG, Airbnb, Spotify, Red Bull, and TikTok in the private sector, as well as numerous local authorities and ministerial references in the public sector.

    Read more →
  • Apache Kudu

    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".

    Read more →